Design and validate quantitative trading strategies for DeFi and crypto derivatives.
npx skills add https://github.com/BarisSozen/claude --skill hft-quant-expert
Please help me install this Agent Skill. Skill: hft-quant-expert Source: https://github.com/BarisSozen/claude/tree/main/.claude/skills/hft-quant-expert Command: npx skills add https://github.com/BarisSozen/claude --skill hft-quant-expert
This Skill provides a framework for designing and validating quantitative trading strategies in DeFi and crypto derivatives, helping users quantify risk and optimize position sizing.
Define a basic strategy: generate a signal from a simple metric, compute a Kelly-based position size, backtest the strategy, and account for costs such as gas and slippage.
High-intent search queries and answers about installing and using this skill.
To size positions for DeFi trading strategies using the Kelly criterion, you calculate optimal capital allocation per asset, typically applying a fractional multiplier like 0.25x to control risk and account for gas fees and slippage costs.
Backtesting crypto derivatives strategies without lookahead bias requires enforcing strict bias checks during validation, analyzing performance metrics like alpha, Sharpe ratio, and drawdowns to ensure realistic strategy expectations.
Generating trading signals for quantitative crypto market analysis requires calculating statistical metrics such as z-scores, Sharpe ratios, and volatility from market data to drive objective entry decisions across DeFi protocols.
Yes, backtesting DeFi strategies accounts for gas fees and slippage in profit calculations through scenario analysis, ensuring cost-aware profit calculations that maintain realistic expectations for crypto derivatives trading.
Risk management rules for multiple crypto assets in a quantitative portfolio include applying fractional Kelly criterion position sizing, monitoring cross-asset risk exposure, and validating strategy performance against survivorship bias to control drawdowns.
Avoid using quantitative trading frameworks for crypto derivatives when transaction costs like gas and slippage consume projected alpha, or when insufficient historical data prevents reliable backtesting and survivorship bias validation.
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