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 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 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 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 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_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_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_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 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 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_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_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 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_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 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_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_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_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 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_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 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 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 plot_2d_graph(self, data_frame, gp, chart_type):
sns.set()
if gp is None: gp = GraphProperties()
if gp.chart_type is None and chart_type is None: chart_type = 'line'
if gp.resample is not None: data_frame = data_frame.asfreq(gp.resample)
self.apply_style_sheet(gp)
if chart_type == 'heatmap':
# Draw a heatmap with the numeric values in each cell
sns_plot = sns.heatmap(data_frame,
annot=True,
fmt="d",
linewidths=.5)
fig = sns_plot.fig
# TODO
pass
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_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 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_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_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 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
def plot_2d_graph(self, data_frame, gp, chart_type):
plotly.tools.set_credentials_file(username=gp.plotly_username, api_key=gp.plotly_api_key)
mode = 'line'
if gp is None: gp = GraphProperties()
if gp.chart_type is None:
if chart_type is None: chart_type = 'line'
else:
chart_type = gp.chart_type
marker_size = 1
scale_factor = gp.scale_factor
x = ''; y = ''; z = ''
if chart_type == 'line':
chart_type = 'scatter'
elif chart_type == 'scatter':
mode = 'markers'
marker_size = 5
elif chart_type == 'bubble':
x = data_frame.columns[0]
y = data_frame.columns[1]
z = data_frame.columns[2]
# special case for choropleth which has yet to be implemented in Cufflinks
# will likely remove this in the future
elif chart_type == 'choropleth':
import plotly.plotly as py
for col in data_frame.columns:
try:
data_frame[col] = data_frame[col].astype(str)
except:
pass
if gp.color != []:
color = gp.color
else:
color = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
text = ''
if 'text' in data_frame.columns:
text = data_frame['Text']
data = [ dict(
type='choropleth',
colorscale = color,
autocolorscale = False,
locations = data_frame['Code'],
z = data_frame[gp.plotly_choropleth_field].astype(float),
locationmode = gp.plotly_location_mode,
text = text,
marker = dict(
line = dict (
color = 'rgb(255,255,255)',
width = 1
)
),
colorbar = dict(
title = gp.units
)
) ]
layout = dict(
title = gp.title,
geo = dict(
scope=gp.plotly_scope,
projection=dict( type=gp.plotly_projection ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)',
),
)
fig = dict( data=data, layout=layout )
url = py.plot(fig, validate=False, filename=gp.plotly_url, world_readable=gp.plotly_world_readable)
return
# check other plots implemented by Cufflinks
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
# need to change to strings for cufflinks
color_spec = []
color_list = self.create_color_list(gp, data_frame)
# if no colors are specified strip the list
if color_list == [None] * len(color_list):
color_spec = None
else:
# otherwise assume all the colors are rgba
for color in color_list:
color = 'rgba' + str(color)
