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Ultimate Beginner Guide to Pro Trading — Transcript

by Andrea Cimi · 53,024 words · 7,645 segments · language en · Watch on YouTube

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0:00Hi, let's be real. If you clicked here,
0:02it's probably because you're another
0:04financially illiterate who just goes on
0:07YouTube to search how to make some money
0:09with trading. Maybe you saw a guy
0:10claiming to have become rich by flipping
0:12some memecoin or you saw a forex guru
0:15flaunting his Lambo and his lifestyle.
0:17Just know that's all [ __ ] It's all
0:19a scam. Forget about it. That is not
0:21professional trading. Plus, every video
0:23on YouTube that has a title similar to
0:25this one will likely show you how to
0:27draw a bunch of lines on a chart, follow
0:29some price [music] patterns, or some
0:31magical combination of indicators, and
0:33you'll end up trusting people with zero
0:35credentials. Try trading with a broker
0:37they have an affiliate link with so that
0:39they can earn commissions from the money
0:41you will eventually lose. So, you'll
0:43lose money. You'll have to buy their
0:44premium program or some signal room or
0:46some other shortcut that will not lead
0:48you to trading success anyway. You'll
0:50become a gambling addict, fall for more
0:52scams, and lose money for years. And I'm
0:54not joking. This is literally what
0:56happens to millions of people that
0:58approach trading. But lucky for both of
1:00us, today you've clicked on a video,
1:01which is slightly different. A video
1:02where someone, yours truly, will finally
1:05wake you up on how trading actually
1:07works, where you'll understand it's not
1:09all fun and games and quick money on a
1:11chart. I will be brutally honest with
1:13you so you know how it really works. see
1:16if trading is even the right business
1:18model for you and what it actually takes
1:20to be successful. And I am in a unique
1:23position to teach you this because
1:24unlike most of trading YouTubers, over
1:26the last 6 years, I've been studying and
1:28trading the market side by side in the
1:30professional trading floors of world
1:33trading champions like Patrick Mill, two
1:35times world trading champion,
1:37professional portfolio manager, or Jan
1:39Smolen, three times world trading
1:41champion, who's also a macro hedge fund
1:43trader. people who have documented in
1:45the world stage three-digit returns per
1:47year consistently for more than 5 years.
1:50One of my mentors have managed [music]
1:51$20 billion in a European bank and is
1:54now a hedge fund manager for example.
1:56And we're talking about people who are
1:57barely on social medias at all that I
1:59had to look really hard for and pay tens
2:01if not hundreds of thousands of dollar
2:02to learn their trading strategies which
2:04unlike most retail traders on YouTube
2:06who just show you some screenshots taken
2:09from offshore brokers god knows where
2:11they're from which are 99% fake. These
2:14people have audited results and I have
2:16managed thanks to this professional
2:18education to become consistently
2:20profitable and generate alpha. And I'm
2:22one of the few people you're going to
2:22find online that has actually shared a
2:25live broker login with a regulated
2:27broker. And I'll keep sharing my
2:28performance and my journey fully
2:30transparently on this channel. So if
2:31you're tired of the [ __ ] and you
2:33want to cut to the real stuff, I can
2:35confidently say this is one of the few
2:36places you're going to be able to find
2:37it. This is not going to be that kind of
2:39dopamine friendly video where by the end
2:41of it you'll be automatically profitable
2:42and be able to make $5,000 a day type of
2:45thing cuz that, as we have established,
2:47is complete [ __ ] This will be a
2:49brutally realistic video where I've
2:51condensed literally the best [ __ ]
2:53knowledge you can possibly find on the
2:55face of the internet gathered in 6 years
2:57of trading education, academic research,
3:00and personal experience with world top
3:02traders on how to become a pro trader, a
3:06professional of the trading business.
3:11And like, bro, we're talking about
3:14finance, okay? This is no [ __ ]
3:15[music] game, okay? People study for
3:17decades and have PhDs for this [ __ ] And
3:20it's not an online [music] business
3:21model either where you have to sell a
3:23product to some people. We're talking
3:25about becoming an emotionally neutral
3:28ninja sniper that reads where the banks
3:30and the smart [music] money are moving
3:32their money and being able to predict
3:35this flow of money consistently through
3:37time. It's not quick and easy money.
3:38It's a skill that will take time to
3:40build, likely years. And whoever tells
3:42you that a video is enough or 6 months
3:44is enough, they're lying to your face.
3:46So, this is how it's going to go down. I
3:48will first place your expectations in
3:49the right place so you don't lose your
3:51money like a degenerate gambling right
3:54away. You're welcome. I will show you
3:55what financial [music] markets really
3:57are, the difference between an average
3:59retail trader and a protrader who can
4:02manage to build generational wealth with
4:04trading. How professionals read the
4:06markets. how they analyze it through
4:09fundamentals, [music] order flow and
4:10option flow. How do they build a
4:12strategy and what strategy they use and
4:15being able to not only survive in the
4:17game but thriving in it, making two,
4:19three, even fourdigit return per year.
4:21So, buckle up, save this video in a
4:23watch later list or something. Going to
4:25be a long video packed with value that
4:27normally gets charged tens of thousands
4:29of dollars for, but that will give you
4:30professional trading knowledge and a
4:32clear road map. So, pause the video now,
4:34take pen and paper cuz we're getting
4:35started. So, let's start from the basic.
4:37What is trading? Is it like trying to
4:39buy a meme coin at $1 and then selling
4:40it at $20 for a 20x profit? Technically,
4:43yes. Or trying to trade forex every time
4:44price crosses a weird line. Technically,
4:46that's also trading. Those are retail
4:47trading strategies that 99% of people
4:50use to lose the savings they were
4:52supposed to use to pay off their student
4:53loans. But that's just a more advanced
4:55and cooler form of gambling, right? It's
4:57a casino. Very few get lucky. Very few
4:59times. 99% don't. That is not a reliable
5:03business model. What professional
5:04trading really is is the most advanced
5:07and at the same time the most accessible
5:09glitch in the matrix. It's not a
5:11business, okay? You're not creating a
5:13product and then selling it to a market
5:14for a profit. But for the purpose of
5:16this video, for those who seriously want
5:18to learn the skill of trading and become
5:20able with a couple of smart decisions
5:22every day, repeated for a long time to
5:24potentially make fuckloads of money, we
5:26need to properly differentiate retail
5:28trading from real professional trading.
5:30And for us, a definition of a prot
5:32trader is a person who is capable of
5:34consistently milking money out of
5:36financial markets by executing a
5:38predetermined riskadjusted strategy that
5:41has a statistically valid edge. So when
5:44you want to become a trader, you're not
5:45just starting a new business. You're
5:47embarking on a journey to become a more
5:50disciplined person, a more stoic person,
5:52a patient person who doesn't let
5:54emotions influence his decisional
5:57process. And you have to become capable
5:58of consistently milking money out of
6:01financial markets. Not once, not a
6:03one-time flip. You have to build a skill
6:05and keep that skill sharp, refining it
6:07and sharpen it like a sword, just like a
6:10chess grandmaster or a professional
6:12fighter or a higher ranked sniper who
6:14worked both hard and smart for years
6:16building and refining a skill. Second
6:18point, you don't make money, you milk
6:20it. And I specifically chose this word
6:22because in trading again, you're not
6:24creating something and making money out
6:25of it. You're just taking money from
6:27someone else. Thanks to a smart decision
6:29to click a button on a screen, which is
6:31something that just doesn't happen in
6:33any other business model. And only in
6:34the trading model can you scale to six,
6:37seven, nine figures without a team,
6:40without building websites or brands,
6:42without having to know about marketing,
6:43about fulfillment, customer care, sales,
6:46managing team, dealing with angry
6:47customers, or with any people at all for
6:49that matter. None of this. You literally
6:51just need to push a button, right? buy
6:53at the right moment, sell at the right
6:54moment, any financial asset, and get
6:56paid. But you don't just guess the right
6:58moment. By finding the right moment, I
7:00actually mean executing a predetermined
7:02risk adjusted strategy that has a
7:04statistically valid edge. Again, yes,
7:06you're pushing a button, but you're not
7:07just improvising like just pushing a
7:09button in slot machine and seeing what
7:11happens. Your job is to apply a strategy
7:13and find a strategy that has an edge
7:16before you even think of entering a
7:18trade. Your edge is your boat in this
7:20crazy ocean. So what exactly is an edge
7:22in trading? You have an edge when you
7:24have a profitable expectancy. So when
7:27you can confidently expect your trades
7:29to be profitable on a large enough
7:31sample and there's three main element of
7:33your trading edge. The first one is the
7:34win rate. And the win rate is on 100
7:37trades how many of them are wins versus
7:39how many of them are losses. So you have
7:40a win rate and a loss rate. If you win
7:4260 trades out of 100, you have a 60% win
7:44rate. The second element is the
7:46riskto-reward ratio, also known as RR.
7:48And this means if I risk $1 per trade,
7:51how many dollars do I earn? For example,
7:53if I risk $10 to make $20, that is a 1:2
7:57risk-to-reward ratio. So, if I'm trading
7:59a stock and I buy it, I can place a
8:01stop-loss $10 below the current price
8:03where I choose to accept a loss and put
8:05a takerit 20 points above where I expect
8:07to take a profit. And these are two
8:08levels that I'm willing to sell at close
8:10my trade. But the relationship between
8:12your stop-loss and your take-profit is
8:14your risk-to-reward ratio. So, for
8:15example, if you have a 50% win rate with
8:17a 1:2 risk-to-reward ratio, and you're
8:20able to achieve this consistently as a
8:22result of your trading, you will be
8:23profitable. You will have a positive
8:25expectancy. So, you would expect to be
8:27profitable and have a profit factor
8:29higher than one. A profit factor is the
8:31total sum of your wins divided by the
8:33total sum of your losses. A profitable
8:35edge is anything above [music] one. So
8:36in zero sum games in the field of
8:39stochastic events there is a
8:40relationship between win rate and
8:42risk-to-reward rate which is win rate
8:44equals 1 over 1 + reward or your reward
8:47to risk ratio. So technically if you
8:49were to trade completely randomly
8:51flipping a coin with a fixed 1:1
8:53risk-to-reward ratio on a large enough
8:55data sample you'll tend to have a 50%
8:58win rate. If you were to trade
8:59completely randomly with a 1:2 fixed
9:01risk-to-reward ratio you will tend to
9:03have a 33.3% win rate. And if you plot
9:06on a chart all possible combination,
9:08this line forms, which is the break even
9:10line. This is where random trading
9:13technically brings you. But since for
9:15trading you need to pay commissions and
9:17likely a spread, if you trade randomly,
9:18you will technically lose money. But
9:20let's say below this line there is the
9:22losing area and above this line there's
9:24the profitability area. An area with a
9:26profit factor below one and an area with
9:28a profit factor above one. Your goal is
9:30that your strategy is an anomaly that
9:33manages to beat randomness and have a
9:35combination between win rate and
9:37risk-to-reward ratio above the break
9:38even line. That makes it profitable. And
9:41if you're just going to gamble, you're
9:42going to non-randomly be in the losing
9:44area, which is statistically speaking as
9:46impressive as being in the profitability
9:47area because you are achieving
9:49non-random results. So you could just
9:51take a bunch of unprofitable trader,
9:53copy their trade the opposite way, and
9:55be profitable. That's what brokers do,
9:57specifically CFD Forex brokers. But if
9:59you're here, you want to become
10:00profitable. And there's three ways to
10:03find an edge that gives you a positive
10:05expectancy. The first one is algorithmic
10:08trading or systematic trading. This is a
10:10path that you can choose to follow,
10:11which is not going to be covered in this
10:13video, but it's probably one of the most
10:15common ways to trade in the professional
10:16space. So, you learn how to code with
10:18Python, MLQ, Easy Language. You build an
10:21automated trading algorithm with a
10:23series of if then functions where if all
10:26conditions are met, opens trades on your
10:28behalf. And this is very powerful
10:30because you don't have to worry about
10:31opening the trades yourself and you're
10:33way less likely to incur in
10:35psychological mistake, cognitive biases,
10:37and emotional trading. But that comes
10:39with a very unique set of problems
10:41because someone else out there will tell
10:43you that systematic trading or
10:44algorithmic trading is the best way of
10:46trading that you can simply let money
10:48work for you. But that's absolute
10:50[ __ ] I've met with a lot of
10:51algorithmic traders. I run some
10:53algorithms myself. and algorithms since
10:55they have a fixed set of rules that are
10:58being applied to a very dynamic entity
11:00such as financial markets. [music] These
11:02bots just at some point will break
11:04because markets tend to be efficient
11:07when someone else finds the same edge.
11:09And since the strategy is very
11:11mechanical and very rule-based, very
11:13likely that someone else will find the
11:14same edge or that the counterpart that
11:17made that edge profitable will
11:18disappear. And because of this efficient
11:20nature in markets, algorithmic
11:22systematic edges at some point stop
11:24working. So as an algorithmic trader,
11:26you constantly have to look for new
11:28edges and maybe run a portfolio of 20,
11:3130, 50, 100 different trading systems
11:34and monitor their performance. Maybe
11:36choose to switch some systems off,
11:37switch some systems on based on how
11:39markets condition vary. And how you find
11:41an edge here is you build a hypothesis,
11:44you test it in the past, also known as
11:46back beck testing [music] or insample
11:48data collection and you see if that idea
11:51works in the past. Then there is usually
11:52a fitting phase where you try to
11:54optimize the performance of the bot by
11:56tweaking the entry rules here and there.
11:58And the main risk here is overfitting.
12:01So to perfectly calibrate the strategy
12:03on past data and make it so good on past
12:06data that it will not work with new data
12:08in the present in the future. Plus if
12:10HID has worked in the past there's no
12:12guarantee that it will work in the
12:14future. So it takes a lot of research
12:16and a lot of constant monitoring and if
12:18you want to achieve outstanding
12:20performance with algorithms it is a
12:21full-time job. So it has pros and cons
12:24and in my experience what I've seen is
12:26that most trading system will tend to be
12:28[music] slightly above the break even
12:30line and achieve a profit factor of 1.2
12:331.3 1.5 [music]
12:341.6 maybe two in the best cases. So
12:36that's the first way to find an edge.
12:38The second way to find an edge is with
12:40manual trading or discretionary trading
12:42which is completely different and
12:44requires you to build a different set of
12:46skills because with discretionary
12:48trading you trade manually and you trade
12:50based on your personal view of the
12:53markets on the way you analyze markets
12:56the way you analyze price the way you
12:57analyze volume the way you analyze
12:59macroeconomic [music] trend and market
13:01sentiment. So in discretionary trading,
13:04you are the edge. That's why no one can
13:06copy you. And that's why you are not a
13:08victim of edge decay or alpha decay in
13:12the same way that algorithmic traders
13:13do. You don't have to learn how to code,
13:15but you have to learn how to analyze
13:16markets [music] and how to get in tune
13:18with the markets and being able to
13:20analyze the sentiment of the markets.
13:22And [music] by training your pattern
13:24recognition abilities to recognize
13:27patterns through hours and hours of
13:29sitting in front of the charts and
13:31patiently executing your smartest trade
13:32ideas, which is still a logicbased and
13:35rational way of doing trading, but it's
13:37not based on statistical mathematical
13:40probability. It's based on subjective
13:42probability. Something similar to baian
13:44probability where as new factors come
13:47into place the probability of an event
13:49happening vary through time. As you
13:51gather new information and discretionary
13:53trading is a skill that takes time to
13:56master. But it's like training your own
13:58neural network. And not a lot of people
14:00are able to have the mental resilience
14:03to be able to become successful [music]
14:05discretionary traders. But those few who
14:08manage to become one, their edge is way
14:11above the profitability area. The most
14:12successful traders I've met, even though
14:14they do run also algorithms, they've all
14:16built a skill of discretionary trading.
14:19They know how to read orderflow. They
14:20know how to understand macroeconomics.
14:22They've seen the reactions of [music]
14:24market during specific times of day or
14:26when some news come out and they're able
14:28to join the trend with an extremely high
14:31accuracy and potentially with both a
14:33high win rate and a high risk-to-reward.
14:35and that brings them deep in the
14:37profitability area. But a lot of people
14:39since discretionary trading becomes a
14:41mental game where you have to trust your
14:43cognitive abilities and learn how to
14:46refine them as you build them, the
14:48majority of aspiring discretionary
14:49traders fail because they don't have the
14:52necessary mental resilience and
14:54discipline. And that's why there is a
14:55third way which I define as hybrid
14:57trading which is a trading that is
14:59manual but is more mechanical. So it's a
15:02set of rules where on top of which you
15:05add your own interpretation of price
15:07action and orderflow dynamics and your
15:09own interpretation of market behavior to
15:12base your edge on something rational and
15:14solid and rule-based and then gradually
15:16build your skill on top which is I think
15:18the best way for beginners [music] to
15:20start building the right habits.
15:24So we have understood what an edge
15:25technically is and the three different
15:27ways to find an edge. But before you
15:29choose how to trade, there's also other
15:30things you need to consider. And a very
15:32crucial choice you have to take is which
15:34style of trading you're going to
15:36implement because you could either be a
15:38scalper or a day trader. So trading
15:41inside of a single day and take short
15:44price movements and maybe your trades
15:46will last some minutes or some hours.
15:48And that is a trading style that
15:49requires time in front of the charts for
15:52at least 4 to 5 hours every day, which
15:54is likely compatible if you have another
15:56stream of income as a freelancer, for
15:58example. Because in the early stages,
16:00you're not going to make a lot of money
16:01and you still need some stream of cash
16:03flow to survive. And becoming profitable
16:06as a day trader in under one year is
16:08very irrealistic. Or if you work a 9
16:10to-5 for example, you can start with
16:12swing trading which is a much better
16:14option if you don't have a lot of time
16:15to trade because swing trading requires
16:17way less active management of positions
16:20and you open a trade to stay inside for
16:22days, weeks or even months if you are a
16:25position trader. So with swing trading,
16:27you try and join longerterm price
16:30swings. So based on how much time you
16:32can invest in trading, you should choose
16:34a trading style that fits your schedule.
16:36And again, in this video, we're going to
16:37take a look at day trading strategies
16:39and swing trading strategies. Or if you
16:41think that you don't feel ready to
16:43embark on the trading journey that is
16:44not worth for you, you should consider
16:46investing instead and gradually build
16:48your capital through time with other
16:50sources of cash flow and simply use
16:53financial markets as a place to park
16:55your capital. not have any active
16:58management of your capital at all and
17:00have a business, a job or a freelance
17:02profession as your source of cash flow
17:04to pour money into your investment
17:06account to avoid your capital being
17:09eroded by inflation, which is something
17:11I strongly advise everyone to do. And
17:13maybe we'll do a video about investing
17:15in the future here, but now we're
17:16talking about trading. So, in this next
17:18chapter, we'll start understanding
17:19financial markets, how they really work,
17:22and understanding the money flow of the
17:24markets.
17:29So in order to analyze the market and
17:31analyze it in the most logical possible
17:34way, let's ask ourself these five
17:36crucial questions. What is moving? Who
17:38is moving it? When, where, and how,
17:40where, what moves of course are prices
17:43of financial asset. And the analysis of
17:46prices is also known as price action
17:49analysis. traders. We earn from price
17:52swinging up, swinging down, buying at a
17:54low price, selling at a higher price, or
17:56sell short a high price hoping to buy
17:58back at a good price. The second
18:00question we need to ask is who's moving
18:01price? And these are market
18:03participants. And there's a lot of
18:06different types of market participants
18:08which operate inside of the market for
18:10different reasons. And I personally like
18:12to divide them into three main
18:13categories or actually four main
18:15categories. These categories being big
18:18money, smart money, market makers, and
18:21retail traders. And let's define big
18:23money as big market participants such as
18:26central banks, commercial banks, pension
18:28funds, sovereign funds, university
18:31endowments, investment funds, and big
18:33companies. Smart money instead are hedge
18:36funds, investment banks, HFT firms. And
18:39they are still big money as in they are
18:42a big portion of the who the who moves
18:45the market. And we need to make a
18:46distinction between smart money and big
18:48money. Because what we mean by smart
18:51money is not just a big money market
18:53participants such as central banks,
18:55commercial banks, pension funds,
18:56sovereign funds, blah blah blah that are
18:58inside of the market to stay for a long
19:00time and they just pour money into the
19:02markets without caring too much about
19:04filling of their orders. Smart money
19:06participants instead engage and invest a
19:09lot of money in something called
19:10execution alpha which is basically a
19:13field of trading that refers to how do
19:16we optimize the execution of such big
19:19orders in such a way that we pour this
19:22big money without impacting price and
19:24basically not caring about where you get
19:26filled. these market participants are
19:28putting a little more effort into having
19:30an alpha also in how they place these
19:32big trades in the market and they will
19:34engage in smart order routings maybe
19:37some orderflow manipulation tactics and
19:39this is for example a screenshot that
19:40I've taken from a company that as a main
19:42job and business model is providing best
19:45execution algorithms and they have
19:47several way that they implement this
19:49execution alpha such as volumedriven
19:51algorithm and price driven algorithms
19:53where big orders are poured inside of
19:56the market based on volume weighted
19:58average price. So from the open close of
20:00the session as price moves and volumes
20:02moves throughout the day. So the market
20:04volume goes up and the volume that
20:05they're feeling is kind of following how
20:08volume averages throughout the session.
20:10So they don't get slipped too much or
20:12they have time weighted average price.
20:13So they just place the orders gradually
20:15throughout a single session but at the
20:17same level of volume. There's
20:19participation target close only at the
20:21closing of the session or priced driven
20:23algorithms so at steps or momentum
20:26value. These are all techniques that are
20:28widely known and used in the
20:30institutional space to improve the
20:32firm's alpha also in the execution
20:34stage. Then you have the small money
20:36that encompasses not only retails, small
20:39prop firms, maybe some CPOS, some
20:41commodity pool operators, some commodity
20:43trading advisors. And by prop trading
20:45firms, I mean small firms managing maybe
20:48a couple million dollars, 10, 20, 50,
20:50100 up to a hundred million. Let's say
20:52it's considerable a small proprietary
20:54trading firm. So people trading with
20:55their own money or CPOS, CTAs, AMC's
20:58which are a form of let's call it a
20:59small fund and retail traders which can
21:02vary drastically. You have retail
21:04traders that are managing tens of
21:06millions of dollars or retail traders
21:08that are managing a couple hundred
21:09thousand dollars or people just trading
21:11with the $100 in their account. Let's
21:13consider all of this to be small money.
21:15And then you have market makers. And
21:17market firms could be considered smart
21:20money, but their business model is so
21:22unique because market makers are not
21:24trying to speculate. They're not trying
21:26to hedge with long positions. The only
21:28thing that market makers are doing is
21:30providing liquidity. So, as we will see
21:32later, every market has a bid price and
21:35an ask price. The bid is the closest buy
21:37offer. The ask is the closest sell
21:40offer. And the distance between these
21:42two is called spread cuz maybe if you
21:44want to sell, you can sell to a buyer at
21:4799. And if you want to buy, you're going
21:49to buy maybe at 100. And so this $1
21:52spread is what people pay to be able to
21:56trade market. And market makers are the
21:59one selling here and buying here. And so
22:01they and so the spread that is paid by
22:03trader that's what they earn. So they
22:05quote both the ask and the bid and earn
22:07a spread every time price goes up and
22:09down up and down up down a single tick.
22:11That's where and how they earn money in
22:13all sorts of market. And this will later
22:16be important to understand and we will
22:18explain it thoroughly because a lot of
22:20for example the volume that is moved in
22:22stock market indices futures contracts
22:25comes from options market makers for
22:27example but again we'll talk about this
22:28later but it's important to identify
22:30them as a specific market entity. And so
22:33all of these are market participants
22:35that engage in trading and investing and
22:38in pouring money in opening trades,
22:41buying and selling inside of financial
22:43markets and they have different needs
22:45and they're engaging in the market for
22:47different reasons. And this makes us
22:49move on to the next question which is
22:50why do market participants interact in
22:53the market? Well, it could be for
22:55speculative purposes, for capital
22:57preservation and growth or for hedging
22:59purposes. So these are the main reasons
23:02people trade in the market and the
23:04reason why they take such a decision and
23:06the analysis of why traders and
23:08investors might act in a certain way in
23:11the future is called fundamental
23:13analysis. And fundamental analysis is
23:16the analysis of the fundamentals. The
23:18analysis of the nature of markets. So
23:21for example, if someone is trading a
23:23stock, the reason why he's buying that
23:25stock, regardless of the purpose, for
23:27example, why a rich person might want to
23:30buy gold instead of keeping cash, for
23:32example, for capital preservation and
23:34growth purposes, might be for
23:36macroeconomic reasons. So macroeconomics
23:38is one type of fundamental analysis that
23:41leads market participants to take
23:43decisions. If the expectations on
23:45inflation are going to be very high, you
23:47would expect a lot of money flow from
23:49all sorts of market participants inside
23:51of gold because gold is the ultimate
23:53inflation hedge asset or fundamental
23:56analysis declines also in the real
23:58fundamentals of each asset class. So
24:01each market has different fundamentals,
24:03different drivers that drive the flow of
24:06money inside of that particular market.
24:09For example, we just said gold as an
24:11inflation hedge. Stocks, for example,
24:14are driven mostly by in risk appetite.
24:17Bonds, for example, are also used as a
24:19hedge for inflation, but more of a way
24:21to park money, but also used if an
24:24investor wants a fixed income. So for
24:27stock, for example, if Apple has a new
24:29amazing CEO or something bad goes wrong
24:32about the company, that will move price
24:34because it will move the fundamentals of
24:37the market. So the analysis of
24:39fundamentals of each markets answers the
24:41question why the perceived value of that
24:44specific asset or asset class is
24:47changing through time and is as a
24:49consequence going to affect prices. So
24:52as you become a trader it's going to be
24:54very important for you to be
24:55consistently finding reasonable answers
24:58to why the perceived value of an asset
25:01which is its fundamentals will drive
25:04what ultimately moves which is price.
25:07because prices will always be a
25:08reflection of the market participants
25:11opinion of what the fundamentals of that
25:14assets are. So we understand that
25:16financial market prices are moved by
25:18market participants that act based on
25:20what type of market participants they
25:22are for different purposes and basing
25:24their decision on fundamentals. So
25:26fundamentals answer to the reason why
25:28something might happen especially in
25:30long-term price movements. For shorttime
25:33price movement, it might be because of
25:35execution alpha or market makers hedging
25:38activity or some retail traders doing
25:40some crazy stuff like what happened for
25:42example in GameStop. Now how does all of
25:45this happen? So let's answer the
25:47question how how do market participants
25:50move money for fundamental reasons that
25:52move prices. But how does that happen?
25:55This happens with a constant flow of buy
25:58and sell order also known as supply and
26:01demand that enters the market in the
26:06form of order flow or volume. And supply
26:09demands is a very easy concept to
26:11understand. If there's more supply, if
26:12there's a lot of a certain asset, the
26:15prices are going to be low. For example,
26:16water's price is decently low for most
26:19people, but gold as it's rare and
26:21there's less of it has a higher price,
26:23right? Supply and demand. So in
26:24financial markets, supply and demand
26:27exists in the form of buy and sell
26:29orders. And there's a constant flow of
26:31buy and sell orders, which we call order
26:34flow that we measure through something
26:37called volume. So one stock traded from
26:40a buyer and a seller that trade with
26:43each other equals one volume. So volume
26:47is literally the amount of transactions
26:49that happen in the marketplace. And we
26:51can analyze this flow of orders and we
26:55can access through a data feed this
26:57constant flow of buy and sell orders to
27:00understand if there's any sort of
27:02imbalance in the auction of these orders
27:05because these are traded in a so-called
27:08double auction which we'll get deep into
27:11later. And the analysis of the market
27:13based on this double auction mechanic is
27:15also called the liquidity auction theory
27:18or auction market theory which is what
27:21we will use to analyze both volume and
27:24price. And then we have the last two
27:26questions which are relatively less
27:28important but still important which is
27:31where so in which markets is the money
27:35flowing. The answer to this comes from
27:37something called intermarket analysis.
27:39So analyzing how certain markets perform
27:42compared to others and understanding if
27:44money is flowing out of a certain market
27:46in which other market is it being poured
27:48in and when can be anything related to
27:51analyzing market cycles such as seasonal
27:54analysis or intraday seasonals also
27:56known as situational analysis. So by
27:59answering the basic question of logical
28:01analysis, we can understand what we need
28:04to consider, what we need to study and
28:06the main things we will need to study is
28:08what moves, how it moves and why it
28:11moves and then we can add where exactly
28:14in which markets through intermarket
28:15analysis and market cycles and when as
28:18in cycles. One more thing that I forgot
28:21to add is sentiment analysis which is
28:24also something very crucial that is
28:26multifaced. It's diverse and there's
28:29many ways to do sentiment analysis but
28:32it's basically the analysis of
28:34participation. So seeing what's the
28:37overall sentiment, see how different
28:39market participants are participating in
28:41different asset classes and you can you
28:43know use instruments like retail
28:45sentiment there's a lot of retail
28:47sentiment tools that tells you what
28:48retails are technically doing or
28:50institutional sentiment with something
28:52called a coot report etc etc or a lot of
28:54people for example using Twitter analyze
28:56the overall sentiment of traders around
28:59the world. So to recap, what moves
29:02prices? Price action. Who moves the
29:04market? Market participants. Why do they
29:07move the market? Because of decisions
29:09they take based on speculative purposes,
29:11hedging purposes, or capital
29:13preservation for fundamental reasons
29:16that can be due to macroeconomics,
29:18single asset fundamentals, or market
29:20sentiment. How do they move prices?
29:22Through a constant flow of buy and sell
29:25order, also known as order flow. So
29:28through volume where do they do it in
29:30different types of markets for different
29:32reasons when do they do it in different
29:34times of the year in different times of
29:36the day in different times of the
29:38macroeconomic cycle and in different
29:40time of each day. So this is the
29:41clearest framework you can have to
29:43understand the basics of the markets.
29:45Now before getting deep into the why
29:48let's first understand the how. So
29:50understanding how prices move through
29:53what we call the liquidity auction
29:55theory. And do you know what? I think
29:57I'll just give you the whole map so you
29:59can review it if you want. I'll send it
30:00on my Telegram chat that you can find in
30:02the description below. So the first step
30:04of the liquidity auction theory is
30:06market mechanics. So let's understand
30:08that one first. This is the price
30:10ladder. So lower prices, higher prices.
30:12And let's say the current price is 100.
30:15So as we know what drives price up and
30:18down is supply and demand. So a constant
30:20flow of buy and sell orders. And let's
30:21see how these orders actually interact
30:23because there's two types of orders. You
30:25got sell orders at higher prices and you
30:27got buy orders at lower prices. These
30:30are called the bids. These are called
30:31the offers. And let's imagine this is
30:33the price for example of Bitcoin. Just
30:35to make a relatable example for you
30:37gamblers. And let's understand it at a
30:39glance with this animated video. So
30:41these are people offering to sell
30:43Bitcoin. These are people offering to
30:44buy Bitcoin at different prices. So
30:47these are buy and sell offers. And this
30:50is the first side of the liquidity.
30:52These are also called the market makers.
30:54This is passive liquidity. Imagine it
30:57just like in an auction where they sell
30:59paintings of a famous artist. Imagine
31:02all of these being the goods being sold
31:04at the auction being offered at the
31:05auction. Price will not move if someone
31:08in the audience will raise their hands
31:10and says, "Hey, I am willing to pay a
31:11higher price." But the difference is
31:13there's not just things being sold.
31:15There are also things being bought.
31:17That's why we call the auction of of
31:20financial markets a double auction
31:22because it works both for price going up
31:24and also for price going down. Now let's
31:26put them all on this side. So if this is
31:29the paintings of Dainci sold at the
31:31auction then you have the auctioneer or
31:33the middleman the guy with the little
31:35hammer which is the matching algorithm
31:37of for example the exchange in can be it
31:40can be Coinbase or Binance or whatever.
31:43For futures, it can be the CME. For
31:45stocks, it can be the New York Stock
31:46Exchange or whatever stock exchange.
31:48Doesn't change. It works exactly the
31:49same for all financial markets. So maybe
31:51a guy named Fabio decides he wants to
31:55buy market one of the one of these one
31:57of these bitcoins that are being sold.
31:59Which one will he take? Of course, the
32:01one at the lowest price, which is 101.
32:03Let's say he wants to buy three
32:05bitcoins, right? So his order will be
32:07sent from the broker to the matching
32:09algorithms that through a first in first
32:11out system will match it with this order
32:13and this order because he needs to buy
32:15three of them, right? So he will be able
32:18to buy one here and two here. So these
32:20will go here into the matching algorithm
32:23and Fabio will be long three bitcoin,
32:26one bitcoin from here and two bitcoin
32:28from here. And so the current price from
32:30here will move first here and then here.
32:33So if there was a candle that opened
32:35here, the the the candle will go first
32:38here and then here. Now let's say now
32:41let's say another person comes named
32:43Patrick and he decides that they want to
32:45sell Bitcoin. So he wants to sell for
32:48Bitcoin. His order will be sent as well
32:49to the matching algorithm that will try
32:51to match that sell request with the best
32:54possible price where there's at least a
32:56buy offer. So he will buy three from
32:58here out of those four and one will be
33:01filled here. B. So all of these orders
33:04that were filled are not going to be in
33:06the order book anymore. Here we'll only
33:08have three. One of them got here. And
33:11now the price would have gone here and
33:14then here cuz that's where the last
33:16order got filled. The last match was
33:18made. And so the candle will tick below,
33:22turn red, and live a wick above where it
33:24used to be because this is now the new
33:26price. So as you see price is always
33:29determined when an aggressive buyer or
33:32an aggressive sellers choose to accept
33:35any of the offers made in the order
33:38book. So aggressors are the price mover
33:42because because remember Fabio could
33:44have just you know placed a buy offer
33:46there without having to accept a
33:49slightly a slightly worse price and he
33:51could have just placed a buy limit at 98
33:53instead of having to buy at worse
33:55prices. He could have paid 98 for the
33:58same thing he paid 102 for, but there
34:00was no guarantee that a seller would
34:02have accepted that offer, right? So, the
34:05fact that he was not willing to wait,
34:07but he was okay to pay a higher price, a
34:11slightly higher price, that's why we
34:12call it aggressive orders because
34:14they're not waiting. They're kind of in
34:16a FOMO. I I need to buy now and I am
34:18willing to pay a slightly higher price.
34:20I'm willing to pay something called the
34:23spread, which is the difference between
34:25the best ask and the best bid. This is
34:27called spread. So whenever there's a low
34:29level of liquidity between the bid and
34:31ask, we say the book is thin because
34:34there's not a lot of liquidity. And by
34:35liquidity, we mean this. We mean orders
34:38in the book. Someone who can be our
34:40counterpart and makes it easy for us to
34:43trade. So the second step of
34:46understanding the liquidity auction
34:47theory is the auction market theory and
34:52as we have understood price are mostly
34:54driven by big operators whales big banks
34:57hedge funds institutions people with big
35:00amounts of money and you understand that
35:02for example if Fabio were to buy in the
35:05previous example a thousand bitcoins he
35:08would have to buy four here eight here
35:1010 here and so on and so forth and
35:13gradually ally accept really really
35:15worse prices in order to to get that
35:18thousand contracts thousand bitcoin
35:21fail. So the first pillar of auction
35:23market theory is that smart money
35:24prefers slow and liquid markets because
35:30they have such big orders they will
35:32fraction them and instead of buying a
35:35thousand contracts right away they will
35:37buy them bit by bit. So you will see
35:40often price stay in a situation of we
35:44say consolidation where there's no a
35:46clear direction of price and sometimes
35:50yes price do but smart money prefers
35:52slow and liquid markets. So they will
35:54split their orders and slowly put them
35:57into the market rather than having to
35:59put them in the market all at once and
36:01move price super super fast with one big
36:03order. And this happens when the market
36:06is agreeing on a price because the
36:09aggressive selling pressure and the
36:11aggressive buying pressure is pretty
36:13even on both sides and it's creating a
36:15situation of balance. So whenever price
36:18is like this, we call this a balanced
36:20market or a situation of fair value. And
36:24what fair value means is is that since
36:28the market is influencing prices through
36:31buying and selling aggressive pressure
36:34that's based on the value of the
36:36underlying asset or the perceived future
36:39value of the underlying asset. Both
36:41aggressive sellers and aggressive buyers
36:43are agreeing that this is a fair price
36:46both to trade sell aggressively or buy
36:49aggressively. Then this is where a smart
36:50money prefers to trade. But then
36:52something might change in the perception
36:54of the future value of the underlying
36:57asset that will drive aggressive buyers
37:01to be ready to accept higher and higher
37:03and higher prices as we saw in the book
37:06because if this is the current price as
37:08we said we have sell liquidity, sell
37:10liquidity, sell liquidity, sell
37:12liquidity, sell liquidity, right? So if
37:14the value of the underlying asset is
37:16such that aggressive buyers are ready to
37:18pay a slightly higher price just to get
37:21filled just to get their hands on that
37:22asset that's what drives price up. So
37:25for example the market will buy some
37:26here some here some here some here some
37:28here some here some here and every time
37:30they buy some tuck tuck tuck tuck tuck
37:32price goes up up up up but it's not like
37:34buyer necessarily like this ideally they
37:36would like to buy just here or even
37:39lower without having to pay a higher
37:41price every single time. So price going
37:43up and accepting all of these sell after
37:45is showing us is a search for liquidity.
37:49They would hope that there's a huge
37:51liquidity over here already ready to
37:53sell back to all of this aggressive
37:54buyers but there's none. There's not
37:56enough liquidity. And so what's
37:58basically happening is there is this
38:00phase of imbalance or price discovery
38:04which some people like to call fair
38:06value gap which is a name that overall
38:08I'd say makes sense because it's a not
38:10exactly a gap but it's absence of fair
38:13value because the market is not agreeing
38:15that that's the fair valuation for that
38:17asset and so this phase where aggressive
38:20buyers are willing to accept higher
38:22prices will eventually stop or at some
38:26point they will find more resistance
38:28from passive sellers and aggressive
38:33sellers will start to consider these
38:34prices fair as well because if they were
38:37willing to sell here, they're probably
38:39still willing to sell here even though
38:40at not at the same rhythm as buyers. But
38:43hey, if I were selling Bitcoin at 100, I
38:46might also sell it at 110. And so as the
38:49market starts considering these prices
38:51to be fair again we will have another
38:53situation of balance another situation
38:56where both aggressive sellers and
38:58aggressive buyers are agreeing that this
39:00is a fair price to trade and sometimes
39:03price will try and exit from fair value
39:06and it will happen at times that price
39:10breaks this situation of consolidation
39:12and buyers start accepting higher prices
39:16but sellers will still consider these to
39:18these super premium prices to sell at.
