区块链技术概括起来是指通过去中心化和去信任的方式集体维护一个可靠数据库的技术。其实，区块链技术并不是一种单一的、全新的技术，而是多种现有技术整合的结果，这些技术与数据库巧妙地组合在一起，形成了一种新的数据记录、传递、存储与呈现的方式
什么是量化交易？确切地说，量化交易属于人工智能的一个应用分支，它利用计算机的强大运算能力，用数学模型来模仿人的思维作出决策，通过数据建模、统计学分析、程序设计等工具
区块链技术是一种新型互联网技术，它是一种公开的、分布式的账簿，可以有效核查和永久记录各方之间的交易。具体来说，它是由网络上一个个储存区块组成的相互连接的链条，在每个区块中，储存着一定时间内网络链条中所有交流信息的数据。由于数据在每个区块上都有实时记录，也就使得区块链具备去中心化和不可篡改的特点。
1.移动平均
def MA(df,n):
MA=Series(rolling_mean(df['Close'],n),name='MA_'+str(n))
df=df.join(MA)
return df
2.指数移动平均
def EMA(df,n):
EMA=Series(ewma(df['Close'],span=n,min_periods=n-1),name='EMA_'+str(n))
df=df.join(EMA)
return df
3.动量
def MOM(df,n):
M=Series(df['Close'].diff(n),name='Momentum_'+str(n))
df=df.join(M)
return df
4.变化率
def ROC(df,n):
M=df['Close'].diff(n-1)
N=df['Close'].shift(n-1)
ROC=Series(M/N,name='ROC_'+str(n))
df=df.join(ROC)
return df
5.均幅指标
def ATR(df,n):
i=0
TR_l=[0]
while i<df.index[-1]:
TR=max(df.get_value(i+1,'High'),df.get_value(i,'Close'))-min(df.get_value(i+1,'Low'),df.get_value(i,'Close'))
TR_l.append(TR)
i=i+1
TR_s=Series(TR_l)
ATR=Series(ewma(TR_s,span=n,min_periods=n),name='ATR_'+str(n))
df=df.join(ATR)
return df
6.布林线
def BBANDS(df,n):
MA=Series(rolling_mean(df['Close'],n))
MSD=Series(rolling_std(df['Close'],n))
b1=4*MSD/MA
B1=Series(b1,name='BollingerB_'+str(n))
df=df.join(B1)
b2=(df['Close']-MA+2MSD)/(4MSD)
B2=Series(b2,name='Bollinger%b_'+str(n))
df=df.join(B2)
return df
7.转折、支撑、阻力点
def PPSR(df):
PP=Series((df['High']+df['Low']+df['Close'])/3)
R1=Series(2*PP-df['Low'])
S1=Series(2*PP-df['High'])
R2=Series(PP+df['High']-df['Low'])
S2=Series(PP-df['High']+df['Low'])
R3=Series(df['High']+2*(PP-df['Low']))
S3=Series(df['Low']-2*(df['High']-PP))
psr={'PP':PP,'R1':R1,'S1':S1,'R2':R2,'S2':S2,'R3':R3,'S3':S3}
PSR=DataFrame(psr)
df=df.join(PSR)
return df
8.随机振荡器（%K线）
def STOK(df):
SOk=Series((df['Close']-df['Low'])/(df['High']-df['Low']),name='SO%k')
df=df.join(SOk)
return df
9.随机振荡器（%D线）
def STO(df,n):
SOk=Series((df['Close']-df['Low'])/(df['High']-df['Low']),name='SO%k')
SOd=Series(ewma(SOk,span=n,min_periods=n-1),name='SO%d_'+str(n))
df=df.join(SOd)
return df
10.三重指数平滑平均线
def TRIX(df,n):
EX1=ewma(df['Close'],span=n,min_periods=n-1)
EX2=ewma(EX1,span=n,min_periods=n-1)
EX3=ewma(EX2,span=n,min_periods=n-1)
i=0
ROC_l=[0]
while i+1<=df.index[-1]:
ROC=(EX3[i+1]-EX3<i>)/EX3<i>
ROC_l.append(ROC)
i=i+1
Trix=Series(ROC_l,name='Trix_'+str(n))
df=df.join(Trix)
return df
11.平均定向运动指数
def ADX(df,n,n_ADX):
i=0
UpI=[]
DoI=[]
while i+1<=df.index[-1]:
UpMove=df.get_value(i+1,'High')-df.get_value(i,'High')
DoMove=df.get_value(i,'Low')-df.get_value(i+1,'Low')
if UpMove>DoMove and UpMove>0:
UpD=UpMove
else:UpD=0
UpI.append(UpD)
if DoMove>UpMove and DoMove>0:
DoD=DoMove
else:DoD=0
DoI.append(DoD)
i=i+1
i=0
TR_l=[0]
while i<df.index[-1]:
TR=max(df.get_value(i+1,'High'),df.get_value(i,'Close'))-min(df.get_value(i+1,'Low'),df.get_value(i,'Close'))
TR_l.append(TR)
i=i+1
TR_s=Series(TR_l)
ATR=Series(ewma(TR_s,span=n,min_periods=n))
UpI=Series(UpI)
DoI=Series(DoI)
