I Built a 5-Minute VRP Trading Scanner in Python — Here's the Code

Options implied volatility overestimates realized vol. This is the variance risk premium (VRP) — and it's one of the most persistent edges in financial markets. The problem: most people who know this still lose money selling premium. Not because the edge isn't there, but because they lack a repeatable process for deciding when, what, and how much to trade. I built a 5-step daily workflow that answers all three questions using two API calls per symbol. It runs in under 5 minutes. Here's the full code. The Stack Data: FlashAlpha API — pre-computed VRP analytics, GEX regime data, dealer positioning levels across 4,000+ US equities/ETFs Language: Python (also works in JS/cURL) Dependencies: requests (that's it) Step 1: Screen for VRP Signals The /v1/vrp/{symbol} endpoint returns a full VRP dashboard per symbol. The two fields that matter for screening: z_score — how many standard deviations above the 252-day mean. Above +1.0 = elevated. vrp_20d — raw IV minus realized vol over 20 days. Pos