[7/7] enable + (re)start services"
+sudo systemctl daemon-reload
+sudo systemctl enable --now kronos.service
+sudo systemctl enable --now kronos-watchdog.service
+sleep 2
+sudo systemctl --no-pager --lines=5 status kronos.service || true
+echo "==> done. app on 0.0.0.0:$APP_PORT (reach via SSH tunnel)."
diff --git a/forecast_dashboard/forecast_engine.py b/forecast_dashboard/forecast_engine.py
new file mode 100644
index 000000000..c49b1c083
--- /dev/null
+++ b/forecast_dashboard/forecast_engine.py
@@ -0,0 +1,683 @@
+"""forecast_engine.py — THE CORE. Single source of truth for all model + data logic.
+
+Every client (Streamlit app, MCP server, notebook) imports from here. No model or
+data logic is duplicated anywhere else.
+
+Honesty principles baked into the engine (see SCOPE.md §0):
+ - Forecast is a DISTRIBUTION of independent sample paths, never a single point.
+ - We expose p10..p90 bands + the full sample matrix so clients can show the fan.
+ - We carry the per-instrument confidence caveat through to every result.
+ - The model sees price candles ONLY. It cannot see news / macro events.
+
+Run the smoke test: python forecast_engine.py
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import math
+import threading
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from instruments import Instrument, get_instrument, all_instruments
+
+# --------------------------------------------------------------------------- #
+# Kronos repo discovery. This dashboard ships *inside* the Kronos repo (at
+# Kronos/forecast_dashboard/), so by default the model package lives in the
+# parent directory. We still honour an explicit KRONOS_DIR override and fall
+# back to a sibling ./Kronos clone, so the engine also works if this folder is
+# copied out and used standalone.
+# --------------------------------------------------------------------------- #
+_HERE = os.path.dirname(os.path.abspath(__file__))
+_PARENT = os.path.dirname(_HERE) # Kronos repo root when shipped inside it
+
+
+def _discover_kronos_dir() -> str:
+ # 1) explicit override
+ env = os.environ.get("KRONOS_DIR")
+ if env:
+ return env
+ # 2) shipped inside the Kronos repo: model package is in the parent dir
+ if os.path.isdir(os.path.join(_PARENT, "model")):
+ return _PARENT
+ # 3) standalone: a sibling ./Kronos clone next to this folder
+ return os.path.join(_HERE, "Kronos")
+
+
+KRONOS_DIR = _discover_kronos_dir()
+if KRONOS_DIR and KRONOS_DIR not in sys.path and os.path.isdir(KRONOS_DIR):
+ sys.path.insert(0, KRONOS_DIR)
+
+
+# --------------------------------------------------------------------------- #
+# Defaults (overridable via .env / function args)
+# --------------------------------------------------------------------------- #
+DEFAULT_MODEL = os.environ.get("KRONOS_MODEL", "NeoQuasar/Kronos-small")
+DEFAULT_TOKENIZER = os.environ.get("KRONOS_TOKENIZER", "NeoQuasar/Kronos-Tokenizer-base")
+DEFAULT_DEVICE = os.environ.get("KRONOS_DEVICE", "cpu")
+MAX_CONTEXT = 512 # hard cap for Kronos-small/base
+TRADING_DAYS_PER_YEAR = 252
+
+# --------------------------------------------------------------------------- #
+# Timeframes — daily + intraday. Kronos treats minute candles the same way it
+# treats daily candles (it just reads OHLCV tokens), so the ONLY things that
+# change per timeframe are: the yfinance interval, how far back free data goes,
+# how we step future timestamps, and the volatility annualization factor.
+#
+# yfinance free-data limits (hard caps we respect):
+# 1m -> last 7 days 5m/15m -> last 60 days 1h -> last 730 days
+# Sub-minute / tick data is NOT available on free feeds, so we stop at 1m.
+# --------------------------------------------------------------------------- #
+TIMEFRAMES: dict[str, dict] = {
+ "1d": {"interval": "1d", "period": None, "freq": None,
+ "label": "Daily", "intraday": False, "periods_per_year": 252,
+ "horizon_unit": "d"},
+ "1h": {"interval": "60m", "period": "729d", "freq": "60min",
+ "label": "1 hour", "intraday": True, "periods_per_year": 24 * 252,
+ "horizon_unit": "h"},
+ "15m": {"interval": "15m", "period": "59d", "freq": "15min",
+ "label": "15 min", "intraday": True, "periods_per_year": 96 * 252,
+ "horizon_unit": "bar"},
+ "5m": {"interval": "5m", "period": "59d", "freq": "5min",
+ "label": "5 min", "intraday": True, "periods_per_year": 288 * 252,
+ "horizon_unit": "bar"},
+ "1m": {"interval": "1m", "period": "7d", "freq": "1min",
+ "label": "1 min", "intraday": True, "periods_per_year": 1440 * 252,
+ "horizon_unit": "bar"},
+}
+DEFAULT_TIMEFRAME = "1d"
+
+
+def timeframe_config(timeframe: str) -> dict:
+ tf = (timeframe or DEFAULT_TIMEFRAME).lower()
+ if tf not in TIMEFRAMES:
+ raise KeyError(f"Unknown timeframe '{timeframe}'. "
+ f"Valid: {', '.join(TIMEFRAMES)}")
+ return TIMEFRAMES[tf]
+
+
+# --------------------------------------------------------------------------- #
+# Result containers
+# --------------------------------------------------------------------------- #
+@dataclass
+class ForecastResult:
+ """Everything a client needs to honestly render a forecast distribution."""
+
+ instrument_key: str
+ display_name: str
+ ticker_used: str
+ used_volume_path: bool # True if OHLC-only (no-volume) path was used
+ has_reliable_volume: bool
+ confidence_caveat: str
+ event_sensitive: bool
+
+ last_close: float
+ last_history_date: pd.Timestamp
+ generated_at: datetime
+
+ # Forecast horizon
+ pred_len: int
+ n_samples: int
+ future_timestamps: pd.DatetimeIndex
+
+ # Per-horizon percentile arrays (length == pred_len)
+ p10: np.ndarray
+ p25: np.ndarray
+ median: np.ndarray # p50
+ p75: np.ndarray
+ p90: np.ndarray
+
+ # Full close-price sample matrix (n_samples x pred_len) for the fan plot
+ sample_matrix: np.ndarray
+
+ # Recent history kept for plotting context (DataFrame: timestamps, ohlcv)
+ history: pd.DataFrame
+
+ # Risk metrics
+ realized_vol_annualized: float # from recent daily log returns (HISTORICAL)
+ skew_metrics: pd.DataFrame # per-horizon skew + downside probability
+
+ # Sampling params used (for logging / reproducibility framing)
+ T: float = 1.0
+ top_p: float = 0.9
+ model_name: str = DEFAULT_MODEL
+
+ # Timeframe metadata (daily vs intraday)
+ timeframe: str = DEFAULT_TIMEFRAME
+ timeframe_label: str = "Daily"
+ is_intraday: bool = False
+
+ def price_lean(self, horizon: Optional[int] = None) -> str:
+ """Directional lean of the median vs last close at a horizon.
+
+ Used by the combined (price + news) read. Returns 'higher' | 'lower' |
+ 'flat'. 'flat' when the median move is within a small fraction of price
+ (noise), so we don't over-read tiny drifts.
+ """
+ if horizon is None:
+ horizon = min(4, self.pred_len - 1)
+ horizon = max(0, min(horizon, self.pred_len - 1))
+ med = float(self.median[horizon])
+ if self.last_close == 0:
+ return "flat"
+ move = (med - self.last_close) / self.last_close
+ # 0.25% threshold for daily; scale down for intraday noise.
+ thresh = 0.0025 if not self.is_intraday else 0.0008
+ if move > thresh:
+ return "higher"
+ if move < -thresh:
+ return "lower"
+ return "flat"
+
+ def horizon_table(self) -> pd.DataFrame:
+ """Daily Low/Median/High forecast-distribution table for display."""
+ return pd.DataFrame(
+ {
+ "date": self.future_timestamps,
+ "Low (p10)": self.p10,
+ "p25": self.p25,
+ "Median (p50)": self.median,
+ "p75": self.p75,
+ "High (p90)": self.p90,
+ }
+ )
+
+ def summary_text(self) -> str:
+ """Plain-text summary with honesty framing — reused by the MCP server."""
+ lines = []
+ lines.append(f"{self.display_name} (ticker used: {self.ticker_used})")
+ lines.append(f"Last close: {self.last_close:,.4f} as of {self.last_history_date.date()}")
+ lines.append(
+ f"Forecast: DISTRIBUTION of {self.n_samples} independent sample paths over "
+ f"{self.pred_len} business days (NOT a single prediction)."
+ )
+ lines.append("")
+ lines.append("Per-horizon band (Low p10 / Median p50 / High p90):")
+ # Show a few representative horizons
+ idxs = sorted(set([0,
+ min(4, self.pred_len - 1),
+ min(9, self.pred_len - 1),
+ self.pred_len - 1]))
+ for i in idxs:
+ d = self.future_timestamps[i].date()
+ lines.append(
+ f" +{i+1:>2}d {d}: {self.p10[i]:,.4f} | {self.median[i]:,.4f} | {self.p90[i]:,.4f}"
+ )
+ lines.append("")
+ # Skew interpretation
+ lines.append(self.skew_sentence())
+ lines.append(f"Historical realized volatility (annualized): {self.realized_vol_annualized*100:,.1f}%")
+ lines.append("")
+ if self.used_volume_path is False and not self.has_reliable_volume:
+ lines.append("[!] No-volume forecast path was used (volume data unreliable).")
+ lines.append(f"CAVEAT: {self.confidence_caveat}")
+ lines.append("CAVEAT: The model sees PAST PRICE CANDLES ONLY. It does not see Fed/BoE/BoJ "
+ "decisions, CPI, OPEC headlines, or any news/risk events.")
+ return "\n".join(lines)
+
+ def skew_sentence(self) -> str:
+ """One plain-English sentence interpreting the downside/upside skew."""
+ # Use a ~2-week horizon if available, else the last horizon
+ h = min(9, self.pred_len - 1)
+ row = self.skew_metrics.iloc[h]
+ ratio = row["downside_upside_ratio"]
+ below = row["prob_below_last_close"] * 100
+ day = h + 1
+ if not math.isfinite(ratio) or ratio <= 0:
+ return (f"At the +{day}-day horizon, ~{below:.0f}% of sample paths are below the current price.")
+ if ratio >= 1.15:
+ shape = f"the downside tail is ~{ratio:.1f}x the upside"
+ elif ratio <= 0.87:
+ shape = f"the upside tail is ~{1/ratio:.1f}x the downside"
+ else:
+ shape = "the tails are roughly symmetric"
+ return (f"At the +{day}-day horizon, {shape}; "
+ f"~{below:.0f}% of sample paths end below the current price.")
+
+ def to_log_dict(self) -> dict:
+ """Compact dict for storage.py logging (one row per forecast run)."""
+ return {
+ "instrument_key": self.instrument_key,
+ "ticker_used": self.ticker_used,
+ "model_name": self.model_name,
+ "generated_at": self.generated_at.isoformat(),
+ "last_history_date": self.last_history_date.isoformat(),
+ "last_close": float(self.last_close),
+ "pred_len": int(self.pred_len),
+ "n_samples": int(self.n_samples),
+ "used_volume_path": bool(self.used_volume_path),
+ "T": float(self.T),
+ "top_p": float(self.top_p),
+ "future_timestamps": [ts.isoformat() for ts in self.future_timestamps],
+ "p10": [float(v) for v in self.p10],
+ "p25": [float(v) for v in self.p25],
+ "median": [float(v) for v in self.median],
+ "p75": [float(v) for v in self.p75],
+ "p90": [float(v) for v in self.p90],
+ }
+
+
+# --------------------------------------------------------------------------- #
+# Predictor loading (expensive — cache and reuse)
+# --------------------------------------------------------------------------- #
+_PREDICTOR_CACHE: dict = {}
+_PREDICTOR_LOCK = threading.Lock()
+
+
+def load_predictor(model_name: str = DEFAULT_MODEL,
+ tokenizer_name: str = DEFAULT_TOKENIZER,
+ device: str = DEFAULT_DEVICE,
+ max_context: int = MAX_CONTEXT):
+ """Load tokenizer + model + KronosPredictor once and reuse.
+
+ Loading downloads weights from HuggingFace on first run (~100MB cached locally)
+ and is slow, so we cache by (model, tokenizer, device, max_context).
+
+ `device` is passed explicitly to KronosPredictor (do NOT rely on its default).
+ """
+ key = (model_name, tokenizer_name, device, max_context)
+ if key in _PREDICTOR_CACHE:
+ return _PREDICTOR_CACHE[key]
+ with _PREDICTOR_LOCK:
+ if key in _PREDICTOR_CACHE:
+ return _PREDICTOR_CACHE[key]
+ try:
+ from model import Kronos, KronosTokenizer, KronosPredictor
+ except ImportError as exc:
+ raise ImportError(
+ "Could not import Kronos. Clone it and set KRONOS_DIR, e.g.\n"
+ " git clone https://github.com/shiyu-coder/Kronos.git\n"
+ f"Expected repo root on sys.path. KRONOS_DIR={KRONOS_DIR!r}"
+ ) from exc
+
+ tokenizer = KronosTokenizer.from_pretrained(tokenizer_name)
+ model = Kronos.from_pretrained(model_name)
+ predictor = KronosPredictor(model, tokenizer, device=device, max_context=max_context)
+ _PREDICTOR_CACHE[key] = predictor
+ return predictor
+
+
+# --------------------------------------------------------------------------- #
+# Data fetching
+# --------------------------------------------------------------------------- #
+def _flatten_columns(df: pd.DataFrame) -> pd.DataFrame:
+ """yfinance returns multi-index (tuple) columns — flatten + lowercase them."""
+ df = df.copy()
+ df.columns = [
+ (c[0].lower() if isinstance(c, tuple) else str(c).lower()) for c in df.columns
+ ]
+ return df
+
+
+def _volume_is_reliable(volume: pd.Series) -> bool:
+ """Heuristic: volume is junk if it's empty, all-zero, or all-NaN."""
+ if volume is None or len(volume) == 0:
+ return False
+ v = pd.to_numeric(volume, errors="coerce").fillna(0.0)
+ if (v <= 0).all():
+ return False
+ # If the vast majority of rows are zero, treat as unreliable.
+ nonzero_frac = float((v > 0).mean())
+ return nonzero_frac >= 0.30
+
+
+def _download(ticker: str, lookback_days: int, timeframe: str = DEFAULT_TIMEFRAME) -> pd.DataFrame:
+ """Download OHLCV via yfinance and return a clean flat DataFrame.
+
+ For daily we request a day window; for intraday we use the per-timeframe
+ `period` cap (yfinance refuses longer windows for fine intervals).
+ """
+ import yfinance as yf
+
+ tf = timeframe_config(timeframe)
+ interval = tf["interval"]
+ if tf["intraday"]:
+ period = tf["period"]
+ raw = yf.download(
+ ticker, period=period, interval=interval,
+ auto_adjust=False, progress=False, threads=False,
+ )
+ else:
+ period_days = max(lookback_days + 60, 120)
+ raw = yf.download(
+ ticker, period=f"{period_days}d", interval=interval,
+ auto_adjust=False, progress=False, threads=False,
+ )
+ if raw is None or len(raw) == 0:
+ return pd.DataFrame()
+ raw = _flatten_columns(raw)
+ raw = raw.reset_index()
+ # The datetime column may be 'date' or 'datetime' after flattening/reset.
+ ts_col = None
+ for cand in ("date", "datetime", "index"):
+ if cand in raw.columns:
+ ts_col = cand
+ break
+ if ts_col is None:
+ ts_col = raw.columns[0]
+ raw = raw.rename(columns={ts_col: "timestamps"})
+ keep = ["timestamps", "open", "high", "low", "close", "volume"]
+ have = [c for c in keep if c in raw.columns]
+ raw = raw[have].copy()
+ # Intraday timestamps are tz-aware (UTC/exchange); drop tz to keep one code path.
+ ts = pd.to_datetime(raw["timestamps"])
+ try:
+ if getattr(ts.dt, "tz", None) is not None:
+ ts = ts.dt.tz_convert("UTC").dt.tz_localize(None)
+ else:
+ ts = ts.dt.tz_localize(None)
+ except (TypeError, AttributeError):
+ ts = ts.dt.tz_localize(None)
+ raw["timestamps"] = ts
+ for c in ("open", "high", "low", "close", "volume"):
+ if c in raw.columns:
+ raw[c] = pd.to_numeric(raw[c], errors="coerce")
+ raw = raw.dropna(subset=["open", "high", "low", "close"]).reset_index(drop=True)
+ return raw
+
+
+def fetch_ohlcv(instrument_key: str, lookback_days: int = 900,
+ timeframe: str = DEFAULT_TIMEFRAME) -> pd.DataFrame:
+ """Fetch OHLCV for an instrument with primary -> fallback ticker logic.
+
+ Returns a clean DataFrame with columns:
+ timestamps, open, high, low, close, volume, amount
+ plus attributes on `df.attrs`:
+ ticker_used (str), volume_is_reliable (bool)
+
+ The `amount` column is SYNTHETIC (volume * close); Yahoo does not provide it.
+ """
+ inst = get_instrument(instrument_key)
+ candidates = [inst.primary_ticker]
+ if inst.fallback_ticker:
+ candidates.append(inst.fallback_ticker)
+
+ df = pd.DataFrame()
+ ticker_used = inst.primary_ticker
+ for ticker in candidates:
+ attempt = _download(ticker, lookback_days, timeframe=timeframe)
+ if len(attempt) >= 50: # need enough rows to be useful
+ df = attempt
+ ticker_used = ticker
+ # If this instrument is supposed to have reliable volume but the primary
+ # returned junk volume, try the fallback before giving up.
+ if inst.has_reliable_volume and not _volume_is_reliable(df.get("volume")) \
+ and ticker == inst.primary_ticker and inst.fallback_ticker:
+ continue
+ break
+
+ if len(df) == 0:
+ raise RuntimeError(
+ f"No usable data for {inst.display_name} "
+ f"(tried tickers: {', '.join(candidates)})."
+ )
+
+ if "volume" not in df.columns:
+ df["volume"] = 0.0
+ df["volume"] = pd.to_numeric(df["volume"], errors="coerce").fillna(0.0)
+
+ volume_reliable = inst.has_reliable_volume and _volume_is_reliable(df["volume"])
+ # Synthetic 'amount' column (documented as volume*close).
+ df["amount"] = df["volume"] * df["close"]
+
+ df = df[["timestamps", "open", "high", "low", "close", "volume", "amount"]].copy()
+ df.attrs["ticker_used"] = ticker_used
+ df.attrs["volume_is_reliable"] = bool(volume_reliable)
+ df.attrs["instrument_key"] = instrument_key
+ return df
+
+
+# --------------------------------------------------------------------------- #
+# Skew / downside-tail metrics
+# --------------------------------------------------------------------------- #
+def compute_skew_metrics(sample_matrix: np.ndarray, last_close: float) -> pd.DataFrame:
+ """Per-horizon skew + downside probability from the close-price sample matrix.
+
+ Args:
+ sample_matrix: shape (n_samples, pred_len) of forecast close prices.
+ last_close: most recent actual close.
+
+ Returns a DataFrame indexed by horizon (1..pred_len) with columns:
+ median, p10, p90,
+ downside (median - p10), upside (p90 - median),
+ downside_upside_ratio (downside / upside; >1 means fatter left tail),
+ prob_below_last_close (fraction of samples below current price).
+ """
+ sm = np.asarray(sample_matrix, dtype=float)
+ if sm.ndim != 2:
+ raise ValueError("sample_matrix must be 2-D (n_samples x pred_len)")
+ pred_len = sm.shape[1]
+
+ median = np.median(sm, axis=0)
+ p10 = np.percentile(sm, 10, axis=0)
+ p90 = np.percentile(sm, 90, axis=0)
+ downside = median - p10
+ upside = p90 - median
+ with np.errstate(divide="ignore", invalid="ignore"):
+ ratio = np.where(upside > 0, downside / upside, np.nan)
+ prob_below = (sm < last_close).mean(axis=0)
+
+ return pd.DataFrame(
+ {
+ "horizon": np.arange(1, pred_len + 1),
+ "median": median,
+ "p10": p10,
+ "p90": p90,
+ "downside": downside,
+ "upside": upside,
+ "downside_upside_ratio": ratio,
+ "prob_below_last_close": prob_below,
+ }
+ ).set_index("horizon")
+
+
+def realized_volatility(close: pd.Series, window: int = 30,
+ periods_per_year: int = TRADING_DAYS_PER_YEAR) -> float:
+ """Annualized realized volatility from recent log returns (HISTORICAL).
+
+ `periods_per_year` lets intraday timeframes annualize correctly (e.g. hourly
+ bars use 24*252). The number is still HISTORICAL realized vol, distinct from
+ the forecast band width.
+ """
+ c = pd.to_numeric(close, errors="coerce").dropna()
+ if len(c) < 5:
+ return float("nan")
+ log_ret = np.log(c / c.shift(1)).dropna()
+ recent = log_ret.tail(window)
+ if len(recent) < 2:
+ return float("nan")
+ return float(recent.std(ddof=1) * math.sqrt(periods_per_year))
+
+
+# --------------------------------------------------------------------------- #
+# Future timestamps — business days for daily, fixed-frequency bars for intraday
+# --------------------------------------------------------------------------- #
+def _future_business_days(last_date: pd.Timestamp, pred_len: int) -> pd.DatetimeIndex:
+ """pred_len business days strictly after last_date."""
+ start = pd.Timestamp(last_date).normalize() + pd.offsets.BDay(1)
+ return pd.bdate_range(start=start, periods=pred_len)
+
+
+def _future_timestamps(last_date: pd.Timestamp, pred_len: int,
+ timeframe: str = DEFAULT_TIMEFRAME) -> pd.DatetimeIndex:
+ """Future timestamps after last_date, stepping by the timeframe.
+
+ Daily uses business days. Intraday steps by the bar frequency (e.g. 60min);
+ these are naive continuation stamps for display/logging — markets have gaps,
+ but Kronos itself only consumes the candle sequence, not wall-clock spacing.
+ """
+ tf = timeframe_config(timeframe)
+ if not tf["intraday"]:
+ return _future_business_days(last_date, pred_len)
+ freq = tf["freq"]
+ start = pd.Timestamp(last_date) + pd.tseries.frequencies.to_offset(freq)
+ return pd.date_range(start=start, periods=pred_len, freq=freq)
+
+
+# --------------------------------------------------------------------------- #
+# The main forecast call
+# --------------------------------------------------------------------------- #
+def run_forecast(instrument_key: str,
+ lookback: int = 400,
+ pred_len: int = 30,
+ n_samples: int = 30,
+ T: float = 1.0,
+ top_p: float = 0.9,
+ top_k: int = 0,
+ device: str = DEFAULT_DEVICE,
+ model_name: str = DEFAULT_MODEL,
+ tokenizer_name: str = DEFAULT_TOKENIZER,
+ history_tail: int = 60,
+ timeframe: str = DEFAULT_TIMEFRAME,
+ predictor=None) -> ForecastResult:
+ """Generate a forecast DISTRIBUTION for an instrument.
+
+ We generate `n_samples` INDEPENDENT paths. The Kronos `sample_count` arg
+ AVERAGES internally — that destroys the spread we need — so we instead request
+ n_samples independent series via predict_batch (each with sample_count=1) and
+ collect the close-price arrays ourselves to form the fan.
+
+ `timeframe` selects daily ('1d') or intraday ('1h'/'15m'/'5m'/'1m'). Kronos
+ reads the candle sequence identically; only data interval, future-stamp
+ spacing, and vol annualization differ.
+
+ Returns a fully-populated ForecastResult (see dataclass).
+ """
+ inst = get_instrument(instrument_key)
+ tf = timeframe_config(timeframe)
+ lookback = int(min(lookback, MAX_CONTEXT))
+ n_samples = int(max(2, n_samples))
+ pred_len = int(max(1, pred_len))
+
+ # 1. Data
+ df = fetch_ohlcv(instrument_key, lookback_days=max(lookback + 60, 200),
+ timeframe=timeframe)
+ ticker_used = df.attrs["ticker_used"]
+ volume_reliable = df.attrs["volume_is_reliable"]
+
+ if len(df) < lookback:
+ lookback = len(df)
+ hist = df.tail(lookback).reset_index(drop=True)
+
+ last_close = float(hist["close"].iloc[-1])
+ last_history_date = pd.Timestamp(hist["timestamps"].iloc[-1])
+ future_ts = _future_timestamps(last_history_date, pred_len, timeframe=timeframe)
+
+ # 2. Build the model input frame. Route to no-volume path when volume is junk.
+ use_volume = bool(volume_reliable)
+ if use_volume:
+ x_cols = ["open", "high", "low", "close", "volume", "amount"]
+ else:
+ # No-volume path: OHLC only; predictor zero-fills volume + amount.
+ x_cols = ["open", "high", "low", "close"]
+ x_df = hist[x_cols].copy()
+ x_timestamp = hist["timestamps"].copy()
+
+ # 3. Predictor
+ if predictor is None:
+ predictor = load_predictor(model_name=model_name,
+ tokenizer_name=tokenizer_name,
+ device=device,
+ max_context=MAX_CONTEXT)
+
+ # 4. Generate n_samples INDEPENDENT paths via predict_batch (sample_count=1).
