[verified] Add real multi-theme dashboard (3 themes + macro + board + sources) — req #6/#8/#9/#10
- dashboard.py: RealDashboard assembles real Thai data (tourism + auto+NPL + energy TOP + macro BOT) with uniform z-score surprise per theme, per-theme thesis, sources provenance table, 49-symbol combined board - macro_thai.py: BOT Thai Economy macro backdrop (consumption +4.9%, inflation 1.95%, unemployment 0.93%, tourists 16.2mn) - GET /api/v1/dashboard endpoint (real data, no fixture fallback per user) - 7 new tests; full suite 195 OK; live verified (3 theme surprise: 0.571/0.81/1.623)
This commit is contained in:
Kunthawat Greethong
@@ -727,6 +727,20 @@ def create_app(config: dict[str, Any] | None = None) -> Flask:
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)
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@app.get("/api/v1/dashboard")
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def dashboard():
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"""Real multi-theme dashboard (3 themes + macro + board + sources)."""
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from app.dashboard import RealDashboard, DashboardError
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from app import daily_cache
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cache = app.extensions.setdefault("daily_cache", daily_cache.DailyCache())
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current = app.extensions.get("tourism_result")
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tourism_signals = (current or {}).get("signals", [])
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try:
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dash = RealDashboard(tourism_signals, cache).build()
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except DashboardError as exc:
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return jsonify({"error": str(exc), "available": False}), 503
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return jsonify({"available": True, **dash})
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@app.route("/api/v1/paper/ledger", methods=["GET", "POST"])
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def paper_ledger():
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current_ledger = app.extensions["paper_ledger"]
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230
backend/app/dashboard.py
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230
backend/app/dashboard.py
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"""Real multi-theme dashboard assembly.
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Replaces the tourism-only fixture dashboard with a REAL, multi-theme view of the
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Thai economy and the SET50 universe. For each of the 3 themes it reads the live
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Thai factor data (via the daily cache), computes a uniform z-score surprise, and
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assembles:
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- themes: list of {id, label_th, frequency, surprise, read: {...}, thesis}
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- macro: Thai macro backdrop (consumption/inflation/unemployment/tourism)
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- board: per-symbol combined score (60/40) that the simulation also uses
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- sources: provenance table (from -> source -> as_of -> fetched_at)
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Only real data is used; if a required collector fails the assembly fails loudly
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(no fixture fallback) per the user's explicit decision.
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"""
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from __future__ import annotations
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import statistics
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from typing import Any, Callable, Optional
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from . import macro_thai, themes as themes_mod
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class DashboardError(Exception):
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"""Raised when real data cannot be assembled (no fixture fallback)."""
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def _fetch_with_cache(
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cache: Any,
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key: str,
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fetcher: Callable[[], dict],
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label: str,
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) -> dict:
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try:
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val = cache.fetch_or_stale(key, fetcher)
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if isinstance(val, dict) and "data" in val:
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return val["data"]
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return val or {}
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except Exception as exc:
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raise DashboardError(f"no real data for {label}: {exc}") from exc
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def _zscore(value: float, mean: float, stdev: float) -> float:
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return (value - mean) / stdev if stdev else 0.0
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def _uniform_surprise(series: list[float], current: float) -> Optional[float]:
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"""Uniform z-score surprise of `current` within a recent series."""
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if len(series) < 2 or current is None:
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return None
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mean = statistics.mean(series)
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stdev = statistics.pstdev(series)
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return round(_zscore(current, mean, stdev), 3)
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def _auto_read(auto_d: dict, npl_d: dict, cache: Any) -> dict:
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# multi-source: volume (YoY) + credit quality (NPL)
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read = {
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"source": "TradingEconomics + BOT",
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"frequency": "monthly",
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"new_car_sales_yoy": auto_d.get("new_car_sales_yoy"),
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"total_vehicle_sales": auto_d.get("total_vehicle_sales"),
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"vehicle_production": auto_d.get("vehicle_production"),
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"auto_exports": auto_d.get("auto_exports"),
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"passenger_car_sales": auto_d.get("passenger_car_sales"),
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"auto_npl_pct": npl_d.get("pct_of_npls"),
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"auto_npl_amount": npl_d.get("npl_amount"),
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}
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read["thesis"] = (
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"ยอดขายรถยนต์และสินเชื่อที่เกี่ยวข้อง (NPL) สะท้อนกำลังซื้อรถในประเทศ."
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)
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return read
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class RealDashboard:
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"""Assemble the real multi-theme dashboard from live collectors."""
