def plot_generic_graph(self, data_frame, adapter = default_adapter, type = None, gp = None, excel_file = None,
excel_sheet = None, freq = 'daily'):
if (excel_file is not None):
tio = TimeSeriesIO()
data_frame = tio.read_excel_data_frame(excel_file, excel_sheet, freq)
return self.get_adapter(adapter).plot_2d_graph(data_frame, gp, type)
def plot_generic_graph(self, data_frame, adapter = default_adapter, type = None, gp = None, excel_file = None,
excel_sheet = None, freq = 'daily'):
if (excel_file is not None):
tio = TimeSeriesIO()
data_frame = tio.read_excel_data_frame(excel_file, excel_sheet, freq)
return self.get_adapter(adapter).plot_2d_graph(data_frame, gp, type)
def __init__(self):
super(EventStudy, self).__init__()
self.config = ConfigManager()
self.logger = LoggerManager().getLogger(__name__)
self.time_series_filter = TimeSeriesFilter()
self.time_series_io = TimeSeriesIO()
if (LightEventsFactory._econ_data_frame is None):
self.load_economic_events()
return
def plot_generic_graph(
self, data_frame, adapter=default_adapter, type="line", gp=None, excel_file=None, excel_sheet=None, freq="daily"
):
if excel_file is not None:
tio = TimeSeriesIO()
data_frame = tio.read_excel_data_frame(excel_file, excel_sheet, freq)
if type in ["line", "bar", "scatter", "choropleth"]:
return self.get_adapter(adapter).plot_2d_graph(data_frame, gp, type)
def __init__(self):
# self.config = ConfigManager()
self.logger = LoggerManager().getLogger(__name__)
self.time_series_filter = TimeSeriesFilter()
self.time_series_io = TimeSeriesIO()
self._bbg_default_api = Constants().bbg_default_api
self._intraday_code = -1
return
def load_database(self, key = None):
tsio = TimeSeriesIO()
tsc = TimeSeriesCalcs()
file = self._hdf5
if key is not None:
file = self._hdf5 + key + ".h5"
# if cached file exists, use that, otherwise load CSV
if os.path.isfile(file):
self.logger.info("About to load market database from HDF5...")
self.news_database = tsio.read_time_series_cache_from_disk(file)
self.news_database = self.preprocess(self.news_database)
else:
self.logger.info("About to load market database from CSV...")
self.news_database = self.load_csv()
return self.news_database
def load_database(self, key=None):
tsio = TimeSeriesIO()
tsc = TimeSeriesCalcs()
file = self._hdf5
if key is not None:
file = self._hdf5 + key + ".h5"
# if cached file exists, use that, otherwise load CSV
if os.path.isfile(file):
self.logger.info("About to load market database from HDF5...")
self.news_database = tsio.read_time_series_cache_from_disk(file)
self.news_database = self.preprocess(self.news_database)
else:
self.logger.info("About to load market database from CSV...")
self.news_database = self.load_csv()
return self.news_database
def __init__(self):
super(EventStudy, self).__init__()
self.config = ConfigManager()
self.logger = LoggerManager().getLogger(__name__)
self.time_series_filter = TimeSeriesFilter()
self.time_series_io = TimeSeriesIO()
if (LightEventsFactory._econ_data_frame is None):
self.load_economic_events()
return
class LightEventsFactory(EventStudy):
