feat: Implement new bot architecture including handlers, services, analysis modules, and comprehensive tests.

src/analysis/market_alert_engine.py

@@ -1009,6 +1009,8 @@ def _build_telegram_messages_mispricing(
     city_weather: Dict[str, Any],
     rules: Dict[str, Dict[str, Any]],
     market_snapshot: Optional[Dict[str, Any]] = None,
+    suppression: Optional[Dict[str, Any]] = None,
+    map_url: Optional[str] = None,
 ) -> Dict[str, str]:
     temp_symbol = str(city_weather.get("temp_symbol") or "°C")
     city_name = city_weather.get("display_name") or city_weather.get("name", "").title()
@@ -1019,8 +1021,12 @@ def _build_telegram_messages_mispricing(
 
     snapshot = market_snapshot or _extract_market_snapshot(city_weather)
     momentum = rules.get("momentum_spike", {})
+    center_deb = rules.get("ankara_center_deb_hit", {})
     local_time = str(city_weather.get("local_time") or "").strip()
     obs_time = str(current.get("obs_time") or "").strip()
+    suppressed = bool((suppression or {}).get("suppressed"))
+    has_active_trigger = any(rule.get("triggered") for rule in rules.values())
+    types_cn = "高温已过（暂停推送）" if suppressed else (_join_trigger_types_cn(rules) or "天气状态快照")
 
     delta_temp = _sf(momentum.get("delta_temp"))
     delta_min = momentum.get("delta_minutes")
@@ -1055,9 +1061,34 @@ def _build_telegram_messages_mispricing(
         or snapshot.get("primary_market_url")
         or ""
     ).strip()
+    final_map = map_url or "https://polyweather-pro.vercel.app/"
+    advice = _build_advice_cn(rules, temp_symbol, suppression=suppression)
 
-    lines_zh = [f"🚨 PolyWeather 错价雷达 [{city_name}]"]
+    center_line = ""
+    if center_deb.get("triggered"):
+        center_station = center_deb.get("center_station") or {}
+        center_name = center_station.get("name") or "Ankara Center"
+        center_temp = _sf(center_station.get("temp"))
+        deb_prediction = _sf(center_deb.get("deb_prediction"))
+        airport_temp = _sf(center_deb.get("airport_temp"))
+        lead_gap = _sf(center_deb.get("center_lead_vs_airport"))
+        if center_temp is not None and deb_prediction is not None:
+            center_line = (
+                f"Center信号：{center_name} {center_temp:.1f}{temp_symbol} 已达到 DEB {deb_prediction:.1f}{temp_symbol}"
+            )
+            if airport_temp is not None:
+                center_line += f" | 机场 {airport_temp:.1f}{temp_symbol}"
+            if lead_gap is not None:
+                center_line += f" | 领先 {lead_gap:+.1f}{temp_symbol}"
+
+    if snapshot.get("available"):
+        title_zh = "🚨 PolyWeather 错价雷达"
+    else:
+        title_zh = "🚨 PolyWeather 异动预警" if (has_active_trigger or suppressed) else "📍 PolyWeather 状态快照"
+
+    lines_zh = [f"{title_zh} [{city_name}]"]
     lines_zh.append("")
+    lines_zh.append(f"类型：{types_cn}")
     if anchor_high_c is not None and anchor_settle is not None:
         if anchor_model:
             lines_zh.append(
@@ -1071,6 +1102,8 @@ def _build_telegram_messages_mispricing(
         lines_zh.append("基准：多模型最高温 --（结算参考 --）")
     lines_zh.append(f"命中桶：{match_bucket_label} | Yes: {match_bucket_yes}")
     lines_zh.append("触发：该桶 Yes 价格 < 10c，疑似低估")
+    if center_line:
+        lines_zh.append(center_line)
     lines_zh.append("")
     lines_zh.append(f"动态：{dynamic_text}")
     if local_time or obs_time:
@@ -1080,17 +1113,21 @@ def _build_telegram_messages_mispricing(
             lines_zh.append(f"时间：当地 {local_time}")
         else:
             lines_zh.append(f"时间：观测 {obs_time}")
+    lines_zh.append(f"建议：{advice}")
     lines_zh.append("")
     if market_url:
         lines_zh.append(f"市场链接：{market_url}")
+    lines_zh.append(f"地图：{final_map}")
 
     lines_en = [
         f"🚨 PolyWeather Mispricing Radar [{city_name}]",
         "",
+        f"Type: {types_cn}",
         f"Now: {dynamic_text}",
     ]
     if market_url:
         lines_en.append(f"Market link: {market_url}")
+    lines_en.append(f"Map: {final_map}")
 
     return {"zh": "\n".join(lines_zh), "en": "\n".join(lines_en)}
 
@@ -1297,6 +1334,8 @@ def build_trading_alerts(
         city_weather=city_weather,
         rules=rules,
         market_snapshot=market_snapshot,
+        suppression=suppression,
+        map_url=map_url,
     )
     evidence = _build_alert_evidence(
         city_weather=city_weather,

src/analysis/trend_engine.py

@@ -385,34 +385,43 @@ def analyze_weather_trend(
     probabilities: List[Dict[str, Any]] = []
     forecast_miss_deg = 0.0
 
