feat(ai): add causal holding research and price replay dashboards

backend/app/alpha/support_resistance.py

@@ -0,0 +1,175 @@
+"""Causal five-minute support/resistance *descriptors*, not trading rules.
+
+Input timestamps are timezone-aware bar OPEN times. A row is usable only after
+that five-minute bar closes. In particular, the 09:40 row's opening-range values
+become available at 09:45; callers must exclude an unfinished current bar.
+
+Levels are derived from the preceding XNYS session, the first 15 minutes, and
+strict pivots with two bars on each side. A pivot at 10:00 is confirmed only on
+the 10:10 row (available 10:15). Crossing/rejection always uses levels known on
+the PREVIOUS row, never a pivot newly confirmed by the current row. No ticker,
+date, trade-size, directional veto, or transaction-frequency rules are included.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import exchange_calendars as xcals
+
+VERSION = "support_resistance_v1"
+FEATURE_DESCRIPTIONS = {
+    "sr_previous_high_distance_bps": "Close / preceding full session high minus one, in bps.",
+    "sr_previous_low_distance_bps": "Close / preceding full session low minus one, in bps.",
+    "sr_previous_close_distance_bps": "Close / preceding session final bar close minus one, in bps.",
+    "sr_opening_high_distance_bps": "Close distance to first 15-minute high; available after 09:45 ET.",
+    "sr_opening_low_distance_bps": "Close distance to first 15-minute low; available after 09:45 ET.",
+    "sr_pivot_high_distance_bps": "Close distance to last confirmed intraday 2-left/2-right strict high pivot.",
+    "sr_pivot_low_distance_bps": "Close distance to last confirmed intraday 2-left/2-right strict low pivot.",
+    "sr_vwap_distance_bps": "Close distance to cumulative typical-price volume-weighted OHLCV proxy, not tick VWAP.",
+    "sr_running_range_position": "Close within observed session low/high; missing if range is zero or incomplete.",
+    "sr_resistance_cross_up": "Close crosses nearest previously known resistance above/equal previous close (0/1).",
+    "sr_support_cross_down": "Close crosses nearest previously known support below/equal previous close (0/1).",
+    "sr_resistance_rejection_bps": "High overshoot of prior resistance if close returned below it; otherwise zero.",
+    "sr_support_rebound_bps": "Low undershoot of prior support if close returned above it; otherwise zero.",
+    "sr_last_upbreak_close_distance_bps": "Close distance to the latest resistance crossed by a close this session.",
+    "sr_last_upbreak_low_distance_bps": "Current low distance to that retained resistance; describes potential retest.",
+    "sr_last_downbreak_close_distance_bps": "Close distance to latest support crossed downward by a close this session.",
+    "sr_last_downbreak_high_distance_bps": "Current high distance to that retained support; describes potential retest.",
+    "sr_upbreak_age_bars": "Elapsed five-minute grid slots since latest confirmed upward close crossing.",
+    "sr_downbreak_age_bars": "Elapsed five-minute grid slots since latest confirmed downward close crossing.",
+}
+FEATURE_COLUMNS = tuple(FEATURE_DESCRIPTIONS)
+
+
+def _distance(value: float, level: float) -> float:
+    if not np.isfinite(value) or not np.isfinite(level) or level <= 0:
+        return np.nan
+    return (value / level - 1.0) * 10_000.0
+
+
+def support_resistance_features(frame: pd.DataFrame) -> pd.DataFrame:
+    """Return 19 same-index numeric features without imputation or future data.
+
+    The preceding *calendar trading session* must be present, rather than merely
+    the preceding observed date. Prior high/low need every scheduled five-minute
+    high/low (including early closes); a missing final close stays missing.
+    Opening range requires all three opening bars. Missing slots are explicit in
+    pivot windows, running extrema and VWAP. Non-session rows remain NaN. Missing
+    current OHLCV makes that output row NaN. A zero imbalance/return is never used
+    to stand in for unavailable evidence. Intraday states reset each session.
+    """
+    required = ["open", "high", "low", "close", "volume"]
+    if not isinstance(frame.index, pd.DatetimeIndex) or frame.index.tz is None:
+        raise ValueError("Timezone-aware bar-open DatetimeIndex required")
+    if not frame.index.is_monotonic_increasing or not frame.index.is_unique:
+        raise ValueError("Bar-open timestamps must be sorted and unique")
+    if not set(required).issubset(frame.columns):
+        raise ValueError("open/high/low/close/volume columns required")
+    result = pd.DataFrame(np.nan, index=frame.index, columns=FEATURE_COLUMNS)
+    result.attrs.update(feature_version=VERSION, feature_descriptions=FEATURE_DESCRIPTIONS.copy(),
+                        timestamp_convention="bar_open_available_at_end", frequency="5min",
+                        vwap_kind="OHLCV typical-price proxy; not true trade VWAP")
+    if frame.empty:
+        return result
+    bars = frame[required].apply(pd.to_numeric, errors="coerce").copy()
+    bars.index = bars.index.tz_convert("America/New_York")
+    for column in required[:4]:
+        bars[column] = bars[column].where(bars[column] > 0)
+    bars["volume"] = bars.volume.where(bars.volume >= 0)
+    bars = bars.replace([np.inf, -np.inf], np.nan)
+    calendar = xcals.get_calendar("XNYS", start=bars.index[0].date()-pd.Timedelta(days=15),
+                                  end=bars.index[-1].date()+pd.Timedelta(days=1))
+    output = result.copy()
+    output.index = bars.index
+    step = pd.Timedelta(minutes=5)
+
+    for day, observed in bars.groupby(bars.index.date):
+        session = pd.Timestamp(day)
+        if not calendar.is_session(session):
+            continue
+        start = calendar.session_open(session).tz_convert("America/New_York")
+        end = calendar.session_close(session).tz_convert("America/New_York")
+        regular = observed.loc[(observed.index >= start) & (observed.index < end)]
+        if regular.empty:
+            continue
+        if any((regular.index.asi8 - start.value) % step.value):
+            raise ValueError("Regular-session rows must align with the five-minute grid")
+        grid = pd.date_range(start, regular.index[-1], freq=step)
+        b = regular.reindex(grid)
+        previous = calendar.previous_session(session)
+        prior_grid = pd.date_range(calendar.session_open(previous), calendar.session_close(previous)-step, freq=step)
+        prior = bars.reindex(prior_grid)
+        previous_high = prior.high.max() if prior.high.notna().all() else np.nan
+        previous_low = prior.low.min() if prior.low.notna().all() else np.nan
+        previous_close = prior.close.iloc[-1]
+        opening_high = opening_low = pivot_high = pivot_low = np.nan
+        broken_up = broken_down = np.nan
+        up_at = down_at = None
+        previous_close_in_day = np.nan
+        running_high = running_low = np.nan
+        cumulative_pv = cumulative_volume = 0.0
+        range_valid = vwap_valid = True
+        values = b.to_numpy(dtype=float)
+        rows = []
+
+        for i, (o, h, l, c, v) in enumerate(values):
+            row = dict.fromkeys(FEATURE_COLUMNS, np.nan)
+            current_valid = bool(np.isfinite([o, h, l, c, v]).all())
+            # Candidates are snapshotted before current opening-range/pivot updates.
+            if current_valid and np.isfinite(previous_close_in_day):
+                resistances = [x for x in (previous_high, opening_high, pivot_high)
+                               if np.isfinite(x) and x >= previous_close_in_day]
+                supports = [x for x in (previous_low, opening_low, pivot_low)
+                            if np.isfinite(x) and x <= previous_close_in_day]
+                if resistances:
+                    resistance = min(resistances)
+                    crossed = c > resistance
+                    row["sr_resistance_cross_up"] = float(crossed)
+                    row["sr_resistance_rejection_bps"] = max(_distance(h, resistance), 0.0) if c < resistance else 0.0
+                    if crossed:
+                        broken_up, up_at = resistance, i
+                if supports:
+                    support = max(supports)
+                    crossed = c < support
+                    row["sr_support_cross_down"] = float(crossed)
+                    row["sr_support_rebound_bps"] = max(-_distance(l, support), 0.0) if c > support else 0.0
+                    if crossed:
+                        broken_down, down_at = support, i
+
+            if i == 2 and np.isfinite(values[:3, 1:3]).all():
+                opening_high = float(values[:3, 1].max())
+                opening_low = float(values[:3, 2].min())
+            if i >= 4:
+                highs, lows = values[i-4:i+1, 1], values[i-4:i+1, 2]
+                if np.isfinite(highs).all() and highs[2] > np.max(highs[[0, 1, 3, 4]]):
+                    pivot_high = highs[2]
+                if np.isfinite(lows).all() and lows[2] < np.min(lows[[0, 1, 3, 4]]):
+                    pivot_low = lows[2]
+
+            range_valid = range_valid and bool(np.isfinite([h, l]).all())
+            if range_valid:
+                running_high = h if i == 0 else max(running_high, h)
+                running_low = l if i == 0 else min(running_low, l)
+            vwap_valid = vwap_valid and bool(np.isfinite([h, l, c, v]).all())
+            if vwap_valid:
+                cumulative_pv += (h+l+c) / 3 * v
+                cumulative_volume += v
+            vwap = cumulative_pv/cumulative_volume if vwap_valid and cumulative_volume > 0 else np.nan
+            for name, level in (("previous_high", previous_high), ("previous_low", previous_low),
+                                ("previous_close", previous_close), ("opening_high", opening_high),
+                                ("opening_low", opening_low), ("pivot_high", pivot_high),
+                                ("pivot_low", pivot_low), ("vwap", vwap)):
+                row[f"sr_{name}_distance_bps"] = _distance(c, level)
+            if range_valid and running_high > running_low:
+                row["sr_running_range_position"] = (c-running_low)/(running_high-running_low)
+            row.update(sr_last_upbreak_close_distance_bps=_distance(c, broken_up),
+                       sr_last_upbreak_low_distance_bps=_distance(l, broken_up),
+                       sr_last_downbreak_close_distance_bps=_distance(c, broken_down),
+                       sr_last_downbreak_high_distance_bps=_distance(h, broken_down),
+                       sr_upbreak_age_bars=i-up_at if up_at is not None else np.nan,
+                       sr_downbreak_age_bars=i-down_at if down_at is not None else np.nan)
+            rows.append(row if current_valid else dict.fromkeys(FEATURE_COLUMNS, np.nan))
+            previous_close_in_day = c if current_valid else np.nan
+        output.loc[regular.index] = pd.DataFrame(rows, index=grid).loc[regular.index, FEATURE_COLUMNS].to_numpy()
+    output.index = frame.index
+    return output

backend/app/dashboard/__init__.py

@@ -0,0 +1 @@
+"""Read-only, source-labelled dashboard datasets."""

backend/app/dashboard/price_replay.py

@@ -0,0 +1,36 @@
+"""Serve portable saved research, without loading models or contacting a broker."""
+from __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+
+
+BUNDLE_ROOT = Path(__file__).resolve().parents[2] / "data" / "price_replay"
+
+
+def price_replay(ticker="SNDK", date="", variant="levels_error_risk", *, root=None):
+    root = Path(root) if root is not None else BUNDLE_ROOT
+    ticker = ticker.strip().upper()
+    request = {"ticker": ticker, "date": date, "variant": variant}
+    metadata = {"available_dates": [], "variants": [], "tickers": []}
+    try:
+        manifest = json.loads((root / "manifest.json").read_text())
+        metadata = {key: manifest[key] for key in metadata}
+        date = date or max(metadata["available_dates"])
+        request["date"] = date
+        if (ticker not in metadata["tickers"] or date not in metadata["available_dates"]
+                or variant not in metadata["variants"]):
+            raise ValueError("No saved research for the requested ticker, date and model.")
+        name = f"{date}/{variant}/{ticker}.json"
+        raw = (root / name).read_bytes()
+        if hashlib.sha256(raw).hexdigest() != manifest["files"][name]:
+            raise ValueError("Saved replay integrity check failed.")
+        payload = json.loads(raw)
+        if any(payload[key] != value for key, value in request.items()):
+            raise ValueError("Saved replay identity does not match the requested data.")
+        return {**payload, **metadata, "success": True}
+    except (OSError, ValueError, KeyError, TypeError):
+        return {"success": False, "status": "unavailable", **request, **metadata,
+                "error": "该股票、日期或模型的已保存回放不可用，或数据校验失败。",
+                "bars": [], "fills": [], "marks": [], "summary": None}

backend/app/research/calibrated_holding.py

@@ -4,7 +4,8 @@ import json
 import numpy as np
 import pandas as pd
 from sklearn.linear_model import Ridge
-from .holding_policy import HoldingModel,HoldingSpec,holding_features,executable_labels
+from .holding_policy import HoldingModel,HoldingSpec,holding_features,executable_labels,curve_from_cumulative
+from sklearn.covariance import LedoitWolf
 
 VERSION='calibrated_direction_holding_v1'
 
@@ -37,6 +38,48 @@ class CalibratedHoldingModel(HoldingModel):
                     last_label_end=end.reindex(p.index)[valid].max().isoformat())
             model.calibration[s]=dict(mean=mean.tolist(),scale=scale.tolist(),state=state,
                 validation_days=[str(d) for d in validation_days],base_trained_before=validation_start.isoformat())
+        if spec.forecast_error_risk:
+            # Forward-only second-stage evaluation: earlier base forecasts are
+            # frozen; each calibrator learns exclusively from preceding days.
+            k=max(spec.horizons);blocks=[];folds=[];last_ends=[]
+            labels={s:{h:executable_labels(frames[s],h) for h in spec.horizons} for s in symbols}
+            one={s:executable_labels(frames[s],1) for s in symbols}
+            for day in validation_days[2:]:
+                fold_cutoff=pd.Timestamp(day,tz='America/New_York')
+                estimates={};truth={};ends={};training_ends=[]
+                for s in symbols:
+                    p=pd.DataFrame(predictions[s]);past=p.loc[p.index<fold_cutoff]
+                    mu=past.mean();sd=past.std(ddof=0).replace(0,1)
+                    z=(p-mu)/sd
+                    test_index=p.index[p.index.date==day]
+                    cumulative=[]
+                    for h in spec.horizons:
+                        y,end=labels[s][h];y=y.reindex(p.index);end=end.reindex(p.index)
+                        valid=(p.index<fold_cutoff)&end.lt(fold_cutoff)&y.notna()&np.isfinite(z).all(axis=1)
+                        if valid.sum()<2:raise ValueError('Insufficient forward error calibration labels')
+                        fit=Ridge(alpha=spec.ridge_alpha,solver='svd').fit(z.loc[valid],y.loc[valid])
+                        cumulative.append(fit.predict(z.loc[test_index]))
+                        training_ends.append(end.loc[valid].max())
+                    estimates[s]=pd.DataFrame(np.array(cumulative).T,index=test_index,columns=spec.horizons)
+                    yy,ee=one[s]
+                    yy=yy.loc[yy.index.date==day];ee=ee.reindex(yy.index)
+                    truth[s]=pd.concat([yy.shift(-j) for j in range(k)],axis=1).reindex(test_index)
+                    ends[s]=ee.shift(-(k-1)).reindex(test_index)
+                count=0
+                for stamp in test_index:
+                    actual=np.column_stack([truth[s].loc[stamp].to_numpy() for s in symbols])
+                    if not np.isfinite(actual).all() or any(pd.isna(ends[s].loc[stamp]) or ends[s].loc[stamp]>=cutoff for s in symbols):continue
+                    if any(ends[s].loc[stamp]-stamp!=pd.Timedelta(minutes=5*(k+1)) for s in symbols):continue
+                    cumulative=np.column_stack([estimates[s].loc[stamp].to_numpy() for s in symbols])
+                    blocks.append((actual-curve_from_cumulative(cumulative,spec.horizons,k)).ravel())
+                    last_ends.extend(ends[s].loc[stamp] for s in symbols);count+=1
+                folds.append(dict(day=str(day),rows=count,calibrator_last_label_end=max(training_ends).isoformat()))
+            if len(blocks)<2:raise ValueError('Insufficient prior forward prediction errors')
+            errors=np.array(blocks);fit=LedoitWolf().fit(errors);bias=errors.mean(axis=0)
+            model.forecast_error_covariance=fit.covariance_+np.outer(bias,bias)
+            model.error_training=dict(method='prior_forward_calibrator_path_error_second_moment',
+                rows=len(blocks),folds=folds,last_label_end=max(last_ends).isoformat(),
+                base_trained_before=validation_start.isoformat(),overlapping_windows=True)
         return model
 
     def allocation(self,forecasts,current,stamp,allowed=None,shortable=None,l1_adjustment=None,liquidation_at=None):

backend/app/research/direction_policy.py

@@ -55,18 +55,25 @@ def direction_features(frames):
         own[s]=f.replace([np.inf,-np.inf],np.nan)
     return own
 
-def planned_target(forecasts,covariance,current,symbols,eligible,spec,*,shortable=None):
+def planned_target(forecasts,covariance,current,symbols,eligible,spec,*,shortable=None,
+                   forecast_error_covariance=None):
     """Trade only the first step of a fully costed hypothetical holding plan.
 
