feat: allow shorter data history when using model for forecasts only …

…and no training

Signed-off-by: ivelin <[email protected]>

forecast.sh

@@ -39,4 +39,5 @@ set -exv
 #./canswim.sh forecast --forecast_start_date "2024-08-26"
 #./canswim.sh forecast --forecast_start_date "2024-09-02"
 #./canswim.sh forecast --forecast_start_date "2024-09-09"
-./canswim.sh forecast --forecast_start_date "2024-09-16"
+#./canswim.sh forecast --forecast_start_date "2024-09-16"
+./canswim.sh forecast --forecast_start_date "2024-09-23"

src/canswim/forecast.py

@@ -24,7 +24,7 @@ def __init__(self):
         logger.info(f"n_stocks: {self.n_stocks}")
         self.forecast_subdir = os.getenv("forecast_subdir", "forecast/")
         logger.info(f"Forecast data path: {self.forecast_subdir}")
-        self.canswim_model = CanswimModel()
+        self.canswim_model = CanswimModel(forecast_only=True)
         self.hfhub = HFHub()
 
     def download_model(self):

src/canswim/model.py

@@ -39,7 +39,8 @@ def optuna_print_callback(study, trial):
 
 
 class CanswimModel:
-    def __init__(self):
+    def __init__(self, forecast_only=False):
+        self.forecast_only = forecast_only
         self.n_stocks: int = 50
         self.n_epochs: int = 10
         self.train_series = {}
@@ -76,7 +77,12 @@ def n_test_range_days(self):
     def min_samples(self):
         # minimum amount of historical data required to train on a stock series
         # stocks that are too new off IPOs, are not a good fit for training this model
-        m = self.n_test_range_days * 3
+        # m = self.n_test_range_days * 3
+        # update Sep 11, 2024:
+        # min required samples for training and forecast encompasses the lookback history window plus val prediction window plus test prediction window
+        m = self.train_history + self.pred_horizon + self.pred_horizon
+        if not self.forecast_only:
+            m += self.train_history
         return m
 
     @property