Category: Quant Investment with Python
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Real-Time Trading Systems and Deployment Best Practices
Your backtest won't survive production. Latency budgets, order rejection handling, kill switches, and monitoring that catches bugs before losses.
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Pairs Trading Is Dead (Unless You Know Where to Look)
Classic equity pairs trading is arbitraged out, but Kalman-filtered cross-asset pairs and multi-leg baskets still show alpha in these 3 markets.
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ML Stock Prediction: Why Most Fail and What Works
Most ML stock models fail live trading within weeks. Here's what survives transaction costs, regime shifts, and the reality of slippage.
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Risk Management and Portfolio Optimization Techniques in Python
VaR underestimates tail risk, Markowitz explodes on estimation error. Fix it with shrinkage estimators and Kelly criterion position sizing.
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Backtesting Frameworks: Building Your First Trading Strategy
Most backtests lie about profitability through lookahead bias. Build a point-in-time engine that doesn't cheat on order fills or data access.
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Quant Feature Engineering with Pandas and TA Indicators
Why shift(1) is the most important line in your feature engineering pipeline โ and how to avoid the temporal leakage that kills strategies.
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Data Collection and Preprocessing for Quant Trading in Python
Stock split adjustments, survivorship bias, and timestamp alignment pitfalls that corrupt your trading data โ with yfinance and Parquet fixes.
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Getting Started with Quantitative Investment in Python
Build a quant trading infrastructure in Python: data pipelines, log return calculations, and backtesting that includes transaction costs.