+ df_list = [x_df] * n_samples
+ xts_list = [x_timestamp] * n_samples
+ yts_list = [pd.Series(future_ts)] * n_samples
+
+ pred_dfs = predictor.predict_batch(
+ df_list=df_list,
+ x_timestamp_list=xts_list,
+ y_timestamp_list=yts_list,
+ pred_len=pred_len,
+ T=T,
+ top_k=top_k,
+ top_p=top_p,
+ sample_count=1,
+ verbose=False,
+ )
+ sample_matrix = np.vstack([p["close"].to_numpy(dtype=float) for p in pred_dfs])
+
+ # 5. Percentile bands from the samples
+ p10 = np.percentile(sample_matrix, 10, axis=0)
+ p25 = np.percentile(sample_matrix, 25, axis=0)
+ median = np.percentile(sample_matrix, 50, axis=0)
+ p75 = np.percentile(sample_matrix, 75, axis=0)
+ p90 = np.percentile(sample_matrix, 90, axis=0)
+
+ # 6. Risk metrics
+ skew = compute_skew_metrics(sample_matrix, last_close)
+ rvol = realized_volatility(hist["close"], window=min(30, len(hist) - 1),
+ periods_per_year=tf["periods_per_year"])
+
+ history_for_plot = df.tail(history_tail).reset_index(drop=True)
+
+ return ForecastResult(
+ instrument_key=instrument_key,
+ display_name=inst.display_name,
+ ticker_used=ticker_used,
+ used_volume_path=use_volume,
+ has_reliable_volume=inst.has_reliable_volume,
+ confidence_caveat=inst.confidence_caveat,
+ event_sensitive=inst.event_sensitive,
+ last_close=last_close,
+ last_history_date=last_history_date,
+ generated_at=datetime.now(timezone.utc),
+ pred_len=pred_len,
+ n_samples=n_samples,
+ future_timestamps=future_ts,
+ p10=p10, p25=p25, median=median, p75=p75, p90=p90,
+ sample_matrix=sample_matrix,
+ history=history_for_plot,
+ realized_vol_annualized=rvol,
+ skew_metrics=skew,
+ T=T, top_p=top_p, model_name=model_name,
+ timeframe=timeframe.lower(),
+ timeframe_label=tf["label"],
+ is_intraday=tf["intraday"],
+ )
+
+
+# --------------------------------------------------------------------------- #
+# Smoke test (SCOPE.md §9 step 2)
+# --------------------------------------------------------------------------- #
+def _smoke_test():
+ print("=" * 70)
+ print("forecast_engine smoke test")
+ print("=" * 70)
+ print(f"KRONOS_DIR = {KRONOS_DIR}")
+ print(f"device = {DEFAULT_DEVICE}")
+ print(f"model = {DEFAULT_MODEL}")
+ print("Loading predictor (downloads weights on first run)...")
+ predictor = load_predictor(device=DEFAULT_DEVICE)
+ print("Predictor loaded.\n")
+
+ for inst in all_instruments():
+ print("-" * 70)
+ print(f"Instrument: {inst.display_name} ({inst.key})")
+ try:
+ res = run_forecast(
+ inst.key,
+ lookback=200,
+ pred_len=5,
+ n_samples=5,
+ predictor=predictor,
+ )
+ print(res.summary_text())
+ assert res.sample_matrix.shape == (res.n_samples, res.pred_len)
+ assert (res.p90 >= res.p10).all(), "p90 should be >= p10"
+ vol_note = "no-volume path" if not res.used_volume_path else "volume path"
+ print(f"[OK] shape={res.sample_matrix.shape} ({vol_note})")
+ except Exception as exc: # noqa: BLE001 - smoke test should report all
+ print(f"[FAIL] {inst.display_name}: {exc!r}")
+ print()
+
+
+if __name__ == "__main__":
+ _smoke_test()
diff --git a/forecast_dashboard/instruments.py b/forecast_dashboard/instruments.py
new file mode 100644
index 000000000..7b3c90e8e
--- /dev/null
+++ b/forecast_dashboard/instruments.py
@@ -0,0 +1,105 @@
+"""instruments.py — configuration for the four traded instruments.
+
+Single source of truth for tickers, data quirks, and the honesty caveats that
+MUST be surfaced in every client (dashboard, MCP server, notebook).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+@dataclass(frozen=True)
+class Instrument:
+ """Static config for a single tradeable instrument.
+
+ Attributes:
+ key: stable internal id (used in storage + APIs).
+ display_name: human-friendly name shown in the UI.
+ primary_ticker: preferred Yahoo Finance ticker (futures = real volume).
+ fallback_ticker: used if the primary returns no/garbage data.
+ has_reliable_volume: True when volume can be trusted; False routes the
+ fetch to the no-volume forecast path when volume is junk.
+ confidence_caveat: short string shown verbatim as a badge in the UI.
+ event_sensitive: True for instruments driven by scheduled headlines
+ the daily-candle model cannot see (e.g. OPEC for WTI).
+ """
+
+ key: str
+ display_name: str
+ primary_ticker: str
+ fallback_ticker: Optional[str]
+ has_reliable_volume: bool
+ confidence_caveat: str
+ event_sensitive: bool = False
+ tags: tuple = field(default_factory=tuple)
+
+
+# The four instruments — exactly these, in this order.
+INSTRUMENTS: dict[str, Instrument] = {
+ "silver": Instrument(
+ key="silver",
+ display_name="Silver (XAG/USD)",
+ primary_ticker="SI=F",
+ fallback_ticker="XAGUSD=X",
+ has_reliable_volume=True,
+ confidence_caveat="Futures volume is real. Model still sees price only.",
+ event_sensitive=False,
+ tags=("metal",),
+ ),
+ "gold": Instrument(
+ key="gold",
+ display_name="Gold (XAU/USD)",
+ primary_ticker="GC=F",
+ fallback_ticker="XAUUSD=X",
+ has_reliable_volume=True,
+ confidence_caveat="Futures volume is real. Model still sees price only.",
+ event_sensitive=False,
+ tags=("metal",),
+ ),
+ "wti": Instrument(
+ key="wti",
+ display_name="WTI Crude Oil",
+ primary_ticker="CL=F",
+ fallback_ticker=None,
+ has_reliable_volume=True,
+ confidence_caveat="EVENT-SENSITIVE: OPEC / inventory headlines are NOT seen by the model.",
+ event_sensitive=True,
+ tags=("energy",),
+ ),
+ "gbpjpy": Instrument(
+ key="gbpjpy",
+ display_name="GBP/JPY",
+ primary_ticker="GBPJPY=X",
+ fallback_ticker=None,
+ has_reliable_volume=False,
+ confidence_caveat="LOW CONFIDENCE: no reliable volume — forecast uses the no-volume path.",
+ event_sensitive=True,
+ tags=("fx",),
+ ),
+}
+
+# Stable display order for "all four at a glance" views.
+INSTRUMENT_ORDER: list[str] = ["silver", "gold", "wti", "gbpjpy"]
+
+
+def get_instrument(key: str) -> Instrument:
+ """Return the Instrument config for a key, raising a clear error if unknown."""
+ try:
+ return INSTRUMENTS[key]
+ except KeyError as exc:
+ valid = ", ".join(INSTRUMENTS)
+ raise KeyError(f"Unknown instrument '{key}'. Valid keys: {valid}") from exc
+
+
+def all_instruments() -> list[Instrument]:
+ """Return all instruments in canonical display order."""
+ return [INSTRUMENTS[k] for k in INSTRUMENT_ORDER]
+
+
+if __name__ == "__main__":
+ for inst in all_instruments():
+ vol = "real-volume" if inst.has_reliable_volume else "NO-VOLUME PATH"
+ print(f"{inst.key:8s} {inst.display_name:18s} {inst.primary_ticker:10s} "
+ f"[{vol}] -> {inst.confidence_caveat}")
diff --git a/forecast_dashboard/mcp_server.py b/forecast_dashboard/mcp_server.py
new file mode 100644
index 000000000..8f4a179ff
--- /dev/null
+++ b/forecast_dashboard/mcp_server.py
@@ -0,0 +1,169 @@
+"""mcp_server.py — MCP server exposing the forecast engine as tools.
+
+A THIN client over forecast_engine + storage (same core, no duplication). Lets the
+user ask Claude to run forecasts live instead of opening the dashboard.
+
+Every tool response carries the honesty framing (SCOPE.md §6):
+ - the forecast is a DISTRIBUTION (band), never a naked point;
+ - the per-instrument confidence caveat is included;
+ - "the model did NOT see news / macro events" is always stated;
+ - no fabricated confidence/sentiment score is ever returned.
+
+Transport: stdio (the standard for local MCP servers).
+Run: python mcp_server.py
+"""
+
+from __future__ import annotations
+
+import json
+import os
+
+try:
+ from dotenv import load_dotenv
+ load_dotenv()
+except Exception: # noqa: BLE001
+ pass
+
+from mcp.server.fastmcp import FastMCP
+
+import forecast_engine as fe
+import storage
+from instruments import all_instruments, get_instrument
+
+mcp = FastMCP("kronos-forecast")
+
+GLOBAL_CAVEAT = (
+ "The Kronos model sees PAST DAILY PRICE CANDLES ONLY. It cannot see Fed/BoE/BoJ "
+ "decisions, CPI prints, OPEC headlines, or any risk events. This is a forecast "
+ "DISTRIBUTION (a band), never a single prediction, and carries no confidence score."
+)
+
+DEF_DEVICE = os.environ.get("KRONOS_DEVICE", "cpu")
+_predictor = None
+
+
+def _get_predictor():
+ global _predictor
+ if _predictor is None:
+ _predictor = fe.load_predictor(device=DEF_DEVICE)
+ return _predictor
+
+
+@mcp.tool()
+def list_instruments():
+ """List the four supported instruments and their honesty caveats. Returns text."""
+ out = ["The four instruments (price-only daily forecasting):", ""]
+ for inst in all_instruments():
+ vol = "real volume" if inst.has_reliable_volume else "NO reliable volume (no-volume path)"
+ out.append(f"- {inst.display_name} [{inst.key}] ticker={inst.primary_ticker} "
+ f"({vol})")
+ out.append(f" caveat: {inst.confidence_caveat}")
+ out.append("")
+ out.append(GLOBAL_CAVEAT)
+ return "\n".join(out)
+
+
+@mcp.tool()
+def get_forecast(instrument: str, pred_len: int = 14, n_samples: int = 30):
+ """Run a forecast for an instrument and return the band + skew + caveats.
+
+ Args:
+ instrument: one of silver | gold | wti | gbpjpy.
+ pred_len: forecast horizon in business days (<= 60 recommended).
+ n_samples: number of independent sample paths forming the distribution.
+
+ Returns structured text including median/p10/p90 per horizon, the skew
+ summary, the ticker used, the volume-path flag, and the confidence caveats.
+ """
+ try:
+ inst = get_instrument(instrument)
+ except KeyError as exc:
+ return f"Error: {exc}"
+
+ res = fe.run_forecast(
+ inst.key,
+ pred_len=int(pred_len),
+ n_samples=int(n_samples),
+ device=DEF_DEVICE,
+ predictor=_get_predictor(),
+ )
+ try:
+ storage.log_forecast(res)
+ except Exception: # noqa: BLE001 - logging is best-effort here
+ pass
+
+ # Structured payload + the human-readable honesty-framed summary.
+ payload = {
+ "instrument": inst.key,
+ "display_name": res.display_name,
+ "ticker_used": res.ticker_used,
+ "used_volume_path": res.used_volume_path,
+ "last_close": round(res.last_close, 6),
+ "last_history_date": str(res.last_history_date.date()),
+ "pred_len": res.pred_len,
+ "n_samples": res.n_samples,
+ "horizons": [
+ {
+ "h": i + 1,
+ "date": str(res.future_timestamps[i].date()),
+ "p10": round(float(res.p10[i]), 6),
+ "median": round(float(res.median[i]), 6),
+ "p90": round(float(res.p90[i]), 6),
+ }
+ for i in range(res.pred_len)
+ ],
+ "realized_vol_annualized_pct": round(res.realized_vol_annualized * 100, 2),
+ "skew_note": res.skew_sentence(),
+ "confidence_caveat": res.confidence_caveat,
+ "global_caveat": GLOBAL_CAVEAT,
+ }
+ return res.summary_text() + "\n\nJSON:\n" + json.dumps(payload, indent=2)
+
+
+@mcp.tool()
+def get_accuracy(instrument: str):
+ """Return the calibration / accuracy summary for an instrument from storage."""
+ try:
+ inst = get_instrument(instrument)
+ except KeyError as exc:
+ return f"Error: {exc}"
+ summary = storage.accuracy_summary(inst.key)
+ lines = [f"Accuracy so far for {inst.display_name}:"]
+ if summary["n_reconciled"] == 0:
+ lines.append(" No reconciled forecasts yet. Run get_forecast daily, then "
+ "refresh_actuals once target dates pass.")
+ else:
+ hr = summary["band_hit_rate"]
+ lines.append(f" Reconciled points: {summary['n_reconciled']}")
+ lines.append(f" Band hit-rate (p10-p90): "
+ f"{hr*100:.0f}% (nominal {summary['band_nominal']*100:.0f}%)")
+ if summary["median_abs_error"] is not None:
+ lines.append(f" Median abs error: {summary['median_abs_error']:.4f}")
+ if summary["directional_accuracy"] is not None:
+ lines.append(f" Directional accuracy: {summary['directional_accuracy']*100:.0f}%")
+ lines.append(f" Note: {summary['calibration_note']}")
+ lines.append("")
+ lines.append(GLOBAL_CAVEAT)
+ return "\n".join(lines)
+
+
+@mcp.tool()
+def refresh_actuals(instrument: str = ""):
+ """Reconcile logged forecasts against realized actuals.
+
+ Args:
+ instrument: optional single instrument key; empty = all instruments.
+ """
+ key = instrument.strip() or None
+ if key is not None:
+ try:
+ get_instrument(key)
+ except KeyError as exc:
+ return f"Error: {exc}"
+ n = storage.update_actuals(key)
+ scope = key if key else "all instruments"
+ return f"Reconciled {n} forecast point(s) against actuals for {scope}."
+
+
+if __name__ == "__main__":
+ mcp.run()
diff --git a/forecast_dashboard/news_context.py b/forecast_dashboard/news_context.py
new file mode 100644
index 000000000..00acc818f
--- /dev/null
+++ b/forecast_dashboard/news_context.py
@@ -0,0 +1,285 @@
+"""news_context.py — "things the model did NOT see" + text-based analysis.
+
+Two honest jobs (SCOPE.md §0, §5.6):
+
+1. Surface RECENT HEADLINES for an instrument, framed as context the price-only
+ model could NOT have seen (Fed/BoE/BoJ, CPI, OPEC, risk events). This is NEVER
+ a forecast input and NEVER produces a numeric reliability/confidence score.
+
+2. Produce a TEXT-BASED ANALYSIS ("bot") that describes the forecast DISTRIBUTION
+ in plain English — bands, skew, realized vol, calibration so far — and the blind
+ spot. If an LLM is configured it can phrase the read, but it is explicitly
+ labelled as an UNVALIDATED impression and is forbidden from emitting a score.
+
+Everything degrades gracefully: with no API keys, the panel still works using a
+deterministic, rule-based analysis built straight from the ForecastResult.
+"""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from typing import Optional
+
+# Per-instrument search query + the macro events the daily model cannot see.
+NEWS_QUERIES: dict[str, dict] = {
+ "silver": {
+ "query": "silver price XAG today Fed rate inflation industrial demand",
+ "blind_spots": ["Fed rate decisions", "CPI / inflation prints",
+ "industrial demand shifts", "USD moves", "risk-on/off swings"],
+ },
+ "gold": {
+ "query": "gold price XAU today Fed rate inflation safe haven central banks",
+ "blind_spots": ["Fed rate decisions", "CPI / inflation prints",
+ "central-bank buying", "geopolitical risk", "real yields"],
+ },
+ "wti": {
+ "query": "WTI crude oil price today OPEC inventory EIA supply demand",
+ "blind_spots": ["OPEC+ production decisions", "EIA/API inventory reports",
+ "supply disruptions / sanctions", "demand outlook", "geopolitics"],
+ },
+ "gbpjpy": {
+ "query": "GBP/JPY pound yen today BoE BoJ intervention rate decision",
+ "blind_spots": ["BoE rate decisions", "BoJ policy / YCC", "MoF FX intervention",
+ "UK CPI / jobs", "global risk sentiment"],
+ },
+}
+
+DISCLAIMER = ("LLM impression of headline tone — UNVALIDATED, do not weight heavily. "
+ "This is NOT a forecast input and carries NO confidence score.")
+
+
+@dataclass
+class NewsItem:
+ title: str
+ source: str = ""
+ url: str = ""
+ published: str = ""
+
+
+@dataclass
+class NewsContext:
+ instrument_key: str
+ blind_spots: list
+ items: list # list[NewsItem]
+ tone_note: Optional[str] # optional LLM impression (labelled), or None
+ enabled: bool # whether live search ran
+ notice: str # explanation shown when search is disabled/empty
+
+
+# --------------------------------------------------------------------------- #
+# Live headline search (optional; Tavily if configured)
+# --------------------------------------------------------------------------- #
+def _search_tavily(query: str, max_results: int = 6) -> list:
+ """Search headlines via Tavily if TAVILY_API_KEY is set. Returns [NewsItem]."""
+ api_key = os.environ.get("TAVILY_API_KEY", "").strip()
+ if not api_key:
+ return []
+ try:
+ import requests
+ resp = requests.post(
+ "https://api.tavily.com/search",
+ json={
+ "api_key": api_key,
+ "query": query,
+ "topic": "news",
+ "max_results": max_results,
+ "search_depth": "basic",
+ },
+ timeout=20,
+ )
+ resp.raise_for_status()
+ data = resp.json()
+ items = []
+ for r in data.get("results", [])[:max_results]:
+ items.append(NewsItem(
+ title=r.get("title", "")[:200],
+ source=(r.get("url", "").split("/")[2] if r.get("url") else ""),
+ url=r.get("url", ""),
+ published=r.get("published_date", "") or "",
+ ))
+ return items
+ except Exception: # noqa: BLE001 - news is best-effort
+ return []
+
+
+def _llm_tone_note(instrument_display: str, items: list) -> Optional[str]:
+ """Ask an LLM for a one-line tone impression IF ANTHROPIC_API_KEY is set.
+
+ Hard constraint: the prompt forbids any numeric score and requires the output
+ to be a qualitative impression only. We still wrap it with the disclaimer.
+ """
+ api_key = os.environ.get("ANTHROPIC_API_KEY", "").strip()
+ if not api_key or not items:
+ return None
+ try:
+ import anthropic
+ client = anthropic.Anthropic(api_key=api_key)
+ model = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-haiku-latest")
+ headlines = "\n".join(f"- {it.title}" for it in items[:8])
+ prompt = (
+ "You are summarizing recent headline TONE for context only. "
+ "Rules you MUST follow:\n"
+ "1. Output ONE short sentence (max 25 words).\n"
+ "2. Do NOT output any number, percentage, score, or probability.\n"
+ "3. Do NOT give trading advice or a directional prediction.\n"
+ "4. Only describe the qualitative tone (e.g. cautious, bullish chatter, "
+ "mixed, focused on supply).\n\n"
+ f"Instrument: {instrument_display}\nHeadlines:\n{headlines}\n\n"
+ "One-sentence tone impression:"
+ )
+ msg = client.messages.create(
+ model=model,
+ max_tokens=80,
+ messages=[{"role": "user", "content": prompt}],
+ )
+ text = "".join(getattr(b, "text", "") for b in msg.content).strip()
+ return text or None
+ except Exception: # noqa: BLE001 - LLM is best-effort
+ return None
+
+
+def get_news_context(instrument_key: str, instrument_display: str) -> NewsContext:
+ """Build the 'context the model did NOT see' panel data."""
+ cfg = NEWS_QUERIES.get(instrument_key, {"query": instrument_display, "blind_spots": []})
+ items = _search_tavily(cfg["query"])
+ enabled = bool(os.environ.get("TAVILY_API_KEY", "").strip())
+
+ tone = _llm_tone_note(instrument_display, items) if items else None
+
+ if not enabled:
+ notice = ("Live news search is OFF (no TAVILY_API_KEY). The blind-spot list "
+ "below still shows the event types the model cannot see.")
+ elif not items:
+ notice = "No recent headlines returned. The blind-spot list still applies."
+ else:
+ notice = ""
+
+ return NewsContext(
+ instrument_key=instrument_key,
+ blind_spots=cfg.get("blind_spots", []),
+ items=items,
+ tone_note=tone,
+ enabled=enabled,
+ notice=notice,
+ )
+
+
+# --------------------------------------------------------------------------- #
+# Text-based analysis "bot" — deterministic, honesty-first
+# --------------------------------------------------------------------------- #
+def analyze_forecast_text(result, accuracy: Optional[dict] = None) -> str:
+ """Generate a plain-English read of a ForecastResult.
+
+ Always deterministic and rule-based (no API needed). Describes the
+ distribution, band width, skew, realized vol, and calibration-so-far, and
+ always restates the blind spot. Never emits a confidence score.
+ """
+ r = result
+ last = r.last_close
+ # Horizons to talk about: ~1wk and end.
+ h_wk = min(4, r.pred_len - 1)
+ h_end = r.pred_len - 1
+
+ def band_pct(i):
+ lo, hi = r.p10[i], r.p90[i]
+ return (hi - lo) / last * 100 if last else float("nan")
+
+ med_wk = r.median[h_wk]
+ med_end = r.median[h_end]
+ dir_wk = "higher" if med_wk > last else ("lower" if med_wk < last else "flat")
+
+ lines = []
+ lines.append(f"### Text analysis — {r.display_name}")
+ lines.append("")
+ lines.append(
+ f"As of {r.last_history_date.date()} the last close was **{last:,.4f}** "
+ f"(ticker `{r.ticker_used}`). The forecast is a **distribution of "
+ f"{r.n_samples} independent sample paths**, not a single number."
+ )
+ lines.append("")
+ lines.append(
+ f"- **~1 week (+{h_wk+1}d):** median leans **{dir_wk}** at "
+ f"{med_wk:,.4f}; the p10-p90 band spans {r.p10[h_wk]:,.4f} to "
+ f"{r.p90[h_wk]:,.4f} (~{band_pct(h_wk):.1f}% of price wide)."
+ )
+ lines.append(
+ f"- **End (+{h_end+1}d):** median {med_end:,.4f}; p10-p90 "
+ f"{r.p10[h_end]:,.4f} to {r.p90[h_end]:,.4f} "
+ f"(~{band_pct(h_end):.1f}% wide). The band widening with horizon is the "
+ f"model telling you it knows less further out."
+ )
+ lines.append("")
+ lines.append(f"- **Skew:** {r.skew_sentence()}")
+ lines.append(
+ f"- **Historical volatility:** {r.realized_vol_annualized*100:,.1f}% "
+ f"annualized (from recent daily returns — this is realized history, "
+ f"distinct from the forecast band width)."
+ )
+
+ # Calibration so far
+ if accuracy and accuracy.get("n_reconciled", 0) > 0:
+ if accuracy.get("enough_data"):
+ lines.append(
+ f"- **Track record:** {accuracy['calibration_note']} "
+ f"(over {accuracy['n_reconciled']} reconciled points; "
+ f"directional accuracy "
+ f"{(accuracy.get('directional_accuracy') or 0)*100:.0f}%)."
+ )
+ else:
+ lines.append(f"- **Track record:** {accuracy['calibration_note']}")
+ else:
+ lines.append(
+ "- **Track record:** no reconciled forecasts yet — calibration is "
+ "unknown until actuals come in. Do not assume the band is right."
+ )
+
+ lines.append("")
+ if r.event_sensitive:
+ lines.append(
+ f"> **Blind spot:** {r.confidence_caveat} The model sees price candles "
+ f"only; scheduled events can move this instrument outside the band."
+ )
+ else:
+ lines.append(
+ "> **Blind spot:** the model sees price candles only — no Fed/BoE/BoJ "
+ "decisions, CPI, or headlines. Treat the band as price-history-implied, "
+ "not all-knowing."
+ )
+ return "\n".join(lines)
+
+
+def llm_narrative(result, accuracy: Optional[dict] = None) -> Optional[str]:
+ """OPTIONAL: an LLM-phrased narrative of the deterministic analysis.
+
+ Returns None unless ANTHROPIC_API_KEY is set. The LLM is given the already-
+ computed numbers and asked to rephrase honestly; it is forbidden from adding
+ a confidence score or directional advice. Output is labelled as unvalidated.
+ """
+ api_key = os.environ.get("ANTHROPIC_API_KEY", "").strip()
+ if not api_key:
+ return None
+ try:
+ import anthropic
+ client = anthropic.Anthropic(api_key=api_key)
+ model = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-haiku-latest")
+ facts = analyze_forecast_text(result, accuracy)
+ prompt = (
+ "Rewrite the following forecast read in 4-6 sentences of plain, honest "
+ "English for a paper-trader. RULES:\n"
+ "1. Keep all uncertainty framing; the forecast is a distribution.\n"
+ "2. Do NOT invent any confidence score, probability, or new number.\n"
+ "3. Do NOT give a buy/sell recommendation.\n"
+ "4. Keep the blind-spot caveat.\n\n"
+ f"{facts}"
+ )
+ msg = client.messages.create(
+ model=model, max_tokens=400,
+ messages=[{"role": "user", "content": prompt}],
+ )
+ text = "".join(getattr(b, "text", "") for b in msg.content).strip()
+ if not text:
+ return None
+ return text + "\n\n_(LLM-phrased; unvalidated, no confidence score implied.)_"
+ except Exception: # noqa: BLE001
+ return None
diff --git a/forecast_dashboard/news_sentiment.py b/forecast_dashboard/news_sentiment.py
new file mode 100644
index 000000000..02bbc80a8
--- /dev/null
+++ b/forecast_dashboard/news_sentiment.py
@@ -0,0 +1,543 @@
+"""news_sentiment.py — Layer 2: live news sentiment (the qualitative read).
+
+This is a FIRST-CLASS layer, not a footnote. The application now shows two
+honest, independent views side by side:
+
+ Layer 1 Kronos price model -> the distribution / band (quantitative)
+ Layer 2 News sentiment (here) -> sentiment gauge + drivers (qualitative)
+
+and then a COMBINED read that states whether they AGREE or DIVERGE.
+
+Why this is honest (the whole point of the project):
+ * We do NOT claim Kronos "read the news" — it is a price-only model. Pretending
+ otherwise would be the lie. Instead we compute sentiment SEPARATELY and show
+ where the two layers line up or conflict. Divergence is itself the signal.
+ * Headlines are real (Yahoo Finance, free, no key). Sentiment scoring uses a
+ proper LLM when available (Gemini via Vertex on GCP needs NO api key — the VM
+ authenticates itself), and degrades to a transparent keyword baseline that is
+ clearly LABELLED as crude, so the method is never hidden.
+ * The sentiment score is about HEADLINE TONE, not a probability of price moves.
+ It is never presented as a calibrated confidence.
+
+Scoring methods, in priority order:
+ 1. gemini — Vertex AI Gemini (no key on GCP) or GEMINI_API_KEY (dev)
+ 2. anthropic— if ANTHROPIC_API_KEY set
+ 3. lexicon — deterministic keyword baseline (always works, clearly labelled)
+"""
+
+from __future__ import annotations
+
+import os
+import json
+import re
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Optional
+
+# --------------------------------------------------------------------------- #
+# Instrument framing: search hints + the macro drivers that matter, and how the
+# instrument typically reacts (used for the lexicon baseline + LLM context).
