SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Order Book Analyzer
Develops a framework to analyze high-frequency limit order book data and predict market outcomes using machine learning algorithms.
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
2k stars
99 watching
670 forks
Language: Jupyter Notebook
last commit: over 2 years ago
Linked from 2 awesome lists
algorithmic-tradingbacktesting-trading-strategiesfeature-engineeringfeature-selectionhigh-frequency-tradinginvestmentlimit-order-bookmachine-learningmarket-makermarket-makingmarket-microstructuremodel-selectionorderbookorderbook-tick-datapythonquantquantitative-tradingtradingtrading-strategies
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a limit order book data structure for high-frequency trading in C. | 1,023 |
| A high-performance, GPU-accelerated library for building quantitative trading strategies using factor analysis and backtesting | 655 |
| An analysis tool for studying high frequency trading patterns on Bitcoin exchanges | 151 |
| A fast and efficient database for storing high-frequency trading order book data in binary format. | 691 |
| An algorithmic trading system using machine learning and historical stock data to predict price movements. | 113 |
| Teaches data analysts and developers to create smart investment strategies using machine learning algorithms | 1,516 |
| 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 |
| A framework for simulating high-frequency trading and market-making strategies with realistic latency and order book simulations. | 2,066 |
| An open-source trading framework providing a flexible and modular system for developing quantitative trading strategies | 2,261 |
| Enables quick analysis and backtesting of trading strategies using technical indicators and chart data | 377 |
| This project implements 101 algorithmic trading strategies using Python to generate superior returns relative to a benchmark. | 16 |
| A collection of notebooks and blogs on quantitative finance and trading strategies, including machine learning, deep reinforcement learning, and backtesting. | 2,157 |
| A repository providing examples and tools for developing and backtesting algorithmic trading strategies in Python. | 76 |
| This is an implementation of a trading algorithm using RNNs to predict stock return prices based on S&P 500 index movements. | 32 |