Create algo/tbats.py

algo/tbats.py

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+from tbats import TBATS
+import pandas as pd
+
+def tbats_forecast(data, forecast_horizon):
+    """
+    Forecast future values using the TBATS model, with a dynamic forecast horizon.
+
+    Parameters:
+    - data: Pandas Series of historical closing prices.
+    - forecast_horizon: Integer specifying the number of days to forecast.
+
+    Returns:
+    - Pandas Series containing the forecasted values with a datetime index.
+    """
+    # Instantiate the TBATS estimator with seasonal periods. Adjust these as necessary.
+    estimator = TBATS(seasonal_periods=(7, 365.25), use_trend=True, use_box_cox=False)
+
+    # Fit the model to the historical data
+    model = estimator.fit(data)
+
+    # Forecast for the specified horizon
+    forecast = model.forecast(steps=forecast_horizon)
+
+    # Creating a pandas Series for the forecast, indexed by future dates
+    future_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon)
+    forecast_series = pd.Series(forecast, index=future_dates)
+
+    return forecast_series