Access Delhi Silver (DELH-XAG) Historical Prices for Financial Modeling using this API

Delhi Silver (DELH-XAG) historical prices power a wide range of financial modeling workflows—from calibrating cost curves for electronics manufacturing to backtesting hedging strategies and stress-testing supply chain budgets. In this guide, you’ll learn how to access high-quality historical Silver (XAG) data via the Metals-API, structure it for models, handle timezone and unit nuances, and integrate it into production systems. We’ll focus on repeatable steps you can apply whether you’re building pricing engines for jewelry, running quantitative strategies in commodities, or benchmarking industrial procurement in the Delhi market. If you need the exact market symbol for “Delhi Silver,” consult the live directory first; symbol availability varies and you should confirm the correct ticker on the Symbols page before coding.

What “Delhi Silver (DELH-XAG) Historical Prices” Means in Practice

In many teams, “Delhi Silver (DELH-XAG)” refers to Silver exposure relevant to the Delhi region—typically a locally referenced price, benchmark, or basis to international Silver (XAG). The Metals-API provides XAG as a first-class symbol, and you can combine XAG history with your own basis adjustments (freight, duties, local premiums/discounts) to derive a Delhi-representative series. If the precise DELH-XAG symbol is supported in your plan and visible on the symbols list, you can query it directly. Otherwise, the standard operational flow is:

Fetch historical XAG series via the Historical or Time-Series endpoints.
Apply your Delhi basis or location factor in your model.
Validate the final series against your internal procurement benchmarks.

You can browse the official symbol directory at Metals-API Supported Symbols to confirm the exact code you need before implementation.

Why Silver Data Drives Better Financial Modeling

Silver (XAG) sits at the intersection of precious metal investment and industrial demand. It’s heavily used in electronics, photovoltaics, medical devices, and smart manufacturing sensors—so its price reflects both macro factors and supply-chain specific cycles. For teams in Delhi and across India, reliable historical prices enable:

Backtesting hedges and coverage ratios for import-exposed bills of materials.
Scenario analysis for product launch pricing under volatile input costs.
Stress tests of working capital and cashflow under extreme price shocks.
Optimal reorder and lock-in strategies to reduce price slippage.
Fair-value benchmarking in RFQs and supplier negotiations.

Using an API with consistent response structures and unified units lowers the friction to pipe this data into Jupyter notebooks, spreadsheet models, and in-house pricing engines.

Key Concepts You Must Get Right (Base, Units, and Time)

Base Currency: By default, rates are relative to USD, meaning responses show how many troy ounces of a metal one USD buys (i.e., “per USD”), unless you specify an alternative base in your plan. For example, an XAG rate of 0.03815 means 1 USD = 0.03815 troy ounces of silver.
Units: Metals-API quotes metals per troy ounce. 1 troy ounce = 31.1034768 grams. If your ERP or quoting logic uses grams or kilograms, convert consistently.
Timestamps & Timezone: Responses include a UNIX timestamp and a date string. Historical and time-series values are aligned to the API’s daily snapshots. If you calculate returns or align with exchange closings, maintain a canonical timezone in your data pipeline.
Market Days: Precious metals have weekend and holiday behavior. When querying multi-day spans, expect flat or missing updates on closures; always validate continuity and consider forward-filling rules explicitly in your modeling code.

Confirm the Symbol Before You Code

If you specifically require a Delhi-quoted silver symbol, verify availability on the symbols endpoint. If not present, use XAG and apply your own Delhi basis. The dynamic list is here: Metals-API Supported Symbols. This step avoids hardcoding an unsupported ticker and saves refactoring time later.

Quick Start: Get Your API Key and First Response

Sign up for an API key at the Metals-API Website. With your key, you can hit historical data endpoints right away. For complete parameter and response details, see the official Metals-API Documentation. Below, we’ll walk through historical Silver (XAG) retrieval and how to adapt it for Delhi-specific financial modeling.

Historical Silver (XAG) for Modeling: Typical Workflow

Fetch a clean historical series of XAG for your modeling horizon (e.g., from 2019 onward).
Normalize units and currency, ensuring “per troy ounce per USD” is consistently interpreted.
Apply a Delhi basis or local adjustment, if you are tracking a Delhi reference.
Derive features: returns, realized volatility, rolling averages, drawdowns.
Backtest procurement locks, hedge thresholds, or dynamic reorder strategies.
Cache results and create a data quality monitor to catch anomalies and holidays.

