oilpriceapi 1.10.2

Official Python SDK for OilPriceAPI - Real-time and historical oil prices

OilPriceAPI Python SDK

Real-time oil, gas, LNG, carbon and fuel prices in your Python app in under 60 seconds — typed client, pandas DataFrames, async + WebSocket streaming, and technical indicators built in.

Get a Free API Key · Documentation · Pricing · API Explorer · Quick start ↓

The official Python SDK for OilPriceAPI, the commodity price API behind fintech dashboards, fleet & logistics tools, maritime compliance platforms and energy analytics products — serving 2M+ API requests every month.

📝 Documentation Status: All code examples shown are tested and working. Technical indicators are available as of v1.10.0 (see Technical Indicators); see our GitHub Issues for the roadmap.

What can you get?

110+ commodities across the energy complex. The ones our customers poll the most:

Code	What it is	Typical use
BRENT_CRUDE_USD	Brent crude (global)	dashboards, market context, deal models
WTI_USD	WTI crude (US)	trading tools, macro models
NATURAL_GAS_USD	Henry Hub natural gas	energy analytics, procurement
DUTCH_TTF_EUR	TTF gas (Europe)	European energy, LNG analytics
JKM_LNG_USD	JKM LNG (Asia)	LNG trading & shipping
EU_CARBON_EUR	EU ETS carbon allowances	CBAM reporting, maritime compliance, ESG
DIESEL_USD	Diesel (Gulf Coast)	fleet fuel-surcharge calculators, logistics
JET_FUEL_USD	Jet fuel	aviation ops & charter pricing
VLSFO_USD	Marine bunker fuel (0.5%S)	voyage costing, bunker procurement
GOLD_USD	Gold	macro & portfolio context

🚀 Quick Start

Installation

pip install oilpriceapi

Basic Usage

from oilpriceapi import OilPriceAPI

# Initialize client (uses OILPRICEAPI_KEY env var by default)
client = OilPriceAPI()

# Get latest Brent Crude price
brent = client.prices.get("BRENT_CRUDE_USD")
print(f"Brent Crude: ${brent.value:.2f}")
# Output: Brent Crude: $71.45

# Get multiple prices
prices = client.prices.get_multiple(["BRENT_CRUDE_USD", "WTI_USD", "NATURAL_GAS_USD"])
for price in prices:
    print(f"{price.commodity}: ${price.value:.2f}")

Beyond Oil — Gas, LNG, Carbon & Fuels

The same client covers EU ETS carbon, European gas (TTF), LNG (JKM), and marine/road/aviation fuels — for maritime compliance (EU ETS / FuelEU Maritime), fleet & logistics fuel costing, LNG and European gas analytics, and CBAM reporting.

# EU ETS carbon allowances (EUAs) — spot price in EUR
eua = client.prices.get("EU_CARBON_EUR")
print(f"EU carbon: €{eua.value:.2f}")  # €XX.XX per tonne CO2

# Dutch TTF natural gas — futures curve via slug helpers
# (other slugs: "lng-jkm", "eua-carbon", "uk-carbon", "natural-gas")
ttf = client.futures.latest("ttf-gas")
print(ttf["front_month"]["last_price"])  # front-month, €XX.XX/MWh

# Marine bunker fuels (VLSFO / MGO / IFO380) at a specific port
rotterdam = client.bunker_fuels.port("RTM")  # 3-letter port codes: SIN, RTM, FUJ, ...
for fuel in rotterdam["prices"]:
    print(f"{fuel['grade']}: ${fuel['price']}/{fuel['unit']}")  # VLSFO: $XXX.XX/MT

Spot codes for these markets also work with client.prices.get() / get_multiple(): DUTCH_TTF_EUR, JKM_LNG_USD, VLSFO_USD, JET_FUEL_USD, DIESEL_USD, NATURAL_GAS_USD. (Futures and bunker endpoints require a plan with futures data — spot prices work on every tier.)

Historical Data with Pandas

# Get historical data as DataFrame
df = client.prices.to_dataframe(
    commodity="BRENT_CRUDE_USD",
    start="2024-01-01",
    end="2024-12-31",
    interval="daily"
)

print(f"Retrieved {len(df)} data points")
print(df.head())

Technical Indicators (New in v1.10.0)

Add technical analysis indicators to any price DataFrame. Implemented in pure pandas/numpy, so no extra dependencies beyond the optional [pandas] extra.

