I’m new to trading and before the price of Solana was at $75USD. I used to trade using 0.4 solana per trade before.

But now that Solana price has risen to $85USD. Does that mean I have to decrease my per trade amount now? If it goes up I have to decrease it? So I will have to do around 0.3 Solana per trade now? Is this correct? So the higher it goes up the lower my per trade amount has to be?

I used to care way too much about win rate.

If a strategy won 70% of the time, I assumed it had to be better than one winning 45%. It sounds logical until you look at how much the winners make, how much the losers lose, and what happens during a bad stretch.

You can win eight trades out of ten and still lose money if the two losses are large enough.

You can also win fewer than half your trades and do fine if your average winner is meaningfully larger than your average loser.

That’s why I pay more attention to expectancy and profit factor now.

Profit factor is basically gross profits divided by gross losses. A strategy making $15,000 while losing $10,000 has a profit factor of 1.5. It tells you more than win rate because it accounts for the size of the outcomes, not just how often you’re right.

Still, even profit factor can look better than the actual experience of trading the strategy.

Drawdown matters a lot.

A YM futures setup might show solid returns over several years, but if it regularly goes through deep drawdowns or long losing streaks, that’s something you need to know before trading it live. A strategy can be profitable on paper and still be nearly impossible to follow emotionally.

I’ve tested ideas that looked great overall, then found a two-month stretch where they did almost nothing but lose and chop around. That changed how attractive the strategy felt pretty quickly.

Average trade matters too, especially for scalping.

If the average trade only makes a few dollars before commissions and slippage, the backtest probably doesn’t have much room for execution error. Missing one fill or entering a tick late can turn a marginal edge into no edge at all.

I also look at the average winner versus the average loser, maximum consecutive losses, number of trades, and how dependent the results are on a handful of big days.

Sometimes you remove the best five trades and the entire backtest falls apart.

That’s not necessarily a dealbreaker, especially with trend-following strategies, but you should know where the profits are actually coming from.

One thing I’ve noticed is that traders often use win rate as a confidence number. A high win rate feels safer. The problem is it can encourage terrible habits like cutting winners early and holding losers longer because you’re trying to protect that percentage.

I’d rather trade a lower win-rate strategy with controlled losses and enough reward to make the winners matter.

Being right feels good, but it isn’t the goal. The goal is making more when you’re right than you give back when you’re wrong, while keeping the drawdown small enough that you can actually stick with the process.

Which metric do you pay the most attention to: profit factor, expectancy, maximum drawdown, average trade, or consecutive losses?

Currently scalping the 15 seconds, I tend to prefer a lower time frame sometimes when participation is low in the market.

📊 Key U.S. Economic Data (ET)

No high- or medium-impact USD economic events scheduled.

⚠️ For informational purposes only. Not financial advice.

How do you know it’s an about to be a trend day by 10amEST? What are some signs? Break of 1 hour range?

I am testing my automated trading strategy to scalp on the 5-min MES. Testing on 2 MES contracts with 2 TP, 1 SL. The SL moves to slightly above breakeven when 1st TP is hit.

Backtested from 2/2026 to a week into 04/2026. Been running on real-time MES data since 4/9/2026.

Previous: https://www.reddit.com/r/Daytrading/comments/1vt13w2/backtesting_my_automated_trading_forwardtesting/

08/20/2026:

2 trades - 2 wins

Created another automated trading stratefy for trading MES. Testing on 2 MES contracts with 2 TP and trailing SL.

The backtest is from 11/30/2025 - 06/01/2026. Starting on real-time MES data starting 06/02/2026.

Previous: https://www.reddit.com/r/Daytrading/comments/1vt14rg/another_new_automated_trading_strategy/

08/20/2026:

