Exemplo n.º 1
0
    def add_summary_page(self):
        """Build a table which is shown on the first page which gives an overview of the portfolios"""
        s = PortfolioSummary()
        s.include_long_short()
        pieces = []
        for r in self.results:
            tmp = s(r.port, PortfolioSummary.analyze_returns)
            tmp['desc'] = r.desc
            tmp['sid'] = r.sid
            tmp = tmp.set_index(['sid', 'desc'],
                                append=1).reorder_levels([2, 1, 0])
            pieces.append(tmp)
        frame = pd.concat(pieces)

        tf = self.pdf.table_formatter(frame)
        tf.apply_basic_style(cmap=self.table_style)
        # [col.guess_format(pcts=1, trunc_dot_zeros=1) for col in tf.cells.iter_cols()]
        tf.cells.match_column_labels(
            ['nmonths', 'cnt', 'win cnt', 'lose cnt', 'dur max']).int_format()
        tf.cells.match_column_labels(['sharpe ann', 'sortino',
                                      'dur avg']).float_format(precision=1)
        tf.cells.match_column_labels(['maxdd dt']).apply_format(
            new_datetime_formatter('%d-%b-%y'))
        tf.cells.match_column_labels([
            'cagr', 'mret avg', 'mret std ann', 'ret std', 'mret avg ann',
            'maxdd', 'avg dd', 'winpct', 'ret avg', 'ret min', 'ret max'
        ]).percent_format()

        self.pdf.build_page('summary', {'F1': tf.build()})
Exemplo n.º 2
0
    def add_summary_page(self):
        """Build a table which is shown on the first page which gives an overview of the portfolios"""
        s = PortfolioSummary()
        s.include_long_short()
        pieces = []
        for r in self.results:
            tmp = s(r.port, PortfolioSummary.analyze_returns)
            tmp["desc"] = r.desc
            tmp["sid"] = r.sid
            tmp = tmp.set_index(["sid", "desc"],
                                append=1).reorder_levels([2, 1, 0])
            pieces.append(tmp)
        frame = pd.concat(pieces)

        tf = self.pdf.table_formatter(frame)
        tf.apply_basic_style(cmap=self.table_style)
        # [col.guess_format(pcts=1, trunc_dot_zeros=1) for col in tf.cells.iter_cols()]
        tf.cells.match_column_labels(
            ["nmonths", "cnt", "win cnt", "lose cnt", "dur max"]).int_format()
        tf.cells.match_column_labels(["sharpe ann", "sortino",
                                      "dur avg"]).float_format(precision=1)
        tf.cells.match_column_labels(["maxdd dt"]).apply_format(
            new_datetime_formatter("%d-%b-%y"))
        tf.cells.match_column_labels([
            "cagr",
            "mret avg",
            "mret std ann",
            "ret std",
            "mret avg ann",
            "maxdd",
            "avg dd",
            "winpct",
            "ret avg",
            "ret min",
            "ret max",
        ]).percent_format()

        self.pdf.build_page("summary", {"F1": tf.build()})
Exemplo n.º 3
0
    def add_position_page(self, result):
        def dofmt(t):
            t.apply_basic_style(cmap=self.table_style)
            [
                row.guess_format(pcts=1, trunc_dot_zeros=1)
                for row in t.cells.iter_rows()
            ]
            ncols = len(t.formatted_values.columns)
            t.set_col_widths(pcts=[1. / ncols] * ncols)
            return t

        def do_rename(df):
            d = {
                'consecutive_win_cnt_max': 'win_streak',
                'consecutive_loss_cnt_max': 'lose_streak'
            }
            return df.rename(index=lambda c: d.get(c, c))

        pdf = self.pdf
        figures = self.figures
        port = result.port
        buyhold = result.buyhold

        sframe = pd.DataFrame({
            'all': port.positions.stats.series,
            'long': port.long.positions.stats.series,
            'short': port.short.positions.stats.series
        })
        tf = pdf.table_formatter(insert_level(sframe, 'Position', copy=True))
        stable = dofmt(tf).build()

        s = PortfolioSummary()
        s.include_long_short().include_win_loss()
        dframe = s(port, PortfolioSummary.analyze_returns).T.ix['pos']
        tf = pdf.table_formatter(
            do_rename(insert_level(dframe, 'Position', copy=True)))
        dtable = dofmt(tf).build()

