Algorithm monthly return series

To extract the monthly return series with higher precision in QuantConnect, you can follow these steps:

1. Initialize the Algorithm: Set up your algorithm with the desired start and end dates, initial capital, and add the securities you want to analyze.

2. Access Historical Data: Use the History method to retrieve daily historical data for the securities you're interested in, specifying a sufficient lookback period. The History method can return a pandas.DataFrame containing the historical data.

3. Calculate Daily Returns: Compute the daily returns from the historical price data by calculating the percentage change between consecutive days.

4. Aggregate Monthly Returns: Group the daily returns by month and calculate the cumulative product of daily returns for each month to obtain the monthly return.

5. Store Monthly Returns: Save the monthly returns in a data structure, such as a dictionary or DataFrame, for further analysis or reporting.

6. Adjust Precision: Ensure that the monthly returns are stored with the desired precision by formatting the output or using a data type that supports higher precision.

7. Output the Results: Use the Log method or another output mechanism to display or save the monthly return series with the specified precision.

Here's a sample implementation in Python:

class MonthlyReturnAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2020, 1, 1)
        self.set_end_date(2021, 1, 1)
        self.set_cash(100000)
        self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol

    def on_data(self, data: Slice) -> None:
        pass

    def on_end_of_algorithm(self) -> None:
        history = self.history(self.symbol, 365, Resolution.DAILY)
        history['daily_return'] = history['close'].pct_change()
        history['month'] = history.index.to_period('M')
        monthly_returns = history.groupby('month')['daily_return'].apply(lambda x: (1 + x).prod() - 1)

        for month, monthly_return in monthly_returns.items():
            self.log(f"Month: {month}, Monthly Return: {monthly_return:.4f}")

This code will calculate the monthly returns with four decimal places of precision. Adjust the precision as needed by modifying the format string in the log method.

Sources: Residual Momentum, Log Return - QuantConnect.com, Combining Mean Reversion And Momentum In Forex Market, Seasonality Effect Based On Same Calendar Month Returns, History Algorithm

Was meant to extract month return of my backtest instead of handling data into the algorithm