def test_05_(self) :
msg = {}
for stock, ret in T.extract() :
value_1 = ret['Adj Close'].pct_change() + 1
_risk, _returns = HELPER.findRiskAndReturn(value_1, period=HELPER.YEAR)
value_1 = [_returns,_risk,_returns/_risk]
value_1 = dict(zip(['returns','risk','sharpe'],value_1))
value_2 = ret['Adj Close'].pct_change()
_risk, _returns = HELPER.findRiskAndReturn(value_2, period=HELPER.YEAR)
value_2 = [_returns,_risk,_returns/_risk]
value_2 = dict(zip(['returns','risk','sharpe'],value_2))
msg[stock] = value_2
msg[stock + "+ 1"] = value_1
msg = pd.DataFrame(msg)
logging.info(msg.T)
def test_04_(self) :
msg = {}
for stock, ret in T.extract() :
daily = HELPER.findDailyReturns(ret)
_risk, _returns = HELPER.findRiskAndReturn(daily, period=HELPER.YEAR)
value = [_returns,_risk,_returns/_risk]
value = dict(zip(['returns','risk','sharpe'],value))
msg[stock] = value
msg = pd.DataFrame(msg).T
logging.info(msg)
def find(cls, data, **kwargs):
target = "period"
period = kwargs.get(target, 0)
target = "risk_free_rate"
risk_free_rate = kwargs.get(target, 0.02)
target = "span"
span = kwargs.get(target, 2 * FINANCE.YEAR)
data, risk_free_rate, period, span = cls.validate(
data, risk_free_rate, period, span)
data = FINANCE.findDailyReturns(data, period)
if data is None:
ret = dict(zip(cls.key_list, [0, 0, 0, 0]))
return ret
risk, returns = FINANCE.findRiskAndReturn(data, span)
sharpe = 0
if risk != 0:
sharpe = (returns - risk_free_rate) / risk
ret = dict(zip(cls.key_list, [returns, risk, sharpe, len(data)]))
return ret
def _getSharpe(cls, data):
risk, returns = FINANCE.findRiskAndReturn(data, period=FINANCE.YEAR)
ret = TRANSFORM_SHARPE.sharpe(risk, returns, cls.risk_free_rate)
return ret