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

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