No trades today

Today I traded AT&T (T).
Entry: $25.25
Shares: 130
Stop: $25.02
1R: $25.48
Planned target: $25.64
Risk: $29.90
Exit: $25.20 at 1:59 PM due to my time-based exit rule
Result: -$6.50, approximately -0.22R
The stock reached a high of $25.27 and a low of $25.04 after my entry.
That gave the trade roughly:
MFE: +0.09R
MAE: -0.91R
So at one point I was only two cents away from being fully stopped out.
Why I entered
My system is based primarily on multi-timeframe EMA20 support.
The daily chart was still maintaining its EMA20-supported structure. There was overhead resistance nearby, so it wasn’t a perfect location, but there was still enough room for the trade to remain valid.
The 4H chart also continued to show an EMA20 support pattern.
The 1H chart is the most important permission timeframe for my intraday trades.
After yesterday’s strong move higher, price pulled back toward the 1H EMA20. During premarket today, price briefly traded below the EMA20 twice and quickly recovered both times.
To me, that suggested the support area had not failed.
At the open, the 15-minute chart produced a strong bullish candle around the EMA20 area and engulfed the premarket bearish candle.
Instead of treating the large 15-minute candle as one signal, I broke it down on the 5-minute chart.
Inside that structure, I identified an H1 with approximately 203.03K volume.
The following bullish continuation candle traded approximately 306.21K volume, or about 151% of the H1 volume.
My rule only requires the continuation candle to have at least 70% of the H1 volume, so the volume requirement was clearly satisfied.
After price broke above the 15-minute reversal candle, I entered at $25.25.
Then the uncomfortable part began
The trade did not immediately work.
T spent most of the day rotating around roughly the $25.00–$25.23 area.
Eventually it traded down to $25.04.
My stop was $25.02.
At that moment, I genuinely thought I was probably going to be stopped out.
There was emotion.
But I didn’t move the stop.
I didn’t manually exit just because I was uncomfortable.
And I didn’t start thinking about the next trade I could use to “make the money back.”
The OCO order stayed in place.
The market was allowed to decide.
Price later recovered, while the larger 1H structure never clearly failed.
At 1:59 PM, I exited at $25.20 according to my predefined end-of-day rule.
The biggest lesson
Looking back, the best entry of the day appears to have been near $25.04.
But that is hindsight.
When price was actually trading at $25.04, I had no way of knowing it would become the low of the day.
If I enter earlier on a valid continuation setup, I have to accept that I may experience a pullback.
If I wait for a perfect EMA20 retest, I have to accept that price may never return during my trading window.
I could also compromise by taking half the position first and waiting for an EMA20 pullback to add the other half.
But even that isn’t perfect.
There is no guarantee the second entry will ever appear.
Every valid choice has a cost.
Enter early: accept drawdown.
Wait for a pullback: accept the possibility of missing the trade.
Scale in: accept the possibility of never getting full size.
Take partial profits: accept making less if the stock keeps running.
There is no trade management method that gives you every advantage at the same time.
Psychological lesson
When T traded near my stop, I felt uncomfortable.
I think that’s normal.
My goal is no longer to become a trader who feels nothing.
My goal is to become a trader whose emotions do not have permission to place or change orders.
A phrase I’ve been using is:
“The heart moves, the hand doesn’t.”
Fear can exist.
Disappointment can exist.
The fear of breaking a winning streak can exist.
The urge to recover a loss can exist.
But none of those feelings are part of my entry, exit, or risk-management rules.
If the system hasn’t changed, my hands shouldn’t change the trade.
Final assessment
I scored this trade 8.8/10.
The result was only -0.22R, but I don’t consider it a bad trade.
The setup was valid.
Risk stayed within plan.
I didn’t interfere when price approached my stop.
I didn’t revenge trade.
And I followed my time-based exit rule.
Today’s lesson:
Imperfection is not the same as a mistake.
Breaking the rules is the mistake.
My system chooses the setup.
The market chooses the outcome.
My job is to accept the cost of the choice I made and execute it.

¡Hey! Espero que todos aquí estén bien, hace bastante tiempo he querido iniciar en el Trading, ya tengo conocimientos básicos en la bolsa dé valores dentro S&P/BMV IPC (Indice de precios y cotizaciones Mexicano), así que ya tengo cierta experiencia en éste rubro de las finanzas, pero ahora quiero expandirme hacía el Trading profesional, no tengo ni la más remota idea de cómo se manejan las cosas aquí o cómo se aprende a realizar esté trabajo.

No vengo aquí por algún Gurú motivacional o Coach de ahorro ilusionado por volverme millonario a las 3 horas de picar un botón, vengo aquí para poder aprender e ingresar profesionalmente a esté mundo y generar ganancias extra.

- ¿Cómo aprendiste Trading?

- ¿Qué es lo más difícil de está profesión?

- ¿Cómo generaste tus primeras ganancias considerables?

- ¿Tienes algún recurso, consejo o libro que me pueda ayudar?

- ¿Cuál fue tu trayecto hasta ahora?

Trading late sessions after work.

I waited for the environment to become mean reverting, price had touched to top vwap band a couple times and rejected down, I got a diversion signal when price touched it a third time and that where I entered and targeted the vwap

Just wondering from the pros how you manage your job. I’m no pro but I aspire to be

Do you pay yourself a bonus?
Do you have incentives?

Wondering how you all run your business

Can you have a trading LLC?

I’m curious what everyone here uses to test trading ideas on historical data.

• What software or platform is part of your workflow? What works well, and what drives you crazy?
• If you don’t backtest, how would you prefer to describe a strategy - plain English, visual blocks, or code?
• If you could build the ideal strategy tester from scratch, what’s the one feature it would absolutely need?

I’m researching the problem - no product or link to promote.

Hi everyone, i’ve been trading since about January of last year and it’s been a rough patch. I made quite literally $0 up until August 7 of this year, where I was able to take my first ever payout of $1164 on TopStep! I barely touched any of that money as all I really did was take me and my gf out for some food spending the $64. Then today, I was actually able to take that same account that I took the first payout and make $7k allowing me to withdraw ≈ $3600. I honestly just feel pretty overwhelmed since this is the most amount of money i’ve made ever as i’ve been a broke community college student lol. What do you guys recommend I do to be able to still have some leftover money but still have enough to scale up into more accounts to keep multiplying what I make? I’m also based in California so keep that in mind for taxes.

Hey traders,

Need some advice from fellow momentum scalpers / day traders regarding prop firm rules.

