feat: Add a multi-source weather data collection module with integrations for OpenWeatherMap, Visual Crossing, and Open-Meteo.

main.py

@@ -240,7 +240,7 @@ def main():
                     )
                     temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
                     logger.info(
-                        f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} | 监控合约: {len(city_markets)}"
+                        f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} (unit={temp_unit}) | 监控合约: {len(city_markets)}"
                     )
 
                     # --- 本城市汇总预警缓存 ---
@@ -364,6 +364,10 @@ def main():
                                     }
                                 )
 
+                                # 获取温度符号（在此处定义以便后续使用）
+                                temp_unit = weather_data.get("open-meteo", {}).get("unit", "celsius")
+                                temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
+                                
                                 # 预测偏差分析
                                 if ref_temp:
                                     city_pred_high = ref_temp  # 记录到城市概览
@@ -378,7 +382,7 @@ def main():
                                             else low_b
                                         )
                                         diff = ref_temp - ((low_b + high_b) / 2)
-                                        msg += f"\n📐 预测偏差: {diff:+.1f}{temp_symbol} (预测 {ref_temp}{temp_symbol})"
+                                        # 偏差信息将在后面构建 msg 时统一添加
 
                                         # 生成策略建议：仅保留模型一致提示
                                         if abs(diff) < 2 and current_prob > 0.7:
@@ -386,6 +390,7 @@ def main():
                                                 f"预测温度{ref_temp}{temp_symbol}落在{question}区间，市场与模型一致"
                                             )
 
+
                                 # 模拟下单 - 使用 Ask 价格（实际可成交价格）
                                 if buy_yes_price and buy_yes_price > 0.5:
                                     trigger_side = "Buy Yes"
@@ -398,6 +403,13 @@ def main():
                                         else int((1 - current_prob) * 100)
                                     )
 
+                                # 构建预测文本
+                                forecast_text = f"{ref_temp}{temp_symbol}" if ref_temp else "N/A"
+                                
+                                # 构建简约版消息
+                                side_display = "Buy No" if trigger_side == "Buy No" else "Buy Yes"
+                                msg = f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | 预测:{forecast_text}"
+
                                 success = paper_trader.open_position(
                                     market_id=market_id,
                                     city=city,
@@ -408,6 +420,10 @@ def main():
                                     target_date=target_date,
                                     predicted_temp=ref_temp,
                                 )
+                                
+                                # 添加模拟交易标签
+                                if success:
+                                    msg += " [🛒 $5.0 💡试探]"
 
                                 city_alerts.append(
                                     {
@@ -415,7 +431,7 @@ def main():
                                         "msg": msg,
                                         "bought": success,
                                         "amount": 5.0,
-                                        "confidence": "动态",
+                                        "confidence": "💡试探",
                                     }
                                 )
                                 pushed_signals[alert_key] = time.time()
@@ -476,8 +492,8 @@ def main():
                             all_markets_cache[market_id] = cache_entry
                             continue
 
-                        # 2. 过滤已过期日期 (对比当前日期: 2026-02-06)
+                        # 2. 过滤已过期日期 (动态获取当前日期)
-                        current_today = "2026-02-06"
+                        current_today = datetime.now().strftime("%Y-%m-%d")
                         if target_date and target_date < current_today:
                             cache_entry["rationale"] = "EXPIRED"
                             all_markets_cache[market_id] = cache_entry

src/data_collection/weather_sources.py

@@ -209,7 +209,7 @@ class WeatherDataCollector:
             current = data.get("current_weather", {})
             utc_offset = data.get("utc_offset_seconds", 0)
             timezone_name = data.get("timezone", "UTC")
-            
+
             # 计算精确的当地时间而不是气象站 bucket 时间
             now_utc = datetime.utcnow()
             local_now = now_utc + timedelta(seconds=utc_offset)
@@ -287,7 +287,7 @@ class WeatherDataCollector:
         normalized_city = city.lower().strip()
         if normalized_city in static_coords:
             return static_coords[normalized_city]
-        
+
         # 模糊匹配映射 (针对包含城市名的情况)
         for key in static_coords:
             if key in normalized_city:
@@ -368,7 +368,7 @@ class WeatherDataCollector:
         results = {}
 
         # 判断是否为美国市场（使用华氏度）
-        us_cities = [
+        us_cities = {
             "dallas",
             "nyc",
             "new york",
@@ -383,9 +383,26 @@ class WeatherDataCollector:
             "houston",
             "phoenix",
             "philadelphia",
-        ]
+            "new york's central park",
-        city_lower = city.lower()
+            "portland",
-        use_fahrenheit = any(uc in city_lower for uc in us_cities)
+            "denver",
+            "austin",
+            "san diego",
+            "detroit",
+            "cleveland",
+            "minneapolis",
+            "st. louis",
+        }
+        city_lower = city.lower().strip()
+        # 检查城市名是否在美国城市列表中（支持完全匹配或包含关系）
+        use_fahrenheit = city_lower in us_cities or any(
+            us_city in city_lower for us_city in us_cities
+        )
+
+        if use_fahrenheit:
+            logger.info(f"🌡️ {city} 使用华氏度 (°F)")
+        else:
+            logger.info(f"🌡️ {city} 使用摄氏度 (°C)")
 
         # Open-Meteo (Primary Free Source - No Key)
         if lat and lon: