TradeAI News
The structure behind the move — never the direction
What it does
Gamma exposure, short interest and FTDs, insider Form 4s, analyst-accuracy backtests, and a trade journal that knows the conditions behind each trade. Every number traces to a named source, with the date it was true. Significance and magnitude — never direction. I audited my own marketing and deleted every figure that didn't trace, including one claiming "2M+ historical situations" when the real dataset is 7,931 events.
Structural market context for US equities: gamma exposure, short interest, insider activity and a context-aware trade journal. Significance and magnitude, never direction.
Estimated gamma exposure, short interest and failures-to-deliver, insider filings, analyst accuracy measured against what actually happened, and a trade journal that knows the market conditions behind every trade you took — in one platform. We measure significance and magnitude, never direction. Real dashboard, real data — SPY gamma exposure with walls, flip and the methodology note on the chart. News and regulatory feeds from named providers monitored continuously — FDA decisions, earnings wires, press releases, exchange halts, the options tape. Captured as it is published, not after it shows up on a chart. The event is classified into one of 40+ catalyst types and scored 0–100 on…from tradeainews.com
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I’ve been experimenting with a graph-based approach to a classic trading problem: why most dip-buying strategies can’t tell the difference between a temporary overreaction and a genuine structural collapse. Most systems treat a −5% move the same regardless of context. My hypothesis was that where a company sits in the market’s structure matters more than the price move itself. The engineering idea I built a knowledge graph of the U.S. public markets with ~207k edges across ~21 relationship types, organized into four layers: Operational: supply-chain relationships (SUPPLIES_TO, PRODUCES)…
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