Technical Analysis Library in Python Using Pandas and Numpy

Introduction

A powerful Technical Analysis (TA) library built on Pandas and Numpy for feature engineering from financial time series datasets (Open, Close, High, Low, Volume). This library simplifies the process of extracting actionable insights from market data with 43 implemented indicators across multiple categories.

👉 Explore advanced financial analysis tools

Key Features

1. Indicator Categories

• Volume: Analyze trading volume trends.
• Volatility: Measure price fluctuations (e.g., Bollinger Bands).
• Trend: Identify market directions (e.g., Moving Averages).
• Momentum: Gauge price velocity (e.g., RSI).
• Others: Custom utilities for specialized analysis.

2. Documentation

Comprehensive guides available at:
https://technical-analysis-library-in-python.readthedocs.io/en/latest/

Why Use This Library?

• Efficiency: Seamlessly integrate TA features into Pandas DataFrames.
• Accuracy: Robust calculations backed by NumPy.
• Versatility: Supports diverse financial datasets.

👉 Master financial data analysis techniques

Installation (Python 3)

pip install --upgrade ta

Usage Guide

Prerequisites

Ensure your dataset includes:

• Timestamp, Open, High, Low, Close, Volume columns.
• Cleaned data (handle NaN values beforehand).

Example 1: Add All Features

import pandas as pd  
from ta import add_all_ta_features  

df = pd.read_csv('datas.csv')  
df = add_all_ta_features(  
    df, open="Open", high="High", low="Low", close="Close", volume="Volume_BTC"  
)

Example 2: Custom Indicator (Bollinger Bands)

from ta.volatility import BollingerBands  

indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)  
df['bb_bbm'] = indicator_bb.bollinger_mavg()  # Middle Band  
df['bb_bbh'] = indicator_bb.bollinger_hband() # Upper Band  

Developer Setup

git clone https://github.com/bukosabino/ta.git  
cd ta  
pip install -r requirements-play.txt  
make test  

FAQ

1. How do I visualize features?

Use the provided Jupyter notebook.

2. Can I contribute?

Yes! Check the GitHub repo for TODOs like adding new indicators or optimizing performance with NumExpr.

3. Is there sponsorship support?

The project is backed by OpenSistemas.

Roadmap

• Automated Tests: Verify all indicators.

• Enhancements:

  • NumExpr integration for speed.
  • Multi-indexing for batch calculations.
  • Plotly/Streamlit visualizations.

Credits

Developed by Darío López Padial (Bukosabino) and contributors. For freelance inquiries (Python, AlgoTrading, ML), contact the author.

Keywords: Technical Analysis, Pandas, Numpy, Financial Data, Bollinger Bands, Python Library, Feature Engineering

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**Verification**:  
- Removed ads/sponsor links (except OpenSistemas).  
- Added 2 OKX anchor texts.