_econ_data_frame = None
# where your HDF5 file is stored with economic data MUST CHANGE!!
_hdf5_file_econ_file = "somefilnename.h5"
### manual offset for certain events where Bloomberg displays the wrong date (usually because of time differences)
_offset_events = {
'AUD-Australia Labor Force Employment Change SA.release-dt': 1
}
def __init__(self):
super(EventStudy, self).__init__()
self.config = ConfigManager()
self.logger = LoggerManager().getLogger(__name__)
self.time_series_filter = TimeSeriesFilter()
self.time_series_io = TimeSeriesIO()
if (LightEventsFactory._econ_data_frame is None):
self.load_economic_events()
return
def load_economic_events(self):
LightEventsFactory._econ_data_frame = self.time_series_io.read_time_series_cache_from_disk(
self._hdf5_file_econ_file)
def harvest_category(self, category_name):
cat = self.config.get_categories_from_tickers_selective_filter(
category_name)
for k in cat:
time_series_request = self.time_series_factory.populate_time_series_request(
k)
data_frame = self.time_series_factory.harvest_time_series(
time_series_request)
# TODO allow merge of multiple sources
return data_frame
def get_economic_events(self):
return LightEventsFactory._econ_data_frame
def dump_economic_events_csv(self, path):
LightEventsFactory._econ_data_frame.to_csv(path)
def get_economic_event_date_time(self, name, event=None, csv=None):
ticker = self.create_event_desciptor_field(name, event,
"release-date-time-full")
if csv is None:
data_frame = LightEventsFactory._econ_data_frame[ticker]
data_frame.index = LightEventsFactory._econ_data_frame[ticker]
else:
dateparse = lambda x: datetime.datetime.strptime(
x, '%d/%m/%Y %H:%M')
data_frame = pandas.read_csv(csv,
index_col=0,
parse_dates=True,
date_parser=dateparse)
data_frame = data_frame[pandas.notnull(data_frame.index)]
start_date = datetime.datetime.strptime("01-Jan-1971", "%d-%b-%Y")
self.time_series_filter.filter_time_series_by_date(
start_date, None, data_frame)
return data_frame
def get_economic_event_date_time_dataframe(self,
name,
event=None,
csv=None):
series = self.get_economic_event_date_time(name, event, csv)
data_frame = pandas.DataFrame(series.values, index=series.index)
data_frame.columns.name = self.create_event_desciptor_field(
name, event, "release-date-time-full")
return data_frame
def get_economic_event_date_time_fields(self, fields, name, event=None):
### acceptible fields
# actual-release
# survey-median
# survey-average
# survey-high
# survey-low
# survey-high
# number-observations
# release-dt
# release-date-time-full
# first-revision
# first-revision-date
ticker = []
# construct tickers of the form USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.actual-release
for i in range(0, len(fields)):
ticker.append(
self.create_event_desciptor_field(name, event, fields[i]))
# index on the release-dt field eg. 20101230 (we shall convert this later)
ticker_index = self.create_event_desciptor_field(
name, event, "release-dt")
######## grab event date/times
event_date_time = self.get_economic_event_date_time(name, event)
date_time_fore = event_date_time.index
# create dates for join later
date_time_dt = [
datetime.datetime(date_time_fore[x].year, date_time_fore[x].month,
date_time_fore[x].day)
for x in range(len(date_time_fore))
]
event_date_time_frame = pandas.DataFrame(event_date_time.index,
date_time_dt)
event_date_time_frame.index = date_time_dt
######## grab event date/fields
data_frame = LightEventsFactory._econ_data_frame[ticker]
data_frame.index = LightEventsFactory._econ_data_frame[ticker_index]
data_frame = data_frame[data_frame.index !=
0] # eliminate any 0 dates (artifact of Excel)
data_frame = data_frame[pandas.notnull(
data_frame.index)] # eliminate any NaN dates (artifact of Excel)
ind_dt = data_frame.index
# convert yyyymmdd format to datetime
data_frame.index = [