     Risk uses a covariance matrix of sequential, cross-stock returns estimated
     before the decision date. Intermediate turnover and terminal liquidation
     both cost money. Future weights are plans, never claimed future fills.
+    An optional prior-only forecast-error covariance expresses uncertainty in
+    the return curve in the same interval-by-symbol order and return units.
+    Its quadratic penalty is added to market risk without overriding direction.
     """
     symbols=tuple(symbols);n=len(symbols);mu=np.asarray(forecasts,dtype=float)
     if mu.ndim!=2 or mu.shape[1]!=n or not np.isfinite(mu).all():raise ValueError('Finite horizon-by-symbol forecasts required')
     k=len(mu);m=k*n
     if k<1 or any(v!=0 for s,v in current.items() if s not in symbols):raise ValueError('All current exposure must be budgeted')
     old=np.array([current.get(s,0.) for s in symbols]);cov=_validated_covariance(covariance,m)
+    error_cov = (None if forecast_error_covariance is None else
+                 _validated_covariance(forecast_error_covariance,m))
+    risk_cov = cov if error_cov is None else cov + error_cov
     if not np.isfinite(old).all():raise ValueError('Nonfinite holdings')
     limits=np.tile([spec.symbol_limit if eligible.get(s,False) else 0 for s in symbols],k)
     d=np.eye(m)
@@ -82,8 +89,8 @@ def planned_target(forecasts,covariance,current,symbols,eligible,spec,*,shortabl
     bounds=Bounds(np.r_[lower_weights,np.zeros(2*m)],np.r_[limits,np.full(2*m,np.inf)])
     cost=spec.cost_bps/10000
     terminal=np.r_[np.zeros(m-n),np.full(n,cost)]
-    scale=max(abs(mu).max(),spec.risk_aversion*abs(cov).max(),cost,np.finfo(float).eps)
-    p=sparse.block_diag([spec.risk_aversion*cov/scale,sparse.csc_matrix((2*m,2*m))],format='csc')
+    scale=max(abs(mu).max(),spec.risk_aversion*abs(risk_cov).max(),cost,np.finfo(float).eps)
+    p=sparse.block_diag([spec.risk_aversion*risk_cov/scale,sparse.csc_matrix((2*m,2*m))],format='csc')
     q=np.r_[-mu.ravel(),np.full(m,cost),terminal]/scale
     matrix=sparse.vstack([sparse.csc_matrix(a),sparse.eye(3*m)],format='csc')
     lo=np.r_[lower,bounds.lb];hi=np.r_[np.full(len(lower),np.inf),bounds.ub]
@@ -108,7 +115,10 @@ def planned_target(forecasts,covariance,current,symbols,eligible,spec,*,shortabl
     return dict(zip(symbols,map(float,planned[0]))),dict(solver=solver,planned_steps=k,
         planned_weights=planned.tolist(),forecast_curve_bps=(mu*10000).tolist(),
         estimated_plan_turnover_cost=float(cost*abs(changes).sum()),estimated_terminal_cost=float(cost*abs(planned[-1]).sum()),
-        forecast_portfolio_return=float((mu*planned).sum()),forecast_variance=float(planned.ravel()@cov@planned.ravel()))
+        forecast_portfolio_return=float((mu*planned).sum()),forecast_variance=float(planned.ravel()@cov@planned.ravel()),
+        forecast_error_risk_enabled=error_cov is not None,
+        forecast_error_variance=0. if error_cov is None else float(planned.ravel()@error_cov@planned.ravel()),
+        total_risk_variance=float(planned.ravel()@risk_cov@planned.ravel()))
 
 class DirectionRuntime(LiquidRuntime):
     def __init__(self,*args,**kwargs):

backend/app/research/holding_policy.py

@@ -33,10 +33,15 @@ class HoldingSpec:
     seasonal_sessions: int = 20
     min_price: float = 10.
     min_adv: float = 50_000_000.
+    forecast_error_risk: bool = False
 
     def __post_init__(self):
-        if self.family not in {'legacy', 'paper', 'context'}:
+        if self.family not in {'legacy', 'paper', 'context', 'session_context', 'support_context'}:
             raise ValueError('Unknown feature family')
+        if not isinstance(self.forecast_error_risk, bool):
+            raise ValueError('forecast_error_risk must be boolean')
+        if self.forecast_error_risk and not self.planning:
+            raise ValueError('Forecast error risk requires a multi-period plan')
         if not self.horizons or tuple(sorted(set(self.horizons))) != tuple(self.horizons):
             raise ValueError('Sorted unique horizons required')
         if any(type(h) is not int or h < 1 or h > 12 for h in self.horizons):
@@ -62,7 +67,7 @@ def holding_features(frames, symbols=DEFAULT_SYMBOLS, spec=HoldingSpec()):
     else:
         panel = bar_alpha_panel(bars, BarAlphaSpec(), rank_symbols=symbols)
         result = {s: panel[s] for s in symbols}
-    if spec.family == 'context':
+    if spec.family in {'context', 'session_context', 'support_context'}:
         for s, f in result.items():
             b = bars[s]
             # Every denominator uses only earlier sessions at the SAME time slot.
@@ -86,9 +91,41 @@ def holding_features(frames, symbols=DEFAULT_SYMBOLS, spec=HoldingSpec()):
             previous = pd.Series(index.date, index=index).map(prior_close)
             f['overnight_gap'] = b.open.groupby(index.date).transform('first') / previous - 1
             f['session_fraction'] = (index.hour * 60 + index.minute - 570) / 390
+    if spec.family == 'session_context':
+        for s, f in result.items():
+            b = bars[s]
+            groups = b.groupby(b.index.date)
+            opening = groups.open.transform('first')
+            high, low = groups.high.cummax(), groups.low.cummin()
+            typical = (b.high + b.low + b.close) / 3
+            vwap_proxy = (typical*b.volume).groupby(index.date).cumsum() / groups.volume.cumsum().replace(0,np.nan)
+            # Intraday OHLCV state, not an order-flow or institutional-identity claim.
+            f['state_open_return'] = b.close/opening-1
+            f['state_high_drawdown'] = b.close/high-1
+            f['state_low_rebound'] = b.close/low-1
+            f['state_range_location'] = (b.close-low)/(high-low).replace(0,np.nan)
+            f['state_bar_vwap_distance'] = b.close/vwap_proxy-1
+            for ref in REFERENCES:
+                r = bars[ref]
+                observed = r.close/r.groupby(index.date).open.transform('first')-1
+                f[f'state_{ref}_open_return'] = observed
+                f[f'state_{ref}_open_residual'] = f.state_open_return-observed
+                f[f'state_{ref}_bar_return'] = r.close/r.open-1
+            # Expose lack of lookback explicitly instead of confusing imputation
+            # with an actually observed neutral market state.
+            f['state_momentum_12_available'] = f.momentum_12.notna().astype(float)
+            f['state_beta_available'] = f.xs_market_beta.notna().astype(float)
+    if spec.family == 'support_context':
+        from ..alpha.support_resistance import support_resistance_features
+        for s, f in result.items():
+            extra=support_resistance_features(bars[s])
+            for name in extra:
+                f[name]=extra[name]
     for f in result.values():
         f.replace([np.inf, -np.inf], np.nan, inplace=True)
-        f.attrs.update(feature_version=VERSION, spec=asdict(spec), panel_symbols=list(bars),
+        feature_spec=asdict(spec)
+        feature_spec.pop('forecast_error_risk')  # Allocation-only; preserve old feature contracts.
+        f.attrs.update(feature_version=VERSION, spec=feature_spec, panel_symbols=list(bars),
                        rank_symbols=list(symbols), timestamp_convention='bar_open_available_at_end',
                        window_unit='5min', peer_groups=PEERS)
     return result
@@ -192,7 +229,13 @@ class HoldingModel:
         eligible = {s: s in allowed and self.eligibility[s]['eligible'] for s in symbols}
         cov = self.covariance[:k*len(symbols), :k*len(symbols)]
         if self.spec.planning:
-            weights, explanation = planned_target(curve, cov, current, symbols, eligible, self.spec,shortable=shortable)
+            error_cov = None
+            if self.spec.forecast_error_risk:
+                if not hasattr(self, 'forecast_error_covariance'):
+                    raise ValueError('Prior prediction error evidence required')
+                error_cov = self.forecast_error_covariance[:k*len(symbols), :k*len(symbols)]
+            weights, explanation = planned_target(curve, cov, current, symbols, eligible, self.spec,
+                shortable=shortable, forecast_error_covariance=error_cov)
         else:
             selected=tuple(s for s in symbols if eligible[s]);indices=[symbols.index(s) for s in selected]
             weights=dict.fromkeys(symbols,0.)
@@ -236,6 +279,9 @@ class HoldingModel:
             covariance=self.covariance.tolist(),eligibility=self.eligibility,
             history={s:dict(index=[t.isoformat() for t in b.index],values=b.to_numpy().tolist()) for s,b in self.history.items()},
             performance_verified=False,deployment='paper_candidate_pending_review')
+        if self.spec.forecast_error_risk:
+            payload.update(forecast_error_covariance=self.forecast_error_covariance.tolist(),
+                           error_training=self.error_training)
         Path(path).write_text(json.dumps(payload,allow_nan=False)+'\n')
 
     @classmethod
@@ -245,6 +291,18 @@ class HoldingModel:
         obj=cls(); spec=a['spec'];spec['horizons']=tuple(spec['horizons']);obj.spec=HoldingSpec(**spec)
         obj.symbols=tuple(a['symbols']);obj.input_symbols=tuple(a['input_symbols'])
         obj.trained_before=a['trained_before'];obj.eligibility=a['eligibility'];obj.covariance=np.array(a['covariance'])
+        if obj.spec.forecast_error_risk:
+            from ..quant_policy import _validated_covariance
+            obj.forecast_error_covariance=_validated_covariance(a['forecast_error_covariance'],max(obj.spec.horizons)*len(obj.symbols))
+            obj.error_training=a['error_training']
+            error_cutoff=pd.Timestamp(obj.trained_before,tz='America/New_York')
+            if pd.Timestamp(obj.error_training['last_label_end'])>=error_cutoff:
+                raise ValueError('Immature forecast error evidence')
+            for fold in obj.error_training['folds']:
+                fold_start=pd.Timestamp(fold['day'],tz='America/New_York')
+                if (fold_start>=error_cutoff or pd.Timestamp(fold['calibrator_last_label_end'])>=fold_start
+                        or pd.Timestamp(obj.error_training['base_trained_before'])>=fold_start):
+                    raise ValueError('Noncausal forecast error fold')
         obj.models={}
         for s,hs in a['models'].items():
             obj.models[s]={}

backend/data/charts/dashboard_replay.js

@@ -2,10 +2,16 @@
   'use strict';
 
   const symbols = ['TSLA', 'NVDA', 'SNDK', 'PLTR'];
-  const comparison = Boolean(document.getElementById('datePicker'));
-  let currentTicker = 'TSLA';
-  let currentDate = '';
-  let currentTimeframe = '5m';
+  const parentOrigin = (() => {
+    try {
+      const referrer = new URL(document.referrer);
+      return ['http:', 'https:'].includes(referrer.protocol) ? referrer.origin : window.location.origin;
+    } catch { return window.location.origin; }
+  })();
+  const initial = new URLSearchParams(window.location.search);
+  let currentTicker = symbols.includes(initial.get('ticker')) ? initial.get('ticker') : 'TSLA';
+  let currentDate = /^\d{4}-\d{2}-\d{2}$/.test(initial.get('date') || '') ? initial.get('date') : '';
+  let currentTimeframe = ['1m', '5m', '15m', '30m'].includes(initial.get('interval')) ? initial.get('interval') : '5m';
   let currentMode = 'real';
   let market = null;
   let fills = [];
@@ -17,6 +23,28 @@
   const byId = (id) => document.getElementById(id);
   const setText = (id, value) => { const node = byId(id); if (node) node.textContent = value; };
   const escapeHtml = (value) => String(value).replace(/[&<>"']/g, c => ({ '&': '&amp;', '<': '&lt;', '>': '&gt;', '"': '&quot;', "'": '&#39;' }[c]));
+  const etTime = new Intl.DateTimeFormat('en-GB', { timeZone: 'America/New_York', hour: '2-digit', minute: '2-digit', hourCycle: 'h23' });
+  const etSeconds = new Intl.DateTimeFormat('en-GB', { timeZone: 'America/New_York', hour: '2-digit', minute: '2-digit', second: '2-digit', hourCycle: 'h23' });
+  const actions = {
+    BUY: { letter: 'B', label: '买入', color: '#fb7185', rotation: 0 },
+    SELL: { letter: 'S', label: '卖出', color: '#10b981', rotation: 180 },
+    SHORT: { letter: 'X', label: '做空', color: '#f59e0b', rotation: 180 },
+    COVER: { letter: 'C', label: '平空', color: '#a78bfa', rotation: 0 }
+  };
+
+  function fillTimestamp(fill, data) {
+    // Broker archives may store an ET wall-clock timestamp without a UTC offset.
+    // Anchor those records to the selected session's explicit market-data offset,
+    // never the browser's local timezone or a rounded five-minute label.
+    let value = String(fill.time || '').replace(' ', 'T');
+    if (/^\d{2}:\d{2}/.test(value)) value = `${data.date}T${value}`;
+    if (!/(Z|[+-]\d{2}:?\d{2})$/i.test(value)) {
+      const offset = String(data.full_time?.[0] || '').match(/(Z|[+-]\d{2}:?\d{2})$/i);
+      if (!offset) return NaN;
+      value += offset[0];
+    }
+    return Date.parse(value);
+  }
 
   function ema(values, period) {
     const factor = 2 / (period + 1);
@@ -39,50 +67,65 @@
 
