Portfolio Object interaction

In [2]: import mpt
In [3]: p = mpt.Portfolio(symbols=["AAPL", "GT", "MMM", "GNW", "KO"], startdate="2005-1-1", enddate="2008-8-23", dbfilename="data/stocks.db")
Adding: AAPL
Adding: GNW
Adding: GT
Adding: KO
Adding: MMM

In [4]: rt = 2.0 # Risk tolerance, equivalent to a 200% annual volatility

In [5]: drt = rt/252.0 # Convert to a daily volatility using trading days per year

In [6]: lb = 0.0 # lower bound of allowed weighting for an asset

In [7]: ub = 1.0 # upper bound of allowed weighting for an asset

In [8]: p.optimize_portfolio(rt=drt, lower_bound_weight=lb, upper_bound_weight=ub)   Optimization completed in [ 5 ] iterations.
Ending weights:
{'MMM': 0.0, 'AAPL': 1.0, 'GT': 0.0, 'KO': 0.0, 'GNW': 0.0}

Optimized Variance: 0.155 and Portfolio Return: 54.093%

In [9]: rt = 0.5 # Risk tolerance, equivalent to a 50% annual volatility

In [10]: drt = rt/252.0 # Convert to a daily volatility using trading days per year

In [11]: del p.port_opt # Clear out the cached optimized portfolio

In [12]: p.optimize_portfolio(rt=drt, lower_bound_weight=lb, upper_bound_weight=ub)
Optimization completed in [ 5 ] iterations.
Ending weights:
{'MMM': 0.0, 'AAPL': 0.79253926811721775, 'GT': 0.0, 'KO': 0.20746073188278225, 'GNW': 0.0}

Optimized Variance: 0.103 and Portfolio Return: 44.936%