本文整理汇总了Python中pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs.rolling_corr方法的典型用法代码示例。如果您正苦于以下问题：Python TimeSeriesCalcs.rolling_corr方法的具体用法？Python TimeSeriesCalcs.rolling_corr怎么用？Python TimeSeriesCalcs.rolling_corr使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs的用法示例。
在下文中一共展示了TimeSeriesCalcs.rolling_corr方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: ticker
# 需要导入模块: from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs [as 别名]
# 或者: from pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs import rolling_corr [as 别名]
'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示例2: TimeSeriesRequest
# 需要导入模块: from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs [as 别名]
# 或者: from pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs import rolling_corr [as 别名]
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)