Signed-off-by: Matt Bruce <mbrucedogs@gmail.com>

web/src/ONBOARDING.md

@@ -224,8 +224,8 @@ Treats low-volume bars as fake:
 
 ### Hide Market-Closed Gaps (Stocks)
 Compresses non-trading time on stock charts:
-- `1d`: removes weekend spacing
+- `1d`: removes weekends and full missing days (for example exchange holidays with no bars)
-- intraday (`1m`..`1h`): removes weekends and overnight closed hours
+- intraday (`1m`..`1h`): removes weekends, full missing days, and overnight closed hours; x-axis labels show one date per trading day
 
 Use OFF for 24/7 markets (for example many crypto workflows) when you want continuous time.
 
@@ -284,8 +284,9 @@ streamlit run app.py --server.port 8502
 
 ### I still see some time gaps
 - For stocks, keep `Hide market-closed gaps (stocks)` ON.
-- Daily charts remove weekends; intraday removes weekends + closed hours.
+- Daily charts remove weekends + full no-data days; intraday removes weekends + full no-data days + closed hours.
-- Some exchange holidays/half-days can still produce spacing depending on the data feed.
+- Day-based periods (`1d`, `5d`) backfill to target trading-day count when provider limits allow.
+- Half-days can still look visually compressed because they are partial sessions, not missing sessions.
 
 ### Exports crash with timestamp errors
 - Pull latest project changes (export logic now handles named index columns)

web/src/PRD.md

@@ -25,6 +25,7 @@ Provide an analysis-only charting tool that classifies OHLC bars as real/fake, t
 - filter/toggle settings
  - optional advanced controls (alerts, replay, compare symbols, backtest controls, regime filter)
 2. App fetches OHLCV via Yahoo Finance (`yfinance`).
+ - For day-based periods (for example `1d`, `5d`), fetch widens calendar lookback as needed, then trims to the latest N trading days that actually contain bars.
 3. Optional last-bar drop (live-bar guard) for intraday intervals.
 4. Bars are classified (`real_bull`, `real_bear`, `fake`, `unclassified` for first bar).
 5. Trend state is derived from classification sequence.
@@ -158,7 +159,8 @@ Important:
 
 Gap handling (`hide_market_closed_gaps`):
 - Always removes weekend gaps (`sat` -> `mon`).
-- For intraday intervals, also removes inferred overnight hours using session bounds.
+- Removes full missing calendar days between first/last bar (for example market holidays with no bars).
+- For intraday intervals, uses contiguous bar-order x-axis (no closed-session spacing) and day-level tick labels.
 - For daily interval, weekend break removal is applied.
 
 ## 8. Help and Onboarding Behavior

web/src/tests/test_charting.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+import pandas as pd
+
+from web_core.charting import _missing_calendar_day_values, build_figure
+from web_core.constants import TREND_NEUTRAL
+
+
+def _make_daily_df(days: list[str]) -> pd.DataFrame:
+    index = pd.DatetimeIndex([pd.Timestamp(day, tz="UTC") for day in days])
+    count = len(index)
+    return pd.DataFrame(
+        {
+            "Open": [100 + i for i in range(count)],
+            "High": [101 + i for i in range(count)],
+            "Low": [99 + i for i in range(count)],
+            "Close": [100.5 + i for i in range(count)],
+            "Volume": [1000 + i for i in range(count)],
+            "classification": ["fake"] * count,
+            "trend_state": [TREND_NEUTRAL] * count,
+        },
+        index=index,
+    )
+
+
+def _make_intraday_df(days: list[str]) -> pd.DataFrame:
+    index_values: list[pd.Timestamp] = []
+    for day in days:
+        session = pd.date_range(
+            start=f"{day} 09:30:00",
+            end=f"{day} 15:45:00",
+            freq="15min",
+            tz="America/New_York",
+        )
+        index_values.extend(session.to_list())
+
+    index = pd.DatetimeIndex(index_values)
+    count = len(index)
+    return pd.DataFrame(
+        {
+            "Open": [100 + i for i in range(count)],
+            "High": [101 + i for i in range(count)],
+            "Low": [99 + i for i in range(count)],
+            "Close": [100.5 + i for i in range(count)],
+            "Volume": [1000 + i for i in range(count)],
+            "classification": ["fake"] * count,
+            "trend_state": [TREND_NEUTRAL] * count,
+        },
+        index=index,
+    )
+
+
+def test_missing_calendar_day_values_include_weekday_holidays_only() -> None:
+    df = _make_daily_df(["2026-02-13", "2026-02-17"])
+    missing = _missing_calendar_day_values(df)
+
+    assert "2026-02-15" not in missing
+    assert "2026-02-16" in missing
+
+
+def test_build_figure_adds_missing_day_rangebreak_values() -> None:
+    df = _make_daily_df(["2026-02-13", "2026-02-17"])
+    fig = build_figure(
+        df,
+        gray_fake=False,
+        interval="1d",
+        hide_market_closed_gaps=True,
+    )
+
+    rangebreak_values: list[str] = []
+    for rb in fig.layout.xaxis.rangebreaks:
+        values = list(getattr(rb, "values", ()) or ())
+        rangebreak_values.extend(str(v) for v in values)
+
+    assert "2026-02-16" in rangebreak_values
+    assert "2026-02-15" not in rangebreak_values
+
+
+def test_build_figure_intraday_uses_category_axis_when_hiding_gaps() -> None:
+    df = _make_intraday_df(["2026-02-13", "2026-02-17"])
+    fig = build_figure(
+        df,
+        gray_fake=False,
+        interval="15m",
+        hide_market_closed_gaps=True,
+    )
+
+    assert fig.layout.xaxis.type == "category"
+    assert len(fig.layout.xaxis.rangebreaks) == 0
+    assert fig.layout.xaxis.tickmode == "array"
+    assert list(fig.layout.xaxis.ticktext) == ["2/13", "2/17"]
+    assert len(fig.layout.xaxis.tickvals) == 2

web/src/tests/test_data.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from datetime import datetime
+
+import pandas as pd
+
+from web_core import data as data_module
+
+
+def _make_intraday_df(days: list[str]) -> pd.DataFrame:
+    index_values: list[pd.Timestamp] = []
+    for day in days:
+        index_values.append(pd.Timestamp(f"{day} 14:30:00", tz="UTC"))
+        index_values.append(pd.Timestamp(f"{day} 15:30:00", tz="UTC"))
+
+    index = pd.DatetimeIndex(index_values)
+    return pd.DataFrame(
+        {
+            "Open": [100.0 + i for i in range(len(index))],
+            "High": [101.0 + i for i in range(len(index))],
+            "Low": [99.0 + i for i in range(len(index))],
+            "Close": [100.5 + i for i in range(len(index))],
+            "Volume": [1000 + i for i in range(len(index))],
+        },
+        index=index,
+    )
+
+
+def test_fetch_ohlc_day_period_backfills_until_target_trading_days(monkeypatch) -> None:
+    fixed_now = datetime(2026, 2, 17, 20, 0, 0)
+    four_day_df = _make_intraday_df(["2026-02-12", "2026-02-13", "2026-02-14", "2026-02-17"])
+    five_day_df = _make_intraday_df(["2026-02-11", "2026-02-12", "2026-02-13", "2026-02-14", "2026-02-17"])
+    calls: list[dict[str, object]] = []
+
+    class FakeTicker:
+        def history(self, **kwargs: object) -> pd.DataFrame:
+            calls.append(kwargs)
+            start = kwargs.get("start")
+            if start is None:
+                return four_day_df.copy()
+            lookback_days = (fixed_now - pd.Timestamp(start).to_pydatetime()).days
+            return five_day_df.copy() if lookback_days >= 12 else four_day_df.copy()
+
+    monkeypatch.setattr(data_module.yf, "Ticker", lambda symbol: FakeTicker())
+    monkeypatch.setattr(data_module, "_utc_now", lambda: fixed_now)
+    data_module.fetch_ohlc.clear()
+
+    out = data_module.fetch_ohlc(symbol="TSLA", interval="2m", period="5d")
+    session_days = pd.DatetimeIndex(out.index).normalize().unique()
+
+    assert len(session_days) == 5
+    assert pd.Timestamp("2026-02-11", tz="UTC") in session_days
+    assert len(calls) >= 2
+    assert all("start" in call and "end" in call for call in calls)
+
+
+def test_fetch_ohlc_non_day_period_uses_period_request(monkeypatch) -> None:
+    calls: list[dict[str, object]] = []
+    month_df = _make_intraday_df(["2026-01-05", "2026-01-06", "2026-01-07"])
+
+    class FakeTicker:
+        def history(self, **kwargs: object) -> pd.DataFrame:
+            calls.append(kwargs)
+            return month_df.copy()
+
+    monkeypatch.setattr(data_module.yf, "Ticker", lambda symbol: FakeTicker())
+    data_module.fetch_ohlc.clear()
+
+    out = data_module.fetch_ohlc(symbol="AAPL", interval="1h", period="1mo")
+
+    assert len(out) == len(month_df)
+    assert len(calls) == 1
+    assert calls[0].get("period") == "1mo"
+    assert "start" not in calls[0]
+    assert "end" not in calls[0]

web/src/web_core/charting.py

@@ -15,30 +15,36 @@ def _is_daily_interval(interval: str) -> bool:
     return interval == "1d"
 
