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
class ExampleStrategy(bt.Strategy):  # You must leave this name ExampleStrategy the same or the strategy will not run, you can take out this comment in your generated code
    # implement new params based on user specification
    params = (
        ('short_period', 12),  # Short term EMA period
        ('long_period', 26),  # Long term EMA period
    )

    def __init__(self):
        # Initialize EMA indicators with dynamic periods based on strategy parameters
        self.short_ema = bt.indicators.EMA(self.datas[0], period=self.params.short_period)
        self.long_ema = bt.indicators.EMA(self.datas[0], period=self.params.long_period)
        self.ema_diff = self.short_ema - self.long_ema
        self.prev_ema_diff = self.ema_diff(-1)
        logging.info(f'EMA Indicators Initialized: Short={self.params.short_period}, Long={self.params.long_period}')

    def next(self):
        # Calculate the current and previous EMA differences
        curr_ema_diff = self.ema_diff[0]
        prev_ema_diff = self.prev_ema_diff[0]

        # Log for tracking
        logging.info(f'Date: {self.datas[0].datetime.date(0)}, Current EMA Diff: {curr_ema_diff}, Previous EMA Diff: {prev_ema_diff}')

        # Check for buy signal
        if curr_ema_diff > prev_ema_diff and not self.getposition(self.datas[0]).size:
            size = int(self.broker.get_cash() / self.datas[0].close[0])  # Calculate size based on current cash
            self.buy(size=size)  # Execute buy order
            logging.info(f'BUY ORDER PLACED on {self.datas[0].datetime.date(0)}, Size: {size}, EMA Diff: {curr_ema_diff}, Cash: {self.broker.get_cash()}')

        # Check for sell signal
        elif curr_ema_diff < prev_ema_diff and self.getposition(self.datas[0]).size:
            self.sell(size=self.position.size)  # Execute sell order using current position size
            logging.info(f'SELL ORDER PLACED on {self.datas[0].datetime.date(0)}, Size: {self.position.size}, EMA Diff: {curr_ema_diff}, Cash: {self.broker.get_cash()}')

# Adjust start and end dates for data fetching to account for the lookback period
lookback_period = max(26, 12)  # The largest period used in EMA calculation
adjusted_start_date = (start_date - pd.Timedelta(days=lookback_period)).strftime('%Y-%m-%d')
extended_end_date = (end_date + pd.Timedelta(days=lookback_period)).strftime('%Y-%m-%d')

# Fetch and add data to Cerebro
# Note: Adjust the stock ticker to TQQQ as specified by the user
leveraged_etf_data = bt.feeds.PandasData(dataname=yf.download('TQQQ', start=adjusted_start_date, end=extended_end_date), name='TQQQ')
cerebro.adddata(leveraged_etf_data)

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