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diff --git a/tests/features/test_labeling_schemes.py b/tests/features/test_labeling_schemes.py
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+import pytest
+import pandas as pd
+import numpy as np
+from features.labeling_schemes import calculate_future_returns
+
+def test_calculate_future_returns_happy_path():
+    # Setup data
+    data = {"close": [100.0, 105.0, 102.0, 110.0, 115.0]}
+    df = pd.DataFrame(data)
+
+    # Test horizon 1
+    result_h1 = calculate_future_returns(df.copy(), horizon=1)
+
+    # Expected:
+    # idx 0: (105 - 100) / 100 = 0.05
+    # idx 1: (102 - 105) / 105 = -0.028571
+    # idx 2: (110 - 102) / 102 = 0.078431
+    # idx 3: (115 - 110) / 110 = 0.045455
+    # idx 4: NaN
+    assert len(result_h1) == 4
+    np.testing.assert_allclose(result_h1["future_returns"].iloc[0], 0.05, atol=1e-5)
+    np.testing.assert_allclose(result_h1["future_returns"].iloc[1], -0.028571, atol=1e-5)
+
+    # Test horizon 2
+    result_h2 = calculate_future_returns(df.copy(), horizon=2)
+    # idx 0: (102 - 100) / 100 = 0.02
+    # idx 1: (110 - 105) / 105 = 0.047619
+    # idx 2: (115 - 102) / 102 = 0.127451
+    # idx 3: NaN
+    # idx 4: NaN
+    assert len(result_h2) == 3
+    np.testing.assert_allclose(result_h2["future_returns"].iloc[0], 0.02, atol=1e-5)
+    np.testing.assert_allclose(result_h2["future_returns"].iloc[1], 0.047619, atol=1e-5)
+    np.testing.assert_allclose(result_h2["future_returns"].iloc[2], 0.127451, atol=1e-5)
+
+def test_calculate_future_returns_tiny_df():
+    # Rationale: Testing with a tiny DataFrame length < horizon to see if it correctly returns empty or handles it gracefully.
+    data = {"close": [100.0, 105.0]}
+    df = pd.DataFrame(data)
+
+    # Test horizon 5 where df length is 2
+    result = calculate_future_returns(df.copy(), horizon=5)
+
+    # Should return empty DataFrame gracefully
+    assert result.empty
+    assert "future_returns" in result.columns
+
+def test_calculate_future_returns_missing_close_column():
+    data = {"open": [100.0, 105.0, 102.0]}
+    df = pd.DataFrame(data)
+
+    with pytest.raises(KeyError):
+        calculate_future_returns(df.copy(), horizon=1)