   function chartOption(data, limit, includeFills) {
     const end = Math.max(1, Math.min(limit, data.time.length));
-    const times = data.time.slice(0, end);
+    const intervalMs = Number.parseInt(data.interval, 10) * 60000;
+    const starts = data.full_time.map(value => Date.parse(value));
+    // A closing price becomes available at the end of its bar.
+    const times = starts.slice(0, end).map(value => value + intervalMs);
     const closes = data.close.slice(0, end);
-    const candles = times.map((_, i) => [data.open[i], data.close[i], data.low[i], data.high[i]]);
+    const candles = times.map((time, i) => [time, data.open[i], data.close[i], data.low[i], data.high[i]]);
     const volume = data.volume.slice(0, end).map((value, i) => ({
-      value,
+      value: [times[i], value],
       itemStyle: { color: data.close[i] >= data.open[i] ? '#10b981' : '#ef4444' }
     }));
-    const annotations = includeFills ? fills.filter(fill => times.includes(fill.time.slice(11, 16))) : [];
-    const markerSeries = ['BUY', 'SELL', 'SHORT', 'COVER'].map(action => ({
-      name: action,
+    const annotations = includeFills ? fills.map(fill => ({ ...fill, timestamp: fillTimestamp(fill, data) }))
+      .filter(fill => Number.isFinite(fill.timestamp) && fill.timestamp >= starts[0] && fill.timestamp <= times[end - 1]) : [];
+    const markerSeries = Object.entries(actions).map(([action, style]) => ({
+      name: `${style.letter} ${style.label}`,
       type: 'scatter',
-      symbol: action === 'BUY' || action === 'COVER' ? 'triangle' : 'pin',
-      symbolRotate: action === 'BUY' || action === 'COVER' ? 0 : 180,
-      symbolSize: 13,
-      itemStyle: { color: action === 'BUY' || action === 'COVER' ? '#22c55e' : '#ef4444' },
-      data: annotations.filter(fill => fill.action === action).map(fill => [fill.time.slice(11, 16), fill.price]),
-      tooltip: { valueFormatter: value => `$${Number(value).toFixed(2)}` }
+      symbol: 'triangle', symbolRotate: style.rotation, symbolSize: 12, z: 8,
+      itemStyle: { color: style.color },
+      label: { show: true, position: style.rotation ? 'bottom' : 'top', color: style.color, fontSize: 10,
+        formatter: params => `${style.letter}${params.data.shares}` },
+      labelLayout: { hideOverlap: true },
+      data: annotations.filter(fill => fill.action === action).map(fill => ({ value: [fill.timestamp, fill.price], shares: fill.shares, source: fill.source })),
+      tooltip: { trigger: 'item', formatter: params => `${escapeHtml(etSeconds.format(params.value[0]))} ET · ${style.letter} ${style.label}<br>${params.data.shares} 股 · $${Number(params.value[1]).toFixed(4)}<br>${escapeHtml(params.data.source || 'broker_archive')}` }
     }));
+    const pair = values => values.map((value, index) => [times[index], value]);
+    const timeAxis = { type: 'time', min: starts[0], max: starts[starts.length - 1] + intervalMs,
+      axisLine: { lineStyle: { color: '#334155' } }, axisLabel: { color: '#94a3b8', formatter: value => etTime.format(value) }, splitLine: { show: false } };
     return {
       animation: false,
       backgroundColor: '#131722',
       title: {
         text: `${data.ticker} · ${data.date} · ${data.interval.toUpperCase()}`,
-        subtext: `${data.data_provenance.bar_count} 根已观测 K 线 · 覆盖至 ${data.data_provenance.last_bar_end.slice(11, 16)} · ${data.data_provenance.source}`,
+        subtext: `${end}/${data.time.length} 根已完成 · 复盘至 ${etTime.format(times[end - 1])} ET · ${data.data_provenance.source}`,
         left: 12, top: 8, textStyle: { color: '#e2e8f0', fontSize: 14 }, subtextStyle: { color: '#94a3b8', fontSize: 11 }
       },
-      legend: { top: 10, right: 18, textStyle: { color: '#94a3b8' }, data: ['K线', 'EMA9', 'EMA21', 'VWAP'] },
-      tooltip: { trigger: 'axis', axisPointer: { type: 'cross' }, backgroundColor: '#0f172a', borderColor: '#334155', textStyle: { color: '#e2e8f0' } },
+      legend: { type: 'scroll', top: 54, left: 12, right: 18, textStyle: { color: '#94a3b8', fontSize: 10 },
+        data: ['PRICE', 'OHLCV VWAP 近似', 'B 买入', 'S 卖出', 'X 做空', 'C 平空', 'K线', 'EMA9', 'EMA21'],
+        selected: { K线: false, EMA9: false, EMA21: false } },
+      tooltip: { trigger: 'axis', axisPointer: { type: 'cross', label: { formatter: params => params.axisDimension === 'x' ? `${etTime.format(params.value)} ET` : Number(params.value).toFixed(2) } },
+        formatter: params => {
+          const rows = params.filter(row => row.seriesType !== 'scatter');
+          if (!rows.length) return '';
+          return `${etTime.format(rows[0].value[0])} ET<br>` + rows.map(row => row.seriesType === 'candlestick'
+            ? `${row.marker} K线 O ${row.value[1]} · C ${row.value[2]} · L ${row.value[3]} · H ${row.value[4]}`
+            : `${row.marker} ${escapeHtml(row.seriesName)}: ${row.seriesName === '成交量' ? Number(row.value[1]).toLocaleString() : '$' + Number(row.value[1]).toFixed(2)}`).join('<br>');
+        }, backgroundColor: '#0f172a', borderColor: '#334155', textStyle: { color: '#e2e8f0' } },
       axisPointer: { link: [{ xAxisIndex: 'all' }] },
-      grid: [{ left: 58, right: 22, top: 58, height: '65%' }, { left: 58, right: 22, top: '78%', height: '14%' }],
-      xAxis: [
-        { type: 'category', data: times, boundaryGap: true, axisLine: { lineStyle: { color: '#334155' } }, axisLabel: { color: '#94a3b8', interval: Math.max(0, Math.floor(times.length / 8) - 1) }, splitLine: { show: false } },
-        { type: 'category', gridIndex: 1, data: times, boundaryGap: true, axisLine: { lineStyle: { color: '#334155' } }, axisLabel: { show: false }, splitLine: { show: false } }
-      ],
+      grid: [{ left: 58, right: 22, top: 92, bottom: '29%' }, { left: 58, right: 22, top: '78%', bottom: 28 }],
+      xAxis: [timeAxis, { ...timeAxis, gridIndex: 1 }],
       yAxis: [
         { scale: true, axisLabel: { color: '#94a3b8' }, splitLine: { lineStyle: { color: '#1e293b' } } },
-        { gridIndex: 1, scale: true, axisLabel: { color: '#64748b' }, splitLine: { show: false } }
+        { gridIndex: 1, scale: true, axisLabel: { color: '#64748b', formatter: value => Math.abs(value) >= 1000000 ? `${(value / 1000000).toFixed(1)}M` : Math.abs(value) >= 1000 ? `${(value / 1000).toFixed(0)}k` : value }, splitLine: { show: false } }
       ],
       dataZoom: [{ type: 'inside', xAxisIndex: [0, 1], start: 0, end: 100 }],
       series: [
-        { name: 'K线', type: 'candlestick', data: candles, itemStyle: { color: '#10b981', color0: '#ef4444', borderColor: '#10b981', borderColor0: '#ef4444' } },
-        { name: 'EMA9', type: 'line', data: ema(closes, 9), showSymbol: false, lineStyle: { color: '#38bdf8', width: 1.4 } },
-        { name: 'EMA21', type: 'line', data: ema(closes, 21), showSymbol: false, lineStyle: { color: '#f59e0b', width: 1.2 } },
-        { name: 'VWAP', type: 'line', data: vwap({ ...data, close: closes, high: data.high.slice(0, end), low: data.low.slice(0, end), volume: data.volume.slice(0, end) }), showSymbol: false, lineStyle: { color: '#a855f7', width: 1.2, type: 'dashed' } },
+        { name: 'PRICE', type: 'line', data: pair(closes), smooth: false, showSymbol: false, itemStyle: { color: '#38bdf8' }, lineStyle: { color: '#38bdf8', width: 2 } },
+        { name: 'OHLCV VWAP 近似', type: 'line', data: pair(vwap({ ...data, close: closes })), smooth: false, showSymbol: false, itemStyle: { color: '#f59e0b' }, lineStyle: { color: '#f59e0b', width: 1.6 } },
+        { name: 'K线', type: 'candlestick', data: candles, encode: { x: 0, y: [1, 2, 3, 4] }, itemStyle: { color: '#10b981', color0: '#ef4444', borderColor: '#10b981', borderColor0: '#ef4444' } },
+        { name: 'EMA9', type: 'line', data: pair(ema(closes, 9)), showSymbol: false, itemStyle: { color: '#a78bfa' }, lineStyle: { color: '#a78bfa', width: 1.2 } },
+        { name: 'EMA21', type: 'line', data: pair(ema(closes, 21)), showSymbol: false, itemStyle: { color: '#94a3b8' }, lineStyle: { color: '#94a3b8', width: 1.2 } },
         { name: '成交量', type: 'bar', xAxisIndex: 1, yAxisIndex: 1, data: volume },
         ...markerSeries
       ]
@@ -93,7 +136,10 @@
     const node = byId(id);
     if (!node || !market || typeof echarts === 'undefined') return;
     const chart = echarts.getInstanceByDom(node) || echarts.init(node, null, { renderer: 'canvas' });
-    chart.setOption(chartOption(market, limit, showFills), true);
+    const option = chartOption(market, limit, showFills);
+    const selected = chart.getOption()?.legend?.[0]?.selected;
+    if (selected) option.legend.selected = { ...option.legend.selected, ...selected };
+    chart.setOption(option, true);
   }
 
   function showError(message) {
@@ -148,28 +194,33 @@
     }
     const pills = byId('tickerPills');
     if (pills) pills.innerHTML = symbols.map(symbol => `<button class="ticker-pill${symbol === currentTicker ? ' active' : ''}" onclick="setTicker('${symbol}')">${symbol}</button>`).join('');
+    document.querySelectorAll('.tf-pill').forEach(node => node.classList.toggle('active', node.textContent.toLowerCase() === currentTimeframe));
   }
 
   async function loadData(preferredDate) {
     const sequence = ++requestSequence;
+    const requestedTicker = currentTicker;
+    const requestedTimeframe = currentTimeframe;
     stopReplay();
     showError('正在读取完整历史行情…');
     try {
-      const query = new URLSearchParams({ ticker: currentTicker, interval: currentTimeframe });
+      const query = new URLSearchParams({ ticker: requestedTicker, interval: requestedTimeframe });
       if (preferredDate) query.set('date', preferredDate);
       const response = await fetch(`/api/dashboard/market_data?${query}`);
       const data = await response.json();
       if (!response.ok || !data.success) throw new Error(data.error || '历史行情不可用');
       if (sequence !== requestSequence) return;
+      const fillResponse = await fetch(`/api/dashboard/trades?ticker=${encodeURIComponent(requestedTicker)}&date=${encodeURIComponent(data.date)}`);
+      const fillData = await fillResponse.json();
+      if (sequence !== requestSequence) return;
       market = data;
       currentDate = data.date;
-      const fillResponse = await fetch(`/api/dashboard/trades?ticker=${encodeURIComponent(currentTicker)}&date=${encodeURIComponent(currentDate)}`);
-      const fillData = await fillResponse.json();
       fills = fillData.success && Array.isArray(fillData.fills) ? fillData.fills : [];
       replayIndex = data.time.length;
       updateStaticLabels();
       populateControls();
       renderAll();
+      if (window.parent !== window) window.parent.postMessage({ type: 'quant-replay-selection-change', ticker: currentTicker, date: currentDate, interval: currentTimeframe }, parentOrigin);
     } catch (error) {
       if (sequence === requestSequence) showError(error instanceof Error ? error.message : '历史行情不可用');
     }
@@ -185,7 +236,8 @@
   window.togglePlay = function () {
     if (!market) return;
     if (timer) { stopReplay(); return; }
-    if (replayIndex >= market.time.length) replayIndex = Math.min(5, market.time.length);
+    if (replayIndex >= market.time.length) replayIndex = 1;
+    draw('dynamicChart', replayIndex, currentMode === 'real');
     const button = byId('btnPlay');
     if (button) button.textContent = '⏸ 暂停';
     timer = window.setInterval(() => {
@@ -197,7 +249,7 @@
 
   window.resetReplay = function () {
     stopReplay();
-    if (market) replayIndex = market.time.length;
+    if (market) replayIndex = 1;
     renderAll();
     const stream = byId('orderStream');
     if (stream) stream.innerHTML = '<div style="color:#64748b;font-size:12px;text-align:center;padding:15px">播放使用真实历史 K 线；不会自动生成交易。</div>';
@@ -205,9 +257,11 @@
   };
 
   window.setSpeed = function (value, button) {
+    const playing = Boolean(timer);
     speed = Number(value) || 1;
     document.querySelectorAll('.speed-btn').forEach(node => node.classList.remove('active'));
     if (button) button.classList.add('active');
+    if (playing) { stopReplay(); window.togglePlay(); }
   };
   window.setTicker = function (symbol) { currentTicker = symbols.includes(symbol) ? symbol : 'TSLA'; loadData(currentDate); };
   window.setTimeframe = function (interval) {
@@ -235,6 +289,17 @@
   window.addEventListener('resize', () => ['dynamicChart', 'plotlyChart'].forEach(id => {
     const node = byId(id); const chart = node && typeof echarts !== 'undefined' ? echarts.getInstanceByDom(node) : null; chart?.resize();
   }));
+  window.addEventListener('message', event => {
+    if (event.source !== window.parent || event.origin !== parentOrigin || event.data?.type !== 'quant-replay-selection') return;
+    const { ticker, date, interval } = event.data;
+    const symbol = symbols.includes(ticker) ? ticker : currentTicker;
+    const day = /^\d{4}-\d{2}-\d{2}$/.test(date || '') ? date : currentDate;
+    const timeframe = ['1m', '5m', '15m', '30m'].includes(interval) ? interval : currentTimeframe;
+    if (symbol === currentTicker && day === currentDate && timeframe === currentTimeframe) return;
+    currentTicker = symbol;
+    currentTimeframe = timeframe;
+    loadData(day);
+  });
   updateStaticLabels();
-  loadData('');
+  loadData(currentDate);
 })();

backend/data/price_replay/2026-09-14/context/NVDA.json

@@ -0,0 +1 @@
+{"schema_version":1,"ticker":"NVDA","date":"2026-09-14","variant":"context","source_label":"已保存历史行情与模型模拟成交","price_source":"Saved Yahoo OHLCV","is_simulated":true,"as_of":"2026-09-14T16:00:00-04:00","partial":false,"starting_equity":47006.42,"capital_scope":"four_stock_portfolio","cost_bps":5,"cost_label":"单边模拟摩擦假设；非实际券商收费","actual_fees_verified":false,"opening_shares":0,"session_open":"2026-09-14T09:30:00-04:00","session_close":"2026-09-14T16:00:00-04:00","bar_interval_minutes":5,"bar_timing":"completed_bar_availability","vwap_label":"OHLCV VWAP 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backend/data/price_replay/manifest.json

@@ -0,0 +1,48 @@
+{
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+    "2026-09-15"
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+  ],
+  "study": "manual_style_research_20260915",
+  "source_registry_sha256": "440c6827502028fb50e50437057c721cd8797733b9c770c72cfbea08bd5d65db",
+  "source_evidence_sha256": "cf5dffeadd99db2940e0135e76cb27a3839a464a34a6ea8ceb396cb0aa0cbb08",
+  "export_script_sha256": "1b2764d22706db56bbbef875a539bcbf7eca2e6e906c07d46b6a22c0d1b45ba8",
+  "files": {
+    "2026-09-14/context/SNDK.json": "880f0973549d0dcfb6adb16b9e5458a10472024751a751bff332ee451638527a",
+    "2026-09-14/context/TSLA.json": "0d2d388a8d4f201ca6815299db47becc996e7f306aed0c993b50f2e1f50c7171",
+    "2026-09-14/context/PLTR.json": "01fd690140fe735115e3407954c62c9260ff5d02eca5e659000bd9711f272a21",
+    "2026-09-14/context/NVDA.json": "7251e71358afbc87aa45325e0303538852a23a7eb32f67be0df07e833fce1b02",
+    "2026-09-14/levels/SNDK.json": "ef9359230f72f9be54ed6c3c3782d7cfa1e8afdcd6463cd6504a93700d76b480",
+    "2026-09-14/levels/TSLA.json": "c5337b6fac8932ef825673dd0908dd75ae2e6be1158632ea815b5ff0bfb312af",
+    "2026-09-14/levels/PLTR.json": "afa0f1cf6d7046f57b4d0adc8a1a4b293ea7092bad6a738a27e47f18d88dae36",
+    "2026-09-14/levels/NVDA.json": "581b783589eaf182dbe74f6442be70684b52007f65e3490565557557fb3386d4",
+    "2026-09-14/levels_error_risk/SNDK.json": "2c21de72c583a9164c408cf8491015b52f610e81b5376c311ef93ed99a25fd08",
+    "2026-09-14/levels_error_risk/TSLA.json": "1c4daae67f47c80e12686db60bee45815fe77dcbc27c4bf2c48dfed13a8815b6",
+    "2026-09-14/levels_error_risk/PLTR.json": "a3d52d4d47a461228b35bdd205ba4320195c43ecf783b89ce9a1033ded25c566",
+    "2026-09-14/levels_error_risk/NVDA.json": "ff0ed8402b2bff74cee2d4f7d0fcc99e35fc4573113aad408257f8bb8add19b5",
+    "2026-09-15/context/SNDK.json": "bae01ef17e0a30226f8cd973fcb2f3f9c597a8d5caee836995477753c3054d7a",
+    "2026-09-15/context/TSLA.json": "210b5ce6998ba8a8889d216d083dec4151d7e35aa362744698791e79ae77e6db",
+    "2026-09-15/context/PLTR.json": "86e824c71c713b333dd81afc8611467ee3624f74f4435880466e5003ed5a40ed",
+    "2026-09-15/context/NVDA.json": "82752b749197cb2aa2a1c1448921131ce0e54d9f928ece5bde15210d80de0321",
+    "2026-09-15/levels/SNDK.json": "33b64b685c80f62bea383fa9b0a9753a64f95d4bfed05a21835f85457ebf392c",
+    "2026-09-15/levels/TSLA.json": "665b53354488e50da9148a8b96be76667cf135bc08cc6a18a0074c4c802e540b",
+    "2026-09-15/levels/PLTR.json": "8f3208ba961de099aa89f30006b0dce2f4069e9a20e251768968e3bf67356edd",
+    "2026-09-15/levels/NVDA.json": "81e42e48bf669c7547a57bd66c9509e4c780a874646d76e9318dba136e869ee5",
+    "2026-09-15/levels_error_risk/SNDK.json": "e64694f35b4d0edc2883802854637dd6a399d72dbb2f586d9b99b1b48bd32a59",
+    "2026-09-15/levels_error_risk/TSLA.json": "5ccd4051e3bde559105a3426872cb2bde2dbbbef1b40b014eb4239aad7c75580",
+    "2026-09-15/levels_error_risk/PLTR.json": "64b18cc639f2073594d07565ec7cb3d08d69d43200024d7e43f0f09a80ed634f",
+    "2026-09-15/levels_error_risk/NVDA.json": "d14f8d4b56f31ccd9d3db54527800f1c0eeeb8137b028244c86c4e4f18972709"
+  }
+}

backend/main_api.py

@@ -1852,6 +1852,13 @@ def get_dashboard_market_data(ticker: str = "TSLA", date: str = "", interval: st
         return {"success": False, "error": str(exc)}
 
