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Step-by-Step Guide to Get Ferro Silicon (FE-SI) Historical Prices using this API

Step-by-Step Guide to Get Ferro Silicon (FE-SI) Historical Prices using this API

Step-by-Step Guide to Get Ferro Silicon (FE-SI) Historical Prices using this API

If your goal is to pull Ferro Silicon (often abbreviated “FeSi” or shown as FE-SI in some market data contexts) historical prices into a quant model, pricing engine, or procurement dashboard, Metals-API gives you a clean, scalable way to do it. This guide walks you through how to discover whether Ferro Silicon is available in the Metals-API Supported Symbols, how to retrieve historical prices with reproducible requests, and how to productionize the workflow so it’s robust to weekends, market holidays, and data edge cases. As we go, we’ll also show working requests using Gold (XAU) as a concrete example of the exact same API flow for historical data retrieval, because Gold is included in every sample response below. You can use the same patterns for FE-SI if it’s listed for your plan and symbol set.

What you will build: repeatable extraction of Ferro Silicon historical prices

By the end of this tutorial, you will be able to:

  • Verify the correct symbol code for Ferro Silicon (FE-SI or a variant) via the live symbols endpoint reference.
  • Pull daily historical rates for any date range since the dataset’s historical coverage using the time-series and historical endpoints.
  • Normalize units (troy ounces vs metric units) and base currency handling to make the data immediately usable in your systems.
  • Cache and backfill data safely around weekends and exchange holidays.
  • Convert between currencies or metals if you need price expression in a different base.
  • Extend the same logic to related ferroalloys, base metals, or precious metals for cross-commodity analytics.

We will anchor the concrete examples to XAU (Gold) to align with the JSON samples provided, then show exactly how you would swap in Ferro Silicon once you confirm its symbol code on the Metals-API Supported Symbols page. If your plan includes LME historical coverage and you’re sourcing Ferro Silicon via exchange-referenced pricing, we’ll also outline integration patterns that leverage the same retrieval logic.

Before you start: check the symbol and get an API key

Do two quick checks up front:

  1. Confirm whether Ferro Silicon is currently listed and the exact symbol spelling. Visit the live symbols directory and search for “Ferro Silicon,” “FeSi,” or “FE-SI.” Symbols can vary by venue or data source. Do not assume the code; copy it exactly as listed.
  2. Obtain your API key from the Metals-API Website. You can start with a free key to prototype. See the Metals-API Documentation for the latest on authentication and parameters.

If Ferro Silicon is not currently listed for your plan, you can still follow this tutorial with XAU (Gold) and later substitute your target symbol. The workflows, endpoints, and response semantics are identical.

How Metals-API expresses prices: base currency, units, and timestamps

Understand three critical facets of the response to avoid downstream math or logic errors:

  • Base currency: By default, exchange rates are delivered relative to USD (“base”: “USD”). When you see "rates": {"XAU": 0.000482}, read that as 1 USD buys 0.000482 troy ounces of Gold. To invert to USD per troy ounce, compute 1 / 0.000482.
  • Units: The "unit" field clarifies “per troy ounce” for many precious and industrial metals responses. If your procurement or manufacturing requirements are in kilograms or metric tons, convert carefully. 1 troy ounce = 31.1034768 grams. Always persist the "unit" value to your database alongside the raw rate to prevent unit drift.
  • Timestamps and dates: Responses include "timestamp" (Unix epoch, seconds) and "date" (YYYY-MM-DD). Normalized time is critical for backtesting and chart backfills. Treat the date field as the session day and the timestamp as the retrieval snapshot time. Cache at the date granularity for historical pulls.

Core workflow for FE-SI historical prices

The pattern for getting Ferro Silicon historical prices is typically:

  1. Find the FE-SI symbol on the symbols list.
  2. Use the Historical endpoint for a single date backfill, or the Time-series endpoint for a date range.
  3. Optional: Use the Fluctuation endpoint to compute period-over-period changes without extra math.
  4. Optional: Use Convert if you need values expressed in EUR, JPY, or in another metal basis.
  5. Optional: For intraday monitoring or bid/ask spreads on liquid metals, use Latest, Bid/Ask, or OHLC.

Below we walk through each of these capabilities using XAU examples that mirror what you’d do with FE-SI once you confirm the symbol code.