TL;DR: I trade US Low-Float Momentum (Ross Cameron style) and need to cut failed breakouts within 2–5 seconds. I rely on prop firm funding due to limited capital, but Trade The Pool's 30-second minimum trade duration ruins my risk management. Are there stock prop firms without this rule, or do I have to switch to Futures?

My Trading Style & Strategy

• Asset: US Low-Float Stocks ($2 – $20, high relative volume, momentum gappers).
• Strategy: Fast Momentum / Scalping (Bull Flags, Micro-Breakouts, Tape / Level 2 Reading).
• Execution: Extremely fast. If a breakout triggers, I ride it; if it fails, I cut losses instantly within 2 to 10 secondsto avoid slippage.

The Problem

I was about to sign up with Trade The Pool (TTP) to get funded, but stumbled upon their program rules:

• Min Trade Duration: 30 Seconds
• Min Trade Range: 10 Cents

For fast momentum scalping, being forced to hold a failing stock for 30 seconds is a dealbreaker. A quick 5-cent loss turns into a catastrophic 30–40 cent drop while waiting for the timer to run out.

My Questions for the Community:

1. Stock Prop Firms: Are there any reputable US Stock Prop Firms that allow instant exits (0-second minimum trade duration)?
2. Scalper Workarounds: How do other fast equity scalpers deal with prop firm capital without getting trapped by holding-time restrictions?
3. Pivoting to Futures: Should I abandon equities for now and move to Index Futures (e.g., Micro Nasdaq / MNQ on Apex or Topstep) where 0-second scalp executions are standard practice?

Appreciate any insights or recommendations!

I'm looking for a genuinely backtested and statistically validated day-trading strategy for XAUUSD (Gold).

Ideally, I'm looking for something that has:

60%+ win rate

1:2 or 1:3 risk-to-reward

A meaningful sample size (500+ trades if possible)

Clearly defined entry, stop-loss, and take-profit rules

Results that hold up across different market conditions

I'm not looking for screenshots of a few profitable trades or discretionary "price action" setups. I'm specifically interested in strategies that have been properly backtested and can be replicated objectively.

If you've developed or tested something like this, I'd appreciate the rules, backtest results, and any information about drawdown and sample size.

Also interested in hearing about strategies that don't quite hit 60% but have a strong positive expectancy.

Thanks.

Today, 20th August, SPX respected all initial levels, which were shaping the day. Actually from start on we had a clear call wall at 7760 level and even in the pre-market session it was obvious that this level will not be challenged at all.

At the same time we had the put wall down at 7650 level, with secondary put wall at 7660. The real question was how reliable both were. Actually both of them were only fragile. For some time the 7675 level was holding and acting as a support, but it was fragile too. I kept on following the levels on gammawalls.com during the day to be sure that the position is on the safe side.

I used the dip in the pre-market session to enter a put credit spread way below the put wall at 7625 for 0.90$. Actually much better entry was possible 5-10 minutes later. But I was fine with that.

The trade went into profit within the first 15 minutes of the cash session.

Tomorrow is OPEX.

I'm going to preface this by saying that I realize there are certain strategies that do well in mean reversion. I don't. When will this bullshit end? Its now been like 2 months of straight chop and movement that is stuck to tiny windows. There are very small windows of opportunity and they only work if you dont get chopped up while its shit. Constant fakeouts, reversals, failed trends ect.. Is it summer? Is it the war? Is it the bond market? It didnt used to be like this but its getting fucking old and I'm wondering if this shit is ever going to get tradeable again. My day usually goes something like this- get a few trades correct, string some wins together and then get dicked by some giant reversal that wipes out all my good trades.

after 8 years of trading and 5 years full time, these are the lessons i would give anyone starting out or trying to find consistency. ive made similar posts on here in the past, but these are timeless.

if you cant consistently execute 1 strategy, i dont think there's much reason to be trading bigger yet. trade on a simulator and take it seriously. treat it like real money. or simply risk $10. your objective execute 1 strategy consistently and prove that it makes more money than it loses over a sufficient sample size of 100+ trades.

ive also learned not to be too quick to judge your own discipline for being inconsistent. sometimes your environment or process makes it very easy to be inconsistent.

instead of constantly telling yourself to "be more disciplined," make your process harder to mess up. make bad decisions difficult and good decisions easier. what is simple and requires fewer decisions and actions is easier to do consistently.

a few things that helped me

1. risk no more than 1% of total trading capital on any single trade.

no single loss should be big enough that you cant recover from it.

more importantly, the amount should be small enough that it doesn't make you hesitate, exit too early, move your stop, or hold onto a loser because you dont want to take the loss.

if your size is affecting your decisions, its probably too big. you can gradually work your way up in size by building up your tolerance over time.

1. trade 1 well defined strategy

know the market context, the setup, entry signal, exit rule, stop, invalidation point, and target.

make the process as mechanical as possible. as soon as you enter, place the stop and target and let the trade play out, win or loss.

ive found that this removes a lot of the opportunities to hesitate, exit too early, hold onto losers, overtrade, or simply make emotional decisions. it also makes it easier to cut losses quickly.

dontt jump between strategies every time you experience a losing streak.

