result_plotter.py 文件源码

def plot_backtest_result_curve(log_file):
    data_dict = read_result_from_log(log_file)
    #benchmark_dates, benchmark_value = sort_dict_buy_key(data_dict)
    portfolio_history_data = pd.DataFrame(data_dict).T
    portfolio_history_data.columns = ['total_value', 'cash', 'stock_value', 'bench_value']

    dates_ = list(portfolio_history_data.index)
    dates_.pop()
    dates = []
    for d in dates_:
        if d == None: 
            d = config.start_date

        date = datetime.datetime.strptime(d, '%Y-%m-%d').date() 
        dates.append(date)
    #dates = [datetime.datetime.strptime(d, '%Y-%m-%d').date() for d in dates if d != None else config.start_date]

    total_value = list(portfolio_history_data["total_value"].values)
    cash = list(portfolio_history_data["cash"].values)
    cash_value = list(portfolio_history_data["stock_value"].values)
    bench_value = list(portfolio_history_data["bench_value"].values)
    total_value.pop()
    bench_value.pop()
    # xaxis tick formatter
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
    # xaxis ticks
    #plt.gca().xaxis.set_major_locator(mdates.MonthLocator())

    # figure title and legend
    plt.gca().legend()
    plt.gca().set(title="event factor backtesting porfolio value",
                  ylabel = "porfolio total value",
                  xlabel = "backtesting time")
    plt.locator_params(axis='x', nticks=10)
    plt.plot(dates, total_value)
    plt.plot(dates, bench_value)
    plt.gcf().autofmt_xdate()
    plt.show()

#data = read_result_from_log(sys.argv[1])
#plot_backtest_result_curve(data)