def g10_line_plot_gdp(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' gdp = self.get_GDP_QoQ(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 GDP" gp.units = 'Rebased' gp.scale_factor = Constants.plotfactory_scale_factor gp.file_output = today_root + 'G10 UNE ' + str( gp.scale_factor) + '.png' gdp.columns = [x.split('-')[0] for x in gdp.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United Kingdom'] from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs tsc = TimeSeriesCalcs() gdp = gdp / 100 gdp = tsc.create_mult_index_from_prices(gdp) pf.plot_generic_graph(gdp, type='line', adapter='pythalesians', gp=gp)
def usa_plot_une(self, start_date, finish_date): country_group = 'usa-states' source = 'bloomberg' une = self.get_UNE(start_date, finish_date, country_group, source='bloomberg') une = self.hist_econ_data_factory.grasp_coded_entry(une, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'USA-states' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'usa' gp.plotly_projection = 'albers usa' gp.plotly_world_readable = False gp.plotly_url = country_group + "-unemployment" gp.title = "USA Unemployment" gp.units = 'pc' pf.plot_generic_graph(une, type='choropleth', adapter='plotly', gp=gp)
def g10_plot_gdp_cpi_une(self, start_date, finish_date, data_type = 'cpi'): country_group = 'g10' if data_type == 'cpi': df = self.get_CPI_YoY(start_date, finish_date, country_group) elif data_type == 'gdp': df = self.get_GDP_YoY(start_date, finish_date, country_group) elif data_type == 'une': df = self.get_UNE(start_date, finish_date, country_group) df = self.hist_econ_data_factory.grasp_coded_entry(df, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'world' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'world' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = country_group + "-" + data_type gp.title = "G10 " + data_type gp.units = '%' pf.plot_generic_graph(df, type = 'choropleth', adapter = 'plotly', gp = gp)
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 g10_plot_gdp_cpi_une(self, start_date, finish_date, data_type='cpi'): country_group = 'g10' if data_type == 'cpi': df = self.get_CPI_YoY(start_date, finish_date, country_group) elif data_type == 'gdp': df = self.get_GDP_QoQ(start_date, finish_date, country_group) elif data_type == 'une': df = self.get_UNE(start_date, finish_date, country_group) df = self.hist_econ_data_factory.grasp_coded_entry(df, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'world' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'world' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = country_group + "-" + data_type gp.title = "G10 " + data_type gp.units = '%' pf.plot_generic_graph(df, type='choropleth', adapter='plotly', gp=gp)
def plot_strategy_signals(self, date=None, strip=None): ######## plot signals strategy_signal = self._strategy_signal strategy_signal = 100 * (strategy_signal) if date is None: last_day = strategy_signal.ix[-1].transpose().to_frame() else: last_day = strategy_signal.ix[date].transpose().to_frame() if strip is not None: last_day.index = [x.replace(strip, '') for x in last_day.index] print(last_day) pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY + " positions (% portfolio notional)" gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Positions) ' + str( gp.scale_factor) + '.png' pf.plot_generic_graph(last_day, adapter='pythalesians', type='bar', gp=gp)
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_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_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_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_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_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 g10_line_plot_cpi(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' cpi = self.get_CPI_YoY(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 CPI YoY" gp.units = '%' gp.scale_factor = 3 gp.file_output = today_root + 'G10 CPI YoY ' + str(gp.scale_factor) + '.png' cpi.columns = [x.split('-')[0] for x in cpi.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United States'] pf.plot_generic_graph(cpi, type = 'line', adapter = 'pythalesians', gp = gp)
