The Yahoo fetch and batch paths agree about the current session's placeholder bar

src/ml4t/data/providers/yahoo.py

@@ -53,6 +53,22 @@ def _chunks(lst: list[Any], n: int) -> Iterator[list[Any]]:
         yield lst[i : i + n]
 
 
+def _drop_priceless_rows(df: pl.DataFrame) -> pl.DataFrame:
+    """Drop bars that carry no price at all.
+
+    From the US close until Yahoo consolidates the daily bar, ``yf.download`` returns a
+    final row for the current exchange date with volume and null OHLC. It is not a
+    session: keeping it writes null prices into storage and into everything derived from
+    them. A row with prices and zero volume is a real halted session and is kept.
+    """
+    price_columns = ("open", "high", "low", "close")
+    present = [column for column in price_columns if column in df.columns]
+    if not present:
+        return df
+    has_price = pl.any_horizontal(pl.col(column).is_not_null() for column in present)
+    return df.filter(has_price)
+
+
 class YahooFinanceProvider(BaseProvider):
     """
     Thin wrapper around yfinance for API consistency and incremental updates.
@@ -194,6 +210,14 @@ def _fetch_and_transform_data(
             # Convert to Polars with symbol column
             df = self._convert_to_polars(df_pandas, symbol)
 
+            if df.is_empty():
+                # Every row was the current session's placeholder; the window holds no bar.
+                raise SymbolNotFoundError(
+                    "yahoo",
+                    symbol,
+                    details={"start": start, "end": end_str, "frequency": frequency},
+                )
+
             logger.info("Successfully fetched data", symbol=symbol, rows=len(df))
             return df
 
@@ -279,6 +303,7 @@ def _convert_to_polars(self, df_pandas: pd.DataFrame, symbol: str) -> pl.DataFra
                 pl.col("volume").cast(pl.Float64),
             )
             .with_columns(pl.lit(symbol.upper()).alias("symbol"))
+            .pipe(_drop_priceless_rows)
             .select(["timestamp", "symbol", "open", "high", "low", "close", "volume"])
         )
 
@@ -587,10 +612,7 @@ def _convert_batch_to_polars(
                     ]
                 )
 
-                # Drop rows where all OHLCV are null (symbol had no data for that date)
-                df_symbol = df_symbol.filter(
-                    pl.col("close").is_not_null() | pl.col("open").is_not_null()
-                )
+                df_symbol = _drop_priceless_rows(df_symbol)
 
                 if len(df_symbol) > 0:
                     records.append(df_symbol)

tests/futures/test_continuous.py

@@ -8,7 +8,7 @@
 import pytest
 
 from ml4t.data.core.config import resolve_storage_path
-from ml4t.data.futures.adjustment import BackAdjustment, RatioAdjustment
+from ml4t.data.futures.adjustment import BackAdjustment, NoAdjustment, RatioAdjustment
 from ml4t.data.futures.continuous import (
     ContinuousContractBuilder,
     build_continuous_contract,
@@ -557,3 +557,71 @@ def test_storage_path_as_pathlib(self, sample_raw_data, sample_continuous_data):
             )
 
             mock_raw.assert_called_once()
+
+
+class TestContinuousContractRemovesRollGap:
+    """End-to-end: the series a reader gets back carries no artificial roll-day move."""
+
+    @staticmethod
+    def _two_contract_panel() -> pl.DataFrame:
+        """Front month at 100 rising by 1/day, deferred 10 points above it in contango.
+
+        Volume moves to the deferred contract on 2024-01-04, so a roll follows.
+        """
+        dates = [date(2024, 1, day) for day in range(1, 7)]
+        front_close = [100.0, 101.0, 102.0, 103.0, 104.0, 105.0]
+        back_close = [close + 10.0 for close in front_close]
+        front_volume = [10000.0, 10000.0, 10000.0, 1000.0, 1000.0, 1000.0]
+        back_volume = [1000.0, 1000.0, 1000.0, 10000.0, 10000.0, 10000.0]
+        return pl.DataFrame(
+            {
+                "date": dates * 2,
+                "symbol": ["ESH24"] * 6 + ["ESM24"] * 6,
+                "open": front_close + back_close,
+                "high": front_close + back_close,
+                "low": front_close + back_close,
+                "close": front_close + back_close,
+                "volume": front_volume + back_volume,
+            }
+        )
+
+    def _build(self, adjustment_method) -> pl.DataFrame:
+        builder = ContinuousContractBuilder(
+            roll_strategy=VolumeBasedRoll(min_days_between_rolls=0),
+            adjustment_method=adjustment_method,
+        )
+        with patch("ml4t.data.futures.continuous.parse_quandl_chris_raw") as mock_raw:
+            mock_raw.return_value = self._two_contract_panel()
+            return builder.build("ES")
+
+    def test_unadjusted_series_carries_the_roll_gap(self):
+        """The premise: without adjustment the roll day shows a jump no contract made."""
+        result = self._build(NoAdjustment())
+        roll_index = result["is_roll_date"].to_list().index(True)
+
+        assert result["is_roll_date"].sum() == 1
+        raw_move = result["adjusted_close"][roll_index] - result["adjusted_close"][roll_index - 1]
+        assert raw_move == pytest.approx(11.0)  # 10.0 of spread, 1.0 of genuine move
+
+    def test_back_adjusted_roll_day_moves_by_the_old_contract_only(self):
+        result = self._build(BackAdjustment())
+        roll_index = result["is_roll_date"].to_list().index(True)
+
+        move = result["adjusted_close"][roll_index] - result["adjusted_close"][roll_index - 1]
+        assert move == pytest.approx(1.0)
+
+    def test_ratio_adjusted_roll_day_returns_the_old_contract_return(self):
+        result = self._build(RatioAdjustment())
+        roll_index = result["is_roll_date"].to_list().index(True)
+
+        adjusted_return = (
+            result["adjusted_close"][roll_index] / result["adjusted_close"][roll_index - 1] - 1
+        )
+        # The outgoing contract rose 1 point off its own prior close of 103 on the roll day.
+        assert adjusted_return == pytest.approx(1.0 / 103.0)
+
+    def test_adjusted_series_ends_on_the_traded_price(self):
+        """Back-adjustment anchors the most recent bar; only history is shifted."""
+        for method in (BackAdjustment(), RatioAdjustment()):
+            result = self._build(method)
+            assert result["adjusted_close"][-1] == pytest.approx(result["close"][-1])

