progress

src/tools/data_loader.py

@@ -91,32 +91,6 @@ def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
     return df
 
 
-def transform_dataframe(df: pd.DataFrame, price_column: str):
-    # Select only the columns we need
-    df_selected = df[["tstamp", "symbol", price_column]]
-
-    # Start with unique timestamps
-    result_df: pd.DataFrame = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
-
-    # For each unique symbol, add a corresponding close price column
-    for symbol in df_selected["symbol"].unique():
-        # Filter rows for this symbol
-        df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(drop=True)
-
-        # Create column name like "close-COIN"
-        new_price_column = f"{price_column}_{symbol}"
-
-        # Create temporary dataframe with timestamp and price
-        temp_df = pd.DataFrame({
-            "tstamp": df_symbol["tstamp"],
-            new_price_column: df_symbol[price_column]
-        })
-
-        # Join with our result dataframe
-        result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
-        result_df = result_df.reset_index(drop=True) # do not dropna() since irrelevant symbol would affect dataset
-
-    return result_df
 
 
 # if __name__ == "__main__":