        # Plot Position Returns
        f, ax = self.create_ax()
        port.positions.plot_rets(ax=ax)
        plt.tight_layout()
        figures.savefig(key='pos_ls', clear=1)
        # Plot Position Ranges
        f, ax = self.create_ax(figsize=(8, 3))
        port.positions.plot_ret_range(ls=1, dur=1, ax=ax)
        plt.tight_layout()
        figures.savefig(key='pos_rng', clear=1)
        # Plot Long Short Positions with regression line
        tmp = port.position_frame[['side', 'ret']].reset_index()
        g = sns.lmplot("pid", "ret", col="side", hue="side", data=tmp, size=3)
        AxesFormat().Y.percent().apply()
        figures.savefig(key='pos_ls', clear=1)
        # Plot Return vs Duration
        tmp = port.position_frame[['ret', 'duration', 'side']]
        diag_kws = {}
        if len(port.position_frame.index) <= 1:
            diag_kws = {'range': (-100, 100)}
        sns.pairplot(tmp, hue="side", size=3, diag_kws=diag_kws)
        figures.savefig(key='pos_pair', clear=1)

        toimg = lambda path: rlab.new_dynamic_image(path)
        itms = {
            'F1':
            toimg(figures['pos_rng']),
            'F3':
            toimg(figures['pos_ls']),
            'F2':
            toimg(figures['pos_pair']),
            'F5':
            stable,
            'F6':
            dtable,
            'HDR':
            self.title_bar('{0} - {1} - position summary'.format(
                result.sid, result.desc))
        }
        pdf.build_page('positions', itms)
Exemplo n.º 4
0
    def add_portfolio_page(self, result):
        def alpha_beta(p, bm):
            model = pd.ols(x=bm.rets, y=p.rets)
            beta = model.beta[0]
            alpha = p.total_ann - beta * bm.total_ann
            s = pd.Series({'alpha': alpha, 'beta': beta})
            return s

        def rs(port1, port2, kind='dly_ret_stats'):
            stats = getattr(port1, kind)
            ab = alpha_beta(stats, getattr(port2, kind))
            tmp = stats.series.append(ab)
            tmp.name = stats.series.name
            return tmp

        def dofmt(t):
            t.apply_basic_style(cmap=self.table_style)
            [
                row.guess_format(pcts=1, trunc_dot_zeros=1)
                for row in t.cells.iter_rows()
            ]
            ncols = len(t.formatted_values.columns)
            t.set_col_widths(pcts=[1. / ncols] * ncols)

        def do_rename(df):
            d = {
                'consecutive_win_cnt_max': 'win_streak',
                'consecutive_loss_cnt_max': 'lose_streak'
            }
            return df.rename(index=lambda c: d.get(c, c))

        # Build the pdf tables
        pdf = self.pdf
        figures = self.figures
        port = result.port
        buyhold = result.buyhold
        sframe = pd.DataFrame([
            rs(port, buyhold, 'dly_ret_stats'),
            rs(port, buyhold, 'weekly_ret_stats'),
            rs(port, buyhold, 'monthly_ret_stats'),
            rs(port, buyhold, 'quarterly_ret_stats')
        ]).T

        tf = pdf.table_formatter(insert_level(sframe, 'Portfolio', copy=True))
        dofmt(tf)
        stable = tf.build()

        s = PortfolioSummary()
        s.include_long_short().include_win_loss()
        dframe = s(port, PortfolioSummary.analyze_returns).T
        tf = pdf.table_formatter(
            do_rename(insert_level(dframe.ix['port'], 'Portfolio', copy=True)))
        dofmt(tf)
        dtable = tf.build()

