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()
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()
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()
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()