A computed table of every ticker × every signal. Query it live, as of any past moment, or as a webhook subscription.
Free plan. Every ticker, every signal, real-time data, all-time history.
the table
Every ticker is a row. Every signal is a column. Live to the minute, plus all-time history. Rows: every US equity, the US Treasury curve, major FX pairs, spot metals, major crypto. Columns: 421+ signals across price, volume, price action, technicals, fundamentals, events, company profile, analyst ratings, and news.
the api
Compose signals into a SQL WHERE clause and pass it as q to /v2/scan. Every matching ticker comes back as a full computed row, not as a bare symbol. The same query runs live, as of any past moment, or as a webhook subscription. The same grammar, columns, and tenses apply across the API: /v2/tickers for one symbol’s full row, /v2/signals for every ticker matching one signal.
{
"count": 100,
"results": [
{
"ticker": "TRV",
"price": 368.74, "market_cap": 72620289106,
"above_sma_200": true,
...
},
{
"ticker": "STX",
"price": 785.87, "market_cap": 194726146156,
"above_sma_200": true,
...
},
...
]
}where Tickerbot fits
A raw price feed hands you trades, quotes, and bars — the right buy for teams building the rest themselves. Everything between that and a working trading algorithm — or a shipped fintech feature — is the rest: technical indicators to compute, a stock screener to build, history to store for backtesting, and alerts to deliver. Tickerbot is that layer, out of the box.
| You need | Build it yourself | Tickerbot |
|---|---|---|
| Technical indicators & signals | Indicator code + ingest pipelines, or a quant hire | 421+ signals precomputed, live |
| A stock screener | A database + glue code | One SQL WHERE clause to /v2/scan |
| Backtest-ready history | Point-in-time indexing + backfill | ?asof= on every read |
| Stock alerts | Polling loops, or your own webhook infrastructure | Signed, retried webhooks — plus a websocket stream |
early days
What people are saying.
“dude. whoah.”
“Tickerbot is insane. It turns Claude into a quant.”
“Best value for hobbyists and advanced traders alike.”
for agents
Raw data doesn’t fit in a context window. Computed state does — your agent spends its context on decisions, not data.
get started
Free plan. Every ticker, every signal, real-time data, all-time history.