color_spec.append(color)
data_frame.iplot(kind=chart_type,
filename=gp.plotly_url,
title=gp.title,
xTitle=gp.x_title,
yTitle=gp.y_title,
x=x, y=y, z=z,
mode=mode,
size=marker_size,
theme=gp.plotly_theme,
bestfit=gp.line_of_best_fit,
world_readable=gp.plotly_world_readable,
legend=gp.display_legend,
color = color_spec,
dimensions=(gp.width * scale_factor, gp.height * scale_factor),
asPlot=not(gp.silent_display),
asImage=gp.plotly_as_image)
vendor_tickers = ['EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'AUDUSD BGN Curncy'],
vendor_fields = ['PX_LAST'], # 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)
tsc = TimeSeriesCalcs()
df = tsc.calculate_returns(df)
df = tsc.rolling_corr(df['EURUSD.close'], 20, data_frame2 = df[['GBPUSD.close', 'AUDUSD.close']])
gp = GraphProperties()
gp.title = "1M FX rolling correlations"
gp.scale_factor = 3
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
###### download daily data from Bloomberg for AUD/JPY, NZD/JPY spot with S&P500, then calculate correlation
if True:
time_series_request = TimeSeriesRequest(
start_date="01 Jan 2015", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='bloomberg', # use Bloomberg as data source
tickers=['AUDJPY', # ticker (Thalesians)
'NZDJPY',
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 = "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()
fields=['close'], # which fields to download
vendor_tickers=[
'EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'JPYUSD BGN Curncy',
'AUDUSD BGN Curncy'
],
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()
pf = PlotFactory()
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(df)
pf.plot_line_graph(df, adapter='pythalesians', gp=gp)
###### download daily data from Quandl (via FRED) for EUR/USD and GBP/USD spot and then plot
if True:
time_series_request = TimeSeriesRequest(
start_date="01 Jan 1970", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
def plot_2d_graph(self, data_frame, gp, chart_type):
if gp is None: gp = GraphProperties()
if gp.chart_type is None and chart_type is None: chart_type = 'line'
if gp.resample is not None: data_frame = data_frame.asfreq(gp.resample)
self.apply_style_sheet(gp)
# create figure & add a subplot
fig = plt.figure(figsize=((gp.width * gp.scale_factor) / gp.dpi,
(gp.height * gp.scale_factor) / gp.dpi),
dpi=gp.dpi)
ax = fig.add_subplot(111)
if gp.x_title != '': ax.set_xlabel(gp.x_title)
if gp.y_title != '': ax.set_ylabel(gp.y_title)
plt.xlabel(gp.x_title)
plt.ylabel(gp.y_title)
fig.suptitle(gp.title, fontsize=14 * gp.scale_factor)
# format Y axis
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
# create a second y axis if necessary
ax2 = []
if gp.y_axis_2_series != []:
ax2 = ax.twinx()
# do not use a grid with multiple y axes
ax.yaxis.grid(False)
ax2.yaxis.grid(False)
# matplotlib 1.5
try:
cyc = matplotlib.rcParams['axes.prop_cycle']
color_cycle = [x['color'] for x in cyc]
except KeyError:
# pre 1.5
pass
# color_cycle = matplotlib.rcParams['axes.color_cycle']
bar_ind = np.arange(0, len(data_frame.index))
# for bar charts, create a proxy x-axis (then relabel)
xd, bar_ind, has_bar, no_of_bars = self.get_bar_indices(
data_frame, gp, chart_type, bar_ind)
# plot the lines (using custom palettes as appropriate)
try:
# get all the correct colors (and construct gradients if necessary eg. from 'blues')
color_spec = self.create_color_list(gp, data_frame)
# for stacked bar
yoff_pos = np.zeros(len(data_frame.index.values)
) # the bottom values for stacked bar chart
yoff_neg = np.zeros(len(data_frame.index.values)
) # the bottom values for stacked bar chart
zeros = np.zeros(len(data_frame.index.values))
# for bar chart
bar_space = 0.2
bar_width = (1 - bar_space) / (no_of_bars)
bar_index = 0
has_matrix = False
# some lines we should exclude from the color and use the default palette
for i in range(0, len(data_frame.columns.values)):
if gp.chart_type is not None:
if isinstance(gp.chart_type, list):
chart_type = gp.chart_type[i]
else:
chart_type = gp.chart_type
if chart_type == 'heatmap':
