feat: Introduce comprehensive weather data querying, analysis, and display services, integrate Polymarket data collection, and add Telegram notification utilities.

2026-03-11 10:23:33 +08:00
parent 878e3280d1
commit b3f46430ad
9 changed files with 505 additions and 63 deletions
@@ -58,6 +58,17 @@ def _render_local_time(
    metar: Dict[str, Any],
    fallback_utc_offset: int,
 ) -> str:
+    utc_offset = open_meteo.get("utc_offset")
+    if utc_offset is None:
+        utc_offset = fallback_utc_offset
+    try:
+        local_now = datetime.now(timezone.utc).astimezone(
+            timezone(timedelta(seconds=int(utc_offset)))
+        )
+        return local_now.strftime("%H:%M")
+    except Exception:
+        pass
+
    local_time = (open_meteo.get("current") or {}).get("local_time", "")
    if " " in str(local_time):
        return str(local_time).split(" ")[1][:5]
@@ -79,13 +90,49 @@ def _render_local_time(
    if metar_obs:
        return str(metar_obs)[:5]

-    try:
-        local_now = datetime.now(timezone.utc).astimezone(
-            timezone(timedelta(seconds=int(fallback_utc_offset)))
-        )
-        return local_now.strftime("%H:%M")
-    except Exception:
-        return "N/A"
+    return "N/A"
+
+
+def _derive_mgm_daily_highs_from_hourly(
+    mgm: Dict[str, Any],
+    fallback_utc_offset: int,
+) -> Dict[str, float]:
+    if not isinstance(mgm, dict):
+        return {}
+    hourly = mgm.get("hourly")
+    if not isinstance(hourly, list) or not hourly:
+        return {}
+
+    daily_highs: Dict[str, float] = {}
+    local_tz = timezone(timedelta(seconds=int(fallback_utc_offset)))
+    for row in hourly:
+        if not isinstance(row, dict):
+            continue
+        temp = _sf(row.get("temp"))
+        raw_time = str(row.get("time") or "").strip()
+        if temp is None or not raw_time:
+            continue
+
+        date_key = None
+        if "T" in raw_time:
+            try:
+                dt = datetime.fromisoformat(raw_time.replace("Z", "+00:00"))
+                if dt.tzinfo is not None:
+                    dt = dt.astimezone(local_tz)
+                date_key = dt.strftime("%Y-%m-%d")
+            except Exception:
+                if len(raw_time) >= 10 and raw_time[4] == "-" and raw_time[7] == "-":
+                    date_key = raw_time[:10]
+        elif len(raw_time) >= 10 and raw_time[4] == "-" and raw_time[7] == "-":
+            date_key = raw_time[:10]
+
+        if not date_key:
+            continue
+
+        prev = daily_highs.get(date_key)
+        daily_highs[date_key] = temp if prev is None else max(prev, temp)
+
+    return daily_highs


 def _append_future_forecast_lines(
@@ -98,6 +145,12 @@ def _append_future_forecast_lines(
 ) -> None:
    mgm = weather_data.get("mgm") or {}
    mgm_daily = (mgm.get("daily_forecasts") or {}) if isinstance(mgm, dict) else {}
+    mgm_hourly_daily = _derive_mgm_daily_highs_from_hourly(mgm, fallback_utc_offset)
+    if not isinstance(mgm_daily, dict):
+        mgm_daily = {}
+    for date_key, day_high in mgm_hourly_daily.items():
+        if date_key not in mgm_daily:
+            mgm_daily[date_key] = day_high
    mm_raw = weather_data.get("multi_model") or {}
    mm_daily = mm_raw.get("daily_forecasts", {}) if isinstance(mm_raw, dict) else {}
    mb_daily = (weather_data.get("meteoblue") or {}).get("daily_highs", []) or []
@@ -108,8 +161,9 @@ def _append_future_forecast_lines(
        for d, t in zip(dates[1:], max_temps[1:]):
            mgm_value = mgm_daily.get(d) if isinstance(mgm_daily, dict) else None
            if mgm_value is not None:
+                mgm_display = f"{float(mgm_value):.1f}"
                future_forecasts.append(
-                    f"{d[5:]}: {t}{temp_symbol} | 🇺🇸 <b>MGM: {mgm_value}{temp_symbol}</b>"
+                    f"{d[5:]}: {t}{temp_symbol} | 🇺🇸 <b>MGM: {mgm_display}{temp_symbol}</b>"
                )
            else:
                future_forecasts.append(f"{d[5:]}: {t}{temp_symbol}")
@@ -473,6 +473,18 @@ def _bucket_label(bucket: Any) -> Optional[str]:
    return None


