"tradesense team on Alpaca AI Trading Agents Hackathon Hackathon"
Team Idea
Building: forecast how much a name will actually move over the next week, compare that to how much the options market is charging for the same window, sell where the market is overpaying. Everything is defined-risk so the tail stays capped. Contract selection goes through Alpaca's MCP server. Risk stuff (sizing, exposure caps, stops) is plain code. Previously built a daily equities strategy with Alpaca. Four years of out-of-sample testing, no edge worth trading. Direction is really hard to forecast at a daily horizon. Reusing the pipeline for this. There's already six years of daily OHLCV, a feature pipeline and a backtest harness to work with. Who I need: Quant-minded engineers, Go or Python. - the vol forecasting model, and how we establish whether it's real - options data and contract selection through Alpaca's MCP server - live risk exposure caps, tail control, kill switch - the demo: dashboard, video, deck (25% of the score, and nobody ever volunteers)
Team Members
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Jamie Min
jamin131
Mark Adamov
ThinWhale
Submission
Team Leader hasn't made a submission yet