What can you build with FinFeedAPI?

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

Most APIs let you fetch prices. FinFeedAPI lets you build deterministic systems on top of structured, normalized market data.

You get:

• Stocks API — historical + intraday OHLCV via REST
• Prediction Markets API — Polymarket, Kalshi, Myriad, Manifold (metadata, OHLCV, order books)
• SEC API — structured filings (10-K, 10-Q, 8-K, etc.)
• Currencies API — real-time + historical FX (REST / WebSocket / JSON-RPC)
• Flat Files API — bulk T+1 canonical datasets via S3-compatible access

The value is not “fetch candles.”
The value is:

• reproducible research
• cross-market normalization
• event-driven workflows
• infrastructure-level ingestion

Below are 7 possible builds — with actual system design.

1. Rebuild and Replay a Full Trading Day

Product used

Stock API + Flat Files API

Goal

Reconstruct an entire trading session deterministically for backtesting, debugging, and execution analysis.

Core idea

Use T+1 canonical OHLCV (or bulk flat files) as the source of truth, store it in your own time-series layer, and drive a replay engine that emits market-time events to strategy logic. The replay system becomes a controlled simulation of real market state — not synthetic data.

Architecture

Data layer

• Pull historical OHLCV (minute/second/trade-level if available).
• Or ingest T+1 bulk flat files for canonical truth.
• Store in columnar format (Parquet + ZSTD).

Storage schema (example)

1CREATE TABLE stock_ohlcv (
2    symbol TEXT,
3    exchange TEXT,
4    timeframe TEXT,      -- 1m, 1s, etc.
5    time_start TIMESTAMP,
6    open DOUBLE,
7    high DOUBLE,
8    low DOUBLE,
9    close DOUBLE,
10    volume DOUBLE,
11    PRIMARY KEY (symbol, timeframe, time_start)
12);

Replay engine design

A replay loop should:

1. Load bars ordered by time_start.
2. Advance a simulated clock.
3. Emit:
   • price events
   • derived indicator events
4. Execute strategy callbacks.
5. Record fills and PnL.

Pseudo-flow:

1for bar in bars:
2    clock.set(bar.time_start)
3    strategy.on_bar(bar)
4    broker.simulate_execution()

Engineering considerations

• Canonical truth: use T+1 for backtests to avoid late corrections.
• Idempotent ingestion: key by (symbol, timeframe, time_start).
• Session modeling: define session boundaries explicitly (e.g. 13:30–20:00 UTC for US equities).
• Slippage models:
  • next bar open
  • mid-price proxy
  • volatility-adjusted spread model

You’re not just replaying data — you’re recreating market state.

Key Takeaways

This is a medium to advanced difficulty project, especially if you include execution simulation and slippage modeling. The main benefit is deterministic backtesting using canonical T+1 stock market data, which improves research accuracy and strategy validation. It also helps debug trading signals and investigate specific intraday moments with precision. You’ll need solid time-series storage and careful timestamp alignment. It’s worth building if you care about reliable backtests and reproducible trading research.

2. Cross-Market Arbitrage Dashboard

Product used

Prediction Markets API + Stock API

Goal

Detect when a prediction contract’s implied probability diverges from related public-market behavior.

Core idea

Normalize both systems into comparable signals — contract price as probability and stock movement as a modeled proxy — then compute structured divergence metrics while controlling for liquidity and spread. The result is a cross-market signal engine, not just a price comparison tool.

Data normalization

Prediction contract:

1implied_probability = contract_price

Stock side (simple proxy):

1prob_proxy = logistic(beta * return + intercept)

Or use a regime-based proxy (volatility + direction).

Data model (simplified)

1CREATE TABLE cross_market_signal (
2    contract_id TEXT,
3    symbol TEXT,
4    timestamp TIMESTAMP,
5    implied_prob DOUBLE,
6    modeled_prob DOUBLE,
7    divergence DOUBLE,
8    spread DOUBLE,
9    depth DOUBLE
10);

Pipeline

1. Pull prediction OHLCV + metadata.
2. Pull stock OHLCV.
3. Align timestamps (same bar resolution).
4. Compute divergence.
5. Filter low-liquidity contracts:
   • minimum volume
   • minimum order book depth

Critical detail

Prediction markets often have thin books.

Include liquidity guards:

1if depth_usd < threshold:
2    ignore signal

This is cross-market signal engineering — not just price comparison.

