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HP_lib.py
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HP_lib.py
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"""
#功能：通通股票分析软件框架函数库 Ver1.00
#版本：Ver1.00
#设计人：独狼荷蒲
#电话:18578755056
#QQ：2775205
#百度：荷蒲指标
#开始设计日期: 2018-07-08
#公众号:独狼股票分析
#使用者请同意最后<版权声明>
#最后修改日期:2018年9月14日
#主程序：HP_main.py
###################基本函数库##############################
使用说明
df 指标序列
tp 指标字段,例如close
n 周期数
al 字段别名,可以省略
----------------------------------------------------
import hplibx as mylib
平均移动线函数
def MA(df,tp, n) #移动平均,Moving Average
使用 df=(df,'close',5)
def EMA(df,tp, n) #指数移动平均.Exponential Moving Average
上穿函数
def CROSS(df,tp1,tp2)
#取前n周期数值函数
def REF(df,tp,n)
#取后n周期数值函数
def REFX(df,tp, n)
#Standard Deviation#标准偏差
def STDDEV(df,tp,n):
#取前n周期数值的最高价
def HHV(df,tp, n,al=''):
#取前n周期数值的最低价
def LLV(df,tp, n,al=''):
#取前n周期数值大于0的次数
def COUNT(df,tp, n,al=''):
#求前n周期数值和
def SUM(df,tp, n,al=''):
#Winner当前价格获利率
def WINNER(df,price, tp1,al=''):
#求动态移动平均。
#DMA(X,A),求X的A日动态移动平均。
def DMA(df,tp1,tp2,al=''):
#大于等于函数
def EGT(df,tp1,tp2,al=''):
#小于等于函数
def ELT(df,tp1,tp2,al=''):
#等于函数
def EQUAL(df,tp1,tp2,al=''):
#大于函数
def GT(df,tp1,tp2,al=''):
#小于函数
def LT(df,tp1,tp2,al=''):
#并且函数
def AND(df,tp1,tp2,al=''):
#或者函数
def OR(df,tp1,tp2,al=''):
###################指标库########################
def ACCDIST(df, n): #积累/分配,Accumulation/Distribution
def ADX(df, n, n_ADX): #定向运动平均指数,Average Directional Movement Index
def ATR(df, n): #平均真实范围.Average True Range
def BBANDS(df, n): #布林带.Bollinger Bands
def CCI(df, n): #商品通道指数,Commodity Channel Index
def COPP(df, n): #COPPOCK曲线,Coppock Curve
def Chaikin(df): #蔡金振荡器,Chaikin Oscillator
def DONCH(df, n): #奇安通道,Donchian Channel
def EOM(df, n): #缓解运动,Ease of Movement
def FORCE(df, n): #力指数,Force Index
def KELCH(df, n): #Keltner通道,Keltner Channel
def KST(df, r1, r2, r3, r4, n1, n2, n3, n4): # KST振荡器,KST Oscillator
def MACD(df, n_fast, n_slow):
#MACD指标信号和MACD的区别, MACD Signal and MACD difference
def MFI(df, n): #资金流量指标和比率,Money Flow Index and Ratio
def MOM(df, n): #动量.Momentum
def MassI(df): #质量指数,Mass Index
def OBV(df, n): #平衡量,On-balance volume
def PPSR(df): #支点，支撑和阻力.Pivot Points, Supports and Resistances
def ROC(df, n): #变化率.Rate of Change
def RSI(df, n): #相对强弱指标,Relative Strength Index
def STDDEV(df, n): #标准偏差,#Standard Deviation
def STO(df, n): #随机指标D,Stochastic oscillator %D
def STOK(df): #随机指标K,Stochastic oscillator %K
def TRIX(df, n): #矩阵,#Trix
def TSI(df, r, s): #真实强度指数,True Strength Index
def ULTOSC(df): #最终振荡器,Ultimate Oscillator
def Vortex(df, n): #涡指标,#Vortex Indicator
"""
import platform
import pandas as pd
import numpy
import math as m
from HP_global import *
#版本号
def VER():
return 1.00
#版本号
def Ver():
return 1.00
#聚宽股票代码转换
def jqsn(s):
if (len(s)<6 and len(s)>0):
s=s.zfill(6)+'.XSHE'
if len(s)==6:
if s[0:1]=='0':
s=s+'.XSHE'
else:
s=s+'.XSHG'
return s
##############内部函数库########################
def ema(c_list,n=12):
y_list=[]
_n = 1
for c in c_list:
if c == c_list[0]:
y = c
elif _n<n:
y= c*2/(_n+1) + (1- 2/(_n+1))*y_list[-1]
else:
y=c*2/(n+1)+(1-2/(n+1))*y_list[-1]
y_list.append(y)
_n = _n+1
return y_list
##############基本函数库#########################
def G_MA(Series,n):
G_pyver=int(platform.python_version()[0:1])
G_ma=None
if G_pyver==2:
