feat(Evaluate): increase transactions costs, to get a more realistic view on performance (#137)

data_loader/load_data.py

@@ -56,6 +56,15 @@ def __load_data(assets: DataCollection,
     ) for data_source in target_file]
     target_asset_df = ray.get(target_asset_future)
     
+    target_asset_only_returns_future = __load_df.remote(
+        data_source=target_file[0],
+        prefix=target_file[0][1],
+        returns='returns',
+        feature_extractors=[],
+        narrow_format=narrow_format,
+    )
+    df_target_asset_only_returns = ray.get(target_asset_only_returns_future)
+
     asset_futures = [__load_df.remote(
         data_source=data_source,
         prefix=data_source[1],
@@ -86,14 +95,16 @@ def __load_data(assets: DataCollection,
 
     if index_column == 'int':
         dfs.reset_index(drop=True, inplace=True)
+        df_target_asset_only_returns.reset_index(drop=True, inplace=True)
 
     if narrow_format:
         dfs = dfs.drop(index=dfs.index[0], axis=0)
+        df_target_asset_only_returns = df_target_asset_only_returns.drop(index=dfs.index[0], axis=0)
 
     ## Create target 
     target_col = 'target'
     returns_col = target_asset[1] + '_returns'
-    forward_returns = __create_target_cum_forward_returns(dfs, returns_col, forecasting_horizon)
+    forward_returns = __create_target_cum_forward_returns(df_target_asset_only_returns, returns_col, forecasting_horizon)
     if method == 'regression':
         dfs[target_col] = forward_returns
     elif method == 'classification':

utils/evaluate.py

@@ -6,7 +6,7 @@ from utils.helpers import get_first_valid_return_index
 import pandas as pd
 import numpy as np
 
-def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.002) -> pd.Series:
+def backtest(returns: pd.Series, signal: pd.Series, transaction_cost = 0.008) -> pd.Series:
     delta_pos = signal.diff(1).abs().fillna(0.)
     costs = transaction_cost * delta_pos
     return (signal * returns) - costs