def g10_line_plot_une(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' une = self.get_UNE(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 Unemployment Rate (%)" gp.units = '%' gp.scale_factor = Constants.plotfactory_scale_factor gp.file_output = today_root + 'G10 UNE ' + str(gp.scale_factor) + '.png' une.columns = [x.split('-')[0] for x in une.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United States'] pf.plot_generic_graph(une, type = 'line', adapter = 'pythalesians', gp = gp)
def g10_line_plot_une(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' une = self.get_UNE(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 Unemployment Rate (%)" gp.units = '%' gp.scale_factor = Constants.plotfactory_scale_factor gp.file_output = today_root + 'G10 UNE ' + str( gp.scale_factor) + '.png' une.columns = [x.split('-')[0] for x in une.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United States'] pf.plot_generic_graph(une, type='line', adapter='pythalesians', gp=gp)
def g10_line_plot_cpi(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' cpi = self.get_CPI_YoY(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 CPI YoY" gp.units = '%' gp.scale_factor = 3 gp.file_output = today_root + 'G10 CPI YoY ' + str( gp.scale_factor) + '.png' cpi.columns = [x.split('-')[0] for x in cpi.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United States'] pf.plot_generic_graph(cpi, type='line', adapter='pythalesians', gp=gp)
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 usa_plot_une(self, start_date, finish_date): country_group = 'usa-states'; source = 'bloomberg' une = self.get_UNE(start_date, finish_date, country_group, source = 'bloomberg') une = self.hist_econ_data_factory.grasp_coded_entry(une, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'USA-states' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'usa' gp.plotly_projection = 'albers usa' gp.plotly_world_readable = False gp.plotly_url = country_group + "-unemployment" gp.title = "USA Unemployment" gp.units = 'pc' pf.plot_generic_graph(une, type = 'choropleth', adapter = 'plotly', gp = gp)
def europe_plot_une(self, start_date, finish_date): country_group = 'all-europe' une = self.get_UNE(start_date, finish_date, country_group) une = self.hist_econ_data_factory.grasp_coded_entry(une, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'europe' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'europe' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = country_group + "-unemployment"; gp.title = "Europe Unemployment" gp.units = '%' pf.plot_generic_graph(une, type = 'choropleth', adapter = 'plotly', 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): 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 g10_line_plot_gdp(self, start_date, finish_date): today_root = datetime.date.today().strftime("%Y%m%d") + " " country_group = 'g10-ez' gdp = self.get_GDP_QoQ(start_date, finish_date, country_group) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.title = "G10 GDP" gp.units = 'Rebased' gp.scale_factor = Constants.plotfactory_scale_factor gp.file_output = today_root + 'G10 UNE ' + str(gp.scale_factor) + '.png' gdp.columns = [x.split('-')[0] for x in gdp.columns] gp.linewidth_2 = 3 gp.linewidth_2_series = ['United Kingdom'] from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs tsc = TimeSeriesCalcs() gdp = gdp / 100 gdp = tsc.create_mult_index_from_prices(gdp) pf.plot_generic_graph(gdp, type = 'line', adapter = 'pythalesians', gp = gp)
def world_plot_cpi(self, start_date, finish_date): country_group = 'world-liquid' cpi = self.get_CPI_YoY(start_date, finish_date, country_group) cpi = self.hist_econ_data_factory.grasp_coded_entry(cpi, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'world' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'world' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = str(country_group) + "-cpi" gp.title = "World Liquid CPI YoY" gp.units = '%' pf.plot_generic_graph(cpi, type='choropleth', adapter='plotly', gp=gp)
