Institutional-Grade
Strategy Validation
A 7-stage multi-LLM kill pipeline that generates, tests, and deploys quantitative trading strategies. 96% are killed. You learn from the 4% that survived.
Strategy Generation as a Service
AI generates hundreds of quantitative trading hypotheses grounded in economic logic — not curve fitting. Every hypothesis must have a thesis, a counterparty (who loses), and an explanation of why the edge persists. Ideas are restricted to liquid instruments (SPY, QQQ, TLT, GLD, and other major ETFs) to ensure realistic execution.
Automated Kill Pipeline
3 automated kill gates plus 1 human-assisted gate ruthlessly filter out weak strategies. The pipeline checks for minimum Sharpe ratio (≥ 0.5), minimum trade count (≥ 25), maximum drawdown thresholds, and walk-forward performance decay. Only 2–4% of generated strategies survive all gates.
Automated execution: downloads real market data, runs backtest, checks Sharpe ≥ 0.5 and minimum 25 trades. 60–80% of strategies die here.
5 hostile market scenarios: triple volatility, remove best month, crash injection, time shift, correlation breakdown. Kill if worst drawdown exceeds -50%.
5 years training, 1 year out-of-sample test. Kill if Sharpe decay > 50% or out-of-sample Sharpe ≤ 0. This catches overfitting — the most dangerous trap.
Multi-LLM Adversarial Testing
Using a single AI model creates systematic bias. Our pipeline uses 4 different LLM providers — OpenAI o3 for hypothesis generation and implementation, DeepSeek Reasoner for adversarial interrogation, and Google Gemini for stress test analysis — reducing blind spots through cognitive diversity.
Stress Testing
Every surviving strategy is tested against 5 hostile market scenarios designed to break it:
Amplifies all daily returns by 3× to simulate extreme market regimes.
Injects a 35% market drop over 5 trading days at a random point in the backtest.
Strips the single best-performing month to test if returns depend on one lucky period.
Shifts the entire data window by 6 months to check time-period sensitivity.
Walk-Forward Validation
The most important gate. Data is split: 5 years for training (2020–2025), 1 year for out-of-sample testing (2025–2026). The strategy is run on both independently. If the out-of-sample Sharpe degrades by more than 50% versus in-sample, the strategy is killed. This is the single most effective defense against overfitting — the most dangerous and common trap in quantitative trading.
Complete Audit Trail
Every decision, every kill, every result is recorded in a structured manifest.yaml per strategy. You can trace exactly which gate killed a strategy, what thresholds it failed, and what the metrics looked like at time of death. Full transparency — no black boxes.
Deploy Reports
Every strategy that survives all 7 stages receives a comprehensive, human-readable deploy report. The report summarizes all stage results, metrics, stress test performance, walk-forward validation, and the adversarial critique. A human reviews the report and gives final approval before the strategy moves to paper trading with minimum capital.
Disclaimer: AlgoWorkout is an educational platform. All strategies are for educational and paper-trading purposes only. Past backtest performance does not guarantee future results. This is not financial advice.