 
+@app.get("/api/dashboard/price_replay")
+def get_dashboard_price_replay(ticker: str = "SNDK", date: str = "", variant: str = "levels_error_risk"):
+    """Read saved price, simulated fills and ledger; never run research or trade."""
+    from app.dashboard.price_replay import price_replay
+    return price_replay(ticker, date, variant)
+
+
 @app.get("/api/dashboard/trades")
 def get_dashboard_trades(ticker: str = "TSLA", date: str = ""):
     from app.ml.dashboard_market import archived_fills

backend/tests/test_direction_forecast_risk.py

@@ -0,0 +1,65 @@
+"""Forecast uncertainty changes sizing without hard direction or time rules."""
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from app.quant_policy import PolicySpec, target_weights
+from app.research.direction_policy import planned_target
+
+
+def plan(mu, covariance, current=0., **kwargs):
+    return planned_target(np.asarray(mu, dtype=float).reshape(-1, 1), covariance,
+                          {'stock': current}, ('stock',), {'stock': True},
+                          PolicySpec('uncertainty_test', risk_aversion=50., cost_bps=2.), **kwargs)
+
+
+def test_none_matches_one_step_objective_including_terminal_cost():
+    mu = .0015
+    covariance = np.array([[.00008]])
+    weights, info = plan([mu], covariance, current=.2)
+    explicit_none, _ = plan([mu], covariance, current=.2, forecast_error_covariance=None)
+    # The existing one-period solver can express terminal liquidation cost as
+    # its absolute-position penalty, independently reproducing the old planner.
+    reference = target_weights({'stock': mu}, covariance, {'stock': .2},
+                               PolicySpec('reference', risk_aversion=50., cost_bps=2.),
+                               symbols=('stock',), uncertainty=[.0002],
+                               uncertainty_aversion=1., solver='osqp')
+    assert weights == pytest.approx(reference, abs=1e-7)
+    assert explicit_none == pytest.approx(weights, abs=1e-9)
+    assert info['forecast_error_risk_enabled'] is False
+    assert info['forecast_error_variance'] == 0
+    assert info['total_risk_variance'] == pytest.approx(info['forecast_variance'])
+
+
+def test_forecast_uncertainty_reduces_extreme_exposure_and_accounts_for_risk():
+    covariance = np.array([[.000001]])
+    without, _ = plan([.0015], covariance)
+    weights, info = plan([.0015], covariance, forecast_error_covariance=np.array([[.0001]]))
+    assert without['stock'] == pytest.approx(.7, abs=1e-7)
+    assert 0 < weights['stock'] < without['stock']
+    assert info['forecast_error_risk_enabled'] is True
+    assert info['forecast_error_variance'] > 0
+    assert info['total_risk_variance'] == pytest.approx(
+        info['forecast_variance'] + info['forecast_error_variance'])
+
+
+def test_strong_negative_forecast_still_reverses_a_long_position():
+    weights, info = plan([-.01], np.array([[.000001]]), current=.5,
+                         forecast_error_covariance=np.array([[.0001]]))
+    assert weights['stock'] < 0
+    assert info['estimated_plan_turnover_cost'] > .5 * .0002
+
+
+@pytest.mark.parametrize('invalid', [
+    np.zeros((1, 1)),  # The plan below has two intervals.
+    np.array([[1., np.nan], [np.nan, 1.]]),
+    np.array([[1., 1.], [0., 1.]]),
+    np.array([[1., 2.], [2., 1.]]),
+])
+def test_invalid_forecast_error_covariance_is_rejected(invalid):
+    with pytest.raises(ValueError, match='Covariance'):
+        plan([.001, .002], np.eye(2) * .00001, forecast_error_covariance=invalid)

backend/tests/test_price_replay.py

@@ -0,0 +1,90 @@
+"""Saved chart data must preserve fills, partial exposure and portfolio accounting."""
+import hashlib
+import json
+from datetime import datetime, timedelta
+
+import pytest
+
+from app.dashboard.price_replay import BUNDLE_ROOT, price_replay
+
+
+def test_all_portable_replays_reconcile_without_double_charging_friction():
+    manifest = json.loads((BUNDLE_ROOT / "manifest.json").read_text())
+    assert len(manifest["files"]) == 24
+    for day in manifest["available_dates"]:
+        for variant in manifest["variants"]:
+            for ticker in manifest["tickers"]:
+                payload = price_replay(ticker, day, variant)
+                assert payload["success"]
+                assert payload["is_simulated"] and not payload["actual_fees_verified"]
+                assert payload["capital_scope"] == "four_stock_portfolio"
+                position = 0
+                for fill in payload["fills"]:
+                    assert fill["shares_before"] == position
+                    assert abs(fill["quantity"]) == fill["shares"]
+                    assert fill["action"] == (
+                        "B" if fill["quantity"] > 0 and position >= 0 else
+                        "C" if fill["quantity"] > 0 else "S" if position > 0 else "X")
+                    position += fill["quantity"]
+                    assert position == fill["shares_after"]
+                for bar, mark in zip(payload["bars"], payload["marks"]):
+                    assert datetime.fromisoformat(bar["time"]) - datetime.fromisoformat(bar["bar_open"]) == timedelta(minutes=5)
+                    assert bar["time"] == mark["time"]
+                    seen = [fill for fill in payload["fills"] if fill["time"] < mark["time"]]
+                    shares = sum(fill["quantity"] for fill in seen)
+                    cash = -sum(fill["quantity"] * fill["price"] + fill["commission_cost"] for fill in seen)
+                    assert mark["shares"] == shares
+                    # Fill prices already include assumed slippage. Subtracting
+                    # assumed_cost here a second time would fail this equality.
+                    assert mark["pnl"] == pytest.approx(cash + shares * bar["close"], abs=1e-7)
+                summary = payload["summary"]
+                assert summary["net_pnl"] == payload["marks"][-1]["pnl"]
+                assert summary["cost"] == pytest.approx(sum(f["assumed_cost"] for f in payload["fills"]))
+                assert summary["gross_pnl"] - summary["cost"] == pytest.approx(summary["net_pnl"])
+                assert summary["ending_shares"] == position
+                assert summary["fill_count"] == len(payload["fills"])
+
+
+def test_partial_session_keeps_open_short_and_original_cutoff():
+    data = price_replay(" sndk ", "2026-09-15", "levels_error_risk")
+    assert data["success"] and data["partial"]
+    assert data["as_of"] == "2026-09-15T15:50:00-04:00"
+    assert data["bars"][-1]["time"] == data["as_of"]
+    assert len(data["bars"]) == 76
+    assert data["summary"]["ending_shares"] == -3
+    assert data["summary"]["net_pnl"] == pytest.approx(-147.1863435058549)
+    complete = price_replay("SNDK", "2026-09-14")
+    assert not complete["partial"]
+    assert complete["summary"]["ending_shares"] == 0
+
+
+@pytest.mark.parametrize("kwargs", [{"ticker": "MSTR"}, {"date": "2026-09-11"},
+                                    {"variant": "does_not_exist"}, {"ticker": "../SNDK"}])
+def test_unknown_request_does_not_fall_back_to_wrong_prices_or_zero_pnl(kwargs):
+    data = price_replay(**kwargs)
+    assert not data["success"]
+    assert data["status"] == "unavailable"
+    assert data["summary"] is None
+    assert data["bars"] == []
+    assert data["available_dates"]
+
+
+def test_missing_or_tampered_bundle_is_unavailable(tmp_path):
+    assert not price_replay(root=tmp_path)["success"]
+    manifest = json.loads((BUNDLE_ROOT / "manifest.json").read_text())
+    name = "2026-09-15/levels_error_risk/SNDK.json"
+    destination = tmp_path / name
+    destination.parent.mkdir(parents=True)
+    raw = (BUNDLE_ROOT / name).read_bytes()
+    destination.write_bytes(raw + b" ")
+    (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+    assert not price_replay(root=tmp_path)["success"]
+    destination.write_bytes(raw)
+    assert price_replay(root=tmp_path)["success"]
+    # Even a hash-updated wrong-symbol payload must not masquerade as SNDK.
+    changed = json.loads(raw)
+    changed["ticker"] = "TSLA"
+    destination.write_text(json.dumps(changed))
+    manifest["files"][name] = hashlib.sha256(destination.read_bytes()).hexdigest()
+    (tmp_path / "manifest.json").write_text(json.dumps(manifest))
+    assert not price_replay(root=tmp_path)["success"]

backend/tests/test_session_context_risk.py

@@ -0,0 +1,84 @@
+"""Opening observations and prediction-error evidence are causal model inputs."""
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from app.research.calibrated_holding import CalibratedHoldingModel
+from app.research.holding_policy import DEFAULT_SYMBOLS, REFERENCES, HoldingSpec, holding_features
+from test_holding_policy import frames
+
+
+def test_opening_context_is_observed_without_fabricating_long_lookbacks(frames):
+    features = holding_features(frames, spec=HoldingSpec(family='session_context'))
+    opening = pd.Timestamp('2026-08-10 09:30', tz='America/New_York')
+    for symbol in DEFAULT_SYMBOLS:
+        row = features[symbol].loc[opening]
+        expected = frames[symbol].loc[opening, 'close'] / frames[symbol].loc[opening, 'open'] - 1
+        assert row.state_open_return == pytest.approx(expected)
+        assert np.isnan(row.momentum_12)
+        assert row.state_momentum_12_available == 0
+        assert row.state_beta_available == 0
+        for ref in REFERENCES:
+            ref_expected = frames[ref].loc[opening, 'close'] / frames[ref].loc[opening, 'open'] - 1
+            assert row[f'state_{ref}_open_return'] == pytest.approx(ref_expected)
+            assert row[f'state_{ref}_open_residual'] == pytest.approx(expected - ref_expected)
+            assert np.isnan(row[f'{ref}_return_12'])
+
+
+def test_session_context_matches_prefix_and_ignores_future_extrema(frames):
+    spec = HoldingSpec(family='session_context')
+    last_closed = pd.Timestamp('2026-08-10 10:05', tz='America/New_York')
+    original = holding_features(frames, spec=spec)
+    prefix = holding_features({s: b.loc[:last_closed] for s, b in frames.items()}, spec=spec)
+    changed = {s: b.copy() for s, b in frames.items()}
+    for bars in changed.values():
+        bars.loc[bars.index > last_closed, ['open', 'high', 'low', 'close', 'volume']] *= 3
+    mutated = holding_features(changed, spec=spec)
+    for symbol in DEFAULT_SYMBOLS:
+        pd.testing.assert_frame_equal(original[symbol].loc[:last_closed], prefix[symbol])
+        pd.testing.assert_frame_equal(original[symbol].loc[:last_closed], mutated[symbol].loc[:last_closed])
+
+
+def test_error_covariance_uses_prior_forward_folds_and_survives_roundtrip(frames, tmp_path):
+    cutoff = pd.Timestamp('2026-08-10', tz='America/New_York')
+    spec = HoldingSpec(family='session_context', horizons=(1, 3), seasonal_sessions=2,
+                       forecast_error_risk=True)
+    model = CalibratedHoldingModel.fit(frames, cutoff, spec, validation_sessions=3)
+    changed = {s: b.copy() for s, b in frames.items()}
+    for bars in changed.values():
+        # Mutate all not-yet-observed prices and volume while preserving OHLC.
+        bars.loc[bars.index >= cutoff, ['open', 'high', 'low', 'close', 'volume']] *= 2
+    other = CalibratedHoldingModel.fit(changed, cutoff, spec, validation_sessions=3)
+    np.testing.assert_allclose(model.forecast_error_covariance, other.forecast_error_covariance,
+                               rtol=0, atol=1e-14)
+    assert model.error_training == other.error_training
+    assert len(model.error_training['folds']) == 1
+    for fold in model.error_training['folds']:
+        assert pd.Timestamp(fold['calibrator_last_label_end']) < pd.Timestamp(fold['day'], tz='America/New_York')
+        assert pd.Timestamp(fold['day'], tz='America/New_York') < cutoff
+    assert pd.Timestamp(model.error_training['last_label_end']) < cutoff
+    assert np.linalg.eigvalsh(model.forecast_error_covariance).min() >= -1e-14
+    assert model.forecast_error_covariance.shape == (12, 12)
+
+    path = tmp_path / 'candidate.json'
+    model.save(path)
+    restored = CalibratedHoldingModel.load(path)
+    np.testing.assert_allclose(restored.forecast_error_covariance, model.forecast_error_covariance)
+    kwargs = dict(allowed=DEFAULT_SYMBOLS, shortable=dict.fromkeys(DEFAULT_SYMBOLS, True),
+                  as_of=cutoff + pd.Timedelta(hours=10, minutes=35))
+    before, info = model.live_target(frames, dict.fromkeys(DEFAULT_SYMBOLS, 0.), **kwargs)
+    after, _ = restored.live_target(frames, dict.fromkeys(DEFAULT_SYMBOLS, 0.), **kwargs)
+    assert after == pytest.approx(before, abs=1e-7)
+    assert info['forecast_error_risk_enabled'] is True
+
+    artifact = json.loads(path.read_text())
+    artifact['error_training']['last_label_end'] = cutoff.isoformat()
+    path.write_text(json.dumps(artifact))
+    with pytest.raises(ValueError, match='Immature forecast error'):
+        CalibratedHoldingModel.load(path)

backend/tests/test_support_resistance.py

@@ -0,0 +1,135 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from app.alpha.support_resistance import FEATURE_COLUMNS, support_resistance_features
+
+
+def _day(day="2026-09-14", periods=78):
+    index = pd.date_range(day+" 09:30", periods=periods, freq="5min", tz="America/New_York")
+    rng = np.random.default_rng(17)
+    c = 100 + np.cumsum(rng.normal(0, 0.4, periods))
+    return pd.DataFrame(dict(open=c-.1, high=c+.5, low=c-.5, close=c, volume=1000.), index=index)
+
+
+def test_prefix_equivalence_and_future_mutation():
+    bars = pd.concat([_day(), _day("2026-09-15", 20)])
+    full = support_resistance_features(bars)
+    for n in (1, 2, 3, 5, 12, 79, 86, 98):
+        pd.testing.assert_frame_equal(full.iloc[:n], support_resistance_features(bars.iloc[:n]))
+    changed = bars.copy()
+    changed.iloc[86:] *= 10
+    pd.testing.assert_frame_equal(full.iloc[:86], support_resistance_features(changed).iloc[:86])
+
+
+def test_opening_range_available_only_after_third_bar_completes():
+    bars = _day(periods=8)
+    f = support_resistance_features(bars)
+    assert f.sr_opening_high_distance_bps.iloc[:2].isna().all()
+    assert f.sr_opening_high_distance_bps.iloc[2] == pytest.approx((bars.close.iloc[2]/bars.high.iloc[:3].max()-1)*1e4)
+    assert bars.index[2]+pd.Timedelta(minutes=5) == pd.Timestamp("2026-09-14 09:45", tz="America/New_York")
+    missing = support_resistance_features(bars.drop(bars.index[1]))
+    assert missing.sr_opening_high_distance_bps.isna().all()
+
+
+def test_pivot_requires_two_right_bars_and_same_prior_level_for_crossing():
+    bars = _day(periods=8)
+    bars.loc[:, "high"] = [101, 102, 110, 104, 103, 112, 113, 114]
+    bars.loc[:, "low"] = [98, 97, 90, 96, 97, 98, 99, 100]
+    bars.loc[:, "close"] = [100, 101, 105, 102, 101, 111, 112, 113]
+    bars.loc[:, "open"] = bars.close
+    f = support_resistance_features(bars)
+    assert f.sr_pivot_high_distance_bps.iloc[:4].isna().all()
+    assert f.sr_pivot_low_distance_bps.iloc[:4].isna().all()
+    assert f.sr_pivot_high_distance_bps.iloc[4] == pytest.approx((101/110-1)*1e4)
+    assert f.sr_pivot_low_distance_bps.iloc[4] == pytest.approx((101/90-1)*1e4)
+    assert f.sr_resistance_cross_up.iloc[5] == 1
+    assert f.sr_last_upbreak_low_distance_bps.iloc[6] == pytest.approx((99/110-1)*1e4)
+    assert f.sr_upbreak_age_bars.iloc[6] == 1
+
+
+def test_newly_confirmed_level_cannot_create_same_bar_cross():
+    bars = _day(periods=8)
+    # Opening high 120 is not crossed; the lower pivot 108 is confirmed on bar 6.
+    bars.loc[:, "high"] = [120, 102, 103, 104, 108, 106, 107, 110]
+    bars.loc[:, "low"] = 90
+    bars.loc[:, "open"] = bars.loc[:, "close"] = [100, 100, 100, 100, 103, 102, 106, 109]
+    f = support_resistance_features(bars)
+    assert f.sr_resistance_cross_up.iloc[6] == 0
+    assert f.sr_resistance_cross_up.iloc[7] == 1
+    assert f.sr_last_upbreak_close_distance_bps.iloc[7] == pytest.approx((109/108-1)*1e4)
+
+
+def test_previous_trading_day_respects_session_and_missing_dates():
+    friday, monday = _day("2026-09-11"), _day("2026-09-14", 8)
+    f = support_resistance_features(pd.concat([friday, monday]))
+    assert f.loc[monday.index[0], "sr_previous_high_distance_bps"] == pytest.approx((monday.close.iloc[0]/friday.high.max()-1)*1e4)
+    assert f.loc[monday.index[0], "sr_pivot_high_distance_bps"] != f.loc[monday.index[0], "sr_pivot_high_distance_bps"]
+    missing_friday = support_resistance_features(pd.concat([_day("2026-09-10"), monday]))
+    assert missing_friday.loc[monday.index, "sr_previous_high_distance_bps"].isna().all()
+    incomplete_friday = support_resistance_features(pd.concat([friday.iloc[:-1], monday]))
+    assert incomplete_friday.loc[monday.index, "sr_previous_high_distance_bps"].isna().all()
+    assert incomplete_friday.loc[monday.index, "sr_previous_close_distance_bps"].isna().all()
+
+
+def test_missing_rows_are_not_zero_filled_and_break_cumulative_measurements():
+    bars = _day(periods=12)
+    bars.loc[bars.index[5], :] = np.nan
+    f = support_resistance_features(bars)
+    assert f.iloc[5].isna().all()
+    assert f.sr_vwap_distance_bps.iloc[5:].isna().all()
+    assert f.sr_running_range_position.iloc[5:].isna().all()
+    assert f.sr_opening_high_distance_bps.iloc[6:].notna().all()
+    assert len(f.columns) == 19 and tuple(f.columns) == FEATURE_COLUMNS
+
+
+def test_early_close_previous_session_and_timezone_preservation():
+    # Friday after US Thanksgiving closes at 13:00 ET (42 five-minute bars).
+    prior, following = _day("2025-11-28", 42), _day("2025-12-01", 5)
+    b = pd.concat([prior, following]).tz_convert("UTC")
+    f = support_resistance_features(b)
+    assert f.index.equals(b.index)
+    assert f.iloc[42].sr_previous_high_distance_bps == pytest.approx((following.close.iloc[0]/prior.high.max()-1)*1e4)
+
+
+def test_invalid_timestamp_contract_fails_explicitly():
+    with pytest.raises(ValueError, match="Timezone-aware"):
+        support_resistance_features(_day().tz_localize(None))
+    with pytest.raises(ValueError, match="sorted and unique"):
+        support_resistance_features(_day().iloc[::-1])
+
+
+def test_holding_support_context_preserves_context_and_live_prefix_contract():
+    from app.research.holding_policy import (
+        DEFAULT_SYMBOLS, REFERENCES, HoldingSpec, holding_features,
+    )
+
+    # Three complete historical sessions provide prior-day and seasonal history;
+    # the fourth is a live prefix whose later bars must never affect its features.
+    history = pd.concat([_day("2026-09-10"), _day("2026-09-11"),
+                         _day("2026-09-14"), _day("2026-09-15", 20)])
+    frames = {symbol: history * (1 + i / 10)
+              for i, symbol in enumerate((*DEFAULT_SYMBOLS, *REFERENCES))}
+    support_spec = HoldingSpec(family="support_context", seasonal_sessions=2)
+    context = holding_features(frames, spec=HoldingSpec(family="context", seasonal_sessions=2))
+    full = holding_features(frames, spec=support_spec)
+    for symbol in DEFAULT_SYMBOLS:
+        assert list(full[symbol].columns) == [*context[symbol].columns, *FEATURE_COLUMNS]
+        pd.testing.assert_frame_equal(full[symbol][context[symbol].columns], context[symbol])
+
+    # At 09:40 only 09:30/09:35 bars have closed. The opening range is unavailable.
+    for n in (3 * 78 + 2, 3 * 78 + 12):
+        prefix = {symbol: frame.iloc[:n] for symbol, frame in frames.items()}
+        live_features = holding_features(prefix, spec=support_spec)
+        for symbol in DEFAULT_SYMBOLS:
+            pd.testing.assert_frame_equal(live_features[symbol], full[symbol].iloc[:n])
+            if n == 3 * 78 + 2:
+                assert np.isnan(live_features[symbol].iloc[-1].sr_opening_high_distance_bps)
+
+    cutoff = 3 * 78 + 12
+    mutated = {symbol: frame.copy() for symbol, frame in frames.items()}
+    for frame in mutated.values():
+        frame.iloc[cutoff:] *= 20
+    recomputed = holding_features(mutated, spec=support_spec)
+    for symbol in DEFAULT_SYMBOLS:
+        pd.testing.assert_frame_equal(full[symbol].iloc[:cutoff], recomputed[symbol].iloc[:cutoff])

