feat: implement ML filter with LightGBM for trading signal validation

- 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

tests/test_bot.py

@@ -37,10 +37,8 @@ def sample_df():
 
 @pytest.mark.asyncio
 async def test_bot_processes_signal(config, sample_df):
-    with patch("src.bot.BinanceFuturesClient") as MockExchange, \
-         patch("src.bot.TradeRepository") as MockRepo:
+    with patch("src.bot.BinanceFuturesClient") as MockExchange:
         MockExchange.return_value = AsyncMock()
-        MockRepo.return_value = MagicMock()
         bot = TradingBot(config)
 
     bot.exchange = AsyncMock()
@@ -48,8 +46,8 @@ async def test_bot_processes_signal(config, sample_df):
     bot.exchange.get_position = AsyncMock(return_value=None)
     bot.exchange.place_order = AsyncMock(return_value={"orderId": "123"})
     bot.exchange.set_leverage = AsyncMock(return_value={})
-    bot.db = MagicMock()
-    bot.db.save_trade = MagicMock(return_value={"id": "trade1"})
+    bot.exchange.calculate_quantity = MagicMock(return_value=100.0)
+    bot.exchange.MIN_NOTIONAL = 5.0
 
     with patch("src.bot.Indicators") as MockInd:
         mock_ind = MagicMock()

tests/test_label_builder.py

@@ -0,0 +1,73 @@
+import pandas as pd
+import numpy as np
+import pytest
+from src.label_builder import build_labels
+
+
+def make_signal_df():
+    """
+    신호 발생 시점 이후 가격이 TP에 도달하는 시나리오
+    entry=100, TP=103, SL=98.5
+    """
+    future_closes = [100.5, 101.0, 101.8, 102.5, 103.1, 103.5]
+    future_highs  = [c + 0.3 for c in future_closes]
+    future_lows   = [c - 0.3 for c in future_closes]
+    return future_closes, future_highs, future_lows
+
+
+def test_label_tp_reached():
+    closes, highs, lows = make_signal_df()
+    label = build_labels(
+        future_closes=closes,
+        future_highs=highs,
+        future_lows=lows,
+        take_profit=103.0,
+        stop_loss=98.5,
+        side="LONG",
+    )
+    assert label == 1, "TP 먼저 도달해야 레이블 1"
+
+
+def test_label_sl_reached():
+    future_closes = [99.5, 99.0, 98.8, 98.4, 98.0]
+    future_highs  = [c + 0.3 for c in future_closes]
+    future_lows   = [c - 0.3 for c in future_closes]
+    label = build_labels(
+        future_closes=future_closes,
+        future_highs=future_highs,
+        future_lows=future_lows,
+        take_profit=103.0,
+        stop_loss=98.5,
+        side="LONG",
+    )
+    assert label == 0, "SL 먼저 도달해야 레이블 0"
+
+
+def test_label_neither_reached_returns_none():
+    future_closes = [100.1, 100.2, 100.3]
+    future_highs  = [c + 0.1 for c in future_closes]
+    future_lows   = [c - 0.1 for c in future_closes]
+    label = build_labels(
+        future_closes=future_closes,
+        future_highs=future_highs,
+        future_lows=future_lows,
+        take_profit=103.0,
+        stop_loss=98.5,
+        side="LONG",
+    )
+    assert label is None, "미결 시 None 반환"
+
+
+def test_label_short_tp():
+    future_closes = [99.5, 99.0, 98.0, 97.0]
+    future_highs  = [c + 0.3 for c in future_closes]
+    future_lows   = [c - 0.3 for c in future_closes]
+    label = build_labels(
+        future_closes=future_closes,
+        future_highs=future_highs,
+        future_lows=future_lows,
+        take_profit=97.0,
+        stop_loss=101.5,
+        side="SHORT",
+    )
+    assert label == 1

tests/test_ml_features.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import numpy as np
+import pytest
+from src.ml_features import build_features, FEATURE_COLS
+
+
+def make_df(n=100):
+    """테스트용 최소 DataFrame 생성"""
+    np.random.seed(42)
+    close = 100 + np.cumsum(np.random.randn(n) * 0.5)
+    df = pd.DataFrame({
+        "open":   close * 0.999,
+        "high":   close * 1.002,
+        "low":    close * 0.998,
+        "close":  close,
+        "volume": np.random.uniform(1000, 5000, n),
+    })
+    return df
+
+
+def test_build_features_returns_series():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    assert isinstance(features, pd.Series)
+
+
+def test_build_features_has_all_cols():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    for col in FEATURE_COLS:
+        assert col in features.index, f"피처 누락: {col}"
+
+
+def test_build_features_no_nan():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    features = build_features(df_ind, signal="LONG")
+    assert not features.isna().any(), f"NaN 존재: {features[features.isna()]}"
+
+
+def test_side_encoding():
+    from src.indicators import Indicators
+    df = make_df(100)
+    ind = Indicators(df)
+    df_ind = ind.calculate_all()
+    long_feat  = build_features(df_ind, signal="LONG")
+    short_feat = build_features(df_ind, signal="SHORT")
+    assert long_feat["side"] == 1
+    assert short_feat["side"] == 0