+# --------------------------------------------------------------------------- #
+INSTRUMENT_NEWS: dict[str, dict] = {
+ "silver": {
+ "aliases": ["silver", "xag", "precious metal", "bullion"],
+ "drivers": ["Fed rate path", "CPI / inflation", "industrial demand",
+ "USD strength", "risk sentiment"],
+ # safe-haven-ish but also industrial; hawkish Fed / strong USD = bearish
+ "haven": True,
+ },
+ "gold": {
+ "aliases": ["gold", "xau", "bullion", "precious metal"],
+ "drivers": ["Fed rate path", "real yields", "central-bank buying",
+ "geopolitical risk", "USD strength"],
+ "haven": True,
+ },
+ "wti": {
+ "aliases": ["oil", "crude", "wti", "brent", "opec", "petroleum"],
+ "drivers": ["OPEC+ supply", "inventories (EIA/API)", "supply disruptions",
+ "demand outlook", "geopolitics (Hormuz, sanctions)"],
+ "haven": False,
+ },
+ "gbpjpy": {
+ "aliases": ["pound", "sterling", "gbp", "yen", "jpy", "boe", "boj"],
+ "drivers": ["BoE policy", "BoJ policy / intervention", "UK data (CPI/jobs/GDP)",
+ "risk sentiment (carry)", "USD/rate differentials"],
+ "haven": False,
+ },
+}
+
+# Sentiment is about the INSTRUMENT'S price, from a long perspective:
+# positive score = headlines lean supportive of a HIGHER price.
+SENTIMENT_LABELS = [
+ (-1.01, -0.5, "strongly bearish"),
+ (-0.5, -0.15, "bearish lean"),
+ (-0.15, 0.15, "neutral / mixed"),
+ (0.15, 0.5, "bullish lean"),
+ (0.5, 1.01, "strongly bullish"),
+]
+
+DISCLAIMER = ("Sentiment reflects HEADLINE TONE only — it is not a probability or a "
+ "confidence score, and it is a SEPARATE signal from the price model.")
+
+
+# --------------------------------------------------------------------------- #
+# Data model
+# --------------------------------------------------------------------------- #
+@dataclass
+class Headline:
+ title: str
+ source: str = ""
+ url: str = ""
+ published: str = ""
+ summary: str = ""
+
+
+@dataclass
+class ScoredHeadline:
+ title: str
+ source: str
+ url: str
+ published: str
+ score: float # -1..+1 (bullish positive) for THIS instrument
+ rationale: str = "" # short why (LLM) or matched terms (lexicon)
+
+
+@dataclass
+class SentimentResult:
+ instrument_key: str
+ headlines: list = field(default_factory=list) # list[ScoredHeadline]
+ aggregate_score: float = 0.0 # -1..+1
+ label: str = "neutral / mixed"
+ drivers: list = field(default_factory=list) # key themes / drivers
+ method: str = "lexicon" # gemini | anthropic | lexicon
+ method_note: str = "" # human description of method
+ n_headlines: int = 0
+ as_of: str = ""
+ notice: str = ""
+
+ def to_dict(self) -> dict:
+ return {
+ "instrument": self.instrument_key,
+ "aggregate_score": round(self.aggregate_score, 4),
+ "label": self.label,
+ "drivers": self.drivers,
+ "method": self.method,
+ "method_note": self.method_note,
+ "n_headlines": self.n_headlines,
+ "as_of": self.as_of,
+ "notice": self.notice,
+ "disclaimer": DISCLAIMER,
+ "headlines": [
+ {"title": h.title, "source": h.source, "url": h.url,
+ "published": h.published, "score": round(h.score, 3),
+ "rationale": h.rationale}
+ for h in self.headlines
+ ],
+ }
+
+
+# --------------------------------------------------------------------------- #
+# 1. Fetch headlines (Yahoo Finance via yfinance — free, no key). Robust to both
+# the old and new yfinance news schemas.
+# --------------------------------------------------------------------------- #
+def _parse_news_item(item: dict) -> Optional[Headline]:
+ # New schema: {'content': {'title','summary','pubDate','provider':{'displayName'},
+ # 'canonicalUrl':{'url'} or 'clickThroughUrl':{'url'}}}
+ c = item.get("content") if isinstance(item.get("content"), dict) else None
+ if c:
+ title = (c.get("title") or "").strip()
+ summary = (c.get("summary") or "").strip()
+ prov = c.get("provider") or {}
+ source = (prov.get("displayName") if isinstance(prov, dict) else "") or ""
+ url = ""
+ for k in ("canonicalUrl", "clickThroughUrl"):
+ u = c.get(k)
+ if isinstance(u, dict) and u.get("url"):
+ url = u["url"]
+ break
+ published = (c.get("pubDate") or c.get("displayTime") or "").strip()
+ if title:
+ return Headline(title=title, source=source, url=url,
+ published=published, summary=summary)
+ return None
+ # Old schema: {'title','publisher','link','providerPublishTime'(epoch)}
+ title = (item.get("title") or "").strip()
+ if not title:
+ return None
+ published = ""
+ ts = item.get("providerPublishTime")
+ if ts:
+ try:
+ published = datetime.fromtimestamp(int(ts), tz=timezone.utc).isoformat()
+ except Exception: # noqa: BLE001
+ published = ""
+ return Headline(
+ title=title,
+ source=item.get("publisher", "") or "",
+ url=item.get("link", "") or "",
+ published=published,
+ summary=(item.get("summary") or "").strip(),
+ )
+
+
+def fetch_headlines(ticker: str, max_items: int = 10) -> list:
+ """Fetch recent headlines for a yfinance ticker. Best-effort, never raises."""
+ try:
+ import yfinance as yf
+ raw = yf.Ticker(ticker).news or []
+ except Exception: # noqa: BLE001
+ return []
+ out, seen = [], set()
+ for item in raw:
+ h = _parse_news_item(item)
+ if not h:
+ continue
+ key = h.title.lower()
+ if key in seen:
+ continue
+ seen.add(key)
+ out.append(h)
+ if len(out) >= max_items:
+ break
+ return out
+
+
+# --------------------------------------------------------------------------- #
+# 2a. Lexicon baseline scorer (deterministic, always available, clearly crude).
+# --------------------------------------------------------------------------- #
+# Generic price-direction polarity. Positive = supportive of higher price.
+_POS = {
+ "rebound": 1, "rebounds": 1, "rally": 1, "rallies": 1, "surge": 1, "surges": 1,
+ "gain": 1, "gains": 1, "rise": 1, "rises": 1, "rose": 1, "jump": 1, "jumps": 1,
+ "climb": 1, "climbs": 1, "soar": 1, "soars": 1, "higher": 1, "boost": 1,
+ "boosts": 1, "support": 1, "supported": 1, "strength": 1, "strengthens": 1,
+ "demand": 1, "tighten": 1, "tightens": 1, "shortage": 1, "upgrade": 1,
+ "bullish": 1, "record": 1, "stabilize": 0.5, "stabilizes": 0.5, "steadies": 0.4,
+ "steady": 0.4, "firm": 0.6, "firmer": 0.6, "optimism": 0.6, "recovers": 1,
+}
+_NEG = {
+ "slide": 1, "slides": 1, "fall": 1, "falls": 1, "fell": 1, "drop": 1, "drops": 1,
+ "retreat": 1, "retreats": 1, "decline": 1, "declines": 1, "slump": 1, "slumps": 1,
+ "plunge": 1, "plunges": 1, "tumble": 1, "tumbles": 1, "weak": 1, "weaker": 1,
+ "lower": 1, "pressure": 1, "pressured": 1, "pullback": 1, "pull back": 1,
+ "cut": 0.8, "cuts": 0.8, "downgrade": 1, "bearish": 1, "glut": 1, "oversupply": 1,
+ "selloff": 1, "sell-off": 1, "loss": 0.8, "losses": 0.8, "drag": 0.8, "drags": 0.8,
+ "sinks": 1, "sink": 1, "dips": 0.7, "dip": 0.7, "soft": 0.6, "softer": 0.6,
+}
+# Macro modifiers whose sign depends on the instrument.
+# For HAVEN metals (gold/silver): hawkish/strong-USD/higher-yields = bearish;
+# dovish/risk-off/geopolitics = bullish.
+# For OIL: supply disruption/sanctions/OPEC cut = bullish; demand fears/glut = bearish.
+# For GBPJPY (carry/risk): hawkish BoE / risk-on = bullish; dovish BoE / risk-off = bearish.
+_MACRO = {
+ "hawkish": {"gold": -1, "silver": -1, "gbpjpy": +0.6, "wti": -0.4},
+ "dovish": {"gold": +1, "silver": +1, "gbpjpy": -0.6, "wti": +0.2},
+ "rate hike": {"gold": -0.8, "silver": -0.8, "gbpjpy": +0.6, "wti": -0.3},
+ "rate cut": {"gold": +0.8, "silver": +0.8, "gbpjpy": -0.6, "wti": +0.2},
+ "strong dollar": {"gold": -0.9, "silver": -0.9, "wti": -0.6, "gbpjpy": -0.3},
+ "stronger dollar": {"gold": -0.9, "silver": -0.9, "wti": -0.6, "gbpjpy": -0.3},
+ "weak dollar": {"gold": +0.9, "silver": +0.9, "wti": +0.5},
+ "safe haven": {"gold": +0.8, "silver": +0.5},
+ "geopolitical": {"gold": +0.6, "silver": +0.3, "wti": +0.6},
+ "tension": {"gold": +0.4, "wti": +0.6},
+ "sanction": {"wti": +0.7}, "sanctions": {"wti": +0.7},
+ "disruption": {"wti": +0.8}, "supply": {"wti": +0.2},
+ "inventory build": {"wti": -0.7}, "stockpile": {"wti": -0.4},
+ "peace": {"wti": -0.6, "gold": -0.3}, "ceasefire": {"wti": -0.7, "gold": -0.4},
+ "opec": {"wti": +0.2},
+}
+
+
+def _lexicon_score_one(instrument_key: str, text: str) -> tuple[float, str]:
+ t = " " + text.lower() + " "
+
+ # Relevance gate: if the headline doesn't mention this instrument or any of
+ # its driver topics, the keyword baseline is too unreliable to take a side.
+ cfg = INSTRUMENT_NEWS.get(instrument_key, {})
+ aliases = cfg.get("aliases", [])
+ macro_terms = [p for p in _MACRO if instrument_key in _MACRO[p]]
+ relevant = any(a in t for a in aliases) or any(p in t for p in macro_terms)
+ if not relevant:
+ return 0.0, "off-topic for this instrument"
+
+ raw, matched = 0.0, []
+ for w, wt in _POS.items():
+ if re.search(rf"\b{re.escape(w)}\b", t):
+ raw += wt; matched.append(f"+{w}")
+ for w, wt in _NEG.items():
+ if re.search(rf"\b{re.escape(w)}\b", t):
+ raw -= wt; matched.append(f"-{w}")
+ for phrase, table in _MACRO.items():
+ if phrase in t and instrument_key in table:
+ raw += table[instrument_key]
+ matched.append(f"{'+' if table[instrument_key] > 0 else ''}{phrase}")
+ # squash to -1..+1
+ score = max(-1.0, min(1.0, raw / 2.5))
+ return score, ", ".join(matched[:5])
+
+
+def _score_lexicon(instrument_key: str, headlines: list) -> list:
+ scored = []
+ for h in headlines:
+ s, why = _lexicon_score_one(instrument_key, f"{h.title}. {h.summary}")
+ scored.append(ScoredHeadline(
+ title=h.title, source=h.source, url=h.url, published=h.published,
+ score=s, rationale=(why or "no strong sentiment terms")))
+ return scored
+
+
+# --------------------------------------------------------------------------- #
+# 2b. LLM scorer — Gemini (Vertex, no key on GCP) or Anthropic. Returns per-
+# headline scores + rationale + extracted drivers, as strict JSON.
+# --------------------------------------------------------------------------- #
+def _llm_prompt(instrument_key: str, display: str, headlines: list) -> str:
+ cfg = INSTRUMENT_NEWS.get(instrument_key, {})
+ drivers = ", ".join(cfg.get("drivers", []))
+ items = "\n".join(f"{i+1}. {h.title}" for i, h in enumerate(headlines))
+ return (
+ f"You are a markets analyst scoring HEADLINE TONE for {display}.\n"
+ f"Key drivers for this instrument: {drivers}.\n\n"
+ "For EACH headline, give a sentiment score from -1.0 to +1.0 for the "
+ f"instrument's PRICE, where +1 = clearly supportive of a HIGHER {display} "
+ "price and -1 = clearly supportive of a LOWER price. Account for context "
+ "(e.g. a hawkish Fed / stronger dollar is bearish for gold; a supply "
+ "disruption is bullish for oil). Use 0 for irrelevant or neutral.\n\n"
+ "Also extract 2-4 short DRIVER phrases summarising what is moving the "
+ "narrative right now.\n\n"
+ "RULES:\n"
+ "- Output STRICT JSON only, no prose, no markdown.\n"
+ "- Do NOT output probabilities or a confidence score; this is tone only.\n"
+ "- Do NOT give buy/sell advice.\n\n"
+ 'Schema: {"headlines":[{"i":1,"score":-0.4,"why":"short reason"}],'
+ '"drivers":["phrase","phrase"]}\n\n'
+ f"Headlines:\n{items}\n\nJSON:"
+ )
+
+
+def _extract_json(text: str) -> Optional[dict]:
+ if not text:
+ return None
+ # strip code fences
+ text = re.sub(r"^```(?:json)?|```$", "", text.strip(), flags=re.MULTILINE).strip()
+ try:
+ return json.loads(text)
+ except Exception: # noqa: BLE001
+ m = re.search(r"\{.*\}", text, flags=re.DOTALL)
+ if m:
+ try:
+ return json.loads(m.group(0))
+ except Exception: # noqa: BLE001
+ return None
+ return None
+
+
+def _score_gemini(instrument_key, display, headlines):
+ """Gemini via google-genai. Uses Vertex (ADC, no key) on GCP if
+ GOOGLE_GENAI_USE_VERTEXAI=true, else GEMINI_API_KEY. Returns (scored, drivers)."""
+ try:
+ from google import genai
+ except Exception: # noqa: BLE001
+ return None
+ use_vertex = os.environ.get("GOOGLE_GENAI_USE_VERTEXAI", "").lower() in ("1", "true", "yes")
+ api_key = os.environ.get("GEMINI_API_KEY", "").strip()
+ if not use_vertex and not api_key:
+ return None
+ try:
+ if use_vertex:
+ client = genai.Client(
+ vertexai=True,
+ project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
+ location=os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
+ )
+ else:
+ client = genai.Client(api_key=api_key)
+ model = os.environ.get("GEMINI_MODEL", "gemini-2.0-flash-001")
+ prompt = _llm_prompt(instrument_key, display, headlines)
+ resp = client.models.generate_content(model=model, contents=prompt)
+ data = _extract_json(getattr(resp, "text", "") or "")
+ if not data:
+ return None
+ return _apply_llm_scores(headlines, data)
+ except Exception: # noqa: BLE001
+ return None
+
+
+def _score_anthropic(instrument_key, display, headlines):
+ api_key = os.environ.get("ANTHROPIC_API_KEY", "").strip()
+ if not api_key:
+ return None
+ try:
+ import anthropic
+ client = anthropic.Anthropic(api_key=api_key)
+ model = os.environ.get("ANTHROPIC_MODEL", "claude-3-5-haiku-latest")
+ prompt = _llm_prompt(instrument_key, display, headlines)
+ msg = client.messages.create(model=model, max_tokens=700,
+ messages=[{"role": "user", "content": prompt}])
+ text = "".join(getattr(b, "text", "") for b in msg.content)
+ data = _extract_json(text)
+ if not data:
+ return None
+ return _apply_llm_scores(headlines, data)
+ except Exception: # noqa: BLE001
+ return None
+
+
+def _apply_llm_scores(headlines, data) -> tuple:
+ by_i = {}
+ for row in data.get("headlines", []):
+ try:
+ by_i[int(row["i"])] = (float(row.get("score", 0.0)),
+ str(row.get("why", ""))[:140])
+ except Exception: # noqa: BLE001
+ continue
+ scored = []
+ for idx, h in enumerate(headlines, start=1):
+ s, why = by_i.get(idx, (0.0, ""))
+ s = max(-1.0, min(1.0, s))
+ scored.append(ScoredHeadline(
+ title=h.title, source=h.source, url=h.url, published=h.published,
+ score=s, rationale=why))
+ drivers = [str(d)[:60] for d in data.get("drivers", [])][:4]
+ return scored, drivers
+
+
+# --------------------------------------------------------------------------- #
+# 3. Public entry point
+# --------------------------------------------------------------------------- #
+def _label_for(score: float) -> str:
+ for lo, hi, lab in SENTIMENT_LABELS:
+ if lo <= score < hi:
+ return lab
+ return "neutral / mixed"
+
+
+def get_sentiment(instrument_key: str, display_name: str, ticker: str,
+ max_items: int = 10) -> SentimentResult:
+ """Fetch headlines and score sentiment for an instrument (Layer 2)."""
+ cfg = INSTRUMENT_NEWS.get(instrument_key, {})
+ headlines = fetch_headlines(ticker, max_items=max_items)
+ as_of = datetime.now(timezone.utc).isoformat()
+
+ if not headlines:
+ return SentimentResult(
+ instrument_key=instrument_key, headlines=[], aggregate_score=0.0,
+ label="no headlines", drivers=cfg.get("drivers", []),
+ method="none", method_note="No headlines returned by the news source.",
+ n_headlines=0, as_of=as_of,
+ notice="No recent headlines available; sentiment unavailable right now.")
+
+ scored = None
+ method = "lexicon"
+ method_note = ""
+ drivers = []
+
+ llm = _score_gemini(instrument_key, display_name, headlines)
+ if llm:
+ scored, drivers = llm
+ method = "gemini"
+ method_note = "Scored by Gemini (Vertex AI) with instrument-aware context."
+ else:
+ llm = _score_anthropic(instrument_key, display_name, headlines)
+ if llm:
+ scored, drivers = llm
+ method = "anthropic"
+ method_note = "Scored by Claude with instrument-aware context."
+
+ if scored is None:
+ scored = _score_lexicon(instrument_key, headlines)
+ method = "lexicon"
+ method_note = ("Keyword baseline (no LLM configured). Crude — it matches "
+ "sentiment words and macro phrases; trust it less than the "
+ "LLM method.")
+ drivers = cfg.get("drivers", [])
+
+ # Aggregate: emphasise headlines that actually carry sentiment. We average
+ # the non-zero scores (off-topic / neutral headlines should not drag the
+ # signal toward a false "neutral"). If nothing carries signal, it IS neutral.
+ nonzero = [s.score for s in scored if abs(s.score) > 1e-6]
+ if nonzero:
+ agg = sum(nonzero) / len(nonzero)
+ else:
+ agg = 0.0
+ agg = max(-1.0, min(1.0, agg))
+
+ return SentimentResult(
+ instrument_key=instrument_key,
+ headlines=scored,
+ aggregate_score=agg,
+ label=_label_for(agg),
+ drivers=drivers or cfg.get("drivers", []),
+ method=method,
+ method_note=method_note,
+ n_headlines=len(scored),
+ as_of=as_of,
+ notice="",
+ )
+
+
+# --------------------------------------------------------------------------- #
+# 4. Combined read — where Layer 1 (price model) and Layer 2 (news) agree/diverge.
+# THIS is the headline output of the reframed app.
+# --------------------------------------------------------------------------- #
+def combine(price_lean: str, sentiment: SentimentResult) -> dict:
+ """price_lean is 'higher' | 'lower' | 'flat' from the model's median vs last.
+
+ Returns a structured agree/diverge verdict. We never collapse the two layers
+ into one number; we report alignment honestly.
+ """
+ s = sentiment.aggregate_score
+ news_lean = "higher" if s > 0.15 else ("lower" if s < -0.15 else "flat")
+
+ if price_lean == "flat" or news_lean == "flat":
+ status = "partial"
+ if price_lean == "flat" and news_lean == "flat":
+ verdict = "Both the price model and the news tone are roughly neutral."
+ elif price_lean == "flat":
+ verdict = (f"The price model is roughly flat while headlines lean "
+ f"{news_lean}. Mild tilt from news only.")
+ else:
+ verdict = (f"The price model leans {price_lean} while news tone is "
+ f"neutral. Tilt from price history only.")
+ elif price_lean == news_lean:
+ status = "aligned"
+ verdict = (f"Price model and news BOTH lean {price_lean}. The two "
+ f"independent views agree — higher conviction, but both can "
+ f"still be wrong together.")
+ else:
+ status = "divergent"
+ verdict = (f"DIVERGENCE: the price model leans {price_lean} but news tone "
+ f"leans {news_lean}. Treat with caution — the price-only model "
+ f"may be missing what the headlines are pricing in (or vice "
+ f"versa).")
+
+ return {
+ "status": status, # aligned | divergent | partial
+ "price_lean": price_lean,
+ "news_lean": news_lean,
+ "sentiment_score": round(s, 4),
+ "sentiment_label": sentiment.label,
+ "verdict": verdict,
+ }
+
+
+# --------------------------------------------------------------------------- #
+# Smoke test
+# --------------------------------------------------------------------------- #
+if __name__ == "__main__":
+ import sys
+ sys.stdout.reconfigure(encoding="utf-8", errors="replace")
+ tickers = {"silver": "SI=F", "gold": "GC=F", "wti": "CL=F", "gbpjpy": "GBPJPY=X"}
+ names = {"silver": "Silver (XAG/USD)", "gold": "Gold (XAU/USD)",
+ "wti": "WTI Crude Oil", "gbpjpy": "GBP/JPY"}
+ for k, tk in tickers.items():
+ r = get_sentiment(k, names[k], tk)
+ print(f"\n==== {names[k]} | method={r.method} | n={r.n_headlines} ====")
+ print(f"aggregate={r.aggregate_score:+.3f} -> {r.label}")
+ print(f"drivers: {r.drivers}")
+ for h in r.headlines[:5]:
+ print(f" {h.score:+.2f} {h.title[:80]} [{h.rationale[:40]}]")
+ # demo combine
+ for lean in ("higher", "lower", "flat"):
+ c = combine(lean, r)
+ print(f" price={lean:6} -> {c['status']:9} {c['verdict'][:80]}")
+ print("\nDONE")
diff --git a/forecast_dashboard/notebook_sync.ipynb b/forecast_dashboard/notebook_sync.ipynb
new file mode 100644
index 000000000..df034cf8f
--- /dev/null
+++ b/forecast_dashboard/notebook_sync.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# notebook_sync.ipynb \u2014 Kronos forecast experimentation\n",
+ "\n",
+ "A **thin** notebook. It imports `forecast_engine` and calls the SAME functions the\n",
+ "dashboard and MCP server use. There is **zero duplicated model logic** here \u2014 that is\n",
+ "the \"sync\": notebook and dashboard literally share `forecast_engine.py`.\n",
+ "\n",
+ "Honesty reminders (see SCOPE.md \u00a70):\n",
+ "- The forecast is a **distribution** of sample paths, not a point.\n",
+ "- The model sees **price candles only** \u2014 no news / macro events.\n",
+ "- No fabricated confidence score anywhere."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Setup \u2014 add Kronos repo to path & import the engine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os, sys\n",
+ "# Make the project importable if running from elsewhere.\n",
+ "PROJECT_DIR = os.path.dirname(os.path.abspath('notebook_sync.ipynb')) if '__file__' not in dir() else os.path.dirname(__file__)\n",
+ "if PROJECT_DIR not in sys.path:\n",
+ " sys.path.insert(0, PROJECT_DIR)\n",
+ "\n",
+ "# Optional: point at a non-default Kronos clone via env before importing.\n",
+ "# os.environ['KRONOS_DIR'] = r'C:\\\\path\\\\to\\\\Kronos'\n",
+ "\n",
+ "import importlib\n",
+ "import forecast_engine as fe\n",
+ "import instruments\n",
+ "importlib.reload(fe)\n",
+ "\n",
+ "print('Kronos dir:', fe.KRONOS_DIR)\n",
+ "print('Device :', fe.DEFAULT_DEVICE)\n",
+ "print('Instruments:', [i.key for i in instruments.all_instruments()])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Pull + inspect raw data for one instrument"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "INSTRUMENT = 'gold' # silver | gold | wti | gbpjpy\n",
+ "\n",
+ "df = fe.fetch_ohlcv(INSTRUMENT, lookback_days=500)\n",
+ "print('ticker used :', df.attrs['ticker_used'])\n",
+ "print('volume reliable :', df.attrs['volume_is_reliable'])\n",
+ "print('rows :', len(df))\n",
+ "df.tail()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. Run a forecast and plot the fan inline\n",
+ "\n",
+ "The shaded band (p10\u2013p90) is the point of the chart \u2014 it shows what the model does\n",
+ "*not* know. The dashed median line is **not** \"the answer\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Loads the model once (downloads weights on first run) and reuses it.\n",
+ "predictor = fe.load_predictor(device=fe.DEFAULT_DEVICE)\n",
+ "\n",
+ "res = fe.run_forecast(INSTRUMENT, lookback=400, pred_len=20, n_samples=30, predictor=predictor)\n",
+ "print(res.summary_text())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_fan(res):\n",
+ " hist = res.history\n",
+ " fig, ax = plt.subplots(figsize=(11, 5))\n",
+ " ax.plot(hist['timestamps'], hist['close'], color='black', lw=1.6, label='history')\n",
+ " fx = res.future_timestamps\n",
+ " # faint sample paths\n",
+ " for i in range(res.sample_matrix.shape[0]):\n",
+ " ax.plot(fx, res.sample_matrix[i], color='black', alpha=0.05, lw=0.8)\n",
+ " # p10-p90 band (most prominent)\n",
+ " ax.fill_between(fx, res.p10, res.p90, color='black', alpha=0.12, label='p10-p90 band')\n",
+ " ax.fill_between(fx, res.p25, res.p75, color='black', alpha=0.20, label='p25-p75 band')\n",
+ " ax.plot(fx, res.median, color='black', lw=1.6, ls='--', label='median (p50)')\n",
+ " ax.axhline(res.last_close, color='gray', lw=1, ls=':')\n",
+ " ax.set_title(f'{res.display_name} \u2014 forecast distribution (NOT a single prediction)')\n",
+ " ax.legend(loc='upper left', fontsize=9)\n",
+ " ax.grid(alpha=0.2)\n",
+ " plt.tight_layout(); plt.show()\n",
+ "\n",
+ "plot_fan(res)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Distribution table + skew\n",
+ "display(res.horizon_table().round(4))\n",
+ "display(res.skew_metrics.round(4))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Scratch cell \u2014 tweak params (T, top_p, lookback) and re-plot\n",
+ "\n",
+ "Higher `T` / lower `top_p` => wider, more diverse paths. This changes the band\n",
+ "width \u2014 a reminder that the band is a modelling choice, not ground truth."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "res2 = fe.run_forecast(\n",
+ " INSTRUMENT,\n",
+ " lookback=400,\n",
+ " pred_len=20,\n",
+ " n_samples=40,\n",
+ " T=1.1,\n",
+ " top_p=0.9,\n",
+ " predictor=predictor,\n",
+ ")\n",
+ "plot_fan(res2)\n",
+ "print(res2.skew_sentence())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. (Optional) log this forecast & check accuracy over time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import storage\n",
+ "run_id = storage.log_forecast(res)\n",
+ "print('logged run_id:', run_id)\n",
+ "n = storage.update_actuals(INSTRUMENT)\n",
+ "print('reconciled points:', n)\n",
+ "storage.accuracy_summary(INSTRUMENT)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/forecast_dashboard/requirements.txt b/forecast_dashboard/requirements.txt
new file mode 100644
index 000000000..c734b134f
--- /dev/null
+++ b/forecast_dashboard/requirements.txt
@@ -0,0 +1,30 @@
+# Kronos Forecasting Dashboard — our dependencies.
