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target.py
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target.py
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# -*- coding: utf-8 -*-
import math
import pandas as pd
from init import *
np.seterr(divide='ignore', invalid='ignore') # 忽略除数为0的警告
#------------------ 0级：核心工具函数 --------------------------------------------
def RD(N,D=3): return np.round(N,D) #四舍五入取3位小数
def RET(S,N=1): return np.array(S)[-N] #返回序列倒数第N个值,默认返回最后一个
def ABS(S): return np.abs(S) #返回N的绝对值
def LN(S): return np.log(S) #求底是e的自然对数,
def POW(S,N): return np.power(S,N) #求S的N次方
def SQRT(S): return np.sqrt(S) #求S的平方根
def MAX(S1,S2): return np.maximum(S1,S2) #序列max
def MIN(S1,S2): return np.minimum(S1,S2) #序列min
def IF(S,A,B): return np.where(S,A,B) #序列布尔判断 return=A if S==True else B
def REF(S, N=1): #对序列整体下移动N,返回序列(shift后会产生NAN)
return pd.Series(S).shift(N).values
def DIFF(S, N=1): #前一个值减后一个值,前面会产生nan
return pd.Series(S).diff(N).values #np.diff(S)直接删除nan，会少一行
def STD(S,N): #求序列的N日标准差，返回序列
return pd.Series(S).rolling(N).std(ddof=0).values
def SUM(S, N): #对序列求N天累计和，返回序列 N=0对序列所有依次求和
return pd.Series(S).rolling(N).sum().values if N>0 else pd.Series(S).cumsum().values
def CONST(S): #返回序列S最后的值组成常量序列
return np.full(len(S),S[-1])
def HHV(S,N): #HHV(C, 5) 最近5天收盘最高价
return pd.Series(S).rolling(N).max().values
def LLV(S,N): #LLV(C, 5) 最近5天收盘最低价
return pd.Series(S).rolling(N).min().values
def HHVBARS(S,N): #求N周期内S最高值到当前周期数, 返回序列
return pd.Series(S).rolling(N).apply(lambda x: np.argmax(x[::-1]),raw=True).values
def LLVBARS(S,N): #求N周期内S最低值到当前周期数, 返回序列
return pd.Series(S).rolling(N).apply(lambda x: np.argmin(x[::-1]),raw=True).values
def MA(S,N): #求序列的N日简单移动平均值，返回序列
return pd.Series(S).rolling(N).mean().values
def EMA(S,N): #指数移动平均,为了精度 S>4*N EMA至少需要120周期 alpha=2/(span+1)
return pd.Series(S).ewm(span=N, adjust=False).mean().values
def SMA(S, N, M=1): #中国式的SMA,至少需要120周期才精确 (雪球180周期) alpha=1/(1+com)
return pd.Series(S).ewm(alpha=M/N,adjust=False).mean().values #com=N-M/M
def WMA(S, N): #通达信S序列的N日加权移动平均 Yn = (1*X1+2*X2+3*X3+...+n*Xn)/(1+2+3+...+Xn)
return pd.Series(S).rolling(N).apply(lambda x:x[::-1].cumsum().sum()*2/N/(N+1),raw=True).values
def DMA(S, A): #求S的动态移动平均，A作平滑因子,必须 0<A<1 (此为核心函数，非指标）
if isinstance(A,(int,float)): return pd.Series(S).ewm(alpha=A,adjust=False).mean().values
A=np.array(A); A[np.isnan(A)]=1.0; Y= np.zeros(len(S)); Y[0]=S[0]
