feat: Integrate multi-model and ensemble weather forecasts, update READMEs with new architecture and betting strategies, and resolve merge conflicts.

2026-02-23 21:04:44 +08:00
parent b89b137e83
commit be90e610ee
3 changed files with 0 additions and 190 deletions
@@ -8,11 +8,7 @@ An intelligent weather bot for prediction markets and professional weather betti

 - **Python 3.11+**
 - Dependencies: `pip install -r requirements.txt`
-<<<<<<< HEAD
- **Environment**: Configure `METEOBLUE_API_KEY` in `.env` to enable high-precision London forecasts.
-=======
 - **Environment Variables**: Set `TELEGRAM_BOT_TOKEN` in `.env` (required). Optionally set `METEOBLUE_API_KEY` for London high-precision forecasts.
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

 ### VPS Deployment (Recommended)

@@ -110,17 +106,7 @@ py -3.11 run.py
 | **NWS**                 | Official (US)           | US Only         | US National Weather Service high-fidelity forecasts                         |
 | **MGM**                 | Observations (Turkey)   | Ankara Only     | Turkish State Met Service: pressure, cloud cover, feels-like, 24h rainfall  |

-<<<<<<< HEAD
-| Source             | Role                    | Coverage        | Strength                                                                           |
-| :----------------- | :---------------------- | :-------------- | :--------------------------------------------------------------------------------- |
-| **Open-Meteo**     | Base Forecast           | Global          | Provides detailed 72-hour temperature curves for all cities.                       |
-| **Meteoblue (MB)** | **Precision Consensus** | London Only     | **Traders' choice**. Aggregates multiple models; excellent for microclimates.      |
-| **METAR**          | **Settlement Standard** | Global Airports | The absolute truth for Polymarket settlement; real-time station data.              |
-| **NWS**            | Official (US)           | US Only         | High-fidelity forecasts for US cities, critical for extreme weather events.        |
-| **MGM**            | Official (Turkey)       | Ankara          | Direct access to Turkish State Meteorological Service for local official accuracy. |
-=======
 > ⚠️ **All NWP model queries use airport coordinates** (matching METAR station), not city center. This eliminates systematic bias between forecast and settlement locations.
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

 **Open-Meteo API Architecture**: Three API calls go through the same platform, each serving a different purpose:

@@ -240,19 +226,12 @@ graph TD
    Bot --> Collector[WeatherDataCollector]

    subgraph "Data Engine"
-<<<<<<< HEAD
-        Collector --> OM[Open-Meteo API]
-        Collector --> MB[Meteoblue Weather API]
-        Collector --> NOAA[METAR Data Center]
-        Collector --> MGM[Turkish MGM API]
-=======
        Collector --> MM[Multi-Model API<br/>ECMWF/GFS/ICON/GEM/JMA]
        Collector --> OM[Open-Meteo Forecast]
        Collector --> ENS[Open-Meteo Ensemble]
        Collector --> MB[Meteoblue API]
        Collector --> NOAA[METAR / NOAA]
        Collector --> MGM[MGM Observations]
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
        Collector --> NWS[US NWS API]
    end

@@ -271,12 +250,6 @@ graph TD

 ## 🎯 Betting Strategy Tips

-<<<<<<< HEAD
-1. **Check Consensus**: Compare Open-Meteo and Meteoblue (MB). Consensus usually implies higher probability.
-2. **Watch the Peak**: Use `/city` frequently during predicted peak windows to catch momentum.
-3. **Weighting Hierarchy**: Settlement is **METAR**; high-accuracy trend is **MB** (London); Official (NWS/MGM) is the "anchor."
-4. **Geographic Risk**: Pay close attention to cities where "Bias will significantly amplify."
-=======
 1. **Check Model Consensus**: The 🎯/⚖️/⚠️ rating tells you immediately if the forecast is reliable.
 2. **Use the Entry Signal**: Wait for ⏰ **Ideal** or **Good** timing before placing bets. Don't bet early when uncertainty is high.
 3. **Watch Ensemble Spread**: A tight 90% band (< 2°) means model confidence is high — this is where edges live.
@@ -289,4 +262,3 @@ graph TD
 ---

 _Last updated: 2026-02-22_
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
@@ -8,11 +8,7 @@

 - **Python 3.11+**
 - 依赖安装: `pip install -r requirements.txt`
-<<<<<<< HEAD
- **环境变量**: 需在 `.env` 中配置 `METEOBLUE_API_KEY` 以激活伦敦高精度预报。
-=======
 - **环境变量**: 在 `.env` 中设置 `TELEGRAM_BOT_TOKEN`（必需）。可选设置 `METEOBLUE_API_KEY` 以激活伦敦高精度预报。
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

