WorldQuant

Research Consultant

Conducted quantitative research on systematic equity strategies, designing and testing predictive signals across global markets with a focus on the US and China universes.

US and China Markets Systematic Equity Signals 2+ Sharpe Alphas

Research Focus

Integrated fundamental accounting ratios, return momentum, volatility filters, and z-score logic to identify short-term mispricings in liquid equities.

Method

Refined strategies through iterative experimentation, empirical validation, and robustness checks, consistently producing alphas with Sharpe ratios above 2.

Alpha Spotlight

Fundamental Anchoring in Microstructure Noise. A signal that combines intraday dislocations with persistent profitability to capture liquidity-driven mean reversion.

Performance Snapshot

Backtest highlights from the research note.

View Note
Sharpe 2.03
Fitness 1.40
Avg Annual Return 16.24%
  • Strong performance in volatile, dislocated regimes; more fragile in momentum-driven markets.
  • Targets microstructure mean reversion while filtering for profitability persistence.
  • Validated using Monte Carlo style robustness checks and empirical stress scenarios.

Signal Architecture

  • Price dislocation: z-scored VWAP vs close to capture end-of-day mispricing.
  • Volatility filter: compressed intraday ranges to identify post-stabilization reversals.
  • Fundamental anchor: operating profitability rank to avoid structurally weak firms.
  • Contrarian tilt: recent underperformers to capture short-term reversal premium.
alpha = zscore(VWAP / Close) * (1 - rank(High / Low)) * ts_rank(OperatingIncome, 252) * rank(-Returns)

Origin Story

Inspired by reading The Man Who Solved the Market about Jim Simons, I discovered my love of coding through alpha research. That curiosity led me to learn Python and begin building systematic research projects from scratch.

Core Tools Python, factor research
Focus Systematic equity signals
Markets US and China