# Get historical data
df = client.prices.to_dataframe(
    commodity="BRENT_CRUDE_USD",
    start="2024-01-01",
    interval="daily",
)

# Method 1: DataFrame helper (non-mutating, returns a new DataFrame)
df = client.analysis.with_indicators(
    df,
    indicators=["sma_20", "sma_50", "rsi", "bollinger_bands", "macd"],
)
# Adds columns: sma_20, sma_50, rsi, bb_upper, bb_middle, bb_lower,
#               macd, macd_signal, macd_histogram
print(df.tail())

# Method 2: Direct calculation on a Series
df["sma_20"] = client.analysis.sma(df["value"], period=20)
df["ema_12"] = client.analysis.ema(df["value"], period=12)
df["rsi"] = client.analysis.rsi(df["value"], period=14)
bands = client.analysis.bollinger_bands(df["value"], period=20, std=2)

Supported indicators: SMA, EMA, RSI, MACD, Bollinger Bands, and ATR.

Diesel Prices (New in v1.3.0)

# Get state average diesel price (free tier)
ca_price = client.diesel.get_price("CA")
print(f"California diesel: ${ca_price.price:.2f}/gallon")
print(f"Source: {ca_price.source}")
print(f"Updated: {ca_price.updated_at}")

# Get nearby diesel stations (paid tiers)
result = client.diesel.get_stations(
    lat=37.7749,   # San Francisco
    lng=-122.4194,
    radius=8047    # 5 miles in meters
)

print(f"Regional average: ${result.regional_average.price:.2f}/gallon")
print(f"Found {len(result.stations)} stations")

# Find cheapest station
cheapest = min(result.stations, key=lambda s: s.diesel_price)
print(f"Cheapest: {cheapest.name} at {cheapest.formatted_price}")
print(f"Savings: ${abs(cheapest.price_delta):.2f}/gal vs average")

# Get diesel prices as DataFrame
df = client.diesel.to_dataframe(states=["CA", "TX", "NY", "FL"])
print(df[["state", "price", "updated_at"]])

# Station data as DataFrame
df_stations = client.diesel.to_dataframe(
    lat=34.0522,   # Los Angeles
    lng=-118.2437,
    radius=5000
)
print(df_stations[["name", "diesel_price", "price_vs_average"]])

Price Alerts (New in v1.5.0)

# Create a price alert with webhook notification
alert = client.alerts.create(
    name="Brent High Alert",
    commodity_code="BRENT_CRUDE_USD",
    condition_operator="greater_than",
    condition_value=85.00,
    webhook_url="https://your-server.com/webhook",  # Optional
    enabled=True,
    cooldown_minutes=60  # Min time between triggers
)

print(f"Alert created: {alert.id}")
print(f"Monitoring: {alert.commodity_code}")
print(f"Condition: {alert.condition_operator} ${alert.condition_value}")

# List all alerts
alerts = client.alerts.list()
for alert in alerts:
    print(f"{alert.name}: {alert.enabled} ({alert.trigger_count} triggers)")

# Update an alert
updated = client.alerts.update(
    alert.id,
    condition_value=90.00,
    enabled=False
)

# Test webhook endpoint
test_result = client.alerts.test_webhook("https://your-server.com/webhook")
if test_result.success:
    print(f"Webhook OK: {test_result.status_code} in {test_result.response_time_ms}ms")
else:
    print(f"Webhook failed: {test_result.error}")

# Delete an alert
client.alerts.delete(alert.id)

# Get alerts as DataFrame
df = client.alerts.to_dataframe()
print(df[["name", "commodity_code", "condition_value", "trigger_count"]])

Supported operators:

• greater_than - Price exceeds threshold
• less_than - Price falls below threshold
• equals - Price matches threshold
• greater_than_or_equal - Price meets or exceeds threshold
• less_than_or_equal - Price meets or falls below threshold

Webhook Payload:

{
  "alert_id": "550e8400-e29b-41d4-a716-446655440000",
  "alert_name": "Brent High Alert",
  "commodity_code": "BRENT_CRUDE_USD",
  "current_price": 86.5,
  "condition_operator": "greater_than",
  "condition_value": 85.0,
  "triggered_at": "2025-12-15T10:30:00Z"
}

