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Go Backtester
A custom backtesting engine built for strategies that stopped fitting neatly inside off-the-shelf Python frameworks.
2 connected postsTags / Backtests
Trading infrastructureActive
A custom backtesting engine built for strategies that stopped fitting neatly inside off-the-shelf Python frameworks.
2 connected postsThe Python tools I used to backtest trading strategies, what they are good at, and the exact point where I outgrew them.

Gartley, Bat, Butterfly, Crab on ES from 2020 to 2024. One looked tradeable; the cross-instrument check killed the universal claim.

Six candlestick patterns tested on NQ from 2020 to 2024. One held up. Two failed cleanly. One fired too rarely. Two were NQ-only artifacts.

I tested moving-average crossover on NQ 1H, ES 1H, GC 1H, and the daily 50/200 golden cross. Three of four told a different story.

I ran the same regime-filtered breakout on four configurations. Three broke in different ways. Here is what that taught me about strategy fit.

I ran a plain Donchian-channel breakout on NQ 1H, NQ Daily, and Gold 1H. One worked, one was lukewarm, one lost money.

A backtest of random trading signals on NQ futures - from seed 42 experiments to massive 10,000-generator ensembles. Explore probability patterns, profit curves, and what happens when chaos meets code.

Many traders chase high win rates, but this can backfire on them. Learn why low reward-to-risk setups can break you, even if you’re often right.

I used ChatGPT to backtest an EMA + ATR system with stop-loss, slippage, commissions, take-profit, and real P&L tracking.
Manual backtests build feel, but code (or AI tools) helps you test faster and uncover deeper insights.