def europe_plot_une(self, start_date, finish_date): country_group = 'all-europe' une = self.get_UNE(start_date, finish_date, country_group) une = self.hist_econ_data_factory.grasp_coded_entry(une, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'europe' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'europe' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = country_group + "-unemployment" gp.title = "Europe Unemployment" gp.units = '%' pf.plot_generic_graph(une, type='choropleth', adapter='plotly', gp=gp)
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_signals(self, date = None, strip = None): ######## plot signals strategy_signal = self._strategy_signal strategy_signal = 100 * (strategy_signal) if date is None: last_day = strategy_signal.ix[-1].transpose().to_frame() else: last_day = strategy_signal.ix[date].transpose().to_frame() if strip is not None: last_day.index = [x.replace(strip, '') for x in last_day.index] print(last_day) pf = PlotFactory() gp = GraphProperties() gp.title = self.FINAL_STRATEGY + " positions (% portfolio notional)" gp.scale_factor = self.SCALE_FACTOR gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Positions) ' + str(gp.scale_factor) + '.png' pf.plot_generic_graph(last_day, adapter = 'pythalesians', type = 'bar', gp = gp)
def world_plot_cpi(self, start_date, finish_date): country_group = 'world-liquid' cpi = self.get_CPI_YoY(start_date, finish_date, country_group) cpi = self.hist_econ_data_factory.grasp_coded_entry(cpi, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'world' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'world' gp.plotly_projection = 'Mercator' gp.plotly_world_readable = False gp.plotly_url = str(country_group) + "-cpi" gp.title = "World Liquid CPI YoY" gp.units = '%' pf.plot_generic_graph(cpi, type = 'choropleth', adapter = 'plotly', gp = gp)
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 run_arbitrary_sensitivity(self, strat, parameter_list = None, parameter_names = None, pretty_portfolio_names = None, parameter_type = None): asset_df, spot_df, spot_df2, basket_dict = strat.fill_assets() port_list = None for i in range(0, len(parameter_list)): br = strat.fill_backtest_request() current_parameter = parameter_list[i] # for calculating P&L for k in current_parameter.keys(): setattr(br, k, current_parameter[k]) strat.br = br # for calculating signals signal_df = strat.construct_signal(spot_df, spot_df2, br.tech_params) cash_backtest = CashBacktest() self.logger.info("Calculating... " + pretty_portfolio_names[i]) cash_backtest.calculate_trading_PnL(br, asset_df, signal_df) stats = str(cash_backtest.get_portfolio_pnl_desc()[0]) port = cash_backtest.get_cumportfolio().resample('B') port.columns = [pretty_portfolio_names[i] + ' ' + stats] if port_list is None: port_list = port else: port_list = port_list.join(port) pf = PlotFactory() gp = GraphProperties() gp.color = 'Blues' gp.resample = 'B' gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + '.png' gp.scale_factor = self.scale_factor gp.title = strat.FINAL_STRATEGY + ' ' + parameter_type pf.plot_line_graph(port_list, adapter = 'pythalesians', gp = gp)
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_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
ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() 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.plotly_username = '******' gp.plotly_world_readable = True pf = PlotFactory() pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp) pf.plot_generic_graph(df, type = 'line', adapter = 'cufflinks', gp = gp) # test simple Plotly bar charts - average differences in EURUSDV1M-1Y vol and USDJPYV1M-1Y slope over past sixth months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory() end = datetime.datetime.utcnow() start = end - timedelta(days=180) tickers = ['EURUSDV1M', 'EURUSDV1Y', 'USDJPYV1M', 'USDJPYV1Y'] vendor_tickers = ['EURUSDV1M BGN Curncy', 'EURUSDV1Y BGN Curncy', 'USDJPYV1M BGN Curncy', 'USDJPYV1Y BGN Curncy'] time_series_request = TimeSeriesRequest(
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[ ]:
tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices( ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() 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.plotly_username = '******' gp.plotly_world_readable = True pf = PlotFactory() pf.plot_generic_graph(df, type='line', adapter='pythalesians', gp=gp) pf.plot_generic_graph(df, type='line', adapter='cufflinks', gp=gp) # test simple Plotly bar charts - average differences in EURUSDV1M-1Y vol and USDJPYV1M-1Y slope over past sixth months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory() end = datetime.datetime.utcnow() start = end - timedelta(days=180) tickers = ['EURUSDV1M', 'EURUSDV1Y', 'USDJPYV1M', 'USDJPYV1Y'] vendor_tickers = [ 'EURUSDV1M BGN Curncy', 'EURUSDV1Y BGN Curncy', 'USDJPYV1M BGN Curncy', 'USDJPYV1Y BGN Curncy'
ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() 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.plotly_username = "******" gp.plotly_world_readable = True pf = PlotFactory() pf.plot_generic_graph(df, type="line", adapter="pythalesians", gp=gp) pf.plot_generic_graph(df, type="line", adapter="cufflinks", gp=gp) # test simple Plotly bar charts - average differences in EURUSDV1M-1Y vol and USDJPYV1M-1Y slope over past sixth months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory() end = datetime.datetime.utcnow() start = end - timedelta(days=180) tickers = ["EURUSDV1M", "EURUSDV1Y", "USDJPYV1M", "USDJPYV1Y"] vendor_tickers = ["EURUSDV1M BGN Curncy", "EURUSDV1Y BGN Curncy", "USDJPYV1M BGN Curncy", "USDJPYV1Y BGN Curncy"]
start_date="01 Jan 1970", # start date finish_date=datetime.date.today(), # finish date freq='daily', # daily data data_source='quandl', # use Quandl as data source tickers=[ 'EURUSD', # ticker (Thalesians) 'GBPUSD' ], fields=['close'], # which fields to download vendor_tickers=['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl) vendor_fields=['close'], # which Bloomberg fields to download cache_algo='internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() daily_vals = ltsf.harvest_time_series(time_series_request) techind = TechIndicator() tech_params = TechParams() tech_params.sma_period = 20 techind.create_tech_ind(daily_vals, 'SMA', tech_params=tech_params) sma = techind.get_techind() signal = techind.get_signal() combine = daily_vals.join(sma, how='outer') pf = PlotFactory() pf.plot_line_graph(combine, adapter='pythalesians')
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'] time_series_request = TimeSeriesRequest(
time_series_request = TimeSeriesRequest( start_date="01 Jan 1970", # start date finish_date=datetime.date.today(), # finish date freq='daily', # daily data data_source='quandl', # use Quandl as data source tickers=['EURUSD', # ticker (Thalesians) 'GBPUSD'], fields=['close'], # which fields to download vendor_tickers=['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl) vendor_fields=['close'], # which Bloomberg fields to download cache_algo='internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() daily_vals = ltsf.harvest_time_series(time_series_request) techind = TechIndicator() tech_params = TechParams() tech_params.sma_period = 20 techind.create_tech_ind(daily_vals, 'SMA', tech_params=tech_params) sma = techind.get_techind() signal = techind.get_signal() combine = daily_vals.join(sma, how='outer') pf = PlotFactory() pf.plot_line_graph(combine, adapter='pythalesians')
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs # displaying data from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties # logging from pythalesians.util.loggermanager import LoggerManager import datetime seasonality = Seasonality() tsc = TimeSeriesCalcs() logger = LoggerManager().getLogger(__name__) pf = PlotFactory() # just change "False" to "True" to run any of the below examples ###### calculate seasonal moves in EUR/USD and GBP/USD (using Quandl data) if True: time_series_request = TimeSeriesRequest( start_date="01 Jan 1970", # start date finish_date=datetime.date.today(), # finish date freq='daily', # daily data data_source='quandl', # use Quandl as data source tickers=[ 'EURUSD', # ticker (Thalesians) 'GBPUSD' ], fields=['close'], # which fields to download