+# (Kronos's own deps are also listed so a single install covers everything.)
+
+# --- our app stack ---
+# Bespoke web dashboard (FastAPI + vendored ECharts). Streamlit kept as a
+# legacy/fallback UI (app.py) but the primary UI is server.py + web/.
+fastapi>=0.110
+uvicorn>=0.27
+jinja2>=3.1
+streamlit>=1.36,<1.40
+yfinance>=0.2.40
+pandas>=2.0
+numpy>=1.24
+plotly>=5.20
+matplotlib>=3.7
+python-dotenv>=1.0
+mcp>=1.0
+
+# --- news / sentiment scoring (OPTIONAL, graceful fallback) ------------------
+# Layer 2 scorer priority: Gemini (Vertex, keyless on GCP) -> Anthropic ->
+# deterministic keyword baseline. The baseline always works with neither SDK.
+google-genai>=0.3 # Vertex AI Gemini (keyless via ADC on GCP)
+anthropic>=0.34 # fallback scorer / news context
+
+# --- Kronos's own deps (the cloned repo imports these) ---
+torch>=2.0.0
+einops>=0.8
+huggingface_hub>=0.30
+safetensors>=0.4
+tqdm>=4.66
diff --git a/forecast_dashboard/server.py b/forecast_dashboard/server.py
new file mode 100644
index 000000000..59f431b32
--- /dev/null
+++ b/forecast_dashboard/server.py
@@ -0,0 +1,474 @@
+"""server.py — FastAPI backend for the custom Kronos dashboard.
+
+This replaces the Streamlit UI with a bespoke frontend. It is still a THIN client:
+all model/data logic lives in forecast_engine.py and storage.py (single source of
+truth). This module only does HTTP plumbing + serialization.
+
+Honesty framing is carried into every payload (caveats, distribution-not-point,
+blind-spot). No fabricated confidence score is ever produced.
+
+Run:
+ python server.py # http://localhost:8000
+ uvicorn server:app --host 0.0.0.0 --port 8000 # GCP
+"""
+
+from __future__ import annotations
+
+import os
+import threading
+from datetime import datetime, timezone
+
+import numpy as np
+import pandas as pd
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.responses import FileResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+
+try:
+ from dotenv import load_dotenv
+ load_dotenv()
+except Exception: # noqa: BLE001
+ pass
+
+import forecast_engine as fe
+import storage
+import news_context as nc
+import news_sentiment as ns
+from instruments import all_instruments, get_instrument, INSTRUMENT_ORDER
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+WEB_DIR = os.path.join(HERE, "web")
+DEVICE = os.environ.get("KRONOS_DEVICE", "cpu")
+
+app = FastAPI(title="Kronos Forecasting Dashboard", version="1.0")
+
+# --------------------------------------------------------------------------- #
+# Activity heartbeat — touched on every API request. A separate idle-watchdog
+# (deploy/watchdog.sh) reads ACTIVITY_FILE to auto-stop the GCP VM after a
+# period with no traffic, so an idle demo box stops billing. Opt-in: set
+# ACTIVITY_FILE on the VM; it's a no-op locally (env unset).
+# --------------------------------------------------------------------------- #
+ACTIVITY_FILE = os.environ.get("ACTIVITY_FILE", "").strip()
+
+
+def _touch_activity() -> None:
+ if not ACTIVITY_FILE:
+ return
+ try:
+ with open(ACTIVITY_FILE, "w") as fh:
+ fh.write(str(datetime.now(timezone.utc).timestamp()))
+ except Exception: # noqa: BLE001
+ pass
+
+
+@app.middleware("http")
+async def _activity_middleware(request, call_next):
+ # Health checks shouldn't count as "user activity" (the watchdog polls it),
+ # so the box can still idle-stop while only being pinged.
+ if ACTIVITY_FILE and not request.url.path.startswith(("/api/health", "/api/prewarm-status")):
+ _touch_activity()
+ return await call_next(request)
+
+
+# --------------------------------------------------------------------------- #
+# Predictor + simple in-process caches (forecasts are expensive)
+# --------------------------------------------------------------------------- #
+_predictor = None
+_predictor_lock = threading.Lock()
+_forecast_cache: dict = {} # key -> result
+_cache_lock = threading.Lock()
+_inflight: dict = {} # key -> threading.Lock (dedup concurrent computes)
+_inflight_lock = threading.Lock()
+FORECAST_TTL = 60 * 60 * 6 # 6h
+
+# Sentiment is cheap (network + LLM) but we still cache it briefly so repeated
+# views / the combined read don't re-hit the news source and LLM every request.
+_sentiment_cache: dict = {} # instrument -> (timestamp, SentimentResult)
+_sentiment_lock = threading.Lock()
+SENTIMENT_TTL = int(os.environ.get("SENTIMENT_TTL", "900")) # 15 min
+
+
+def _get_predictor():
+ global _predictor
+ if _predictor is None:
+ with _predictor_lock:
+ if _predictor is None:
+ _predictor = fe.load_predictor(device=DEVICE)
+ return _predictor
+
+
+def _cache_key(instrument, lookback, pred_len, n_samples, T, top_p, timeframe):
+ # Daily forecast cache is per-DAY; intraday is per (5-min) bucket so it
+ # refreshes through the session without recomputing on every click.
+ if timeframe == "1d":
+ bucket = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+ else:
+ now = datetime.now(timezone.utc)
+ bucket = now.strftime("%Y-%m-%d %H:") + f"{(now.minute // 5) * 5:02d}"
+ return (instrument, lookback, pred_len, n_samples, round(T, 3), round(top_p, 3),
+ timeframe, bucket)
+
+
+def _compute_forecast(instrument, lookback, pred_len, n_samples, T, top_p, timeframe):
+ res = fe.run_forecast(
+ instrument, lookback=lookback, pred_len=pred_len, n_samples=n_samples,
+ T=T, top_p=top_p, device=DEVICE, predictor=_get_predictor(),
+ timeframe=timeframe,
+ )
+ try:
+ storage.log_forecast(res)
+ except Exception: # noqa: BLE001
+ pass
+ return res
+
+
+def _get_forecast(instrument, lookback, pred_len, n_samples, T, top_p,
+ timeframe="1d", force=False):
+ key = _cache_key(instrument, lookback, pred_len, n_samples, T, top_p, timeframe)
+ if not force:
+ with _cache_lock:
+ hit = _forecast_cache.get(key)
+ if hit is not None:
+ return hit
+
+ # Per-key lock so a prewarm + a visitor (or two visitors) asking for the same
+ # forecast don't each run expensive CPU inference; the second waits and reuses.
+ with _inflight_lock:
+ lock = _inflight.setdefault(key, threading.Lock())
+ with lock:
+ if not force:
+ with _cache_lock:
+ hit = _forecast_cache.get(key)
+ if hit is not None:
+ return hit
+ res = _compute_forecast(instrument, lookback, pred_len, n_samples, T, top_p, timeframe)
+ with _cache_lock:
+ _forecast_cache[key] = res
+ return res
+
+
+def _get_sentiment(instrument_key: str, force: bool = False) -> ns.SentimentResult:
+ """Cached news-sentiment for an instrument (Layer 2). Cheap but networked."""
+ import time
+ inst = get_instrument(instrument_key)
+ now = time.time()
+ if not force:
+ with _sentiment_lock:
+ hit = _sentiment_cache.get(instrument_key)
+ if hit and (now - hit[0]) < SENTIMENT_TTL:
+ return hit[1]
+ res = ns.get_sentiment(inst.key, inst.display_name, inst.primary_ticker)
+ with _sentiment_lock:
+ _sentiment_cache[instrument_key] = (now, res)
+ return res
+
+
+# --------------------------------------------------------------------------- #
+# Serialization
+# --------------------------------------------------------------------------- #
+def _fmt_ts(ts, intraday: bool) -> str:
+ """Daily -> 'YYYY-MM-DD'; intraday -> 'YYYY-MM-DD HH:MM' (UTC, naive)."""
+ t = pd.Timestamp(ts)
+ return t.strftime("%Y-%m-%d %H:%M") if intraday else str(t.date())
+
+
+def _result_to_dict(res, include_samples=True, max_sample_paths=40):
+ inst = get_instrument(res.instrument_key)
+ hist = res.history
+ skew = res.skew_metrics.reset_index()
+
+ sample_paths = []
+ if include_samples:
+ n = min(res.n_samples, max_sample_paths)
+ sample_paths = [[round(float(v), 6) for v in res.sample_matrix[i]] for i in range(n)]
+
+ return {
+ "instrument": res.instrument_key,
+ "display_name": res.display_name,
+ "ticker_used": res.ticker_used,
+ "timeframe": res.timeframe,
+ "timeframe_label": res.timeframe_label,
+ "is_intraday": res.is_intraday,
+ "used_volume_path": res.used_volume_path,
+ "has_reliable_volume": res.has_reliable_volume,
+ "event_sensitive": res.event_sensitive,
+ "confidence_caveat": res.confidence_caveat,
+ "last_close": round(res.last_close, 6),
+ "last_history_date": _fmt_ts(res.last_history_date, res.is_intraday),
+ "generated_at": res.generated_at.isoformat(),
+ "pred_len": res.pred_len,
+ "n_samples": res.n_samples,
+ "realized_vol_annualized": round(res.realized_vol_annualized, 6),
+ "skew_sentence": res.skew_sentence(),
+ "price_lean": res.price_lean(),
+ "history": {
+ "dates": [_fmt_ts(d, res.is_intraday) for d in hist["timestamps"]],
+ "open": [round(float(v), 6) for v in hist["open"]],
+ "high": [round(float(v), 6) for v in hist["high"]],
+ "low": [round(float(v), 6) for v in hist["low"]],
+ "close": [round(float(v), 6) for v in hist["close"]],
+ },
+ "forecast": {
+ "dates": [_fmt_ts(d, res.is_intraday) for d in res.future_timestamps],
+ "p10": [round(float(v), 6) for v in res.p10],
+ "p25": [round(float(v), 6) for v in res.p25],
+ "median": [round(float(v), 6) for v in res.median],
+ "p75": [round(float(v), 6) for v in res.p75],
+ "p90": [round(float(v), 6) for v in res.p90],
+ },
+ "skew": {
+ "horizon": [int(h) for h in skew["horizon"]],
+ "downside": [round(float(v), 6) for v in skew["downside"]],
+ "upside": [round(float(v), 6) for v in skew["upside"]],
+ "prob_below_last_close": [round(float(v), 6) for v in skew["prob_below_last_close"]],
+ "downside_upside_ratio": [
+ (None if not np.isfinite(v) else round(float(v), 4))
+ for v in skew["downside_upside_ratio"]
+ ],
+ },
+ "sample_paths": sample_paths,
+ }
+
+
+# --------------------------------------------------------------------------- #
+# API
+# --------------------------------------------------------------------------- #
+@app.get("/api/instruments")
+def api_instruments():
+ return {
+ "order": INSTRUMENT_ORDER,
+ "instruments": [
+ {
+ "key": i.key,
+ "display_name": i.display_name,
+ "primary_ticker": i.primary_ticker,
+ "has_reliable_volume": i.has_reliable_volume,
+ "event_sensitive": i.event_sensitive,
+ "confidence_caveat": i.confidence_caveat,
+ }
+ for i in all_instruments()
+ ],
+ "global_caveat": (
+ "The model sees PAST DAILY PRICE CANDLES ONLY. It cannot see Fed/BoE/BoJ "
+ "decisions, CPI, OPEC headlines, or risk events. Forecast is a distribution "
+ "(a band), never a single prediction, and carries no confidence score."
+ ),
+ }
+
+
+@app.get("/api/forecast/{instrument}")
+def api_forecast(
+ instrument: str,
+ lookback: int = Query(400, ge=100, le=512),
+ pred_len: int = Query(30, ge=5, le=60),
+ n_samples: int = Query(30, ge=10, le=80),
+ T: float = Query(1.0, ge=0.5, le=1.5),
+ top_p: float = Query(0.9, ge=0.5, le=1.0),
+ timeframe: str = Query("1d"),
+ include_sentiment: bool = Query(True),
+ force: bool = Query(False),
+):
+ try:
+ get_instrument(instrument)
+ except KeyError:
+ raise HTTPException(status_code=404, detail=f"Unknown instrument '{instrument}'")
+ if timeframe.lower() not in fe.TIMEFRAMES:
+ raise HTTPException(status_code=400,
+ detail=f"Unknown timeframe '{timeframe}'. "
+ f"Valid: {', '.join(fe.TIMEFRAMES)}")
+ try:
+ res = _get_forecast(instrument, lookback, pred_len, n_samples, T, top_p,
+ timeframe=timeframe.lower(), force=force)
+ except Exception as exc: # noqa: BLE001
+ raise HTTPException(status_code=500, detail=f"Forecast failed: {exc}")
+ payload = _result_to_dict(res)
+ payload["accuracy"] = storage.accuracy_summary(instrument)
+ payload["analysis_text"] = nc.analyze_forecast_text(res, payload["accuracy"])
+
+ # Layer 2 (news sentiment) + the COMBINED agree/diverge read. This is the
+ # reframed headline output: two independent views shown side by side.
+ if include_sentiment:
+ try:
+ sent = _get_sentiment(instrument, force=force)
+ payload["sentiment"] = sent.to_dict()
+ payload["combined"] = ns.combine(res.price_lean(), sent)
+ except Exception as exc: # noqa: BLE001 - sentiment is best-effort
+ payload["sentiment"] = None
+ payload["combined"] = None
+ payload["sentiment_error"] = str(exc)
+ return payload
+
+
+@app.get("/api/glance")
+def api_glance(
+ lookback: int = Query(400, ge=100, le=512),
+ n_samples: int = Query(30, ge=10, le=80),
+):
+ # Reuse the SAME 30-day daily forecast the detail view uses (shared cache key),
+ # then slice the first 7 days for the 1-week summary. This avoids computing a
+ # second forecast per instrument on CPU. Glance is always DAILY.
+ GLANCE_PRED_LEN = 30
+ SPARK_DAYS = 7
+ out = []
+ for inst in all_instruments():
+ try:
+ res = _get_forecast(inst.key, lookback, GLANCE_PRED_LEN, n_samples, 1.0, 0.9,
+ timeframe="1d", force=False)
+ h = min(SPARK_DAYS, res.pred_len) - 1
+ card = {
+ "instrument": inst.key,
+ "display_name": res.display_name,
+ "ticker_used": res.ticker_used,
+ "last_close": round(res.last_close, 6),
+ "median_1w": round(float(res.median[h]), 6),
+ "p10_1w": round(float(res.p10[h]), 6),
+ "p90_1w": round(float(res.p90[h]), 6),
+ "has_reliable_volume": res.has_reliable_volume,
+ "event_sensitive": res.event_sensitive,
+ "confidence_caveat": res.confidence_caveat,
+ "price_lean": res.price_lean(),
+ "forecast_dates": [str(d.date()) for d in res.future_timestamps[:SPARK_DAYS]],
+ "median_series": [round(float(v), 6) for v in res.median[:SPARK_DAYS]],
+ "p10_series": [round(float(v), 6) for v in res.p10[:SPARK_DAYS]],
+ "p90_series": [round(float(v), 6) for v in res.p90[:SPARK_DAYS]],
+ }
+ # Cheap sentiment + combined verdict per card (cached).
+ try:
+ sent = _get_sentiment(inst.key)
+ card["sentiment_score"] = round(sent.aggregate_score, 4)
+ card["sentiment_label"] = sent.label
+ card["sentiment_method"] = sent.method
+ card["combined"] = ns.combine(res.price_lean(), sent)
+ except Exception: # noqa: BLE001
+ card["sentiment_score"] = None
+ card["combined"] = None
+ out.append(card)
+ except Exception as exc: # noqa: BLE001
+ out.append({"instrument": inst.key, "display_name": inst.display_name,
+ "error": str(exc)})
+ return {"cards": out}
+
+
+@app.get("/api/accuracy/{instrument}")
+def api_accuracy(instrument: str):
+ try:
+ get_instrument(instrument)
+ except KeyError:
+ raise HTTPException(status_code=404, detail=f"Unknown instrument '{instrument}'")
+ return storage.accuracy_summary(instrument)
+
+
+@app.post("/api/refresh-actuals")
+def api_refresh_actuals(instrument: str = Query("")):
+ key = instrument.strip() or None
+ if key is not None:
+ try:
+ get_instrument(key)
+ except KeyError:
+ raise HTTPException(status_code=404, detail=f"Unknown instrument '{key}'")
+ n = storage.update_actuals(key)
+ return {"reconciled": n, "scope": key or "all"}
+
+
+@app.get("/api/news/{instrument}")
+def api_news(instrument: str):
+ try:
+ inst = get_instrument(instrument)
+ except KeyError:
+ raise HTTPException(status_code=404, detail=f"Unknown instrument '{instrument}'")
+ ctx = nc.get_news_context(inst.key, inst.display_name)
+ return {
+ "instrument": inst.key,
+ "blind_spots": ctx.blind_spots,
+ "items": [{"title": it.title, "source": it.source, "url": it.url,
+ "published": it.published} for it in ctx.items],
+ "tone_note": ctx.tone_note,
+ "enabled": ctx.enabled,
+ "notice": ctx.notice,
+ "disclaimer": nc.DISCLAIMER,
+ }
+
+
+@app.get("/api/sentiment/{instrument}")
+def api_sentiment(instrument: str, force: bool = Query(False)):
+ """Layer 2 only: live news-sentiment for an instrument (no model run)."""
+ try:
+ get_instrument(instrument)
+ except KeyError:
+ raise HTTPException(status_code=404, detail=f"Unknown instrument '{instrument}'")
+ try:
+ sent = _get_sentiment(instrument, force=force)
+ except Exception as exc: # noqa: BLE001
+ raise HTTPException(status_code=500, detail=f"Sentiment failed: {exc}")
+ return sent.to_dict()
+
+
+@app.get("/api/timeframes")
+def api_timeframes():
+ """Available forecast timeframes (daily + intraday)."""
+ return {
+ "default": fe.DEFAULT_TIMEFRAME,
+ "timeframes": [
+ {"key": k, "label": v["label"], "intraday": v["intraday"]}
+ for k, v in fe.TIMEFRAMES.items()
+ ],
+ "note": ("Intraday data is delayed ~15 min on free feeds. Sub-minute / tick "
+ "data is not available. 1m history is limited to ~7 days, 5m/15m to "
+ "~60 days, 1h to ~2 years."),
+ }
+
+
+@app.get("/api/health")
+def api_health():
+ return {"status": "ok", "device": DEVICE,
+ "model": fe.DEFAULT_MODEL, "time": datetime.now(timezone.utc).isoformat()}
+
+
+# --------------------------------------------------------------------------- #
+# Background prewarm — so a fresh GCP boot serves instantly instead of making
+# the first visitor wait ~2-3 min per instrument on CPU. Cache is per-day, so
+# this runs once after startup. Disable with KRONOS_PREWARM=0.
+# --------------------------------------------------------------------------- #
+_prewarm_state = {"running": False, "done": [], "started_at": None}
+
+
+def _prewarm_worker():
+ _prewarm_state["running"] = True
+ _prewarm_state["started_at"] = datetime.now(timezone.utc).isoformat()
+ for inst in INSTRUMENT_ORDER:
+ try:
+ # detail + glance now share this one 30-day forecast (cache key match)
+ _get_forecast(inst, 400, 30, 30, 1.0, 0.9, force=False)
+ _prewarm_state["done"].append(inst)
+ except Exception: # noqa: BLE001
+ pass
+ _prewarm_state["running"] = False
+
+
+@app.on_event("startup")
+def _on_startup():
+ if os.environ.get("KRONOS_PREWARM", "1") != "0":
+ threading.Thread(target=_prewarm_worker, daemon=True).start()
+
+
+@app.get("/api/prewarm-status")
+def api_prewarm_status():
+ return {**_prewarm_state, "instruments": INSTRUMENT_ORDER}
+
+
+# --------------------------------------------------------------------------- #
+# Static frontend
+# --------------------------------------------------------------------------- #
+@app.get("/")
+def index():
+ return FileResponse(os.path.join(WEB_DIR, "index.html"))
+
+
+app.mount("/web", StaticFiles(directory=WEB_DIR), name="web")
+
+
+if __name__ == "__main__":
+ import uvicorn
+ port = int(os.environ.get("PORT", "8000"))
+ host = os.environ.get("HOST", "127.0.0.1")
+ uvicorn.run(app, host=host, port=port, log_level="info")
diff --git a/forecast_dashboard/storage.py b/forecast_dashboard/storage.py
new file mode 100644
index 000000000..411161cdb
--- /dev/null
+++ b/forecast_dashboard/storage.py
@@ -0,0 +1,411 @@
+"""storage.py — forecast logging + accuracy tracking (SQLite, file-based).
+
+This is the point of the demo account: every forecast is logged at generation
+time, and later reconciled against the actual realized close. The dashboard then
+surfaces calibration as NUMBERS (band hit-rate, errors), not a vibe.
+
+Headline metric: band hit-rate — a well-calibrated p10-p90 band should contain
+the actual ~80% of the time. We report whether it actually does.
+
+Smoke test: python storage.py
+"""
+
+from __future__ import annotations
+
+import os
+import json
+import sqlite3
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+DEFAULT_DB = os.environ.get(
+ "FORECAST_DB",
+ os.path.join(os.path.dirname(os.path.abspath(__file__)), "forecasts.db"),
+)
+
+# Band convention: p10-p90 is a nominal 80% interval.
+BAND_NOMINAL_COVERAGE = 0.80
+
+
+# --------------------------------------------------------------------------- #
+# Schema
+# --------------------------------------------------------------------------- #
+# forecast_runs : one row per run_forecast() call.
+# forecast_points: one row per (run, horizon) with the band + later the actual.
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS forecast_runs (
+ run_id INTEGER PRIMARY KEY AUTOINCREMENT,
+ instrument_key TEXT NOT NULL,
+ ticker_used TEXT NOT NULL,
+ model_name TEXT NOT NULL,
+ generated_at TEXT NOT NULL, -- ISO UTC
+ last_history_date TEXT NOT NULL, -- ISO date of last actual candle
+ last_close REAL NOT NULL,
+ pred_len INTEGER NOT NULL,
+ n_samples INTEGER NOT NULL,
+ used_volume_path INTEGER NOT NULL,
+ T REAL,
+ top_p REAL
+);
+
+CREATE TABLE IF NOT EXISTS forecast_points (
+ point_id INTEGER PRIMARY KEY AUTOINCREMENT,
+ run_id INTEGER NOT NULL REFERENCES forecast_runs(run_id) ON DELETE CASCADE,
+ instrument_key TEXT NOT NULL,
+ horizon INTEGER NOT NULL, -- 1..pred_len
+ target_date TEXT NOT NULL, -- ISO date the forecast is for
+ p10 REAL NOT NULL,
+ p25 REAL,
+ median REAL NOT NULL,
+ p75 REAL,
+ p90 REAL NOT NULL,
+ last_close REAL NOT NULL, -- close at generation (for direction calc)
+ actual_close REAL, -- filled by update_actuals()
+ reconciled_at TEXT
+);
+
+CREATE INDEX IF NOT EXISTS idx_points_instrument ON forecast_points(instrument_key);
+CREATE INDEX IF NOT EXISTS idx_points_target ON forecast_points(target_date);
+CREATE INDEX IF NOT EXISTS idx_points_actual ON forecast_points(actual_close);
+"""
+
+
+@contextmanager
+def _connect(db_path: str = DEFAULT_DB):
+ conn = sqlite3.connect(db_path)
+ conn.row_factory = sqlite3.Row
+ try:
+ conn.execute("PRAGMA foreign_keys = ON;")
+ yield conn
+ conn.commit()
+ finally:
+ conn.close()
+
+
+def init_db(db_path: str = DEFAULT_DB) -> None:
+ """Create tables if they do not exist."""
+ with _connect(db_path) as conn:
+ conn.executescript(_SCHEMA)
+
+
+# --------------------------------------------------------------------------- #
+# Logging
+# --------------------------------------------------------------------------- #
+def log_forecast(result, db_path: str = DEFAULT_DB) -> int:
+ """Persist a ForecastResult. Returns the run_id.
+
+ Accepts either a ForecastResult object or its to_log_dict() output.
+ Idempotent-ish: if an identical run (same instrument + generated date +
+ last_history_date) already exists, we skip re-inserting and return that id.
+ """
+ init_db(db_path)
+ data = result.to_log_dict() if hasattr(result, "to_log_dict") else dict(result)
+
+ instrument_key = data["instrument_key"]
+ generated_at = data["generated_at"]
+ last_history_date = data["last_history_date"]
+ gen_date = generated_at[:10]
+
+ with _connect(db_path) as conn:
+ # Dedup: same instrument + same calendar day of generation + same history anchor.
+ existing = conn.execute(
+ """SELECT run_id FROM forecast_runs
+ WHERE instrument_key = ? AND substr(generated_at,1,10) = ?
+ AND last_history_date = ?""",
+ (instrument_key, gen_date, last_history_date),
+ ).fetchone()
+ if existing:
+ return int(existing["run_id"])
+
+ cur = conn.execute(
+ """INSERT INTO forecast_runs
+ (instrument_key, ticker_used, model_name, generated_at,
+ last_history_date, last_close, pred_len, n_samples,
+ used_volume_path, T, top_p)
+ VALUES (?,?,?,?,?,?,?,?,?,?,?)""",
+ (
+ instrument_key,
+ data["ticker_used"],
+ data["model_name"],
+ generated_at,
+ last_history_date,
+ float(data["last_close"]),
+ int(data["pred_len"]),
+ int(data["n_samples"]),
+ 1 if data["used_volume_path"] else 0,
+ float(data.get("T", 1.0)),
+ float(data.get("top_p", 0.9)),
+ ),
+ )
+ run_id = int(cur.lastrowid)
+
+ rows = []
+ future_ts = data["future_timestamps"]
+ for i, ts in enumerate(future_ts):
+ target_date = ts[:10]
+ rows.append(
+ (
+ run_id,
+ instrument_key,
+ i + 1,
+ target_date,
+ float(data["p10"][i]),
+ float(data["p25"][i]) if data.get("p25") else None,
+ float(data["median"][i]),
+ float(data["p75"][i]) if data.get("p75") else None,
+ float(data["p90"][i]),
+ float(data["last_close"]),
+ )
+ )
+ conn.executemany(
+ """INSERT INTO forecast_points
+ (run_id, instrument_key, horizon, target_date,
+ p10, p25, median, p75, p90, last_close)
+ VALUES (?,?,?,?,?,?,?,?,?,?)""",
+ rows,
+ )
+ return run_id
+
+
+def latest_run(instrument_key: str, db_path: str = DEFAULT_DB) -> Optional[dict]:
+ """Return the most recent forecast run row for an instrument (or None)."""
