data_source = 'bloomberg', # use Bloomberg as data source
tickers = ['USDBRL'] , # ticker (Thalesians)
fields = ['close'], # which fields to download
vendor_tickers = ['USDBRL BGN Curncy'], # ticker (Bloomberg)
vendor_fields = ['PX_LAST'], # 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]
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
data_source='bloomberg', # use Bloomberg as data source
tickers=['USDBRL'], # ticker (Thalesians)
fields=['close'], # which fields to download
vendor_tickers=['USDBRL BGN Curncy'], # ticker (Bloomberg)
vendor_fields=['PX_LAST'], # 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]
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
]
