We’ve been building trading tools — first for ourselves, then for others — for years. This page is what we kept hitting, what we tried first, and what we decided to ship.
why we built this
Every time we wanted to act on the market — backtest a hypothesis, time an entry, ship an alert app — we ended up rebuilding the same stack. Pull OHLCV from a feed. Stand up a warehouse. Compute indicators. Schedule the scan. Wire up webhook delivery. Validate that your RSI matches everyone else’s. Maintain it as data corrections come in.
The feed was the cheap step. The work was everything after. We built that work for ourselves a dozen times — paper-trading ideas, custom alerts, dashboards for our own portfolios — and every project started the same way: six weeks of plumbing before the first signal fired.
So we built it once, productized it, and put a real API around it. That’s Tickerbot.
what came before
The first was Tickerterm — a full hosted trading terminal. Agent chat for exploring ideas and generating strategy code, an in-browser editor for the algorithms themselves, charts on par with TradingView, portfolio management, and an API that did everything Tickerbot does today plus order execution through Alpaca. It worked. The audience for a hosted terminal was narrower than the audience for the signal layer underneath it.
The second was a consumer mobile app — describe a setup in natural language, get push notifications when it fires. The engine still worked. The retail audience wanted simpler tools than we were building, and our most engaged users kept asking the same question: “can I get this as an API?”
This is the third, and the right one. The data, indicators, SQL grammar, and webhook delivery — the layer doing the real work inside both prior products — is what we ship now. The terminal you’d build on top of us, you build yourself. The agent you’d write trading code with, you bring yourself. The broker you execute through, you bring yourself. The signal layer underneath is ours.
what’s in the box
Tickerbot runs a per-minute scan across every US equity (~12,000) and the top 100 cryptos. Stocks during market hours; crypto 24/7. For each ticker we compute ~59 indicators (RSI, MAs, ATR, gap math, position metrics, volatility) and resolve ~56 named condition flags (gap_up_3pct, at_52w_high, breakout, rsi_oversold, …).
The result is queryable in SQL. Four endpoints front it:
/v1/tickers — current state per ticker/v1/signals — historical time series for any field/v1/scan — SQL WHERE clause across the live universe/v1/webhooks — POST your endpoint when a condition firesUnder the hood:
the team
We’re David Spiro (co-founder & CEO) and Marc Auger (co-founder & CTO). We met as two of the first few employees at Bubble nearly a decade ago, where we helped build a visual programming language for shipping web apps without writing code. That put us up close with a generation of AI tools — agents, code generators, model integrations — long before they were mainstream. We started applying what we’d learned to financial markets. Tickerbot is what came out of it.
try it