# TODO experimental!
# ax.set_frame_on(False)
ax.pcolor(data_frame, cmap=plt.cm.Blues, alpha=0.8)
# plt.colorbar()
has_matrix = True
break
label = str(data_frame.columns[i])
ax_temp = self.get_axis(ax, ax2, label, gp.y_axis_2_series)
yd = data_frame.ix[:, i]
if color_spec[i] is None:
color_spec[i] = color_cycle[i % len(color_cycle)]
if (chart_type == 'line'):
linewidth_t = self.get_linewidth(label, gp.linewidth,
gp.linewidth_2,
gp.linewidth_2_series)
if linewidth_t is None:
linewidth_t = matplotlib.rcParams['axes.linewidth']
ax_temp.plot(xd,
yd,
label=label,
color=color_spec[i],
linewidth=linewidth_t)
elif (chart_type == 'bar'):
# for multiple bars we need to allocate space properly
bar_pos = [
k - (1 - bar_space) / 2. + bar_index * bar_width
for k in range(0, len(bar_ind))
]
ax_temp.bar(bar_pos,
yd,
bar_width,
label=label,
color=color_spec[i])
bar_index = bar_index + 1
elif (chart_type == 'stacked'):
bar_pos = [
k - (1 - bar_space) / 2. + bar_index * bar_width
for k in range(0, len(bar_ind))
]
yoff = np.where(yd > 0, yoff_pos, yoff_neg)
ax_temp.bar(bar_pos,
yd,
label=label,
color=color_spec[i],
bottom=yoff)
yoff_pos = yoff_pos + np.maximum(yd, zeros)
yoff_neg = yoff_neg + np.minimum(yd, zeros)
# bar_index = bar_index + 1
elif (chart_type == 'scatter'):
ax_temp.scatter(xd, yd, label=label, color=color_spec[i])
if gp.line_of_best_fit is True:
self.trendline(ax_temp,
xd.values,
yd.values,
order=1,
color=color_spec[i],
alpha=1,
scale_factor=gp.scale_factor)
# format X axis
self.format_x_axis(ax, data_frame, gp, has_bar, bar_ind,
has_matrix)
except:
pass
if gp.display_source_label == True:
ax.annotate('Source: ' + gp.source,
xy=(1, 0),
xycoords='axes fraction',
fontsize=7 * gp.scale_factor,
xytext=(-5 * gp.scale_factor, 10 * gp.scale_factor),
textcoords='offset points',
ha='right',
va='top',
color=gp.source_color)
if gp.display_brand_label == True:
self.create_brand_label(ax,
anno=gp.brand_label,
scale_factor=gp.scale_factor)
leg = []
leg2 = []
loc = 'best'
# if we have two y-axis then make sure legends are in opposite corners
if ax2 != []: loc = 2
try:
leg = ax.legend(loc=loc, prop={'size': 10 * gp.scale_factor})
leg.get_frame().set_linewidth(0.0)
leg.get_frame().set_alpha(0)
if ax2 != []:
leg2 = ax2.legend(loc=1, prop={'size': 10 * gp.scale_factor})
leg2.get_frame().set_linewidth(0.0)
leg2.get_frame().set_alpha(0)
except:
pass
try:
if gp.display_legend is False:
if leg != []: leg.remove()
if leg2 != []: leg.remove()
except:
pass
try:
plt.savefig(gp.file_output, transparent=False)
except:
pass
####### various matplotlib converters are unstable
# convert to D3 format with mpld3
try:
# output matplotlib charts externally to D3 based libraries
import mpld3
if gp.display_mpld3 == True:
mpld3.save_d3_html(fig, gp.html_file_output)
mpld3.show(fig)
except:
pass
# FRAGILE! convert to Bokeh format
# better to use direct Bokeh renderer
try:
if (gp.convert_matplotlib_to_bokeh == True):
from bokeh.plotting import output_file, show
from bokeh import mpl
output_file(gp.html_file_output)
show(mpl.to_bokeh())
except:
pass
# FRAGILE! convert matplotlib chart to Plotly format
# recommend using AdapterCufflinks instead to directly plot to Plotly
try:
import plotly.plotly as py
import plotly
import plotly.tools as tls
if gp.convert_matplotlib_to_plotly == True:
plotly.tools.set_credentials_file(username=gp.plotly_username,
api_key=gp.plotly_api_key)
py_fig = tls.mpl_to_plotly(fig, strip_style=True)
plot_url = py.plot_mpl(py_fig, filename=gp.plotly_url)
except:
pass
# display in matplotlib window
try:
if Constants.plotfactory_silent_display == True:
pass
elif gp.silent_display == False:
plt.show()
except:
pass
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)
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 = "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()
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
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
tickers=tickers, # ticker (Thalesians)
fields=['close'], # which fields to download
vendor_tickers=vendor_tickers, # ticker (Bloomberg)
vendor_fields=['PX_LAST'], # which Bloomberg fields to download
cache_algo='internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
gp = GraphProperties()
gp.title = 'Spot values'
gp.file_output = 'output_data/demo.png'
gp.html_file_output = 'output_data/demo.htm'
gp.source = 'Thalesians/BBG'
# plot using PyThalesians
pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp)