39:20So they will push the market back in a
39:23situation of balance. Or it could happen
39:25that sellers are considering these
39:28prices to be very premium, very
39:31convenient to sell at and they will
39:34start pushing price down again out of
39:37the balance. But the same buyers that
39:39consider these prices to be fair are
39:41likely to consider it again. So there's
39:43a good chance they will push price back
39:46into a situation of balance. And we call
39:49these phases failed auctions. And this
39:52is the most accurate model to analyze
39:55market structure and price action
39:57dynamics as well. Okay, let's say price
40:00does this. What Charles Dao did in the
40:0318th century was trying to analyze price
40:07swings. So you would basically mark the
40:11highs, the lows, the highs, the lows,
40:13the highs, the lows of price. And in
40:16order to assess a trend, you would see
40:18where the highs and where the lows are
40:21going. And if there's a higher high and
40:25a higher low, we're clearly in an
40:27uptrend. As soon as a higher low for
40:30example gets broken then we understand
40:33we are in a situation of a bearish
40:35market environment [snorts] or bearish
40:37market structure. Then since this model
40:40presents itself a lot in the market,
40:43people have tried to find again more
40:46visual patterns just like the concepts
40:48of highs and lows and highs and lows
40:50because this is just visual references,
40:52right? And because they repeat so often,
40:54they've tried to predict it by finding
40:56some shapes that are visually easy to
40:59identify. For example, technical
41:01analysts might define this as a pennant
41:05or as a wedge or as a head and shoulder
41:09pattern or a triangle pattern to find
41:13again a type of wedge pattern. And so
41:15people have tried for decades, for years
41:17to find patterns in price and try and
41:20see if they have any statistical
41:22validity whatsoever, but they never
41:24really proved it. But what all of these
41:26are are failed attempts to rationalize
41:30market structure just through price
41:32without understanding the mechanics
41:34behind it. But if we simply think of the
41:36auction market theory model, we would
41:38just define this as an area of fair
41:41value followed by a phase of imbalance
41:44and then another situation of fair value
41:46where yes, at some point there have been
41:49some failed attempts by sellers to
41:51consider these fair prices and push
41:54price lower followed by phases where
41:56buyers are still considering these
41:58prices to be cheap. So they push the
42:00auction up until they eventually stop
42:02and aggressive sellers take control of
42:04the auction. But then again, you would
42:06see a situation of fair value followed
42:08by another situation of balance where
42:10yes, you had some failed auctions
42:12followed by a situation of imbalance
42:14followed by a situation of balance and
42:17so on and so forth. And by rationalizing
42:19market mechanics in a way that we just
42:22look at where money is agreeing and
42:24where money is not agreeing, we can
42:26easily follow where the money is flowing
42:27in the market because ranges is where
42:29the most money was traded. And so if
42:32most of the money was traded here and
42:34now most of the money was traded here,
42:36we have an absolute objective indication
42:39of where the money is going because as
42:41we discussed in our model here is where
42:44most of the time is spent. Most of the
42:47big orders are slowly slowly put in the
42:49market in fraction. We don't spend a lot
42:51of time in these prices. But again we
42:53spend a lot of time here. And here we
42:56move on to the next step which is
42:58volume. If we go back here and we
43:00remember that three orders were matched
43:02around here, four orders were matched
43:04around there. So we had two contracts
43:07traded here, one contract traded here
43:10and then we had three contracts traded
43:12here and then one contract traded here
43:15again. The total amount of contracts, of
43:17bitcoins, of stocks, of gold ounces,
43:21whatever that is traded as every single
43:23level of price is what we call the
43:27volume profile. And the volume profile
43:29shows you basically how much money was
43:31traded at each level of price. And it
43:34may sound obvious but if we had to draw
43:37a volume profile of this whole price
43:39action we would see that where there was
43:43a lot of time spent that's the areas
43:46where volume is really really high.
43:49These areas are the areas where volume
43:51was really really low. There was not a
43:53lot of trading going on but then we
43:55started spending a lot of time here and
43:58this is where a lot of volume of trading
44:01happened and maybe a little bit also
44:04here and this makes you understand that
44:06where the ranges are that's where the
44:09money is and that when price ranges move
44:12upwards that's where the money is
44:15flowing. Now let's take a software like
44:16deep charts and open a new book advanced
44:20depth of market which basically means
44:22exactly what we saw here the market
44:24micro mechanics and for this example
44:26we've selected the e- mini S&P 500 let's
44:30zoom in and we can see exactly what we
44:33just talked about and we can clearly see
44:35in this column all the passive sell
44:37orders in the ask column and here all
44:40the buy limit orders or passive buy
44:43orders in the bid and we can see how
44:45They vary through time. So, for example,
44:47we can activate the volume profile and
44:50we're going to reset it to start from
44:52scratch. And we can see that now the
44:54price is here. That's where the volume
44:57is being traded. For example, 15
44:59contracts have been traded here. Now,
45:01we're trading back here. And now we're
45:03trading downwards. And you see how price
45:05moves up and down depending on where the
45:08volume is traded. Exactly as we said. So
45:11for example, if I were a bank and I need
45:13to buy a,000 contracts, I will have to
45:16accept all of these offers and take 88,
45:1996, 206, 83, 120, 90, 9090 and gradually
45:24buy at worse and worse and worse and
45:26worse and worse prices because that's
45:27where sell offers are. Same thing if I
45:30were to sell, there needs to be enough
45:32liquidity. Now, let's just have this on
45:34the side of the screen and have a normal
45:36price chart on this side of the screen.
45:38And if you look closely, you can
45:40literally see how price moves ups and
45:42down, up and down. And how candles are
45:45created by volume being traded on the
45:49ask or traded on the bid because of
45:51aggressive buyers accepting sell offers
45:54or aggressive sellers accepting these
45:57buy offers. And you can see how now
45:59someone accepted to purchase one of
46:01these 65 64 and they've bought more all
46:05up through here and a spike formed,
46:07right? Because the candle has gone up
46:09here and then down again. And these
46:10movements happen because contracts are
46:12traded upwards or downwards. This is how
46:15the market works. No big deal, right? So
46:18we understand that these are only
46:19passive orders and there but they can
46:22vary. These numbers change through time
46:24because I could, for example, put a
46:25bunch of buy limits here and then just
46:27cancel them. So these aren't there to
46:30stay necessarily. They just express an
46:32intention, not an actual order getting
46:35filled. This is a very different thing.
46:37The that's why we call these passive
46:39orders. The only orders that are
46:41executed out of all of these are the
46:43ones that you actually see traded in the
46:46volume profile. Or if you go up here in
46:48the indicators and you activate the
46:49orderflow analyzer, you can basically
46:52have a split version of the volume
46:54profile where you see exactly how many
46:56contracts were traded in the ask or in
46:59the bid. So, if it was aggressive buyers
47:02in green or aggressive sellers in
47:04purple, this is called a footprint
47:06chart, a deep candle, call it however
47:08you like it, but it's basically showing
47:10you how the candle was formed and a
47:13summary of all the volume and all the
47:15orders that either hit the bid in purple
47:18or lifted the ask in green. Now, someone
47:20is selling here, so price drops, someone
47:23is buying again here. And all of this
47:25data, by the way, comes from a data
47:26feed. I'm currently using DX feed to
47:29gather all of this very important data.
47:31Now, I'm gonna disconnect it for a
47:33second to show you how there's always
47:35sellers one tick up and buyers one tick
47:38down. Right? That's why if I zoom here
47:41on a candle, you will see nine here, two
47:44here, and then zero zero because these
47:47two contract were bought here and these
47:50nine contract were sold here. So when
47:53you read this type of chart, the
47:55footprint chart, you always read how the
47:57auction unfolds diagonally because it's
48:00always one tick up, one tick down, one
48:02tick up, one tick down. This is what we
48:04call an auction. And when you see also
48:06here, also here you have 17 and two.
48:08This was that auction. This was another
48:10auction. This was another auction. This
48:12was another auction. And you have zero
48:14zero because technically here there
48:17would also be a zero, right? Because
48:19there's always buyers one tick down and
48:21sellers one tick up. And that's how the
48:23auction unfolds. So if I wanted to buy,
48:25for example, I could buy submitted and
48:28place a limit order and be here in the
48:30order book or cancel
48:32>> and simply buy. And if I click the buy
48:35market button, I will get filled here
48:37[snorts]
48:38where there's at least a sell offer. So
48:40if I buy, I click buy now.
48:42>> Order filled.
48:43>> You can clearly see I bought exactly
48:45there. My order got filled here, which
48:47is exactly that side of the auction. So,
48:50I basically accepted a slightly higher
48:54price, a 0.25 points higher price as
48:57long as I could get my hands on a
48:59seller, right? Cuz I didn't want to wait
49:01until someone sold to me. If this
49:04mechanism of the auction is not clear,
49:06please rewatch it a lot of times until
49:08you fully grasped and understood the
49:11concept because we're going to need it
49:12later. Now, let's reconnect to the data
49:13feed.
49:14>> Connected. price eventually went up and
49:16a lot of buyers started stepping in and
49:19I'm currently earning money in this.
49:20This is just a demo account though and
49:22for example I could put my take profit.
49:24I could drag the takerit and as you can
49:26see it's minus one limit
49:29>> order submitted. It's a limit order,
49:32right? Because I'm basically to close my
49:34buy position, I have to sell, right? And
49:36I have to sell at a higher price. So, I
49:38can put a resting order, a sell limit as
49:42my takerit as the area where I'm going
49:44to close the trade. Now, I'm going to
49:46put it here and see if I can order
49:47submitted. Order submitted.
49:48>> Put it slightly lower.
49:49>> Order submitted. Order submitted. Order
49:52submitted.
49:52>> And now I'm out of the position. Let's
49:54buy once more.
49:55>> Order filled
49:56>> and buy from the best ask. I can also
49:59click on SL and what this will do is
50:02placing a sell stop
50:03>> order submitted.
50:05>> So a sell order that will not stay in
50:08the book but will simply get triggered
50:10if price drops and I'm going to lose
50:12basically $150 and cap my loss to a
50:16maximum amount. That's a stop-loss,
50:18right? You're probably familiar with it
50:19already. But this stop order is not in
50:22the book. It's only inside of my
50:24platform and will be executed as a
50:27market order because the only sell
50:30limits that are allowed are above the
50:32price. If I place a stop, it will be
50:33executed like a sell market orders. It's
50:36like I'm basically telling my platform,
50:38hey, when price reaches this 6803.75,
50:42execute my order at the best price, like
50:44you were selling market. And this will
50:46close my trade.
50:47>> Order cancelled.
50:48>> Or to close my position, I could simply
50:49sell market. As you can see, I sold here
50:52in this side of the auction. And with
50:54this, the mechanism of the auction
50:55should be clear for you. And we can move
50:57on to a deeper level. Since markets
50:59often go this way, they often go up one
51:02tick, down one tick, up one tick, down
51:04one tick, up one tick, down one tick,
51:06and so on and so forth. As you see, it's
51:08doing now. Constantly going up, down,
51:10up, down, up, down, up, down. There are
51:12some specialized firms that are called
51:15marketmaking firms. And what they do is
51:18exactly this. They always sell at the
51:20best ask and they always sell the best
51:23bid. So they always quote both the ask
51:27and the bid to earn the uptick down tick
51:30uptick down tick movement and earn the
51:32spreads that traders pay. And this is a
51:35massively profitable business model by
51:36the way. And why this is so important is
51:39because it helps us understand even
51:41deeper the nature of the markets. So the
51:44next thing we need to understand is the
51:47different types of matching algorithms.
51:50Now let's make the exact same example.
51:52We have one contract in the bid and one
51:55contract on the ask. Let's put it this
51:57for the sake of this example. The first
51:58type of matching algorithm is the first
52:01in first out algorithm also known as
52:04FIFO. This is the most common type of
52:07matching algorithm in let's say most
52:09exchanges. And how this works is if I
52:12place a sell limit order here and then
52:14someone else places it after me in a
52:17chronological order even let's say it's
52:19a bigger order then someone else put
52:21another order and someone else puts
52:23another order and the same thing happens
52:25for example in the bid one more order
52:27one more order the first order that was
52:30placed here chronologically speaking so
52:32the earlier you place an order the
52:34earlier you're going to get filled so
52:36even though you ultimately end up seeing
52:38only one number. For example, hey, there
52:40is five orders here. Okay, five
52:43contracts and here you have six
52:44contracts. So, slightly more. In the
52:46normal order book, you will only see
52:48this. But in the back, this could be six
52:52different people that place one single
52:53order. Or in this case, four different
52:55people, one order, one order, two
52:57orders, two orders, right? Or it could
52:59be just one person placing six contracts
53:02all at once. But what happens in the
53:04background really is if a buyer comes
53:06and buys one contract market, the
53:08matching algorithm will match it with
53:11the first sell order that was put in the
53:13queue. So this order as it was the first
53:16one to be placed here will be matched
53:18with this buyer. That's why we call it
53:20first in first out. The first order to
53:22be placed in the queue will be the first
53:24one to be filled. But this is not the
53:26only type of matching algorithm. Another
53:28form of matching algorithm is the FIFO
53:31with LMM that stands for first in first
53:35out with lead market maker and this is
53:38slightly different. So in this model
53:40there is a lead market maker. So a
53:42market making firm that is both quoting
53:46the ask and the bid and there's
53:48basically an agreement between the
53:50exchange let's say it's the CME the
53:52Chicago Merkantile exchange and a
53:55marketmaking firm let's say it's Citadel
53:57Securities one of the biggest market
53:59makers in the world and with this type
54:01of relationship the market maker will
54:03make sure to always provide liquidity to
54:05the CME and the CME is happy because
54:08because people want to trade there
54:09because there's always someone to buy
54:11from and someone to sell to aka the
54:13market maker. This service is also
54:15called liquidity provision. So the
54:17market maker acts as a liquidity
54:20provider for the CME. In exchange for
54:22this service, the CME will grant the
54:25market maker with different formulas,
54:27but for example, let's say 40% of
54:30aggressive volume. This way the market
54:33maker can profitably run his business
54:35and have some guaranteed flow of buyers
54:37and sellers, buyers and sellers and
54:39basically do like we just did and earn
54:41from uptick down tick uptick down tick
54:43uptick down tick and earn a spread. So
54:46the market making business model is to
54:48earn a spread. The business model of the
54:49CME is to facilitate trading and the
54:52goal of traders is to get filled at a
54:54decent price and not pay a huge spread.
54:57So if this matching algorithm is in
54:59place and let's say that 10 buyarket
55:02contracts are entering the market and
55:04let's say this is our market maker
55:06liquidity and let's say we have 10
55:08orders here and some of them are placed
55:11here by the market making firm. Well in
55:13this case out of these 10 four of them
55:16will be saved for the market maker and
55:18six will be granted to the rest of the
55:20market participants that place their
55:22orders here. So in this case, even
55:24though someone might have placed orders
55:26here before market makers did, market
55:28makers will still have a priority up
55:31until 40% of the total aggressive volume
55:34coming in the market. But there's only
55:35one problem here. And let's get back to
55:37our chart. What happens if let's say I
55:40do this, I sell here and buy here,
55:42right? If price happens to go the other
55:45way, I'm losing money, right? Minus 12.
55:47Let's see if price starts going to one
55:50direction, right? Okay, now I just
55:52earned some money. I'm going to sell
55:53back again here. See, now price is
55:55moving lower and I'm losing money as a
55:57market maker and I'm again buying and
56:00selling one tick, buying one tick down,
56:02selling one tick up. If the market take
56:04a clear aggressive direction, I will be
56:06losing more and more money. And for
56:08example, here I will be still buying
56:10here and selling here. Right now, let's
56:12do it again. Let's sell here and buy
56:13here. And what a market making firms
56:15looks like, it's actually like this,
56:17right? This is the book of a market
56:19making firm. It will always sell to the
56:20ask, sell in the ask, buy in the bid and
56:22up and down and up and down. Well, you
56:24clearly understand if price suddenly
56:26takes a very firm and constant
56:29direction, you will sell, sell, sell,
56:31sell, sell and keep losing money
56:33basically. So the risk of a market maker
56:35is that price will start going in one
56:38direction without doing down ticks.
56:40Because if it does this, this this the
56:42market maker business is still
56:43profitable, right? But if price just
56:45goes tick tick up tick up tick up tick
56:47up and without any tick down they don't
56:49get a chance to close profitably their
56:51position as you can see also it's
56:53happening now I'm losing $62 now we got
56:56very lucky because price is just going
56:58up and down so market makers are now
56:59happy whenever there's a flat action
57:01they're happy now we're booking some of
57:03that profit let's see yeah so what will
57:05happen is the market makers will provide
57:08liquidity under one condition only they
57:10can not provide liquidity during
57:13macroeconom economic data releases. This
57:16is the only condition because when a new
57:18macroeconomic data is released, let's
57:20say for example, let's say for example
57:22in 2021 inflation was a big problem and
57:24the stock market was super bearish
57:26because of fear of inflation. And if a
57:29new inflation data came out and it was
57:31suddenly really positive and inflation
57:33was going lower, coming down more than
57:35the market expected, it's very likely
57:37that that the stock bulls will be happy
57:40and keep buying, buying, buying, buying,
57:41buying, buying. Well, who they're going
57:43to buy from? are dear market makers
57:45because if they're constantly quoting
57:47all of the ask and suddenly they're all
57:49bought, they're losing a lot of money.
57:51So what they can do during macroeconomic
57:54news release is basically delete all of
57:57their orders. Okay, for example, let's
57:59take the latest FOMC data release which
58:02happened September 17th and let's see
58:04what happened in the footprint chart
58:07during that release. Well, you do see
58:09something interesting here. See a lot of
58:11zeros. A lot of zeros. What this means
58:14and what this signals us that is
58:16happening is there's a lot of buyers
58:18accepting all of these sell offers even
58:21though they're very little as you can
58:22see and there's little to no aggressive
58:24selling literally zero aggressive
58:26selling. If market makers were to
58:28constantly be the sellers of this
58:29movement, they will be losing money all
58:31the way through, right? And they do not
58:32want to take that risk. That's why they
58:34delete all of the liquidity. And I'm
58:36going to share with you a clip now that
58:37will make you exactly understand this
58:40phenomena of spread widening. When a
58:43news is released, such as NFP, CDI, or
58:46FOMC, here's what's happening behind the
58:48scenes. In every market, there's two
58:50types of traders. Market makers in the
58:52order book, buy and sell orders resting
58:55above and below price, and market
58:57takers, people actually buying and
59:00selling to the best price. When a market
59:02maker and a market taker agree on a
59:04price and trade, that price becomes the
59:06current market price. So a market making
59:08firm will provide liquidity by placing
59:10both buy and sell limits. So its
59:13business model is to sell at a slightly
59:15higher price and buy at a slightly lower
59:17price from and to all traders who buy
59:20and sell market to earn a spread. This
59:23is a massively profitable business. But
59:25if the market would start rising all of
59:27a sudden, maybe because the Fed has
59:29finally cut interest rates.
59:30>> Good afternoon.
59:31>> And stock bulls are happy for a market
59:33maker, that means trouble because it
59:35would have to keep selling at a higher
59:36and higher price and lose a lot of
59:38money. So to prevent this risk, market
59:41making firms before any news release
59:43have the ability to cancel all the
59:45orders and stop providing liquidity for
59:47some seconds. This way, the spread
59:49between the best sell offer and the best
59:51buy offer will be really wide. All it
59:54takes is a buy market order which will
59:55be matched with wherever there is at
59:57least one sell offer. So price will
59:59immediately jump wherever there was a
1:00:01sell limit in a matter of milliseconds.
1:00:03If someone in this time frame sells
1:00:05market it will be matched with the first
1:00:07buy offer which could be substantially
1:00:09lower and in a matter of milliseconds
1:00:11price will drop and just like that with
1:00:14two very small order that can be a huge
1:00:16volatility simply because of a lack of
1:00:18liquidity from market makers.
1:00:21So we have understood the basic of
1:00:23market mechanics also in depth with how
1:00:26the different types of matching
1:00:27algorithms work and why since the market
1:00:30works like an auction the liquidity
1:00:31auction theory is the best model to
1:00:34analyze market structure and basically
1:00:36follow where the money is going and
1:00:38instead of simply using highs and lows
1:00:40as visual references or weird shapes
1:00:42simply look at price with the lens of
1:00:44volume and with the idea of following
1:00:47the flow of big money. And this is what
1:00:49we will ultimately do. We will try to
1:00:51find the better ways to rationally
1:00:53follow the big money by looking in real
1:00:55time at the activity of buyers and
1:00:57sellers through the footprint chart if
1:00:59we are day traders or in general to
1:01:02price action and volume if for example
1:01:04we're swing traders. And this is what
1:01:05we're going to talk about now in this
1:01:06next chapter. But even before we get
1:01:08into all of that good stuff and how do
1:01:11we actually study the auction market
1:01:13theory and find models to enter the
1:01:15market for intraday setups for swing
1:01:17setups I think it's important that we
1:01:18clarify first what are the reason that
1:01:21will push market participants to either
1:01:23buy or sell through the matching
1:01:25algorithm and all the liquidity auction
1:01:27theory that we've modeled out and hence
1:01:30causes price to move in such a way but
1:01:32why so let's get a little bit deeper
1:01:34into fundamental analysis and the first
1:01:36element I want to address is
1:01:37fundamentals themselves and every market
1:01:40has its own fundamentals. For example,
1:01:42one of the most famous market for sure
1:01:44is the stock market and to understand
1:01:46what moves the stock market, we need to
1:01:47understand what the stock market is. And
1:01:49the stock market is the market of stocks
1:01:51which are shares of companies that are
1:01:54listed in the stock exchange. So in the
1:01:56stock market you basically trade company
1:01:58shares and you can either trade single
1:02:01stocks for example Apple, Microsoft,
1:02:03Tesla, Nvidia and so you basically trade
1:02:06by buying and selling stocks of the
1:02:08single companies or you trade index
1:02:10funds for example the S&P 500, the
1:02:13Nasdaq, the Dow Jones or the Russell
1:02:16where for example the S&P 500 holds
1:02:19together every single stock the top 500
1:02:22single stocks and by top 500 I mean the
1:02:25500 00 stock with the highest market
1:02:27capitalization or market cap of the
1:02:30entire American stock market. The NASDAQ
1:02:32100 takes the top 100 companies listed
1:02:35at the NASDAQ. The Dow Jones Industrial
1:02:37Average Index or Dow Jones 30 takes into
1:02:39consideration only the top 30 companies
1:02:41but mostly from the industrial sector
1:02:43and the Russell 2000 takes for example
1:02:45small cap stocks. So you have different
1:02:47index funds that are composed of
1:02:50slightly different companies and they
1:02:52have a slightly different composition of
1:02:54single stocks. And here is an example of
1:02:57the entire S&P 500 visualized. This is a
1:03:00graphics from visual capitalist. Shout
1:03:02out to them. And in 2023 for example,
1:03:05these are the different sectors. You
1:03:07have the info technology sector. You
1:03:09have the financial sector. So companies
1:03:11like Apple, Microsoft, Nvidia, Adobe,
1:03:14Salesforce, Intel, AMD are all tech
1:03:17companies. Then you have for example
1:03:19Fizer, Johnson and Johnson that are part
1:03:21of the healthcare sector. In the
1:03:22financial sector you have Birkshshire
1:03:24Hathaways, JP Morgan, Mastercard, Visa.
1:03:27Then you have the consumer discretionary
1:03:29sector that includes stuff like Amazon,
1:03:31Tesla, McDonald, Nike, Home Depot. And
1:03:34they're considered consumer
1:03:35discretionary because they're
1:03:37discretionary. So they're not primary
1:03:39goods such as, you know, food or water.
1:03:42They're discretionary. Consumers don't
1:03:43always buy from these companies. They're
1:03:46secondary. Unlike, for example, consumer
1:03:48staples like Proctor and Gamble,
1:03:50Coca-Cola, Pepsi, Costco, Walmart,
1:03:53Mundles, all of those companies that
1:03:55sell staples, stuff that people buy all
1:03:57the time. And this is already something
1:03:59you can start understanding. These type
1:04:01of stocks are more solid, more stable.
1:04:04They pay dividends because they
1:04:05constantly have revenue. While consumer
1:04:08discretionary, for example, if the
1:04:09economy is going good, they might
1:04:11perform really well. But for example,
1:04:13during a phase of recession, people will
1:04:15care less about buying new stuff from
1:04:17Amazon or eating outside at McDonald's
1:04:19or buy a new pair of Nikes or buy a new
1:04:21Tesla or even buy a new iPhone. But for
1:04:24sure, they'll keep spending on consumer
1:04:26staples. They'll they for sure do their
1:04:27groceries at Walmart, do their groceries
1:04:29at Proctor and Gamble. So consumer
1:04:31staples for example is one of those
1:04:33sectors that maybe has a better
1:04:35performance during bare markets that are
1:04:37mainly affecting consumer discretionary
1:04:39sector and the infochnology sector.
1:04:41Financials also normally are really
1:04:44solid but if there's a financial crisis
1:04:46this is the kind of sector that is going
1:04:48to perform less or healthcare for
1:04:50example is another really evergreen set
1:04:52of stocks because there's always going
1:04:54to be a need for healthcare whether the
1:04:56economy is good or the economy is bad.
1:04:58Then you have the energy sector which is
1:05:00heavily influenced for example by oil
1:05:02prices. You have materials, utilities,
1:05:04real estate that for example is very
1:05:06much affected by interest rate policies
1:05:08decision because as you know most of the
1:05:10real estate is bought through loans and
1:05:12the interest rate you see in loans are
1:05:14determined by the interest rates set by
1:05:17central banks. So central bank decisions
1:05:19on monetary policies will affect the
1:05:21financial sector a lot and the real
1:05:22estate a lot. And so you already start
1:05:24understanding in general the different
1:05:26types of sector how would they respond
1:05:28to the economy but in general the stock
1:05:30market the reason why it moves. So the
1:05:33fundamental reasons one of the main
1:05:35drivers of the stock market is risk
1:05:37appetite. So in general the stock market
1:05:40when you invest in a company so why an
1:05:43investor a person with big money should
1:05:45or shouldn't invest in a stock it is
1:05:48typically because they expect the
1:05:49company valuation and the price of that
1:05:52stock to grow so for growth or because
1:05:55for example it's a company that pays a
1:05:57lot of dividends. For example, a company
1:05:59like Tesla didn't pay dividends at all
1:06:01to its investor, but it had an intense
1:06:04growth in its price that generated a
1:06:06return for investors, but it does not
1:06:09pay dividends. Coca-Cola instead is a
1:06:11company that pays a lot of dividends,
1:06:12right? So another thing that influences
1:06:15risk appetite so incentivizes investors
1:06:18to put on capital into stocks and to
1:06:21invest in the stock market or in some
1:06:23specific stocks is expectations on the
1:06:26company's earnings. And as of today,
1:06:28every 3 months, all publicly traded
1:06:31companies have to release their earnings
1:06:33once every 3 months or quarterly because
1:06:35earnings are both a driver of growth and
1:06:38also earnings which for all of those who
1:06:41don't know is simply revenue minus
1:06:43expenses. So the net profit of the
1:06:46company is the earnings is equally
1:06:48divided and distributed to shareholders.
1:06:50So if you bought a stock, you bought a
1:06:52share and a lot of companies will pay
1:06:54you earnings because you hold a share.
1:06:56you're a shareholder. And so a lot of
1:06:58investors might invest in a company for
1:07:00income, not for growth, income. So
1:07:03income/ dividends. So this is everything
1:07:05that relates to the company itself,
1:07:08right? And each company has its own
1:07:10fundamentals. And by fundamentals, that
1:07:12could mean for example, who is the
1:07:14founder or the CEO of that company? How
1:07:18trustworthy is him? How are the
1:07:20financials of the company? So you
1:07:21basically take the balance sheet of each
1:07:23company and analyze their earnings,
1:07:26their EIDA, their leverage ratio. So how
1:07:29much in depth they are and their
1:07:31financial solidity overall. Another
1:07:33crucial thing is how how is the
1:07:35sentiment of markets towards the sector
1:07:38they operate in. These are all parts of
1:07:41the fundamentals of the company. And of
1:07:43course it's a part of the fundamentals
1:07:45of the stock market in general.
1:07:47everything that relates to the economy
1:07:49they operate in or the macroeconomic
1:07:53context. Of course, if the overall
1:07:55economy is expecting to shrink
1:07:58drastically, that's not going to help
1:08:00stocks go high. It's going to decrease
1:08:02the risk appetite and investors maybe
1:08:05take money out of the stock market into
1:08:07a safer type of asset. So, if the
1:08:09macroeconomic content is overall good
1:08:11and there's a positive sentiment about
1:08:13the economy, the stocks tend to be
1:08:15bullish. If there's likely to be a
1:08:17recession, this is typically bearish.
1:08:19But always remember, if during recession
1:08:21stocks are bearish, doesn't mean that
1:08:23that money is being lost or burned. Like
1:08:25some newspaper like to say, trillions of
1:08:28dollars burned in the stock market in a
1:08:29single day. Yeah, but it doesn't burn.
1:08:31It just moves somewhere else because
1:08:33markets are just this money moving where
1:08:36it thinks it will get a better
1:08:38treatment. And the macroeconomic context
1:08:41is heavily heavily influenced by
1:08:44monetary policies which we'll get deep
1:08:46into shortly and fiscal policies which
1:08:49we'll also get deep into later. Now,
1:08:50another important thing about the stock
1:08:52market and the reason also why the stock
1:08:54market is one of my favorite market to
1:08:56trade and this is where I mostly trade
1:08:58by the way. I mostly trade the S&P 500
1:09:01is because it's in some sense more
1:09:04predictable because we can truly
1:09:05understand what the intentions of the
1:09:07market are very very clearly much more
1:09:09clearly than a lot of other markets I
1:09:11would dare to say. And the main market
1:09:14participants of the stock market are for
1:09:17sure investors. investors who invest in
1:09:20the stock market and they create an
1:09:22upward bullish pressure by constantly
1:09:25buying, buying, buying, buying, buying
1:09:28and accumulating money into the stock
1:09:31market for capital preservation and
1:09:33capital growth reasons. And these are
1:09:35long-term traders. They affect the
1:09:37long-term direction of the stock market.
1:09:40And as you can see in most big economies
1:09:43since more money is being printed as we
1:09:46will see later investors have more and
1:09:48more money to invest in the stock market
1:09:50and the market tends always to go up. So
1:09:53the fact that there is investors creates
1:09:55a skew in the probabilities of prices to
1:09:59go up more than they go down most of the
1:10:01time which is already in and of itself a
1:10:04great edge already which is the reason
1:10:06why simple trading setups like the
1:10:08opening range breakout always works in
1:10:10stocks. And then of course you have
1:10:12speculators and speculators can affect
1:10:14more let's say the short-term price
1:10:16action and the short-term volatility.
1:10:18And while investors might simply buy
1:10:21stocks or investors often for example
1:10:24buy/sell
1:10:26ETFs of certain index funds or of some
1:10:30specific sectors cuz for example each
1:10:32one of these sectors of the S&P 500 has
1:10:34its own ETF. speculators instead
1:10:37together with just using single stocks
1:10:39or trading ETFs. They will also use
1:10:42derivative contracts such as futures of
1:10:45index funds and I would say mostly
1:10:47options and all of these are derivatives
1:10:50but they are such huge markets that they
1:10:53end up affecting the underlying asset
1:10:56cuz you should know that a future an
1:10:58option a CFD it's a derivative contracts
1:11:00because it deres it price from the
1:11:03underlying asset right but if most of
1:11:05the volume is traded in future contracts
1:11:07and in options the hedging activity or
1:11:09the arbitrage that can happen between
1:11:11different markets will affect the stock
1:11:14prices itself. So [snorts] as you will
1:11:15see later sometimes options especially
1:11:18are the underlying asset themselves and
1:11:20also both speculators and investors but
1:11:23just the big ones trade in something
1:11:25called dark pools especially single
1:11:28stocks and dark pools are a different
1:11:30type of exchange that is not transparent
1:11:33is not regulated. It's not public, but
1:11:35it's a private pool of institutional
1:11:38liquidity where big investors, big money
1:11:41participants can more comfortably trade
1:11:44big amounts of money and trade in
1:11:46blocks. Okay. And get a feel to their
1:11:49so-called exactly block trades. And this
1:11:52is a considerably big part of the
1:11:55market. I'm not sure what's the current
1:11:57volume overall, but at some point I'm
1:11:59sure it was around 40%. And for the rest
1:12:01of the market, there's also a lot of
1:12:03calculations of where is the most money
1:12:05traded in single stocks, in ETFs of the
1:12:08index funds, in the futures of the index
1:12:10funds or in the options of both stocks
1:12:12and and index funds. And the answer is
1:12:15options. Most of the market, most of the
1:12:17public market of stocks and index funds
1:12:19are not traded in ETFs, not in futures.
1:12:22Most of the daily volume happens in
1:12:24options, specifically zerodte options,
1:12:28which became very popular in the last
1:12:30few years for both retail traders and
1:12:32institutional traders. And this leads us
1:12:35to the third big market participant of
1:12:37the stock market, which are market
1:12:40makers or marketmaking firms, the same
1:12:42ones we saw here like Citadel
1:12:44Securities, which are market
1:12:45participants that are basically
1:12:46liquidity providers that earn a spread.
1:12:48And the biggest one and most influential
1:12:50ones are option market makers for sure
1:12:52because as most of the notional volume
1:12:55in the stock market is traded through
1:12:56through options and specifically zero
1:12:59DTE. The way market makers stay neutral
1:13:02and basically hedge their position
1:13:04creates a flow of hedging orders in the
1:13:08futures market and in the stock market
1:13:10that according to some estimate is
1:13:12around 10% to 15% of the total volume
1:13:15which is a lot. So hedging flows from
1:13:19market makers specifically in the option
1:13:21market are really considerable in
1:13:23specifically the short-term market
1:13:25action and we will get deep into that
1:13:27later but I already want to show you
1:13:28something which I consider very
1:13:30interesting. Go on squeezemetrics
1:13:33squeezemetrics.com and you get this
1:13:36chart that basically has the S&P 500 but
1:13:38you also get two very insightful
1:13:41indicators. The first one is the DIX,
1:13:44which which could be kind of a funny
1:13:46name, but is the darkpool indicator or
1:13:49dark index, which basically take
1:13:51darkpool data from all of the stocks of
1:13:54the S&P 500 and basically creates an
1:13:56index of darkpool activity. So for free
1:13:59on squeeze metrics, you can get a daily
1:14:02recap of darkpool activity. And
1:14:05typically whenever you see big peaks up
1:14:08or down, they happen because some huge
1:14:11market participants are whether buying a
1:14:13lot of stocks or selling a lot of
1:14:15stocks, which can be a crucial data
1:14:17point because you understand that if
1:14:19someone this big is joining the party or
1:14:21quitting the party, then he might know
1:14:23something you don't. And we might want
1:14:25to be careful. It is not random that
1:14:27these short peaks in the in the market
1:14:30happen right before a big stock crash
1:14:33and instead the high peaks happen right
1:14:36before big bull runs. And this is one of
1:14:38the indicators you get here. Another one
1:14:40you get here which is very insightful is
1:14:42the gam exposure. And the gam exposure
1:14:44is to properly explain it. It takes a
1:14:47little more knowledge on how options
1:14:49work and we will do that in this video
1:14:50because I truly want you to understand
1:14:52it. But for now just know this. When the
1:14:54gam exposure is in the purple level, we
1:14:57typically expect volatility to compress
1:15:01and when the gam exposure is in the
1:15:03yellow area, we expect swings to be much
1:15:06more volatile because of the hedging
1:15:09flows of options market maker that I was
1:15:11mentioning, but we'll get deep into how
1:15:13that work later. Now, this is the chart
1:15:15of the S&P 500 index or the S&P on
1:15:18Trading View. Let's use the monthly
1:15:20chart and set the chart on logarithmic
1:15:22scale. Well, you can clearly see it has
1:15:25a very clear direction. Let's put a line
1:15:27chart and let's walk through a little
1:15:29bit of the history behind it because
1:15:30you're probably familiar with the stock
1:15:32market bubble of 1929 and the following
1:15:35stock market crash where the stock
1:15:37market lost 85% of its value. This was a
1:15:41clear example of a bare market
1:15:43unfolding. a bare market where investors
1:15:46who invested their money here basically
1:15:48saw their value wiped out and had to
1:15:52wait more than 20 years to see a profit.
1:15:55But in general markets tend to go up and
1:15:58sometimes a crash happens. This is the
1:16:00crash of the '60s, the crash of 1966, of
1:16:031969 and the crash of the '7s. And
1:16:06unlike the great recession of the 1930s,
1:16:09all of these bare markets recovered
1:16:12pretty quickly. And so the stock market
1:16:14has this V-shaped reversals that happen
1:16:16because people bought the dip, right?
1:16:19Because we expect stocks to go high. And
1:16:21when everyone panics, typically it's a
1:16:23good time to buy. Another famous stock
1:16:25market crash happened in 1987. In the
1:16:282000s because of the explosion of the
1:16:32stock market.com bubble and then again
1:16:34in 2008 during the housing crisis and
1:16:37the great financial crisis. And then
1:16:39other important bare markets happened in
1:16:412015. in 2018 and during COVID. Then we
1:16:44had another bare market during the
1:16:45inflation crisis of 2022. And the last
1:16:48big buy the dip happened during the
1:16:50Trump tariff war. And these are the main
1:16:52things you need to know about the stock
1:16:54market. And I would say the little
1:16:55brother of the stock market is the
1:16:57crypto market for sure because one of
1:16:59the main drivers of crypto is risk
1:17:03appetite. So in some way it is similar
1:17:06to the stock market but it's completely
1:17:08different because cryptocurrencies you
1:17:10have the main one which is Bitcoin of
1:17:12course which is a completely different
1:17:14cryptocurrency for example than than
1:17:17most of the other altcoin and the
1:17:18drivers of cryptos are risk appetite and
1:17:21purely growthbased. Some people might
1:17:24say they are a valid alternative payment
1:17:26method and for some things they are.
