Build a Real-Time Energy Price Alert System with Python and the Volatility API
Learn how to build automated energy price monitoring and threshold alerts using the Energy Volatility API — with Python, webhooks, and email notifications.
In early April 2026, Brent crude futures tumbled to around $100/barrel amid geopolitical tensions in the Strait of Hormuz, while natural gas prices surged on the back of data center energy demand and LNG supply tightening. For energy traders, procurement teams, and industrial companies with significant energy cost exposure, these moves happened fast — and the teams that had automated monitoring systems in place responded hours faster than those relying on manual checks.
Building a real-time energy price alert system is no longer a tool reserved for quantitative hedge funds with dedicated engineering teams. With the Energy Volatility API and Python, you can have a working alert system running in under two hours.
What We're Building
A Python service that:
- Polls the Energy Volatility API for current commodity prices (Brent crude, Henry Hub natural gas, European TTF gas, US electricity)
- Evaluates prices against configurable threshold rules
- Fires alerts via email, Slack, or webhook when thresholds are breached
- Tracks volatility metrics (not just price) to catch unusual market conditions before they move against you
Why Volatility Alerts Matter More Than Price Alerts
Most amateur monitoring systems alert on price levels: "notify me when Brent crosses $95." This misses the real risk signal. A 2% move in a day when markets are calm is significant. A 2% move during a geopolitical crisis when markets are already pricing in instability may be noise.
Volatility-adjusted alerts — "notify me when the realized volatility for Brent exceeds 3 standard deviations of the 30-day average" — give you signal with much less noise. The Energy Volatility API provides these calculated metrics directly, eliminating the need to compute them yourself.
Prerequisites
pip install requests python-dotenv schedule slack-sdkRAPIDAPI_KEY=YOUR_API_KEY
SLACK_BOT_TOKEN=xoxb-your-token
SLACK_CHANNEL_ID=C0123456789
[email protected]Step 1: Fetch Real-Time Price and Volatility Data
import requests
import os
from datetime import datetime
from typing import Dict, Any, List
RAPIDAPI_KEY = os.environ["RAPIDAPI_KEY"]
ENERGY_HOST = "energy-volatility-api.p.rapidapi.com"
HEADERS = {
"X-RapidAPI-Key": RAPIDAPI_KEY,
"X-RapidAPI-Host": ENERGY_HOST,
"Content-Type": "application/json"
}
def get_current_prices(commodities: List[str] = None) -> Dict[str, Any]:
"""
Fetch current prices and volatility metrics for specified energy commodities.
Available commodities: "brent", "wti", "henry_hub", "ttf", "jkm", "uk_nbp",
"eu_power", "us_power_pjm", "coal_api2"
"""
if commodities is None:
commodities = ["brent", "henry_hub", "ttf"]
response = requests.get(
f"https://{ENERGY_HOST}/prices/current",
headers=HEADERS,
params={"commodities": ",".join(commodities)}
)
response.raise_for_status()
return response.json()
def get_volatility_metrics(commodity: str, lookback_days: int = 30) -> Dict[str, Any]:
"""
Get volatility metrics for a commodity: realized vol, implied vol,
historical percentile, and standard deviation bands.
"""
response = requests.get(
f"https://{ENERGY_HOST}/volatility/{commodity}",
headers=HEADERS,
params={"lookback_days": lookback_days}
)
response.raise_for_status()
return response.json()
# Quick test
prices = get_current_prices(["brent", "henry_hub"])
print(f"Brent: ${prices['brent']['price']:.2f}/bbl | "
f"Realized 30d vol: {prices['brent']['volatility']['realized_30d']:.1f}%")
print(f"Henry Hub: ${prices['henry_hub']['price']:.3f}/MMBtu | "
f"Realized 30d vol: {prices['henry_hub']['volatility']['realized_30d']:.1f}%")Step 2: Define Alert Rules
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional, Callable
class AlertSeverity(Enum):
INFO = "info"
WARNING = "warning"
CRITICAL = "critical"
@dataclass
class PriceAlertRule:
"""Alert when price crosses a static threshold."""
name: str
commodity: str
direction: str # "above" | "below"
threshold: float
severity: AlertSeverity = AlertSeverity.WARNING
cooldown_minutes: int = 60 # Minimum time between repeat alerts
@dataclass
class VolatilityAlertRule:
"""Alert when volatility exceeds N standard deviations from the mean."""
