Benchmarking Intraday Trading Algorithms: A Reproducible Framework Using Energy API Market Feeds and Synthetic Latency Injection

Benchmarking intraday trading algorithms in energy markets is hard for a very practical reason: the data firehose doesn’t speak a common language. Electricity auctions publish hourly or 15-minute curves. Gas and oil typically publish once per day. Carbon allowances and coal arrive in yet another cadence. Each source uses different symbols, currencies, calendars, and holiday rules. When your task is to evaluate strategy performance with realistic fills, you end up spending 80% of your time normalizing feeds and 20% testing your logic. The result is fragile infrastructure, inconsistent backtests, and a production move that’s far slower than it needs to be.

This post presents a reproducible benchmarking framework for intraday trading algorithms that uses normalized market feeds from Energy API and a simple, configurable synthetic latency injector. You’ll learn how to stitch together electricity intraday curves with daily gas, oil, coal, and carbon prices through one consistent REST surface, generate realistic replay streams, add controlled delays to mimic venue and network slippage, and compare algorithmic fills under varied market conditions. Whether you’re a quant on a power desk, a fintech team building exposure hedges, or a utility data engineer supporting risk, this framework gives you the shortest path from concept to evaluation-ready datasets.

By the end, you’ll have a clear picture of which energy symbols are most suitable for intraday strategies, how to query and align them at minute granularity, how to bundle multiple commodities in one call for cross-asset signals, how to inject reproducible latency into your replay, and how to report P&L deltas attributable to data delay, quote staleness, and order type assumptions. Let’s get hands-on.

Why Energy API

Energy trading is a multi-commodity exercise: intraday electricity often responds to gas supply conditions, carbon allowance costs, and expected grid carbon intensity. Getting those pieces in one place with consistent timestamps and currency metadata is the key to shipping strategies quickly. Here’s how Energy API accelerates your workflow and improves quality:

• One normalized JSON schema across electricity, gas, oil, coal, carbon allowances, and grid carbon intensity. In practical terms, you write your parsing and validation once. Your feature extraction pipeline doesn’t splinter per commodity, reducing test surface area and chance of silent format drift.
• Cross-asset queries in a single call. Need TTF gas, Brent crude, and EUAs to compute a synthetic marginal cost-of-generation factor for your intraday power signal? Request them together, get date stamps and currencies in one response, and standardize conversion in the same pass.
• Intraday electricity curves where sources publish them. Benchmarking intraday algos needs high-resolution prices, not just day-ahead settles. With hourly and 15-minute curves accessible through a dedicated endpoint, you can reconstruct session microstructure and stress-test fills against the price shape.
• A complete suite of endpoints for discovery, latest ticks, historical snapshots, timeseries, OHLC candles, forecasts for auction symbols, status checks, and basic cost estimation. This allows you to combine research-time analytics (e.g., OHLC volatility) with operational health checks (provider status) without extra vendors or bespoke scrapers.

The bottom line: with Energy API you replace a pile of scrapers and weekly CSV wrangling with one clean REST interface that scales from exploratory notebooks to production-grade backtests and real-time monitoring. You’ll spend your hours engineering strategies, not ETL.

Quick Start

All endpoints reside under the base URL:

https://energy-api.com/api/v1

Authentication is performed by including an api_key query parameter. The examples below show the structure so you can plug in your own key in local setups, CI jobs, or serverless functions.

To grab the most recent prices for multiple commodities in a single call:

curl -G https://energy-api.com/api/v1/latest \
--data-urlencode "symbols=BRENT_CRUDE,TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

Typical JSON response:

{
"success": true,
"date": "2026-06-11",
"base": "MIXED",
"rates": {
"BRENT_CRUDE": 74.82,
"TTF_GAS": 38.15,
"EUA_CO2": 67.40
},
"dates": {
"BRENT_CRUDE": "2026-06-11",
"TTF_GAS": "2026-06-11",
"EUA_CO2": "2026-06-11"
},
"currencies": {
"BRENT_CRUDE": "USD",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR"
}
}

Key fields:

• rates: latest values per symbol. Use for current exposure calculation and sanity checks.
• dates: the effective publishing date per symbol, crucial for correct alignment in backtests when various assets update at different local times.
• currencies: currency per symbol to ensure consistent P&L and hedging comparisons. Convert once to your reporting currency and propagate that invariant downstream.