-    if (ens_p10 is not None and ens_p90 is not None) or fallback_sigma:
-        if not is_dead_market:
-            # Forecast miss magnitude
-            if max_so_far is not None and forecast_median is not None:
-                forecast_miss_deg = round(forecast_median - max_so_far, 1)
-    
-            fallback_center = forecast_median if forecast_median is not None else (forecast_high if forecast_high is not None else cur_temp)
-            center = ens_median if ens_median is not None else fallback_center
-    
-            # Reality-anchored μ
-            if (
-                max_so_far is not None
-                and forecast_median is not None
-                and peak_status in ("past", "in_window")
-                and max_so_far < forecast_median - 2.0
-            ):
-                if is_cooling or peak_status == "past":
-                    mu = max_so_far
-                else:
-                    mu = max_so_far + 0.5
+    if is_dead_market:
+        settled_wu = wu_round(max_so_far) if max_so_far is not None else 0
+        dead_msg = f"🎲 <b>结算预测</b>：已锁定 {settled_wu}{temp_symbol} (死盘确认)"
+        insights.append(dead_msg)
+        ai_features.append("🎲 状态: 确认死盘，结算已无悬念。")
+        if max_so_far is not None:
+            mu = max_so_far
+            probabilities = [
+                {"value": settled_wu, "range": f"[{settled_wu-0.5}~{settled_wu+0.5})", "probability": 1.0}
+            ]
+    elif (ens_p10 is not None and ens_p90 is not None) or fallback_sigma:
+        # Forecast miss magnitude
+        if max_so_far is not None and forecast_median is not None:
+            forecast_miss_deg = round(forecast_median - max_so_far, 1)
+
+        fallback_center = forecast_median if forecast_median is not None else (forecast_high if forecast_high is not None else cur_temp)
+        center = ens_median if ens_median is not None else fallback_center
+
+        # Reality-anchored μ
+        if (
+            max_so_far is not None
+            and forecast_median is not None
+            and peak_status in ("past", "in_window")
+            and max_so_far < forecast_median - 2.0
+        ):
+            if is_cooling or peak_status == "past":
+                mu = max_so_far
             else:
-                mu = (
-                    forecast_median * 0.7 + center * 0.3
-                    if forecast_median is not None and center is not None
-                    else center
-                )
-                if max_so_far is not None and mu is not None and max_so_far > mu:
-                    mu = max_so_far + (0.3 if not is_cooling else 0.0)
+                mu = max_so_far + 0.5
+        else:
+            mu = (
+                forecast_median * 0.7 + center * 0.3
+                if forecast_median is not None and center is not None
+                else center
+            )
+            if max_so_far is not None and mu is not None and max_so_far > mu:
+                mu = max_so_far + (0.3 if not is_cooling else 0.0)
 
         # Forecast miss severity for AI
         if forecast_miss_deg > 2.0 and peak_status in ("past", "in_window"):
@@ -431,7 +440,7 @@ def analyze_weather_trend(
         mu = probs_result.get("mu", mu)
         probabilities = probs_result.get("probabilities", [])
         sorted_probs = probs_result.get("sorted_probs", [])
-        
+
         if sorted_probs:
             prob_parts = [
                 f"{int(t)}{temp_symbol} [{t - 0.5}~{t + 0.5}) {p * 100:.0f}%"
@@ -442,17 +451,6 @@ def analyze_weather_trend(
                 insights.append(f"🎲 <b>结算概率</b> (μ={mu:.1f})：{prob_str}")
                 ai_features.append(f"🎲 数学概率分布：{prob_str}")
 
-    elif is_dead_market:
-        settled_wu = wu_round(max_so_far) if max_so_far is not None else 0
-        dead_msg = f"🎲 <b>结算预测</b>：已锁定 {settled_wu}{temp_symbol} (死盘确认)"
-        insights.append(dead_msg)
-        ai_features.append("🎲 状态: 确认死盘，结算已无悬念。")
-        if max_so_far is not None:
-            mu = max_so_far
-            probabilities = [
-                {"value": settled_wu, "range": f"[{settled_wu-0.5}~{settled_wu+0.5})", "probability": 1.0}
-            ]
-
     # === Actual exceeds forecast ===
     if max_so_far is not None and forecast_high is not None:
         if max_so_far > forecast_high + 0.5: