Thread by @heynavtoor

🚨 BREAKING: AI can now build trading algorithms like Goldman Sachs' algorithmic trading desk (for free).

Here are 15 insane Claude prompts that replace $500K/year quant strats (Save for later)

1. The Goldman Sachs Quant Strategy Architect

"You are a managing director on Goldman Sachs' algorithmic trading desk who designs systematic trading strategies managing $10B+ in institutional capital across global equity markets.

I need a complete quantitative trading strategy designed from scratch.

Architect:

- Strategy thesis: the specific market inefficiency or pattern this strategy exploits
- Universe selection: which instruments to trade and why (stocks, ETFs, futures, options)
- Signal generation logic: the exact mathematical rules that produce buy and sell signals
- Entry rules: precise conditions that must all be true before opening a position
- Exit rules: profit targets, stop losses, time-based exits, and signal reversal exits
- Position sizing model: how much capital to allocate per trade based on conviction and risk
- Risk parameters: maximum drawdown, position limits, sector exposure caps, and correlation limits
- Backtesting framework: how to properly test this strategy against historical data
- Benchmark selection: what to measure performance against and why
- Edge decay monitoring: how to detect when the strategy stops working

Format as a Goldman Sachs-style quantitative strategy memo with mathematical formulas, pseudocode logic, and risk parameter tables.

My trading focus: [DESCRIBE YOUR CAPITAL, PREFERRED MARKETS, TIME HORIZON, RISK TOLERANCE, AND ANY STRATEGIES YOU'VE EXPLORED]"

2. The Renaissance Technologies Backtesting Engine

"You are a senior quantitative researcher at Renaissance Technologies who builds rigorous backtesting systems that separate real alpha from overfitted noise across decades of market data.

I need a complete backtesting framework that gives me honest, reliable results.

Build:

- Data requirements: which historical data feeds I need, minimum time periods, and data quality checks
- Backtesting engine architecture: event-driven or vectorized with pros and cons for my strategy type
- Transaction cost modeling: commissions, slippage, bid-ask spread, and market impact estimates
- Lookahead bias prevention: safeguards that ensure no future data leaks into past decisions
- Survivorship bias handling: accounting for delisted stocks and failed companies in historical data
- Walk-forward optimization: train on past data, test on unseen data in rolling windows
- Out-of-sample testing protocol: how to split data so results aren't just curve-fitting
- Monte Carlo simulation: randomize trade sequences to understand the range of possible outcomes
- Statistical significance tests: is the backtest return real or could it happen by random chance
- Complete Python backtesting code ready to run with sample data and visualization

Format as a quantitative research document with full Python code, statistical validation methodology, and result interpretation guidelines.

My strategy: [DESCRIBE YOUR TRADING STRATEGY, PREFERRED MARKET, TIME FRAME, AND AVAILABLE HISTORICAL DATA]"

3. The Two Sigma Risk Management System

"You are a senior portfolio risk manager at Two Sigma who builds risk management frameworks protecting $60B+ in assets from catastrophic losses during black swan events and market crashes.

I need a complete risk management system for my trading operations.

Build:

- Position sizing algorithm: Kelly Criterion or fractional Kelly with exact implementation
- Stop-loss framework: fixed, trailing, volatility-adjusted, and time-based stops with rules for each
- Maximum drawdown controls: hard limits that automatically reduce position size or halt trading
- Correlation monitoring: detect when supposedly uncorrelated positions start moving together
- Value at Risk (VaR) calculation: estimate maximum daily loss at 95% and 99% confidence levels
- Stress testing scenarios: simulate portfolio behavior during 2008 crash, COVID crash, and flash crashes
- Leverage limits: maximum margin utilization rules with automatic deleveraging triggers
- Sector and factor exposure caps: prevent hidden concentration risk across positions
- Liquidity risk assessment: ensure every position can be exited within acceptable timeframe and cost
- Daily risk dashboard: every metric I should check every morning before markets open

Format as a Two Sigma-style risk management specification with formulas, Python implementation code, and a daily risk monitoring checklist.

My portfolio: [DESCRIBE YOUR TRADING CAPITAL, STRATEGY TYPES, POSITION COUNT, LEVERAGE USAGE, AND BIGGEST RISK CONCERN]"

4. The Citadel Alpha Signal Research Lab

"You are a senior quantitative researcher at Citadel who discovers and validates new alpha signals by analyzing alternative data, market microstructure, and statistical patterns across thousands of securities.

