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Not Trading is Part of the Edge — Transcript

by Christopher Creamer | Orderflow Trader · 2,923 words · 459 segments · language en · Watch on YouTube

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0:00If you don't understand this key
0:01concept, then you're going to continue
0:03to blow all of your funded accounts and
0:05all of your evals. Just like 95% of
0:08traders, when I first started trading
0:11before I was getting consistent payouts,
0:13I had an entry model and anytime I saw
0:15that entry model, I was just immediately
0:17taking the trade and I was getting
0:19absolutely cooked and I thought it was
0:21the strategy. I thought maybe the
0:23strategy doesn't work. So, I went to
0:24different strategies. But what I didn't
0:26understand is that context matters more
0:29than any signal or entry signal that you
0:32may have. And so in this video, I'm
0:34going to go over why context matters
0:36more than any signal. And hopefully you
0:38learn something from this video if
0:40you're a newer trader that could
0:42potentially help you have a better
0:43equity curve and maybe find some
0:46success. Now, most traders think that
0:48their edge is their entry model. But in
0:51reality, the edge starts before the
0:54setup even appears. So, most traders
0:56lose because they treat every condition
0:58like it's the same market. But we have
1:00different environments. We have
1:01different regimes. And that's something
1:02that we need to take into account. So,
1:05professionals define edge as environment
1:07plus location plus participation plus
1:10execution. Signals do not create the
1:13edge. the filters before the signals
1:16actually create the edge. And so what is
1:19edge? Well, edge is just positive
1:22expectancy over a large sample. So if
1:24you want to know the actual formula for
1:26it, it's win rate times the average win
1:29minus the loss rate times the average
1:31loss. And the edge is going to degrade
1:33when the win rate drops, when the follow
1:36through decreases, when loss frequency
1:38increases, and when chop increases
1:40variance. So environment is going to
1:43directly impact expectancy and the same
1:46setup performs different across regimes.
1:48And so the way that a lot of traders
1:50like to look at it is they just want to
1:52know the entry model. What's the entry
1:53model? How do you enter a trade? And so
1:55they look for that signal, then they try
1:58and justify it and then they enter and
2:00then they manage damage. The correct way
2:02that we should be looking at this or
2:04approaching the market is first you
2:07classify the environment. Then you
2:09identify location and then you confirm
2:11participation and then you execute. The
2:14signal comes last not first. And
2:17confirmation does not override context.
2:20So how do we classify environments? What
2:22type of environments are there in the
2:24market? Well, there's three primary
2:26market environments. The first one is
2:28initiative expansion. The second one is
2:30balanced rotation and the third one is
2:33low participation or compression or what
2:36some people may consider chop. Now all
2:39trading conditions basically fall into
2:41one of these three states. So your model
2:44most likely does not perform equally
2:48across all three environments. So what
2:50is initiative expansion? Initiative
2:53expansion is a directional auction where
2:55price is discovering value. These are
2:58things like trending days or where we
3:00see price essentially have higher time
3:02frame alignment. The breaks are going to
3:04hold and extend pullbacks actually
3:07resolve and there's acceptance outside
3:09prior range. And there's also follow-th
3:11through after imbalance. And so in an
3:14initiative expansion, the best type of
3:16models for this type of environment are
3:18going to be continuation models. They're
3:20going to perform best. And you can use
3:22reversal models, but they're going to
3:23require extreme location. In this type
3:27of environment, you have lower chop, you
3:29have cleaner invalidation, and you're
3:31going to have clear directional bias.
