This repository contains a trading algorithm implemented using the NEAT (NeuroEvolution of Augmenting Topologies) algorithm in Python. The trading algorithm aims to generate high profits by autonomously trading stocks based on historical price data.
The algorithm follows the following steps:
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Data Collection: The algorithm collects historical price data for stocks from one or more sources. This data is saved to a database for further processing.
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Preprocessing: Over a certain amount of time, the algorithm takes rolling 90-day trading windows of the stock data. Within each window, the data is normalized to account for variations in stock prices.
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Neural Network Evolution: NEAT is employed to evolve neural network architectures and parameters that represent trading strategies. Each neural network is trained using historical price data to predict optimal trading decisions.
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Trading: The evolved neural networks are applied to the normalized data to generate buy/sell signals. The algorithm executes trades based on these signals, aiming to achieve a high profit percentage over time.
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Evaluation and Optimization: The algorithm continuously evaluates the performance of the trading strategies and adjusts the neural networks through evolutionary processes to improve profitability.
- See requirements.txt in base directory
To use the trading algorithm, follow these steps:
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Install the required dependencies using pip install -r requirements.txt.
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Update base paremeters in main.py and config.txt
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Run the main script main.py to start the algorithm.
You can configure various aspects of the trading algorithm, including:
- NEAT algorithm parameters (e.g., population size, mutation rates)
- Database connection settings
- Stock selection and data retrieval parameters Modify the configuration files (config.txt) to customize the algorithm according to your requirements.
With a fitness threshold of 75 (75 percent gains) I was able to train and find a model saved in the best.pickle file.
Trying the best model with multiple runs shows consistently positive returns with backtesting.
