sugar_collector.py

"""
AgriQuant AI - Sugar #11 Weather & Data Collector
Regions: Brazil (Center-South), India (Maharashtra/UP), Thailand
Ticker: SB (ICE)
Primary risks: Brazil drought, India monsoon failure, Thailand floods
"""
import requests, logging
from datetime import datetime
from typing import Dict, List
logger = logging.getLogger(__name__)

SUGAR_REGIONS = {
    'Brazil_CenterSouth': {
        'lat': -22.0, 'lon': -47.9, 'country': 'Brazil',
        'production_share': 0.38, 'harvest_months': [4,5,6,7,8,9,10,11],
        'drought_threshold_mm': 40, 'priority': 1,
        'notes': 'Sao Paulo state - largest sugar producing region globally'
    },
    'India_Maharashtra': {
        'lat': 17.5, 'lon': 75.3, 'country': 'India',
        'production_share': 0.18, 'harvest_months': [10,11,12,1,2,3],
        'monsoon_months': [6,7,8,9], 'priority': 2,
        'normal_monsoon_mm': 650,
        'notes': 'Western Maharashtra - Pune/Kolhapur belt'
    },
    'India_UttarPradesh': {
        'lat': 26.5, 'lon': 80.9, 'country': 'India',
        'production_share': 0.15, 'harvest_months': [10,11,12,1,2,3],
        'monsoon_months': [6,7,8,9], 'priority': 3,
        'normal_monsoon_mm': 880,
        'notes': 'UP belt - second largest India state producer'
    },
    'Thailand_Central': {
        'lat': 14.5, 'lon': 100.5, 'country': 'Thailand',
        'production_share': 0.12, 'harvest_months': [12,1,2,3],
        'priority': 4,
        'notes': 'Central plains - flood risk Oct-Nov'
    },
}

HISTORICAL_SUPPLY_EVENTS = [
    {'year': 2022, 'cause': 'India export ban', 'sb_impact_pct': 25.0, 'type': 'policy'},
    {'year': 2021, 'cause': 'Brazil drought + frost', 'sb_impact_pct': 18.5, 'type': 'weather'},
    {'year': 2020, 'cause': 'Thailand drought', 'sb_impact_pct': 20.3, 'type': 'weather'},
    {'year': 2015, 'cause': 'El Nino India drought', 'sb_impact_pct': 12.1, 'type': 'weather'},
    {'year': 2010, 'cause': 'India monsoon failure', 'sb_impact_pct': 32.4, 'type': 'weather'},
]

class SugarCollector:
    """
    Sugar #11 weather and supply data collection.
    Monitors Brazil drought, India monsoon, Thailand floods, and policy signals.
    """
    def __init__(self):
        self.noaa_base = 'https://api.weather.gov'
        self.imd_base = 'https://imdpune.gov.in'  # India Meteorological Dept

    def get_brazil_sugarcane_forecast(self, lat: float, lon: float) -> Dict:
        """GFS forecast for Sao Paulo sugarcane belt"""
        try:
            resp = requests.get(f"{self.noaa_base}/points/{lat},{lon}", timeout=10)
            if resp.status_code == 200:
                grid = resp.json()['properties']
                f = requests.get(grid['forecast'], timeout=10)
                if f.status_code == 200:
                    return {'source': 'NOAA_GFS', 'region': 'Brazil_CenterSouth',
                            'periods': f.json()['properties']['periods'][:8],
                            'timestamp': datetime.utcnow().isoformat()}
        except Exception as e:
            logger.error(f"Brazil sugar forecast error: {e}")
        return {'source': 'unavailable', 'timestamp': datetime.utcnow().isoformat()}

    def assess_india_monsoon(self, region: str, rainfall_data: Dict) -> Dict:
        """
        India monsoon assessment. June-September rainfall vs normal.
        IMD defines: >110% = excess, 90-110% = normal, 75-90% = deficient,
        <75% = large deficient. Large deficient = sugar supply shock.
        """
        region_data = SUGAR_REGIONS[region]
        normal_mm = region_data.get('normal_monsoon_mm', 700)
        actual_mm = rainfall_data.get('cumulative_monsoon_mm', normal_mm)
        pct_of_normal = (actual_mm / normal_mm) * 100

        if pct_of_normal < 75:
            category, price_impact = 'large_deficient', (18, 35)
        elif pct_of_normal < 90:
            category, price_impact = 'deficient', (8, 18)
        elif pct_of_normal <= 110:
            category, price_impact = 'normal', (0, 3)
        else:
            category, price_impact = 'excess', (-5, 0)  # bearish signal

        return {
            'region': region, 'actual_monsoon_mm': actual_mm,
            'normal_monsoon_mm': normal_mm, 'pct_of_normal': round(pct_of_normal, 1),
            'imd_category': category, 'price_impact_range_pct': price_impact,
            'signal': 'LONG' if pct_of_normal < 90 else ('SHORT' if pct_of_normal > 115 else 'NEUTRAL'),
            'production_share': region_data['production_share'],
        }

    def assess_brazil_drought(self, rainfall_data: Dict) -> Dict:
        """Drought assessment for Brazil Center-South sugarcane"""
        monthly_mm = rainfall_data.get('monthly_mm', 60)
        region = SUGAR_REGIONS['Brazil_CenterSouth']
        threshold = region['drought_threshold_mm']
        current_month = datetime.utcnow().month
        is_harvest = current_month in region['harvest_months']

        deficit_pct = max(0, (threshold - monthly_mm) / threshold)
        if deficit_pct > 0.6 and is_harvest:
            level, impact = 'critical', (12, 22)
        elif deficit_pct > 0.4:
            level, impact = 'high', (6, 14)
        elif deficit_pct > 0.2:
            level, impact = 'moderate', (2, 8)
        else:
            level, impact = 'low', (0, 2)

        return {
            'monthly_rainfall_mm': monthly_mm, 'threshold_mm': threshold,
            'drought_level': level, 'is_harvest_season': is_harvest,
            'price_impact_range_pct': impact,
            'production_share': region['production_share'],
        }

    def collect_all_regions(self) -> Dict:
        """Collect all sugar production region data"""
        results = {}
        logger.info("Collecting sugar production region data...")
        for region_name, data in SUGAR_REGIONS.items():
            forecast = self.get_brazil_sugarcane_forecast(data['lat'], data['lon'])
            if 'India' in region_name:
                risk = self.assess_india_monsoon(region_name,
                         {'cumulative_monsoon_mm': 600})
            else:
                risk = self.assess_brazil_drought({'monthly_mm': 55})
            results[region_name] = {'forecast': forecast, 'risk': risk,
                                    'priority': data['priority']}
        return {'commodity': 'SB', 'timestamp': datetime.utcnow().isoformat(),
                'regions': results}