Quant Research

StockAPIS gives quant teams one normalized API for historical and real-time market data — prices, OHLCV, order books, news and sentiment — so you can backtest strategies and develop signals across crypto, equities and more.

Turn raw market data into tested, production-ready signals with StockAPIS.

The Challenge

Quant researchers face a critical problem: building signals on incomplete, inconsistent data.

Manual Data Collection

• Scraping each exchange and vendor separately takes time
• Stitching CSVs into research datasets by hand
• Limited backtesting capacity
• Snapshots drift out of sync across sources
• Result: strategies that look great in-sample and fail live

Siloed Data

• Different APIs for crypto, equities, news and on-chain
• No unified symbology or timestamp convention
• Cannot align order books with price moves
• Result: survivorship bias and lookahead leakage

The Solution

StockAPIS provides instant access to market data from major exchanges, brokers and data vendors through a single platform.

Platform Coverage

We support 80+ data sources across every asset class:

• Binance, Coinbase, Kraken and other crypto exchanges
• NYSE and CME Group on the stock exchanges side
• Brokers for retail order flow and execution data
• Polygon, Alpha Vantage and Finnhub for aggregated financial data
• Bloomberg and Reuters for financial news
• Social signals from Reddit, StockTwits and more

Research-Grade Data

• Historical OHLCV down to the minute and tick, with corporate actions handled
• Full order book snapshots and depth for microstructure research
• Point-in-time news and sentiment to avoid lookahead bias
• On-chain metrics and price-change events for crypto factor models

Quick Start

from stockapis import StockAPIS
 
api = StockAPIS(api_key='your_api_key')
 
# Pull a year of daily OHLCV for backtesting
candles = api.platforms.binance.get_ohlcv(
    symbol='BTCUSDT',
    interval='1d',
    start='2025-01-01',
    end='2025-12-31',
)
 
returns = candles['close'].pct_change()
sharpe = (returns.mean() / returns.std()) * (252 ** 0.5)
print(f"Buy-and-hold Sharpe: {sharpe:.2f}")

Ready to backtest your first signal? Start with the API getting-started guide, review pricing, or contact us to discuss research data volumes.