Algorithmic investing reinvented.

Generated Code

# Setup logging configuration
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# Define your strategy for trading leveraged ETFs
class ExampleStrategy(bt.Strategy):
    params = (
        ('short_period', 10),  # Short period for the moving average
        ('long_period', 30),   # Long period for the moving average
    )

    def __init__(self):
        # Initialize short and long moving averages for leveraged ETFs
        self.short_ma = bt.indicators.SimpleMovingAverage(self.datas[0].close, period=self.p.short_period)
        self.long_ma = bt.indicators.SimpleMovingAverage(self.datas[0].close, period=self.p.long_period)
        logging.info('Short and Long MAs for Leveraged ETF initialized.')

    def next(self):
        short_ma = self.short_ma[0]
        long_ma = self.long_ma[0]
        close_price = self.datas[0].close[0]
        
        # Log current cash and portfolio value for better trade visibility
        cash = self.broker.get_cash()
        value = self.broker.get_value()

        # Buy/Sell signals based on moving averages.
        if short_ma > long_ma and not self.position:
            size = int(cash / close_price)  # Calculate size
            self.buy(size=size)
            logging.info(f'BUY Signal: Short MA > Long MA | Date: {self.datas[0].datetime.date(0)} | Size: {size} | Portfolio Value: {value}')

        elif short_ma < long_ma and self.position:
            self.sell(size=self.position.size)
            logging.info(f'SELL Signal: Short MA < Long MA | Date: {self.datas[0].datetime.date(0)} | Size: {self.position.size} | Portfolio Value: {value}')

# Define the lookback period based on the longest indicator params
lookback_period = 30  # Using the largest number in params as lookback period

# Fetch and add data for leveraged ETFs (e.g., UPRO - a 3x leveraged S&P 500 ETF)
leveraged_etf_data = bt.feeds.PandasData(dataname=yf.download('UPRO', start_date, end_date), name='UPRO')
cerebro.adddata(leveraged_etf_data)

globals()['lookback_period'] = lookback_period  # Update global variable for lookback period usage