finish_date=datetime.date.today(), # finish date freq="daily", # daily data data_source="bloomberg", # use Bloomberg as data source tickers=["EURUSD", "GBPUSD"], # ticker (Thalesians) fields=["close", "high", "low"], # which fields to download vendor_tickers=["EURUSD BGN Curncy", "GBPUSD BGN Curncy"], # ticker (Bloomberg) vendor_fields=["PX_LAST", "PX_HIGH", "PX_LOW"], # which Bloomberg fields to download cache_algo="internet_load_return", ) # how to return data ltsf = LightTimeSeriesFactory() df = None df = ltsf.harvest_time_series(time_series_request) pf = PlotFactory() pf.plot_line_graph(df, adapter="pythalesians") ###### download event dates for non farm payrolls and then print if False: time_series_request = TimeSeriesRequest( start_date="01 Jan 2014", # start date finish_date=datetime.date.today(), # finish date category="events", freq="daily", # daily data data_source="bloomberg", # use Bloomberg as data source tickers=["FOMC", "NFP"], fields=["release-date-time-full", "release-dt", "actual-release"], # which fields to download vendor_tickers=["FDTR Index", "NFP TCH Index"], # ticker (Bloomberg) vendor_fields=[
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)
# data_type = 'House Price Index'; title = 'House Price Index (YoY change %)'; yoy_ret = True; freq = 12; source = 'fred' df = hist.get_economic_data_history(start_date, finish_date, country_group, data_type, source=source) df = df.fillna(method='pad') if yoy_ret is True: df = 100 * (df / df.shift(freq) - 1) df = hist.grasp_coded_entry(df, -1) from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties gp = GraphProperties() pf = PlotFactory() gp.plotly_location_mode = 'USA-states' gp.plotly_choropleth_field = 'Val' gp.plotly_scope = 'usa' gp.plotly_projection = 'albers usa' gp.plotly_url = country_group + data_type.replace(' ', '-') gp.plotly_world_readable = False gp.title = title gp.units = 'Value' # do a map plot by US state pf.plot_generic_graph(df, type = 'choropleth', adapter = 'cufflinks', gp = gp) #### uses CommonEconDataFactory to get more common forms of economic data and plot ####
) # 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_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)
plotfactory_examples Examples to show how to use PlotFactory to make charts (at present only line charts have a lot of support). """ import datetime from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties # just change "False" to "True" to run any of the below examples if True: pf = PlotFactory() # test simple PyThalesians/Bokeh time series line charts if False: ltsf = LightTimeSeriesFactory() start = '01 Jan 2000' end = datetime.datetime.utcnow() tickers = ['AUDJPY', 'USDJPY'] vendor_tickers = ['AUDJPY BGN Curncy', 'USDJPY BGN Curncy'] time_series_request = TimeSeriesRequest( start_date=start, # start date finish_date=datetime.date.today(), # finish date freq='daily', # daily data data_source='bloomberg', # use Bloomberg as data source
end = datetime.datetime.utcnow() start_date = end.replace(hour=0, minute=0, second=0, microsecond=0) # Returns a copy time_series_request = TimeSeriesRequest( start_date = start_date, # start date finish_date = datetime.datetime.utcnow(), # finish date freq = 'intraday', # intraday data data_source = 'bloomberg', # use Bloomberg as data source tickers = ['EURUSD'] , # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = ['EURUSD BGN Curncy'], # ticker (Bloomberg) vendor_fields = ['close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() df = ltsf.harvest_time_series(time_series_request) df.columns = [x.replace('.close', '') for x in df.columns.values] gp = GraphProperties() gp.title = 'EURUSD stuff!' gp.file_output = 'EURUSD.png' gp.source = 'Thalesians/BBG (created with PyThalesians Python library)' pf = PlotFactory() pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp) pytwitter.update_status("check out my plot of EUR/USD!", picture = gp.file_output)