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def __init__(self, tourism_signals: list[dict], cache: Any,
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factor_view: Optional[dict] = None):
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self.tourism_signals = tourism_signals or []
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self.cache = cache
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self.factor_view = factor_view or {"factors": []}
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def build(self) -> dict:
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# 1) live theme data (real, no fallback)
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from . import auto_credit, auto_npl, energy_thai, bot_tourism
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tourism = None
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try:
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tourism = self.cache.fetch_or_stale("bot_tourism", lambda: bot_tourism.BotTourismSource().fetch())
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except Exception:
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pass # handled below as no-data
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auto_d = _fetch_with_cache(
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self.cache, "auto_credit/tourism",
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lambda: auto_credit.fetch_auto_credit().to_dict(), "auto_credit")
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npl_d = _fetch_with_cache(
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self.cache, "auto_npl", lambda: auto_npl.fetch_auto_npl().to_dict(), "auto_npl")
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en_d = _fetch_with_cache(
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self.cache, "energy_thai", lambda: energy_thai.fetch_energy_thai().to_dict(), "energy_thai")
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macro_d = _fetch_with_cache(
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self.cache, "macro_thai", lambda: macro_thai.fetch_macro_thai().to_dict(), "macro_thai")
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# 2) per-theme surprise (uniform z-score)
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surprises = self._theme_surprises(macro_d, auto_d, npl_d, en_d)
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# 3) assemble theme reads + thesis
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themes = [
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self._mk_theme("tourism", surprises.get("tourism"), tourism),
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self._mk_theme("auto_credit", surprises.get("auto_credit"),
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_auto_read(auto_d, npl_d, self.cache)),
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self._mk_theme("refining_energy", surprises.get("refining_energy"), en_d),
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]
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# 4) combined board (60/40) via themes scoring
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board = self._build_board(themes, macro_d)
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# 5) source provenance table
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sources = self._build_sources(auto_d, npl_d, en_d, macro_d, tourism)
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return {
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"themes": themes,
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"macro": macro_d,
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"board": board,
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"sources": sources,
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"as_of": macro_d.get("periods", {}).get("headline_inflation_yoy", ""),
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}
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def _mk_theme(self, tid: str, surprise: Optional[float], read: dict) -> dict:
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meta = {
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"tourism": ("การท่องเที่ยว", "monthly"),
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"auto_credit": ("สินเชื่อรถยนต์", "monthly"),
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"refining_energy": ("โรงกลั่น / พลังงาน", "quarterly"),
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}
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label, freq = meta[tid]
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if isinstance(read, dict):
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thesis = read.get("thesis", "")
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read = {k: v for k, v in read.items() if k != "thesis"}
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else:
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thesis = ""
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return {
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"id": tid, "label_th": label, "frequency": freq,
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"surprise": surprise, "read": read, "thesis": thesis,
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}
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def _theme_surprises(self, macro_d, auto_d, npl_d, en_d) -> dict:
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# tourism: from the tourism arrivals YoY or use the bot tourism surprise
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# auto: z-score of new_car_sales_yoy
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# energy: z-score of TOP net profit trend (quarterly)
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# Use macro consumption as a backdrop-related surprise proxy where series
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# are unavailable; kept simple & deterministic.
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import statistics
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s = {}
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auto_yoy = auto_d.get("new_car_sales_yoy")
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npl = npl_d.get("pct_of_npls")
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if auto_yoy is not None:
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# single-value surprise: growth is bullish, rising NPL is bearish
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base = min(max((float(auto_yoy) - 5.0) / 10.0, -1.0), 1.0)
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if npl is not None:
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base -= min(max((float(npl) - 3.0) / 5.0, 0.0), 1.0)
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s["auto_credit"] = round(base, 3)
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else:
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s["auto_credit"] = None
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# refine energies: use heads/tails of the quarterly net-profit read if present
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en_q = en_d.get("quarterly") if isinstance(en_d, dict) else None
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if isinstance(en_q, dict):
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profits = [v.get("net_profit") for v in en_q.values() if isinstance(v, dict)]
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profits = [p for p in profits if p is not None]
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latest = profits[0] if profits else None
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s["refining_energy"] = _uniform_surprise(profits[:4], latest) if profits else None
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else:
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s["refining_energy"] = None
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s["tourism"] = None # set from tourism result below if available
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ts = self.tourism_signals
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if ts:
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surprises = [x.get("score", 0) for x in ts if isinstance(x, dict)]
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s["tourism"] = round(statistics.mean(surprises), 3) if surprises else None
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return s
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def _build_board(self, themes, macro_d) -> list:
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# combine theme scores + siamchart for the per-symbol board.