For intraday strategy benchmarking, combine this latest snapshot with intraday electricity curves (hourly or 15-minute) to build minute-level replays that include daily drivers (gas/oil/carbon) and high-resolution execution targets (power curves).

Core Endpoints for Intraday Benchmarking

The following endpoints form the backbone of a reproducible intraday benchmarking pipeline. Each example includes a request, a realistic response, and implementation notes for trading use cases.

1) Discover Symbols: GET /symbols

Purpose: enumerate available symbols, their categories, frequency, and currencies. You’ll use this to dynamically configure your backtest baskets and to validate user-provided inputs in production services.

curl -G https://energy-api.com/api/v1/symbols \
--data-urlencode "category=gas" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"count": 3,
"symbols": [
{
"symbol": "TTF_GAS",
"name": "TTF Natural Gas Day-Ahead",
"category": "gas",
"country_code": "EU",
"currency_code": "EUR",
"frequency": "daily",
"description": "TTF day-ahead price published by EEX."
}
]
}

Notes:

• frequency tells you what cadence to expect (daily vs intraday). Build your replay loader to handle mixed frequencies.
• currency_code ensures your portfolio P&L is consistent. Maintain a conversion map or lock to a reporting base.
• description and name are useful for UIs and logs; strategy metadata becomes clearer and easier to audit.

2) Latest Cross-Asset Snapshot: GET /latest

Purpose: align cross-asset signals at one reference timestamp. For intraday electricity trading, pull the latest Brent, TTF, and EUA to contextualize the current session’s marginal cost and emissions cost backdrop.

curl -G https://energy-api.com/api/v1/latest \
--data-urlencode "symbols=BRENT_CRUDE,TTF_GAS,EUA_CO2,OMIE_ES_DA" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"date": "2026-06-11",
"base": "MIXED",
"rates": {
"BRENT_CRUDE": 74.82,
"TTF_GAS": 38.15,
"EUA_CO2": 67.40,
"OMIE_ES_DA": 92.10
},
"dates": {
"BRENT_CRUDE": "2026-06-11",
"TTF_GAS": "2026-06-11",
"EUA_CO2": "2026-06-11",
"OMIE_ES_DA": "2026-06-11"
},
"currencies": {
"BRENT_CRUDE": "USD",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR",
"OMIE_ES_DA": "EUR"
}
}

Practical use:

• Compute a composite driver for intraday bids: e.g., a standardized z-score blend of TTF_GAS and EUA_CO2, then regress the hourly OMIE shape residuals against that blend in a rolling window. Test fill sensitivity with latency injection (details later).
• If your risk system mandates a single currency, convert BRENT_CRUDE (USD) to EUR upfront using your FX feed; store both original and converted for auditability.

3) Intraday Electricity Curves: GET /electricity/hourly

Purpose: retrieve the full intraday curve for a given symbol/date (hourly or 15-minute depending on source). Use this as your execution target path for intraday backtesting and slippage modeling.

