I will build and debug your algorithmic strategy backtest in python

Your backtest is lying to you unless it's built to tell the truth.

Most trading backtests look profitable because they're quietly cheating: look-ahead bias, survivorship bias, overfitting to noise. I've spent [X] years on the buy-side at [a systematic hedge fund / multi-strategy fund] watching strategies that shine in backtest die in production and I build to prevent exactly that.

What I'll do for you:

• Build a clean, reproducible backtest of your strategy in Python (pandas, NumPy, [backtrader / vectorbt / custom engine])
• Compute the metrics that actually matter: Sharpe, Sortino, max drawdown, hit rate, turnover, exposure
• Hunt down and eliminate the biases that make backtests lie
• Stress-test with walk-forward and parameter-sensitivity analysis (Premium)

Who this is for: retail algo traders, quant students, and small funds who want to know whether an edge is real before risking capital.

What you'll get: clean commented code, a clear performance report, and an honest verdict including when a strategy doesn't work, which is the most valuable thing a backtest can tell you.

Not sure which package fits? Message me your strategy idea and I'll point you to the right one.