def __init__(self):
self.data_access = DataAccess()
self.symbols = []
self.start_date = None
self.end_date = None
self.field = 'Adj Close'
self.condition = Condition()
self.matrix = None
self.num_events = 0
self.oneEventPerEquity = True
def __init__(self):
self.data_access = DataAccess()
self.symbols = []
self.start_date = None
self.end_date = None
self.field = 'adjusted_close'
self.condition = Condition()
self.matrix = None
self.num_events = 0
self.oneEventPerEquity = True
class EventFinder(object):
def __init__(self):
self.data_access = DataAccess()
self.symbols = []
self.start_date = None
self.end_date = None
self.field = 'adjusted_close'
self.condition = Condition()
self.matrix = None
self.num_events = 0
self.oneEventPerEquity = True
def generate_filename(self):
return '%s%s%s%s%s%s' % (''.join(self.symbols), self.start_date.strftime('%Y-%m-%d'),
self.end_date.strftime('%Y-%m-%d'), self.field, self.condition.id,
str(self.oneEventPerEquity))
def search(self, oneEventPerEquity=True, useCache=True, save=True):
self.oneEventPerEquity = oneEventPerEquity
# 1. Load the data if requested and available
self.matrix = self.data_access.load(self.generate_filename(), '.evt_matrix')
if useCache and self.matrix is not None:
pass
else:
# 2. Data was not loaded
# 2.1 Get the dates, and Download/Import the data
nyse_dates = DateUtils.nyse_dates(start=self.start_date, end=self.end_date)
data = self.data_access.get_data(self.symbols, nyse_dates[0], nyse_dates[-1], self.field)
# Special case
if len(data.columns) == 1:
data.columns = self.symbols
# 2.2 Create and fill the matrix of events
data = data[self.start_date:self.end_date]
self.matrix = pd.DataFrame(index=data.index, columns=self.symbols)
for symbol in self.symbols:
i = 0
for item in data[symbol][1:]:
e = self.condition.function(i, item, data[symbol][1:])
if e:
self.matrix[symbol][i+1] = 1
if oneEventPerEquity == True:
break
i = i + 1
# 3. Calculate other results and save if requested
# Reduce Matrix: Sum each row and columns: if is greater than 0 there is an event
self.matrix = self.matrix[self.matrix.fillna(value=0).sum(axis=1) > 0]
valid_cols = self.matrix.columns[self.matrix.fillna(value=0).sum(axis=0) > 0].values
self.matrix = self.matrix[valid_cols]
# 3.2 Create list of events
self.list = pd.Series(index=self.matrix.index, name='Equity')
for idx, row in self.matrix.iterrows():
equity = row[row == 1].index[0]
self.list.loc[idx] = equity
# 3.3 Save
self.num_events = len(self.list)
if save:
self.data_access.save(self.matrix, self.generate_filename(), '.evt_matrix')
def decrease(decrease):
condition = Condition()
condition.id = 'decrease' + str(decrease)
condition.function = lambda i, item, data: (data[i - 1] - item > decrease)
return condition
class EventFinder(object):
def __init__(self):
self.data_access = DataAccess()
self.symbols = []
self.start_date = None
self.end_date = None
self.field = 'Adj Close'
self.condition = Condition()
self.matrix = None
self.num_events = 0
self.oneEventPerEquity = True
def generate_filename(self):
return '%s%s%s%s%s%s' % (
''.join(self.symbols), self.start_date.strftime('%Y-%m-%d'),
self.end_date.strftime('%Y-%m-%d'), self.field, self.condition.id,
str(self.oneEventPerEquity))
def search(self, oneEventPerEquity=True, useCache=True, save=True):
self.oneEventPerEquity = oneEventPerEquity
# 1. Load the data if requested and available
self.matrix = self.data_access.load(self.generate_filename(),
'.evt_matrix')
if useCache and self.matrix is not None:
pass
else:
# 2. Data was not loaded
# 2.1 Get the dates, and Download/Import the data
nyse_dates = DateUtils.nyse_dates(start=self.start_date,
end=self.end_date)
data = self.data_access.get_data(self.symbols, nyse_dates[0],
nyse_dates[-1], self.field)
# Special case
if len(data.columns) == 1:
data.columns = self.symbols
# 2.2 Create and fill the matrix of events
data = data[self.start_date:self.end_date]
self.matrix = pd.DataFrame(index=data.index, columns=self.symbols)
for symbol in self.symbols:
i = 0
for item in data[symbol][1:]:
e = self.condition.function(i, item, data[symbol][1:])
if e:
self.matrix[symbol][i + 1] = 1
if oneEventPerEquity == True:
break
i = i + 1
# 3. Calculate other results and save if requested
# Reduce Matrix: Sum each row and columns: if is greater than 0 there is an event
self.matrix = self.matrix[self.matrix.fillna(value=0).sum(axis=1) > 0]
valid_cols = self.matrix.columns[self.matrix.fillna(value=0).sum(
axis=0) > 0].values
self.matrix = self.matrix[valid_cols]
# 3.2 Create list of events
self.list = pd.Series(index=self.matrix.index,
name='Equity',
dtype=str)
for idx, row in self.matrix.iterrows():
equity = row[row == 1].index[0]
self.list.ix[idx] = equity
# 3.3 Save
self.num_events = len(self.list)
if save:
self.data_access.save(self.matrix, self.generate_filename(),
'.evt_matrix')
def went_below(below):
condition = Condition()
condition.id = 'went_below' + str(below)
condition.function = lambda i, item, data: (data[i - 1] >= below and item <
below)
return condition
def went_above(above):
condition = Condition()
condition.id = 'went_above' + str(above)
condition.function = lambda i, item, data: (data[i - 1] <= above and item >
above)
return condition
def increase(increase):
condition = Condition()
condition.id = 'increase' + str(increase)
condition.function = lambda i, item, data: (item - data[i - 1] > increase)
return condition
def increase(increase):
condition = Condition()
condition.id = 'increase' + str(increase)
condition.function = lambda i, item, data: (item - data[i-1] > increase)
return condition
def went_above(above):
condition = Condition()
condition.id = 'went_above' + str(above)
condition.function = lambda i, item, data: (data[i-1] <= above and item > above)
return condition
def decrease(decrease):
condition = Condition()
condition.id = 'decrease' + str(decrease)
condition.function = lambda i, item, data: (data[i-1] - item > decrease)
return condition
def went_below(below):
condition = Condition()
condition.id = 'went_below' + str(below)
condition.function = lambda i, item, data: (data[i-1] >= below and item < below)
return condition