1:17:29Some altcoins maybe could be better than
1:17:31bitcoin as a payment method. Then you
1:17:33have all the world of stable coins. And
1:17:36for example, especially with Bitcoin, we
1:17:37have seen a very consistent, even though
1:17:40not always, but pretty consistent
1:17:42correlation between the price of Bitcoin
1:17:45and the price of stock indices such as
1:17:47the S&P 500. They tend to move not the
1:17:50same way, but a lot of the time they do
1:17:52because of the fact that they're both
1:17:54risk assets. But a lot of the investors
1:17:56of Bitcoin or the traders of Bitcoin,
1:17:59the holders or the hodlers truly believe
1:18:02in Bitcoin. And so through time, Bitcoin
1:18:05is likely to become not just a risky
1:18:08asset, but treat it almost like a
1:18:10digital form of gold, so a store of
1:18:13value. Most altcoins, I would say 80% of
1:18:16the altcoins are mostly attractive to
1:18:20gamblers. And with altcoins, there's a
1:18:22lot of insider trading. You could trade
1:18:24them with market sentiment because the
1:18:26cool thing about cryptos is they're more
1:18:28transparent than most market thanks to
1:18:29the blockchain which is mostly public.
1:18:32So you can follow the trades of all
1:18:34market participants, the big ones and
1:18:36the small ones with a very high level of
1:18:39detail and do the so-called onchain
1:18:41analysis. And specifically with
1:18:43altcoins, meme coins are the favorite
1:18:46tool for pump and dump schemes from
1:18:47scammers, influencers, and even
1:18:49politicians at time where they pump
1:18:51price up, they sell before anyone else
1:18:54can, and then they lose all of their
1:18:56value because they have no intrinsic
1:18:58value whatsoever. So while with Bitcoin
1:19:01and also other altcoins such as XRP or
1:19:04even Ethereum, you could argue that
1:19:06there is some level of intrinsic value,
1:19:08with memecoins, it's just a pure
1:19:10lottery. It's pure casino and some
1:19:12people might get lucky, some people
1:19:14might not. Or some people might be aware
1:19:16that is a casino and place themselves
1:19:18smartly on the right side. There's a lot
1:19:20of successful traders of meme coins that
1:19:23simply take advantage of the dump money
1:19:25there and take smart decisions instead.
1:19:27But this is not going to be part of this
1:19:30course that we're doing. Even though for
1:19:31more liquid cryptocurrencies like
1:19:33Bitcoin, you could use roughly the same
1:19:36models of the liquidity auction theory
1:19:37because there's a lot of big
1:19:39participants now involved and now ETFs
1:19:41are involved and there is more and more
1:19:43institutional interest in Bitcoin and it
1:19:46will likely keep rising in the future.
1:19:47But for sure this is a market worth
1:19:49mentioning as one of the types of
1:19:50financial markets. The next market is
1:19:52the commodities market. for example,
1:19:54oil, natural gas, or even water or
1:19:57cocoa, coffee, live cattle, even orange
1:20:00juice, wood, cotton, copper, lithium,
1:20:03sugar, and so on and so forth. All of
1:20:05these commodities of prime materials
1:20:08that are used to then produce other
1:20:10stuff. They're the basics to create
1:20:13other products. We will not get deep
1:20:14into every single one of them now
1:20:16because it would be a very, very long
1:20:18video. It's already pretty long. But
1:20:20they mostly revolve around expectations
1:20:22around supply and demand. These are the
1:20:25main fundamentals of each market. So for
1:20:27example for oil there are producers that
1:20:30are the supply and the global industry
1:20:32which is the demand. So for oil the
1:20:35supply can be the OPEC plus countries.
1:20:38Big producers is the US, Russia and
1:20:41Canada. So the global supply is the
1:20:44producers of oil. The demand is the
1:20:48global industry as oil is used in every
1:20:52possible industry whatsoever. So the
1:20:54expectations around how much production
1:20:57of oil there will be and how much demand
1:20:59there will be because of for example how
1:21:02well the global manufacturing industry
1:21:04is likely to be active in producing new
1:21:07stuff. These are the main drivers that
1:21:09drive oil prices. And for example, we
1:21:11have seen a lot of volatility in oil in
1:21:14many instances throughout history where
1:21:15there was a supply shock. So this is the
1:21:17chart of oil. And for example, in 1973
1:21:21during a war that exploded in the Middle
1:21:23East that and remember this was a period
1:21:25of time where most of the global oil
1:21:27supply was Arab countries, Iran decided
1:21:30to stop oil production and stop
1:21:31supplying oil and that created not one
1:21:34but two oil shocks where price simply
1:21:37exploded creating if we take the
1:21:40inflation rate and put it on top we can
1:21:42see these two big waves of inflation
1:21:44that were caused by this shock in the
1:21:47prices of oil And this was all a supply
1:21:50shock. Then also during ' 07 there was
1:21:52this huge also speculative move in the
1:21:55price of oil that then dropped
1:21:57drastically because of the global
1:21:59financial crisis where we would expect
1:22:01the demand of oil to radically get
1:22:03lower. Same thing happened during COVID
1:22:05where all the world shut down. So the
1:22:07expected demand of oil dropped
1:22:10significantly and drove traders and
1:22:12investors and hedggers to basically sell
1:22:14oil and even go below zero at some point
1:22:17because if everything's closed, there's
1:22:19no transportation, there's no
1:22:21production, that's a shock in the demand
1:22:24of oil. So supply shocks and demand
1:22:26shocks are the main driver. For example,
1:22:28during 2021 2022, because of the war,
1:22:31both oil prices and natural gas prices
1:22:34had a huge supply shock. And that all
1:22:36happened because of the expectations
1:22:38around the supply and the demand of that
1:22:40asset. The same thing happens with
1:22:41natural gas with all of these for
1:22:43example CCOA where when you see this you
1:22:45could think this is basically a
1:22:47cryptocurrency but what happened here
1:22:48was a shock in prices caused by
1:22:51unexpected weather condition in the
1:22:53countries that are the highest producers
1:22:55of cocoa and that drove prices up
1:22:58significantly. And this is basically the
1:23:00driver behind commodities. And the
1:23:02participants of this market are big
1:23:05companies that use these commodities to
1:23:07produce and they are the demand usually
1:23:10and big producers. And they are a big
1:23:12part a big portion of the volume because
1:23:14for example they hedge the risk of
1:23:16prices increases suddenly through
1:23:18futures contract which you know it's the
1:23:20most common type of contract to trade
1:23:22commodities in even though also here you
1:23:24can find options, CFDs etc. But futures
1:23:27are the main one. And the reason why
1:23:29participants engage in trading
1:23:31commodities is also because of hedging.
1:23:33But also there's a lot of speculation.
1:23:36So also you have big and small
1:23:38speculator as one of the significant
1:23:41participants in this market. But in
1:23:43general, I would suggest you if you want
1:23:44to be a commodities trader to study the
1:23:46fundamentals of each market one by one.
1:23:49Take your time and truly understand what
1:23:51is influencing the supply and what is
1:23:53influencing the demand. And of course
1:23:55even here the constant and as you will
1:23:57see it's the constant in all market is
1:23:59macroeconomic conditions because if the
1:24:01overall economy is going really slow oil
1:24:04prices might fall and so on and so forth
1:24:06and especially for stuff like oil global
1:24:09international conflicts and geopolitical
1:24:12dynamics are a huge influence. And the
1:24:15next big important market is precious
1:24:17metals such as gold and silver and they
1:24:20are technically commodities but they
1:24:23deserve a category of their own. And
1:24:25together with the bond market and the
1:24:29forex market before explaining you the
1:24:32fundamentals I cannot explain you the
1:24:34fundamentals of these markets without
1:24:37explaining you how monetary policies
1:24:39work and how money creation works. what
1:24:42is inflation and laying down some basics
1:24:45of macroeconomics. So the next step is
1:24:48macroeconomics and one video is not
1:24:51enough to properly explain you
1:24:53everything there everything that someone
1:24:54should know about macroeconomics but I
1:24:57will try my best to summarize the best
1:24:59and most crucial information for traders
1:25:01specifically because the sentiment of
1:25:03the market around macroeconomics is one
1:25:06of the main drivers of all sorts of
1:25:09markets. So, it's really important to
1:25:11know if you want to become a
1:25:12professional trader. When we're talking
1:25:13about macroeconomics,
1:25:15we mostly talk about the economics of
1:25:19big systems such as nations or the
1:25:22global economy. And whenever we're
1:25:24talking about macroeconomics, when we
1:25:26talk about the economy, we have what
1:25:29exactly do we mean? How do we measure
1:25:31how the macroeconomic landscape or the
1:25:34current economic scenario the current
1:25:36economic status let's say we imagine the
1:25:39economy as being a person if a person is
1:25:42healthy or unhealthy we have some key
1:25:45metrics some data points that we need to
1:25:47kind of connect to understand if a
1:25:49person is healthy or not right the same
1:25:50thing we do with an economy and the most
1:25:53obvious metrics are GDP which is the
1:25:57gross and by gross it doesn't mean it's
1:26:00and disgusting. It means it's not net.
1:26:02The gross domestic product and the gross
1:26:05domestic product basically takes into
1:26:07account how much in dollar terms a
1:26:10nation is able to produce. What's the
1:26:13output of the economy including how much
1:26:16investment there is, expenses there are,
1:26:19import, export, everything that relates
1:26:21to the wealth that the nation was able
1:26:24to output in a single year. Typically,
1:26:26for example, now the GDP of the United
1:26:29States is above $30 trillion. And other
1:26:33important metrics in macroeconomics is
1:26:36employment. And the current status of
1:26:38employment in a nation can be understood
1:26:41with something called the unemployment
1:26:43rate, which is the percentage of the
1:26:46labor force that is not currently
1:26:48employed. Another important metric in
1:26:50employment is job openings. So is there
1:26:53new job offers being open? Because also
1:26:56the employment works with supply and
1:26:59demand, the supply being the workers and
1:27:01the demand being the businesses asking
1:27:03for labor. And another important metrics
1:27:06for example is new monthly payrolls. So
1:27:09were there new people employed this
1:27:12month in an economy? And we can see this
1:27:14for example in the US with the ADP
1:27:16report or with the NFP, the non-farm
1:27:19payrolls, right? The next important
1:27:21metric is for sure inflation and the
1:27:24current unemployment rate in the US is
1:27:26around 4% which is not much. Anything
1:27:29above 4% indicating a not so healthy
1:27:32economy because if people has no jobs
1:27:35they buy less. So consumer spending
1:27:38declines, business revenues decline and
1:27:41so businesses will have to lay off
1:27:44workers which will bring this even
1:27:46higher and bring more unemployment to
1:27:48the nation for example. Right? So
1:27:50probably since Kanes which is one of the
1:27:52greatest economists of the last century
1:27:53we've understood that achieving maximum
1:27:56employment in a country and we keep
1:27:58people spending money will make everyone
1:28:02earn more money and have an overall
1:28:04stable growth in the economy. The second
1:28:07or third most important macroeconomical
1:28:10metric in an economy is for sure
1:28:12inflation. And what inflation is growth
1:28:15in consumer prices. Let's say for
1:28:18example a coffee now costs $5. If next
1:28:22[snorts] year the prices of the same
1:28:24coffee is $55,
1:28:27that's a 1% increase in prices or a 1%
1:28:32inflation rate for the prices of coffee,
1:28:35right? And you have multiple metrics for
1:28:37inflation. For example, in the US, and
1:28:39we're mostly talking about the US
1:28:41because it's the one with the most
1:28:43amount of data and the amount of
1:28:45transparency with economic data compared
1:28:47to the rest of the world. We're not
1:28:48saying it's perfect, but it's probably
1:28:50the best one. We have different metrics
1:28:52for inflation. The first and most famous
1:28:54one is the CPI or the consumer price
1:28:58index. And for example, I can look for
1:29:00US CPI, United States consumer price
1:29:03index. And I can clearly see that prices
1:29:06mostly go up. Okay. And you can clearly
1:29:09understand here if prices go up which
1:29:13means that with $5 I was able to buy one
1:29:15coffee. Now $5 are taking me 0.95
1:29:20coffees. Right? So an increase in prices
1:29:24means that the value of the money is
1:29:27actually going lower. So actually if I
1:29:30divide one by the US CPI, so one over
1:29:35CPI, I get and maybe I put it in
1:29:38percentage terms, I can see that over
1:29:40the last 75 years, the US dollar lost
1:29:44around 92% of its value. There's other
1:29:47ways to calculate inflation. Another
1:29:49pretty famous one and one of probably
1:29:51the most realistic one is the PCE
1:29:55or the personal consumption expenditures
1:29:59which is a kind of more accurate
1:30:00representation of inflation because
1:30:02consumer prices just takes the prices of
1:30:05apples, the prices of oranges and
1:30:08averages out the overall inflation rate.
1:30:10The personal consumption expenditure
1:30:11instead takes into account the behavior
1:30:14of consumers. So for example, if apples
1:30:17are way more used by consumers and way
1:30:20more common commonly bought by consumers
1:30:22rather than oranges when then apples
1:30:25will have a higher weight in the overall
1:30:27calculation of the inflation rate. So
1:30:29the personal consumption expenditures
1:30:31takes into account consumer's behavior.
1:30:33So it's a kind of more accurate
1:30:35representation of how prices are
1:30:36growing. But as you can see this is a
1:30:38number, right? This is a number. This is
1:30:40not a percentage term. Indeed, both the
1:30:43CPI and the PCE are typically expressed
1:30:46in yearoveryear
1:30:48increase. So, how much did inflation
1:30:51increase over the last year? By the way,
1:30:54also the gross domestic product is
1:30:56typically measured in year-over-year
1:30:58growth rate. So, here we're talking
1:31:00about the GDP growth rate yearover-year
1:31:04or even quarter over quarter. And here
1:31:07we talk about not inflation but the
1:31:10inflation rate year-over-year or quarter
1:31:12over quarter. In fact, if I write USI,
1:31:15so US inflation rate R year over year, I
1:31:20get this chart instead, which basically
1:31:23is measuring the speed of US CPI. And as
1:31:26you can see, the steepness of this blue
1:31:29curve is pretty stable. It's not really
1:31:31vertical, and the inflation rate is
1:31:33here. But as soon as the speed of this
1:31:36line rising goes higher here we measure
1:31:39the speed basically the rate of change
1:31:42of this particular economic data. So
1:31:44when it rises fast the inflation rate is
1:31:46high because it's comparing this to
1:31:48maybe this. So one year prior and as
1:31:51soon as prices tend to flatten you can
1:31:53see the inflation rate goes down. And a
1:31:55high inflation is typically very problem
1:31:58problematic because it consumes wealth
1:32:01especially from poor people especially
1:32:03from the middle class especially from
1:32:05consumers and it's very bad in the
1:32:07economy. Typically an inflation around
1:32:102% is considered healthy because let's
1:32:13say the GDP is growing by 2%. If the
1:32:16economy grows by 2% it's fair to expect
1:32:19an inflation rate of 2% because yes the
1:32:23economy grows there's more money into
1:32:25the economy more money has being spent
1:32:27by consumers and if consumers spend
1:32:31prices of goods gets higher. So if
1:32:33inflation is driven by consumer spending
1:32:37it's typically healthy and will stay
1:32:40around a healthy range of 2%. But let's
1:32:42say for example that oil prices in
1:32:45suddenly increase. Look at what happens
1:32:47to inflation. Yeah, that's exactly what
1:32:49happens. And you can see a clear pattern
1:32:51here, right? Boom in oil prices, big
1:32:53wave of inflation. Boom in oil prices,
1:32:55big wave of inflation again. And these
1:32:58were the wars in the Middle East and the
1:32:59oil shocks. And here you have oil prices
1:33:02getting really low, inflation dropping
1:33:04down, boom in oil prices, war in
1:33:06Ukraine, inflation going up. These booms
1:33:08of inflation did not come because of an
1:33:11increased consumer spending but because
1:33:13of global conflicts. So these are what
1:33:16we also call hard data. But there is
1:33:18also soft data. And soft data are mostly
1:33:23surveys such as business sentiment
1:33:26surveys such as the PMI, the purchasing
1:33:28managers index, which basically tracks
1:33:31how confident are businesses, how much
1:33:34products or commodities they're
1:33:35warehousing, if they're investing in new
1:33:37productions, if they're hiring more
1:33:39people, blah blah blah. And also talking
1:33:41about businesses, another type of
1:33:43inflation is the PPI, which is the
1:33:46producers price index. So if the
1:33:49consumer price index is based on prices
1:33:52that consumers pay for while buying
1:33:56groceries, while buying new car, buying
1:33:58a new house, producer prices instead
1:34:00measure the inflation that businesses
1:34:03feel that businesses pay for. And
1:34:05typically inflation will first hit
1:34:07producers and then consumers. Because if
1:34:10oil prices go up first, the businesses
1:34:13will have higher costs for production
1:34:14and then those higher costs will be
1:34:17reflected into the consumer prices. So
1:34:19if for example you add US PPI
1:34:22year-over-year, you'll also tend to see
1:34:24a pattern where typically producer
1:34:26prices peak before consumer prices do,
1:34:29they drop before they do, they rise
1:34:32before these do. So they tend to have
1:34:34some level of predictive effect
1:34:36understandably. And now that we know the
1:34:39main metrics of an economy, there's way
1:34:41more by the way. I'm just summarizing
1:34:43the most important ones. We now need to
1:34:45understand macroeconomic cycles. Now, in
1:34:47order to understand the macroeconomic
1:34:50cycles, we first need to understand how
1:34:51money is created. Right? In the early
1:34:53stages of our civilization, people used
1:34:55to trade goods and services in exchange
1:34:58for goods and services. So, hey, here I
1:35:01have five apples. Give me 10 potatoes in
1:35:03exchange. And they would exchange this.
1:35:05Then this thing evolved to exchanging
1:35:08goods for some more measurable units of
1:35:11some stuff to make trade easier. For
1:35:13example, pounds of rice or pounds of
1:35:17salt. Something measurable that is easy
1:35:19to use as a currency to buy from others
1:35:22goods and services. And then they
1:35:24started using coins made of precious
1:35:26metals such as gold, silver, copper,
1:35:29because they were a much more easily
1:35:30measurable unit. So if I want to buy two
1:35:33oranges, that's going to cost you three
1:35:35coins. And so precious metals became the
1:35:37currency. But then carrying around huge
1:35:40amounts of gold became sort of
1:35:42dangerous, right? So Jewish people
1:35:43invented banks, places which basically
1:35:46said, "Hey, we are going to keep your
1:35:49money safe." So people started
1:35:50depositing gold into banks and in
1:35:53exchange for the gold, the bank would
1:35:55release something known as a bank note.
1:35:58the note of the bank. It was basically
1:36:00an I an I owe you. So whenever you want
1:36:03your gold back, you just give me back
1:36:04this bank note. I know it's yours and
1:36:06I'll give you your coins, your gold
1:36:08coins back. But then since banks started
1:36:10having a lot of money that was sitting
1:36:12there for no reason and they realized
1:36:14that people were not often coming and
1:36:16picking all of that money up, they used
1:36:17to keep it there as savings. So they
1:36:19started thinking, hey, it doesn't make
1:36:20sense that I keep all of this money. I
1:36:22can start for example lending it and
1:36:24earn an interest rates and only keep in
1:36:26the bank what I am confidently sure
1:36:28people will come and ask for for their
1:36:30daily expenses and the rest of the money
1:36:32I will just put it to work and basically
1:36:34lend it to someone else. And gradually
1:36:36banks started issuing more bank notes
1:36:39even though they did not really have all
1:36:40of that gold to back all of those
1:36:42banknotes because they just cared of
1:36:44earning an interest rate counting
1:36:46counting on the fact that the money then
1:36:47would be given back. And this is the way
1:36:49fractional reserve banking was born. And
1:36:51up until 1971, you could still somewhat
1:36:54exchange your bank notes for gold at any
1:36:57bank. This era was called the gold
1:37:00standard. You could exchange your money
1:37:02for gold or silver. But gradually
1:37:04throughout the 20th century,
1:37:05specifically 1971, the world decided to
1:37:07abandon the gold standard and decide
1:37:09that money itself was the currency even
1:37:12though it was not backed by gold. And
1:37:13that was the birth of the fiat currency
1:37:16system. And fiat is a Latin word that
1:37:19means faith. That's why it's also called
1:37:21fiduciary currency because we all trust
1:37:24that these dollars or euros or yens have
1:37:27intrinsic value because we all agree on
1:37:30it. But they're just pieces of paper.
1:37:31They don't really have value. They have
1:37:33value because we all have faith in it in
1:37:35its value. We trust the value of money
1:37:37because other people will accept it to
1:37:39exchange for goods and services. It
1:37:41started even earlier but in 1971 when it
1:37:44really became the only way of creating
1:37:46new money. Money was not created through
1:37:47gold. money was not created through
1:37:49anything to back it up other than debt.
1:37:52So for example, a government would go to
1:37:54the central bank and the central bank is
1:37:57in charge of printing money and deciding
1:37:59monetary policy. The government would
1:38:01issue a IOU or a debt security also
1:38:07known as a government bond for let's say
1:38:10$100,000 and the central bank will print
1:38:12$100,000 lend it to the government. The
1:38:15government will give the bond to the
1:38:17central bank will pay an interest on
1:38:20this loan basically to the central bank
1:38:22and lastly give the money back. The debt
1:38:25doesn't exist anymore and also the money
1:38:27is canceled basically. Or with a normal
1:38:29bank a person who needs money will ask
1:38:32for a loan. The bank will create money
1:38:35out of thin air, loan it to the person
1:38:37that will have a debt that will owe a
1:38:40debt to the bank, will pay an interest
1:38:43to the bank, and then give the money
1:38:45back. The money is canceled, the debt
1:38:47doesn't exist anymore, and the bank has
1:38:49earned interest rates. And that's how
1:38:51money is created. Money is created in
1:38:53central banks and in the commercial
1:38:55banks out of thin air through the debt
1:38:58system. And because this system is in
1:39:00place, new money is created through
1:39:03debt. And since a debt will have to be
1:39:06repaid, we have cycles in the economy.
1:39:08Because if now I can spend this, but
1:39:11through debt, I'm able to spend more. So
1:39:14because of debt, at first I can spend
1:39:16more than I earn, but then I'll have to
1:39:18use part of my earnings to give back and
1:39:21to pay the debt. So I will have to spend
1:39:23less. And this cycle of a lot of
1:39:26spending at first, a lot of wealth and
1:39:29perceived wellness at first will result
1:39:32later in having to spend less blah blah
1:39:34blah. And this does not happen just to
1:39:36people. It happens to the economy
1:39:38overall. So if we plot on a chart the
1:39:40GDP of a nation through time in an
1:39:43economy without debt, the only way to
1:39:46increase GDP, which basically means to
1:39:49increase productivity, is with
1:39:51technological innovation. And for
1:39:52example with a strong demography. So if
1:39:55technological innovation and strong
1:39:57demography contributes to an increase in
1:39:59productivity that's what we call
1:40:01structural growth because there's more
1:40:03people working and we can increase the
1:40:05production increase productivity and
1:40:06increase the economic output in a
1:40:09system. But through the debt system, I
1:40:11can input more money, more gasoline into
1:40:14the economy and basically the economy
1:40:16can grow at a much higher pace at least
1:40:18at first because people and businesses
1:40:20and governments will ha will ask for
1:40:23loans and they will be able to spend
1:40:25more and that will increase the economic
1:40:27activity and the economic output because
1:40:29if we have more money we can make more
1:40:31stuff, people can spend more and the GDP
1:40:34grows and this is called a leveraged
1:40:36growth because it's growing through the
1:40:39leverage of debt. But at some point
1:40:42businesses, individuals and governments
1:40:44will have to pay back that loan. And so
1:40:46there will be a phase that in economy is
1:40:48called deleveraging. And then at some
1:40:50point people will start getting into
1:40:52debt a little more and that will fuel a
1:40:54new phase of leverage growth followed by
1:40:57a phase of deleveraging. And we create
1:40:59cycles in the economy. The stages where
1:41:01the economy grows are also called
1:41:04expansions that is normally followed by
1:41:07a slowdown until we reach a peak
1:41:10followed by a phase of contraction and
1:41:12then a phase of recession. For example,
1:41:14this is the chart of the GDP of the USA.
1:41:17And you can see there's been some
1:41:18instances of cycles, but it still tend
1:41:21to go up, right? In 2008, we had a clear
1:41:23example of deleveraging just like the
1:41:25one we had in 1989. Pretty similar. And
1:41:27there is two cycles. This is called the
1:41:30big cycle that happens every 75 to 100
1:41:32years. And then you have smaller inner
1:41:35cycles of ups and down in the economy.
1:41:38These are the shortterm debt cycles. And
1:41:41especially the short-term debt cycles
1:41:43are basically driven by central banks
1:41:46and their monetary policies. And central
1:41:48banks basically manage monetary policy
1:41:52including interest rates and open market
1:41:55operations such as quantitative easing
1:41:58or tightening. I will explain them now
1:42:00but just know first that they use
1:42:02monetary policies to keep stable prices
1:42:05aka an inflation rate below 2% and keep
1:42:09maximum employment which means a low
1:42:11unemployment rate. even though they
1:42:12don't have a specific target. Anything
1:42:14above 4% 5% can start to be a little too
1:42:17much for an economy like the US right
1:42:19now. So in their constitution their goal
1:42:22needs to be st price stability and
1:42:25maximum employment inflation rate below
1:42:262% and low unemployment. And how they
1:42:29manage to do this is by playing with
1:42:32these two things interest rate decisions
1:42:34and open market operations. But before
1:42:36we dive in deep into interest rates,
1:42:38just know this video is taking me days
1:42:40to make and it's taken me years to learn
1:42:42all of this stuff. So, I would
1:42:43appreciate you to leave a like to help
1:42:45me out with the algorithm. Now, what are
1:42:47interest rates? Well, we all know
1:42:48interest rates. For example, if you ask
1:42:50a loan to a bank, they will, let's say,
1:42:52loan you $100,000 plus 5% interest rate,
1:42:55which means on those 100,000, every year
1:42:58you have to pay 5%. Which means that on
1:43:01those $100,000 loan you take, you have
1:43:03to pay 5% annual interest rate on that
1:43:07100,000, which for example in one year
1:43:08is going to be $5,000, right? But
1:43:11specifically the interest rates that the
1:43:13central banks are setting, they are
1:43:15overnight interest rates on interbank
1:43:18deposits specifically. So for example,
1:43:20you have bank A, you have bank B and
1:43:23then you have the Fed, the Federal
1:43:25Reserve, which is the central bank of
1:43:26the United States. So why is it
1:43:28overnight interest rates on interbank
1:43:29deposits? Because basically sometimes
1:43:32bank A at the end of the day might have
1:43:34some extra cash, some extra reserves. So
1:43:36he will basically loan that deposit to
1:43:39bank B overnight. So the interest rate
1:43:42at which this transaction happen is
1:43:44always within a range defined by the
1:43:46Federal Reserve. In this case, they're
1:43:48called the federal funds rates. And the
1:43:51same thing happen if for example the
1:43:53banks are choosing to deposit that money
1:43:55overnight to the Federal Reserve and the
1:43:57Federal Reserve will pay these banks an
1:43:59interest rate. This is also called the
1:44:01IORB or interest on reserve balances or
1:44:05it can also happen the Federal Reserve
1:44:07will lend some reserves of cash to bank
1:44:10A or bank B and then the bank will have
1:44:13to pay the discount rate and these are
1:44:15typically on a range of 0.25 basis
1:44:18points. For example, the target federal
1:44:21fund rates could be between 4 and 4.25%.
1:44:25So the Federal Reserve sets the target
1:44:27of the interbank federal fund rates. So
1:44:29interest rates that banks make between
1:44:31each other somewhere in between the
1:44:33discount rate and the interest on
1:44:35reserves. And if I open the Fed funds
1:44:37chart, this is the history of the
1:44:39Federal Reserve interest rates. And as
1:44:41you can see during the last year, for
1:44:42example, during COVID, they dropped them
1:44:44significantly up to the point where it
1:44:46was zero. And then when inflation came
1:44:49up after the COVID, they rose interest
1:44:51rates. This is also known as the hiking
1:44:53cycles where they hike rates. And now
1:44:55we're in a phase of lowering interest
1:44:57rates. And as you can see, this happens
1:45:00in cycles. Similar to what we said are
1:45:03the economic cycles that the economy
1:45:06goes through. And the Fed decides to
1:45:08either hike or cut interest rates for
1:45:11one main reason, which is to incentivize
1:45:14access to credit. So they cut interest
1:45:16rates to incentivize access to credit.
1:45:19This way if the target rates of the
1:45:21federal fund rates are at 0% if an
1:45:24individual goes to the bank and asks for
1:45:26a loan, the interest rates on this loan
1:45:30will likely be closer to 0%. While if
1:45:32the interest rates are high, this is
1:45:34disincentivizing people to ask for
1:45:37loans. So since the interest rates are
1:45:39high, it's less convenient to ask for a
1:45:41loan if the interest rates are are at
1:45:445%. rather than 0%. And at the end of
1:45:47the day, the federal fund rates or any
1:45:50central bank's interest rates are
1:45:51nothing more than the cost of creating
1:45:54new money. Because as we said, whenever
1:45:56there's a loan, new money is created. So
1:45:58following its dual mandate to keep
1:46:01stable prices and maximum employment, we
1:46:03can already understand that if inflation
1:46:06is super high because people are
1:46:08spending a lot, maybe hiking interest
1:46:10rates will disincentivize people to ask
1:46:13for loans. they will consume less, they
1:46:15will buy less, the overall economic
1:46:17activity will be shrunk and this can
1:46:19lead to a drop in inflation. Or if their
1:46:22goal is to keep maximum employment and
1:46:25suddenly the unemployment rises because
1:46:27there's a recession coming in, the
1:46:28central bank might drop interest rate so
1:46:31that people are more incentivized to
1:46:33leverage and take on new loans. More
1:46:36money will be created. So there will be
1:46:37an expansion in the money supply. Hence
1:46:39in the economy as well, companies will
1:46:41start hiring more and the unemployment
1:46:43rate will go down again. Let's view that
1:46:45in a cycle. Let's draw our GDP chart in
1:46:48our macro cycle and let's say we are
1:46:50just coming out of a period of
1:46:52recession. At this point in this period
1:46:55of time, it's very likely that the
1:46:57employment is low or the unemployment is
1:46:59really high. People don't have a lot of
1:47:01jobs. The economy is [ __ ] So the Fed
1:47:03will intervene by lowering interest
1:47:05rates. This will cause economic activity
1:47:07to pick up and eventually to expand
1:47:10because people take on more debt blah
1:47:12blah blah and the economy grows, right?
1:47:13But it can come up to a point where the
1:47:16economy is doing so good that inflation
1:47:18starts being the problem instead. And
1:47:19when inflation start being the problem,
1:47:21that's where they hike interest rates.
1:47:23And that will typically slow down
1:47:25economic activity because people will
1:47:27have less loans. The cost of previous
1:47:29debt will rise and that will lead to a
1:47:32contraction in economic activity. and
1:47:35sometimes to a recession. And the second
1:47:37thing they can do is so-called open
1:47:38market operation or quantitative
1:47:40tightening or easing. And this is a more
1:47:43complicated mechanism that is often not
1:47:45understood when studying monetary
1:47:47policies. Most people would just call
1:47:49this money printing, but it's not that
1:47:51easy. But in order to understand how
1:47:53open market operations work, we need to
1:47:55first understand how a balance sheet
1:47:58works. For example, if you go here and
1:48:00you write WCL,
1:48:03you have the balance sheet of the
1:48:04Federal Reserve Bank. And you can see
1:48:07that their balance sheet is sometimes
1:48:10expanding, sometimes contracting,
1:48:12sometime expanding, sometimes
1:48:13contracting, sometime huge expansions
1:48:16followed by a contraction. So you also
1:48:18see this idea of a cycle here, right?
1:48:20But let's first understand what it is.
1:48:22The balance sheet of let's say a
1:48:24company. It's basically a summary an
1:48:26accounting sheet of all the company's
1:48:29assets and liabilities and that
1:48:32basically helps us understand the
1:48:34financial situation of the company.
1:48:36Typically in the assets you will have if
1:48:38they have for example any machinery or
1:48:40some intellectual property rights for
1:48:42example trademark or patents or lands
1:48:45any type of assets or maybe software
1:48:47that's part of their assets. And let's
1:48:49say for example this amounts to $1,000.
1:48:52And then you have cash which is an
1:48:54asset. You have bank accounts balances.
1:48:58So bank balances and other forms of cash
1:49:01for a total of let's say $500. And on
1:49:03the liabilities side you have capital
1:49:06contributions from the owner who for
1:49:08example he is the one who have bought
1:49:09the machinery have the IP rights and
1:49:12owns the software. So that's the $1,000.
1:49:15Then in the liabilities you typically
1:49:17have the profit which could be let's say
1:49:19$200 and also debt. And let's say the
1:49:23company has a debt of $300 because maybe
1:49:26out of those $500 they have in cash,
1:49:29200s comes from the profit that the
1:49:31company made that year. And that profit
1:49:33is in the liabilities because it's
1:49:36actually money that has to be given back
1:49:38to the owner through dividends. The
1:49:40capital contribution is some sort of a
1:49:42debt to the owner himself. And the debt
1:49:44is simply a debt to someone else, maybe
1:49:46a bank or an investor. But the point is
1:49:49assets and liabilities always balance.
1:49:51So, you're always going to have $1,500
1:49:53here and $1,500 here. And for example,
1:49:56when you do the fundamental analysis of
1:49:59a stock, you typically take a look at
1:50:01the composition of the balance sheet.
1:50:03How much of that is debt, how much is
1:50:05capital contribution, how much liquidity
1:50:08they have compared to how much debt they
1:50:09have. And so this thing called balance
1:50:11sheet is basically a summary of the
1:50:14financial situation of the company. And
1:50:15the total assets is always evening out
1:50:18with the total liabilities. Let's do now
1:50:20the balance sheet of a bank. A balance
1:50:21sheet of a bank will look something like
1:50:23this. You will have reserves of
1:50:25liquidity or cash for let's say a
1:50:28th00and loans to businesses or to
1:50:31individuals. So basically money that
1:50:33other people owns the bank which is a
1:50:36credit for the bank. So it goes into the
1:50:38assets and for example they will have
1:50:41securities such as bonds, stocks and so
1:50:45on for let's say another $1,000 and the
1:50:47total will be $3,000. And in the
1:50:49liabilities instead they will have of
1:50:51course the owner's equity. So the
1:50:53capital of the owner can who for example
1:50:56can be $15 $1,500. And then they have
1:50:59all the deposits from people and from
1:51:01businesses. All the money that they are
1:51:03holding in the bank for other people or
1:51:06for other businesses that is basically a
1:51:08debt to everyone else, right? The money
1:51:10they hold for other people. And let's
1:51:12say that's also 1,500. And also here the
1:51:14balance checks out 3,000 3,000. Now
1:51:17let's see the balance sheet of the
1:51:18Federal Reserve or the central bank.
1:51:20This is the central bank's balance
1:51:22sheet. It can be for example the Federal
1:51:24Reserve banks. And typically in their
1:51:26assets you will see securities mostly in
1:51:29the form of government bonds. As you
1:51:31remember a bond is basically an IOU a
1:51:34debt that the government has and it's
1:51:36part of their assets because the
1:51:38government owes the money that it's
1:51:40written on those bonds. Right? Then in
1:51:42the asset class they have loans. So
1:51:45money or reserves that they have loaned
1:51:47to let's say banks they will have
1:51:49typically some reserves of foreign
1:51:52exchange currencies that sometimes
1:51:54central banks use to influence the forex
1:51:56market and kind of rebalance the
1:51:58exchange rates at times. And then they
1:52:00have gold reserves which is something
1:52:02that central banks in the last few years
1:52:04have been absolutely hungry about. In
1:52:06the liabilities they have money
1:52:08specifically currency so banknotes and
1:52:10coins that are circulating in the
1:52:12economy. They all come from the Federal
1:52:14Reserve Bank. Then at times they have
1:52:16something called the reverse repo
1:52:17facility which we'll not get deep into
1:52:19now. And then they hold the bank account
1:52:21for the government. So the US Treasury
1:52:23general account which you can also find
1:52:25on Trading View. For example, if you
1:52:27write Wre Gen Treasury General Account
1:52:30and this is basically the current bank
1:52:32account of the government at the Federal
1:52:34Reserve. And then they have bank reserve
1:52:36balances. Remember when I told you that
1:52:38a bank deposits some bank reserves, some
1:52:41liquidity, some cash at the Federal
1:52:43Reserve? Well, that's it. So now you
1:52:45have understood what is a balance sheet.
1:52:47What's the composition for example of a
1:52:48company balance sheet versus a bank's
1:52:50balance sheet versus the central bank's
1:52:52balance sheet. But how does this help us
1:52:55into understanding how this WCL
1:52:59is typically used in cycles of expansion
1:53:02of the balance sheet and contraction of
1:53:04the balance sheet? expansion and
1:53:06contraction, also known as QE or
1:53:09quantitative easing or QT, quantitative
1:53:12tightening. In order to properly
1:53:14understand that, we need to add another
1:53:16concept into our map, a crucial concept
1:53:18called interbank liquidity. Let's get
1:53:20back to our initial drawing of a normal
1:53:23bank loaning money to an individual.
1:53:25Now, let's say this dude has $100,000
1:53:28and deposits this money into his
1:53:30checking account or savings account.
1:53:32Now, by law, the bank is only required
1:53:36to keep 2% of this money as reserve. So,
1:53:39for example, $2,000 as a reserve and
1:53:43basically use that 98,000
1:53:46remaining to loan it out to some other
1:53:49person that might need it. But these 98
1:53:51are not actually taken from this guy's
1:53:54money. They're basically created out of
1:53:56thin air as soon as someone decides to
1:53:58take it as a loan. So whenever someone
1:54:00comes and asks a loan to a bank, the
1:54:03bank will create new money out of thin
1:54:05air based on based on the fact that they
1:54:08just need a 2% reserve. This mechanism
1:54:10is also called the fractional reserve
1:54:13system. Then let's say this person takes
1:54:15a loan take these 9 $98,000 and then
1:54:19redeposits this money into the bank in
1:54:21his bank account. Now, the bank will
1:54:23only have to keep 2% of 98% which is
1:54:27exactly $1,960
1:54:30as a 2% reserve. The remaining money,
1:54:33which is $96,40,
1:54:35can be used as a loan to other
1:54:37customers. That will again redeposit the
1:54:39money and the cycle could continue
1:54:41endlessly. And out of those $100,000
1:54:44that initially were deposited into the
1:54:45bank, a lot of new money can be created.
