'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"
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
'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
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)
