Python Technical Indicator Library Built from Rolling Time-Series Functions

Summary

This document presents a Python library for building common trading indicators from a small set of time-series operations. Core helpers cover rolling averages, standard deviation, shifts, differences, extrema, cumulative sums, exponential smoothing, and linear regression slope. Higher-level utilities count or detect conditions across bars and identify crossings.

The library then defines indicators including MACD, KDJ, RSI, Bollinger Bands, ATR, directional movement, Donchian and Keltner channels, momentum, volume measures, and VWAP. The examples show how familiar indicators can be assembled from reusable operations and return series suitable for analysis. This is reference material rather than a trading strategy: it offers no market data, validation, or evidence that any indicator produces an edge. Users should also verify implementation details and data conventions before relying on the outputs, since indicator formulas and warm-up behavior can vary across platforms.

Key ideas

• The library separates foundational time-series functions from indicator calculations.
• Rolling operations support common moving averages, volatility measures, extrema, and sums.
• Higher-level functions detect recurring conditions and price crossings.
• The collection includes trend, momentum, volatility, and volume-based indicators.
• The document provides formulas but no empirical evidence of trading performance.

Tags

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