marketsim.py 文件源码

def testcode_marketsim(symbol = 'ML_based', base_dir = './orders/', \
                       sv = 100000, leverLimit = True, verbose = True):
###    Use one of the order folders below    ###
#    of = "./orders/benchmark.csv"
#    of = "./orders/bestPossibleStrategy.csv"
#    of = "./orders/rule_based.csv"
#    of = "./orders/ML_based.csv"
    of = symbol_to_path(symbol, base_dir)

#    sv = 100000 # starting value of portfolio, i.e. initial cash available

    # Process orders
    portVals = compute_portvals(of, sv, leverLimit)
    if isinstance(portVals, pd.DataFrame):
        portVals = portVals[portVals.columns[0]] # just get the first column as a Series
    else:
        "warning, code did not return a DataFrame"

    start_date = portVals.index[0]
    end_date = portVals.index[-1]
    pricesSPX = get_data(['$SPX'], pd.date_range(start_date, end_date))
    pricesSPX = pricesSPX['$SPX']

    cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = get_portfolio_stats(portVals, \
                                                daily_rf = 0, samples_per_year = 252)
    cum_ret_SPY, avg_daily_ret_SPY, std_daily_ret_SPY, sharpe_ratio_SPY = \
            get_portfolio_stats(pricesSPX, daily_rf = 0, samples_per_year = 252)

    # Compare portfolio against $SPX
    if verbose == True:
        dfTemp = pd.concat([portVals, pricesSPX], axis = 1, keys = ['portfolio', '$SPX'])
        plot_normalized_data(dfTemp,'', '', '')

        print "\nDate Range: {} to {}".format(start_date.date(), end_date.date())
        print
        print "Sharpe Ratio of Fund: {}".format(sharpe_ratio)
        print "Sharpe Ratio of SPY : {}".format(sharpe_ratio_SPY)
        print
        print "Cumulative Return of Fund: {}".format(cum_ret)
        print "Cumulative Return of SPY : {}".format(cum_ret_SPY)
        print
        print "Standard Deviation of Fund: {}".format(std_daily_ret)
        print "Standard Deviation of SPY : {}".format(std_daily_ret_SPY)
        print
        print "Average Daily Return of Fund: {}".format(avg_daily_ret)
        print "Average Daily Return of SPY : {}".format(avg_daily_ret_SPY)
        print
        print "Final Portfolio Value: {}".format(portVals[-1])

    return cum_ret, portVals