Carteras

# Create a Stratey class TestStrategy_B(bt.Strategy): params = ( ('betain', 6), ('printlog', True) ) def log(self, txt, dt=None, doprint=True): ''' Logging function fot this strategy''' if self.params.printlog or doprint: dt = dt or self.datas[0].datetime.date(0) #print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close self.databetaCloseL=self.datas[0].betaCloseL self.databetaCloseC=self.datas[0].betaCloseC self.datavar_betaCloseL=self.datas[0].var_betaCloseL self.datavar_betaCloseC=self.datas[0].var_betaCloseC self.datacoef_10=self.datas[0].coef_10 self.datacoef_5=self.datas[0].coef_5 self.dataproducto=self.datas[0].producto self.dataindecile=self.datas[0].indecile self.dataoutdecile=self.datas[0].outdecile self.dataventaja=self.datas[0].ventaja self.params.betain=self.params.betain self.track_last_order = 0 self.track_last_order_date=0 self.track_last_day_order="" #self.params.betaout=self.params.betaout self.atr=bt.ind.ATR(self.datas[0],period=7) #self.stochrsi=StochRSI() # To keep track of pending orders and buy price/commission self.order = None self.buyprice = None self.buycomm = None def notify_order(self, order): if order.status in [order.Submitted, order.Accepted]: # Buy/Sell order submitted/accepted to/by broker - Nothing to do return # Check if an order has been completed # Attention: broker could reject order if not enough cash if order.status in [order.Completed]: if order.isbuy(): self.log( 'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.buyprice = order.executed.price self.buycomm = order.executed.comm else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference self.log('Close, %.2f' % self.dataclose[0]) # Check if an order is pending ... if yes, we cannot send a 2nd one if self.order: return # Check if we are in the market if not self.position: orderSize=int((self.broker.getvalue()/self.datas[0].close[0])*0.99) # Not yet ... we MIGHT BUY if ... if self.dataproducto[0]<self.dataproducto[-1]<self.dataproducto[-2]<self.dataindecile[0] and self.databetaCloseL[-1]>self.databetaCloseC[0]: self.track_last_order="Comprar" # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order=self.buy(size=orderSize) #self.order=self.buy() else: if (self.dataproducto[0] >self.dataoutdecile[0] and self.databetaCloseL[0]<self.databetaCloseC[0]) or self.dataproducto[0] >0.5 :# >self.dataproducto[-2]:#>self.dataproducto[-1]:# or self.dataproducto[0] <0.1 :# and self.dataproducto[-5]<0.25 : self.track_last_order_date = self.datas[0].datetime.date(0) self.track_last_order="Vender" # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell(size = self.position.size) # elif self.position.price *1.20 < self.datas[0].close[0] or elif self.position.price *0.70 > self.datas[0].close[0]: #self.order=self.sell(size=self.position.size) def stop(self): if self.track_last_order_date==self.datas[0].datetime.date(0): self.track_last_day_order=self.track_last_order self.log(' Ending Value %.2f' % ( self.broker.getvalue()), doprint=True)

def betas(ticker,start,end,largo, corto,window): datb=yf.download(ticker,start=start,end=end, interval="1d", progress=False) datb=datb.dropna() #introducimos el calculo de coeficiente de las lineas de regresion para largo y corto for n in [largo,corto]: lista=[] for i in range(0,len(datb)): lista.append(i) datb["X"]=lista coef=[] for i in range(0,len(datb)): X=datb["X"].iloc[i:i+n] X=np.array(X) X=X.reshape(-1,1) Y=datb["Close"].iloc[i:i+n] Y=np.array(Y) reg = LinearRegression() reg.fit(X, Y) coef.append(reg.coef_[0]) n1=str(n) datb["coef_"+n1]=coef datb[["coef_"+n1]]=datb[["coef_"+n1]].shift(n) data=pd.DataFrame() data=datb.copy() #data=data.dropna() k=str(largo) l=str(corto) prices=["Close"] dataf=pd.DataFrame() for price in prices: data["Log"+price]=np.log(data[price]).diff() for i in [largo,corto]: j=str(i) data["Log"+price+j+"P"]=data["Log"+price].where(data["Log"+price]>0.0,1e-6) data["Log"+price+j+"N"]=-data["Log"+price].where(data["Log"+price]<0.0,-1e-6) data["Log"+price+j+"P"]=data["Log"+price+j+"P"].cumsum()-data["Log"+price+j+"P"].shift(i).cumsum()+0.000025 # Atencion: añadimos este valor, con numeros bajos la distirbucion beta nos puede dar NaN data["Log"+price+j+"N"]=data["Log"+price+j+"N"].cumsum()-data["Log"+price+j+"N"].shift(i).cumsum()+0.000025 # Calulamos las 2 betas (largo y corto) #data["beta"+price+j],data["var_beta"+price+j]=beta.stats(data["Log"+price+j+"P"],data["Log"+price+j+"N"],moments="ms") data["beta"+price+j],data["var_beta"+price+j]=beta.stats(data["Log"+price+j+"P"],data["Log"+price+j+"N"],moments="ms") dataf=data[["Open","High","Low","Close","LogClose","betaClose"+k,"betaClose"+l,"var_betaClose"+k,"var_betaClose"+l]]#,"coef_16","coef_8"]] dataf['betaCloseL'] = dataf["betaClose"+k] dataf['betaCloseC'] = dataf["betaClose"+l] dataf['var_betaCloseL'] = dataf["var_betaClose"+k] dataf['var_betaCloseC'] = dataf["var_betaClose"+l] dataf["producto"]=0.5 dataf["indecile"]=0.1 dataf["outdecile"]=0.28 dataf=dataf.dropna().copy() #print(dataf) for j in range(0,len(dataf)): if j==0: a= dataf["betaCloseL"][j]+ dataf["betaCloseC"][j]+0.5 b= 3-dataf["betaCloseL"][j]- dataf["betaCloseC"][j] -0.5 dataf['producto'][j]= beta.stats(a,b,moments="m") elif j>0: A= dataf["betaCloseL"][j] +dataf["betaCloseC"][j] + dataf["producto"][j-1] B= 3- dataf["betaCloseL"][j] - dataf["betaCloseC"][j]- dataf["producto"][j-1] dataf['producto'][j] = beta.stats(A,B,moments="m") dataf["indecile"][j]=np.percentile(dataf["producto"][0:j], 2.5) dataf["outdecile"][j]=np.percentile(dataf["producto"][0:j], 35) dataf=dataf.dropna().copy() #dataf=dataf.iloc[50:] return dataf

beta.stats(50,10, moments="m")