Risk Management on FX Backtest Diary

A 75.5% win rate that lost 26%: every exit and take-profit fix I tested failed

Fri, 24 Jul 2026 00:00:00 +0000

There is a one-parameter change that lifts my trading system’s win rate from 36.8% to 75.5%. It also turns ten years of +122% into a 26% loss.

That is not a typo, and it is the thread running through this article. I spent four separate studies trying to improve the exits of my trend-following EA (automated trading program): a take-profit target, a smarter trailing stop, a wider exit window, and averaging down into losers. Every single attempt made things worse, and all four failed for the same reason. This is the story of that reason, the win-rate trap.

A PF 1.03 YouTube strategy became my gold sleeve, but its winning filter didn't transplant

Fri, 24 Jul 2026 00:00:00 +0000

The strategy from the video measured out at PF 1.03. That is a coin flip, and normally the story ends right there with a “rejected” stamp. Yet today that same strategy trades gold inside my live system. And there is an epilogue: the filter that rescued it, the one piece that turned a coin flip into a keeper, did absolutely nothing when I transplanted it into my other gold logic.

An index sleeve broke my 0.47% monthly ceiling, then shorting indices sank to PF 0.13

Fri, 24 Jul 2026 00:00:00 +0000

My trading system was stuck at a monthly return of 0.47% no matter what I tuned. Adding one stock index sleeve on the long side lifted that to 0.53% monthly at PF 1.45, and the drawdown got smaller at the same time, from -9.0% to -8.3%. Meanwhile, a strategy that shorted the very same indices finished at -29.6% overall with a PF of 0.13. Same market, opposite direction, opposite fate.

Changing only when and how much I withdraw cut my first prop payout from 530 to 169 days

Fri, 24 Jul 2026 00:00:00 +0000

I did not touch the trading logic. I did not change what the system risks per trade. And yet the median time to my first prop firm payout fell from 530 trading days to 169, roughly a third of the original wait. The only thing that changed was the payout policy: when to withdraw, and how much to leave behind.

Chasing 3% a month means a 70% drawdown, and every smart allocation lost to fixed weights

Fri, 24 Jul 2026 00:00:00 +0000

I once asked my own data a simple question: what would a 3% monthly return actually cost? The answer came back as a 70% drawdown, which is another way of saying “your account, eventually”. And a second finding from the same line of work: five clever schemes for shifting money between strategies all lost to plain fixed weights.

Five times I doubted the system that won 10 of 11 years, five times the answer was keep it

Fri, 24 Jul 2026 00:00:00 +0000

The most frequent verdict I have handed down on my own trading system over the past half year is “change nothing.” Is the filter symbol right? Shouldn’t I add to winning positions? Is the track record just one lucky year? Are the parameter values a fluke? Is it a bug that the system holds a long and a short on the same currency at once? And after I found real bugs in my data pipeline, did the conclusions even survive? Five audits, five different angles of attack, and five times the numbers came back: the current configuration is optimal.

I tested 12 mechanisms to diversify my EA and rejected every one, even at PF 1.81

Fri, 24 Jul 2026 00:00:00 +0000

PF 1.81, a 0.07 correlation to my existing system, and a clean pass through out-of-sample walk-forward testing. A candidate with credentials that good got rejected anyway. So did eleven others. Twelve mechanisms tested, zero adopted.

This article merges six studies (research notes 124, 128, 129, 131, 132, 133) into one story: the hunt for an uncorrelated sleeve to bolt onto my trading system. It is a record of total failure, but the structure that emerged at the end, the reason nothing could be added, turned out to be worth more than any single candidate.

My AI position sizer lost to a shuffled placebo, and six more smart ideas died with it

Fri, 24 Jul 2026 00:00:00 +0000

A reinforcement learner studied my trading system’s history and carefully learned how much leverage to use in each market state. Its result: +0.72% per month. Then I took its learned rules, shuffled them into deliberate nonsense, and ran the test again: +1.57% per month. The intelligence was worth less than nothing.

My prop challenge math said +¥71,227, then a zero-edge test and one -6.32% day broke it

Fri, 24 Jul 2026 00:00:00 +0000

A 38% pass rate and +¥71,227 of expected value. That is what my first prop-challenge simulation handed me, and for a moment it looked like a green light. Then a sanity check showed the same model paying +¥45,753 to a strategy with zero edge, which is impossible, and a minute-by-minute rebuild of the account found one intervention day where equity sank 6.32% intraday. Instant disqualification, on a day my daily-bar backtest recorded as spotless.

Six famous YouTube trading methods coded as taught: zero edges, one blown-up account

Fri, 24 Jul 2026 00:00:00 +0000

“The 10-billion-yen trader.” “Win from zero.” “Gold-only, high win rate EA, free to download.” Over the past months I took six famous methods from YouTube videos and trading courses, coded each one exactly as taught, and measured it on real data. The punchline up front: zero new edges. Almost everything was either no edge at all or a re-labeling of an edge I already trade, and the giveaway EA, run on its own disclosed parameters, wiped out the account two months in.

The gold strategy that won on every setting, then dropped 7.2% in a single day

Fri, 24 Jul 2026 00:00:00 +0000

A strategy that made money on every single parameter setting nearly lost 7.2% of the account in one day. The culprit was not a failed stop loss. It was the open profit the strategy had spent months accumulating. This article traces the family history of the gold trend strategy inside my EA: how it was born, how a 1-minute stress test exposed its structural weakness, and how a two-entry experiment ended up choosing its final form. Three studies (16, 17 and 50 in my research log), one story.

Why a 0.85% monthly backtest becomes 0.7% in a live account, measured cost by cost

Fri, 24 Jul 2026 00:00:00 +0000

A system that backtests at 0.85% per month earns about 0.7% in a live account. That is my measured answer, and the interesting part is where the missing piece goes. The monthly return itself barely bends. What deepens is the drawdown, by an extra 1.5 to 2 percentage points, because costs attack the risk side of the ledger before they touch the profit side.

Making the defense continuous bought 27% more payout at the same risk

Tue, 21 Jul 2026 00:00:00 +0000

Turn a defensive switch from on/off into continuously variable. That single change chained through eight studies and ended with the prop account’s steady-state monthly payout rising from 1.92% to 2.43%, a 27% raise. Risk did not increase; crisis-time ruin probability actually fell.

This article records the whole arc (research notes 223, 226 through 230, plus two follow-ups) as one story, including why each decision went the way it did. An EA improvement is never one number; it is a chain of judgments.

The Numbers Behind the Current Logic — How Long a Prop Pass Takes, and What Monthly Return Is Possible Without Limits

Fri, 03 Jul 2026 00:00:00 +0000

This site has accumulated 167 verification studies. This article answers the obvious question: “So with the current logic, what can it actually do?” — in one page.

Everything below is a theoretical value from backtests and Monte Carlo simulation. It is not a guarantee of live results (reasons at the end).