Key Takeaways

This is an advanced analytics project, because it requires cross-market normalization and liquidity filtering. The benefit is early detection of pricing inefficiencies between prediction markets and public equities. It can generate trading signals, sentiment indicators, or risk alerts. You must account for thin liquidity and contract lifecycle changes. It’s worth pursuing if you want structured cross-market intelligence using prediction market data.

3. SEC Filing Change Detection & Alert System

Product used

SEC API

Goal

Trigger structured alerts when new filings (10-K, 10-Q, 8-K) appear or change — without scraping EDGAR.

Core idea

Treat filings as versioned structured events keyed by stable identifiers (CIK, accession number). Build an ingestion index that detects inserts and revisions, and turn regulatory disclosures into machine-readable triggers for trading, compliance, or monitoring systems.

Canonical index table

Store a compact filing index:

1CREATE TABLE sec_filings_index (
2    company_cik TEXT,
3    accession_number TEXT PRIMARY KEY,
4    filing_type TEXT,
5    filed_at TIMESTAMP,
6    period_end DATE,
7    amendment_flag BOOLEAN,
8    source_url TEXT,
9    document_id TEXT,
10    ingestion_timestamp TIMESTAMP DEFAULT NOW()
11);
12

This is your control table.

Change detection logic

On each poll:

1. Fetch filings filtered by:
   • form type
   • date range
   • CIK
2. For each filing:
   • If accession_number not in table → INSERT + alert
   • If exists but metadata differs → UPDATE + revision alert

Pseudo:

1if not exists(accession_number):
2    insert()
3    send_alert()
4elif metadata_changed:
5    update()
6    send_revision_alert()
7

Backfill strategy

• Initial run: fetch last N days.
• Persist.
• Switch to incremental polling.

Alert routing example

1if filing_type == '8-K':
2    route = 'high_priority'
3elif filing_type in ('10-Q', '10-K'):
4    route = 'review_queue'
5

This turns filings into event triggers for trading or risk systems.

Key Takeaways

This is a low to medium difficulty project with high practical value. It converts structured SEC filings into automated alerts without scraping EDGAR. The benefit is real-time awareness of earnings reports, material events, and regulatory disclosures. You need proper de-duplication using accession numbers and incremental polling logic. It’s worth building if you want event-driven trading or compliance monitoring based on official SEC data.

4. Multi-Exchange Prediction Market Comparator

Product used

Prediction Markets API

Goal

Compare pricing, liquidity, and market quality for the same event across Polymarket, Kalshi, Myriad, and Manifold.

Core idea

Leverage standardized schemas to model all venues under a unified structure. Match contracts across platforms, normalize depth and spreads, and compute venue-level efficiency metrics. This exposes structural differences, not just price gaps.

Unified schema example

1CREATE TABLE prediction_contracts (
2    venue TEXT,
3    contract_id TEXT,
4    title TEXT,
5    resolution_date TIMESTAMP,
6    state TEXT,
7    PRIMARY KEY (venue, contract_id)
8);
9

Order book snapshot:

1CREATE TABLE order_book_snapshot (
2    venue TEXT,
3    contract_id TEXT,
4    timestamp TIMESTAMP,
5    best_bid DOUBLE,
6    best_ask DOUBLE,
7    spread DOUBLE,
8    depth_usd DOUBLE
9);

Matching logic

• Exact mapping if known.
• Otherwise fuzzy match on:
  • normalized title
  • resolution date
  • category

Comparison metrics

• Spread %
• Depth within 1% of mid
• Realized volatility
• Volume turnover

Normalize depth to USD for comparability.

Venue differences matter — resolution rules, settlement mechanics, suspension behavior.

Store those explicitly.

Key Takeaways

This is a medium to advanced difficulty project due to contract matching and liquidity normalization. The benefit is visibility into pricing differences and execution quality across Polymarket, Kalshi, Myriad, and Manifold. It helps identify the best venue for execution and better signal sources. You must handle resolution rules and inconsistent trading activity across platforms. It’s worth it if you trade or analyze prediction markets seriously and want structured venue comparison.

5. Stock + FX Exposure Calculator

Product used

Stock API + Currencies API

Goal

Calculate true performance of international equity positions in a chosen base currency and separate equity return from FX impact.