plotfactory_examples Examples to show how to use PlotFactory to make charts (at present only line charts have a lot of support). """ import datetime from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties # just change "False" to "True" to run any of the below examples if True: pf = PlotFactory() # test simple PyThalesians/Bokeh time series line charts if False: ltsf = LightTimeSeriesFactory() start = "01 Jan 2000" end = datetime.datetime.utcnow() tickers = ["AUDJPY", "USDJPY"] vendor_tickers = ["AUDJPY BGN Curncy", "USDJPY BGN Curncy"] time_series_request = TimeSeriesRequest( start_date=start, # start date finish_date=datetime.date.today(), # finish date freq="daily", # daily data data_source="bloomberg", # use Bloomberg as data source
def run_arbitrary_sensitivity(self, strat, parameter_list=None, parameter_names=None, pretty_portfolio_names=None, parameter_type=None): asset_df, spot_df, spot_df2, basket_dict = strat.fill_assets() port_list = None tsd_list = [] for i in range(0, len(parameter_list)): br = strat.fill_backtest_request() current_parameter = parameter_list[i] # for calculating P&L for k in current_parameter.keys(): setattr(br, k, current_parameter[k]) strat.br = br # for calculating signals signal_df = strat.construct_signal(spot_df, spot_df2, br.tech_params, br) cash_backtest = CashBacktest() self.logger.info("Calculating... " + pretty_portfolio_names[i]) cash_backtest.calculate_trading_PnL(br, asset_df, signal_df) tsd_list.append(cash_backtest.get_portfolio_pnl_tsd()) stats = str(cash_backtest.get_portfolio_pnl_desc()[0]) port = cash_backtest.get_cumportfolio().resample('B') port.columns = [pretty_portfolio_names[i] + ' ' + stats] if port_list is None: port_list = port else: port_list = port_list.join(port) # reset the parameters of the strategy strat.br = strat.fill_backtest_request() pf = PlotFactory() gp = GraphProperties() ir = [t.inforatio()[0] for t in tsd_list] # gp.color = 'Blues' # plot all the variations gp.resample = 'B' gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + '.png' gp.scale_factor = self.scale_factor gp.title = strat.FINAL_STRATEGY + ' ' + parameter_type pf.plot_line_graph(port_list, adapter='pythalesians', gp=gp) # plot all the IR in a bar chart form (can be easier to read!) gp = GraphProperties() gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' ' + parameter_type + ' IR.png' gp.scale_factor = self.scale_factor gp.title = strat.FINAL_STRATEGY + ' ' + parameter_type summary = pandas.DataFrame(index=pretty_portfolio_names, data=ir, columns=['IR']) pf.plot_bar_graph(summary, adapter='pythalesians', gp=gp) return port_list
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'] time_series_request = TimeSeriesRequest(
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs # displaying data from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties # logging from pythalesians.util.loggermanager import LoggerManager import datetime seasonality = Seasonality() tsc = TimeSeriesCalcs() logger = LoggerManager().getLogger(__name__) pf = PlotFactory() ###### calculate seasonal moves in EUR/USD and GBP/USD (using Quandl data) if True: time_series_request = TimeSeriesRequest( start_date = "01 Jan 1970", # start date finish_date = datetime.date.today(), # finish date freq = 'daily', # daily data data_source = 'quandl', # use Quandl as data source tickers = ['EURUSD', # ticker (Thalesians) 'GBPUSD'], fields = ['close'], # which fields to download vendor_tickers = ['FRED/DEXUSEU', 'FRED/DEXUSUK'], # ticker (Quandl) vendor_fields = ['close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data
""" plotfactory_examples Examples to show how to use PlotFactory to make charts (at present only line charts have a lot of support). """ import datetime from pythalesians.market.loaders.lighttimeseriesfactory import LightTimeSeriesFactory from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest from pythalesians.graphics.graphs.plotfactory import PlotFactory from pythalesians.graphics.graphs.graphproperties import GraphProperties if True: pf = PlotFactory() # test simple PyThalesians/Bokeh time series line charts if True: ltsf = LightTimeSeriesFactory() start = '01 Jan 2000' end = datetime.datetime.utcnow() tickers = ['AUDJPY', 'USDJPY'] vendor_tickers = ['AUDJPY BGN Curncy', 'USDJPY BGN Curncy'] time_series_request = TimeSeriesRequest( start_date = start, # start date finish_date = datetime.date.today(), # finish date freq = 'daily', # daily data data_source = 'bloomberg', # use Bloomberg as data source