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{
"examples": [
{
"task": "Create a Monte Carlo simulation for stock price forecasting",
"domain": "quantitative_analysis",
"input": {
"historical_data": "3 years of daily closing prices",
"forecast_period": "30 days",
"volatility_method": "historical"
},
"output": {
"code": """import numpy as np\nimport pandas as pd\n\ndef monte_carlo_simulation(prices, days=30, simulations=1000):\n # Calculate returns\n returns = prices.pct_change().dropna()\n mean_return = returns.mean()\n std_dev = returns.std()\n \n # Generate random returns\n random_returns = np.random.normal(mean_return, std_dev, (days, simulations))\n \n # Calculate future prices\n last_price = prices.iloc[-1]\n future_prices = last_price * (1 + random_returns).cumprod()\n \n return future_prices\n""",
"analysis": {
"confidence": 0.85,
"risk_profile": "moderate",
"computational_complexity": "medium"
}
}
},
{
"task": "Optimize a portfolio using mean-variance optimization",
"domain": "quantitative_analysis",
"input": {
"assets": ["AAPL", "GOOGL", "MSFT", "AMZN"],
"risk_tolerance": 0.1,
"expected_returns": [0.12, 0.15, 0.10, 0.13]
},
"output": {
"code": """import numpy as np\nimport cvxpy as cp\n\ndef optimize_portfolio(returns, risk_tolerance):\n n = len(returns)\n weights = cp.Variable(n)\n \n # Objective: maximize return\n objective = cp.Maximize(returns @ weights)\n \n # Constraints: sum of weights = 1, risk <= tolerance\n constraints = [cp.sum(weights) == 1,\n cp.quad_form(weights, np.cov(returns)) <= risk_tolerance]\n \n prob = cp.Problem(objective, constraints)\n prob.solve()\n \n return weights.value\n""",
"analysis": {
"confidence": 0.9,
"risk_profile": "low",
"computational_complexity": "high"
}
}
}
]
}