Collect monthly High Grade Copper Jan 2025 (HGF26) prices using this API

If your goal is to collect monthly High Grade Copper Jan 2025 (often referenced as HGF26 on some venues) prices into a pricing engine, charting pipeline, or backtesting framework, the Metals-API gives you the building blocks to get there without wrestling with bespoke data scrapers. In this guide, we’ll show you how to use copper spot and historical daily data (XCU) from the Metals-API to construct a clean, auditable monthly dataset for January 2025, and then discuss practical modeling choices to approximate a contract-specific series when a direct futures symbol is not available. We’ll cover endpoint selection, data normalization (units, base currency), time-alignment, sampling, caching, and guardrails for weekends and market closures—plus how to automate it in production. You’ll see concrete requests with realistic JSON responses, learn how to parse them, and leave with a blueprint you can copy into a quant research notebook, a fintech backend, or an ERP pricing workflow.

Why monthly High Grade Copper Jan 2025 data—and how to build it with Metals-API

Monthly copper pricing for a specific delivery month (e.g., January 2025) is an essential input for hedging analytics, cost-plus manufacturing models, and macro research. While exchange futures symbols like HGF26 are contract-specific, Metals-API focuses on robust real-time and historical spot and reference rates for metals such as Copper (XCU), plus OHLC, bid/ask, fluctuation, time-series, and historical LME data, all via a clean JSON REST interface. This means you can:

Collect daily XCU prices for 2025-01-01 to 2025-01-31 and compute monthly aggregates (settle, average, high/low, last business day close) that proxy the futures contract.
Manage multiple currencies with a common base (default USD) and convert to per-pound or per-metric-ton units if needed.
Build robust pipelines that factor in weekends/market closures and resample to end-of-month (EOM) or business-day conventions.

For full endpoint documentation, refer to the Metals-API Documentation, and for metal symbol definitions including XCU, bookmark the Metals-API Supported Symbols. If you’re new to the service, get started with a key at the Metals-API Website.

The copper symbol (XCU) and what it represents

Metals-API exposes copper with the symbol XCU. Data is, by default, quoted relative to USD and expressed “per troy ounce.” This is a crucial nuance: futures contracts and many OTC references often use pounds (lb) or metric tons (mt). You’ll need to convert carefully if your downstream models require different units. Typical conversions:

1 troy ounce ≈ 31.1034768 grams
1 pound ≈ 453.59237 grams ⇒ 1 pound ≈ 14.5833333 troy ounces
1 metric ton = 1,000 kilograms = 1,000,000 grams ⇒ 1 metric ton ≈ 32,150.7466 troy ounces

Because Metals-API returns rates as “USD per troy ounce” by default (base USD, unit per troy ounce), XCU values are easy to integrate if you standardize your entire analytics stack to one unit and convert just once at ingest or just-in-time at query.

Mapping monthly HGF26 to Metals-API data

Many trading users refer to the January 2025 High Grade Copper futures as HGF26. If a directly named futures contract symbol is not provided by the API, the standard approach is to approximate the contract’s monthly profile using daily spot (XCU) data and robust aggregation logic. Common patterns:

Settlement proxy: Use the last business day’s close in January 2025.
Volume-weighted (if you have volumes elsewhere): Resample daily closes weighted by volume to get a monthly value.
Average-of-daily (arithmetic): Compute the simple average of daily XCU values for all trading days in the month.
OHLC-based composite: Use monthly open (first trading day’s open), high (max of highs), low (min of lows), close (last trading day’s close) from the OHLC endpoint.

These transformations are straightforward when your API provides a daily time-series and OHLC. You can extract the series for 2025-01-01 to 2025-01-31, then produce your monthly metrics in a repeatable way. If you use reference data from LME historical feeds where available, align the time zone and settlement convention to avoid mismatches. See the API’s “Historical LME” notes in the docs for what’s supported.

End-to-end architecture to capture January 2025 monthly copper

A minimal yet robust pipeline could be:

Daily data collection (Time-series endpoint) for XCU from 2025-01-01 to 2025-01-31.
Supplementary daily OHLC and Bid/Ask snapshots where your plan includes them, for richer analytics.
Unit normalization to per-pound or per-metric-ton if required.
Aggregation to monthly metrics (e.g., last business day close, monthly average, monthly OHLC).
Caching responses by date to minimize calls; backfill only new or missing days; handle weekends and holidays.
Persist results to a time-series database (e.g., PostgreSQL with Timescale, InfluxDB, or your data lake).

