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    Binance Alpha Quant & Risk Engine

    1

    Institutional market regime detection and VaR/CVaR risk analytics for Binance crypto pairs.

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    Binance Alpha Quant & Risk Engine

    Binance Alpha Quant & Risk Engine

    Example session with this skill installed

    Analyze the current BTC/USDT market regime and give me a 99% VaR risk report based on recent volatility.

    • Read your context and instructions
    • Compiled the binance alpha quant

    I have analyzed the BTC/USDT pair using the Binance MCP server. The market is currently exhibiting a Volatility Expansion regime following a breakout from a 7-day mean-reverting range.

    BTC/USDT Quantitative Risk Report

    MetricValueInterpretation
    Market RegimeVolatility ExpansionHigh momentum with increasing ATR; trend following favored.
    Orderbook Imbalance+12.4% (Buy Side)Heavy bid support within 0.5% of mid-price.
    1-Day VaR (95%)4.21%$4,210 risk per $100k at 95% confidence.
    1-Day VaR (99%)6.88%$6,880 risk per $100k at 99% confidence.
    Expected Shortfall (CVaR)8.15%Average loss in the worst 1% of scenarios.
    Max Drawdown (24h)3.45%Peak-to-trough decline in the current session.
    Sharpe Ratio (Daily)2.14Strong risk-adjusted returns in the current window.

    Next steps

    • Monitor the 99% VaR threshold; if BTC exceeds 7% volatility, consider reducing position size.
    • Audit depth bands again in 4 hours to see if buy-side liquidity remains stable during the expansion.
    • To execute trades based on this regime with slippage protection, consider the Binance Smart Execution skill.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Manual technical analysis is too slow for volatile crypto markets. This skill provides an institutional-grade quant layer for Binance, automating market regime classification and portfolio risk math. It moves beyond basic price alerts to identify structural market shifts and liquidity imbalances.

    What it does

    • Regime Detection classifies price action into Trending Bull, Trending Bear, Mean-Reverting Range, or Volatility Expansion.
    • Orderbook Analytics evaluates bid/ask volume skew across the top 20 depth levels to detect hidden pressure.
    • Risk Calculation computes 95% and 99% Value at Risk (VaR) and Expected Shortfall (CVaR).
    • Health Metrics generates Sharpe/Sortino ratios and max drawdown analysis for risk-adjusted performance.

    How it works

    1. Data Ingestion: Fetch 24hr ticker data and klines using the krmaazha-binance-mcp server.
    2. Structural Analysis: Classify the current market regime based on trend and volatility metrics.
    3. Liquidity Audit: Analyze order book depth bands to identify slippage risks or imbalance.
    4. Risk Reporting: Output a quantitative health check with VaR and CVaR metrics.

    Frameworks & tools

    Built for the krmaazha-binance-mcp server. Requires uvx for transport. All math utilizes high-precision decimal typing.

    Why this beats prompting it yourself

    Generic LLM prompts often hallucinate financial math or miss subtle regime shifts. This skill enforces a structured quantitative protocol and uses direct orderbook data to ensure risk metrics like VaR are calculated with precision rather than estimated.

    Use cases

    • Identify when a market has shifted from a range into a high-volatility expansion.
    • Audit bid/ask imbalance before entering a large position to minimize slippage.
    • Calculate daily portfolio risk budgets during periods of high market stress.

    Known limitations

    Does not support trade execution or private key management. Analysis is limited to data available through the Binance MCP server.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Krmaazha Binance (Uvx Krmaazha Binance Mcp) MCP
    Python 3.10+
    Krmaazha Binance MCP
    uv (Python tool runner)

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