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BREAKING: AI can now build ML models like Goldman Sachs' AI trading desk (for free). Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later)
1/ Time Series Forecasting Model You are a Quantitative Researcher at Goldman Sachs Global Markets. I need a complete time series forecasting model for [STOCK/ASSET]. Please provide: - Data preprocessing: How to clean price data and handle missing values - Feature engineering: Technical indicators (moving averages, RSI, MACD, Bollinger Bands) - Model selection: Compare ARIMA, LSTM neural networks, and Prophet models - Training approach: Train-test split ratios and cross-validation strategy - Performance metrics: MAE, RMSE, directional accuracy for predictions - Backtesting framework: How to test strategy on historical data - Risk management: Stop-loss rules and position sizing based on confidence - Implementation code: Python pseudocode with library recommendations Format as quantitative research report with model specifications and expected accuracy. Asset: [DESCRIBE STOCK/CRYPTO/COMMODITY, TIME PERIOD, DATA SOURCE]
2/ Mean Reversion Trading Strategy You are a VP of Quantitative Trading at JP Morgan's Systematic Trading desk. I need a mean reversion algorithm for [MARKET/ASSET]. Please provide: - Statistical foundation: Z-score calculation and standard deviation bands - Entry signals: When price deviates X standard deviations from mean - Exit signals: When price returns to mean or stop-loss triggers - Pair selection: How to find correlated assets for pairs trading - Cointegration testing: Statistical tests to validate pair relationships - Position sizing: Kelly Criterion or fixed-fraction approach - Risk parameters: Maximum drawdown limits and exposure caps - Backtesting results: Expected Sharpe ratio and win rate over 3+ years Format as algorithmic trading strategy document with entry/exit rules. Market: [DESCRIBE ASSET CLASS, TIMEFRAME, TRADING STYLE]
3/ Sentiment Analysis Trading Model You are a Machine Learning Engineer at Citadel's NLP trading team. I need a sentiment-based trading model for [STOCKS/SECTOR]. Please provide: - Data sources: Twitter, Reddit, news APIs, earnings call transcripts - Sentiment scoring: How to rate text as bullish/neutral/bearish (-1 to +1 scale) - NLP preprocessing: Tokenization, stop word removal, entity recognition - Model architecture: BERT, FinBERT, or custom transformer for financial text - Signal generation: How sentiment changes trigger buy/sell decisions - Volume weighting: Adjusting for tweet/article volume and source credibility - Lag analysis: Time delay between sentiment spike and price movement - Performance tracking: Correlation between sentiment and actual returns Format as machine learning model specification with training pipeline. Sector: [DESCRIBE STOCKS, SENTIMENT SOURCES, TARGET RETURNS]
4/ Portfolio Optimization Algorithm You are a Portfolio Manager at BlackRock's Systematic Strategies group. I need a portfolio optimization model for [ASSET UNIVERSE]. Please provide: - Modern Portfolio Theory: Efficient frontier calculation with mean-variance optimization - Sharpe ratio maximization: Finding optimal risk-adjusted return portfolio - Constraints definition: Sector limits, individual position caps, liquidity requirements - Covariance matrix: How assets move together (correlation and volatility) - Rebalancing rules: When and how much to adjust positions - Transaction costs: Incorporating trading fees and slippage into optimization - Risk budgeting: Allocating risk across assets based on contribution to portfolio variance - Scenario testing: How portfolio performs in market crash, rally, or sideways conditions Format as portfolio construction framework with allocation percentages. Portfolio: [DESCRIBE ASSETS, RISK TOLERANCE, CONSTRAINTS]
5/ Machine Learning Feature Selection You are a Senior Quant at Two Sigma's Research Platform. I need a feature engineering pipeline for [TRADING STRATEGY]. Please provide: - Raw features: Price, volume, volatility, bid-ask spread, market depth - Derived features: Returns, log returns, rolling statistics, momentum indicators - Alternative data: Satellite imagery, web traffic, credit card transactions - Feature importance: Which variables actually predict price movements - Dimensionality reduction: PCA or factor models to reduce feature count - Feature correlation: Removing redundant features that don't add information - Forward-looking bias: Ensuring no data leakage from future into training - Feature stability: Which features remain predictive across different market regimes Format as feature engineering documentation with correlation matrix. Strategy: [DESCRIBE TRADING APPROACH, PREDICTION TARGET, DATA AVAILABLE]
6/ High-Frequency Trading Signal Detection You are an Algorithmic Trader at Virtu Financial's Market Making desk. I need a microstructure-based signal system for [LIQUID ASSETS]. Please provide: - Order book analysis: Bid-ask spread, depth imbalance, order flow toxicity - Tick data processing: How to handle millisecond-level price updates - Signal triggers: Imbalances, large orders, quote stuffing detection - Execution logic: Market orders vs. limit orders vs. hidden orders - Latency requirements: Infrastructure needs for sub-10ms execution - Slippage estimation: Expected cost of trading at different sizes - Market impact: How your orders move the price and how to minimize it - Profitability calculation: Edge per trade minus costs (commissions, exchange fees) Format as high-frequency trading playbook with signal specifications. Assets: [DESCRIBE LIQUID INSTRUMENTS, EXCHANGE, HOLDING PERIOD]