With this setup, your January 2025 monthly record becomes deterministic and reproducible across runs. You can run it nightly, weekly, or at the end of the month. For intraday strategies, combine intraday endpoint data (where available) with your daily snapshots for precise cutoffs.

Developer quick start: symbols, base currency, and units

Before coding:

Get your API key from the Metals-API Website.
Confirm the copper symbol and any related symbols you intend to use by checking the Metals-API Supported Symbols.
Note that the default base is USD and unit is per troy ounce, as indicated in responses.

If your application needs different bases (e.g., EUR), either request the relevant currency via API where supported, or use the Convert endpoint to translate amounts. Always document your unit conversion in code comments and database metadata.

Concrete curl request: daily copper (XCU) for Jan 2025

Use the time-series endpoint to fetch daily values across January 2025. Your plan determines date range limits and refresh cadence. The example below illustrates a typical workflow. Replace YOUR_API_KEY with your key.

curl -s "https://metals-api.com/api/timeseries?access_key=YOUR_API_KEY&start_date=2025-01-01&end_date=2025-01-31&base=USD&symbols=XCU"

While the time-series JSON below shows sample fields, exact availability per symbol depends on plan and data coverage. The Metals-API documentation outlines fields and behaviors in detail: Metals-API Documentation.

Example JSON: Latest copper with other metals

This is a realistic example of a latest rates response showing that XCU appears with other metals. You can use the latest endpoint for quick spot checks or intra-day dashboards.

{
"success": true,
"timestamp": 1789604906,
"base": "USD",
"date": "2026-09-17",
"rates": {
"XAU": 0.000482,
"XAG": 0.03815,
"XPT": 0.000912,
"XPD": 0.000744,
"XCU": 0.294118,
"XAL": 0.434783,
"XNI": 0.142857,
"XZN": 0.344828
},
"unit": "per troy ounce"
}

Key fields:

success: Boolean—if false, check error object for details.
timestamp: Unix epoch seconds—use it to time-align with other feeds; consider time zone in your ETL.
base: USD by default—important for pricing math and conversions.
date: ISO string for the snapshot date.
rates: Map of symbol to quote. For XCU, interpret as USD per troy ounce unless unit is explicitly changed.
unit: Confirms per troy ounce; store alongside values for auditability.

Example JSON: Time-series (daily) across a date window

Use time-series for historical spans such as a monthly window. You can pull Jan 2025 and aggregate to monthly. The structure typically includes start date, end date, and per-day rate objects.

{
"success": true,
"timeseries": true,
"start_date": "2026-09-10",
"end_date": "2026-09-17",
"base": "USD",
"rates": {
"2026-09-10": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915
},
"2026-09-12": {
"XAU": 0.000483,
"XAG": 0.0382,
"XPT": 0.000913
},
"2026-09-17": {
"XAU": 0.000482,
"XAG": 0.03815,
"XPT": 0.000912
}
},
"unit": "per troy ounce"
}

Field usage:

rates[YYYY-MM-DD][XCU]: The daily rate you’ll parse for copper (XCU). When you query with symbols=XCU, expect XCU keys on each date where coverage exists.
Gaps: Some days (e.g., weekends) might be absent—handle missing days via forward-fill or business-day conventions as your model requires.
units and base currency: Always check and normalize.

Building monthly aggregates for January 2025

Once you pull daily XCU data for 2025-01-01 to 2025-01-31, choose your aggregation:

Last business day close: Identify the last available date in the period (skipping weekends/holidays) and use its rate as the monthly “close.”
Monthly average: Mean of all daily XCU rates observed in the month.
Monthly OHLC: You can synthesize from daily series or, when supported, use the OHLC endpoint for a given date to complement your calculations.

Whichever you choose, document your method so downstream consumers know exactly which convention you used for the “Jan 2025 copper” figure.

Example JSON: Fluctuation over a window

The fluctuation endpoint helps quantify changes between two dates. You can use it to compute month-over-month deltas or intramonth variability for reporting.