for i in range(1,len(S)): Y[i]=A[i]*S[i]+(1-A[i])*Y[i-1] #A支持序列 by jqz1226
return Y
def AVEDEV(S, N): #平均绝对偏差 (序列与其平均值的绝对差的平均值)
return pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean()).values
def SLOPE(S, N): #返S序列N周期回线性回归斜率
return pd.Series(S).rolling(N).apply(lambda x: np.polyfit(range(N),x,deg=1)[0],raw=True).values
def FORCAST(S, N): #返回S序列N周期回线性回归后的预测值， jqz1226改进成序列出
return pd.Series(S).rolling(N).apply(lambda x:np.polyval(np.polyfit(range(N),x,deg=1),N-1),raw=True).values
def LAST(S, A, B): #从前A日到前B日一直满足S_BOOL条件, 要求A>B & A>0 & B>=0
return np.array(pd.Series(S).rolling(A+1).apply(lambda x:np.all(x[::-1][B:]),raw=True),dtype=bool)
#------------------ 1级：应用层函数(通过0级核心函数实现）使用方法请参考通达信--------------------------------
def COUNT(S, N): # COUNT(CLOSE>O, N): 最近N天满足S_BOO的天数 True的天数
return SUM(S,N)
def EVERY(S, N): # EVERY(CLOSE>O, 5) 最近N天是否都是True
return IF(SUM(S,N)==N,True,False)
def EXIST(S, N): # EXIST(CLOSE>3010, N=5) n日内是否存在一天大于3000点
return IF(SUM(S,N)>0,True,False)
def FILTER(S, N): # FILTER函数，S满足条件后，将其后N周期内的数据置为0, FILTER(C==H,5)
for i in range(len(S)): S[i+1:i+1+N]=0 if S[i] else S[i+1:i+1+N]
return S # 例：FILTER(C==H,5) 涨停后，后5天不再发出信号
def BARSLAST(S): #上一次条件成立到当前的周期, BARSLAST(C/REF(C,1)>=1.1) 上一次涨停到今天的天数
M=np.concatenate(([0],np.where(S,1,0)))
for i in range(1, len(M)): M[i]=0 if M[i] else M[i-1]+1
return M[1:]
def BARSLASTCOUNT(S): # 统计连续满足S条件的周期数 by jqz1226
rt = np.zeros(len(S)+1) # BARSLASTCOUNT(CLOSE>OPEN)表示统计连续收阳的周期数
for i in range(len(S)): rt[i+1]=rt[i]+1 if S[i] else rt[i+1]
return rt[1:]
def BARSSINCEN(S, N): # N周期内第一次S条件成立到现在的周期数,N为常量 by jqz1226
return pd.Series(S).rolling(N).apply(lambda x:N-1-np.argmax(x) if np.argmax(x) or x[0] else 0,raw=True).fillna(0).values.astype(int)
def CROSS(S1, S2): # 判断向上金叉穿越 CROSS(MA(C,5),MA(C,10)) 判断向下死叉穿越 CROSS(MA(C,10),MA(C,5))
return np.concatenate(([False], np.logical_not((S1>S2)[:-1]) & (S1>S2)[1:])) # 不使用0级函数,移植方便 by jqz1226
def LONGCROSS(S1,S2,N): # 两条线维持一定周期后交叉,S1在N周期内都小于S2,本周期从S1下方向上穿过S2时返回1,否则返回0
return np.array(np.logical_and(LAST(S1<S2,N,1),(S1>S2)),dtype=bool) # N=1时等同于CROSS(S1, S2)
def VALUEWHEN(S, X): # 当S条件成立时,取X的当前值,否则取VALUEWHEN的上个成立时的X值 by jqz1226
return pd.Series(np.where(S,X,np.nan)).ffill().values
def BETWEEN(S, A, B): # S处于A和B之间时为真。 包括 A<S<B 或 A>S>B
return ((A<S) & (S<B)) | ((A>S) & (S>B))
def TOPRANGE(S): # TOPRANGE(HIGH)表示当前最高价是近多少周期内最高价的最大值 by jqz1226
