Stock analysis/prediction model using machine learning using the impact between different out-of-the-market factors (weather, etc.) and the stock prices.
There are three ML model that are being implemented:
- A simple feedforward neural network
- A recurrent neural network with LSTM (long short term memory)
- A convolutional neural network
The pipeline implemented is using backtrader to implement backtesting in order to test each individual strategy. In the future, it is worthwhile to try using a genetic algorithm to better figure the accuracy of the model.
There are three main usages for this project:
run python driver.py -t model_name to train
run python driver.py -b model_name to backtest the model