Debug order execution with bid/ask price, instead of open price

lumibot/backtesting/backtesting_broker.py

@@ -580,6 +580,8 @@ def process_pending_orders(self, strategy):
             #############################
             # Get OHLCV data for the asset
             #############################
+            bid = None
+            ask = None
 
             # Get the OHLCV data for the asset if we're using the YAHOO, CCXT data source
             data_source_name = self.data_source.SOURCE.upper()
@@ -637,13 +639,21 @@ def process_pending_orders(self, strategy):
                 low = df["low"].iloc[0]
                 close = df["close"].iloc[0]
                 volume = df["volume"].iloc[0]
+                if 'bid' in df.columns and 'ask' in df.columns:
+                    bid = df["bid"].iloc[0]
+                    ask = df["ask"].iloc[0]
 
             #############################
             # Determine transaction price.
             #############################
 
             if order.type == "market":
-                price = open
+                if order.side == "buy" and ask is not None:
+                    price = ask
+                elif order.side == "sell" and bid is not None:
+                    price = bid
+                else:
+                    price = open
 
             elif order.type == "limit":
                 price = self.limit_order(order.limit_price, order.side, open, high, low)

lumibot/entities/data.py

@@ -555,13 +555,6 @@ def get_bars(self, dt, length=1, timestep=MIN_TIMESTEP, timeshift=0):
         if timestep != "minute" and timestep != "day":
             raise ValueError(f"Only minute and day are supported for timestep. You provided: {timestep}")
 
-        agg_column_map = {
-            "open": "first",
-            "high": "max",
-            "low": "min",
-            "close": "last",
-            "volume": "sum",
-        }
         if timestep == "day" and self.timestep == "minute":
             # If the data is minute data and we are requesting daily data then multiply the length by 1440
             length = length * 1440
@@ -576,6 +569,20 @@ def get_bars(self, dt, length=1, timestep=MIN_TIMESTEP, timeshift=0):
         if data is None:
             return None
 
+        agg_column_map = {
+            "open": "first",
+            "high": "max",
+            "low": "min",
+            "close": "last",
+            "volume": "sum",
+        }
+        # check if 'bid' and 'ask' are in the data and add them to the agg_column_map
+        if 'bid' in data and 'ask' in data:
+            agg_column_map['bid'] = "max"
+            agg_column_map['ask'] = "min"
+            agg_column_map['bid_size'] = "sum"
+            agg_column_map['ask_size'] = "sum"
+
         df = pd.DataFrame(data).assign(datetime=lambda df: pd.to_datetime(df['datetime'])).set_index('datetime')
         df_result = df.resample(f"{quantity}{unit}").agg(agg_column_map)
 

tests/backtest/test_thetadata.py

@@ -284,7 +284,7 @@ def verify_backtest_results(self, theta_strat_obj):
         assert 130.0 < theta_strat_obj.prices[asset_order_id] < 140.0, "Valid asset price between 130 and 140"
         assert 130.0 < stock_order.get_fill_price() < 140.0, "Valid asset price between 130 and 140"
         assert theta_strat_obj.prices[option_order_id] == 4.5, "Price is $4.5 on 08/01/2023 12:30pm"
-        assert option_order.get_fill_price() == 4.5, "Fills at 1st candle open price of $4.10 on 08/01/2023"
+        assert option_order.get_fill_price() == 4.55, "Fills at 1st candle open price of $4.10 on 08/01/2023"
 
         assert option_order.is_filled()