+ init_db(db_path)
+ with _connect(db_path) as conn:
+ row = conn.execute(
+ """SELECT * FROM forecast_runs
+ WHERE instrument_key = ?
+ ORDER BY generated_at DESC LIMIT 1""",
+ (instrument_key,),
+ ).fetchone()
+ return dict(row) if row else None
+
+
+# --------------------------------------------------------------------------- #
+# Reconciliation against actuals
+# --------------------------------------------------------------------------- #
+def update_actuals(instrument_key: Optional[str] = None,
+ db_path: str = DEFAULT_DB,
+ fetch_fn=None) -> int:
+ """Fetch realized closes for past forecast points whose target date has passed.
+
+ Args:
+ instrument_key: limit to one instrument, or None for all.
+ fetch_fn: callable(instrument_key) -> DataFrame with
+ columns 'timestamps','close'. Defaults to
+ forecast_engine.fetch_ohlcv.
+
+ Returns the number of points newly reconciled.
+ """
+ init_db(db_path)
+ if fetch_fn is None:
+ from forecast_engine import fetch_ohlcv as fetch_fn # lazy import
+
+ today = pd.Timestamp(datetime.now(timezone.utc).date())
+
+ with _connect(db_path) as conn:
+ instruments = (
+ [instrument_key]
+ if instrument_key
+ else [r["instrument_key"] for r in conn.execute(
+ "SELECT DISTINCT instrument_key FROM forecast_points")]
+ )
+
+ total = 0
+ for inst_key in instruments:
+ pending = conn.execute(
+ """SELECT point_id, target_date FROM forecast_points
+ WHERE instrument_key = ? AND actual_close IS NULL
+ AND target_date <= ?""",
+ (inst_key, today.date().isoformat()),
+ ).fetchall()
+ if not pending:
+ continue
+
+ try:
+ actuals = fetch_fn(inst_key)
+ except Exception: # noqa: BLE001 - skip instrument on fetch failure
+ continue
+ if actuals is None or len(actuals) == 0:
+ continue
+
+ close_by_date = {
+ pd.Timestamp(t).date().isoformat(): float(c)
+ for t, c in zip(actuals["timestamps"], actuals["close"])
+ }
+
+ now_iso = datetime.now(timezone.utc).isoformat()
+ for row in pending:
+ actual = close_by_date.get(row["target_date"])
+ if actual is None:
+ continue # market may have been closed that day; try later
+ conn.execute(
+ """UPDATE forecast_points
+ SET actual_close = ?, reconciled_at = ?
+ WHERE point_id = ?""",
+ (actual, now_iso, row["point_id"]),
+ )
+ total += 1
+ return total
+
+
+# --------------------------------------------------------------------------- #
+# Accuracy summary
+# --------------------------------------------------------------------------- #
+def accuracy_summary(instrument_key: str,
+ db_path: str = DEFAULT_DB,
+ min_points: int = 10) -> dict:
+ """Compute calibration / accuracy stats for reconciled forecast points.
+
+ Returns a dict with:
+ n_reconciled, enough_data (bool), min_points,
+ band_hit_rate (fraction of actuals inside p10-p90),
+ band_nominal (0.80 — what a calibrated band should hit),
+ calibration_note (plain-English read),
+ median_abs_error, (|actual - median|, absolute)
+ median_abs_pct_error, (as % of actual)
+ directional_accuracy, (did median imply the realized direction?)
+ by_horizon (DataFrame-as-records for per-horizon hit-rate)
+ """
+ init_db(db_path)
+ with _connect(db_path) as conn:
+ rows = conn.execute(
+ """SELECT horizon, target_date, p10, median, p90, last_close, actual_close
+ FROM forecast_points
+ WHERE instrument_key = ? AND actual_close IS NOT NULL
+ ORDER BY target_date""",
+ (instrument_key,),
+ ).fetchall()
+
+ n = len(rows)
+ result = {
+ "instrument_key": instrument_key,
+ "n_reconciled": n,
+ "min_points": min_points,
+ "enough_data": n >= min_points,
+ "band_nominal": BAND_NOMINAL_COVERAGE,
+ "band_hit_rate": None,
+ "median_abs_error": None,
+ "median_abs_pct_error": None,
+ "directional_accuracy": None,
+ "calibration_note": None,
+ "by_horizon": [],
+ }
+ if n == 0:
+ result["calibration_note"] = "No reconciled forecasts yet."
+ return result
+
+ df = pd.DataFrame([dict(r) for r in rows])
+ inside = (df["actual_close"] >= df["p10"]) & (df["actual_close"] <= df["p90"])
+ abs_err = (df["actual_close"] - df["median"]).abs()
+ abs_pct = (abs_err / df["actual_close"].abs()).replace([np.inf, -np.inf], np.nan)
+
+ pred_dir = np.sign(df["median"] - df["last_close"])
+ real_dir = np.sign(df["actual_close"] - df["last_close"])
+ # Only count points where the forecast actually implied a direction.
+ dir_mask = pred_dir != 0
+ dir_acc = float((pred_dir[dir_mask] == real_dir[dir_mask]).mean()) if dir_mask.any() else None
+
+ band_hit = float(inside.mean())
+ result["band_hit_rate"] = band_hit
+ result["median_abs_error"] = float(abs_err.median())
+ result["median_abs_pct_error"] = float(abs_pct.median()) if abs_pct.notna().any() else None
+ result["directional_accuracy"] = dir_acc
+
+ # Per-horizon hit-rate
+ by_h = (
+ df.assign(inside=inside)
+ .groupby("horizon")
+ .agg(n=("inside", "size"), hit_rate=("inside", "mean"),
+ median_abs_error=("actual_close", lambda s: float((s - df.loc[s.index, "median"]).abs().median())))
+ .reset_index()
+ )
+ result["by_horizon"] = by_h.to_dict(orient="records")
+
+ # Plain-English calibration note (honest about small samples).
+ if n < min_points:
+ result["calibration_note"] = (
+ f"Not enough logged forecasts yet ({n} reconciled, need >= {min_points}). "
+ "Treat numbers below as provisional."
+ )
+ else:
+ pct = band_hit * 100
+ nominal = BAND_NOMINAL_COVERAGE * 100
+ if band_hit >= 0.75:
+ cal = f"Band hit-rate {pct:.0f}% vs nominal {nominal:.0f}% -> roughly calibrated."
+ elif band_hit >= 0.6:
+ cal = (f"Band hit-rate {pct:.0f}% vs nominal {nominal:.0f}% -> band is somewhat "
+ "too NARROW (overconfident).")
+ else:
+ cal = (f"Band hit-rate {pct:.0f}% vs nominal {nominal:.0f}% -> band is much too "
+ "NARROW; the model is overconfident on this instrument.")
+ result["calibration_note"] = cal
+
+ return result
+
+
+# --------------------------------------------------------------------------- #
+# Smoke test (SCOPE.md §9 step 3)
+# --------------------------------------------------------------------------- #
+def _smoke_test():
+ import tempfile
+
+ tmp = os.path.join(tempfile.gettempdir(), "kronos_storage_smoke.db")
+ if os.path.exists(tmp):
+ os.remove(tmp)
+ print("storage smoke test ->", tmp)
+ init_db(tmp)
+
+ # Build a fake forecast for dates in the past so update_actuals can reconcile.
+ base = pd.Timestamp(datetime.now(timezone.utc).date()) - pd.Timedelta(days=10)
+ future_ts = [(base + pd.offsets.BDay(i + 1)).isoformat() for i in range(5)]
+ fake = {
+ "instrument_key": "gold",
+ "ticker_used": "GC=F",
+ "model_name": "NeoQuasar/Kronos-small",
+ "generated_at": base.isoformat(),
+ "last_history_date": base.isoformat(),
+ "last_close": 2000.0,
+ "pred_len": 5,
+ "n_samples": 30,
+ "used_volume_path": True,
+ "T": 1.0,
+ "top_p": 0.9,
+ "future_timestamps": future_ts,
+ "p10": [1950, 1940, 1930, 1920, 1910],
+ "p25": [1975, 1970, 1965, 1960, 1955],
+ "median": [2005, 2010, 2015, 2020, 2025],
+ "p75": [2035, 2045, 2055, 2065, 2075],
+ "p90": [2060, 2080, 2100, 2120, 2140],
+ }
+ run_id = log_forecast(fake, db_path=tmp)
+ print(f"Logged run_id={run_id}")
+
+ # Fake actuals fetcher: returns a close inside the band for each target date.
+ def fake_fetch(instrument_key):
+ dates = [pd.Timestamp(t) for t in future_ts]
+ closes = [2003, 2012, 1925, 2030, 2200] # mix: in-band, in-band, below p10, in-band, above p90
+ return pd.DataFrame({"timestamps": dates, "close": closes})
+
+ n = update_actuals("gold", db_path=tmp, fetch_fn=fake_fetch)
+ print(f"Reconciled {n} points")
+
+ summary = accuracy_summary("gold", db_path=tmp, min_points=3)
+ print("Accuracy summary:")
+ for k, v in summary.items():
+ if k == "by_horizon":
+ print(f" by_horizon: {len(v)} rows")
+ else:
+ print(f" {k}: {v}")
+
+ assert summary["n_reconciled"] == 5
+ # 3 of 5 inside band -> 0.6 hit rate
+ assert abs(summary["band_hit_rate"] - 0.6) < 1e-9, summary["band_hit_rate"]
+ print("\n[OK] storage smoke test passed.")
+
+
+if __name__ == "__main__":
+ _smoke_test()
diff --git a/forecast_dashboard/tests/README.md b/forecast_dashboard/tests/README.md
new file mode 100644
index 000000000..b5cecad7f
--- /dev/null
+++ b/forecast_dashboard/tests/README.md
@@ -0,0 +1,23 @@
+# Tests / smoke checks
+
+These validate the system end-to-end against the real Kronos model and live data.
+
+| File | What it checks |
+|---|---|
+| `test_accuracy_e2e.py` | log -> reconcile -> accuracy_summary loop with REAL yfinance closes. Fast (no model load). |
+| `test_mcp_server.py` | spawns `mcp_server.py` over stdio, calls all four tools, asserts the honesty framing is present. Loads the model (cached after first run). |
+
+Run from the project root:
+
+```bash
+python tests/test_accuracy_e2e.py
+python tests/test_mcp_server.py
+```
+
+Module-level smoke tests also live in the core files:
+
+```bash
+python forecast_engine.py # forecasts all 4 instruments, prints bands
+python storage.py # logging + reconciliation on a fake forecast
+python instruments.py # prints the instrument config
+```
diff --git a/forecast_dashboard/tests/test_accuracy_e2e.py b/forecast_dashboard/tests/test_accuracy_e2e.py
new file mode 100644
index 000000000..55d40a927
--- /dev/null
+++ b/forecast_dashboard/tests/test_accuracy_e2e.py
@@ -0,0 +1,72 @@
+"""End-to-end accuracy flow test against REAL data.
+
+1. Run a real forecast for gold, but backdate its target dates into the past so
+ update_actuals() can fetch real closes and reconcile.
+2. Verify accuracy_summary returns real numbers (band hit-rate etc).
+This proves the log -> reconcile -> summarize loop works with live yfinance data.
+"""
+import os
+import sys
+import tempfile
+import pandas as pd
+from datetime import datetime, timezone
+
+# Make the project root importable when run from tests/.
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if PROJECT_ROOT not in sys.path:
+ sys.path.insert(0, PROJECT_ROOT)
+
+# Use an isolated DB so we don't pollute the real one.
+TMP_DB = os.path.join(tempfile.gettempdir(), "kronos_e2e.db")
+os.environ["FORECAST_DB"] = TMP_DB
+if os.path.exists(TMP_DB):
+ os.remove(TMP_DB)
+
+import forecast_engine as fe
+import storage
+
+INST = "gold"
+
+# Pull real history so we can place target dates on actual past trading days.
+hist = fe.fetch_ohlcv(INST, lookback_days=120)
+past = hist.iloc[-12:-6] # 6 trading days that already happened
+target_dates = [pd.Timestamp(t) for t in past["timestamps"]]
+anchor = pd.Timestamp(hist["timestamps"].iloc[-13])
+last_close = float(hist["close"].iloc[-13])
+
+# Build a forecast-like dict whose target dates are those past trading days.
+# Use a deliberately WIDE band so most actuals fall inside (sanity of hit-rate).
+n = len(target_dates)
+fake = {
+ "instrument_key": INST,
+ "ticker_used": hist.attrs["ticker_used"],
+ "model_name": "test",
+ "generated_at": anchor.isoformat(),
+ "last_history_date": anchor.isoformat(),
+ "last_close": last_close,
+ "pred_len": n,
+ "n_samples": 30,
+ "used_volume_path": True,
+ "T": 1.0, "top_p": 0.9,
+ "future_timestamps": [t.isoformat() for t in target_dates],
+ "p10": [last_close * 0.90] * n,
+ "p25": [last_close * 0.95] * n,
+ "median": [last_close] * n,
+ "p75": [last_close * 1.05] * n,
+ "p90": [last_close * 1.10] * n,
+}
+run_id = storage.log_forecast(fake, db_path=TMP_DB)
+print("logged run_id:", run_id, "with", n, "past target dates")
+
+n_recon = storage.update_actuals(INST, db_path=TMP_DB)
+print("reconciled:", n_recon, "points against REAL closes")
+
+summary = storage.accuracy_summary(INST, db_path=TMP_DB, min_points=3)
+print("band_hit_rate:", summary["band_hit_rate"])
+print("median_abs_error:", summary["median_abs_error"])
+print("directional_accuracy:", summary["directional_accuracy"])
+print("note:", summary["calibration_note"])
+
+assert n_recon >= 3, "should reconcile several real trading days"
+assert summary["band_hit_rate"] is not None
+print("\n[OK] end-to-end accuracy flow works with real data.")
diff --git a/forecast_dashboard/tests/test_mcp_server.py b/forecast_dashboard/tests/test_mcp_server.py
new file mode 100644
index 000000000..dbf8ef5f9
--- /dev/null
+++ b/forecast_dashboard/tests/test_mcp_server.py
@@ -0,0 +1,56 @@
+"""Smoke test for the MCP server: spawn it over stdio and call each tool.
+
+Verifies the four tools are exposed and that responses carry the honesty framing.
+"""
+import asyncio
+import os
+import sys
+
+from mcp import ClientSession, StdioServerParameters
+from mcp.client.stdio import stdio_client
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+async def main():
+ params = StdioServerParameters(
+ command=sys.executable,
+ args=["mcp_server.py"],
+ cwd=PROJECT_ROOT,
+ )
+ async with stdio_client(params) as (read, write):
+ async with ClientSession(read, write) as session:
+ await session.initialize()
+ tools = await session.list_tools()
+ names = [t.name for t in tools.tools]
+ print("TOOLS:", names)
+ assert set(names) == {"list_instruments", "get_forecast",
+ "get_accuracy", "refresh_actuals"}, names
+
+ r = await session.call_tool("list_instruments", {})
+ text = r.content[0].text
+ print("\n--- list_instruments (head) ---")
+ print(text[:400])
+ assert "PAST DAILY PRICE CANDLES ONLY" in text
+
+ r = await session.call_tool("get_forecast",
+ {"instrument": "gold", "pred_len": 4, "n_samples": 6})
+ text = r.content[0].text
+ print("\n--- get_forecast gold (head) ---")
+ print(text[:600])
+ assert "DISTRIBUTION" in text
+ assert "CAVEAT" in text
+
+ r = await session.call_tool("get_accuracy", {"instrument": "gold"})
+ print("\n--- get_accuracy gold ---")
+ print(r.content[0].text[:300])
+
+ r = await session.call_tool("refresh_actuals", {})
+ print("\n--- refresh_actuals ---")
+ print(r.content[0].text[:200])
+
+ print("\n[OK] MCP server smoke test passed.")
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/forecast_dashboard/ui_theme.py b/forecast_dashboard/ui_theme.py
new file mode 100644
index 000000000..210593c5e
--- /dev/null
+++ b/forecast_dashboard/ui_theme.py
@@ -0,0 +1,232 @@
+"""ui_theme.py — minimalist black & white theming for the dashboard.
+
+The brief asks for a clean, honest, data-dense UI (SCOPE.md §5): restraint over
+decoration, uncertainty visually prominent, no "trading terminal" gravitas.
+
+We keep a strictly monochrome palette and offer a few theme variants the user can
+switch between live. Each theme provides:
+ - CSS variables + overrides injected into the Streamlit page
+ - a matching Plotly template (paper/plot bg, font, gridlines)
+ - semantic colors for the fan chart (history, samples, median, band)
+
+All themes are grayscale only — color is never used to imply confidence.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Theme:
+ key: str
+ name: str
+ bg: str # page background
+ panel: str # secondary/panel background
+ text: str # primary text
+ muted: str # secondary text
+ border: str # hairlines / borders
+ accent: str # the single emphasis ink (median line, buttons)
+ grid: str # chart gridlines
+ band: str # p10-p90 fill (rgba)
+ band_inner: str # p25-p75 fill (rgba)
+ sample: str # individual sample paths (rgba, faint)
+ history: str # historical price line
+ is_dark: bool
+
+
+# A small set of monochrome themes. Black & white first, by design.
+THEMES: dict[str, Theme] = {
+ "paper": Theme(
+ key="paper", name="Paper (white)",
+ bg="#ffffff", panel="#f4f4f4", text="#111111", muted="#666666",
+ border="#dddddd", accent="#000000", grid="#ececec",
+ band="rgba(0,0,0,0.12)", band_inner="rgba(0,0,0,0.20)",
+ sample="rgba(0,0,0,0.06)", history="#111111", is_dark=False,
+ ),
+ "ink": Theme(
+ key="ink", name="Ink (black)",
+ bg="#0b0b0b", panel="#161616", text="#f2f2f2", muted="#9a9a9a",
+ border="#2a2a2a", accent="#ffffff", grid="#1f1f1f",
+ band="rgba(255,255,255,0.12)", band_inner="rgba(255,255,255,0.22)",
+ sample="rgba(255,255,255,0.06)", history="#f2f2f2", is_dark=True,
+ ),
+ "slate": Theme(
+ key="slate", name="Slate (dim gray)",
+ bg="#15171a", panel="#1d2024", text="#e6e8ea", muted="#8b9197",
+ border="#2c3036", accent="#e6e8ea", grid="#23272c",
+ band="rgba(230,232,234,0.10)", band_inner="rgba(230,232,234,0.20)",
+ sample="rgba(230,232,234,0.06)", history="#e6e8ea", is_dark=True,
+ ),
+ "e-ink": Theme(
+ key="e-ink", name="E-ink (warm white)",
+ bg="#f7f5f0", panel="#efece4", text="#1a1a1a", muted="#5f5b53",
+ border="#d8d3c8", accent="#1a1a1a", grid="#e4e0d6",
+ band="rgba(26,26,26,0.12)", band_inner="rgba(26,26,26,0.20)",
+ sample="rgba(26,26,26,0.06)", history="#1a1a1a", is_dark=False,
+ ),
+ "high-contrast": Theme(
+ key="high-contrast", name="High contrast",
+ bg="#ffffff", panel="#ffffff", text="#000000", muted="#333333",
+ border="#000000", accent="#000000", grid="#bdbdbd",
+ band="rgba(0,0,0,0.18)", band_inner="rgba(0,0,0,0.30)",
+ sample="rgba(0,0,0,0.10)", history="#000000", is_dark=False,
+ ),
+}
+
+THEME_ORDER = ["paper", "ink", "slate", "e-ink", "high-contrast"]
+DEFAULT_THEME = "paper"
+
+
+def get_theme(key: str) -> Theme:
+ return THEMES.get(key, THEMES[DEFAULT_THEME])
+
+
+def plotly_template(theme: Theme) -> dict:
+ """A minimal Plotly layout template matching the theme (grayscale)."""
+ return {
+ "layout": {
+ "paper_bgcolor": theme.bg,
+ "plot_bgcolor": theme.bg,
+ "font": {"color": theme.text, "family": "ui-monospace, SFMono-Regular, Menlo, monospace", "size": 12},
+ "colorway": [theme.accent, theme.muted, theme.border],
+ "xaxis": {
+ "gridcolor": theme.grid, "zerolinecolor": theme.grid,
+ "linecolor": theme.border, "tickcolor": theme.border,
+ "showline": True,
+ },
+ "yaxis": {
+ "gridcolor": theme.grid, "zerolinecolor": theme.grid,
+ "linecolor": theme.border, "tickcolor": theme.border,
+ "showline": True,
+ },
+ "legend": {"bgcolor": "rgba(0,0,0,0)", "bordercolor": theme.border},
+ "margin": {"l": 50, "r": 20, "t": 30, "b": 40},
+ "hoverlabel": {"font": {"family": "ui-monospace, monospace"}},
+ }
+ }
+
+
+def css(theme: Theme) -> str:
+ """Global CSS overriding Streamlit chrome for a minimalist monochrome look."""
+ return f"""
+
+ """
+
+
+def badge(text: str, kind: str = "line") -> str:
+ """Return an inline HTML badge. kind: warn | soft | line."""
+ cls = {"warn": "badge-warn", "soft": "badge-soft", "line": "badge-line"}.get(kind, "badge-line")
+ return f'{text} '
diff --git a/forecast_dashboard/web/app.js b/forecast_dashboard/web/app.js
new file mode 100644
index 000000000..ff6a4d6e5
--- /dev/null
+++ b/forecast_dashboard/web/app.js
@@ -0,0 +1,633 @@
+/* ============================================================================
+ Kronos dashboard — frontend app.
+ Talks to the FastAPI backend (/api/*), draws charts with ECharts.
+ Honesty-first: band is the most prominent chart element, median is dashed
+ (clearly "not the answer"), no confidence score anywhere.
+ ========================================================================== */
+
+const THEMES = [
+ { key: "paper", name: "Paper" },
+ { key: "ink", name: "Ink" },
+ { key: "slate", name: "Slate" },
+ { key: "eink", name: "E-ink" },
+];
+
+const state = {
+ instruments: [],
+ timeframes: [],
+ timeframe: "1d",
+ current: null,
+ view: "detail",
+ themeIdx: 0,
+ charts: {}, // id -> echarts instance
+ lastForecast: null,
+};
+
+const $ = (sel) => document.querySelector(sel);
+const $$ = (sel) => Array.from(document.querySelectorAll(sel));
+
+function cssVar(name) {
+ return getComputedStyle(document.documentElement).getPropertyValue(name).trim();
+}
+
+function fmt(n, d = 2) {
+ if (n === null || n === undefined || Number.isNaN(n)) return "—";
+ return Number(n).toLocaleString(undefined, { minimumFractionDigits: d, maximumFractionDigits: d });
+}
+
+function decimals(v) {
+ const a = Math.abs(v || 0);
+ if (a >= 1000) return 1;
+ if (a >= 100) return 2;
+ if (a >= 1) return 3;
+ return 4;
+}
+
+/* ---------------- API ---------------- */
+async function api(path, opts) {
+ const res = await fetch(path, opts);
+ if (!res.ok) {
+ const t = await res.text().catch(() => "");
+ throw new Error(`${res.status} ${t}`);
+ }
+ return res.json();
+}
+
+/* ---------------- Theme ---------------- */
+function applyTheme(idx) {
+ state.themeIdx = (idx + THEMES.length) % THEMES.length;
+ const t = THEMES[state.themeIdx];
+ document.documentElement.setAttribute("data-theme", t.key);
+ $("#themeName").textContent = t.name;
+ localStorage.setItem("kronos_theme", String(state.themeIdx));
+ // redraw charts with new palette
+ redrawAll();
+}
+
+/* ---------------- Overlay ---------------- */
+function showOverlay(text) {
+ $("#overlayText").textContent = text || "Working…";
+ $("#overlay").classList.remove("hidden");
+}
+function hideOverlay() { $("#overlay").classList.add("hidden"); }
+
+/* ---------------- Instrument tabs ---------------- */
+function renderTabs() {
+ const nav = $("#instrumentTabs");
+ nav.innerHTML = "";
+ state.instruments.forEach((inst) => {
+ const b = document.createElement("button");
+ b.className = "itab" + (inst.key === state.current ? " is-active" : "");
+ let flag = "";
+ if (!inst.has_reliable_volume) flag = 'LOW CONF ';
+ else if (inst.event_sensitive) flag = 'EVENT ';
+ b.innerHTML = inst.display_name + flag;
+ b.onclick = () => selectInstrument(inst.key);
+ nav.appendChild(b);
+ });
+}
+
+/* ---------------- Timeframe switcher ---------------- */
+function renderTimeframes() {
+ const box = $("#tfToggle");
+ if (!box) return;
+ box.innerHTML = "";
+ state.timeframes.forEach((tf) => {
+ const b = document.createElement("button");
+ b.className = "seg-btn" + (tf.key === state.timeframe ? " is-active" : "");
+ b.dataset.tf = tf.key;
+ b.textContent = tf.key === "1d" ? "Daily" : tf.key;
+ b.title = tf.label + (tf.intraday ? " (intraday, ~15min delayed)" : "");
+ b.onclick = () => selectTimeframe(tf.key);
+ box.appendChild(b);
+ });
+}
+
+function selectTimeframe(key) {
+ if (key === state.timeframe) return;
+ state.timeframe = key;
+ localStorage.setItem("kronos_tf", key);
+ renderTimeframes();
+ // intraday horizon ranges differ from daily; update slider hints + unit
+ const intraday = key !== "1d";
+ const unit = $("#predLenUnit");
+ if (unit) unit.textContent = intraday ? (key === "1h" ? "h" : " bars") : "d";
+ runForecast(false);
+}
+
+/* lean -> arrow + word */
+function leanDisplay(lean) {
+ if (lean === "higher") return { arrow: "▲", word: "leans higher", cls: "up" };
+ if (lean === "lower") return { arrow: "▼", word: "leans lower", cls: "down" };
+ return { arrow: "▬", word: "flat", cls: "flat" };
+}
+
+/* ---------------- Charts ---------------- */
+function getChart(id) {
+ if (!state.charts[id]) {
+ state.charts[id] = echarts.init(document.getElementById(id), null, { renderer: "canvas" });
+ }
+ return state.charts[id];
+}
+
+function baseGrid() {
+ return {
+ textStyle: { fontFamily: "IBM Plex Mono, monospace", color: cssVar("--ink2") },
+ grid: { left: 54, right: 18, top: 16, bottom: 28 },
+ tooltip: {
+ trigger: "axis",
+ backgroundColor: cssVar("--panel"),
+ borderColor: cssVar("--line2"),
+ textStyle: { color: cssVar("--ink"), fontFamily: "IBM Plex Mono, monospace", fontSize: 11 },
+ axisPointer: { lineStyle: { color: cssVar("--ink2") } },
+ },
+ };
+}
+
+function drawFan(d) {
+ const chart = getChart("fanChart");
+ const ink = cssVar("--ink"), ink2 = cssVar("--ink2"), grid = cssVar("--grid");
+ const band = cssVar("--band"), band2 = cssVar("--band2"), sample = cssVar("--sample");
+ const accent = cssVar("--accent");
+
+ const histDates = d.history.dates;
+ const fDates = d.forecast.dates;
+ const allDates = histDates.concat(fDates);
+ const nh = histDates.length;
+ const pad = (arr) => new Array(nh).fill(null).concat(arr);
+
+ const dp = decimals(d.last_close);
+
+ // bands as stacked areas: base = p10, then (p25-p10), (median-p25)... we instead
+ // use two stacked sets to render p10-p90 and p25-p75.