docs/MANUAL_TRADING_TRANSLATION_20260915.md

@@ -0,0 +1,80 @@
+# 把人工日内交易判断转成可核对的 SNDK 策略
+
+用户截图里的 SNXX 买卖是用户本人手动操作。它用于说明希望达到的节奏：分批买入、确认后加仓、持有上涨段、走弱后退出。本次研究交易对象是 SNDK 股票。没有用户的精确成交时间和股数，不能把截图直接当训练标签，也不能计算用户那组交易的收益。
+
+## 已完成的实现
+
+- `backend/app/alpha/support_resistance.py` 提供19列支撑压力、开盘区间、确认高低点、突破及回踩描述。
+- `holding_policy.py` 的 `support_context` 在原量价、同时间段相对成交量、市场和行业参考特征上追加这些列。训练和候选推断使用同一函数。
+- 继续预测5/15/30/60分钟累计收益，由既有多周期组合规划决定目标仓位；另一组对照加入以前向校准误差估计的风险。没有把支撑位写成必买规则，也没有把历史高点写成必卖时点。
+- 生成两天、三组候选的实际模拟买卖点、每笔股数、持仓、预测及盈亏。当前运行中的模型没有被替换，UI和README未改。
+
+这只是把人工判断中的可观察信息转成机器输入，不代表机器已经学会用户的交易能力。尤其是“多加几个特征”未必解决目标函数、校准或退出方式的问题。
+
+## 人的观察如何对应数据
+
+| 人工观察 | 可以计算的输入 | 用途与限制 |
+|---|---|---|
+| 回踩某价位后稳住 | 当时已知的前日高低、开盘区间、确认低点、跌破后收回距离 | 判断候选入场；低点需等待确认，不能提前知道全天底部 |
+| 突破后有没有人继续买 | 突破与回踩距离、同时间段相对成交量、价格推进幅度、市场相对强弱 | 区分有延续的突破和迅速回落；总成交量不能确定机构身份 |
+| 先买一点，走对再加 | 多周期预期收益、校准误差、现有仓位、波动及股票间相关性 | 联合配仓；本次股数由模型和资金决定，不按截图写死 |
+| 持有一段，而非每根K线翻向 | 15/30/60分钟预测曲线、维持仓位与改变仓位的成本 | 原规划器已经使用；两天结果显示短时反复交易仍未解决 |
+| 接近压力位后分批出 | 受阻幅度、较低高点、回落、剩余预测收益 | 测试退出质量；碰到某价位本身不证明应该卖出 |
+| 期权是否活跃 | 延迟Call/Put成交量、到期日与行权价分布、带日期的OI | 待建立完整留存；OI不带看涨方向，也不能识别机构买入 |
+
+## 两天对照结果
+
+9月14日为完整交易日；9月15日仅到**15:50美东**，仍有估值中的未平仓股数，不是当日最终盈亏。
+
+| SNDK模拟净盈亏 | 原Context | 加支撑压力 | 加支撑压力与误差风险 |
+|---|---:|---:|---:|
+| 9月14日 | +$107.66 | −$126.72 | −$95.05 |
+| 9月15日截至15:50 | −$262.61 | −$182.33 | −$147.19 |
+| 两次独立实验结果之和 | −$154.95 | −$309.05 | −$242.24 |
+
+9月14日四股组合起始资金$47,006.42；9月15日$47,958.07。每天独立零持仓开始，**不是连续账户复利**。SNDK资金按四股组合动态分配，不是将以上本金全部用于SNDK。规划成本2bps、实际模拟成交单边摩擦5bps，两者均为假设，非确认的Alpaca扣费。滑点已包含在模拟成交价，净盈亏不重复扣减。未验证借券可得性、真实成交概率和额外借券/规费。
+
+加风险的候选今天比原Context少亏$115.42，但昨天少赚/多亏$202.71，两次结果之和反而差$87.28。不能用今天改善宣称新Alpha成功。完整四股组合结果见[原始实验报告](../reports/manual_style_research_20260915/REPORT.md)，三组候选两天的组合结果也均亏损。
+
+## 仍失败的具体交易
+
+以下均为模型模拟，价位为未加滑点的参考价。含滑点成交价与每笔股数见CSV。
+
+- **9月14日09:50**：新风险候选仍追买9股，参考$1,546.70；09:55又卖11股，参考$1,524.79。它缩小了部分风险，却没有识别这次假突破。
+- **9月14日13:30之后**：新候选先卖完剩余2股，随后13:45、13:50、14:00又在更高价加回。持有上涨段的质量仍差。
+- **9月15日上午**：先做空并在10:15平空，净亏约$65.63；10:20参考$1,564.22转多4股，至10:45卖完，再亏约$41.26。减亏主要来自仓位变化，方向错误依然存在。
+- **9月15日11:15与11:45**：两次买入都在五分钟后退出，分别净亏约$10.19和$7.36。仅增加支撑压力输入还不足以形成用户希望的持仓节奏。
+
+## 可直接查看的产物
+
+- [9月14日新候选：价格、成交量、每笔股数、持仓和盈亏](../reports/manual_style_review_20260915/2026-09-14/levels_error_risk/SNDK_volume_orders.png)
+- [9月15日新候选：同样口径截至15:50](../reports/manual_style_review_20260915/2026-09-15/levels_error_risk/SNDK_volume_orders.png)
+- [9月14日全部特征、原始/校准预测和目标仓位](../reports/manual_style_review_20260915/2026-09-14/levels_error_risk/SNDK_review.csv)
+- [9月15日全部特征、原始/校准预测和目标仓位](../reports/manual_style_review_20260915/2026-09-15/levels_error_risk/SNDK_review.csv)
+- [9月14日逐笔成交](../reports/manual_style_research_20260915/2026-09-14/levels_error_risk/SNDK_orders.csv)
+- [9月15日逐笔成交](../reports/manual_style_research_20260915/2026-09-15/levels_error_risk/SNDK_orders.csv)
+
+图中的 B=买入开多、S=卖出多仓、C=平空、X=开空；后面的数字为股数。持仓阶梯按实际模拟成交时刻变化。CSV每行同时提供 `bar_open` 和 `available_at`；最后一根K线产生的预测不一定已经执行。原实验文件保留，复盘导出在独立目录并校验输入哈希。
+
+## 期权实际进度
+
+完成[免费期权数据审计](OPTIONS_DATA_AUDIT_20260915.md)。本账户SNDK/TSLA样本的合约资料、免费indicative快照及较早历史K线返回成功。不过样本OI日期均为9月11日；9月15日下午取得的快照不能倒填昨天或今天早盘。免费indicative报价是修改过的衍生报价，成交有15分钟延迟。[Alpaca官方说明](https://docs.alpaca.markets/us/docs/historical-option-data)
+
+这次两天实验**没有加入期权或L1输入**。可建设完整合约池的延迟成交量、Call/Put活动比例和每天OI留存，再按真实可用时间做对照。两份单Call样本不能计算全链Put/Call比例，更不能推导可信的Dealer Gamma或机构资金方向。
+
+## 验证与下一轮研究依据
+
+支撑特征和统一训练/推断契约的9项测试通过。包括追加未来行情不改变过去、确认点可用时间、缺失数据、提前收盘以及原Context列保持一致。六条模拟路径以成交现金流加期末库存独立重算，盈亏一致；训练与校准标签均早于目标交易日。这两天已经看过，属于回顾开发，不是未触碰的样本外证明。
+
+下一轮应围绕具体失败检查：原始预测经过校准后是否错误翻向；退出后很快高价回补是否来自过短的收益目标；同一预测下持仓调整是否比继续持有更有价值。记录全部尝试，以后续未用于修改的日期验证，不能把这两天调到漂亮后当作泛化成绩。期权数据先积累可用时间记录，再评价是否增加信息。
+
+重现本次原实验（必须指定新的输出目录，避免覆盖）：
+
+```bash
+python3 scripts/research_manual_style.py \
+  --today /Users/yuliangpeng/.local/share/quant-l1/reports/manual_style_20260915/bars_today \
+  --output reports/manual_style_reproduction
+python3 scripts/export_manual_style_review.py \
+  --study reports/manual_style_reproduction \
+  --output reports/manual_style_reproduction_review
+```

docs/OPTIONS_DATA_AUDIT_20260915.md

@@ -0,0 +1,91 @@
+# 免费期权信息可用性审计 — 2026-09-15
+
+**结论：本账户可读取 SNDK/TSLA 期权合约资料、indicative 最新快照以及较早的历史期权 K 线。可以据此建设延迟成交量与每日持仓量研究；尚不能把这些数据当作真实实时期权订单流，更没有证明它们提升股票策略收益。**
+
+审计范围是给股票交易补充上下文，不涉及购买期权、订阅付费行情或改变账户设置。本次只发送 GET 请求，每只股票查询一个合约、一份快照、五根历史 K 线，未下载全链。
+
+## 1. 本地实现核查
+
+检查了 `backend`、`scripts`、`frontend/src` 中的 Python/TypeScript 源码。
+
+- `backend/app/config.py:228` 有 `OPTIONS_QUANT_TRADER` 角色配置，包含 IV Rank、Skew、Delta/Gamma 等名称及固定权重。
+- 当前未发现对应的 Alpaca 期权链/OI/OPRA 采集器，也未发现这些期权字段进入股票预测特征管道的实现。
+- 因此“有期权策略角色配置”不等于“已经使用真实期权 Alpha”。这些配置数值是策略模板参数，不是来自市场的观察值。
+- 本次保留所有现有界面及配置，仅增加审计文件与只读数据样本。
+
+## 2. 官方数据口径
+
+| 数据 | 已核实的官方含义 | 项目中的正确用法 |
+|---|---|---|
+| Indicative options feed | 免费衍生行情；报价经过修改，成交也是衍生数据并延迟15分钟 | 可用于接口联调；不能命名为真实实时期权盘口、扫单或机构买入 |
+| OPRA feed | 期权市场合并最优买卖报价源；实时权限取决于订阅 | 仅在账户权限和数据来源核实后使用；本次未验证实时 OPRA 权限 |
+| Historical option bars | 支持按合约、区间与周期查询；无实时权限时文档的默认截止时间为当前时间减15分钟 | 研究延迟可用的成交量、价格与合约活跃度；K线须完整闭合，并计入可获取延迟 |
+| Option chain snapshot | 返回各合约最新成交、最新报价、Greeks等 | 是查询时快照，不是任意历史时点期权链；不能倒填昨天或今天早盘 |
+| Open interest | 合约未平仓数量，并带有 `open_interest_date` | 是带日期的存量资料；不能视为当下新增买盘或盘中实时流量 |
+
+来源：[Alpaca历史期权数据](https://docs.alpaca.markets/us/docs/historical-option-data)、[期权历史K线参数](https://docs.alpaca.markets/us/reference/optionbars)、[期权链快照](https://docs.alpaca.markets/us/reference/optionchain)、[OptionContract字段](https://alpaca.markets/sdks/python/api_reference/trading/models.html#optioncontract)。
+
+Alpaca WebSocket 支持 `indicative` 与 `opra`，期权流采用 MsgPack；连接可用不改变 feed 本身的数据性质。[实时期权数据接口](https://docs.alpaca.markets/us/docs/real-time-option-data)
+
+OCC需在日终清算配对后计算新的未平仓量。OI上升本身不能证明看涨，因为一份新增合约同时有买方和卖方。[OIC/OCC关于OI的解释](https://www.optionseducation.org/referencelibrary/faq/general-information)
+
+## 3. 当前账户只读验证结果
+
+查询时间：**2026-09-15 15:54:25 美东 / 19:54:25 UTC**。使用机器上已有的 Alpaca 配置；证据文件不保存密钥、请求认证头或账户标识。
+
+| 项目 | SNDK样本 | TSLA样本 |
+|---|---|---|
+| 合约 | `SNDK260918C01500000` | `TSLA260918C00350000` |
+| 到期日/类型 | 2026-09-18 / Call | 2026-09-18 / Call |
+| 行权价 | $1,500 | $350 |
+| 合约资料HTTP状态 | 200 | 200 |
+| OI | 1,631 | 5,812 |
+| **OI数据日期** | **2026-09-11** | **2026-09-11** |
+| 最新 indicative 快照HTTP状态 | 200 | 200 |
+| 历史5分钟K线HTTP状态 | 200 | 200 |
+| 历史样本数 | 5，存在下一页 | 5，存在下一页 |
+| 样本最新K线起点 | 2026-09-15 15:30 ET | 2026-09-15 15:30 ET |
+
+两个合约按明确的到期、类型及行权区间取第一条，仅用于验证接口，不代表全链、最活跃合约或策略选择结果。
+
+**实际核实到的OI甚至不是9月14日，而是9月11日。** 在9月15日查询时可见这个值，不足以证明该值在9月14日09:35已由项目接收到。`open_interest_date`是资料对应日期，不能代替发布时间和接收时间。
+
+快照里的 `latestQuote` 接近查询时刻，`latestTrade` 分别约在15:37:53与15:39:19，符合延迟来源的特征。报价“时间新”不能消除 indicative 报价经过修改的性质。快照内 `dailyBar`、`minuteBar`、`prevDailyBar`、IV、Greeks保留原始返回值，但各字段的来源和更新周期未逐一独立验证。
+
+历史K线请求使用官方 `/v1beta1/options/bars` 参数，未加入该端点文档未列出的 `feed` 参数。HTTP 200验证了本账户当前可取得这些较早的价格与成交量记录；响应没有显式 feed 字段，不据此宣称取得实时 OPRA 或完成全交易所逐笔核验。
+
+原始市场数据及请求参数证据：[options_data_probe_20260915.json](../reports/options_data_probe_20260915.json)。这个文件只有两份合约样本，**不足以计算标的全链Put/Call比或Gamma敞口**。
+
+## 4. 可以补充哪些股票交易特征
+
+这些是待检验候选，先做数据采集和研究，再与原股票模型做同资金、同成本的对照。
+
+| 候选 | 所需输入 | 时间与含义限制 |
+|---|---|---|
+| 延迟期权相对成交量 | 预先确定的合约池、已闭合历史bars、过去同时间段成交量 | 只使用真实可获取时刻之前的记录；不能将全日成交量用于上午预测 |
+| 延迟Call/Put成交量比例 | 两边合约的完整采样口径、按到期和价内外程度分组 | 反映交易活动，不直接区分买入/卖出、开仓/平仓；单一Call合约无法计算 |
+| 到期与行权价OI分布 | 每日保存的完整合约元数据和OI日期 | 是背景持仓集中度；不能把最大OI行权价当成必然支撑/压力 |
+| 当日量/OI | 延迟累计成交量、最近实际可用OI及其日期 | OI较旧时显示资料年龄；分母缺失或零时该比值不可用 |
+| IV期限结构/偏斜 | 同时点可靠报价、完整合约、明确估值方法 | 当前indicative IV仅记录为衍生快照；未获得可信历史快照前不用于回放早盘 |
+
+免费情况下，近期最可执行的是**有延迟标注的期权成交量上下文**与**每日OI存档**。它们可与股价相对VWAP、当时已知支撑压力、成交量变化一起研究30/60分钟走势，不能用15分钟后才到的数据解释5分钟前已经发出的订单。
+
+不从公开OI、总量或单笔大成交直接推断机构身份。Dealer Gamma的正负需要持仓方与买卖方向等假设，当前数据无法验证，不将“Gamma wall”或“主力扫单”作为实测事实。
+
+## 5. 必须保存的数据时间及缺失信息
+
+建议每条新记录保留 `provider`、`endpoint`、`requested_feed`、`response_feed_if_present`、`event_time`、`received_at`、`available_at`、`contract_symbol`、`expiration`、`strike`、`option_type`、`open_interest_date`、`coverage`、`quality_status`。
+
+- 实时接收记录只能在 `received_at` 之后用于决策。
+- 历史5分钟bar用开始时间标记时，最早可用时间先经过bar闭合，再加文档规定的延迟；真实采集还要考虑轮询/网络延迟。历史回填的到达时间属于假设，应单独标记。
+- 最新快照不能回填旧时点的IV/Greeks/OI可见性。
+- 未下载、未授权、过期、缺失、未定义分别保留状态；缺失不写成0，也不伪造正常的默认比率。
+- 真实0成交量与缺失bar需要按数据源含义区分；不能仅因没有返回记录就声称没有成交。
+
+## 6. 已完成与仍未知
+
+已完成：官方文档核对、本地源码查找、两只股票小样本API验证及证据存档。
+
+仍未知：实时OPRA是否授权、全链覆盖、各合约真实发布时间、历史OI快照、Greeks更新口径、延迟期权信息是否增加净收益。未启用任何期权驱动股票交易规则。
+
+本报告为研究与工程可用性审计，不把9月14/15日走势或人工B/S截图作为已验证Alpha；账户费用也不从模拟滑点假设推定。

