Swing Strategy Backtest & Automation
Budget: $30 – $250 AUD
I trade a rules-based swing strategy and need clear proof of how it would have held up in the past before I let a bot touch my capital. Your first task is to recreate the logic in Python, or C++ back-test it over robust historical data, and present objective metrics—equity curve, max drawdown, Sharpe, win-rate, and any other insights you feel are useful.
Deliverables
• Clean, well-commented Python script of the strategy will need to have a good command of Quant Connect platform
• Comprehensive back-test report (PDF or notebook) showing data sources, parameters tested, and performance stats
• Automation module wired to Interactive Brokers, with instructions so I can launch it locally or on a VPS
• Short call or video walkthrough to confirm everything compiles, connects, and trades as expected
If you’ve worked with pandas, backtrader, zipline Quant Connect platform , or similar frameworks and have deployed live code through IBKR, I’d love to see examples. Let’s make sure the strategy proves itself on paper first, then put it to work in the market.
Deliverables
• Clean, well-commented Python script of the strategy will need to have a good command of Quant Connect platform
• Comprehensive back-test report (PDF or notebook) showing data sources, parameters tested, and performance stats
• Automation module wired to Interactive Brokers, with instructions so I can launch it locally or on a VPS
• Short call or video walkthrough to confirm everything compiles, connects, and trades as expected
If you’ve worked with pandas, backtrader, zipline Quant Connect platform , or similar frameworks and have deployed live code through IBKR, I’d love to see examples. Let’s make sure the strategy proves itself on paper first, then put it to work in the market.