I need a systematic process for discovering profitable trading signals.

Research:

- Signal idea generation framework: 20 categories of potential alpha signals to investigate
- Data source inventory: price data, fundamental data, sentiment data, and alternative data sources
- Feature engineering pipeline: transform raw data into testable trading signals step by step
- Signal strength testing: information coefficient, hit rate, and risk-adjusted return for each signal
- Decay analysis: how quickly each signal loses its predictive power after formation
- Correlation check: ensure new signals aren't just repackaging existing known factors
- Signal combination methodology: how to blend multiple weak signals into one strong composite
- Regime detection: identify which signals work in trending vs mean-reverting vs volatile markets
- Turnover analysis: how often the signal forces trades and whether the alpha survives transaction costs
- Signal monitoring dashboard: track live signal performance against backtested expectations

Format as a Citadel-style quantitative research report with signal definitions, statistical test results, and Python code for signal generation.

My focus: [DESCRIBE YOUR MARKET, AVAILABLE DATA SOURCES, TRADING FREQUENCY, AND TYPES OF SIGNALS YOU'RE INTERESTED IN]"

5. The Jane Street Market Making Engine

"You are a senior quantitative trader at Jane Street who designs market-making algorithms that profit from bid-ask spreads while managing inventory risk across thousands of trades per day.

I need a complete market-making strategy framework.

Design:

- Spread calculation model: how to set bid and ask prices based on volatility, volume, and inventory
- Inventory management: rules for staying neutral and avoiding large directional bets
- Quote adjustment logic: how to shift prices when inventory builds up on one side
- Adverse selection detection: identify when informed traders are picking off your quotes
- Speed and latency requirements: how fast order placement and cancellation need to be
- Hedging strategy: when and how to offset accumulated directional risk
- Market microstructure analysis: understanding order book dynamics, tick sizes, and queue priority
- PnL decomposition: separate profit from spread capture vs directional moves vs hedging costs
- Risk limits: maximum inventory, maximum loss per day, and automatic shutdown triggers
- Performance metrics: spread captured, inventory turnover, Sharpe ratio, and fill rate targets

Format as a Jane Street-style trading system specification with mathematical models, pseudocode, and risk parameter tables.

My interest: [DESCRIBE THE MARKET YOU WANT TO MAKE IN, YOUR CAPITAL, TECHNOLOGY AVAILABLE, AND EXPERIENCE LEVEL WITH MARKET MAKING]"

6. The AQR Factor Model Builder

"You are a senior researcher at AQR Capital Management who builds multi-factor models used to construct portfolios that systematically harvest risk premiums across global markets.

I need a complete factor model for portfolio construction.

Build:

- Factor selection: which factors to include (value, momentum, quality, size, low volatility) with evidence
- Factor definition: exact calculation formula for each factor using available financial data
- Factor portfolio construction: how to build long-short portfolios for each individual factor
- Factor exposure measurement: how to calculate my current portfolio's exposure to each factor
- Factor correlation matrix: how factors move relative to each other and diversification benefits
- Multi-factor combination: how to weight and blend factors into a single composite score
- Rebalancing methodology: when to rebalance factor portfolios and how to minimize turnover
- Factor timing analysis: can we increase exposure to factors when conditions favor them
- Performance attribution: decompose returns into factor contributions and stock-specific alpha
- Complete Python implementation with data loading, factor calculation, and portfolio construction

Format as an AQR-style factor research paper with mathematical definitions, empirical results framework, and production-ready code.

My investment universe: [DESCRIBE YOUR MARKET (US STOCKS, GLOBAL, ETFs), CAPITAL SIZE, REBALANCING FREQUENCY, AND FACTOR PREFERENCES]"

7. The D.E. Shaw Statistical Arbitrage System

"You are a senior portfolio manager at D.E. Shaw who builds statistical arbitrage systems that exploit pricing relationships between related securities using advanced statistical methods.

I need a complete pairs trading and statistical arbitrage framework.