 
-def _infer_session_bounds(df: pd.DataFrame) -> tuple[float, float] | None:
+def _intraday_day_ticks(index: pd.DatetimeIndex) -> tuple[list[pd.Timestamp], list[str]]:
+    if len(index) == 0:
+        return [], []
+
+    normalized = index.normalize()
+    first_mask = ~normalized.duplicated()
+    tickvals = [index[pos] for pos, keep in enumerate(first_mask) if bool(keep)]
+    ticktext = [f"{ts.month}/{ts.day}" for ts in tickvals]
+    return tickvals, ticktext
+
+
+def _missing_calendar_day_values(df: pd.DataFrame) -> list[str]:
     if df.empty:
-        return None
+        return []
 
     index = pd.DatetimeIndex(df.index)
-    if index.tz is None:
+    session_days = pd.DatetimeIndex(index.normalize().unique()).sort_values()
-        return None
+    if len(session_days) < 2:
+        return []
 
-    minutes = index.hour * 60 + index.minute
+    if session_days.tz is None:
-    session_df = pd.DataFrame({"date": index.date, "minute": minutes})
+        all_days = pd.date_range(start=session_days[0], end=session_days[-1], freq="D")
-    day_bounds = session_df.groupby("date")["minute"].agg(["min", "max"])
-    if day_bounds.empty:
-        return None
-
-    start_minute = float(day_bounds["min"].median())
-    # Include the final candle width roughly by adding one median step when possible.
-    if len(index) > 1:
-        deltas = pd.Series(index[1:] - index[:-1]).dt.total_seconds().div(60.0)
-        step = float(deltas[deltas > 0].median()) if not deltas[deltas > 0].empty else 0.0
     else:
-        step = 0.0
+        all_days = pd.date_range(start=session_days[0], end=session_days[-1], freq="D", tz=session_days.tz)
-    end_minute = float(day_bounds["max"].median() + step)
 
-    return end_minute / 60.0, start_minute / 60.0
+    missing_days = all_days.difference(session_days)
+    # Weekend gaps are already handled by sat->mon bounds; keep explicit values
+    # for weekday closures (e.g., exchange holidays) to avoid overlap artifacts.
+    weekday_missing = [day for day in missing_days if day.dayofweek < 5]
+    return [day.strftime("%Y-%m-%d") for day in weekday_missing]
 
 
 def build_figure(
@@ -143,17 +149,26 @@ def build_figure(
         height=760,
     )
     if hide_market_closed_gaps:
-        rangebreaks: list[dict[str, object]] = [dict(bounds=["sat", "mon"])]
         if _is_intraday_interval(interval):
-            # Collapse inferred overnight closed hours from the data's timezone/session.
+            # Intraday rangebreak combinations can produce axis rendering artifacts
-            inferred_bounds = _infer_session_bounds(df)
+            # with some feeds/timezones. Categorical axis keeps chronological bars
-            hour_bounds = list(inferred_bounds) if inferred_bounds else [16, 9.5]
+            # contiguous and removes closed-session gaps reliably.
-            rangebreaks.append(dict(pattern="hour", bounds=hour_bounds))
+            tickvals, ticktext = _intraday_day_ticks(pd.DatetimeIndex(df.index))
+            fig.update_xaxes(
+                type="category",
+                categoryorder="array",
+                categoryarray=list(df.index),
+                tickmode="array",
+                tickvals=tickvals,
+                ticktext=ticktext,
+                tickangle=0,
+            )
         elif _is_daily_interval(interval):
-            # Daily charts still show weekend spacing on a continuous date axis.
+            rangebreaks: list[dict[str, object]] = [dict(bounds=["sat", "mon"])]
-            # Weekend rangebreak removes these non-trading gaps.
+            missing_days = _missing_calendar_day_values(df)
-            pass
+            if missing_days:
-        fig.update_xaxes(rangebreaks=rangebreaks)
+                rangebreaks.append(dict(values=missing_days))
+            fig.update_xaxes(rangebreaks=rangebreaks)
 
     fig.update_yaxes(title_text="Price", row=1, col=1)
     fig.update_yaxes(title_text="Volume", row=2, col=1)

