Beispiel #1
0
def queryVolatile(sym,startdate,dbconn):
    
    df=stockeod.getAllDataFrame(sym,startdate,dbconn)
    #df['chg']=pd.Series(np.random.randn(sLength), index=df.index)
    df['chg']=1
    df['lschg']=1
    p1=0.0
    p0=0.0
    for index, row in df.iterrows():
        if index==0:
            df.ix[0,['chg']] = 0
        else:            
            p1 =  df.ix[index,'sadjclose']
            p0 =  df.ix[index-1,'sadjclose']
            pclose = df.ix[index,'sclose']
            plow = df.ix[index,'slow']
            chg = 100*(p1 / p0 - 1)
            #prev_close = pclose / (chg/100+1)
            lschg = abs((plow/pclose-1)*100)
            #print index,plow,prev_close,chg,lschg
            #lschg = lschg - chg
            
                
            #if chg>=0:
            #    chg+=0.4
            #else:
            #    chg-=0.4
                    
            df.ix[index,'chg'] = chg #int(round(chg))
            df.ix[index,'lschg'] = lschg
            
    #print df[['symbol','sdate','sopen','sadjclose','chg','lschg']]
    print df
    #bins = [-1000,-5,-3,-1,1,3,5,1000]
    #cats = pd.cut(df['chg'],bins)
    #cats.plot(kind='kde')
    fig = plt.figure()
    ax1 = fig.add_subplot(2,1,1)
    ax2 = fig.add_subplot(2,1,2)
    ax1.set_xlim([-20,20])
    mybins=[-15,-5,-3,-1,1,3,5,15]
    
    cats = pd.cut(df['chg'],mybins)
    print "change percent\n", pd.value_counts(cats)
    
    shadowbins=[0,1,3,5,15]
    shadowcats = pd.cut(df['lschg'],shadowbins)
    print "shadow line change percent\n", pd.value_counts(shadowcats)
    df['chg'].hist(ax=ax1,bins=mybins)
    df['chg'].plot(ax=ax1,kind='kde')
    df['lschg'].hist(ax=ax2,bins=shadowbins)
    df['lschg'].plot(ax=ax2,kind='kde')
    plt.show()
Beispiel #2
0
def queryVolatile(sym, startdate, dbconn):

    df = stockeod.getAllDataFrame(sym, startdate, dbconn)
    #df['chg']=pd.Series(np.random.randn(sLength), index=df.index)
    df['chg'] = 1
    df['lschg'] = 1
    p1 = 0.0
    p0 = 0.0
    for index, row in df.iterrows():
        if index == 0:
            df.ix[0, ['chg']] = 0
        else:
            p1 = df.ix[index, 'sadjclose']
            p0 = df.ix[index - 1, 'sadjclose']
            pclose = df.ix[index, 'sclose']
            plow = df.ix[index, 'slow']
            chg = 100 * (p1 / p0 - 1)
            #prev_close = pclose / (chg/100+1)
            lschg = abs((plow / pclose - 1) * 100)
            #print index,plow,prev_close,chg,lschg
            #lschg = lschg - chg

            #if chg>=0:
            #    chg+=0.4
            #else:
            #    chg-=0.4

            df.ix[index, 'chg'] = chg  #int(round(chg))
            df.ix[index, 'lschg'] = lschg

    #print df[['symbol','sdate','sopen','sadjclose','chg','lschg']]
    print df
    #bins = [-1000,-5,-3,-1,1,3,5,1000]
    #cats = pd.cut(df['chg'],bins)
    #cats.plot(kind='kde')
    fig = plt.figure()
    ax1 = fig.add_subplot(2, 1, 1)
    ax2 = fig.add_subplot(2, 1, 2)
    ax1.set_xlim([-20, 20])
    mybins = [-15, -5, -3, -1, 1, 3, 5, 15]

    cats = pd.cut(df['chg'], mybins)
    print "change percent\n", pd.value_counts(cats)

    shadowbins = [0, 1, 3, 5, 15]
    shadowcats = pd.cut(df['lschg'], shadowbins)
    print "shadow line change percent\n", pd.value_counts(shadowcats)
    df['chg'].hist(ax=ax1, bins=mybins)
    df['chg'].plot(ax=ax1, kind='kde')
    df['lschg'].hist(ax=ax2, bins=shadowbins)
    df['lschg'].plot(ax=ax2, kind='kde')
    plt.show()
Beispiel #3
0
def quotient(sym,startdate,dbconn):    
    df=stockeod.getAllDataFrame(sym,startdate,dbconn)

    df['hp']=0.
    df['filt']=0.
    df['nrf']=1.0
    df['quo']=1.0
    df['peak']=0.
    #print type(df.loc[1,'sadjclose'])
    #print df.loc[0,'sadjclose']
    #return
    print "alpha1,",alpha1
    print "a1,",a1
    print "b1,",b1
    #peak1=0.0
    for index, row in df.iterrows():
        if index>=2:
            price = df.loc[index,'sadjclose']
            price1= df.loc[index-1,'sadjclose']
            price2= df.loc[index-2,'sadjclose']
            