 ### VPS 部署 (推荐)

@@ -110,17 +106,7 @@ py -3.11 run.py
 | **NWS**                 | 官方预测(美)   | 仅限美国   | 美国国家气象局高精度预报                                     |
 | **MGM**                 | 实测数据(土)   | 仅限安卡拉 | 土耳其气象局：气压、云量、体感温度、24h 降水                 |

-<<<<<<< HEAD
-| 数据源             | 数据角色       | 覆盖范围   | 优势                                             |
-| :----------------- | :------------- | :--------- | :----------------------------------------------- |
-| **Open-Meteo**     | 基础预测       | 全球       | 提供所有城市的 72 小时精细化温度曲线             |
-| **Meteoblue (MB)** | **高精度共识** | 仅限伦敦   | **交易员首选**。聚合多家模型，对微气候处理极佳   |
-| **METAR**          | **结算标准**   | 全球机场   | Polymarket 结算参考的绝对真理，实时机场观测      |
-| **NWS**            | 官方预测(美)   | 仅限美国   | 美国国家气象局，对美国城市的极端天气预判准确     |
-| **MGM**            | 官方预测(土)   | 仅限安卡拉 | 土耳其气象局，提供安卡拉 Esenboğa 机场的官方数据 |
-=======
 > ⚠️ **所有 NWP 模型查询使用机场坐标**（与 METAR 站点一致），而非市中心。这消除了预报位置与结算位置之间的系统性偏差。
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

 **Open-Meteo API 架构关系**：三个 API 调用都经过 Open-Meteo 平台，但获取的是不同维度的数据：

@@ -244,13 +230,6 @@ graph TD
    User[/Telegram User/] --> Bot[bot_listener.py]
    Bot --> Collector[WeatherDataCollector]

-<<<<<<< HEAD
-    subgraph "Data Engine"
-        Collector --> OM[Open-Meteo API]
-        Collector --> MB[Meteoblue Weather API]
-        Collector --> NOAA[METAR Data Center]
-        Collector --> MGM[Turkish MGM API]
-=======
    subgraph "数据引擎"
        Collector --> MM[多模型 API<br/>ECMWF/GFS/ICON/GEM/JMA]
        Collector --> OM[Open-Meteo 预报]
@@ -258,7 +237,6 @@ graph TD
        Collector --> MB[Meteoblue API]
        Collector --> NOAA[METAR / NOAA]
        Collector --> MGM[MGM 实测数据]
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
        Collector --> NWS[US NWS API]
    end

@@ -277,12 +255,6 @@ graph TD

 ## 🎯 博弈策略提示

-<<<<<<< HEAD
-1. **检查模型共识**：查看 Open-Meteo 和 Meteoblue (MB) 是否达成共识。
-2. **关注峰值窗口**：在预测的峰值时段多次使用 `/city` 刷新。
-3. **数据权重优先级**：结算以 **METAR** 为准，趋势预测以 **MB** 为准（仅限伦敦）。
-4. **地理风险评估**：重点关注提示中的“偏差会显著放大”警告（如安卡拉、伦敦）。
-=======
 1. **看模型共识**：🎯/⚖️/⚠️ 评级让你一眼判断预报是否可靠。高共识 + 市场低定价 = 套利机会。
 2. **用入场信号**：等 ⏰ **理想** 或 **较好** 时机再下注。不确定性高时绝不提前入场。
 3. **关注集合散度**：90% 区间越窄（< 2°），模型置信越高 — 这才是 edge 所在。
@@ -295,4 +267,3 @@ graph TD
 ---

 _最后更新: 2026-02-22_
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
@@ -37,29 +37,21 @@ def analyze_weather_trend(weather_data, temp_symbol):
        forecast_highs.append(mb["today_high"])
    if nws.get("today_high") is not None:
        forecast_highs.append(nws["today_high"])
-<<<<<<< HEAD
-    if mgm.get("today_high") is not None:
-        forecast_highs.append(mgm["today_high"])
-=======
    # 加入多模型预报 (ECMWF, GFS, ICON, GEM, JMA)
    for mv in weather_data.get("multi_model", {}).get("forecasts", {}).values():
        if mv is not None:
            forecast_highs.append(mv)
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
    
    forecast_highs = [h for h in forecast_highs if h is not None]
    # 取预报中的最高值作为风险防御基准
    forecast_high = max(forecast_highs) if forecast_highs else None
    # 取最低值用于判断是否“已触及预报高位”
    min_forecast_high = min(forecast_highs) if forecast_highs else forecast_high
-<<<<<<< HEAD
-=======
    # 取中位数作为用户可见的"预期值"（避免极端模型误导）
    forecast_median = None
    if forecast_highs:
        sorted_fh = sorted(forecast_highs)
        forecast_median = sorted_fh[len(sorted_fh) // 2]
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
    
    wind_speed = metar.get("current", {}).get("wind_speed_kt", 0)
    
