Author: admin

Everyone shows you the equity curve going up. Here’s mine — including the year it went down.

In Part 1, I told you how a lookahead bug almost made me fund a fantasy. This is what survived the bug hunt: Setup B, a breakout system, tested on 11.75 years of data. Every number below is real. Including the ugly ones.

The system in 30 seconds

Setup B trades breakouts from 12-bar consolidations on XAU/USD M15: ATR-based stop (1.5×), minimum 3:1 reward-to-risk, take half at the first target and move the stop to breakeven, exit after 24 hours max. One position at a time, max three trades a day. No AI, no discretion — pure rules.

The numbers

• 819 trades (January 2015 – August 2026, Dukascopy M5 data, ~833,000 bars)
• +196.05R total, profit factor 1.37
• Win rate 23.8% — yes, it loses three out of four trades
• +0.239R per trade on average
• 414 buys / 405 sells — no directional bias
• 11 out of 12 years positive

Read that win rate again: 23.8%. Most “gurus” would be embarrassed to publish it. I’m not, because the math works: small losses, occasional large wins, breakeven stop doing the heavy lifting.

The year I lost money

2021: −2.62R. There it is, in public.

A system that never has a losing year is either curve-fit or lying. 2021 was a choppy, directionless year for gold, and Setup B bled slowly. If you’re evaluating any system — mine or anyone’s — ask to see the worst year. If they won’t show it, walk away.

The surprise

Here’s what I didn’t expect: the strongest period wasn’t the gold bull market. It was 2015–2019 — when gold mostly went sideways or down. Profit factor 1.53, +123.3R.

That kills the obvious objection (“your system just rides the bull market”). The edge doesn’t come from the trend. It comes from the structure: tight consolidations, asymmetric payoffs, ruthless exits.

The honest caveats

No honest report ends without these:

1. No spread, commission, or slippage in these numbers. Real life will shave the edge — my estimate is ~0.1–0.15R per trade after costs.
2. Dukascopy feed ≠ my broker’s feed. Volatility on Dukascopy runs higher (ATR ~$9 vs ~$5.5). The final verdict comes from live forward-testing, which started this week.
3. The backtest stopped at August 2026; data timestamps are GMT.

Anyone who shows you a backtest without caveats is selling you something.

The point

819 trades. PF 1.37. One losing year, shown proudly. This is what a real, testable edge looks like: modest, ugly, and honest.

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This is Part 2 of a 4-part series. In Part 3, I trained an AI to predict gold prices on these 819 signals. It flipped coins — AUC 0.52. Here’s the autopsy.

New here? Start with Part 1: the bug that lied to me. For the unfiltered numbers as they happen, subscribe to the newsletter.

My backtest showed 14 wins out of 15 trades. It was a lie my own code told me.

The scanner finished running, and I stared at the screen. 14 wins out of 15 trades. +64R. On a Fibonacci pullback system for gold that I had coded myself.

My finger hovered over the deposit button on my broker’s website.

I didn’t click it. Not because I’m disciplined — because I’ve been burned before. In backtesting, “too good to be true” almost always means one thing: a bug.

The system

I was validating a trading system distilled from two classic books on Fibonacci and Gann applications in financial markets. The rules were fully deterministic: specific Fibonacci retracement zones, RSI confirmation, ATR-based stops, strict risk management. I coded everything into an MQL5 scanner and ran it over 17 months of my broker’s M5 data on XAU/USD.

Setup A — the Fibonacci pullback setup — was the star. 15 signals. 14 winners. +64R.

The discovery

Here is the iron rule of backtesting: your code may only see the past. Every signal must be computed from bars that already closed. The moment your code peeks at a single future bar, your results are fiction.

Something about 14 out of 15 bothered me enough to go back into the MQL5 code — specifically the loop that searches for swing highs and lows. In MQL5, arrays can be set “as-series,” which means index 0 is the newest bar and higher indexes are older bars. My search loop was traversing in the wrong direction: it was scanning toward the future instead of the past.

My scanner was trading with tomorrow’s newspaper.

The fix

One loop direction flipped. Spec bumped to v1.2.1. I re-ran everything on the exact same data.

Setup A: 1 trade. −1R.

Then I ran it on 11.75 years of Dukascopy data — 833,000 M5 bars. Across 11.6 years, Setup A produced 8 signals and lost −6.09R. The “holy grail” was completely, permanently dead.

How I verify everything now

That bug rewired how I work:

1. Two independent implementations. I rewrote the scanner in Python and ran it on the raw broker data. 37 of 45 signals overlapped; 35 of 37 outcomes matched. Two codebases, one verdict.
2. Chronological discipline. No signal may use data past its own bar close. Ever. No exceptions.
3. If it looks too good, it’s a bug. Real edges are modest and ugly. The setup that survived — Setup B, a breakout system — has a 23.8% win rate. It looks terrible, and it works: +196R over 819 trades, 11 out of 12 years positive.

The point

Most trading content shows you the 14 out of 15. Nobody shows you the bug hunt.

I vet trading systems for a living — including my own. Especially my own.

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This is Part 1 of a 4-part series on honest system validation. In Part 2, I open the full 11.75-year backtest report — every number, including the losing year.

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