curl -G https://energy-api.com/api/v1/electricity/hourly \
--data-urlencode "symbol=OMIE_ES_DA" \
--data-urlencode "date=2026-06-11" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"symbol": "OMIE_ES_DA",
"date": "2026-06-11",
"frequency": "hourly",
"currency": "EUR",
"curve": [
{"time": "2026-06-11T00:00:00+02:00", "price": 86.40},
{"time": "2026-06-11T01:00:00+02:00", "price": 84.10},
{"time": "2026-06-11T02:00:00+02:00", "price": 82.75},
{"time": "2026-06-11T03:00:00+02:00", "price": 80.10},
{"time": "2026-06-11T04:00:00+02:00", "price": 78.95},
{"time": "2026-06-11T05:00:00+02:00", "price": 79.80},
{"time": "2026-06-11T06:00:00+02:00", "price": 88.20},
{"time": "2026-06-11T07:00:00+02:00", "price": 96.10},
{"time": "2026-06-11T08:00:00+02:00", "price": 101.30},
{"time": "2026-06-11T09:00:00+02:00", "price": 104.90},
{"time": "2026-06-11T10:00:00+02:00", "price": 102.40},
{"time": "2026-06-11T11:00:00+02:00", "price": 98.70},
{"time": "2026-06-11T12:00:00+02:00", "price": 92.60},
{"time": "2026-06-11T13:00:00+02:00", "price": 89.50},
{"time": "2026-06-11T14:00:00+02:00", "price": 88.00},
{"time": "2026-06-11T15:00:00+02:00", "price": 90.40},
{"time": "2026-06-11T16:00:00+02:00", "price": 95.20},
{"time": "2026-06-11T17:00:00+02:00", "price": 101.50},
{"time": "2026-06-11T18:00:00+02:00", "price": 109.80},
{"time": "2026-06-11T19:00:00+02:00", "price": 112.30},
{"time": "2026-06-11T20:00:00+02:00", "price": 107.10},
{"time": "2026-06-11T21:00:00+02:00", "price": 98.40},
{"time": "2026-06-11T22:00:00+02:00", "price": 92.70},
{"time": "2026-06-11T23:00:00+02:00", "price": 88.50}
]
}

Implementation details:

• time is ISO8601 with timezone. Always align on the provided zone to avoid DST artifacts in backtests. Store both local-time and UTC indices in your parquet files for traceability.
• Use curve as the “execution price” for each interval. If you simulate market orders, your fill can be set to the published price. For limit orders, you can test different acceptance rules relative to each interval’s price.
• For venues that publish 15-minute granularity, your curve length will be 96 items. Be prepared to resample to the model’s expected horizon if needed.

4) Historical Timeseries: GET /timeseries

Purpose: obtain multi-day windows for feature engineering and to train your signal models. The timeseries endpoint returns date-keyed values for each symbol and makes it easy to build rolling means, volatility, and cointegration diagnostics.

curl -G https://energy-api.com/api/v1/timeseries \
--data-urlencode "start=2025-01-01" \
--data-urlencode "end=2025-03-31" \
--data-urlencode "symbols=BRENT_CRUDE,TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"base": "MIXED",
"start_date": "2025-01-01",
"end_date": "2025-03-31",
"rates": {
"BRENT_CRUDE": {
"2025-01-02": 76.30,
"2025-01-03": 75.90
},
"TTF_GAS": {
"2025-01-02": 46.80,
"2025-01-03": 47.10
},
"EUA_CO2": {
"2025-01-02": 81.50,
"2025-01-03": 82.10
}
},
"frequencies": {
"BRENT_CRUDE": "daily",
"TTF_GAS": "daily",
"EUA_CO2": "daily"
},
"currencies": {
"BRENT_CRUDE": "USD",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR"
}
}

Usage tips:

• Use start_date and end_date to confirm coverage before fitting models. If gaps exist (weekends/holidays), roll-forward or interpolate only where justified—document choices.
• frequencies confirms cadence. Don’t resample daily series to hourly unless you explicitly encode a structural assumption (e.g., carry-forward for cost drivers).
• Pair timeseries windows with intraday curves by left-joining on date. This keeps daily drivers stable across the intraday session to mimic how traders contextualize hourly shapes with daily macro inputs.