7/ Risk Management & VaR Model You are a Risk Manager at Morgan Stanley's Quantitative Risk group. I need a Value at Risk model for [PORTFOLIO/STRATEGY]. Please provide: - VaR calculation: Historical simulation, parametric, or Monte Carlo approach - Confidence level: 95% or 99% probability of maximum loss - Time horizon: Daily, weekly, or monthly VaR estimation - Stress testing: How portfolio performs in 2008 crisis, COVID crash scenarios - Expected Shortfall: Average loss when VaR threshold is breached - Greeks calculation: Delta, gamma, vega for options portfolios - Correlation breakdown: How individual positions contribute to total risk - Risk limits: Position limits, leverage caps, concentration restrictions Format as risk management framework with loss scenario projections. Portfolio: [DESCRIBE HOLDINGS, LEVERAGE, RISK APPETITE]
8/ Options Pricing & Greeks Model You are a Derivatives Trader at Citadel Securities' Options desk. I need an options pricing and hedging model for [UNDERLYING ASSET]. Please provide: - Black-Scholes model: Theoretical price calculation with assumptions - Implied volatility: Extracting market's volatility expectation from option prices - Greeks computation: Delta, gamma, theta, vega, rho for risk management - Volatility smile: How implied vol changes across strike prices - Delta hedging: How many shares to hold to be market-neutral - Gamma scalping: Profiting from volatility through dynamic hedging - Option strategies: Spreads, strangles, iron condors with P&L profiles - Scenario analysis: How position performs if stock moves ±5%, ±10% Format as options trading manual with pricing formulas and hedge ratios. Underlying: [DESCRIBE STOCK/INDEX, OPTION TYPE, EXPIRATION]
9/ Pairs Trading Cointegration Model You are a Statistical Arbitrage Trader at Renaissance Technologies. I need a pairs trading model for [CORRELATED ASSETS]. Please provide: - Pair selection: Finding stocks that move together historically - Cointegration test: Augmented Dickey-Fuller test for statistical relationship - Spread calculation: Price difference or ratio between the two assets - Z-score threshold: Entry when spread is 2+ standard deviations from mean - Mean reversion speed: Half-life of spread returning to equilibrium - Position sizing: Dollar-neutral or beta-neutral pair construction - Exit rules: Close position when spread returns to mean or hits stop-loss - Risk monitoring: What if cointegration breaks down during holding period Format as statistical arbitrage strategy with quantitative entry/exit criteria. Pairs: [DESCRIBE ASSET PAIR, SECTOR, RELATIONSHIP TYPE]
10/ Machine Learning Backtesting Framework You are a Quantitative Developer at AQR Capital's Research Infrastructure team. I need a robust backtesting system for [TRADING STRATEGY]. Please provide: - Data pipeline: Historical price data ingestion and storage - Signal generation: How strategy produces buy/sell/hold decisions - Transaction simulation: Market orders, limit orders, realistic fill assumptions - Cost modeling: Commissions, slippage, market impact, borrowing costs - Performance metrics: Sharpe ratio, max drawdown, win rate, profit factor - Overfitting detection: Walk-forward testing, out-of-sample validation - Regime analysis: How strategy performs in bull, bear, sideways markets - Production readiness: Code structure, error handling, monitoring dashboards Format as backtesting specification document with validation procedures. Strategy: [DESCRIBE TRADING LOGIC, UNIVERSE, FREQUENCY]
11/ Reinforcement Learning Trading Agent You are an AI Researcher at JP Morgan's Machine Learning Center of Excellence. I need a reinforcement learning agent for [TRADING TASK]. Please provide: - Environment setup: State space (prices, positions, cash), action space (buy/sell/hold) - Reward function: Profit minus transaction costs minus risk penalty - RL algorithm: Deep Q-Learning, PPO, or Actor-Critic approach - Neural network architecture: Input layers, hidden layers, output layer specifications - Training approach: Episodes, experience replay, exploration vs. exploitation - Hyperparameter tuning: Learning rate, discount factor, batch size optimization - Performance benchmarks: Compare to buy-and-hold and simple moving average strategies - Risk constraints: Maximum position size, drawdown limits built into reward Format as reinforcement learning project specification with training plan. Task: [DESCRIBE ASSET, GOAL, TRAINING DATA PERIOD]
12/ Factor Investing Model You are a Quantitative Portfolio Manager at AQR's Factor Investing group. I need a multi-factor model for [EQUITY UNIVERSE]. Please provide: - Factor definitions: Value (P/E, P/B), momentum (12-month return), quality (ROE, debt ratio) - Factor scoring: Ranking stocks within universe on each factor - Weight calculation: Combining multiple factors into single composite score - Portfolio construction: Long top quintile, short bottom quintile for each factor - Rebalancing frequency: Monthly, quarterly, or annual turnover - Capacity analysis: How much capital can strategy absorb before returns degrade - Factor timing: When to overweight/underweight certain factors - Attribution analysis: Which factors drove returns in each period Format as factor investing strategy document with stock rankings. Universe: [DESCRIBE STOCK UNIVERSE, FACTORS, TARGET RETURN]
Each of these models replaces work that costs: - Junior Quant: $180K/year - Senior Researcher: $300K/year - VP Quant Trader: $400K+/year Goldman Sachs-level quantitative models in 30 minutes instead of 30 days. Copy any prompt. Replace the brackets. Get Wall Street quant quality. No PhD in physics needed.
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