From b3f892c4b3dde1964ce18605d817e3476d56c9a7 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Wed, 11 Mar 2026 18:35:17 +0000
Subject: [PATCH] Add tests for create_labels_multi_bar

Co-authored-by: maghdam <63883156+maghdam@users.noreply.github.com>
---
 tests/test_labeling_schemes.py | 67 ++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 tests/test_labeling_schemes.py

diff --git a/tests/test_labeling_schemes.py b/tests/test_labeling_schemes.py
new file mode 100644
index 0000000..8c2a89f
--- /dev/null
+++ b/tests/test_labeling_schemes.py
@@ -0,0 +1,67 @@
+import pandas as pd
+import numpy as np
+import pytest
+from features.labeling_schemes import create_labels_multi_bar
+
+def test_create_labels_multi_bar():
+    """
+    Test create_labels_multi_bar correctly assigns labels based on future returns.
+    """
+    # Create a simple dummy dataframe
+    # We want future returns over horizon=2 to be:
+    # index 0: (10.5 / 10.0) - 1 = 0.05 (should be +1, since >= 0.05 is not met if threshold=0.06, wait let's use exact)
+
+    df = pd.DataFrame({
+        "close": [100.0, 100.0, 105.0, 95.0, 100.0, 100.0]
+    })
+
+    # Let's set horizon=2, threshold=0.04
+    # future returns for horizon=2:
+    # i=0: (105.0 - 100.0)/100.0 = 0.05 => >= 0.04 => 1
+    # i=1: (95.0 - 100.0)/100.0 = -0.05 => <= -0.04 => -1
+    # i=2: (100.0 - 105.0)/105.0 = -0.0476 => <= -0.04 => -1
+    # i=3: (100.0 - 95.0)/95.0 = 0.0526 => >= 0.04 => 1
+    # i=4: NaN
+    # i=5: NaN
+
+    labeled_df = create_labels_multi_bar(df, horizon=2, threshold=0.04)
+
+    # Check that df wasn't modified in place
+    assert "multi_bar_label" not in df.columns
+
+    # Ensure correct columns exist in result
+    assert "future_return_h" in labeled_df.columns
+    assert "multi_bar_label" in labeled_df.columns
+
+    # Since the original drops NaN, it should have 4 rows
+    assert len(labeled_df) == 4
+
+    # Check calculated future returns roughly match expected
+    expected_returns = [0.05, -0.05, -0.047619047619047616, 0.052631578947368474]
+    np.testing.assert_allclose(labeled_df["future_return_h"].values, expected_returns, rtol=1e-5)
+
+    # Check assigned labels
+    expected_labels = [1, -1, -1, 1]
+    np.testing.assert_array_equal(labeled_df["multi_bar_label"].values, expected_labels)
+
+
+def test_create_labels_multi_bar_neutral():
+    """
+    Test create_labels_multi_bar handles neutral labels correctly (returns inside threshold).
+    """
+    df = pd.DataFrame({
+        "close": [100.0, 101.0, 102.0, 99.0, 100.0]
+    })
+
+    # Let's set horizon=1, threshold=0.02
+    # future returns for horizon=1:
+    # i=0: (101 - 100)/100 = 0.01 (neutral -> 0)
+    # i=1: (102 - 101)/101 = 0.0099 (neutral -> 0)
+    # i=2: (99 - 102)/102 = -0.0294 (down -> -1)
+    # i=3: (100 - 99)/99 = 0.0101 (neutral -> 0)
+
+    labeled_df = create_labels_multi_bar(df, horizon=1, threshold=0.02)
+
+    assert len(labeled_df) == 4
+    expected_labels = [0, 0, -1, 0]
+    np.testing.assert_array_equal(labeled_df["multi_bar_label"].values, expected_labels)