7e4e9315c2
- Added MLFilter class to load and evaluate LightGBM model for trading signals. - Introduced retraining mechanism to update the model daily based on new data. - Created feature engineering and label building utilities for model training. - Updated bot logic to incorporate ML filter for signal validation. - Added scripts for data fetching and model training. Made-with: Cursor
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import pandas as pd
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import numpy as np
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import pytest
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from src.ml_features import build_features, FEATURE_COLS
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def make_df(n=100):
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"""테스트용 최소 DataFrame 생성"""
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np.random.seed(42)
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close = 100 + np.cumsum(np.random.randn(n) * 0.5)
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df = pd.DataFrame({
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"open": close * 0.999,
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"high": close * 1.002,
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"low": close * 0.998,
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"close": close,
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"volume": np.random.uniform(1000, 5000, n),
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})
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return df
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def test_build_features_returns_series():
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from src.indicators import Indicators
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df = make_df(100)
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ind = Indicators(df)
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df_ind = ind.calculate_all()
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features = build_features(df_ind, signal="LONG")
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assert isinstance(features, pd.Series)
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def test_build_features_has_all_cols():
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from src.indicators import Indicators
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df = make_df(100)
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ind = Indicators(df)
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df_ind = ind.calculate_all()
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features = build_features(df_ind, signal="LONG")
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for col in FEATURE_COLS:
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assert col in features.index, f"피처 누락: {col}"
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def test_build_features_no_nan():
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from src.indicators import Indicators
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df = make_df(100)
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ind = Indicators(df)
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df_ind = ind.calculate_all()
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features = build_features(df_ind, signal="LONG")
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assert not features.isna().any(), f"NaN 존재: {features[features.isna()]}"
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def test_side_encoding():
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from src.indicators import Indicators
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df = make_df(100)
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ind = Indicators(df)
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df_ind = ind.calculate_all()
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long_feat = build_features(df_ind, signal="LONG")
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short_feat = build_features(df_ind, signal="SHORT")
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assert long_feat["side"] == 1
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assert short_feat["side"] == 0
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