Single-day historical retrieval (swap XAU for FE-SI)

When you need to backfill a single day (for example, to correct a missing value), use the Historical Rates endpoint by appending a date in YYYY-MM-DD format.

Example JSON response (Historical)

{
"success": true,
"timestamp": 1789346269,
"base": "USD",
"date": "2026-09-14",
"rates": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915,
"XPD": 0.000748
},
"unit": "per troy ounce"
}

Interpretation for XAU on 2026-09-14: 1 USD = 0.000485 troy ounces of gold. To convert to USD per troy ounce: price = 1 / 0.000485 = ~2061.86 USD/oz. For Ferro Silicon, replace "XAU" with the exact FE-SI symbol and read the rate the same way. Always inspect the "unit" field; if it’s per troy ounce, convert as needed for your product specs.

Fields you will actually use

  • success: Boolean guardrail for error handling.
  • timestamp: Use for cache versioning and reconciliation.
  • date: The quote day. Store as your data partition key.
  • base: Typically USD, affects inversion math.
  • rates: Map of symbol to rate. Pull the FE-SI key once confirmed.
  • unit: Persist with the row; powers downstream conversions.

Multi-day backfill for FE-SI using time series

When you need a rolling history (e.g., for FE-SI charting or moving-average inputs), use the Time-series endpoint to retrieve a block of dates in a single call. This reduces round trips and is ideal for first-time backfills or weekly refreshes.

Example JSON response (Time-series)

{
"success": true,
"timeseries": true,
"start_date": "2026-09-08",
"end_date": "2026-09-15",
"base": "USD",
"rates": {
"2026-09-08": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915
},
"2026-09-10": {
"XAU": 0.000483,
"XAG": 0.0382,
"XPT": 0.000913
},
"2026-09-15": {
"XAU": 0.000482,
"XAG": 0.03815,
"XPT": 0.000912
}
},
"unit": "per troy ounce"
}

For FE-SI, once you confirm the symbol, you will select its rate from each day in "rates". Note that non-trading days may be absent or carry the last good value depending on venue aggregation. Always validate that your date index is complete before computing indicators.

Practical handling tips for time-series

  • Weekend gaps: Many metals do not update on weekends. Forward-fill cautiously only when your analytics explicitly allow it, and tag imputed values to avoid contaminating training sets.
  • Partial trading days: Holidays can truncate sessions. Treat these carefully when computing volume-weighted indicators or realized volatility proxies.
  • Cache per day: Store normalized USD-per-unit and the raw rate together. That way, users can choose the representation best suited for their workflow.

Track changes for FE-SI with the fluctuation endpoint

To understand how FE-SI moved between two dates, the Fluctuation endpoint returns start and end rates, absolute change, and percentage change—removing the need to diff two time-series calls on your side.

Example JSON response (Fluctuation)

{
"success": true,
"fluctuation": true,
"start_date": "2026-09-08",
"end_date": "2026-09-15",
"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"
}

Swap XAU for FE-SI and you have period-over-period deltas in one call. Store both start_rate and end_rate for transparency, and compute corresponding USD-per-unit values if that is your canonical measure.

Get intraday context with latest, bid/ask, and OHLC

While this guide focuses on historical backfills, many FE-SI workflows benefit from intraday context—especially for automated alerts or procurement timing decisions.

Latest snapshot

{
"success": true,
"timestamp": 1789432669,
"base": "USD",
"date": "2026-09-15",
"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"
}

Use this for monitoring FE-SI in real time if it’s supported for your plan and symbol. The “timestamp” and “date” help you align snapshots in your UI and avoid stale updates.

Bid/Ask for spread-aware decisions

{
"success": true,
"timestamp": 1789432669,
"base": "USD",
"date": "2026-09-15",
"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"
}

If FE-SI appears in your symbol set with bid/ask, you can compute execution-aware pricing. For example, if you buy FE-SI, you likely care about the ask-side equivalent in your base currency.

OHLC for day-range context

{
"success": true,
"timestamp": 1789432669,
"base": "USD",
"date": "2026-09-15",
"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 FE-SI, OHLC lets you compute intraday ranges, realized range-based volatility, and detect breakouts relative to prior day highs/lows—all without extra aggregation code.

Currency and unit conversion in one step

When your ERP or quoting engine needs FE-SI denominated in a non-USD currency or cross-metal terms, the Convert endpoint handles the math server-side, saving round trips and potential floating-point divergence.

