def cal_earning(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
stock_quote = StockAsset.get_stock(stock_id).quote
p = stock_quote.tclose
pe = stock_fdmt.pettm
eps = p / pe
return eps
def cal_halpha(self, stock_id):
stock_quote = StockAsset.get_stock(stock_id).quote
close = stock_quote.tcloseaf
close = close.replace(0.0, method='pad')
ret = close.pct_change()
sz = Asset.load_nav_series('120000016')
bret = sz.pct_change()
ret = ret.resample('m').sum().iloc[:-1]
bret = bret.resample('m').sum().iloc[:-1]
common_index = ret.index.intersection(bret.index)
ret = ret.loc[common_index]
bret = bret.loc[common_index]
ser = pd.Series()
if len(common_index) < 60:
return ser
for i in range(60, len(common_index)):
tmp_dates = common_index[i - 59:i + 1]
y = ret.loc[tmp_dates].values
x = bret.loc[tmp_dates].values.reshape(-1, 1)
mod = LinearRegression().fit(x, y)
ser.loc[tmp_dates[-1]] = mod.intercept_
today = get_today()
ser.loc[today] = np.nan
ser = ser.resample('d').last().fillna(method='pad')
return ser
def cal_dastd(self, stock_id, period=23):
stock_quote = StockAsset.get_stock(stock_id).quote
close = stock_quote.tclose
close = close.replace(0.0, method='pad')
ret = close.pct_change()
ret = ret.rolling(period).apply(lambda x: pow(pow(x, 2).mean(), 0.5))
return ret
def cal_mom(self, stock_id, period=23):
stock_quote = StockAsset.get_stock(stock_id).quote
close = stock_quote.tclose
close = close.replace(0.0, method='pad')
ret = close.pct_change()
tr = stock_quote.turnrate
ret_tr = (ret * tr).rolling(period).sum()
weight = tr.rolling(period).sum()
# mom = ret.rolling(period).mean()
mom = ret_tr / weight
return mom
def cal_hilo(self, stock_id, period=23):
stock_quote = StockAsset.get_stock(stock_id).quote
high = stock_quote.thigh
high = high.replace(0.0, method='pad')
hi = high.rolling(period).max()
low = stock_quote.tlow
low = low.replace(0.0, method='pad')
lo = low.rolling(period).min()
hilo = np.log(hi / low)
return hilo
def cal_indexposure(self, stock_id):
stock_quote = StockAsset.get_stock(stock_id).quote
stock_info = StockAsset.all_stock_info()
sf = pd.DataFrame(index=stock_quote.index)
sf_ind = stock_info.loc[stock_id].sk_swlevel1code
if sf_ind == self.sf_ind:
sf_exposure = 1
else:
sf_exposure = 0
sf['exposure'] = sf_exposure
return sf.exposure
def cal_egro(self, stock_id):
def cal_egro_single(x):
mod = LinearRegression().fit(np.arange(5).reshape(-1, 1), x)
return mod.coef_[0] / np.mean(x)
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
stock_quote = StockAsset.get_stock(stock_id).quote
p = stock_quote.tclose
pe = stock_fdmt.pettm
eps = p / pe
eps = eps[eps.diff() > 0.001]
eps_y = pd.Series()
for k, v in eps.groupby(eps.index.strftime('%Y')):
eps_y.loc[v.index[-1]] = v.values[-1]
eps_y = eps_y.rolling(5).apply(cal_egro_single)
today = datetime.now()
today_idx = pd.tslib.Timestamp(today.year, today.month, today.day)
eps_y.loc[today_idx] = np.nan
eps_y = eps_y.resample('d').last().fillna(method='pad').dropna()
return eps_y
def cal_cmra(self, stock_id):
stock_quote = StockAsset.get_stock(stock_id).quote
close = stock_quote.tcloseaf
close = close.replace(0.0, method='pad')
nav = close / close.iloc[0]
zt = np.log(nav)
zt_max = zt.rolling(window=252 * 5).max()
zt_min = zt.rolling(window=252 * 5).min()
cmra = np.log((1 + zt_max) / (1 + zt_min))
cmra = cmra.fillna(method='pad')
cmra = cmra.dropna()
return cmra
def cal_btsg(self, stock_id):
stock_quote = StockAsset.get_stock(stock_id).quote
close = stock_quote.tcloseaf
close = close.replace(0.0, method='pad')
ret = close.pct_change()
sz = Asset.load_nav_series('120000016')
bret = sz.pct_change()
ret = ret.resample('m').sum().iloc[:-1]
bret = bret.resample('m').sum().iloc[:-1]
common_index = ret.index.intersection(bret.index)
ret = ret.loc[common_index]
bret = bret.loc[common_index]
ser = pd.Series()
if len(common_index) < 60:
return ser
for i in range(60, len(common_index)):
tmp_dates = common_index[:i + 1]
y = ret.loc[tmp_dates].values
x = bret.loc[tmp_dates].values.reshape(-1, 1)
x = sm.add_constant(x)
mod = sm.OLS(y, x).fit()
beta = mod.params[1]
sigma = mod.resid.std()
btsg = pow(beta * sigma, 0.5)
ser.loc[tmp_dates[-1]] = btsg
today = get_today()
ser.loc[today] = np.nan
ser = ser.resample('d').last().fillna(method='pad')
return ser
def cal_cashrt(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
cashrt = stock_fdmt.cashrt
return cashrt
def cal_ltmliabtota(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
ltmliabtota = stock_fdmt.ltmliabtota
return ltmliabtota
def cal_sgpmargin(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
sgpmargin = stock_fdmt.sgpmargin
return sgpmargin
def cal_currentrt(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
currentrt = stock_fdmt.currentrt
return currentrt
def cal_roe(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
roe = stock_fdmt.roedilutedcut
return roe
def cal_roa(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
roa = stock_fdmt.roa
return roa
def cal_bp(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
bp = 1 / stock_fdmt.pb
return bp
def cal_turnover(self, stock_id, period=23):
stock_quote = StockAsset.get_stock(stock_id).quote
tr = stock_quote.turnrate
tr = tr.rolling(period).mean()
return tr
def cal_equtotliab(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
equtotliab = stock_fdmt.equtotliab
return equtotliab
def cal_size(self, stock_id):
stock_quote = StockAsset.get_stock(stock_id).quote
totmktcap = stock_quote.totmktcap
return totmktcap
def cal_ep(self, stock_id):
stock_fdmt = StockAsset.get_stock(stock_id).fdmt
ep = 1 / stock_fdmt.pettm
return ep