Commodities Catalog (New in v1.5.0)

# Get all available commodities
commodities = client.commodities.list()
for commodity in commodities:
    print(f"{commodity['code']}: {commodity['name']}")

# Get details for specific commodity
brent = client.commodities.get("BRENT_CRUDE_USD")
print(f"Category: {brent['category']}")
print(f"Unit: {brent['unit']}")

# Get commodities grouped by category
categories = client.commodities.categories()
crude_oils = categories.get('Crude Oil', [])

Futures Contracts (New in v1.5.0)

# Get latest front month WTI futures
price = client.futures.latest("CL.1")
print(f"WTI Front Month: ${price['price']:.2f}")

# Get OHLC data
ohlc = client.futures.ohlc("CL.1")
print(f"Open: ${ohlc['open']:.2f}, High: ${ohlc['high']:.2f}")

# Get futures curve
curve = client.futures.curve("CL")
for point in curve:
    print(f"{point['month']}: ${point['price']:.2f}")

# Spread analysis between contracts
spread = client.futures.spreads("CL.1", "CL.2")
print(f"Calendar Spread: ${spread['current_spread']:.2f}")

Oil Storage & Inventory (New in v1.5.0)

# Get Cushing, OK inventory
cushing = client.storage.cushing()
print(f"Cushing Inventory: {cushing['value']} barrels")
print(f"Weekly Change: {cushing['change']} barrels")

# Strategic Petroleum Reserve
spr = client.storage.spr()
print(f"SPR Inventory: {spr['value']} barrels")

# Regional storage (PADD regions)
regional = client.storage.regional(region="PADD3")
print(f"Gulf Coast: {regional['value']} barrels")

# Historical storage data
history = client.storage.history("cushing", start_date="2024-01-01")

Rig Counts (New in v1.5.0)

# Get latest rig counts
rig_counts = client.rig_counts.latest()
print(f"Oil Rigs: {rig_counts['oil']}")
print(f"Gas Rigs: {rig_counts['gas']}")
print(f"Total: {rig_counts['total']}")

# Get rig count summary with changes
summary = client.rig_counts.summary()
print(f"Week Change: {summary['week_change']}")
print(f"Year Change: {summary['year_change']}")

# Historical rig counts
history = client.rig_counts.historical(start_date="2024-01-01")

Bunker Fuels (New in v1.5.0)

# Get bunker prices for Singapore
singapore = client.bunker_fuels.port("SINGAPORE")
print(f"VLSFO: ${singapore['vlsfo']['price']}")
print(f"MGO: ${singapore['mgo']['price']}")

# Compare prices across ports
comparison = client.bunker_fuels.compare(["SINGAPORE", "ROTTERDAM", "HOUSTON"])
for port, data in comparison.items():
    print(f"{port}: ${data['vlsfo']['price']}")

# Spread analysis
spreads = client.bunker_fuels.spreads()
print(f"VLSFO-MGO Spread: ${spreads['vlsfo_mgo']:.2f}")

Price Analytics (New in v1.5.0)

# Get 30-day performance
perf = client.analytics.performance("BRENT_CRUDE_USD", days=30)
print(f"30-day Return: {perf['return_pct']}%")
print(f"Volatility: {perf['volatility']}")

# Statistical analysis
stats = client.analytics.statistics("WTI_USD", days=90)
print(f"Mean: ${stats['mean']:.2f}, Std Dev: ${stats['std_dev']:.2f}")

# Correlation between commodities
corr = client.analytics.correlation("BRENT_CRUDE_USD", "WTI_USD", days=90)
print(f"Correlation: {corr['correlation']:.3f}")

# Trend analysis
trend = client.analytics.trend("NATURAL_GAS_USD", days=30)
print(f"Direction: {trend['direction']}, Strength: {trend['strength']}")

# Price forecast
forecast = client.analytics.forecast("BRENT_CRUDE_USD")
print(f"7-day Forecast: ${forecast['7_day']['price']:.2f}")

Drilling Intelligence (New in v1.5.0)

# Get latest drilling data
latest = client.drilling.latest()
print(f"Total rigs: {latest['total_rigs']}")
print(f"Frac spreads: {latest['frac_spreads']}")