{
"success": true,
"fluctuation": true,
"start_date": "2026-09-10",
"end_date": "2026-09-17",
"base": "USD",
"rates": {
"XAU": {
"start_rate": 0.000485,
"end_rate": 0.000482,
"change": -3.0e-6,
"change_pct": -0.62
},
"XAG": {
"start_rate": 0.03825,
"end_rate": 0.03815,
"change": -0.0001,
"change_pct": -0.26
},
"XPT": {
"start_rate": 0.000915,
"end_rate": 0.000912,
"change": -3.0e-6,
"change_pct": -0.33
}
},
"unit": "per troy ounce"
}

In practice for XCU, this lets you explain how January’s last rate compares with December’s last rate, or summarize the intramonth move.

Example JSON: OHLC for a given date

OHLC is invaluable when you need to attach a formal open/high/low/close profile. You can query per day and then compile a monthly OHLC by combining each daily datapoint across the month. Below is a representative response structure:

{
"success": true,
"timestamp": 1789604906,
"base": "USD",
"date": "2026-09-17",
"rates": {
"XAU": {
"open": 0.000485,
"high": 0.000487,
"low": 0.000481,
"close": 0.000482
},
"XAG": {
"open": 0.03825,
"high": 0.0383,
"low": 0.0381,
"close": 0.03815
},
"XPT": {
"open": 0.000915,
"high": 0.000918,
"low": 0.00091,
"close": 0.000912
}
},
"unit": "per troy ounce"
}

For copper, when available via your plan, you’d expect an XCU object with open, high, low, close. To compute monthly OHLC:

Monthly open: The “open” from the first trading day in January 2025.
Monthly high: Max of all daily “high” values in January 2025.
Monthly low: Min of all daily “low” values in January 2025.
Monthly close: The “close” from the last trading day in January 2025.

Example JSON: Bid/Ask for quoting engines

If you run a quoting engine or need to account for spreads in your internal cost models, use the bid/ask endpoint to incorporate microstructure realistically. Here’s a representative response:

{
"success": true,
"timestamp": 1789604906,
"base": "USD",
"date": "2026-09-17",
"rates": {
"XAU": {
"bid": 0.000481,
"ask": 0.000483,
"spread": 2.0e-6
},
"XAG": {
"bid": 0.0381,
"ask": 0.0382,
"spread": 0.0001
},
"XPT": {
"bid": 0.000911,
"ask": 0.000913,
"spread": 2.0e-6
}
},
"unit": "per troy ounce"
}

In a copper workflow, insert XCU’s bid and ask when provided to propagate conservative pricing through downstream calculations (e.g., quote mid = (bid + ask) / 2; apply spread stress tests).

Example JSON: Convert endpoint

The convert endpoint is convenient when you need to translate money amounts into troy ounces of a metal or vice versa. This lets you price invoices or inventory quickly.

{
"success": true,
"query": {
"from": "USD",
"to": "XAU",
"amount": 1000
},
"info": {
"timestamp": 1789604906,
"rate": 0.000482
},
"result": 0.482,
"unit": "troy ounces"
}

While this example shows XAU, for copper-relevant math you can translate amounts to/from XCU as appropriate. Be explicit with units in your UI and data models.

JavaScript example: Fetching Jan 2025 daily XCU and computing monthly aggregates

The snippet below demonstrates how to retrieve the daily series for January 2025 using fetch, then compute the last business day close and the monthly average. Make sure to replace YOUR_API_KEY.

// Node.js or browser (with appropriate CORS/keys storage)
// 1) Fetch Jan 2025 daily copper (XCU) rates
async function fetchJan2025XCU() {
const url = "https://metals-api.com/api/timeseries"
+ "?access_key=YOUR_API_KEY"
+ "&start_date=2025-01-01"
+ "&end_date=2025-01-31"
+ "&base=USD"
+ "&symbols=XCU";
const res = await fetch(url);
if (!res.ok) throw new Error("HTTP " + res.status);
const json = await res.json();
if (!json.success) throw new Error(JSON.stringify(json.error || { message: "API unsuccessful" }));
return json;
}

function computeMonthlyFromDaily(timeseriesJson) {
const { rates, unit, base } = timeseriesJson;
const entries = Object.entries(rates)
.sort(([d1], [d2]) => new Date(d1) - new Date(d2))
.map(([date, obj]) => ({ date, value: obj["XCU"] }))
.filter(x => typeof x.value === "number" && !Number.isNaN(x.value));

if (entries.length === 0) {
return { base, unit, monthlyAverage: null, lastBusinessDay: null, lastBusinessDayValue: null };
}

