quant-analyst
Build Trading Strategies and Financial Models
Develop and backtest algorithmic trading strategies with risk metrics and portfolio optimization. Get actionable insights for quantitative finance without requiring deep expertise in financial modeling.
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正在使用「quant-analyst」。 Backtest a momentum strategy on tech stocks
預期結果:
Strategy: Momentum with 12-month lookback, 1-month holding period
Backtest Results (2019-2024):
- Total Return: 124.5% (vs S&P 500: 89.2%)
- Sharpe Ratio: 1.42
- Max Drawdown: -18.3%
- Win Rate: 58%
- Avg Trade: 2.1%
Risk Metrics:
- VaR (95%): -4.2%
- Volatility: 15.8%
Note: Past performance does not guarantee future results.
正在使用「quant-analyst」。 Optimize my portfolio for maximum Sharpe ratio
預期結果:
Optimal Portfolio Allocation:
- AAPL: 25%
- MSFT: 22%
- GOOGL: 18%
- AMZN: 15%
- NVDA: 12%
- JNJ: 8%
Expected Metrics:
- Expected Return: 14.2%
- Expected Volatility: 18.5%
- Sharpe Ratio: 0.68
Efficient Frontier: Available for review
Rebalancing Recommendation: Quarterly
安全審計
安全This is a prompt-only skill with no executable code. The static analyzer found 0 files scanned and 0 potential security issues. The skill provides guidance for quantitative finance tasks using pandas, numpy, and scipy without making any system-level operations. No dangerous patterns detected.
品質評分
你能建構什麼
Strategy Development
Create and backtest new trading strategies with realistic market simulation
Risk Assessment
Evaluate portfolio risk exposure and calculate key risk metrics
Portfolio Optimization
Optimize asset allocation using modern portfolio theory
試試這些提示
Help me backtest a trading strategy. I want to test a [moving average crossover strategy] on [AAPL] stock with data from [2020-2024]. Include transaction costs of [0.1%] and slippage of [0.05%]. Calculate Sharpe ratio, maximum drawdown, and total return.
Calculate the following risk metrics for my portfolio [SPY 60%, AGG 40%]: Value at Risk (VaR) at 95% confidence, Expected Shortfall, Sharpe ratio, and maximum drawdown over the last 3 years. Use historical simulation method.
Perform Markowitz mean-variance optimization for my portfolio with these assets: [AAPL, MSFT, GOOGL, AMZN, JNJ, XOM]. Use 5 years of historical data. Maximize Sharpe ratio with a target return of [8%]. Include the efficient frontier in your analysis.
Build a statistical arbitrage pairs trading strategy between [KO and PEP] using the last 2 years of daily data. Include cointegration testing, hedge ratio calculation, entry/exit signals with z-score thresholds, and backtest results with performance metrics.
最佳實務
- Always use out-of-sample testing to validate strategies before production deployment
- Include realistic transaction costs, slippage, and market impact in all backtests
- Focus on risk-adjusted returns (Sharpe ratio) rather than absolute returns
避免
- Do not overfit strategies to historical data without proper cross-validation
- Avoid using future information (look-ahead bias) in backtesting
- Never skip the separation between research and production code environments