1:54:48And of course this creates an expansion
1:54:50of the overall money supply and this is
1:54:53how new money basically is created in
1:54:55the banking system in the private
1:54:56banking system. But some of it stays
1:54:58there this fractional reserve and they
1:55:01keep only 2% because on average out of
1:55:04all of the deposits that all of these
1:55:06people made 2% is what is statistically
1:55:09required if people goes to the bank and
1:55:12for example withdraw some money at the
1:55:14ATM let's say $100. So that 2% is only
1:55:17there to make up for people going into
1:55:20the ATM and withdrawing some of their
1:55:22money. But if all of a sudden all of the
1:55:25people went to the bank and wanted to
1:55:27withdraw all of their money, they will
1:55:29quickly find out that the bank does not
1:55:31have it. This typically does not happen
1:55:33very often, but when it happens, it can
1:55:35create a great distress in the financial
1:55:38system. This is for example what
1:55:39happened in 2008 during the great
1:55:41financial crisis. It's also what almost
1:55:44happened in 2023 during the regional
1:55:46bank crisis. So these bank reserves are
1:55:50exactly what we saw here in the
1:55:52liabilities of the balance sheet at the
1:55:54Federal Reserve Bank. And so what
1:55:56happened for example during 2008 is that
1:55:58all of these people started asking money
1:56:00to the banks but the banks only had some
1:56:03reserves, some deposits and then they
1:56:06had some securities. So they had some
1:56:09assets such as bonds, such as stocks and
1:56:12such as for example MBS's or mortgage
1:56:15back securities. In the 2008 financial
1:56:18crisis, it became clear that in the
1:56:20average balance sheet of a bank, these
1:56:23securities were not liquid enough, were
1:56:25not high quality enough to eventually be
1:56:28liquidated and sold back to the market
1:56:31in case they needed to face a high
1:56:33volume of withdrawals and run out of
1:56:36reserves. So in 2008 a new law came to
1:56:39life that required the banks to have a
1:56:41better liquidity coverage ratio. So
1:56:44basically this new rule, this new law on
1:56:46the liquidity coverage ratio basically
1:56:48meant that the reserve of highly liquid
1:56:51high quality so with a good credit
1:56:53rating assets or securities the ratio
1:56:56between this part of the balance sheet
1:56:59of a bank and the expected and the
1:57:01expected outflow of cash in the next 30
1:57:05days based on a stress test should be
1:57:08equal or above 100%. And by reserve of
1:57:11highly liquid assets or highquality
1:57:13assets, we basically mean cash, central
1:57:16bank reserves, government bonds or other
1:57:19forms of bonds such as qualifying
1:57:21corporate bonds rated AA minus or
1:57:23higher. So even corporate bonds but with
1:57:25a high credit rating based on the Basil
1:57:273 international framework. The total
1:57:30amount of these assets should be equal
1:57:32or more than 100% of the expected cash
1:57:35flow in the next 30 days. This way, if
1:57:37people start suddenly asking for all of
1:57:39their money, the banks can quickly
1:57:41liquidate some of their bonds, some of
1:57:43their stocks, and eventually use some of
1:57:46their reserves to let people withdraw
1:57:48their money and not create a stress in
1:57:49the financial system. But as we said in
1:57:512008 this was not there basically and
1:57:54that's why the first really big
1:57:56expansion in the Federal Reserve balance
1:57:58sheet happened exactly the first great
1:58:01quantitative easing movement happened
1:58:03exactly in '08 during the financial
1:58:06crisis. Another big boom, not a gradual
1:58:09rise up, a big boom happened during the
1:58:12COVID crash. And there was a little bump
1:58:14that happened right when we were about
1:58:16to see a regional banking crisis. And
1:58:19all of these huge open market operations
1:58:22that the Federal Reserve has implemented
1:58:24was for example because of this type of
1:58:27situation where there was a high
1:58:28distress in the financial system. And so
1:58:30what the central bank did was to
1:58:32basically being a net purchaser of
1:58:34government bonds so that the banks could
1:58:37easily liquidate some of those
1:58:39securities, sell it to the central banks
1:58:41that would print bank reserves to pay
1:58:44for these bonds so that the banks could
1:58:47have enough reserves to let their
1:58:49customers withdraw and being overall
1:58:51financially stable. So what quantitative
1:58:53easing does really is not printing money
1:58:56is printing new bank reserve balances to
1:58:59basically purchase bonds from the
1:59:01interbank market to give banks more
1:59:04reserves. So new bank reserves also
1:59:07known as interbank liquidity is this.
1:59:10That's it. So in phases of quantitative
1:59:12easing the central bank is flooding the
1:59:14market with liquidity with bank reserves
1:59:16and purchasing government bonds. And the
1:59:19opposite happens during quantitative
1:59:20tightening where interbank liquidity and
1:59:22reserves are not a problem anymore.
1:59:24They're not in stress anymore. And so
1:59:26the central bank is shrinking their
1:59:28balance sheet instead. And there's also
1:59:30two important phases. The quantitative
1:59:32easing before it moves to quantitative
1:59:35tightening. It has some sort of
1:59:36slowdown. That's when we talk about
1:59:38tapering because they taper the
1:59:41quantitative easing. They slow it down.
1:59:42or after a phase of quantitative
1:59:45tightening they slow down and that's
1:59:47also called a phase of tapering. So they
1:59:50slow down the sales of these bonds. So
1:59:52open market operation in general are
1:59:54used to lubricate the financial system
1:59:57to keep it liquid and to suppress bond
2:00:00volatility. This is for example what
2:00:02happened here during the COVID crash.
2:00:05There was a huge problem in the
2:00:07liquidity of government bonds and the
2:00:09Federal Reserve stepped in and purchased
2:00:12awful amount of government bonds that
2:00:14basically no one wanted to buy to
2:00:16suppress the volatility of the bond
2:00:18market. So if interest rate, we could
2:00:20say they have a more direct impact on
2:00:23the economy. Open market operation,
2:00:25quantitative easing and quantitative
2:00:26tightening, they have an effect that is
2:00:28more direct to the financial system to
2:00:30be able to support the economy. So now
2:00:32you've basically understood the core of
2:00:34literally how money works from who
2:00:36prints it to who manages it to who
2:00:39actually gets it. So how do we
2:00:41contextualize what we've learned now to
2:00:43understand and possibly predict where is
2:00:47the macroeconomic cycle going to go and
2:00:49how is that going to affect all of the
2:00:51different markets fundamentals so that
2:00:53we can read them through the liquidity
2:00:55auction theory with a macroeconomic
2:00:57context. You could imagine, for example,
2:00:59the economy as a car with Jerome Powell
2:01:02driving and it has one foot on the
2:01:04accelerator and one foot on the brake.
2:01:06And let's say we're coming out from a
2:01:08phase of recession. Typically, the
2:01:10unemployment is up. Inflation is
2:01:13typically low and the central bank at
2:01:15this point typically cuts interest rates
2:01:18very aggressively to stimulate the
2:01:20economy and at the same time to
2:01:22lubricate the financial system and
2:01:24provide liquidity to the bond market.
2:01:26They will also start quote unquote to
2:01:28print money and expand their balance
2:01:31sheet. So this is the balance sheet.
2:01:32These are the interest rates in yellow.
2:01:34We have inflation in blue and
2:01:36unemployment in red. These are the two
2:01:38economic metrics that the Federal
2:01:41Reserve is taking care of. And these are
2:01:43the two tools of monetary policies they
2:01:45have. So because inflation is not a
2:01:46problem, they have to deal with
2:01:48unemployment and the fact that the
2:01:49economy is really struggling. So in
2:01:51order to make it pick it up, they will
2:01:53lower interest rates. So people will be
2:01:55more incentivized to take loans. So
2:01:57people will take loans to invest in
2:01:59their business to buy a car to buy a new
2:02:02house which means that people will spend
2:02:04more. The companies will earn more money
2:02:07and they will be able to invest more for
2:02:09example in hiring new people and this
2:02:12can take the unemployment down and the
2:02:14Federal Reserve is happy. So the economy
2:02:16overall catches up. Typically the bank
2:02:18will keep the interest rates sort of low
2:02:20in this period and will sort of keep
2:02:22this money printing this bank reserve
2:02:24printing to facilitate the bond market
2:02:26overall and it's likely that at some
2:02:28point because of all this rising
2:02:30economic activity people will buy more
2:02:33stuff and stuff will hence cost more. So
2:02:36inflation will start picking back up
2:02:38slowly and whenever unemployment is not
2:02:41a problem anymore because everything's
2:02:42fine, everyone has a job, there's a 3%
2:02:45unemployment, but then inflation start
2:02:48rising. We're typically very close to
2:02:50the peak of the cycle. The central banks
2:02:52will have to hike interest rates because
2:02:54now its problem is starting to be
2:02:57inflation instead. Now employment is not
2:02:59a problem anymore, but inflation is. So
2:03:02because of overheat in the economy, the
2:03:04prices start rising or maybe god forbids
2:03:06a war starts in the Middle East and oil
2:03:09prices explode up and so inflation
2:03:12explodes as well. At this point, they
2:03:13will hike interest rates and basically
2:03:16tighten the economic conditions because
2:03:18people will have higher interest rates
2:03:20to pay on their loans. They will be less
2:03:21incentivized to take on more debt. And
2:03:24at the same time they will likely slow
2:03:26down this expansion of the balance sheet
2:03:28and slowly start printing less and less
2:03:31bank reserves and actually start
2:03:33shrinking their balance sheet. Normally
2:03:34in history most of the times we've seen
2:03:37a hiking cycle of the interest rates. We
2:03:40have also seen some form of contraction
2:03:42in the economy. This happens typically
2:03:44after inflation has cooled down. But
2:03:47this will slowly bring unemployment up.
2:03:50And if we do indeed get into a state of
2:03:53recession where the economy is really
2:03:55really suffering and employment becomes
2:03:58a problem again while inflation is not
2:04:01anymore. That's where the cycle will
2:04:04invert again and central banks will
2:04:06start cutting rates very quickly and
2:04:08start printing money again. This way the
2:04:10economy can slowly rise back up and the
2:04:14cycle continues. Now, let's take a look
2:04:16at some historical examples by looking
2:04:18at the S&P 500 and adding the Fed funds
2:04:21rate, the inflation rate, and the
2:04:23unemployment rate to truly see what
2:04:25exactly happened. Let's use the same
2:04:27colors we used in the drawing. Let's
2:04:30start whenever we have a decent amount
2:04:31of data. We see for example that in 1957
2:04:35after a slow but sure rate hike because
2:04:39of inflation starting to picking up and
2:04:41reaching about 4%. After this rate hike
2:04:45we had a burst in unemployment. So the
2:04:47Federal Reserve decided to cut interest
2:04:49rates. In the meantime inflation was not
2:04:51a problem anymore. They kept rates low
2:04:53and as soon as the unemployment rate was
2:04:56not a problem anymore they slowly hiked
2:04:58rates. Something similar happened in the
2:05:00late60s when inflation picked up. So the
2:05:02bank hiked interest rates and that
2:05:04caused another recession and we see it
2:05:06from the unemployment quickly going up,
2:05:08inflation dumping down because of slower
2:05:11economic activity. Hence the Federal
2:05:13Reserves lowers interest rates again.
2:05:15Then unemployment starts not being a
2:05:17problem anymore, but inflation picks up
2:05:19again and so the Fed hikes rates once
2:05:22more and the inflation rate this time is
2:05:24really really bad. So they hike interest
2:05:26rates really really fast and that causes
2:05:29another recession. Unemployment picking
2:05:31up and because of this while
2:05:33unemployment is picking up inflation
2:05:35rate goes down because people don't
2:05:37spend. So the prices of stuff goes lower
2:05:39and they can afford to lower interest
2:05:41rates instead. Keep them low for a
2:05:43decent period of time to let all of this
2:05:45recession to kind of cool off. And as
2:05:47soon as the unemployment rate is not a
2:05:49problem anymore, but inflation start to
2:05:51picks up again. Boom. You have another
2:05:53hiking cycle. After this other hiking
2:05:55cycle, they had to drop rates because
2:05:57they caused another recession and prices
2:06:00dropping, but they didn't drop as fast
2:06:02as they imagined. So, they had to rehike
2:06:04interest rates, cause another recession,
2:06:06and then prices eventually calmed down.
2:06:08This was a big mess in the 1980s. A new
2:06:12hiking rate happened during a new rise
2:06:14in inflation in the 80s and the '90s,
2:06:16and this hike again caused a new
2:06:18recession with unemployment starting to
2:06:20pick up. So the Fed had to cut interest
2:06:22rates all over again, keep them pretty
2:06:24low. Inflation was pretty much stable.
2:06:27The unemployment rate was gradually
2:06:28getting lower. Interest rates were
2:06:30pretty much stable overall. And after a
2:06:33hike, inflation started picking up
2:06:35again. So the Fed hiked [snorts]
2:06:37interest rates again. And that caused
2:06:38another recession. This also happened in
2:06:40this also happened at the same times
2:06:42with a stock market bubble and then
2:06:44again slowly inflation starting picking
2:06:46up. So they hike interest rates and
2:06:49boom, welcome to the 2008 financial
2:06:51crisis. Big unemployment. So they cut
2:06:54rates drastically. Inflation goes down.
2:06:56And as soon as this is not a problem
2:06:58anymore, they can finally start to
2:07:01slowly rise interest rate again. So a
2:07:03new hiking cycle begins. And here we
2:07:05have the highest peak in history because
2:07:07of COVID. So the Fed cuts interest
2:07:09rates. Then unemployment is not a
2:07:11problem anymore. Inflation picks back
2:07:13up. So the Fed has to hike rates again.
2:07:15then inflation is not a problem anymore.
2:07:17Employment starts to be worrying the Fed
2:07:19a little bit. So they start cutting
2:07:21interest rates. So this is a cycle that
2:07:23repeats eternally. And if you plot the
2:07:26S&P 500 here, you will see that
2:07:29financial markets feel this very deeply.
2:07:32We typically see during hiking cycles a
2:07:34lot of bare markets. For example, new
2:07:37hiking cycle, the stock market drops
2:07:39because it expects a recession that
2:07:42after materializes. And again here new
2:07:45hiking cycle stock market crash
2:07:47recession coming again in the 80s quick
2:07:49hiking cycles bare market again during a
2:07:52new recession new hiking cycle.com
2:07:55bubble burst recession new hiking cycle
2:07:58market expects a recession which then
2:08:01materializes same thing happened over
2:08:03here during COVID. So you kind of start
2:08:05understanding how the money flows in and
2:08:07out of the stock market based based on
2:08:09in which part of the economic cycle we
2:08:11are. Or to be even more accurate, if
2:08:14this is the representation of the cycle,
2:08:16the stock market will try to anticipate
2:08:20what the economy will do typically with
2:08:22a time window of 6 to9 months. Because
2:08:26all of the economic data that builds the
2:08:28cycle is somewhat lagging because it's
2:08:31coming month after month, quarter after
2:08:34quarter. the market will try to place a
2:08:37bet based on every single data point
2:08:39from employment, inflation and for
2:08:42example price in a recession before the
2:08:45recession actually materializes. And
2:08:47this is probably the most important
2:08:49thing you need to understand when you
2:08:50want to analyze or trade the market and
2:08:53join long-term money flow trends based
2:08:56on macro. You need to understand that
2:08:58there's this lag and the markets are
2:09:00very efficient behind the markets.
2:09:02There's people with degrees in
2:09:05macroeconomics. They have crazy
2:09:07predictive models which sometimes work,
2:09:09sometimes don't. So when we're starting
2:09:11to study the macroeconomic cycles, this
2:09:13is just one piece of the puzzle because
2:09:15often times the data point that build
2:09:17the current cycle might point towards
2:09:19somewhere and sometimes financial
2:09:22markets might go in a direction that
2:09:24seem completely irrational and is not
2:09:26actually matching your macro view. But
2:09:28you have to understand that whoever is
2:09:29placing billions of dollars might be
2:09:32smarter than you and actually understand
2:09:34macroeconomics better than you. That's
2:09:36why we want to look not just at macro by
2:09:38itself, but we want to understand the
2:09:41macro sentiment. So by looking at price
2:09:44and volume of financial markets, we can
2:09:47understand what type of macroeconomic
2:09:49scenario they believe will happen and
2:09:52not just out of sheer belief but money
2:09:54put on the table. So what is the market
2:09:56betting the next 6 to9 months of economy
2:09:59will look like? But now that we have
2:10:01thoroughly understood how the macro
2:10:03cycles work, what's the role of the
2:10:04central bank in kind of driving and
2:10:07facilitating the money flow in the real
2:10:10economy through interest rate decisions
2:10:12and through lubricating the financial
2:10:13system through quantitative easing and
2:10:15quantitative tightening. We pretty much
2:10:16get the basics of macroeconomics. Then
2:10:18there's a lot more data points that we
2:10:20can look at, but I want to keep it
2:10:22simple. There's it's already a lot of
2:10:23stuff. I understand it. But I want to
2:10:24keep it simple. We only have the only
2:10:26thing I need you to worry about and the
2:10:27only thing I need you to start caring
2:10:29about is where's the employment going
2:10:31and where is the inflation going and
2:10:33always look at how the markets is
2:10:35reacting to these news. Not just in the
2:10:37short term, not just short-term price
2:10:39action, but the days and the weeks
2:10:41following some specific market data.
2:10:43Now, let's have a throwback to the last
2:10:45years. When COVID happened, the first
2:10:47thing that the Federal Reserve did was
2:10:48dropping interest rate at zero. Plus, if
2:10:51we add the balance sheet of the Federal
2:10:53Reserve, they were granting a lot of
2:10:55liquidity in the financial system. And
2:10:56when there's a lot of liquidity in
2:10:58financial system, usually this
2:10:59translates into a higher risk appetite
2:11:02and stocks typically going higher. When
2:11:04you have super low interest rates, a lot
2:11:06of money printing, stocks just go up.
2:11:09There's not much more to say. But as
2:11:11soon as inflation rate started to pick
2:11:13up, the Federal Reserve was telling us,
2:11:14hey, this inflation is temporary. Don't
2:11:16worry, guys. But then it started picking
2:11:18up again and again and the market
2:11:20started not believing the Federal
2:11:22Reserve anymore and already started
2:11:24pricing in the fact that the Fed will
2:11:26eventually hike interest rates which
2:11:28happened here in March April 2022. But
2:11:30typically the Federal Reserve will
2:11:32announce this way earlier. So as soon as
2:11:35they started announcing a new hiking
2:11:37cycle, typically a new hiking cycles
2:11:39together with this high inflation
2:11:41historically has always brought some
2:11:43level of recession in the American
2:11:45economy. And so the market already
2:11:47started pricing in the fact that a new
2:11:50recession will eventually start to
2:11:52unfold and we had a new bare market. But
2:11:54as soon as the inflation started running
2:11:57really really low but at the same time
2:11:59the unemployment rate was not moving was
2:12:02just staying flat then the market
2:12:04understood okay inflation is going down
2:12:06no sign of recession seems to be
2:12:07materializing. So they started buying
2:12:09back up. So in this case, a bare market
2:12:11was trying to price in a potential
2:12:15recession that didn't happen. And then
2:12:17you had probably one of the craziest
2:12:19bull market that America and the US
2:12:22stock market has ever seen. And during
2:12:24this crazy bull market, which was also
2:12:26fueled by the what people call the AI
2:12:28bubble, these big candles that you see
2:12:30here all happened during news releases.
2:12:33So whenever a new data of for example
2:12:36inflation data or NFP data came out you
2:12:39would see these booms in prices boom and
2:12:42it was not just a one-time volatility
2:12:44but these news were eventually drivers
2:12:47of trend. So the sentiment of the market
2:12:49around macroeconomics is what ultimately
2:12:52drives the long-term trend. And the same
2:12:54thing by the way was happening whenever
2:12:56there was a news release maybe around
2:12:58inflation that was not particularly
2:13:01positive. For example, here we had one
2:13:03we had another inflation data coming out
2:13:05here worse than expected and inflation
2:13:07was going crazy, right? And so price
2:13:09dropped significantly and it was not
2:13:11just a news release that faded. So just
2:13:13some short-term volatility. It was a
2:13:16trend setting data, right? Same thing
2:13:18over here in here or here another
2:13:20inflation data came out or unemployment
2:13:22data came out and price dropped because
2:13:25in this period in this specific
2:13:27historical period the market was scared
2:13:29about this and the main narrative of the
2:13:31markets inside of this historical period
2:13:33inflation is the problem right so the
2:13:36market follows a narrative for example
2:13:38during the tariff war of Trump the main
2:13:41narrative was tariffs and nowadays again
2:13:44it is tariffs that's why we talk about
2:13:46macro sentiment ment we want to see the
2:13:48reaction of the market to macroeconomic
2:13:51news data so that we can understand
2:13:53where the long-term trend of money is
2:13:56going towards. Now that we have
2:13:58understood the basic of macroeconomics,
2:14:00we need to put the final pieces of the
2:14:02puzzles to understand the fundamentals
2:14:03of each markets. And the last market we
2:14:05need to explain are precious metals,
2:14:07bond market and the forex market. And I
2:14:09would like to start with the bond
2:14:10market. So the bond market is the market
2:14:13of government treasury securities or
2:14:16government bonds. You have the treasury
2:14:18bills, the treasury bonds and the
2:14:20treasury notes. And a bond looks
2:14:23something like this. Me, I, the US
2:14:26government, owe the bearer of this bond,
2:14:28let's say $100,000 plus 5% interest
2:14:32rates one year from now. Okay, this is
2:14:34basically all a bond is. It's a debt
2:14:37security. So basically someone it can be
2:14:40a person or it can be a bank can be a
2:14:43foreign bank it can be a domestic bank
2:14:45or it can be a company will lend money
2:14:47to a government in this case and buy
2:14:50this bond. So the government in order to
2:14:52finance all of its activities will issue
2:14:55bonds will basically create debt
2:14:58securities that people can purchase. So
2:15:00the government gets the money and the
2:15:02lender whether it is an individual, a
2:15:05bank or a company can earn an interest
2:15:08rate. They come in different forms.
2:15:10There's zero coupon bonds. So just bonds
2:15:13that you know give you the whole amount
2:15:15plus the interest rates at the end of
2:15:17the expiration. Or there's bonds that
2:15:19give you a 6 months or a 3 months coupon
2:15:22where they slowly slowly give you the
2:15:245,000 throughout the year. That's why
2:15:26they're also called fixed income assets.
2:15:29But the basic principle is after some
2:15:31time the government gives the money back
2:15:34to the lender with some interest rates
2:15:36and now that doesn't exist anymore. But
2:15:38at the same time the person who bought
2:15:40this can also sell it to other people.
2:15:42Right? So in the government bond market
2:15:44you typically have something called the
2:15:46primary market where freshly created new
2:15:50bonds are sold in auctions to basically
2:15:53a group of big banks. So these banks
2:15:56purchase these bonds and they lend this
2:15:59money to the government. So the
2:16:00government have money to spend and banks
2:16:02have a way to earn an interest rate. But
2:16:05they can also sell it and trade it and
2:16:08even speculate on it in the secondary
2:16:10market which is the market we all know
2:16:12where there's traders, banks, investors,
2:16:15companies, individuals, all sorts of
2:16:17market participants. And so these can be
2:16:19traded, right? This is of course not the
2:16:21only type of bonds that exist. These are
2:16:23government bonds. But also companies can
2:16:25issue bonds and they're also called
2:16:27corporate bonds. So the company for
2:16:29example in order to in order to finance
2:16:32its operation and invest in stuff will
2:16:34issue bonds that banks and other
2:16:36investors can purchase to earn an
2:16:39interest rate. And for all of these
2:16:40bonds, there is a system that basically
2:16:42tells you how creditw worthy is the
2:16:45issuer of this bond. So how risky is it
2:16:48to lend this guy money? Typically, the
2:16:50government is always kind of the safest
2:16:53entity to lend money to because you're
2:16:55pretty sure the government will pay its
2:16:57bills. Typically, it's not always like
2:16:58that. The big corporates, for example,
2:17:00Apple or, you know, companies that are
2:17:02very liquid can also be creditworthy.
2:17:05Some companies, they might be kind of
2:17:07risky. And there's a score, a metric
2:17:09that the so-called rating agencies such
2:17:12as standard and pores or Moody's or
2:17:15Fitch. These companies will basically
2:17:17give a rating to all sorts of bonds. And
2:17:20they will typically look something like
2:17:22this. A double A triple B
2:17:26triple C
2:17:27and finally D. A tier bond maybe even
2:17:30down to B or double B and are often
2:17:33referred to as investment grade bonds.
2:17:36So bonds that are worth investing.
2:17:38Anything that is below is typically
2:17:41referred to as junk bonds, very high
2:17:44risky bonds. So starting here, you have
2:17:46super high credibility, super high
2:17:48creditworthiness, then gradually less
2:17:50and less and less and less until you go
2:17:52to the D that stands for default. It's a
2:17:55bond that it's highly likely that will
2:17:57not get paid. And that's by the way what
2:17:59we mean when a company or a government
2:18:02defaults on its debt. basically means
2:18:04that is not able to pay either all of
2:18:08the money. So maybe they will pay just a
2:18:10fraction of it by restructuring debt and
2:18:12or not within the promised time frame.
2:18:15So this is the definition of default.
2:18:18And you can already start understanding
2:18:19that if you're going to lend money to a
2:18:21very creditworthy person and not take
2:18:23much risk, maybe you can ask for a 2%
2:18:25return. Maybe, right? Let's say you ask
2:18:27for a 2% return. Well, if you're going
2:18:29to lend it to a not so creditworthy
2:18:31entity, you might want to ask maybe for
2:18:33a slightly higher return to an entity
2:18:36that has a higher risk of not giving it
2:18:38back. Well, at least if I have to risk
2:18:39that, I want a higher return. So, one of
2:18:42the element that eventually determines
2:18:44the interest rates is creditworthiness.
2:18:46Because to lend to a high-risisk
2:18:48borrower, I want a premium for my risk.
2:18:51That's why it's also called the risk
2:18:53premium. And another thing you can
2:18:54understand is if I'm going to lend you
2:18:56money for 1 year or for 30 years, well
2:18:59for 30 years I might want a higher
2:19:01interest rate because I'm depriving
2:19:03myself of money for 30 years. So maybe
2:19:05to the same person a one-year bond might
2:19:08ask for a 3% interest rates. But if I
2:19:12have to give it to you for 30 years,
2:19:14well a lot of stuff can happen in 30
2:19:16years. So I want to paid more because
2:19:18it's a lot of time. So let's say this
2:19:20one is 6%. This is called the risk
2:19:23premium. So the difference depend in
2:19:25return in interest rates based on
2:19:27creditworthiness of the borrower. This
2:19:30is called the term premium. So an extra
2:19:33percentage point that I want because of
2:19:35the time frame. And remember the bond
2:19:37market is probably the biggest market in
2:19:39the world for capitalization. Like
2:19:41there's a lot of money in the bond
2:19:42market, like trillions of dollars,
2:19:45especially government bonds, right?
2:19:47Because government bonds are the only
2:19:49way the government can literally print
2:19:52money by issuing new debt. They don't
2:19:54really create new money, but they kind
2:19:55of do. We'll understand it. And another
2:19:57important thing about the bond market
2:19:58that you can understand with the concept
2:20:00of term premium is something called the
2:20:02yield curve. This is a chart that is
2:20:05widely known specifically in in, you
2:20:07know, in all sorts of bonds, but
2:20:08especially in the government bond
2:20:09market. So if we say that for example a
2:20:1230-year Treasury bond rewards me 6% per
2:20:15year interest rate while a one-year
2:20:18rewards me a 3% interest rate then you
2:20:21have you know 3 months bond you have 6
2:20:23months bonds you have 1 year you have
2:20:252ear bonds 3 years 5 years 7 years 10
2:20:29years 20 years and finally 30 years.
2:20:32Well, in a normal situation, you would
2:20:34expect the three-month bond to ask for a
2:20:36lower interest rate and the longer
2:20:38maturities to ask for a higher interest
2:20:40rate, right? So, what you typically see
2:20:42is interest rates getting higher and
2:20:45higher depending on maturity. And you
2:20:47can basically plot a line also known as
2:20:50the yield curve. And in a normal
2:20:52scenario, this all makes sense. But
2:20:54again, the term premium is not the only
2:20:56thing that affects the yield of these
2:20:58bonds, the interest rates of these
2:20:59bonds. So yes, one thing is the term
2:21:02premium, another thing is the risk
2:21:03premium. But another thing that affects
2:21:05how much my bond is yielding is for
2:21:08example inflation expectations. If I
2:21:10expect inflation to be 3% over the last
2:21:1310 years, I want at least 3%. Likely a
2:21:18little bit more because I don't want my
2:21:19money to be eroded by inflation. So if
2:21:22the inflation expectations for the next
2:21:24let's say 3 years it's 3%, I am going to
2:21:27at least ask for 5%. Right? So just an
2:21:30example and bonds are the most common
2:21:33and at the same time kind of safest way
2:21:36to hedge against inflation. And the last
2:21:39very important thing is the central
2:21:41bank's interest rates. If the central
2:21:44bank's interest rates is 4% let's say
2:21:47well I can just go to a bank deposit
2:21:50money and I will get 4% on my deposit.
2:21:52So you kind of need to be competitive at
2:21:54least. So you would like at least to
2:21:55have a 5% right if I have to put my
2:21:57money into your hands. So all of these
2:22:00things are affecting the yield of the
2:22:02bonds and the yield curve. For example,
2:22:04when inflation expect when the market is
2:22:06expecting the central bank to rise
2:22:08interest rates, you would typically see
2:22:10in the shortterm maturities very high
2:22:13numbers sometimes even higher than the
2:22:16long-term maturities and you can see the
2:22:18yield curve going down inverting. That's
2:22:21what we call an inverted yield curve.
2:22:23This phenomena is also known as
2:22:25backwardation. While a normal yield
2:22:27curve is a curve in contango. Just some
2:22:30cool finance terms that you might want
2:22:31to learn. For example, this is the
2:22:33current yield curve and it has this
2:22:34really weird shape. Let's go back to
2:22:36before inflation was ever a problem.
2:22:39Back to April 2021. Look at this
2:22:42beautiful yield curve. Interest rates
2:22:44are at zero now. So all the shortterm
2:22:47maturities are very close to zero. And
2:22:50you have to know that especially the
2:22:52shortterm maturities. So one month, two,
2:22:55three, four, six months up to one year,
2:22:57they are much more affected by the
2:23:00central bank interest rates because
2:23:02remember central bank interest rates are
2:23:03by definition overnight interest rates.
2:23:05So they are very shortterm. So this blue
2:23:08area is where the target interest rates
2:23:10for the Federal Reserve are. And you can
2:23:12clearly see that the bonds were exactly
2:23:14yielding somewhere close to zero. But as
2:23:18you moved on, let's move on for example
2:23:21to November 2021, you gradually start
2:23:24see the front end of the line flattening
2:23:27down. One year starting to rise. Let's
2:23:29move to July 2022 and we see something
2:23:32starts changing. Now the interest rates
2:23:34are at 1.5. Let's move on even further.
2:23:37November 2022. Also, the very shortterm
2:23:40maturities are now at 4% interest rates.
2:23:43The one-year Treasury bond is yielding
2:23:45more than the 30-year. And in 2023, we
2:23:48have a full inversion of the yield
2:23:50curve, completely inverted. A 3mon bond
2:23:53is yielding way more than a 30-year
2:23:56bond. And this happens because, as I
2:23:58said, the shortterm end of the curve is
2:24:01more impacted by interest rate decisions
2:24:03of the central bank. And if you go on
2:24:05Trading View, you can see the bond yield
2:24:07yourself. For example, you can write US
2:24:09maturity 01. For example, year one year.
2:24:12So, US United States Treasury bonds one
2:24:15year yield. So the Y stands for yield.
2:24:18And if I plot on top of this the Fed
2:24:20funds rates, you can clearly see there's
2:24:23clearly a pattern. And the pattern
2:24:25actually is that the bonds will
2:24:26anticipate what the Fed will do later.
2:24:29The Fed will hike rates. Yeah. Well, I
2:24:31mean the bonds knew it for at least
2:24:33October since March. So yeah, as I told
2:24:36you, 6 to9 months. That's the timing of
2:24:38financial markets. Even here started
2:24:40dropping way before the Fed was saying
2:24:42it. Let's plot it with the inflation
2:24:44rates now. Well, there still is some
2:24:46sort of pattern, but not as cool, right?
2:24:49Not as exactly there. Now, let's take a
2:24:52US 30-year Treasury bonds yield. Well,
2:24:54we still see somewhat of a pattern, but
2:24:56for example, here when inflation started
2:24:58picking up, well, the 30 years were
2:25:00starting to pick up much more quickly
2:25:02than the one-year because on things like
2:25:0430-year bonds, long-term inflation
2:25:07expectation and the risk premium, the
2:25:09credit rating of the issuer play a
2:25:11bigger role. And you can create your
2:25:13sort of yield curve here. For example,
2:25:15if I write US 30-year yields and I do
2:25:18minus US 3 months yield, I have this
2:25:22chart that shows me the differential,
2:25:24the spread between 30-year bond yields
2:25:27and 3 months bond yields. Now, let's
2:25:29plot Fed funds. And typically during Fed
2:25:32rate hikes, the short end of the curve
2:25:34will lead a curve inversion. And
2:25:36whenever we reach zero, it means that
2:25:38the 30 years and the 3 months yield
2:25:41exactly the same. If we go below zero,
2:25:43it means that the 3 months yields more
2:25:45than the third year. And we can see that
2:25:46this clearly happens whenever there's an
2:25:48interest rate spike. So this is
2:25:50basically a simpler way to identify a
2:25:52yield inversion. Whenever it's below
2:25:54zero, that's what we call an inverted
2:25:55yield curve. And this is a wildly maybe
2:25:58overused indicator to anticipate a
2:26:01recession. This is for example on Fred,
2:26:03the Federal Reserve Bank of St. Louis.
2:26:05They basically release economic data and
2:26:07they have a this great bank of economic
2:26:09data. And as you can see in history,
2:26:11throughout history, whenever this
2:26:12inversion happened, this is the 10-year
2:26:14versus the 2 years. So still a long-term
2:26:16maturity versus a kind of short-term
2:26:18maturity. Every time this line went
2:26:21below zero, you had a recession. And you
2:26:23can see the recessions with this gray
2:26:25area. Had a recession here, a recession
2:26:27here, yield curve inverts, steepens,
2:26:29recession, inverts, steepens, recession,
2:26:32inverts, steepens, recession. Slightly
2:26:34inverts, steepens, recession, inverts,
2:26:37steepens. And that's why a lot of people
2:26:39is expecting a recession anytime soon.
2:26:41But this is not the only indicator that
2:26:44one should take into consideration
2:26:45because the yield curve is inverted. But
2:26:47it can happen because of the interest
2:26:49rates, the short-term bank interest
2:26:51rates that are choking the leveraged
2:26:53part of the economy. And the bond market
2:26:55is really cool because there's no retail
2:26:58traders. Not a lot of retail traders
2:27:00speculating on bond market moves. Also,
2:27:02the bonds are very low volatility
2:27:05markets. Generally speaking, you never
2:27:07hear bond traders in the retail space.
2:27:09But in the institutional space, they are
2:27:12they are one of the biggest markets.
2:27:14Absolutely, hands down. And I would say
2:27:16they are one of the main drivers in
2:27:18general of financial market because the
2:27:20bond market is just the market of debt.
2:27:22It's the market of money literally. It's
2:27:25money parked in the future basically. So
2:27:28it greatly reflects the real expectation
2:27:31of the money of really wealthy people
2:27:34about what the economy might be going to
2:27:36do. So also reading sentiment through
2:27:38the lens of the bond market. It's
2:27:40something that retail traders likely
2:27:41don't do. Now another reason why it's
2:27:43crucial to understand how the bond
2:27:45market works is specifically to
2:27:47understand another piece of the
2:27:50macroeconomics puzzle because government
2:27:52play a huge role into how the
2:27:55macroeconomic landscape forms. So if
2:27:57these are the metrics and we have
2:27:59understood the role of central banks in
2:28:01the macroeconomic cycle well also the
2:28:04government plays a role. So government
2:28:06policies in general play a huge role in
2:28:09each nation's economy. Specifically the
2:28:12policies that relate to the fiscal space
2:28:15to taxes and in general what is called
2:28:18fiscal policies. And just like any other
2:28:20entity, the government also have an
2:28:22income that comes from taxes and all
2:28:25sorts of expenses. Expenses to build
2:28:28roads, pay for the military, to pay for
2:28:30the police, to pay for all sorts of
2:28:32government services and public services
2:28:35in general. And in an ideal world,
2:28:37governments should earn more than they
2:28:39actually spend. And the balance between
2:28:41income and expenses is also in some way
2:28:44the difference between socialism and
2:28:47liberalism which are typically the right
2:28:50and the left political parties. Where
2:28:52typically liberalism believes in free
2:28:54market, socialisms believe more in
2:28:56equality and welfare. So typically
2:28:58states that are more socialist will tend
2:29:00to have higher expenses because they
2:29:02have a lot of government services. For
2:29:04example, in Italy, which is the country
2:29:05I'm from, the government provides a lot
2:29:07of welfare. There's a lot of government
2:29:10in the economy which I personally
2:29:11believe is [ __ ] I'm more in the
2:29:13free market, right? But for example,
2:29:15healthcare is public, school is public,
2:29:17public transportation is publicly owned.