# DUC (Drilled but Uncompleted) wells
ducs = client.drilling.duc_wells()
for duc in ducs:
    print(f"{duc['basin']}: {duc['count']} DUCs")

# Basin-specific data
permian = client.drilling.basin("permian")
print(f"Permian rigs: {permian['rig_count']}")

# Completion trends
completions = client.drilling.completions()

Well Production (Beta)

US well production data. State/national monthly aggregates come from the EIA API; per-well history and cycle-time analytics are beta and only cover states where regulatory data has been collected — this is not a complete US well-level production dataset. Requires a plan with the Drilling Intelligence feature (403 ENTERPRISE_REQUIRED otherwise).

# National overview + top producing states
overview = client.well_production.summary()
for state in overview["top_states"]:
    print(f"{state['state']}: {state['oil_bbl']:,} bbl ({state['period']})")

# State-level production for a month
states = client.well_production.states(period="2026-04")

# Production history for one state
tx = client.well_production.state("TX", start_date="2026-01-01")

# Per-well history (beta; 14-digit API number, dashes OK)
well = client.well_production.well("42-285-34329-00-00")

# Top producing wells in a state (beta)
top = client.well_production.top_producers("NM", limit=10, months=12)

# Permit-to-production cycle times (beta)
ct = client.well_production.cycle_time(state="TX")
print(f"Median cycle: {ct['cycle_time_stats']['median_days']} days")
cohorts = client.well_production.cycle_time_cohorts(state="TX", group_by="quarter")

Webhooks (New in v1.5.0)

# Create webhook for price updates
webhook = client.webhooks.create(
    url="https://myapp.com/webhook",
    events=["price.updated", "alert.triggered"],
    description="Price alerts webhook",
    enabled=True
)
print(f"Webhook created: {webhook['id']}")

# List all webhooks
webhooks = client.webhooks.list()
for wh in webhooks:
    print(f"{wh['url']}: {wh['events']}")

# Test webhook endpoint
result = client.webhooks.test(webhook['id'])
print(f"Test status: {result['status']}")

# View webhook event history
events = client.webhooks.events(webhook['id'])
for event in events:
    print(f"{event['created_at']}: {event['type']} - {event['status']}")

# Delete webhook
client.webhooks.delete(webhook['id'])

EIA Forecasts (New in v1.5.0)

# Get monthly EIA forecasts
forecasts = client.forecasts.monthly()
for forecast in forecasts:
    print(f"{forecast['period']}: ${forecast['price']:.2f}")

# Get specific commodity forecast
wti_forecast = client.forecasts.get("2025-03", commodity="WTI_USD")
print(f"March 2025 WTI: ${wti_forecast['price']:.2f}")
print(f"Range: ${wti_forecast['low']:.2f} - ${wti_forecast['high']:.2f}")

# Check forecast accuracy
accuracy = client.forecasts.accuracy()
print(f"30-day Accuracy: {accuracy['30_day']['accuracy']}%")

Data Quality Monitoring (New in v1.5.0)

# Get overall data quality summary
summary = client.data_quality.summary()
print(f"Overall Quality Score: {summary['score']}")
print(f"Total Issues: {summary['total_issues']}")

# Get quality report for specific commodity
report = client.data_quality.report("BRENT_CRUDE_USD")
print(f"Quality Score: {report['quality_score']}%")
print(f"Completeness: {report['completeness']}%")
print(f"Last Update: {report['last_update']}")

# Get all quality reports
reports = client.data_quality.reports()
for report in reports:
    print(f"{report['commodity']}: {report['quality_score']}%")

Energy Intelligence (New in v1.5.0)

# Access EI sub-resources for government energy data
# EI rig counts
ei_rigs = client.ei.rig_counts.latest()
print(f"Total rigs: {ei_rigs['total']}")

# EI oil inventories (EIA weekly data)
inventories = client.ei.oil_inventories.latest()
print(f"Crude stocks: {inventories['crude']} barrels")

# OPEC production data
opec = client.ei.opec_production.latest()
for country, data in opec.items():
    print(f"{country}: {data['production']} bbl/day")

# Drilling productivity
productivity = client.ei.drilling_productivity.latest()
print(f"Permian: {productivity['permian']['boe_per_rig']} BOE/rig")

