StockAPIS for Market Researchers
StockAPIS gives market researchers one normalized feed across 80+ crypto exchanges, stock markets, data providers, and news sources — so cross-venue prices, OHLCV, order books, and sentiment land ready for analysis and reports.
Key Benefits
- Comprehensive Coverage: One API across 80+ platforms — crypto exchanges, stock markets, brokers, data APIs, and news.
- Historical Data: Years of tick, OHLCV, and order-book history for backtesting and longitudinal studies.
- Cross-Venue Normalization: Unified symbols, timestamps (UTC), and schemas across Binance, Coinbase, NYSE, and Polygon.
- Multi-Asset: Crypto, equities, ETFs, FX, and on-chain metrics in a single workflow.
- Export Options: CSV, JSON, and Parquet for analysis in Excel, R, pandas, or notebooks.
Use Cases
Cross-Venue Price Analysis
Compare prices for the same asset across exchanges:
from stockapis import StockAPIS
api = StockAPIS(api_key='your_api_key')
def compare_venues(symbol, venues):
results = []
for venue in venues:
quote = api.platforms.get_quote(venue=venue, symbol=symbol)
results.append({
'venue': venue,
'price': quote.last,
'bid': quote.bid,
'ask': quote.ask,
'volume_24h': quote.volume_24h
})
return results
# Compare BTC across major crypto exchanges
venues = ['binance', 'coinbase', 'kraken', 'okx']
spread = compare_venues('BTC-USD', venues)
for v in spread:
print(f"{v['venue']}: ${v['price']:,.2f} vol ${v['volume_24h']:,.0f}")See the full list of crypto exchanges and the dedicated Binance integration.
Liquidity and Order-Book Studies
Measure depth and spreads for microstructure research:
def analyze_liquidity(venue, symbol):
book = api.platforms.get_order_book(venue=venue, symbol=symbol, depth=50)
best_bid = book.bids[0].price
best_ask = book.asks[0].price
spread = best_ask - best_bid
mid = (best_bid + best_ask) / 2
bid_depth = sum(level.size for level in book.bids)
ask_depth = sum(level.size for level in book.asks)
print(f"Liquidity Analysis - {symbol} @ {venue}")
print(f"Spread: {spread:.4f} ({spread / mid:.3%})")
print(f"Bid Depth: {bid_depth:,.2f}")
print(f"Ask Depth: {ask_depth:,.2f}")
if spread / mid < 0.0005:
print("Status: Deep / tight market")
else:
print("Status: Thin / wide market")OHLCV Trend Forecasting
Pull historical candles for longitudinal trend work:
def analyze_price_trends(venue, symbol):
candles = api.platforms.get_ohlcv(
venue=venue, symbol=symbol, interval='1d', limit=365
)
first = candles[0].close
last = candles[-1].close
yoy_change = (last / first - 1) * 100
print(f"Trend Analysis - {symbol} @ {venue}")
print(f"Current Close: ${last:,.2f}")
print(f"1-Year Change: {yoy_change:+.1f}%")
# Simple projection
forecast = last * (1 + yoy_change / 100)
print(f"12-Month Projection: ${forecast:,.0f}")News and Sentiment Studies
Blend market data with financial news and social signals:
def sentiment_snapshot(symbol):
news = api.platforms.get_news(symbol=symbol, sources=['bloomberg', 'reuters'])
social = api.platforms.get_sentiment(symbol=symbol, sources=['stocktwits', 'reddit'])
bullish = sum(1 for n in news if n.sentiment > 0)
bearish = sum(1 for n in news if n.sentiment < 0)
print(f"Sentiment Snapshot - {symbol}")
print(f"News: {bullish} bullish / {bearish} bearish ({len(news)} items)")
print(f"Social Score: {social.score:+.2f} Mentions: {social.mentions:,}")Comparative Asset Studies
Rank a basket of assets across venues:
def rank_assets(symbols, venue='binance'):
ranking = []
for symbol in symbols:
candles = api.platforms.get_ohlcv(
venue=venue, symbol=symbol, interval='1d', limit=30
)
change_30d = (candles[-1].close / candles[0].close - 1) * 100
ranking.append({'symbol': symbol, 'change_30d': change_30d})
ranking.sort(key=lambda x: x['change_30d'], reverse=True)
print("Asset Rankings by 30-Day Return:")
for i, a in enumerate(ranking, 1):
print(f"{i}. {a['symbol']} - {a['change_30d']:+.1f}%")Quick Start
from stockapis import StockAPIS
api = StockAPIS(api_key='your_api_key')
# Get a normalized quote
quote = api.platforms.get_quote(venue='coinbase', symbol='ETH-USD')
print(f"Last Price: ${quote.last:,.2f}")
print(f"Bid/Ask: ${quote.bid:,.2f} / ${quote.ask:,.2f}")
print(f"24h Volume: ${quote.volume_24h:,.0f}")Ready to dig in? Read the Getting Started guide, browse all platforms and the financial data APIs, compare pricing, or contact us for research-tier access.
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