Portfolio Audits
BlockMind's Portfolio Audits feature provides an in-depth, quantitative analysis of a user's cryptocurrency portfolio. This service is designed to offer sophisticated insights into portfolio risk, diversification, and optimal asset allocation strategies, leveraging advanced financial modeling techniques.
Volatility and Covariance Calculation
Central to the portfolio audit is the calculation of Exponentially Weighted Moving Average (EWMA) mean returns and the covariance matrix of asset returns.
This process involves:
Extracting Price Series: 120 days of historical price data for each asset in the portfolio is extracted.
Computing Log Returns: Simple logarithmic returns are calculated for each asset's price series.
EWMA Weight Vector: An EWMA weight vector is constructed to give more weight to recent observations.
EWMA Means: The EWMA mean return for each asset is computed.
EWMA Covariance Matrix: A covariance matrix is calculated, reflecting the interdependencies between asset returns.
Portfolio Optimization
Utilizing the calculated mean returns and covariance matrix, the system performs portfolio optimization to determine optimal asset weights for various strategies, such as minimum variance and maximum Sharpe ratio.
This involves:
Baseline Volatility: Bitcoin's volatility is used as a baseline for comparison.
Altcoin Isolation: The portfolio's altcoins are identified and isolated for specific analysis.
Covariance Matrix Inversion: The covariance matrix for the altcoins are inverted using the Gauss-Jordan method.
Minimum Variance and Maximum Sharpe Ratio Weights: Optimal weights are computed for portfolios aiming to minimize variance (volatility) or maximize the Sharpe ratio (risk-adjusted returns).
Portfolio Optimization Logic:
The system calculates two primary portfolio configurations: one that minimizes variance (risk) and another that maximizes the Sharpe ratio (risk-adjusted return).
Weight Dampening and Sharpening
To refine the calculated optimal weights, a dampening and sharpening process is applied. This involves:
Smoothing and Temperature: Parameters α (smoothing) and T (temperature) are introduced to control the distribution of weights.
Softmax Transformation: The weights are transformed using a Softmax function, ensuring they sum to one.
Sharpening: Weights are raised to a power and then renormalized, which can either flatten or sharpen the distribution, depending on the power applied. This allows for fine-tuning the concentration of the portfolio. The power of which the weights are raised to depends dynamically on the number of coins sent into the optimization engine.
Report Generation and Delivery
The final stage involves generating a comprehensive portfolio analysis report. This report includes key metrics such as average daily gain, daily swing, Sharpe ratio, diversification score, effective bets, and worst-case loss scenarios. The report also identifies top risk contributors and includes various charts for visual representation of the portfolio's characteristics.
These reports are designed to be delivered in a structured format, enabling users to gain a clear understanding of their portfolio's performance and risk profile.
Last updated