featured imageMarika
February 11, 2026

What can you build with FinFeedAPI?

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Most market data providers give you endpoints. FinFeedAPI gives you infrastructure.

FinFeedAPI is a set of APIs that give you clean, structured market data across stocks, FX, SEC filings, prediction markets, and bulk historical flat files. The interesting part (for devs) isn’t “you can fetch candles” — it’s that you can build systems that construct market behavior, compare markets that were never meant to be compared, and trigger workflows from real disclosures.

Below are 7 advanced builds (simple language, real engineering) using:
• Stock API (historical + intraday market data via REST) — see Stock API docs
• Prediction Markets API (Polymarket, Kalshi, Myriad, Manifold; metadata, OHLCV, order books) — see Prediction Markets API docs
• SEC API (structured filings access via REST) — see SEC API docs
• Currencies API (real-time + historical FX via REST/WebSocket/JSON-RPC) — see Currencies API docs
• Flat Files API (bulk historical OHLCV, timezone-aligned T+1) — see Flat Files docs

Reconstruct an entire trading day for a symbol and replay it deterministically for backtests, debugging, and “what happened at 10:32:05?” analysis.

Use T+1 canonical stock data (historical OHLCV and/or bulk flat files) to rebuild the price/volume sequence. Then drive a replay engine that emits “market time” events to strategy code.

  1. Pick replay resolution: per-trade (if available), per-second bars, or per-minute bars.
  2. Pull historical OHLCV for a UTC window (or trading-session window if you model sessions).
  3. Store to your own time-series store (Parquet + DuckDB, ClickHouse, TimescaleDB, etc.).
  4. Implement a replay loop that:
    • advances a simulated clock
    • publishes OHLCV bars (and optionally derived signals)
    • records strategy actions + PnL
  • Canonical vs real-time: use T+1 for “final truth” (late fixes, consolidation).
  • Time boundaries: decide bar alignment (e.g., minute bars pinned to :00). Keep everything in UTC internally.
  • Idempotent ingestion: key by (symbol, time_start, period) so re-runs don’t duplicate.
  • Volatility/volume spike detection: compute rolling std-dev, ATR, z-scores on volume, and “range expansion” flags during replay.
  • Execution simulation: start simple (fill at next bar open/close), then add slippage models (spread proxy, volatility-adjusted).
  • Stock historical OHLCV
  • Stock symbol/metadata listing (symbol identifiers, exchange mapping)
  • Flat Files catalog + object listing via S3-compatible API (T+1 bulk datasets)

Spot when a prediction contract’s implied probability drifts away from related public-market price action.

Normalize both sides into comparable signals:
• prediction market: contract price → implied probability
• public market: stock/ETF move → implied probability proxy (if you have a model) or simple direction/volatility regime

  1. Choose mappings: contract ticker/ETF (e.g., “Candidate wins” sector ETF + poll index proxy).
  2. Stream or poll:
    • prediction market latest price + OHLCV
    • stock OHLCV
  3. Compute divergence metrics:
    • probability gap (contract price – model probability)
    • spread/liquidity risk (from order book if used)
  4. Visualize: heatmap of gaps + time series overlay.
  • Normalization: always convert to a single quote basis (e.g., probability in [0,1]) and consistent timestamps.
  • Latency vs stability: real-time is for “now”; T+1 is for post-mortems. Use both.
  • Outliers: prediction markets can have thin liquidity; include a “minimum depth/volume” guard.
  • Contract lifecycle: handle suspended/settled/rolled markets as state transitions.
  • Prediction Markets metadata (venues, markets, contracts)
  • Prediction Markets current + historical OHLCV
  • Prediction Markets current order books (liquidity/spread)
  • Stock historical OHLCV

Trigger alerts when new filings land (10-K, 10-Q, 8-K) without scraping EDGAR or parsing raw documents yourself.

Treat filings as structured events keyed by stable identifiers (CIK/accession). Build an ingestion pipeline that detects “new row appeared” or “known filing updated”.

  1. Poll on a schedule (or near-real-time if supported) for new filings.
  2. Store a compact index table: company_id (CIK), accession_number, filing_type, filed_at, period_end, amendment_flag, source_url/doc_id (if provided).
  3. Detect changes:
    • new accession → new alert
    • same accession but updated metadata → update alert
  4. Push alerts to email/Slack/webhooks.
  • De-dup: accession number is your friend.
  • Backfill: on first run, backfill last N days/weeks, then switch to incremental polling.
  • Alert routing: different severity for 8-K vs 10-Q vs 10-K.
  • Rate control: batch requests by issuer; cache issuer metadata.
  • SEC filings search/listing (filter by form type, company, date)
  • SEC filing detail (structured fields)
  • SEC company/identifier lookup (CIK mapping)

Compare the same “event” across venues (Polymarket, Kalshi, Myriad, Manifold): pricing, liquidity, and market quality.