G_MAstr='pd.rolling_mean(Series,n)'
G_ma=eval(G_MAstr)
else :
G_MAstr='Series.rolling(window=n,center=False).mean()'
G_ma=eval(G_MAstr)
return G_ma
#复制函数
def COPY(df,tp1,al=''):
if (al.strip()==''):
na=tp1+'_2'
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
y=df.get_value(i, tp1)
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#替换函数
def REPLACE(df,tp1,x):
df[tp1]=x
return df
#ONLYONE函数
def ONLYONE(df,tp1):
i=0
while i < len(df):
if df[tp1][i]>0 :
df[tp1]=0
df[tp1][i]=1
break
i=i+1
return df
#大于等于函数
def EGTN(df,tp1,x,al=''):
if (al.strip()==''):
na=tp1+'_EGTN_' + str(x)
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) >= x) :
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#小于等于函数
def ELTN(df,tp1,x,al=''):
if (al.strip()==''):
na=tp1+'_ELTN' + str(x)
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) <= x) :
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#大于等于函数
def EGT(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_EGT_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) >= df.get_value(i, tp2)):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#小于等于函数
def ELT(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_ELT_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) <= df.get_value(i, tp2)):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#等于函数
def EQUAL(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_EQUAL_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) == df.get_value(i, tp2)):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#大于函数
def GT(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_GT_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) > df.get_value(i, tp2)):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#小于函数
def LT(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_LT_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) < df.get_value(i, tp2)):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#并且函数
def AND(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_AND_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, tp1) >0 and df.get_value(i, tp2)>0):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#或者函数
def OR(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_AND_' + tp2
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df)-1:
if (df.get_value(i, tp1) >0 or df.get_value(i, tp2)>0):
y=1
else:
y=0
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#WW:= IF(L>CC, 0, IF(H<CC, 1, (CC-L+0.01)/(H-L+0.01))); { 每日获利盘 }
#Winner2:DMA(ww, VOL/CAPITAL)*100; { 获利盘 };
#Winner当前价格获利率
def WINNER(df,price, tp1,al=''):
if price==0.0:
price=df.get_value(len(df)-1, 'close')
if (al.strip()==''):
na='WINNER_' + tp1
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df):
if (df.get_value(i, 'low') <price):
y=0.0
elif (df.get_value(i, 'high') >price):
y=1.00
else:
y=(price-df.get_value(i, 'low')+0.01)/(df.get_value(i, 'high')-df.get_value(i, 'low')+0.01)
yy=y
ZB_l.append(yy)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#Moving Average 平均线
def MA(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_MA_' + str(n)
else:
na=al
#MA = pd.Series(pd.rolling_mean(df[tp1],window=n,center=False), name =na)
MA = pd.Series(df[tp1].rolling(window=n,center=False).mean(), name =na )
df = df.join(MA)
