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)
df = ltsf.harvest_time_series(time_series_request)
df.columns = [x.replace('.close', '') for x in df.columns.values]
df = tsc.calculate_returns(df) * 100
df = df.dropna()
df_sorted = tsc.get_bottom_valued_sorted(df, "USDBRL", n = 20)
# df = tsc.get_top_valued_sorted(df, "USDBRL", n = 20) # get biggest up moves
# get values on day after
df2 = df.shift(-1)
df2 = df2.ix[df_sorted.index]
df2.columns = ['T+1']
df_sorted.columns = ['T']
df_sorted = df_sorted.join(df2)
df_sorted.index = [str(x.year) + '/' + str(x.month) + '/' + str(x.day) for x in df_sorted.index]
gp = GraphProperties()
gp.title = 'Largest daily falls in USDBRL'
gp.scale_factor = 3
gp.display_legend = True
gp.chart_type = 'bar'
gp.x_title = 'Dates'
gp.y_title = 'Pc'
gp.file_output = "usdbrl-biggest-downmoves.png"
pf = PlotFactory()
pf.plot_line_graph(df_sorted, adapter = 'pythalesians', gp=gp)
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)