// Last available day in Jan 2025 (skips missing/weekends by design)
const last = entries[entries.length - 1];

// Simple average across available trading days
const sum = entries.reduce((acc, x) => acc + x.value, 0);
const avg = sum / entries.length;

return {
base,
unit, // expected "per troy ounce"
startDate: entries[0].date,
endDate: last.date,
monthlyAverage: avg,
lastBusinessDay: last.date,
lastBusinessDayValue: last.value,
countTradingDays: entries.length
};
}

(async () => {
try {
const json = await fetchJan2025XCU();
const summary = computeMonthlyFromDaily(json);
console.log("Jan 2025 XCU monthly summary:", summary);
} catch (e) {
console.error("Pipeline error:", e);
}
})();

What to pay attention to:

Sort dates explicitly. Do not assume object key order.
Filter out nulls or missing symbols; handle sparse days.
Carry the “unit” and “base” fields through your pipeline for clarity.
Secure your API key appropriately; do not hardcode in client apps without protection.

Historical single-date snapshots for validation

It’s often useful to validate your monthly aggregates with a specific day’s historical endpoint. For example, you may want to confirm the last business day’s value independently. The historical endpoint returns rates for a single past date:

{
"success": true,
"timestamp": 1789518506,
"base": "USD",
"date": "2026-09-16",
"rates": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915,
"XPD": 0.000748
},
"unit": "per troy ounce"
}

In your copper pipeline, this cross-check could be done for the final day in January 2025: query the historical endpoint with that date and verify that the value matches your time-series data for the same day.

From spot to futures-like series: methodology notes

When a contract-specific futures symbol (e.g., HGF26) is not directly accessible via the API, the common strategy is to approximate:

Use daily XCU as a core reference—with awareness that futures and spot may diverge due to carry, storage, and calendar effects.
If you maintain a separate curve model, apply calendar spreads or basis adjustments to XCU to estimate the January 2025 contract’s fair value.
For compliance-sensitive reporting, label the series clearly: “XCU-derived Jan 2025 monthly proxy (method: last business day close)” or similar.

If you have access to LME historical references via Metals-API and your jurisdiction permits, align your chosen instrument/venue and settlement times to keep your monthly definition consistent.

Caching, retries, and weekend behavior

Best practices for production:

Cache daily responses keyed by date and symbol; if you re-run the same month, you’ll hit cache for all but new days.
Schedule runs after typical market close or at end-of-day to avoid partial-day values if that matters to your process.
Handle weekends and holidays: daily time-series might omit non-trading days. Don’t attempt to “create” data—fill appropriately.
Implement exponential backoff and idempotency in your ETL to recover gracefully from transient HTTP or network failures.

Security and key management

Security fundamentals for using the API in production:

Keep your API key in a secure secret store (e.g., environment variables, HashiCorp Vault, AWS Secrets Manager).
Restrict where possible: run server-side calls rather than exposing keys in browser clients.
Log only the minimal metadata necessary; avoid logging full responses with secrets.
Pin TLS and verify HTTPS in client libraries as a baseline practice.

OTC, spot, and exchange alignment

Monthly copper analytics benefit from consistent definitions. If you compare to exchange futures like High Grade Copper (e.g., contract code references on CME/COMEX), understand differences in:

Trading hours and time zone cutoffs.
Settlement rules and holiday calendars.
Units (troy ounce vs pound/metric ton).

As a companion, it can be useful to bookmark venue-specific contract specs for reference. For educational context outside of this API guide, consult the official exchange pages, for example the CME Group’s information portal for copper contracts (searchable on their site) and LME copper contract specs on the LME website. Always align time zones explicitly in your code to avoid off-by-one-day errors at month-end.

Smart technology for metals data: from spreadsheets to APIs

Copper sits at the intersection of industrial demand, green transition infrastructure, and global macro cycles. The digital transformation in metal markets favors programmatic access to consistent data. With Metals-API, you can move beyond manual spreadsheet imports into automated, testable, versioned data flows that power:

E-commerce repricing for copper-bearing product lines.
ERP-driven cost updates synchronized with monthly copper inputs.
Research dashboards that merge copper with FX, gold, silver, and PGMs for cross-asset context.
Quant models evaluating copper’s role as a growth proxy alongside energy and base metals.

