-Implementation of genetic algorithm and LSTM-based recurrent neural network (RNN) to find optimal weighting of these indicators for a single-stock portfolio

-Implementation of multiple-stock genetic algorithm to find optimal weighting of these indicators for a multiple-stock portfolio

-Stock screener based on genetic algorithm implementation

Setup: python3 -m pip install pyalgotrade, keras, yfinance

Usage: python3 pat_papertrade.py [stock] [period=1y] [interval=1d] # single-stock genetic algorithm

python3 rnn_algotrade.py [stock] [period=1y] [interval=1d] # single-stock LSTM RNN algorithm

python3 screener.py [period] [interval] # genetic algorithm stock screener

python3 multiple_series.py [stocks...] [period=1y] [interval=1d] # multiple-stock genetic algorithm

python3 yfinance_csv.py (*) [stock(s...)] [period=1y] [interval=1d] # wrapper for yfinance library

-Can also specify custom strategies in strategies.py and add them to single_strat.py to backtest them