# plot using Bokeh (still needs a lot of work!)
pf.plot_line_graph(daily_vals, adapter='bokeh', gp=gp)
# do more complicated charts using several different Matplotib stylesheets (which have been customised)
if False:
ltsf = LightTimeSeriesFactory()
'JPYUSD',
'AUDUSD'],
fields = ['close'], # which fields to download
vendor_tickers = ['EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'JPYUSD BGN Curncy',
'AUDUSD BGN Curncy'],
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()
pf = PlotFactory()
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(df)
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
###### download daily data from Quandl (via FRED) for EUR/USD and GBP/USD spot and then plot
if False:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 1970", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
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 = "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
tsc = TimeSeriesCalcs() 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[ ]:
'JPYUSD',
'AUDUSD'],
fields = ['close'], # which fields to download
vendor_tickers = ['EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'JPYUSD BGN Curncy',
'AUDUSD BGN Curncy'],
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()
pf = PlotFactory()
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
tsc = TimeSeriesCalcs()
df = tsc.create_mult_index_from_prices(df)
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
###### download daily data from Quandl (via FRED) for EUR/USD and GBP/USD spot and then plot
if False:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 1970", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
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.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:
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
tickers = tickers, # ticker (Thalesians)
fields = ['close'], # which fields to download
vendor_tickers = vendor_tickers, # ticker (Bloomberg)
vendor_fields = ['PX_LAST'], # which Bloomberg fields to download
cache_algo = 'internet_load_return') # how to return data
daily_vals = ltsf.harvest_time_series(time_series_request)
pf = PlotFactory()
gp = GraphProperties()
gp.title = 'Spot values'
gp.file_output = 'demo.png'
gp.html_file_output = 'demo.htm'
gp.source = 'Thalesians/BBG'
# plot using PyThalesians
pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp)
# plot using Bokeh (still needs a lot of work!)
pf.plot_line_graph(daily_vals, adapter = 'bokeh', gp = gp)
# do more complicated charts using several different Matplotib stylesheets (which have been customised)
if True:
ltsf = LightTimeSeriesFactory()
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)
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 2014", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
data_source = 'bloomberg', # use Bloomberg as data source
tickers = ['EURUSD', # ticker (Thalesians)
'GBPUSD',
'AUDUSD'],
fields = ['close'], # which fields to download
vendor_tickers = ['EURUSD BGN Curncy', # ticker (Bloomberg)
'GBPUSD BGN Curncy',
'AUDUSD BGN Curncy'],
vendor_fields = ['PX_LAST'], # 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)
tsc = TimeSeriesCalcs()
df = tsc.calculate_returns(df)
df = tsc.rolling_corr(df['EURUSD.close'], 20, data_frame2 = df[['GBPUSD.close', 'AUDUSD.close']])
gp = GraphProperties()
gp.title = "1M FX rolling correlations"
pf = PlotFactory()
pf.plot_line_graph(df, adapter = 'pythalesians', gp = gp)
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
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.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:
###### 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
ltsf = LightTimeSeriesFactory()
df = ltsf.harvest_time_series(time_series_request)
df_ret = tsc.calculate_returns(df)
day_of_month_seasonality = seasonality.bus_day_of_month_seasonality(df_ret, partition_by_month = False)
day_of_month_seasonality = tsc.convert_month_day_to_date_time(day_of_month_seasonality)
gp = GraphProperties()
gp.date_formatter = '%b'
gp.title = 'FX spot moves by time of year'
gp.scale_factor = 3
gp.file_output = "output_data/20150724 FX spot seas.png"
pf.plot_line_graph(day_of_month_seasonality, adapter='pythalesians', gp = gp)
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 = "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()