Requesting Historical Silver Data

The Historical Rates endpoint returns a daily snapshot for a specific date. For multi-day windows, the Time-Series endpoint is more efficient. All examples below are representative; always refer to your plan’s supported parameters and the docs for up-to-date constraints.

Historical Endpoint: Single-Date Silver (XAG)

Use this when you need a single trading day’s rate for XAG and other metals.

{
"success": true,
"timestamp": 1789433029,
"base": "USD",
"date": "2026-09-15",
"rates": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915,
"XPD": 0.000748
},
"unit": "per troy ounce"
}

Fields that matter for modeling:

success: Boolean; verify before using values.
timestamp: UNIX epoch; critical for temporal alignment in databases.
base: Typically “USD”. All rates are relative to this base.
date: Daily reference date for the snapshot.
rates: Key-value map; XAG is Silver per USD troy ounce.
unit: Clarifies that metals are per troy ounce.

Use the XAG value to compute costs or convert to INR per gram if needed. If you maintain a Delhi basis (e.g., a premium), apply it after unit conversion to align with your local quoting convention.

Time-Series Endpoint: Multi-Day Silver (XAG)

Use the Time-Series endpoint to fetch a continuous block of XAG daily data. This is ideal for backtesting and factor derivation.

{
"success": true,
"timeseries": true,
"start_date": "2026-09-09",
"end_date": "2026-09-16",
"base": "USD",
"rates": {
"2026-09-09": {
"XAU": 0.000485,
"XAG": 0.03825,
"XPT": 0.000915
},
"2026-09-11": {
"XAU": 0.000483,
"XAG": 0.0382,
"XPT": 0.000913
},
"2026-09-16": {
"XAU": 0.000482,
"XAG": 0.03815,
"XPT": 0.000912
}
},
"unit": "per troy ounce"
}

Modeling tips:

Dates may skip weekends or holidays; don’t assume consecutive calendar days.
Forward-fill or business-day align depending on your backtest’s ground truth.
Compute daily log returns from XAG to estimate volatility, then calibrate stop-loss or hedge triggers.

Step-by-Step: From API to a Delhi-Adjusted Series

Pull XAG time series for your desired window.
Convert XAG (troy ounces per USD) to your working unit and currency. For example, USD per troy ounce is the reciprocal of the XAG rate; then multiply by the USD-INR rate if you need INR.
Apply your known Delhi basis (e.g., import duty, VAT, logistics, and local premium/discount). Keep these as separate columns so you can revise inputs without re-scraping data.
Store the final “Delhi Silver” series in your analytics database or cache for downstream models.

Core Endpoints You’ll Use Most

Although “Delhi Silver” may be a local designation, your data backbone will usually rely on core endpoints. Below we highlight how each supports financial modeling for XAG and related workflows.

Latest Rates: Real-Time Context for Today’s Pricing

While this article focuses on historical data, you often need the current snapshot to anchor today’s decisions or to mark your portfolio to market. Depending on your subscription, update frequency varies.

{
"success": true,
"timestamp": 1789519429,
"base": "USD",
"date": "2026-09-16",
"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 timestamp and date to log arrival times in your market-data bus.
If you capture intraday snapshots, store them with a consistent sampling schedule.

Fluctuation: Summaries for Risk Reports

To communicate weekly or monthly moves succinctly to stakeholders, the Fluctuation endpoint calculates net changes and percentage deltas across a window.

{
"success": true,
"fluctuation": true,
"start_date": "2026-09-09",
"end_date": "2026-09-16",
"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"
}

This is helpful for dashboards, VaR commentary, or supplier negotiation briefs where you need precise yet readable change stats without recomputing them in your own stack.

OHLC: Daily Open/High/Low/Close for Quant Features

Some strategies (e.g., breakout or mean-reversion rules) derive signals from OHLC bars rather than closes alone. The OHLC endpoint gives you day-level bars per metal.

{
"success": true,
"timestamp": 1789519429,
"base": "USD",
"date": "2026-09-16",
"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"
}

With OHLC, you can compute range-based volatility, true range, and intraday skew. If you track a Delhi-local series, you can apply your basis to each OHLC field consistently.

Bid/Ask: Execution-Aware Valuation

For treasury or trading teams that simulate executable prices or slippage, bid/ask spreads matter. Use these to estimate realistic entry/exit costs on XAG.