+def _row_yes_buy_prob(row: Dict[str, Any]) -> Optional[float]:
+    if not isinstance(row, dict):
+        return None
+    return _norm_probability(row.get("yes_buy"))
+
+
+def _has_actionable_yes_buy_quote(row: Dict[str, Any]) -> bool:
+    quote = _row_yes_buy_prob(row)
+    # 0 usually means no actionable orderbook bid, not a tradable quote.
+    return quote is not None and quote > 0.0
+
+
 def _to_celsius(temp: Optional[float], temp_symbol: str) -> Optional[float]:
    if temp is None:
        return None
@@ -555,6 +567,7 @@ def _pick_bucket_for_forecast(

    best_row: Optional[Dict[str, Any]] = None
    best_distance: Optional[float] = None
+    best_has_quote = False
    best_probability = -1.0
    best_rank = 10**9

@@ -564,12 +577,14 @@ def _pick_bucket_for_forecast(
            continue

        distance = _distance_to_bucket(target, bounds)
+        has_quote = _has_actionable_yes_buy_quote(row)
        probability = _norm_probability(row.get("probability"))
        probability_rank = probability if probability is not None else -1.0

        if best_row is None:
            best_row = row
            best_distance = distance
+            best_has_quote = has_quote
            best_probability = probability_rank
            best_rank = idx
            continue
@@ -578,19 +593,32 @@ def _pick_bucket_for_forecast(
        if distance < best_distance:
            best_row = row
            best_distance = distance
+            best_has_quote = has_quote
            best_probability = probability_rank
            best_rank = idx
            continue

        if abs(distance - best_distance) <= 1e-9:
-            if probability_rank > best_probability:
+            if has_quote and not best_has_quote:
                best_row = row
                best_distance = distance
+                best_has_quote = has_quote
                best_probability = probability_rank
                best_rank = idx
-            elif abs(probability_rank - best_probability) <= 1e-9 and idx < best_rank:
+            elif has_quote == best_has_quote and probability_rank > best_probability:
                best_row = row
                best_distance = distance
+                best_has_quote = has_quote
+                best_probability = probability_rank
+                best_rank = idx
+            elif (
+                has_quote == best_has_quote
+                and abs(probability_rank - best_probability) <= 1e-9
+                and idx < best_rank
+            ):
+                best_row = row
+                best_distance = distance
+                best_has_quote = has_quote
                best_probability = probability_rank
                best_rank = idx

@@ -972,7 +1000,12 @@ def _build_telegram_messages_mispricing(
    om_settle = snapshot.get("open_meteo_settlement")
    forecast_bucket = snapshot.get("forecast_bucket") or {}
    match_bucket_label = str(forecast_bucket.get("label") or "--").strip() or "--"
-    match_bucket_yes = _fmt_cents(forecast_bucket.get("yes_buy"))
+    match_bucket_yes_prob = _norm_probability(forecast_bucket.get("yes_buy"))
+    match_bucket_yes = (
+        _fmt_cents(match_bucket_yes_prob)
+        if match_bucket_yes_prob is not None and match_bucket_yes_prob > 0.0
+        else "--"
+    )
    market_url = str(
        snapshot.get("market_url")
        or snapshot.get("primary_market_url")