Python Stock Trading Backtest
Budget: ₹1,500 – ₹12,500 INR
I need a complete Python workflow that takes my technical-analysis rules for equities, pulls historical price data, runs a clean, reproducible back-test, and reports the results in an easy-to-read format. I will give you the exact entry, exit, and position-sizing logic; you convert that logic into code using standard libraries such as pandas, numpy, TA-Lib (or ta), and a back-testing engine like Backtrader or Zipline. Feel free to suggest alternatives if they improve speed or clarity.
The script—or notebook—must be parameterised so I can swap tickers, date ranges, and indicator settings without digging through the code. Please comment each section so I understand the data pull, signal generation, order handling, and performance calculation.
When the run is finished, I want to see an equity curve plotted against an appropriate benchmark together with key statistics: CAGR, max drawdown, Sharpe ratio, total trades, hit rate, and average trade return. Include a transaction log so I can verify that the strategy behaved exactly as specified.
Deliverables that will mark the job as complete:
• Well-documented Python code or Jupyter Notebook
• Summary report/plots of performance metrics
• Brief note explaining how to adjust parameters and re-run the test
If you keep the code modular, vectorised, and free from hard-coded paths, review and payment will be quick.
The script—or notebook—must be parameterised so I can swap tickers, date ranges, and indicator settings without digging through the code. Please comment each section so I understand the data pull, signal generation, order handling, and performance calculation.
When the run is finished, I want to see an equity curve plotted against an appropriate benchmark together with key statistics: CAGR, max drawdown, Sharpe ratio, total trades, hit rate, and average trade return. Include a transaction log so I can verify that the strategy behaved exactly as specified.
Deliverables that will mark the job as complete:
• Well-documented Python code or Jupyter Notebook
• Summary report/plots of performance metrics
• Brief note explaining how to adjust parameters and re-run the test
If you keep the code modular, vectorised, and free from hard-coded paths, review and payment will be quick.