Find setups, in SQL

Sweep ~12,000 US equities and the top 100 cryptos for setups that match your shape. Compose any condition from 100+ queryable fields, then check the historical rhythm of every match — point-in-time signal fires going back up to all-time, per ticker.

GET /v2/scan + /v2/signals

Sweep the universe, then check the history

Start with a SQL WHERE clause to filter the live universe. Pull historical fires for any signal × ticker to see whether the pattern shows up often or rarely. Compose with pre-computed indicators (rsi, macd, …) and named flags (breakout, gap_up_3pct, …) — same vocabulary across every endpoint.

WHERE clause
-- small caps pattern-matching repeat breakouts
breakout
  AND volume_unusual_2x
  AND market_cap < 2_000_000_000
response
200·4 matches · 138ms
tickerbreakoutrel_volfires (5y)
RKLB3.1×12
IONQ5.2×7
ACHR2.8×5
BBAI2.3×9

primitives you’ll use

Three primitives, one composable surface

Discovery sweeps the universe and confirms the pattern repeats. Three primitives combined.

Screeners

SQL filter against the live universe. Every field and flag composable, no fixed library.

See screeners →

Signals

50+ named flags plus historical fires per ticker. Confirm a setup is real, not a one-off.

See signals →

Indicators

RSI, MACD, ATR, Bollinger Bands as queryable columns. Compose into any setup.

See indicators →

why this works

Built for finding net-new edges

comparison

Manual chart-flipping vs Tickerbot

Manual chart-flipping

Browser-based discovery, one ticker at a time

  • Flip through hundreds of charts; miss anything you don’t look at
  • Eye-ball the setup; no way to verify the pattern repeats
  • Fixed screener categories; can’t compose your own conditions
  • Re-do the whole sweep tomorrow

Tickerbot

SQL across the universe + signal history

  • One query scans ~12,000 tickers in milliseconds
  • Historical fires per signal × ticker — confirm the pattern repeats
  • Compose any conditions from 100+ queryable fields
  • Save the scan as a rule; rerun on a schedule or subscribe to it

works with your agent

Agents are experts at SQL. Hand them the loop.

Hand the discovery loop to your agent. Same SQL grammar for the live scan and the historical confirmation, so the agent can iterate on dozens of setup hypotheses without swapping mental models.

See the agent integration →

ship it

Get started