Natural Gas Price Forecast 2026: Seasonal Patterns & Data-Driven Analysis

import requests
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class NatGasForecaster:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://api.oilpriceapi.com/v1"
        self.headers = {"Authorization": f"Token {api_key}"}

    def get_historical_prices(self, months=12):
        """Get historical natural gas prices."""
        response = requests.get(
            f"{self.base_url}/prices/past_month",
            params={"by_code": "NATURAL_GAS_USD", "interval": "1d"},
            headers=self.headers
        )
        return response.json()["data"]["prices"]

    def calculate_seasonal_factors(self, prices):
        """Calculate monthly seasonal adjustment factors."""
        df = pd.DataFrame(prices)
        df["date"] = pd.to_datetime(df["created_at"])
        df["month"] = df["date"].dt.month

        monthly_avg = df.groupby("month")["price"].mean()
        overall_avg = df["price"].mean()
        seasonal_factors = monthly_avg / overall_avg

        return seasonal_factors.to_dict()

    def simple_forecast(self, current_price, months_ahead, seasonal_factors):
        """Generate simple seasonal forecast."""
        forecasts = []
        current_month = datetime.now().month

        for i in range(months_ahead):
            target_month = ((current_month + i - 1) % 12) + 1
            factor = seasonal_factors.get(target_month, 1.0)
            forecast_price = current_price * factor

            forecasts.append({
                "months_ahead": i + 1,
                "month": target_month,
                "forecast_price": round(forecast_price, 2),
                "seasonal_factor": round(factor, 3)
            })

        return forecasts

    def get_storage_context(self):
        """Get current storage situation from Energy Intelligence."""
        response = requests.get(
            f"{self.base_url}/ei/oil_inventories/latest",
            headers=self.headers
        )
        return response.json()

# Usage
forecaster = NatGasForecaster("YOUR_API_KEY")

# Get current price
current_response = requests.get(
    "https://api.oilpriceapi.com/v1/prices/latest?by_code=NATURAL_GAS_USD",
    headers={"Authorization": "Token YOUR_API_KEY"}
)
current_price = current_response.json()["data"]["price"]

# Historical seasonal factors (example values)
seasonal_factors = {
    1: 1.15, 2: 1.11, 3: 1.02, 4: 0.92,
    5: 0.91, 6: 0.96, 7: 1.00, 8: 1.02,
    9: 0.98, 10: 1.00, 11: 1.07, 12: 1.13
}

# Generate 12-month forecast
forecast = forecaster.simple_forecast(current_price, 12, seasonal_factors)

print(f"Current Price: ${current_price:.2f}/MMBtu")
print("\n12-Month Seasonal Forecast:")
for f in forecast:
    print(f"  Month {f['month']}: ${f['forecast_price']:.2f} (factor: {f['seasonal_factor']})")

def storage_adjustment(storage_vs_5yr_pct):
    """Adjust forecast based on storage levels."""
    if storage_vs_5yr_pct < -15:
        return 1.15  # 15% bullish adjustment
    elif storage_vs_5yr_pct < -5:
        return 1.05
    elif storage_vs_5yr_pct < 5:
        return 1.00
    elif storage_vs_5yr_pct < 15:
        return 0.95
    else:
        return 0.85  # 15% bearish adjustment

# Apply storage adjustment to forecast
storage_vs_5yr = -8  # Example: 8% below 5-year average
adjustment = storage_adjustment(storage_vs_5yr)

adjusted_forecast = [
    {**f, "adjusted_price": round(f["forecast_price"] * adjustment, 2)}
    for f in forecast
]

def calculate_annual_gas_budget(monthly_consumption_mmbtu, api_key):
    """Calculate annual gas budget with seasonal adjustments."""
    forecaster = NatGasForecaster(api_key)

    # Get current price
    current = requests.get(
        "https://api.oilpriceapi.com/v1/prices/latest?by_code=NATURAL_GAS_USD",
        headers={"Authorization": f"Token {api_key}"}
    ).json()["data"]["price"]

    # Seasonal factors
    seasonal = {
        1: 1.15, 2: 1.11, 3: 1.02, 4: 0.92,
        5: 0.91, 6: 0.96, 7: 1.00, 8: 1.02,
        9: 0.98, 10: 1.00, 11: 1.07, 12: 1.13
    }

    # Calculate monthly costs
    monthly_costs = []
    total_cost = 0

    for month in range(1, 13):
        expected_price = current * seasonal[month]
        monthly_cost = monthly_consumption_mmbtu * expected_price
        monthly_costs.append({
            "month": month,
            "expected_price": round(expected_price, 2),
            "consumption": monthly_consumption_mmbtu,
            "cost": round(monthly_cost, 2)
        })
        total_cost += monthly_cost

    return {
        "current_price": current,
        "monthly_costs": monthly_costs,
        "annual_cost": round(total_cost, 2),
        "bear_case": round(total_cost * 0.8, 2),
        "bull_case": round(total_cost * 1.4, 2)
    }

# Example: Facility consuming 10,000 MMBtu/month
budget = calculate_annual_gas_budget(10000, "YOUR_API_KEY")
print(f"Annual Budget: ${budget['annual_cost']:,.0f}")
print(f"Bear Case: ${budget['bear_case']:,.0f}")
print(f"Bull Case: ${budget['bull_case']:,.0f}")