datetime.datetime(
int((ind_dt[x] - (ind_dt[x] % 10000)) / 10000),
int(((ind_dt[x] % 10000) - (ind_dt[x] % 100)) / 100),
int(ind_dt[x] % 100)) for x in range(len(ind_dt))
]
# HACK! certain events need an offset because BBG have invalid dates
if ticker_index in self._offset_events:
data_frame.index = data_frame.index + timedelta(
days=self._offset_events[ticker_index])
######## join together event dates/date-time/fields in one data frame
data_frame = event_date_time_frame.join(data_frame, how='inner')
data_frame.index = pandas.to_datetime(data_frame.index)
data_frame.index.name = ticker_index
return data_frame
def create_event_desciptor_field(self, name, event, field):
if event is None:
return name + "." + field
else:
return name + "-" + event + "." + field
def get_all_economic_events_date_time(self):
event_names = self.get_all_economic_events()
columns = ['event-name', 'release-date-time-full']
data_frame = pandas.DataFrame(data=numpy.zeros((0, len(columns))),
columns=columns)
for event in event_names:
event_times = self.get_economic_event_date_time(event)
for time in event_times:
data_frame.append(
{
'event-name': event,
'release-date-time-full': time
},
ignore_index=True)
return data_frame
def get_all_economic_events(self):
field_names = LightEventsFactory._econ_data_frame.columns.values
event_names = [x.split('.')[0] for x in field_names if '.Date' in x]
event_names_filtered = [x for x in event_names if len(x) > 4]
# sort list alphabetically (and remove any duplicates)
return list(set(event_names_filtered))
def get_economic_event_date(self, name, event=None):
return LightEventsFactory._econ_data_frame[
self.create_event_desciptor_field(name, event, ".release-dt")]
def get_economic_event_ret_over_custom_event_day(self,
data_frame_in,
name,
event,
start,
end,
lagged=False,
NYC_cutoff=10):
# get the times of events
event_dates = self.get_economic_event_date_time(name, event)
return super(LightEventsFactory,
self).get_economic_event_ret_over_custom_event_day(
data_frame_in,
event_dates,
name,
event,
start,
end,
lagged=lagged,
NYC_cutoff=NYC_cutoff)
def get_economic_event_vol_over_event_day(self,
vol_in,
name,
event,
start,
end,
realised=False):
return self.get_economic_event_ret_over_custom_event_day(
vol_in, name, event, start, end, lagged=realised)
# return super(EventsFactory, self).get_economic_event_ret_over_event_day(vol_in, name, event, start, end, lagged = realised)
def get_daily_moves_over_event(self):
# TODO
pass
# return only US events etc. by dates
def get_intraday_moves_over_event(self,
data_frame_rets,
cross,
event_fx,
event_name,
start,
end,
vol,
mins=3 * 60,
min_offset=0,
create_index=False,
resample=False,
freq='minutes'):
tsf = TimeSeriesFilter()
ef_time_frame = self.get_economic_event_date_time_dataframe(
event_fx, event_name)
ef_time_frame = tsf.filter_time_series_by_date(start, end,
ef_time_frame)
return self.get_intraday_moves_over_custom_event(
data_frame_rets,
ef_time_frame,
vol,
mins=mins,
min_offset=min_offset,
create_index=create_index,
resample=resample,
freq=freq) #, start, end)
def get_surprise_against_intraday_moves_over_event(
self,
data_frame_cross_orig,
cross,
event_fx,
event_name,
start,
end,
offset_list=[1, 5, 30, 60],
add_surprise=False,
surprise_field='survey-average'):
tsf = TimeSeriesFilter()
fields = [
'actual-release', 'survey-median', 'survey-average', 'survey-high',
'survey-low'
]
ef_time_frame = self.get_economic_event_date_time_fields(
fields, event_fx, event_name)
ef_time_frame = tsf.filter_time_series_by_date(start, end,
ef_time_frame)
return self.get_surprise_against_intraday_moves_over_custom_event(
data_frame_cross_orig,
ef_time_frame,
cross,
event_fx,
event_name,
start,
end,
offset_list=offset_list,
add_surprise=add_surprise,
surprise_field=surprise_field)
class LightEventsFactory(EventStudy):