return df
#Exponential Moving Average
def EMA(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_EMA_' + str(n)
else:
na=al
#EMA = pd.Series(pd.ewma(df[tp1], span = n, min_periods = n - 1), name = na)
EMA = pd.Series(df[tp1].ewm(span = n, min_periods = n - 1,adjust=True,ignore_na=False).mean(), name = na)
df = df.join(EMA)
return df
#求动态移动平均。
#DMA(X,A),求X的A日动态移动平均。
#算法: 若Y=DMA(X,A)
#则 Y=A*X+(1-A)*Y',其中Y'表示上一周期Y值,A必须小于1。
#例如：DMA(CLOSE,VOL/CAPITAL)表示求以换手率作平滑因子的平均价
def DMA(df,tp1,tp2,al=''):
if (al.strip()==''):
na='DMA_'+tp1+'_' + tp2
else:
na=al
i = 1
ZB_l = [0]
y=df.get_value(i-1, tp1)*df.get_value(i-1, tp2)
i=i+1
while i < df.index[-1]:
y=df.get_value(i-1, tp1)*df.get_value(i-1, tp2)+(1-df.get_value(i-1, tp2))*y
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#上穿函数
def CROSS(df,tp1,tp2,al=''):
if (al.strip()==''):
na=tp1+'_CROSS_' + tp2
else:
na=al
i = 1
CR_l = [0]
y=0
while i < len(df):
if ((df.get_value(i-1, tp1) <df.get_value(i-1, tp2)) and (df.get_value(i, tp1) >=df.get_value(i, tp2))):
y=1
else:
y=0
CR_l.append(y)
i = i + 1
CR_s = pd.Series(CR_l)
CR = pd.Series(CR_s, name = na)
df = df.join(CR)
return df
#取前n周期数值函数
def REF(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_REF_' + str(n)
else:
na=al
i = 0
ZB_l = []
y = 0
while i < n:
y=df.get_value(i, tp1)
ZB_l.append(y)
i=i+1
while i < len(df):
y=df.get_value(i-n, tp1)
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#取后n周期数值函数
def REFX(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_REFX_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=0
while i < len(df)-n:
y=df.get_value(i+n, tp1)
ZB_l.append(y)
i=i+1
while i < len(df):
y=df.get_value(i, tp1)
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#Standard Deviation#标准偏差
def STDDEV(df,tp,n):
df = df.join(pd.Series(pd.rolling_std(df[tp], n), name = tp+'_STD_' + str(n)))
return df
#取前n周期数值的最高价
def HHV(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_HHV_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=df.get_value(i, tp1)
while i < n:
if y<df.get_value(i, tp1):
y=df.get_value(i, tp1)
ZB_l.append(y)
i=i+1
while i < len(df):
j=1
y=df.get_value(i, tp1)
while j < n:
if y<df.get_value(i-j, tp1) :
y=df.get_value(i-j, tp1)
j=j+1
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#取前n周期数值的最低价
def LLV(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_HHV_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=df.get_value(0, tp1)
while i < n:
if y>df.get_value(i, tp1):
y=df.get_value(i, tp1)
ZB_l.append(y)
i=i+1
while i < len(df):
j=1
y=df.get_value(i, tp1)
while j < n:
if y>df.get_value(i-j, tp1) :
y=df.get_value(i-j, tp1)
j=j+1
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#取前n周期数值大于0的次数
def COUNT(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_COUNT_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=0
while i < n:
if df.get_value(i, tp1)>0:
y=y+1
ZB_l.append(y)
i=i+1
while i < len(df)+1:
j=1
y=0
while j < n:
if df.get_value(i-j, tp1)>0 :
y=y=y+1
j=j+1
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#求前n周期数值和
def SUM(df,tp, n,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_SUM_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=0
while i < n:
y=y+ df.get_value(i, tp1)
ZB_l.append(y)
i=i+1
while i < len(df):
j=1
y=0