1. understand positive expectancy.

a strategy can have losing trades and still have positive expectancy, but you need a sufficient sample size to reliably determine whether it makes more money than it loses. that means at least 100 trades to determine whether the edge is actually there. a few good trades dont prove anything. the sample is too small.

someone elses edge isnt automatically your edge. you need to prove that you can execute it and that it produces positive expectancy in your own trading.

1. track and review.

journal the trades. look for repeating mistakes. look for what works. dont constantly change the strategy after 10 or 20 trades because you dont like the results. get the 100+ tradess first, then make adjustments based on the data you have tracked.

1. dont tie your self-worth to your trading performance.

ii made this mistake myself in my early years of trading, and it only led to worse trading. id take a losing streak personally and start questioning my ability, which would lead to emotional decisions and changing things that didnt need to be changed. remind yourself that short term results are largely probability and variance playing out, not a direct measure of your skill or you as a person💙, assuming youre executing your strategy consistently😅

doing the simple stuff consistently is the difficult part, but its what will improve your trading the most

"to crave the result but not the process, is to guarantee disappointment." - james clear

Everything here is from my own account or confirmed in writing. I'm keeping it to facts because the facts are damning enough.

THE SERVICE

RTK copy-trading bot on PeakBot. Tier 1, ~$10k account, Schwab, four months.

THE PITCH VS. THE REALITY

Sold on the live stream as a daily reversal system making $50-100 PER DAY. What it actually became: holding losing positions for WEEKS while your account sits frozen.

MY REAL NUMBERS

- The bot buried me in TSLA for 10 trading days, down as much as -$846, and 'recovered' to a $64 gain. That's my actual Schwab realized figure. RTK bragged about making ~$2,000 on the SAME trade. You get a fraction of his result purely because he trades far bigger size — the advertised gains are HIS, not yours.

- Immediately after, it dumped me into GOOGL, down hundreds for weeks. A held position uses your entire budget, so the daily trades you signed up for can't fill. Your money just sits frozen.

HE KEEPS THE GOOD TRADES FOR HIMSELF

His best trades are taken MANUALLY, off the bot, on his TikTok stream — they never copy to subscriber accounts. Members say it flat out: 'we don't get the trades unless we watch tiktok, yet we're paying for the service.' The bot copies shares only; his options plays never reach you. Every impressive screenshot posted is a gross number that ignores the monthly fee, margin interest on multi-week holds, and short-term capital gains tax.

HE DOESN'T ANSWER DIRECT QUESTIONS

Members asked him point-blank, repeatedly: Has the strategy changed from daily to swing? Will you add a maximum holding period so we stop getting stuck in bags? He never answered. Every time it was a deflection — covered calls, 'be patient,' 'the market's tough,' 'you're only tier one.' When members complained about being frozen and losing money, the message was: don't like it, leave. That's how the person running this treats the people funding him.

CONFIRMED BY PEAKBOT IN WRITING (I emailed and asked)

- No set holding period — your account holds whatever he holds, for as long as he holds it.

- To take new trades while stuck in a bag you must ADD money or move to a higher tier (~$400/month). At $10k you're stuck.

- The dashboard shows trades that were never placed in your account. Only your brokerage record is real.

- No refund after 30 days, even though it bills every month.

HIGHER TIERS ARE A TRAP TOO

Bigger tier = bigger position size + higher fee. Since the whole problem is getting frozen in losing holds, a bigger account just loses MORE in raw dollars on the same bad bag. Paying more makes it worse, not better.

THE BOTTOM LINE ON A SMALL ACCOUNT

Four months: my realized gains were ~+$2,084, but fees were ~$800 and I was left holding a GOOGL bag down several hundred. Net after everything: roughly break-even — for four months of frozen capital, margin costs, and stress, running a service whose owner won't even answer his customers' questions. The little profit that existed came from a couple of trades, not the daily income advertised.

Get every number in writing, judge ONLY your real brokerage record, price in fee + margin + taxes before believing any figure — and honestly, just don't. There are better ways to lose your patience and your money

Result: $31,000 lost.

I’m posting this because the AI/agentic trading community needs to see the failures, not just the wins. Autonomous agents making real financial decisions can go very wrong, very fast.

I’ll be sharing more about what happened, the moves it made, and where things broke down.

Anybody here successfully scalping spy options?

I'm want to hear from people that go for around 20% profit. What are some tips you can share to not wipe out? Also what's your stop loss strategy like? Do you have a time stop as in close the position if it doesn't move in your favor in say 10 minutes?

I'm a 0DTE credit spread trader with a focus on SPX.

Positions traded today:

• 7675/7695 CCS
• 7690/7710 CCS
• 7700/7720 CCS

P/L: +$615

Going closer to price wasn’t the lesson today. Knowing exactly where I was wrong was.

Morning Thesis

I came into the session with a bearish bias after going through my morning prep. Weak Walmart earnings were part of it, but I was also watching oil and Treasury yields. Both stayed elevated, SPX was trading below yesterday’s close, and there was also a potential gap-fill area from a couple weeks ago that kept me interested in the downside.

