Alpha Intelligence™ APIs

Alpha Intelligence™ APIs

NEWS_SENTIMENT — Market News & Sentiment

Returns live/historical news articles with sentiment scores for tickers, sectors, and topics.

Optional:

• tickers — comma-separated ticker symbols (e.g., IBM,AAPL)
• topics — comma-separated topics: blockchain, earnings, ipo, mergers_and_acquisitions, financial_markets, economy_fiscal, economy_monetary, economy_macro, energy_transportation, finance, life_sciences, manufacturing, real_estate, retail_wholesale, technology
• time_from / time_to — format YYYYMMDDTHHMM
• sort — LATEST, EARLIEST, or RELEVANCE
• limit — max articles returned (default 50, max 1000)

# Get news for specific ticker
data = av_get("NEWS_SENTIMENT", tickers="AAPL", sort="LATEST", limit=10)
articles = data["feed"]

for a in articles[:3]:
    print(a["title"])
    print(a["url"])
    print(a["time_published"])
    print(a["overall_sentiment_label"])   # "Bullish", "Bearish", "Neutral", etc.
    print(a["overall_sentiment_score"])   # -1.0 to 1.0
    for ts in a["ticker_sentiment"]:
        if ts["ticker"] == "AAPL":
            print(f"  AAPL sentiment: {ts['ticker_sentiment_label']} ({ts['ticker_sentiment_score']})")
            print(f"  Relevance: {ts['relevance_score']}")

# Article fields: "title", "url", "time_published", "authors", "summary",
#                 "source", "source_domain", "topics", "overall_sentiment_score",
#                 "overall_sentiment_label", "ticker_sentiment"
# Sentiment labels: "Bearish", "Somewhat-Bearish", "Neutral", "Somewhat-Bullish", "Bullish"

# Get news by topic
data = av_get("NEWS_SENTIMENT", topics="earnings,technology", time_from="20240101T0000", limit=50)

EARNINGS_CALL_TRANSCRIPT — Earnings Call Transcript

Returns full earnings call transcripts (requires premium).

Required: symbol, quarter (format YYYYQN, e.g., 2023Q4)

data = av_get("EARNINGS_CALL_TRANSCRIPT", symbol="AAPL", quarter="2023Q4")
transcript = data["transcript"]

for segment in transcript[:5]:
    print(f"[{segment['speaker']}]: {segment['content'][:200]}")
# Fields: "symbol", "quarter", "transcript" (list of {speaker, title, content})

TOP_GAINERS_LOSERS — Top Market Movers

Returns top 20 gainers, losers, and most actively traded US stocks for the current/most recent trading day.

data = av_get("TOP_GAINERS_LOSERS")

for g in data["top_gainers"][:5]:
    print(g["ticker"], g["price"], g["change_amount"], g["change_percentage"], g["volume"])

for l in data["top_losers"][:5]:
    print(l["ticker"], l["price"], l["change_amount"], l["change_percentage"])

# Fields: "ticker", "price", "change_amount", "change_percentage", "volume"
# Also: data["most_actively_traded"]

INSIDER_TRANSACTIONS — Insider Trading Data

Returns insider transactions (Form 4) for a given company (requires premium).

Required: symbol

data = av_get("INSIDER_TRANSACTIONS", symbol="AAPL")
transactions = data["data"]

for t in transactions[:5]:
    print(
        t["transaction_date"],
        t["executive"],         # insider name
        t["executive_title"],   # e.g., "CEO"
        t["action"],            # "Buy" or "Sell"
        t["shares"],
        t["share_price"],
        t["total_value"]
    )

ANALYTICS_FIXED_WINDOW — Portfolio Analytics (Fixed Window)

Returns mean return, variance, covariance, correlation, and alpha/beta for a set of tickers over a fixed historical window.

Required:

• SYMBOLS — comma-separated tickers (e.g., AAPL,MSFT,IBM)
• RANGE — date range format: 2year, 6month, 30day, or YYYY-MM-DD&YYYY-MM-DD
• INTERVAL — DAILY, WEEKLY, or MONTHLY
• OHLC — close, open, high, or low
• CALCULATIONS — comma-separated: MEAN, STDDEV, MAX_DRAWDOWN, CORRELATION, COVARIANCE, VARIANCE, CUMULATIVE_RETURN, MIN, MAX, MEDIAN, HISTOGRAM

data = av_get(
    "ANALYTICS_FIXED_WINDOW",
    SYMBOLS="AAPL,MSFT,IBM",
    RANGE="1year",
    INTERVAL="DAILY",
    OHLC="close",
    CALCULATIONS="MEAN,STDDEV,CORRELATION,MAX_DRAWDOWN"
)
payload = data["payload"]
print(payload["MEAN"])        # {"AAPL": 0.0012, "MSFT": 0.0009, ...}
print(payload["STDDEV"])
print(payload["CORRELATION"])  # correlation matrix
print(payload["MAX_DRAWDOWN"])

ANALYTICS_SLIDING_WINDOW — Portfolio Analytics (Sliding Window)

Same as fixed window but with rolling calculations over time.

Required: Same as fixed window, plus:

• WINDOW_SIZE — number of periods (e.g., 20 for 20-day rolling window)

data = av_get(
    "ANALYTICS_SLIDING_WINDOW",
    SYMBOLS="AAPL,MSFT",
    RANGE="1year",
    INTERVAL="DAILY",
    OHLC="close",
    CALCULATIONS="MEAN,STDDEV",
    WINDOW_SIZE=20
)
# Returns time series of rolling calculations