Building a Commodity Price Dashboard with Open Source Tools

Hook: Stop Chasing Spreadsheets — Get Real-Time Commodity Prices Your Team Can Trust

If you work on cloud or SaaS platforms for ag-tech, trading desks, or internal analytics, you know the pain: scattered CSVs, delayed data feeds, and dashboards that break on volume spikes. You need a reliable internal dashboard that tracks cotton, corn, wheat and soybean movements in near real-time — one that developers can maintain with open source tools. This guide shows exactly how to build that using Prometheus to collect and model price metrics and Grafana to visualize, alert, and analyze them.

What you'll deliver (fast)

• A Docker-based stack: Prometheus + Alertmanager + Grafana + a lightweight custom exporter.
• Canonical metric model for commodity prices with low cardinality and high signal fidelity.
• Grafana dashboards: time-series panels, spread calculations, moving averages, and alerts tied to business thresholds.
• Scalability and long-term storage options (VictoriaMetrics / Mimir) and secure secret handling.

Why this matters in 2026

By 2026, cloud-native observability and OpenTelemetry patterns are mainstream. Teams expect metrics to be queryable, annotated, and alertable by default. Grafana's unified alerting and plugin ecosystem (matured through 2024–2025) make it the natural UI layer. Prometheus still excels for short-term, high-cardinality operational metrics. Combining them gives dev teams low-cost, open-source control over ag market observability — which is critical as trading desks and supply chain systems demand millisecond visibility and programmatic access to price changes.

Architecture Overview

High-level architecture is simple and robust:

• Data sources: USDA Market News, CmdtyView, exchange APIs (CME/CBOT, ICE for cotton) or paid vendors (Quandl / Nasdaq Data Link) depending on licensing.
• Polling/ingest: lightweight exporter (Python/Go) that polls APIs and exposes metrics at /metrics in Prometheus format.
• Prometheus: scrapes exporter, evaluates recording rules, forwards long-term data to remote storage (optional).
• Grafana: queries Prometheus, draws dashboards, handles unified alerting and annotations (USDA reports, weather alerts).
• Alertmanager: routes alerts to Slack, MS Teams, PagerDuty, or webhook endpoints.

Step 1 — Pick data sources & handle licensing

Before any code, decide where the commodity prices come from. Public sources are handy for prototypes; production needs reliable feeds and clear licensing.

• USDA Market News — great for cash prices and domestic market context.
• CmdtyView — referenced in 2025 / 2026 market summaries; useful for national averages and cash quotes.
• CME/ICE market data — futures and front-month contracts. Often requires a subscription or paid API access for tick-level data.
• Quandl / Nasdaq Data Link — has commodity futures and spot datasets. Paid tiers give lower latency.

Operational tip: keep a backup source for each commodity. If API A fails, fall back to API B. Always respect terms of service: cache results, rate-limit, and consider storing raw responses for auditing.

Step 2 — Design your Prometheus metric model

Prometheus metrics should be simple, low-cardinality, and expressive. Use gauges for prices and expose key metadata as labels.

Recommended metric names and labels:

commodity_price_usd{symbol="CORN", contract="Z26", source="cme", venue="cbot"} 3.845
commodity_volume{symbol="CORN", contract="Z26", source="cme"} 15230
commodity_last_update_timestamp{symbol="CORN", source="cme"} 1672531200

• symbol: CORN, WHEAT, SOY, COTTON — keep short and consistent.
• contract: front-month code when using futures (e.g., Z26) — optional for spot-only flows.
• source & venue: for provenance and filtering.

Cardinality rule: avoid adding high-cardinality labels like trade_id, account_id inside metrics. Use logs or traces for that detail.

Step 3 — Build a lightweight exporter (example)

Make a small, resilient service (Python example) that polls the chosen APIs every 15–60s and exposes Prometheus-format metrics at /metrics. Use retries, exponential backoff, and local caching.

#!/usr/bin/env python3
from flask import Flask, Response
import requests
import time

app = Flask(__name__)

def fetch_price(symbol):
    # Replace with real API call and auth
    r = requests.get(f'https://api.example.com/price/{symbol}')
    data = r.json()
    return data['price'], data.get('volume')

@app.route('/metrics')
def metrics():
    out = []
    for symbol in ['CORN','WHEAT','SOY','COTTON']:
        price, volume = fetch_price(symbol)
        out.append(f'commodity_price_usd{{symbol="{symbol}",source="myapi"}} {price}')
        if volume is not None:
            out.append(f'commodity_volume{{symbol="{symbol}",source="myapi"}} {volume}')
    return Response('\n'.join(out)+'\n', mimetype='text/plain')

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=9100)

Production tips: containerize this exporter, run it with a process supervisor, and handle API keys via environment variables injected from a secrets manager (HashiCorp Vault, AWS Secrets Manager).

