from datetime import datetime from bokeh.plotting import figure, output_file, show from myToolbox import downloadStock, firstDayOfData, plotTimeSeriesDF, getYearlyReturn, solveMarkowitzWithShorting, getStats, calculateWeightedPortfolio, getWeeklyReturn, getWeeklyLogReturn # Pick your stocks stockNameVec = ['BND', 'O', 'SPLV'] # This one is good at 10%! # Pick the year to start your analysis (probably corresponds to earliest data) y1 = 2008 # Make this zero to turn off the lower bound subDict = dict() for curName in stockNameVec: # Download the data stock = downloadStock(curName, y1,1,1,2010,0,0) # Strip out everything except for the Adjusted Close Price and put it in a dictionary as a Series subDict[curName] = pd.Series(stock['Adj Close'].as_matrix(), index=stock.Date) tickerDF = pd.DataFrame(subDict) # Then make a new data frame with just the adjusted closes. firstDayOfData(tickerDF) filename = "ticker.html" plotTitle = "Stocks to Plot" plotTimeSeriesDF(filename, plotTitle, tickerDF) # Translate the daily prices into yearly rates of return subDict = dict() for curName in tickerDF: yearRecord, rate = getYearlyReturn(tickerDF[curName], 2000, 2016)