Example JSON response (Convert)

{
"success": true,
"query": {
"from": "USD",
"to": "XAU",
"amount": 1000
},
"info": {
"timestamp": 1789432669,
"rate": 0.000482
},
"result": 0.482,
"unit": "troy ounces"
}

For FE-SI, set “to” to the FE-SI symbol once confirmed. You can also invert: convert from a metal to USD by swapping “from” and “to”. Always log “rate” and “timestamp” to explain price formation to auditors and clients.

Step-by-step: pulling FE-SI history with one cURL and one JavaScript example

Below, we show a complete cURL request pattern and a JavaScript snippet. Replace the XAU symbol with FE-SI once you’ve confirmed its availability in the symbols catalog, and insert your access_key.

cURL: Time-series backfill for a date range

curl -G "https://metals-api.com/api/timeseries" \
--data-urlencode "access_key=YOUR_API_KEY" \
--data-urlencode "start_date=2026-09-08" \
--data-urlencode "end_date=2026-09-15" \
--data-urlencode "base=USD" \
--data-urlencode "symbols=XAU"

Swap symbols=XAU for your Ferro Silicon symbol (e.g., symbols=FE-SI if listed that way). Use this for initial backfills and periodic refresh jobs.

JavaScript: Fetch historical price for one day

async function getHistoricalMetalRate(date, symbol, apiKey) {
const url = `https://metals-api.com/api/${date}?access_key=${encodeURIComponent(apiKey)}&base=USD&symbols=${encodeURIComponent(symbol)}`;
const resp = await fetch(url, { method: 'GET' });
if (!resp.ok) throw new Error(`HTTP ${resp.status}`);
const data = await resp.json();
if (!data.success) throw new Error(`API error: ${JSON.stringify(data)}`);
const rate = data.rates[symbol];
const unit = data.unit; // e.g., "per troy ounce"
// Convert to USD per unit if needed:
const usdPerUnit = 1 / rate;
return { date: data.date, rate, usdPerUnit, unit, base: data.base, timestamp: data.timestamp };
}

// Example usage:
// const result = await getHistoricalMetalRate("2026-09-14", "XAU", "YOUR_API_KEY");
// console.log(result);

In production, wrap this with retries and cache the JSON blob keyed by date and symbol. When you switch to FE-SI, pass its symbol string, and keep the conversion math identical unless the “unit” indicates a different base measure.

Important: Units for Ferro Silicon vs precious metals

Ferroalloys like Ferro Silicon are often quoted in metric tons or kilograms in procurement contexts, whereas the examples above are per troy ounce. Always check the "unit" field in the response and normalize into your canonical unit (e.g., USD/MT). If you receive per troy ounce and need USD/MT, compute:

  • USD per troy ounce = 1 / rate (if rate is troy ounces per USD).
  • USD per gram = (USD per troy ounce) / 31.1034768.
  • USD per kilogram = (USD per gram) * 1000.
  • USD per metric ton = (USD per kilogram) * 1000.

Persist both the original rate and the converted unit price with their units to avoid ambiguity downstream. For auditability, store the access_key-agnostic fields date, symbol, base, unit, and timestamp alongside the computed values.

Discoverability and symbol hygiene

FE-SI naming can vary. Some vendors list it as FE-SI, others as FESI, and some split by grade (e.g., FeSi 75%). You should:

  • Programmatically fetch and diff the symbols catalog weekly to detect changes.
  • Store a map of your internal commodity codes to Metals-API symbols. Include fields like “purity,” “grade,” and “spec” if your internal taxonomy distinguishes them.
  • Validate symbols at job submission time. Fail fast if a requested symbol is unsupported for your plan tier.

Caching, retries, and performance

For stable, cost-effective operations across FE-SI and related metals:

  • Cache historical responses by (symbol, date). Historical data does not change retroactively in normal circumstances.
  • Use ETag or “last retrieved” semantics in your own data layer. Even if your plan allows frequent calls, it’s more efficient to reuse historical snapshots.
  • Backoff and retry with jitter on transient network or 5xx errors. Avoid tight retry loops that consume quota.
  • Batch requests using Time-series for contiguous days instead of one-day-at-a-time loops. This reduces overhead and improves throughput.
  • Shard workloads by date ranges to parallelize large backfills, but respect rate limits and quotas.