        # Return on $1 image
        f, ax = self.create_ax()
        buyhold.plot_ret_on_dollar('B', label='Buy & Hold', ax=ax)
        port.plot_ret_on_dollar('B', label=result.desc, ax=ax, color='k')
        ax.legend(loc='upper left')
        ax.set_title('vs Buy & Hold')
        plt.tight_layout()
        figures.savefig(key='buyhold', clear=1)
        # Drawdown image
        f, ax = self.create_ax()
        port.dly_ret_stats.plot_ltd(ax=ax)
        plt.tight_layout()
        figures.savefig(key='dd', clear=1)
        # Long / Short Returns
        f, ax = self.create_ax()
        port.plot_ret_on_dollar('B', label='All', color='k', ax=ax)
        port.long.plot_ret_on_dollar('B', label='Long', ax=ax)
        port.short.plot_ret_on_dollar('B', label='Short', ax=ax)
        ax.legend(loc='upper left')
        figures.savefig(key='ls', clear=1)
        # Sharpe / Ann Vol
        f, ax = self.create_ax()
        perf.sharpe_annualized(port.monthly_rets,
                               expanding=1).iloc[3:].plot(ax=ax,
                                                          color='k',
                                                          label='sharpe')
        ax.set_ylabel('sharpe ann', color='k')
        ax2 = ax.twinx()
        perf.std_annualized(port.monthly_rets,
                            expanding=1).iloc[3:].plot(ax=ax2,
                                                       label='vol',
                                                       color='b',
                                                       alpha=1)
        ax2.set_ylabel('vol ann', color='b')
        plt.tight_layout()
        figures.savefig(key='sharpe', clear=1)
        # Monthly Returns Bar Chart
        f, ax = self.create_ax()
        tmp = pd.DataFrame({
            'All': port.monthly_rets.to_period('M'),
            'Long': port.long.monthly_rets.to_period('M'),
            'Short': port.short.monthly_rets.to_period('M')
        })
        tmp.plot(kind='bar',
                 ax=ax,
                 color=['k', self.long_color, self.short_color])
        AxesFormat().Y.percent().X.rotate().apply()
        plt.tight_layout()
        ax.set_title('Monthly Returns')
        figures.savefig(key='mrets', clear=1)
        # Monthly Returns Box Plot
        f, ax = self.create_ax()
        sns.boxplot(tmp,
                    ax=ax,
                    color=['gray', self.long_color, self.short_color])
        ax.set_title('Monthly Returns')
        AxesFormat().Y.percent().apply()
        plt.tight_layout()
        figures.savefig(key='mrets_box', clear=1)
        # Build the PDF Page
        toimg = lambda path: rlab.new_dynamic_image(path)
        itms = {
            'F1':
            toimg(figures['buyhold']),
            'F2':
            toimg(figures['dd']),
            'F3':
            toimg(figures['ls']),
            'F4':
            toimg(figures['mrets']),
            'F5':
            toimg(figures['sharpe']),
            'F6':
            toimg(figures['mrets_box']),
            'F7':
            stable,
            'F8':
            dtable,
            'HDR':
            self.title_bar('{0} - {1} - portfolio summary'.format(
                result.sid, result.desc))
        }
        pdf.build_page('portfolio', itms)
Exemplo n.º 5
0
    def add_position_page(self, result):
        def dofmt(t):
            t.apply_basic_style(cmap=self.table_style)
            [
                row.guess_format(pcts=1, trunc_dot_zeros=1)
                for row in t.cells.iter_rows()
            ]
            ncols = len(t.formatted_values.columns)
            t.set_col_widths(pcts=[1.0 / ncols] * ncols)
            return t

