How to Avoid Overfitted EAs — A Practical Pre-Purchase Checklist
Introduction
“Amazing backtest,” “90% win rate” — attractive claims, but they may signal overfitting. An overfitted EA is over-optimized to past data and tends to fail in live trading. Below is a practical, quick-to-use flow to help buyers spot overfitting before they commit.
What is overfitting? (1-minute read)
- It’s when parameters are tuned to random past noise, creating a past-only optimizer that won’t generalize.
- Typical symptom: Backtests show a high PF, but in live (or a different period) the PF collapses and DD jumps.
How to spot an overfitted EA
1) Unnaturally high win rate × small RR
If win rate sits around 80–95% while RR < 1 (average win < average loss) or RR is very small, that’s the classic “many tiny wins, occasional huge loss” profile. Grid/martingale risk is common here.
- Aim for RR = Avg win ÷ Avg loss ≥ 1.2–1.5.
- Check that Largest loss is not several times the average win.
- Be wary if the backtest shows almost no losses — inspect how the EA loses, not just how it wins.
Related article: Stop Chasing Win Rate: Expectancy & Risk-Reward (RR)
2) High PF with too few trades
Example: 100–200 trades with PF ≈ 2.3 → could be luck or over-tuning. The fewer the trades, the easier it is to overfit.
- Realistic cue: same logic with ≥ 500 trades (ideally ≥ 1,000) and PF ≈ 1.3–1.8.
- If PF is much higher, re-check trade count and cost realism (commissions/spread/slippage).
Related article: Profit Factor (PF) Explained: Benchmarks & Pitfalls
3) User backtesting is restricted
If you can’t backtest it yourself under your broker’s conditions, avoid it. The vendor may have tuned to one broker’s overly tight costs.
- Prefer EAs you can backtest on your own and verify similar tendencies.
4) Backtest period is restricted/cherry-picked
Some EAs limit trading to a subset of dates in code (e.g., only trading after 2018 even if you test from 2010, or skipping specific “bad” days).
- Inspect the trade history and the chart with orders — look for suspicious no-trade spans.
Live forward results are essential
Over-optimized systems draw beautiful curves in backtests but often fail live. Verified real-account forward is the most important cross-check.
- On Myfxbook / FXBlue, confirm it’s a Real account (demos are reference only).
- Look for Track Record Verified and Trading Privileges Verified.
- MQL5 Signals are also useful; they list real accounts only.
- Compare monthly win rate, RR, PF, Max DD with the backtest. Patterns should be in a similar range.
Below are the results of our Gold Galaxy Express EA on Myfxbook and MQL5 real accounts (as of August 24, 2025).
Ideal sanity check: works on other pairs too
Overfitted EAs often succeed only on a single pair × timeframe. Run the same parameters on GBPUSD / USDJPY / XAUUSD, etc.
- If it doesn’t fall apart elsewhere, that hints at more robust logic.
- If only one pair looks great and others are terrible, suspect overfitting.
Lot Size: 0.1
10-minute due diligence before you buy
- Broker & account type: Which broker, account type (ECN/Raw/Cent), leverage, symbol suffix? Results that require one exotic account are fragile.
- Costs & execution: Were commissions, realistic spreads, slippage included in tests? Ask for exact numbers. “Fixed 0.1 pip” everywhere is not believable.
- Lot behavior: Check for stepping up lots after losses (hidden martingale). Verify Max lot vs starting balance and the rationale.
- Equity vs balance: On Myfxbook, compare equity curve to balance. Large gaps → floating drawdown or grid exposure.
- Deposits/withdrawals: Repeated deposits can mask drawdowns. Inspect the history line for cashflows aligned with DDs.
- Trade distribution: Look at average vs largest loss, consecutive losses, and average holding time. Very long tails with tiny average wins = danger.
- Time-of-day bias: If almost all profits come from one hour window, test sensitivity. Narrow edges often die first live.
- Stagnation: Ask for worst month, longest flat period, and Recovery Factor (Net profit ÷ Max DD). Vendors who share these are usually more trustworthy.
Red flags vs. green flags
Red flags
- Perfect equity with near-zero DD and very few trades.
- High win rate × low RR; largest loss dwarfs average win.
- No real-account forward or it’s frequently set to private.
- User backtests are blocked; broker/cost details are vague.
- Backtest dates look curated (no trades in tough regimes).
Green flags
- Real-account forward with verified tags, clear broker & costs.
- Reasonable stats: PF 1.3–1.8, RR ≥ 1.2–1.5, ≥ 500 trades on the same logic.
- Vendor shares .set files, cost assumptions, worst-case months, and parameter sensitivity tests.
- EA behavior remains similar on secondary pairs (not necessarily profitable, but not catastrophic).
Summary: checklist you can use today
- Real-account public performance is available (top priority)
- Balance among trade count / PF / RR / DD looks reasonable
- Not just high win rate × low RR
- No grid/martingale signs (check lot progression & largest loss)
- No restrictions on user backtesting
- Doesn’t collapse on other pairs with the same settings
- Vendor discloses broker, costs, worst month, stagnation
Key idea: choose robustness over looks, and repeatability over headline numbers. Stick to these two and you’ll greatly reduce the odds of buying an overfitted EA.