Exemple #1
0
    def _plot(self,
              x,
              y,
              labels=None,
              x_label='Return [%]',
              y_label='-',
              title='Backtesting results',
              plot_title='',
              xlim=None,
              ylim=None,
              pres_name=None):

        if pres_name is None:
            pres_name = self.presname
        pres = Presenter(pres_name)
        fig = plt.figure()

        plt.scatter(x, y)
        if xlim is not None:
            plt.xlim(xlim)
        if ylim is not None:
            plt.ylim(ylim)
        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.title(plot_title)
        plt.grid()
        img_path = 'temp.png'
        plt.savefig(img_path)
        plt.close(fig)

        pres.add_picture_slide(img_path, title=title)
        pres.save_presentation(pres_name)
Exemple #2
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    def _cm_plot(self,
                 x,
                 y,
                 labels=None,
                 x_label='Transaction Value [monetary unit]',
                 y_label='Transaction Costs [monetary unit]',
                 title='Transaction costs',
                 plot_title=''):

        pres = Presenter(self.presname)
        fig = plt.figure()

        if isinstance(y[0], np.ndarray) | isinstance(y[0], list):
            for i in range(0, len(y)):
                plt.plot(x, y[i], label=labels[i])
            plt.legend(loc='upper left')
        else:
            plt.plot(x, y, 'b', label=labels)
            if labels is not None:
                plt.legend(loc='upper left')

        plt.xlabel(x_label)
        plt.ylabel(y_label)
        plt.title(plot_title)
        img_path = 'temp.png'
        plt.savefig(img_path)
        plt.close(fig)

        pres.add_picture_slide(img_path, title=title)
        pres.save_presentation(self.presname)

        os.remove(img_path)
Exemple #3
0
    def compare_to_moving_average(self, window):
        if not os.path.exists('Data_Checks'):
            os.makedirs('Data_Checks')
        location = 'Data_Checks'

        pres = Presenter()
        pres.start_presentation(
            title=''.join(['Data Checks ', self.ticker]),
            subtitle=dt.datetime.today().date().isoformat())
        mov_df = pd.DataFrame(index=[],
                              columns=[
                                  'id', 'price_date', 'item', 'value',
                                  'lower_limit', 'upper_limit'
                              ])

        for name in self.orig_names:
            mov_avg = pd.rolling_mean(self.unchecked_data[name],
                                      window=window,
                                      min_periods=1)
            mov_std = pd.rolling_std(self.unchecked_data[name],
                                     window=window,
                                     min_periods=1)

            num_std = 3
            # Determine range
            up_lim = mov_avg + num_std * mov_std
            low_lim = mov_avg - num_std * mov_std

            # Plot it
            dates = pd.to_datetime(self.unchecked_data['price_date'])
            fig = plt.figure()
            plt.xticks(rotation=45)
            plt.gcf().subplots_adjust(bottom=0.15)
            plt.plot(dates.values,
                     self.unchecked_data[name],
                     'r',
                     label='Raw data')
            plt.plot(dates.values, mov_avg, 'b', label='Moving average')
            plt.plot(dates.values,
                     up_lim,
                     'g',
                     label=''.join([str(num_std), 'std']))
            plt.plot(dates.values, low_lim, 'g')
            plt.xlabel('Time')
            plt.ylabel('Value')
            plt.legend(loc='upper left')
            img_path = ''.join(['temp', self.ticker, '.png'])
            plt.savefig(img_path)
            plt.close(fig)

            pres.add_picture_slide(img_path, title=name)

            # Find all values that lie outside of this range
            extr = self.unchecked_data.loc[
                (self.unchecked_data[name] > up_lim) |
                (self.unchecked_data[name] < low_lim)]

            # Add to list
            extr_select = extr.ix[:, ['id', 'price_date', name]]

            extr_select.loc[:, 'item'] = [name] * len(extr_select)
            extr_select.loc[:, 'upper_limit'] = up_lim[extr_select.index]
            extr_select.loc[:, 'lower_limit'] = low_lim[extr_select.index]
            extr_select.rename(columns={name: 'value'}, inplace=True)

            mov_df = pd.concat(
                [mov_df,
                 extr_select.reindex_axis(mov_df.columns, axis=1)])

        # Print list
        mov_df.to_csv(''.join([
            location, '/', self.ticker, '_', self.symbol_id,
            '_moving_avg_data.csv'
        ]),
                      index=False)
        pres.save_presentation(''.join(
            [location, '/', self.ticker, '_', self.symbol_id, '_plots.pptx']))

        os.remove(img_path)

        return mov_df