5) OHLC Candles: GET /ohlc

Purpose: compute volatility regimes, detect structural breaks, and design risk budgets. Weekly, monthly, and quarterly candles are great for zoomed-out views while still being easy to store and plot.

curl -G https://energy-api.com/api/v1/ohlc \
--data-urlencode "symbols=BRENT_CRUDE,TTF_GAS" \
--data-urlencode "period=monthly" \
--data-urlencode "start=2024-01-01" \
--data-urlencode "end=2025-12-31" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"period": "monthly",
"data": {
"BRENT_CRUDE": [
{"period": "2024-01", "open": 78.10, "high": 83.40, "low": 75.20, "close": 80.05, "data_points": 23},
{"period": "2024-02", "open": 80.05, "high": 84.90, "low": 79.10, "close": 83.70, "data_points": 21}
],
"TTF_GAS": [
{"period": "2024-01", "open": 39.80, "high": 46.00, "low": 37.50, "close": 42.20, "data_points": 21},
{"period": "2024-02", "open": 42.20, "high": 47.90, "low": 41.10, "close": 45.30, "data_points": 20}
]
}
}

Interpretation:

• data_points indicates how many observations contributed to the candle, handy for quality checks.
• Use high-low ranges to size protective stops for intraday electricity strategies that reference daily gas/oil risk context.
• Regime-switch detection (e.g., rolling window variance shifts) helps decide whether to tighten latency budgets or relax thresholds when volatility spikes.

6) Day-Ahead Forecast Lookup: GET /forecast

Purpose: fetch already-published auction results for the next day for auction-driven electricity symbols. This is not a predictive model; it’s a deterministic lookup. Great for day-ahead strat planning and verifying your pre-open positioning assumptions.

curl -G https://energy-api.com/api/v1/forecast \
--data-urlencode "symbol=OMIE_ES_DA" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"symbol": "OMIE_ES_DA",
"target_date": "2026-06-12",
"published_at": "2026-06-11T13:45:00+02:00",
"frequency": "hourly",
"currency": "EUR",
"curve": [
{"time": "2026-06-12T00:00:00+02:00", "price": 84.2},
{"time": "2026-06-12T01:00:00+02:00", "price": 82.9}
]
}

Trading note: incorporate forecast curves as the baseline and then measure intraday slippage relative to forecast. This uncovers whether your short-horizon tactics add value beyond simple forecast-following.

A Complete, Finance-Focused Endpoint Catalogue

Below is a concise map of all category and core endpoints you can use to build end-to-end trading research and live monitoring. Where relevant, we add performance and error-handling notes to keep your pipelines robust.

Core endpoints

• GET /symbols — Discovery and validation for baskets and UI pickers.
• GET /latest — Multi-asset snapshot in one call; aligns drivers for same-day context.
• GET /historical — Point-in-time lookup on a past date. If a date is a non-publishing day, the endpoint returns the most recent value before it, which is ideal for deterministic backfills.
• GET /timeseries — Multi-day historical series; feed it to features and charts.
• GET /fluctuation — Directly returns start_value, end_value, change, change_pct across a period; ideal for risk summaries and dashboards without manual differencing.
• GET /ohlc — Higher timeframe candles for regime checks and volatility budgets.

Electricity category endpoints

• GET /electricity/latest — Latest prices for all electricity symbols; filter by country with country=ISO-2 if needed.
• GET /electricity/hourly — High-resolution hourly/15-minute curves for replay and execution benchmarks.
• GET /electricity/pvpc — Hourly Spanish PVPC retail reference prices; useful to analyze retail exposure or to price B2C risk products while benchmarking portfolio hedges.
• GET /forecast — Next published day-ahead for auction symbols only; returns 404 for non-auction symbols by design.

Gas, oil, carbon, coal, carbon-intensity endpoints

• GET /gas/latest — TTF_GAS and HENRY_HUB in one call; great for cross-region risk context.
• GET /emissions/latest — EU ETS EUA_CO2; emissions cost is frequently embedded in marginal cost-of-generation models that influence intraday power prices.
• GET /coal/latest — COAL_ROTTERDAM (API2), COAL_NEWCASTLE; helpful in broader fuel stack comparisons and power market regime analysis.
• GET /carbon-intensity — Grid carbon intensity by country; feed into ESG overlays or penalty-adjusted P&L metrics.
• GET /status — Provider fetch health; use for observability and to trip circuit breakers or switch to fallback logic if a provider is delayed.
• POST /cost-estimate — Quick monthly wholesale electricity cost estimate; useful for pricing analytics and retail product what-ifs alongside trading models.

