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Estimating efficient frontier and constructing portfolio by regularized mean-variance optimizaton

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Portfolio-constructor

  • Constructs portfolios by constrained quadratic optimization --- minizing volatility, maxizing returns, and reducing over-fitting by encouraging to equally invest in all positions
  • Data is sourced from Yahoo finance.

Screenshot

  1. Frontend tested in Chrome 44 and Firefox 39
  • Some D3.js animation is turned off in Firefox.
  1. Backend written in Python 3.4, requires the following libraries:
  • Cvxopt 1.1.7
  • Flask 0.10.1
  • Numpy 1.9.2
  • Pandas 0.16.2
  1. Get started
  • Run "python3 index.py" in terminal.
  • Open 127.0.0.1:5000 in a browser.
  • Select start- and end-date; add more tickers.
  • Click "Quote" to pull data and fit model.
  • Fit the model according to your risk-appetite.
  • Click red circles in the panel of "efficient frontier" to get constructed portfolios.
  1. (Over-)simplification about this constructor
  • Reinvest dividends.
  • Ignore tax, commission fees, price spreads, and price fluctuation in a day.

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Estimating efficient frontier and constructing portfolio by regularized mean-variance optimizaton

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