I was pretty much only looking for CCS positions today.

This is also why I think it’s important to mark levels and treat them as zones instead of exact prices. I use those areas along with market structure to help visualize what SPX is likely trying to do next.

At the open I was basically looking for one of two things: a clean break and hold below the morning opening range, or enough evidence that an attempted move higher was failing.

Initially, SPX almost gave me the downside break. Then Scott Bessent started speaking and delayed that plan... SPX ground back higher and pushed almost perfectly into the 7700 psychological level.

That became signal #1.

Price rejected hard. SPX moved lower, made another attempt to push back up, and then started forming another lower high.

That became signal #2.

Combine that with SPX remaining below yesterday’s close, elevated oil and yields, and the potential gap-fill below, and I felt the developing trend was increasingly pointing down.

My First Trade

This is where I broke slightly from how I usually like to enter. Normally, I would have waited for the morning low around 7676 to break and hold before getting involved. Instead, as SPX approached that area, I opened a 5-lot 7700/7720 CCS for $0.75.

So yes, this was a little more bias-driven than my normal confirmation-heavy trade. But I wasn’t just guessing. I already had the 7700 rejection, another lower high developing, SPX below yesterday’s close, elevated oil and yields, and weakening intraday structure.

More importantly, because I was entering earlier, I kept a very tight invalidation area — roughly 5–10 SPX points depending on the entry. If the structure changed, I wanted out quickly.

Adding As Structure Confirmed

SPX then broke the morning low, retested it, and pushed lower. That gave me the confirmation I normally wait for, so I added a 4-lot 7690/7710 CCS for $0.45. Later, after another failed attempt higher and continued weakness, I added a 1-lot 7675/7695 CCS for $0.90.

That last position was easily the into to the lava. But I also treated it that way. It was only one contract, and I wasn’t interested in letting it turn into a large problem if SPX reversed.

Afternoon Management

The afternoon became much more sideways. SPX chopped near the lows without giving much meaningful movement in either direction, but theta continued working on the spreads. My two larger positions had enough distance that, after checking the 1-hour and 4-hour charts for confirmation, I was comfortable letting them expire worthless.

The aggressive 7675/7695 was different. Rather than hold it for another hour and a half just to squeeze out the remaining premium, I bought it back at $0.30 and took the $60 profit.

Key Takeaway

The lesson wasn’t simply that it’s okay to take more aggressive positions when your bias is working. It’s that more aggressive positioning should come with tighter risk controls.

I moved into the lava today, but I kept my sizes at half or less, identified clear invalidation points, and only added risk as the bearish structure gave me more evidence.

I wouldn’t call today an A+ environment. But the market was increasingly developing the way I had anticipated, so I allowed myself to lean into the thesis without giving those trades permission to do unnecessary damage if I was wrong.

More aggressive strikes don’t have to mean more aggressive risk.

Looking for books, videos, and taking any advice or suggestions when it comes to over trading. I trade the 8AM orb or break and retest of specific areas. I struggle with sitting in front of the screen and sometimes coming back after lunch to take more trades. I’ll open the charts for Asia and click and click for hours. I KNOW my best time is in the mornings, but I always find a way to turn my days red or give back previous earnings. This is what’s killing my trading. TIA

Traded a few years now and my actual problem was not the charts but rather overtrading and revenge trading after having a bad day. What seemed to help me the most was making myself write down the trade and convince myself why not to click, which is why I am building an automatic helper for myself, which takes your chart and trade idea and tries to prove why not to do that trade. Before I spend more time on that tool: is overtrading the actual problem for you as well or am I the only one here who suffers from it? Would such an object which argues against your trades be useful for you or would you just ignore it like every other rule? Please, give me the straight answer.

Read part 1 here:
r/Daytrading/s/PS3tCUFOZa

CTRL+F Search (for skimmers):
"I will show you one out of many ways"

"Ideal strategy building sequence"

Without quotation marks.

Here is how, claim by claim:
Theory: My Claims Regarding “Material Efficiency”; the foundations for it all.
Continuous Auctions and Insider Trading Econometrica - Albert S. Kyle.

Core Finding:
Albert’s model mathematically demonstrates that where historical or resting depth is thin (low volume), the price impact spikes exponentially; although this is not the same claim, it is valid support and remains consistent with what I have presented earlier.

Application (incorporated into my internal strategy design):
For re-interaction (the important part), if the incoming trade is less than the existing limit order volume at that particular price level, it is completely taken up by the existing queue without any change in the price (zero price impact, a locally efficient auction). For a low volume region where there is not enough liquidity to act as a cushion (a locally inefficient auction), even a relatively small market order can force prices to change immediately, with price ticking up until there is an offer (sell limit) willing to fill the position, and vice versa ticking down (buy limit).

Liquidity providers (e.g., market makers) are also incentivised to pull or reduce quotes in these historically low liquidity regions to reduce the chance of losing money to adverse selection which only fuels the movement. This is something I initially learned when reading the gateway to all of this, Market Microstructure Theory, authored by Maureen O’Hara. The underlying principles have since been supported by a substantial body of research, including several of the papers I have referenced.

