Quant-Python-Jumpstart is a comprehensive repo for someone who have ML experience and looking to swich their toolkits into quantitative finance (even as an interest).
Using Python and Qlib, this repo provide basic overview and scripts you need to start crunching financial data, backtesting models, and visualizing your strategies using Qlib. This is also a record of my personal learning journey.
This repository, designed for both beginners and experts in quantitative finance, embraces Python. It leverages MicroSoft Qlib, as it is a powerful and comprehensive lib for quantization.
- Overall architecture: When I consider Quantization system as an machine learning engineering system, what it should be like?
- Data Collection and Preprocessing
- Feature Extraction and Selection
- Model Generation and Training
- Backtesting and Performance Measurement
- Visualization Tools for Analysis
Before you begin, ensure you have met the following requirements:
- You have installed Python 3.7 or later.
- You have installed the necessary libraries. Update later.
- Update
First, clone the repository.
git clone https://github.com/duanyiqun/quant-jumpstart.gitThen, navigate to the cloned directory and install the dependencies.
bash cd quant-python-jumpstart pip install -r requirements.txt
This repository is organized into several modules, each addressing a different aspect of quantitative finance:
data_collection_and_preprocessing: Collect and preprocess data for modeling. feature_extraction_and_selection: Extract and select the most significant features. model_generation_and_training: Generate and train models using the prepared data. backtesting_and_performance_measurement: Backtest strategies and measure performance. visualization_tools: Tools for data and result visualization.
This project is licensed under the MIT License - see the LICENSE file for details.
Microsoft Qlib