orderflow: adds cache_size to config

docs/advanced-orderflow.md

@@ -21,6 +21,7 @@ This guide walks you through utilizing public trade data for advanced orderflow
 
 2. **Configure Orderflow Processing:** Define your desired settings for orderflow processing within the orderflow section of config.json. Here, you can adjust factors like:
 
+- `cache_size`: How many previous orderflow candles are saved into cache instead of calculated every new candle
 - `max_candles`: Filter how many candles get processed from the tail
 - `scale`: This controls the price bin size for the footprint chart.
 - `stacked_imbalance_range`: Defines the minimum consecutive imbalanced price levels required for consideration.
@@ -29,6 +30,7 @@ This guide walks you through utilizing public trade data for advanced orderflow
 
 ```json
 "orderflow": {
+    "cache_size": 1000, 
     "max_candles": 1500, 
     "scale": 0.5, 
     "stacked_imbalance_range": 3, //  needs at least this amount of imbalance next to each other

freqtrade/constants.py

@@ -537,6 +537,7 @@ CONF_SCHEMA = {
         "orderflow": {
             "type": "object",
             "properties": {
+                "cache_size": {"type": "number", "minimum": 1, "default": 1000},
                 "max_candles": {"type": "number", "minimum": 1, "default": 1500},
                 "scale": {"type": "number", "minimum": 0.0},
                 "stacked_imbalance_range": {"type": "number", "minimum": 0},

freqtrade/data/converter/orderflow.py

@@ -14,6 +14,9 @@ from collections import OrderedDict
 
 logger = logging.getLogger(__name__)
 
+# Global cache dictionary
+cached_grouped_trades = OrderedDict()
+
 
 def _init_dataframe_with_trades_columns(dataframe: pd.DataFrame):
     """
@@ -56,11 +59,6 @@ def _calculate_ohlcv_candle_start_and_end(df: pd.DataFrame, timeframe: str):
         df.drop(columns=["datetime"], inplace=True)
 
 
-# Global cache dictionary
-cache_size = 1000  # TODO move that in config
-cached_grouped_trades = OrderedDict()  # TODO move that where?
-
-
 def populate_dataframe_with_trades(config, dataframe, trades):
     """
     Populates a dataframe with trades
@@ -68,8 +66,9 @@ def populate_dataframe_with_trades(config, dataframe, trades):
     :param trades: Trades to populate with
     :return: Dataframe with trades populated
     """
-    config_orderflow = config["orderflow"]
     timeframe = config["timeframe"]
+    config_orderflow = config["orderflow"]
+    cache_size = config_orderflow["cache_size"]
 
     # create columns for trades
     _init_dataframe_with_trades_columns(dataframe)

tests/data/test_converter_public_trades.py

@@ -90,6 +90,7 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
     config = {
         "timeframe": "5m",
         "orderflow": {
+            "cache_size": 1000,
             "max_candles": 1500,
             "scale": 0.005,
             "imbalance_volume": 0,
@@ -201,6 +202,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
     config = {
         "timeframe": "5m",
         "orderflow": {
+            "cache_size": 1000,
             "max_candles": 1500,
             "scale": 0.5,
             "imbalance_volume": 0,
@@ -243,7 +245,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
     assert 169.442 == row["ask"]
 
     # Assert the number of trades
-    assert 151 == len(row.trades)
+    assert 151 == len(row["trades"])
 
     # Assert specific details of the first trade
     t = row["trades"].iloc[0]
@@ -367,6 +369,7 @@ def test_public_trades_config_max_trades(
     orderflow_config = {
         "timeframe": "5m",
         "orderflow": {
+            "cache_size": 1000,
             "max_candles": 1,
             "scale": 0.005,
             "imbalance_volume": 0,