Error handling and validation

Build robust error handling around:

  • success flag: Check success === true before trusting payload.
  • Missing symbols: If data.rates[symbol] is undefined, log and alert. Fallback strategies might include trying the nearest prior business day.
  • Unit changes: If “unit” changes across periods or endpoints for the same symbol, halt downstream processing and investigate.
  • Empty date blocks: On time-series, confirm your expected date count. If fewer entries than expected, reconcile around weekends/holidays.

Security best practices

  • Keep your access_key secret. Do not hardcode in front-end code for public apps; proxy via your server.
  • Rotate keys periodically. Update deployment secrets with zero-downtime strategies.
  • Rate-limit your own API that fronts Metals-API to prevent accidental fan-out from clients.
  • Log only non-sensitive request metadata and anonymize user context where applicable.

Comparing common symbols and units

Symbol Metal Typical Unit in Responses Notes
XAU Gold per troy ounce Used in examples; invert rate for USD/oz.
XAG Silver per troy ounce Often paired with Gold for ratio analysis.
XPT Platinum per troy ounce Industrial and jewelry demand sensitivity.
XPD Palladium per troy ounce Auto-catalyst exposure.
XCU Copper per troy ounce Industrial barometer; check unit before MT conversions.
XAL Aluminum per troy ounce Often modeled with energy input proxies.
XNI Nickel per troy ounce Battery and stainless steel exposure.
XZN Zinc per troy ounce Construction and galvanization signal.

For Ferro Silicon, verify its exact symbol and typical unit via the supported symbols directory before coding. Do not assume a troy-ounce basis for ferroalloys.

Advanced analytics with FE-SI: spreads, basis, and hedging

Once you have a clean historical FE-SI series, you can:

  • Compute rolling means and volatilities to drive procurement timing and inventory hedging rules.
  • Analyze spreads versus input metals (e.g., silicon, iron pricing proxies) to understand margin risk.
  • Run correlations versus energy prices or macro indices to detect cost shocks; persist alignment timestamps for any regression analysis.
  • Price escalation clauses: backtest your clauses with realized FE-SI history and measure budget variance.

If your firm leverages collateral or mark-to-market processes, persist both the original rate and the inverted USD/unit for audit, and archive the response JSON for each day to facilitate SOX/ISAE assurance needs.

Extending your FE-SI stack with related endpoints

Several features can elevate your workflow:

  • Carat endpoint: If your catalog includes gold jewelry lines that must price by carat alongside FeSi quotes, the carat endpoint returns Gold rates by Carat. Use it to unify your pricing backend with a single provider.
  • Lowest/Highest Price: Use the lowest-highest/YYYY-MM-DD endpoint to retrieve session extremes for risk controls or to validate intraday alerts on your side.
  • Open/High/Low/Close (OHLC): As shown above, compute day range-based indicators without rolling your own aggregator.
  • Historical LME: If your FE-SI exposure is benchmarked to LME-referenced instruments, leverage the historical-lme endpoint for LME symbols going back to 2008 (when available under your plan). Align FE-SI curves with LME proxies in one warehouse.
  • Intraday: For high-frequency dashboards, use the intraday endpoint for a single symbol to power alerting on threshold moves.

Always refer to the Metals-API Documentation for the latest on endpoint parameters, plan availability, and date limits.

Production architecture patterns

  • Data lake staging: Land raw JSON per day and symbol. Downstream jobs convert to USD/unit and write to a curated table with schema: date, symbol, rate_raw, unit_raw, usd_per_unit, base, timestamp.
  • Service facade: Expose an internal API like GET /prices?symbol=FE-SI&date=YYYY-MM-DD that returns from your cache and only falls back to Metals-API on cache miss.
  • Observability: Emit metrics for miss rates, API latency, and non-success responses. Alert when symbols disappear or the “unit” changes.
  • Governance: Version your processing pipeline. If you change conversion constants or rounding, bump the version and reprocess backfills deterministically.

Quality controls for FE-SI time series

  • Continuity checks: Ensure no unintentional zeroes or NaNs enter the series. Fail the job if anomalies are detected.
  • Outlier detection: Z-score or median absolute deviation to flag jumps; cross-check with Latest and OHLC snapshots.
  • Holiday calendars: Maintain a calendar for major exchanges and commodity markets. Align your expectations for update frequency accordingly.
  • Reconciliation: Periodically re-pull a random sample of historical dates and compare to stored values. Investigate meaningful drifts.

