مكتبات Python للتداول

The essential library for working with financial time series data, OHLCV manipulation, and vectorized backtesting.

Feature-rich backtesting framework with built-in indicators, analyzers, and optimization capabilities.

Official Python package for connecting to MetaTrader 5, accessing market data, and executing trades.

Download historical market data from Yahoo Finance for stocks, forex, cryptocurrencies, and indices.

Industry-standard library with 150+ technical indicators and pattern recognition functions.

Lightning-fast backtesting using vectorization, 100x faster than event-driven frameworks.

Apply machine learning models to predict market movements and build intelligent trading strategies.

Unified API for 100+ cryptocurrency exchanges with consistent interface for trading and data.

The fundamental package for numerical computing in Python, essential for financial calculations and vectorized operations.

Comprehensive technical analysis library with 130+ indicators that integrates seamlessly with pandas.

Flexible backtesting framework with tree-based strategy structure for portfolio-focused testing.

Pythonic algorithmic trading library with Pipeline API for factor-based quantitative strategies.

Modern async Python wrapper for Interactive Brokers API with event-driven trading.

Modern commission-free trading API for stocks, ETFs, and crypto with paper trading support.

Google's deep learning library for building neural networks for price prediction and pattern recognition.

Dynamic deep learning framework favored by researchers for custom architectures and flexibility.

High-performance gradient boosting library excelling at tabular data and feature-based prediction.

Lightning-fast technical analysis with 100+ indicators. Easy pip install — no external C library required like TA-Lib.

Elegant technical analysis with 80+ indicators. One-line syntax: TA.RSI(df) — built entirely on pandas.

Popular library with 40+ indicators organized by type. Add all indicators to a DataFrame with one function call.

Create publication-quality candlestick, OHLC, Renko, and Point & Figure charts with minimal code.

Discover recurring patterns, detect anomalies, and identify regime changes using matrix profiles. By TD Ameritrade.

Machine learning for time series: DTW pattern matching, clustering, and classification of chart patterns.

Detect support/resistance with find_peaks(), smooth noisy data, find market cycles with FFT, and optimize parameters.

Time series forecasting (ARIMA), cointegration for pairs trading, stationarity testing, and factor regression.