        def do_rename(df):
            d = {
                "consecutive_win_cnt_max": "win_streak",
                "consecutive_loss_cnt_max": "lose_streak",
            }
            return df.rename(index=lambda c: d.get(c, c))

        pdf = self.pdf
        figures = self.figures
        port = result.port
        buyhold = result.buyhold

        sframe = pd.DataFrame({
            "all": port.positions.stats.series,
            "long": port.long.positions.stats.series,
            "short": port.short.positions.stats.series,
        })
        tf = pdf.table_formatter(insert_level(sframe, "Position", copy=True))
        stable = dofmt(tf).build()

        s = PortfolioSummary()
        s.include_long_short().include_win_loss()
        dframe = s(port, PortfolioSummary.analyze_returns).T.ix["pos"]
        tf = pdf.table_formatter(
            do_rename(insert_level(dframe, "Position", copy=True)))
        dtable = dofmt(tf).build()

        # Plot Position Returns
        f, ax = self.create_ax()
        port.positions.plot_rets(ax=ax)
        plt.tight_layout()
        figures.savefig(key="pos_ls", clear=1)
        # Plot Position Ranges
        f, ax = self.create_ax(figsize=(8, 3))
        port.positions.plot_ret_range(ls=1, dur=1, ax=ax)
        plt.tight_layout()
        figures.savefig(key="pos_rng", clear=1)
        # Plot Long Short Positions with regression line
        tmp = port.position_frame[["side", "ret"]].reset_index()
        g = sns.lmplot("pid", "ret", col="side", hue="side", data=tmp, size=3)
        AxesFormat().Y.percent().apply()
        figures.savefig(key="pos_ls", clear=1)
        # Plot Return vs Duration
        tmp = port.position_frame[["ret", "duration", "side"]]
        diag_kws = {}
        if len(port.position_frame.index) <= 1:
            diag_kws = {"range": (-100, 100)}
        sns.pairplot(tmp, hue="side", size=3, diag_kws=diag_kws)
        figures.savefig(key="pos_pair", clear=1)

        toimg = lambda path: rlab.new_dynamic_image(path)
        itms = {
            "F1":
            toimg(figures["pos_rng"]),
            "F3":
            toimg(figures["pos_ls"]),
            "F2":
            toimg(figures["pos_pair"]),
            "F5":
            stable,
            "F6":
            dtable,
            "HDR":
            self.title_bar("{0} - {1} - position summary".format(
                result.sid, result.desc)),
        }
        pdf.build_page("positions", itms)
Exemplo n.º 6
0
    def add_portfolio_page(self, result):
        def alpha_beta(p, bm):
            model = pd.ols(x=bm.rets, y=p.rets)
            beta = model.beta[0]
            alpha = p.total_ann - beta * bm.total_ann
            s = pd.Series({"alpha": alpha, "beta": beta})
            return s

        def rs(port1, port2, kind="dly_ret_stats"):
            stats = getattr(port1, kind)
            ab = alpha_beta(stats, getattr(port2, kind))
            tmp = stats.series.append(ab)
            tmp.name = stats.series.name
            return tmp

        def dofmt(t):
            t.apply_basic_style(cmap=self.table_style)
            [
                row.guess_format(pcts=1, trunc_dot_zeros=1)
                for row in t.cells.iter_rows()
            ]
            ncols = len(t.formatted_values.columns)
            t.set_col_widths(pcts=[1.0 / ncols] * ncols)

        def do_rename(df):
            d = {
                "consecutive_win_cnt_max": "win_streak",
                "consecutive_loss_cnt_max": "lose_streak",
            }
            return df.rename(index=lambda c: d.get(c, c))

