feat: add OHLCV market data updater

tests/test_market_data.py

@@ -0,0 +1,123 @@
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+import pandas as pd
+
+import data_manager
+
+
+class UpdateMarketDataTests(unittest.TestCase):
+    def test_update_market_data_accepts_lowercase_fields_and_does_not_fill_volume(self):
+        dates = pd.to_datetime(["2024-01-02", "2024-01-03", "2024-01-04"])
+        raw = pd.DataFrame(
+            {
+                ("Close", "AAA"): [10.0, 11.0, 12.0],
+                ("Close", "BBB"): [20.0, float("nan"), 22.0],
+                ("Open", "AAA"): [9.5, 10.5, 11.5],
+                ("Open", "BBB"): [19.5, 20.5, 21.5],
+                ("High", "AAA"): [10.5, 11.5, 12.5],
+                ("High", "BBB"): [20.5, 21.5, 22.5],
+                ("Low", "AAA"): [9.0, 10.0, 11.0],
+                ("Low", "BBB"): [19.0, 20.0, 21.0],
+                ("Volume", "AAA"): [1000, 1100, 1200],
+                ("Volume", "BBB"): [2000, float("nan"), 2200],
+            },
+            index=dates,
+        )
+        raw.columns = pd.MultiIndex.from_tuples(raw.columns)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            with mock.patch.object(data_manager, "DATA_DIR", tmpdir):
+                with mock.patch("data_manager.yf.download", return_value=raw) as mocked_download:
+                    panels = data_manager.update_market_data(
+                        "us",
+                        ["AAA", "BBB"],
+                        ["close", "open", "high", "low", "volume"],
+                    )
+                self.assertEqual(set(panels), {"close", "open", "high", "low", "volume"})
+                self.assertEqual(panels["close"].loc[dates[1], "BBB"], 20.0)
+                self.assertTrue(pd.isna(panels["volume"].loc[dates[1], "BBB"]))
+                self.assertTrue((Path(tmpdir) / "us.csv").exists())
+                self.assertTrue((Path(tmpdir) / "us_open.csv").exists())
+                self.assertTrue((Path(tmpdir) / "us_high.csv").exists())
+                self.assertTrue((Path(tmpdir) / "us_low.csv").exists())
+                self.assertTrue((Path(tmpdir) / "us_volume.csv").exists())
+
+                saved_high = pd.read_csv(Path(tmpdir) / "us_high.csv", index_col=0, parse_dates=True)
+                pd.testing.assert_frame_equal(saved_high, panels["high"], check_freq=False)
+
+                self.assertEqual(mocked_download.call_args.args[0], ["AAA", "BBB"])
+                self.assertEqual(mocked_download.call_args.kwargs["auto_adjust"], True)
+                self.assertIn("start", mocked_download.call_args.kwargs)
+
+    def test_update_market_data_rejects_unsupported_fields(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            with mock.patch.object(data_manager, "DATA_DIR", tmpdir):
+                with self.assertRaisesRegex(ValueError, "Unsupported market data field: adjusted_close"):
+                    data_manager.update_market_data("us", ["AAA"], ["adjusted_close"])
+
+    def test_update_market_data_preserves_existing_cache_columns_and_dates(self):
+        existing_dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
+        new_dates = pd.to_datetime(["2024-01-02", "2024-01-03"])
+        existing_close = pd.DataFrame(
+            {
+                "AAA": [9.0, 10.0],
+                "CCC": [30.0, 31.0],
+            },
+            index=existing_dates,
+        )
+        downloaded_close = pd.DataFrame({"Close": [10.5, 11.5]}, index=new_dates)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            existing_close.to_csv(Path(tmpdir) / "us.csv")
+            with mock.patch.object(data_manager, "DATA_DIR", tmpdir):
+                with mock.patch("data_manager.yf.download", return_value=downloaded_close):
+                    panels = data_manager.update_market_data("us", ["AAA"], ["close"])
+
+            expected = pd.DataFrame(
+                {
+                    "AAA": [9.0, 10.5, 11.5],
+                    "CCC": [30.0, 31.0, float("nan")],
+                },
+                index=pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03"]),
+            )
+            saved_close = pd.read_csv(Path(tmpdir) / "us.csv", index_col=0, parse_dates=True)
+
+        pd.testing.assert_frame_equal(panels["close"], expected, check_freq=False)
+        pd.testing.assert_frame_equal(saved_close, expected, check_freq=False)
+
+    def test_update_market_data_volume_merge_can_clear_stale_cached_values(self):
+        existing_dates = pd.to_datetime(["2024-01-01", "2024-01-02"])
+        new_dates = pd.to_datetime(["2024-01-02", "2024-01-03", "2024-01-04"])
+        existing_volume = pd.DataFrame(
+            {
+                "AAA": [1000.0, 9999.0],
+                "CCC": [3000.0, 3100.0],
+            },
+            index=existing_dates,
+        )
+        downloaded_volume = pd.DataFrame({"Volume": [float("nan"), 1200.0, 1300.0]}, index=new_dates)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            existing_volume.to_csv(Path(tmpdir) / "us_volume.csv")
+            with mock.patch.object(data_manager, "DATA_DIR", tmpdir):
+                with mock.patch("data_manager.yf.download", return_value=downloaded_volume):
+                    panels = data_manager.update_market_data("us", ["AAA"], ["volume"])
+
+            expected = pd.DataFrame(
+                {
+                    "AAA": [1000.0, float("nan"), 1200.0, 1300.0],
+                    "CCC": [3000.0, 3100.0, float("nan"), float("nan")],
+                },
+                index=pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04"]),
+            )
+            saved_volume = pd.read_csv(Path(tmpdir) / "us_volume.csv", index_col=0, parse_dates=True)
+
+        pd.testing.assert_frame_equal(panels["volume"], expected, check_freq=False)
+        pd.testing.assert_frame_equal(saved_volume, expected, check_freq=False)
+
+
+if __name__ == "__main__":
+    unittest.main()