Step 4 — Prometheus config & recording rules

Minimal scrape config (prometheus.yml):

scrape_configs:
  - job_name: 'commodity_exporter'
    static_configs:
      - targets: ['commodity-exporter:9100']

Recording rules speed up queries and reduce load on Prometheus. Add rules for 1h moving averages and percent changes:

groups:
- name: commodity.rules
  rules:
  - record: commodity_price_1h_avg
    expr: avg_over_time(commodity_price_usd[1h])
  - record: commodity_price_24h_pct_change
    expr: (commodity_price_usd - last_over_time(commodity_price_usd[24h])) / last_over_time(commodity_price_usd[24h]) * 100

PromQL examples you’ll use often:

• Latest price for corn: commodity_price_usd{symbol="CORN"}
• 24h percent change: commodity_price_24h_pct_change{symbol="SOY"}
• 30m trend (slope estimate): increase(commodity_price_usd[30m])

Step 5 — Docker Compose for quick dev stack

Spin up a reproducible local environment with Docker Compose. This example runs Prometheus, Grafana, and the exporter together so your dev team can iterate fast.

version: '3.8'
services:
  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
      - ./rules.yml:/etc/prometheus/rules.yml:ro
    ports:
      - 9090:9090
  grafana:
    image: grafana/grafana:latest
    ports:
      - 3000:3000
  commodity-exporter:
    build: ./exporter
    ports:
      - 9100:9100

After launch, point Grafana to Prometheus at http://prometheus:9090 within the compose network or your localhost port for dev.

Step 6 — Craft Grafana panels and dashboards

Start with these panels to get immediate value:

1. Multi-series Time Series: One panel with CORN, WHEAT, SOY, COTTON price series. Use distinct colors and dashed futures vs solid spot.
2. Table — Latest Prices & Deltas: Column for last price, 1h %, 24h % and 7d %. Use transformations to calculate deltas if not recording rules.
3. Spread / Ratio Panel: e.g., soybean-to-corn ratio: commodity_price_usd{symbol="SOY"} / commodity_price_usd{symbol="CORN"}. Use this for arbitrage or crop-switch signals.
4. Heatmap / Sparkline Grid for quick day-over-day movement across commodities and contracts.
5. Annotations: Integrate USDA reports, weather warnings, export announcements as dashboard annotations. Grafana can load annotations via a simple JSON endpoint or use Prometheus events.

Use Grafana's alerting to notify when thresholds are crossed. For example, trigger an alert if corn drops more than 4% in 24 hours or soybean-to-corn ratio exceeds a business-defined threshold.

Step 7 — Alerts: Prometheus Alertmanager or Grafana Unified Alerts?

Both are viable in 2026. Prometheus Alertmanager remains great for pure metric-based rules and mature routing. Grafana unified alerting offers tighter UX and support for composite rules, silence sharing, and notification policies.

Example Prometheus alert rule:

- alert: CornLargeDrop
  expr: commodity_price_24h_pct_change{symbol="CORN"} < -4
  for: 15m
  labels:
    severity: critical
  annotations:
    summary: "Corn down >4% in 24h"
    description: "Check exports and weather reports."

Route these through Alertmanager to Slack or Ops channels and include dashboard links and query examples for fast incident triage.

Step 8 — Scale & long-term storage

Prometheus is ideal for short-term retention (days to a few weeks). For 6–24+ month retention and heavy queries across years, integrate remote_write to:

• VictoriaMetrics — efficient, easy to run single binary for larger retention.
• Grafana Mimir / Cortex — multi-tenant options if you host dashboards for multiple internal teams.

Downsampling strategy: keep raw high-resolution metrics for 7–14 days, then store 1m or 5m downsampled series long-term. This reduces cost and preserves analytics usability.

Step 9 — Security & secret management

• Never store API keys in container images. Use a secrets manager and mount at runtime.
• Enable TLS for Grafana and Prometheus endpoints in production. Use an ingress or sidecar for cert management.
• Restrict Grafana access with SSO / LDAP and per-dashboard permissions. Audit dashboard changes in Git.
• Log all outbound requests from exporters for troubleshooting and compliance.