@@ -186,14 +178,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
    is_breakthrough = False
    if max_so_far is not None and forecast_high is not None:
        if max_so_far > forecast_high + 0.5:
-<<<<<<< HEAD
-            # 实测已超所有预报！
-            exceed_by = max_so_far - forecast_high
-            insights.append(f"🚨 <b>预报已被击穿</b>：实测最高 {max_so_far}{temp_symbol} 已超所有预报上限 {forecast_high}{temp_symbol} 约 {exceed_by:.1f}°！")
-            insights.append(f"💡 <b>博弈建议</b>：市场需重新评估，当前可能存在极端异常增温。")
-            return "\n💡 <b>态势分析</b>\n" + "\n".join(insights)
-
-=======
            is_breakthrough = True
            exceed_by = max_so_far - forecast_high
            insights.append(f"🚨 <b>实测已超预报</b>：实测最高 {max_so_far}{temp_symbol} 超过了所有预报的天花板 {forecast_high}{temp_symbol}，多了 {exceed_by:.1f}°！")
@@ -221,7 +205,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
                    f"刚刚越过进位线，再降 <b>{fractional - 0.5:.1f}°</b> 就会回落到 {settled - 1}{temp_symbol}。"
                )

->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
    # --- 峰值时刻预测逻辑 (仍以 Open-Meteo 逐小时数据为准) ---
    hourly = open_meteo.get("hourly", {})
    times = hourly.get("time", [])
@@ -236,48 +219,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
                    hour = t_str.split("T")[1][:5]
                    peak_hours.append(hour)
        
-<<<<<<< HEAD
-        if peak_hours:
-            window = f"{peak_hours[0]} - {peak_hours[-1]}" if len(peak_hours) > 1 else peak_hours[0]
-            insights.append(f"⏱️ <b>预计峰值时刻</b>：今天 <b>{window}</b> 之间。")
-            # 只有在还没进入峰值时段且还没达到预报高点时才给这个建议
-            if local_hour < int(peak_hours[0].split(":")[0]) and (max_so_far is None or max_so_far < forecast_high):
-                insights.append(f"🎯 <b>博弈建议</b>：关注该时段实测能否站稳 {forecast_high}{temp_symbol}。")
-
-    is_peak_passed = False
-    if curr_temp is not None and forecast_high is not None:
-        diff_max = forecast_high - curr_temp
-        
-        # 1. 气温节奏判定 (动态参考峰值时刻)
-        last_peak_h = int(peak_hours[-1].split(":")[0]) if peak_hours else 15
-        first_peak_h = int(peak_hours[0].split(":")[0]) if peak_hours else 13
-        
-        if local_hour > last_peak_h:
-            # 已经过了预报的峰值时段
-            is_peak_passed = True
-            # 如果实测已经接近“任一”主流预报的最高温 (使用 min_forecast_high)
-            if max_so_far and max_so_far >= min_forecast_high - 0.5:
-                insights.append(f"✅ <b>今日峰值已过</b>：气温已触及或接近预报最高，目前处于高位波动或缓慢回落。")
-            else:
-                # 虽然时间过了，但离最高温还有差距
-                insights.append(f"📉 <b>处于降温期</b>：已过预报峰值时段，且当前气温乏力 ({curr_temp}{temp_symbol})，冲击最高预报 {forecast_high}{temp_symbol} 的概率降低。")
-        elif first_peak_h <= local_hour <= last_peak_h:
-            # 正在峰值窗口内
-            if diff_max <= 0.8:
-                insights.append(f"⚖️ <b>高位横盘</b>：正处于预测峰值时段，气温将在当前水平小幅波动。")
-            else:
-                insights.append(f"⏳ <b>峰值窗口中</b>：虽在预报高点时段，但目前仍有差距，紧盯最后冲刺。")
-        elif local_hour < first_peak_h:
-            # 还没到峰值窗口
-            if diff_max > 1.2:
-                insights.append(f"📈 <b>升温进程中</b>：距离峰值时段还有 {first_peak_h - local_hour}h，正向高点冲击。")
-            else:
-                insights.append(f"🌅 <b>临近峰值</b>：即将进入高点时段，气温已处于预报高位。")
-        else:
-            # 回退逻辑
-            insights.append(f"🌌 <b>夜间/早间</b>：等待日出后的新一轮波动。")
-
-=======
    # 确定用于逻辑判断的峰值小时
    if peak_hours:
        first_peak_h = int(peak_hours[0].split(":")[0])
@@ -331,43 +272,20 @@ def analyze_weather_trend(weather_data, temp_symbol):
            # 回退逻辑
            insights.append(f"🌌 <b>夜间</b>：等明天太阳出来后再看新一轮升温。")

