From 2d6408a3638250b88a46777b92b6689fd0efbb5a Mon Sep 17 00:00:00 2001
From: Joe Schr <8218910+TheJoeSchr@users.noreply.github.com>
Date: Wed, 19 Jun 2024 19:22:49 +0200
Subject: [PATCH] feat: adds `max_candles` to orderflow config

---
 docs/advanced-orderflow.md                 |  2 ++
 freqtrade/constants.py                     |  9 ++++++++-
 freqtrade/data/converter/orderflow.py      |  5 ++++-
 tests/data/test_converter_public_trades.py | 23 ++++++++++++++++++++++
 4 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/docs/advanced-orderflow.md b/docs/advanced-orderflow.md
index b5100064a..54655d488 100644
--- a/docs/advanced-orderflow.md
+++ b/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:
 
+- `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.
 - `imbalance_volume`: Filters out imbalances with volume below this threshold.
@@ -28,6 +29,7 @@ This guide walks you through utilizing public trade data for advanced orderflow
 
 ```json
 "orderflow": {
+    "max_candles": 1500, 
     "scale": 0.5, 
     "stacked_imbalance_range": 3, //  needs at least this amount of imbalance next to each other
     "imbalance_volume": 1, //  filters out below
diff --git a/freqtrade/constants.py b/freqtrade/constants.py
index f56e14503..1105f4ffe 100644
--- a/freqtrade/constants.py
+++ b/freqtrade/constants.py
@@ -537,12 +537,19 @@ CONF_SCHEMA = {
         "orderflow": {
             "type": "object",
             "properties": {
+                "max_candles": {"type": "number", "minimum": 1, "default": 1500},
                 "scale": {"type": "number", "minimum": 0.0},
                 "stacked_imbalance_range": {"type": "number", "minimum": 0},
                 "imbalance_volume": {"type": "number", "minimum": 0},
                 "imbalance_ratio": {"type": "number", "minimum": 0.0},
             },
-            "required": ["scale", "stacked_imbalance_range", "imbalance_volume", "imbalance_ratio"],
+            "required": [
+                "max_candles",
+                "scale",
+                "stacked_imbalance_range",
+                "imbalance_volume",
+                "imbalance_ratio",
+            ],
         },
     },
     "definitions": {
diff --git a/freqtrade/data/converter/orderflow.py b/freqtrade/data/converter/orderflow.py
index 8d60d5aaa..1b1d7a565 100644
--- a/freqtrade/data/converter/orderflow.py
+++ b/freqtrade/data/converter/orderflow.py
@@ -68,8 +68,11 @@ def populate_dataframe_with_trades(
         # calculate ohlcv candle start and end
         _calculate_ohlcv_candle_start_and_end(trades, timeframe)
 
+        # get date of earliest max_candles candle
+        max_candles = config_orderflow["max_candles"]
+        start_date = df.tail(max_candles).date.iat[0]
         # slice of trades that are before current ohlcv candles to make groupby faster
-        trades = trades.loc[trades.candle_start >= df.date[0]]
+        trades = trades.loc[trades.candle_start >= start_date]
         trades.reset_index(inplace=True, drop=True)
 
         # group trades by candle start
diff --git a/tests/data/test_converter_public_trades.py b/tests/data/test_converter_public_trades.py
index 07e17b1b7..9baadb868 100644
--- a/tests/data/test_converter_public_trades.py
+++ b/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": {
+            "max_candles": 1500,
             "scale": 0.005,
             "imbalance_volume": 0,
             "imbalance_ratio": 3,
@@ -200,6 +201,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
     config = {
         "timeframe": "5m",
         "orderflow": {
+            "max_candles": 1500,
             "scale": 0.5,
             "imbalance_volume": 0,
             "imbalance_ratio": 3,
@@ -355,6 +357,27 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
     assert 52.7199999 == pytest.approx(df["delta"].iat[0])  # delta
 
 
+def test_public_trades_config_max_trades(
+    default_conf, populate_dataframe_with_trades_dataframe, populate_dataframe_with_trades_trades
+):
+    dataframe = populate_dataframe_with_trades_dataframe.copy()
+    trades = populate_dataframe_with_trades_trades.copy()
+    default_conf["exchange"]["use_public_trades"] = True
+    orderflow_config = {
+        "timeframe": "5m",
+        "orderflow": {
+            "max_candles": 1,
+            "scale": 0.005,
+            "imbalance_volume": 0,
+            "imbalance_ratio": 3,
+            "stacked_imbalance_range": 3,
+        },
+    }
+
+    df = populate_dataframe_with_trades(default_conf | orderflow_config, dataframe, trades)
+    assert df.delta.count() == 1
+
+
 def test_public_trades_testdata_sanity(
     candles,
     public_trades_list,