spectre
Trading strategy builder
A high-performance, GPU-accelerated library for building quantitative trading strategies using factor analysis and backtesting
GPU-accelerated Factors analysis library and Backtester
655 stars
21 watching
111 forks
Language: Python
last commit: about 1 year ago
Linked from 1 awesome list
algorithmic-tradingbacktesterbacktestingfactor-analysisquantitative-analysisspectre
Related projects:
Repository | Description | Stars |
---|---|---|
| An open-source trading framework providing a flexible and modular system for developing quantitative trading strategies | 2,261 |
| A collection of code examples focused on systematic trading strategies and related algorithms using Python. | 375 |
| A Python library for creating and backtesting algorithmic trading strategies using machine learning and technical indicators. | 104 |
| Develops a framework to analyze high-frequency limit order book data and predict market outcomes using machine learning algorithms. | 1,963 |
| Enables quick analysis and backtesting of trading strategies using technical indicators and chart data | 377 |
| A Python-based backtesting and live trading package for quantitative traders. | 541 |
| A collection of notebooks and blogs on quantitative finance and trading strategies, including machine learning, deep reinforcement learning, and backtesting. | 2,157 |
| Framework for testing and evaluating trading strategies using Python | 718 |
| A repository providing examples and tools for developing and backtesting algorithmic trading strategies in Python. | 76 |
| A backtesting and trading engine for algorithmic traders, designed to facilitate rapid experimentation and research iteration. | 206 |
| A backtesting and trading library for various assets and markets | 930 |
| A quant framework for stock market analysis and trading | 1,146 |
| Automates backtesting of investment algorithms with live pricing data from IEX Cloud, Tradier, and FinViz for training AI models. | 1,048 |
| A framework for simulating high-frequency trading and market-making strategies with realistic latency and order book simulations. | 2,066 |
| A Python framework for developing algorithmic trading strategies using machine learning and data science techniques. | 2,113 |