Trading Signals

StockAPIS turns raw prices, order books, news, and social chatter from 80+ market data sources into structured signals, sentiment scores, and real-time alerts you can wire straight into trading products.

Build signal feeds, sentiment indicators, and automated alerts from normalized market data across crypto exchanges, stock markets, brokers, data APIs, and news and social sources.

The Trading Signal Challenge

Quant teams and signal-product builders spend more time stitching together fragmented data feeds than they do on alpha, and most off-the-shelf signals are stale, opaque, or impossible to backtest.

Fragmented Data Sources

• Separate integrations for each exchange (Binance, Coinbase, Kraken, OKX)
• Different schemas for prices, OHLCV, and order books per venue
• Equities data from Polygon or Finnhub in yet another format
• No single normalized feed across crypto and stocks

Opaque Third-Party Signals

• Black-box signal vendors with no underlying data access
• No way to verify how a score was computed
• Signals shared with thousands of subscribers, eroding edge
• High subscription cost per strategy

Missing Sentiment & News Context

• Price-only signals ignore narrative-driven moves
• No structured feed of Bloomberg, Reuters, or CNBC headlines
• Social sentiment from Twitter, Reddit, and StockTwits left untapped
• On-chain flows and whale activity invisible to price-only models

Stale or Lagging Data

• Quotes delayed by seconds during volatile sessions
• Sentiment refreshed hourly instead of streaming
• No real-time updates on order book imbalances
• Backtests built on inconsistent historical snapshots

Inefficient Alerting

• Manual chart watching for breakouts and reversals
• No programmatic trigger on price, volume, or news events
• Cannot prioritize high-conviction setups
• Missing trigger events such as funding-rate flips and unusual options volume

The StockAPIS Solution

StockAPIS provides normalized market data and signal-ready feeds from 80+ sources including Binance, Coinbase, NYSE, Polygon, Bloomberg, and StockTwits, so you can compute your own signals and ship alerts in real time.

Key Capabilities

• Price & OHLCV Feeds: Real-time and historical candles across crypto and equities
• Order Book Data: Level 2 depth, spreads, and imbalance for microstructure signals
• News Sentiment: Structured headlines from Bloomberg, Reuters, CNBC with sentiment scoring
• Social Signals: Aggregated sentiment from Twitter, Reddit, StockTwits
• On-Chain Signals: Whale transfers, exchange flows, and funding rates for crypto
• Custom Indicators: Compute RSI, momentum, and volume spikes from raw data
• Streaming Alerts: Webhook and stream triggers on price, volume, and news events
• Backtest-Ready History: Consistent, normalized historical data for strategy testing

Data Coverage

Access signal data from sources including:

• Crypto Exchanges: Binance, Coinbase, Kraken, Bybit, OKX
• Stock Exchanges: NYSE, CME Group
• Data APIs: Polygon, Finnhub, Alpha Vantage, Twelve Data
• News & Social: Bloomberg, Reuters and Reddit, StockTwits
• 80+ total sources with consistent, normalized schemas

How It Works

Build Price-Based Momentum Signals

Detect breakouts and momentum shifts from streaming OHLCV data.

Volume-Confirmed Breakouts

Find symbols breaking out on above-average volume:

from stockapis import StockAPIS
 
api = StockAPIS(api_key='your_api_key')
 
# Pull recent candles for momentum scoring
candles = api.platforms.binance.get_ohlcv(
    symbol='BTCUSDT',
    interval='1h',
    limit=200
)
 
for signal in compute_breakouts(candles):
    print(f"{signal.symbol}")
    print(f"Price: ${signal.price:,.2f} (breakout above ${signal.resistance:,.2f})")
    print(f"Volume vs 20-period avg: {signal.volume_ratio:.1f}x")
    print(f"Momentum Score: {signal.score}/100")

Order Book Imbalance

Surface microstructure pressure from Level 2 depth:

# Detect bid/ask imbalance for short-term direction
book = api.platforms.binance.get_order_book(
    symbol='ETHUSDT',
    depth=50
)
 
imbalance = (book.bid_volume - book.ask_volume) / (book.bid_volume + book.ask_volume)
print(f"ETHUSDT imbalance: {imbalance:+.2%} ({'buy' if imbalance > 0 else 'sell'} pressure)")

Score News & Social Sentiment

Blend narrative context with price signals:

# Aggregate sentiment across news and social sources
sentiment = api.signals.sentiment(
    symbol='AAPL',
    sources=['bloomberg', 'reuters', 'stocktwits', 'reddit'],
    window='4h'
)
 
print(f"AAPL sentiment: {sentiment.score:+.2f} ({sentiment.label})")
print(f"Headlines: {sentiment.news_count}  Social mentions: {sentiment.social_count}")

Stream Real-Time Alerts

Trigger on price, volume, and news events as they happen:

# Subscribe to a multi-condition alert stream
for alert in api.signals.stream(
    symbols=['BTCUSDT', 'AAPL', 'NVDA'],
    triggers=['price_breakout', 'volume_spike', 'news_sentiment']
):
    print(f"ALERT {alert.symbol}: {alert.trigger}")
    print(f"  {alert.message} @ {alert.timestamp}")

Quick Start

from stockapis import StockAPIS
 
api = StockAPIS(api_key='your_api_key')
 
# Compute a simple momentum signal from live candles
candles = api.platforms.binance.get_ohlcv(
    symbol='BTCUSDT',
    interval='1h',
    limit=100
)
 
for signal in compute_breakouts(candles):
    print(f"{signal.symbol} - {signal.score}/100 momentum")

For detailed code examples and advanced usage, see our API Documentation, explore all data sources, or view pricing. Need a custom signal feed? Contact us.