2:29:19So it's mostly provided by the
2:29:22government in a predominantly liberalist
2:29:24economy or nation. Healthcare is
2:29:26private, education is private,
2:29:27transportation is private. And by free
2:29:29market, we mean a purer version of
2:29:32capitalism where you just let privates
2:29:34do their own thing. And I personally
2:29:36believe this is more representative of a
2:29:38meritocracy and has proven to really
2:29:40work. If you live people freed to do
2:29:42entrepreneurship, they will create new
2:29:44jobs. And if you incentivize people to
2:29:47entrepreneurship, the wealth of the
2:29:48country will rise. But also socialists
2:29:51have proven to be an effective antidote
2:29:53to the inequality that liberalism can
2:29:55cause at times. So with higher taxes on
2:29:58rich people, they manage to rebalance
2:30:00inequality. So most western societies
2:30:02try to find a balance between socialism
2:30:05and liberalism and free capitalism. So
2:30:07we also understand that a very socialist
2:30:09state will have a lot of expenses
2:30:11because the government is providing for
2:30:13healthare for education for
2:30:16transportation for pensions and
2:30:18retirement plans. All things that are
2:30:20private here. So a socialist state will
2:30:22have more expenses. So we'll have to
2:30:25have more taxes as well. Whereas a more
2:30:27liberalist state that promotes free
2:30:29market and capitalism will tend to have
2:30:31lower expenses and so a lesser need for
2:30:34taxes. But for example, if the
2:30:36government takes as taxes 20 trillion,
2:30:38ideally the expenses should also be 20
2:30:41trillion. But a lot of times this does
2:30:44not happen. Sometimes it might happen
2:30:45that there's more expenses, for example,
2:30:4725 trillion. So for that extra $5
2:30:50trillion, that's where the government
2:30:52needs to issue more debt. issue $5
2:30:55trillion of debt. So when income is not
2:30:59enough to cover all of the expenses that
2:31:00the government had and it has to use
2:31:02debt, that's what we call a fiscal
2:31:05deficit, which is the opposite of a
2:31:08government running a fiscal surplus,
2:31:10which happens when the income are more
2:31:12than the expenses. And you can
2:31:14understand that a fiscal deficit is
2:31:17basically taking this $5 trillion and
2:31:19put it right into the economy because
2:31:21the government is spending that money.
2:31:23So that money is going into the economy.
2:31:25So it typically has at least in the
2:31:27short to medium-term a positive effect
2:31:30on the overall growth of the economy.
2:31:32While a fiscal surplus where the
2:31:35government is earning more money and
2:31:36collecting more money with taxes than
2:31:38it's putting into the economy by
2:31:40spending money, a fiscal surplus is
2:31:42positive for the government because it's
2:31:44earning more money, but it's negative
2:31:45technically to the economy because we're
2:31:47taking money away from the economy and
2:31:49not spending as much. And typically when
2:31:51governments announce that they will run
2:31:54a very strong fiscal deficit just like
2:31:57Trump is planning to, the stock market
2:31:59typically roars and just explodes in a
2:32:02very intense bull market because this
2:32:04will mean more money into the pocket of
2:32:06customers that will spend money into the
2:32:09companies and buy products and services
2:32:11from companies will brings the earnings
2:32:13of those companies higher. And so the
2:32:15stock market which is where these
2:32:17companies are traded will anticipate
2:32:18that this will happen and will start
2:32:20buying stock and pumping stock prices
2:32:22up. But this has a limit because in
2:32:24order to run on fiscal deficit a
2:32:26government needs to issue debt needs to
2:32:28issue bonds and those bonds are
2:32:31expensive because there's interest
2:32:33rates. As we know the public debt of a
2:32:36nation is the total amount of
2:32:39outstanding government bonds that have
2:32:41been issued by a government. For
2:32:43example, this is the US debt. US public
2:32:45debt has broken the ceiling of $30
2:32:48trillion. Irregardless, by the way,
2:32:51there was a Republican or a Democrat
2:32:53president. So, more of a liberalist
2:32:55party or a socialist party. Regardless
2:32:57of this, depth just kept going higher
2:32:59and higher. And you remember we were
2:33:01talking about the GDP as the measure for
2:33:04how much wealth a country is producing
2:33:08in a year. And even though that is very
2:33:10important metric in the economy because
2:33:12a very important metric to assess the
2:33:14productivity of a country as we
2:33:16discussed we also understood that it's
2:33:18one thing if the growth is structural
2:33:20because of strong demography and
2:33:22technological innovation but because of
2:33:24the debt system if the government is
2:33:26running on a deficit the economy will
2:33:28grow faster right so the relationship
2:33:30between how much debt there is in an
2:33:33economy and the GDP of that economy is
2:33:36the truly important metric to assess in
2:33:39a way how virtuous that economy really
2:33:41is. So between the important metrics in
2:33:44an economy, we want to add the total
2:33:47public debt to GDP ratio aka public debt
2:33:51over GDP in percentage. And this is a
2:33:53chart of the US debt to GDP ratio. And
2:33:56we typically consider a nation virtuous
2:33:58when the debt to GDP ratio stays below
2:34:02100 because it means that the growth and
2:34:05the wealth that that nation produces in
2:34:07that year it's driven by real
2:34:10productivity not just debt leverage and
2:34:13typically everything above 100% will
2:34:15start to raise some alarms because it
2:34:18means that the government is constantly
2:34:20issuing new debt and constantly running
2:34:22on a deficit which in the long term All
2:34:26of this economic expansion built on
2:34:28leverage will have to live some sort of
2:34:31contraction. And the bigger the
2:34:32leverage, the bigger the contraction.
2:34:34Plus, because of interest rates, if the
2:34:36debt is increasingly higher than GDP,
2:34:39the money I will earn from taxes will
2:34:41start to be less and less compared to
2:34:43the interest rates that I will need to
2:34:45pay on my debt that will continue
2:34:47rising. Because if the GDP grows, but
2:34:50the debt grows at a higher rate than the
2:34:52GDP, my expenses on the debt, which is
2:34:55the interest rate, and the taxes that I
2:34:57earn from the wealth generated in the
2:35:00country, the gap between them will force
2:35:02me to keep running on an even crazier
2:35:05deficit. And this will spiral into what
2:35:07we're seeing now in the American economy
2:35:09and in a lot of economies in general.
2:35:12And the highest risk of this is exactly
2:35:14inflation because with the government
2:35:16printing basically a lot of new money
2:35:19but because since the government is
2:35:21issuing so much debt it's basically like
2:35:23creating new money right especially if
2:35:25the central bank is also doing
2:35:26quantitative easing and constantly
2:35:28buying those bonds from the secondary
2:35:30market. So fiscal deficit is actual real
2:35:33economy money printing and if people
2:35:36spend more prices will rise. So the risk
2:35:38of constantly running on a deficit and
2:35:40constantly printing new money is that
2:35:42the money will lose its value because of
2:35:45inflation. And we've seen this thing
2:35:46happening in all sorts of nations in the
2:35:48world starting from Germany in the 30s
2:35:50or in Argentina and Venezuela or even in
2:35:53Turkey where you had episode of what is
2:35:55called hyperinflation which is whenever
2:35:57inflation goes above 100% year-over-year
2:36:00which is crazy but for what concerns the
2:36:02money flow whenever there's fiscal
2:36:05deficit just think that the stocks will
2:36:07just typically in the US and the most
2:36:09western economies fiscal deficit
2:36:11typically means big growth leveraged
2:36:14growth grow in an economy. So now that
2:36:16we have understood the role of
2:36:18government policies in the money world
2:36:21and how the national debt and the fiscal
2:36:24deficit affects the economy and the
2:36:26markets overall, we truly understand the
2:36:28role of bonds in the economy of the bond
2:36:31market overall and we can finally start
2:36:33talking about the forex market. And the
2:36:35forex market or foreign exchange is the
2:36:38market of foreign currencies. It's also
2:36:40the market that is mostly traded by
2:36:42wannabe traders, trading newbies for the
2:36:45wrong reasons because the brokerage
2:36:47industry and the guru industry managed
2:36:50to create a casino out of the forex
2:36:52market which is actually one of the most
2:36:54untransparent market there is. It's
2:36:56completely traded OTC. So it's like but
2:36:59now we want to understand it in a
2:37:00different way. We want to understand the
2:37:02fundamentals of the forex market. And as
2:37:04we know as it's the currency market you
2:37:06have all sorts of currency pairs market.
2:37:08So in the forex market, you always have
2:37:10one currency, for example, the dollar
2:37:12versus, for example, the Japanese yen,
2:37:15JPY. And when you trade the forex
2:37:17market, you're basically betting against
2:37:19the rising and falling of the exchange
2:37:21rate between two currencies. And there's
2:37:23all sorts of currency pairs. For
2:37:25example, the Euro dollar, euro USD,
2:37:27pound USD, also known as the cable,
2:37:29AUDUSD, also known as the Aussie, you
2:37:32have the NZDU USD, the New Zealand
2:37:34dollars, and so on and so forth. And you
2:37:36can just shuffle them and mix them
2:37:38however you like. You have euro GBP,
2:37:40Euro OD or GBP, Canadian dollar and so
2:37:44on and so forth. Now if for example the
2:37:46exchange rate of this is 1.15, it means
2:37:49that for one euro I can get $1.15,
2:37:53right? And if the euro is stronger than
2:37:56the dollar, the exchange rate will go
2:37:57up. If the dollar is stronger than the
2:37:59euro, the currency pair will go down.
2:38:01Very basic. But the real question we
2:38:02need to ask is what makes a currency
2:38:05stronger than the other one? What are
2:38:06the fundamental drivers of the forex
2:38:08market? The main drivers of the forex
2:38:10market are central bank interest rates
2:38:12and bond yields. That's why before
2:38:16coming to the forex market, we went
2:38:18through literally everything that you
2:38:20need to know about the economy because
2:38:22you cannot understand the driver of the
2:38:24forex market if you don't understand
2:38:26macroeconomics overall. And I can assure
2:38:28you if you were a forex trader and you
2:38:30did not know anything about
2:38:31macroeconomics and you were just like
2:38:33trading forex CFDs on a meta trader prop
2:38:35firm whatever this will truly open your
2:38:38mind. Let's keep Euro dollar as an
2:38:39example. Let's say that the interest
2:38:42rates of the central bank of the euro
2:38:44let's make an example at 1% and the bond
2:38:46yields for let's say a one-year bond are
2:38:501.5%. And now for the dollar, let's say
2:38:52that the Federal Reserve has set the
2:38:54interest rates at 5% and the bond deals
2:38:57are around like 5.2%. If you had to park
2:39:00your money somewhere, let's say in a
2:39:03bank or let's say in a government bond
2:39:05and park your money there to hedge
2:39:07yourself safely for inflation, blah blah
2:39:09blah. Which one will you choose? Would
2:39:11you choose to keep euros that yields you
2:39:151% per year or dollars that give you
2:39:17like a fat 5% per year? Of course, you
2:39:20would prefer to invest in a dollarbased
2:39:23bond because the yield is higher. So,
2:39:26the main fundamental driver of the forex
2:39:28market is the spread between interest
2:39:31rates or the spread between yields. And
2:39:34something really cool can happen because
2:39:35of the spread between interest rates.
2:39:37Cuz in a situation like this, I can, for
2:39:39example, borrow €100,000
2:39:42where I pay a 1% interest rate. And with
2:39:45this money, I can buy €100,000
2:39:49worth of US government bonds and that
2:39:52will pay me 5% interest. So by borrowing
2:39:56this money and rebarorrowing it to
2:39:58another government that pays better
2:40:00interest rates, I basically made for
2:40:02free a 4% return on the capital. This is
2:40:05called a carry trade. Exploiting the
2:40:08interest rate differential to borrow
2:40:10money at a cheap interest rates and
2:40:12reend it to someone else at a higher
2:40:15interest rate. Typically buying
2:40:16government bonds. The most famous carry
2:40:18trade of the last 20 centuries was the
2:40:22yen carry trade because the bank of
2:40:24Japan for probably the last 30 years has
2:40:27kept interest rates at zero. So imagine
2:40:30borrowing yens at basically 0% interest
2:40:34rates and then buying any other bond
2:40:37that maybe can yield you 5%. That's just
2:40:39literally free money. And this trade was
2:40:42huge. And the yen carry trade is a very
2:40:44interesting case study because it caused
2:40:46some instabilities in August 2023 on the
2:40:50yen, on the dollar, and on the entire
2:40:52world stock market. We'll maybe make a
2:40:54video about it. So the spread between
2:40:56interest rates of two currencies
2:40:57determines the trend of a forex pair.
2:41:00But as always, just as I told you that
2:41:03the cycles in the economy are
2:41:05anticipated by the stock market exactly
2:41:08the same way, the spread between the
2:41:10interest rates is always anticipated by
2:41:12the forex market. So the smart money in
2:41:14the forex market is trying to predict
2:41:16what the spread will be. And if for
2:41:19example they expect at some point in the
2:41:21future interest rates on the euro going
2:41:23high and interest rates on the dollars
2:41:25going low 6 months before the market
2:41:28will price it in and push Euro dollar
2:41:30up. So it's not the spread between the
2:41:33interest rates but it's the expectations
2:41:35on what the spread between these two
2:41:37will be in the future. Always remember.
2:41:39And how does the market try to
2:41:40anticipate this? Well, it's pretty easy
2:41:43because inflation and employment data as
2:41:46we discussed directly affect monetary
2:41:49policy decisions which includes interest
2:41:51rates. So both the yields of the bonds
2:41:54and the interest rate decisions will be
2:41:56highly affected by for example how
2:41:57inflation goes but also how employment
2:41:59goes. So whenever a new inflation data
2:42:01or employment data comes out, if the
2:42:04inflation and the employment has a clear
2:42:06direction already that the market is
2:42:09starting to bet in favor of, that's when
2:42:11big trends happen. Let me go on Trading
2:42:13View real quick. On my profile, a trade
2:42:16idea that I probably shared, I don't
2:42:18know, a lot of years ago. It's written
2:42:20in Italian, but I was saying basically
2:42:22the FOMC is starting to spread around
2:42:24some rumors on tapering. And just so you
2:42:27remember, the tapering is slowing down
2:42:30this printing of money from the Fed,
2:42:32which is the first phase of the hiking
2:42:34cycle that will lead to higher the
2:42:36interest rates on the dollar. So less
2:42:38dollars being printed, higher interest
2:42:39rates on the dollars. So we would expect
2:42:41the US rates to go up while the GBP
2:42:44rates at that point will still flat. So
2:42:46we would expect the dollar to finally
2:42:49catch up. And we were just coming out of
2:42:50COVID, by the way, where the dollar lost
2:42:53all of its value because of all the
2:42:54money printing. And now after this huge
2:42:57trend, they've just decided to stop all
2:43:00of this money printing and hire interest
2:43:02rates instead. And rates were at zero at
2:43:04that point. So this was the beginning of
2:43:06the hiking cycles. And in June 15, 2021,
2:43:10this was the area where I made this
2:43:11analysis at the top of the range and I
2:43:14said this is likely where we're going to
2:43:16short because this was a complete
2:43:18reversal in monetary policies. That's
2:43:21where the biggest short impulse in
2:43:23history probably on the GBPUSD happened
2:43:26with 4,000 pips from the entry I called.
2:43:29I mean, that's awesome. And then, of
2:43:30course, everything changed because then
2:43:31Trump got into power with a clear aim to
2:43:34lower interest rates. And well, now you
2:43:36know the story. And that's why when you
2:43:37see inflation data or employment data
2:43:39coming up, you see a lot of volatility
2:43:41in forex. And if you always read it
2:43:43through the lens of what will the
2:43:45central banks do with this data you the
2:43:48volatility that will happen in the forex
2:43:50will not be a mystery to you anymore.
2:43:51And by the way just with this stuff
2:43:53world trading champion Yan Smolen in
2:43:552021 2022 2023 won three times the world
2:43:59trading championships just with this
2:44:01concept. So these are the main
2:44:02fundamentals you need to know about the
2:44:04forex market. And we can move to our
2:44:06final markets which is the precious
2:44:08metal market. And we're specifically
2:44:10talking about gold and silver that could
2:44:12be considered commodities. But but
2:44:14unlike normal commodities that are
2:44:16simply driven by expectations around
2:44:18supply and demand, the driver of these
2:44:20markets are completely different because
2:44:22they're not seen as commodities. They
2:44:24are seen as store of value for some
2:44:27reason. So for example, these are used
2:44:30especially gold as a hedge against
2:44:32inflation. So because they are a store
2:44:34of value, they are used as a hedge
2:44:36against inflation, but also a hedge in
2:44:38general against economical or
2:44:41geopolitical uncertainty. So whenever
2:44:43there's wars, whenever there's
2:44:44economical uncertainty, typically we see
2:44:47gold and silver go really really up. And
2:44:49another driver which more than a driver
2:44:52is probably a very strong historical
2:44:54correlation specifically with gold and
2:44:56its little brother silver because
2:44:58they're a hedge against inflation. The
2:45:00second biggest hedge against inflation,
2:45:02as we said, is bonds, right? Bonds is
2:45:04the safest asset to invest to have a
2:45:06yield and just hedge a little bit
2:45:08against inflation, right? So, we could
2:45:09say that bonds yields specifically are
2:45:12the main competitor of gold.
2:45:14Specifically, something called real bond
2:45:17yields. And in finance or in economy,
2:45:19let's say, anything that we define as
2:45:21real is net of inflation. For example,
2:45:24if the bonds yields 5.5%,
2:45:27that's the interest rate on the bonds.
2:45:29But we have a 2% inflation rate, then
2:45:32the real yield I get on my money is
2:45:353.5%. Right? And the same thing, by the
2:45:37way, goes for the GDP growth rate. If
2:45:40the GDP growth rate is 2%, but the
2:45:43inflation rate year-over-year is 1%,
2:45:46then the real growth that happened in
2:45:48that year, so the real GDP growth was
2:45:521%. because the GDP is calculated in
2:45:55dollars and if those dollars lost value
2:45:58because of inflation then the real GDP
2:46:00growth is 1% not two. So because gold
2:46:02was always a hedge against inflation
2:46:04there is an inverse correlation between
2:46:07real bond yields and gold because if the
2:46:10real yield of bond is really high and
2:46:12for example I don't know I can get an 8%
2:46:15return because bonds are basically
2:46:16risk-f free I would probably prefer to
2:46:18put my money in bonds if I have to hedge
2:46:21against inflation instead of silver or
2:46:23gold that can be more volatile. If
2:46:25instead the real bond yields are like
2:46:27one or 2%. Well, that's not a lot of
2:46:30growth to be honest. So, I might just as
2:46:32well buy gold. So, this is the chart of
2:46:35gold and let's add on top of this real
2:46:38yield. So, US 5 years yields minus break
2:46:42even inflation rates which basically are
2:46:44the expectations of 5 years inflation.
2:46:47So, this is the chart of the so-called
2:46:48real interest rates. If we plot the gold
2:46:51chart on top of this and look at the
2:46:52historical charts. So bonds were not
2:46:55considered to be a good hedge against
2:46:56inflation. That's exactly when gold
2:46:59pumped. When real rates or real bond
2:47:02yields started growing up, that's
2:47:04typically when gold suffered. And
2:47:06especially when they started rising
2:47:08really fast, that's when gold dropped.
2:47:10And spoiler alert also here, we can
2:47:13anticipate when the bond yields are
2:47:15going to go up with monetary policies.
2:47:17Then after this huge rise in real yields
2:47:20and consequent gold bare market as soon
2:47:22as we started stabilizing and started
2:47:24kind of going lower that's when boom
2:47:27gold started going up and then they
2:47:29picked up again. So gold decided to go
2:47:31down a little bit but then they dropped
2:47:33again and that's where gold [ __ ] and
2:47:36throughout all these years while real
2:47:38yields started dropping gold had the run
2:47:40of its life and also here not so long
2:47:44ago I posted this which is now hidden
2:47:46for some reason because it's violating
2:47:48one of more house rules whatever I was
2:47:50exactly highlighting a long idea on gold
2:47:53this trade idea was on September 2023
2:47:56and since we were expecting because we
2:47:59were having high inflation, but also we
2:48:01were expecting the end of the hawkish
2:48:03cycle and finally interest rates
2:48:05starting to gradually go slightly lower,
2:48:07we could expect a new long cycle in
2:48:10gold. And guess what happened? Yep,
2:48:12that's where we're here today. So you
2:48:13can slowly start understanding that if
2:48:16you learn what the market is currently
2:48:18betting based on newly coming news and
2:48:22based on newly published macroeconomic
2:48:24data by understanding macroeconomics and
2:48:28especially the role that they play in
2:48:30financial markets, you can have an edge
2:48:32on long-term trades and on swing trading
2:48:35that is just something else. It's not
2:48:38just a candlestick based strategy. It's
2:48:40learning how to be in the flow of smart
2:48:43money that is trying to following
2:48:45macroeconomics and that is [ __ ]
2:48:48awesome. And you can do it to anticipate
2:48:50what the stock market will do, hence
2:48:52what the crypto market will do, also
2:48:53some commodities, but especially what
2:48:55the bond market will do, hence where the
2:48:58forex market will go with ridiculous
2:49:00degree of accuracy as well as gold. And
2:49:03there's two main practical ways you can
2:49:05use fundamentals. You can use
2:49:07fundamentals and macroeconomics news
2:49:09data either to write news, also known as
2:49:12news trading, which to be honest
2:49:14requires some experience, but it's one
2:49:16of the main strategies of one of the
2:49:17best traders I know. It's not for
2:49:19beginners, but it's really cool. Or you
2:49:21can ride long-term trends with swing
2:49:25trading or even position
2:49:27trading/investing
2:49:29where you ride long-term trends, which
2:49:32is by the way swing trading and position
2:49:35trading. one of the if not the main
2:49:37trading type of smart money because of
2:49:40the fact that macroeconomic trends
2:49:43fundamentals are so reliable because
2:49:46they're based on the reality of each
2:49:49market. Not only they provide a great
2:49:52edge historically big money and smart
2:49:55money as we said they have huge orders.
2:49:58One order can even take a whole day to
2:50:01be filled or even days, even weeks maybe
2:50:04for what we know because as we said,
2:50:06they are so big that if they were to buy
2:50:09all at once, they will move price. So
2:50:12the main kind of trading that these guys
2:50:14do is long-term trading. They are
2:50:16investing in the market. A lot of the
2:50:18greatest hedge funds in the world are
2:50:21so-called global macro hedge funds. So
2:50:23even though retail traders that smart
2:50:25money trading is daily price action
2:50:28hunting stop-loss [ __ ] to be honest
2:50:30real smart money trading is long-term
2:50:32trading is swing trading position
2:50:34trading macroeconomics big trends that
2:50:37even the big money because they're so
2:50:39big can take advantage of they shortterm
2:50:42price action is not liquid enough to
2:50:44create a substantial edge to smart money
2:50:47it's not enough most unless they're
2:50:49doing HFT but even there the edge is
2:50:52very limited That's why global macro
2:50:54hedge funds and long-term trading
2:50:56investing is the main business of smart
2:50:59money when they're trading the markets.
2:51:02And that's why the day trading action
2:51:04when the movement of prices throughout
2:51:06the day can be more random, but still we
2:51:09can read them through how these
2:51:12participants feel their long-term orders
2:51:14and through auction market theory
2:51:17realize that hey, this is where maybe
2:51:18they're buying or selling here.
2:51:20something clearly happened that shifted
2:51:22maybe a news event happened that drove
2:51:25the fair valuation of this asset up and
2:51:28now they found liquidity again and now
2:51:30they're trading here. So, I'm going to
2:51:32follow the big money in the intraday or
2:51:35let's say the shortterm market price
2:51:37action and look at where the money is
2:51:40going with order flow and with volume
2:51:43analysis while still being aware and
2:51:46aligned with the global macro view and
2:51:49the long-term trends. Welcome to
2:51:51professional trading. And there's a lot
2:51:52of way we can follow this by the way in
2:51:54the daily actions. We can follow it
2:51:56through order flow and auction market
2:51:58theory and option flow blah blah blah.
2:52:00And we will use volume analysis for
2:52:02swing trading. We'll get deep into how a
2:52:04strategy based on auction market theory
2:52:06for swing trades might work. But also we
2:52:08can track big money and smart money
2:52:10through something called the commitment
2:52:12of trader report which is another
2:52:14[ __ ] cheat code for swing trading
2:52:16because the coot report basically tells
2:52:18us how much the smart money are buying
2:52:20or selling in a week. So once we have a
2:52:23global macro narrative and we identify
2:52:25the trend, we can confirm with the coot
2:52:28report if the smart money is actually
2:52:30going that way and through volume we can
2:52:32actually follow the money by seeing it
2:52:35on the charts. Isn't this absolutely
2:52:38phenomenal? Isn't this awesome? And all
2:52:40of this without a secret algorithm,
2:52:42without inventing [ __ ] about the
2:52:44market, but but but simply looking at
2:52:46how the world works, how money works in
2:52:49each market, rationally analyzing market
2:52:52participation, and through order flow
2:52:54and auction market theory, time our
2:52:56entry like [ __ ] snipers. Now, I am
2:52:59really tired. I've recorded all night.
2:53:01Leave a [ __ ] like to this video once
2:53:03and for all. Now I'm going to stop
2:53:04recording and restart tomorrow so that
2:53:06we can take everything that we've
2:53:08learned and start building a solid
2:53:10strategy and first start building a
2:53:13solid swing strategy and after that we
2:53:15will be able to move on to more
2:53:17shortterm day trading strategies as
2:53:19well. So now I will share with you some
2:53:20trading models both for position trading
2:53:22for swing trading and news trading. For
2:53:26position trading, I am not a huge
2:53:28position trader, but if you want to re
2:53:31really write the long-term trends of
2:53:33fundamental analysis, there's already a
2:53:36widely used uh model by, you know,
2:53:40professional investors and institutional
2:53:42investor that basically follows
2:53:45something also known as the Mary Lynch
2:53:47investment clock model or the sector
2:53:50rotation model where for example on this
2:53:52chart, this is the economic cycle.
2:53:55This is the market cycle. Same thing
2:53:58over here. Very similar. And during each
2:54:01phase of the cycle, so during the
2:54:03recession where we're falling recession,
2:54:05that's where typically the market
2:54:06bottoms because the market tries to
2:54:08anticipate the fact that the economy
2:54:09will recover. Then we have an early
2:54:11recover and that's where we're in the
2:54:13full bull market. Full recovery. That's
2:54:16where they say the market tops even
2:54:18though I don't fully agree. And in the
2:54:21early stages of a recession or slightly
2:54:24before a recession recession starts
2:54:26happening, that's where you actually see
2:54:28a bare market. And in the different
2:54:31stages of this, the risk aversion goes
2:54:35up and down. And there are some sectors
2:54:38that performs best than others. For
2:54:40example, in the early stages during
2:54:42market bottoms and during bull markets,
2:54:44we see sectors like the technology
2:54:46sector, like the communication services
2:54:49sectors, the discretionary sector
2:54:51typically pumping up and outperforming
2:54:53most other sectors and stuff like
2:54:56energy, healthcare, consumer staples,
2:54:58utilities typically keep performing
2:55:01better than these ones in the phases of
2:55:03bare market because they are considered
2:55:05as more stable type of sectors because
2:55:08as I saiduring during a race session,
2:55:10you're still going to buy groceries, but
2:55:13you're not going to buy a a new iPhone,
2:55:14maybe, right? So, different sectors
2:55:16perform better, and you can rotate a
2:55:19portfolio based on ETFs of every single
2:55:22sector depending on where we're likely
2:55:25to be heading with the economy, which is
2:55:27very similar to the investment clock of
2:55:30Meil Lynch, where you have growth
2:55:32recovers. So, we're in the upway of the
2:55:35cycle. Inflation rises. That's the top
2:55:37of the cycle where inflation is getting
2:55:39really high. Then growth weakens. We're
2:55:41at the top of the cycle going to this
2:55:43part of the cycle. And then inflation
2:55:45falling when the cycle is in its final
2:55:48step of recession before again growth
2:55:51recovers. And you basically divide this
2:55:55cycle in quadrants where you have
2:55:57recovery, overheat, stagflation, and
2:56:00then reflation. And in each quarter of
2:56:03this model, different asset classes
2:56:05perform best. So bonds typically perform
2:56:07good here. Stocks perform best during
2:56:09recoveries. Commodities in the phase of
2:56:11overheat is where they're performing
2:56:14best because they're also the reason why
2:56:17overheat is happening because if all the
2:56:18commodity prices goes up, all of the
2:56:20prices go up and inflation rises and
2:56:23then you get to stackflation. So, these
2:56:26models are great and are definitely
2:56:28worth getting deep into if you're
2:56:30looking to invest in the market with a
2:56:33capital allocation type of perspective,
2:56:35but position trading is, as I said,
2:56:37riding longterm trends. These cycles can
2:56:41take years, and this is great if you're
2:56:43looking to just use a capital allocation
2:56:46model and basically switch your
2:56:47investments smartly by following the
2:56:50global macroeconomic landscape. But this
2:56:52is not the type of trading that
2:56:53personally I have engaged into. So I'd
2:56:55rather spend most of the time explaining
2:56:57you the way both me and my partner Fabio
2:57:00and also Patrick Neil the world trading
2:57:02champion and also Jansming partly trades
2:57:05using both macroeconomics but also the
2:57:07rest of the fundamentals of each market.
2:57:10So we take the macroeconomic plot chart
2:57:12and we kind of take inspiration in a way
2:57:14from this type of thing but we help
2:57:17ourselves with unemployment data,
2:57:19inflation data, interest rate data,
2:57:21balance sheet data and intermarket
2:57:23analysis and do something in a similar
2:57:26fashion of the Meil Lynch investment
2:57:28clock model, but just to assess the
2:57:32trend, but we time the market better
2:57:34thanks to the auction market theory
2:57:36model and we follow the big money with
2:57:40technical analysis and with the
2:57:42commitment of traders report. So the
2:57:43checklist to build the context here is
2:57:47first looking at macros. So monetary
2:57:50policies, which stage of the cycle we
2:57:52are, fiscal policies, how is the
2:57:54government handling money printing, how
2:57:56is unemployment, how is inflation, and
2:57:58what is the soft data telling us. And
2:58:00while here we can just take a look at
2:58:03which point we are in the cycle here we
2:58:05can do the same but typically we take a
2:58:07look at how fast these data points
2:58:11change. So what's the rate of change of
2:58:14these data points and let's do a
2:58:16practical example of where we're at now.
2:58:19We take the Fed funds rates we take the
2:58:21unemployment rate. We take inflation and
2:58:24let's start with this. And just like
2:58:26this we can already understand in which
2:58:27phase of the cycle we are. We're in a
2:58:29phase where inflation is steadily
2:58:31getting lower but maybe in a phase where
2:58:34it's starting to pick up a little bit
2:58:35and we are in a situation where
2:58:37employment is slowly starting to pick up
2:58:40but at the same times the tightening
2:58:42stance of monetary policies policies is
2:58:45getting lower. We take real GDP growth
2:58:48quarteron quarter and we can see that
2:58:50growth is doing fine. The economy is
2:58:52growing quarter over quarter. So overall
2:58:55the economy is doing great. Employment
2:58:57is doing good. Inflation is also doing
2:59:01okay. So there's a good likelihood that
2:59:03the Fed will keep cutting rates. But as
2:59:05always in macroeconomics, there's
2:59:07multiple scenarios possible. So first we
2:59:09build scenario one, scenario two, and
2:59:12maybe a third scenario. Scenario one is
2:59:15unemployment stays low, the economy is
2:59:18resilient, and inflation is pretty
2:59:19stable. And that will bring the Federal
2:59:22Reserve to cut rates. The unemployment
2:59:24of course stays low but slowly picks up
2:59:27but not in a recession type of fashion.
2:59:30If this scenario is great in all of
2:59:33this, the government is running a
2:59:36deficit and that brings money into the
2:59:38real economy. If this is the scenario
2:59:40that ultimately happens, we're going to
2:59:42be long on stocks, short on the dollar,
2:59:46and long on gold because all of this
2:59:48money printing and the Fed cuts will
2:59:50boost the economy. The economy is
2:59:52already telling us it resilient. We
2:59:54don't have to worry about inflation and
2:59:56hence the Fed having to hike interest
2:59:59rates and the unemployment is okay. So
3:00:02the expectations on earnings on stocks
3:00:04are higher. But at the same times all of
3:00:06this money printing the Fed is going to
3:00:08cut rates because the inflation is going
3:00:10down. If the inflation is going down and
3:00:14the interest rates are going down, it
3:00:16means that the bond yields on the dollar
3:00:19will also go down and fixed income bond
3:00:22investors will likely not keep a lot of
3:00:25dollars. And that's really useful for,
3:00:27for example, the forex markets, right?
3:00:29Because if we can find, for example, a
3:00:31currency that has the opposite problem,
3:00:34so a high inflation, so higher interest
3:00:36rates and higher bond yields, that
3:00:38currency will be really strong. So the
3:00:40next step is to do the same thing with
3:00:42another currency and spoiler alert GBP
3:00:46could be one of them in the near future
3:00:48and basically have a trade there. The
3:00:50economies is resilient but there's at
3:00:52the same times a lot of uncertainty but
3:00:54especially because the inflation is
3:00:56going down and the Feds are cutting
3:00:58rates. It's likely that the real yield
3:01:01of bonds will also go down then we're
3:01:03long gold because bonds are not anymore
3:01:06a good inflation hedge. So gold is now
3:01:09we look at scenario two. Unemployment
3:01:12starts rising higher than expected.
3:01:16Inflation drops drastically. The Fed has
3:01:19to cut rates more aggressively and the
3:01:22government of course keeps printing
3:01:23money. This does not change anything as
3:01:26a short on the US dollar, a long on
3:01:29gold. Stocks might start being not so
3:01:33attractive. They might still go up
3:01:36because they have a bias of kind of
3:01:38hoping for the best, but it will not be
3:01:40a trend, but it will likely be a trend
3:01:43with huge retracements and a lot of buy
3:01:45the dip going on. But if this becomes
3:01:49and unfolds into a recession, even
3:01:51though the Fed is cutting rates, you
3:01:53will likely see the stock market going
3:01:55down. So stocks short if recession, so
3:01:59if the expectation of a recession start
3:02:02rising. And scenario three, inflation
3:02:05rises unexpectedly, unemployment stays
3:02:08relatively low. This will bring the Fed
3:02:12to kind of want to hike rates. And for
3:02:14now, let's say the government just keeps
3:02:15printing money. Well, the fact that the
3:02:17Fed will likely hike rates will
3:02:20completely shift our bias on the USD. We
3:02:23will likely see the big short trend that
3:02:26has been the protagonist of the forex
3:02:29market in the last months at least
3:02:31retracing. So probably long USD maybe
3:02:34finally the time for a retracement of on
3:02:36gold and we're kind of neutral on stock.
3:02:39And by neutral I mean that there's could
3:02:41be some short-term retracement but it's
3:02:44probably going to just buy the dip again
3:02:46like it always happens. So these are the
3:02:48three scenarios and now we look at what
3:02:51the markets are actually doing to see on
3:02:54which scenario the market is currently
3:02:57actually putting its money. So, we look
3:02:59at the DXY, we go on to the daily time
3:03:02frame, and well, the trend has been
3:03:05pretty clear. It seems like the market
3:03:07is still betting on the first scenario
3:03:10to happen. So, inflation down,
3:03:13unemployment
3:03:15slightly up, Fed cutting rates. So, this
3:03:19is what the market is betting will be
3:03:22happening slash is happening. So, we've
3:03:25built our three macroeconomic scenarios.
3:03:27And the next question is what is the
3:03:31market currently pricing in aka betting
3:03:35on? And for example, if the market is
3:03:38betting on scenario one, we also define
3:03:40it as our macro narrative, which is what
3:03:45the market is believing will happen in
3:03:48the next 3 to six to nine months. And we
3:03:50do that by looking at the DXY, by
3:03:53looking at the S&P 500, by looking at
3:03:56bonds, and by looking at gold. Now, what
3:03:59often will happen is that new
3:04:02macroeconomic data points will come out
3:04:04in the future, and they will either
3:04:06confirm the narrative, be somewhat
3:04:08neutral to the narrative, or radically
3:04:12negate the narrative. For example, if
3:04:15scenario one was inflation down,
3:04:19unemployment
3:04:21slightly up, Fed cutting, if the next
3:04:24CPI for example is confirming the
3:04:26narrative or let's say it like that, if
3:04:28it is exacerbating the narrative, for
3:04:32example, CPI really down, then this will
3:04:36help the current trend to continue. This
3:04:39could happen for example if the
3:04:40unemployment at the same time is also
3:04:43going down. So these two data points are
3:04:46exacerbating the narrative slash
3:04:48confirming the narrative and they will
3:04:50either cause price to continue betting
3:04:53on that. But that's when we have to look
3:04:54at price because if the market has been
3:04:58pricing in this narrative for a long
3:04:59time like it has for example here all
3:05:03throughout this time the market has been
3:05:05pricing in pricing in pricing in pricing
3:05:07in pricing in this scenario. What
3:05:10happened here for example during the
3:05:12last FOMC meeting is that the Fed
3:05:14confirmed the scenario and what happened
3:05:16which sometime does the market will take
3:05:19profits on this bet. So a buy the news
3:05:24event will happen. So depending at which
3:05:26point we are in the trend, if we are at
3:05:28very discount prices, this will push the
3:05:31price towards the trend. If we already
3:05:34priced it in and we are basically at
3:05:36all-time highs, it can be that even
3:05:39though the data is confirming the
3:05:40narrative, you would have a sell the
3:05:42news event. So option one, we keep the
3:05:45trend going. Option two, if we're really
3:05:47high and we've already priced in the
3:05:49narrative, we'll sell the news. But for
3:05:51this, of course, we need technicals. If
3:05:53the data is neutral to to the narrative,
3:05:55typically you will not see a lot of
3:05:57market movement. If it is radically
3:06:00negating the narrative, especially in a
3:06:02period like this where the markets are
3:06:05kind of waiting to see what will happen,
3:06:08that's when you can see trend
3:06:09inversions. And when trend inverts,
3:06:13that's when you have to be really
3:06:15careful if a new scenario or a new
3:06:19narrative is currently being priced in.
3:06:21Now, with a practical example, let's go
3:06:23to the GBPUSD and and currently if we
3:06:27take a look at the UK CPI. So, we can
3:06:30just search for inflation rates in the
3:06:33economy part for the United Kingdom. And
3:06:35we take the United Kingdom inflation
3:06:37rate year-over-year. And for example, we
3:06:40compare it with the United States
3:06:41inflation rate year-over-year. Well, we
3:06:43can see that the UK one has been picking
3:06:45up pretty aggressively. We are at 3.8
3:06:49versus a mere 2.9. So inflation is way
3:06:53more problematic in the UK as of today.