Example: Fluctuation analysis

curl -G https://energy-api.com/api/v1/fluctuation \
--data-urlencode "start=2026-05-01" \
--data-urlencode "end=2026-06-11" \
--data-urlencode "symbols=TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"base": "MIXED",
"period": {"start": "2026-05-01", "end": "2026-06-11"},
"results": {
"TTF_GAS": {
"start_value": 35.10,
"end_value": 38.15,
"change": 3.05,
"change_pct": 8.69,
"currency": "EUR"
},
"EUA_CO2": {
"start_value": 63.20,
"end_value": 67.40,
"change": 4.20,
"change_pct": 6.64,
"currency": "EUR"
}
}
}

Use cases:

• Risk summary tiles in trader dashboards with direct period-over-period changes.
• Pre-trade checks: if change_pct exceeds a threshold, your algo can switch to a conservative latency configuration to reduce slippage risk.

Reproducible Benchmarking with Synthetic Latency Injection

To fairly compare intraday trading algorithms, you need to control for data freshness and transport delays. Synthetic latency injection creates standardized conditions. Here’s a practical approach you can implement in any language:

1. Data acquisition layer: Pull electricity intraday curves via GET /electricity/hourly for target dates and relevant countries. Pull daily drivers (TTF_GAS, BRENT_CRUDE, EUA_CO2) via GET /latest or GET /historical/GET /timeseries for deterministic windows. Persist raw responses as canonical JSON with metadata (fetch_time_utc, source_endpoint, params).
2. Normalization: Convert all timestamps to UTC while retaining original local offset in a parallel field. Normalize currencies to a single reporting base (e.g., EUR) and record conversion rates used. Save normalized parquet files keyed by date and symbol.
3. Replay clock: For each benchmark run, define a wall-clock schedule that “releases” data to the algo according to historical publish times and the configured synthetic latency L (e.g., 50 ms, 500 ms, 2 s). If an hourly power price was published at T+00:00 local, your replay releases it at T+L.
4. Order simulator: Given your algo’s order intent at minute granularity, decide fills versus the intraday curve price at that minute (market) or conditional rules (limit/stop). Apply an execution slippage model that can include spread-like penalties or impact proxies when volatility is high (see OHLC regime notes).
5. Metrics and fairness: Record P&L, hit ratio, and average slippage per latency bucket. Repeat runs with identical seeds and only change L to quantify latency sensitivity. Store run configs to allow exact reproduction.

Below is a minimal Python-style pseudocode to illustrate the mechanics around Energy API responses:

# Pseudocode illustrating a latency-injected replay using Energy API data
from datetime import datetime, timedelta, timezone
import time
import requests

BASE = "https://energy-api.com/api/v1"
API_KEY = "YOUR_API_KEY"

def fetch_intraday_curve(symbol: str, date: str):
r = requests.get(
f"{BASE}/electricity/hourly",
params={"symbol": symbol, "date": date, "api_key": API_KEY},
timeout=20,
)
r.raise_for_status()
payload = r.json()
assert payload["success"]
return payload["curve"], payload["currency"], payload["frequency"]

def fetch_daily_drivers(symbols, date):
# Deterministic point-in-time lookup
r = requests.get(
f"{BASE}/historical",
params={"date": date, "symbols": ",".join(symbols), "api_key": API_KEY},
timeout=15,
)
r.raise_for_status()
return r.json()

