支持1d级别插针模型

tests/test_artifact_paths.py

@@ -15,6 +15,8 @@ class ArtifactPathTests(unittest.TestCase):
         self.assertEqual(train.artifact_dir, Path("artifacts/v1"))
         self.assertEqual(run.artifact_dir, Path("artifacts/v1"))
         self.assertEqual(predict.artifact, Path("artifacts/v1/model.joblib"))
+        daily = parser.parse_args(["prepare", "--timeframe", "1d"])
+        self.assertEqual(daily.timeframe, "1d")
 
 
 if __name__ == "__main__":

tests/test_latest_predict.py

@@ -6,7 +6,7 @@ from unittest.mock import patch
 
 import pandas as pd
 
-from cbpred.latest_predict import OUTPUT_COLUMNS, build_predictions
+from cbpred.latest_predict import OUTPUT_COLUMNS, build_predictions, output_columns_for_timeframe
 
 
 class LatestPredictTests(unittest.TestCase):
@@ -27,6 +27,9 @@ class LatestPredictTests(unittest.TestCase):
             ),
         )
 
+    def test_output_columns_follow_timeframe(self):
+        self.assertEqual(output_columns_for_timeframe("1d")[4], "quote_volume_usdt_1d")
+
     def test_batch_skips_symbols_without_training_profiles(self):
         with tempfile.TemporaryDirectory() as directory:
             root = Path(directory)

tests/test_pipeline.py

@@ -10,7 +10,9 @@ import pandas as pd
 
 from cbpred.pipeline import (
     CSV_COLUMNS,
+    DAY_MS,
     EIGHT_H_MS,
+    aggregate_ohlcv,
     aggregate_8h,
     build_feature_dataset,
     compute_indicators,
@@ -45,6 +47,33 @@ class PipelineTests(unittest.TestCase):
         self.assertEqual(frame.iloc[0]["timestamp"], 0)
         self.assertAlmostEqual(frame.iloc[0]["quote"], sum(row[6] for row in rows[:8]))
 
+    def test_aggregate_1d_drops_incomplete_buckets(self):
+        rows = make_hour_rows(0, 49)
+        frame = aggregate_ohlcv(rows, "1d")
+        self.assertEqual(len(frame), 2)
+        self.assertEqual(frame.iloc[0]["timestamp"], 0)
+        self.assertEqual(frame.iloc[1]["timestamp"], DAY_MS)
+
+    def test_daily_feature_dataset_uses_daily_continuity(self):
+        raw = aggregate_ohlcv(make_hour_rows(0, 24 * 80), "1d")
+        profiles = {
+            "x": {
+                "asset_atr_price_top20_median": 0.01,
+                "asset_quote_volume_top20_median_log": 12.0,
+            }
+        }
+        scaler = fit_asset_scaler(profiles)
+        dataset, _, _ = build_feature_dataset(
+            {"x": raw},
+            1,
+            int(raw.iloc[55]["timestamp"]),
+            int(raw.iloc[-1]["timestamp"] + DAY_MS),
+            scaler,
+            volatility_rank_threshold=0.0,
+            step_ms=DAY_MS,
+        )
+        self.assertFalse(dataset.empty)
+
     def test_future_target_changes_but_past_features_do_not(self):
         start = 0
         raw = aggregate_8h(make_hour_rows(start, 8 * 70))