de-shaw skill

---
name: de-shaw-computational-finance
description: Build trading systems in the style of D.E. Shaw, the pioneering computational finance firm. Emphasizes systematic strategies, rigorous quantitative research, and world-class technology infrastructure. Use when building research platforms, systematic trading strategies, or quantitative finance infrastructure.
---

# D.E. Shaw Style Guide

## Overview

D.E. Shaw, founded in 1988 by computer scientist David E. Shaw, is one of the original quantitative hedge funds. They pioneered the application of computational methods to finance, treating trading as a scientific and engineering problem. The firm manages ~$60B and is known for hiring exceptional technologists and scientists.

## Core Philosophy

> "We approach problems in finance the same way scientists approach problems in physics or biology."

> "The best ideas often come from people who aren't finance experts."

> "Technology is not a cost center; it's a competitive advantage."

D.E. Shaw believes that finance is fundamentally a computational problem. By applying rigorous scientific methods and world-class technology, systematic approaches can outperform discretionary ones.

## Design Principles

1. **Science Over Intuition**: Hypothesize, test, validate, or reject.

2. **Research Infrastructure**: The platform enables the research, not the other way around.

3. **Hire Generalists**: The best quants aren't necessarily from finance.

4. **Long-Term Thinking**: Build systems that will work for decades.

5. **Risk First**: Understand what can go wrong before what can go right.

## When Building Systematic Trading Systems

### Always

- Formulate clear, testable hypotheses
- Separate alpha research from execution
- Build robust risk management into every layer
- Version control everything: code, data, models, configs
- Design for extensibility and maintainability
- Document assumptions and limitations

### Never

- Rely on intuition without empirical validation
- Conflate in-sample and out-of-sample performance
- Ignore regime changes and structural breaks
- Assume correlations are stable
- Deploy without thorough testing
- Optimize for a single metric

### Prefer

- Modular, composable architectures
- Clear separation of concerns
- Reproducible research pipelines
- Defensive programming practices
- Extensive logging and monitoring
- Gradual rollouts with kill switches

## Code Patterns

### Research Pipeline Architecture

```python
class ResearchPipeline:
    """
    D.E. Shaw's approach: systematic research with reproducibility.
    Every experiment is tracked, versioned, and reproducible.
    """
    
    def __init__(self, experiment_tracker, data_warehouse, compute_cluster):
        self.tracker = experiment_tracker
        self.data = data_warehouse
        self.compute = compute_cluster
    
    def run_experiment(self,
                       hypothesis: Hypothesis,
                       config: ExperimentConfig) -> ExperimentResult:
        """
        Run a single experiment with full tracking.
        """
        # Create experiment record
        experiment_id = self.tracker.create_experiment(
            hypothesis=hypothesis.description,
            config=config.to_dict(),
            git_commit=get_git_commit(),
            data_version=self.data.get_version()
        )
        
        try:
            # Load data with point-in-time correctness
            data = self.data.load(
                universe=config.universe,
                start_date=config.start_date,
                end_date=config.end_date,
                as_of_date=config.as_of_date  # Prevent lookahead
            )
            
            # Validate data quality
            quality_report = self.validate_data(data)
            self.tracker.log_artifact(experiment_id, 'data_quality', quality_report)
            
            # Run the actual analysis
            result = hypothesis.evaluate(data, config)
            
            # Compute statistical significance
            significance = self.assess_significance(result, config)
            
            # Log results
            self.tracker.log_metrics(experiment_id, {
                'sharpe_ratio': result.sharpe_ratio,
                'information_ratio': result.information_ratio,
                't_statistic': significance.t_stat,
                'p_value': significance.p_value,
                'num_observations': result.n_obs
            })
            
            return ExperimentResult(
                experiment_id=experiment_id,
                hypothesis=hypothesis,
                result=result,
                significance=significance,
                reproducible=True
            )
            
        except Exception as e:
            self.tracker.log_failure(experiment_id, str(e))
            raise
    
    def run_hypothesis_suite(self, 
                             hypotheses: List[Hypothesis],
                             config: ExperimentConfig) -> SuiteResult:
        """
        Run multiple hypotheses and correct for multiple testing.
        """
        results = []
        
        for hypothesis in hypotheses:
            result = self.run_experiment(hypothesis, config)
            results.append(result)
        
        # Apply Benjamini-Hochberg FDR correction
        corrected = self.apply_fdr_correction(results)
        
        return SuiteResult(
            results=corrected,
            significant_count=sum(1 for r in corrected if r.is_significant),
            total_count=len(corrected)
        )
```