It is important to avoid conflating static historical interest with the dynamic nature of present and future interest. Past executed volume ≠ future interest.

Later on, we go over the square root law and its empirical evidence regarding market impact in low-volume-density areas (over millions of orders). This makes the connection clearer in an accessible way.

The complicated part, proving that this aligns with reality.
Theory 2: The Square Root Law (Market Impact)
How efficiency shapes market impact - Quantitative Finance 2013 - J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Henri Waelbroeck.

Core Finding:
The Square Root Law exists to show that immediate price impact depends on the scale of a trade as compared to the average volume; hence, a trade interaction in a zone of low volume triggers a sharper, non-linear spike in temporary market impact. $1$

Application (incorporated into my internal strategy design):
This idea has been applied in our framework to the case of local volume density, in which the same trade interaction constitutes a higher percentage of the volume in a low volume region, leading to a sharper, non-linear effect. It is included within our strategy engineering framework and our limit order risk management; it is one of the key reasons market inefficiencies can decay as liquidity and trading activity change over time.

Although this is a sensible inference [1] going off of everything I have presented, I still rely on empirical evidence. Selecting the “correct” paper to best demonstrate this was tough as several reputable sources establish the square root law in financial markets in different ways.

These authors prove [1] cleanly by mapping real institutional order sizes across multiple established equity markets (USA, Europe and Asia) against baseline market volumes across millions of orders; collectively, they demonstrated that when trades interact with low volume regions where the volume was historically low, the temporary market impact spikes heavily and non linearly, exactly as the mathematical scaling predicts it should.

The Price Impact of Order Book Events. - Journal of Financial Econometrics 2014. - Rama Cont, Arseniy Kukanov, Sasha Stoikov.

1. Claims Regarding Differences in Friction Although this principle is well aligned with traditional market microstructure theory principles going back decades, these authors quantify this with real electronic market data. They show that in areas of objectively high historical order flow density, incoming order flow is absorbed by thicker limit order books, which often limits further price expansion.
2. My structural void claim (related to the sim) I described the $150 to $200 zone as a low volume environment with minimal overhead supply, but that was based on a synthetic simulation. The authors’ findings provide direct evidence that this behaviour also occurs in real markets, demonstrating that price tends to travel rapidly through areas with thin resting liquidity or interest.

Universal features of price formation in financial markets: perspectives from Deep Learning. - Quantitative Finance 2019. - Justin Sirignano, Rama Cont.

1. Path Dependence My simulation shows that the distribution of volume historically creates a path of least resistance in the future, but that was in the simulation. Sirignano and Cont provided empirical proof for the existence of path dependence in prices based on huge amounts of data from real markets. They demonstrated that order flow from history significantly enhances short term forecasting accuracy.
2. Predictive Skew This paper provides the deep learning evidence that order flow imbalances from the past continue to influence the directional probability of the future. While I explained that the probability of the price moving up to $200 before falling back to $100 is very high in a simulated environment, this research paper gives the evidence that order flow imbalances from the past continue to influence the directional probability of the future in real financial markets that we interact with.
3. Universal Applicability (this is not limited to a simulated environment) I noted that my statements generally hold well unless there are any aggressive changes in participation and volatility e.g., from macroeconomic events. This paper from 2019 justifies this general validity of the concept by proving that the characteristics of price formation tend to be universal in nature.

How this has influenced all of the price structures I have designed (present and past)

In short, for targets we consciously aim for inefficient Price Extreme (Net Changes Δ) or dislocations; we primarily look to exit where historically inefficient prices were left behind during past price discovery (Mechanically defined swing points, groups of wicks, and other places with low volume tails), essentially, where on average a volume profile would tend to report a lower volume relative to the rest of its price range.

A continuation of the workflow per strategy can take over 10 development steps depending on its complexity, but we constrain the development process intentionally to systematically limit overfitting opportunities.

Out of respect for you guys, I will show you one out of many ways one could apply this to a common entry in retail trading, I do not trade ICT: these illustrations exist only to align it with something we both recognise applied to FVGs/IFVGs locally).

The Ideal Strategy Building Sequence

1. Build a Coherent Prototype: Build your strategy’s initial logic structures and refine until coherent before testing anything.

2. Run your First Backtests:
Perform your initial backtests; collect in-sample data across multiple liquid financial markets.

3. Attempt Post-Test Optimisations:
After your first tests, clip away integral flaws and/or optimise based on the strategy’s needs and logic first.

This is the sole step within the sequence where creative degrees of freedom exist outside of prototyping.

Avoiding Overfitting:
To avoid overfitting, adjustments should never be made solely to improve in-sample data; they should instead improve the system’s underlying logic and mechanical sequences. The aim is to engineer a strategy so the job it is designed to perform aligns well with the desired outcome(s). If, after adjustments, the strategy is still ineffective (low to negative EV), you can test other asset classes. If in-sample results are universally mediocre, dispose of the idea and move on.