Build:

- Pair selection methodology: how to find stocks that move together using correlation and cointegration
- Cointegration testing: Engle-Granger and Johansen tests to verify the pair relationship is real
- Spread calculation: how to measure the price difference between paired securities correctly
- Z-score signal generation: when the spread deviates enough from normal to trigger a trade
- Entry and exit thresholds: exact z-score levels for opening, adding to, and closing positions
- Hedge ratio calculation: how many shares of each stock to trade to stay market-neutral
- Mean reversion speed analysis: how quickly the spread typically returns to normal
- Regime change detection: identify when a pair relationship breaks down permanently
- Portfolio of pairs: how to run 20+ pairs simultaneously with capital allocation rules
- Complete Python code with pair screening, signal generation, and backtesting

Format as a D.E. Shaw-style quantitative research document with statistical test outputs, strategy rules, and full implementation code.

My market: [DESCRIBE YOUR PREFERRED SECTOR OR MARKET, AVAILABLE DATA, TRADING CAPITAL, AND EXPERIENCE WITH PAIRS TRADING]"

8. The Bridgewater Macro Trading Strategist

"You are a senior investment strategist at Bridgewater Associates who designs systematic macro trading strategies based on Ray Dalio's economic machine framework, trading global currencies, bonds, commodities, and equities.

I need a complete systematic macro trading strategy.

Design:

- Economic indicator dashboard: 15 macro signals to monitor (GDP, inflation, employment, yield curves)
- Regime classification system: growth/inflation matrix creating 4 market environments
- Asset class behavior map: how stocks, bonds, commodities, and currencies perform in each regime
- Signal construction: how to combine macro indicators into actionable portfolio allocation signals
- All-Weather inspired allocation: a baseline portfolio designed to perform in any environment
- Tactical overlay rules: how to tilt away from baseline when regime signals are strong
- Instrument selection: specific ETFs or futures for expressing each macro view
- Rebalancing triggers: calendar-based, threshold-based, or signal-based with rules for each
- Correlation regime monitoring: how asset correlations change during crises and how to prepare
- Geopolitical risk framework: how to adjust positioning for elections, wars, and policy changes

Format as a Bridgewater-style investment strategy memo with economic frameworks, allocation tables, and Python code for regime detection.

My focus: [DESCRIBE YOUR INVESTABLE CAPITAL, PREFERRED INSTRUMENTS, RISK TOLERANCE, AND MACRO VIEWS YOU WANT TO TRADE]"

9. The Bloomberg Terminal Data Pipeline Builder

"You are a senior quantitative data engineer at Bloomberg who builds the real-time and historical data pipelines feeding algorithmic trading systems at the world's largest hedge funds.

I need a complete market data pipeline for my trading system.

Build:

- Data source architecture: free and paid sources for price, fundamental, sentiment, and alternative data
- Real-time data feed: WebSocket connections to live market data with reconnection handling
- Historical data storage: database design for efficiently storing years of tick, minute, and daily data
- Data cleaning pipeline: handle missing values, stock splits, dividends, and delistings automatically
- Corporate action adjustment: automatically adjust historical prices for splits, mergers, and spinoffs
- Feature store: pre-computed technical indicators and fundamental ratios ready for signal generation
- Data validation rules: automated checks that catch bad data before it triggers false trades
- API layer: clean endpoints your trading strategy can query for any data point instantly
- Scheduling system: automated daily updates, weekly fundamental refreshes, and monthly recalculations
- Complete Python data pipeline code with database setup, data ingestion, and API serving

Format as a data engineering specification with pipeline diagrams, database schemas, and production-ready Python code.

My needs: [DESCRIBE YOUR TRADING MARKETS, DATA SOURCES YOU HAVE ACCESS TO, UPDATE FREQUENCY NEEDED, AND STORAGE PREFERENCES]"

10. The Virtu Financial Execution Algorithm Designer

"You are a senior execution algorithm developer at Virtu Financial who builds smart order routing and execution algorithms that minimize market impact and slippage for institutional-sized orders.

I need execution algorithms that get me into and out of positions at the best possible prices.

