Quant Fund Improves Signal Coverage with Unified Market Data

A systematic quant fund used StockAPIS to consolidate prices, order books, news, and sentiment from dozens of venues into a single schema, expanding signal coverage from 3 to 40+ tradable instruments and cutting data-engineering time by 50 hours per week.

Fund Profile

Strategy: Systematic, cross-asset (crypto + equities)
AUM: ~$120M under management
Horizon: Intraday to multi-day holding
Team Size: 6 (3 researchers, 2 engineers, 1 PM)

The Challenge

The fund’s alpha relied on combining price action with alternative data, but every venue and vendor spoke a different dialect. Onboarding a new exchange or news source meant weeks of parsing, normalization, and reconciliation, so coverage stalled at a handful of liquid instruments.

Key Problems

Each venue (Binance, Coinbase, NYSE feeds) returned a different OHLCV and order-book format
News and sentiment lived in separate, unstructured pipelines
No unified timestamp or symbology across crypto and equities
Backtests skewed by gaps and survivorship in self-collected history
Adding one new data source took 2-3 engineer-weeks

The Solution

The team standardized on StockAPIS as a single ingestion layer across crypto exchanges, stock exchanges, financial data APIs, financial news, and social signals — all delivered in one normalized schema.

Implementation

The unified layer let them:

Pull synchronized OHLCV and L2 order books across venues
Merge news headlines and sentiment scores onto the same symbology
Backfill clean historical bars for reliable backtests via crypto data
Stream real-time updates into the live signal engine

Technology Stack


from stockapis import StockAPIS
 
api = StockAPIS(api_key='api_key')
 
# Unified signal builder across venues
symbols = ['BTC-USD', 'ETH-USD', 'AAPL', 'NVDA']
 
for symbol in symbols:
    bars = api.market_data.ohlcv(
        symbol=symbol,
        interval='1m',
        limit=1440,
    )
 
    book = api.market_data.order_book(symbol=symbol, depth=20)
    news = api.news.latest(symbol=symbol, with_sentiment=True)
 
    imbalance = (book.bid_volume - book.ask_volume) / book.total_volume
    sentiment = sum(n.sentiment for n in news) / max(len(news), 1)
 
    if imbalance > 0.3 and sentiment > 0.2:
        # Confluence of flow + sentiment — flag for the signal engine
        emit_signal(symbol, score=imbalance + sentiment)

Results

Quantitative Impact

Signal Coverage: Expanded from 3 to 40+ tradable instruments
Time Saved: 50 hours per week of data-engineering and reconciliation
Onboarding Speed: New source integration cut from weeks to hours
Data Quality: Backtest gaps reduced by 90% with clean historical bars
Asset Classes: Unified crypto and equities under one schema

Operational Impact

Faster Research: New signal hypotheses tested in days, not weeks
Cost Savings: ~$140K annually in reduced engineering time
StockAPIS Cost: $4,788 annually
Net Benefit: ~$135K first year, before any alpha uplift
Engineer Focus: Shifted from plumbing to strategy research

Key Learnings

What Worked

One normalized schema removed per-venue parsing entirely
Sentiment fused onto price symbology surfaced cross-asset confluence
Clean OHLCV history made backtests reproducible and trustworthy
Real-time order-book streams powered intraday flow signals

Best Practices

Define a single symbology before merging crypto and equities
Validate sentiment scores against realized moves before trading them
Use unified timestamps to align news, prices, and order books
Stream order books for flow signals; poll OHLCV for slower features

Testimonial

“StockAPIS let us stop building parsers and start building strategies. We went from three instruments to over forty, and our researchers test ideas in days instead of weeks. Having Binance, Coinbase, and equities in one schema — with news and sentiment attached — was the unlock.”

— Head of Research

Implementation Timeline

Week 1: API integration and schema mapping
Week 2: Backfilled historical OHLCV for backtests
Week 3: Wired news and sentiment into the feature pipeline
Week 4: Live order-book streaming across venues
Month 2: First cross-asset signal in production
Month 6: 40+ instruments under coverage

Get Started

Ready to unify your market data? Explore the Binance integration, browse all platforms, see the API getting-started guide, or contact us and review pricing.