Extract global cached_grouped_trades_per_pair into IStrategy

freqtrade/data/converter/orderflow.py

@@ -7,20 +7,18 @@ import time
 import typing
 from collections import OrderedDict
 from datetime import datetime
-from typing import Dict, Tuple
+from typing import Tuple
 
 import numpy as np
 import pandas as pd
 
 from freqtrade.constants import DEFAULT_ORDERFLOW_COLUMNS
+from freqtrade.enums import RunMode
 from freqtrade.exceptions import DependencyException
 
 
 logger = logging.getLogger(__name__)
 
-# Global cache dictionary
-cached_grouped_trades_per_pair: Dict[str, OrderedDict[Tuple[datetime, datetime], pd.DataFrame]] = {}
-
 
 def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame):
     """
@@ -64,8 +62,11 @@ def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
 
 
 def populate_dataframe_with_trades(
-    pair: str, config, dataframe: pd.DataFrame, trades: pd.DataFrame
+    cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], pd.DataFrame],
-):
+    config,
+    dataframe: pd.DataFrame,
+    trades: pd.DataFrame,
+) -> Tuple[pd.DataFrame, OrderedDict[Tuple[datetime, datetime], pd.DataFrame]]:
     """
     Populates a dataframe with trades
     :param dataframe: Dataframe to populate
@@ -74,15 +75,11 @@ def populate_dataframe_with_trades(
     """
     timeframe = config["timeframe"]
     config_orderflow = config["orderflow"]
-    cache_size = config_orderflow["cache_size"]
 
     # create columns for trades
     _init_dataframe_with_trades_columns(dataframe)
 
     try:
-        cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], pd.DataFrame] = (
-            cached_grouped_trades_per_pair.get(pair, OrderedDict())
-        )
         start_time = time.time()
         # calculate ohlcv candle start and end
         _calculate_ohlcv_candle_start_and_end(trades, timeframe)
@@ -184,7 +181,10 @@ def populate_dataframe_with_trades(
                 )
 
                 # Maintain cache size
-                if len(cached_grouped_trades) > cache_size:
+                if (
+                    config.get("runmode") in (RunMode.DRY_RUN, RunMode.LIVE)
+                    and len(cached_grouped_trades) > config_orderflow["cache_size"]
+                ):
                     cached_grouped_trades.popitem(last=False)
             else:
                 logger.debug(f"Found NO candles for trades starting with {candle_start}")
@@ -196,16 +196,12 @@ def populate_dataframe_with_trades(
         dataframe["imbalances"] = imbalances_series
         dataframe["stacked_imbalances_bid"] = stacked_imbalances_bid_series
         dataframe["stacked_imbalances_ask"] = stacked_imbalances_ask_series
-        # dereference old cache
-        if pair in cached_grouped_trades_per_pair:
-            del cached_grouped_trades_per_pair[pair]
-        cached_grouped_trades_per_pair[pair] = cached_grouped_trades
 
     except Exception as e:
         logger.exception("Error populating dataframe with trades")
         raise DependencyException(e)
 
-    return dataframe
+    return dataframe, cached_grouped_trades
 
 
 def trades_to_volumeprofile_with_total_delta_bid_ask(

freqtrade/strategy/interface.py

@@ -5,6 +5,7 @@ This module defines the interface to apply for strategies
 
 import logging
 from abc import ABC, abstractmethod
+from collections import OrderedDict
 from datetime import datetime, timedelta, timezone
 from math import isinf, isnan
 from typing import Dict, List, Optional, Tuple, Union
@@ -140,6 +141,11 @@ class IStrategy(ABC, HyperStrategyMixin):
     # A self set parameter that represents the market direction. filled from configuration
     market_direction: MarketDirection = MarketDirection.NONE
 
+    # Global cache dictionary
+    _cached_grouped_trades_per_pair: Dict[
+        str, OrderedDict[Tuple[datetime, datetime], DataFrame]
+    ] = {}
+
     def __init__(self, config: Config) -> None:
         self.config = config
         # Dict to determine if analysis is necessary
@@ -1603,8 +1609,19 @@ class IStrategy(ABC, HyperStrategyMixin):
 
             config = self.config
             config["timeframe"] = self.timeframe
+            pair = metadata["pair"]
             # TODO: slice trades to size of dataframe for faster backtesting
-            dataframe = populate_dataframe_with_trades(metadata["pair"], config, dataframe, trades)
+            cached_grouped_trades: OrderedDict[Tuple[datetime, datetime], DataFrame] = (
+                self._cached_grouped_trades_per_pair.get(pair, OrderedDict())
+            )
+            dataframe, cached_grouped_trades = populate_dataframe_with_trades(
+                cached_grouped_trades, config, dataframe, trades
+            )
+
+            # dereference old cache
+            if pair in self._cached_grouped_trades_per_pair:
+                del self._cached_grouped_trades_per_pair[pair]
+            self._cached_grouped_trades_per_pair[pair] = cached_grouped_trades
 
             logger.debug("Populated dataframe with trades.")
 

tests/data/test_converter_public_trades.py

@@ -1,3 +1,5 @@
+from collections import OrderedDict
+
 import numpy as np
 import pandas as pd
 import pytest
@@ -47,15 +49,6 @@ def public_trades_list_simple(testdatadir):
     return read_csv(testdatadir / "orderflow/public_trades_list_simple_example.csv").copy()
 
 
-@pytest.fixture
-def reset_cache(request):
-    import freqtrade.data.converter.orderflow as orderflow
-
-    global orderflow  # noqa F811
-    orderflow.cached_grouped_trades_per_pair = {}
-    yield
-
-
 def test_public_trades_columns_before_change(
     populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
 ):
@@ -80,7 +73,7 @@ def test_public_trades_columns_before_change(
 
 
 def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
-    reset_cache, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+    populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
 ):
     """
     Tests the `populate_dataframe_with_trades` function's order flow calculation.
@@ -108,7 +101,7 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
         },
     }
     # Apply the function to populate the data frame with order flow data
-    df = populate_dataframe_with_trades("BTC/UDST", config, dataframe, trades)
+    df, _ = populate_dataframe_with_trades(OrderedDict(), config, dataframe, trades)
     # Extract results from the first row of the DataFrame
     results = df.iloc[0]
     t = results["trades"]
@@ -179,7 +172,7 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
 
 
 def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
-    reset_cache, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+    populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
 ):
     """
     Tests the `populate_dataframe_with_trades` function's handling of trades,
@@ -221,7 +214,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
     }
 
     # Populate the DataFrame with trades and order flow data
-    df = populate_dataframe_with_trades("BTC/UDST", config, dataframe, trades)
+    df, _ = populate_dataframe_with_trades(OrderedDict(), config, dataframe, trades)
 
     # --- DataFrame and Trade Data Validation ---
 
@@ -387,8 +380,8 @@ def test_public_trades_config_max_trades(
         },
     }
 
-    df = populate_dataframe_with_trades(
+    df, _ = populate_dataframe_with_trades(
-        "BTC/UDST", default_conf | orderflow_config, dataframe, trades
+        OrderedDict(), default_conf | orderflow_config, dataframe, trades
     )
     assert df.delta.count() == 1