Quant Kavin

Multi-feature logistic regression with competition importance weighting (World Cup and tournament matches weighted up to 3× higher than friendlies) and exponential recency decay applied to match history pulled from football-data.org. Wraps the model in Platt scaling via CalibratedClassifierCV for better-calibrated probabilities, pulls squad size from the teams endpoint as a depth feature, and runs a sensitivity analysis sweeping the WC weight multiplier across 7 values. Outputs form trend charts, competition mix breakdown, aggregate stat comparisons, and a sensitivity curve for their June 16 World Cup 2026 Group I matchup.

1 file

Quant Kavin

Jun 8, 2026

Fetches full match history for Brazil and Morocco from football-data.org and applies exponential time-decay weighting to prioritize recent form. Builds Poisson attack/defense strength ratings to generate a scoreline probability grid, trains a logistic regression on recency-weighted differential features, then blends both models into a final win/draw/loss probability for their June 13 World Cup 2026 Group C clash. Outputs a scoreline heatmap, blended probability bars, and expected goals comparison.

Other1 file

Quant Kavin

Jun 8, 2026

Pulls recent international match history for USA and Paraguay via the football-data.org free API, engineers rolling form features (win rate, goals for/against, goal difference) over an 8-game window, and trains a logistic regression to output head-to-head win probabilities for their June 12 World Cup 2026 Group D opener. Includes feature coefficient analysis and a rolling form comparison chart.

Other1 file

Quant Kavin

Jun 8, 2026

AAPL WWDC 2026 — Event-Day Volatility Arbitrage A quantitative options strategy built around Apple's annual WWDC keynote (June 8, 2026). The core idea is implied vs realised volatility arbitrage — options markets have historically overpriced AAPL's expected 1-day move on keynote day, creating a structural edge for volatility sellers. By collecting 11 years of WWDC event-day returns (2015–2025), comparing them against the ATM straddle implied move, and backtesting a short straddle strategy, we quantify that edge and size it using the Kelly Criterion. Quant concepts used: implied volatility, realised volatility, volatility risk premium, ATM straddle pricing (call + put ÷ spot), 1-day IV derivation (annual IV ÷ √252), Shapiro-Wilk normality testing, skewness and kurtosis analysis, Kelly Criterion position sizing (½ Kelly for safety), short straddle and iron condor payoff mechanics, Sharpe ratio, and max drawdown. Libraries: yfinance (data), pandas + numpy (analysis), scipy.stats (distribution testing), matplotlib + matplotlib.animation (static and animated charts), pillow + ffmpeg (GIF and MP4 export).

Other1 file

Quant Kavin

Jun 7, 2026

Maryland Keychain I made 8 years ago. Keep in mind your filament diameter and file size, as there is small text on this keychain. Enjoy!

1 3D model

Quant Kavin

Jun 7, 2026

Some old 3D printing files I made almost 10 years ago (unc). I didn't know where to upload this publicly, so I posted it here. Feel free to print and use it yourself!

Manufacturing1 3D model

Quant Kavin

Jun 7, 2026