daniloaleixo / NeuroEvolutionMarketTrader

Neuro evolution agent to buy and sell stocks atumatically

Neuro Evolution Market Trader

Neuro evolution agent to trade

If you like this project you can support me.

It took me several experiments to get to this agent. I tried several deep learning architectures and technical analysis parameters.

Agent v 3.0141

The agent has the following characteristics:

• Using neuro evolution
• Receives OHLC as parameters
• Generates RSI and MACD technical indicators to be used as parameters
• Also run through a pretrained CNN buy and sell classifier to get the last set of parameters

Running

# Building notebook
docker build -t my-notebook -f docker/Dockerfile .

# Running notebook
docker run --rm -p 8888:8888 -p 6006:6006 -e JUPYTER_ENABLE_LAB=yes -v "$PWD":/home/jovyan/work my-notebook

# Optional: Tensorboard
docker exec -it <container_id> /bin/bash
tensorboard --logdir work/GA/tensorboard_Market_v3.0141/

Results

Right now the best agent v3.0141, the agent uses OHLC, RSI and MACD inputs, as well as the output of a pretrained CNN buy and sell classifier. The agent has these results applied to EURUSD with 30M candles:

• Mean over backtest returns: 14.17%
• Points above buy and hold mean: 12.26%
• Winning Percent mean: 80%

Training

Max fitness for each generation

Mean fitness for each generation

Backtesting

2016 15M candles

2017 30M candles

2018 30M candles

RUNINFO: v3.0141 - OHLC + CNN Classifier B/S w10 15x15 + RSI&MACD

Params

• layers: 16, 32, 64, 32, 16
• population_size: 256
• generations: 100
• episodes: 10
• mutation_variance: 0.005
• survival_ratio: 0.3
• both_parent_percentage: 0.8
• one_parent_percentage: 0.1
• reward_function: SimpleProfit
• initial_cash: 5.0
• profit_window_size: 10
• close_col: 0
• large_holdings_penalty: 0
• lost_all_cash_penalty: -1e2
• inaction_penalty: 0

Results

• After 100 generations
  • Max rewards: 2.508929
  • Mean rewards: 0.963821
  • Std rewrads: 1.400484
  • Best Profit (Env 10k - 50k): 37.23
  • Mean profit over all best genome (Env 10k - 50k): 14.35
• Backtesting:
  • Mean of 2016 + 2017 + 2018 returns: 14.17
  • Points above BH mean: 12.26
  • Winning Percent mean: 80%
  • 2016 15M
    • Return [%] 24.8711
    • Buy & Hold Return [%] -3.144688847131627
    • Max. Drawdown [%] 2.58
    • Avg. Drawdown [%] 0.64
    • n Trades 7860
    • Win Rate [%] 81.40
    • Best Trade [%] 0.12363999999999997
    • Worst Trade [%] -0.08284999999999965
    • SQN 4.67
    • Sharpe Ratio 0.0
    • Sortino Ratio -2.1676
  • 2017 30M
    • Return [%] 8.5952
    • Buy & Hold Return [%] 13.862653618570775
    • Max. Drawdown [%] 3.55
    • Avg. Drawdown [%] 0.75
    • n Trades 3160
    • Win Rate [%] 80.54
    • Best Trade [%] 0.02414999999999967
    • Worst Trade [%] -0.04445000000000032
    • SQN 2.20
    • Sharpe Ratio 0.0
    • Sortino Ratio -2.3002
  • 2018 30M
    • Return [%] 9.0453
    • Buy & Hold Return [%] -5.008444957273516
    • Max. Drawdown [%] 2.72
    • Avg. Drawdown [%] 0.12
    • n Trades 3318
    • Win Rate [%] 78.99
    • Best Trade [%] 0.024970000000000603
    • Worst Trade [%] -0.038909999999999556
    • SQN 2.31
    • Sharpe Ratio 0.0
    • Sortino Ratio -2.8040

Next Steps

• Develop an API
• Develop a training API
• Use socket inside API
• Develop a front end to visualize the training