docs/PRICE_REPLAY_DISPLAY.md

@@ -0,0 +1,46 @@
+# 价格线与买卖点回放展示
+
+用户授权用价格折线、买卖标记和股数替换复盘大屏主图，保留 Dynamic Replay 与既有交互。该变更仅涉及数据展示，不改变交易模型或下单逻辑。
+
+## 页面
+
+- `TradeComparisonPanel` 默认显示新的价格回放；经典规则轨迹与K线保留在旁边的切换按钮。
+- `PriceActionReplayChart` 显示蓝色价格折线、橙色OHLCV均价代理、B/S/C/X及股数、成交量、持仓和净盈亏。支持股票、日期、研究模型、播放/暂停、倍速、进度拖动、时段与缩放。
+- 原Dynamic Replay保留沙盒、播放按钮、倍速和其他页面结构，图表默认改为价格线与均价代理。K线及EMA可通过图例开启。
+- 股票在两个展示区域联动。研究回放和券商归档的日期独立选择，因为保存的日期范围可能不同；不能把券商归档日期强塞给研究回放。
+- 修复原 `trade_comparison` 页面重复渲染两套复盘面板的问题。
+
+## 数据
+
+新增只读接口：
+
+```text
+GET /api/dashboard/price_replay?ticker=SNDK&date=2026-09-15&variant=levels_error_risk
+```
+
+当前可移植数据包位于 `backend/data/price_replay/`，覆盖SNDK、TSLA、NVDA、PLTR，9月14日与9月15日，Context/支撑压力/支撑压力与误差风险三种研究模型，共24份。该接口读取已保存数据，不训练模型、不查询券商、不自动生成交易。缺失组合显示不可用。
+
+9月15日仅截至15:50美东，仍保留未平仓库存；不能标成完整日或实际账户盈亏。单边5bps为模拟摩擦，已体现在模拟成交价，不能再次从净盈亏扣除。组合期初资金不等于某只股票独占的资金。
+
+价格时间使用已完成5分钟K线的可用时刻，成交使用实际模拟执行时间。持仓阶梯直接来自成交事件。保存的K线收盘估值发生在同刻下一根开盘成交之前，因此前端追加同刻成交后的现金与库存估值，使回放不会出现新仓位却仍显示成交前盈亏的情况。重置/播放不显示尚未发生的成交标记。
+
+原Dynamic Replay保留券商归档来源。其真实成交标记现在按完整时间戳定位，不再要求成交分钟恰好等于5分钟K线时间点。
+
+## 更新研究数据包
+
+使用导出脚本读取经过校验的研究产物，命令行参数见：
+
+```bash
+python3 scripts/export_price_replay_bundle.py --help
+```
+
+不要手动修改保存的盈亏或股数；清单哈希不匹配的产物会被拒绝展示。新报告加入页面需要更新数据包，而不是将当前两天样本假装成实时推断。
+
+## 验证
+
+- `PYTHONPATH=backend python3 -m pytest -q backend/tests/test_price_replay.py`：7项通过，包含24份数据的逐次现金/库存重算、未平仓、时间可用性、错误请求及哈希异常。
+- `npm run build`：TypeScript和Vite构建通过。
+- Chrome离线浏览器检查：四股、日期/模型切换、原报告盈亏与成交数、重置、播放/暂停/倍速、缩放、窄屏、研究和券商日期独立；无浏览器运行异常。新主图使用真实保存的研究数据，测试不连接券商。
+- Dynamic Replay浏览器检查：历史价格线、可选K线、09:32:26这类非整5分钟成交标记、播放中无未来标记、图例保持以及快速换股票后的请求竞争。
+
+截图与检查摘要：`reports/price_replay_ui_20260915/`。本次未修改 `live_runner.py` 或替换线上模型。

docs/TSLA_REVERSAL_RESEARCH_20260915.md

@@ -0,0 +1,60 @@
+# TSLA反转逻辑修改与阶段性结果
+
+评估区间：2026-09-15 09:30—11:00 ET。这是已查看行情后的开发诊断，非未触碰样本外验证。
+资金：$47,958.07，来自当日只读账户last_equity字段；期初空仓是假设，非账户库存复刻。
+行情：固定Yahoo OHLCV快照，与runner同提供方；原始实时到达与修订历史无法复刻。
+所有版本交易同样的SNDK、TSLA、NVDA，PLTR仅参与特征。单边5bps为模拟摩擦，非Alpaca实收手续费。
+未平仓按11:00可知的最后完整K线收盘估值，不强行提前平仓，不补全天未来数据。
+
+## 已修改逻辑
+
+1. 新增可选session_context特征族：相对开盘收益、截至当前的高低点位置、OHLCV VWAP代理偏离、SPY/QQQ/SOXX相对开盘走势及长回看是否可用。
+2. 多期限组合新增可选预测误差风险：优化目标使用市场收益协方差加预测误差矩阵，沿用同一风险权重；没有新增按ticker、时间或当日涨跌决定买卖的规则。
+3. 误差矩阵来自9月10、11、14日的前向校准预测，基底模型先冻结，每日校准只用更早日期；最后标签在9月14日15:55成熟。矩阵为误差协方差加偏差外积，非置信区间。
+4. 默认配置兼容既有工件，预测误差开关不改变特征契约；加载时核查误差fold训练/标签日期。新模型候选已生成，尚未激活。
+
+## 同口径结果
+
+|版本|TSLA净模拟盈亏|三股组合净模拟盈亏|
+|---|---:|---:|
+|旧冻结5m|-$985.96|-$940.61|
+|既有Context多期限|-$718.64|-$981.62|
+|既有Context＋误差风险|-$628.34|-$881.12|
+|新增session_context|-$863.70|-$1,125.46|
+|新增session_context＋误差风险|-$732.66|-$977.93|
+
+TSLA“既有Context＋误差风险”比旧冻结少亏$357.62，但组合只少亏$59.49。
+隔离风险项的对照：比既有Context TSLA少亏$90.30，组合少亏$100.50。
+新增开盘特征未改善收益，应保留失败记录，不据今天结果部署。
+
+## 反转是否解决
+
+没有。所有模型09:35仍先看空。旧冻结09:45模拟从空91股翻到多91股；
+Context多期限当时目标多29股，加入误差风险后多16股，09:50转为空仓。
+减亏主要是改变持仓规模和路径，尚未学到用户希望的“先多后空”。
+Context风险版本组合成交40笔，旧冻结18笔，因此也不能声称已经减少交易次数或解决所有换手。
+新风险版本11:00产生的目标没有下一根已保存开盘价可成交，在报表中明确标记未执行。
+
+## 验证与产物
+
+22项测试通过：原持仓模型、校准、接入、误差优化及新增开盘状态；覆盖未来数据变更不影响过去、
+保存加载一致性、旧模型兼容、误差增加时缩减简单极端仓位、强反向预测仍可反转、无效协方差拒绝。
+24个最终产物哈希通过，组合权益与逐股盈亏核对通过。
+
+- 最终对照：`reports/tsla_reversal_ablation_20260915/REPORT.md`
+- 逐时决策：`reports/tsla_reversal_ablation_20260915/TSLA_decision_comparison.csv`
+- 全部盈亏：`reports/tsla_reversal_ablation_20260915/summary.csv`
+- 候选：`reports/tsla_reversal_ablation_20260915/context_error_curve.json`
+- 第一轮结果完整保留：`reports/tsla_reversal_research_20260915/`
+
+复现（使用新输出目录，避免覆盖已登记实验）：
+
+```bash
+python3 scripts/research_tsla_reversal.py \
+  --today /Users/yuliangpeng/.local/share/quant-l1/reports/tsla_reversal_20260915/bars_today/runner_yahoo \
+  --output reports/tsla_reversal_rerun_20260915 \
+  --capital 47958.07
+```
+
+正式考虑接入之前，应固定候选与参数，按用户顺序继续检验此前已保存交易日及未来纸盘；
+不能因为今天减亏就宣称学会稳定识别拐点。

frontend/dist/assets/{index-DCSOhC_L.css → index-7xTfuMOi.css}

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var(--color-border);gap:1.5rem;padding:1rem}.pnl-header-container{flex-direction:column;align-items:flex-start;gap:1rem}.interval-picker-container{align-items:flex-start;width:100%}.time-tabs{flex-wrap:wrap}.portfolio-value{font-size:2rem}.strategy-patterns-grid{grid-template-columns:1fr;gap:1.5rem}.chart-wrapper{height:300px}.sidebar{padding:1.5rem 1rem}.chat-config-grid{grid-template-columns:1fr}.chat-risk-header{flex-direction:column;align-items:flex-start;gap:10px}.equity-curve-stats{flex-wrap:wrap;gap:.75rem}}.research-panel{border:1px solid var(--color-border);background-color:#000;border-radius:16px;flex-direction:column;height:calc(100vh - 120px);display:flex;overflow:hidden}.research-layout{grid-template-columns:240px 1fr;height:100%;display:grid;overflow:hidden}.research-toc{border-right:1px solid var(--color-border);background-color:#0d0d0e;flex-direction:column;gap:1.5rem;padding:1.5rem 1rem;display:flex;overflow-y:auto}.toc-title{text-transform:uppercase;letter-spacing:.5px;color:#fff;border-bottom:1px solid var(--color-border);padding-bottom:.5rem;font-size:.95rem;font-weight:800}.toc-list{flex-direction:column;gap:4px;display:flex}.toc-item{color:var(--color-text-secondary);text-align:left;cursor:pointer;white-space:nowrap;text-overflow:ellipsis;background:0 0;border:none;border-radius:6px;padding:8px 12px;font-size:.85rem;font-weight:600;transition:all .2s;overflow:hidden}.toc-item:hover{color:#fff;background-color:#ffffff0a}.toc-item.active{color:#000;background-color:var(--color-green);font-weight:700}.toc-apply-widget{border:1px solid var(--color-border);background-color:#ffffff05;border-radius:8px;margin-top:auto;padding:12px}.toc-widget-title{color:#fff;text-transform:uppercase;margin-bottom:6px;font-size:.78rem;font-weight:750}.btn-apply-preset{cursor:pointer;text-align:left;border:none;border-radius:6px;align-items:center;width:100%;margin-bottom:6px;padding:8px 10px;font-size:.75rem;font-weight:700;transition:all .2s;display:flex}.btn-apply-preset.dynamic{color:var(--color-green);background:#00c8051a;border:1px solid #00c80533}.btn-apply-preset.dynamic:hover{background:var(--color-green);color:#000}.btn-apply-preset.trend{color:#bf5af2;background:#af52de1a;border:1px solid #af52de33}.btn-apply-preset.trend:hover{color:#000;background:#bf5af2}.btn-apply-preset.reversion{color:#ff9f0a;background:#f5a6231a;border:1px solid #f5a62333}.btn-apply-preset.reversion:hover{color:#000;background:#ff9f0a}.research-doc-body{scroll-behavior:smooth;background-color:#000;flex-direction:column;gap:3rem;padding:2.5rem;display:flex;overflow-y:auto}.doc-section-card{border:1px solid var(--color-border);background-color:#121214;border-radius:12px;padding:1.5rem;transition:border-color .4s,box-shadow .4s}.doc-section-card.highlight{border-color:#00c8054d;box-shadow:0 0 20px #00c8050d}.doc-section-title{color:#fff;border-left:4px solid var(--color-green);margin-top:0;margin-bottom:1.25rem;padding-left:10px;font-size:1.4rem;font-weight:800}.doc-heading3{color:#fff;border-bottom:1px solid #ffffff0a;margin-top:1.5rem;margin-bottom:.75rem;padding-bottom:4px;font-size:1.05rem;font-weight:700}.doc-paragraph{color:#e5e5e7;letter-spacing:-.01em;margin-bottom:1.25rem;font-size:.98rem;line-height:1.75}.doc-paragraph code,.doc-list li code,.doc-alert-box code{color:#ff9f0a;background:#ffffff14;border-radius:4px;padding:2px 5px;font-family:SFMono-Regular,Consolas,Liberation Mono,Menlo,monospace;font-size:.85rem}.doc-list{margin:1rem 0 1.25rem;padding-left:1.5rem}.doc-list li{color:#e5e5e7;letter-spacing:-.01em;margin-bottom:.6rem;font-size:.98rem;line-height:1.75}.code-lang-label{text-transform:uppercase;color:var(--color-text-secondary);font-family:monospace;font-size:.65rem;font-weight:700;position:absolute;top:8px;right:12px}.doc-code-block{border:1px solid var(--color-border);border-radius:6px;margin:0;overflow-x:auto}.doc-alert-box.alert-info,.doc-alert-box.alert-note{color:#a4d3ff;border-left-color:#0a84ff}.doc-alert-box.alert-tip{border-left-color:var(--color-green);color:#d0ffd4}.doc-alert-box.alert-important{color:#f5d6ff;border-left-color:#bf5af2}.doc-alert-box.alert-warning{color:#ffebcc;border-left-color:#ff9f0a}.doc-alert-box.alert-caution{border-left-color:var(--color-red);color:#ffd6d4}.widget-header{color:#fff;align-items:center;gap:8px;margin-top:0;margin-bottom:.75rem;font-size:1rem;font-weight:700;display:flex}.calc-input-group label{color:#dedede;margin-bottom:4px;font-size:.82rem;display:block}.calc-stat-box{flex-direction:column;display:flex}.calc-stat-label{color:var(--color-text-secondary);text-transform:uppercase;letter-spacing:.5px;font-size:.72rem;font-weight:700}.feasibility-badge{border-radius:6px;width:fit-content;padding:4px 10px;font-size:.8rem;display:inline-block}.feasibility-badge.danger{color:var(--color-red);background:#ff3b3026}.feasibility-badge.warning{color:#ff9f0a;background:#f5a62326}.feasibility-badge.moderate{color:#bf5af2;background:#af52de26}.feasibility-badge.success{color:var(--color-green);background:#00c80526}.market-detail-row{color:#dedede;margin-bottom:8px;font-size:.82rem;line-height:1.5}.market-detail-row strong{color:#fff}.candlestick-card{transition:all .25s;overflow:hidden}.candlestick-card:hover{border-color:#ffffff26;transform:translateY(-2px);box-shadow:0 4px 12px #0000004d}.candlestick-card.border-bullish{border-left:3px solid var(--color-green)!important}.candlestick-card.border-bearish{border-left:3px solid var(--color-red)!important}.candlestick-card.border-neutral{border-left:3px solid var(--color-text-secondary)!important}.badge-type.bullish{color:var(--color-green);background:#00c80526}.badge-type.bearish{color:var(--color-red);background:#ff3b3026}.badge-type.neutral{color:var(--color-text-secondary);background:#8e8e9326}.checklist-item-row:hover{background-color:#ffffff05!important;border-color:#ffffff1a!important}.code-links-grid{grid-template-columns:1fr 1fr;gap:10px;display:grid}.code-link-item{background:#111;border:1px solid #222;border-radius:8px;align-items:center;gap:12px;padding:12px;text-decoration:none;transition:all .2s;display:flex}.code-link-item:hover{border-color:var(--color-green);background:#ffffff08;transform:translate(2px)}.file-icon{opacity:.8;font-size:1.5rem}.code-link-item div{flex-direction:column;display:flex}.code-link-item div strong{color:#fff;font-family:monospace;font-size:.85rem}.code-link-item div span{color:var(--color-text-secondary);margin-top:2px;font-size:.72rem}@keyframes spin{0%{transform:rotate(0)}to{transform:rotate(360deg)}}@keyframes pulse{0%{opacity:.6;transform:scale(1)}50%{opacity:1;transform:scale(1.1)}to{opacity:.6;transform:scale(1)}}@keyframes fadeIn{0%{opacity:0;transform:translateY(8px)}to{opacity:1;transform:translateY(0)}}.price-replay{color:#e2e8f0;background:linear-gradient(#0c121c,#080d15);border:1px solid #243042;border-radius:14px;min-width:0;padding:22px}.price-replay-heading,.price-replay-selectors,.price-replay-toolbar,.price-replay-buttons,.price-replay-readout,.price-replay-legend,.price-replay-context,.price-replay-seek{flex-wrap:wrap;align-items:center;gap:12px;display:flex}.price-replay-heading{justify-content:space-between;align-items:flex-start;gap:18px}.price-replay-eyebrow{color:#38bdf8;letter-spacing:.8px;font-size:12px;font-weight:800}.price-replay-price{color:#f8fafc;font-variant-numeric:tabular-nums;font-size:clamp(28px,3vw,42px);font-weight:800;line-height:1.4}.price-replay-price span{color:#a5b4c8;margin-left:8px;font-size:17px}.price-replay-subtitle,.price-replay-context,.price-replay-footnote{color:#8d9bb0;font-size:12px;line-height:1.7}.price-replay-selectors{justify-content:flex-end}.price-replay-selectors label{color:#94a3b8;align-items:center;gap:8px;font-size:12px;display:flex}.price-replay select,.price-replay button{color:#cbd5e1;font:inherit;cursor:pointer;background:#121d2b;border:1px solid #2b394d;border-radius:6px;padding:7px 10px;font-size:12px}.price-replay button{white-space:nowrap;font-weight:700}.price-replay button.active{color:#7dd3fc;background:#12364c;border-color:#288bb1}.price-replay button:hover:not(:disabled){color:#fff;border-color:#38bdf8}.price-replay button:disabled{opacity:.45;cursor:wait}.price-replay button:focus-visible,.price-replay select:focus-visible,.price-replay input:focus-visible{outline-offset:3px;outline:2px solid #38bdf8}.price-replay-symbols{flex-wrap:wrap;gap:4px;display:flex}.price-replay-context{border-bottom:1px solid #213044;gap:8px 16px;margin-top:14px;padding:9px 0}.price-replay-source{color:#c4b5fd;background:#28213d;border-radius:4px;padding:2px 8px;font-weight:700}.price-replay-toolbar{justify-content:space-between;padding:13px 0}.price-replay-buttons{gap:5px}.price-replay-readout{color:#8da1ba;font-variant-numeric:tabular-nums;background:#111b28;border-radius:6px;gap:8px 20px;padding:9px 12px;font-size:12px}.price-replay-readout b{color:#e2e8f0;margin-left:5px}.price-replay-legend{color:#94a3b8;gap:8px 20px;padding:14px 0 4px;font-size:11px}.price-replay-canvas{width:100%;overflow-x:auto}.price-replay-canvas svg{cursor:crosshair;width:100%;min-width:700px;display:block}.price-replay-seek{color:#94a3b8;white-space:nowrap;flex-wrap:nowrap;padding-top:10px;font-size:12px}.price-replay-seek input{accent-color:#38bdf8;cursor:pointer;flex:1;min-width:50px}.price-replay-footnote{padding-top:10px}.price-replay-message{color:#7dd3fc;text-align:center;place-content:center;min-height:310px;font-size:14px;display:grid}.price-replay-message.error{color:#fbbf24}@media (width<=800px){.price-replay{padding:14px 10px}.price-replay-selectors{justify-content:flex-start}.price-replay-toolbar{align-items:flex-start}.price-replay-context{font-size:11px}}