        # Build the pdf tables
        pdf = self.pdf
        figures = self.figures
        port = result.port
        buyhold = result.buyhold
        sframe = pd.DataFrame([
            rs(port, buyhold, "dly_ret_stats"),
            rs(port, buyhold, "weekly_ret_stats"),
            rs(port, buyhold, "monthly_ret_stats"),
            rs(port, buyhold, "quarterly_ret_stats"),
        ]).T

        tf = pdf.table_formatter(insert_level(sframe, "Portfolio", copy=True))
        dofmt(tf)
        stable = tf.build()

        s = PortfolioSummary()
        s.include_long_short().include_win_loss()
        dframe = s(port, PortfolioSummary.analyze_returns).T
        tf = pdf.table_formatter(
            do_rename(insert_level(dframe.ix["port"], "Portfolio", copy=True)))
        dofmt(tf)
        dtable = tf.build()

        # Return on $1 image
        f, ax = self.create_ax()
        buyhold.plot_ret_on_dollar("B", label="Buy & Hold", ax=ax)
        port.plot_ret_on_dollar("B", label=result.desc, ax=ax, color="k")
        ax.legend(loc="upper left")
        ax.set_title("vs Buy & Hold")
        plt.tight_layout()
        figures.savefig(key="buyhold", clear=1)
        # Drawdown image
        f, ax = self.create_ax()
        port.dly_ret_stats.plot_ltd(ax=ax)
        plt.tight_layout()
        figures.savefig(key="dd", clear=1)
        # Long / Short Returns
        f, ax = self.create_ax()
        port.plot_ret_on_dollar("B", label="All", color="k", ax=ax)
        port.long.plot_ret_on_dollar("B", label="Long", ax=ax)
        port.short.plot_ret_on_dollar("B", label="Short", ax=ax)
        ax.legend(loc="upper left")
        figures.savefig(key="ls", clear=1)
        # Sharpe / Ann Vol
        f, ax = self.create_ax()
        perf.sharpe_annualized(port.monthly_rets,
                               expanding=1).iloc[3:].plot(ax=ax,
                                                          color="k",
                                                          label="sharpe")
        ax.set_ylabel("sharpe ann", color="k")
        ax2 = ax.twinx()
        perf.std_annualized(port.monthly_rets,
                            expanding=1).iloc[3:].plot(ax=ax2,
                                                       label="vol",
                                                       color="b",
                                                       alpha=1)
        ax2.set_ylabel("vol ann", color="b")
        plt.tight_layout()
        figures.savefig(key="sharpe", clear=1)
        # Monthly Returns Bar Chart
        f, ax = self.create_ax()
        tmp = pd.DataFrame({
            "All": port.monthly_rets.to_period("M"),
            "Long": port.long.monthly_rets.to_period("M"),
            "Short": port.short.monthly_rets.to_period("M"),
        })
        tmp.plot(kind="bar",
                 ax=ax,
                 color=["k", self.long_color, self.short_color])
        AxesFormat().Y.percent().X.rotate().apply()
        plt.tight_layout()
        ax.set_title("Monthly Returns")
        figures.savefig(key="mrets", clear=1)
        # Monthly Returns Box Plot
        f, ax = self.create_ax()
        sns.boxplot(tmp,
                    ax=ax,
                    color=["gray", self.long_color, self.short_color])
        ax.set_title("Monthly Returns")
        AxesFormat().Y.percent().apply()
        plt.tight_layout()
        figures.savefig(key="mrets_box", clear=1)
        # Build the PDF Page
        toimg = lambda path: rlab.new_dynamic_image(path)
        itms = {
            "F1":
            toimg(figures["buyhold"]),
            "F2":
            toimg(figures["dd"]),
            "F3":
            toimg(figures["ls"]),
            "F4":
            toimg(figures["mrets"]),
            "F5":
            toimg(figures["sharpe"]),
            "F6":
            toimg(figures["mrets_box"]),
            "F7":
            stable,
            "F8":
            dtable,
            "HDR":
            self.title_bar("{0} - {1} - portfolio summary".format(
                result.sid, result.desc)),
        }
        pdf.build_page("portfolio", itms)