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docs: Clean up merge conflict markers and update betting strategy tips in README.

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2026-02-23 21:23:52 +08:00
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@@ -8,10 +8,7 @@ An intelligent weather bot for prediction markets and professional weather betti
- **Python 3.11+**
- Dependencies: `pip install -r requirements.txt`
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- # **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.
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### VPS Deployment (Recommended)
@@ -69,21 +66,21 @@ py -3.11 run.py
### Supported Cities
| City | Aliases | METAR Station | Extra Sources |
| :----------- | :--------------------- | :------------------------ | :------------ |
| London | `lon`, `伦敦` | EGLC (City Airport) | Meteoblue |
| Paris | `par`, `巴黎` | LFPG (Charles de Gaulle) | — |
| Ankara | `ank`, `安卡拉` | LTAC (Esenboğa) | MGM |
| New York | `nyc`, `ny`, `纽约` | KLGA (LaGuardia) | NWS |
| Chicago | `chi`, `芝加哥` | KORD (O'Hare) | NWS |
| Dallas | `dal`, `达拉斯` | KDAL (Love Field) | NWS |
| Miami | `mia`, `迈阿密` | KMIA (International) | NWS |
| Atlanta | `atl`, `亚特兰大` | KATL (Hartsfield-Jackson) | NWS |
| Seattle | `sea`, `西雅图` | KSEA (Sea-Tac) | NWS |
| Toronto | `tor`, `多伦多` | CYYZ (Pearson) | — |
| Seoul | `sel`, `首尔` | RKSI (Incheon) | — |
| Buenos Aires | `ba`, `布宜诺斯艾利斯` | SAEZ (Ezeiza) | — |
| Wellington | `wel`, `惠灵顿` | NZWN (Wellington) | — |
| City | Aliases | METAR Station | Extra Sources |
|:---|:---|:---|:---|
| London | `lon`, `伦敦` | EGLC (City Airport) | Meteoblue |
| Paris | `par`, `巴黎` | LFPG (Charles de Gaulle) | — |
| Ankara | `ank`, `安卡拉` | LTAC (Esenboğa) | MGM |
| New York | `nyc`, `ny`, `纽约` | KLGA (LaGuardia) | NWS |
| Chicago | `chi`, `芝加哥` | KORD (O'Hare) | NWS |
| Dallas | `dal`, `达拉斯` | KDAL (Love Field) | NWS |
| Miami | `mia`, `迈阿密` | KMIA (International) | NWS |
| Atlanta | `atl`, `亚特兰大` | KATL (Hartsfield-Jackson) | NWS |
| Seattle | `sea`, `西雅图` | KSEA (Sea-Tac) | NWS |
| Toronto | `tor`, `多伦多` | CYYZ (Pearson) | — |
| Seoul | `sel`, `首尔` | RKSI (Incheon) | — |
| Buenos Aires | `ba`, `布宜诺斯艾利斯` | SAEZ (Ezeiza) | — |
| Wellington | `wel`, `惠灵顿` | NZWN (Wellington) | — |
### Example
@@ -99,15 +96,15 @@ py -3.11 run.py
### 1. 🏛️ Multi-Source Data Fusion
| Source | Role | Coverage | Strength |
| :---------------------- | :---------------------- | :-------------- | :-------------------------------------------------------------------------------- |
| **Multi-Model (5 NWP)** | **Consensus Scoring** | Global | ECMWF, GFS, ICON, GEM, JMA — 5 fully independent NWP models via Open-Meteo |
| Source | Role | Coverage | Strength |
| :---------------------- | :---------------------- | :-------------- | :-------------------------------------------------------------------------- |
| **Multi-Model (5 NWP)** | **Consensus Scoring** | Global | ECMWF, GFS, ICON, GEM, JMA — 5 fully independent NWP models via Open-Meteo |
| **Open-Meteo** | Base Forecast | Global | 72h hourly curves, sunrise/sunset, **sunshine duration**, **shortwave radiation** |
| **Open-Meteo Ensemble** | **Uncertainty Range** | Global | 51-member ensemble: median, P10, P90 spread for confidence assessment |
| **Meteoblue (MB)** | Precision Consensus | London Only | Multi-model aggregation; excellent for microclimates |
| **METAR** | **Settlement Standard** | Global Airports | Polymarket settlement source; real-time airport observations |
| **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 |
| **Open-Meteo Ensemble** | **Uncertainty Range** | Global | 51-member ensemble: median, P10, P90 spread for confidence assessment |
| **Meteoblue (MB)** | Precision Consensus | London Only | Multi-model aggregation; excellent for microclimates |
| **METAR** | **Settlement Standard** | Global Airports | Polymarket settlement source; real-time airport observations |
| **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 |
> ⚠️ **All NWP model queries use airport coordinates** (matching METAR station), not city center. This eliminates systematic bias between forecast and settlement locations.