Step 10 — Observability for the observability stack

Monitor exporter health with a simple heartbeat metric:

commodity_exporter_up{source="myapi"} 1

Alert if exporter is down for more than two scrape intervals. Also run synthetic tests: query key dashboard panels and ensure expected metrics are fresh.

2026 Trends & Future-Proofing

Keep these trends in mind as you build:

• OpenTelemetry for metrics is standard. Consider emitting OpenMetrics and integrating with OTLP receivers for future compatibility.
• Edge ingestion and streaming: low-latency feeds now use Kafka / Pulsar; you can transform streams into metrics with lightweight stream processors if you need sub-second telemetry.
• AI-driven anomaly detection is increasingly available as managed plugins in Grafana (late 2025 brought multiple vendors). Consider adding ML anomaly panels to highlight unexpected moves rather than just threshold alerts.
• Regulatory & data lineage: business teams increasingly demand lineage. Keep raw API responses alongside your metrics for auditability.

Example sprint — deliver this in 2 weeks

1. Day 1–2: Choose data sources & validate API access for four commodities.
2. Day 3–6: Implement exporter, containerize, and run end-to-end scrape with Prometheus in Docker Compose.
3. Day 7–9: Build Grafana dashboard with 5 panels and basic alerts.
4. Day 10–12: Add recording rules, tune PromQL, and add annotations for USDA/WASDE reports.
5. Day 13–14: Harden: secrets, TLS, remote_write to VictoriaMetrics for retention, and automated tests.

Success metrics: dashboard refresh latency < 60s, per-commodity alerts working, and a documented on-call runbook.

Real-world considerations & pitfalls

• Avoid high cardinality: do not label per-trade or per-counterparty.
• Respect API rate limits: implement backoff and caching.
• Be thoughtful about contract rollovers: front-month futures change month codes — implement logic to roll gracefully and annotate roll dates on dashboards.
• Handle market holidays and overnight gaps when computing moving averages and percent changes.

Pro tip: store contract metadata (expiry, tick size, multiplier) in a small DB and expose it to Grafana via a lookup table to display correct notional values and make spreads meaningful.

Actionable checklist

• Choose at least two data sources per commodity.
• Create exporter and expose commodity_price_usd and commodity_volume gauges.
• Configure Prometheus scrape and two recording rules (1h avg, 24h pct change).
• Build a Grafana dashboard with time series, table, and spread panels.
• Set up alerts to route to Slack/Teams and test them.
• Plan remote_write to VictoriaMetrics or Mimir for long-term retention.

Further reading & resources (quick list)

• USDA Market News and WASDE reports (for annotations)
• CmdtyView / market data providers (for national averages and cash prices)
• Prometheus docs: metric naming and best practices
• Grafana docs: transformations, annotations, and unified alerting
• VictoriaMetrics / Mimir docs for long-term storage

Final example: useful PromQL snippets

• Current price per commodity: last_over_time(commodity_price_usd[5m])
• 24h percent change: ((last_over_time(commodity_price_usd[5m]) - last_over_time(commodity_price_usd[24h])) / last_over_time(commodity_price_usd[24h])) * 100
• Soy/Corn ratio: (last_over_time(commodity_price_usd{symbol="SOY"}[5m])) / (last_over_time(commodity_price_usd{symbol="CORN"}[5m]))
• 30m volatility (stddev): stddev_over_time(commodity_price_usd[30m])

Wrap-up: Deliver value, then iterate

Start small: focus on reliable prices, a clean metric model, and a couple of high-value dashboards. Once business stakeholders trust the data, add forecasting, anomaly detection, or expanded coverage (contracts, implied vol, option data). In 2026, teams that combine open-source observability with responsible data engineering will move faster and reduce blind spots in ag markets.

Call to action

Ready to build your commodity price dashboard? Clone the sample repo, spin up the Docker Compose stack, and adapt the exporter to your data feeds. If you want a review of your Prometheus metrics model or grafana panels, share your dashboard JSON and I’ll provide optimization suggestions tailored to ag-market use cases.

Related Reading

• Microwaveable Warmers vs Traditional Bottles: What to Offer in Your Cafe Waiting Area
• Pamper Your Pooch: What the Best Dog-Friendly Homes Teach Resorts About Pet Amenities
• From Streams to Stadiums: How Bluesky’s LIVE Badges and Twitch Integration Will Change Football Fandom
• Phone Plans for Freelancers and Gig Workers: Choosing a Plan That Supports Your Side Hustle
• The Best Hot-Water Bottles for Winter 2026: Comfort, Safety and Value