def replay_with_latency(curve, latency_ms=500):
# curve: list of {"time": "...", "price": x}
fills = []
latency = timedelta(milliseconds=latency_ms)
for pt in curve:
published = datetime.fromisoformat(pt["time"])
available = published + latency
now = datetime.now(published.tzinfo)
if now < available:
# Wait until data becomes "visible" to the algo
sleep_s = (available - now).total_seconds()
time.sleep(max(0, sleep_s))
# Simulate a market order fill at the published price
fills.append({"time": available.isoformat(), "fill_price": pt["price"]})
return fills

def run_benchmark(symbol, date, drivers):
curve, ccy, freq = fetch_intraday_curve(symbol, date)
drivers_pt = fetch_daily_drivers(drivers, date)
# Normalize currency and store metadata omitted for brevity...
results = {}
for L in [50, 200, 500, 2000]:
fills = replay_with_latency(curve, latency_ms=L)
results[L] = analyze_fills(fills, drivers_pt) # user-defined P&L logic
return results

Best practices:

• Always persist raw API responses. If results differ between two runs, you can prove whether the difference is in the replay logic or in upstream data shifts.
• Use GET /status before your run to confirm provider health; if a source shows stale, either pause or route to a fallback date with clear labeling in outputs.
• Store your latency parameter and execution assumptions per run. Benchmarks without run config metadata are not reproducible.

Error Handling, Health Checks, and Observability

Production-grade benchmarking needs robust failure handling and clear diagnostics. Energy API publishes consistent error codes and a health endpoint to support operational excellence.

• 401 — Missing or invalid api_key (ensure secure injection of your key at runtime).
• 404 — No data for the given symbols or date (e.g., requesting intraday data for a non-intraday symbol).
• 422 — Validation error (missing or malformed param, unsupported value).
• 429 — Rate limit exceeded; on the client side, implement exponential backoff and jitter.

Error shape:

{
"success": false,
"error": "Human-readable message."
}

Health checks:

curl -G https://energy-api.com/api/v1/status \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"providers": [
{"provider": "omie", "last_fetch": "2026-06-11T13:45:10Z", "status": "ok"},
{"provider": "entsoe", "last_fetch": "2026-06-11T13:44:51Z", "status": "ok"},
{"provider": "eia", "last_fetch": "2026-06-11T12:02:10Z", "status": "ok"},
{"provider": "fred", "last_fetch": "2026-06-11T12:02:00Z", "status": "ok"},
{"provider": "esios", "last_fetch": "2026-06-11T13:43:05Z", "status": "ok"}
]
}

Operational tips:

• If /status indicates a provider delay, log a warning and continue, but tag all downstream outputs with a “stale-provider” flag to preserve audit integrity.
• Wrap calls with retries, timeouts, and idempotent request semantics where applicable. At the orchestration layer, use circuit breakers to fail open to cached data for short windows to keep scheduled benchmarks running.

Electricity, Gas, Oil, Coal, Carbon, and Carbon Intensity: Symbols That Matter for Finance

The following high-signal symbols and categories map directly to common financial hypotheses and hedging constructs:

• Gas: TTF_GAS (EUR/MWh, EU), HENRY_HUB (USD/MMBtu, US) — fuel cost drivers and cross-region spreads.
• Oil: BRENT_CRUDE (USD/barrel), WTI_CRUDE (USD/barrel) — macro risk proxies that often correlate with sentiment and broad commodity cycles.
• Electricity: OMIE_ES_DA (Spain, EUR/MWh), EPEX_DE_DA (Germany), PVPC_ES_2TD (Spain retail), AEMO_NSW1 (Australia) — intraday execution targets and day-ahead baselines.
• Carbon (ETS): EUA_CO2 (EUR/MT) — emissions cost integrated into marginal cost-of-generation.
• Coal: COAL_ROTTERDAM (API2), COAL_NEWCASTLE — alternative fuel stack considerations.
• Carbon Intensity: CARBON_INT_DE, CARBON_INT_EU (gCO2eq/kWh) — ESG overlays and green premium modeling.