PosDI=Series(ewma(UpI,span=n,min_periods=n-1)/ATR)
NegDI=Series(ewma(DoI,span=n,min_periods=n-1)/ATR)
ADX=Series(ewma(abs(PosDI-NegDI)/(PosDI+NegDI),span=n_ADX,min_periods=n_ADX-1),name='ADX_'+str(n)+'_'+str(n_ADX))
df=df.join(ADX)
return df
12.MACD
def MACD(df,n_fast,n_slow):
EMAfast=Series(ewma(df['Close'],span=n_fast,min_periods=n_slow-1))
EMAslow=Series(ewma(df['Close'],span=n_slow,min_periods=n_slow-1))
MACD=Series(EMAfast-EMAslow,name='MACD_'+str(n_fast)+'_'+str(n_slow))
MACDsign=Series(ewma(MACD,span=9,min_periods=8),name='MACDsign_'+str(n_fast)+'_'+str(n_slow))
MACDdiff=Series(MACD-MACDsign,name='MACDdiff_'+str(n_fast)+'_'+str(n_slow))
df=df.join(MACD)
df=df.join(MACDsign)
df=df.join(MACDdiff)
return df
13.梅斯线（高低价趋势反转）
def MassI(df):
Range=df['High']-df['Low']
EX1=ewma(Range,span=9,min_periods=8)
EX2=ewma(EX1,span=9,min_periods=8)
Mass=EX1/EX2
MassI=Series(rolling_sum(Mass,25),name='Mass Index')
df=df.join(MassI)
return df
14.涡旋指标
def Vortex(df,n):
i=0
TR=[0]
while i<df.index[-1]:
Range=max(df.get_value(i+1,'High'),df.get_value(i,'Close'))-min(df.get_value(i+1,'Low'),df.get_value(i,'Close'))
TR.append(Range)
i=i+1
i=0
VM=[0]
while i<df.index[-1]:
Range=abs(df.get_value(i+1,'High')-df.get_value(i,'Low'))-abs(df.get_value(i+1,'Low')-df.get_value(i,'High'))
VM.append(Range)
i=i+1
VI=Series(rolling_sum(Series(VM),n)/rolling_sum(Series(TR),n),name='Vortex_'+str(n))
df=df.join(VI)
return df
15.KST振荡器
def KST(df,r1,r2,r3,r4,n1,n2,n3,n4):
M=df['Close'].diff(r1-1)
N=df['Close'].shift(r1-1)
ROC1=M/N
M=df['Close'].diff(r2-1)
N=df['Close'].shift(r2-1)
ROC2=M/N
M=df['Close'].diff(r3-1)
N=df['Close'].shift(r3-1)
ROC3=M/N
M=df['Close'].diff(r4-1)
N=df['Close'].shift(r4-1)
ROC4=M/N
KST=Series(rolling_sum(ROC1,n1)+rolling_sum(ROC2,n2)2+rolling_sum(ROC3,n3)3+rolling_sum(ROC4,n4)*4,name='KST_'+str(r1)+'_'+str(r2)+'_'+str(r3)+'_'+str(r4)+'_'+str(n1)+'_'+str(n2)+'_'+str(n3)+'_'+str(n4))
df=df.join(KST)
return df
16.相对强度指标
def RSI(df,n):
i=0
UpI=[0]
DoI=[0]
while i+1<=df.index[-1]:
UpMove=df.get_value(i+1,'High')-df.get_value(i,'High')
DoMove=df.get_value(i,'Low')-df.get_value(i+1,'Low')
if UpMove>DoMove and UpMove>0:
UpD=UpMove
else:UpD=0
UpI.append(UpD)
if DoMove>UpMove and DoMove>0:
DoD=DoMove
else:DoD=0
DoI.append(DoD)
i=i+1
UpI=Series(UpI)
DoI=Series(DoI)
PosDI=Series(ewma(UpI,span=n,min_periods=n-1))
NegDI=Series(ewma(DoI,span=n,min_periods=n-1))
RSI=Series(PosDI/(PosDI+NegDI),name='RSI_'+str(n))
df=df.join(RSI)
return df
17.真实强度指标
def TSI(df,r,s):
M=Series(df['Close'].diff(1))
aM=abs(M)
EMA1=Series(ewma(M,span=r,min_periods=r-1))
aEMA1=Series(ewma(aM,span=r,min_periods=r-1))
EMA2=Series(ewma(EMA1,span=s,min_periods=s-1))
aEMA2=Series(ewma(aEMA1,span=s,min_periods=s-1))
TSI=Series(EMA2/aEMA2,name='TSI_'+str(r)+'_'+str(s))
df=df.join(TSI)
return df
18.吸筹/派发指标
def ACCDIST(df,n):
ad=(2df['Close']-df['High']-df['Low'])/(df['High']-df['Low'])df['Volume']
M=ad.diff(n-1)
N=ad.shift(n-1)
ROC=M/N
AD=Series(ROC,name='Acc/Dist_ROC_'+str(n))
