Trading

TA-Lib is the most widely used technical analysis library in quantitative trading. 158 functions covering technical indicators and candlestick pattern recognition, all backed by a C implementation. If you do any kind of systematic trading in Python, you’ll run into it sooner or later. This tutorial covers everything from installation to a working strategy: the two API styles and when to use each, parameter selection tips for core indicators, candlestick pattern recognition, Pandas/Polars integration, and how TA-Lib compares to pandas-ta. If you’ve already set up yfinance for data, TA-Lib is the next step in turning that data into trading signals. ...

The backtest shows 80% annualized returns with a 5% max drawdown. Three months after going live, the strategy is down 30%. This scenario plays out repeatedly in quant trading, and the root cause is almost always the same: the backtest itself was wrong. Not a code bug — systematic flaws in the assumptions, data, and statistical methods that made the strategy look profitable when it wasn’t. This article breaks down the most common backtesting pitfalls into four categories: data pitfalls, statistical pitfalls, execution pitfalls, and psychological pitfalls. Each comes with a wrong-vs-right Python code comparison, followed by a self-check checklist. ...

The worst part about quantitative trading isn’t having a bad strategy. It’s not knowing whether your strategy is good or bad. A strategy with 30% annualized returns sounds great, until you realize the max drawdown was 60% — you’d never have held through it. A Sharpe ratio of 2.0 looks impressive, but if it’s propped up by a few windfall trades in extreme market conditions, the Sortino ratio will tell a very different story. Quantitative trading metrics aren’t decorations for backtesting reports. They’re the tools that help you decide whether a strategy is worth putting real money behind. ...