Momentum Trading in Python

Momentum SPY Trading Strategy.py

import pandas as pd
import backtrader as bt

# Define the strategy
class MyStrategy(bt.Strategy):
    params = dict(
        ma_length=50,
        obv_ma_length=200,
        target=5000,
        stop=4000
    )

    def __init__(self):
        self.ma = bt.indicators.SimpleMovingAverage(
            self.data.close, period=self.params.ma_length
        )
        self.obv = bt.indicators.OnBalanceVolume(
            self.data.close, self.data.volume
        )
        self.obv_ma = bt.indicators.SimpleMovingAverage(
            self.obv, period=self.params.obv_ma_length
        )

    def next(self):
        if self.data.close[0] > self.ma[0]:
            if (
                self.data.open[0] > self.data.low[-1]
                or self.data.open[-1] > self.ma[-1] > self.data.low[-1]
            ):
                if (
                    self.data.close[0] > self.ma[0]
                    and self.obv[0] > self.obv_ma[0]
                    and self.data.datetime.date(0).weekday() in [0, 2, 3, 4]
                ):
                    self.buy(
                        size=self.params.target / self.data.close[0],
                        exectype=bt.Order.Market
                    )

    def notify_order(self, order):
        if order.status in [bt.Order.Completed, bt.Order.Canceled, bt.Order.Rejected]:
            self.order = None

# Download data for SPY
data = bt.feeds.YahooFinanceData(
    dataname='SPY',
    fromdate=datetime(2007, 1, 1),
    todate=datetime(2022, 4, 7),
    reverse=False
)

# Create a cerebro instance
cerebro = bt.Cerebro()

# Add the data feed
cerebro.adddata(data)

# Add the strategy
cerebro.addstrategy(MyStrategy)

# Set the broker and cash
cerebro.broker.setcash(1000000)

# Set the commission and slippage
cerebro.broker.setcommission(commission=0.001)
cerebro.broker.set_slippage_fixed(
    fixed=0.01, slip_open=True, slip_limit=True, slip_match=True, slip_out=True
)

# Set the position sizing
cerebro.addsizer(bt.sizers.PercentSizer, percents=10)

# Add the analyzers
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')

# Run the backtest
results = cerebro.run()

# Print the final results
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
print('Sharpe Ratio:', results[0].analyzers.sharpe.get_analysis()['sharperatio'])
print('Total Returns:', results[0].analyzers.returns.get_analysis()['rtot'])
print('Max Drawdown:', results[0].analyzers.drawdown.get_analysis()['max']['drawdown'])
print('Returns:', results[0].analyzers.pyfolio.get_analysis()['returns'])
print('Positions:', results[0].analyzers