rt = np.zeros(len(S))
for i in range(1,len(S)): rt[i] = np.argmin(np.flipud(S[:i]<S[i]))
return rt.astype('int')
def LOWRANGE(S): # LOWRANGE(LOW)表示当前最低价是近多少周期内最低价的最小值 by jqz1226
rt = np.zeros(len(S))
for i in range(1,len(S)): rt[i] = np.argmin(np.flipud(S[:i]>S[i]))
return rt.astype('int')
#------------------ 2级：技术指标函数(全部通过0级，1级函数实现） ------------------------------
def MACD(CLOSE,SHORT=12,LONG=26,M=9): # EMA的关系，S取120日，和雪球小数点2位相同
DIF = EMA(CLOSE,SHORT)-EMA(CLOSE,LONG);
DEA = EMA(DIF,M); MACD=(DIF-DEA)*2
return RD(DIF),RD(DEA),RD(MACD)
def KDJ(CLOSE,HIGH,LOW, N=9,M1=3,M2=3): # KDJ指标
RSV = (CLOSE - LLV(LOW, N)) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
K = EMA(RSV, (M1*2-1)); D = EMA(K,(M2*2-1)); J=K*3-D*2
return K, D, J
def RSI(CLOSE, N=24): # RSI指标,和通达信小数点2位相同
DIF = CLOSE-REF(CLOSE,1)
return RD(SMA(MAX(DIF,0), N) / SMA(ABS(DIF), N) * 100)
def WR(CLOSE, HIGH, LOW, N=10, N1=6): #W&R 威廉指标
WR = (HHV(HIGH, N) - CLOSE) / (HHV(HIGH, N) - LLV(LOW, N)) * 100
WR1 = (HHV(HIGH, N1) - CLOSE) / (HHV(HIGH, N1) - LLV(LOW, N1)) * 100
return RD(WR), RD(WR1)
def BIAS(CLOSE,L1=6, L2=12, L3=24): # BIAS乖离率
BIAS1 = (CLOSE - MA(CLOSE, L1)) / MA(CLOSE, L1) * 100
BIAS2 = (CLOSE - MA(CLOSE, L2)) / MA(CLOSE, L2) * 100
BIAS3 = (CLOSE - MA(CLOSE, L3)) / MA(CLOSE, L3) * 100
return RD(BIAS1), RD(BIAS2), RD(BIAS3)
def BOLL(CLOSE,N=20, P=2): #BOLL指标，布林带
MID = MA(CLOSE, N);
UPPER = MID + STD(CLOSE, N) * P
LOWER = MID - STD(CLOSE, N) * P
return RD(UPPER), RD(MID), RD(LOWER)
def PSY(CLOSE,N=12, M=6):
PSY=COUNT(CLOSE>REF(CLOSE,1),N)/N*100
PSYMA=MA(PSY,M)
return RD(PSY),RD(PSYMA)
def CCI(CLOSE,HIGH,LOW,N=14):
TP=(HIGH+LOW+CLOSE)/3
return (TP-MA(TP,N))/(0.015*AVEDEV(TP,N))
def ATR(CLOSE,HIGH,LOW, N=20): #真实波动N日平均值
TR = MAX(MAX((HIGH - LOW), ABS(REF(CLOSE, 1) - HIGH)), ABS(REF(CLOSE, 1) - LOW))
return MA(TR, N)
def BBI(CLOSE,M1=3,M2=6,M3=12,M4=20): #BBI多空指标
return (MA(CLOSE,M1)+MA(CLOSE,M2)+MA(CLOSE,M3)+MA(CLOSE,M4))/4
def DMI(CLOSE,HIGH,LOW,M1=14,M2=6): #动向指标：结果和同花顺，通达信完全一致
TR = SUM(MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(LOW - REF(CLOSE, 1))), M1)
HD = HIGH - REF(HIGH, 1); LD = REF(LOW, 1) - LOW
DMP = SUM(IF((HD > 0) & (HD > LD), HD, 0), M1)
DMM = SUM(IF((LD > 0) & (LD > HD), LD, 0), M1)
PDI = DMP * 100 / TR; MDI = DMM * 100 / TR
ADX = MA(ABS(MDI - PDI) / (PDI + MDI) * 100, M2)
ADXR = (ADX + REF(ADX, M2)) / 2
return PDI, MDI, ADX, ADXR