->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
        # 2. 湿度与露点分析 (仅在傍晚以后)
        humidity = metar.get("current", {}).get("humidity")
        dewpoint = metar.get("current", {}).get("dewpoint")
        
        if local_hour >= 18:
            if humidity and humidity > 80:
-<<<<<<< HEAD
-                insights.append(f"💦 <b>闷热高湿</b>：湿度极高 ({humidity}%)，将显著锁住夜间热量。")
-            if dewpoint is not None and curr_temp - dewpoint < 2.0:
-                insights.append(f"🌡️ <b>触及露点支撑</b>：气温已跌至露点支撑位，降温将变慢。")
-=======
                insights.append(f"💦 <b>湿度很高</b>：湿度 {humidity}%，空气很潮湿，夜里热量散不掉，降温会很慢。")
            if dewpoint is not None and curr_temp - dewpoint < 2.0:
                insights.append(f"🌡️ <b>降温快到底了</b>：温度已经接近露点（空气中水汽开始凝结的温度），再往下降会很困难。")
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

        # 3. 风力
        if wind_speed >= 15:
            insights.append(f"🌬️ <b>风很大</b>：风速 {wind_speed}kt，温度可能会忽高忽低。")
        elif wind_speed >= 10:
-<<<<<<< HEAD
-            insights.append(f"🍃 <b>清劲风</b>：空气流动快，虽然有助于散热，但在升温期可能带来暖平流加速。")
-
-        # 4. 云层遮挡分析 (仅在升温期/峰值期有意义)
-        clouds = metar.get("current", {}).get("clouds", [])
-        if clouds and local_hour <= last_peak_h + 1:
-            main_cloud = clouds[-1]
-            cover = main_cloud.get("cover", "")
-            if cover == "OVC":
-                insights.append(f"☁️ <b>全阴锁温</b>：机场上空完全遮挡，阳光增温几乎停滞，很难再冲高点。")
-            elif cover == "BKN":
-                insights.append(f"🌥️ <b>云层显著</b>：天空大部被遮挡，日照受限，升温斜率受阻。")
-            elif cover in ["SKC", "CLR", "FEW"]:
-                if not is_peak_passed:
-                    insights.append(f"☀️ <b>晴空万里</b>：日照强烈，无云层遮挡，气温有冲向预报上限甚至超出的动能。")
-=======
            insights.append(f"🍃 <b>有风</b>：风速适中 ({wind_speed}kt)，会加速空气流动，具体影响看风向。")

        # 4. 云层遮挡分析 (仅在升温期/峰值期有意义)
@@ -381,38 +299,12 @@ def analyze_weather_trend(weather_data, temp_symbol):
                insights.append(f"🌥️ <b>云比较多</b>：天空大部分被云挡住了，日照不足，升温会比较慢。")
            elif cover in ["SKC", "CLR", "FEW"]:
                insights.append(f"☀️ <b>大晴天</b>：阳光直射，没什么云，有利于温度继续往上冲。")
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

