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② One strategy in this book returned 2.3× the S&P 500 on a risk-adjusted basis over 5 years.
Fully coded in Python. Yours to run today.
The 2026 Playbook — 30+ backtested strategies,
full code included, ready to deploy.
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⑤ Most quant courses teach you to watch. This one makes you build.
Live. Weekly. With feedback on your actual code.
The AlgoEdge Quant Finance Bootcamp — 12 weeks of stochastic models, Black-Scholes, Heston, volatility surfaces, and exotic options. Built from scratch in Python.
Not pre-recorded. Not self-paced. Live sessions, weekly homework, direct feedback, and a full code library that's yours to keep.
Cohort size is limited intentionally — so every question gets answered.
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Premium Members – Your Full Notebook Is Ready
The complete Google Colab notebook from today’s article (with live data, full Hidden Markov Model, interactive charts, statistics, and one-click CSV export) is waiting for you.
Preview of what you’ll get:
Inside the Strategy Lab
Install and imports — installs all 6 libraries (yfinance, xlsxwriter, pygal, pycirclize, radarchart-py, plotly) plus core imports for numpy, pandas, matplotlib, and plotly
Basic stock comparison (Matplotlib) — fetches live P/E, ROE, Debt/Equity, Profit Margin, and Revenue Growth for MSFT, AAPL, META, and NVDA via yfinance and plots one raw polygon per stock on a polar axis
Optimized stock comparison (Matplotlib) — fetches EPS, Beta, Current Ratio, Revenue/Share, and Earnings Growth for ORCL, AMZN, GOOG, and TSLA, applies min-max normalization (0–1 scale), and plots the normalized comparison for fairer cross-stock analysis
Portfolio analysis via XlsxWriter — writes an Equal-Weighted vs Mean-Variance portfolio comparison across Return, Volatility, Sharpe, CVaR, and Omega Ratio directly into an Excel
.xlsxfile with a filled radar chart, plus a Matplotlib inline previewPerformance measures via Pygal — renders an SVG radar chart comparing EW, RP, and 60/40 portfolios across 5 metrics (1999–2021 including COVID), plus a Matplotlib backup for guaranteed inline display
Multivariate visualization with radar_chart — defines a full custom
radar_chart_fnhelper with configurable markers, grid, fill, and legend, then plots VAMA vs VAMA1 vs VAMA2 backtesting results across 8 performance axes, plus a simpler agricultural portfolio weights exampleFinancial radar charts with pyCirclize — two charts: NVDA vs AMD on 5 profitability ratios (2023), and a 4-year MSFT income statement (2022–2025) with per-year line styling (dotted for 2025, solid for prior years) and square markers
DuPont analysis with Plotly — interactive
line_polarradar comparing AAPL vs MSFT on the 6-component Extended DuPont framework (Interest Burden, Tax Burden, Operating Margin, Asset Turnover, Equity Multiplier, ROE) for FY 2024Options Greeks comparison — Plotly
Scatterpolarchart comparing Long Call, Long Put, and Iron Condor across Delta, Gamma, Vega, Theta, Rho, and Implied Volatility, with hover tooltips showing strategy name and exact valuesMacroeconomic benchmarking + MA SWOT analysis — radarchart-py comparison of USA/China/India on 4 macro indicators (Oct 2025) with a Matplotlib fallback, followed by a full 10-MA SWOT radar scored 1–5 across Lag Reduction, Smoothness, Adaptivity, Noise Robustness, and Complexity, plus a summary scores table sorted by total
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
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