while j < n:
y=y+ df.get_value(i-j, tp1)
j=j+1
ZB_l.append(y)
i = i + 1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
#SMA(X,N,M)，求X的N日移动平均，M为权重。算法:若Y=SMA(X,N,M) 则 Y=(M*X+(N-M)*Y')/N，其中Y'表示上一周期Y值，N必须大于M。
def SMA(df,tp,n,m,al=''):
if (tp.strip()==''):
tp1='close'
else:
tp1=tp
if (al.strip()==''):
na=tp1+'_SMA_' + str(n)
else:
na=al
i = 0
ZB_l = []
y=1
while i < len(df):
y=(df.get_value(i, tp1)*m+(n-m)*y)/n
ZB_l.append(y)
i=i+1
ZB_s = pd.Series(ZB_l)
ZB = pd.Series(ZB_s, name = na)
df = df.join(ZB)
return df
################指标库#########################
#RSV:=(CLOSE-LLV(LOW,N))/(HHV(HIGH,N)-LLV(LOW,N))*100;
#K:SMA(RSV,M1,1);
#D:SMA(K,M2,1);
#J:3*K-2*D;
def KDJ(df,n,m1,m2):
i = 0
RSV=0.0000
ZB_l = []
yl= df.get_value(0, 'low')
yh= df.get_value(0, 'high')
while i < n:
if yl>df.get_value(i, 'low') :
yl=df.get_value(i, 'low')
if yh<df.get_value(i, 'high') :
yh=df.get_value(i, 'high')
i=i+1
RSV= (df.get_value(i, 'close')-yl)/(yh-yl)*100.0000
ZB_l.append(RSV)
while i < len(df):
j=0
yl= df.get_value(i, 'low')
yh= df.get_value(i, 'high')
while j < n:
if yl>df.get_value(i-j, 'low') :
yl=df.get_value(i-j, 'low')
if yh<df.get_value(i-j, 'high') :
yh=df.get_value(i-j, 'high')
j=j+1
if yh !=yl :
RSV= (df.get_value(i, 'close')-yl)/(yh-yl)*100.0000
else:
RSV=50
ZB_l.append(RSV)
i = i + 1
ZB_s = pd.Series(ZB_l)
rsv=ZB_s
ZB = pd.Series(ZB_s, name = 'RSV')
df = df.join(ZB)
i = 0
ZB_l = []
y=1
while i < len(df):
y=(rsv[i]*1+(m1-1)*y)/m1
ZB_l.append(y)
i=i+1
ZB_s = pd.Series(ZB_l)
k=ZB_s
ZB = pd.Series(ZB_s, name = 'K')
df = df.join(ZB)
i = 0
ZB_l = []
y=1
while i < len(df):
y=(k[i]*1+(m2-1)*y)/m2
ZB_l.append(y)
i=i+1
ZB_s = pd.Series(ZB_l)
d=ZB_s
ZB = pd.Series(ZB_s, name = 'D')
df = df.join(ZB)
j=3*k-2*d
ZB = pd.Series(j, name = 'J')
df = df.join(ZB)
return df
def OBVX(df, n,m):
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 = pd.Series(OBV,name = 'OBV')
df=df.join(OBV)
OBV_ma = pd.Series(pd.rolling_mean(OBV, n), name = 'OBV_' + str(n))
df = df.join(OBV_ma)
OBV_ma = pd.Series(pd.rolling_mean(OBV, m), name = 'OBV_' + str(m))
df = df.join(OBV_ma)
return df
#Relative Strength Index
def RSIX(df, n,al=''):
if (al.strip()==''):
na=tp1+'RSI_' + str(n)
else:
na=al
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 = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1))
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1))
RSI = pd.Series(PosDI*100.00 / (PosDI + NegDI), name = na)
df = df.join(RSI)
return df
def MACDX(df, n_long, n_short,m):
d1=pd.Series(pd.ewma(df['close'], span = n_long, min_periods = n_long - 1))
#d1= pd.Series(df['close'].ewm(span = n_long, min_periods = n_long - 1,adjust=True,ignore_na=False).mean())
d2=pd.Series(pd.ewma(df['close'], span = n_short, min_periods = n_short - 1))
#d2= pd.Series(df['close'].ewm(span = n_short, min_periods = n_short - 1,adjust=True,ignore_na=False).mean())
diff = pd.Series(d1 - d2)
#dea=pd.Series(pd.ewma(diff, span = m, min_periods = m - 1))
dea= pd.Series(diff.ewm(span = m, min_periods = m - 1,adjust=True,ignore_na=False).mean())
DIFF= pd.Series(diff,name='DIFF')
DEA= pd.Series(dea,name='DEA')
MACD = pd.Series(2*(diff-dea), name = 'MACD')
df = df.join(DIFF)
df = df.join(DEA)
df = df.join(MACD)
return df
#MACD, MACD Signal and MACD difference