### Multi-Factor Risk Model

```python
class RiskModel:
    """
    D.E. Shaw's risk approach: understand and control risk at multiple levels.
    """
    
    def __init__(self, factor_returns, factor_covariance, specific_risk):
        self.factor_returns = factor_returns  # Historical factor returns
        self.factor_cov = factor_covariance   # Factor covariance matrix
        self.specific_risk = specific_risk    # Idiosyncratic risk by asset
    
    def estimate_portfolio_risk(self,
                                 positions: pd.Series,
                                 factor_exposures: pd.DataFrame) -> RiskEstimate:
        """
        Decompose portfolio risk into systematic and idiosyncratic components.
        """
        # Factor risk: w' * B * Σ_f * B' * w
        portfolio_exposures = factor_exposures.T @ positions
        factor_var = portfolio_exposures @ self.factor_cov @ portfolio_exposures
        
        # Specific risk: Σ(w_i^2 * σ_i^2)
        specific_var = (positions ** 2 * self.specific_risk ** 2).sum()
        
        # Total risk
        total_var = factor_var + specific_var
        
        return RiskEstimate(
            total_volatility=np.sqrt(total_var * 252),  # Annualized
            factor_volatility=np.sqrt(factor_var * 252),
            specific_volatility=np.sqrt(specific_var * 252),
            factor_contribution=self.calculate_factor_contributions(
                positions, factor_exposures
            )
        )
    
    def calculate_factor_contributions(self, positions, factor_exposures):
        """
        Break down risk by factor for attribution.
        """
        portfolio_exposures = factor_exposures.T @ positions
        
        contributions = {}
        for factor in self.factor_cov.columns:
            # Marginal contribution to risk
            factor_exposure = portfolio_exposures[factor]
            factor_vol = np.sqrt(self.factor_cov.loc[factor, factor])
            contributions[factor] = {
                'exposure': factor_exposure,
                'volatility': factor_vol,
                'contribution': factor_exposure * factor_vol
            }
        
        return contributions
    
    def stress_test(self, 
                    positions: pd.Series,
                    scenarios: Dict[str, Dict[str, float]]) -> Dict[str, float]:
        """
        Apply historical or hypothetical stress scenarios.
        """
        results = {}
        
        for scenario_name, factor_shocks in scenarios.items():
            pnl = 0.0
            
            for factor, shock in factor_shocks.items():
                factor_exposure = self.get_portfolio_exposure(positions, factor)
                pnl += factor_exposure * shock
            
            results[scenario_name] = pnl
        
        return results
```

### Strategy Composition Framework

```python
class StrategyFramework:
    """
    D.E. Shaw's modular strategy architecture.
    Strategies are composed from reusable components.
    """
    
    def __init__(self):
        self.alpha_models = {}
        self.risk_models = {}
        self.execution_models = {}
        self.portfolio_constructors = {}
    
    def register_alpha_model(self, name: str, model: AlphaModel):
        """Alpha models generate return predictions."""
        self.alpha_models[name] = model
    
    def register_risk_model(self, name: str, model: RiskModel):
        """Risk models estimate covariances and factor exposures."""
        self.risk_models[name] = model
    
    def create_strategy(self, config: StrategyConfig) -> Strategy:
        """
        Compose a strategy from registered components.
        """
        alpha = self.alpha_models[config.alpha_model]
        risk = self.risk_models[config.risk_model]
        execution = self.execution_models[config.execution_model]
        constructor = self.portfolio_constructors[config.portfolio_constructor]
        
        return ComposedStrategy(
            alpha_model=alpha,
            risk_model=risk,
            execution_model=execution,
            portfolio_constructor=constructor,
            constraints=config.constraints,
            risk_limits=config.risk_limits
        )


class ComposedStrategy:
    """
    A strategy composed from modular components.
    """
    
    def __init__(self, alpha_model, risk_model, execution_model,
                 portfolio_constructor, constraints, risk_limits):
        self.alpha = alpha_model
        self.risk = risk_model
        self.execution = execution_model
        self.constructor = portfolio_constructor
        self.constraints = constraints
        self.risk_limits = risk_limits
    
    def generate_trades(self, 
                        current_positions: pd.Series,
                        market_data: MarketData) -> List[Trade]:
        """
        Full strategy pipeline: alpha → portfolio → trades.
        """
        # 1. Generate alpha signals
        alpha_scores = self.alpha.predict(market_data)
        
        # 2. Estimate risk
        risk_estimate = self.risk.estimate(market_data)
        
        # 3. Construct optimal portfolio
        target_positions = self.constructor.optimize(
            alpha_scores=alpha_scores,
            risk_model=risk_estimate,
            current_positions=current_positions,
            constraints=self.constraints,
            risk_limits=self.risk_limits
        )
        
        # 4. Generate trades to move from current to target
        trades = self.calculate_trades(current_positions, target_positions)
        
        # 5. Optimize execution
        scheduled_trades = self.execution.schedule(trades, market_data)
        
        return scheduled_trades
```