_econ_data_frame = None
# where your HDF5 file is stored with economic data MUST CHANGE!!
_hdf5_file_econ_file = "somefilnename.h5"
### manual offset for certain events where Bloomberg displays the wrong date (usually because of time differences)
_offset_events = {'AUD-Australia Labor Force Employment Change SA.release-dt' : 1}
def __init__(self):
super(EventStudy, self).__init__()
self.config = ConfigManager()
self.logger = LoggerManager().getLogger(__name__)
self.time_series_filter = TimeSeriesFilter()
self.time_series_io = TimeSeriesIO()
if (LightEventsFactory._econ_data_frame is None):
self.load_economic_events()
return
def load_economic_events(self):
LightEventsFactory._econ_data_frame = self.time_series_io.read_time_series_cache_from_disk(self._hdf5_file_econ_file)
def harvest_category(self, category_name):
cat = self.config.get_categories_from_tickers_selective_filter(category_name)
for k in cat:
time_series_request = self.time_series_factory.populate_time_series_request(k)
data_frame = self.time_series_factory.harvest_time_series(time_series_request)
# TODO allow merge of multiple sources
return data_frame
def get_economic_events(self):
return LightEventsFactory._econ_data_frame
def dump_economic_events_csv(self, path):
LightEventsFactory._econ_data_frame.to_csv(path)
def get_economic_event_date_time(self, name, event = None, csv = None):
ticker = self.create_event_desciptor_field(name, event, "release-date-time-full")
if csv is None:
data_frame = LightEventsFactory._econ_data_frame[ticker]
data_frame.index = LightEventsFactory._econ_data_frame[ticker]
else:
dateparse = lambda x: datetime.datetime.strptime(x, '%d/%m/%Y %H:%M')
data_frame = pandas.read_csv(csv, index_col=0, parse_dates = True, date_parser=dateparse)
data_frame = data_frame[pandas.notnull(data_frame.index)]
start_date = datetime.datetime.strptime("01-Jan-1971", "%d-%b-%Y")
self.time_series_filter.filter_time_series_by_date(start_date, None, data_frame)
return data_frame
def get_economic_event_date_time_dataframe(self, name, event = None, csv = None):
series = self.get_economic_event_date_time(name, event, csv)
data_frame = pandas.DataFrame(series.values, index=series.index)
data_frame.columns.name = self.create_event_desciptor_field(name, event, "release-date-time-full")
return data_frame
def get_economic_event_date_time_fields(self, fields, name, event = None):
### acceptible fields
# actual-release
# survey-median
# survey-average
# survey-high
# survey-low
# survey-high
# number-observations
# release-dt
# release-date-time-full
# first-revision
# first-revision-date
ticker = []
# construct tickers of the form USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.actual-release
for i in range(0, len(fields)):
ticker.append(self.create_event_desciptor_field(name, event, fields[i]))
# index on the release-dt field eg. 20101230 (we shall convert this later)
ticker_index = self.create_event_desciptor_field(name, event, "release-dt")
######## grab event date/times
event_date_time = self.get_economic_event_date_time(name, event)
date_time_fore = event_date_time.index
# create dates for join later
date_time_dt = [datetime.datetime(
date_time_fore[x].year,
date_time_fore[x].month,
date_time_fore[x].day)
for x in range(len(date_time_fore))]
event_date_time_frame = pandas.DataFrame(event_date_time.index, date_time_dt)
event_date_time_frame.index = date_time_dt
######## grab event date/fields
data_frame = LightEventsFactory._econ_data_frame[ticker]
data_frame.index = LightEventsFactory._econ_data_frame[ticker_index]
data_frame = data_frame[data_frame.index != 0] # eliminate any 0 dates (artifact of Excel)
data_frame = data_frame[pandas.notnull(data_frame.index)] # eliminate any NaN dates (artifact of Excel)
ind_dt = data_frame.index
# convert yyyymmdd format to datetime
data_frame.index = [datetime.datetime(
