Gas Optimization, Transaction Simulation, and Portfolio Tracking: A Safer DeFi Workflow

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A common misconception in DeFi is that a transaction is safe if the wallet shows a familiar token amount and the gas fee looks reasonable. In reality, low cost and low risk are different properties. A cheap transaction can authorize a malicious contract, route assets through an unexpected call, or leave an approval active long …

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Comparing Open-Source Wallets: Cake Wallet vs Alternatives in Code Transparency and Security Audits

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A developer evaluating wallet software for custody of significant holdings faces a practical puzzle: how to distinguish between claims of security and evidence of security. Marketing materials often describe features such as “open-source” or “non-custodial” without explaining what those terms actually require in terms of code review, update cadence, or third-party verification. The distinction matters …

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Uniswap V3 Myths: What the Protocol Really Does—and Where the Risks Begin

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In a decentralized exchange, the quoted price is not necessarily waiting for a buyer and seller to agree. On Uniswap, the trade itself changes the market. That counterintuitive fact explains both the power and the risk of the Uniswap protocol: liquidity pools provide continuous execution, but every swap interacts with mathematical rules, available capital, network …

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Covid-19 Progression Analysis

Machine Learning, Predictive analytics / By

Covid-19 Progression Analysis Introduction This updates a prior post analyzing the growth rate of Covid-19 infections and deaths by country. The analysis is conducted as of 2020-03-24 The daily growth rate of cumulative confirmed cases and deaths are calculated as: \(G_{confirmed}=growth~rate~confirmed=log(Confirmed_t / Confirmed_{t-1})\) \(G_{deaths}=growth~rate~deaths=log(Deaths_t / Deaths_{t-1})\) Using this formulation, the total growth after \(d\) days …

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II. Probabilistic Programming: Survival Analysis

Actuarial, Machine Learning, Predictive analytics / By

Introduction This is a second article in the series of Probabilistic Programming for Actuarial Science. The first article introduced the Bayesian approach to inferring risk rates and predicting future claims in an actuarial context and built a practical model in Greta, a probabilistic programming language, to conduct the analysis. In a beta binomial model, a …

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I. Probabilistic Programming for Actuarial Science

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Introduction A core task of actuarial analysis is estimation of risk rates for insured events. This includes analyses such as estimating mortality rates, lapse rates, incidence rates, termination rates and so on. In addition to determining best estimates for risk rates, understanding the uncertainty around the best estimates is a critical component of the overall …

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Life Expectancies in the US Worsen (but with some bright spots)

Actuarial / By

The CDC has just published its new set of reports analyzing the trends in mortality and life expectancy from 2016 to 2017. Unfortunately, there has been a decline in overall life expectancies in the US.  Sources of mortality that have shown statistically significant increases in mortality are (from highest to lowest impact): Uninentional Injuries Influenza …

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