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financial-modeling

Computes risk metrics, Monte Carlo portfolios and constrained SLSQP optimizations for trading strategies.

6|1|Updated Apr 7, 2026
Search Tags:#backtesting#financial-modeling#monte-carlo#risk-metrics#sharpe-ratio#portfolio-optimization#aster-dex
One-click install
npx skills add https://github.com/kmshihab7878/claude-code-setup --skill financial-modeling-kmshihab7878
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: financial-modeling
Source: https://github.com/kmshihab7878/claude-code-setup/tree/main/skills/financial-modeling
Command: npx skills add https://github.com/kmshihab7878/claude-code-setup --skill financial-modeling-kmshihab7878

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables quantitative traders and analysts to automate performance evaluation, risk measurement, and portfolio construction so manual spreadsheets and ad-hoc scripts no longer limit strategy development.

Core Features & Use Cases

  • Risk Metrics: Compute Sharpe, Sortino, max drawdown, VaR, and conditional VaR for strategy returns.
  • Monte Carlo & Backtesting: Run Monte Carlo portfolio simulations and backtests against historical klines for robust performance estimates.
  • Portfolio Optimization: Perform mean-variance optimization with constrained SLSQP and target-return workflows.
  • DEX Integration: Retrieve market data and positions via Aster DEX MCP endpoints to run live-synced backtests and portfolio state checks.

Quick Start

Backtest a mean-variance portfolio on BTC-USD one-hour klines for the past 500 periods and report total return, sharpe, and max drawdown.

Frequently Asked Questions about financial-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I backtest a quantitative trading strategy using historical price data?▼

Run Monte Carlo portfolio simulations against historical klines to backtest quantitative trading strategies and compute total return, Sharpe, and max drawdown for robust performance estimates.

What is Monte Carlo portfolio simulation and how does it estimate risk metrics?▼

Monte Carlo portfolio simulation generates ensembles of possible return paths from historical inputs to estimate risk metrics like VaR, conditional VaR, and max drawdown for strategy evaluation.

Can I perform mean-variance portfolio optimization with target-return constraints?▼

Mean-variance portfolio optimization applies constrained SLSQP algorithms to historical price returns and covariance inputs to construct efficient portfolios with specific target-return workflows.

Does this portfolio optimization approach work with crypto assets on Aster DEX?▼

Aster DEX MCP endpoints integrate with the portfolio optimization workflow to retrieve live crypto market data and positions, enabling live-synced backtests and portfolio state checks.

What financial risk metrics can I calculate for algorithmic trading returns?▼

Calculate Sharpe ratio, Sortino ratio, max drawdown, Value at Risk (VaR), and conditional VaR to evaluate algorithmic trading strategy performance and measure downside risk exposure.

Do I need historical covariance inputs for quantitative portfolio backtesting?▼

Historical price returns and covariance inputs are required to run constrained SLSQP mean-variance optimization and Monte Carlo ensembles for accurate quantitative portfolio backtesting.

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