Mboya Jeffers - Data Analyst | ContraWork by Mboya Jeffers
Mboya Jeffers

Mboya Jeffers

Data engineer who builds full-stack analytics platforms.

New to Contra

Mboya is ready for their next project!

Cover image for Equity Portfolio Overview
Equity Portfolio Overview
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Cover image for Automated equity portfolio analytics report
Automated equity portfolio analytics report — live performance vs SPY benchmark (Sharpe ratio, Beta, R-squared, annualized return, max drawdown). Macroeconomic environment section pulls directly from FRED: GDP, CPI, unemployment, Fed Funds rate, 10-year yield. All figures source-attributed with pull date. Built with Python — Yahoo Finance + FRED API, fully automated from data pull through PDF generation.
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Cover image for Q2 2026 S&P 500 sector
Q2 2026 S&P 500 sector performance scoreboard — 11 GICS sectors ranked by quarterly return, YTD, 52-week return, annualized volatility, and momentum signal. Technology led Q2 at +45.5%, Utilities lagged at -5.2%. SPY benchmark included for comparison. Momentum classification (Bullish/Neutral/Bearish) based on 20-day vs 50-day MA crossover with price confirmation. Live data pulled from Yahoo Finance. Fully automated — Python handles the data pull, computation, ranking, and PDF generation.
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Cover image for Automated crypto portfolio intelligence report
Automated crypto portfolio intelligence report — 20 digital assets ranked by market cap, weight, and multi-timeframe return (24h, 7d, 30d). Includes executive summary with portfolio-level Sharpe ratio, weighted return, VaR at 95% confidence, and concentration risk flag. Live data pulled from CoinGecko API. Built with Python — fully automated from data pull through PDF generation.
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Cover image for Automated S&P 500 sector performance
Automated S&P 500 sector performance report — 11 GICS sectors ranked by Q2 return, YTD performance, 52-week return, annualized volatility, and momentum signal. Live data pulled from Yahoo Finance. Includes SPY benchmark comparison and Bullish/Neutral/Bearish momentum classification per sector. Delivered as a branded PDF on a quarterly cadence. Built entirely with Python — automated data pull, computation, and report generation. No manual inputs.
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