3:06:57And this has some likelihood to bring
3:06:59the UK central bank to hike rates. We
3:07:02plot the UK interest rates and yes they
3:07:05have been cutting but now they are kind
3:07:07of thinking about stopping because the
3:07:09inflation rate is getting high or even
3:07:12maybe rehiking them a little bit and all
3:07:14of this is contributing to the strength
3:07:16of the GBP in contrast with the weakness
3:07:19of the dollar. So this could be a trade
3:07:21idea and this is where we start using
3:07:23the auction market theory model to try
3:07:26and catch the best trades. So when we
3:07:28have to answer what is the market
3:07:29currently pricing in or betting on we
3:07:33rely on two main factors. First
3:07:36technical analysis through the auction
3:07:39market theory model and two the analysis
3:07:41of participation. So if through the
3:07:44auction market theory model we're
3:07:45basically taking a look at okay where is
3:07:48price going but not only price where is
3:07:50the volume going right where is the
3:07:52money going. In participation analysis,
3:07:55we may take a look at, for example, the
3:07:57COT report, which exactly tells us what
3:08:00the institutional traders, the big
3:08:02speculators are doing. And depending on
3:08:04the market, we can use option data. We
3:08:07can take a look, for example, at what
3:08:08the retail sentiment is doing to kind of
3:08:11try and understand, okay, where is the
3:08:12smart money going and when is the dumb
3:08:14money going. And for the auction market
3:08:16theory models, we take this beautiful
3:08:19model that we have drawn back here. We
3:08:21paste it all the way here and we
3:08:23basically create two ideal setups.
3:08:26Whenever we see that a range formed and
3:08:30price went up and created a new range,
3:08:33this means that a new fair value has
3:08:37been accepted. So we take the volume
3:08:39profile of this part of price, which
3:08:43will likely look something like this.
3:08:45Not a lot of volume and then big volume
3:08:48because there's a lot of trading
3:08:49happening here. This is where the money
3:08:51is and then again slow. This will be the
3:08:53top of the range which will likely
3:08:54coincide with our value area. This is
3:08:56the bottom. And as we said in the model,
3:08:58either price starts pumping up or price
3:09:03tries to pump up but fails or tries to
3:09:05pump down and fails. So these are the
3:09:08three options basically. So the first
3:09:09model here is to wait for price to drop
3:09:12and fall again to prices that were
3:09:14considered really cheap by aggressive
3:09:17buyers by the same buyers that were
3:09:19considering these prices cheap and kept
3:09:22buying because they couldn't get enough.
3:09:25And these are the same prices where the
3:09:27sellers that were considering these
3:09:29prices to be cheap were like, you know
3:09:31what, yeah, they're not so cheap.
3:09:33Actually, the best prices to sell are
3:09:35these ones and these ones and these
3:09:37ones. So here you had an imbalance in
3:09:39the auction because these prices were
3:09:42considered unfair by sellers and fair by
3:09:46buyers. So as soon as sellers maybe
3:09:49because of some mechanical selling
3:09:51pressure that normally sits below market
3:09:54lows push the auction higher, we want to
3:09:57see these same buyers that consider
3:10:00these prices cheap to now kick back in
3:10:02and push price back into the range.
3:10:04maybe with a big daily candle that
3:10:07closes back inside of the daily range or
3:10:09with a very strong movement anyways. And
3:10:12this is our first setup. We'll wait for
3:10:14price to trace back here and we'll
3:10:16either buy to take profit to the other
3:10:18side of the range or wait for a test
3:10:20before going up. That is setup number
3:10:23one. Setup number two is after price has
3:10:26done this is we wait for a strong
3:10:29breakout with a lot of volume and
3:10:32position ourselves as soon as price
3:10:34retraces a little bit maybe onto this
3:10:36area to look for a continuation. And
3:10:38these are the same models by the way
3:10:40that I have learned from Tom Forvault
3:10:42and Patrick Neil the world trading
3:10:44champions themselves. These are only two
3:10:47of the many models that they teach. And
3:10:49for example they call this the breakin
3:10:51and the breakout. So, for example, in
3:10:54this Forex example, I would take a fixed
3:10:56range volume profile, draw it from the
3:10:58beginning of the last impulse to the
3:11:01current price. And the volume profile
3:11:04will basically tell me, hey, this is
3:11:05where most money was traded. And you can
3:11:08see one part is highlighted. That's
3:11:10because I have set this volume profile
3:11:12with the value area. You don't 100% need
3:11:14it. This is a statistical reference. We
3:11:16can also put it at 100 and just think
3:11:19about where the biggest chunk of volume
3:11:22is, right? And we understand it's here.
3:11:24And what I do is I take the end of these
3:11:27areas. From a situation of high volume,
3:11:29we dropped into a situation of low
3:11:31volume. Okay, that's the lower area. And
3:11:34here from a situation of high volume, we
3:11:36dropped, right? So this is the higher
3:11:39value area. And I always want to see
3:11:41what's happening here, here, here. So we
3:11:44failed auctioning lower and the same
3:11:46buyers that bought here and bought here
3:11:48kept buying here and they also tried
3:11:51buying outside of this range but sellers
3:11:55were not ready yet. They still consider
3:11:57these prices to be fair. So they pushed
3:12:01the price back in until the other side
3:12:03of the range. So the market is clearly
3:12:06still in a situation of balance. But we
3:12:08did this. We tested here. And this was
3:12:11for example a good trade idea to go
3:12:13until here. If a new failed auction
3:12:16might happen with a strong rejection and
3:12:20some value created here, then this is
3:12:22still a good setup. So I would have
3:12:24either bought here with the idea of
3:12:26going to the other side of the range or
3:12:28wait for a strong breakout. Some time
3:12:30spent outside here to keep auctioning up
3:12:33if the macroeconomic data plus the coot
3:12:37reports tells us that the economy is
3:12:39going the same way and the institutions
3:12:41are too. Or for example here in the S&P
3:12:43500 we clearly had this as the main
3:12:46let's say distribution era. Trump came
3:12:49in with the tariffs. Scare the [ __ ] out
3:12:51of the market. But the economy was doing
3:12:54great. He literally said, "Guys, buy the
3:12:56dip." This was the value area high. This
3:12:58was the valier area low. And already
3:13:00since here, you started seeing this
3:13:02happening. Market kind of consolidating
3:13:04here and then breaking out. Then
3:13:06consolidating again, and then breaking
3:13:08out again. And the auction is clearly
3:13:10telling us where the money is going. And
3:13:12so this was just a huge breakin. So we
3:13:14break back in, spend some time there,
3:13:16test. That's the first trade to the
3:13:19other side. Second trade at the
3:13:20breakout. But we're clearly looking at
3:13:22we basically almost weekly charts. But
3:13:25even here, what happens again? A
3:13:26situation of out of balance, finding
3:13:29balance. Same concept here. Price breaks
3:13:32back, tests. That's another long setup.
3:13:34Then price moves up, create a situation
3:13:37of balance, breaks, and test it right
3:13:39here. by the way. So this is a very
3:13:41simple model that simply follows the
3:13:44basic rules of the market to easily
3:13:46follow the big money. And if we couple
3:13:48this with where is the institutional
3:13:51money going with the COT report, which
3:13:53by the way you can find for free here at
3:13:56tradingstair.com/cot.
3:13:58And in the COOT report, you basically
3:14:00see non-commercials with which are large
3:14:03speculator, commercials, which are
3:14:05hedgers, and non-reportables, which are
3:14:07technically retails, even though, you
3:14:09know, they're still kind of sort of big.
3:14:11But we want to look at the
3:14:12non-commercials. And the
3:14:14non-commercials, which are the banks,
3:14:16big banks, and institutions that are
3:14:18speculating and not engaging in futures
3:14:20trading for hedging purposes, will
3:14:22basically tell us all the story. They
3:14:24went long and increased their long
3:14:26exposure for 4,000 contracts which are
3:14:29almost 5% of the total exposure and they
3:14:33closed almost 900 short contracts. So we
3:14:36can go also in this chart take off the
3:14:37commercials, take off the
3:14:38non-reportables and we can clearly see
3:14:41that they have been accumulating and
3:14:43buying all of this time. Even though
3:14:45their net exposure was short, it's
3:14:49gradually drifting back up to being a
3:14:51net long position. Now, unfortunately,
3:14:53the last data is from September 23rd,
3:14:55and we're missing a lot of data because
3:14:57the government, the US government is
3:14:59currently in shutdown. But, for example,
3:15:01we can see what the retails are doing.
3:15:02So, we can go on my FX book retail
3:15:04sentiment, look at GBPUSD, and wow, and
3:15:08clearly see that most retail traders are
3:15:11absolutely shorting this market. So
3:15:14recap the scenario is telling us that
3:15:18likely GBP is rising and USD is falling
3:15:21because of the spread between the
3:15:23expected bond yields in both currencies.
3:15:26The COT reports tells us institution are
3:15:28buying and retail sentiment is telling
3:15:30us that retails are selling and the
3:15:32price action is is pretty clear. And me
3:15:34and Fabio, by the way, I've used this
3:15:36model for months in public live
3:15:38sessions, calling hundreds of trades
3:15:41with a really high win rate and
3:15:42risk-to-reward ratio, which is a really
3:15:44great case study of how to use this
3:15:47approach to the markets. We understand
3:15:49where is the money likely to flow from
3:15:51the fundamental side. We look at the
3:15:53money flow through the COT report. We
3:15:55look at the money flow through the
3:15:56technicals and we use them to time our
3:15:58entry perfectly. And this is a trading
3:16:00model that once a month we're also
3:16:03applying live in the worldass edge
3:16:05channel for free with a live sessions
3:16:07where we use this model to analyze the
3:16:09market and see potential trading setups.
3:16:11So if you want to join you can find the
3:16:13link in the description. It's completely
3:16:14free. So now thanks to the macro plot
3:16:16chart checklist through the building of
3:16:18a scenario through the liquidity auction
3:16:20theory model we have a very powerful
3:16:22swing trading model. But this model also
3:16:24is really really powerful for everything
3:16:26that relates to day trading because it's
3:16:28clearly allowing us to identify where
3:16:30the money is being traded. So this is a
3:16:33fractal concept that you can apply in
3:16:35most time frames with of course due
3:16:38adaptations. So we can zoom out and get
3:16:41back to the micro mechanics. Remember
3:16:43how we talked about how market orders
3:16:46are interacting with each other to make
3:16:47price move? And that's the basic micro
3:16:50mechanic, the granular micro mechanics
3:16:52that shows how money intent through
3:16:56accepting liquidity is the only driver
3:16:58of price movements. So the same way we
3:17:00use this to interpret price action in
3:17:02the swing trading, 4hour time frame, in
3:17:04the 1 hour time frame, in the daily time
3:17:05frame, we can also use this in lower
3:17:07time frames. And we can use this model
3:17:09for a shorterterm type of trading which
3:17:11is day trading. And unlike swing
3:17:13trading, day trading is for all intents
3:17:16and purposes a profession that requires
3:17:18time that could have a higher return,
3:17:20but it's way more stressful. The
3:17:22statistics are the same. 90% of traders
3:17:24fail and there's a much higher
3:17:26adrenaline involved. So trading
3:17:27psychology, especially in day trading,
3:17:30is one of the main issues for beginner
3:17:32traders. So, as I said before, the best
3:17:34way to start day trading is to start
3:17:35with a simple strategy. have a
3:17:38mechanical approach, an objective set of
3:17:40rules that takes advantage of a
3:17:43structural edge. So, first we start with
3:17:45something very simple, something
3:17:47profitable, very mechanical. But the
3:17:49goal in day trading needs to be becoming
3:17:51a proficient discretionary trader cuz
3:17:54this is not a algorithmic trading
3:17:55course. And in order to become a
3:17:57proficient discretionary day trader, a
3:17:59super simple, super mechanical, super
3:18:02objective, almost algorithmical trading
3:18:04strategy, I would say is enough to make
3:18:06the first profits. But to become a
3:18:08really successful discretionary trader,
3:18:09you need to do a lot of reps. You need
3:18:11to work on your intuition, not just on
3:18:14information and develop a subjective
3:18:16probability. This is how you can scale
3:18:19as a discretionary trader. And as I
3:18:21said, this requires time and the proper
3:18:23mindset. So, what we're going to do now
3:18:25in the next step of this video is we're
3:18:26going to first learn five simple trading
3:18:29strategies that you can start as a
3:18:31beginner that are very mechanical. And
3:18:32then we're going to take a look at how
3:18:34to read market action to learn how build
3:18:38a shortterm narrative. These five simple
3:18:41trading strategies are the oops strategy
3:18:44by world trading champion Larry
3:18:46Williams. We're going to learn a gap
3:18:48fill strategy that I've learned from
3:18:51Patrick Nil, world trading champion.
3:18:53We're going to learn a opening range
3:18:55breakout which is a great classic that I
3:18:58personally learned with Fabio Valentini.
3:19:01four times world top ranked trader in
3:19:03the Robbins Cup. The same with Larry
3:19:05Williams and Patrick Nil, but was
3:19:06originally coded by a guy named Tobby
3:19:09Crrael in the '9s. Then we're going to
3:19:12learn the rule of four by Tom Hogart,
3:19:16one of the best traders I've ever met,
3:19:18author of the book Best Losers Win, and
3:19:20the PBD strategy by Tom Forvald, which
3:19:24is the mentor of Patrick Neil himself,
3:19:27also mentored Fabio Valentini and
3:19:29myself. And please keep in mind all of
3:19:31these have decades of data and have been
3:19:35back tested and for tested and these are
3:19:38strategies that perform even though they
3:19:40are all a very basic set of rules. So
3:19:42they have been through a process of
3:19:44statistical validation which is
3:19:46something that I strongly suggest you to
3:19:49do as well which is something not a lot
3:19:51of traders do because they believe is
3:19:53not good but I believe it's super
3:19:55crucial especially as a beginner and
3:19:57observe it and look for patterns that
3:19:59repeat and that you can see constantly
3:20:02and once you've observed them you come
3:20:04up with a hypothesis. So for example,
3:20:06every time the first 30 minute of this
3:20:08session is broken, most of the time
3:20:10price will continue rising. So that's a
3:20:12good strategy, right? Which is also the
3:20:14principle of the opening range breakout
3:20:15strategy that we will learn. So now we
3:20:17have to take this hypothesis and do some
3:20:20in simple testing also known as back
3:20:23test. And when you back test, you have
3:20:24to have of course clear rules. So entry
3:20:28and exit rules. For example, where's my
3:20:30entry? Where do I place my stop loss?
3:20:32Where do I place my takerit? Is it a
3:20:34fixed riskto-reward take-profit? Is it a
3:20:37fixed percentage or points take-profit?
3:20:40Same with a stop-loss. And these have to
3:20:42be fixed. And for example, you can build
3:20:44an Excel sheet like this one. I'm going
3:20:46to leave this one in the description so
3:20:48you can use it yourself where you can
3:20:49gather all of your data. If you're
3:20:50buying, if you're selling, what was the
3:20:52outcome, the date, the time, if you had
3:20:54multiple take profits, the type of data
3:20:56maybe you're collecting, the
3:20:57confirmations for the entry, maybe a
3:20:59screenshot of the analysis before and
3:21:01after, and some notes. And this sheet
3:21:03will automatically track all of your
3:21:04data. You can write all of your rules
3:21:06here. So you can either do it with a
3:21:08Google sheet or a notion template. And
3:21:11you take any charting platform. It can
3:21:13be trading view. It can be deep charts.
3:21:15You go in the past and you start
3:21:17applying this strategy. And your goal,
3:21:19your only goal is to gather data on your
3:21:22hypothetical edge and eventually
3:21:25optimize the strategy. So doing a
3:21:27process called fitting. So for example,
3:21:30you see that this strategy does not
3:21:32really perform good on Mondays or on
3:21:35Fridays. Maybe because on Fridays very
3:21:37often some news are released in the
3:21:40macroeconomic side. So a simple
3:21:41technical strategy might fail. So you
3:21:44decide maybe to take away that day of
3:21:45the week or maybe you see that a certain
3:21:48stop-loss for example a 20 point
3:21:50stop-loss does not work and if you put a
3:21:5230 point stop-loss then your performance
3:21:55is incredibly higher. These are all
3:21:57thing that you can edit and tweak, but
3:22:00you should be careful about not falling
3:22:02into the rookie mistake of overfitting.
3:22:04As in, for example, you see that if you
3:22:07sum up all of the trades from 9:46 a.m.
3:22:12and 10:21
3:22:14a.m., but excluding the time frame from
3:22:17102
3:22:19and 1004, then your strategy is
3:22:22profitable. Well, I mean, you've
3:22:25cherrypicked the best times to make it
3:22:28profitable. You cheated, right? So, if
3:22:31you do this kind of things, your
3:22:33strategy will be overfitted as in it
3:22:36works so well on past data because it's
3:22:40only trained on past data that as soon
3:22:44as you come to the next phase, which is
3:22:46forward testing, won't work in the
3:22:48present or out of sample data. So always
3:22:51be careful about this. So just optimize
3:22:54what makes sense to optimize for a clear
3:22:57reason. Don't try to put your data on
3:22:59steroids. So at this point, you're ready
3:23:01to go to the next step which is probably
3:23:04even more important which is forward
3:23:06testing or out of sample test where you
3:23:10basically take the optimized strategy
3:23:11and try to apply it in real time to the
3:23:14actual markets. And here you keep
3:23:17tracking and monitoring the performance.
3:23:20This can happen in a demo account or a
3:23:23small real account just to get the
3:23:25feeling of what it is to trade with real
3:23:27money or with a small prop firm account
3:23:30so that you already start training your
3:23:33psychology and getting used to applying
3:23:35a strategy in real time. So once you've
3:23:37gathered data in the back test and
3:23:39gather data in the forward test and your
3:23:41strategy is profitable then you can
3:23:43finally go live with your strategy. And
3:23:46that comes with this whole sets of
3:23:47problems that we will get deep into when
3:23:49we'll talk about how to become a
3:23:51proficient discretionary trader.
3:23:52Everything that relates to market
3:23:54psychology, how to monitor your data,
3:23:56and how to actually go live. But for
3:23:58anything that relates the statistical
3:24:00validation protocol before going live,
3:24:02this is how it works. Algorithmical
3:24:04trader do this exactly the same way. And
3:24:06here you have strategies that have
3:24:08already gone through this process on
3:24:10both in sample and out of sample data
3:24:13for decades. and they consistently
3:24:15outperform the market and by outperform
3:24:18the market I mean generating alpha or
3:24:21performing better than just buying and
3:24:22holding the S&P 500. So first we will
3:24:24learn these four but now you also know
3:24:26how to do this process yourself and you
3:24:28have the tools to do it and then on top
3:24:30of this you will need to build your
3:24:33discretion your intuition and your
3:24:35subjective probability. So, we will
3:24:36learn how to read the market action to
3:24:38learn how to build a short-term
3:24:40narrative through order flow and how it
3:24:43interacts and causes price action. Of
3:24:47course, order flow and price action are
3:24:49tied together because one causes the
3:24:52other and the other is the consequence
3:24:55of order flow. We will learn how to
3:24:57understand the rhythm of the daily money
3:25:01flow and understand the impact of option
3:25:05flow which is something legitimately
3:25:07crazy like I I don't want to say it's a
3:25:09cheat code but it's it's a cheat code.
3:25:11So let's start with the first strategy
3:25:13which is called the oops strategy by
3:25:15Larry Williams and it's basically this
3:25:17model. Let's say this is the previous
3:25:19day. If the next day opens here, so with
3:25:22a gap compared to the high of the
3:25:25previous session and the candle breaks
3:25:27inside of this level, we basically sell
3:25:30here until the next candle closes with a
3:25:32fixed stop-loss, for example, of a
3:25:34certain number of points or above this
3:25:36level. Ideally, the gap should be
3:25:38minimum 20 points. Same thing here. If
3:25:40it's a short candle and there is a gap
3:25:43down, we wait for price to break above
3:25:45and that's where we buy. This is a very
3:25:48simple trading model that you can apply
3:25:50in in so many different markets. I don't
3:25:52advise you to just start using it. I
3:25:55always suggest you to statistically
3:25:56validate it yourself. So you can also
3:25:58trained to look for this pattern and
3:26:01trade it in a simulated environment
3:26:02while gathering data as well. But this
3:26:04is a statistical validation that was
3:26:07made by was made by an algorithmic
3:26:08trader on the DAX which is the German
3:26:12stock market index. And since 2012 to
3:26:142024 it had a great performance overall.
3:26:16For example, let's take this session.
3:26:18This had a clear gap where the price
3:26:20opened here. And as soon as we break in
3:26:23this level, we can sell. So this was the
3:26:26previous session high. This was the
3:26:28following session open. And like this,
3:26:30our short trade would be around here.
3:26:32And even with a 1:1 risk-to-reward
3:26:35ratio, there was this was a pretty
3:26:36decent profit. And that's the first
3:26:38trading model that you can start
3:26:39testing. The next 3D model you can start
3:26:41testing is the gap fail strategy that I
3:26:43learned from Patrick Neil but has been
3:26:44there also for a very long time which is
3:26:47sort of similar but you basically try
3:26:49and find sell opportunities before the
3:26:52gap closes. So unlike the oops strategy
3:26:55that goes for this trade you anticipate
3:26:57that trade and try and fill the gap from
3:27:00the previous day close to the next day
3:27:03open. Same thing over here. If we close
3:27:05the previous session here and we open
3:27:07here, we want to trade the gap fill and
3:27:09buy expecting that price will rise. This
3:27:12also has a lot of statistical
3:27:13validation. This type of pattern has
3:27:15been studied for decades and it has a
3:27:18clear statistical validity in the S&P
3:27:20500. 65 to 70% of the gaps are filled in
3:27:23the NASDAQ is even higher. So we can
3:27:26take the same example and drop into some
3:27:28lower time frame. This was the close of
3:27:30the previous session. This was the open
3:27:32of the new session. This is our gap. And
3:27:35maybe here we can wait for a break of
3:27:37structure. So market telling us it's
3:27:40going to start dropping lower. Or we can
3:27:42wait for another breakup structure and
3:27:44either trade this breakout here up until
3:27:46here. So more aggressively on the first
3:27:49breakup structure or conservatively on
3:27:51the second breakup structure with a low
3:27:53risk-to-reward ratio of course. And
3:27:55that's our second strategy. The third
3:27:57strategy is probably one of my favorite
3:27:58ones is the opening range breakout. So
3:28:00you basically take the first 15minute
3:28:02range and you wait for a five-minute
3:28:04candle to close either above or below.
3:28:07You can also add some volume analysis
3:28:09here and see if this candle has a lot of
3:28:11participation, but we'll get deep into
3:28:12that later. This also has a very long
3:28:16statistical validation. This is a back
3:28:17test that a friend of mine, Luke, he's a
3:28:20quantitative analyst has done, and it
3:28:22outperforms the S&P 500. And remember,
3:28:24this is the 15-minute opening range
3:28:26breakout for the 9:30 a.m. stock market
3:28:29open. So let's go to the 15-minut time
3:28:30frame. This was the first 15inut candle.
3:28:33We take the high, take the low, go to
3:28:35the five minute time frame, and we wait
3:28:37for a breakout. Breakout happens here.
3:28:40Buy, place our stop slightly below the
3:28:42other side. Now we're already running at
3:28:44a 1:1 risk-to-reward ratio, potentially
3:28:47targeting a 1:2 risk-to-reward ratio. Of
3:28:49course, this works best in very
3:28:51directional sessions like these ones
3:28:53where you take your first 15-minute
3:28:55candle, you go to the 5-minute chart,
3:28:57and as soon as this, you wait for this
3:28:59candle to close, sell, and this was a
3:29:02very profitable day that almost reached
3:29:04a 1:3 risk-to-reward ratio, and that's
3:29:06the opening range breakout. The fourth
3:29:09one is the rule of four by Tom Hogart.
3:29:12And this only happens during NFP news
3:29:14release or FOMC news release on the DAX
3:29:18and on the Footsc 100 which is the UK
3:29:21stock market index. So after the news
3:29:23event we wait for the fourth 5minut
3:29:26candle. So this is the 5minut chart and
3:29:29we wait one candle, two candle, three
3:29:31candle, four candle and we basically do
3:29:33the same thing that we did here with the
3:29:34opening range breakout but we simply buy
3:29:37whenever there's a breakout here and
3:29:38sell whenever there's a breakout here.
3:29:40The next one is the PBD strategy by Tom
3:29:43Forvvald and where you're either in an
3:29:45uptrend, so you have a P. And this is
3:29:47based on the auction market theory model
3:29:48by the way, or you're in a downtrend and
3:29:50you have a B, or you're consolidating
3:29:53and you have a D. P B D. And here you
3:29:56have two trend following models where as
3:29:59soon as you have a failed auction here,
3:30:01so price breaks out of a range and then
3:30:03it breaks back inside, you wait for a
3:30:05test or you buy directly until the other
3:30:08side of the range. The second option is
3:30:10you wait for a strong breakout and trade
3:30:13the breakout. Break in breakout just
3:30:15like the previous one for the swing
3:30:16trading model. The other one is a
3:30:18reversal setup where you basically wait
3:30:19for a range to form. Price breaks out
3:30:22and as soon as it breaks out again we
3:30:24sell here, stop above here targeting the
3:30:27beginning of the impulse. Uh here you
3:30:29have the same thing but in a downtrend.
3:30:31So whenever you have a failed auction
3:30:32here with a break below inside, you sell
3:30:34to the other side. here. If you have a
3:30:36strong breakout, you follow the trend.
3:30:39Or if maybe this is happening at a
3:30:41previous very important zone, you wait
3:30:43for a double breakout to trade the
3:30:45reversal up until the beginning of the
3:30:47impulse. Or if you are in a
3:30:49consolidation situation, you wait for a
3:30:51breakout and then a breakin on either
3:30:53side to go to the other side of the
3:30:55range and you do this kind of pingpong
3:30:57thing. This is the track record of
3:30:59Patrick Nil that used these exact setups
3:31:02in the World Trading Championships. Now
3:31:04you can take all of these strategies and
3:31:06test them one by one and already have an
3:31:08arsenal of strategies that are very
3:31:11basic, very profitable and very
3:31:13mechanical that does not require you a
3:31:15lot of thinking at least at the
3:31:16beginning. But to make these perform
3:31:18even better and truly become profitable,
3:31:21it's important to understand how to read
3:31:23orderflow, price action, the rhythm of
3:31:25the daily money flow, understanding the
3:31:27impact of option flow so that we can
3:31:29take these very basic setup that have a
3:31:32statistical validity historically
3:31:34speaking and build our discretion on top
3:31:36of these. Now, in order to truly
3:31:38understand this, let's start again from
3:31:39the basics and kind of remind ourselves
3:31:41how market mechanics work and try to
3:31:43read orderflow first in its purest form.
3:31:46So let's open deep chart and add a new
3:31:48advanced time in sales. This is the time
3:31:51in sales and it's the rawest form of
3:31:52order flow you can have together with
3:31:55the depth of market. So the depth of
3:31:57market is showing us sell offers and buy
3:31:59offers. And if these orders being
3:32:01offered are actually sold to market
3:32:04takers, you get the time and sales. And
3:32:07time and sales basically tells you at
3:32:08what time one of these orders that was
3:32:10offered was taken. So if the order book
3:32:13is the summary of the menu, time and
3:32:16sales is the summary of what was taken
3:32:19from the menu. And for example, you can
3:32:20also put a filter and enable for example
3:32:23a filter of minimum 25 contracts. So you
3:32:26only see the big trades and this is how
3:32:28it used to be done. They literally had a
3:32:31tape, a physical tape that they were
3:32:33reading where all of this time in sales
3:32:35used to be. That's why when we talk
3:32:36about the speed of the market, we also
3:32:38talk about tape speed. But this was a
3:32:41very raw visualization of volume which
3:32:44then translated in this chart also known
3:32:46as footprint charts. So as much as you
3:32:48can see price ticking up and down up and
3:32:50down also here this amazing chart
3:32:52basically summarizes all of the orders
3:32:55that were traded in the ask. So you got
3:32:57the bid you got the ask. Here you got
3:32:59the bid here you got the ask. All of
3:33:00these were aggressive buyers who
3:33:03accepted some offers on the ask. All of
3:33:06these aggressive sellers accepted buy
3:33:08offers made in the bid. And as you see a
3:33:11lot of colors in these candlesticks, you
3:33:13should know what they are. So you have
3:33:14two elements. You have the colors of the
3:33:16background and the color of the text
3:33:18that can vary. The color of the
3:33:19background is determined by comparing
3:33:22the volume traded here and the volume
3:33:24traded right on the side. If there was
3:33:26more aggressive buy volume, this will be
3:33:29green. Same like here and like here or
3:33:31here for example. And here the
3:33:33background is sort of purple because
3:33:35there's way more volume here than here.
3:33:37We also call this horizontal delta
3:33:40because if this minus this is positive
3:33:43will be purple. If this minus this is
3:33:45positive this will be green. So it's
3:33:47calculated through a differential a
3:33:49delta a subtraction. The second element
3:33:51as we discussed is the color of the
3:33:53text. Most of it is black but sometimes
3:33:55it becomes pink or it becomes blue and
3:33:58it becomes a certain color if for
3:34:00example compared to the other side of
3:34:03the auction because typically there's a
3:34:05bid and there's an ask and price goes up
3:34:07and down up and down like this as you
3:34:08can see here as well goes up tick down
3:34:10tick up tick down tick. So you have one
3:34:12bid, one ask. One bid, one ask. That's
3:34:14what we call an auction, right? So
3:34:16whenever there's an imbalance in the
3:34:18auction of these orders, considering
3:34:20only what was traded in the bid compared
3:34:22to what was traded in the relative level
3:34:24of the ask, if the ratio between these
3:34:26two is above 200%, aka this one is two
3:34:30times this one, the twice as much bigger
3:34:33number will color itself with a special
3:34:35color. For example, here 91 is at least
3:34:37twice 92. 143 and 214 don't have that
3:34:41difference. For example, 78 and 581.
3:34:45Well, 581 is more than four times. So,
3:34:47it will be shown even fattier and even
3:34:50more highlighted. Same thing here. 221
3:34:53with 37. 26 with zero is not colored
3:34:56because if there's a zero, the imbalance
3:34:58is not considered. 81 versus 10. Yeah,
3:35:00that one is pretty bigger. 75, 120, none
3:35:03of them is at least twice as much. Same
3:35:04thing with 58 and 29, but 124 and 21,
3:35:08there's a strong imbalance between the
3:35:10two. And actually, if you go to our deep
3:35:11charts website, you can see a Easter egg
3:35:13that few people probably noticed, which
3:35:15is this. If you hover on top, you see
3:35:18exactly what I mean. When we're watching
3:35:19the auction imbalance, we look
3:35:21diagonally, right? So, you see auction
3:35:23imbalance. You can see it in the text
3:35:24over here. Auction imbalance 202%, 174,
3:35:28273. It's more than 200%. So, the color
3:35:31is green. Same thing over here. And the
3:35:33color of these instead is based on delta
3:35:35and it's considered horizontally with
3:35:37these two numbers. Right? And this also
3:35:39represent the total volume. You see it
3:35:41here price tick total volume. And this
3:35:43is for example this type of
3:35:44visualization of the footprint chart.
3:35:46And with these type of candles you can
3:35:48see even more clearly not just the
3:35:50numbers of the auction but also a little
3:35:52volume profile that tells you how much
3:35:54money was traded in each level. And it's
3:35:57specifically with these kind of charts
3:35:58that I like to trade because you can see
3:36:00this type of stuff. You can see
3:36:02anomalies in volume where you can see
3:36:04that there was 92 contracts, 100, 100,
3:36:07and so on and so forth. Pretty decent
3:36:09numbers similar to the previous candles.
3:36:11But on this level specifically, look at
3:36:13this. 1,600
3:36:15contracts traded all at the same level.
3:36:18This, let me tell you, this is not a
3:36:20retail trader. This is a big market
3:36:23participant who was absorbing all of the
3:36:25selling pressure. So probably there was
3:36:27a huge order, for example, here. The
3:36:29market was trying to sell into it but
3:36:31couldn't make it. This order was like a
3:36:33wall that price could not go through at
3:36:35least at first and then price eventually
3:36:37dropped. So these type of orderflow
3:36:39anomalies is some things that orderflow
3:36:41trading often look at. Also here for
3:36:43example there were aggressive buyers
3:36:45trying to buy this market but they were
3:36:47completely blocked because they were
3:36:49trying to buy from the ask but in the
3:36:51ask there was this huge seller and
3:36:53eventually price dropped. Also here
3:36:55there was some sort of anomaly and this
3:36:57way you can kind of track what the big
3:36:59traders are doing in the market. So
3:37:01let's start building a framework to
3:37:03truly understand how to read these type
3:37:05of chart and to do that let's help us
3:37:07with another great indicator which is
3:37:08orderflow values which is nothing more
3:37:10than statistics then data about each
3:37:14candle and these are very very simple
3:37:16values regarding the orderflow of each
3:37:19candle. So you have total volume here so
3:37:21how many contracts were traded in this
3:37:23specific candle. We have the delta of
3:37:25this candle. So if the total candle
3:37:28volume is the sum of all volume traded
3:37:32in the ask. So aggressive buyers plus
3:37:34the total sum of the volume traded in
3:37:37the bid. So that equals volume. So if
3:37:39you sum all of this column with all of
3:37:41this column, all of these number add up
3:37:43to 4,000. The delta takes all the
3:37:47aggressive buyers trader who accepted
3:37:49the ask minus all of these contracts
3:37:52that accepted the bid. So ask minus bit
3:37:55equals delta. So this is ask plus bit.
3:37:58This is ask minus bit. And then you have
3:38:00this number which is the delta
3:38:02percentage which is basically this
3:38:04number divided by this number expressed
3:38:07in percentage. So 957
3:38:11over 4,000 that's around 24%.
3:38:15And that's this number. So how much
3:38:18crowded was this candle? who was the
3:38:20most aggressive participant in this
3:38:22candle and how much aggressive he was
3:38:25compared to the total volume. Super
3:38:27super easy. And as we discussed in the
3:38:29auction market theory model, we know
3:38:30that there's some periods of the market
3:38:33where the auction is in a situation of
3:38:35balance and moments where we go in a
3:38:37situation of imbalance. When we look at
3:38:39footprint charts, we will define these
3:38:42as responsive auctions and these as
3:38:46initiative. For example, let's look at
3:38:48this session on the S&P 500 on the 21st
3:38:52of October. And what we saw happening
3:38:54here was first a situation of balance,
3:38:56then a situation of imbalance, then a
3:38:57long situation of balance, and at the
3:38:59end of the session, imbalance again. So,
3:39:01let's look at what happened in these
3:39:03candles, in these candles, these ones,
3:39:06and this one. Let's zoom in and dissect
3:39:09these candles. What you typically see in
3:39:12slow price action and a phase of balance
3:39:15and responsive auction. What you will
3:39:17see is at the top of the candles a lot
3:39:20of green. A lot of green here, green
3:39:22here, green at the tops. And you will
3:39:24also tend to see more darkness at the
3:39:27bottom. Also here you can clearly see
3:39:29more dark at the tops. You see a lot of
3:39:32dark green. And all of this means one
3:39:34thing that buyers are trying to buy into
3:39:37these levels. But there's a lot of
3:39:39people as passive sellers in the ask. So
3:39:42a lot of people selling in this part of
3:39:44the book absorbing all of this upward
3:39:48pressure. That's what you call a
3:39:49responsive auction because there is a
3:39:51response from sellers here. And
3:39:55typically what you will see in these
3:39:57types of situation is an overall kind of
3:39:59flat delta percentage low numbers right
3:40:031 7 8 typically below 10. And what you
3:40:07will tend to see is also low volume. A
3:40:10thousand contracts, couple thousand
3:40:11contracts, couple thousand contracts.
3:40:13Not a lot of volume going on, right? So
3:40:15this is slow movement of price and slow
3:40:18movement of orders. Then once we get out
3:40:20of the balance, that's where you see the
3:40:23volume numbers going up. So you see a
3:40:25lot of aggressive volume and you see a
3:40:28lot of imbalances. When you see three
3:40:31levels of imbalance all at one place, we
3:40:34call this an imbalance cluster. And
3:40:36that's the clearest example of
3:40:38initiation because sellers here are
3:40:41willing to keep paying lower and lower
3:40:42prices to get their hands on a buyer. So
3:40:45sellers are eating every single level
3:40:47and when buyers try to push up they're
3:40:50again absorbed at the top absorbed at
3:40:52the top and there's more aggressive
3:40:54sellings with all these imbalances. Then
3:40:56a phase of retracement starts and you
3:40:58see we had 12 8 7,000 volumes 6,000
3:41:02volumes 4,000 volumes. As soon as we go
3:41:05up in the retracement part, you see that
3:41:07volume kind of starts to dry out. That's
3:41:09a typical thing in a phase of
3:41:10retracement. In an impulse, you will see
3:41:13high volume. In a retracement, you will
3:41:15see kind of lower volume. Then, as much
3:41:18as we move up, we start seeing more of
3:41:20that patterns. At the top, more
3:41:21absorption. We keep moving up and again,
3:41:24what happens at the top of the candle? A
3:41:26lot of dark green. A lot of dark, a lot
3:41:28of dark green. We're getting responsive
3:41:30again. So we can understand that all of
3:41:31this area has been protected by passive
3:41:34sellers. Let's see also when price comes
3:41:36back here again we see the same pattern
3:41:38happening. Buyers absorbed, buyers
3:41:40absorbed, buyers absorbed. And the next
3:41:42thing you notice is yes, we have low
3:41:45volume. And as soon as we break out of
3:41:47this balance and this responsive
3:41:50auctions, that's when we have an
3:41:52initiative candle. Boom. 1 2 3 4
3:41:55imbalance cluster volume increasing. We
3:41:58had 700 1,000 1,000 1,000 1,000 2,000.
3:42:02We start getting more aggressive and
3:42:04that's where we slowly slowly start
3:42:06falling back down, but we're not ready
3:42:08yet apparently. Let's see what happens
3:42:09once we come back here again at the top.
3:42:12What happens? Absorption responsiveness.