tests/test_yahoo_provider.py

@@ -6,6 +6,7 @@
 import polars as pl
 import pytest
 
+from ml4t.data.core.exceptions import SymbolNotFoundError
 from ml4t.data.providers.yahoo import YahooFinanceProvider
 
 
@@ -215,3 +216,69 @@ def test_frequency_mapping(self, mock_download: MagicMock) -> None:
             # Check that the correct interval was used
             call_kwargs = mock_download.call_args[1]
             assert call_kwargs["interval"] == expected_interval
+
+
+class TestYahooCurrentSessionPlaceholderBar:
+    """From the US close until Yahoo consolidates the daily bar it returns a row with
+    volume and no prices. The single-symbol and batch paths must agree about that row."""
+
+    @staticmethod
+    def _frame_with_placeholder() -> pd.DataFrame:
+        """The frame observed on 2026-09-03: a final row carrying volume and NaN OHLC."""
+        return pd.DataFrame(
+            {
+                ("Close", "AAPL"): [324.959991, float("nan")],
+                ("High", "AAPL"): [328.399994, float("nan")],
+                ("Low", "AAPL"): [323.529999, float("nan")],
+                ("Open", "AAPL"): [326.869995, float("nan")],
+                ("Volume", "AAPL"): [33776400, 37197362],
+            },
+            index=pd.DatetimeIndex(
+                [pd.Timestamp("2026-09-02"), pd.Timestamp("2026-09-03")], name="Date"
+            ),
+        )
+
+    @patch("ml4t.data.providers.yahoo.yf.download")
+    def test_fetch_ohlcv_drops_the_placeholder_bar(self, mock_download: MagicMock) -> None:
+        mock_download.return_value = self._frame_with_placeholder()
+
+        df = YahooFinanceProvider().fetch_ohlcv("AAPL", "2026-09-02", "2026-09-03", "daily")
+
+        assert len(df) == 1
+        assert df["timestamp"].dt.date().to_list() == [pd.Timestamp("2026-09-02").date()]
+        assert df["open"].to_list() == [326.869995]
+
+    @patch("ml4t.data.providers.yahoo.yf.download")
+    def test_fetch_and_batch_agree_on_the_placeholder_bar(self, mock_download: MagicMock) -> None:
+        """The defect was the disagreement: batch_load succeeded where update() raised."""
+        provider = YahooFinanceProvider()
+
+        mock_download.return_value = self._frame_with_placeholder()
+        single = provider.fetch_ohlcv("AAPL", "2026-09-02", "2026-09-03", "daily")
+
+        mock_download.return_value = self._frame_with_placeholder()
+        batch = provider.fetch_batch_ohlcv(["AAPL"], "2026-09-02", "2026-09-03", "daily")
+
+        assert single["timestamp"].dt.date().to_list() == batch["timestamp"].dt.date().to_list()
+
+    @patch("ml4t.data.providers.yahoo.yf.download")
+    def test_a_row_with_prices_and_no_volume_is_kept(self, mock_download: MagicMock) -> None:
+        """Only a priceless row is a placeholder. A halted session with zero volume is a bar."""
+        frame = self._frame_with_placeholder()
+        frame.loc[pd.Timestamp("2026-09-03")] = [325.0, 325.0, 325.0, 325.0, 0]
+        mock_download.return_value = frame
+
+        df = YahooFinanceProvider().fetch_ohlcv("AAPL", "2026-09-02", "2026-09-03", "daily")
+
+        assert len(df) == 2
+
+    @patch("ml4t.data.providers.yahoo.yf.download")
+    def test_a_response_of_nothing_but_placeholders_is_not_data(
+        self, mock_download: MagicMock
+    ) -> None:
+        """Dropping every row must not report an empty fetch as a successful one."""
+        frame = self._frame_with_placeholder().iloc[1:]
+        mock_download.return_value = frame
+
+        with pytest.raises(SymbolNotFoundError):
+            YahooFinanceProvider().fetch_ohlcv("AAPL", "2026-09-03", "2026-09-03", "daily")