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PasinduUpendra
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quant-finance-strategy-risk

Compute risk metrics, position sizing, and volatility models for trade signals.

Updated Mar 15, 2026
Search Tags:#risk-management#backtesting#position-sizing#regime-detection#quantitative-finance#volatility-modeling
One-click install
npx skills add https://github.com/PasinduUpendra/Binance-Futures-Trading --skill quant-finance-strategy-risk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quant-finance-strategy-risk
Source: https://github.com/PasinduUpendra/Binance-Futures-Trading/tree/main/.github/skills/quant-finance-strategy-risk
Command: npx skills add https://github.com/PasinduUpendra/Binance-Futures-Trading --skill quant-finance-strategy-risk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quantitative finance teams require a unified framework to evaluate trade signals and quantify risk across strategies. This Skill delivers risk metrics, position sizing models, volatility forecasting, regime detection, and backtesting support to inform robust decision-making.

Core Features & Use Cases

  • Risk metrics (Sharpe, Sortino, VaR, CVaR, drawdown) for strategy evaluation
  • Position sizing models (Kelly, half-Kelly, confidence-based sizing)
  • Volatility modeling (GARCH, EWMA) and regime detection
  • Multi-timeframe analysis and correlation checks for risk budgeting
  • Backtesting results interpretation and decision support for deployment
  • Use cases: evaluate new signals, calibrate leverage, compare strategies, and enforce risk budgets

Quick Start

Run a backtest on a proposed signal and output key risk metrics (Sharpe, Sortino, VaR, CVaR) along with a recommended position size for a given balance.

Frequently Asked Questions about quant-finance-strategy-risk

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

FAQPage Schema
How do I calculate risk metrics like Sharpe ratio and VaR for my trading strategy?▼

Backtesting a proposed trade signal outputs key risk metrics including Sharpe, Sortino, VaR, and CVaR, along with a recommended position size for a given balance. This helps evaluate new signals and enforce risk budgets.

What is the best way to determine position sizing using Kelly criterion models?▼

Volatility modeling supports GARCH and EWMA techniques to forecast market volatility. Combined with regime detection, it enables multi-timeframe analysis and correlation checks necessary for effective risk budgeting across trading strategies.

How do I detect market regimes for quantitative finance strategies?▼

Yes, you can compare multiple trading strategies by running backtests and interpreting the resulting risk metrics. This backtesting support enables strategy comparison, leverage calibration, and informed deployment decisions based on quantified risk.

Do I need a specific framework to run quantitative finance backtesting and risk computation?▼

You need a unified framework that integrates risk computation, strategy design, and backtesting modules. The framework requires a SKILL.md with YAML frontmatter and may include optional scripts, references, and assets directories to house necessary tooling.

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