+ const p10 = pad(d.forecast.p10);
+ const p90 = pad(d.forecast.p90);
+ const p25 = pad(d.forecast.p25);
+ const p75 = pad(d.forecast.p75);
+ const med = pad(d.forecast.median);
+
+ const diff = (hi, lo) => hi.map((v, i) => (v === null || lo[i] === null ? null : +(v - lo[i]).toFixed(6)));
+
+ const series = [];
+
+ // Outer band p10..p90 (stack outer): base p10 (transparent) + (p90-p10)
+ series.push({ name: "_p10base", type: "line", data: p10, stack: "outer", lineStyle: { opacity: 0 }, symbol: "none", areaStyle: { opacity: 0 }, silent: true });
+ series.push({ name: "p10–p90 (80%)", type: "line", data: diff(p90, p10), stack: "outer", lineStyle: { opacity: 0 }, symbol: "none", areaStyle: { color: band }, silent: true });
+
+ // Inner band p25..p75 (stack inner)
+ series.push({ name: "_p25base", type: "line", data: p25, stack: "inner", lineStyle: { opacity: 0 }, symbol: "none", areaStyle: { opacity: 0 }, silent: true });
+ series.push({ name: "p25–p75 (50%)", type: "line", data: diff(p75, p25), stack: "inner", lineStyle: { opacity: 0 }, symbol: "none", areaStyle: { color: band2 }, silent: true });
+
+ // faint sample paths
+ (d.sample_paths || []).forEach((sp) => {
+ series.push({ type: "line", data: pad(sp), lineStyle: { color: sample, width: 1 }, symbol: "none", silent: true, z: 1 });
+ });
+
+ // median (dashed = not the answer)
+ series.push({ name: "median (p50)", type: "line", data: med, lineStyle: { color: accent, width: 2, type: "dashed" }, symbol: "none", z: 4 });
+
+ // history (solid)
+ series.push({ name: "history", type: "line", data: histDates.map((_, i) => d.history.close[i]).concat(new Array(fDates.length).fill(null)), lineStyle: { color: ink, width: 2 }, symbol: "none", z: 3 });
+
+ chart.setOption({
+ ...baseGrid(),
+ legend: { show: false },
+ xAxis: {
+ type: "category", data: allDates, boundaryGap: false,
+ axisLine: { lineStyle: { color: cssVar("--line2") } },
+ axisLabel: { color: ink2, fontSize: 10, hideOverlap: true },
+ splitLine: { show: false },
+ axisTick: { show: false },
+ },
+ yAxis: {
+ type: "value", scale: true,
+ axisLine: { show: false }, axisTick: { show: false },
+ axisLabel: { color: ink2, fontSize: 10, formatter: (v) => fmt(v, v >= 100 ? 0 : 2) },
+ splitLine: { lineStyle: { color: grid } },
+ },
+ tooltip: {
+ ...baseGrid().tooltip,
+ formatter: (params) => {
+ const date = params[0].axisValue;
+ const find = (n) => { const p = params.find((x) => x.seriesName === n); return p ? p.value : null; };
+ const h = find("history"), m = find("median (p50)");
+ let rows = `${date}
`;
+ if (h !== null && h !== undefined) rows += `history ${fmt(h, dp)} ${fmt(d.forecast.median[idx], dp)} low confidence · no volume `);
+ if (d.event_sensitive) badges.push(`event-sensitive `);
+ badges.push(`${d.used_volume_path ? "volume path" : "no-volume path"} `);
+ badges.push(`${d.timeframe_label} `);
+ $("#badges").innerHTML = badges.join("");
+
+ // stats
+ $("#stripStats").innerHTML = `
+ Last close ${fmt(d.last_close, dp)}
+ Ticker ${d.ticker_used}
+ As of ${d.last_history_date}
+ Realized vol ${fmt(d.realized_vol_annualized * 100, 1)}%
`;
+
+ // table
+ const f = d.forecast;
+ const col = d.is_intraday ? "Time (UTC)" : "Date";
+ let rows = `${col} Low p10 p25 Median p75 High p90 `;
+ f.dates.forEach((date, i) => {
+ rows += `${date} ${fmt(f.p10[i], dp)} ${fmt(f.p25[i], dp)} ${fmt(f.median[i], dp)} ${fmt(f.p75[i], dp)} ${fmt(f.p90[i], dp)} `;
+ $("#distTable").innerHTML = rows;
+
+ // charts
+ drawFan(d);
+ drawSkew(d);
+ drawProb(d);
+ $("#skewSentence").textContent = d.skew_sentence;
+
+ // accuracy
+ renderAccuracy(d.accuracy);
+
+ // analysis (markdown-ish -> html)
+ $("#analysisBody").innerHTML = mdToHtml(d.analysis_text);
+
+ // Layer 2 + combined read (sentiment is in the forecast payload now)
+ renderCombined(d);
+ renderSentiment(d.sentiment);
+ renderHeadlines(d.sentiment);
+}
+
+/* ---------------- Combined read (the headline) ---------------- */
+function renderCombined(d) {
+ const c = d.combined;
+ const priceL = leanDisplay(d.price_lean);
+ $("#priceLean").innerHTML = `${priceL.arrow} ${priceL.word}`;
+ $("#priceSub").textContent = `${d.timeframe_label} median vs last close`;
+
+ if (!c) {
+ $("#newsLean").innerHTML = "—";
+ $("#verdictStatus").textContent = "no news";
+ $("#verdictStatus").className = "verdict-status";
+ $("#verdictText").textContent = "News sentiment unavailable; showing the price model only.";
+ $("#combinedBanner").dataset.status = "partial";
+ return;
+ }
+ const newsL = leanDisplay(c.news_lean);
+ $("#newsLean").innerHTML = `${newsL.arrow} ${newsL.word}`;
+ $("#newsSub").textContent = `${c.sentiment_label} (${c.sentiment_score >= 0 ? "+" : ""}${fmt(c.sentiment_score, 2)})`;
+
+ const statusLabel = { aligned: "Agree", divergent: "Diverge", partial: "Partial" }[c.status] || c.status;
+ $("#verdictStatus").textContent = statusLabel;
+ $("#verdictStatus").className = "verdict-status " + c.status;
+ $("#verdictText").textContent = c.verdict;
+ $("#combinedBanner").dataset.status = c.status;
+}
+
+/* ---------------- News sentiment (Layer 2) ---------------- */
+function renderSentiment(s) {
+ const body = $("#sentimentBody");
+ const methodEl = $("#sentMethod");
+ if (!s) {
+ methodEl.textContent = "unavailable";
+ body.innerHTML = `News sentiment unavailable right now.
`;
+ return;
+ }
+ const methodLabel = { gemini: "Gemini (Vertex)", anthropic: "Claude", lexicon: "keyword baseline", none: "no headlines" }[s.method] || s.method;
+ methodEl.textContent = methodLabel;
+
+ // gauge: -1..+1 mapped to 0..100%
+ const score = s.aggregate_score;
+ const pct = Math.round((score + 1) / 2 * 100);
+ const scoreCls = score > 0.15 ? "up" : (score < -0.15 ? "down" : "flat");
+
+ let html = `
+
+
+
bearish neutral bullish
+
+
${score >= 0 ? "+" : ""}${fmt(score, 2)}
+
${s.label}${s.n_headlines} headline${s.n_headlines === 1 ? "" : "s"}
+
` + s.drivers.map((d) => `${esc(d)} `).join("") + `
`;
+ }
+ if (s.method === "lexicon") {
+ html += `${esc(s.method_note)}
`;
+ }
+ html += `${esc(s.disclaimer)}
`;
+ body.innerHTML = html;
+}
+
+function esc(s) {
+ return String(s == null ? "" : s).replace(/&/g, "&").replace(//g, ">");
+}
+
+/* scored headlines list */
+function renderHeadlines(s) {
+ const body = $("#newsBody");
+ if (!s || !s.headlines || !s.headlines.length) {
+ body.innerHTML = `No recent headlines.
`;
+ return;
+ }
+ let html = "";
+ s.headlines.forEach((h) => {
+ const cls = h.score > 0.1 ? "up" : (h.score < -0.1 ? "down" : "flat");
+ const sign = h.score >= 0 ? "+" : "";
+ const link = h.url ? `${esc(h.title)} ` : esc(h.title);
+ html += `
+ ${sign}${fmt(h.score, 2)}
+ ${link}
+ ${h.source ? `${esc(h.source)} ` : ""}
+ ${h.rationale ? `${esc(h.rationale)} ` : ""}
+
`;
+ });
+ body.innerHTML = html;
+}
+
+function renderAccuracy(acc) {
+ const body = $("#accBody");
+ if (!acc || acc.n_reconciled === 0) {
+ body.innerHTML = `No reconciled forecasts yet. Run forecasts over several days, then hit Refresh actuals once target dates pass. Calibration is unknown until then — do not assume the band is right.
`;
+ return;
+ }
+ const pct = (v) => (v === null || v === undefined ? "—" : `${Math.round(v * 100)}%`);
+ body.innerHTML = `
+
+
Band hit-rate
${pct(acc.band_hit_rate)}
+
Median abs err
${fmt(acc.median_abs_error, 3)}
+
Directional
${pct(acc.directional_accuracy)}
+
${acc.calibration_note} ${acc.n_reconciled} reconciled points · nominal band coverage ${Math.round(acc.band_nominal * 100)}%.
`;
+}
+
+/* tiny markdown subset -> html (bold, italics, headings, lists, blockquote) */
+function mdToHtml(md) {
+ if (!md) return "—";
+ const esc = (s) => s.replace(/&/g, "&").replace(//g, ">");
+ const lines = md.split("\n");
+ let html = "", inList = false;
+ const inline = (s) => esc(s)
+ .replace(/\*\*(.+?)\*\*/g, "$1 ")
+ .replace(/\*(.+?)\*/g, "$1 ")
+ .replace(/`(.+?)`/g, "$1");
+ for (let raw of lines) {
+ const line = raw.trimEnd();
+ if (!line.trim()) { if (inList) { html += ""; inList = false; } continue; }
+ if (line.startsWith("### ")) { if (inList) { html += ""; inList = false; } html += `${inline(line.slice(4))} `; }
+ else if (line.startsWith("> ")) { if (inList) { html += ""; inList = false; } html += `${inline(line.slice(2))} `; }
+ else if (line.startsWith("- ")) { if (!inList) { html += ""; inList = true; } html += `${inline(line.slice(2))} `; }
+ else { if (inList) { html += " "; inList = false; } html += `${inline(line)}
`; }
+ }
+ if (inList) html += "";
+ return html;
+}
+
+/* ---------------- Glance ---------------- */
+async function loadGlance() {
+ showOverlay("Loading all four…");
+ try {
+ const data = await api(`/api/glance`);
+ const grid = $("#glanceGrid");
+ grid.innerHTML = "";
+ data.cards.forEach((c) => {
+ const div = document.createElement("div");
+ div.className = "gcard";
+ if (c.error) {
+ div.innerHTML = `${c.display_name}
error: ${c.error}
`;
+ grid.appendChild(div);
+ return;
+ }
+ const dp = decimals(c.last_close);
+ const lean = leanDisplay(c.price_lean || (c.median_1w > c.last_close * 1.001 ? "higher" : (c.median_1w < c.last_close * 0.999 ? "lower" : "flat")));
+ const flag = !c.has_reliable_volume ? "LOW CONF" : (c.event_sensitive ? "EVENT" : "");
+ const sparkId = `spark_${c.instrument}`;
+ // combined verdict chip
+ let verdict = "";
+ if (c.combined) {
+ const st = c.combined.status;
+ const lbl = { aligned: "agree", divergent: "diverge", partial: "partial" }[st] || st;
+ verdict = `${lbl} `;
+ }
+ const sScore = (c.sentiment_score === null || c.sentiment_score === undefined)
+ ? "" : `news ${c.sentiment_score >= 0 ? "+" : ""}${fmt(c.sentiment_score, 2)} `;
+ div.innerHTML = `
+
+
${c.display_name}
${c.ticker_used}${flag ? " · " + flag : ""}
+ ${verdict}
+
${fmt(c.last_close, dp)}
+ 1wk median ${fmt(c.median_1w, dp)} · p10–p90 ${fmt(c.p10_1w, dp)}–${fmt(c.p90_1w, dp)}
+ ${lean.arrow} price ${lean.word}
+
`;
+ div.onclick = () => { selectInstrument(c.instrument); setView("detail"); };
+ grid.appendChild(div);
+ setTimeout(() => drawSpark(sparkId, c), 0);
+ });
+ } catch (e) {
+ $("#glanceGrid").innerHTML = `Failed: ${e.message}
`;
+ } finally {
+ hideOverlay();
+ }
+}
+
+/* ---------------- Actions ---------------- */
+async function runForecast(force) {
+ const inst = state.current;
+ const pred_len = +$("#predLen").value;
+ const lookback = +$("#lookback").value;
+ const n_samples = +$("#nSamples").value;
+ const tf = state.timeframe;
+ showOverlay(force ? "Running forecast…" : "Loading forecast…");
+ try {
+ const d = await api(`/api/forecast/${inst}?pred_len=${pred_len}&lookback=${lookback}&n_samples=${n_samples}&timeframe=${tf}&force=${force ? "true" : "false"}`);
+ renderDetail(d);
+ $("#footStatus").textContent = `${d.timeframe_label} forecast · generated ${new Date(d.generated_at).toLocaleString()}`;
+ } catch (e) {
+ alert("Forecast failed: " + e.message);
+ } finally {
+ hideOverlay();
+ }
+}
+
+async function refreshActuals() {
+ showOverlay("Reconciling against realized actuals…");
+ try {
+ const r = await api(`/api/refresh-actuals`, { method: "POST" });
+ $("#footStatus").textContent = `reconciled ${r.reconciled} point(s)`;
+ if (state.lastForecast) {
+ const acc = await api(`/api/accuracy/${state.current}`);
+ renderAccuracy(acc);
+ }
+ } catch (e) {
+ alert("Refresh failed: " + e.message);
+ } finally {
+ hideOverlay();
+ }
+}
+
+function selectInstrument(key) {
+ state.current = key;
+ localStorage.setItem("kronos_inst", key);
+ renderTabs();
+ if (state.view === "detail") runForecast(false);
+}
+
+function setView(view) {
+ state.view = view;
+ $$("#viewToggle .seg-btn").forEach((b) => b.classList.toggle("is-active", b.dataset.view === view));
+ $("#detailView").classList.toggle("hidden", view !== "detail");
+ $("#glanceView").classList.toggle("hidden", view !== "glance");
+ if (view === "glance") loadGlance();
+ else { renderTabs(); if (!state.lastForecast) runForecast(false); else redrawAll(); }
+}
+
+/* ---------------- Init ---------------- */
+async function init() {
+ // theme
+ const savedTheme = +(localStorage.getItem("kronos_theme") || 0);
+ applyTheme(Number.isNaN(savedTheme) ? 0 : savedTheme);
+
+ $("#themeBtn").onclick = () => applyTheme(state.themeIdx + 1);
+ $$("#viewToggle .seg-btn").forEach((b) => (b.onclick = () => setView(b.dataset.view)));
+ $("#runBtn").onclick = () => runForecast(true);
+ $("#refreshBtn").onclick = refreshActuals;
+
+ // control labels
+ const bind = (id, out) => { const el = $(id), o = $(out); el.oninput = () => (o.textContent = el.value); };
+ bind("#predLen", "#predLenOut");
+ bind("#lookback", "#lookbackOut");
+ bind("#nSamples", "#nSamplesOut");
+
+ window.addEventListener("resize", () => Object.values(state.charts).forEach((c) => c.resize()));
+
+ // load instruments
+ try {
+ const data = await api("/api/instruments");
+ state.instruments = data.order.map((k) => data.instruments.find((i) => i.key === k));
+ const savedInst = localStorage.getItem("kronos_inst");
+ state.current = (savedInst && state.instruments.some((i) => i.key === savedInst)) ? savedInst : state.instruments[0].key;
+ renderTabs();
+
+ // load timeframes + restore selection
+ try {
+ const tfData = await api("/api/timeframes");
+ state.timeframes = tfData.timeframes || [];
+ const savedTf = localStorage.getItem("kronos_tf");
+ if (savedTf && state.timeframes.some((t) => t.key === savedTf)) state.timeframe = savedTf;
+ else state.timeframe = tfData.default || "1d";
+ renderTimeframes();
+ } catch (e) { /* timeframes optional */ }
+
+ const h = await api("/api/health");
+ $("#footStatus").textContent = `${h.model} · ${h.device}`;
+ runForecast(false);
+ } catch (e) {
+ document.body.innerHTML = `Backend not reachable: ${e.message}
`;
+ }
+}
+
+init();
diff --git a/forecast_dashboard/web/index.html b/forecast_dashboard/web/index.html
new file mode 100644
index 000000000..a6b13e891
--- /dev/null
+++ b/forecast_dashboard/web/index.html
@@ -0,0 +1,188 @@
+
+
+
+ Kronos — forecast distribution & calibration
+
+ Two honest layers, side by side: a price-history forecast (Kronos,
+ a distribution — not a prediction) and a live news-sentiment read. Kronos never
+ "reads the news"; we show where the two agree or diverge . No confidence score is implied.
+
+
+
+
+
+
+
+
+
+
+
+
Layer 1 · Price model (Kronos)
+
—
+
price-history distribution
+
+
+
—
+
Run a forecast to compare the two layers.
+
+
+
Layer 2 · News sentiment
+
—
+
live headline tone
+
+
+
+
+
+
+
+
Forecast fan Layer 1
+ band width = uncertainty
+
+
+
+
+
+
+
+
+
News sentiment Layer 2
+ headline tone
+
+
+
+
+
+
+
+
Daily distribution
+ not "predicted price"
+
+
+
+
+
+
+
Skew / downside tail
+
+ —
+
+
+
+
+
% paths below current
+
+
+
+
+
+
+
Calibration so far
+ headline: band hit-rate
+
+
+
+
+
+
+
Read deterministic, no score
+ —
+
+
+
+
+
+
Scored headlines
+ free · yahoo finance
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Running forecast…
+
First run loads the model (CPU). Subsequent runs are cached.
