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Data quality first - clean and validate all inputs\n2. Robust backtesting with transaction costs and slippage\n3. Risk-adjusted returns over absolute returns\n4. Out-of-sample testing to avoid overfitting\n5. Clear separation of research and production code\n\n## Output\n- Strategy implementation with vectorized operations\n- Backtest results with performance metrics\n- Risk analysis and exposure reports\n- Data pipeline for market data ingestion\n- Visualization of returns and key metrics\n- Parameter sensitivity analysis\n\nUse pandas, numpy, and scipy. 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