Tellurium (TE) side note: innovation and data-driven metals strategies

While your current focus might be Ferro Silicon, it’s worth noting how emerging metals like Tellurium (often tied to advanced semiconductors and solar technologies) illustrate the broader digital transformation in metals data. As materials science advances, supply chains pivot quickly, and data-driven insights become essential. APIs like Metals-API enable rapid onboarding of new symbols, analytics, and alerting logic across the stack—so when your product line adds Tellurium exposure, your architecture is already prepared. Integrate smart technology (stream processors, serverless compute) with your historical time series to unlock forward-looking risk analytics, scenario modeling, and automated procurement recommendations for both legacy metals and frontier materials like TE.

Endpoint-by-endpoint guidance and pitfalls

Latest

Purpose: Obtain a current snapshot for one or multiple symbols. Use for dashboards and alert checks.

  • Parameters: access_key, base (default USD), symbols (comma-separated).
  • Response highlights: timestamp, date, rates{}, unit.
  • Pitfalls: Do not treat Latest as a historical source. Cache with a TTL and persist snapshots if you need a record.
  • Performance: Request only the symbols you actually display; avoid all-symbol calls unless needed.
  • Security: Avoid exposing your key client-side. Call from your backend and forward minimal data to the UI.

Historical

Purpose: Get the rate for a single date. Use for precise backfills and corrections.

  • Parameters: access_key, date path segment (YYYY-MM-DD), base, symbols.
  • Response highlights: date, rates{}, unit.
  • Pitfalls: If a requested date is a weekend/holiday, consider pulling the last business day prior or using Time-series to frame context.
  • Performance: Batch separate days with Time-series if pulling many dates.

Time-series

Purpose: Multi-day ranges in one call. Best for first-loads and recurring refresh.

  • Parameters: access_key, start_date, end_date, base, symbols.
  • Response highlights: timeseries flag, start_date, end_date, rates keyed by date, unit.
  • Pitfalls: Do not assume every date is present; validate and reconcile gaps.
  • Performance: Chunk large ranges to respect plan limits; parallelize responsibly.

Fluctuation

Purpose: Period-over-period change metrics for symbols.

  • Parameters: access_key, start_date, end_date, base, symbols.
  • Response highlights: rates[SYMBOL].start_rate/end_rate/change/change_pct, unit.
  • Pitfalls: Confirm you understand sign conventions; negative change indicates end_rate < start_rate in the base representation.

Convert

Purpose: Amount conversion between currencies/metals.

  • Parameters: access_key, from, to, amount.
  • Response highlights: query, info.timestamp, info.rate, result, unit.
  • Pitfalls: Always persist the rate used to support audit queries from finance teams.

OHLC

Purpose: Open, high, low, close context for a date.

  • Parameters: access_key, date, base, symbols.
  • Response highlights: rates[SYMBOL].open/high/low/close, unit.
  • Pitfalls: If you compute returns, pick a consistent convention (close-to-close or open-to-close) and stick to it across assets.

Bid/Ask

Purpose: Execution-aware pricing via bid/ask spread.

  • Parameters: access_key, base, symbols.
  • Response highlights: rates[SYMBOL].bid/ask/spread, unit.
  • Pitfalls: Be explicit about which side you use for buy/sell marking to avoid P&L drift.

Carat

Purpose: Gold by carat for jewelry pricing. If you price gold products alongside FE-SI inputs, this consolidates vendors.

  • Tip: Append the correct base to retrieve carat-specific rates; check docs for latest parameterization.

Lowest/Highest

Purpose: Get session extremes for risk checks or anomaly alerts.

  • Tip: Use alongside OHLC to validate that day’s range and eliminate inconsistent outliers.

Historical LME

Purpose: Deep LME history (back to 2008 for supported symbols). Useful if your FE-SI exposure references LME benchmarks or if you’re correlating FE-SI with LME-traded inputs.

  • Tip: Align trading calendars and settlement conventions when joining LME series with FE-SI curves.

Intraday

Purpose: High-frequency snapshots for a single symbol, enabling alerting and short-window analytics.

  • Tip: Avoid over-polling; respect plan frequency limits and cache for UI refresh intervals.