The ability to pull BRENT_CRUDE, TTF_GAS, and EUA_CO2 together in one GET /latest call and then align them to an hourly electricity curve on the same date is the key differentiator that compresses weeks of integration work into a single afternoon of modeling.

Additional Endpoint Examples You’ll Use in Finance Workflows

Electricity latest snapshot

curl -G https://energy-api.com/api/v1/electricity/latest \
--data-urlencode "country=ES" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"country": "ES",
"base": "EUR",
"symbols": [
{"symbol": "OMIE_ES_DA", "price": 92.10, "date": "2026-06-11"},
{"symbol": "PVPC_ES_2TD", "price": 104.30, "date": "2026-06-11"}
]
}

Use this endpoint to confirm day-of-session baselines before running intraday replay. If your observed PVPC retail reference is unusually high vs. OMIE_es_dA, flag potential retail margin compression in B2C products or investigate anomalies.

Gas latest bundle

curl -G https://energy-api.com/api/v1/gas/latest \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"base": "MIXED",
"rates": {
"TTF_GAS": 38.15,
"HENRY_HUB": 2.81
},
"currencies": {
"TTF_GAS": "EUR",
"HENRY_HUB": "USD"
},
"dates": {
"TTF_GAS": "2026-06-11",
"HENRY_HUB": "2026-06-10"
}
}

Cross-region risk: If HENRY_HUB lags a day behind due to publication schedules, don’t unintentionally align it to the current European intraday session without explicit carry-forward logic; store the as-of date and mark forward usage distinctly.

Emissions latest

curl -G https://energy-api.com/api/v1/emissions/latest \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"symbol": "EUA_CO2",
"price": 67.40,
"currency": "EUR",
"date": "2026-06-11"
}

Incorporate EUA_CO2 directly into your marginal cost calculation that feeds intraday electricity bid/ask thresholds, and analyze how latency changes the alpha attributable to this driver.

Coal latest

curl -G https://energy-api.com/api/v1/coal/latest \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"rates": {
"COAL_ROTTERDAM": 119.50,
"COAL_NEWCASTLE": 135.20
},
"currencies": {
"COAL_ROTTERDAM": "USD",
"COAL_NEWCASTLE": "USD"
},
"dates": {
"COAL_ROTTERDAM": "2026-06-11",
"COAL_NEWCASTLE": "2026-06-11"
}
}

Even if coal is not your primary driver, it can provide context for fuel-switching regimes that affect power price shapes, especially during stress periods.

Carbon intensity

curl -G https://energy-api.com/api/v1/carbon-intensity \
--data-urlencode "country=DE" \
--data-urlencode "api_key=YOUR_API_KEY"

{
"success": true,
"country": "DE",
"units": "gCO2eq/kWh",
"date": "2026-06-11",
"value": 328
}

For finance teams producing ESG-aware returns, you can compute an emissions-adjusted P&L or “green alpha” where strategies with lower implied emissions intensity get a positive adjustment. Benchmarking with and without the ESG overlay can guide investor reporting and mandate alignment.

Implementing the Latency-Injection Benchmark: Practical Walkthrough

This walkthrough ties the endpoints together to produce a reproducible benchmark run.

1. Select symbols and dates:
   • Electricity: OMIE_ES_DA for 2026-06-11.
   • Drivers: TTF_GAS, BRENT_CRUDE, EUA_CO2 on the same calendar date (via GET /historical to be point-in-time correct).
2. Fetch data:
   • GET /electricity/hourly with symbol/date.
   • GET /historical with date and symbols=TTF_GAS,BRENT_CRUDE,EUA_CO2.
3. Normalize:
   • Convert BRENT_CRUDE from USD to EUR if your reporting currency is EUR; store fx_rate_used and reference source for compliance.
   • Keep a time_index_utc and a time_index_local in your curve dataset.
4. Latency-injected replay:
   • Define L in milliseconds; produce runs for L in {10, 50, 200, 500, 2000}.
   • Release each hourly price at published_time + L.
5. Execution model:
   • Market-order fill at curve price to isolate data staleness effect.
   • Optional: Add a spread/impact penalty function that scales with recent OHLC volatility.
6. Reporting:
   • Produce per-run metrics: total P&L, mean slippage, 95% tail slippage, and trade count.
   • Store run metadata: symbols, dates, latency_ms, currency_base, endpoints_used, response_checksums.

