Python Time-Series Functions for Trading Indicators

Summary

This reference presents Python functions for common time-series operations and technical indicators. Core utilities cover shifting and differencing series, rolling sums and standard deviations, extrema, moving averages, and dynamic smoothing. Higher-level functions detect conditions such as crosses, consecutive signals, and time since an event. The indicator collection includes MACD, RSI, Bollinger Bands, ATR, directional movement, Donchian and Keltner channels, momentum, and volume-based measures.

The functions are intended to make indicator calculations reusable on market data, with rolling-window operations implemented through pandas and NumPy. The document gives formulas and code but no market tests, trading rules, or performance evidence. It notes that some exponential or smoothed averages need a longer history to stabilize and mentions decimal rounding for precision. The code should be checked before use: formatting and several expressions appear malformed or incomplete, and behavior around missing values, indexing, and zero denominators is not explained.

Key ideas

• The reference groups reusable time-series helpers with higher-level technical indicator calculations.
• Rolling windows support averages, dispersion, extrema, and event detection.
• The indicator examples span trend, momentum, volatility, and volume measures.
• Some smoothed averages may need substantial historical data before stabilizing.
• The code is presented without validation, and apparent formatting or expression issues warrant review.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.