RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen
Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati
This project provides technical documentation and reference guides for spot trading, including specifications for REST, WebSocket, and FIX protocols. It serves as a comprehensive resource for integrating with spot trading endpoints to execute trades, query account data, and fetch market statistics. The project distinguishes itself by supporting institutional-grade connectivity through the Financial Information eXchange standard and simple binary encoding to reduce latency and payload size. It also includes a dedicated sandbox environment for validating trading logic and strategies without fin
pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated trading strategies. It provides a comprehensive framework for financial strategy backtesting, a technical analysis library for computing mathematical indicators, and connectors for cryptocurrency exchange integration. The project distinguishes itself by supporting sentiment-based trading through the integration of real-time social media feeds and keyword streams. It features a quantitative trading visualization tool for plotting price action and portfolio equity curves, along with
tqsdk-python एक क्वांटिटेटिव ट्रेडिंग SDK और फ्रेमवर्क है जिसे Python का उपयोग करके फ्यूचर्स, ऑप्शन्स और स्टॉक्स के लिए स्वचालित रणनीतियाँ विकसित करने के लिए डिज़ाइन किया गया है। यह एक एल्गोरिथमिक ट्रेडिंग इंजन और वित्तीय बाज़ार डेटा API के रूप में कार्य करता है, जो रणनीतियों को बैकटेस्ट करने, ऐतिहासिक डेटा का विश्लेषण करने और कई ब्रोकरेज खातों में लाइव ट्रेड्स निष्पादित करने के लिए आवश्यक टूल्स प्रदान करता है।
shinnytech/tqsdk-python की मुख्य विशेषताएं हैं: Algorithmic Trading Engines, Quantitative Trading Platforms, Technical Indicator Calculators, Financial Data APIs, Live Trading Execution, Stock Trade Executions, Multi-Account Portfolio Management, Multi-Account Trading Protocols।
shinnytech/tqsdk-python के ओपन-सोर्स विकल्पों में शामिल हैं: ricequant/rqalpha — RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies… binance/binance-spot-api-docs — This project provides technical documentation and reference guides for spot trading, including specifications for… gbeced/pyalgotrade — pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated… fasiondog/hikyuu — Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading… edtechre/pybroker — pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for…