        # 5. 特殊天气现象
        wx_desc = metar.get("current", {}).get("wx_desc")
        has_mgm = bool(mgm.get("current"))
        mgm_rain = mgm.get("current", {}).get("rain_24h")
        if wx_desc:
-<<<<<<< HEAD
-            if any(x in wx_desc.upper() for x in ["RA", "DZ", "RAIN", "DRIZZLE"]):
-                insights.append(f"🌧️ <b>降雨压制</b>：当前有降雨，蒸发吸热将显著抑制升温。")
-            elif any(x in wx_desc.upper() for x in ["SN", "SNOW", "GR", "GS"]):
-                insights.append(f"❄️ <b>固态降水</b>：正在降雪或冰雹，气温将持续低迷。")
-            elif any(x in wx_desc.upper() for x in ["FG", "BR", "HZ", "FOG", "MIST"]):
-                insights.append(f"🌫️ <b>能见度受限</b>：当前有雾/霭，阻挡阳光并带来高湿，会大幅延缓升温周期。")
-
-        # 6. 风向平流分析 (仅在未进入降温期前显示)
-        if not is_peak_passed or local_hour <= last_peak_h + 2:
-            try:
-                wind_dir = float(metar.get("current", {}).get("wind_dir", 0))
-                # 北半球简化逻辑：北风 cold，南风 warm
-                if 315 <= wind_dir or wind_dir <= 45:
-                    insights.append(f"🌬️ <b>偏北风</b>：冷空气处于主导地位，午后增温阻力较大。")
-                elif 135 <= wind_dir <= 225:
-                    # 只有在当前温度离最高预测还有距离时，南风才有意义
-                    if diff_max > 0.5:
-                        if is_peak_passed:
-                            insights.append(f"🔥 <b>偏南风</b>：存在暖平流支撑，但已过传统峰值时段，冲击上限 {forecast_high}{temp_symbol} 的动能正在衰减。")
-                        else:
-                            insights.append(f"🔥 <b>偏南风</b>：正从低纬度输送暖平流，气温仍有向上突围的潜力。")
-            except (TypeError, ValueError):
-                pass
-=======
            wx_upper = wx_desc.upper().strip()
            wx_tokens = wx_upper.split()
            # 用分词匹配，避免 "METAR" 中的 "RA" 误判
@@ -483,7 +375,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
                    insights.append(f"🌬️ <b>吹东风</b>（{wind_source} {wd:.0f}°）：对温度影响较小，主要看日照和云量。")
        except (TypeError, ValueError):
            pass
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

        try:
            visibility = metar.get("current", {}).get("visibility_mi")
@@ -494,16 +385,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
        except (TypeError, ValueError):
            pass

-<<<<<<< HEAD
-        # 7. 模型准确度预警 (针对用户反馈的 MB 偏高问题)
-        if is_peak_passed and max_so_far is not None:
-            model_checks = []
-            if om_high and om_high > max_so_far + 1.5:
-                model_checks.append(f"Open-Meteo ({om_high}{temp_symbol})")
-            mb_h = mb.get("today_high")
-            if mb_h and mb_h > max_so_far + 1.5:
-                model_checks.append(f"Meteoblue ({mb_h}{temp_symbol})")
-=======
        # 7. 模型准确度预警（使用多模型数据）
        if is_peak_passed and max_so_far is not None:
            model_checks = []
@@ -514,16 +395,11 @@ def analyze_weather_trend(weather_data, temp_symbol):
            mb_h = mb.get("today_high")
            if mb_h and mb_h > max_so_far + 1.5:
                model_checks.append(f"MB ({mb_h}{temp_symbol})")
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
            nws_h = nws.get("today_high")
            if nws_h and nws_h > max_so_far + 1.5:
                model_checks.append(f"NWS ({nws_h}{temp_symbol})")
            
            if model_checks:
-<<<<<<< HEAD
-                insights.append(f"⚠️ <b>预报偏高</b>：目前实测远低于 " + "、".join(model_checks) + "，判定预报模型今日表现过度乐观。")
-
-=======
                insights.append(f"⚠️ <b>预报偏高了</b>：实测远低于 " + "、".join(model_checks) + "，这些模型今天报高了。")

        # 8. MGM 气压分析 (仅安卡拉)
@@ -638,7 +514,6 @@ def analyze_weather_trend(weather_data, temp_symbol):
            insights.append(f"⏰ <b>入场时机：谨慎</b> — {factors_str}。建议继续观察。")
        else:
            insights.append(f"⏰ <b>入场时机：不建议</b> — {factors_str}。不确定性大，等更多数据。")
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729

    if not insights:
        return ""
@@ -708,13 +583,6 @@ def start_bot():
                "par": "paris", "巴黎": "paris",
            }
            
-<<<<<<< HEAD
-            # 1. 第一优先级：严格全字匹配
-            city_name = STANDARD_MAPPING.get(city_input)
-            
-            # 2. 第二优先级：如果长度 >= 3，尝试前缀匹配
-            if not city_name and len(city_input) >= 3:
-=======
            # 支持的城市全名列表（用于模糊匹配）
            SUPPORTED_CITIES = list(set(STANDARD_MAPPING.values()))
            
@@ -728,7 +596,6 @@ def start_bot():
            # 3. 第三优先级：前缀匹配（在别名和城市全名中搜索）
            if not city_name and len(city_input) >= 2:
                # 先搜别名
->>>>>>> e575440acfd8b5f1e8c30e83dfcb972d26175729
                for k, v in STANDARD_MAPPING.items():
                    if k.startswith(city_input):
                        city_name = v