By structuring your data model around Metals-API’s JSON responses, you can build next-generation applications in fintech, manufacturing, or trading operations with less friction and more reliability.

Field-by-field: what matters in the responses

Every response includes metadata you should capture:

success: Fails fast if false—don’t assume the “rates” object exists.
timestamp/date: Use both. The ISO date helps with human-readable logs; the epoch timestamp helps precise time alignment.
base: Price math relies on this. If you change base currencies, re-derive your conversions downstream accordingly.
rates: The payload you’ll actually store and analyze. For XCU, store the number as a float in base units plus a unit field.
unit: Per troy ounce unless changed—persist this with the value for audit and conversions.

Optimizing requests and quotas

To stay within plan quotas and maximize throughput:

Batch queries using the time-series endpoint for daily windows instead of hitting historical day-by-day if possible.
Cache immutable responses (historical data does not change once finalized, apart from rare corrections).
Avoid redundant intraday polls if your SLA only requires end-of-day prices.
Compress storage by keeping raw JSON for audit and a normalized columnar schema for analytics.

Error handling patterns

Common pitfalls and safeguards:

Access denied or invalid key: On errors, log the error object and alert ops. Rotate credentials via secret manager.
Missing symbol: Consult the Metals-API Supported Symbols before deploying to production to avoid surprises.
Empty rates object: Could indicate a weekend, holiday, or symbol not included in your plan. Implement fallback logic or skip days.
Unit confusion: Always check the “unit” field; standardize in one canonical unit in your DB.

Putting it all together: January 2025 copper monthly record

Let’s outline a reproducible method to compute a single monthly record named “Jan 2025 XCU (proxy for HGF26)” in your database:

Query time-series for XCU from 2025-01-01 to 2025-01-31 (base=USD).
Parse daily values; drop non-trading days (missing entries), validate floats.
Compute:
- monthly_close = last available date’s value
- monthly_open = first available date’s value
- monthly_high = max of daily values (if you only have closes) or of daily highs if you also queried OHLC
- monthly_low = min as above
- monthly_avg = arithmetic mean of daily closes across the month
Convert units if needed (e.g., per lb = (USD per troy ounce) × (1 troy ounce / 1/14.5833333 lb) ⇒ divide by 14.5833333 to go from per troy ounce to per pound; confirm your unit math in code comments).
Persist the final record with metadata: base=USD, unit=per troy ounce, method notes, run timestamp, and source URL for lineage.

curl example: single-date validation for end-of-month

Once you determine the last trading day of January 2025, validate it with a historical endpoint call. For illustration, if 2025-01-31 is the date you want to verify (replace with actual EOM business day as needed):

curl -s "https://metals-api.com/api/2025-01-31?access_key=YOUR_API_KEY&base=USD&symbols=XCU"

Compare the returned XCU value with your time-series entry for the same date before writing the final monthly_close to your warehouse.

Carrying context across metals (gold, silver, and beyond)

While this guide centers on copper (XCU), a multi-metal stack benefits from consistent handling for gold (XAU), silver (XAG), platinum (XPT), and others. Correlation studies, hedging overlays, and cross-asset indices work best when your ETL adheres to a common schema. Here’s a representative latest response with multiple metals again for quick reference (repeated here to emphasize structure):

{
"success": true,
"timestamp": 1789604906,
"base": "USD",
"date": "2026-09-17",
"rates": {
"XAU": 0.000482,
"XAG": 0.03815,
"XPT": 0.000912,
"XPD": 0.000744,
"XCU": 0.294118,
"XAL": 0.434783,
"XNI": 0.142857,
"XZN": 0.344828
},
"unit": "per troy ounce"
}

Downstream, you can normalize and store these side-by-side with copper for richer analytics and dashboards.

Granularity and intraday strategies

If your plan provides support for intraday or near-real-time updates, pair the daily time-series with intraday snapshots to ensure your monthly close coincides with your target cut-off. This is vital for systems that require precise “as-of” times, e.g., end-of-London-session versus end-of-New-York-session. Always document the timestamp you’re using as your defined close.