Core idea

Join stock OHLCV with aligned FX rates, convert pricing consistently, and decompose returns into equity, currency, and interaction components. This transforms raw price data into exposure-aware performance analytics.

Attribution math

Let:

• P_local
• FX_local_base

Then:

1P_base = P_local / FX_local_base

Return decomposition:

Total ≈ Equity + FX + Interaction

Schema example

1CREATE TABLE fx_adjusted_returns (
2    symbol TEXT,
3    base_currency TEXT,
4    timestamp TIMESTAMP,
5    equity_return DOUBLE,
6    fx_return DOUBLE,
7    total_return DOUBLE
8);

Alignment rules

• Use same timeframe (1m, 1h, 1d).
• Join by exact timestamp when possible.
• Otherwise nearest lower boundary.

Be explicit about rate basis:

• mid
• bid
• ask

Never mix.

Key Takeaways

This is a medium difficulty analytics project that requires accurate FX rate alignment. The benefit is clean performance attribution between equity return and currency impact. It improves portfolio reporting and international investment analysis. Careful timestamp joins and consistent rate selection (mid, bid, ask) are critical. It’s worth building if you manage global portfolios or want accurate currency-adjusted returns.

6. Large-Scale Historical Warehouse

Product used

Flat Files API

Goal

Build an internal research warehouse for large-scale backtesting and ML without excessive per-request API calls.

Core idea

Ingest T+1 bulk datasets via S3-compatible access, convert to columnar storage, partition intelligently, and expose through SQL engines. This shifts from request-based data access to infrastructure-level research pipelines.

Ingestion pipeline

1. Enumerate datasets via S3-compatible listing.
2. Download daily partitions.
3. Convert to Parquet.
4. Register partitions.

Partition strategy:

1/dataset/
2    date=YYYY-MM-DD/
3        exchange=NYSE/
4            symbol=AAPL.parquet

Validation checks

• Row count vs expected.
• No negative prices.
• High >= Low.
• Volume >= 0.

Schema evolution

Version tables:

1ohlcv_v1
2ohlcv_v2

Never assume static schema.

This becomes your ML + backtesting backbone.

Key Takeaways

This is an advanced infrastructure project that requires storage design and partition strategy. The benefit is scalable backtesting and machine learning without excessive API calls. It enables faster research queries and long-term data consistency. You must validate data quality and manage schema evolution carefully. It’s worth the investment if you run systematic research or quantitative models on large historical datasets.

7. Prediction Market Event Reaction Analyzer

Product used

Prediction Markets API

Goal

Quantify how prediction markets react around real-world events such as CPI releases, earnings, debates, or legal rulings.

Core idea

Build an event-study pipeline that pulls OHLCV and order book snapshots around defined timestamps, then compute jump size, liquidity shock, spread changes, and repricing speed. The focus is on microstructure dynamics — not just last price.

Event table

1CREATE TABLE market_events (
2    event_id TEXT PRIMARY KEY,
3    event_time_utc TIMESTAMP,
4    event_type TEXT,
5    description TEXT
6);

Reaction metrics

For each event + contract:

• Pre/post return
• Jump size at T0
• Time to half-move
• Spread widening %
• Depth collapse %

Pseudo-windowing:

1window_pre  = [-24h, 0]
2window_post = [0, +24h]

Use multiple windows (1h, 6h, 24h).

Spreads often move before price.
Depth often collapses before volatility spikes.

That’s microstructure insight.

Key Takeaways

This is a medium to advanced quantitative project focused on event studies and microstructure analysis. The benefit is measurable insight into how prediction markets react to news, earnings, CPI releases, or political events. It helps quantify repricing speed, liquidity shock, and spread behavior. You need careful event timestamp alignment and multi-window analysis. It’s worth building if you want structured research into prediction market behavior around real-world events.

Getting Started (Developer Path)

1. Create your API key in the API BRICKS console and authenticate your first request in minutes.
2. Start with REST endpoints for clean, deterministic historical pulls and structured testing.
3. Add WebSocket streaming when you need real-time FX updates or low-latency workflows.
4. Scale with Flat Files once your research grows and you need bulk historical datasets for backtesting or ML pipelines.

New organizations can unlock $25 in free credits after creating an API key and adding a verified payment method, purchasing credits, or starting a subscription.

Build small. Validate fast. Scale when the system proves itself.

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• Prediction Markets Data: Corporate Use-Cases & Real-World Applications