3:42:14And the next thing you know is a
3:42:16beautiful sell candle with 5,000
3:42:19contracts. And that's where the new
3:42:21trend starts. The following candle,
3:42:2334,000 contracts. Look at all of this
3:42:26crazy aggressive selling volume. So to
3:42:28summarize order flow, we have seen that
3:42:30the depth of market is basically the
3:42:32menu with the bids and the ask, buy
3:42:35limits and sell limits above and below
3:42:37price. This is the central limit order
3:42:39book, also known as the depth of market,
3:42:41our menu. And then we have the time and
3:42:43sales, which is basically telling us how
3:42:45much volume was actually traded at every
3:42:47level of price at a specific time, which
3:42:50is the rawest form of actual order flow.
3:42:52We can then see it through the lens of a
3:42:55candlestick chart. So we can still have
3:42:57our time frame candlestick with uh open,
3:43:00low, high, and close levels. And on the
3:43:03side having this our bid ask footprint
3:43:05where on the left we have all of the
3:43:07orders that hit the bid. So aggressive
3:43:10sellers that accepted these buy offers
3:43:12and on this side the orders that lifted
3:43:15the ask or the aggressive buyers that
3:43:18accepted these sell offer on the ask. If
3:43:20these numbers are colored is because of
3:43:23a situation of imbalance in the auction
3:43:25that we calculate diagonally because we
3:43:28always have one bid and one ask and the
3:43:30auction follows like this. And we can
3:43:33have a volume profile that is calculated
3:43:35horizontally on every level of price.
3:43:38And if we sum the bid and the ask we get
3:43:40the total volume. If we do ask minus bid
3:43:43we get the volume delta. So the net
3:43:46aggression. And if we divide this by
3:43:48this we get the delta percentage and
3:43:51they respectively answer the question
3:43:53how much participation there is who was
3:43:55more aggressive if buyers or sellers and
3:43:58exactly how aggressive were they and
3:44:00these are the basic tools of orderflow
3:44:02and we can read these tools by
3:44:04remembering what the auction market
3:44:06theory model looks like where we have
3:44:08situations of balance and out of
3:44:10balance. So ranging markets and
3:44:12impulsive markets and we read a
3:44:14situation of balance in the order flow
3:44:16as a responsive type of auction of
3:44:18liquidity where you typically have low
3:44:20volume and low delta in the orderflow
3:44:22values and buyers absorbed at the tops
3:44:25if we're looking at the top balance. Of
3:44:28course, the same thing goes for sellers
3:44:29at the bottoms. If we are, let's say
3:44:31here in the situations of out ofbalance
3:44:33price discovery, aka these ones, that's
3:44:36what we call an initiative auction that
3:44:39typically has a lot of imbalance
3:44:41clusters, a lot of really colorful
3:44:43one-sided volume activity, aggression,
3:44:46and typically higher delta and higher
3:44:48participation, higher volume. And this
3:44:50is how we distinguish looking at
3:44:51orderflow the two different phases of
3:44:54the auction market theory that we also
3:44:56see in the super high time frames but
3:44:58with the numbers in the shortterm
3:45:01auction. Let's start for example to see
3:45:03what is going on here and we can already
3:45:06preview the fact that we have an
3:45:07impulsive move and then a consolidation
3:45:10right so we have the two phases of the
3:45:12auction and to verify let's see what
3:45:14happened where earlier in the auction we
3:45:16had a lot of absorptions at the tops
3:45:18absorption absorption absorption a lot
3:45:20of greens at the top of the candles in
3:45:22the top part of the range we keep going
3:45:24with low volume low delta and then what
3:45:27happens big candle with a lot of green
3:45:296,000 contracts high delta and the
3:45:33initiation starts 4,000 4,000 3,000. You
3:45:36see also this candle has a lot of green,
3:45:39a lot of imbalance clustering. Keeps
3:45:41going up, keeps going up, a lot of
3:45:42volume. And then at some point right
3:45:44over here, we find the sellers willing
3:45:47to sell. So green on the top, green on
3:45:49the top, a lot of green on the top. Look
3:45:51at this one. 1,000 contracts in a single
3:45:54cell. This is a big seller absorbing
3:45:56like it's absorbing here. It's absorbing
3:45:58here. It's absorbing here. 800
3:46:00contracts. A lot of absorption. We keep
3:46:02going forward. And here again, all these
3:46:04buyers are absorbed at the tops. A lot
3:46:06of green at the tops, but overall
3:46:08auction volume is drying up and slowing
3:46:11down. Now, this is the short-term order
3:46:12flow. And we will need it to properly
3:46:14understand how to analyze the market
3:46:16when we day trade. But in order to do
3:46:18that, let me load another important
3:46:21chart that will help us understand,
3:46:22let's say, the medium-term auction. Now
3:46:25let's take a very clean chart and add on
3:46:27top volume profile and the volume chart.
3:46:30So if price is what as we said volume is
3:46:34how volume is money and if we zoom out
3:46:38we see that we have volume profile which
3:46:40is total volume per level of price and
3:46:42then we have horizontal volume or volume
3:46:45by candle volume by time frame which is
3:46:48the total volume traded in every single
3:46:50one of these candles every single one of
3:46:52these time frames. And you can clearly
3:46:54see there's a rhythm in the money flow,
3:46:57right? We have hours where there's a lot
3:46:58of volume and hours where there's zero
3:47:00to nonvolume. This is what we call the
3:47:03RH or regular trading hour sessions.
3:47:06Also commonly referred to as the New
3:47:08York session or the cash session. And
3:47:11then we have the Asian session and the
3:47:13London session also commonly referred to
3:47:15as electronic trading hours. And since
3:47:18we are currently looking at the chart of
3:47:20the ES, which is the E- mini S&P 500
3:47:23future contract, which basically tracks
3:47:25the S&P 500, and it's probably the most
3:47:28traded future contract in the world and
3:47:30one of the most traded assets
3:47:32volume-wise in the world. And and this
3:47:34contract, like many futures contracts,
3:47:36is traded in the CME, the Chicago
3:47:38Merkantile Exchange. And the trading at
3:47:40the CME starts at 9:30 local time and
3:47:43finishes at 400 p.m. local time or 3
3:47:45p.m. local time. Anyway, in most of
3:47:48these since we have this differentiation
3:47:50between cash session and the rest of the
3:47:52sessions, we want to mostly look at
3:47:54where the most money is traded clearly.
3:47:56And so we have this volume profile that
3:47:58as you can see begins and ends in the
3:48:02cash session and then we have a volume
3:48:04profile for everything that is not cash
3:48:07session. So one volume profile for the
3:48:09cash session, one volume profile for
3:48:11Asian and London session. And also if we
3:48:13zoom into the horizontal volume chart,
3:48:16we can clearly see a pattern, right? We
3:48:19see there's a lot of volume in the open,
3:48:21then volume dries down and then jumps
3:48:23right back at the end. Also here, a lot
3:48:26of volume at the start, maybe some
3:48:28spikes here and there, and then one big
3:48:30spike at the end. And that's the flow of
3:48:32volume throughout the day. This part of
3:48:35the session is also called the opening
3:48:37auction. The last part of the day is
3:48:40also called the closing auction. And in
3:48:42both these instances, something very
3:48:44interesting happens. Basically
3:48:45institutional traders and you know
3:48:47traders of all sorts will basically
3:48:49pre-market place something called market
3:48:52on open orders which is basically
3:48:56contracts trades that are placed
3:48:58slightly below market opens so that the
3:49:01exchange can fill them during the first
3:49:03few minutes of the session. The same
3:49:04thing happens throughout the day. If for
3:49:06example a big bank or an institution
3:49:09couldn't manage to fill all of their
3:49:10orders during the trading session, they
3:49:12will also have markets on close orders
3:49:17that will be executed all at once at the
3:49:19end of the auction all at once at the
3:49:21end of one cash session. And if for
3:49:23example we go to financialju.com
3:49:26which is my favorite uh news feed and
3:49:28every trader's probably favorite
3:49:29newsfeed and we go back at the beginning
3:49:32of the session I am currently in Turkey.
3:49:34So, the session begins at 4:30 p.m.
3:49:36There you go. You have the M imbalance
3:49:39or the market on open imbalance, which
3:49:42basically tells you in the S&P 500,
3:49:45there's an imbalance between buy and
3:49:47sell of plus $14 million or shares. I'm
3:49:51not sure if it's expressed in numbers of
3:49:53shares, but anyway, 104 million in
3:49:56market on open imbalance, which
3:49:58basically mean there's 104 more buyers
3:50:00than sellers. Same with the NASDAQ, with
3:50:02the Dow, and with the Magnificent 7. To
3:50:04be honest, this is not a huge imbalance.
3:50:06But let's go back to yesterday, and you
3:50:08will see that 10 minutes before the
3:50:10closing of the session, we have the MOC
3:50:13imbalance or market on close imbalance.
3:50:15And typically, there's more volume. You
3:50:17can see here S&P 500 almost a billion
3:50:20imbalance. Same thing on the NASDAQ.
3:50:22This is a huge positive imbalance. And
3:50:24these are not necessarily reliable to
3:50:26see what type of movements the market
3:50:28will do in the open or in the closing
3:50:30auction, but sometime they might. I'll
3:50:32invite you to back test it yourself. And
3:50:34then for each cash session, we have this
3:50:36volume profile. And every volume profile
3:50:39typically will have this colorful area
3:50:41and then a gray area. This is called the
3:50:44value area. And it is where 70% of the
3:50:48volume is traded. Why 70% you ask? Glad
3:50:52you asked. This is a bell curve also
3:50:54known as the Gaussian curve because of
3:50:56the mathematician that came up with it
3:50:58and basically observed this sort of law
3:51:01we could say that I will explain you
3:51:02with an example. This is a chart
3:51:04representing the distribution of penis
3:51:06length in the I think US population. You
3:51:09have a few people with a very small dick
3:51:10and then an increasing amount of people
3:51:13having averagesized dick and then a
3:51:16decreasing number of people having a
3:51:18bigger dick. This for example is the
3:51:20distribution of male height. This
3:51:22instead is the distribution of IQ in the
3:51:25population of the US. And as you can
3:51:27see, all of these elements in a sample
3:51:30will tend to be for the 78%
3:51:33within the average, a little less below
3:51:36the average, and even lesser the extreme
3:51:39below average or above average part. And
3:51:41this happens in almost any observable
3:51:44phenomenon or data sample. And so Gaus
3:51:46came up with a calculation of the
3:51:48distribution of probability. So how much
3:51:50is the probability that a person will be
3:51:53between the mean 68%. That interval is
3:51:55what we call a standard deviation with
3:51:57this sigma. This is minus and plus a
3:52:00standard deviation. This is the second
3:52:02standard deviation and this is the third
3:52:03standard deviation. So within the first
3:52:05standard deviation there's 68% or 70% of
3:52:09the elements in a sample. 95% will be
3:52:12under the second standard deviation. 99%
3:52:15will be under the third standard
3:52:17deviation. And well also in the volume
3:52:19profile we do exactly the same thing. We
3:52:21take one standard deviation of volume
3:52:24see where the most volume was traded and
3:52:26how statistically speaking volume was
3:52:29distributed inside of an auction. And
3:52:31this is a very interesting and useful
3:52:33data point because we will likely see
3:52:35that around these areas is where we get
3:52:37with a higher probability a return to
3:52:40the mean. Same over here. This is where
3:52:42we're more likely to see a return to the
3:52:45mean. So the level of one standard
3:52:47deviation in the volume profile tells us
3:52:49where it's likely that maybe in the next
3:52:51session we will see price bouncing back
3:52:54and up from not based on price not based
3:52:57on a pattern but based on the
3:52:59statistical distribution of volume in
3:53:02the previous session. So this standard
3:53:04deviation is also called the value area.
3:53:08The upper end is called the value area
3:53:11high. The lower area is called the value
3:53:14area low. And then you have this line
3:53:17which is not the mean exactly. It's the
3:53:20mode. It's the level with the highest
3:53:22concentration of samples. Also known as
3:53:25the point of control, which is the
3:53:29simply the level with the highest amount
3:53:30of volume. All of these peaks in volume,
3:53:34we will call them high volume nodes. All
3:53:38of these areas with low volume, we will
3:53:40call them low volume nodes, also known
3:53:43as peaks and valleys. Let's take for
3:53:47example this cash session. Let's take
3:53:49the valary low and the valary high and
3:53:52extend them. And we can clearly see that
3:53:54the following session exactly respects
3:53:57these levels with utmost precision. This
3:54:00is because we are in a situation of
3:54:01balance. And also in the following
3:54:03London and Asian session, we stay in a
3:54:05situation of balance because that's
3:54:07where the market is agreeing is a fair
3:54:10valuation, a fair price. But as soon as
3:54:14we open the following cash session and
3:54:16Trump maybe does some tweets. That's
3:54:18where we have a bump right in the bottom
3:54:21and then we break out of it, test it and
3:54:24drop. And actually, if we go back, this
3:54:26is exactly what happened here. This is
3:54:27the volume standard deviation, the upper
3:54:29part and the lower part. We drag them
3:54:32forward. And here you can see we opened
3:54:34here. We bounced back. We bounced back.
3:54:36We tried to move out. Failed auction. We
3:54:39dropped back in. Test this side and go
3:54:42to the other one. This is how you use
3:54:43the volume profile, by the way. And then
3:54:45you can go inside of these areas. And to
3:54:47time your entry, you can look at the
3:54:49short-term auction. We have a phase of
3:54:51responsive auction, initiative auction,
3:54:54then again responsive auction. So we can
3:54:56enter on the responsive auction as soon
3:54:58as we get a indication. And as we see
3:55:01for example when we see aggressive
3:55:02seller kicking in and we trade to the
3:55:04other side of the range. This is how you
3:55:06follow the money inside of a daily
3:55:08cycle. And as you can see these were
3:55:10also the high and the low of this
3:55:13current price distribution. Right? And
3:55:15that's exactly where today we've had the
3:55:18same sellers that out here broke out of
3:55:22the range then came back tested this
3:55:24area and dropped. these same aggressive
3:55:27sellers that consider these prices to be
3:55:29cheap enough to sell or premium enough
3:55:31to sell, but at the same time not as
3:55:33many buyers considering these prices
3:55:36cheap enough to outweigh the selling
3:55:38pressure in that same level. That's
3:55:40exactly what happened here. If we follow
3:55:42the money, the sellers who dominated the
3:55:44previous auction at these levels kick in
3:55:47again once and twice. And that's exactly
3:55:50where a responsive type of auction
3:55:52starts happening here. And as you can
3:55:54see in the first part, we had a lot of
3:55:56volume. As we approach this phase of
3:55:59consolidation, volume kind of dries up.
3:56:02And when does it spike again? When we
3:56:04break out. So we break out, we test the
3:56:06area and we go to the other side. Now
3:56:08what is likely to happen? Since we are
3:56:11on this side, I can for example draw
3:56:13this volume profile over here. So I have
3:56:16the full area and I know this is the
3:56:18value area high. And this is where I
3:56:20would expect sellers to consider these
3:56:22prices to be premium enough to sort of
3:56:25be present. And that's exactly where if
3:56:28we take a lower time frame chart and
3:56:31maybe draw in the tools from the other
3:56:33chart. Let's see what happens here.
3:56:35Well, that's exactly the area where we
3:56:37were exactly seeing responsiveness.
3:56:39Responsiveness again here. More
3:56:41responsiveness, absorption, absorption,
3:56:45and sellers getting more aggressive at
3:56:46the top of the candles with these
3:56:48imbalances. Now, for example, I can take
3:56:50a volume profile of this area and see
3:56:52that here where is where it's most
3:56:54likely to see sellers joining the party
3:56:56and going at least until here and maybe
3:56:59have a failed auction below here or a
3:57:01failed auction about here. So,
3:57:02summarizing all the steps of the auction
3:57:05analysis, now that we have understood
3:57:06the basics of orderflow, we know that
3:57:08the volume profile, it's basically the
3:57:10distribution of volume and the value
3:57:11area is the standard deviation. is the
3:57:14first standard deviation where 70% of
3:57:16the volume is traded where we have a
3:57:18valer high a valera low and a point of
3:57:21control which is the area and the level
3:57:23specifically where most volume was
3:57:25traded. The high volume nodes are peaks
3:57:27in volume. The low volume nodes are
3:57:30valleys in the volume profile just like
3:57:32a canyon. And then we move on to the
3:57:35auction analysis in the sessions. How do
3:57:38the session work? We have cash session
3:57:40where a lot of money is traded.
3:57:42Pre-market electronic trading hour
3:57:44sessions where not a lot of money is
3:57:45traded. It depends on the market of
3:57:46course. For example, in the DAX which is
3:57:49the German stock market index, you have
3:57:51much more volume here. And the regular
3:57:53trading hours are here. But for the
3:57:55stock market, the American stock market,
3:57:56the S&P 500, this is the cash session
3:57:59from 9:30 to around 4 p.m. And the
3:58:02section structure is like this. You have
3:58:04an opening auction, a lot of volume in
3:58:06the beginning, less volume throughout
3:58:08the session, and then a final burst of
3:58:09volume with the closing auction and the
3:58:12market on close orders. In the opening
3:58:14auction, you have a market on open
3:58:16orders. And in both of these cases, you
3:58:18can have an imbalance that can push
3:58:20price either up or down. And we can use
3:58:23the levels. And when a trend is clearly
3:58:26set, we can wait for a consolidation to
3:58:28happen. Draw an area around the value
3:58:30area high and the value area low. And in
3:58:32the next few session either sell from
3:58:34the top, buy from the bottom or wait for
3:58:37a failed auction either up or down and
3:58:39then a test to go to the other side of
3:58:41the range. Either way and looking inside
3:58:43of these areas how the money is behaving
3:58:46to look for areas of responsive auction
3:58:49and initiative auction which are
3:58:50explained right here. And we can also by
3:58:52the way use all of this with these
3:58:54models over here. If you implement and
3:58:56basically put together this together
3:58:59with this, you realize it's pretty much
3:59:01the same thing. And this is why I
3:59:03personally consider this built by Tom
3:59:05Forvville, the mentor of the world
3:59:06trading champion, my mentor and also
3:59:08partly Fabio's mentor as well as a
3:59:11revolutionary way of understanding
3:59:13market mechanics because it simplifies
3:59:15everything we've seen in four to five
3:59:17different patterns. And another thing
3:59:18you might notice is that the session
3:59:20structure of the volume is exactly what
3:59:23we see here in how institution fill
3:59:26their order with the VWAP logic where
3:59:28there's more volume in the open, less
3:59:30volume throughout the session and then
3:59:32even more volume in the closing auction.
3:59:34Exactly the same shape because this is
3:59:36how the institutional money flows inside
3:59:38of the market. And starting to observe
3:59:41these situations combined with these
3:59:43type of patterns. For example, we can
3:59:45look for these setups and look for
3:59:48confirmation with these type of
3:59:50dynamics. Are we in a situation where
3:59:52price can have a lot of movement because
3:59:53there's a lot of volume? Maybe I'm going
3:59:55to look for it in the cash session. For
3:59:56example, this opening range breakout
3:59:59strategy is specifically looking for the
4:00:01first 15-minute candle of the cash
4:00:03session and a breakout of that range
4:00:05from a at least 5 minute candle. And for
4:00:07example, in this previous session, we're
4:00:09on the five-minute chart. Let's get the
4:00:11volume going. We see this is where the
4:00:12cash session start. This is the top of
4:00:15the first 15-inut range. This is the
4:00:16bottom of the first 15-minute range. We
4:00:18have 1 2 3 5minute candle. That's 15
4:00:21minutes. And for example, we can wait
4:00:22for a breakout below a 5-minut candle
4:00:25open closing below this low. And then we
4:00:28can look for an extra confirmation with
4:00:29our volume analyzer. And what do we see
4:00:32here? Exactly during the breakout, we
4:00:34can see a huge level of imbalance. This
4:00:37is an imbalance cluster that coincides
4:00:39also with the breakout below the big
4:00:41value area here. And as soon as price
4:00:43reaches this level, sellers clearly
4:00:45start absorbing over here. So we have a
4:00:47phase of absorption that continues
4:00:49followed by aggressive selling. There's
4:00:51a big buyer here trying to resist, but
4:00:54eventually it fails. And this is a great
4:00:56entry. Let's look at it from a shorter
4:00:58time frame perspective. This is exactly
4:01:00the area we're looking at. What we want
4:01:02to see is first a responsive auction and
4:01:04then an initiative auction as a
4:01:05confirmation. And what do we see? Low
4:01:07delta percentage 0 41. lot of green at
4:01:10the top of the candle which indicates
4:01:12absorption of buyers and right after
4:01:15this phase of responsive auction a phase
4:01:18of initiative auction increasing volume
4:01:20more imbalance clusters this is the last
4:01:23confirmation for our entry so we sell
4:01:25here place our stop even slightly above
4:01:27generously above the area and I dare to
4:01:30say this was a pretty solid session I
4:01:33mean this is an exemplary example so not
4:01:35all session will be this awesome but
4:01:37this is the same principle that we can
4:01:39apply For example, here in today's
4:01:40session, if we approach the market in
4:01:43the exact same way, this is the first 5
4:01:45minute. This is the opening range. As it
4:01:48breaks out, we clearly have a long bias
4:01:51for the day. But we're still below this
4:01:53value error. We might want to wait until
4:01:55price breaks in gives us a test here.
4:01:58And here we look for longs to the other
4:02:00side. Even just an a very basic opening
4:02:02breakout strategy by the way here with a
4:02:04stop-loss below this level would have
4:02:06been profitable anyways with a lower
4:02:08risk-to-reward ratio. But that's why we
4:02:10want to wait for a confirmation for
4:02:11example, right? So when the break inside
4:02:13of this level happens, we see volume
4:02:16increasing in the breakin and then
4:02:18increasing again. We can check what
4:02:20happened here also with this footprint
4:02:22chart. You have buyers slightly being
4:02:23absorbed here also here. But there's a
4:02:26good momentum. Even buyers are still
4:02:28absorbed here. There's a big seller
4:02:29clearly here trying his best. So, what I
4:02:32would personally do since it's also
4:02:33doing it here, I would wait for this.
4:02:36Let's say it's a guy that is constantly
4:02:38blocking price from going up to be fully
4:02:41walked through. And after that, these
4:02:43guys won. This is a block of orders.
4:02:45This is another block of orders. Another
4:02:47block of orders. Another blocks of
4:02:49orders. Another block of orders. Also
4:02:50known as liquidity pockets. And that's
4:02:53your order block, guys, because that's
4:02:54where the war happens again. And we move
4:02:57back up. Now I get it. This takes time
4:02:59to properly master and understand. We
4:03:01will make more video about this for
4:03:03sure. But also in occasion of the launch
4:03:05of this software which is deep charts,
4:03:08me and Fabio are planning to host a boot
4:03:10camp where we go exactly through this
4:03:12type of strategies. And Fabio used these
4:03:14exact concepts to win four times at the
4:03:17World Scalping Championship. By the way,
4:03:19we are still here by we're still here
4:03:21where we were analyzing shortly before
4:03:23and see what happens here again.
4:03:24Responsive auction. Responsive auction
4:03:26absorption at the top. Absorption at the
4:03:28top. Now we are getting some initiative
4:03:30short. So we're likely to see more
4:03:32volume in the breakout typically. And
4:03:34there's two options here as we're
4:03:36dropping below all of these lows, we'll
4:03:37see typically some aggressive selling.
4:03:40And this is where I would expect some
4:03:42more pump up. But if by any chance since
4:03:45we are in this area and that's where
4:03:46we're expecting some aggressive selling
4:03:48as we discussed if we do this and then
4:03:51maybe break even below here with huge
4:03:53imbalance this is could be actually a
4:03:55good idea for selling. If below the
4:03:57breakout we have high volume and this is
4:04:00basically how you can follow through
4:04:02order flow and through volume analysis
4:04:04the auction of markets with a very
4:04:07objective understanding of the markets.
4:04:10And well, look at what happened. Exactly
4:04:12as we were saying, we were expecting
4:04:13some bearishness. Same sellers as we
4:04:15were talking about here. We had the
4:04:17responsive auction here. We started with
4:04:19some aggressiveness and with some 4,000
4:04:21contracts. We pulled back a little bit
4:04:23and then we melted down and the closing
4:04:27auction has just started by the way and
4:04:29we see a lot of sell orders, a lot of
4:04:31selling balance. We can check if there's
4:04:34have been a market on close imbalance on
4:04:37the short side. So, we move all the way
4:04:39up. Wow, that's a big imbalance. That's
4:04:423 billion worth of imbalance on the S&P,
4:04:451 and a.5 billion on the NASDAQ, and
4:04:47we're keeping going lower apparently for
4:04:49now. And now we need to understand what
4:04:51happens after this candle. And this is,
4:04:53by the way, the second thing that I like
4:04:56to go kind of deeper that we didn't
4:04:57delve too much deep into, but the real
4:05:00game happened around these value areas,
4:05:02right? Because as we discussed here,
4:05:04these are the areas where it's
4:05:06comfortable to trade for smart money
4:05:07because smart money prefers slow and
4:05:09liquid markets. These moments of price
4:05:11discovery is where one side is stronger
4:05:14than the other in the auction. And then
4:05:15these failed auctions happen where
4:05:18aggressive buyers keep buying higher but
4:05:20aggressive sellers find this very high
4:05:22so they bring price back in. So what
4:05:24happens during these failed auction is
4:05:26also crucial to kind of understand how
4:05:28to act when a new initiation outside of
4:05:31a phase of balance happens. So we can
4:05:33approach a phase where price let's say
4:05:36is moving high and has just created a
4:05:38new situation of balance and we have a
4:05:40volume distribution similar to this one.
4:05:43This is the value area approximately. So
4:05:45there's realistically four things that
4:05:47can happen. The first thing that can
4:05:48happen is that we break above and keep
4:05:50going higher. Let's call this scenario
4:05:52number one. Scenario number two is that
4:05:54we break below and keep dropping below
4:05:58the scenario two. So these are
4:05:59breakouts, very normal breakouts. Option
4:06:01three is price breaks out and then
4:06:04breaks back in and then moves to the
4:06:06other side. Another option is market
4:06:08tries to auction higher but fails and
4:06:10gets back into the comfort area. And of
4:06:12course there's also an option where we
4:06:13just bounce back from this area or
4:06:16bounce back up from this area. So the
4:06:18key is understanding that these areas
4:06:20are the one that we need to take most
4:06:22care of.
4:06:24[bell] Well, apparently we closed our
4:06:27day, we closed our auction and there you
4:06:29go. A lot of volume in the last part of
4:06:31the session. The closing auction is over
4:06:33and now we have the settlement and then
4:06:35a new session will begin tomorrow. So
4:06:37basically this was a comfort area. This
4:06:39was a big failed auction and then we
4:06:41went back into balance and likely we're
4:06:43going to stay here for a while. If not,
4:06:45we're going to break out or let's see
4:06:47back at our explanation. What we truly
4:06:49want to see is how price behaves around
4:06:51these areas, right? And what does it
4:06:53mean on an auction level? So, the key
4:06:55thing is here the buyers were dominating
4:06:58the auction higher. There was a
4:07:00situation of imbalance and high
4:07:03initiative where we have a high positive
4:07:05delta and a lot of volume. And all of
4:07:08this happens because buyers are
4:07:10constantly willing to pay higher prices
4:07:12to get their hands on the underlying
4:07:14asset. And then at some point both this
4:07:16buying pressure and selling pressure
4:07:18agrees that this is a fair price to
4:07:20trade, right? And that's why they trade
4:07:22a long time here. There's a lot of
4:07:24volume of transactions. So it's
4:07:26reasonable to expect at least at the
4:07:28beginning in the early stages of a
4:07:29consolidation that most breakouts will
4:07:32fail. So in the early stages of a new
4:07:34fair valuation, there's a higher
4:07:36likelihood that we'll see something like
4:07:37this or this. So setup three and four.
4:07:40And how we want to basically trace these
4:07:42is that when price is going down, of
4:07:45course, there will be a burst of volume,
4:07:47but then the following candles go lower
4:07:49in volume. There's less and less
4:07:51interest. Again, less volume. Volume
4:07:54dries again. And then when we start
4:07:56auctioning back inside of the range, we
4:07:58see more volume coming in and an
4:08:01initiation phase. This is what we call a
4:08:04failed auction. And this is a good
4:08:05indication that price might reverse
4:08:07after this. If instead what happens
4:08:10here, the same thing of course would
4:08:11happen above here, but of course on the
4:08:13other side. So all of these are
4:08:15confirmations and then we can look
4:08:16inside of the candles with orderflow to
4:08:18see if we can recognize some of the
4:08:21responsive auction but most of all
4:08:23initiative auction when we drop back
4:08:24inside of the range. So this is the
4:08:26first option and this is the first let's
4:08:28say trade idea right the second idea is
4:08:30when we have an actual breakout. How do
4:08:32we want to evaluate a breakout? Well,
4:08:35what we'd like to see here and here is
4:08:38ideally a situation where for the sell
4:08:41setup drop lower, we drop lower with a
4:08:44high intensity and high volume with a
4:08:46higher participation with initiative and
4:08:48intensive activity. And as we move back
4:08:51to test this area, either an absorption
4:08:54here, so a responsive auction from
4:08:55sellers. So, we want to see the same
4:08:57sellers that caused their strength. And
4:09:00if they actually do that and maybe even
4:09:03break below, then we know it's a
4:09:05qualified sell setup. So, again, we want
4:09:07to see increase in volume and again
4:09:09increase in volume. We don't want to see
4:09:10a dry up in volume. We want to see a lot
4:09:12of activity here. Same thing over here.
4:09:14As we break above the value area, we
4:09:16want to see a good initiative and then a
4:09:19pullback and then again either some form
4:09:21of absorption here or a form of
4:09:23exhaustion. So basically a decrease in
4:09:26sell activity at the bottom of the
4:09:27candles which is the ideal scenario and
4:09:29that's already a good buy setup. And
4:09:32unlike this one, this is in favor of the
4:09:34trend. So we might be a little more
4:09:36aggressive than this one which is a
4:09:37reversal setup because we're clearly
4:09:39long. And then you have these situations
4:09:41that could happen. I wouldn't say
4:09:42they're more rare, but I normally prefer
4:09:44to wait for this to happen instead. But
4:09:47this could still be a potential setup
4:09:49where we wait for an absorption here and
4:09:51an initiative. absorption and
4:09:53initiative. So responsive plus
4:09:55initiative. And these are the principles
4:09:58behind for buying setups and for selling
4:10:00setups that you can kind of implement
4:10:02together with with this logic and that
4:10:04you can even more objectify if you build
4:10:07them on top of a gap fill strategy and
4:10:09opening range breakup strategy and stuff
4:10:10like that. So this is basically
4:10:12everything you need to know about
4:10:13auction analysis and order flow and the
4:10:16rest you know is practice. You need to
4:10:18look a lot. As I said, this is part of
4:10:21building a discretionary narrative. And
4:10:23you're not just going to do it by
4:10:24looking at price action. You want to do
4:10:26it looking at order flow, looking at
4:10:27what happens behind the candles, look at
4:10:29the money flow inside of the market. And
4:10:31that's how then, as I said, you can
4:10:33build this discretion on top of
4:10:36preackaged
4:10:38edges that have proven to work for a
4:10:40long time to then improve your edge and
4:10:42your trading. And if you together with
4:10:44this you give also a context of where we
4:10:47are at at the macroeconomic level. What
4:10:50are the fundamentals of the asset and
4:10:52what is likely the next macroeconomic
4:10:55scenario. You can even align yourself
4:10:57with a big long-term money flow thanks
4:11:00to fundamentals with a clear
4:11:02understanding on how the money is moving
4:11:05and with clear strategies and models to
4:11:08be able to ride the waves and serve the
4:11:10waves of money by being on the right
4:11:13side of the money flow with a decent
4:11:16statistical edge. And I would say to get
4:11:18even deeper and let's also say the final
4:11:20step of this course especially since
4:11:23we're talking mostly about indices
4:11:24because remember that indices are
4:11:26typically going up. They have these
4:11:29edges that have been working for
4:11:30decades. So they are very solid and we
4:11:33have access to all of this money flow
4:11:36but also and the reason why I like them
4:11:38is because they're fully transparent not
4:11:40only on the futures orderflow not only
4:11:42the order flow that you can also see
4:11:44through the individual stocks of the S&P
4:11:47500 but the next thing the last piece of
4:11:50the puzzle that you need to know to
4:11:51understand properly how the daily rhythm
4:11:54of the money flow is set inside of a
4:11:57session is understanding the impact of
4:12:00options. option flow. And in order to
4:12:02understand option flow, we need to first
4:12:03understand what options are. By the way,
4:12:05this map is absolutely huge. Probably
4:12:08the I've been shooting this video for
4:12:10like weeks now. Even though you see the
4:12:13same setup all the time, if you notice,
4:12:15my hair and my beard kind of grew and
4:12:17then they and then I cut it and you can
4:12:18see the passage of time in my face.
4:12:21That's crazy. So, uh, let me understand
4:12:23where can I put them the option flow.
4:12:25Let's put it here. So options are a very
4:12:28fascinating yet complex financial
4:12:31instrument and market and options are a
4:12:35financial derivative just like futures
4:12:37as you know you know there's underlying
4:12:39assets let's say it's the S&P 500 the
4:12:41S&P and you can basically have
4:12:44derivatives right so we have you can
4:12:45trade it to through futures you can
4:12:47trade a CFD you can trade an ETF and you
4:12:50can trade options on the S&P or you can
4:12:54trade you know the single stocks of the
4:12:56S&P, for example, the Magnificent 7 and
4:12:59stuff like that. But that would be kind
4:13:00of trading the underlying asset, right?
4:13:02And the reason why options are so
4:13:03important is that if you sum the total
4:13:06volume, the nominal volume of single
4:13:09stocks, ETFs, CFDs, futures on the
4:13:13underlying asset of the American stock
4:13:15market. All of this is combined not as
4:13:19big as the option market. So the option
4:13:22market is absolutely the biggest
4:13:24financial derivative in the stock
4:13:26market. So big that this became the
4:13:29underlying asset itself which is kind of
4:13:32crazy. And the flow of options contracts
4:13:35that flows inside of the market. It's
4:13:37part of the causes of the futures and
4:13:40the underlying price movement. And you
4:13:42can basically follow the option flow to
4:13:45kind of spot where this type of flow
4:13:47will affect the market and how. Now you
4:13:49have to understand that options are a
4:13:50more complicated financial instrument
4:13:52right and they kind of work like an
4:13:54insurance and you have two types of
4:13:57option contracts calls and puts. A call
4:14:02option basically gives the owner the
4:14:04right to buy a certain let's say stock
4:14:07for example at a price defined strike
4:14:10price within a specific date also known
4:14:13as the expiration. That's the call
4:14:15option. The put option gives the owner
4:14:18the right to sell a certain stock at a
4:14:20price called strike within a specific
4:14:22date. So if you buy a call is because
4:14:25you want to buy. So you typically buy a
4:14:27call when you expect price to rise. You
4:14:30buy a put when you expect price to drop.
4:14:33And why would you do that? Well, for
4:14:35example, a lot of big funds are long US
4:14:38equity market. So they are long stocks.
4:14:41They bought stocks. So imagine for
4:14:43example, you're a hedge fund. You bought
4:14:44Apple at this price. Now the price is
4:14:47here and you expect a retracement. You
4:14:50want to kind of mitigate this draw down.
4:14:52You want to hedge this draw down without
4:14:55having necessarily to sell the stock and
4:14:58close your trade. So what you can do,
4:15:00you can buy a put option. And when you
4:15:02buy a put option, you're basically
4:15:04insuring yourself against a possible
4:15:07bare market on the stock. So you buy the
4:15:10option, literally the option of selling
4:15:12it at this price. Let's say this price
4:15:15is $1,000. So you would buy the put at
4:15:19$1,000. And to buy this, you pay a
4:15:23premium, which is the price of the
4:15:24option, for example. Let's say you pay
4:15:27$100. So that's what you pay. That's
4:15:28kind of your stop-loss, if you will. And
4:15:30if price drops and the expiration comes,
4:15:34so your option expires and let's say now
4:15:36price is $800. So the price has dropped
4:15:40$200. When you buy a put option and
4:15:43let's say for example you buy one
4:15:45contract, so one option contract, one
4:15:47option contract typically corresponds to
4:15:50100 stocks. So 100 units of the
4:15:54underlying stock. So this $200 of price
4:15:57drop in the stock of Apple will be
4:15:59basically your profit multiplied by 100
4:16:02times. So your option at expiration is
4:16:05worth $20,000 because if you were to
4:16:08exercise the option of selling that
4:16:11stock actually at 1,000 and you had a
4:16:14100 of them, you could basically resell
4:16:15it right away at the current price and
4:16:17have a profit of $20,000. So you have a
4:16:20leverage effect, a multiplier effect of
4:16:23a 100. And so typically in options you
4:16:25have something called the payoff chart
4:16:28where you have the strike. So the price
4:16:29of the underlying asset the stock and
4:16:31let's say we were here right when we
4:16:34bought the put option at $1,000 of price
4:16:37of Apple. Now the price is $800. So if
4:16:41normally you see price going down on the
4:16:43y ais here you have price on the xaxis.
4:16:47On the y- axis instead you have the p
4:16:49and l. So the profit and loss. So your
4:16:52payoff chart and let's say this line is
4:16:56$0. So at $1,000 you paid a premium of
4:17:01$100, right? So this is let's say the
4:17:03negative level. This is what you paid
4:17:05$100. So if the price stays exactly
4:17:08where it is, that's what you will pay.
4:17:10And if price by any chance goes up, so
4:17:13so the price of Apple goes up, you still
4:17:15paid $100. That's your maximum loss.
4:17:17You're not going to lose more than that.
4:17:19So even if price reaches $1,200, it does
4:17:2220% up, you still lose only $100. But as
4:17:26we said, as soon as price drops, you
4:17:28start earning money. So you see your
4:17:29profit line going up and then keeps
4:17:32going up until, let's say, you reach uh
4:17:34$800 and your P&L here is, as we said,
4:17:37$20,000. So as soon as you buy it,
4:17:39you're basically losing for a little
4:17:41bit. Then you pass the break even line
4:17:43and you're in profit. Potentially
4:17:45unlimited profit until we reach of
4:17:47course the strike of $0. And that's the
4:17:50payoff chart of a put option. For call
4:17:52option is exactly the same but opposite.