Design:

- TWAP algorithm: split large orders evenly across a time window to reduce market impact
- VWAP algorithm: execute proportional to historical volume patterns throughout the trading day
- Implementation shortfall optimizer: balance urgency against market impact cost
- Iceberg order logic: show only a small portion of the total order to hide true size
- Smart order routing: how to choose between exchanges and dark pools for best execution
- Slippage measurement: track the difference between signal price and actual execution price
- Market impact model: estimate how my order size will move the price against me
- Execution quality analytics: metrics to evaluate whether my execution is getting better or worse
- Pre-trade cost estimation: predict total execution cost before placing the order
- Post-trade transaction cost analysis: detailed breakdown of where costs came from

Format as an execution algorithm specification with mathematical models, pseudocode for each algorithm, and performance measurement frameworks.

My trading: [DESCRIBE YOUR AVERAGE ORDER SIZE, TRADING FREQUENCY, MARKETS TRADED, AND CURRENT EXECUTION CHALLENGES]"

11. The Point72 Machine Learning Alpha Researcher

"You are a senior ML researcher at Point72's Cubist division who builds machine learning models that predict short-term stock price movements using hundreds of features and alternative data signals.

I need a complete ML-based trading signal using modern machine learning techniques.

Build:

- Feature engineering: 50+ features from price, volume, fundamental, and technical data
- Label construction: how to define the target variable (future returns, direction, or risk-adjusted returns)
- Model selection: compare gradient boosting (XGBoost, LightGBM), random forests, and neural networks
- Cross-validation strategy: purged k-fold that prevents lookahead bias in time-series data
- Hyperparameter tuning: systematic search with proper out-of-sample validation
- Feature importance analysis: which inputs drive predictions and which are noise
- Overfitting prevention: regularization, early stopping, and ensemble techniques
- Prediction-to-signal conversion: transform raw model scores into portfolio weights
- Model monitoring: detect model degradation and trigger retraining alerts
- Complete Python ML pipeline: data prep, model training, evaluation, and signal generation code

Format as a Point72-style ML research report with feature definitions, model comparison tables, and a complete reproducible Python pipeline.

My data: [DESCRIBE YOUR MARKET, AVAILABLE DATA SOURCES, PREDICTION HORIZON, AND MACHINE LEARNING EXPERIENCE LEVEL]"

12. The Man Group Portfolio Optimization Engine

"You are a senior portfolio manager at Man Group who builds portfolio optimization systems that allocate capital across multiple strategies and assets to maximize risk-adjusted returns.

I need a complete portfolio optimization system for multi-asset or multi-strategy allocation.

Optimize:

- Mean-variance optimization: classic Markowitz with expected returns, covariance matrix, and constraints
- Black-Litterman model: combine market equilibrium with my personal views on specific assets
- Risk parity allocation: equal risk contribution from each asset or strategy
- Hierarchical risk parity: cluster-based allocation that avoids unstable covariance matrix inversion
- Constraint framework: position limits, sector caps, turnover constraints, and long-only rules
- Robust optimization: techniques that work even when return estimates are noisy or wrong
- Rebalancing optimizer: minimize trading costs while keeping portfolio close to optimal weights
- Scenario analysis: how the optimal portfolio changes under different market assumptions
- Performance attribution: decompose returns into allocation effect, selection effect, and timing
- Complete Python optimization code with visualization of efficient frontiers and allocation recommendations

Format as a Man Group-style portfolio construction document with optimization methodology, constraint specifications, and interactive Python code.

My portfolio: [DESCRIBE YOUR ASSETS OR STRATEGIES, CAPITAL, CONSTRAINTS, RISK TARGET, AND RETURN EXPECTATIONS]"

13. The Millennium Management Live Trading System

"You are a senior systems architect at Millennium Management who builds production trading systems that execute algorithmic strategies in real-time with institutional-grade reliability and monitoring.

I need a complete live trading system architecture that executes my strategy in real markets.

Build:

- System architecture: how the signal generator, order manager, and execution engine connect
- Broker API integration: connect to Interactive Brokers, Alpaca, or other broker with order placement
- Order management system: track every order from creation to fill with state machine logic
- Position tracking: real-time portfolio state showing current holdings, P&L, and exposure
- Real-time signal processing: consume market data, calculate signals, and generate orders automatically
- Paper trading mode: test everything with simulated money before risking real capital
- Kill switch: one-click emergency shutdown that cancels all orders and flattens all positions
- Reconciliation engine: compare your internal records against broker statements to catch discrepancies
- Alerting system: SMS and email alerts for fills, errors, drawdown breaches, and system failures
- Logging and audit trail: record every decision, order, and fill for post-trade analysis

Format as a trading system architecture document with component diagrams, API specifications, and complete Python implementation code.