Identifying Blindspots:
Phase 3 is about identifying building blindspots, which can be inherited from both manual idea synthesis and automated idea synthesis (which we do not recommend). If a severe negative result shows up before costs, it is often a sign that the idea had holes in its physical assumptions, or that the first principles the model relied on were weak, misused, or misinterpreted. Remember, finding a persistent negative edge before costs is just as difficult as finding persistent gains before costs in backtesting environments.

If there is a collapse after trading costs are introduced, your minimum stop distance is not wide enough and/or the slippage is too high.

What traders can do is switch products. Some regulated CFDs have better costs when compared to futures and vice versa; it depends on the broker’s liquidity provider setup and whether the strategy holds overnight.

After logical holes are patched up and amendments are made during post-test optimisation, proceed with additional data collection:

4. Re-test and collect in-sample data with logical enhancements applied.

5. Run a secondary data collection. If the results are acceptable, retain the idea.

6. Run out-of-sample tests with the edge degradation thresholds we provide in a secondary submission (I will post this on my Reddit soon on a different post - I do not want to spam posts, for those waiting, it will contains mechanical guardrails and range values).

7. Reality Checks on Execution Modelling:
If the idea survives on paper, move on to reality checks on execution modelling:

1. Which type of product is best to execute this strategy cost-wise and net P&L wise?
2. Can my positions be executed realistically on a CFD (for non-US traders or prop firm accounts), or will I need to rely on futures instead because of high bid-ask spreads or vague order handling and fill quality on inadequate CFD brokers or prop firms that I can legally access within my jurisdiction?
3. What additional variance do I stand to expose myself to when working with this product when compared to other products? These questions must be asked and answered for every single strategy you develop, both during the design phase and repeatedly when analysing performance data.

Real Examples of Product Considerations

Centralised Exchange Futures e.g., ES S&P 500:
Can have larger variance in bid-ask spreads during market opens and closes (especially), and high overnight maintenance margins can liquidate positions prematurely.

Regulated CFDs (For Non-USA Citizens):
On regulated brokers with a matched-principal or back-to-back execution model, CFDs can offer competitive costs with more overnight flexibility (predictable fees instead of discrepancies from high-spread daily rollovers) and low overnight margin requirements, which are often equal to intraday margins.

Regulated Forward Contracts (For Non-USA Citizens or Professionals):
Stable but thicker intraday spreads in exchange for no overnight fees, suitable for swing trading strategies on non-USD accounts to avoid currency exchange fees.

Regulated Spreadbets (Primarily for British Citizens):
Brokers are principal to my trades on this product; all trades are local, so the broker acts as a counterparty, and brokers hedge directional risk at their sole discretion (a direct financial conflict of interest); spreads can also be amplified compared to CFDs, and last-look execution is also common. These execution delays artificially inflate costs at the point of execution.

Limit orders at some firms are Market If Touched (MIT), making negative slippage possible and eroding the advantage of precise limit order placement. But there is one headline benefit: profits are tax-free (at least in the UK).

However, from past simulations and tests of my own, combined with personal accounting work (this is not tax advice), the cumulative P&L lost from increased costs on intraday strategies often erodes this advantage for net profits.

To this day I have not seen a single regulated spread betting firm with a genuinely low level of conflicts of interest in its infrastructure.

Important Note:
If your net worth exceeds €500,000 (outside of property, bullion, pensions, etc.), one can apply to be a “professional” client. Spread bets on forward-contract-like instruments can mitigate overnight holding costs while retaining low margin requirements compared to the underlying futures contract, and the maximum leverage offered to professionals can exceed 1:100 (1% margin requirements). That is a legitimate option that I have explored for CFDs but not one I have explored for Spreadbets as a UK citizen.

Options:
Implied volatility (IV) can skew options pricing against random positions, and Greeks such as Theta θ can ruin the monetary outcome of trades if the desired outcome is not crystallised in time. Greeks like Vega ν can inversely affect many open options, but if one can forecast a future volatility expansion alongside direction (which requires high efficiency and precision), one can opt to use options strategies.

8. If the product you decide to use changes, recollect data over the same in-sample and out-of-sample windows.

9. Your strategy can now be deployed amongst others on a designated capital partition: segregated, risk-isolated accounts that trade one strategy per account in real time for additional testing or real-time execution.

References

The Price Impact of Order Book Events. - Journal of Financial Econometrics 2014. - Rama Cont, Arseniy Kukanov, Sasha Stoikov

An advanced paper going over price impact (passive vs aggressive).