# Well timeline data
timeline = client.ei.well_timeline("42-123-45678")
for event in timeline['events']:
    print(f"{event['date']}: {event['type']}")

Data Sources (New in v1.5.0)

# List all configured data sources
sources = client.data_sources.list()
for source in sources:
    print(f"{source['name']}: {source['type']} - {source['status']}")

# Check health of a data source
health = client.data_sources.health("123")
print(f"Status: {health['status']}")
print(f"Last successful fetch: {health['last_success']}")

# View data source logs
logs = client.data_sources.logs("123", limit=100)
for log in logs:
    print(f"{log['timestamp']}: {log['level']} - {log['message']}")

# Test connection
result = client.data_sources.test("123")
print(f"Test status: {result['status']}")

Market Brief (New in v1.9.0)

Get a multi-commodity structured summary (latest price, 24h change, and a 1-month forecast per commodity) in a single request. Pass narrative=True to also receive a natural-language summary.

brief = client.market_brief(["BRENT_CRUDE_USD", "WTI_USD"], narrative=True)

print(f"As of: {brief.as_of}")
for c in brief.commodities:
    print(f"{c.code}: ${c.price} ({c.change_24h_pct:+.2f}% 24h)")
    if c.forecast_1m:
        print(f"  1m forecast: {c.forecast_1m.point} "
              f"[{c.forecast_1m.low}–{c.forecast_1m.high}] ({c.forecast_1m.confidence})")

if brief.narrative:
    print(brief.narrative)

Agent Subscriptions (New in v1.9.0)

Create persistent "watches" that periodically evaluate commodities and emit events your agent can poll for. The interval accepts a friendly string ("5m", "1h", "daily") or raw seconds.

# Create a subscription
sub = client.subscriptions.create(
    codes=["BRENT_CRUDE_USD"],
    interval="5m",            # also accepts "1h", "daily", or 300
    name="Brent watch",
)
print(sub.id, sub.interval_seconds)  # -> 300

# List subscriptions
for s in client.subscriptions.list():
    print(s.name, s.codes, s.status)

# Poll for events using a cursor
page = client.subscriptions.events(since=0)
for event in page:
    print(event.type, event.code)
# Persist page.cursor and pass it as `since` on the next poll
next_page = client.subscriptions.events(since=page.cursor)

# Delete a subscription
client.subscriptions.delete(sub.id)

Attribution headers are sent automatically (X-OPA-Source defaults to sdk-python). MCP tools can override them:

client.subscriptions.create(
    ["WTI_USD"], interval="1h", source="mcp", tool="claude-desktop"
)

All of the above is mirrored on the async client:

async with AsyncOilPriceAPI() as client:
    brief = await client.market_brief(["BRENT_CRUDE_USD"])
    sub = await client.subscriptions.create(["WTI_USD"], interval="daily")
    page = await client.subscriptions.events(since=0)
    await client.subscriptions.delete(sub.id)

📊 Features

• ✅ Simple API - Intuitive methods for all endpoints
• ✅ Type Safe - Full type hints for IDE autocomplete
• ✅ Pandas Integration - First-class DataFrame support
• ✅ Technical Indicators - SMA, EMA, RSI, MACD, Bollinger Bands, ATR (pure pandas/numpy)
• ✅ Price Alerts - Automated monitoring with webhook notifications 🔔
• ✅ Diesel Prices - State averages + station-level pricing ⛽
• ✅ Futures Contracts - OHLC, curves, spreads, and continuous data
• ✅ Storage & Inventory - Cushing, SPR, and regional PADD data
• ✅ Rig Counts - Baker Hughes rig counts with historical trends
• ✅ Bunker Fuels - Marine fuel prices across major ports
• ✅ Price Analytics - Performance, correlations, trends, and forecasts
• ✅ Drilling Intelligence - DUC wells, permits, completions, and basin data
• ✅ Well Production (beta) - State/national production aggregates, per-well history, cycle times
• ✅ Webhooks - Manage event subscriptions and notifications
• ✅ EIA Forecasts - Official monthly price forecasts with accuracy tracking
• ✅ Energy Intelligence - EIA data, OPEC production, drilling productivity
• ✅ Data Quality - Real-time quality monitoring and reporting
• ✅ Data Sources - Connector management with health checks and logging
• ✅ Market Brief - Multi-commodity structured + narrative summary in one call 🧠
• ✅ Agent Subscriptions - Persistent watches + event polling for AI agents 🤖
• ✅ Async Support - High-performance async client
• ✅ WebSocket Streaming - Real-time price stream via ActionCable (Professional+)
• ✅ Smart Caching - Reduce API calls automatically
• ✅ Rate Limit Handling - Automatic retries with backoff
• ✅ Error Handling - Comprehensive exception classes

