Category: Finance & Quant
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Exploratory Data Analysis (EDA) for Stock Price Prediction
Master exploratory data analysis for stock prediction: visualization, moving averages, volatility metrics, and correlation analysis with Python.
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Navigating the Landscape of Financial Datasets on Kaggle
Master financial data analysis with Kaggle datasets. Learn time-series characteristics, environment setup, and explore key datasets for stocks, credit risk, and fraud detection.
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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.