4:17:54So let's say you are a hedge fund which
4:17:57is short a stock. So you have a short
4:17:59position here. Let's say you're short
4:18:01Tesla at $800. But you expect the Tesla
4:18:05price to kind of retrace before going
4:18:07down. How do you hedge yourself from a
4:18:08possible upward movement? You buy a call
4:18:11option and say you buy one call. Let's
4:18:13say here the price is $650. So you buy
4:18:17one call at 650. So if price goes high
4:18:20and let's say reaches $750, the stock
4:18:24has risen of $100 times 100, your profit
4:18:28would be $10,000.
4:18:30So this is how the payoff chart of your
4:18:32call option would look like. At 650, you
4:18:35basically bought your call option and
4:18:37you paid your $100. Then you're losing
4:18:40until you reach the break even line. So
4:18:41price goes a little bit up, a little bit
4:18:43up, then it shoots up to 750. You earn
4:18:46$10,000 and price can even go to zero
4:18:49and you'll still lose only $100. So this
4:18:52is what happens when you buy a put
4:18:54option or you buy a call option. And
4:18:57here you're basically, as I said, buying
4:18:59an insurance against price going up or
4:19:02buying an insurance against price going
4:19:04down. you will truly understand the
4:19:05meaning of insurance while we look at um
4:19:08the Greeks what determines eventually
4:19:10the price of a option. But as we said,
4:19:13buying a call or buying a put gives the
4:19:16buyer the right to sell or buy a certain
4:19:20stock at a price within a specific date
4:19:23or expiration. But how about the seller?
4:19:26So if I am the insurer is the seller of
4:19:29option. If you sell an option, it gives
4:19:31you the obligation to buy a certain
4:19:34stock at a price within a specific date
4:19:36if that option is exercised. So for
4:19:39example, a sell call payoff chart. If
4:19:42this was the buy, what the insurer does
4:19:46is he is the one earning that $100 and
4:19:49he's in profit throughout all this time
4:19:52and his potential loss is virtually
4:19:54unlimited. So the premium that the buyer
4:19:56pays is the profit of the seller. The
4:20:00profit that eventually the called buyer
4:20:02will be paid by the seller and the loss
4:20:04could be potentially unlimited. In the
4:20:06buy put pay of chart is exactly the
4:20:08opposite. The profit of the put buyer is
4:20:11the loss of the put sellers and the
4:20:14premium that $100 that the put buyer
4:20:17paid is actually the profit of the
4:20:20option seller while the potential loss
4:20:22is technically unlimited. Now, here I am
4:20:25in my favorite option trading platform,
4:20:27which is Thinkorswim by Charles Schwab.
4:20:29And here you have something called the
4:20:30option chain, which is a huge list of
4:20:33all the options that you can buy or sell
4:20:35in, in this case, the S&P 500. And you
4:20:38have options basically expiring every
4:20:40day of the week down until December 25
4:20:43years from now, 2030. We got options
4:20:46expiring on 2029, 2028, 2027, 2026, all
4:20:49of 2025. And ultimately, we have these
4:20:52daily options. And as you see 0 1 4 5 6
4:20:567 8 11 12 blah blah blah these are the
4:20:59days to expiration. So how many days are
4:21:02missing till the option eventually
4:21:04expires. We also call this DTE
4:21:08right days till expiration. And if I
4:21:11open for example tomorrow's options this
4:21:13is the option chain. How to read it very
4:21:15easily? You have the strike price at the
4:21:17center. So this is the prices of S&P
4:21:20500. If you go all the way down, you can
4:21:22see that price of S&P goes down and down
4:21:23and down and down and down. As I go down
4:21:25and down and down, the price goes up and
4:21:28it goes up basically every five points.
4:21:30660, 65, 70, 75, 80,85 and so on and so
4:21:34forth. And in this side of the screen,
4:21:36you have the puts. In this side of the
4:21:38screen, you have the calls. And this
4:21:39line here basically makes you understand
4:21:41that the current price of the S&P is
4:21:43between 735 and 740. As you can see, the
4:21:46close of today was 6,738, which is
4:21:49exactly in the middle of these two
4:21:50prices. You have the bid and you have
4:21:52the ask. And just like any other market,
4:21:54you buy from asks and you sell to the
4:21:57bid. So for both call options and put
4:22:00option, as we said, you can either buy
4:22:03or sell. So also here, this is basically
4:22:05the order book with the best bid and the
4:22:07best ask. all of these area. So where
4:22:10the current price is, these strikes are
4:22:13called at the money because they're at
4:22:17where the price is now. So for puts,
4:22:20these are at the money. These are out of
4:22:24the money. These are in money. ATM, ATM,
4:22:29OTM. For the calls, these are in the
4:22:32money. These are still at the money. And
4:22:34these are out of the money. just some
4:22:36options slang you might want to know. So
4:22:38we've understood what is a call, we
4:22:40understood what is a put, what are their
4:22:43payoff chart for the buyer and for the
4:22:47seller. We've looked at the option chain
4:22:49and now we're going to go to options.com
4:22:52and build for example a long call. This
4:22:54is a very useful toolkit for option
4:22:57traders. And so we start with S&P that
4:23:00is currently at 6738.
4:23:02And we can choose the expiration. This
4:23:05is the next day expiration expiring in
4:23:07four days in five in six and so on and
4:23:09so forth. If I move this you will see
4:23:11behind is the price the current price of
4:23:13S&P. So when I put it here it's
4:23:16basically at the money and this is the
4:23:18payoff chart. Let's zoom out with this
4:23:21thing over here this controller over
4:23:22here. And here you can see that the
4:23:24maximum loss if you're long a call. So
4:23:27if you buy a call and you buy one call
4:23:29of S&P 500 at the money so where so
4:23:33where the option strike equals the
4:23:35current price of the underlying asset
4:23:36your max profit is infinite your maximum
4:23:39loss is capped to $2,500. This is the
4:23:42price of buying a call option at the
4:23:44money expiring tomorrow. If instead of
4:23:46buying it at the money, so where price
4:23:48is now I go at higher prices. So I go
4:23:51out of the money. You see now that is
4:23:54really less expensive buy a call above
4:23:56the current price because the likelihood
4:23:58of price being there at expiration is
4:24:02really low and the chance of me making
4:24:04profit is very low. That's why we call
4:24:06this out of the money. If instead I were
4:24:08to buy the same call below the current
4:24:11price, look at this number here. As I go
4:24:13lower, my chance of profit goes higher
4:24:16because the price is here and my option
4:24:18is way below the current price. That's
4:24:20why we say it's in the money because I'm
4:24:22basically trying to buy in a situation
4:24:24where I'm already in profit and I have a
4:24:26higher probability of closing it in
4:24:28profit. But of course, that's why the
4:24:30price is higher. Let's now switch to put
4:24:32and do the exact same example. Now I am
4:24:34at the money where the price is
4:24:36currently. If I put if if I buy a put at
4:24:40lower prices, there's a lower chance
4:24:42that price will do all of that
4:24:43excursion, right? So, my chance of
4:24:45profit is really low. That's why we call
4:24:47it out of the money because I'm buying
4:24:49the right to sell at a lower price than
4:24:51the current price. Instead, if I buy it
4:24:54in the money, my chance of profit will
4:24:56be higher, but I will have to pay a way
4:24:58higher premium because I'm reserving the
4:25:00right to sell at a price which is higher
4:25:02than the current price. That's why we
4:25:04call it in the money. Now let's try to
4:25:06instead of buying one contract, selling
4:25:08one contract. So now we're short a put.
4:25:11Our chance of profit is 61% based on a
4:25:14normal distribution of probabilities of
4:25:17where the price will be at expiration.
4:25:20Our maximum loss can be really really
4:25:22high and our maximum profit will be
4:25:25$2,000 which is our credit. This is of
4:25:28course if I sell at the money. If I sell
4:25:31it out of the money, my chance of profit
4:25:33goes really really high, 96% based on a
4:25:36normal statistical distribution of
4:25:38probabilities of where the price will be
4:25:40at expiration. My profit though will be
4:25:42really really low. If I sell in the
4:25:44money instead, my chance of profit is
4:25:47very lower. The profit I will take is
4:25:49much higher. Now I'm selling a call. And
4:25:51if I sell it, my chance of profit will
4:25:53be really high, but my maximum loss can
4:25:55be infinite. Technically speaking, if I
4:25:57stay in the money instead, my chance of
4:25:59profit will be lower, but my max profit
4:26:01will be extremely high, even though the
4:26:03loss can be technically still infinite.
4:26:05And the cool thing about option is that
4:26:07I can buy and sell multiple legs as we
4:26:10say. So let's say now I'm short a put. I
4:26:13can also sell a call. And what will
4:26:15happen is something really interesting.
4:26:17This is called a short straddle. In this
4:26:19way, I'm basically betting that price
4:26:22will stay within this range. And if it
4:26:24stays within this range, I'm making
4:26:25money. If not, I'm losing a lot of
4:26:27money. So, it's more than so more than
4:26:30betting on price going up or price going
4:26:32down. I'm betting on the fact that
4:26:35volatility will be low. If I buy them
4:26:38instead at the money, I'm basically
4:26:40betting that price will be volatile and
4:26:43exit either from one side or the other.
4:26:45I don't care because I'll make money
4:26:47either way. So these are nondirectional
4:26:50strategies because it doesn't matter
4:26:51where price goes if up or down. It
4:26:53matters that is very volatile and we
4:26:56still didn't get into the real sauce
4:26:57yet. But you can already start
4:26:58understanding that being able to bet on
4:27:01volatility gives you a much wider range
4:27:04of ways to express yourformational
4:27:07advantage or your statistical edge. And
4:27:09that's why so many professional traders
4:27:11or investors approach options. I
4:27:14strongly advise against trading options
4:27:16as a first thing to begin with because
4:27:18they're more complicated as we will see
4:27:20now. So don't just jump into options
4:27:22without knowing what you're doing
4:27:23because as we said, especially if you're
4:27:25selling naked puts and calls, the max
4:27:27loss can be unlimited. But now you at
4:27:29least understand why so many
4:27:31institutional traders go for options and
4:27:33why they're such a big market because
4:27:36they're traded in really high volume.
4:27:38plus every single one of these contracts
4:27:40equals 100 times the underlying asset.
4:27:44Now let's make an example. Let's say you
4:27:46are an insurance company selling and
4:27:48let's say you sell fire insurance. And
4:27:51for this fire insurance, of course,
4:27:52you'll charge a premium. And let's
4:27:54imagine two scenarios. In one scenario,
4:27:57you're close to a forest and a fire has
4:28:00just started and it's a very dry season.
4:28:02In the second scenario, you're in a
4:28:05desert in Siberia and there's no signs
4:28:08of fire around. Which one of these
4:28:11insuranceances will be riskier for you?
4:28:13So, you will have to charge a way higher
4:28:15premium to ensure someone against a
4:28:18fire? Well, of course, this one. Why?
4:28:20Because this has a high probability of
4:28:23happening. This instead has a low
4:28:25probability of happening. Now, let's add
4:28:28another scenario on top. Let's say this
4:28:30fire insurance covers you for 10 years.
4:28:33This other fire insurance covers you for
4:28:365 days. Well, in 10 years a lot of stuff
4:28:38can happen. You want to charge a higher
4:28:40premium. In 5 days, there's a lesser
4:28:42probability that a big fire will happen.
4:28:44So, you'll charge less. So, there is a
4:28:46time component to insurance risk. So,
4:28:49there's two factors. The implied
4:28:52probability of an event happening and
4:28:54you have a time component. The same
4:28:56thing happens also with financial
4:28:59options. The implied probability of an
4:29:01event happening is the implied
4:29:04volatility and the time component is
4:29:06also referred to as time decay. These
4:29:09are two very important components of how
4:29:12we calculate the price of an option. If
4:29:14the implied volatility is high, the
4:29:17premiums will be higher because there's
4:29:19a fire happening inside of a forest. If
4:29:21the implied volatility is low because
4:29:23we're in a desert means that the market
4:29:25is not expecting huge price movements
4:29:27then the premiums will be lower and
4:29:28through time as we've also seen for
4:29:31longer expirations the premium will be
4:29:33higher for short-term expiration the
4:29:35premium will be lower and of course if
4:29:37we're talking about financial markets as
4:29:39we have seen with at the money in the
4:29:41money out of the money the underlying
4:29:43price is also a factor. So we have
4:29:46implied volatility, time decay and
4:29:49underlying price. All of these three
4:29:51things contribute to the variations and
4:29:54the fluctuations of price of an option
4:29:57and and the profit or the losses you
4:29:59will incur. And there is a model called
4:30:01the black and schles model. Hope I write
4:30:04that correctly which basically takes
4:30:06into consideration these calculate the
4:30:08price of the so-called European style
4:30:10options. I'm not going to get deep into
4:30:12that now. And these three factors can be
4:30:14summarized in what we call the option
4:30:17Greeks. Greek letters called delta,
4:30:20vega, and theta. Then to be honest,
4:30:22there's also a another factor that I
4:30:24wouldn't say it's less it's sort of less
4:30:26important or less, let's say, affecting
4:30:28the day-to-day uh option pricing
4:30:30movements, which is so-called risk-free
4:30:32interest rates. So, a part of the price
4:30:35of the option is also influenced by, for
4:30:37example, the central bank's interest
4:30:39rates. The Greek of this is the raw.
4:30:41just know it for now. But delta, vega,
4:30:43and theta are way more important. And
4:30:45and delta basically answers the
4:30:46question, how much does price of my
4:30:49option change given a one point movement
4:30:52the price of the underlying asset. So
4:30:55for example, if the price of S&P 500
4:30:57goes up from here to here five points,
4:31:01how much does the price of my option
4:31:02change? Let's go back to option strat.
4:31:05And you have to know that this chart is
4:31:07payoff chart once the option is expired.
4:31:10But as I buy it, I still didn't mature
4:31:13my premium cuz price is still exactly
4:31:16where it was and no time has passed. So
4:31:18if I bought a call option now and resell
4:31:21it right now, I would be at break even.
4:31:23And as you can see, make it even wider
4:31:25to make it more obvious. First, when I
4:31:27buy a call at the money at lower prices,
4:31:30at lower prices of the underlying asset,
4:31:32this line is not really changing, right?
4:31:34Then you go up and it start changing
4:31:36fast. Then it goes up. And if I were to
4:31:38ask you how fast is this curve rising,
4:31:41which basically means as the price of
4:31:43the underlying asset goes up, how does
4:31:45my profit and loss go up? We would
4:31:47probably say that this is rising pretty
4:31:50slow, basically flat. And then it start
4:31:53rising faster. Look at this. Start
4:31:55rising faster. It start rising faster.
4:31:57And it start rising faster because we're
4:32:00going more and more upwards. We're
4:32:02rising faster. We're rising faster. and
4:32:04we basically plateau at around 45
4:32:07degrees right in fact if I take the
4:32:09chart of the delta that's exactly the
4:32:11type of curve you will see at first the
4:32:14curve was flat then it started reaching
4:32:16high and then it started going straight
4:32:18right so if this curves measure the
4:32:20speed at which profit and loss goes
4:32:22higher this is how it would look like
4:32:24when we are at the money as you can see
4:32:26the delta is 50 so every one point of
4:32:29underlying price movement the price of
4:32:31my option will go up by $50 let's go
4:32:33back to the profit and loss. Now, my
4:32:35option is worth, let's say, zero. Let's
4:32:36say I move around 20 points later. 20 *
4:32:4050 is exactly $1,000, which is my
4:32:43profit. So, price moved 20 points. My
4:32:46profit went up $1,000. $1,000 divided by
4:32:5120 points is exactly 50. That is my
4:32:55delta. If I click on delta, that's
4:32:57exactly 50. I hope that's clear. The
4:33:00Vega answers, how much does the price of
4:33:03an option change given a variation in
4:33:06the implied volatility, which means the
4:33:09volatility that the market expects it to
4:33:12be there. Let's make an example. We
4:33:13bought a call. We bought an insurance
4:33:15against a fire happening. And let's say
4:33:18the implied volatility, which is the
4:33:19probability the fire will happen, starts
4:33:22spiking all of a sudden. Well, now my
4:33:24insurance is worth way more because
4:33:27there's a fire going on. So, I can now
4:33:29sell this insurance back to someone else
4:33:31in this market for a profit because the
4:33:33implied volatility, the implied
4:33:36probability that that event will happen
4:33:37is extremely high. Same thing if we
4:33:39bought a put. If the implied volatility
4:33:42is low, then my insurance on on the fire
4:33:45is not going to be worth much. But if
4:33:47the fire starts, that's where my option
4:33:50my insurance is valuable. And then, of
4:33:52course, you have time decay. As much as
4:33:55time goes forward, my option at the
4:33:57money is worth less and less. This is a
4:34:01chart of the Vega and how it's impacting
4:34:04prices. And as you can see, it follows.
4:34:07We have this line, right? We have this
4:34:08line. And then we have the distribution
4:34:10of probabilities. As soon as the implied
4:34:12volatility increases also, the
4:34:13distribution of the probabilities that
4:34:15price will stay in that range is lower
4:34:18and lower and expands the range where
4:34:20price is likely to be. And the influence
4:34:22is really high before expiration. But as
4:34:24soon as we get closer and closer to the
4:34:27expiration, the impact of volatility on
4:34:29the price of the option will be really
4:34:30low because even though there's a fire,
4:34:32the option is about to close. So it will
4:34:34just interfere with all the other
4:34:36strikes until it finally dissolves.
4:34:39Theta instead there's a huge a effect at
4:34:41the money at the beginning is kind of
4:34:43low then it increasingly higher and
4:34:44higher and more important at the money
4:34:47than it is in the money out of the money
4:34:49and then eventually disappears because
4:34:50the time is officially decayed. So theta
4:34:53answers the question how much does the
4:34:55price of an option change through time
4:34:57and all of this is crucial to understand
4:35:00if we want to understand the impact of
4:35:01option flow in the underlying market and
4:35:04also understand some reason behind the
4:35:06intraday movements of stocks and in
4:35:08order to truly understand it we need to
4:35:09introduce one last Greek which is a
4:35:12second great Greek because it's a Greek
4:35:14derived from another Greek because from
4:35:16the delta we can calculate the gamma and
4:35:19the gamma answers the question how much
4:35:22Does delta change given a onepoint
4:35:26movement in the price of the underlying
4:35:29asset? So, back to option strat. Let's
4:35:32recap what the delta was. We said that
4:35:34the delta was measuring how fast this
4:35:37line goes up, right? And as we said,
4:35:40it's not going fast at all here. Then it
4:35:42starts to go faster, then faster up,
4:35:45faster up, faster up, faster up until it
4:35:49basically goes at the same speed up,
4:35:51right? So this is kind of the measure of
4:35:53the speed of the profit and loss line
4:35:55and that's delta, right? But now we can
4:35:58do the same thing here and say for
4:36:00example, how fast is this rising up?
4:36:02Well, here is pretty slow. Then it start
4:36:04going faster up. Here we're at the
4:36:06maximum speed and then the speeds get
4:36:09slower, right? So we're not going so
4:36:11fast anymore. And that in fact is the
4:36:13chart of gamma. It's measuring the
4:36:15change of the delta through price. So
4:36:18recapping real quick, we've understood
4:36:20what options are. We've understood how
4:36:22the payoff chart works. What's the
4:36:24option chain? And with an example of an
4:36:26in of a fire insurance, we've understood
4:36:28what's affecting the price of these
4:36:31insuranceances, quote unquote. The
4:36:32underlying price, of course, the implied
4:36:34volatility or how likely is there going
4:36:36to be a fire? Time decay until how far
4:36:39am I insuring myself against a fire and
4:36:42hence theta how much the price of the
4:36:44option changes through time. How much
4:36:46does it changes if the fire actually
4:36:48starts the delta is how the price of my
4:36:51option varies through price and gamma
4:36:54measures how delta varies through price.
4:36:57I really don't think you can find an
4:36:59easier explanation this stupid easy
4:37:01explanation of option creeks on the
4:37:03internet. You don't even need to
4:37:04understand all the mathematics behind
4:37:06it. And now we can finally introduce the
4:37:09option markets participants. And also
4:37:12here in the option markets you have
4:37:14three main participants. You have hedge
4:37:16funds, big speculators, you have retail
4:37:19traders, you have investors in general
4:37:22and also here you have market makers.
4:37:25And if investor might just use it for
4:37:26hedging their let's say positions on
4:37:28stocks, there's some big smart money
4:37:30participant that will use it to
4:37:32speculate. Retail traders also will use
4:37:34it to speculate and market makers will
4:37:37use it as always to earn a spread just
4:37:41like the market makers we saw on
4:37:43futures. Remember when we explained the
4:37:45types of matching algorithms and how
4:37:47lead market makers are basically earning
4:37:49a bid ask spread which is the spread
4:37:51between the best ask and the best bid.
4:37:53That's the spread. This guy. Well, we do
4:37:55have a bid ask spread in options as
4:37:57well. If we zoom into the option chain,
4:37:59this is the bid and the ask of call
4:38:01options. If you want to buy, it's going
4:38:03to cost 5150. You want to sell, it's
4:38:06going to be 5250. So this $1 spread is
4:38:10the profit of the market maker that is
4:38:13both putting an order here, putting a
4:38:15buy offer here, and putting a sell offer
4:38:17here. And he's not just doing it for
4:38:19this strike. He's doing it for every
4:38:22single strike. So he's earning a spread
4:38:24from here, from here, from here, from
4:38:26here, from here, from here, from here,
4:38:27from here, from here. And not just from
4:38:28calls, but also from puts from here,
4:38:31from here, from here, from here, and all
4:38:32of these little lines. Isn't that crazy?
4:38:34So, you can start to understand how
4:38:35complicated this all is. And because of
4:38:38the nature of these contracts and theta
4:38:41and Vega and Delta and gamma,
4:38:44considering that market makers only goal
4:38:48is to be directionally neutral, so to
4:38:51not have a directional exposure. Because
4:38:54as we said before, if price were to rise
4:38:57and they were to keep selling here,
4:38:59selling here, selling here, they would
4:39:01keep selling at worse and worse and
4:39:02worse prices and they would lose. Also
4:39:05in options, they want to stay
4:39:06directionally neutral. But we have to
4:39:08consider more factor in the calculation
4:39:10on of how do we stay neutral. Let's make
4:39:13an example. Let's say a retail traders
4:39:15buys a put market. In this case, the
4:39:18market maker will sell a put on the ask
4:39:22because the market maker's job is to
4:39:24provide liquidity. So this will be the
4:39:25profit and loss chart of the retail
4:39:28trader who bought the put. The insurer
4:39:30will be the market maker in this case.
4:39:32So this will be the payoff chart of the
4:39:34market maker. So the market maker if
4:39:36price starts going down he will lose
4:39:38money. If price starts going down he
4:39:40will lose a lot of money. So what often
4:39:42market makers will do is to hedge for
4:39:46example if they are shortput they might
4:39:49at the same time to hedge this danger
4:39:52that they might start losing money for
4:39:54example sell one contract of the ES
4:39:57futures. Imagine the profit and loss of
4:40:00a futures sell position as price goes
4:40:03down. The profit and loss of a ES one ES
4:40:07contract will be like this, right? And
4:40:10would probably be green. So it will be
4:40:12something like this. So the profit from
4:40:15shorting the ES and the loss from having
4:40:18shorted an S&P option basically offset
4:40:21each other. So the directional exposure
4:40:24is flattened. So when the market sells
4:40:26this put, she will also have for example
4:40:28to sell at ES futures to neutralize this
4:40:32exposure. Same thing if this was a call,
4:40:35he has just sold a call and if price
4:40:37goes high, he's losing money. So what
4:40:40can he do? He can for example buy one ES
4:40:43contract and as price rises also his
4:40:47profit will. So this will be his profit
4:40:49line. So he'll make profit here
4:40:52offsetting this loss over here. So if
4:40:54market makers are mostly short call,
4:40:57they will have to buy while market is
4:40:59rising. And if they're short put they
4:41:02will have to sell when market is falling
4:41:04but they will not do it systematically.
4:41:07So one point one contract one point one
4:41:09contract. Nope. Because remember we have
4:41:12a delta to consider because if I just
4:41:15sold this option at the money my
4:41:16directional exposure is not uniformly
4:41:19going down at the same pace. Here I have
4:41:22basically no directional exposure. Here
4:41:24I slightly start having a little bit of
4:41:26directional exposure and having it more
4:41:29and more. So maybe here I could sell one
4:41:32ES contract. Here I maybe should sell
4:41:34two. Here I should sell three. So it's a
4:41:37type of dynamic hedging because the
4:41:39directional exposure is not uniformed
4:41:42through price because of gamma and
4:41:46delta. That's why we call the hedging
4:41:48activity of market makers dynamic delta
4:41:52hedging. And the activity of dynamic
4:41:54delta hedging from options market maker
4:41:56creates a flow that we also call hedging
4:41:59flow inside of the future market. And
4:42:01there are rough estimates around this.
4:42:04But I would say that depending on the
4:42:06day 10 to even 20% of the volume that
4:42:10flows inside of the futures market,
4:42:12which is the same orderflow that we read
4:42:15with the footprint chart that we have
4:42:17just learned, where the [ __ ] is it? that
4:42:18we see inside of the footprint chart on
4:42:21the ES actually comes from here, right?
4:42:26And as we said, if their short call or
4:42:28short put and they want to be delta
4:42:31neutral, as we said, if price falls,
4:42:34they have to sell ES futures to hedge
4:42:36themselves. So, they can earn money
4:42:39while price goes down by shorting ES
4:42:42future contract. So they will sell the
4:42:44dip and if their short call and price
4:42:47falls they have to dynamically hedge
4:42:49their delta by buying ES to offset this
4:42:52loss with this profit to stay neutral
4:42:55and so they will keep buying as price
4:42:59rises. So they will also buy the RIP. So
4:43:03if price is rising they will buy into
4:43:06it. If price drops they will sell into
4:43:09it. This means that they will contribute
4:43:12to the expansion of price volatility.
4:43:16This could for example mean that
4:43:17breakouts will have a a gentle push from
4:43:22option market makers. But what if a
4:43:24trader or an institution or whoever is
4:43:27being the counterpart of the market
4:43:28maker, let's say it's an institution
4:43:30sells put market. Well, the market
4:43:32banker will buy that, right? So now this
4:43:35is the profit and loss of our option
4:43:38market maker right and they want to stay
4:43:40delta neutral right they don't want to
4:43:42be exposed to any kind of direction even
4:43:44if it's in profit so if they are long a
4:43:47put and price drops they would buy an ES
4:43:52contract which will basically lose them
4:43:54money so they can basically offset this
4:43:57profit as well because they don't want
4:43:58to be directionally exposed period
4:44:01doesn't matter if it's a profit or a
4:44:02loss so here price is dropping and they
4:44:06dynamically keep buying to offset their
4:44:09exposure. Same thing if they bought a
4:44:12call to offset this directional positive
4:44:15exposure, they will sell a future
4:44:17contract which will basically go to a as
4:44:20a loss position. So this loss will
4:44:23offset the directional exposure from
4:44:25this profit and they will be delta
4:44:27neutral. So as price keeps rising,
4:44:29options market maker will keep
4:44:31dynamically selling. So they will sell
4:44:34when price goes up and buy when price
4:44:38goes down. So if they're either long
4:44:39call or long put, they will buy the dip
4:44:42and sell the rip. So when price is
4:44:44falling, they will buy and contributing
4:44:46to pushing it up. If price is rising,
4:44:49they will sell and they will contribute
4:44:51to pushing it down. So this will
4:44:53contribute to the compression of price
4:44:56volatility. And for a breakout trader,
4:44:59for example, we will have the exact
4:45:01opposite effect of this that breakouts
4:45:04will not have a gentle push but actually
4:45:06more of a gentle pull back. And the
4:45:09first study on gam exposure was proudly
4:45:12presented by squeeze metrics, an amazing
4:45:15website where they display three main
4:45:18data points. the S&P 500, the darkpool
4:45:22index, which basically measures the
4:45:24darkpool activity, and the GAM exposure,
4:45:27which I shortly introduced to you
4:45:28earlier, but let's see, because they've
4:45:30made this research paper that I strongly
4:45:33suggest you watch and read, where they
4:45:35basically analyze the role of options.
4:45:38They've done this in probably 2017. They
4:45:41talk about this dynamic hedging. And of
4:45:43course, this idea of options hedging
4:45:45starts with four assumption. First is
4:45:47that all trades and all traded options
4:45:50are facilitated by delta hedgers. So by
4:45:52option market makers. So all retail
4:45:55traders, all institution all buy and
4:45:58sell from and to option market makers
4:46:01which is an assumption. Probably most
4:46:03trades are but in order to proceed with
4:46:05the hypothesis we have to start with
4:46:07some assumptions. Then the other
4:46:09assumption is that call options are sold
4:46:11by investors bought by market makers.
4:46:13Put options are mostly bought by
4:46:15investor because they want to hedge from
4:46:17price going down specifically because
4:46:19investors mostly invest in the stock
4:46:21market and they buy puts to avoid price
4:46:24going down, right? And so they buy put
4:46:27options and market makers mostly sell
4:46:29them. So the idea is that market makers
4:46:32mostly sell puts and buy calls. And the
4:46:36other assumption is that the market
4:46:37makers hedge precisely to the option
4:46:40delta. So they basically created this
4:46:42formula for the calculation of gamma
4:46:44exposure based on the open interest of
4:46:47options and they've calculated the total
4:46:50gamma exposure and display it with a
4:46:52number expressed in billions of dollars
4:46:54[clears throat] and compared the gamma
4:46:56exposure if it's a negative number. It's
4:46:59a short gamma exposure which basically
4:47:02means they've sold puts and sold calls.
4:47:05So they as we said contribute to price
4:47:08volatility. If the gamma exposure is
4:47:10positive, it means they are long gamma
4:47:13or mostly long calls and long puts and
4:47:16they will buy the dip and sell the rip
4:47:18contributing to price compression and
4:47:21the compression of volatility. And we
4:47:23can clearly see there is a direct
4:47:25correlation between gam exposure and
4:47:28volatility. So the GAM exposure of the
4:47:31previous day from 2004 to 2017 was a
4:47:35direct indication the likelihood of the
4:47:38following day of the S&P 500 being super
4:47:41volatile with returns of 5 10 + 5 + 10%
4:47:46all the way down to - 5% - 10%. So huge
4:47:50volatility and in case of a long gam
4:47:52exposure a compression of volatility
4:47:55with all the samples staying below the
4:47:575% range way closer to the 0% range. So
4:48:01they have actually found a real
4:48:03correlation between these two. Now this
4:48:06was a great indication before 2017 when
4:48:09this was published but after 2017 there
4:48:12was a a huge change in the option market
4:48:14and what started changing since 2017 is
4:48:17that the zerodte options so the daily
4:48:20options the options that expired today
4:48:23starting 2017 and especially 2019 they
4:48:27saw a huge surge in volume activity also
4:48:30in 2019 it was the first time where
4:48:33zerod options were present for every day
4:48:35of the week. So for Monday, Tuesday,
4:48:37Wednesday, Thursday, and Friday. Before
4:48:39there were just three per weeks. And so
4:48:41today, or better 2024, now it's probably
4:48:43more. Half of the volume of the entire
4:48:46option market is traded in zero DTE
4:48:50options, which is [ __ ] crazy. So my
4:48:53and Fabio's mentor, Enri Costuki, was
4:48:56kind enough to basically recreate this
4:48:59data analysis and update this
4:49:02scatterplot chart with more recent data.
4:49:04And maybe because we've been a lot more
4:49:06in a bull market, there's been way less
4:49:10days of gam exposure of totally negative
4:49:13gam exposure based on the open interest.
4:49:16The correlation is still somewhat there,
4:49:18especially if in the data we include
4:49:20days getting back until 2000 until 2011.
4:49:25But specifically because of this extreme
4:49:27skew in the volume where most of the
4:49:29volume is traded in the daily options
4:49:32there's not much open interest and
4:49:34considering that this gam exposure was
4:49:36calculated on the open interest there's
4:49:39not a lot of open interest in zt options
4:49:41because they expire today so there's no
4:49:42open interest at the end of the day plus
4:49:44this assumption which is that all traded
4:49:47option are fac facilitated by market
4:49:49makers they're always buying calls and
4:49:52selling put is somewhat naive That's why
4:49:55their calculation of the GEX is also
4:49:57called the naive GEX. So some new tools
4:50:01started popping up. The most famous of
4:50:03which is spot gamma. And spot gamma is
4:50:05is an absolutely phenomenal tool. You've
4:50:07also might have seen Fabio using one of
4:50:10their indicators. I personally look at
4:50:12trace a lot because trace basically
4:50:15displays on this chart the zero DTE GX
4:50:19per strike. So again here we have long
4:50:22gamma and here we have short gamma. So
4:50:25these are negative levels of gamma
4:50:27exposure that you can also see in this
4:50:30map and then you have big long gamma
4:50:33levels that are then plotted in this
4:50:35part of the chart in purple. Then again
4:50:37super hot area short gamma again in this
4:50:40pinkish color. I call it the hot fire
4:50:43and the cold fire and the hot fire
4:50:45again. And so this is even better than
4:50:47this. you you don't have just the open
4:50:49interest of the day before and you know
4:50:51that the day after is going to be
4:50:53volatile. Here you have literally the
4:50:55areas at which market makers of options
4:50:59are likely to hedge their delta by
4:51:01compressing volatility or hedging delta
4:51:04by expanding volatility. So you will
4:51:07often see throughout these cold kind of
4:51:10areas price actually is consolidating
4:51:12while the spikes of volatility happen
4:51:15exactly here in the hotter areas and as
4:51:17soon as price will break out of this
4:51:19area you start seeing a clear direction
4:51:22and by the way you can take this and
4:51:24beck test it I don't know for until how
4:51:26long but this is a huge data point and
4:51:29also this is not simply calculated on
4:51:31their short puts and their long calls
4:51:33but it's basically taking more
4:51:35specialized data a more specialized
4:51:37option flow from the CBOE that either
4:51:40tells this software directly what the
4:51:43options market makers are actually doing
4:51:45or uristically calculates it based on
4:51:48where trades are getting filled. So at
4:51:51which price between the best bid and the
4:51:54best ask because sometimes they can be
4:51:56also filled mid-pric. So for example, if
4:51:58they're filled at the bid and at the ask
4:52:00there's a higher chance that they will
4:52:01be market maker. If they are filled in
4:52:03the middle, they could be also other
4:52:05traders. So there's more complex. So in
4:52:08these type of charts, there's more,
4:52:09let's say, accurate information than a
4:52:12mere naive version of the old open
4:52:15interest gs. And with this, my friends,
4:52:18we have a clear picture of literally
4:52:21every type of information that we can
4:52:22get about the markets from the
4:52:24fundamental perspective because of the
4:52:26macroeconomical reasons of the money
4:52:28flow and the way we can use it to assess
4:52:31the long-term trend and sentiment to
4:52:33some basic strategies that you can also
4:52:35use in the intraday to the order flow
4:52:38that moves price intraday with the
4:52:40auction analysis and the liquidity
4:52:42auction theory to understanding why
4:52:44option market makers are such a huge and
4:52:47important player not only in the option
4:52:49market but because of their hedging
4:52:51activity of the same order flow that
4:52:53we're looking at in the S&P 500 which is
4:52:56of course probably the most transparent
4:52:57market of all. Hence why I always prefer
4:53:00to trade the S&P 500 or the NASDAQ
4:53:03because unlike other markets like forex
4:53:06for example that have no sign of order
4:53:08flow let alone option flow stock market
4:53:11indices are just a much more transparent
4:53:13market to trade where we have more
4:53:15information and lessformational
4:53:17asymmetry with other market
4:53:19participants. So now if you're a swing
4:53:21trader, you can take macroeconomics and
4:53:24fundamentals to create a bias for swing
4:53:26trades and have a very strong model
4:53:29based on the fundamental reasons of the
4:53:31money flow on the participation analysis
4:53:34with the coot report and the liquidity
4:53:36auction theory for the technical timing
4:53:38of these setups. If you're a day trader
4:53:40now, you have also five strategies that
4:53:43you can use. Four which are much more
4:53:45mechanical and that have years of data
4:53:48backing them up, plus a full-fledged
4:53:50orderflow reading methodology with an
4:53:53integration of option flow so you can
4:53:55truly follow all the big and smart money
4:53:58in the markets. Oh my god, this was the
4:54:01longest video I've ever made in my
4:54:04entire life.
4:54:06So, did you like it? So, I really hope
4:54:09you did because I literally put all of
4:54:11everything I know is in this document
4:54:13basically.
4:54:16I have to think of a cool conclusion for
4:54:18this video. Yeah, I would suggest you to
4:54:20do a lot of back test. I understand it's
4:54:22a lot to process. I need you to rewatch
4:54:24this video multiple times and I
4:54:27specifically need you to practice this.
4:54:29It's going to take time to become a
4:54:31professional trader. This video probably
4:54:33just helped you realize how much you
4:54:35didn't know about the markets. And
4:54:37please, please compare this video with
4:54:40any other complete beginner trader
4:54:43course that you see out there and look
4:54:45at the [ __ ] gap. So, as I was saying,
4:54:47I need you to re-watch this video
4:54:49multiple times and go again through it
4:54:51bit by bit. I'll make sure to put all
4:54:53the chapters of the video and I strongly
4:54:55advise you to keep following the
4:54:56channels because we will go even deeper
4:54:58on all of these concepts even in a more
4:55:00practical way so you can also truly
4:55:02grasp all of this information bit by bit
4:55:05and have the time to digest it and
4:55:07gradually transform it into a very
4:55:10powerful edge. But knowledge is just the
4:55:13beginning. There's a way deeper video
4:55:15that needs to be made that I will do on
4:55:18everything that relates to the mindset
4:55:20of trading. And it's not as simple as,
4:55:22hey, there is FOMO. Don't be fearful.
4:55:25Don't be hopeful. And respect your
4:55:27trading rules and focus on the process.
4:55:29Yes, those are all amazing and very
4:55:31valuable tips, but the way we take
4:55:33trading decisions is subconscious at
4:55:36some levels, and there's much to be told
4:55:38about it. So, I will keep that for a
4:55:40future video. That's why again, if you
4:55:42haven't done it already, subscribe to
4:55:43this [ __ ]

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