Mechanism Over
Optimism.
Qwark is a systematic quantitative research practice building factor-based equity strategies with live out-of-sample validation and institutional-style risk management.
> Now building systematic strategies with economic reasoning, kill conditions, and live validation.
> Not just backtested. Live validated.
Live Projects
Systematic Equity Strategy
FACTOR-BASED LONG EQUITY // US MID-CAPPeriodically-rebalanced systematic strategy in the US mid-cap space. Single fundamental signal with persistent cross-sectional predictive power. 23-year validated backtest with full factor attribution. Live out-of-sample.
Strategy II
QUANTITATIVE RESEARCH // IN DEVELOPMENTSecond systematic strategy currently in research phase. Same discipline — mechanism-validated signal, pre-specified kill conditions, full factor attribution. Details under embargo until validation complete.
Research Process
> Every strategy follows the same pipeline. No shortcuts, no skipping steps. The process is the risk management.
Mechanism
State the economic reason this signal should work. If you can't, stop.
Backtest
Validate signal over full sample. Document every assumption. No data snooping.
Attribution
Multi-factor attribution. Know what drives your returns.
Kill Conditions
Define yellow/red flags before deployment. Breach = stop, then investigate.
Deploy + Monitor
Go live. Quarterly OOS review. Document underperformance — never hide it.
Research Philosophy
> These aren't abstract principles. Each one was learned the hard way — by building something that worked on paper and failed in practice. The philosophy IS the risk management.
Mechanism Before Alpha
Every signal must have a stated economic mechanism — a reason it should work that's grounded in how markets actually operate, not just in what historically correlated. Cross-sectional patterns without return-side implications are measurement artifacts. I've tested this the hard way.
Lock-Before-Search
No factor research until a specific, identified deficiency exists. "Increasing CAGR" is not a valid problem when the strategy already works. The correct question: what risk does this new factor reduce? If you can't answer that, the search is p-hacking with extra steps.
Pre-Specified Kill Conditions
Every strategy ships with yellow and red flags defined before deployment. Not after. If it breaches, you stop — then investigate. Not the other way around. This is the single hardest discipline in systematic trading and the one that saves you from yourself.
Operator Profile
I build systematic strategies that have a mechanism — not just a backtest.
Quantitative researcher and developer. I started in November 2025 building ML price prediction models — they all failed. In May 2026, I stepped back and realized the problem: prediction without mechanism is just curve-fitting. I pivoted to systematic factor research, and within two months shipped a live strategy with a mechanism-validated signal, pre-specified kill conditions, and full factor attribution.
Every strategy I ship has a stated economic mechanism, pre-specified kill conditions, and honest factor attribution. I don't overclaim, I don't post-hoc rationalize drawdowns, and I don't add factors to a strategy that already works. The research process is the edge — not the signal.
Research Discipline
Mechanism-first. Lock-before-search. Kill conditions defined before deployment. No post-hoc rationalization.
Expanding Research
Systematic equity factors with live OOS track record. Second strategy in research phase.
Pivot Over Ego
Six months of failed ML predictions. Pivoted instead of doubling down. Two months to live strategy after course correction. Kill your darlings, ship what works.
Technical Profile
Standing
High school student. Self-taught quant. No finance degree, no prop desk, no mentor handoff. Started November 2025 with ML price prediction — failed. Pivoted May 2026 to mechanism-first factor research. Shipped live strategy within two months of the pivot.
I compete on output — not credentials. The track record either validates or it doesn't. No prestige shield, no network gate.
Trajectory
Second Strategy + Live Deployment
Strategy I goes live July 1. Research begins on Strategy II — same discipline, different mechanism.
Strategy I Research Complete
Backtesting, factor attribution, kill conditions all validated. Signal proves persistent across multiple holding horizons.
The Pivot
Abandoned ML price prediction. Realized directional forecasting is the wrong problem. Stepped back, rethought from first principles — mechanism, not prediction.
ML Price Prediction (Failed)
Built machine learning models for directional price prediction. Every iteration failed. The lesson: prediction without mechanism is curve-fitting.
Self-Directed Learning
Programming, probability, market microstructure. Consumed everything from scratch. No shortcuts — just the work before the work.
Signal
> Open to conversations about systematic strategies, factor research methodology, and quantitative infrastructure. Not looking for pitch decks — looking for people who think in mechanisms.