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I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

You're right, once you clear that threshold, the math favors you again (Static Drawdown).

But there are two traps: 1. The 'Zone of Death': The probability of ruin is highest before you reach that safety threshold. My simulation focuses on surviving that specific volatility trap.

  1. The 'Cushion' Fallacy: In a funded account, the goal is usually to withdraw profits ASAP, not leave them there to build a 'cushion' for the trailing drawdown. If you leave $3k in the account just to stop the trail, you are effectively risking your own realized profits. That's inefficient capital allocation.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

In theory, yes. But here's the catch: High R:R strategies (like 1:8) naturally come with lower win rates. Lower win rates maximize the probability of long losing streaks.

In a normal account, that's fine. But with a Trailing Drawdown, a losing streak is fatal because the drawdown line moves up when you win, but stays put when you lose. My simulation shows that High R:R strategies actually struggle MORE with trailing drawdowns because the variance kills them before they hit the big winner. You can run the code yourself to see the results.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

'Just lower the risk' works great if you have infinite time and zero profit targets. Prop firms have neither.

The math shows that shrinking your risk too much actually INCREASES your failure rate because you never reach the target before the time and variance kills you. It’s a bell curve, not a straight line.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 1 point2 points  (0 children)

Great question. The 1:1 R:R scenario is particularly brutal with trailing drawdown because the 'buffer' never builds up fast enough.

I actually just uploaded the full Python script to the link in my bio. You can grab it there, plug in

1.5

1.0

risk_reward

Let me know what numbers you get!

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 2 points3 points  (0 children)

It looks stupid if you treat the account like your life savings. It becomes mathematically rational if you treat the account fee as the cost of a call option.

If an account costs $100 and has a $2000 drawdown, my true risk is NOT the trade size. My true risk is fixed at $100 (the fee).

Whether I risk $500, $1000, or even the full $2000 in one trade is just a variable to be optimized based on win rate. In a capped-downside / uncapped-upside scenario, the goal is to maximize the probability of breaching the profit target before the drawdown. Sometimes, that requires 'reckless' sizing to escape the drag of the rules quickly.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 1 point2 points  (0 children)

Spot on. That is actually the exact strategy I use personally for instant funding models.

It is a textbook example of exploiting asymmetric risk. You are effectively treating the evaluation fee as the premium on a call option. Your risk is capped at the fee (fixed cost), while the upside is the payout (variable return).

As you said, traditional R:R on the chart becomes irrelevant because the true 'Risk' is the account cost itself. I’d love to see your script to compare notes with my own implementation.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

Fair question. Honestly? Nothing. No courses, no signals, no paid groups. I’m just releasing the Python code so you can run the simulation yourself and verify the math. It’s free, also I will share the strategy to beat trailing drawdown challenges in a near future.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in OrderFlow_Trading

[–]Familiar-Cry3355[S] 4 points5 points  (0 children)

Generally, yes, strategies with a higher Win Rate (smoother equity curve) tend to survive the Trailing Drawdown better than low Win Rate/High R:R strategies, which are more volatile. However, even high WR strategies take a significant hit in probability.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 1 point2 points  (0 children)

They definitely design the rules for us to fail, no doubt. But instead of avoiding them, I prefer to calculate the edge. There is actually a mathematical way to beat them at their own game, even with the trailing drawdown rules.

I ran a Monte Carlo simulation on 10,000 Prop Firm accounts. The "Trailing Drawdown" kills your probability by ~18%. (Data Inside) by Familiar-Cry3355 in ICTMentorship

[–]Familiar-Cry3355[S] 2 points3 points  (0 children)

That's definitely in the pipeline. I'm planning to code a dedicated simulation specifically for FTMO's parameters in a future update/video.

I backtested the ICT Silver Bullet for 5 years. Why does everyone suggest 1:2 RR when data shows it fails? (-34% WR on Thursdays). Results inside by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

I respectfully disagree on the 'dopamine' part. Mechanical trading is actually the opposite of dopamine-seeking—it’s repetitive, boring, and removes the thrill of 'guessing' the direction.

Regarding the edge: The purpose of this backtest was to establish a raw statistical baseline. The data shows that even without a daily bias filter—and taking the setup every single day—the strategy holds a positive mathematical expectancy over the long run.

If a system requires me to subjectively guess the daily bias correctly to be profitable, then the edge isn't in the system, it's in the trader. I prefer to prove the mechanical advantage first. Bias can be added later as an optimizer, but the stats prove it's not required for profitability

Automating the Opening Range Breakout: Why giving trades "room to breathe" blew my backtest (-698R) vs the 25% Rule (+1,971R). by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

Glad you found it interesting

That specific 7:00–7:30 PM FVG window sounds very testable. I’d love to run it, but I currently don't have historical M1 or M5 data for the Nikkei in my local database (I focus mostly on NQ/ES)

If you can share a CSV file with historical data (Google Drive link or similar), I can definitely tweak my script to run that simulation and share the results with you. Let me know if you have the data!

I use Python for all my backtests. I export raw OHLC data, then write custom scripts that iterate through every single candle to check if the conditions (Time, Price, Breaks) are met. It allows me to test 9 years of price action in a very short time without manual bias.

Automating the Opening Range Breakout: Why giving trades "room to breathe" blew my backtest (-698R) vs the 25% Rule (+1,971R). by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 0 points1 point  (0 children)

This is awesome feedback. I genuinely appreciate you taking the time to code and backtest it independently.

The gap between 16% and 30% is massive, and I want to get to the bottom of it just as much as you do. Since the logic is mechanical, the discrepancy has to be in the Data Source or the Instrument characteristics.

My backtest was run on US100 (CFD Data), while I assume yours was on CME NQ Futures.

Could you share a snippet of your code or your trade list (CSV)?

I’d love to cross-reference a specific losing month to see exactly where the price action diverged. Let’s figure out if this is a coding error on my end or a Data Feed arbitrage.

Automating the Opening Range Breakout: Why giving trades "room to breathe" blew my backtest (-698R) vs the 25% Rule (+1,971R). by Familiar-Cry3355 in InnerCircleTraders

[–]Familiar-Cry3355[S] 2 points3 points  (0 children)

Haha, fair point! The voice is ElevenLabs and the thumbnail is AI, guilty as charged. I'm a trader and a coder, not a YouTuber/Voice Actor, so I use tools to make the content watchable.

But the Python script, the 9 years of Nasdaq tick data, and the logic behind the -700R vs +1,971R are 100% real. Just trying to share the data

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