def returns(ts, **kwargs):
'''
Compute returns on the given period
@param ts : time serie to process
@param kwargs.type: gross or simple returns
@param delta : period betweend two computed returns
@param start : with end, will return the return betweend this elapsed time
@param period : delta is the number of lines/periods provided
@param end : so said
@param cumulative: compute cumulative returns
'''
type = kwargs.get('type', 'net')
cumulative = kwargs.get('cumulative', False)
if type == 'net':
relative = 0
else:
relative = 1 # gross
start = kwargs.get('start', None)
end = kwargs.get('end', dt.datetime.today())
delta = kwargs.get('delta', None)
period = kwargs.get('period', 1)
if isinstance(start, dt.datetime):
log.debug('{} / {} -1'.format(ts[end], ts[start]))
return ts[end] / ts[start] - 1 + relative
elif isinstance(delta, pd.DateOffset) or isinstance(delta, dt.timedelta):
#FIXME timezone problem
ts = reIndexDF(ts, delta=delta)
period = 1
rets_df = ts / ts.shift(period) - 1 + relative
if cumulative:
return rets_df.cumprod()
return rets_df[1:]
def returns(ts, **kwargs):
'''
Compute returns on the given period
@param ts : time serie to process
@param kwargs.type: gross or simple returns
@param delta : period betweend two computed returns
@param start : with end, will return the return betweend this elapsed time
@param period : delta is the number of lines/periods provided
@param end : so said
@param cumulative: compute cumulative returns
'''
type = kwargs.get('type', 'net')
cumulative = kwargs.get('cumulative', False)
if type == 'net':
relative = 0
else:
relative = 1 # gross
start = kwargs.get('start', None)
end = kwargs.get('end', dt.datetime.today())
delta = kwargs.get('delta', None)
period = kwargs.get('period', 1)
if isinstance(start, dt.datetime):
log.debug('{} / {} -1'.format(ts[end], ts[start]))
return ts[end] / ts[start] - 1 + relative
elif isinstance(delta, pd.DateOffset) or isinstance(delta, dt.timedelta):
#FIXME timezone problem
ts = reIndexDF(ts, delta=delta)
period = 1
rets_df = ts / ts.shift(period) - 1 + relative
if cumulative:
return rets_df.cumprod()
return rets_df[1:]
def getHistoricalQuotes(self, symbol, index, market=None):
assert (isinstance(index, pd.Index))
source = 'yahoo'
try:
quotes = DataReader(symbol, source, index[0], index[-1])
except:
log.error('** Could not get {} quotes'.format(symbol))
return pd.DataFrame()
if index.freq != pd.datetools.BDay() or index.freq != pd.datetools.Day():
#NOTE reIndexDF has a column arg but here not provided
quotes = utils.reIndexDF(quotes, delta=index.freq, reset_hour=False)
if not quotes.index.tzinfo:
quotes.index = quotes.index.tz_localize(self.tz)
quotes.columns = utils.Fields.QUOTES
return quotes
def getHistoricalQuotes(self, symbol, index, market=None):
assert (isinstance(index, pd.Index))
source = 'yahoo'
try:
quotes = DataReader(symbol, source, index[0], index[-1])
except:
log.error('** Could not get {} quotes'.format(symbol))
return pd.DataFrame()
if index.freq != pd.datetools.BDay() or index.freq != pd.datetools.Day():
#NOTE reIndexDF has a column arg but here not provided
quotes = utils.reIndexDF(quotes, delta=index.freq, reset_hour=False)
if not quotes.index.tzinfo:
quotes.index = quotes.index.tz_localize(self.tz)
quotes.columns = utils.Fields.QUOTES
return quotes
def average_returns(ts, **kwargs):
''' Compute geometric average returns from a returns time serie'''
type = kwargs.get('type', 'net')
if type == 'net':
relative = 0
else:
relative = -1 # gross
start = kwargs.get('start', ts.index[0])
end = kwargs.get('end', ts.index[len(ts.index) - 1])
delta = kwargs.get('delta', ts.index[1] - ts.index[0])
period = kwargs.get('period', None)
if isinstance(period, int):
pass
else:
ts = reIndexDF(ts, start=start, end=end, delta=delta)
period = 1
avg_ret = 1
for idx in range(len(ts.index)):
if idx % period == 0:
avg_ret *= (1 + ts[idx] + relative)
return avg_ret - 1
def average_returns(ts, **kwargs):
''' Compute geometric average returns from a returns time serie'''
type = kwargs.get('type', 'net')
if type == 'net':
relative = 0
else:
relative = -1 # gross
start = kwargs.get('start', ts.index[0])
end = kwargs.get('end', ts.index[len(ts.index) - 1])
delta = kwargs.get('delta', ts.index[1] - ts.index[0])
period = kwargs.get('period', None)
if isinstance(period, int):
pass
else:
ts = reIndexDF(ts, start=start, end=end, delta=delta)
period = 1
avg_ret = 1
for idx in range(len(ts.index)):
if idx % period == 0:
avg_ret *= (1 + ts[idx] + relative)
return avg_ret - 1
