analysis.py 文件源码

def assess_portfolio(sd = dt.datetime(2008,1,1), ed = dt.datetime(2009,1,1), \
    syms = ['GOOG','AAPL','GLD','XOM'], \
    allocs=[0.1,0.2,0.3,0.4], \
    sv=1000000, rfr=0.0, sf=252.0, \
    gen_plot=False):

    # Read in adjusted closing prices for given symbols, date range
    dates = pd.date_range(sd, ed)
    prices_all = get_data(syms, dates)  # automatically adds SPY
    prices = prices_all[syms]  # only portfolio symbols
    prices_SPY = prices_all['SPY']  # only SPY, for comparison later

    portVal = get_portfolio_value(prices, allocs, sv)
    cr, adr, sddr, sr = get_portfolio_stats(portVal, rfr, sf)

    # Compare daily portfolio value with SPY using a normalized plot
    if gen_plot:
        # add code to plot here
        df_temp = pd.concat([portVal, prices_SPY], keys=['Portfolio', 'SPY'], axis=1)
        plot_normalized_data(df_temp,'Daily portfolio value and SPY',\
                             'date', 'normalized price')
#        ( df_temp / df_temp.values[0,:] ).plot()

    # Add code here to properly compute end value
    ev = portVal[-1]

    return cr, adr, sddr, sr, ev