In many desks, simply quantifying the P&L impact of 200 ms extra latency on intraday power fills can justify network and infrastructure investments. A consistent benchmark provides the evidence and helps tune your production SLAs.

Real-World Use Cases

• Intraday electricity price alert and execution assist:
  Use GET /electricity/hourly for the live curve and GET /latest for TTF_GAS and EUA_CO2. When the driver composite exceeds a threshold, trigger alerts in your trade UI and simulate fill quality with your current latency class. Record realized/expected slippage deltas to guide trader behavior in volatile sessions.
• ESG-aware exposure dashboard for a power trading fund:
  Combine GET /latest for cross-asset snapshot, GET /timeseries for trend bands, and GET /carbon-intensity to compute emissions-adjusted exposures. Show both raw and adjusted P&L lines to quantify the “green premium” associated with your strategies in investor materials.
• Wholesale-to-retail risk pricing:
  With GET /electricity/pvpc plus GET /electricity/hourly and GET /ohlc, model expected retail reference prices against day-ahead curves and volatility regimes. Feed POST /cost-estimate to generate what-if monthly costs under various consumption scenarios while your trading stack benchmarks hedge effectiveness versus retail exposure.

FAQ

How often does the TTF gas price update?

TTF_GAS is published on a daily cadence. When aligning it with intraday electricity strategies, carry the daily value across intraday intervals for that date and clearly tag it as a daily driver. Use GET /historical or GET /timeseries for deterministic backtests and GET /latest for operational dashboards.

Can I query multiple commodities in the same API call?

Yes. Use GET /latest with symbols=... to combine gas, oil, carbon, and electricity into a single response. This is particularly useful for constructing composite drivers and ensuring all values are timestamped and currency-labeled consistently for your calculation pipeline.

Do I get intraday electricity curves (hourly/15-minute) for benchmarking?

Yes. Use GET /electricity/hourly. Where a provider publishes 15-minute resolution, you will receive 96 points for a day. Always respect the provided timezone in time fields to avoid DST-related alignment errors in your backtests.

What’s the best way to handle non-publishing days in backtests?

Use GET /historical, which returns the most recent value before the requested date if the date is a non-publishing day. This deterministic behavior is ideal for reproducible backfills and minimizes surprises in point-in-time accuracy.

How should I add synthetic latency for fair algo comparisons?

Release each data point to your strategy clock at published_time + L, where L is your configured latency in milliseconds. Run multiple L buckets (e.g., 50, 200, 500, 2000 ms) with the same seeds and inputs to isolate performance deltas attributable solely to data availability timing.

Conclusion + CTA

Intraday trading in energy markets rewards teams that move quickly from raw data to clean, testable hypotheses. The combination of hourly or 15-minute electricity curves with daily gas, oil, and carbon benchmarks is the core of a realistic execution model—but only if you can normalize formats, currencies, and timestamps reliably. Energy API gives you that foundation in a single, consistent surface so you can spend your time testing strategies, not deciphering public portals.

The reproducible framework outlined here—normalized data ingestion, deterministic replay clocks, and synthetic latency injection—lets you quantify the true edge in your intraday algorithms. It clarifies how much P&L depends on data freshness, which execution assumptions hold up under volatility, and where to invest next in infrastructure or model refinement.

If you’re ready to replace ad hoc scrapers and brittle CSV patches with one reliable source—and to benchmark strategies with confidence—start building with Energy API. Ship your first cross-asset replay today and prove out your intraday edge. Try Energy API for free.