Practical considerations that beginners often miss

Timezone discipline: Convert timestamps to a single canonical timezone (e.g., UTC) in your pipeline. Store original timestamp for audit.
Weekend/holiday gaps: Avoid synthetic prices—use forward fill only when your business logic requires it, and label derived data clearly.
Data provenance: Persist the request URL and response metadata (timestamp, date) with hashes to ensure traceability.
Backfilling: If a job fails mid-month, rerun for the partial window only. Make your time-series idempotent by primary-keying on symbol + date.
Unit conversion: Put the conversion math in a single, tested function; write unit tests to avoid silent drift.

Bid/Ask, spreads, and execution-sensitive analytics

For pricing engines meant to quote customers, the midpoint may be too optimistic. Use bid for sales (conservative) and ask for purchases, or vice versa depending on your business. The bid/ask endpoint lets you track this cleanly. For monthly summaries, some shops compute and store the month’s average spread and max spread to monitor liquidity conditions.

Fluctuation endpoint for monthly deltas

To produce a neat “January vs December” report, run the fluctuation endpoint with start_date=2024-12-31 and end_date=2025-01-31 (or the last business day of each). Use change_pct for dashboards; report both absolute and percent changes. This is helpful for exec summaries in ERP or BI tools.

Historical LME data alignment

When accessing LME-related historical feeds via Metals-API, review the coverage and symbols in the docs to ensure they align with your required venue. Align contract specs, currencies, and settlement definitions as described on the LME’s official documentation. If in doubt, run a small proof-of-concept side-by-side with your incumbent data source to validate equivalence or document systematic differences.

Data validation and sanitization

Production-grade ingestion should:

Validate that numbers are finite and non-negative.
Reject or quarantine days where the rate changes by more than a configurable threshold unless verified.
Checksum responses and keep a short rolling archive of raw JSON for audit/replay.
Use schemas in your data warehouse with constraints (e.g., NOT NULL for key fields, CHECK constraints for expected ranges).

Performance and scaling

When you expand beyond copper and a single month:

Batch time-series requests by month or quarter to reduce overhead.
Parallelize across symbols but keep concurrency under control to respect your plan’s request patterns.
Implement a local cache with TTL for latest/near-real-time endpoints to prevent redundant hits within the same update interval.
Index your warehouse tables on symbol and date; consider partitioning by year-month to accelerate monthly aggregates.

How to test monthly logic

Testing strategy:

Golden files: Save a known-good JSON response for a past month; run your aggregation to produce a known-good monthly record and assert equality.
Edge dates: Test months with holidays at EOM or partial months to ensure “last business day” logic works as intended.
Unit tests for conversion: Verify grams/ounces/pounds/metric tons conversions to high precision (e.g., 6+ decimals).

Comparing relevant symbols and endpoints

Symbol or Feature	What it Represents	Common Use
XCU	Copper (per troy ounce, base USD by default)	Daily pricing, monthly aggregation, ERP pricing
XAU, XAG, XPT, XPD	Gold, Silver, Platinum, Palladium	Cross-asset analysis, hedging context
Latest	Current snapshot (update cadence varies by plan)	Dashboards, health checks, near-real-time references
Historical (single date)	Rates for a past date	Validation, EOM confirmation, backfills
Time-series	Daily rates between start and end dates	Monthly/weekly aggregation, backtesting
Fluctuation	Change and percent change over a window	MoM/DoD summaries, alerting
OHLC	Daily open, high, low, close	Charting, volatility estimation, EOM OHLC
Bid/Ask	Bid, ask, and spread	Quoting engines, conservative pricing
Convert	Convert between currencies and metals	Invoice translation, quantity normalization

End-user communication: labeling and transparency

When you present your “January 2025 copper” figure, label it clearly:

“Source: Metals-API (XCU), base USD, per troy ounce.”
“Aggregation: Last business day close (2025-01-xx).”
“Method: Daily time-series resampled to monthly close.”

This prevents confusion and smooths audits when different teams compare numbers across systems.

Advanced analysis ideas

Volatility estimates: Use the daily series to compute realized volatility for January.
Stress scenarios: Apply percent shocks to the monthly close and simulate impacts on BOM costs or hedges.
Cross-asset correlations: Combine XCU with XAU/XAG for macro dashboards.

