03-custom-optimization-metrics.py

import datetime
import pandas_ta as ta
import pandas as pd
from backtesting import Backtest
from backtesting import Strategy
from backtesting.lib import crossover
from backtesting.test import GOOG

def optim_func(series):
    if series['# Trades'] < 10:
        return -1
    else:
        return series['Equity Final [$]']/series['Exposure Time [%]']


class RsiOscillator(Strategy):
    upper_bound = 70
    lower_bound = 30
    rsi_window = 14

    # Do as much initial computation as possible
    def init(self):
        self.rsi = self.I(ta.rsi, pd.Series(self.data.Close), self.rsi_window)


    # Step through bars one by one
    # Note that multiple buys are a thing here

    def next(self):
        if crossover(self.rsi, self.upper_bound):
            self.position.close()
        elif crossover(self.lower_bound, self.rsi):
            self.buy()


bt = Backtest(GOOG, RsiOscillator, cash=10_000, commission=.002)
stats = bt.optimize(
    upper_bound = range(55, 85, 5),
    lower_bound = range(10, 45, 5),
    rsi_window = range(10, 30, 2),
    maximize = optim_func,
    constraint = lambda param: param.upper_bound > param.lower_bound,
    # Random Grid search
    #max_tries = 100
)
bt.plot()