test: Update tests for stacked imbalances returning lists
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@@ -1,4 +1,3 @@
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import numpy as np
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import pandas as pd
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import pandas as pd
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import pytest
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import pytest
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@@ -185,24 +184,24 @@ def test_public_trades_mock_populate_dataframe_with_trades__check_orderflow(
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assert results["max_delta"] == 17.298
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assert results["max_delta"] == 17.298
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# Assert that stacked imbalances are NaN (not applicable in this test)
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# Assert that stacked imbalances are NaN (not applicable in this test)
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assert np.isnan(results["stacked_imbalances_bid"])
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assert results["stacked_imbalances_bid"] == [np.nan]
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assert np.isnan(results["stacked_imbalances_ask"])
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assert results["stacked_imbalances_ask"] == [np.nan]
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# Repeat assertions for the third from last row
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# Repeat assertions for the third from last row
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results = df.iloc[-2]
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results = df.iloc[-2]
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assert pytest.approx(results["delta"]) == -20.862
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assert pytest.approx(results["delta"]) == -20.862
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assert pytest.approx(results["min_delta"]) == -54.559999
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assert pytest.approx(results["min_delta"]) == -54.559999
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assert 82.842 == results["max_delta"]
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assert 82.842 == results["max_delta"]
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assert 234.99 == results["stacked_imbalances_bid"]
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assert results["stacked_imbalances_bid"] == [234.99]
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assert 234.96 == results["stacked_imbalances_ask"]
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assert results["stacked_imbalances_ask"] == [234.96]
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# Repeat assertions for the last row
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# Repeat assertions for the last row
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results = df.iloc[-1]
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results = df.iloc[-1]
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assert pytest.approx(results["delta"]) == -49.302
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assert pytest.approx(results["delta"]) == -49.302
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assert results["min_delta"] == -70.222
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assert results["min_delta"] == -70.222
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assert pytest.approx(results["max_delta"]) == 11.213
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assert pytest.approx(results["max_delta"]) == 11.213
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assert np.isnan(results["stacked_imbalances_bid"])
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assert results["stacked_imbalances_bid"] == [np.nan]
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assert np.isnan(results["stacked_imbalances_ask"])
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assert results["stacked_imbalances_ask"] == [np.nan]
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def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
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@@ -358,7 +357,8 @@ def test_public_trades_binned_big_sample_list(public_trades_list):
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assert 197.512 == df["bid_amount"].iloc[0] # total bid amount
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assert 197.512 == df["bid_amount"].iloc[0] # total bid amount
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assert 88.98 == df["ask_amount"].iloc[0] # total ask amount
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assert 88.98 == df["ask_amount"].iloc[0] # total ask amount
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assert 26 == df["ask"].iloc[0] # ask price
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assert 26 == df["ask"].iloc[0] # ask price
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assert -108.532 == pytest.approx(df["delta"].iloc[0]) # delta (bid amount - ask amount)
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# delta (bid amount - ask amount)
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assert -108.532 == pytest.approx(df["delta"].iloc[0])
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assert 3 == df["bid"].iloc[-1] # bid price
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assert 3 == df["bid"].iloc[-1] # bid price
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assert 50.659 == df["bid_amount"].iloc[-1] # total bid amount
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assert 50.659 == df["bid_amount"].iloc[-1] # total bid amount
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@@ -534,7 +534,8 @@ def test_analyze_with_orderflow(
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assert col in df1.columns, f"Column {col} not found in df.columns"
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assert col in df1.columns, f"Column {col} not found in df.columns"
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if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
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if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
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assert df1[col].count() == 5, f"Column {col} has {df1[col].count()} non-NaN values"
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assert df1[col].count() == 5, f"Column {col} has {
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df1[col].count()} non-NaN values"
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assert len(strategy._cached_grouped_trades_per_pair[pair]) == 5
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assert len(strategy._cached_grouped_trades_per_pair[pair]) == 5
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@@ -552,7 +553,8 @@ def test_analyze_with_orderflow(
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assert "open" in df2.columns
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assert "open" in df2.columns
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assert spy.call_count == 0
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assert spy.call_count == 0
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for col in ORDERFLOW_ADDED_COLUMNS:
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for col in ORDERFLOW_ADDED_COLUMNS:
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assert col in df2.columns, f"Round2: Column {col} not found in df.columns"
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assert col in df2.columns, f"Round2: Column {
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col} not found in df.columns"
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if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
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if col not in ("stacked_imbalances_bid", "stacked_imbalances_ask"):
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assert (
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assert (
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