            #alpha1 = (math.cosh(math.sqrt(2) * math.pi / 100) + math.sinh (math.sqrt(2) * math.pi / 100) - 1) / math.cosh(math.sqrt(2) * math.pi / 100)
            #alpha1 = (math.cos(0.707*math.pi*2 / 100) + math.sin (0.707*math.pi*2 / 100) - 1) / math.cos(0.707*math.pi*2 / 100)
            hp1 = df.loc[index-1,'hp']
            hp2 = df.loc[index-2,'hp']
            #hp0 = (1 - alpha1 / 2)**2 * (price - 2 * price1 + price2) + 2 * (1 - alpha1) * hp1 - (1 - alpha1)**2 * hp2;
            hp0 = (1 - alpha1 / 2)*(1 - alpha1 / 2) * (price - 2 * price1 + price2) + 2 * (1 - alpha1) * hp1 - (1 - alpha1)*(1 - alpha1) * hp2;
            df.loc[index,'hp'] = hp0
            filt1 = df.loc[index-1,'filt']
            filt2 = df.loc[index-2,'filt'] 
            
            #filt = EhlersSuperSmootherFilter(hp0,hp1,filt1,filt2,cutoffLength);
            filt = EhlersSuperSmootherFilter2(hp0,hp1,filt1,filt2,cutoffLength);
            #fast attack
            peak1 = df.loc[index-1,'peak']
            peak0 = peak1*0.991
            
            af = abs(filt)
            #print type(af),type(filt),type(peak0)
            if af > peak0:
                peak0 = af
            #print index,peak0,peak1
            #peak1 = peak0
            df.loc[index,'peak'] = peak0
            
            NormRoofingFilter = filt / peak0;
            Quotient1 = (NormRoofingFilter + k) / (k * NormRoofingFilter + 1);
            Quotient2 = (NormRoofingFilter + k2) / (k2 * NormRoofingFilter + 1);
            df.loc[index,'quo'] = Quotient1
            df.loc[index,'nrf'] = NormRoofingFilter

    print df
    fig = plt.figure()
    ax1 = fig.add_subplot(2,1,1) 
    ax2 = fig.add_subplot(2,1,2) 
    
    #df['nrf'].plot(ax=ax1)
    df['quo'].plot(ax=ax1)
    rsidata = rsiFunc(df['sadjclose'],14)
    #rsidata.plot(ax=ax2)
    #    date=df['sdate']
    
    ax2.plot(df['sdate'], rsidata)
    
    plt.show()
Beispiel #4
0
def quotient(sym, startdate, dbconn):
    df = stockeod.getAllDataFrame(sym, startdate, dbconn)

    df['hp'] = 0.
    df['filt'] = 0.
    df['nrf'] = 1.0
    df['quo'] = 1.0
    df['peak'] = 0.
    #print type(df.loc[1,'sadjclose'])
    #print df.loc[0,'sadjclose']
    #return
    print "alpha1,", alpha1
    print "a1,", a1
    print "b1,", b1
    #peak1=0.0
    for index, row in df.iterrows():
        if index >= 2:
            price = df.loc[index, 'sadjclose']
            price1 = df.loc[index - 1, 'sadjclose']
            price2 = df.loc[index - 2, 'sadjclose']

            #alpha1 = (math.cosh(math.sqrt(2) * math.pi / 100) + math.sinh (math.sqrt(2) * math.pi / 100) - 1) / math.cosh(math.sqrt(2) * math.pi / 100)
            #alpha1 = (math.cos(0.707*math.pi*2 / 100) + math.sin (0.707*math.pi*2 / 100) - 1) / math.cos(0.707*math.pi*2 / 100)
            hp1 = df.loc[index - 1, 'hp']
            hp2 = df.loc[index - 2, 'hp']
            #hp0 = (1 - alpha1 / 2)**2 * (price - 2 * price1 + price2) + 2 * (1 - alpha1) * hp1 - (1 - alpha1)**2 * hp2;
            hp0 = (1 - alpha1 / 2) * (1 - alpha1 / 2) * (
                price - 2 * price1 + price2) + 2 * (1 - alpha1) * hp1 - (
                    1 - alpha1) * (1 - alpha1) * hp2
            df.loc[index, 'hp'] = hp0
            filt1 = df.loc[index - 1, 'filt']
            filt2 = df.loc[index - 2, 'filt']

            #filt = EhlersSuperSmootherFilter(hp0,hp1,filt1,filt2,cutoffLength);
            filt = EhlersSuperSmootherFilter2(hp0, hp1, filt1, filt2,
                                              cutoffLength)
            #fast attack
            peak1 = df.loc[index - 1, 'peak']
            peak0 = peak1 * 0.991

            af = abs(filt)
            #print type(af),type(filt),type(peak0)
            if af > peak0:
                peak0 = af
            #print index,peak0,peak1
            #peak1 = peak0
            df.loc[index, 'peak'] = peak0

            NormRoofingFilter = filt / peak0
            Quotient1 = (NormRoofingFilter + k) / (k * NormRoofingFilter + 1)
            Quotient2 = (NormRoofingFilter + k2) / (k2 * NormRoofingFilter + 1)
            df.loc[index, 'quo'] = Quotient1
            df.loc[index, 'nrf'] = NormRoofingFilter

    print df
    fig = plt.figure()
    ax1 = fig.add_subplot(2, 1, 1)
    ax2 = fig.add_subplot(2, 1, 2)

    #df['nrf'].plot(ax=ax1)
    df['quo'].plot(ax=ax1)
    rsidata = rsiFunc(df['sadjclose'], 14)
    #rsidata.plot(ax=ax2)
    #    date=df['sdate']

    ax2.plot(df['sdate'], rsidata)

    plt.show()