{
"success": true,
"timestamp": 1789519429,
"base": "USD",
"date": "2026-09-16",
"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 procurement simulations, translate spreads to local units (e.g., INR per kilogram) to capture transaction costs in total landed cost models for Delhi.

Convert: Unit and Cross-Metal Conversion

The Convert endpoint helps convert between metals and currencies for quick what-if calculations or to standardize units before modeling. Below is a representative response converting USD to XAU; you can adapt the approach to XAG and currencies used in your stack.

{
"success": true,
"query": {
"from": "USD",
"to": "XAU",
"amount": 1000
},
"info": {
"timestamp": 1789519429,
"rate": 0.000482
},
"result": 0.482,
"unit": "troy ounces"
}

For XAG, the mechanism is analogous. If your internal systems prefer grams, kilograms, or local currency, chain conversions carefully and document precision rules in your codebase.

Authentication, Security, and Production Hygiene

API Key: Your key goes in the access_key parameter. Keep it secret; never commit it to version control. Use environment variables or a secrets manager.
Transport Security: Use HTTPS endpoints. Enforce TLS minimums in your HTTP client configuration where applicable.
Access Controls: Restrict who and what services can read the key. Rotate on compromise or role changes.
Input Validation: Sanitize parameters before making requests (e.g., dates, symbols from user input). Validate the symbol against the official list to prevent accidental 4xx errors.

Resilience, Error Handling, and Recovery

Check success: Always gate downstream logic on success == true.
Retry Strategy: On transient network failures, retry with exponential backoff and jitter. Avoid hammering the API during partial outages.
Circuit Breakers: For high-availability services, implement circuit breakers to shed load and fail gracefully to cached data.
Fallbacks: If the latest endpoint is temporarily unavailable, use the last known value with a TTL and tag your decision logs accordingly.
Integrity Checks: Validate that XAG exists in rates and is within sanity bounds. Flag sudden zeroes or NaNs for review.

Caching and Cost Optimization

Immutable History: Historical daily snapshots do not change; cache aggressively and persist to your warehouse.
Shared Layers: Implement a small service-side cache (e.g., Redis) keyed by endpoint+params to de-duplicate queries across microservices.
Batch Requests: Prefer the Time-Series endpoint over many single-date calls to reduce overhead.
Update Cadence: Match polling frequency to your plan’s update interval to avoid redundant requests.

Dealing with Weekends, Holidays, and Missing Data

Calendar Alignment: Create a canonical business-day calendar for your models. Align data to that calendar explicitly.
Forward/Backward Fill: Choose a consistent rule for gaps (forward-fill for risk dashboards, no-fill for strict backtests). Document your choice.
OHLC Consistency: If using OHLC for signals, treat missing days consistently across open/high/low/close values to avoid artifacts.

Supply Chain and Smart Manufacturing: Why XAG Granularity Matters

Silver’s industrial applications—from PV cells to medical sensors—mean small price shifts can cascade through product margins. In smart manufacturing and IIoT contexts, you might embed Metals-API data into MES/ERP layers to:

Trigger reorder proposals when adjusted Delhi XAG prices dip below a threshold.
Auto-update configurable BOM cost rollups for custom quotes.
Power dynamic pricing in e-commerce for silver jewelry or components.
Run predictive maintenance economics that weigh parts replacement against silver content and spot prices.

Example: Curl Request to Retrieve Historical XAG

Replace YOUR_ACCESS_KEY with your key. If you need a local symbol for Delhi and it is supported, substitute XAG with the verified symbol from the Metals-API Supported Symbols page.

curl -G https://metals-api.com/api/2026-09-15 \
--data-urlencode "access_key=YOUR_ACCESS_KEY" \
--data-urlencode "base=USD" \
--data-urlencode "symbols=XAG"

The response will follow the Historical Rates format shown earlier. Persist the JSON, extract rates.XAG, and align it to your local time convention if needed.

JavaScript Example: Fetch Time-Series and Compute Basic Returns

This example demonstrates production-friendly steps: checking success, extracting XAG, and computing simple returns. Adapt error handling and caching for your environment.