def MACD(df, n_fast, n_slow):
EMAfast = pd.Series(pd.ewma(df['close'], span = n_fast, min_periods = n_slow - 1))
EMAslow = pd.Series(pd.ewma(df['close'], span = n_slow, min_periods = n_slow - 1))
MACD = pd.Series(EMAfast - EMAslow, name = 'MACD_' + str(n_fast) + '_' + str(n_slow))
MACDsign = pd.Series(pd.ewma(MACD, span = 9, min_periods = 8), name = 'MACDsign_' + str(n_fast) + '_' + str(n_slow))
MACDdiff = pd.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
#Momentum
def MOM(df, n):
M = pd.Series(df['close'].diff(n), name = 'Momentum_' + str(n))
df = df.join(M)
return df
#Rate of Change
def ROC(df, n):
M = df['close'].diff(n - 1)
N = df['close'].shift(n - 1)
ROC = pd.Series(M / N, name = 'ROC_' + str(n))
df = df.join(ROC)
return df
#Average True Range
def ATR(df, n):
i = 0
TR_l = [0]
while i < len(df.index):
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 = pd.Series(TR_l)
ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n), name = 'ATR_' + str(n))
df = df.join(ATR)
return df
#Bollinger Bands
def BBANDS(df, n):
MA = pd.Series(pd.rolling_mean(df['close'], n))
MSD = pd.Series(pd.rolling_std(df['close'], n))
b1 = 4 * MSD / MA
B1 = pd.Series(b1, name = 'BollingerB_' + str(n))
df = df.join(B1)
b2 = (df['close'] - MA + 2 * MSD) / (4 * MSD)
B2 = pd.Series(b2, name = 'Bollinger%b_' + str(n))
df = df.join(B2)
return df
#Pivot Points, Supports and Resistances
def PPSR(df):
PP = pd.Series((df['high'] + df['low'] + df['close']) / 3)
R1 = pd.Series(2 * PP - df['low'])
S1 = pd.Series(2 * PP - df['high'])
R2 = pd.Series(PP + df['high'] - df['low'])
S2 = pd.Series(PP - df['high'] + df['low'])
R3 = pd.Series(df['high'] + 2 * (PP - df['low']))
S3 = pd.Series(df['low'] - 2 * (df['high'] - PP))
psr = {'PP':PP, 'R1':R1, 'S1':S1, 'R2':R2, 'S2':S2, 'R3':R3, 'S3':S3}
PSR = pd.DataFrame(psr)
df = df.join(PSR)
return df
#Stochastic oscillator %K
def STOK(df):
SOk = pd.Series((df['close'] - df['low']) / (df['high'] - df['low']), name = 'SO%k')
df = df.join(SOk)
return df
#Stochastic oscillator %D
def STO(df, n):
SOk = pd.Series((df['close'] - df['low']) / (df['high'] - df['low']), name = 'SO%k')
SOd = pd.Series(pd.ewma(SOk, span = n, min_periods = n - 1), name = 'SO%d_' + str(n))
df = df.join(SOd)
return df
#Trix
def TRIX(df, n):
EX1 = pd.ewma(df['close'], span = n, min_periods = n - 1)
EX2 = pd.ewma(EX1, span = n, min_periods = n - 1)
EX3 = pd.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 = pd.Series(ROC_l, name = 'Trix_' + str(n))
df = df.join(Trix)
return df
#Average Directional Movement Index
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 = pd.Series(TR_l)
ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n))
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1) / ATR)
NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1) / ATR)
ADX = pd.Series(pd.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
#Mass Index
def MassI(df):
Range = df['high'] - df['low']
EX1 = pd.ewma(Range, span = 9, min_periods = 8)
EX2 = pd.ewma(EX1, span = 9, min_periods = 8)
Mass = EX1 / EX2
MassI = pd.Series(pd.rolling_sum(Mass, 25), name = 'Mass Index')
df = df.join(MassI)
return df
#Vortex Indicator: http://www.vortexindicator.com/VFX_VORTEX.PDF
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]