Tearsheets - QSTrader



  • Re: Getting to portfolio performance data.

    Since pyfolio is iffy with its tear sheets and QSTrader's tearsheet seems to be pretty nice, is there any update on this?

    I find tearsheets, esp monthly PnL's to be helpful. I am not that experienced but would gladly help either test or code some of this port unleass this is already done?



  • @Osofuego Hi, I googled the QST tearsheet. Obviously would save time to just be able to connect to this. But also, it wouldn't be that hard for us to code our own. Basic charts and basic stats. What do most people do for viewing performance figures currently?



  • I'm just wondering what you mean by pyfolio tear-sheets are 'iffy'?



  • @CptanPanic
    The output is rather clunky and provides so much useless information. Also, the API has changed and the integration with backtrader is now broken from what I can tell.

    @Richard-O-Regan I currently use some of the backtrader stats but find they are missing some useful things (ie. returns by month) and bm performance.


  • administrators

    @Osofuego said in Tearsheets - QSTrader:

    returns by month

    Use the TimeReturn analyzer with bt.TimeFrame.Months. You can also calculate weekly or daily returns if you wish. Or LogReturnsRolling which does the same but using a logarithmic approach.

    Docs - Analyzers Reference



  • Thx @backtrader for the guidance.

    I have adapted the package monthly_returns to backtrader using backtraders TimeReturn to create a heatmap of monthly returns. Maybe this will help somebody.

    Do you know if pyfolio is going to be re-integrated in backtrader anytime soon? Thx for all your work!

    I have only included the relevant parts (imports, adding analyzer, and then the code i adapted).

    import matplotlib.pyplot as plt
    import seaborn as sns
    import collections
    import pandas as pd
    
    ......
    #add analyzer
    cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='myreturntme',timeframe=bt.TimeFrame.Months)
    .....
    
    #Get analyzer analysis
    tmereturn = strat.analyzers.myreturntme.get_analysis()
    
    
    ....
    # empty list for datetime index
        lst_indx = []
        # empty list for monthly retuns
        lst_mnth_ret = []
    
        #iterate through returns
        for key, value in tmereturn.items():
            lst_indx.append(key)
            lst_mnth_ret.append(value)
    
    
        #Create dataframe for returns
        df_ret_mnth = pd.DataFrame(lst_mnth_ret, index=lst_indx)
        #Change label of column
        df_ret_mnth.columns = ['Returns']
        #multiply returns by 100 
        df_ret_mnth['Returns'] = df_ret_mnth['Returns'] *100
        #Add year month info
        df_ret_mnth['Year'] = df_ret_mnth.index.strftime('%Y')
        df_ret_mnth['Month'] = df_ret_mnth.index.strftime('%b')
        # make pivot table
        df_ret_mnth = df_ret_mnth.pivot('Year', 'Month', 'Returns').fillna(0)
        df_ret_mnth = df_ret_mnth[['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                           'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']]
    
        #Get Plot size
        size = list(plt.gcf().get_size_inches())
        figsize = (size[0], size[0] // 2)
        plt.close()
        #Create subplot
        fig, ax = plt.subplots(figsize=figsize)
        #create heatmap
        ax = sns.heatmap(df_ret_mnth, ax=ax, annot=True,
                         annot_kws={"size": 10}, fmt="0.2f", linewidths=0.5,
                         square=False, cbar=True, cmap='RdYlGn')
        ax.set_title('Monthly Returns (%)', fontsize=12, color='black', fontweight="bold")
    
        fig.subplots_adjust(hspace=0)
        plt.yticks(rotation=0)
        plt.show()
        plt.close()
    

    Here is the result 0_1506527091752_Backtrader Heatmap.png


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