Neuro Evolution Market Trader

Neuro evolution agent to trade

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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 JUPYTERENABLELAB=yes -v "$PWD":/home/jovyan/work my-notebook
Optional: Tensorboard
docker exec -it <container_id> /bin/bash
tensorboard --logdir work/GA/tensorboardMarketv3.0141/

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
• bothparentpercentage: 0.8
• oneparentpercentage: 0.1
• reward_function: SimpleProfit
• initial_cash: 5.0
• profitwindowsize: 10
• close_col: 0
• largeholdingspenalty: 0
• lostallcash_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