simple_es.py

import numpy as np
import backtrader as bt

from es import EvolutionStrategy
from keras.models import Model, Input, Sequential
from keras.layers import Dense, Activation


class ESStrategy(bt.Strategy):
    params = {
        'model': None,
        'ma_period': 200,
        'rsi_period': 14
    }

    def __init__(self):
        self.order = None
        self.dataclose = self.datas[0].close
        self.datavol = self.datas[0].volume
        self.sma = bt.indicators.SimpleMovingAverage(
            self.datas[0],
            period=self.p.ma_period
        )
        self.rsi = bt.indicators.RelativeStrengthIndex(
            period=self.p.rsi_period
        )

    def stop(self):
        cash = self.broker.getvalue()
        print('Result cash: {}'.format(cash))

    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            return

        self.order = None

    def next(self):
        if self.order:
            return

        input_data = [self.dataclose[0], self.datavol[0]]
        # for i in range(7):
        #     input_data.append(self.dataclose[i - 6])
        # for i in range(7):
        #     input_data.append(self.datavol[i - 6])
        # for i in range(7):
        #     input_data.append(self.sma[i - 6])
        # for i in range(7):
        #     input_data.append(self.rsi[i-6])
        inp = np.asanyarray(input_data)
        inp = np.expand_dims(inp, 0)

        predict = self.p.model.predict(inp)[0]
        predict = np.argmax(predict)

        if predict == 2:
            return

        if not self.position:
            if predict == 0:
                self.order = self.buy()
        else:
            if predict == 1:
                self.order = self.sell()

        if not self.position:
            if predict == 1:
                self.order = self.sell()
        else:
            if predict == 0:
                self.order = self.buy()


data = bt.feeds.GenericCSVData(
    dataname='eur_usd_1d.csv',
    separator=',',
    dtformat=('%Y%m%d'),
    tmformat=('%H%M00'),
    datetime=0,
    time=1,
    open=2,
    high=3,
    low=4,
    close=5,
    volume=6,
    openinterest=-1
)

def get_model():
    model = Sequential()
    model.add(Dense(128, input_dim=2, activation='relu'))
    # model.add(Dense(256, activation='relu'))
    model.add(Dense(3, activation='relu'))

    model.compile(optimizer='Adam', loss='mse')
    return model

def get_reward(weights):
    model = get_model()
    model.set_weights(weights)

    cerebro = bt.Cerebro()
    cerebro.addstrategy(ESStrategy, model=model)
    cerebro.adddata(data)
    cerebro.broker.setcash(1000)
    cerebro.addsizer(bt.sizers.FixedSize, stake=50)

    cerebro.run()
    return cerebro.broker.getvalue() - 5000.0


model = get_model()

es = EvolutionStrategy(model.get_weights(), get_reward, population_size=50, sigma=0.2, learning_rate=0.01)
es.run(1000, print_step=1)