short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates,
join='left',
axis=0)
slope = pandas.DataFrame(data=short_dates.values - long_dates.values,
index=short_dates.index,
columns=["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M', how='mean')
slope_monthly.index = [
str(x.year) + '/' + str(x.month) for x in slope_monthly.index
]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter='bokeh', gp=gp)
daily_vals = daily_vals.resample('BM')
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = [str(x.year) + '/' + str(x.month) for x in daily_vals.index]
daily_vals = daily_vals.drop(daily_vals.head(1).index)
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)
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 end of month
daily_vals = daily_vals.resample("BM")
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = [str(x.year) + "/" + str(x.month) for x in daily_vals.index]
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = "Thalesians/BBG"
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)
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 end of month
daily_vals = daily_vals.resample('BM')
daily_vals = daily_vals / daily_vals.shift(1) - 1
daily_vals.index = [
str(x.year) + '/' + str(x.month) for x in daily_vals.index
]
daily_vals = daily_vals.drop(daily_vals.head(1).index)
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
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)
import pandas
df.columns = [x.replace('.close', '') for x in df.columns.values]
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates, join='left', axis = 0)
slope = pandas.DataFrame(data = short_dates.values - long_dates.values, index = short_dates.index,
columns = ["EURUSDV1M-1Y", "USDJPYV1M-1Y"])
# resample fand calculate average over month
slope_monthly = slope.resample('M', how='mean')
slope_monthly.index = [str(x.year) + '/' + str(x.month) for x in slope_monthly.index]
pf = PlotFactory()
gp = GraphProperties()
gp.source = 'Thalesians/BBG'
gp.title = 'Vol slopes in EUR/USD and USD/JPY recently'
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter = 'bokeh', gp = gp)
df.columns = [x.replace(".close", "") for x in df.columns.values]
short_dates = df[["EURUSDV1M", "USDJPYV1M"]]
long_dates = df[["EURUSDV1Y", "USDJPYV1Y"]]
short_dates, long_dates = short_dates.align(long_dates, join="left", axis=0)
slope = pandas.DataFrame(
data=short_dates.values - long_dates.values, index=short_dates.index, columns=["EURUSDV1M-1Y", "USDJPYV1M-1Y"]
)
# resample fand calculate average over month
slope_monthly = slope.resample("M", how="mean")
slope_monthly.index = [str(x.year) + "/" + str(x.month) for x in slope_monthly.index]
pf = PlotFactory()
gp = GraphProperties()
gp.source = "Thalesians/BBG"
gp.title = "Vol slopes in EUR/USD and USD/JPY recently"
gp.scale_factor = 2
gp.display_legend = True
gp.chart_type = "bar"
gp.x_title = "Dates"
gp.y_title = "Pc"
# plot using Cufflinks
pf.plot_bar_graph(slope_monthly, adapter="cufflinks", gp=gp)