My setup: [DESCRIBE YOUR BROKER, STRATEGY TYPE, TRADING FREQUENCY, CAPITAL, AND CURRENT TECHNOLOGY INFRASTRUCTURE]"

14. The Dimensional Fund Advisors Factor Backtester

"You are a senior quantitative researcher at Dimensional Fund Advisors who builds long-term factor investing strategies backed by decades of academic research and institutional-grade backtesting.

I need a complete factor investing strategy backtested with rigorous academic methodology.

Backtest:

- Factor universe: define the investable stock universe with liquidity and size filters
- Factor construction: build long-short portfolios sorted by value, momentum, quality, and size
- Return calculation: daily and monthly returns with proper handling of dividends and corporate actions
- Risk-adjusted metrics: Sharpe ratio, Sortino ratio, maximum drawdown, and Calmar ratio
- Factor premium analysis: is the factor return statistically significant across multiple time periods
- Regime analysis: how factor performance changes in recessions, expansions, and crises
- Factor crowding assessment: are too many people trading this factor now, reducing future returns
- Implementation analysis: does the alpha survive realistic transaction costs and capacity limits
- Multi-factor portfolio: combine factors into a single investable portfolio with rebalancing
- Tear sheet generation: one-page performance summary with all key metrics and drawdown charts

Format as a Dimensional-style factor research paper with complete Python backtesting code and automated tear sheet generation.

My focus: [DESCRIBE YOUR INVESTMENT UNIVERSE, TIME HORIZON, FACTORS OF INTEREST, AND BACKTESTING DATA AVAILABLE]"

15. The Goldman Sachs Algorithmic Trading Compliance Framework

"You are a senior compliance technology officer at Goldman Sachs who builds regulatory compliance frameworks for algorithmic trading operations ensuring adherence to SEC, FINRA, and MiFID II regulations.

I need a complete compliance and governance framework for my algorithmic trading activities.

Build:

- Regulatory inventory: which rules apply to my trading (SEC Rule 15c3-5, Reg SHO, pattern day trader, etc.)
- Pre-trade risk controls: automated checks before every order is sent to the market
- Position limit monitoring: hard and soft limits with automatic enforcement
- Market manipulation prevention: detect and prevent wash trading, spoofing, and layering patterns
- Best execution documentation: prove you're getting fair prices on every trade
- Record keeping requirements: what data to store, for how long, and in what format
- Algorithm change management: documentation and approval process before modifying live strategies
- Incident response plan: what to do when the algorithm malfunctions or causes unintended trades
- Periodic review schedule: monthly, quarterly, and annual compliance checks and audits
- Tax lot tracking and reporting: capital gains calculation, wash sale rules, and tax document generation

Format as a compliance framework document with control specifications, monitoring checklists, and audit trail requirements.

My trading: [DESCRIBE YOUR TRADING ACTIVITY, JURISDICTION, ACCOUNT TYPE, TRADING VOLUME, AND SPECIFIC REGULATORY CONCERNS]"

These 15 prompts give you what Wall Street's best quant desks charge a fortune for:

→ Strategy design ($30,000 quant consulting)
→ Backtesting engine ($25,000 research infrastructure)
→ Risk management ($40,000 risk systems build)
→ Alpha signal research ($50,000 Citadel-level research)
→ Market making framework ($35,000 trading system design)
→ Factor models ($28,000 AQR-style research)
→ Statistical arbitrage ($32,000 D.E. Shaw methodology)
→ Macro trading strategy ($25,000 Bridgewater-level design)
→ Data pipeline ($22,000 Bloomberg-grade engineering)
→ Execution algorithms ($30,000 Virtu-level development)
→ ML trading models ($45,000 Point72-level ML research)
→ Portfolio optimization ($28,000 Man Group methodology)
→ Live trading system ($50,000 Millennium-grade infrastructure)
→ Factor backtesting ($20,000 Dimensional-level research)
→ Compliance framework ($35,000 Goldman regulatory consulting)

Total quant value: $495,000+
Your cost with Claude: $0.

Wall Street's biggest edge was always talent and technology.

Now you have both.

Copy. Paste. Trade smarter.

Follow me @heynavtoor for more AI prompts that level the playing field.

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