I will backtest and validate your trading strategy
Quantitative Trading Research and Backtesting
About this Gig
Have a trading strategy but want to know whether the data actually supports it?
I provide structured Python-based backtesting and quantitative strategy validation to help you understand how your trading rules performed historically and where the weaknesses may be.
Depending on your package, I can provide:
- Historical strategy backtesting
- Win rate, profit factor, drawdown and expectancy
- Equity curve and trade-level analysis
- Long vs short performance comparison
- Fees and slippage assumptions
- Robustness and parameter sensitivity checks
- Out-of-sample validation where appropriate
- Clear findings and a professional research report
I can work with clearly defined strategies for Forex, Gold, Crypto, Futures, Stocks and Indices.
Please provide your entry rules, exit rules, stop loss, take profit, timeframe, market and any filters or conditions.
My goal is to give you clear, reproducible evidence not promises of profitability.
Historical results do not guarantee future performance.
FAQ
What do you need from me to start?
Your complete strategy rules including entry, exit, stop loss, take profit, timeframe, market, indicators, filters, and risk rules. The strategy should be clear and rule-based.
Which markets can you backtest?
I can work with Forex, Gold, Crypto, Futures, Stocks, and Indices, depending on historical data availability.
Do you guarantee that my strategy will be profitable?
No. Backtesting measures historical performance and helps identify strengths, weaknesses, and risks. Past results do not guarantee future profitability.
Do I need to provide historical data?
Not always. If you already have reliable data, you can provide it. Otherwise, please contact me before ordering so I can confirm whether suitable data is available for your market and timeframe.
What will I receive after the backtest?
Depending on the package, you will receive performance metrics, charts, trade-level analysis, strategy findings, and a clear written validation report. Advanced packages include deeper robustness and out-of-sample testing.

