TickerAPI for Python.
Simple client for the TickerAPI HTTP API. Install with pip, initialize with your API key, and start pulling pre-computed market data immediately.
Install and call in seconds.
One pip install, one import, one line to initialize. Every method returns a plain dictionary.
pip install tickerapi
from tickerapi import TickerAPI client = TickerAPI("tapi_your_api_key") # Get a full summary for any ticker summary = client.get_summary("AAPL") print(summary["trend"]["direction"]) # "uptrend" print(summary["momentum"]["rsi_zone"]) # "neutral_high"
Chain calls. Build context.
Scan for oversold stocks, pull a full summary on the top pick, then compare it against peers. Each method returns categorical data you can branch on directly.
Track state changes effortlessly.
Most market data APIs return point-in-time snapshots. TickerAPI tracks state transitions — your agent sees what changed, not just what is.
summary = client.get_summary("AAPL") { "ticker": "AAPL", "changes": [ { "field": "rsi_zone", "from": "neutral", "to": "oversold" }, { "field": "trend", "from": "uptrend", "to": "downtrend" } ] }
What your agent sees.
Every tool returns categorical facts — not raw OHLCV data. Your agent can branch on "oversold" without needing to know what RSI > 70 means.
{ "ticker": "NVDA", "trend": "strong_uptrend", "momentum": { "rsi_zone": "overbought", "macd_signal": "bullish" }, "volatility": "high", "fundamentals": { "pe_zone": "above_historical_avg", "earnings_surprise": "positive" } }
What you can call.
Every method returns categorical, pre-computed data as a plain dictionary. No raw data to parse, no indicators to compute.
Full technical + fundamental snapshot for a single asset.
Side-by-side comparison of multiple assets.
Browse all supported tickers.
List all valid sector values with asset counts.
Batch summaries for a portfolio.
Field-level diffs for your watchlist since the last pipeline run.
Add tickers to your persistent watchlist.
Remove tickers from your watchlist.
Assets in oversold conditions.
Assets in overbought RSI conditions with severity rankings.
Support/resistance breakouts.
Volume anomalies.
Valuation extremes.
Insider buying/selling patterns.
Your plan tier, rate limits, and current API usage.
Register a webhook URL for watchlist change notifications.
List your registered webhook URLs.
Remove a registered webhook.
Built for how code consumes data.
Categorical data, less parsing
Responses like "rsi_zone": "oversold" are ready to branch on. No indicator math, no threshold logic.
Plain dictionaries
Every method returns a dict. No ORM, no custom objects, no serialization steps. JSON in, dict out.
Pre-computed daily
No infrastructure to maintain. No cron jobs, no indicator math, no data pipelines. TickerAPI handles computation and syncing.
Start building.
pip install tickerapi. No credit card required for the free tier.