web/src/web_core/data.py

@@ -1,16 +1,104 @@
 from __future__ import annotations
 
-from datetime import datetime
+from datetime import datetime, timedelta
+import re
 
 import pandas as pd
 import streamlit as st
 import yfinance as yf
 
 
+_DAY_PERIOD_PATTERN = re.compile(r"^([1-9]\d*)d$")
+_DAY_PERIOD_FETCH_ATTEMPTS = 5
+
+
+def _utc_now() -> datetime:
+    return datetime.utcnow()
+
+
+def _parse_day_period(period: str) -> int | None:
+    match = _DAY_PERIOD_PATTERN.match(str(period).strip().lower())
+    if not match:
+        return None
+    return int(match.group(1))
+
+
+def _intraday_max_lookback_days(interval: str) -> int | None:
+    interval_key = str(interval).strip().lower()
+    if interval_key == "1m":
+        return 7
+    if interval_key in {"2m", "5m", "15m", "30m", "60m", "90m", "1h"}:
+        return 60
+    return None
+
+
+def _initial_calendar_lookback_days(trading_days: int) -> int:
+    # Expand beyond target trading days to account for weekends and holidays.
+    return max(trading_days + 2, int((trading_days * 7) / 5) + 3)
+
+
+def _trading_day_count(df: pd.DataFrame) -> int:
+    if df.empty:
+        return 0
+    index = pd.DatetimeIndex(df.index)
+    return int(index.normalize().nunique())
+
+
+def _trim_to_recent_trading_days(df: pd.DataFrame, trading_days: int) -> pd.DataFrame:
+    if df.empty or trading_days <= 0:
+        return df.copy()
+
+    index = pd.DatetimeIndex(df.index)
+    session_days = pd.DatetimeIndex(index.normalize().unique()).sort_values()
+    if len(session_days) <= trading_days:
+        return df.copy()
+
+    keep_days = session_days[-trading_days:]
+    mask = index.normalize().isin(keep_days)
+    return df.loc[mask].copy()
+
+
+def _fetch_history_for_period(ticker: yf.Ticker, interval: str, period: str) -> pd.DataFrame:
+    history_kwargs = {"interval": interval, "auto_adjust": False, "actions": False}
+    day_period = _parse_day_period(period)
+    if day_period is None:
+        return ticker.history(period=period, **history_kwargs)
+
+    lookback_days = _initial_calendar_lookback_days(day_period)
+    max_lookback_days = _intraday_max_lookback_days(interval)
+    if max_lookback_days is not None:
+        lookback_days = min(lookback_days, max_lookback_days)
+
+    now = _utc_now()
+    best_df = pd.DataFrame()
+    for _ in range(_DAY_PERIOD_FETCH_ATTEMPTS):
+        start = now - timedelta(days=lookback_days)
+        df = ticker.history(start=start, end=now, **history_kwargs)
+        if len(df) > len(best_df):
+            best_df = df
+
+        if _trading_day_count(df) >= day_period:
+            return df
+
+        if max_lookback_days is not None and lookback_days >= max_lookback_days:
+            break
+
+        next_lookback = int(lookback_days * 1.6) + 1
+        if max_lookback_days is not None:
+            next_lookback = min(next_lookback, max_lookback_days)
+        if next_lookback <= lookback_days:
+            break
+        lookback_days = next_lookback
+
+    if not best_df.empty:
+        return best_df
+    return ticker.history(period=period, **history_kwargs)
+
+
 @st.cache_data(ttl=60, show_spinner=False)
 def fetch_ohlc(symbol: str, interval: str, period: str) -> pd.DataFrame:
     ticker = yf.Ticker(symbol)
-    df = ticker.history(period=period, interval=interval, auto_adjust=False, actions=False)
+    df = _fetch_history_for_period(ticker=ticker, interval=interval, period=period)
     if df.empty:
         raise ValueError("No data returned. Check symbol/interval/period compatibility.")
 
@@ -20,7 +108,12 @@ def fetch_ohlc(symbol: str, interval: str, period: str) -> pd.DataFrame:
     if missing:
         raise ValueError(f"Missing required columns: {missing}")
 
-    return df[required].dropna().copy()
+    out = df[required].dropna().copy()
+    day_period = _parse_day_period(period)
+    if day_period is not None:
+        out = _trim_to_recent_trading_days(out, day_period)
+
+    return out
 
 
 def maybe_drop_live_bar(df: pd.DataFrame, interval: str, enabled: bool) -> pd.DataFrame: