def plot_strategy_group_benchmark_pnl(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR #gp.color = 'RdYlGn' gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png' # plot cumulative line of returns pf.plot_line_graph(self.reduce_plot(self._strategy_group_benchmark_pnl), adapter = 'pythalesians', gp = gp) # needs write stats flag turned on try: keys = self._strategy_group_benchmark_tsd.keys() ir = [] for key in keys: ir.append(self._strategy_group_benchmark_tsd[key].inforatio()[0]) ret_stats = pandas.DataFrame(index = keys, data = ir, columns = ['IR']) ret_stats = ret_stats.sort_index() gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png' gp.display_brand_label = False # plot ret stats pf.plot_bar_graph(ret_stats, adapter = 'pythalesians', gp = gp) except: pass
def plot_strategy_signal_proportion(self, strip = None): signal = self._strategy_signal long = signal[signal > 0].count() short = signal[signal < 0].count() flat = signal[signal == 0].count() keys = long.index df = pandas.DataFrame(index = keys, columns = ['Long', 'Short', 'Flat']) df['Long'] = long df['Short'] = short df['Flat'] = flat if strip is not None: keys = [k.replace(strip, '') for k in keys] df.index = keys df = df.sort_index() pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).png' try: pf.plot_bar_graph(self.reduce_plot(df), adapter = 'pythalesians', gp = gp) except: pass
def plot_strategy_group_benchmark_pnl(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR #gp.color = 'RdYlGn' gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png' # plot cumulative line of returns pf.plot_line_graph(self.reduce_plot( self._strategy_group_benchmark_pnl), adapter='pythalesians', gp=gp) keys = self._strategy_group_benchmark_tsd.keys() ir = [] for key in keys: ir.append(self._strategy_group_benchmark_tsd[key].inforatio()[0]) ret_stats = pandas.DataFrame(index=keys, data=ir, columns=['IR']) ret_stats = ret_stats.sort() gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png' gp.display_brand_label = False # plot ret stats pf.plot_bar_graph(ret_stats, adapter='pythalesians', gp=gp)
def plot_single_var_regression( self, y, x, y_variable_names, x_variable_names, statistic, tag='stats', title=None, pretty_index=None, output_path=None, scale_factor=Constants.plotfactory_scale_factor, silent_plot=False, shift=[0]): if not (isinstance(statistic, list)): statistic = [statistic] # TODO optimise loop so that we are calculating each regression *once* at present calculating it # for each statistic, which is redundant for st in statistic: stats_df = [] for sh in shift: x_sh = x.shift(sh) stats_temp = self.report_single_var_regression( y, x_sh, y_variable_names, x_variable_names, st, pretty_index) stats_temp.columns = [ x + "_" + str(sh) for x in stats_temp.columns ] stats_df.append(stats_temp) stats_df = pandas.concat(stats_df, axis=1) stats_df = stats_df.dropna(how='all') if silent_plot: return stats_df pf = PlotFactory() gp = GraphProperties() if title is None: title = statistic gp.title = title gp.display_legend = True gp.scale_factor = scale_factor # gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink'] if output_path is not None: gp.file_output = output_path + ' (' + tag + ' ' + st + ').png' pf.plot_bar_graph(stats_df, adapter='pythalesians', gp=gp) return stats_df
def plot_strategy_group_leverage(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY + ' Leverage' gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Leverage).png' pf.plot_line_graph(self.reduce_plot(self._strategy_group_leverage), adapter = 'pythalesians', gp = gp)
def plot_individual_leverage(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY + ' Leverage' gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Individual Leverage).png' try: pf.plot_line_graph(self.reduce_plot(self._individual_leverage), adapter = 'pythalesians', gp = gp) except: pass
def plot_strategy_pnl(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy PnL).png' try: pf.plot_line_graph(self.reduce_plot(self._strategy_pnl), adapter = 'pythalesians', gp = gp) except: pass
def plot_strategy_signal_proportion(self, strip=None): signal = self._strategy_signal # count number of long, short and flat periods in our sample long = signal[signal > 0].count() short = signal[signal < 0].count() flat = signal[signal == 0].count() keys = long.index # how many trades have there been (ignore size of the trades) trades = abs(signal - signal.shift(-1)) trades = trades[trades > 0].count() df_trades = pandas.DataFrame(index=keys, columns=['Trades'], data=trades) df = pandas.DataFrame(index=keys, columns=['Long', 'Short', 'Flat']) df['Long'] = long df['Short'] = short df['Flat'] = flat if strip is not None: keys = [k.replace(strip, '') for k in keys] df.index = keys df_trades.index = keys # df = df.sort_index() pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR try: gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).png' pf.plot_bar_graph(self.reduce_plot(df), adapter='pythalesians', gp=gp) gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy trade no).png' pf.plot_bar_graph(self.reduce_plot(df_trades), adapter='pythalesians', gp=gp) except: pass
def plot_strategy_group_benchmark_annualised_pnl(self, cols = None): # TODO - unfinished, needs checking! if cols is None: cols = self._strategy_group_benchmark_annualised_pnl.columns pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink'] gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark Annualised PnL).png' pf.plot_line_graph(self.reduce_plot(self._strategy_group_benchmark_annualised_pnl[cols]), adapter = 'pythalesians', gp = gp)
def plot_strategy_group_pnl_trades(self): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY + " (bp)" gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Individual Trade PnL).png' # zero when there isn't a trade exit strategy_pnl_trades = self._strategy_pnl_trades.fillna(0) * 100 * 100 try: pf.plot_line_graph(self.reduce_plot(strategy_pnl_trades), adapter = 'pythalesians', gp = gp) except: pass
def plot_single_var_regression(self, y, x, y_variable_names, x_variable_names, statistic, tag = 'stats', title = None, pretty_index = None, output_path = None, scale_factor = Constants.plotfactory_scale_factor, silent_plot = False, shift=[0]): if not(isinstance(statistic, list)): statistic = [statistic] # TODO optimise loop so that we are calculating each regression *once* at present calculating it # for each statistic, which is redundant for st in statistic: stats_df = [] for sh in shift: x_sh = x.shift(sh) stats_temp = self.report_single_var_regression(y, x_sh, y_variable_names, x_variable_names, st, pretty_index) stats_temp.columns = [ x + "_" + str(sh) for x in stats_temp.columns] stats_df.append(stats_temp) stats_df = pandas.concat(stats_df, axis=1) stats_df = stats_df.dropna(how='all') if silent_plot: return stats_df pf = PlotFactory() gp = GraphProperties() if title is None: title = statistic gp.title = title gp.display_legend = True gp.scale_factor = scale_factor # gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink'] if output_path is not None: gp.file_output = output_path + ' (' + tag + ' ' + st + ').png' pf.plot_bar_graph(stats_df, adapter = 'pythalesians', gp = gp) return stats_df
def run_day_of_month_analysis(self, strat): from pythalesians.economics.seasonality.seasonality import Seasonality from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs tsc = TimeSeriesCalcs() seas = Seasonality() strat.construct_strategy() pnl = strat.get_strategy_pnl() # get seasonality by day of the month pnl = pnl.resample('B').mean() rets = tsc.calculate_returns(pnl) bus_day = seas.bus_day_of_month_seasonality(rets, add_average = True) # get seasonality by month pnl = pnl.resample('BM').mean() rets = tsc.calculate_returns(pnl) month = seas.monthly_seasonality(rets) self.logger.info("About to plot seasonality...") gp = GraphProperties() pf = PlotFactory() # Plotting spot over day of month/month of year gp.color = 'Blues' gp.scale_factor = self.scale_factor gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.png' gp.title = strat.FINAL_STRATEGY + ' day of month seasonality' gp.display_legend = False gp.color_2_series = [bus_day.columns[-1]] gp.color_2 = ['red'] # red, pink gp.linewidth_2 = 4 gp.linewidth_2_series = [bus_day.columns[-1]] gp.y_axis_2_series = [bus_day.columns[-1]] pf.plot_line_graph(bus_day, adapter = 'pythalesians', gp = gp) gp = GraphProperties() gp.scale_factor = self.scale_factor gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.png' gp.title = strat.FINAL_STRATEGY + ' month of year seasonality' pf.plot_line_graph(month, adapter = 'pythalesians', gp = gp) return month
def run_day_of_month_analysis(self, strat): from pythalesians.economics.seasonality.seasonality import Seasonality from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs tsc = TimeSeriesCalcs() seas = Seasonality() strat.construct_strategy() pnl = strat.get_strategy_pnl() # get seasonality by day of the month pnl = pnl.resample('B') rets = tsc.calculate_returns(pnl) bus_day = seas.bus_day_of_month_seasonality(rets, add_average=True) # get seasonality by month pnl = pnl.resample('BM') rets = tsc.calculate_returns(pnl) month = seas.monthly_seasonality(rets) self.logger.info("About to plot seasonality...") gp = GraphProperties() pf = PlotFactory() # Plotting spot over day of month/month of year gp.color = 'Blues' gp.scale_factor = self.scale_factor gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.png' gp.title = strat.FINAL_STRATEGY + ' day of month seasonality' gp.display_legend = False gp.color_2_series = [bus_day.columns[-1]] gp.color_2 = ['red'] # red, pink gp.linewidth_2 = 4 gp.linewidth_2_series = [bus_day.columns[-1]] gp.y_axis_2_series = [bus_day.columns[-1]] pf.plot_line_graph(bus_day, adapter='pythalesians', gp=gp) gp = GraphProperties() gp.scale_factor = self.scale_factor gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.png' gp.title = strat.FINAL_STRATEGY + ' month of year seasonality' pf.plot_line_graph(month, adapter='pythalesians', gp=gp) return month
def plot_strategy_group_benchmark_pnl(self, strip=None): pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR #gp.color = 'RdYlGn' gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - cumulative).png' strat_list = self._strategy_group_benchmark_pnl.columns.sort_values() for line in strat_list: self.logger.info(line) # plot cumulative line of returns pf.plot_line_graph(self.reduce_plot( self._strategy_group_benchmark_pnl), adapter='pythalesians', gp=gp) # needs write stats flag turned on try: keys = self._strategy_group_benchmark_tsd.keys() ir = [] for key in keys: ir.append( self._strategy_group_benchmark_tsd[key].inforatio()[0]) if strip is not None: keys = [k.replace(strip, '') for k in keys] ret_stats = pandas.DataFrame(index=keys, data=ir, columns=['IR']) ret_stats = ret_stats.sort_index() gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR).png' gp.display_brand_label = False # plot ret stats pf.plot_bar_graph(ret_stats, adapter='pythalesians', gp=gp) except: pass
def plot_strategy_signal_proportion(self, strip = None): signal = self._strategy_signal # count number of long, short and flat periods in our sample long = signal[signal > 0].count() short = signal[signal < 0].count() flat = signal[signal == 0].count() keys = long.index # how many trades have there been (ignore size of the trades) trades = abs(signal - signal.shift(-1)) trades = trades[trades > 0].count() df_trades = pandas.DataFrame(index = keys, columns = ['Trades'], data = trades) df = pandas.DataFrame(index = keys, columns = ['Long', 'Short', 'Flat']) df['Long'] = long df['Short'] = short df['Flat'] = flat if strip is not None: keys = [k.replace(strip, '') for k in keys] df.index = keys df_trades.index = keys # df = df.sort_index() pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR try: gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).png' pf.plot_bar_graph(self.reduce_plot(df), adapter = 'pythalesians', gp = gp) gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy trade no).png' pf.plot_bar_graph(self.reduce_plot(df_trades), adapter = 'pythalesians', gp = gp) except: pass
def plot_single_var_regression( self, y, x, y_variable_names, x_variable_names, statistic, tag='stats', title=None, pretty_index=None, output_path=None, scale_factor=Constants.plotfactory_scale_factor, silent_plot=False): stats_df = self.report_single_var_regression(y, x, y_variable_names, x_variable_names, statistic, pretty_index) if silent_plot: return stats_df pf = PlotFactory() gp = GraphProperties() if title is None: title = statistic gp.title = title gp.display_legend = True gp.scale_factor = scale_factor # gp.color = ['red', 'blue', 'purple', 'gray', 'yellow', 'green', 'pink'] if output_path is not None: gp.file_output = output_path + ' (' + tag + ').png' pf.plot_bar_graph(stats_df, adapter='pythalesians', gp=gp) return stats_df
def plot_strategy_signal_proportion(self, strip=None): signal = self._strategy_signal long = signal[signal > 0].count() short = signal[signal < 0].count() flat = signal[signal == 0].count() keys = long.index df = pandas.DataFrame(index=keys, columns=['Long', 'Short', 'Flat']) df['Long'] = long df['Short'] = short df['Flat'] = flat if strip is not None: keys = [k.replace(strip, '') for k in keys] df.index = keys df = df.sort_index() pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY gp.display_legend = True gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).png' try: pf.plot_bar_graph(self.reduce_plot(df), adapter='pythalesians', gp=gp) except: pass
if True: time_series_request = TimeSeriesRequest( start_date = "01 Jan 2013", # start date finish_date = datetime.date.today(), # finish date freq = 'daily', # daily data data_source = 'google', # use Bloomberg as data source tickers = ['Apple', 'S&P500 ETF'], # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = ['aapl', 'spy'], # ticker (Google) vendor_fields = ['Close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Plotly (via Cufflinks) # just needs 1 word to change # (although, note that AdapterCufflinks does have some extra parameters that can be set in # GraphProperties) gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp) pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp)
vendor_tickers = ['aapl', 'spy'], # ticker (Google) vendor_fields = ['Close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "output_data/apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Bokeh # just needs 1 word to change gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp) pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp) # test simple Bokeh bar charts - monthly returns over past 6 months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory() end = datetime.datetime.utcnow() start = end - timedelta(days=180) tickers = ['S&P500', 'FTSE', 'Nikkei'] vendor_tickers = ['SPX Index', 'UKX Index', 'NKY Index']
df = tsc.create_mult_index_from_prices(daily_vals) df = df.fillna(method='ffill') # Lastly we can plot the results! We shall plot it with the PlotFactory class using PyThalesians engine (a wrapper on top of matplotlib). We can specify the properties of the plot using a GraphProperties class. The first line, forces matplotlib to give us the plot in the notebook, as opposed to a separate window. # In[ ]: get_ipython().magic(u'matplotlib inline') from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() gp.source = 'Thalesians/BBG (created with PyThalesians Python library)' gp.title = 'Equity Markets' gp.scale_factor = 1 gp.display_legend = True gp.x_title = 'Dates' gp.y_title = 'Index' pf = PlotFactory() pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp) # In[ ]:
vendor_fields=['Close'], # which Bloomberg fields to download cache_algo='internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices( ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "output_data/apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Bokeh # just needs 1 word to change gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type='line', adapter='pythalesians', gp=gp) pf.plot_generic_graph(df, type='line', adapter='bokeh', gp=gp) # test simple Bokeh bar charts - monthly returns over past 6 months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory() end = datetime.datetime.utcnow() start = end - timedelta(days=180) tickers = ['S&P500', 'FTSE', 'Nikkei'] vendor_tickers = ['SPX Index', 'UKX Index', 'NKY Index']
import pandas df.columns = [x.replace('.close', '') for x in df.columns.values] short_dates = df[["EURUSDV1M", "USDJPYV1M"]] long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]] short_dates, long_dates = short_dates.align(long_dates, join='left', axis = 0) slope = pandas.DataFrame(data = short_dates.values - long_dates.values, index = short_dates.index, columns = ["EURUSDV1M-1Y", "USDJPYV1M-1Y"]) # resample fand calculate average over month slope_monthly = slope.resample('M', how='mean') slope_monthly.index = [str(x.year) + '/' + str(x.month) for x in slope_monthly.index] pf = PlotFactory() gp = GraphProperties() gp.source = 'Thalesians/BBG' gp.title = 'Vol slopes in EUR/USD and USD/JPY recently' gp.scale_factor = 2 gp.display_legend = True gp.chart_type = 'bar' gp.x_title = 'Dates' gp.y_title = 'Pc' # plot using Cufflinks pf.plot_bar_graph(slope_monthly, adapter = 'bokeh', gp = gp)
df_event_times.index = df_event_times.index.tz_localize(utc_time) # work in UTC time from pythalesians.economics.events.eventstudy import EventStudy es = EventStudy() # work out cumulative asset price moves moves over the event df_event = es.get_intraday_moves_over_custom_event(df, df_event_times) # create an average move df_event['Avg'] = df_event.mean(axis = 1) # plotting spot over economic data event gp = GraphProperties() gp.scale_factor = 3 gp.title = 'USDJPY spot moves over recent NFP' # plot in shades of blue (so earlier releases are lighter, later releases are darker) gp.color = 'Blues'; gp.color_2 = [] gp.y_axis_2_series = [] gp.display_legend = False # last release will be in red, average move in orange gp.color_2_series = [df_event.columns[-2], df_event.columns[-1]] gp.color_2 = ['red', 'orange'] # red, pink gp.linewidth_2 = 2 gp.linewidth_2_series = gp.color_2_series pf = PlotFactory() pf.plot_line_graph(df_event * 100, adapter = 'pythalesians', gp = gp)