Finovax Quant Internship

Seven research projects spanning signals, stat arb, portfolio construction, and cross-sectional analysis

2025-06 — 2025-08
PythonPandasNumPyBacktraderPlotlystatsmodelsyfinance

A systematic research internship covering the full quantitative workflow — from data-source diligence and technical signals to portfolio construction, stress testing, and cross-sectional fundamental analysis. Delivered seven independent projects processing 500+ S&P 500 tickers across 3+ years of daily data, each with notebook-centric analysis, interactive visualizations, and executive-summary PDF reports.

Key Highlights

  • Pairs Trading: market-neutral stat arb on S&P 500 using rolling quarterly Engle–Granger cointegration, ±2σ spread thresholds, dollar-neutral legs — no look-ahead bias
  • Risk Parity (All Weather): Ray Dalio-style allocation across SPY, TLT, GLD, TIP, BIL with 126-day rolling inverse-volatility weights and stress testing across GFC, COVID, and 2022 drawdowns
  • Benchmarking: risk-parity vs 60/40 vs 100% SPY — Sharpe, Calmar, max drawdown, rolling comparisons with executive-style report
  • Nasdaq Breakout Detection: systematic uptrend identification across Nasdaq-100 with hypothesis testing (t-tests, p-values) proving statistical significance of post-breakout moves
  • Technical Indicator Analysis: 40+ indicators (volume, momentum, volatility, trend) on AAPL using Backtrader with interactive Plotly overlays
  • Market Data Infrastructure: evaluated Alpha Vantage, Tiingo, IEX Cloud, Twelve Data, Polygon as resilient alternatives to yfinance; built Python abstraction layer
  • US Market Sector Study: cross-sectional analysis of all 11 GICS sectors — sector-specific valuation metrics (P/E, P/B, dividend yield, margins), top-5 by market cap, multi-dimensional ranking with exportable CSV