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Neuro Evolution Market Trader

Neuro evolution agent to trade

If you like this project you can support me.

Apoia.se

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

img

Max fitness for each generation

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Mean fitness for each generation

Backtesting

2016 15M candles

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2017 30M candles

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2018 30M candles

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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