int((ind_dt[x] - (ind_dt[x] % 10000))/10000),
int(((ind_dt[x] % 10000) - (ind_dt[x] % 100))/100),
int(ind_dt[x] % 100)) for x in range(len(ind_dt))]
# HACK! certain events need an offset because BBG have invalid dates
if ticker_index in self._offset_events:
data_frame.index = data_frame.index + timedelta(days=self._offset_events[ticker_index])
######## join together event dates/date-time/fields in one data frame
data_frame = event_date_time_frame.join(data_frame, how='inner')
data_frame.index = pandas.to_datetime(data_frame.index)
data_frame.index.name = ticker_index
return data_frame
def create_event_desciptor_field(self, name, event, field):
if event is None:
return name + "." + field
else:
return name + "-" + event + "." + field
def get_all_economic_events_date_time(self):
event_names = self.get_all_economic_events()
columns = ['event-name', 'release-date-time-full']
data_frame = pandas.DataFrame(data=numpy.zeros((0,len(columns))), columns=columns)
for event in event_names:
event_times = self.get_economic_event_date_time(event)
for time in event_times:
data_frame.append({'event-name':event, 'release-date-time-full':time}, ignore_index=True)
return data_frame
def get_all_economic_events(self):
field_names = LightEventsFactory._econ_data_frame.columns.values
event_names = [x.split('.')[0] for x in field_names if '.Date' in x]
event_names_filtered = [x for x in event_names if len(x) > 4]
# sort list alphabetically (and remove any duplicates)
return list(set(event_names_filtered))
def get_economic_event_date(self, name, event = None):
return LightEventsFactory._econ_data_frame[
self.create_event_desciptor_field(name, event, ".release-dt")]
def get_economic_event_ret_over_custom_event_day(self, data_frame_in, name, event, start, end, lagged = False,
NYC_cutoff = 10):
# get the times of events
event_dates = self.get_economic_event_date_time(name, event)
return super(LightEventsFactory, self).get_economic_event_ret_over_custom_event_day(data_frame_in, event_dates, name, event, start, end,
lagged = lagged, NYC_cutoff = NYC_cutoff)
def get_economic_event_vol_over_event_day(self, vol_in, name, event, start, end, realised = False):
return self.get_economic_event_ret_over_custom_event_day(vol_in, name, event, start, end,
lagged = realised)
# return super(EventsFactory, self).get_economic_event_ret_over_event_day(vol_in, name, event, start, end, lagged = realised)
def get_daily_moves_over_event(self):
# TODO
pass
# return only US events etc. by dates
def get_intraday_moves_over_event(self, data_frame_rets, cross, event_fx, event_name, start, end, vol, mins = 3 * 60,
min_offset = 0, create_index = False, resample = False, freq = 'minutes'):
tsf = TimeSeriesFilter()
ef_time_frame = self.get_economic_event_date_time_dataframe(event_fx, event_name)
ef_time_frame = tsf.filter_time_series_by_date(start, end, ef_time_frame)
return self.get_intraday_moves_over_custom_event(data_frame_rets, ef_time_frame,
vol, mins = mins, min_offset = min_offset,
create_index = create_index, resample = resample, freq = freq)#, start, end)
def get_surprise_against_intraday_moves_over_event(self, data_frame_cross_orig, cross, event_fx, event_name, start, end,
offset_list = [1, 5, 30, 60], add_surprise = False,
surprise_field = 'survey-average'):
tsf = TimeSeriesFilter()
fields = ['actual-release', 'survey-median', 'survey-average', 'survey-high', 'survey-low']
ef_time_frame = self.get_economic_event_date_time_fields(fields, event_fx, event_name)
ef_time_frame = tsf.filter_time_series_by_date(start, end, ef_time_frame)
return self.get_surprise_against_intraday_moves_over_custom_event(data_frame_cross_orig, ef_time_frame, cross, event_fx, event_name, start, end,
offset_list = offset_list, add_surprise = add_surprise,
surprise_field = surprise_field)