Core Concept
What is Catalyst-Aware Research?
Most market algorithms are blind to the real world. They see price, volume, and moving averages — but they have no idea that NVDA reports earnings in 3 days, or that the FDA is about to rule on a drug approval. Catalyst-aware research changes that.
The problem with price-only backtesting
Traditional systematic research platforms backtest your strategy against historical price data. This sounds reasonable — until you realize that the biggest market moves are driven by events, not technical patterns.
When NVDA jumped 24% in a single day after reporting Q4 earnings, no moving average flagged that. When the FDA rejected Biogen’s Alzheimer’s drug, no RSI indicator saw it coming. When the Fed surprised markets with a 50bp cut, no Bollinger Band anticipated it.
If your algorithm doesn’t know these events are coming, it’s not really backtesting your thesis. It’s backtesting a simplified version that ignores the most important variable: what happens next.
How catalyst-aware research works
Catalyst-aware research adds an event layer to your algorithm. Instead of just looking at price and volume, your algo also knows about:
- →Earnings dates: Quarterly reports, guidance, revenue surprises
- →FDA decisions: Drug approvals, rejections, advisory committee meetings
- →Fed meetings: Rate decisions, dot plots, Powell press conferences
- →Insider transactions: CEO/CFO buying or selling — a leading indicator
- →Macro events: CPI releases, jobs reports, GDP prints
- →Corporate actions: Splits, buybacks, M&A announcements
Example: A catalyst-aware thesis
Say your thesis is: “AI chip demand will triple NVDA revenue by 2027.”
A traditional algo builder would:
- Select NVDA as a ticker
- Apply a momentum strategy
- Backtest on historical prices
A catalyst-aware algo builder (AlgoThesis) would:
- Select NVDA + correlated tickers (AMD, TSM, AVGO)
- Discover upcoming catalysts: NVDA earnings in 12 days, AMD guidance call next week, TSMC capex report tomorrow
- Build a strategy that scales position size before high-impact events
- Backtest with catalyst-date awareness — not just price
- See a suggested pre-event hedge alongside the basket
Why it matters for individual investors
Institutional investors have teams dedicated to tracking catalysts. They know every earnings date, every FDA advisory committee meeting, every Fed dot plot release. They position accordingly.
Individual investors don’t have those teams. They find out about catalysts when the stock already moved. Catalyst-aware research levels the playing field by automating what the institutions do manually.
Getting started
AlgoThesis is the first platform to offer catalyst-aware research for individual investors. Type your thesis in plain English, and the AI discovers relevant catalysts, builds a strategy around them, and backtests it — all in under 3 minutes.
Test your thesis with catalyst awareness
Free. No credit card. Your algo will know what’s coming.
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