Practical architecture: storage and lineage

A sensible data model in a relational store could include:

raw_responses(symbol, date, response_json, inserted_at)
daily_rates(symbol, date, base, unit, rate, source_timestamp)
monthly_aggregates(symbol, year, month, base, unit, open, high, low, close, average, method, computed_at)

Build lineage by storing request URLs, hashes of responses, and code version hashes (e.g., Git commit SHA) to track exactly how a monthly number was produced.

Example: using Latest + Bid/Ask for daily EOD pricing

Operational workflows may run like this near EOD:

Call latest to snapshot end-of-session prices.
Call bid/ask to capture spreads for conservative quoting.
Call time-series at EOM to finalize the monthly record; cross-check with historical on the exact date.

This approach balances API usage with business accuracy: you don’t need minute-by-minute updates if your SLA is monthly EOM.

Troubleshooting guide

“I’m not seeing XCU in my response.” Confirm you included symbols=XCU and that your plan covers it. Also verify the Metals-API Supported Symbols.
“Unit seems off.” Check the “unit” field in the JSON. If it’s per troy ounce, ensure your UI and DB agree. Convert explicitly if needed.
“Gaps in the time-series.” Weekends/holidays are expected. Do not fabricate values; adjust your resampling to handle business days only.
“Different EOM date than expected.” Ensure you’re using the correct timezone and recognized last business day for your policy. Consider exchange calendars.
“Performance is slow.” Batch requests, enable caching, and reduce frequency to match your required update cadence.

Security and compliance checklist

Store API keys safely (server-side secrets, not client code).
Audit logs: Keep request metadata and response hashes.
Access controls: Limit who can read/write the monthly aggregates table.
Data retention: Archive old raw JSON securely for back-audits; comply with internal retention policies.

A note on gold (XAU) and cross-market context

Although our focus is copper (XCU) and January 2025 monthly construction, many teams benchmark copper movements against gold (XAU) to separate growth/beta effects from risk aversion. Metals-API provides consistent access to XAU, enabling you to add comparative rows to your monthly dashboards without reinventing ingestion. By extending your ETL to XAU and XAG, you can illuminate regime shifts that might influence copper demand and pricing power.

Call to action: get your key and build your monthly copper pipeline

Ready to implement this in your stack? Visit the Metals-API Website to get a free API key and read the Metals-API Documentation for endpoint specifics, data coverage, and advanced features. Keep the Metals-API Supported Symbols page open while you develop to validate symbol availability and semantics.

Additional resources

Metals-API Website — get your API key and explore plans.
Metals-API Documentation — endpoint parameters, examples, and behaviors.
Metals-API Supported Symbols — confirm XCU and other metals.
CME Group — for educational content on copper futures specifications.
London Metal Exchange (LME) — contract specs and calendars for reference.

FAQ

Does Metals-API provide a direct HGF26 futures symbol?

Metals-API focuses on metals rates like copper (XCU) and related endpoints (latest, historical, time-series, fluctuation, convert, OHLC, bid/ask, and historical LME where available). If a contract-specific futures ticker like HGF26 is not listed on the Metals-API Supported Symbols page, construct a monthly proxy from spot/time-series data as described in this guide or combine API data with your own futures curve logic.

What unit are rates returned in?

By default, rates are quoted in USD per troy ounce. The “unit” field in responses confirms this. Convert to pounds or metric tons as required by your application.

How should I handle weekends and holidays?

Do not fabricate prices. Use only available days from the time-series. For monthly close, choose the last business day with a valid rate. Document your convention in code and UI.

Can I convert currency bases?

Yes. Use the Convert endpoint to translate amounts to/from USD and metal units. Always record the base and unit alongside your values.

What about rate limits and quotas?

Your plan determines refresh cadence and date-range limits. Batch where possible, cache immutable data, and avoid unnecessary repeated calls to stay comfortably within quotas. See the documentation for details.

How do I validate my monthly numbers?

Cross-check the final EOM date’s value by calling the historical endpoint for that date. Optionally, compare with intraday snapshots if your SLA requires exact cutoff alignment.

Is there a sandbox?

Use a free API key from the Metals-API Website to explore the endpoints and verify your pipeline before promoting to production.

Ready to use Metals-API?

Try it now!