name: str
commodity: str
std_dev_threshold: float # e.g., 2.0 means alert if vol > 2 stdev above 30d avg
lookback_days: int = 30
severity: AlertSeverity = AlertSeverity.CRITICAL
cooldown_minutes: int = 120
# Configure your rules
PRICE_RULES: List[PriceAlertRule] = [
PriceAlertRule(
name="Brent Above $110",
commodity="brent",
direction="above",
threshold=110.0,
severity=AlertSeverity.CRITICAL
),
PriceAlertRule(
name="Brent Below $85",
commodity="brent",
direction="below",
threshold=85.0,
severity=AlertSeverity.WARNING
),
PriceAlertRule(
name="Henry Hub Above $4.50",
commodity="henry_hub",
direction="above",
threshold=4.50,
severity=AlertSeverity.WARNING
),
PriceAlertRule(
name="TTF Above €45",
commodity="ttf",
direction="above",
threshold=45.0,
severity=AlertSeverity.CRITICAL
),
]
VOLATILITY_RULES: List[VolatilityAlertRule] = [
VolatilityAlertRule(
name="Brent Extreme Volatility",
commodity="brent",
std_dev_threshold=2.5,
severity=AlertSeverity.CRITICAL
),
VolatilityAlertRule(
name="Gas Market Stress",
commodity="henry_hub",
std_dev_threshold=2.0,
severity=AlertSeverity.WARNING
),
]Step 3: Alert Evaluation Engine
from datetime import datetime, timedelta
class AlertEngine:
"""Evaluates rules against current market data and fires alerts."""
def __init__(self):
self._last_fired: Dict[str, datetime] = {}
def _is_in_cooldown(self, rule_name: str, cooldown_minutes: int) -> bool:
if rule_name not in self._last_fired:
return False
elapsed = datetime.utcnow() - self._last_fired[rule_name]
return elapsed < timedelta(minutes=cooldown_minutes)
def evaluate_price_rules(
self,
prices: Dict[str, Any],
rules: List[PriceAlertRule]
) -> List[Dict[str, Any]]:
"""Evaluate price threshold rules against current prices."""
triggered = []
for rule in rules:
commodity_data = prices.get(rule.commodity)
if not commodity_data:
continue
current_price = commodity_data["price"]
breached = (
(rule.direction == "above" and current_price > rule.threshold) or
(rule.direction == "below" and current_price < rule.threshold)
)
if breached and not self._is_in_cooldown(rule.name, rule.cooldown_minutes):
alert = {
"rule_name": rule.name,
"commodity": rule.commodity,
"severity": rule.severity.value,
"current_price": current_price,
"threshold": rule.threshold,
"direction": rule.direction,
"currency": commodity_data.get("currency", "USD"),
"unit": commodity_data.get("unit", ""),
"triggered_at": datetime.utcnow().isoformat()
}
triggered.append(alert)
self._last_fired[rule.name] = datetime.utcnow()
return triggered
def evaluate_volatility_rules(
self,
rules: List[VolatilityAlertRule]
) -> List[Dict[str, Any]]:
"""Evaluate volatility threshold rules using live API data."""
triggered = []
for rule in rules:
metrics = get_volatility_metrics(rule.commodity, rule.lookback_days)
current_vol = metrics["realized_volatility"]
vol_mean = metrics["historical_mean"]
vol_std = metrics["historical_std_dev"]
std_devs_from_mean = (current_vol - vol_mean) / vol_std if vol_std > 0 else 0
if std_devs_from_mean >= rule.std_dev_threshold:
if not self._is_in_cooldown(rule.name, rule.cooldown_minutes):
alert = {
"rule_name": rule.name,
"commodity": rule.commodity,
"severity": rule.severity.value,
"current_volatility": current_vol,
"std_devs_from_mean": round(std_devs_from_mean, 2),
"percentile": metrics.get("historical_percentile"),
"triggered_at": datetime.utcnow().isoformat()
}
triggered.append(alert)
self._last_fired[rule.name] = datetime.utcnow()
return triggeredStep 4: Alert Delivery — Slack and Email
import smtplib
from email.mime.text import MIMEText
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
SLACK_CLIENT = WebClient(token=os.environ.get("SLACK_BOT_TOKEN", ""))
SLACK_CHANNEL = os.environ.get("SLACK_CHANNEL_ID", "")
def send_slack_alert(alert: Dict[str, Any]):
"""Send an energy price alert to Slack."""
