Skip to content

Repository files navigation

Quadratic – LINC Hackathon 2026 Submission

Ranked #1 in Performance

Performance

Summary

We developed a multi-strategy systematic trading approach combining statistical arbitrage, cross-sectional momentum, and index mispricing.

The portfolio is split into three independent sleeves:

  1. Statistical Arbitrage (Pairs / Volatility Spread)

    • Mean-reversion on spreads between indices and equities
    • Idx_01 (VIX-like instrument) used as a volatility leg against equity exposure
    • Rolling beta estimation and z-score normalization
    • Residual-based position sizing with capped signals
    • Asymmetric entry logic for volatility-related trades
  2. Cross-Sectional Momentum + Beta Hedge

    • Monthly ranking of equities based on past returns
    • Long top performers
    • Dynamic beta-neutralization using index hedging (Idx_04)
    • Capital reallocation at rebalance dates
  3. ETF vs Synthetic Basket Arbitrage

    • Constructs a synthetic index from underlying equities
    • Trades mispricing between ETF (Idx_04) and synthetic basket
    • Z-score based entry/exit with capped leverage
    • Dollar-neutral structure

The final portfolio aggregates all three sleeves and applies a global FX hedge at the portfolio level.

Key Characteristics

  • Multi-strategy diversification
  • Volatility harvesting via equity–VIX spreads
  • Explicit beta control
  • Systematic FX exposure neutralization
  • Scalable capital allocation across independent sleeves
  • Rule-based position sizing and execution

Results

  • Total return: ~368% over three years
  • Substantially higher than other submissions (typically ~10–75%)
  • Low drawdown relative to return
  • Consistent performance across the full period

Notes

The implementation follows the competition rules, including the ability to use short exposure to scale positions. The resulting portfolio reflects an aggressive but systematic use of available capital within the defined framework.

Usage

Run the strategy:

python algorithm.py

About

Multi-strategy systematic trading model combining statistical arbitrage, momentum, and ETF mispricing. Achieved ~368% return over three years with low drawdown, ranking #1 in performance among all 15 teams, with ~5x higher returns than the runner-up.

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Languages