StockAPIS for Hedge Funds and Quants

from stockapis import StockAPIS
 
api = StockAPIS(api_key='your_api_key')
 
def load_ohlcv(symbol, exchange='binance', interval='1h', days=365):
    candles = api.platforms.crypto_exchanges.get_ohlcv(
        exchange=exchange,
        symbol=symbol,
        interval=interval,
        lookback_days=days
    )
 
    print(f"OHLCV - {symbol} @ {exchange} ({interval})")
    print(f"Bars: {len(candles)}")
    print(f"Range: {candles[0].timestamp} -> {candles[-1].timestamp}")
 
    # Simple returns series for modeling
    closes = [c.close for c in candles]
    returns = [(closes[i] / closes[i-1]) - 1 for i in range(1, len(closes))]
 
    print(f"Mean return: {sum(returns)/len(returns):+.5f}")
    return candles

def stream_market(symbol, exchange='coinbase'):
    stream = api.platforms.crypto_exchanges.stream(
        exchange=exchange,
        symbol=symbol,
        channels=['trades', 'orderbook']
    )
 
    for msg in stream:
        if msg.type == 'trade':
            print(f"{symbol} trade: {msg.price:,.2f} x {msg.size}")
 
        elif msg.type == 'orderbook':
            best_bid = msg.bids[0].price
            best_ask = msg.asks[0].price
            spread = best_ask - best_bid
            mid = (best_bid + best_ask) / 2
            print(f"{symbol} mid: {mid:,.2f} | spread: {spread:,.4f}")

def cross_exchange_spread(symbol, venues=('binance', 'coinbase', 'kraken')):
    quotes = {}
    for venue in venues:
        q = api.platforms.crypto_exchanges.get_ticker(
            exchange=venue,
            symbol=symbol
        )
        quotes[venue] = q.last
 
    cheapest = min(quotes, key=quotes.get)
    richest = max(quotes, key=quotes.get)
    spread_bps = ((quotes[richest] / quotes[cheapest]) - 1) * 10_000
 
    print(f"Cross-exchange spread - {symbol}")
    for venue, price in quotes.items():
        print(f"  {venue}: {price:,.2f}")
 
    print(f"\nCheapest: {cheapest} | Richest: {richest}")
    print(f"Spread: {spread_bps:.1f} bps")
 
    if spread_bps > 25:
        print("Signal: dislocation above threshold")
 
    return spread_bps

def sentiment_features(symbol, hours=24):
    news = api.platforms.financial_news.search(
        symbol=symbol,
        sources=['bloomberg', 'reuters'],
        lookback_hours=hours
    )
 
    social = api.platforms.social_signals.get_sentiment(
        symbol=symbol,
        sources=['stocktwits', 'reddit'],
        lookback_hours=hours
    )
 
    avg_news = sum(n.sentiment for n in news) / max(len(news), 1)
 
    print(f"Sentiment Features - {symbol} ({hours}h)")
    print(f"News items: {len(news)} | Avg sentiment: {avg_news:+.2f}")
    print(f"Social score: {social.score:+.2f} | Volume: {social.mentions}")
 
    # Feature row for the model
    features = {
        'news_sentiment': avg_news,
        'news_volume': len(news),
        'social_sentiment': social.score,
        'social_mentions': social.mentions,
    }
 
    if avg_news < -0.3 and social.score < -0.3:
        print("Signal: bearish sentiment cluster")
 
    return features

from stockapis import StockAPIS
 
api = StockAPIS(api_key='your_api_key')
 
# Historical OHLCV for a backtest
candles = api.platforms.crypto_exchanges.get_ohlcv(
    exchange='binance',
    symbol='BTC/USDT',
    interval='1h',
    lookback_days=365
)
 
print(f"Loaded {len(candles)} bars")
print(f"Latest close: {candles[-1].close:,.2f}")
 
# Live ticker
ticker = api.platforms.crypto_exchanges.get_ticker(
    exchange='coinbase',
    symbol='BTC/USD'
)
 
print(f"Live: {ticker.last:,.2f}")