tests/test_ml_filter.py

@@ -0,0 +1,63 @@
+import pandas as pd
+import numpy as np
+import pytest
+from unittest.mock import MagicMock, patch
+from pathlib import Path
+from src.ml_filter import MLFilter
+from src.ml_features import FEATURE_COLS
+
+
+def make_features(side="LONG") -> pd.Series:
+    return pd.Series({col: 0.5 for col in FEATURE_COLS} | {"side": 1.0 if side == "LONG" else 0.0})
+
+
+def test_no_model_file_is_not_loaded(tmp_path):
+    f = MLFilter(model_path=str(tmp_path / "nonexistent.pkl"))
+    assert not f.is_model_loaded()
+
+
+def test_no_model_should_enter_returns_true(tmp_path):
+    """모델 없으면 항상 진입 허용 (폴백)"""
+    f = MLFilter(model_path=str(tmp_path / "nonexistent.pkl"))
+    features = make_features()
+    assert f.should_enter(features) is True
+
+
+def test_should_enter_above_threshold():
+    """확률 >= 0.60 이면 True"""
+    f = MLFilter(threshold=0.60)
+    mock_model = MagicMock()
+    mock_model.predict_proba.return_value = np.array([[0.35, 0.65]])
+    f._model = mock_model
+    features = make_features()
+    assert f.should_enter(features) is True
+
+
+def test_should_enter_below_threshold():
+    """확률 < 0.60 이면 False"""
+    f = MLFilter(threshold=0.60)
+    mock_model = MagicMock()
+    mock_model.predict_proba.return_value = np.array([[0.55, 0.45]])
+    f._model = mock_model
+    features = make_features()
+    assert f.should_enter(features) is False
+
+
+def test_reload_model(tmp_path):
+    """reload_model 호출 후 모델 로드 상태 변경"""
+    import joblib
+
+    # 모델 파일이 없는 상태에서 시작
+    model_path = tmp_path / "lgbm_filter.pkl"
+    f = MLFilter(model_path=str(model_path))
+    assert not f.is_model_loaded()
+
+    # _model을 직접 주입해서 is_model_loaded가 True인지 확인
+    mock_model = MagicMock()
+    f._model = mock_model
+    assert f.is_model_loaded()
+
+    # reload_model 호출 시 파일이 없으면 _try_load가 _model을 변경하지 않음
+    # (기존 동작 유지 - 파일 없으면 None으로 초기화하지 않음)
+    f.reload_model()
+    assert f.is_model_loaded()  # mock_model이 유지됨

tests/test_retrainer.py

@@ -0,0 +1,35 @@
+import pytest
+import json
+from pathlib import Path
+from unittest.mock import AsyncMock, MagicMock, patch
+from src.retrainer import Retrainer
+
+
+@pytest.mark.asyncio
+async def test_retrain_calls_train(tmp_path):
+    """재학습 시 train 함수가 호출되는지 확인"""
+    ml_filter = MagicMock()
+    r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
+
+    with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock) as mock_fetch, \
+         patch("src.retrainer.run_training", return_value=0.72) as mock_train, \
+         patch("src.retrainer.get_current_auc", return_value=0.65):
+        await r.retrain()
+
+    mock_fetch.assert_called_once()
+    mock_train.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_retrain_rollback_when_worse(tmp_path):
+    """새 모델이 기존보다 나쁘면 롤백"""
+    ml_filter = MagicMock()
+    r = Retrainer(ml_filter=ml_filter, data_path=str(tmp_path / "data.parquet"))
+
+    with patch("src.retrainer.fetch_and_save", new_callable=AsyncMock), \
+         patch("src.retrainer.run_training", return_value=0.55), \
+         patch("src.retrainer.get_current_auc", return_value=0.70), \
+         patch("src.retrainer.rollback_model") as mock_rollback:
+        await r.retrain()
+
+    mock_rollback.assert_called_once()