severity_emoji = {
"info": ":information_source:",
"warning": ":warning:",
"critical": ":rotating_light:"
}
emoji = severity_emoji.get(alert["severity"], ":bell:")
if "current_price" in alert:
msg = (
f"{emoji} *Energy Price Alert: {alert['rule_name']}*\n"
f"Commodity: `{alert['commodity'].upper()}`\n"
f"Current Price: `{alert['current_price']:.2f} {alert.get('currency', '')} / {alert.get('unit', '')}`\n"
f"Threshold: {alert['direction']} `{alert['threshold']}`\n"
f"Severity: *{alert['severity'].upper()}*\n"
f"Time: {alert['triggered_at']}"
)
else:
msg = (
f"{emoji} *Volatility Alert: {alert['rule_name']}*\n"
f"Commodity: `{alert['commodity'].upper()}`\n"
f"Current Vol: `{alert['current_volatility']:.1f}%`\n"
f"Deviation: `{alert['std_devs_from_mean']}σ` from 30-day mean\n"
f"Severity: *{alert['severity'].upper()}*\n"
f"Time: {alert['triggered_at']}"
)
try:
SLACK_CLIENT.chat_postMessage(channel=SLACK_CHANNEL, text=msg)
except SlackApiError as e:
print(f"Slack error: {e.response['error']}")
def send_email_alert(alert: Dict[str, Any], recipient: str):
"""Send an energy alert via email."""
subject = f"[{alert['severity'].upper()}] Energy Alert: {alert['rule_name']}"
body = f"""Energy Price Alert System
Rule: {alert['rule_name']}
Commodity: {alert['commodity'].upper()}
Severity: {alert['severity'].upper()}
Triggered At: {alert['triggered_at']}
Details:
{'\n'.join(f' {k}: {v}' for k, v in alert.items() if k not in ('rule_name', 'severity', 'triggered_at'))}
---
Automated alert from Energy Volatility API Monitor
"""
msg = MIMEText(body)
msg["Subject"] = subject
msg["From"] = "[email protected]"
msg["To"] = recipient
with smtplib.SMTP("smtp.gmail.com", 587) as server:
server.starttls()
server.login("[email protected]", os.environ["SMTP_PASSWORD"])
server.send_message(msg)Step 5: The Monitoring Loop
import schedule
import time
import logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("energy-monitor")
engine = AlertEngine()
def run_monitoring_cycle():
"""Single monitoring cycle: fetch data, evaluate rules, fire alerts."""
logger.info("Running monitoring cycle...")
try:
# Fetch current prices for all commodities we care about
commodities = list(set(r.commodity for r in PRICE_RULES))
prices = get_current_prices(commodities)
# Evaluate price rules
price_alerts = engine.evaluate_price_rules(prices, PRICE_RULES)
# Evaluate volatility rules (these make separate API calls)
vol_alerts = engine.evaluate_volatility_rules(VOLATILITY_RULES)
all_alerts = price_alerts + vol_alerts
if all_alerts:
logger.warning(f"{len(all_alerts)} alert(s) triggered")
for alert in all_alerts:
send_slack_alert(alert)
# Optionally email CRITICAL alerts
if alert["severity"] == "critical":
send_email_alert(alert, os.environ.get("ALERT_EMAIL", ""))
else:
logger.info("No thresholds breached.")
except Exception as e:
logger.error(f"Monitoring cycle failed: {e}")
# Schedule monitoring every 5 minutes
schedule.every(5).minutes.do(run_monitoring_cycle)
logger.info("Energy price monitor started. Polling every 5 minutes.")
run_monitoring_cycle() # Run immediately on start
while True:
schedule.run_pending()
time.sleep(30)Deployment Options
Option 1: Docker container on Railway/Fly.io
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "monitor.py"]Option 2: AWS Lambda + EventBridge (serverless)
Wrap run_monitoring_cycle() in a Lambda handler and trigger via a 5-minute EventBridge rule. Cost: effectively free for this workload.
Option 3: GitHub Actions scheduled job
Use a schedule: workflow trigger for teams already using GitHub Actions.
What This System Catches
The April 2026 energy volatility event — Brent dropping to $100/barrel while gas prices surged — would have triggered alerts within one polling cycle (5 minutes) using the configuration above. Teams monitoring manually typically noticed the moves 2–4 hours later, after price impacts had already propagated to their cost structures.
An automated alert system doesn't just provide faster notification — it provides consistency. Human monitoring fails during off-hours, weekends, and whenever the person responsible is in a meeting.
Next Steps
- Get your Energy Volatility API key at apivult.com
- Deploy the monitoring loop using Docker or your existing infrastructure
- Tune your thresholds based on your company's actual exposure and risk tolerance
- Add position-adjusted alerts: weight alert severity by your exposure to that commodity
Energy risk management used to require a Bloomberg terminal and a quant. In 2026, the core monitoring infrastructure costs under $50/month in API calls.
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