📚 Documentation

Complete SDK Documentation → | Online Docs →

⚡ Performance Guide → — expected response times, recommended timeouts, optimization best practices, and troubleshooting for slow queries.

Authentication

# Method 1: Environment variable (recommended)
export OILPRICEAPI_KEY="your_api_key"
client = OilPriceAPI()

# Method 2: Direct initialization
client = OilPriceAPI(api_key="your_api_key")

# Method 3: With configuration
client = OilPriceAPI(
    api_key="your_api_key",
    timeout=30,
    max_retries=3,
    cache="memory",
    cache_ttl=300
)

Available Commodities

Oil & Gas:

• BRENT_CRUDE_USD - Brent Crude Oil
• WTI_USD - West Texas Intermediate
• NATURAL_GAS_USD - Natural Gas
• DIESEL_USD - Diesel
• GASOLINE_USD - Gasoline
• HEATING_OIL_USD - Heating Oil

Coal (8 Endpoints):

• CAPP_COAL_USD - Central Appalachian Coal (US Spot)
• PRB_COAL_USD - Powder River Basin Coal (US Spot)
• ILLINOIS_COAL_USD - Illinois Basin Coal (US Spot)
• NEWCASTLE_COAL_USD - Newcastle API6 (International Futures)
• COKING_COAL_USD - Metallurgical Coal (International Futures)
• CME_COAL_USD - CME Coal Futures
• NYMEX_APPALACHIAN_USD - NYMEX Central Appalachian (Historical 2004-2016)
• NYMEX_WESTERN_RAIL_USD - NYMEX Powder River Basin (Historical 2009-2017)

View all 107+ commodities

Error Handling

from oilpriceapi.exceptions import OilPriceAPIError, RateLimitError, DataNotFoundError

try:
    price = client.prices.get("INVALID_CODE")
except DataNotFoundError as e:
    print(f"Commodity not found: {e}")
except RateLimitError as e:
    print(f"Rate limited. Resets in {e.seconds_until_reset}s")
except OilPriceAPIError as e:
    print(f"API error: {e}")

⚡ Async Support

import asyncio
from oilpriceapi import AsyncOilPriceAPI

async def get_prices():
    async with AsyncOilPriceAPI() as client:
        prices = await asyncio.gather(
            client.prices.get("BRENT_CRUDE_USD"),
            client.prices.get("WTI_USD"),
            client.prices.get("NATURAL_GAS_USD")
        )
        return prices

# Run async function
prices = asyncio.run(get_prices())

📡 Real-Time WebSocket Streaming (New in v1.8.0)

Stream live oil and energy prices over WebSocket instead of polling. Streaming is a Professional plan ($99/mo) or higher feature and is exposed through the async client via client.stream.

Install the optional stream extra:

pip install 'oilpriceapi[stream]'

import asyncio
from oilpriceapi import AsyncOilPriceAPI

async def watch_prices():
    async with AsyncOilPriceAPI() as client:
        # Opens an ActionCable subscription to EnergyPricesChannel at /cable.
        async with client.stream.prices(commodities=["BRENT_CRUDE_USD"]) as stream:
            async for update in stream:
                if update.type == "price_update":
                    brent = update.price_update.prices.oil.brent
                    print(f"Brent: ${brent.original_price} "
                          f"({brent.original_currency}) @ {update.price_update.timestamp}")
                elif update.type == "rig_count_update":
                    rc = update.rig_count_update.rig_count
                    print(f"Rig count [{rc.region}]: {rc.count}")

asyncio.run(watch_prices())

Each update is a typed StreamUpdate:

update.type	Populated model	Contents
welcome	update.price_update	Initial price snapshot on subscribe
price_update	update.price_update	Latest Brent/WTI + UK/US/EU natural gas
rig_count_update	update.rig_count_update	Drilling rig count update (premium tiers)

update.raw always holds the decoded payload dict for forward compatibility.

