Category: Quant Investment with Python
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Real-Time Trading Systems and Deployment Best Practices
Build production trading systems that survive real markets: latency budgets, order types, monitoring, kill switches, and the gap between backtest and live.
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Pairs Trading Is Dead (Unless You Know Where to Look)
Classic pairs trading is dead on daily timeframes โ but Kalman filters, cross-asset pairs, and multi-leg baskets might still have edge if you know where to look.
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Machine Learning Models for Stock Price Prediction: Why Most Fail and What Actually Works
Why most ML models fail in live trading, what actually works for stock prediction, and how to build models that survive transaction costs and regime shifts.
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Risk Management and Portfolio Optimization Techniques in Python
Explore risk management and portfolio optimization in Python โ from VaR and CVaR metrics to Markowitz optimization pitfalls, shrinkage estimation, and Kelly criterion sizing.
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Backtesting Frameworks: Building Your First Trading Strategy
Build a point-in-time backtesting engine from scratch, explore vectorbt and backtrader, and learn why most backtests lie about profitability.
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Technical Indicators and Feature Engineering with Pandas for Quant Trading
Build a production-grade feature engineering pipeline for quant trading with Pandas โ RSI, MACD, Bollinger Bands, proper temporal alignment, and why shift(1) matters most.
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Data Collection and Preprocessing for Quant Trading in Python
Learn how to collect, clean, and preprocess stock market data for quant trading in Python using yfinance, Pandas, and Parquet โ with real pitfalls to avoid.
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Getting Started with Quantitative Investment in Python
Learn the infrastructure foundations for quantitative trading in Python. Covers data pipelines, return calculations, and realistic backtesting with transaction costs.