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4:35using code match. Now, let's get back
4:37into the video. Now, what is a balance
4:39rotation? Well, a balance rotation is
4:41essentially where we're in a range. It's
4:42where you have two-sided auction around
4:44an established value. So, some
4:46observable characteristics of this are
4:49going to be repeated rejection at the
4:51extremes, mean reversion behavior, and
4:54where breakouts or breakdowns fail, and
4:56there's going to be overlapping
4:58structure in both sides are active,
5:00meaning both buyers and sellers are
5:01active in this area. The best type of
5:04model fit for this is going to be the
5:06reversal at extremes. continuation
5:09models are going to degrade in this type
5:11of environment because there's more
5:13false breakouts. So, you're typically
5:15going to see higher whips saw, smaller
5:17average extension, and more false
5:20breakouts. Now, what is low
5:22participation or compression? This is
5:24essentially chop. This is essentially
5:26where there's insufficient participation
5:29to produce sustained auction outcomes.
5:32So some things that we might notice in
5:34environment that is low participation or
5:36compression is there's tight overlapping
5:39candles. There's multiple micro
5:41breakouts that fail and then there's
5:43imbalances without continuation. The
5:46tape may even be slow. Liquidity may be
5:48thin and there's no acceptance outside
5:51of structure. And in this type of
5:53environment, most models will actually
5:56degrade and edge is statistically
5:58reduced because there's high noise,
6:01there's poor follow through and there's
6:02going to be frequent stopouts. Now, I
6:05trade orderflow. So, order flow is an
6:07amplifier, but it measures
6:09participation. It does not create
6:11participation. And so, in expansion,
6:14it's going to confirm direction. In
6:17rotation, it shows that two-sided
6:18activity or the activity between buyers
6:21and sellers. And in compression, we're
6:23going to see events occur without any
6:25sustained movement. But order flow
6:27reliability decreases in compression
6:31when participation is low, when the
6:33follow-through is just totally absent,
6:35and when the auction is balanced. At
6:37least for the way that I trade it. Now
6:39the common mistake that people might
6:41make is they may say that they entered a
6:44trade because they saw a stacked
6:45imbalance or they saw absorption happen
6:48or they saw a delta flip. And the
6:50assumption is that the signal guarantees
6:53follow-through. But in reality, signals
6:56represent short-term activity and the
6:59follow-through requires structural
7:01alignment. So a valid signal in a
7:03misaligned environment has lower
7:05probability of extension or lower
7:07probability of working out. So then how
7:09do we think about these environments?
7:12Well, we're going to go over something
7:13called the model environment fit matrix.
7:15We're going to talk about continuation
7:17models, reversal models, and scalping
7:19models. In a continuation model, like I
7:22mentioned earlier, it's going to be
7:24strong in initiative expansion. Why?
7:27Because the directional auction is going
7:29to actually provide us the
7:30follow-through needed for a continuation
7:32model. Pullbacks are going to resolve
7:34rather than reverse. There's going to be
7:36acceptance that supports the
7:38continuation and risk can actually be
7:40defined under structure. So the edge
7:43driver for this is going to be sustained
7:45participation and structural alignment.
7:48Now, the continuation models are weak in
7:50balanced rotation, and the reason why is
7:53because breakouts will frequently fail.
7:57You're going to have two-sided activity
7:58that limits that extension for a
8:00continuation, and you're going to have
8:02mean reversion tendencies that reduce
8:05follow-through. And lastly, trend logic
8:07conflicts with range logic. So, the edge
8:10degression is the win rate drops due to
8:13failed continuation attempts. So, if we
8:15notice that we're in a balanced
8:16rotation, we may want to reconsider
8:19trying to take a continuation model.
8:21Now, a continuation model is going to be
8:24poor in compression because there's no
8:26sustained displacement. We need
8:28displacement for a continuation model,
8:31and there's going to be micro breakouts
8:33that repeatedly fail. The follow-through
8:35is going to absolutely collapse, and
8:37targets rarely get hit before reversion.
8:41So if you're trading a continuation
8:43model in compression, you're going to
8:46have frequent small stopouts with
8:48limited expansion potential just due to
8:50the environment that we're currently in.