def TAQ(HIGH,LOW,N): #唐安奇通道(海龟)交易指标，大道至简，能穿越牛熊
UP=HHV(HIGH,N); DOWN=LLV(LOW,N); MID=(UP+DOWN)/2
return UP,MID,DOWN
def KTN(CLOSE,HIGH,LOW,N=20,M=10): #肯特纳交易通道, N选20日，ATR选10日
MID=EMA((HIGH+LOW+CLOSE)/3,N)
ATRN=ATR(CLOSE,HIGH,LOW,M)
UPPER=MID+2*ATRN; LOWER=MID-2*ATRN
return UPPER,MID,LOWER
def TRIX(CLOSE,M1=12, M2=20): #三重指数平滑平均线
TR = EMA(EMA(EMA(CLOSE, M1), M1), M1)
TRIX = (TR - REF(TR, 1)) / REF(TR, 1) * 100
TRMA = MA(TRIX, M2)
return TRIX, TRMA
def VR(CLOSE,VOL,M1=26): #VR容量比率
LC = REF(CLOSE, 1)
return SUM(IF(CLOSE > LC, VOL, 0), M1) / SUM(IF(CLOSE <= LC, VOL, 0), M1) * 100
def EMV(HIGH,LOW,VOL,N=14,M=9): #简易波动指标
VOLUME=MA(VOL,N)/VOL; MID=100*(HIGH+LOW-REF(HIGH+LOW,1))/(HIGH+LOW)
EMV=MA(MID*VOLUME*(HIGH-LOW)/MA(HIGH-LOW,N),N); MAEMV=MA(EMV,M)
return EMV,MAEMV
def DPO(CLOSE,M1=20, M2=10, M3=6): #区间震荡线
DPO = CLOSE - REF(MA(CLOSE, M1), M2); MADPO = MA(DPO, M3)
return DPO, MADPO
def BRAR(OPEN,CLOSE,HIGH,LOW,M1=26): #BRAR-ARBR 情绪指标
AR = SUM(HIGH - OPEN, M1) / SUM(OPEN - LOW, M1) * 100
BR = SUM(MAX(0, HIGH - REF(CLOSE, 1)), M1) / SUM(MAX(0, REF(CLOSE, 1) - LOW), M1) * 100
return AR, BR
def DFMA(CLOSE,N1=10,N2=50,M=10): #平行线差指标
DIF=MA(CLOSE,N1)-MA(CLOSE,N2); DIFMA=MA(DIF,M) #通达信指标叫DMA 同花顺叫新DMA
return DIF,DIFMA
def MTM(CLOSE,N=12,M=6): #动量指标
MTM=CLOSE-REF(CLOSE,N); MTMMA=MA(MTM,M)
return MTM,MTMMA
def MASS(HIGH,LOW,N1=9,N2=25,M=6): #梅斯线
MASS=SUM(MA(HIGH-LOW,N1)/MA(MA(HIGH-LOW,N1),N1),N2)
MA_MASS=MA(MASS,M)
return MASS,MA_MASS
def ROC(CLOSE,N=12,M=6): #变动率指标
ROC=100*(CLOSE-REF(CLOSE,N))/REF(CLOSE,N); MAROC=MA(ROC,M)
return ROC,MAROC
def EXPMA(CLOSE,N1=12,N2=50): #EMA指数平均数指标
return EMA(CLOSE,N1),EMA(CLOSE,N2);
def OBV(CLOSE,VOL): #能量潮指标
return SUM(IF(CLOSE>REF(CLOSE,1),VOL,IF(CLOSE<REF(CLOSE,1),-VOL,0)),0)/10000
def MFI(CLOSE,HIGH,LOW,VOL,N=14): #MFI指标是成交量的RSI指标
TYP = (HIGH + LOW + CLOSE)/3
V1=SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N)
return 100-(100/(1+V1))
def ASI(OPEN,CLOSE,HIGH,LOW,M1=26,M2=10): #振动升降指标
LC=REF(CLOSE,1); AA=ABS(HIGH-LC); BB=ABS(LOW-LC);
CC=ABS(HIGH-REF(LOW,1)); DD=ABS(LC-REF(OPEN,1));
R=IF( (AA>BB) & (AA>CC),AA+BB/2+DD/4,IF( (BB>CC) & (BB>AA),BB+AA/2+DD/4,CC+DD/4));
X=(CLOSE-LC+(CLOSE-OPEN)/2+LC-REF(OPEN,1));
SI=16*X/R*MAX(AA,BB); ASI=SUM(SI,M1); ASIT=MA(ASI,M2);
return ASI,ASIT
def XSII(CLOSE, HIGH, LOW, N=102, M=7): #薛斯通道II
AA = MA((2*CLOSE + HIGH + LOW)/4, 5) #最新版DMA才支持 2021-12-4
TD1 = AA*N/100; TD2 = AA*(200-N) / 100
