FetchForecast: now able to direct the input df to from_data endpoint

src/fetch_forecast.py

@@ -7,7 +7,7 @@ import requests
 
 
 class FetchForecast:
-    def __init__(self, ticker: str, debug=False) -> None:
+    def __init__(self, ticker: str, df_hist: pd.DataFrame, debug=False) -> None:
         if debug:
             self.logger_level = logging.DEBUG
         else:
@@ -17,9 +17,16 @@ class FetchForecast:
 
         # args
         self.ticker = ticker
+        self.df_hist = df_hist
+        if df_hist is None:
+            self.endpoint = "v1/forecast/from_symbol"
+            logdatasuffix = "without data"
+        else:
+            self.endpoint = "v1/forecast/from_data"
+            logdatasuffix = "with historic data"
+        self.logger.info(f"Initialized FetchForecast for ticker: {self.ticker} {logdatasuffix}")
         # constants
         self.past_horizon = 5  # number of past business days
-        self.endpoint = "v1/forecast/from_symbol"
         # build the api-url based on env variables
         self.api_env = os.environ.get("FORECAST_API_ENV")
         api_url_temp = os.environ.get("API_URL_TEMPLATE")
@@ -30,8 +37,12 @@ class FetchForecast:
         return past_df, fcst_df
 
     def call_api(self) -> tuple:
-        pl_in = {"ticker": self.ticker, "past_horizon": self.past_horizon}
-        self.logger.info(f"Calling forecast API ({self.api_env}) for ticker: {self.ticker}")
+        if self.endpoint.split("/")[-1] in ["from_symbol"]:
+            self.logger.info(f"Sending the ticker symmbol to the forecast API ({self.api_env}) {self.endpoint} endpoint")
+            pl_in = {"ticker": self.ticker, "past_horizon": self.past_horizon}
+        elif self.endpoint.split("/")[-1] in ["from_data"]:
+            self.logger.info(f"Formatting and sending ticker data to the forecast API ({self.api_env}) {self.endpoint} endpoint")
+            pl_in = self.build_payload_with_data(ticker=self.ticker, past_horizon=self.past_horizon)
         resp = requests.post(f"{self.api_url}/{self.endpoint}", json=pl_in, timeout=30)
         if resp.status_code == 200:
             data = resp.json()
@@ -53,6 +64,44 @@ class FetchForecast:
         return past_df, fcst_df
     
 
+
+    def build_payload_with_data(self, ticker: str, past_horizon: int) -> dict:
+        """
+        Takes a dataframe
+        and returns the columnar JSON dict expected by the /from_data endpoint.
+        """
+        df = self.df_hist
+
+        df.columns.name = None
+        # Re-introduce the ticker as a regular column
+        df["Ticker"] = ticker
+        # Make 'Date' a regular column by resetting the index
+        df.reset_index(inplace=True)
+
+        if "Date" not in df.columns or "Close" not in df.columns:
+            raise ValueError("DataFrame must contain 'Date' and 'Close' columns.")
+
+        df = df[["Date", "Close"]].copy()
+        df["Date"] = pd.to_datetime(df["Date"], utc=True, errors="coerce")
+
+        # Clean + order
+        df = (
+            df.dropna(subset=["Date", "Close"])
+            .sort_values("Date")
+            .drop_duplicates(subset=["Date"], keep="last")
+        )
+
+        payload = {
+            "ticker": ticker,
+            "series": {
+                "date": df["Date"].dt.strftime("%Y-%m-%d").tolist(),
+                "close": df["Close"].astype(float).tolist(),
+            },
+            "past_horizon": past_horizon,
+        }
+        return payload
+    
+
 if __name__ == "__main__":
     # load the env variables fom .env file
     dotenv.load_dotenv(dotenv.find_dotenv())