rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2329 stars · 694 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Jupyter Notebook project demonstrating a high-frequency trading strategy on full limit order book tick data using machine learning. It walks through feature extraction (rise ratio, depth ratio), model training with classifiers like Random Forest and Gradient Boosting, short-horizon prediction, and P&L backtesting.

Use cases

• build an HFT strategy on order book tick data
• predict short-term price direction from limit order book features
• learn feature engineering for market microstructure data
• backtest a machine learning trading strategy and compute P&L
• compare classifiers for high-frequency prediction tasks
• study market making and order book dynamics with data science

When to choose

• you want an end-to-end educational example of ML applied to limit order book data
• you need reference code for order book feature extraction and model selection in Python
• you are learning quantitative trading strategy backtesting with tick data

When to avoid

• you need production-ready, low-latency HFT execution infrastructure
• you require a maintained library with a license and API guarantees
• you need live market data connectivity or broker integration

Facets

learning-resource · maturity maintenance

machine-learning data-science trading benchmarking fintech machine-learning data-science python high-frequency-trading limit-order-book quantitative-trading backtesting feature-engineering market-microstructure jupyter-notebook trading-strategy algorithms

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