Quant research · tooling
BTC Threshold Probability Dashboard
Interactive dashboard estimating short-horizon probabilities that Bitcoin crosses a price threshold, with a backtester for Kalshi-implied edges.
- Year
- 2026
- Status
- Working prototype
- Stack
- Python · Streamlit · scikit-learn · pandas
Kalshi lists yes/no contracts on whether Bitcoin will be above a given price at a given time, which basically makes them tradeable probabilities. This project asks whether those implied probabilities are ever measurably off over short horizons.
Model, then compare
A KNN model estimates the probability that BTC crosses a chosen threshold within a chosen horizon, using features built from recent price action. A Streamlit dashboard makes the model interactive: pick a threshold, pick a horizon, see the estimate. A backtester replays history to compare the model's probabilities against market-implied ones and flags where an edge would have existed.
What I would stress-test before trusting it
- Regime shifts: a KNN fitted on calm markets says little about violent ones.
- Fees, spread, and slippage: a paper edge has to survive the costs of being real.
- Calibration: a model that says 70% should be right about 70% of the time, and that deserves its own test.