// Fetch XAG time-series and compute daily returns
async function fetchXagSeries(start, end, apiKey) {
const url = new URL('https://metals-api.com/api/timeseries');
url.searchParams.set('access_key', apiKey);
url.searchParams.set('start_date', start);
url.searchParams.set('end_date', end);
url.searchParams.set('base', 'USD');
url.searchParams.set('symbols', 'XAG');

const res = await fetch(url.toString(), { method: 'GET' });
if (!res.ok) throw new Error('HTTP error ' + res.status);
const json = await res.json();

if (!json.success || !json.timeseries) {
throw new Error('API error or not timeseries response');
}

// Extract date-ordered XAG series
const dates = Object.keys(json.rates).sort(); // ISO date strings sort lexicographically
const series = dates.map(d => ({ date: d, xag: json.rates[d]?.XAG }));

// Validate and compute simple returns (r_t = xag_t / xag_{t-1} - 1)
const cleaned = series.filter(p => typeof p.xag === 'number');
const returns = [];
for (let i = 1; i < cleaned.length; i++) {
const prev = cleaned[i - 1].xag;
const curr = cleaned[i].xag;
if (prev > 0 && curr > 0) {
returns.push({ date: cleaned[i].date, r: (curr / prev) - 1.0 });
}
}

return { meta: { base: json.base, unit: json.unit }, series: cleaned, returns };
}

// Example usage (ensure you store your key securely)
fetchXagSeries('2026-09-09', '2026-09-16', process.env.METALS_API_KEY)
.then(({ meta, series, returns }) => {
console.log('Meta:', meta); // { base: 'USD', unit: 'per troy ounce' }
console.log('Sample series:', series.slice(0, 3));
console.log('Sample returns:', returns.slice(0, 3));
})
.catch(err => console.error('Fetch failed:', err));

Interpreting Responses: What You’ll Actually Use

rates.XAG: Your primary time series for Silver per USD per troy ounce.
timestamp and date: For partitioning tables, indexing time-series databases, or synchronizing to market calendars.
unit: Provides an explicit contract for unit conversions and audit trails.
Derived fields (Fluctuation, OHLC, Bid/Ask): Useful for risk reporting, strategy design, and execution simulation.

Advanced Techniques for Delhi-Specific Financial Models

Local Basis Curve: Build a time-varying basis function that reflects duties, freight, and local market premiums. Backfill missing basis days using business-day rules.
Hedging Thresholds: Combine XAG volatility estimates with cost-of-carry and working capital constraints to set dynamic trigger points for hedges.
Scenario Stressing: Shock XAG by historical drawdowns and add basis shocks to emulate customs policy changes or logistics disruptions.
Dual-Currency Valuation: If you budget in INR but receive revenues in USD, track sensitivity to both XAG and FX. You can integrate currency rates available via Metals-API for a unified pipeline.

Performance, Scaling, and Data Engineering Considerations

Batch Windows: Use the Time-Series endpoint for backfills; stagger large spans to respect operational limits.
Store Once, Compute Many: Persist raw JSON and normalized series so downstream services can recompute features without re-fetching.
Columnar Storage: For analytics at scale, land cleaned series in a columnar store (e.g., Parquet) partitioned by year/month for fast scans.
Idempotent Jobs: Make your ETL idempotent; re-running should not duplicate rows or distort aggregates.

Data Quality and Governance

Schema Contracts: Codify expected fields (success, base, unit, rates.XAG) and assert them in ingestion.
Outlier Detection: Flag sudden zeroes or extreme single-day changes for manual review.
Lineage: Track source URL, query params, timestamp, and hash of content for auditability.
Versioning: If your basis function changes, bump a version and keep old runs reproducible.

Industrial and Technology Use Cases for Silver

Silver plays a central role in modern manufacturing and technology:

Electronics: Conductive pastes and contacts where conductivity and reliability are paramount.
Solar: Photovoltaic cell interconnections—cost volatility directly affects project IRR.
Medical: Antimicrobial applications and precision devices with strict material specs.
Smart Manufacturing: Sensors and connectors in IIoT environments, where BOM costs are tied to real-time metal inputs.

These applications make robust XAG historical data a prerequisite for accurate financial planning and pricing across Delhi’s advanced manufacturing ecosystem.

Practical Tips a Beginner Might Miss

Reciprocal Intuition: If base=USD, XAG is ounces per USD; to get USD per ounce, take 1 / XAG. Always document this in code comments.
Rounding: Keep enough precision during conversions; round only at the presentation layer.
Weekend Behavior: Don’t infer signals from flat weekend values; exclude or tag them in your factor pipeline.
Unit Consistency: If a downstream user expects grams but you feed ounces, results will be off by 31x. Make unit tests for units.

