Most retail "strategy guides" show you a beautiful equity curve from a backtest that was fit on the same data it trades on. I got tired of that, so I built a small honest backtesting toolkit for Micro Nasdaq (MNQ) futures and ran four classic day-trading strategies through proper walk-forward validation.
The honest results (2 years of MNQ 1h data, walk-forward):
| Strategy | OOS trades | Net PnL | Profit factor | Max DD | Verdict |
|---|---|---|---|---|---|
| Gap fade | 163 | +$2,145 | 1.14 | $3,116 | fails DD gate |
| EMA pullback | 118 | -$3,884 | 0.73 | — | fails |
| Donchian breakout | 96 | +$225 | 1.03 | — | noise |
| ATR trailing trend | 104 | -$3,899 | 0.49 | — | fails |
0 of 4 passed my fail-closed gates (>=30 OOS trades, PF >= 1.3, net > 0, max DD <= 50% of net profit). That is the real answer for most public strategies: they don't survive out-of-sample.
What the toolkit gives you (plain Python, no exotic deps, pandas only):
- walk-forward split runner with fail-closed gates
- hand-written indicators (no TA-lib, no pandas-ta segfaults)
- intraday risk caps: daily loss limits, max trades/day, entry cutoff
- a paper-trading runner that replays the same logic live each day
I packaged it as the MNQ Backtest Toolkit ($29): https://stephimp.gumroad.com/l/zqciyo
The point isn't "buy this and get rich" — it's that validation infrastructure is the product. If a strategy can't pass its own out-of-sample gates, no amount of leverage or a prop-firm challenge fixes it.
Happy to answer questions about the walk-forward methodology in the comments.