+
+
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c=t.getColorFromPalette(t.name,null,e.getSeriesCount());o[s]||(o[s]=c,n.setVisual("colorFromPalette",!0)),o.fill="auto"===o.fill||X(o.fill)?c:o.fill,o.stroke="auto"===o.stroke||X(o.stroke)?c:o.stroke}if(n.setVisual("style",o),n.setVisual("drawType",s),!e.isSeriesFiltered(t)&&u)return n.setVisual("colorFromPalette",!1),{dataEach:function(e,n){var i=t.getDataParams(n),r=A({},o);r[s]=u(i),e.setItemVisual(n,"style",r)}}}},$g=new Cc,Jg={createOnAllSeries:!0,performRawSeries:!0,reset:function(t,e){if(!t.ignoreStyleOnData&&!e.isSeriesFiltered(t)){var n=t.getData(),i=t.visualStyleAccessPath||"itemStyle",r=jg(t,i),o=n.getVisual("drawType");return{dataEach:n.hasItemOption?function(t,e){var n=t.getRawDataItem(e);if(n&&n[i]){$g.option=n[i];var a=r($g);A(t.ensureUniqueItemVisual(e,"style"),a),$g.option.decal&&(t.setItemVisual(e,"decal",$g.option.decal),$g.option.decal.dirty=!0),o in a&&t.setItemVisual(e,"colorFromPalette",!1)}}:null}}}},Qg={performRawSeries:!0,overallReset:function(t){var e=yt();t.eachSeries((function(t){var n=t.getColorBy();if(!t.isColorBySeries()){var i=t.type+"-"+n,r=e.get(i);r||(r={},e.set(i,r)),Xg(t).scope=r}})),t.eachSeries((function(e){if(!e.isColorBySeries()&&!t.isSeriesFiltered(e)){var n=e.getRawData(),i={},r=e.getData(),o=Xg(e).scope,a=e.visualStyleAccessPath||"itemStyle",s=qg(e,a);r.each((function(t){var e=r.getRawIndex(t);i[e]=t})),n.each((function(t){var a=i[t];if(r.getItemVisual(a,"colorFromPalette")){var l=r.ensureUniqueItemVisual(a,"style"),u=n.getName(t)||t+"",h=n.count();l[s]=e.getColorFromPalette(u,o,h)}}))}}))}},ty=Math.PI;var ey=function(){function t(t,e,n,i){this._stageTaskMap=yt(),this.ecInstance=t,this.api=e,n=this._dataProcessorHandlers=n.slice(),i=this._visualHandlers=i.slice(),this._allHandlers=n.concat(i)}return t.prototype.restoreData=function(t,e){t.restoreData(e),this._stageTaskMap.each((function(t){var e=t.overallTask;e&&e.dirty()}))},t.prototype.getPerformArgs=function(t,e){if(t.__pipeline){var n=this._pipelineMap.get(t.__pipeline.id),i=n.context,r=!e&&n.progressiveEnabled&&(!i||i.progressiveRender)&&t.__idxInPipeline>n.blockIndex?n.step:null,o=i&&i.modDataCount;return{step:r,modBy:null!=o?Math.ceil(o/r):null,modDataCount:o}}},t.prototype.getPipeline=function(t){return this._pipelineMap.get(t)},t.prototype.updateStreamModes=function(t,e){var n=this._pipelineMap.get(t.uid),i=t.getData().count(),r=n.progressiveEnabled&&e.incrementalPrepareRender&&i>=n.threshold,o=t.get("large")&&i>=t.get("largeThreshold"),a="mod"===t.get("progressiveChunkMode")?i:null;t.pipelineContext=n.context={progressiveRender:r,modDataCount:a,large:o}},t.prototype.restorePipelines=function(t){var e=this,n=e._pipelineMap=yt();t.eachSeries((function(t){var i=t.getProgressive(),r=t.uid;n.set(r,{id:r,head:null,tail:null,threshold:t.getProgressiveThreshold(),progressiveEnabled:i&&!(t.preventIncremental&&t.preventIncremental()),blockIndex:-1,step:Math.round(i||700),count:0}),e._pipe(t,t.dataTask)}))},t.prototype.prepareStageTasks=function(){var t=this._stageTaskMap,e=this.api.getModel(),n=this.api;E(this._allHandlers,(function(i){var r=t.get(i.uid)||t.set(i.uid,{}),o="";lt(!(i.reset&&i.overallReset),o),i.reset&&this._createSeriesStageTask(i,r,e,n),i.overallReset&&this._createOverallStageTask(i,r,e,n)}),this)},t.prototype.prepareView=function(t,e,n,i){var r=t.renderTask,o=r.context;o.model=e,o.ecModel=n,o.api=i,r.__block=!t.incrementalPrepareRender,this._pipe(e,r)},t.prototype.performDataProcessorTasks=function(t,e){this._performStageTasks(this._dataProcessorHandlers,t,e,{block:!0})},t.prototype.performVisualTasks=function(t,e,n){this._performStageTasks(this._visualHandlers,t,e,n)},t.prototype._performStageTasks=function(t,e,n,i){i=i||{};var 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x=Zy(t.getItemVisual(e,"symbolOffset"),n);x&&(f.x=x[0],f.y=x[1]),d&&f.attr("cursor",d);var _=t.getItemVisual(e,"style"),b=_.fill;if(f instanceof Ns){var w=f.style;f.useStyle(A({image:w.image,x:w.x,y:w.y,width:w.width,height:w.height},_))}else f.__isEmptyBrush?f.useStyle(A({},_)):f.useStyle(_),f.style.decal=null,f.setColor(b,r&&r.symbolInnerColor),f.style.strokeNoScale=!0;var S=t.getItemVisual(e,"liftZ"),M=this._z2;null!=S?null==M&&(this._z2=f.z2,f.z2+=S):null!=M&&(f.z2=M,this._z2=null);var I=r&&r.useNameLabel;ic(f,c,{labelFetcher:g,labelDataIndex:e,defaultText:function(e){return I?t.getName(e):pS(t,e)},inheritColor:b,defaultOpacity:_.opacity}),this._sizeX=n[0]/2,this._sizeY=n[1]/2;var T=f.ensureState("emphasis");T.style=o,f.ensureState("select").style=s,f.ensureState("blur").style=a;var C=null==p||!0===p?Math.max(1.1,3/this._sizeY):isFinite(p)&&p>0?+p:1;T.scaleX=this._sizeX*C,T.scaleY=this._sizeY*C,this.setSymbolScale(1),Zl(this,l,u,h)},e.prototype.setSymbolScale=function(t){this.scaleX=this.scaleY=t},e.prototype.fadeOut=function(t,e,n){var i=this.childAt(0),r=rl(this).dataIndex,o=n&&n.animation;if(this.silent=i.silent=!0,n&&n.fadeLabel){var a=i.getTextContent();a&&_h(a,{style:{opacity:0}},e,{dataIndex:r,removeOpt:o,cb:function(){i.removeTextContent()}})}else i.removeTextContent();_h(i,{style:{opacity:0},scaleX:0,scaleY:0},e,{dataIndex:r,cb:t,removeOpt:o})},e.getSymbolSize=function(t,e){return Uy(t.getItemVisual(e,"symbolSize"))},e}(Br);function gS(t,e){this.parent.drift(t,e)}function yS(t,e,n,i){return e&&!isNaN(e[0])&&!isNaN(e[1])&&!(i.isIgnore&&i.isIgnore(n))&&!(i.clipShape&&!i.clipShape.contain(e[0],e[1]))&&"none"!==t.getItemVisual(n,"symbol")}function vS(t){return null==t||q(t)||(t={isIgnore:t}),t||{}}function mS(t){var e=t.hostModel,n=e.getModel("emphasis");return{emphasisItemStyle:n.getModel("itemStyle").getItemStyle(),blurItemStyle:e.getModel(["blur","itemStyle"]).getItemStyle(),selectItemStyle:e.getModel(["select","itemStyle"]).getItemStyle(),focus:n.get("focus"),blurScope:n.get("blurScope"),emphasisDisabled:n.get("disabled"),hoverScale:n.get("scale"),labelStatesModels:rc(e),cursorStyle:e.get("cursor")}}var xS=function(){function t(t){this.group=new Br,this._SymbolCtor=t||fS}return t.prototype.updateData=function(t,e){this._progressiveEls=null,e=vS(e);var n=this.group,i=t.hostModel,r=this._data,o=this._SymbolCtor,a=e.disableAnimation,s=mS(t),l={disableAnimation:a},u=e.getSymbolPoint||function(e){return t.getItemLayout(e)};r||n.removeAll(),t.diff(r).add((function(i){var r=u(i);if(yS(t,r,i,e)){var a=new o(t,i,s,l);a.setPosition(r),t.setItemGraphicEl(i,a),n.add(a)}})).update((function(h,c){var p=r.getItemGraphicEl(c),d=u(h);if(yS(t,d,h,e)){var f=t.getItemVisual(h,"symbol")||"circle",g=p&&p.getSymbolType&&p.getSymbolType();if(!p||g&&g!==f)n.remove(p),(p=new o(t,h,s,l)).setPosition(d);else{p.updateData(t,h,s,l);var y={x:d[0],y:d[1]};a?p.attr(y):vh(p,y,i)}n.add(p),t.setItemGraphicEl(h,p)}else n.remove(p)})).remove((function(t){var e=r.getItemGraphicEl(t);e&&e.fadeOut((function(){n.remove(e)}),i)})).execute(),this._getSymbolPoint=u,this._data=t},t.prototype.updateLayout=function(){var t=this,e=this._data;e&&e.eachItemGraphicEl((function(e,n){var i=t._getSymbolPoint(n);e.setPosition(i),e.markRedraw()}))},t.prototype.incrementalPrepareUpdate=function(t){this._seriesScope=mS(t),this._data=null,this.group.removeAll()},t.prototype.incrementalUpdate=function(t,e,n){function i(t){t.isGroup||(t.incremental=!0,t.ensureState("emphasis").hoverLayer=!0)}this._progressiveEls=[],n=vS(n);for(var r=t.start;r0?n=i[0]:i[1]<0&&(n=i[1]);return n}(r,n),a=i.dim,s=r.dim,l=e.mapDimension(s),u=e.mapDimension(a),h="x"===s||"radius"===s?1:0,c=z(t.dimensions,(function(t){return e.mapDimension(t)})),p=!1,d=e.getCalculationInfo("stackResultDimension");return mx(e,c[0])&&(p=!0,c[0]=d),mx(e,c[1])&&(p=!0,c[1]=d),{dataDimsForPoint:c,valueStart:o,valueAxisDim:s,baseAxisDim:a,stacked:!!p,valueDim:l,baseDim:u,baseDataOffset:h,stackedOverDimension:e.getCalculationInfo("stackedOverDimension")}}function bS(t,e,n,i){var r=NaN;t.stacked&&(r=n.get(n.getCalculationInfo("stackedOverDimension"),i)),isNaN(r)&&(r=t.valueStart);var o=t.baseDataOffset,a=[];return a[o]=n.get(t.baseDim,i),a[1-o]=r,e.dataToPoint(a)}var wS=Math.min,SS=Math.max;function MS(t,e){return isNaN(t)||isNaN(e)}function IS(t,e,n,i,r,o,a,s,l){for(var u,h,c,p,d,f,g=n,y=0;y=r||g<0)break;if(MS(v,m)){if(l){g+=o;continue}break}if(g===n)t[o>0?"moveTo":"lineTo"](v,m),c=v,p=m;else{var x=v-u,_=m-h;if(x*x+_*_<.5){g+=o;continue}if(a>0){for(var b=g+o,w=e[2*b],S=e[2*b+1];w===v&&S===m&&y=i||MS(w,S))d=v,f=m;else{T=w-u,C=S-h;var k=v-u,L=w-v,P=m-h,O=S-m,R=void 0,N=void 0;if("x"===s){var E=T>0?1:-1;d=v-E*(R=Math.abs(k))*a,f=m,D=v+E*(N=Math.abs(L))*a,A=m}else if("y"===s){var z=C>0?1:-1;d=v,f=m-z*(R=Math.abs(P))*a,D=v,A=m+z*(N=Math.abs(O))*a}else R=Math.sqrt(k*k+P*P),d=v-T*a*(1-(I=(N=Math.sqrt(L*L+O*O))/(N+R))),f=m-C*a*(1-I),A=m+C*a*I,D=wS(D=v+T*a*I,SS(w,v)),A=wS(A,SS(S,m)),D=SS(D,wS(w,v)),f=m-(C=(A=SS(A,wS(S,m)))-m)*R/N,d=wS(d=v-(T=D-v)*R/N,SS(u,v)),f=wS(f,SS(h,m)),D=v+(T=v-(d=SS(d,wS(u,v))))*N/R,A=m+(C=m-(f=SS(f,wS(h,m))))*N/R}t.bezierCurveTo(c,p,d,f,v,m),c=D,p=A}else t.lineTo(v,m)}u=v,h=m,g+=o}return y}var TS=function(){this.smooth=0,this.smoothConstraint=!0},CS=function(t){function e(e){var n=t.call(this,e)||this;return n.type="ec-polyline",n}return n(e,t),e.prototype.getDefaultStyle=function(){return{stroke:"#000",fill:null}},e.prototype.getDefaultShape=function(){return new TS},e.prototype.buildPath=function(t,e){var n=e.points,i=0,r=n.length/2;if(e.connectNulls){for(;r>0&&MS(n[2*r-2],n[2*r-1]);r--);for(;i=0){var y=a?(h-i)*g+i:(u-n)*g+n;return a?[t,y]:[y,t]}n=u,i=h;break;case o.C:u=r[l++],h=r[l++],c=r[l++],p=r[l++],d=r[l++],f=r[l++];var v=a?_n(n,u,c,d,t,s):_n(i,h,p,f,t,s);if(v>0)for(var m=0;m=0){y=a?mn(i,h,p,f,x):mn(n,u,c,d,x);return a?[t,y]:[y,t]}}n=d,i=f}}},e}(ks),DS=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e}(TS),AS=function(t){function e(e){var n=t.call(this,e)||this;return n.type="ec-polygon",n}return n(e,t),e.prototype.getDefaultShape=function(){return new DS},e.prototype.buildPath=function(t,e){var n=e.points,i=e.stackedOnPoints,r=0,o=n.length/2,a=e.smoothMonotone;if(e.connectNulls){for(;o>0&&MS(n[2*o-2],n[2*o-1]);o--);for(;r=0;a--){var s=t.getDimensionInfo(i[a].dimension);if("x"===(r=s&&s.coordDim)||"y"===r){o=i[a];break}}if(o){var l=e.getAxis(r),u=z(o.stops,(function(t){return{coord:l.toGlobalCoord(l.dataToCoord(t.value)),color:t.color}})),h=u.length,c=o.outerColors.slice();h&&u[0].coord>u[h-1].coord&&(u.reverse(),c.reverse());var p=function(t,e){var n,i,r=[],o=t.length;function a(t,e,n){var i=t.coord;return{coord:n,color:ti((n-i)/(e.coord-i),[t.color,e.color])}}for(var s=0;se){i?r.push(a(i,l,e)):n&&r.push(a(n,l,0),a(n,l,e));break}n&&(r.push(a(n,l,0)),n=null),r.push(l),i=l}}return r}(u,"x"===r?n.getWidth():n.getHeight()),d=p.length;if(!d&&h)return u[0].coord<0?c[1]?c[1]:u[h-1].color:c[0]?c[0]:u[0].color;var f=p[0].coord-10,g=p[d-1].coord+10,y=g-f;if(y<.001)return"transparent";E(p,(function(t){t.offset=(t.coord-f)/y})),p.push({offset:d?p[d-1].offset:.5,color:c[1]||"transparent"}),p.unshift({offset:d?p[0].offset:.5,color:c[0]||"transparent"});var v=new oh(0,0,0,0,p,!0);return v[r]=f,v[r+"2"]=g,v}}}function FS(t,e,n){var i=t.get("showAllSymbol"),r="auto"===i;if(!i||r){var o=n.getAxesByScale("ordinal")[0];if(o&&(!r||!function(t,e){var n=t.getExtent(),i=Math.abs(n[1]-n[0])/t.scale.count();isNaN(i)&&(i=0);for(var r=e.count(),o=Math.max(1,Math.round(r/5)),a=0;ai)return!1;return!0}(o,e))){var a=e.mapDimension(o.dim),s={};return E(o.getViewLabels(),(function(t){var e=o.scale.getRawOrdinalNumber(t.tickValue);s[e]=1})),function(t){return!s.hasOwnProperty(e.get(a,t))}}}}function GS(t,e){return[t[2*e],t[2*e+1]]}function WS(t){if(t.get(["endLabel","show"]))return!0;for(var e=0;e0&&"bolder"===t.get(["emphasis","lineStyle","width"]))&&(d.getState("emphasis").style.lineWidth=+d.style.lineWidth+1);rl(d).seriesIndex=t.seriesIndex,Zl(d,L,P,O);var R=zS(t.get("smooth")),N=t.get("smoothMonotone");if(d.setShape({smooth:R,smoothMonotone:N,connectNulls:w}),f){var E=a.getCalculationInfo("stackedOnSeries"),z=0;f.useStyle(k(l.getAreaStyle(),{fill:C,opacity:.7,lineJoin:"bevel",decal:a.getVisual("style").decal})),E&&(z=zS(E.get("smooth"))),f.setShape({smooth:R,stackedOnSmooth:z,smoothMonotone:N,connectNulls:w}),$l(f,t,"areaStyle"),rl(f).seriesIndex=t.seriesIndex,Zl(f,L,P,O)}var V=function(t){i._changePolyState(t)};a.eachItemGraphicEl((function(t){t&&(t.onHoverStateChange=V)})),this._polyline.onHoverStateChange=V,this._data=a,this._coordSys=r,this._stackedOnPoints=_,this._points=u,this._step=T,this._valueOrigin=m,t.get("triggerLineEvent")&&(this.packEventData(t,d),f&&this.packEventData(t,f))},e.prototype.packEventData=function(t,e){rl(e).eventData={componentType:"series",componentSubType:"line",componentIndex:t.componentIndex,seriesIndex:t.seriesIndex,seriesName:t.name,seriesType:"line"}},e.prototype.highlight=function(t,e,n,i){var r=t.getData(),o=zo(r,i);if(this._changePolyState("emphasis"),!(o instanceof Array)&&null!=o&&o>=0){var a=r.getLayout("points"),s=r.getItemGraphicEl(o);if(!s){var l=a[2*o],u=a[2*o+1];if(isNaN(l)||isNaN(u))return;if(this._clipShapeForSymbol&&!this._clipShapeForSymbol.contain(l,u))return;var h=t.get("zlevel")||0,c=t.get("z")||0;(s=new fS(r,o)).x=l,s.y=u,s.setZ(h,c);var p=s.getSymbolPath().getTextContent();p&&(p.zlevel=h,p.z=c,p.z2=this._polyline.z2+1),s.__temp=!0,r.setItemGraphicEl(o,s),s.stopSymbolAnimation(!0),this.group.add(s)}s.highlight()}else Og.prototype.highlight.call(this,t,e,n,i)},e.prototype.downplay=function(t,e,n,i){var r=t.getData(),o=zo(r,i);if(this._changePolyState("normal"),null!=o&&o>=0){var a=r.getItemGraphicEl(o);a&&(a.__temp?(r.setItemGraphicEl(o,null),this.group.remove(a)):a.downplay())}else Og.prototype.downplay.call(this,t,e,n,i)},e.prototype._changePolyState=function(t){var e=this._polygon;Dl(this._polyline,t),e&&Dl(e,t)},e.prototype._newPolyline=function(t){var e=this._polyline;return e&&this._lineGroup.remove(e),e=new CS({shape:{points:t},segmentIgnoreThreshold:2,z2:10}),this._lineGroup.add(e),this._polyline=e,e},e.prototype._newPolygon=function(t,e){var n=this._polygon;return n&&this._lineGroup.remove(n),n=new AS({shape:{points:t,stackedOnPoints:e},segmentIgnoreThreshold:2}),this._lineGroup.add(n),this._polygon=n,n},e.prototype._initSymbolLabelAnimation=function(t,e,n){var i,r,o=e.getBaseAxis(),a=o.inverse;"cartesian2d"===e.type?(i=o.isHorizontal(),r=!1):"polar"===e.type&&(i="angle"===o.dim,r=!0);var s=t.hostModel,l=s.get("animationDuration");X(l)&&(l=l(null));var u=s.get("animationDelay")||0,h=X(u)?u(null):u;t.eachItemGraphicEl((function(t,o){var s=t;if(s){var c=[t.x,t.y],p=void 0,d=void 0,f=void 0;if(n)if(r){var g=n,y=e.pointToCoord(c);i?(p=g.startAngle,d=g.endAngle,f=-y[1]/180*Math.PI):(p=g.r0,d=g.r,f=y[0])}else{var v=n;i?(p=v.x,d=v.x+v.width,f=t.x):(p=v.y+v.height,d=v.y,f=t.y)}var m=d===p?0:(f-p)/(d-p);a&&(m=1-m);var x=X(u)?u(o):l*m+h,_=s.getSymbolPath(),b=_.getTextContent();s.attr({scaleX:0,scaleY:0}),s.animateTo({scaleX:1,scaleY:1},{duration:200,setToFinal:!0,delay:x}),b&&b.animateFrom({style:{opacity:0}},{duration:300,delay:x}),_.disableLabelAnimation=!0}}))},e.prototype._initOrUpdateEndLabel=function(t,e,n){var i=t.getModel("endLabel");if(WS(t)){var r=t.getData(),o=this._polyline,a=r.getLayout("points");if(!a)return o.removeTextContent(),void(this._endLabel=null);var s=this._endLabel;s||((s=this._endLabel=new Xs({z2:200})).ignoreClip=!0,o.setTextContent(this._endLabel),o.disableLabelAnimation=!0);var l=function(t){for(var e,n,i=t.length/2;i>0&&(e=t[2*i-2],n=t[2*i-1],isNaN(e)||isNaN(n));i--);return i-1}(a);l>=0&&(ic(o,rc(t,"endLabel"),{inheritColor:n,labelFetcher:t,labelDataIndex:l,defaultText:function(t,e,n){return null!=n?dS(r,n):pS(r,t)},enableTextSetter:!0},function(t,e){var n=e.getBaseAxis(),i=n.isHorizontal(),r=n.inverse,o=i?r?"right":"left":"center",a=i?"middle":r?"top":"bottom";return{normal:{align:t.get("align")||o,verticalAlign:t.get("verticalAlign")||a}}}(i,e)),o.textConfig.position=null)}else this._endLabel&&(this._polyline.removeTextContent(),this._endLabel=null)},e.prototype._endLabelOnDuring=function(t,e,n,i,r,o,a){var s=this._endLabel,l=this._polyline;if(s){t<1&&null==i.originalX&&(i.originalX=s.x,i.originalY=s.y);var u=n.getLayout("points"),h=n.hostModel,c=h.get("connectNulls"),p=o.get("precision"),d=o.get("distance")||0,f=a.getBaseAxis(),g=f.isHorizontal(),y=f.inverse,v=e.shape,m=y?g?v.x:v.y+v.height:g?v.x+v.width:v.y,x=(g?d:0)*(y?-1:1),_=(g?0:-d)*(y?-1:1),b=g?"x":"y",w=function(t,e,n){for(var i,r,o=t.length/2,a="x"===n?0:1,s=0,l=-1,u=0;u=e||i>=e&&r<=e){l=u;break}s=u,i=r}else i=r;return{range:[s,l],t:(e-i)/(r-i)}}(u,m,b),S=w.range,M=S[1]-S[0],I=void 0;if(M>=1){if(M>1&&!c){var T=GS(u,S[0]);s.attr({x:T[0]+x,y:T[1]+_}),r&&(I=h.getRawValue(S[0]))}else{(T=l.getPointOn(m,b))&&s.attr({x:T[0]+x,y:T[1]+_});var C=h.getRawValue(S[0]),D=h.getRawValue(S[1]);r&&(I=Zo(n,p,C,D,w.t))}i.lastFrameIndex=S[0]}else{var A=1===t||i.lastFrameIndex>0?S[0]:0;T=GS(u,A);r&&(I=h.getRawValue(A)),s.attr({x:T[0]+x,y:T[1]+_})}if(r){var k=pc(s);"function"==typeof k.setLabelText&&k.setLabelText(I)}}},e.prototype._doUpdateAnimation=function(t,e,n,i,r,o,a){var s=this._polyline,l=this._polygon,u=t.hostModel,h=function(t,e,n,i,r,o,a,s){for(var l=function(t,e){var n=[];return e.diff(t).add((function(t){n.push({cmd:"+",idx:t})})).update((function(t,e){n.push({cmd:"=",idx:e,idx1:t})})).remove((function(t){n.push({cmd:"-",idx:t})})).execute(),n}(t,e),u=[],h=[],c=[],p=[],d=[],f=[],g=[],y=_S(r,e,a),v=t.getLayout("points")||[],m=e.getLayout("points")||[],x=0;x3e3||l&&ES(p,f)>3e3)return s.stopAnimation(),s.setShape({points:d}),void(l&&(l.stopAnimation(),l.setShape({points:d,stackedOnPoints:f})));s.shape.__points=h.current,s.shape.points=c;var g={shape:{points:d}};h.current!==c&&(g.shape.__points=h.next),s.stopAnimation(),vh(s,g,u),l&&(l.setShape({points:c,stackedOnPoints:p}),l.stopAnimation(),vh(l,{shape:{stackedOnPoints:f}},u),s.shape.points!==l.shape.points&&(l.shape.points=s.shape.points));for(var y=[],v=h.status,m=0;me&&(e=t[n]);return isFinite(e)?e:NaN},min:function(t){for(var e=1/0,n=0;ne&&(e=o,n=r)}return isFinite(n)?n:NaN},nearest:function(t){return t[0]}},ZS=function(t){return Math.round(t.length/2)};function jS(t){return{seriesType:t,reset:function(t,e,n){var i=t.getData(),r=t.get("sampling"),o=t.coordinateSystem,a=i.count();if(a>10&&"cartesian2d"===o.type&&r){var s=o.getBaseAxis(),l=o.getOtherAxis(s),u=s.getExtent(),h=n.getDevicePixelRatio(),c=Math.abs(u[1]-u[0])*(h||1),p=Math.round(a/c);if(isFinite(p)&&p>1){"lttb"===r&&t.setData(i.lttbDownSample(i.mapDimension(l.dim),1/p));var d=void 0;U(r)?d=US[r]:X(r)&&(d=r),d&&t.setData(i.downSample(i.mapDimension(l.dim),1/p,d,ZS))}}}}}var qS=function(t){function e(){var n=null!==t&&t.apply(this,arguments)||this;return n.type=e.type,n}return n(e,t),e.prototype.getInitialData=function(t,e){return _x(null,this,{useEncodeDefaulter:!0})},e.prototype.getMarkerPosition=function(t,e,n){var i=this.coordinateSystem;if(i&&i.clampData){var r=i.clampData(t),o=i.dataToPoint(r);if(n)E(i.getAxes(),(function(t,n){if("category"===t.type&&null!=e){var i=t.getTicksCoords(),a=t.getTickModel().get("alignWithLabel"),s=r[n],l="x1"===e[n]||"y1"===e[n];if(l&&!a&&(s+=1),i.length<2)return;if(2===i.length)return void(o[n]=t.toGlobalCoord(t.getExtent()[l?1:0]));for(var u=void 0,h=void 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n=this.transformCoordToLocal(t,e),i=this.getBoundingRect();return t=n[0],e=n[1],i.contain(t,e)?(this.hoverDataIdx=this.findDataIndex(t,e))>=0:(this.hoverDataIdx=-1,!1)},e.prototype.getBoundingRect=function(){var t=this._rect;if(!t){for(var e=this.shape,n=e.points,i=e.size,r=i[0],o=i[1],a=1/0,s=1/0,l=-1/0,u=-1/0,h=0;h=0&&(l.dataIndex=n+(t.startIndex||0))}))},t.prototype.remove=function(){this._clear()},t.prototype._clear=function(){this._newAdded=[],this.group.removeAll()},t}(),WM=function(t){function e(){var n=null!==t&&t.apply(this,arguments)||this;return n.type=e.type,n}return n(e,t),e.prototype.render=function(t,e,n){var i=t.getData();this._updateSymbolDraw(i,t).updateData(i,{clipShape:this._getClipShape(t)}),this._finished=!0},e.prototype.incrementalPrepareRender=function(t,e,n){var i=t.getData();this._updateSymbolDraw(i,t).incrementalPrepareUpdate(i),this._finished=!1},e.prototype.incrementalRender=function(t,e,n){this._symbolDraw.incrementalUpdate(t,e.getData(),{clipShape:this._getClipShape(e)}),this._finished=t.end===e.getData().count()},e.prototype.updateTransform=function(t,e,n){var i=t.getData();if(this.group.dirty(),!this._finished||i.count()>1e4)return{update:!0};var r=XS("").reset(t,e,n);r.progress&&r.progress({start:0,end:i.count(),count:i.count()},i),this._symbolDraw.updateLayout(i)},e.prototype.eachRendered=function(t){this._symbolDraw&&this._symbolDraw.eachRendered(t)},e.prototype._getClipShape=function(t){if(t.get("clip",!0)){var e=t.coordinateSystem;return e&&e.getArea&&e.getArea(.1)}},e.prototype._updateSymbolDraw=function(t,e){var n=this._symbolDraw,i=e.pipelineContext.large;return n&&i===this._isLargeDraw||(n&&n.remove(),n=this._symbolDraw=i?new GM:new 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KM(t,e,i,r){E(qM,(function(o,a){var s=C(C({},jM[a],!0),r,!0),l=function(t){function i(){var n=null!==t&&t.apply(this,arguments)||this;return n.type=e+"Axis."+a,n}return n(i,t),i.prototype.mergeDefaultAndTheme=function(t,e){var n=Pp(this),i=n?Rp(t):{};C(t,e.getTheme().get(a+"Axis")),C(t,this.getDefaultOption()),t.type=$M(t),n&&Op(t,i,n)},i.prototype.optionUpdated=function(){"category"===this.option.type&&(this.__ordinalMeta=Mx.createByAxisModel(this))},i.prototype.getCategories=function(t){var e=this.option;if("category"===e.type)return t?e.data:this.__ordinalMeta.categories},i.prototype.getOrdinalMeta=function(){return this.__ordinalMeta},i.type=e+"Axis."