Strategy Backtesting

Clearly articulate the trading edge (e.g., momentum, mean reversion) and specify entry/exit rules, position sizing, and risk constraints. Document the time frame, asset universe, and data frequency required. This step ensures every subsequent test is grounded in a testable, falsifiable hypothesis.

Why AudioNotes: AudioNotes converts voice-to-structured text, which can be used to quickly document strategy hypotheses and parameters via dictation, then organize them into notes.

Source clean, adjusted price data (OHLCV) for the target asset(s) and timeframe. Handle splits, dividends, and corporate actions. Align timestamps to a single exchange calendar. Split data into in-sample (training) and out-of-sample (validation) periods to avoid look-ahead bias.

Why Aider: Aider generates code from natural language descriptions, which can be used to write Python scripts (using pandas, yfinance) to acquire and prepare historical data.

Implement the strategy rules programmatically, iterating over historical bars to simulate trades. Include transaction costs (commission, slippage, spread) and realistic order execution (e.g., fill at next open or close). Track portfolio equity, open positions, and trade log in real-time within the loop.

Why AlgoSuite: AlgoSuite provides backtesting capabilities, which can serve as the engine for coding and running strategy backtests.

Execute the backtest over the in-sample period and compute key performance metrics: total return, Sharpe ratio, maximum drawdown, win rate, profit factor, and number of trades. Visualize the equity curve and drawdown chart to spot anomalies or overfitting signs.

Why AlgoSuite: AlgoSuite supports backtesting and strategy optimization, which includes running initial backtests and collecting performance metrics.

Systematically vary key parameters (e.g., lookback periods, stop-loss levels) over a grid or using walk-forward analysis. Check for parameter sensitivity and stability. Apply out-of-sample validation by running the best parameters on the held-out test set without re-optimizing.

Why scikit-learn: scikit-learn provides grid search and cross-validation tools that can be used for parameter optimization and robustness validation in Python.

Vary transaction costs, slippage assumptions, and data frequency to see how performance degrades. Test the strategy during known market regimes (e.g., 2008 crash, 2020 COVID) to assess drawdown resilience. This step reveals hidden weaknesses and sets realistic expectations.

Why AlgoSuite: AlgoSuite's strategy optimization capabilities can be extended to conduct sensitivity and stress tests by varying parameters and market conditions.

Compile all findings into a structured report: strategy description, data sources, methodology, in-sample and out-of-sample metrics, parameter sensitivity, stress test results, and a clear go/no-go recommendation. Include a trade log sample and equity curve charts. Archive the code and data for reproducibility.

Why GitHub Copilot: GitHub Copilot can assist in generating code documentation, explanations, and refactoring for Jupyter Notebooks or Python scripts, and integrates with GitHub for version control.