This repository contains a Python project for optimizing asset allocation to achieve the highest Annual Percentage Yield (APY). The project uses a Random Forest model to predict allocations and includes a simulator for evaluating the performance of different allocation strategies.
forest_allocation.py: Contains theRandomForestAllocationclass which predicts asset allocation using a trained Random Forest model.forward.py: Main script that generates asset and pool data, calculates allocations, and queries the simulator to score the allocations.test.py: Script to run multiple simulations and evaluate the average performance of different allocation strategies.train.py: Script to prepare training data, train the Random Forest model, and save the trained model.src/: Directory containing additional modules required for the project (e.g., pool generation, reward calculation, simulator).
- Python 3.7+
- Libraries:
- numpy
- pandas
- scikit-learn
- tqdm
- Clone the repository:
git clone https://github.com/yourusername/asset-allocation-optimization.git cd asset-allocation-optimization
Create a virtual environment and activate it:
bash
python3 -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activate
Install the required libraries:
bash pip install -r requirements.txt Usage Training the Model Run train.py to generate training data, train the Random Forest model, and save the model to model.pkl: bash python train.py Predicting Allocations Run forward.py to generate asset and pool data, calculate allocations using the trained model, and query the simulator to score the allocations: bash python forward.py Testing the Model Run test.py to execute multiple simulations and evaluate the average performance of different allocation strategies: bash python test.py Logging The project uses Python's logging module to log information during execution. Logs can be adjusted by changing the logging level in the main function of forward.py.