Use FinFeedAPI’s standardized schemas to treat each venue as a data source behind one unified model:

  • market/contract metadata
  • OHLCV
  • order book snapshots
  1. Build a contract matching layer:
    • exact match when venue IDs map cleanly
    • fuzzy match on title/description + resolution date
  2. For each matched set, compute:
    • best bid/ask + spread
    • depth-at-price bands
    • realized volatility from OHLCV
    • volume/turnover
  3. Rank “best venue” for execution vs “best venue” for signal.
  • Liquidity comparisons: normalize depth in USD terms (or shares) consistently.
  • Event risk: settlement rules differ; store venue-specific resolution metadata.
  • Data gaps: some venues may not have continuous trading; handle missing intervals.
  • Prediction Markets venues list
  • Prediction Markets market/contract metadata
  • Prediction Markets OHLCV (current + historical)
  • Prediction Markets order books (current)

Show “true performance” of an international stock position in a base currency, separating:

  • local equity return
  • FX return
  • combined return

Compute currency-adjusted PnL by combining stock OHLCV in local currency with FX rates (spot or VWAP-based) over the same timestamps.

  1. Determine each instrument’s trading currency.
  2. Choose base currency (USD/EUR/etc.).
  3. Pull:
    • stock OHLCV
    • FX rates for local/base
  4. Convert prices and returns:
    • price_base = price_local / fx_local_per_base (or inverse depending on quote)
  5. Output attribution time series.
  • Rate choice: be explicit (mid, bid/ask, VWAP). Use consistent choice across the app.
  • Timestamp alignment: join by nearest timestamp or bar boundary (minute/hour/day).
  • Corporate actions: if you later add splits/dividends, keep a separate adjustment layer.
  • Stock historical OHLCV
  • Currencies real-time rates (REST/WebSocket)
  • Currencies historical rates (REST)

Build an internal research warehouse for large-scale backtesting/ML without making a huge number of per-request API calls.

Ingest T+1 bulk flat files (normalized CSV datasets) into your storage + query layer, and expose it internally via SQL.

  1. Use the Flat Files API to enumerate available datasets (by date/exchange/universe).
  2. Ingest daily partitions into object storage (S3-compatible clients) and convert to columnar format.
  3. 3. Register partitions in your query engine (Spark/Trino/DuckDB/ClickHouse).
  4. Build curated marts:
    • OHLCV feature table
    • corporate calendar/fundamentals table (if you add SEC filings)
  • Partitioning: by date and exchange/venue first; symbol second.
  • Schema evolution: version your tables; don’t assume columns never change.
  • Data validation: row counts, null checks, and “price sanity” bounds per day.
  • Cost controls: compress + prune (Parquet + ZSTD).
  • Flat Files catalog/datasets listing via S3-compatible API
  • Flat Files date partitions/object listing via S3-compatible API

Quantify how prediction markets react around events (debates, CPI prints, earnings, court rulings):

  • probability shifts
  • spread changes
  • liquidity spikes
  • speed of repricing

Build an “event study” pipeline:

  • define event timestamps
  • pull OHLCV + order book snapshots before/after
  • compute pre/post deltas and reaction curves
  1. Create an event table: event_id, event_time_utc, event_type, linked_contracts.
  2. For each event and contract:
    • pull OHLCV for a window (e.g., -24h to +24h)
    • pull order book snapshots around the event (higher frequency near T0)
  3. Compute features:
    • jump size at T0
    • time-to-half-move (reaction speed)
    • spread widening/narrowing
    • depth collapse/rebuild
  4. Aggregate across events to find patterns.
  • Windowing: use multiple windows (1h, 6h, 24h) so results aren’t brittle.
  • Microstructure: spreads and depth are often more informative than last price.
  • Venue differences: keep venue as a first-class dimension in your analysis.
  • Prediction Markets historical OHLCV
  • Prediction Markets order books (current) / snapshots where available
  • Prediction Markets metadata (contract lifecycle/state)
  1. Create an API key in the API BRICKS console
  2. Test endpoints in REST first (simple + debuggable).
  3. If you need streaming FX or fast updates, add WebSocket where supported.

    Trial note: new orgs can unlock $25 free credits after creating an API key and then adding a verified payment method, purchasing credits, or purchasing a subscription.

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