frontend/dist/index.html

@@ -5,8 +5,8 @@
     <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>frontend</title>
-    <script type="module" crossorigin src="/assets/index-BBAp5T1V.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-DCSOhC_L.css">
+    <script type="module" crossorigin src="/assets/index-BouKarZa.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-7xTfuMOi.css">
   </head>
   <body>
     <div id="root"></div>

frontend/src/App.tsx

@@ -840,7 +840,7 @@ function App() {
           )}
 
           {/* Consolidated Master Dashboard Tab */}
-          {(activeTab === 'replay' || activeTab === 'trade_comparison') && (
+          {activeTab === 'replay' && (
             <TradeComparisonPanel
               watchlist={watchlist}
               activeTicker={activeTicker}

frontend/src/components/PriceActionReplayChart.css

@@ -0,0 +1,31 @@
+.price-replay { color: #e2e8f0; background: linear-gradient(180deg, #0c121c, #080d15); border: 1px solid #243042; border-radius: 14px; padding: 22px; min-width: 0; }
+.price-replay-heading, .price-replay-selectors, .price-replay-toolbar, .price-replay-buttons, .price-replay-readout, .price-replay-legend, .price-replay-context, .price-replay-seek { display: flex; align-items: center; flex-wrap: wrap; gap: 12px; }
+.price-replay-heading { justify-content: space-between; align-items: flex-start; gap: 18px; }
+.price-replay-eyebrow { color: #38bdf8; font-size: 12px; font-weight: 800; letter-spacing: .8px; }
+.price-replay-price { font-size: clamp(28px, 3vw, 42px); line-height: 1.4; font-weight: 800; color: #f8fafc; font-variant-numeric: tabular-nums; }
+.price-replay-price span { font-size: 17px; color: #a5b4c8; margin-left: 8px; }
+.price-replay-subtitle, .price-replay-context, .price-replay-footnote { color: #8d9bb0; font-size: 12px; line-height: 1.7; }
+.price-replay-selectors { justify-content: flex-end; }
+.price-replay-selectors label { display: flex; gap: 8px; align-items: center; font-size: 12px; color: #94a3b8; }
+.price-replay select, .price-replay button { background: #121d2b; border: 1px solid #2b394d; border-radius: 6px; color: #cbd5e1; padding: 7px 10px; font: inherit; font-size: 12px; cursor: pointer; }
+.price-replay button { font-weight: 700; white-space: nowrap; }
+.price-replay button.active { color: #7dd3fc; background: #12364c; border-color: #288bb1; }
+.price-replay button:hover:not(:disabled) { border-color: #38bdf8; color: #fff; }
+.price-replay button:disabled { opacity: .45; cursor: wait; }
+.price-replay button:focus-visible, .price-replay select:focus-visible, .price-replay input:focus-visible { outline: 2px solid #38bdf8; outline-offset: 3px; }
+.price-replay-symbols { display: flex; flex-wrap: wrap; gap: 4px; }
+.price-replay-context { margin-top: 14px; padding: 9px 0; gap: 8px 16px; border-bottom: 1px solid #213044; }
+.price-replay-source { color: #c4b5fd; background: #28213d; padding: 2px 8px; border-radius: 4px; font-weight: 700; }
+.price-replay-toolbar { justify-content: space-between; padding: 13px 0; }
+.price-replay-buttons { gap: 5px; }
+.price-replay-readout { border-radius: 6px; padding: 9px 12px; background: #111b28; color: #8da1ba; font-size: 12px; gap: 8px 20px; font-variant-numeric: tabular-nums; }
+.price-replay-readout b { color: #e2e8f0; margin-left: 5px; }
+.price-replay-legend { font-size: 11px; color: #94a3b8; padding: 14px 0 4px; gap: 8px 20px; }
+.price-replay-canvas { width: 100%; overflow-x: auto; }
+.price-replay-canvas svg { display: block; width: 100%; min-width: 700px; cursor: crosshair; }
+.price-replay-seek { flex-wrap: nowrap; font-size: 12px; color: #94a3b8; padding-top: 10px; white-space: nowrap; }
+.price-replay-seek input { flex: 1; min-width: 50px; accent-color: #38bdf8; cursor: pointer; }
+.price-replay-footnote { padding-top: 10px; }
+.price-replay-message { min-height: 310px; display: grid; place-content: center; color: #7dd3fc; font-size: 14px; text-align: center; }
+.price-replay-message.error { color: #fbbf24; }
+@media (max-width: 800px) { .price-replay { padding: 14px 10px; } .price-replay-selectors { justify-content: flex-start; } .price-replay-toolbar { align-items: flex-start; } .price-replay-context { font-size: 11px; } }

frontend/src/components/PriceActionReplayChart.tsx

@@ -0,0 +1,224 @@
+import { useEffect, useId, useMemo, useState } from 'react';
+import type { MouseEvent } from 'react';
+import { API_BASE } from '../config';
+import './PriceActionReplayChart.css';
+
+type Bar = { time: string; open: number; high: number; low: number; close: number; volume: number; vwap: number | null };
+type Fill = { time: string; action: 'B' | 'S' | 'C' | 'X'; shares: number; price: number; reference_price: number; commission_cost: number; shares_after: number };
+type Mark = { time: string; shares: number; pnl: number | null };
+type ReplayData = {
+  success: boolean; error?: string; ticker: string; date: string; available_dates: string[];
+  variants: Array<string | { value?: string; id?: string; label?: string }>;
+  variant: string; source_label: string; is_simulated: boolean; as_of: string;
+  partial: boolean; starting_equity: number; cost_bps: number; opening_shares: number;
+  bars: Bar[]; fills: Fill[]; marks: Mark[];
+  summary: { net_pnl: number; gross_pnl: number; cost: number; fill_count: number; ending_shares: number };
+};
+
+interface Props {
+  ticker: string;
+  watchlist: string[];
+  onSelectTicker: (ticker: string) => void;
+  refreshKey?: number;
+}
+
+const marketTime = new Intl.DateTimeFormat('en-GB', { timeZone: 'America/New_York', hour: '2-digit', minute: '2-digit', second: '2-digit', hourCycle: 'h23' });
+const minute = (time: string) => {
+  const [h, m, s] = marketTime.format(new Date(time)).split(':').map(Number);
+  return h * 60 + m + s / 60 - 570;
+};
+const timeLabel = (m: number) => `${Math.floor((m + 570) / 60).toString().padStart(2, '0')}:${Math.floor((m + 570) % 60).toString().padStart(2, '0')}`;
+const money = (value: number | null | undefined) => Number.isFinite(value) ? `$${Number(value).toLocaleString('en-US', { minimumFractionDigits: 2, maximumFractionDigits: 2 })}` : '—';
+const variants: Record<string, string> = { context: '原 Context', levels: '支撑压力', levels_error_risk: '支撑压力＋误差风险' };
+const actions = { B: { label: '买入开多', color: '#fb7185' }, S: { label: '卖出多仓', color: '#34d399' }, C: { label: '平空', color: '#c4a0f4' }, X: { label: '开空', color: '#fbbf24' } };
+
+function bounds(values: number[], includeZero = false): [number, number] {
+  const finite = values.filter(Number.isFinite);
+  if (includeZero || finite.length === 0) finite.push(0);
+  const low = Math.min(...finite), high = Math.max(...finite);
+  const padding = Math.max((high - low) * .12, high === low ? Math.max(Math.abs(high) * .002, .1) : .01);
+  return [low - padding, high + padding];
+}
+
+export function PriceActionReplayChart({ ticker, watchlist, onSelectTicker, refreshKey }: Props) {
+  const [date, setDate] = useState('');
+  const [variant, setVariant] = useState('levels_error_risk');
+  const [dates, setDates] = useState<string[]>([]);
+  const [data, setData] = useState<ReplayData | null>(null);
+  const [error, setError] = useState('');
+  const [loading, setLoading] = useState(true);
+  const [count, setCount] = useState(0);
+  const [playing, setPlaying] = useState(false);
+  const [speed, setSpeed] = useState(1);
+  const [range, setRange] = useState<[number, number]>([0, 390]);
+  const [hover, setHover] = useState<number | null>(null);
+  const clip = useId().replace(/:/g, '');
+
+  useEffect(() => {
+    const controller = new AbortController();
+    setLoading(true); setData(null); setError(''); setPlaying(false); setHover(null);
+    const query = new URLSearchParams({ ticker, variant });
+    if (date) query.set('date', date);
+    fetch(`${API_BASE}/api/dashboard/price_replay?${query}`, { signal: controller.signal })
+      .then(async response => {
+        const result: ReplayData = await response.json();
+        if (controller.signal.aborted) return;
+        if (result.available_dates?.length) setDates(result.available_dates);
+        if (!response.ok || !result.success || !result.bars?.length) throw new Error(result.error || '这个股票和日期暂无保存的研究回放。');
+        setData(result); setCount(result.bars.length);
+      }).catch(reason => {
+        if (!controller.signal.aborted) setError(reason instanceof Error ? reason.message : '读取回放失败');
+      }).finally(() => { if (!controller.signal.aborted) setLoading(false); });
+    return () => controller.abort();
+  }, [ticker, date, variant, refreshKey]);
+
+  useEffect(() => {
+    if (!playing || !data) return;
+    const timer = window.setInterval(() => setCount(value => Math.min(value + 1, data.bars.length)), 650 / speed);
+    return () => window.clearInterval(timer);
+  }, [playing, speed, data]);
+  useEffect(() => { if (data && count >= data.bars.length) setPlaying(false); }, [count, data]);
+
+  const visible = useMemo(() => {
+    const bars = data?.bars.slice(0, count) || [];
+    const until = bars.length ? Date.parse(bars[bars.length - 1].time) : -Infinity;
+    return { bars, fills: data?.fills.filter(fill => Date.parse(fill.time) <= until) || [], marks: data?.marks.filter(mark => Date.parse(mark.time) <= until) || [] };
+  }, [data, count]);
+  const valuation = useMemo(() => {
+    // Saved close marks precede next-open executions sharing that timestamp.
+    // Append their cash/inventory effect so replay never pairs new holdings
+    // with a pre-fill PnL (nor subtracts embedded slippage twice).
+    return visible.marks.flatMap(mark => {
+      const bar = visible.bars.find(item => item.time === mark.time);
+      const sameTimeFills = visible.fills.filter(fill => fill.time === mark.time);
+      if (!bar || mark.pnl === null || !sameTimeFills.length) return [mark];
+      let cash = mark.pnl - mark.shares * bar.close;
+      let shares = mark.shares;
+      for (const fill of sameTimeFills) {
+        cash -= (fill.shares_after - shares) * fill.price + fill.commission_cost;
+        shares = fill.shares_after;
+      }
+      return [mark, { time: mark.time, shares, pnl: cash + shares * sameTimeFills.at(-1)!.reference_price }];
+    });
+  }, [visible]);
+  const activeBar = visible.bars.length ? visible.bars[hover === null ? visible.bars.length - 1 : Math.min(hover, visible.bars.length - 1)] : undefined;
+  const latestMark = valuation.at(-1);
+  const lastShares = visible.fills.at(-1)?.shares_after ?? data?.opening_shares ?? 0;
+  const width = 1200, left = 76, right = 28, plotWidth = width - left - right;
+  const panels = [{ top: 28, height: 338, label: '价格 ($)' }, { top: 400, height: 82, label: '成交量' }, { top: 513, height: 82, label: '持仓 (股)' }, { top: 626, height: 82, label: '净盈亏 ($)' }];
+  const x = (m: number) => left + (m - range[0]) / (range[1] - range[0]) * plotWidth;
+  const priceBounds = bounds([...visible.bars.flatMap(bar => [bar.close, ...(bar.vwap === null ? [] : [bar.vwap])]), ...visible.fills.map(fill => fill.price)]);
+  const shareBounds = bounds([data?.opening_shares || 0, ...visible.fills.map(fill => fill.shares_after)], true);
+  const pnlBounds = bounds(valuation.flatMap(mark => mark.pnl === null ? [] : [mark.pnl]), true);
+  const volumeMax = Math.max(1, ...visible.bars.map(bar => bar.volume));
+  const domains = [priceBounds, [0, volumeMax * 1.1], shareBounds, pnlBounds];
+  const y = (value: number, panel: number) => panels[panel].top + panels[panel].height * (1 - (value - domains[panel][0]) / (domains[panel][1] - domains[panel][0]));
+  const path = (points: Array<[number, number | null]>, panel: number, step = false) => {
+    let started = false;
+    return points.map(([m, value]) => {
+      if (value === null || !Number.isFinite(value)) { started = false; return ''; }
+      const command = !started ? 'M' : step ? 'H' : 'L'; started = true;
+      return step && command === 'H' ? `H${x(m)}V${y(value, panel)}` : `${command}${x(m)},${y(value, panel)}`;
+    }).join(' ');
+  };
+  const sharePoints: Array<[number, number]> = [[0, data?.opening_shares || 0], ...visible.fills.map(fill => [minute(fill.time), fill.shares_after] as [number, number])];
+  if (visible.bars.length) sharePoints.push([minute(visible.bars.at(-1)!.time), lastShares]);
+  const step = range[1] - range[0] <= 150 ? 15 : 30;
+  const ticks: number[] = [];
+  for (let m = Math.ceil(range[0] / step) * step; m <= range[1]; m += step) ticks.push(m);
+  const handleHover = (event: MouseEvent<SVGSVGElement>) => {
+    const rect = event.currentTarget.getBoundingClientRect();
+    const position = (event.clientX - rect.left) / rect.width * width;
+    const target = range[0] + (position - left) / plotWidth * (range[1] - range[0]);
+    let best = 0;
+    visible.bars.forEach((bar, index) => { if (Math.abs(minute(bar.time) - target) < Math.abs(minute(visible.bars[best].time) - target)) best = index; });
+    setHover(best);
+  };
+  const zoom = (factor: number) => setRange(previous => {
+    const span = Math.min(390, Math.max(30, (previous[1] - previous[0]) * factor));
+    const start = Math.max(0, Math.min(390 - span, (previous[0] + previous[1] - span) / 2));
+    return [start, start + span];
+  });
+  const symbols = [...new Set([ticker, ...watchlist, 'SNDK', 'TSLA', 'NVDA', 'PLTR'])];
+  const variantChoices = data?.variants?.map(item => typeof item === 'string' ? item : item.id || item.value || '').filter(Boolean) || Object.keys(variants);
+
+  return <section className="price-replay" aria-label="价格与买卖点回放">
+    <header className="price-replay-heading">
+      <div>
+        <div className="price-replay-eyebrow">QUANT.AI · 价格与买卖点复盘</div>
+        <div className="price-replay-price">{money(activeBar?.close)} <span>{ticker}</span></div>
+        <div className="price-replay-subtitle">{data ? `${data.date} · 截至 ${marketTime.format(new Date(data.as_of)).slice(0, 5)} 美东 · ${data.partial ? '盘中保存' : '完整交易日'}` : '读取已保存的行情与成交'}</div>
+      </div>
+      <div className="price-replay-selectors">
+        <label>日期 <select aria-label="价格回放日期" value={date || data?.date || ''} onChange={event => setDate(event.target.value)}>
+          {!dates.length && <option value="">最近保存</option>}
+          {date && dates.length > 0 && !dates.includes(date) && <option value={date}>{date} · 暂无研究回放</option>}
+          {dates.map(value => <option key={value} value={value}>{value}</option>)}
+        </select></label>
+        <label>研究模型 <select aria-label="回放研究模型" value={variant} onChange={event => setVariant(event.target.value)}>
+          {variantChoices.map(value => <option key={value} value={value}>{variants[value] || value}</option>)}
+        </select></label>
+        <div className="price-replay-symbols">{symbols.map(symbol => <button key={symbol} className={ticker === symbol ? 'active' : ''} onClick={() => onSelectTicker(symbol)}>{symbol}</button>)}</div>
+      </div>
+    </header>
+    <div className="price-replay-context">
+      <span className="price-replay-source">研究模拟成交</span>
+      <span>{data?.source_label || '已保存的研究回放'}</span>
+      {data && <span>组合期初资金 {money(data.starting_equity)} · 单边摩擦假设 {data.cost_bps} bps</span>}
+    </div>
+    <div className="price-replay-toolbar">
+      <div className="price-replay-buttons">
+        <button disabled={!data || loading} className={playing ? 'active' : ''} onClick={() => { if (!data) return; if (count >= data.bars.length) setCount(1); setPlaying(!playing); }}>{playing ? '⏸ 暂停' : '▶ 播放仿真'}</button>
+        <button disabled={!data || loading} onClick={() => { setPlaying(false); setCount(1); setRange([0, 390]); setHover(null); }}>⟲ 重置</button>
+        {[1, 2, 5, 10].map(value => <button key={value} aria-label={`${value}倍播放速度`} className={speed === value ? 'active' : ''} onClick={() => setSpeed(value)}>{value}x</button>)}
+      </div>
+      <div className="price-replay-buttons">
+        {([{ label: '全天', range: [0, 390] }, { label: '早盘', range: [0, 150] }, { label: '午盘', range: [150, 270] }, { label: '尾盘', range: [270, 390] }] as Array<{ label: string; range: [number, number] }>).map(item => <button key={item.label} className={range[0] === item.range[0] && range[1] === item.range[1] ? 'active' : ''} onClick={() => setRange(item.range)}>{item.label}</button>)}
+        <button aria-label="放大价格图" onClick={() => zoom(.65)}>＋ 放大</button>
+        <button aria-label="缩小价格图" onClick={() => zoom(1.5)}>－ 缩小</button>
+      </div>
+    </div>
+    {loading ? <div className="price-replay-message" role="status">正在读取 {ticker} 的价格与买卖点…</div> : error ? <div className="price-replay-message error" role="status">{error}</div> : data && <>
+      <div className="price-replay-readout">
+        <span>美东时间 <b>{activeBar ? marketTime.format(new Date(activeBar.time)).slice(0, 5) : '—'}</b></span>
+        <span>价格 <b>{money(activeBar?.close)}</b></span><span>均价代理 <b>{money(activeBar?.vwap)}</b></span>
+        <span>该棒成交量 <b>{activeBar?.volume.toLocaleString() ?? '—'}</b></span>
+        <span>回放持仓 <b>{lastShares} 股</b></span>
+        <span>回放净盈亏 <b style={{ color: (latestMark?.pnl ?? 0) >= 0 ? '#34d399' : '#fb7185' }}>{money(latestMark?.pnl)}</b></span>
+      </div>
+      <div className="price-replay-legend"><span style={{ color: '#38bdf8' }}>━ Price 价格线</span><span style={{ color: '#e9ad54' }}>━ OHLCV 均价代理</span>{Object.entries(actions).map(([key, value]) => <span key={key} style={{ color: value.color }}>{key} · {value.label}</span>)}<span>标记数字＝股数</span></div>
+      <div className="price-replay-canvas">
+        <svg viewBox={`0 0 ${width} 744`} role="img" aria-label={`${ticker}价格线、成交量、持仓和模拟盈亏`} onMouseMove={handleHover} onMouseLeave={() => setHover(null)}>
+          <defs>{panels.map((panel, i) => <clipPath id={`${clip}-${i}`} key={i}><rect x={left} y={panel.top - 8} width={plotWidth} height={panel.height + 16} /></clipPath>)}</defs>
+          {panels.map((panel, index) => <g key={panel.label}>
+            <rect x={left} y={panel.top} width={plotWidth} height={panel.height} fill={index % 2 ? '#0d1520' : '#0b121c'} stroke="#263244" />
+            <text x={left} y={panel.top - 10} fill="#94a3b8" fontSize="11">{panel.label}</text>
+            {[0, .5, 1].map(fraction => { const value = domains[index][0] + fraction * (domains[index][1] - domains[index][0]); const py = y(value, index); return <g key={fraction}><line x1={left} x2={width - right} y1={py} y2={py} stroke="#202b3b" /><text x={left - 9} y={py + 4} textAnchor="end" fill="#94a3b8" fontSize="11">{index === 1 ? `${(value / 1000).toFixed(0)}k` : index === 2 ? value.toFixed(0) : value.toFixed(2)}</text></g>; })}
+            {ticks.map(tick => <line key={tick} x1={x(tick)} x2={x(tick)} y1={panel.top} y2={panel.top + panel.height} stroke="#1b2635" />)}
+          </g>)}
+          <g clipPath={`url(#${clip}-0)`}>
+            <path data-testid="replay-price-line" d={path(visible.bars.map(bar => [minute(bar.time), bar.close]), 0)} fill="none" stroke="#38bdf8" strokeWidth="2" vectorEffect="non-scaling-stroke" />
+            <path d={path(visible.bars.map(bar => [minute(bar.time), bar.vwap]), 0)} fill="none" stroke="#e9ad54" strokeWidth="1.6" vectorEffect="non-scaling-stroke" />
+            {visible.fills.map((fill, index) => {
+              const px = x(minute(fill.time)), py = y(fill.price, 0), up = fill.action === 'B' || fill.action === 'C';
+              const size = Math.min(8, 4 + Math.sqrt(fill.shares) / 2), color = actions[fill.action].color;
+              const sameTime = visible.fills.slice(0, index).filter(other => other.time === fill.time).length;
+              return <g key={`${fill.time}-${index}`} data-testid="replay-fill" tabIndex={0} role="img" aria-label={`${marketTime.format(new Date(fill.time))} ${actions[fill.action].label} ${fill.shares}股 ${money(fill.price)}`}>
+                <title>{`${marketTime.format(new Date(fill.time))} · ${actions[fill.action].label} ${fill.shares}股 @ ${money(fill.price)} · 成交后 ${fill.shares_after}股`}</title>
+                <path d={up ? `M${px},${py-size}L${px-size},${py+size}L${px+size},${py+size}Z` : `M${px},${py+size}L${px-size},${py-size}L${px+size},${py-size}Z`} fill={color} />
+                <text x={px} y={py + (up ? -12 : 19) + (up ? -1 : 1) * sameTime * 11} textAnchor="middle" fontSize="10" fontWeight="700" fill={color}>{fill.action}{fill.shares}</text>
+              </g>;
+            })}
+          </g>
+          <g clipPath={`url(#${clip}-1)`}>{visible.bars.map(bar => { const barWidth = Math.min(30, plotWidth * 4 / (range[1] - range[0])); return <rect key={bar.time} x={x(minute(bar.time)) - barWidth / 2} y={y(bar.volume, 1)} width={barWidth} height={y(0, 1) - y(bar.volume, 1)} fill={bar.close >= bar.open ? '#29b991' : '#e25876'} />; })}</g>
+          <g clipPath={`url(#${clip}-2)`}><line x1={left} x2={width - right} y1={y(0, 2)} y2={y(0, 2)} stroke="#536172" /><path d={path(sharePoints, 2, true)} fill="none" stroke="#b5a0ea" strokeWidth="1.8" /></g>
+          <g clipPath={`url(#${clip}-3)`}><line x1={left} x2={width - right} y1={y(0, 3)} y2={y(0, 3)} stroke="#536172" /><path d={path(valuation.map(mark => [minute(mark.time), mark.pnl]), 3)} fill="none" stroke="#36bca9" strokeWidth="1.8" /></g>
+          {hover !== null && activeBar && minute(activeBar.time) >= range[0] && minute(activeBar.time) <= range[1] && panels.map(panel => <line key={panel.top} x1={x(minute(activeBar.time))} x2={x(minute(activeBar.time))} y1={panel.top} y2={panel.top + panel.height} stroke="#8a9bb0" strokeDasharray="4 4" pointerEvents="none" />)}
+          {ticks.map(tick => <text key={tick} x={x(tick)} y={733} textAnchor="middle" fill="#94a3b8" fontSize="12">{timeLabel(tick)}</text>)}
+        </svg>
+      </div>
+      <div className="price-replay-seek"><span>{count} / {data.bars.length} 根</span><input aria-label="回放进度" type="range" min={1} max={data.bars.length} value={Math.max(1, count)} onChange={event => { setPlaying(false); setCount(Number(event.target.value)); setHover(null); }} /><span>{visible.fills.length} 笔成交</span></div>
+      <div className="price-replay-footnote">价格使用已完成 K 线的收盘价；成交标在实际模拟成交时刻。净盈亏包含持仓估值和上述摩擦假设。</div>
+    </>}
+  </section>;
+}