8:53Now for a reversal model, it's a bit
8:55different. So it is selective in
8:58initiative expansion. The reason why is
9:00because initiative pressure is
9:02dominating. So most counter trend
9:05signals are absorbed and reversals
9:08require exhaustion plus failure of
9:11continuation. So a reversal model in
9:14initiative expansion is really only
9:16going to be valid when there's higher
9:18time frame extremes that we can watch
9:20and there's extended moves relative to
9:23the session and there's clear acceptance
9:25failure. So the edge driver for this is
9:29auction failure. It's not just a
9:30footprint signal. It's not just a
9:32signal. while we're watching the
9:33auction. Now, a reversal model is going
9:35to be strong in a balanced rotation at
9:38extremes. Why? Because auction rotates
9:42around value. So, the extremes often
9:44reject and there's liquidity pools
9:46sitting at range highs and lows. The
9:49mean reversion tendencies are
9:51structurally supported that are going to
9:53help with reversal models. So, two-sided
9:56participation is going to create those
9:58opportunities to fade. But again in
10:01compression it is weak because the
10:02extremes are poorly defined. The breaks
10:05are going to lack commitment. Rejections
10:07lack displacement and noise overwhelms
10:10structure. So again even in compression
10:13when you're using a reversal model
10:15there's going to be frequent small
10:16reversals that do not extend. If we
10:18decide that we want to have a scalping
10:20or micro model, then the way that we
10:23have to understand this is a little bit
10:25different because in a scalping or micro
10:28model, it's moderate in initiative
10:30expansion. And the reason why is because
10:33the trend provides directional bias.
10:36There's micro pullbacks that offer
10:38continuation entries and there's going
10:40to be short bursts of momentum that do
10:42exist. So the only limitation with this
10:45is that late entries do get punished if
10:48we're chasing. And it's also going to be
10:51moderate and balanced rotation because
10:54both sides are tradable at extremes. The
10:57micro mean reversion exists and smaller
11:00targets will fit the range conditions.
11:02But the limitation to this is that the
11:05profit ceiling is capped by the range
11:07width. If you're scalping or doing a
11:10micro model, it's highly sensitive to
11:13compression or low participation. And
11:17some characteristics of compression
11:19again are just overlapping structure,
11:22reduced range expansion, there's going
11:24to be failed micro breakouts, there's
11:26inconsistent follow-through, and thin or
11:29erratic liquidity. And it actually
11:31affects scalpers the most because
11:34scalping relies on small bursts of
11:36displacement. and compression reduces
11:39displacement magnitude. And if your
11:42average wind is going to shrink faster
11:44than stop size, then that's going to
11:47cause a problem. Signal frequency stays
11:50high, but expectancy erodess rapidly
11:53because mathematically, if we're in a
11:56compression, if your average wind drops
11:58from five points to three points if
12:01you're scalping, but your stop remains
12:03four points, then your expectancy is
12:05going to collapse. even if the win rate
12:08remains similar. So compression does not
12:11necessarily eliminate signals for
12:13scalping or micro models, but it's just
12:16going to degrade the payoff efficiency.
12:18So if you have a high trade frequency,
12:21but you have low displacement, it's
12:24going to basically give you death by
12:25friction. So the point is just that no
12:29strategy is regime agnostic
12:32and model performance is entirely
12:35environment dependent and if you do not
12:38classify environment then you are
12:40unknowingly changing your expectancy
12:42curve. And so knowing this then we have
12:46to understand that there are times that
12:49we should trade and there are times that
12:51we are we should not be trading and not
12:54trading is part of the edge. Not trading
12:58is a position. And so structural no
13:01trade conditions are going to be things
13:04like multiple consecutive filled
13:07breakouts or overlapping structure with
13:09no expansion, repeated return to value,
13:12no higher time frame alignment, no clean
13:15location, inability to define
13:17continuation path. And so when two or
13:19three of these things occur, expectancy
13:22is degraded. So passing on these types
13:25of environments are going to preserve
13:27our capital distribution. So then what
13:31is the decision tree that we have to
13:32really consider when we're trading and
13:35we're watching the market? Well, the
13:38first step is that we have to think what
13:40environment are we currently watching?
13:42What environment is present? Now does my
13:45model fit this environment and is there
13:47meaningful location for me to execute in
13:51this environment? And lastly, is part
13:54participation aligned with structure? If
13:57the answer to any of these questions is
14:00no, then you pass. You just don't trade
14:03it. Because even if you see your signal,
14:06you do not trade it. Your entry model or
14:09whatever, you do not trade it. Why?
14:11Because execution is conditional and our
14:14participation in the market is going to
14:16be optional, but our risk exposure is
14:20controlled. We want our risk exposure to
14:22be controlled. And so when you're having
14:26a strategy, your edge includes
14:28invalidation conditions. Your edge is
14:31going to include when you should and
14:33should not trade. And you need to define
14:36when your edge works or when your model
14:38works, when it degrades, and when it is
14:41inactive or you shouldn't even be
14:43trading it, regardless of if you see an
14:45entry signal on your chart. Because if
14:48you can't define when your model is
14:51inactive or when you shouldn't be
14:53trading it, then you don't have an edge.
14:56You have a pattern preference. And what
14:59professionals do is they increase
15:01aggression only in aligned environments.
15:05And they're actually going to reduce
15:06their exposure in misaligned ones. And
15:09the final principle is essentially just
15:11signals and entry models are execution
15:14tools. environment is the filter and the
15:18filters are going to protect our
15:20expectancy and they're going to protect
15:22us on our equity curve and not trading
15:26is not hesitation. It's just statistical
15:29preservation. It is a position. It is
15:32part of the edge. And so we need to
15:34constantly
15:35think and remind ourselves that there
15:38are times where statistically our model
15:42or whatever we're trading may not be in
15:44the right environment and if it's not in
15:46the right environment for us to try and
15:48execute that model then we pass. We save
15:51oursel money lost or a headache or you
15:54trying to get out a draw down because
15:57edge is not frequency it's conditional
15:59participation. So our participation in
16:02the market needs to be conditional only
16:05when things align. Only when the
16:06environment aligns with what we're
16:08trying to do only when our model whether
16:11it's continuation or reversal makes
16:14sense for what we're trying to do.
16:16Because often times, let's just say you
16:19have a reversal model that you're trying
16:22to force on the market while we're in
16:24initiative expansion and you just keep
16:27going short when the market is pumping.
16:30Then you're going to get absolutely
16:32destroyed and vice versa. Now, like we
16:35went over, reversal models do work
16:37sometimes in initiative expansion, but
16:40you have to be selective with it. And it
16:42requires extreme location or
16:43understanding on the higher time frame
16:46extremes where that location may be for
16:49us to try and attempt a reversal model.
16:53And the same goes for if we're
16:55rangebound for the day and you keep
16:57trying to trade a continuation model.
16:59Let's say your model is you trade
17:01breakouts of the range and every single
17:03time the market goes to break out of a
17:06range, you enter a trade for a
17:08continuation and we snap back into the
17:10range and you lost going short, then you
17:12lost going long, and then you lost going
17:14short, and then next thing you know,
17:15your account's gone. So, the whole
17:18purpose is if you have an entry model
17:22and it works for you, great. But just
17:26understand that we have different market
17:28environments and realistically your
17:30model probably isn't going to work the
17:33same in all of the market environments.
17:35So we need to be selective on when we do
17:37decide to participate and our our
17:40decision tree needs to be in the correct
17:42order so that we save ourselves from
17:46losing money that we didn't need to
17:48lose.
17:49Now, if you want to understand how
17:52exactly I frame my context and my
17:55structure before I even look at
17:56footprint, before I even consider taking
17:58an entry, I actually have a framework
18:02PDF that I created that I'm going to
18:03include in the description below. It's
18:05free. You can download it. You can read
18:07it. And that's about it for this video.
18:09So, I will catch you guys next time.
18:11Until then, peace.

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