df=df.join(AD)
return df
19.佳庆指标CHAIKIN振荡器
def Chaikin(df):
ad=(2df['Close']-df['High']-df['Low'])/(df['High']-df['Low'])df['Volume']
Chaikin=Series(ewma(ad,span=3,min_periods=2)-ewma(ad,span=10,min_periods=9),name='Chaikin')
df=df.join(Chaikin)
return df
20.资金流量与比率指标
def MFI(df,n):
PP=(df['High']+df['Low']+df['Close'])/3
i=0
PosMF=[0]
while i<df.index[-1]:
if PP[i+1]>PP<i>:
PosMF.append(PP[i+1]*df.get_value(i+1,'Volume'))
else:
PosMF.append(0)
i=i+1
PosMF=Series(PosMF)
TotMF=PP*df['Volume']
MFR=Series(PosMF/TotMF)
MFI=Series(rolling_mean(MFR,n),name='MFI_'+str(n))
df=df.join(MFI)
return df
21.能量潮指标
def OBV(df,n):
i=0
OBV=[0]
while i<df.index[-1]:
if df.get_value(i+1,'Close')-df.get_value(i,'Close')>0:
OBV.append(df.get_value(i+1,'Volume'))
if df.get_value(i+1,'Close')-df.get_value(i,'Close')==0:
OBV.append(0)
if df.get_value(i+1,'Close')-df.get_value(i,'Close')<0:
OBV.append(-df.get_value(i+1,'Volume'))
i=i+1
OBV=Series(OBV)
OBV_ma=Series(rolling_mean(OBV,n),name='OBV_'+str(n))
df=df.join(OBV_ma)
return df
22.强力指数指标
def FORCE(df,n):
F=Series(df['Close'].diff(n)*df['Volume'].diff(n),name='Force_'+str(n))
df=df.join(F)
return df
23.简易波动指标
def EOM(df,n):
EoM=(df['High'].diff(1)+df['Low'].diff(1))(df['High']-df['Low'])/(2df['Volume'])
Eom_ma=Series(rolling_mean(EoM,n),name='EoM_'+str(n))
df=df.join(Eom_ma)
return df
24.顺势指标
def CCI(df,n):
PP=(df['High']+df['Low']+df['Close'])/3
CCI=Series((PP-rolling_mean(PP,n))/rolling_std(PP,n),name='CCI_'+str(n))
df=df.join(CCI)
return df
25.估波指标
def COPP(df,n):
M=df['Close'].diff(int(n*11/10)-1)
N=df['Close'].shift(int(n*11/10)-1)
ROC1=M/N
M=df['Close'].diff(int(n*14/10)-1)
N=df['Close'].shift(int(n*14/10)-1)
ROC2=M/N
Copp=Series(ewma(ROC1+ROC2,span=n,min_periods=n),name='Copp_'+str(n))
df=df.join(Copp)
return df
26.肯特纳通道
def KELCH(df,n):
KelChM=Series(rolling_mean((df['High']+df['Low']+df['Close'])/3,n),name='KelChM_'+str(n))
KelChU=Series(rolling_mean((4df['High']-2df['Low']+df['Close'])/3,n),name='KelChU_'+str(n))
KelChD=Series(rolling_mean((-2df['High']+4df['Low']+df['Close'])/3,n),name='KelChD_'+str(n))
df=df.join(KelChM)
df=df.join(KelChU)
df=df.join(KelChD)
return df
27.终极指标（终极振荡器）
def ULTOSC(df):
i=0
TR_l=[0]
BP_l=[0]
while i<df.index[-1]:
TR=max(df.get_value(i+1,'High'),df.get_value(i,'Close'))-min(df.get_value(i+1,'Low'),df.get_value(i,'Close'))
TR_l.append(TR)
BP=df.get_value(i+1,'Close')-min(df.get_value(i+1,'Low'),df.get_value(i,'Close'))
BP_l.append(BP)
i=i+1
UltO=Series((4rolling_sum(Series(BP_l),7)/rolling_sum(Series(TR_l),7))+(2rolling_sum(Series(BP_l),14)/rolling_sum(Series(TR_l),14))+(rolling_sum(Series(BP_l),28)/rolling_sum(Series(TR_l),28)),name='Ultimate_Osc')
df=df.join(UltO)
return df
28.唐奇安通道指标
def DONCH(df,n):
i=0
DC_l=[]
while i<n-1:
DC_l.append(0)
i=i+1
i=0
while i+n-1<df.index[-1]:
DC=max(df['High'].ix[i:i+n-1])-min(df['Low'].ix[i:i+n-1])
DC_l.append(DC)
i=i+1
DonCh=Series(DC_l,name='Donchian_'+str(n))
DonCh=DonCh.shift(n-1)
df=df.join(DonCh)
return df
