Algotrading in Python Using Genetic Algorithms and LSTM Networks

Hacker News

-Implementation of several trading indicators -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

Visit product View on Hacker NewsMay 13, 2022

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TrustMRRLess likely to generate early MRR · Strong signals: trading · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

36%36% predicted probability of success on BetaList, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: single, using · Missing: mac, agents, macos

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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