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fix: don't sort stacked imbalances, return empty list if no found...

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... also removes helper functions `stacked_imbalance_bid` & `stacked_imbalance_ask`
This commit is contained in:
Joe Schr
2025-01-03 18:23:36 +01:00
parent 11976f11b0
commit 12adbeb7f3
2 changed files with 31 additions and 47 deletions
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37
freqtrade/data/converter/orderflow.py
View File

@@ -164,12 +164,12 @@ def populate_dataframe_with_trades(
dataframe.at[index, "imbalances"] = imbalances.to_dict(orient="index")
stacked_imbalance_range = config_orderflow["stacked_imbalance_range"]
dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance_bid(
imbalances, stacked_imbalance_range=stacked_imbalance_range
dataframe.at[index, "stacked_imbalances_bid"] = stacked_imbalance(
imbalances, label="bid", stacked_imbalance_range=stacked_imbalance_range
)
dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance_ask(
imbalances, stacked_imbalance_range=stacked_imbalance_range
dataframe.at[index, "stacked_imbalances_ask"] = stacked_imbalance(
imbalances, label="ask", stacked_imbalance_range=stacked_imbalance_range
)
bid = np.where(
@@ -256,9 +256,7 @@ def trades_orderflow_to_imbalances(df: pd.DataFrame, imbalance_ratio: int, imbal
return dataframe
def stacked_imbalance(
df: pd.DataFrame, label: str, stacked_imbalance_range: int, should_reverse: bool
):
def stacked_imbalance(df: pd.DataFrame, label: str, stacked_imbalance_range: int):
"""
y * (y.groupby((y != y.shift()).cumsum()).cumcount() + 1)
https://stackoverflow.com/questions/27626542/counting-consecutive-positive-values-in-python-pandas-array
@@ -268,27 +266,14 @@ def stacked_imbalance(
# Group consecutive True values and get their counts
groups = (int_series != int_series.shift()).cumsum()
counts = int_series.groupby(groups).cumsum()
# Find indices where count meets or exceeds the range requirement
valid_indices = counts[counts >= stacked_imbalance_range].index
stacked_imbalance_prices = []
if not valid_indices.empty:
# Get all prices from valid indices from beginning of the range
valid_prices = [imbalance.index.values[idx-(stacked_imbalance_range-1)] for idx in valid_indices]
# Sort prices according to direction
stacked_imbalance_prices = (
sorted(valid_prices)
if not should_reverse
else sorted(valid_prices, reverse=True)
)
return stacked_imbalance_prices if stacked_imbalance_prices else [np.nan]
def stacked_imbalance_ask(df: pd.DataFrame, stacked_imbalance_range: int):
return stacked_imbalance(df, "ask", stacked_imbalance_range, should_reverse=True)
def stacked_imbalance_bid(df: pd.DataFrame, stacked_imbalance_range: int):
return stacked_imbalance(df, "bid", stacked_imbalance_range, should_reverse=False)
stacked_imbalance_prices = [
imbalance.index.values[idx - (stacked_imbalance_range - 1)] for idx in valid_indices
]
return stacked_imbalance_prices if stacked_imbalance_prices else []

41
tests/data/test_converter_orderflow.py
View File

@@ -5,9 +5,9 @@ from freqtrade.constants import DEFAULT_TRADES_COLUMNS
from freqtrade.data.converter import populate_dataframe_with_trades
from freqtrade.data.converter.orderflow import (
ORDERFLOW_ADDED_COLUMNS,
stacked_imbalance,
timeframe_to_DateOffset,
trades_to_volumeprofile_with_total_delta_bid_ask,
stacked_imbalance,
)
from freqtrade.data.converter.trade_converter import trades_list_to_df
from freqtrade.data.dataprovider import DataProvider
@@ -185,8 +185,8 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
assert results["max_delta"] == 17.298
# Assert that stacked imbalances are NaN (not applicable in this test)
assert results["stacked_imbalances_bid"] == [np.nan]
assert results["stacked_imbalances_ask"] == [np.nan]
assert results["stacked_imbalances_bid"] == []
assert results["stacked_imbalances_ask"] == []
# Repeat assertions for the third from last row
results = df.iloc[-2]
@@ -201,8 +201,8 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
assert pytest.approx(results["delta"]) == -49.302
assert results["min_delta"] == -70.222
assert pytest.approx(results["max_delta"]) == 11.213
assert results["stacked_imbalances_bid"] == [np.nan]
assert results["stacked_imbalances_ask"] == [np.nan]
assert results["stacked_imbalances_bid"] == []
assert results["stacked_imbalances_ask"] == []
def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
@@ -575,34 +575,33 @@ def test_stacked_imbalances_multiple_prices():
# Test with empty result
df_no_stacks = pd.DataFrame(
{
'bid_imbalance': [False, False, True, False],
'ask_imbalance': [False, True, False, False]
"bid_imbalance": [False, False, True, False],
"ask_imbalance": [False, True, False, False],
},
index=[234.95, 234.96, 234.97, 234.98]
index=[234.95, 234.96, 234.97, 234.98],
)
no_stacks = stacked_imbalance(df_no_stacks, "bid", stacked_imbalance_range=2, should_reverse=False)
assert no_stacks == [np.nan]
no_stacks = stacked_imbalance(df_no_stacks, "bid", stacked_imbalance_range=2)
assert no_stacks == []
# Create a sample DataFrame with known imbalances
df = pd.DataFrame(
{
'bid_imbalance': [True, True, True, False, False, True, True, False, True],
'ask_imbalance': [False, False, True, True, True, False, False, True, True]
"bid_imbalance": [True, True, True, False, False, True, True, False, True],
"ask_imbalance": [False, False, True, True, True, False, False, True, True],
},
index=[234.95, 234.96, 234.97, 234.98, 234.99, 235.00, 235.01, 235.02, 235.03]
index=[234.95, 234.96, 234.97, 234.98, 234.99, 235.00, 235.01, 235.02, 235.03],
)
# Test bid imbalances (should return prices in ascending order)
bid_prices = stacked_imbalance(df, "bid", stacked_imbalance_range=2, should_reverse=False)
bid_prices = stacked_imbalance(df, "bid", stacked_imbalance_range=2)
assert bid_prices == [234.95, 234.96, 235.00]
# Test ask imbalances (should return prices in descending order)
ask_prices = stacked_imbalance(df, "ask", stacked_imbalance_range=2, should_reverse=True)
assert ask_prices == [235.02, 234.98, 234.97]
ask_prices = stacked_imbalance(df, "ask", stacked_imbalance_range=2)
assert ask_prices == [234.97, 234.98, 235.02]
# Test with higher stacked_imbalance_range
bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3, should_reverse=False)
bid_prices_higher = stacked_imbalance(df, "bid", stacked_imbalance_range=3)
assert bid_prices_higher == [234.95]
def test_timeframe_to_DateOffset():