Framework Philosophy
Probably the most important page of them all. If you read anything, read this. It will save you trillions.
Framework philosophy
We acknowledge three fundamental truths about crypto markets (any markets actually, but we work with crypto):
We CAN NOT reliably predict returns - Token selection and manual market timing are probability games with unfavourable odds
We CAN control costs - Transaction costs, funding rates, and rebalancing expenses are deterministic and highly predictable
We CAN manage risk systematically - Volatility, correlation, and drawdown protection can be quantified and controlled
HyperCroc's framework focuses on what we can control while accepting uncertainty in what we cannot.
Theoretical foundation
Why systematic approach works in DeFi
Despite (and because of) crypto's extreme characteristics, systematic approaches offer advantages:
Emotional Discipline: Crypto's volatility triggers fear and greed. Systematic rules enforce discipline during 40% drawdowns or 300% rallies.
Cost Optimization: With 365 trading days and sub-cent transaction costs on HyperLiquid, systematic rebalancing becomes economically viable at frequencies impossible in TradFi.
Diversification Math: Even with high correlations (0.8), a 5-asset portfolio still provides ~40% volatility reduction vs single-asset exposure. The classical portfolio math still works.
Funding Rate Alpha: Perpetual markets introduce a carry component absent in spot-only strategies. Systematic harvesting of 5-10% APR funding creates uncorrelated returns.
Key crypto-specific adaptations
Shorter Lookback Periods: We use 10/30/60-day momentum vs longer periods to capture crypto's faster regime changes.
Exponential Weighting: All indicators use exponential moving averages (EMA) rather than simple moving averages to give more weight to recent data in fast-moving markets.
Volatility Normalization: All trading signals are scaled by realized volatility to create comparable indicators across assets.
Correlation-Aware Sizing: Position sizes dynamically adjust based on rolling correlations, reducing exposure when correlations spike.
Portfolio optimization for extreme markets
Consider the classic optimization problem: 2 assets, equal expected returns.
Asset 1: 100% annual volatility
Asset 2: 2.5% annual volatility
Correlation: 0.2
Result: Both maximum Sharpe ratio and maximum geometric mean portfolios allocate 100% to the low-volatility asset. This extreme result demonstrates why naive "buy and hold" portfolios of volatile tokens underperform: the volatility drag on geometric returns is severe. Now consider more realistic parameters:
Lending: 5% return, 5% volatility
Tokens: 30% return, 100% volatility
Result: Optimal allocation is only 25% to tokens, 75% to lending.
This is why our flagship portfolio deals with high stablecoin and yield allocations (~70%) and uses leverage selectively on growth assets.
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