A complete example flow for FE-SI backfilling

  1. Discover symbol: Hit Metals-API Supported Symbols and locate the FE-SI code.
  2. Initial load: Use Time-series to fetch the last N years in chunks (e.g., one quarter per request). Store raw JSON and a curated table with normalized USD/unit.
  3. Daily refresh: Schedule a Historical call for yesterday’s date at a deterministic time (e.g., 22:00 UTC), or use Time-series for the last 7 days to heal gaps.
  4. Alerting: Poll Latest and optionally Bid/Ask during your business hours at a cadence aligned to your plan. Trigger notifications on threshold moves or spread widenings.
  5. Analytics: Compute moving averages, volatility, and spreads against related inputs. Store outputs with versioned code references.

Testing strategy

  • Unit tests: Validate parsers for time-series and historical payloads; assert correct inversion to USD/unit.
  • Contract tests: Mock responses with missing fields and ensure your code fails fast with actionable errors.
  • Load tests: Simulate a backfill to measure throughput and confirm you stay within plan constraints.
  • Canary jobs: Run a small daily subset and compare with your main job; alert on divergence.

Troubleshooting common issues

  • Unsupported symbol: Double-check spelling and plan coverage. Consult the documentation or upgrade plan if needed.
  • Empty rates map: Validate that the requested date is a trading day. Try the previous business day if applicable.
  • Unexpected unit: If unit differs from “per troy ounce,” adapt conversion and update metadata schemas.
  • Precision loss: Use decimal-capable types in your database. Avoid float rounding that can create cents-level discrepancies at tonnage scales.
  • Time drift: Use the response “date” as the session key; don’t derive it from local timezones.

Governance and audit-readiness

  • Immutable history: Store raw JSON responses as immutable artifacts with hash checksums.
  • Lineage: Maintain data lineage from Metals-API response to curated price used in a purchase order or invoice.
  • Reproducibility: Tag analytic outputs with code version and conversion constants.
  • Access controls: Restrict who can change symbol mappings or conversion logic.

Why Metals-API for FE-SI and beyond

Metals-API emphasizes clarity of response structure, consistent units, and multi-endpoint coverage spanning latest, historical, time series, OHLC, bid/ask, conversion, and more—so you can evolve from a simple backfill to a fully automated, analytics-driven procurement and pricing system. Start quickly with a free key, then scale up as your workloads move from prototype to production. Visit the Metals-API Website to get an API key and start pulling FE-SI history today.

Additional resources

Conclusion

To reliably obtain Ferro Silicon (FE-SI) historical prices with Metals-API, anchor your workflow around symbol discovery, the Historical and Time-series endpoints, careful unit and base currency normalization, and resilient production patterns for caching, retries, and gap handling. Add Fluctuation, OHLC, Bid/Ask, and Convert where they provide tangible operational value. Keep a strong emphasis on auditability—store the original responses, tag conversions, and enforce consistent units. With these practices, you can turn raw FE-SI price data into a durable, analytics-ready asset that powers procurement strategies, risk controls, and product pricing in real time and at scale. Get started now at the Metals-API Website—claim your free API key and begin your first FE-SI backfill in minutes.

FAQ

How do I find the correct symbol for Ferro Silicon?

Go to the Supported Symbols page and search for Ferro Silicon, FeSi, or FE-SI. Copy the symbol exactly as listed.

What if the FE-SI unit is not per troy ounce?

Always check the "unit" field in each response. If it’s not per troy ounce, adapt your conversion pipeline accordingly (e.g., to USD/kg or USD/MT). Persist the unit alongside the rate.

Can I get intraday FE-SI updates?

If your plan and symbol coverage include FE-SI intraday or Latest/Bid-Ask data, you can poll those endpoints. Respect plan update frequencies and implement caching to avoid unnecessary calls.

How should I handle weekends and holidays?

Do not assume updates on weekends or holidays. Use Time-series for rolling windows and apply forward-fill only where your analytics explicitly allow it. Tag imputed data.

How can I convert FE-SI prices to EUR or JPY?

Use the Convert endpoint to switch bases in one step. Persist the conversion rate and timestamp for auditability.

What’s the best way to backfill several years of FE-SI prices?

Use the Time-series endpoint in date-chunked batches (e.g., monthly or quarterly), save raw JSON, and write normalized USD/unit values into a curated table. Parallelize within your rate limits.

Where do I get started?

Read the Metals-API Documentation, confirm symbols, and request your free API key at the Metals-API Website. You can prototype with Gold (XAU) examples and then swap in FE-SI once confirmed.

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