Features:

• ActionCable JSON subprotocol (welcome → subscribe → confirm → messages, pings handled)
• Token auth via query param + Authorization header
• Auto-reconnect with exponential backoff + jitter (auto_reconnect, max_reconnect_attempts)
• Clean teardown (unsubscribe + close) on context-manager exit
• Pydantic-typed update models

# Tune reconnection behavior:
stream = client.stream.prices(
    commodities=["BRENT_CRUDE_USD", "WTI_USD"],
    auto_reconnect=True,
    max_reconnect_attempts=10,
    reconnect_base_delay=1.0,
    reconnect_max_delay=30.0,
)

WebSocket streaming requires a Professional+ plan. Subscriptions from lower tiers are rejected during the ActionCable handshake.

The whole OilPriceAPI toolbox

Same data, every stack:

Tool	Install
Node/TypeScript SDK	npm install oilpriceapi
Go SDK	go get github.com/OilpriceAPI/oilpriceapi-go
PHP SDK	composer require oilpriceapi/oilpriceapi
MCP server (Claude, Cursor, AI agents)	npx -y oilpriceapi-mcp
WordPress plugin	wordpress.org, no code
Google Sheets Add-on	custom spreadsheet functions
Excel Add-in	energy prices in Excel

🧪 Testing

The SDK uses standard Python testing frameworks. Example using pytest:

import pytest
from oilpriceapi import OilPriceAPI

def test_get_price():
    client = OilPriceAPI(api_key="your_test_key")
    price = client.prices.get("BRENT_CRUDE_USD")

    assert price is not None
    assert price.value > 0
    assert price.commodity == "BRENT_CRUDE_USD"

📈 Examples

Quick Examples

# Example 1: Get multiple commodity prices
from oilpriceapi import OilPriceAPI

client = OilPriceAPI()
commodities = ["BRENT_CRUDE_USD", "WTI_USD", "NATURAL_GAS_USD"]
prices = client.prices.get_multiple(commodities)

for price in prices:
    print(f"{price.commodity}: ${price.value:.2f}")

# Example 2: Historical data analysis with pandas
import pandas as pd
from oilpriceapi import OilPriceAPI

client = OilPriceAPI()
df = client.prices.to_dataframe(
    commodity="BRENT_CRUDE_USD",
    start="2024-01-01",
    end="2024-12-31"
)

# Calculate simple moving average
df['SMA_20'] = df['price'].rolling(window=20).mean()
print(df[['created_at', 'price', 'SMA_20']].tail())

# Example 3: Price alerts with webhooks
from oilpriceapi import OilPriceAPI

client = OilPriceAPI()

# Create alert when oil exceeds $85
alert = client.alerts.create(
    name="High Oil Price Alert",
    commodity_code="BRENT_CRUDE_USD",
    condition_operator="greater_than",
    condition_value=85.00,
    webhook_url="https://your-app.com/webhook",
    enabled=True
)

print(f"Alert created: {alert.id}")

🔧 Development

# Clone repository
git clone https://github.com/oilpriceapi/python-sdk
cd python-sdk

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .

# Type checking
mypy oilpriceapi

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for details.

💬 Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 🧭 Interactive API Explorer: api.oilpriceapi.com/swagger — try every endpoint in the browser (works in demo mode, no key needed)
• 📜 OpenAPI spec: swagger.json — generate clients, mock servers, or import into Postman/Insomnia
• 📖 Docs: Documentation

🔗 Links

• OilPriceAPI Website
• API Documentation
• Pricing
• Status Page

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🌟 Why OilPriceAPI?

OilPriceAPI provides professional-grade commodity price data at 98% less cost than Bloomberg Terminal ($24,000/year vs $45/month). Trusted by energy traders, financial analysts, and developers worldwide.

Key Benefits

• ⚡ Real-time data updated every 5 minutes
• 📊 Historical data for trend analysis and backtesting
• 🔒 99.9% uptime with enterprise-grade reliability
• 🚀 5-minute integration with this Python SDK
• 💰 Free tier with 100 requests (lifetime) to get started

Start Free → | View Pricing → | Read Docs →

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Made with care by the OilPriceAPI Team