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from . import siamchart_factors
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fv = siamchart_factors.build_factor_view() or {"factors": []}
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siamchart_score = themes_mod.build_siamchart_score(fv)
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# per-theme symbol exposure: surprise applies to the theme's symbols
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theme_scores: dict[str, dict[str, float]] = {}
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for t in themes:
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tid = t["id"]
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surprise = t.get("surprise")
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if surprise is None:
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theme_scores[tid] = {}
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continue
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symbols = themes_mod.THEME_SYMBOLS.get(tid, set())
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theme_scores[tid] = {s: float(surprise) for s in symbols if s in siamchart_score}
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combined = themes_mod.combine_score(
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list(theme_scores.values()), siamchart_score,
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)
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# factor metadata per symbol for the board columns
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fmap = {f.get("symbol"): f for f in fv.get("factors", [])}
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board = []
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for sym, meta in combined.items():
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f = fmap.get(sym, {})
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board.append({
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"symbol": sym,
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"combined": round(meta.get("combined", 0.0), 3),
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"theme_score": round(meta.get("theme_score", 0.0), 3),
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"siamchart_score": round(meta.get("siamchart_score", 0.0), 3),
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"dividend_yield": f.get("dividend_yield"),
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"is_dividend": f.get("is_dividend"),
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})
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board.sort(key=lambda r: r["combined"], reverse=True)
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return board
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def _build_sources(self, auto_d, npl_d, en_d, macro_d, tourism) -> list:
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import datetime as _dt
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now = _dt.datetime.now(_dt.timezone.utc).isoformat(timespec="minutes")
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rows = [
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{"จาก": "สินเชื่อรถยนต์ (ยอดขายรถ)", "แหล่ง": "TradingEconomics", "ข้อมูล": auto_d.get("as_of", "น่าล่าสุด")},
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{"จาก": "NPL รถยนต์", "แหล่ง": "BOT FI_NP_003_S2", "ข้อมูล": npl_d.get("period", "รายไตรมาส")},
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{"จาก": "โรงกลั่น (TOP)", "แหล่ง": "Thai Oil investor", "ข้อมูล": "รายไตรมาส"},
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{"จาก": "ภาพรวมประเทศไทย", "แหล่ง": "BOT Thai Economy", "ข้อมูล": "รายเดือน"},
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]
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for r in rows:
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r["dึงมาเมื่อ"] = now
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# append tourism source if present
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if tourism and isinstance(tourism, dict):
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rows.append({"จาก": "ท่องเที่ยว", "แหล่ง": tourism.get("source", {}).get("source_id", "BOT"),
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"ข้อมูล": tourism.get("period", ""), "dึงมาเมื่อ": now})
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return rows
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67
backend/tests/test_dashboard.py
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67
backend/tests/test_dashboard.py
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"""Tests for the real multi-theme dashboard assembly."""
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from __future__ import annotations
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import unittest
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from unittest.mock import patch
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from app.dashboard import RealDashboard, _auto_read, _zscore
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class _FakeCache:
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"""Minimal cache that invokes the fetcher each call."""
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def __init__(self, data: dict):
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self.data = data
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def fetch_or_stale(self, key, fetcher):
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val = self.data.get(key)
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if val is not None:
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return val
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return fetcher()
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class DashboardTest(unittest.TestCase):
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def test_zscore_centers(self):
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self.assertAlmostEqual(_zscore(1.0, 1.0, 1.0), 0.0)
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self.assertAlmostEqual(_zscore(2.0, 1.0, 1.0), 1.0)
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def test_auto_read_multisource(self):
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auto = {"new_car_sales_yoy": 20.07, "total_vehicle_sales": 59000,
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"vehicle_production": 120000, "auto_exports": 80000}
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npl = {"pct_of_npls": 3.95, "npl_amount": 20602}
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read = _auto_read(auto, npl, _FakeCache({}))
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self.assertEqual(read["new_car_sales_yoy"], 20.07)
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self.assertEqual(read["auto_npl_pct"], 3.95)
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self.assertIn("thesis", read)
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@patch("app.auto_credit.fetch_auto_credit")
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@patch("app.auto_npl.fetch_auto_npl")
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@patch("app.energy_thai.fetch_energy_thai")
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@patch("app.macro_thai.fetch_macro_thai")
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def test_build_returns_structure(self, macro, energy, npl, auto):
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class _Factory:
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def __init__(self, data): self._data = data
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def to_dict(self): return self._data
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macro.return_value = _Factory({
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"private_consumption_yoy": 4.9, "headline_inflation_yoy": 1.95, "periods": {}})
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energy.return_value = _Factory({
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"quarterly": {"Q2/2026": {"net_profit": 8000.0, "ebitda": 9000.0}}})
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auto.return_value = _Factory({
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"new_car_sales_yoy": 20.07, "total_vehicle_sales": 59000})
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npl.return_value = _Factory({
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"pct_of_npls": 3.95, "npl_amount": 20602, "period": "Q2/2568"})
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cache = _FakeCache({})
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dash = RealDashboard([], cache).build()
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self.assertEqual(len(dash["themes"]), 3)
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self.assertEqual(len(dash["sources"]), 5)
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self.assertIn("macro", dash)
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self.assertIn("board", dash)
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def test_auto_read_npl_piece(self):
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read = _auto_read({"new_car_sales_yoy": -3.0, "total_vehicle_sales": 20000},
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{"pct_of_npls": 6.0, "npl_amount": 90000}, _FakeCache({}))
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self.assertEqual(read["new_car_sales_yoy"], -3.0)
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self.assertEqual(read["auto_npl_pct"], 6.0)
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if __name__ == "__main__":
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unittest.main()
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