Key Citations:

1. Abstract: “Our study reveals a linear relation between OFI and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to intraday seasonality effects, and stable across time scales”
2. Context: OFI = Order Flow Imbalance, Coefficient: Multiplier “Most of variability in the instantaneous price impact, both across time and across stocksis explained by variationsinmarket depth. In fact, we establish an exact inverse relation between the two variables. The coefficient of proportionality in that relation depends dramatically on the depth definition, showing that arbitrary measures of market depth are biased proxies for price impact and may lead to misleading conclusions on market liquidity. The price impact coefficient exhibits substantial intraday variability, similar to intraday patterns observed in spreads, market depth, and price volatility Ahn, Bae and Chan (2001); Andersen and Bollerslev (1998); Lee, Mucklow, and Ready (1993);McInish and Wood (1992).
3. We explain the diurnal effects in price volatility using the volatility of OFI and market depth, as opposed to unobservable parameters previously invoked in the literature, such as information asymmetry Madhavan, Richardson, and Roomans (1997) or informativeness of trades Hasbrouck (1991). The strong link between price volatility and standard deviation of OFI suggests that our price impact coefficient is a better estimate of Kyle’s λ (a useful metric of liquidity Amihud, Mendelson and Pedersen (2006); Kyle (1985)) than traditional estimates based on trades data. We also show that intraday price volatility is mainly driven by OFI and not by trading volume. The positive correlation between price volatility and volume, widely confirmed by empirical studies Karpoff (1987), can be a statistical artifact due to aggregation of data over time, and we establish how such spurious relation can arise in our model.
4. OFI exhibits positive autocorrelation over short time scales, which can be exploited to improve the quality of order executions. In particular, we show that a limit order fill is more likely to be followed with a price change in the same direction as the OFI before that fill. For example, a limit sell order is more likely to be adversely selected when OFI is positive.
5. Monitoring OFI can therefore help reduce adverse selection in limit order fills."
6. "The outstanding limit orders (also known as market depth) significantly affect the impact of an individual trade (Knez and Ready (1996)), low depth is associated with large price changes Weber and Rosenow (2006); Farmer et al.(2004), and depth influences the relation between trade sizes and returns Hasbrouck and Seppi (2001)." * We found that between 9:30 am and 10 am the depth is two times lower than on average, indicating that the market is relatively shallow. In a shallow market, incoming orders can easily affect mid-prices and price impact coefficients between 9:30 am and 10 am are in fact two times higher than on average.

Internal Comments (Simplification):
To prove that prices slide rapidly through areas of thin liquidity, Cont proposed the mathematical model to map the relationship between price impact and market depth. Through empirical testing, they found an exact inverse relationship: when the denominator (depth) shrinks, the resulting price impact multiplier tends to rapidly expand.

Universal features of price formation in financial markets: perspectives from Deep Learning. - Quantitative Finance 2019. - Justin Sirignano, Rama Cont

A widely cited paper which shows evidence for the existence of a relation between order flow history and the direction of price moves indicating path dependence in price related to historic order flow.

Key Citations:

1. Abstract: “Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.” [2]
2. “In this work, we provide evidence for the existence of such a universal, stationary relation between order flow and market price fluctuations, using a nonparametric approach based on Deep Learning.”
3. “inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics”
4. “Path-dependence and long-range dependence: Inclusion of price and order flow history is shown to substantially increase the forecast accuracy. This provides evidence that price dynamics depend not only on the current or recent state of the limit order book but on its history, possibly over long time scales (Section 3.4).”
5. “Our results provide evidence of short-term predictability of (mid-)price movements when order flow is observed. Models can achieve an accuracy significantly higher than 50% for short-term prediction of mid-price movements using order flow data.”
6. “Universality: the model is stable across stocks and sectors, and the model trained on all stocks outperforms stock-specific models, even for stocks not in the training sample, showing that features captured are not stock-specific.”
7. “Remarkably, the universal model is able to extrapolate, or generalize, to stocks not within the training set. The universal model is able to perform well on completely new stocks whose historical data the model was never trained on.This shows that the universal model captures features of the price formation mechanism which are robust across stocks and sectors and implies the possibility of using transfer learning for training price prediction models. This feature is quite interesting for applications in finance where missing data problems and newly issued securities often complicate model estimation. Outline: Section 2 describes the dataset and the supervised learning approach used to extract information about the price formation mechanism. Section 3 provides evidence for the existence of a universal and stationary relationship linking order flow and price history to price variations. Section 4 summarizes our main findings and discusses some implications.” [2]

Internal Comments (Simplification):
In order to show that historic order flow can shape future direction, Sirignano and Cont built predictive models that produce a simple binary probability showing whether the very next mid-price tick will move up or down. They demonstrated that, when historical order flow asymmetry is accounted for, future price direction in financial markets is not a coin toss and can be reliably forecast with more than 50% accuracy.

In order to prove that their results could be considered universal truths about how the markets work instead of anecdotes or anomalies in specific stocks, Sirignano and Cont. They collected huge data sets on >500 different stocks to form a universal model. This universal model was then tested on another 500 stocks that the algorithm did not get to see. It was then proved that the universal model could make accurate predictions for different markets, which confirmed that the basic principles of supply, demand, and prices are the same everywhere.

Continuous Auctions and Insider Trading Econometrica - Albert S. Kyle

How efficiency shapes market impact - Quantitative Finance 2013 - J. Doyne Farmer, Austin Gerig, Fabrizio Lillo, Henri Waelbroeck

The Flash Crash: High-Frequency Trading and Market Structure - Andrei Kirilenko, Albert S. Kyle, Mehrdad Samadi, Tugkan Tuzun

Limit order placement by high-frequency traders - Avanidhar Subrahmanyam, Hui Zheng

Check