HK Weather Prediction Market Pipeline: WeatherNext + HKO + Polymarket
- Open-Meteo WeatherNext API client for HK forecasts - HKO public data client (current conditions, 9-day forecast, typhoon warnings) - HK-specific weather extraction and calibration - Polymarket market scanning, price discovery, and market creation proposals - Trading strategy engine: edge detection, Kelly criterion sizing, probability calibration - End-to-end pipeline with dry-run mode and scheduled runner - Interactive dashboard with live HK weather + forecasts + trading signals Dependencies: Python 3.10+, openmeteo-requests, pandas No API keys needed for dry-run mode. Polymarket trading requires private key in .env.
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ramseshk
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"""Open-Meteo client for WeatherNext and other weather model data."""
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from datetime import datetime, timedelta
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from typing import Optional
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import numpy as np
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import pandas as pd
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try:
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import openmeteo_requests
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import requests_cache
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from retry_requests import retry
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except ImportError:
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import subprocess, sys
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subprocess.check_call([sys.executable, "-m", "pip", "install", "openmeteo-requests", "requests-cache", "retry-requests"])
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import openmeteo_requests
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import requests_cache
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from retry_requests import retry
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from config import HK_COORDS, HK_BBOX, OPENMETEO_API_KEY
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class OpenMeteoClient:
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"""Fetch weather data from Open-Meteo including WeatherNext model outputs."""
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BASE_URL = "https://api.open-meteo.com/v1/"
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# Available weather models via Open-Meteo
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MODELS = {
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"weathernext": "google_weathernext",
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"ecmwf": "ecmwf_ifs04",
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"gfs": "gfs_seamless",
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}
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def __init__(self, model: str = "weathernext", cache_ttl: int = 3600):
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cache = requests_cache.CachedSession('.openmeteo_cache', expire_after=cache_ttl)
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retry_session = retry(cache, retries=3, backoff_factor=0.2)
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self.client = openmeteo_requests.Client(session=retry_session)
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self.model = model
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self.params = {
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"latitude": HK_COORDS["hko_headquarters"][0],
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"longitude": HK_COORDS["hko_headquarters"][1],
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"timezone": "Asia/Hong_Kong",
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}
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def get_forecast(self, lead_days: int = 7) -> Optional[pd.DataFrame]:
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"""Fetch forecast for Hong Kong from selected model."""
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params = {
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**self.params,
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"daily": [
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"temperature_2m_max",
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"temperature_2m_min",
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"temperature_2m_mean",
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"precipitation_sum",
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"precipitation_probability_max",
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"rain_sum",
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"wind_speed_10m_max",
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"wind_gusts_10m_max",
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"wind_direction_10m_dominant",
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"shortwave_radiation_sum",
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"et0_fao_evapotranspiration",
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"weather_code",
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],
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"hourly": [
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"temperature_2m",
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"relative_humidity_2m",
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"dew_point_2m",
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"apparent_temperature",
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"precipitation_probability",
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"precipitation",
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"rain",
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"cloud_cover",
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"cloud_cover_low",
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"cloud_cover_mid",
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"cloud_cover_high",
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"wind_speed_10m",
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"wind_speed_100m",
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"wind_gusts_10m",
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"wind_direction_10m",
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"wind_direction_100m",
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"surface_pressure",
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"visibility",
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],
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"past_days": 0,
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"forecast_days": lead_days,
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}
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if OPENMETEO_API_KEY:
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params["apikey"] = OPENMETEO_API_KEY
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try:
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responses = self.client.weather_api(self.BASE_URL + "forecast", params=params)
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return self._parse_response(responses[0])
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except Exception as e:
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print(f"Open-Meteo API error: {e}")
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return None
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def _parse_response(self, response) -> pd.DataFrame:
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"""Parse Open-Meteo response into a DataFrame."""
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hourly = response.Hourly()
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daily = response.Daily()
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self._hourly_vars = [
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"temperature_2m", "relative_humidity_2m", "dew_point_2m",
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"apparent_temperature", "precipitation_probability", "precipitation",
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"rain", "cloud_cover", "cloud_cover_low", "cloud_cover_mid",
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"cloud_cover_high", "wind_speed_10m", "wind_speed_100m",
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"wind_gusts_10m", "wind_direction_10m", "wind_direction_100m",
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"surface_pressure", "visibility",
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]
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self._daily_vars = [
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"temperature_2m_max", "temperature_2m_min", "temperature_2m_mean",
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"precipitation_sum", "precipitation_probability_max", "rain_sum",
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"wind_speed_10m_max", "wind_gusts_10m_max",
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"wind_direction_10m_dominant", "shortwave_radiation_sum",
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"et0_fao_evapotranspiration", "weather_code",
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]
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hourly_data = {
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"date": pd.date_range(
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start=pd.Timestamp(hourly.Time(), unit="s", tz="UTC"),
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end=pd.Timestamp(hourly.TimeEnd(), unit="s", tz="UTC"),
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freq=pd.Timedelta(seconds=hourly.Interval()),
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inclusive="left",
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)
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}
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for i in range(hourly.VariablesLength()):
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var = hourly.Variables(i)
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if i < len(self._hourly_vars):
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hourly_data[self._hourly_vars[i]] = var.ValuesAsNumpy()
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hourly_df = pd.DataFrame(hourly_data).set_index("date")
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hourly_df.index = hourly_df.index.tz_convert("Asia/Hong_Kong")
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daily_data = {
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"date": pd.date_range(
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start=pd.Timestamp(daily.Time(), unit="s", tz="UTC"),
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end=pd.Timestamp(daily.TimeEnd(), unit="s", tz="UTC"),
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freq=pd.Timedelta(seconds=daily.Interval()),
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inclusive="left",
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)
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}
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for i in range(daily.VariablesLength()):
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var = daily.Variables(i)
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if i < len(self._daily_vars):
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daily_data[self._daily_vars[i]] = var.ValuesAsNumpy()
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daily_df = pd.DataFrame(daily_data).set_index("date")
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daily_df.index = daily_df.index.tz_convert("Asia/Hong_Kong")
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daily_df.attrs["model"] = self.model
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daily_df.attrs["hourly"] = hourly_df
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daily_df.attrs["fetch_time"] = datetime.now()
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return daily_df
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def get_current_conditions(self) -> dict:
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"""Get current weather conditions at HKO headquarters."""