@@ -151,11 +148,11 @@ py -3.11 run.py
The bot queries **5 independent NWP models** (ECMWF, GFS, ICON, GEM, JMA) to rate forecast agreement:
| Level | Condition (°C / °F) | Meaning |
| :------------ | :--------------------- | :------------------------------------------------------ |
| 🎯 **High** | Spread ≤ 0.8°C / 1.5°F | All 5 models converge — high confidence, low risk |
| ⚖️ **Medium** | Spread ≤ 1.5°C / 3.0°F | Minor disagreement — moderate confidence |
| ⚠️ **Low** | Spread > 1.5°C / 3.0°F | Major divergence — high uncertainty, wait for more data |
| Level | Condition (°C / °F) | Meaning |
|:---|:---|:---|
| 🎯 **High** | Spread ≤ 0.8°C / 1.5°F | All 5 models converge — high confidence, low risk |
| ⚖️ **Medium** | Spread ≤ 1.5°C / 3.0°F | Minor disagreement — moderate confidence |
| ⚠️ **Low** | Spread > 1.5°C / 3.0°F | Major divergence — high uncertainty, wait for more data |
Primary models: **ECMWF IFS** (Europe), **GFS** (US NOAA), **ICON** (Germany DWD), **GEM** (Canada), **JMA** (Japan). Plus Meteoblue (London) and NWS (US) when available. Ensemble median is excluded to avoid double-counting.
@@ -173,22 +170,22 @@ A tight range = high confidence in the forecast. A wide range = the atmosphere i
A composite score combining three factors to advise on betting timing:
| Factor | Score |
| :------------------------------------ | :---- |
| Peak already passed | +3 |
| ≤ 2h to peak | +2 |
| ≤ 4h to peak | +1 |
| Model consensus: High | +2 |
| Model consensus: Medium | +1 |
| Actual ≈ Forecast (gap ≤ 0.5°) | +2 |
| Actual close to Forecast (gap ≤ 1.5°) | +1 |
| Factor | Score |
|:---|:---|
| Peak already passed | +3 |
| ≤ 2h to peak | +2 |
| ≤ 4h to peak | +1 |
| Model consensus: High | +2 |
| Model consensus: Medium | +1 |
| Actual ≈ Forecast (gap ≤ 0.5°) | +2 |
| Actual close to Forecast (gap ≤ 1.5°) | +1 |
| Total ≥ | Signal | Advice |
| :------ | :--------------------- | :---------------------------- |
| 5 | ⏰ **Ideal** | Low uncertainty — good to bet |
| 3 | ⏰ **Good** | Consider small positions |
| 2 | ⏰ **Cautious** | Keep observing |
| <2 | ⏰ **Not Recommended** | High uncertainty — wait |
| Total ≥ | Signal | Advice |
|:---|:---|:---|
| 5 | ⏰ **Ideal** | Low uncertainty — good to bet |
| 3 | ⏰ **Good** | Consider small positions |
| 2 | ⏰ **Cautious** | Keep observing |
| <2 | ⏰ **Not Recommended** | High uncertainty — wait |
### 6. 🧠 Smart Trend Analysis (Plain Language)
@@ -253,23 +250,15 @@ graph TD
## 🎯 Betting Strategy Tips
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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."
5. **Check Model Consensus**: The 🎯/⚖️/⚠️ rating tells you immediately if the forecast is reliable.
6. **Use the Entry Signal**: Wait for ⏰ **Ideal** or **Good** timing before placing bets. Don't bet early when uncertainty is high.
7. **Watch Ensemble Spread**: A tight 90% band (< 2°) means model confidence is high — this is where edges live.
8. **Watch the Peak Window**: Use `/city` frequently during predicted peak hours.
9. **Settlement Priority**: Settlement is always based on **METAR** data, rounded to integer via Wunderground.
10. **Geographic Risk**: Pay attention to bias warnings, especially for high-risk cities like Seoul and Chicago.
11. **Solar Radiation Clues**: If the bot reports "warm advection driven" 🌙, the temperature was pushed by warm air, not sunlight — this pattern often breaks model predictions.
12. **Wind Conflicts**: When METAR and MGM show opposite wind directions, expect temperature volatility.
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.
4. **Watch the Peak Window**: Use `/city` frequently during predicted peak hours.
5. **Settlement Priority**: Settlement is always based on **METAR** data, rounded to integer via Wunderground.
6. **Geographic Risk**: Pay attention to bias warnings, especially for high-risk cities like Seoul and Chicago.
7. **Solar Radiation Clues**: If the bot reports "warm advection driven" 🌙, the temperature was pushed by warm air, not sunlight — this pattern often breaks model predictions.
8. **Wind Conflicts**: When METAR and MGM show opposite wind directions, expect temperature volatility.
---
_Last updated: 2026-02-22_
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