frontend/src/components/TradeComparisonPanel.tsx

@@ -3,6 +3,7 @@
 import React from 'react';
 import { API_BASE } from '../config';
 import { IntradayKlineChart } from './IntradayKlineChart';
+import { PriceActionReplayChart } from './PriceActionReplayChart';
 
 interface TradeComparisonPanelProps {
   watchlist: string[];
@@ -13,6 +14,21 @@ interface TradeComparisonPanelProps {
 export function TradeComparisonPanel({ watchlist, activeTicker, onSelectTicker }: TradeComparisonPanelProps) {
   const [iframeKey, setIframeKey] = React.useState(Date.now());
   const [isRefreshing, setIsRefreshing] = React.useState(false);
+  const [chartView, setChartView] = React.useState<'price' | 'classic'>('price');
+  const iframeRef = React.useRef<HTMLIFrameElement>(null);
+  const iframeQuery = new URLSearchParams({ v: String(iframeKey), ticker: activeTicker || 'TSLA' });
+
+  React.useEffect(() => {
+    const receiveSelection = (event: MessageEvent) => {
+      if (event.origin !== new URL(API_BASE, window.location.href).origin || event.source !== iframeRef.current?.contentWindow) return;
+      if (event.data?.type !== 'quant-replay-selection-change') return;
+      if (typeof event.data.ticker === 'string' && /^[A-Z.]{1,10}$/.test(event.data.ticker)) onSelectTicker(event.data.ticker);
+      // Research and broker archives can cover different dates. Keep each
+      // source's date selector independent while synchronizing the stock.
+    };
+    window.addEventListener('message', receiveSelection);
+    return () => window.removeEventListener('message', receiveSelection);
+  }, [onSelectTicker]);
 
   const handleRefresh = async () => {
     setIsRefreshing(true);
@@ -46,7 +62,7 @@ export function TradeComparisonPanel({ watchlist, activeTicker, onSelectTicker }
         alignItems: 'center'
       }}>
         <div style={{ fontWeight: 800, color: '#38bdf8', fontSize: '15px', display: 'flex', alignItems: 'center', gap: '8px' }}>
-          📈 历史 SIP 走势与券商成交对照大屏 (Robinhood 风格动态曲线 & 买卖点复盘)
+          📈 价格走势与买卖点复盘 · 研究模拟 / 券商成交
         </div>
         <button
           onClick={handleRefresh}
@@ -67,11 +83,21 @@ export function TradeComparisonPanel({ watchlist, activeTicker, onSelectTicker }
         </button>
       </div>
       <div style={{ padding: '16px' }}>
-        <IntradayKlineChart ticker={activeTicker || 'TSLA'} />
+        <div style={{ display: 'flex', gap: 8, marginBottom: 12 }}>
+          {([{ value: 'price', label: '📈 价格线 · 买卖点 · 持仓与盈亏' }, { value: 'classic', label: '📊 经典规则轨迹与 K 线' }] as const).map(view => <button
+            key={view.value} onClick={() => setChartView(view.value)} aria-pressed={chartView === view.value}
+            style={{ padding: '8px 12px', borderRadius: 7, border: `1px solid ${chartView === view.value ? '#288bb1' : '#2b394d'}`, background: chartView === view.value ? '#12364c' : '#121d2b', color: chartView === view.value ? '#7dd3fc' : '#94a3b8', fontWeight: 700, cursor: 'pointer' }}
+          >{view.label}</button>)}
+        </div>
+        {chartView === 'price' ? <PriceActionReplayChart ticker={activeTicker || 'TSLA'} watchlist={watchlist} onSelectTicker={onSelectTicker} refreshKey={iframeKey} /> : <IntradayKlineChart ticker={activeTicker || 'TSLA'} />}
+      </div>
+      <div style={{ padding: '4px 22px 12px', color: '#94a3b8', fontSize: 12 }}>
+        下方 Dynamic Replay 使用券商归档行情与成交，日期在下方独立选择。
       </div>
       <iframe
+        ref={iframeRef}
         key={iframeKey}
-        src={`${API_BASE}/charts/trade_comparison_dashboard.html?v=${iframeKey}`}
+        src={`${API_BASE}/charts/trade_comparison_dashboard.html?${iframeQuery}`}
         title="Trade Comparison Dashboard"
         style={{
           width: '100%',

reports/alpha_followup_audit_20260914/NVDA_quote_quality.csv

@@ -0,0 +1,79 @@
+bar_open_et,quote_count,raw_last_qi,stored_qi,equal_size_quote_fraction,last_quote_age_seconds,max_interquote_gap_seconds,stored_spread_bps,missing_features,last_quote_age_gt_30s,quote_state,trades__at_or_above_ask,trades__at_or_below_bid,trades__inside_spread_unclassified
+2026-09-14 09:30:00-04:00,47238,0.0,0.0,0.655023498031246,0.011128824,0.713110714,10.928182833251048,,False,equal_positive_sizes,177,180,2026
+2026-09-14 09:35:00-04:00,40632,0.0,0.0,0.7380882063398306,0.058368781,0.589749331,5.248968100589967,,False,equal_positive_sizes,201,138,1230
+2026-09-14 09:40:00-04:00,31587,0.0,0.0,0.4374267895020103,0.018041832,0.719045909,2.385439278642348,,False,equal_positive_sizes,183,65,818
+2026-09-14 09:45:00-04:00,35462,0.0,0.0,0.7860526761039986,0.105675441,0.710951816,6.6514633219315265,,False,equal_positive_sizes,124,128,948
+2026-09-14 09:50:00-04:00,29692,0.0,0.0,0.5788427859356056,0.023441589,0.858456197,3.7965973018077745,,False,equal_positive_sizes,79,89,958
+2026-09-14 09:55:00-04:00,19417,0.0,0.0,0.8709378379770304,0.49161506,1.252646427,4.269753540337472,,False,equal_positive_sizes,41,37,477
+2026-09-14 10:00:00-04:00,30462,0.0,0.0,0.5974656949642178,0.06071779,0.910450571,1.8963637225616101,,False,equal_positive_sizes,54,63,463
+2026-09-14 10:05:00-04:00,26390,0.0,0.0,0.7166350890488822,0.016567135,0.820437086,1.8973081950665556,,False,equal_positive_sizes,93,82,641
+2026-09-14 10:10:00-04:00,23233,0.0,0.0,0.6124908535273103,0.003132741,1.248683774,1.891431813882733,,False,equal_positive_sizes,56,56,594
+2026-09-14 10:15:00-04:00,26567,0.0,0.0,0.6202055181239884,3.8312e-05,0.812947536,1.4243323442123543,,False,equal_positive_sizes,50,51,405
+2026-09-14 10:20:00-04:00,26472,0.3333333333333333,0.3333333333333333,0.5608189785433666,0.033637667,0.652504686,1.4267031268577408,,False,unequal_sizes,38,67,558
+2026-09-14 10:25:00-04:00,22378,-0.3333333333333333,-0.3333333333333333,0.5447761194029851,0.01770726,0.696683323,1.4275857146256699,,False,unequal_sizes,74,53,462
+2026-09-14 10:30:00-04:00,21858,0.0,0.0,0.5774087290694483,0.001018867,0.840904001,1.4250765978671893,,False,equal_positive_sizes,35,67,404
+2026-09-14 10:35:00-04:00,25341,-0.5,-0.5,0.5513989187482735,0.089092127,0.747850098,1.4256183619645562,,False,unequal_sizes,46,44,396
+2026-09-14 10:40:00-04:00,16796,0.0,0.0,0.5656703977137414,0.054313223,0.907759076,1.425753867357419,,False,equal_positive_sizes,45,29,296
+2026-09-14 10:45:00-04:00,19260,0.0,0.0,0.5537383177570093,0.058362412,0.753569034,1.424129500842664,,False,equal_positive_sizes,31,21,386
+2026-09-14 10:50:00-04:00,16950,0.0,0.0,0.5418879056047198,0.043639479,0.945240415,1.4247382043550039,,False,equal_positive_sizes,26,20,259
+2026-09-14 10:55:00-04:00,17764,0.0,0.0,0.49346993920288224,0.018474905,1.925368307,1.4256183619645562,,False,equal_positive_sizes,57,36,414
+2026-09-14 11:00:00-04:00,16624,0.0,0.0,0.5406641000962464,0.027772956,1.46052781,1.4256183619645562,,False,equal_positive_sizes,30,28,218
+2026-09-14 11:05:00-04:00,14599,-0.3333333333333333,-0.3333333333333333,0.6995684635933969,0.436958419,2.837971024,2.8423895021082135,,False,unequal_sizes,20,26,363
+2026-09-14 11:10:00-04:00,12071,0.3333333333333333,0.3333333333333333,0.6126253003065197,0.179880324,1.482236664,1.8964536317083274,,False,unequal_sizes,19,12,174
+2026-09-14 11:15:00-04:00,15902,0.0,0.0,0.5817507231794743,0.020873709,0.980295251,1.42304864454622,,False,equal_positive_sizes,23,14,233
+2026-09-14 11:20:00-04:00,15195,0.0,0.0,0.5663705166173083,0.004564744,1.317557455,1.4214304328256206,,False,equal_positive_sizes,19,18,204
+2026-09-14 11:25:00-04:00,14905,0.0,0.0,0.5639718215363971,0.002002103,0.98752346,1.419681518112824,,False,equal_positive_sizes,26,17,239
+2026-09-14 11:30:00-04:00,16848,0.3333333333333333,0.3333333333333333,0.5435066476733144,0.107928921,1.040888242,1.888663298550075,,False,unequal_sizes,51,34,518
+2026-09-14 11:35:00-04:00,12614,0.0,0.0,0.5820516885999683,0.068022526,1.702253897,1.8870594895513735,,False,equal_positive_sizes,19,14,331
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