+a,i.defaultOption=s,i}(i);t.registerComponentModel(l)})),t.registerSubTypeDefaulter(e+"Axis",$M)}function $M(t){return t.type||(t.data?"category":"value")}var JM=function(){function t(t){this.type="cartesian",this._dimList=[],this._axes={},this.name=t||""}return t.prototype.getAxis=function(t){return 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p=aI(t.base);u=[aI(u[0])/p,aI(u[1])/p]}t.setExtent(u[0],u[1]),t.calcNiceExtent({splitNumber:a,fixMin:h,fixMax:c});var d=i.getExtent.call(t);h&&(u[0]=d[0]),c&&(u[1]=d[1]);var f=i.getInterval.call(t),g=u[0],y=u[1];if(h&&c)f=(y-g)/a;else if(h)for(y=u[0]+f*a;yu[0]&&isFinite(g)&&isFinite(u[0]);)f=Dx(f),g=u[1]-f*a;else{t.getTicks().length-1>a&&(f=Dx(f));var v=f*a;(g=Jr((y=Math.ceil(u[1]/f)*f)-v))<0&&u[0]>=0?(g=0,y=Jr(v)):y>0&&u[1]<=0&&(y=0,g=-Jr(v))}var m=(r[0].value-o[0].value)/s,x=(r[a].value-o[a].value)/s;i.setExtent.call(t,g+f*m,y+f*x),i.setInterval.call(t,f),(m||x)&&i.setNiceExtent.call(t,g+f,y-f)}var lI=function(){function t(t,e,n){this.type="grid",this._coordsMap={},this._coordsList=[],this._axesMap={},this._axesList=[],this.axisPointerEnabled=!0,this.dimensions=QM,this._initCartesian(t,e,n),this.model=t}return t.prototype.getRect=function(){return this._rect},t.prototype.update=function(t,e){var n=this._axesMap;function i(t){var e,n=G(t),i=n.length;if(i){for(var 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this.hostTree.data.getRawIndex(this.dataIndex)},t.prototype.getId=function(){return this.hostTree.data.getId(this.dataIndex)},t.prototype.getChildIndex=function(){if(this.parentNode){for(var t=this.parentNode.children,e=0;e=0){var i=n.getData().tree.root,r=t.targetNode;if(U(r)&&(r=i.getNodeById(r)),r&&i.contains(r))return{node:r};var o=t.targetNodeId;if(null!=o&&(r=i.getNodeById(o)))return{node:r}}}function rD(t){for(var e=[];t;)(t=t.parentNode)&&e.push(t);return e.reverse()}function oD(t,e){return P(rD(t),e)>=0}function aD(t,e){for(var n=[];t;){var i=t.dataIndex;n.push({name:t.name,dataIndex:i,value:e.getRawValue(i)}),t=t.parentNode}return n.reverse(),n}var sD=function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e.hasSymbolVisual=!0,e.ignoreStyleOnData=!0,e}return n(e,t),e.prototype.getInitialData=function(t){var e={name:t.name,children:t.data},n=t.leaves||{},i=new 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p=u===h?1:a(u,h)/2,d=p-u.getLayout().x,f=0,g=0,y=0,v=0;if("radial"===i)f=r/(h.getLayout().x+p+d),g=o/(c.depth-1||1),lD(l,(function(t){y=(t.getLayout().x+d)*f,v=(t.depth-1)*g;var e=LC(y,v);t.setLayout({x:e.x,y:e.y,rawX:y,rawY:v},!0)}));else{var m=t.getOrient();"RL"===m||"LR"===m?(g=o/(h.getLayout().x+p+d),f=r/(c.depth-1||1),lD(l,(function(t){v=(t.getLayout().x+d)*g,y="LR"===m?(t.depth-1)*f:r-(t.depth-1)*f,t.setLayout({x:y,y:v},!0)}))):"TB"!==m&&"BT"!==m||(f=r/(h.getLayout().x+p+d),g=o/(c.depth-1||1),lD(l,(function(t){y=(t.getLayout().x+d)*f,v="TB"===m?(t.depth-1)*g:o-(t.depth-1)*g,t.setLayout({x:y,y:v},!0)})))}}}(t,e)}))}function hD(t){t.eachSeriesByType("tree",(function(t){var e=t.getData();e.tree.eachNode((function(t){var n=t.getModel().getModel("itemStyle").getItemStyle();A(e.ensureUniqueItemVisual(t.dataIndex,"style"),n)}))}))}var cD=["treemapZoomToNode","treemapRender","treemapMove"];function pD(t){var e=t.getData().tree,n={};e.eachNode((function(e){for(var 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e=this.getRawData().tree.root;t&&(t===e||e.contains(t))||(this._viewRoot=e)},e.prototype.enableAriaDecal=function(){pD(this)},e.type="series.treemap",e.layoutMode="box",e.defaultOption={progressive:0,left:"center",top:"middle",width:"80%",height:"80%",sort:!0,clipWindow:"origin",squareRatio:.5*(1+Math.sqrt(5)),leafDepth:null,drillDownIcon:"▶",zoomToNodeRatio:.1024,scaleLimit:null,roam:!0,nodeClick:"zoomToNode",animation:!0,animationDurationUpdate:900,animationEasing:"quinticInOut",breadcrumb:{show:!0,height:22,left:"center",top:"bottom",emptyItemWidth:25,itemStyle:{color:"rgba(0,0,0,0.7)",textStyle:{color:"#fff"}},emphasis:{itemStyle:{color:"rgba(0,0,0,0.9)"}}},label:{show:!0,distance:0,padding:5,position:"inside",color:"#fff",overflow:"truncate"},upperLabel:{show:!1,position:[0,"50%"],height:20,overflow:"truncate",verticalAlign:"middle"},itemStyle:{color:null,colorAlpha:null,colorSaturation:null,borderWidth:0,gapWidth:0,borderColor:"#fff",borderColorSaturation:null},emphasis:{upperLabel:{show:!0,position:[0,"50%"],overflow:"truncate",verticalAlign:"middle"}},visualDimension:0,visualMin:null,visualMax:null,color:[],colorAlpha:null,colorSaturation:null,colorMappingBy:"index",visibleMin:10,childrenVisibleMin:null,levels:[]},e}(bg);function fD(t){var e=0;E(t.children,(function(t){fD(t);var n=t.value;Y(n)&&(n=n[0]),e+=n}));var n=t.value;Y(n)&&(n=n[0]),(null==n||isNaN(n))&&(n=e),n<0&&(n=0),Y(t.value)?t.value[0]=n:t.value=n}var gD=function(){function t(t){this.group=new Br,t.add(this.group)}return t.prototype.render=function(t,e,n,i){var r=t.getModel("breadcrumb"),o=this.group;if(o.removeAll(),r.get("show")&&n){var a=r.getModel("itemStyle"),s=r.getModel("emphasis"),l=a.getModel("textStyle"),u=s.getModel(["itemStyle","textStyle"]),h={pos:{left:r.get("left"),right:r.get("right"),top:r.get("top"),bottom:r.get("bottom")},box:{width:e.getWidth(),height:e.getHeight()},emptyItemWidth:r.get("emptyItemWidth"),totalWidth:0,renderList:[]};this._prepare(n,h,l),this._renderContent(t,h,a,s,l,u,i),Lp(o,h.pos,h.box)}},t.prototype._prepare=function(t,e,n){for(var i=t;i;i=i.parentNode){var r=Ro(i.getModel().get("name"),""),o=n.getTextRect(r),a=Math.max(o.width+16,e.emptyItemWidth);e.totalWidth+=a+8,e.renderList.push({node:i,text:r,width:a})}},t.prototype._renderContent=function(t,e,n,i,r,o,a){for(var s,l,u,h,c,p,d,f,g,y=0,v=e.emptyItemWidth,m=t.get(["breadcrumb","height"]),x=(s=e.pos,l=e.box,h=l.width,c=l.height,p=$r(s.left,h),d=$r(s.top,c),f=$r(s.right,h),g=$r(s.bottom,c),(isNaN(p)||isNaN(parseFloat(s.left)))&&(p=0),(isNaN(f)||isNaN(parseFloat(s.right)))&&(f=h),(isNaN(d)||isNaN(parseFloat(s.top)))&&(d=0),(isNaN(g)||isNaN(parseFloat(s.bottom)))&&(g=c),u=vp(u||0),{width:Math.max(f-p-u[1]-u[3],0),height:Math.max(g-d-u[0]-u[2],0)}),_=e.totalWidth,b=e.renderList,w=i.getModel("itemStyle").getItemStyle(),S=b.length-1;S>=0;S--){var M=b[S],I=M.node,T=M.width,C=M.text;_>x.width&&(_-=T-v,T=v,C=null);var D=new Xu({shape:{points:yD(y,0,T,m,S===b.length-1,0===S)},style:k(n.getItemStyle(),{lineJoin:"bevel"}),textContent:new Xs({style:oc(r,{text:C})}),textConfig:{position:"inside"},z2:1e5,onclick:H(a,I)});D.disableLabelAnimation=!0,D.getTextContent().ensureState("emphasis").style=oc(o,{text:C}),D.ensureState("emphasis").style=w,Zl(D,i.get("focus"),i.get("blurScope"),i.get("disabled")),this.group.add(D),vD(D,t,I),y+=T+8}},t.prototype.remove=function(){this.group.removeAll()},t}();function yD(t,e,n,i,r,o){var a=[[r?t:t-5,e],[t+n,e],[t+n,e+i],[r?t:t-5,e+i]];return!o&&a.splice(2,0,[t+n+5,e+i/2]),!r&&a.push([t,e+i/2]),a}function vD(t,e,n){rl(t).eventData={componentType:"series",componentSubType:"treemap",componentIndex:e.componentIndex,seriesIndex:e.seriesIndex,seriesName:e.name,seriesType:"treemap",selfType:"breadcrumb",nodeData:{dataIndex:n&&n.dataIndex,name:n&&n.name},treePathInfo:n&&aD(n,e)}}var mD=function(){function t(){this._storage=[],this._elExistsMap={}}return t.prototype.add=function(t,e,n,i,r){return!this._elExistsMap[t.id]&&(this._elExistsMap[t.id]=!0,this._storage.push({el:t,target:e,duration:n,delay:i,easing:r}),!0)},t.prototype.finished=function(t){return this._finishedCallback=t,this},t.prototype.start=function(){for(var t=this,e=this._storage.length,n=function(){--e<=0&&(t._storage.length=0,t._elExistsMap={},t._finishedCallback&&t._finishedCallback())},i=0,r=this._storage.length;i3||Math.abs(t.dy)>3)){var e=this.seriesModel.getData().tree.root;if(!e)return;var n=e.getLayout();if(!n)return;this.api.dispatchAction({type:"treemapMove",from:this.uid,seriesId:this.seriesModel.id,rootRect:{x:n.x+t.dx,y:n.y+t.dy,width:n.width,height:n.height}})}},e.prototype._onZoom=function(t){var e=t.originX,n=t.originY,i=t.scale;if("animating"!==this._state){var r=this.seriesModel.getData().tree.root;if(!r)return;var o=r.getLayout();if(!o)return;var a,s=new ze(o.x,o.y,o.width,o.height),l=this._controllerHost;a=l.zoomLimit;var u=l.zoom=l.zoom||1;if(u*=i,a){var h=a.min||0,c=a.max||1/0;u=Math.max(Math.min(c,u),h)}var p=u/l.zoom;l.zoom=u;var d=this.seriesModel.layoutInfo,f=[1,0,0,1,0,0];we(f,f,[-(e-=d.x),-(n-=d.y)]),Me(f,f,[p,p]),we(f,f,[e,n]),s.applyTransform(f),this.api.dispatchAction({type:"treemapRender",from:this.uid,seriesId:this.seriesModel.id,rootRect:{x:s.x,y:s.y,width:s.width,height:s.height}})}},e.prototype._initEvents=function(t){var e=this;t.on("click",(function(t){if("ready"===e._state){var n=e.seriesModel.get("nodeClick",!0);if(n){var i=e.findTarget(t.offsetX,t.offsetY);if(i){var r=i.node;if(r.getLayout().isLeafRoot)e._rootToNode(i);else if("zoomToNode"===n)e._zoomToNode(i);else if("link"===n){var o=r.hostTree.data.getItemModel(r.dataIndex),a=o.get("link",!0),s=o.get("target",!0)||"blank";a&&Mp(a,s)}}}}}),this)},e.prototype._renderBreadcrumb=function(t,e,n){var i=this;n||(n=null!=t.get("leafDepth",!0)?{node:t.getViewRoot()}:this.findTarget(e.getWidth()/2,e.getHeight()/2))||(n={node:t.getData().tree.root}),(this._breadcrumb||(this._breadcrumb=new gD(this.group))).render(t,e,n.node,(function(e){"animating"!==i._state&&(oD(t.getViewRoot(),e)?i._rootToNode({node:e}):i._zoomToNode({node:e}))}))},e.prototype.remove=function(){this._clearController(),this._containerGroup&&this._containerGroup.removeAll(),this._storage={nodeGroup:[],background:[],content:[]},this._state="ready",this._breadcrumb&&this._breadcrumb.remove()},e.prototype.dispose=function(){this._clearController()},e.prototype._zoomToNode=function(t){this.api.dispatchAction({type:"treemapZoomToNode",from:this.uid,seriesId:this.seriesModel.id,targetNode:t.node})},e.prototype._rootToNode=function(t){this.api.dispatchAction({type:"treemapRootToNode",from:this.uid,seriesId:this.seriesModel.id,targetNode:t.node})},e.prototype.findTarget=function(t,e){var n;return this.seriesModel.getViewRoot().eachNode({attr:"viewChildren",order:"preorder"},(function(i){var r=this._storage.background[i.getRawIndex()];if(r){var o=r.transformCoordToLocal(t,e),a=r.shape;if(!(a.x<=o[0]&&o[0]<=a.x+a.width&&a.y<=o[1]&&o[1]<=a.y+a.height))return!1;n={node:i,offsetX:o[0],offsetY:o[1]}}}),this),n},e.type="treemap",e}(Og);var CD=E,DD=q,AD=-1,kD=function(){function t(e){var n=e.mappingMethod,i=e.type,r=this.option=T(e);this.type=i,this.mappingMethod=n,this._normalizeData=FD[n];var o=t.visualHandlers[i];this.applyVisual=o.applyVisual,this.getColorMapper=o.getColorMapper,this._normalizedToVisual=o._normalizedToVisual[n],"piecewise"===n?(LD(r),function(t){var e=t.pieceList;t.hasSpecialVisual=!1,E(e,(function(e,n){e.originIndex=n,null!=e.visual&&(t.hasSpecialVisual=!0)}))}(r)):"category"===n?r.categories?function(t){var e=t.categories,n=t.categoryMap={},i=t.visual;if(CD(e,(function(t,e){n[t]=e})),!Y(i)){var r=[];q(i)?CD(i,(function(t,e){var 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t.sort((function(t,e){return"color"===e&&"color"!==t&&0===t.indexOf("color")?1:-1})),t},t.dependsOn=function(t,e){return"color"===e?!(!t||0!==t.indexOf(e)):t===e},t.findPieceIndex=function(t,e,n){for(var i,r=1/0,o=0,a=e.length;ou[1]&&(u[1]=l);var h=e.get("colorMappingBy"),c={type:a.name,dataExtent:u,visual:a.range};"color"!==c.type||"index"!==h&&"id"!==h?c.mappingMethod="linear":(c.mappingMethod="category",c.loop=!0);var p=new kD(c);return WD(p).drColorMappingBy=h,p}(0,r,o,0,u,d);E(d,(function(t,e){if(t.depth>=n.length||t===n[t.depth]){var o=function(t,e,n,i,r,o){var a=A({},e);if(r){var s=r.type,l="color"===s&&WD(r).drColorMappingBy,u="index"===l?i:"id"===l?o.mapIdToIndex(n.getId()):n.getValue(t.get("visualDimension"));a[s]=r.mapValueToVisual(u)}return a}(r,u,t,e,f,i);YD(t,o,n,i)}}))}else s=XD(u),h.fill=s}}function XD(t){var e=UD(t,"color");if(e){var n=UD(t,"colorAlpha"),i=UD(t,"colorSaturation");return i&&(e=ni(e,null,null,i)),n&&(e=ii(e,n)),e}}function UD(t,e){var n=t[e];if(null!=n&&"none"!==n)return n}function ZD(t,e){var n=t.get(e);return Y(n)&&n.length?{name:e,range:n}:null}var jD=Math.max,qD=Math.min,KD=it,$D=E,JD=["itemStyle","borderWidth"],QD=["itemStyle","gapWidth"],tA=["upperLabel","show"],eA=["upperLabel","height"],nA={seriesType:"treemap",reset:function(t,e,n,i){var r=n.getWidth(),o=n.getHeight(),a=t.option,s=kp(t.getBoxLayoutParams(),{width:n.getWidth(),height:n.getHeight()}),l=a.size||[],u=$r(KD(s.width,l[0]),r),h=$r(KD(s.height,l[1]),o),c=i&&i.type,p=iD(i,["treemapZoomToNode","treemapRootToNode"],t),d="treemapRender"===c||"treemapMove"===c?i.rootRect:null,f=t.getViewRoot(),g=rD(f);if("treemapMove"!==c){var y="treemapZoomToNode"===c?function(t,e,n,i,r){var o,a=(e||{}).node,s=[i,r];if(!a||a===n)return s;var l=i*r,u=l*t.option.zoomToNodeRatio;for(;o=a.parentNode;){for(var h=0,c=o.children,p=0,d=c.length;poo&&(u=oo),a=o}ua[1]&&(a[1]=e)}))):a=[NaN,NaN];return{sum:i,dataExtent:a}}(e,a,s);if(0===u.sum)return t.viewChildren=[];if(u.sum=function(t,e,n,i,r){if(!i)return n;for(var o=t.get("visibleMin"),a=r.length,s=a,l=a-1;l>=0;l--){var u=r["asc"===i?a-l-1:l].getValue();u/n*ei&&(i=a));var l=t.area*t.area,u=e*e*n;return l?jD(u*i/l,l/(u*r)):1/0}function oA(t,e,n,i,r){var o=e===n.width?0:1,a=1-o,s=["x","y"],l=["width","height"],u=n[s[o]],h=e?t.area/e:0;(r||h>n[l[a]])&&(h=n[l[a]]);for(var c=0,p=t.length;ci&&(i=e);var o=i%2?i+2:i+3;r=[];for(var a=0;a0&&(m[0]=-m[0],m[1]=-m[1]);var _=v[0]<0?-1:1;if("start"!==i.__position&&"end"!==i.__position){var b=-Math.atan2(v[1],v[0]);u[0].8?"left":h[0]<-.8?"right":"center",p=h[1]>.8?"top":h[1]<-.8?"bottom":"middle";break;case"start":i.x=-h[0]*f+l[0],i.y=-h[1]*g+l[1],c=h[0]>.8?"right":h[0]<-.8?"left":"center",p=h[1]>.8?"bottom":h[1]<-.8?"top":"middle";break;case"insideStartTop":case"insideStart":case"insideStartBottom":i.x=f*_+l[0],i.y=l[1]+w,c=v[0]<0?"right":"left",i.originX=-f*_,i.originY=-w;break;case"insideMiddleTop":case"insideMiddle":case"insideMiddleBottom":case"middle":i.x=x[0],i.y=x[1]+w,c="center",i.originY=-w;break;case"insideEndTop":case"insideEnd":case"insideEndBottom":i.x=-f*_+u[0],i.y=u[1]+w,c=v[0]>=0?"right":"left",i.originX=f*_,i.originY=-w}i.scaleX=i.scaleY=r,i.setStyle({verticalAlign:i.__verticalAlign||p,align:i.__align||c})}}}function S(t,e){var n=t.__specifiedRotation;if(null==n){var i=a.tangentAt(e);t.attr("rotation",(1===e?-1:1)*Math.PI/2-Math.atan2(i[1],i[0]))}else t.attr("rotation",n)}},e}(Br),YA=function(){function t(t){this.group=new Br,this._LineCtor=t||HA}return t.prototype.updateData=function(t){var e=this;this._progressiveEls=null;var n=this,i=n.group,r=n._lineData;n._lineData=t,r||i.removeAll();var o=XA(t);t.diff(r).add((function(n){e._doAdd(t,n,o)})).update((function(n,i){e._doUpdate(r,t,i,n,o)})).remove((function(t){i.remove(r.getItemGraphicEl(t))})).execute()},t.prototype.updateLayout=function(){var t=this._lineData;t&&t.eachItemGraphicEl((function(e,n){e.updateLayout(t,n)}),this)},t.prototype.incrementalPrepareUpdate=function(t){this._seriesScope=XA(t),this._lineData=null,this.group.removeAll()},t.prototype.incrementalUpdate=function(t,e){function n(t){t.isGroup||function(t){return t.animators&&t.animators.length>0}(t)||(t.incremental=!0,t.ensureState("emphasis").hoverLayer=!0)}this._progressiveEls=[];for(var i=t.start;i=0?i+=u:i-=u:f>=0?i-=u:i+=u}return i}function ek(t,e){var n=[],i=Dn,r=[[],[],[]],o=[[],[]],a=[];e/=2,t.eachEdge((function(t,s){var l=t.getLayout(),u=t.getVisual("fromSymbol"),h=t.getVisual("toSymbol");l.__original||(l.__original=[Tt(l[0]),Tt(l[1])],l[2]&&l.__original.push(Tt(l[2])));var c=l.__original;if(null!=l[2]){if(It(r[0],c[0]),It(r[1],c[2]),It(r[2],c[1]),u&&"none"!==u){var p=SA(t.node1),d=tk(r,c[0],p*e);i(r[0][0],r[1][0],r[2][0],d,n),r[0][0]=n[3],r[1][0]=n[4],i(r[0][1],r[1][1],r[2][1],d,n),r[0][1]=n[3],r[1][1]=n[4]}if(h&&"none"!==h){p=SA(t.node2),d=tk(r,c[1],p*e);i(r[0][0],r[1][0],r[2][0],d,n),r[1][0]=n[1],r[2][0]=n[2],i(r[0][1],r[1][1],r[2][1],d,n),r[1][1]=n[1],r[2][1]=n[2]}It(l[0],r[0]),It(l[1],r[2]),It(l[2],r[1])}else{if(It(o[0],c[0]),It(o[1],c[1]),kt(a,o[1],o[0]),Et(a,a),u&&"none"!==u){p=SA(t.node1);At(o[0],o[0],a,p*e)}if(h&&"none"!==h){p=SA(t.node2);At(o[1],o[1],a,-p*e)}It(l[0],o[0]),It(l[1],o[1])}}))}function nk(t){return"view"===t.type}var ik=function(t){function e(){var n=null!==t&&t.apply(this,arguments)||this;return n.type=e.type,n}return n(e,t),e.prototype.init=function(t,e){var n=new xS,i=new YA,r=this.group;this._controller=new nT(e.getZr()),this._controllerHost={target:r},r.add(n.group),r.add(i.group),this._symbolDraw=n,this._lineDraw=i,this._firstRender=!0},e.prototype.render=function(t,e,n){var i=this,r=t.coordinateSystem;this._model=t;var o=this._symbolDraw,a=this._lineDraw,s=this.group;if(nk(r)){var l={x:r.x,y:r.y,scaleX:r.scaleX,scaleY:r.scaleY};this._firstRender?s.attr(l):vh(s,l,t)}ek(t.getGraph(),wA(t));var u=t.getData();o.updateData(u);var h=t.getEdgeData();a.updateData(h),this._updateNodeAndLinkScale(),this._updateController(t,e,n),clearTimeout(this._layoutTimeout);var c=t.forceLayout,p=t.get(["force","layoutAnimation"]);c&&this._startForceLayoutIteration(c,p);var d=t.get("layout");u.graph.eachNode((function(e){var n=e.dataIndex,r=e.getGraphicEl(),o=e.getModel();if(r){r.off("drag").off("dragend");var a=o.get("draggable");a&&r.on("drag",(function(o){switch(d){case"force":c.warmUp(),!i._layouting&&i._startForceLayoutIteration(c,p),c.setFixed(n),u.setItemLayout(n,[r.x,r.y]);break;case"circular":u.setItemLayout(n,[r.x,r.y]),e.setLayout({fixed:!0},!0),TA(t,"symbolSize",e,[o.offsetX,o.offsetY]),i.updateLayout(t);break;default:u.setItemLayout(n,[r.x,r.y]),_A(t.getGraph(),t),i.updateLayout(t)}})).on("dragend",(function(){c&&c.setUnfixed(n)})),r.setDraggable(a,!!o.get("cursor")),"adjacency"===o.get(["emphasis","focus"])&&(rl(r).focus=e.getAdjacentDataIndices())}})),u.graph.eachEdge((function(t){var e=t.getGraphicEl(),n=t.getModel().get(["emphasis","focus"]);e&&"adjacency"===n&&(rl(e).focus={edge:[t.dataIndex],node:[t.node1.dataIndex,t.node2.dataIndex]})}));var f="circular"===t.get("layout")&&t.get(["circular","rotateLabel"]),g=u.getLayout("cx"),y=u.getLayout("cy");u.graph.eachNode((function(t){DA(t,f,g,y)})),this._firstRender=!1},e.prototype.dispose=function(){this.remove(),this._controller&&this._controller.dispose(),this._controllerHost=null},e.prototype._startForceLayoutIteration=function(t,e){var n=this;!function i(){t.step((function(t){n.updateLayout(n._model),(n._layouting=!t)&&(e?n._layoutTimeout=setTimeout(i,16):i())}))}()},e.prototype._updateController=function(t,e,n){var i=this,r=this._controller,o=this._controllerHost,a=this.group;r.setPointerChecker((function(e,i,r){var o=a.getBoundingRect();return o.applyTransform(a.transform),o.contain(i,r)&&!hT(e,n,t)})),nk(t.coordinateSystem)?(r.enable(t.get("roam")),o.zoomLimit=t.get("scaleLimit"),o.zoom=t.coordinateSystem.getZoom(),r.off("pan").off("zoom").on("pan",(function(e){aT(o,e.dx,e.dy),n.dispatchAction({seriesId:t.id,type:"graphRoam",dx:e.dx,dy:e.dy})})).on("zoom",(function(e){sT(o,e.scale,e.originX,e.originY),n.dispatchAction({seriesId:t.id,type:"graphRoam",zoom:e.scale,originX:e.originX,originY:e.originY}),i._updateNodeAndLinkScale(),ek(t.getGraph(),wA(t)),i._lineDraw.updateLayout(),n.updateLabelLayout()}))):r.disable()},e.prototype._updateNodeAndLinkScale=function(){var t=this._model,e=t.getData(),n=wA(t);e.eachItemGraphicEl((function(t,e){t&&t.setSymbolScale(n)}))},e.prototype.updateLayout=function(t){ek(t.getGraph(),wA(t)),this._symbolDraw.updateLayout(),this._lineDraw.updateLayout()},e.prototype.remove=function(){clearTimeout(this._layoutTimeout),this._layouting=!1,this._layoutTimeout=null,this._symbolDraw&&this._symbolDraw.remove(),this._lineDraw&&this._lineDraw.remove()},e.type="graph",e}(Og);function rk(t){return"_EC_"+t}var ok=function(){function t(t){this.type="graph",this.nodes=[],this.edges=[],this._nodesMap={},this._edgesMap={},this._directed=t||!1}return t.prototype.isDirected=function(){return this._directed},t.prototype.addNode=function(t,e){t=null==t?""+e:""+t;var n=this._nodesMap;if(!n[rk(t)]){var i=new ak(t,e);return i.hostGraph=this,this.nodes.push(i),n[rk(t)]=i,i}},t.prototype.getNodeByIndex=function(t){var e=this.data.getRawIndex(t);return this.nodes[e]},t.prototype.getNodeById=function(t){return this._nodesMap[rk(t)]},t.prototype.addEdge=function(t,e,n){var i=this._nodesMap,r=this._edgesMap;if(j(t)&&(t=this.nodes[t]),j(e)&&(e=this.nodes[e]),t instanceof ak||(t=i[rk(t)]),e instanceof ak||(e=i[rk(e)]),t&&e){var o=t.id+"-"+e.id,a=new sk(t,e,n);return a.hostGraph=this,this._directed&&(t.outEdges.push(a),e.inEdges.push(a)),t.edges.push(a),t!==e&&e.edges.push(a),this.edges.push(a),r[o]=a,a}},t.prototype.getEdgeByIndex=function(t){var e=this.edgeData.getRawIndex(t);return this.edges[e]},t.prototype.getEdge=function(t,e){t instanceof ak&&(t=t.id),e instanceof ak&&(e=e.id);var n=this._edgesMap;return this._directed?n[t+"-"+e]:n[t+"-"+e]||n[e+"-"+t]},t.prototype.eachNode=function(t,e){for(var n=this.nodes,i=n.length,r=0;r=0&&t.call(e,n[r],r)},t.prototype.eachEdge=function(t,e){for(var n=this.edges,i=n.length,r=0;r=0&&n[r].node1.dataIndex>=0&&n[r].node2.dataIndex>=0&&t.call(e,n[r],r)},t.prototype.breadthFirstTraverse=function(t,e,n,i){if(e instanceof ak||(e=this._nodesMap[rk(e)]),e){for(var r="out"===n?"outEdges":"in"===n?"inEdges":"edges",o=0;o=0&&n.node2.dataIndex>=0}));for(r=0,o=i.length;r=0&&this[t][e].setItemVisual(this.dataIndex,n,i)},getVisual:function(n){return this[t][e].getItemVisual(this.dataIndex,n)},setLayout:function(n,i){this.dataIndex>=0&&this[t][e].setItemLayout(this.dataIndex,n,i)},getLayout:function(){return this[t][e].getItemLayout(this.dataIndex)},getGraphicEl:function(){return this[t][e].getItemGraphicEl(this.dataIndex)},getRawIndex:function(){return this[t][e].getRawIndex(this.dataIndex)}}}function uk(t,e,n,i,r){for(var o=new ok(i),a=0;a "+p)),u++)}var d,f=n.get("coordinateSystem");if("cartesian2d"===f||"polar"===f)d=_x(t,n);else{var g=wd.get(f),y=g&&g.dimensions||[];P(y,"value")<0&&y.concat(["value"]);var v=px(t,{coordDimensions:y,encodeDefine:n.getEncode()}).dimensions;(d=new cx(v,n)).initData(t)}var m=new cx(["value"],n);return 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