### Portfolio Optimization with Constraints

```python
class PortfolioOptimizer:
    """
    Mean-variance optimization with realistic constraints.
    """
    
    def optimize(self,
                 alpha: pd.Series,
                 covariance: pd.DataFrame,
                 current_positions: pd.Series,
                 constraints: ConstraintSet) -> pd.Series:
        """
        Solve the quadratic programming problem:
        
        max: α'w - λ/2 * w'Σw - γ * ||w - w_0||^2
        s.t.: constraints
        """
        n = len(alpha)
        
        # Objective: maximize alpha, minimize risk, minimize turnover
        P = constraints.risk_aversion * covariance.values
        P += constraints.turnover_aversion * np.eye(n)
        q = -alpha.values + constraints.turnover_aversion * current_positions.values
        
        # Constraints
        G, h = self.build_inequality_constraints(constraints, n)
        A, b = self.build_equality_constraints(constraints, n)
        
        # Solve
        solution = qp_solve(P, q, G, h, A, b)
        
        return pd.Series(solution, index=alpha.index)
    
    def build_inequality_constraints(self, constraints, n):
        """
        Build inequality constraints: Gx <= h
        - Long-only: -w <= 0
        - Position limits: w <= max_position
        - Sector limits: Σw_sector <= max_sector
        """
        G_list = []
        h_list = []
        
        if constraints.long_only:
            G_list.append(-np.eye(n))
            h_list.append(np.zeros(n))
        
        if constraints.max_position:
            G_list.append(np.eye(n))
            h_list.append(np.full(n, constraints.max_position))
        
        for sector, (assets, max_weight) in constraints.sector_limits.items():
            row = np.zeros(n)
            row[assets] = 1.0
            G_list.append(row.reshape(1, -1))
            h_list.append(np.array([max_weight]))
        
        return np.vstack(G_list), np.concatenate(h_list)
    
    def build_equality_constraints(self, constraints, n):
        """
        Build equality constraints: Ax = b
        - Fully invested: Σw = 1
        - Dollar neutral: Σw = 0
        """
        A_list = []
        b_list = []
        
        if constraints.fully_invested:
            A_list.append(np.ones((1, n)))
            b_list.append(np.array([1.0]))
        
        if constraints.dollar_neutral:
            A_list.append(np.ones((1, n)))
            b_list.append(np.array([0.0]))
        
        if A_list:
            return np.vstack(A_list), np.concatenate(b_list)
        return None, None
```

## Mental Model

D.E. Shaw approaches quantitative finance by asking:

1. **Is this a testable hypothesis?** If not, reformulate
2. **What's the null hypothesis?** What are we testing against?
3. **What could go wrong?** Risk analysis before return analysis
4. **Is it reproducible?** Can someone else replicate this result?
5. **Will it scale?** Both computationally and economically

## Signature D.E. Shaw Moves

- Rigorous hypothesis testing framework
- Multi-factor risk models
- Modular strategy composition
- Reproducible research pipelines
- Extensive experiment tracking
- Gradual position sizing and rollout
- Cross-disciplinary hiring
- Long-term infrastructure investment