Verifying Symbol Coverage and Capabilities

Because naming can differ by venue or locale, confirm the exact symbol for your “Delhi Silver” requirement on the Metals-API Supported Symbols page before implementing. When in doubt, use XAG and layer your local adjustments in your model. For deeper guidance, review endpoint specs and examples in the Metals-API Documentation.

Compliance, Audit, and Reporting

Provenance: Log request URLs, symbols, and timestamps for every data point used in financial reports.
Repeatability: Pin exact query params (start/end dates) and snapshot times for reproducible analyses.
Four-Eyes Checks: For major pricing decisions, require a second reviewer to validate series generation steps and basis updates.

Example: From USD Ounces to INR per Kilogram

Suppose you have an XAG rate r (troy ounces per USD). To compute INR per kilogram:

USD per troy ounce = 1 / r
USD per kilogram = (1 / r) * (1 / 31.1034768) * 1000
INR per kilogram = (USD per kilogram) * USD-INR rate

Keep USD-INR synchronized to the same date and timestamp as your XAG rate. If you’re simulating a Delhi purchase, then add your localized basis.

Designing Your ETL for Historical Backfills

Partition Backfills: Pull data year by year, or quarter by quarter, to control job size.
Checkpointing: After each batch, checkpoint the last processed date to enable safe restarts.
Validation per Batch: Compare fresh pulls against rolling mean/volatility to catch anomalies early.

Alerting on Material Moves

Fluctuation Threshold Alerts: Use change_pct to trigger notifications when daily or weekly moves exceed risk limits.
OHLC Breakouts: Alert on intraday high/low thresholds derived from the OHLC endpoint.
Basis Risk Alerts: If your Delhi basis deviates beyond historical bounds, prompt a review of logistics or duty assumptions.

Cost Control in Procurement

Repricing Windows: Schedule repricing during high-liquidity windows to minimize slippage (bid/ask-aware).
Hedged RFQs: Quote customers with hedged input costs using latest XAG and your Delhi basis; log assumptions for audit trails.
Dynamic Safety Stock: Adjust reorder points based on realized volatility from your time-series; higher volatility can warrant higher buffer stock.

Intraday, LME, and Additional Features

If your plan includes intraday snapshots, the Intraday endpoint can refine mark-to-market granularity for short-term hedging or tactical procurement. Historical LME data (for applicable symbols) extends analysis with exchange-referenced histories. Always verify symbol availability and coverage periods on the symbols page and in the documentation. For a comprehensive feature tour and parameter details, start at the Metals-API Documentation.

End-to-End Governance Checklists

Security: Keys in secrets manager, TLS only, scoped environment access.
Data Contracts: Enforce expected fields; track base and units rigorously.
Quality: Outlier filters, missing-data rules, reproducible basis adjustments.
Cost: Caching, batching, and aligned polling schedules.
Observability: Metrics for success ratio, latency, cache hit rate, and data lag vs. expected update intervals.

Call to Action

Ready to integrate historical Silver for your Delhi-focused financial models? Get started with a free API key at the Metals-API Website, verify your symbols on the Supported Symbols directory, and implement using the developer documentation. In a few minutes, you can have robust XAG history flowing into your analytics stack.

Additional Resources

FAQ

Is “DELH-XAG” an official symbol I can query directly?

Symbol availability can change. Always check the Metals-API Supported Symbols page. If a Delhi-specific silver symbol is not listed, use XAG and apply your Delhi basis adjustments within your model.

What unit does the API use for silver?

Metals are quoted per troy ounce by default. The base currency is typically USD. If you need grams, kilograms, or a different currency, convert consistently and document your formula.

How do I handle weekends and non-trading days?

Expect missing or unchanged values. Decide on a clear policy (exclude, forward-fill, business-day indexing) and keep it consistent across all backtests and reports.

Can I get OHLC and bid/ask for XAG?

Yes, subject to your plan. Use the OHLC and Bid/Ask features to build execution-aware models and range-based indicators. See examples above and refer to the Metals-API Documentation.

How should I secure my API key?

Store it in environment variables or a secrets manager, never in code. Limit access to the services that need it, and rotate the key if you suspect exposure.

What’s the fastest way to start?

Get a free API key at the Metals-API Website, confirm your symbols, then pull a short XAG time-series to validate your ingestion, unit conversions, and basis logic. From there, scale to your full historical window and productionize caching and alerting.

Ready to use Metals-API?

Try it now!