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self._current_vars = [
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"temperature_2m", "relative_humidity_2m", "apparent_temperature",
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"precipitation", "rain", "cloud_cover", "wind_speed_10m",
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"wind_direction_10m", "wind_gusts_10m", "surface_pressure",
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"weather_code",
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]
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params = {
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**self.params,
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"current": self._current_vars,
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"forecast_days": 1,
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}
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try:
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responses = self.client.weather_api(self.BASE_URL + "forecast", params=params)
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current = responses[0].Current()
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result = {"timestamp": datetime.now().isoformat()}
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for i in range(current.VariablesLength()):
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if i < len(self._current_vars):
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result[self._current_vars[i]] = current.Variables(i).Value()
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return {
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"temperature": result.get("temperature_2m"),
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"humidity": result.get("relative_humidity_2m"),
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"apparent_temp": result.get("apparent_temperature"),
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"precipitation": result.get("precipitation"),
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"rain": result.get("rain"),
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"cloud_cover": result.get("cloud_cover"),
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"wind_speed": result.get("wind_speed_10m"),
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"wind_direction": result.get("wind_direction_10m"),
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"wind_gusts": result.get("wind_gusts_10m"),
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"surface_pressure": result.get("surface_pressure"),
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"weather_code": result.get("weather_code"),
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"timestamp": datetime.now().isoformat(),
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}
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except Exception as e:
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print(f"Open-Meteo current conditions error: {e}")
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return {}
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def get_precipitation_probability(self, hours_ahead: int = 24) -> float:
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"""Get precipitation probability for next N hours."""
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df = self.get_forecast(lead_days=2)
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if df is not None and hasattr(df, 'attrs') and 'hourly' in df.attrs:
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hourly = df.attrs['hourly']
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now = pd.Timestamp.now(tz="Asia/Hong_Kong")
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future = hourly[hourly.index <= now + pd.Timedelta(hours=hours_ahead)]
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if 'precipitation_probability' in future.columns:
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return float(future['precipitation_probability'].max())
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return 0.0
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def get_scoring_window_summary(self, target_date: str) -> dict:
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"""
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Get a forecast summary for a specific scoring window (target date).
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Used by the signal generator to create trading signals.
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Returns a dict with all relevant forecast variables for market comparison.
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"""
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df = self.get_forecast(lead_days=7)
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if df is None:
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return {}
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target = pd.Timestamp(target_date).tz_localize("Asia/Hong_Kong")
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if target not in df.index:
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return {}
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row = df.loc[target]
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hourly = df.attrs.get("hourly", pd.DataFrame())
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if not hourly.empty:
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day_hourly = hourly[
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(hourly.index >= target) &
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(hourly.index < target + pd.Timedelta(days=1))
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]
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else:
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day_hourly = pd.DataFrame()
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summary = {
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"date": target_date,
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"model": self.model,
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"fetch_time": df.attrs.get("fetch_time", datetime.now()).isoformat(),
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"temperature_2m_max": float(row.get("temperature_2m_max", np.nan)),
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"temperature_2m_min": float(row.get("temperature_2m_min", np.nan)),
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"precipitation_sum": float(row.get("precipitation_sum", 0)),
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"precipitation_probability_max": float(row.get("precipitation_probability_max", 0)),
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"wind_speed_10m_max": float(row.get("wind_speed_10m_max", np.nan)),
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"wind_gusts_10m_max": float(row.get("wind_gusts_10m_max", np.nan)),
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}
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if not day_hourly.empty:
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summary["temperature_2m_max_hourly"] = float(day_hourly["temperature_2m"].max()) if "temperature_2m" in day_hourly else np.nan
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summary["precipitation_probability_max_hourly"] = float(day_hourly["precipitation_probability"].max()) if "precipitation_probability" in day_hourly else 0
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summary["precipitation_sum_hourly"] = float(day_hourly["precipitation"].sum()) if "precipitation" in day_hourly else 0
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return summary
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