CC = ABS((2*CLOSE + HIGH + LOW)/4 - MA(CLOSE,20))/MA(CLOSE,20)
DD = DMA(CLOSE,CC); TD3=(1+M/100)*DD; TD4=(1-M/100)*DD
return TD1, TD2, TD3, TD4
def LLV(S, N): #LLV,支持N为序列版本
## type: (np.ndarray, Optional[int,float, np.ndarray]) -> np.ndarray
"""
LLV(C, 5) # 最近5天收盘最低价
"""
if isinstance(N, (int, float)):
return pd.Series(S).rolling(N).min().values
else:
res = np.repeat(np.nan, len(S))
for i in range(len(S)):
if (not np.isnan(N[i])) and N[i] <= i + 1:
res[i] = S[i + 1 - N[i]:i + 1].min()
return res
def DSMA(X, N): # 偏差自适应移动平均线 type: (np.ndarray, int) -> np.ndarray
"""
Deviation Scaled Moving Average (DSMA) Python by: jqz1226, 2021-12-27
Referred function from myTT: SUM, DMA
"""
a1 = math.exp(- 1.414 * math.pi * 2 / N)
b1 = 2 * a1 * math.cos(1.414 * math.pi * 2 / N)
c2 = b1
c3 = -a1 * a1
c1 = 1 - c2 - c3
Zeros = np.pad(X[2:] - X[:-2],(2,0),'constant')
Filt = np.zeros(len(X))
for i in range(len(X)):
Filt[i] = c1 * (Zeros[i] + Zeros[i-1]) / 2 + c2 * Filt[i-1] + c3 * Filt[i-2]
RMS = np.sqrt(SUM(np.square(Filt), N) / N)
ScaledFilt = Filt / RMS
alpha1 = np.abs(ScaledFilt) * 5 / N
return DMA(X, alpha1)
def SUMBARSFAST(X, A):
## type: (np.ndarray, Optional[np.ndarray, float, int]) -> np.ndarray
"""
通达信SumBars函数的Python实现 by jqz1226
SumBars函数将X向前累加，直到大于等于A, 返回这个区间的周期数。例如SUMBARS(VOL, CAPITAL),求完全换手的周期数。
:param X: 数组。被累计的源数据。 源数组中不能有小于0的元素。
:param A: 数组（一组）或者浮点数（一个）或者整数（一个），累加截止的界限数
:return: 数组。各K线分别对应的周期数
"""
if any(X<=0): raise ValueError('数组X的每个元素都必须大于0！')
X = np.flipud(X) # 倒转
length = len(X)
if isinstance(A * 1.0, float): A = np.repeat(A, length) # 是单值则转化为数组
A = np.flipud(A) # 倒转
sumbars = np.zeros(length) # 初始化sumbars为0
Sigma = np.insert(np.cumsum(X), 0, 0.0) # 在累加值前面插入一个0.0（元素变多1个，便于引用）
for i in range(length):
k = np.searchsorted(Sigma[i + 1:], A[i] + Sigma[i])
if k < length - i: # 找到
sumbars[length - i - 1] = k + 1
return sumbars.astype(int)
#------------------------指标函数---------------------------------------------
def SAR(HIGH, LOW, N=10, S=2, M=20):
"""
求抛物转向。 例如SAR(10,2,20)表示计算10日抛物转向，步长为2%，步长极限为20%
Created by: jqz1226, 2021-11-24首次发表于聚宽(www.joinquant.com)
:param HIGH: high序列
:param LOW: low序列
:param N: 计算周期
:param S: 步长
:param M: 步长极限
:return: 抛物转向
"""
f_step = S / 100; f_max = M / 100; af = 0.0
is_long = HIGH[N - 1] > HIGH[N - 2]
b_first = True
length = len(HIGH)
s_hhv = REF(HHV(HIGH, N), 1) # type: np.ndarray
s_llv = REF(LLV(LOW, N), 1) # type: np.ndarray
