I will backtest your crypto or tradingview strategy in python

Before you risk real money on a trading strategy, find out if it actually has an edge. I turn your rules into Python and backtest them on 12 months of real crypto exchange data (Bybit, Binance and similar) - with taker fees and slippage on every trade.

Most backtests look great because they ignore costs, peek into the future or fit the past too closely. Mine use conservative fills (stop counted first), proper indicator warm-up and no lookahead.

You get:

• trades, win rate, profit factor, max drawdown, average R/R, long/short breakdown
• equity curve and a plain-English summary
• a clear verdict: edge, no edge, or not enough data
• Python source code (Standard and Premium)

Standard adds out-of-sample and parameter sensitivity tests; Premium adds walk-forward, Monte Carlo and a 2x cost stress test.

I hold my own work to the same standard: my signal strategy (627 trades, real fees) scored profit factor 0.58 - no edge - and I published that result.

Good fit: indicator-based crypto strategies, swing or intraday. Not a fit: guaranteed-profit promises or tick-level HFT.

Message me your rules before ordering - I'll confirm what's testable.