temp.py 文件源码

def test_code():
    # this is a helper function you can use to test your code
    # note that during autograding his function will not be called.
    # Define input parameters
    verbose = False
    # # initializating by reading data from files
    data_start = '2007-12-01'
    data_end = '2011-12-31'
    data = get_data(['IBM'], pd.date_range(data_start, data_end))
    data, data['SMA'], data['StDev'] = add_bband(data,N=20)
    data = add_mmt(data)
    data = add_MACD(data)
    # generate the daily return
    data['DRet'] = data.iloc[:,1].slice_shift(-1)/data.iloc[:,1] - 1

    # for testing of bollinger band only
    plt.plot(data.iloc[:,1][20:300])
    plt.plot(data.SMA[20:300]+2*data.StDev[20:300])
    plt.plot(data.SMA[20:300]-2*data.StDev[20:300])
    plt.plot(data.SMA[20:300])
    plt.show()


    # mmt_div = bin_divider(data.mmt)
    # bbp_div = bin_divider(data.bbp)
    # MACD_div = bin_divider(data.MACD)
    #
    # sd = dt.datetime(2008, 1, 1); ed = dt.datetime(2009, 1, 1)
    # temp = data.index[data.index > sd]
    # print temp[0]

    if verbose:
        print 'mmt_div:', mmt_div
        print 'bbp_div:', bbp_div
        print 'MACD_div:', MACD_div
        test = [data.mmt[500],data.bbp[500],data.MACD[500]]
        print test
        s = get_state(test,[mmt_div,bbp_div,MACD_div],1)
        print s
    # # start and end date the learner concerns
    # start_date = '2007-12-31'
    # end_date = '2009-12-31'
    # # generate training data
    # trainX = data[['bband','mmt','MACD']][start_date:end_date].values
    # trainY = data.Y[start_date:end_date].values
    #
    # plt.plot(data.iloc[:,1]['2007-12-31':'2009-12-31'],'r')
    # # adjust prediction Y to scale up with price
    # enlarge = (data.iloc[:,1].max()-data.iloc[:,1].min())/(trainY.max()-trainY.min())
    # plt.plot(data.Y[start_date:end_date]*enlarge+(data.iloc[:,1].min()+data.iloc[:,1].max())/2,'g')
    # plt.show()
    #
    # # orders = generate_order(ana_data)