Oracle Price Feed Service

A real-time oracle price feed system built for low-latency trading, analytics, and risk engines. It ingests live market prices, normalizes them, caches them for ultra-fast access, stores full history, and exposes clean APIs for downstream consumption.

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Why I Built This

Most trading systems fail at the price layer — slow updates, stale data, no confidence metrics, and zero observability.
I built this to design a production-grade pricing backbone focused on:

• Deterministic price normalization
• Sub-second freshness guarantees
• Historical traceability
• Health-based automation
• Zero-trust oracle design (on-chain ready)

This system is meant to be the single source of truth for mark price, funding, and liquidation logic.

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What This Does

• Live oracle price ingestion
• Confidence-aware normalization
• Ultra-low latency caching
• Time-series persistence
• Health & freshness checks
• Prometheus metrics for observability
• Designed on-chain oracle consensus (outlier rejection + median aggregation)

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Core Data Model

struct PriceResponse {
    symbol: String,
    source: String,
    price: f64,
    confidence: f64,
    timestamp: i64,
}

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API Endpoints

GET /oracle/price/{symbol}      # Latest price (cache)
GET /oracle/prices             # All active prices
GET /oracle/history/{symbol}  # Historical data
GET /oracle/health             # Data freshness
GET /metrics                   # Prometheus metrics

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Database Schema

CREATE TABLE price_history (
    id SERIAL PRIMARY KEY,
    symbol TEXT NOT NULL,
    timestamp BIGINT NOT NULL,
    price DOUBLE PRECISION NOT NULL,
    confidence DOUBLE PRECISION NOT NULL,
    source TEXT NOT NULL
);

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Price Ingestion Logic (Simplified)

loop {
    let raw = fetch_price().await?;
    let price = raw.price * 10f64.powi(raw.expo);
    let conf  = raw.conf  * 10f64.powi(raw.expo);

    let payload = PriceResponse {
        symbol: "BTC".into(),
        source: "oracle".into(),
        price,
        confidence: conf,
        timestamp: now(),
    };

    redis.set(key, to_json(&payload))?;
    db.insert(payload)?;
}

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Caching Strategy

• Redis is used as the single-read source
• Database is used only for historical analysis
• No live system ever blocks on database reads

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Monitoring

• Oracle fetch count
• API request volume
• Fetch latency histogram

All exposed via the /metrics endpoint.

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On-Chain Ready (Designed)

• Multi-oracle validation
• Median-based aggregation
• Staleness & confidence filtering
• Outlier & manipulation rejection

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Final Note

This project is built as a real oracle infrastructure layer, not a demo application — designed for:

• Exchanges
• Perpetual futures
• Risk engines
• Quant research
• Backtesting systems

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