sar_x = np.repeat(np.nan, length) # type: np.ndarray
for i in range(N, length):
if b_first: # 第一步
af = f_step
sar_x[i] = s_llv[i] if is_long else s_hhv[i]
b_first = False
else: # 继续多 或者 空
ep = s_hhv[i] if is_long else s_llv[i] # 极值
if (is_long and HIGH[i] > ep) or ((not is_long) and LOW[i] < ep): # 顺势：多创新高 或者 空创新低
af = min(af + f_step, f_max)
#
sar_x[i] = sar_x[i - 1] + af * (ep - sar_x[i - 1])
if (is_long and LOW[i] < sar_x[i]) or ((not is_long) and HIGH[i] > sar_x[i]): # 反空 或者 反多
is_long = not is_long
b_first = True
return sar_x
def TDX_SAR(High, Low, iAFStep=2, iAFLimit=20): # type: (np.ndarray, np.ndarray, int, int) -> np.ndarray
""" 通达信SAR算法,和通达信SAR对比完全一致 by: jqz1226, 2021-12-18
:param High: 最高价序列
:param Low: 最低价序列
:param iAFStep: AF步长
:param iAFLimit: AF极限值
:return: SAR序列
"""
af_step = iAFStep / 100; af_limit = iAFLimit / 100
SarX = np.zeros(len(High)) # 初始化返回数组
# 第一个bar
bull = True
af = af_step
ep = High[0]
SarX[0] = Low[0]
# 第2个bar及其以后
for i in range(1, len(High)):
# 1.更新：hv, lv, af, ep
if bull: # 多
if High[i] > ep: # 创新高
ep = High[i]
af = min(af + af_step, af_limit)
else: # 空
if Low[i] < ep: # 创新低
ep = Low[i]
af = min(af + af_step, af_limit)
# 2.计算SarX
SarX[i] = SarX[i - 1] + af * (ep - SarX[i - 1])
# 3.修正SarX
if bull:
SarX[i] = max(SarX[i - 1], min(SarX[i], Low[i], Low[i - 1]))
else:
SarX[i] = min(SarX[i - 1], max(SarX[i], High[i], High[i - 1]))
# 4. 判断是否：向下跌破，向上突破
if bull: # 多
if Low[i] < SarX[i]: # 向下跌破，转空
bull = False
tmp_SarX = ep # 上阶段的最高点
ep = Low[i]
af = af_step
if High[i - 1] == tmp_SarX: # 紧邻即最高点
SarX[i] = tmp_SarX
else:
SarX[i] = tmp_SarX + af * (ep - tmp_SarX)
else: # 空
if High[i] > SarX[i]: # 向上突破, 转多
bull = True
ep = High[i]
af = af_step
SarX[i] = min(Low[i], Low[i - 1])
# end for
return SarX
# if __name__ == "__main__":
# df = import_csv("000001.SZ")
# if 'trade_date' in df.columns:
# # times = [datetime.datetime.fromtimestamp(int(str(ts.value)[:10])).strftime('%Y%m%d') for ts in df['trade_date'].tolist()]
# times = df['trade_date'].tolist()
# close = df['close'].tolist()
# hpri = df['high'].tolist()
# lpri = df['low'].tolist()
# elif 'Date' in df.columns:
# times = df['Date'].tolist()
# close = df['Close'].tolist()
# hpri = df['High'].tolist()
# lpri = df['Low'].tolist()
# dif, dea, macd = MACD(close) #传入股票代码，加前缀
# for i in range(len(times)):
# print(times[i], close[i], dif[i], dea[i], macd[i])