def plot_strategy_group_benchmark_pnl(self, strip = None):
pf = PlotFactory()
gp = self.create_graph_properties("", "Group Benchmark PnL - cumulative")
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 = self.DEFAULT_PLOT_ENGINE, 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) ' + gp.SCALE_FACTOR + '.png'
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Group Benchmark PnL - IR) ' + gp.SCALE_FACTOR + '.html'
gp.display_brand_label = False
# plot ret stats
pf.plot_bar_graph(ret_stats, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
except: pass
def plot_strategy_signal_proportion(self, strip = None):
signal = self._strategy_signal
# count number of long, short and flat periods in our sample
long = signal[signal > 0].count()
short = signal[signal < 0].count()
flat = signal[signal == 0].count()
keys = long.index
# how many trades have there been (ignore size of the trades)
trades = abs(signal - signal.shift(-1))
trades = trades[trades > 0].count()
df_trades = pandas.DataFrame(index = keys, columns = ['Trades'], data = trades)
df = pandas.DataFrame(index = keys, columns = ['Long', 'Short', 'Flat'])
df['Long'] = long
df['Short'] = short
df['Flat'] = flat
if strip is not None: keys = [k.replace(strip, '') for k in keys]
df.index = keys
df_trades.index = keys
# df = df.sort_index()
pf = PlotFactory()
gp = self.create_graph_properties("", "")
try:
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).png'
gp.html_file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy signal proportion).html'
pf.plot_bar_graph(self.reduce_plot(df), adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
gp.file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy trade no).png'
gp.html_file_output = self.DUMP_PATH + self.FINAL_STRATEGY + ' (Strategy trade no).html'
pf.plot_bar_graph(self.reduce_plot(df_trades), adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
except: pass
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG (created with PyThalesians Python library)'
gp.html_file_output = "output_data/equities.htm"
gp.title = 'Recent monthly changes in equity markets'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = ['bar', 'scatter', 'line']
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Bokeh then PyThalesians
pf.plot_bar_graph(daily_vals * 100, adapter = 'bokeh', gp = gp)
pf.plot_bar_graph(daily_vals * 100, adapter = 'pythalesians', gp = gp)
# plot daily changes in FX
if True:
from datetime import timedelta
ltsf = LightTimeSeriesFactory()
end = datetime.datetime.utcnow()
start = end - timedelta(days=5)
tickers = ['EUR', 'GBP', 'AUD', 'NZD', 'CAD', 'CHF', 'NOK', 'SEK', 'JPY']
vendor_tickers = [x + 'USD BGN Curncy' for x in tickers]
time_series_request = TimeSeriesRequest(
start_date = start, # start date
import pandas
df.columns = [x.replace('.close', '') for x in df.columns.values]
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates, join='left', axis = 0)
slope = pandas.DataFrame(data = short_dates.values - long_dates.values, index = short_dates.index,
columns = ["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M').mean()
slope_monthly.index = [str(x.year) + '/' + str(x.month) for x in slope_monthly.index]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter = 'cufflinks', gp = gp)
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates,
join='left',
axis=0)
slope = pandas.DataFrame(data=short_dates.values - long_dates.values,
index=short_dates.index,
columns=["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M').mean()
slope_monthly.index = [
str(x.year) + '/' + str(x.month) for x in slope_monthly.index
]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter='cufflinks', gp=gp)
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)
# resample for year end
daily_vals = daily_vals.resample('A')
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = daily_vals.index.year
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Yearly changes in spot'
gp.scale_factor = 3
gp.y_title = "Percent Change"
daily_vals = daily_vals * 100
# plot using PyThalesians (stacked & then bar graph)
pf.plot_stacked_graph(daily_vals, adapter='pythalesians', gp=gp)
pf.plot_bar_graph(daily_vals, adapter='pythalesians', gp=gp)