MyTT
MyTT is a Swiss Army knife in your quantitative toolbox. It is refined and efficient. It simply transplants the indicator formula indicators such as tongdaxin, tonghuashun, Mandarin Chinese and wheat language into Python. The core library is a single file with only 100 lines of code. It implements and converts all common indicators of tonghuashun tongdaxin, such as MACD, RSI, BOL, ATR, KDJ, CCI, Psy, etc. all based on function encapsulation of numpy and pandas, which is simple and high-performance, It can be easily applied in the fields of stock market technical analysis, automatic programmed trading of stocks, quantification of digital currency BTC and so on
Functional features
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Core library lightweight: the project library is just a file MyTT Py, no installation, no setting, just use and go (from MyTT import *)
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Code Humanization:) there are no conspicuous programming tricks. Beginners can understand them. They can increase their own indicators and use them in the project immediately.
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There is no need to install TA lib library, which is the core logic implemented by pure python code. Many people have the painful experience of installing TA lib library
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It is completely compatible with the index writing method of tongdaxin and tonghuashun. A new index can be used directly without modification
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Ultra high performance, basically no loop, all rely on the built-in functions of numpy and pandas to achieve various indicators
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Like the Talib library, it is multi-day parameter input and multi-day indicator (sequence input and sequence output), which is convenient for drawing and observing trends
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Various indexes realized by MyTT are consistent with the technical indexes of tongdaxin, flush, snowball and other software to 2 decimal places
Let's start with the simplest example
#Stock market data acquisition and mapping-2
from Ashare import * #Stock database https://github.com/mpquant/Ashare
from MyTT import * #myTT language tool function index library https://github.com/mpquant/MyTT
# Securities codes are compatible with a variety of formats, tongdaxin, flush, jukuan
# sh000001 (000001.XSHG) sz399006 (399006.XSHE) sh600519 ( 600519.XSHG )
df=get_price('000001.XSHG',frequency='1d',count=120) #Get the daily real-time market for the first 120 days today
print('Shanghai Stock Index daily market\n',df.tail(5))
#-------With the data, let's get to the point-------------
CLOSE=df.close.values; OPEN=df.open.values #The basic data definition can be used as long as the sequence is passed in
HIGH=df.high.values; LOW=df.low.values #For example, CLOSE=list(df.close) is the same
MA5=MA(CLOSE,5) #Get the 5-day moving average sequence
MA10=MA(CLOSE,10) #Get the 10 day moving average sequence
up,mid,lower=BOLL(CLOSE) #Obtain Bollinger belt index data
print('BTC5 Daily average', MA5[-1] ) # Take only the last number
print('BTC10 Daily average',RET(MA10)) # RET(MA10) == MA10[-1]
print('Do you wear the 10th line on the 5th line today',RET(CROSS(MA5,MA10)))
print('Are the closing prices of the last five days all greater than the 10 day line?',EVERY(CLOSE>MA10,5) )
#Shanghai Stock Index daily market----------------------------------------------------
open close high low volume
2021-06-07 3597.14 3599.54 3600.38 3581.90 303718677.0
2021-06-08 3598.75 3580.11 3621.52 3563.25 304491470.0
2021-06-09 3576.80 3591.40 3598.71 3572.64 298323296.0
2021-06-10 3587.53 3610.86 3624.34 3584.13 318174808.0
2021-06-11 3614.11 3589.75 3614.40 3587.15 360554970.0
#Guizhou Maotai 60 minute line----------------------------------------------------
open close high low volume
2021-06-10 14:00:00 2237.00 2224.16 2245.00 2222.00 4541.53
2021-06-10 15:00:00 2222.21 2238.48 2240.34 2222.21 4146.88
2021-06-11 10:30:00 2239.00 2220.00 2244.00 2197.86 12030.00
2021-06-11 11:30:00 2220.01 2210.18 2231.80 2200.18 4868.00
2021-06-11 14:00:00 2210.10 2223.35 2224.48 2206.01 4544.00
2021-06-11 15:00:00 2223.33 2178.81 2226.80 2178.81 12529.00
Some tool functions in MyTT Library
#------------------Level 0: core tool functions--------------------------------------------
def RD(N,D=3): return np.round(N,D) #Rounded to 3 decimal places
def RET(S,N=1): return np.array(S)[-N] #Returns the penultimate value of the sequence. The last value is returned by default
def ABS(S): return np.abs(S) #Returns the absolute value of N
def MAX(S1,S2): return np.maximum(S1,S2) #Sequence max
def MIN(S1,S2): return np.minimum(S1,S2) #Sequence min
def MA(S,N): #Find the N-day average of the sequence and return the sequence
return pd.Series(S).rolling(N).mean().values # pd.rolling_mean(S,N) (Python2)
def REF(S, N=1): #Move N down the sequence as a whole and return the sequence (Nan will be generated after shift)
return pd.Series(S).shift(N).values
def DIFF(S, N=1): #If the previous value is subtracted from the next value, nan will be generated in front
return pd.Series(S).diff(N) #np.diff(S) delete nan directly, and one line will be lost
def STD(S,N): #Find the N-day standard deviation of the sequence and return the sequence
return pd.Series(S).rolling(N).std(ddof=0).values
def IF(S_BOOL,S_TRUE,S_FALSE): #Sequence Boolean judgment res=S_TRUE if S_BOOL==True else S_FALSE
return np.where(S_BOOL, S_TRUE, S_FALSE)
def SUM(S, N): #Calculate the N-day cumulative sum of the sequence, return the sequence N=0, and sum all the sequences in turn
return pd.Series(S).rolling(N).sum().values if N>0 else pd.Series(S).cumsum() #pd.rolling_sum(S,N) (Python2)
def HHV(S,N): # HHV(C, 5) # Highest closing price in the last five days
return pd.Series(S).rolling(N).max().values # pd.rolling_max(S,N) (Python2)
def LLV(S,N): # LLV(C, 5) # Lowest closing price in the last five days
return pd.Series(S).rolling(N).min().values # pd.rolling_min(S,N) (Python2)
def EMA(S,N): #Exponential moving average, in order to achieve accuracy s > 4 * n, EMA needs at least 120 cycles
return pd.Series(S).ewm(span=N, adjust=False).mean().values
def SMA(S, N, M=1): #Chinese SMA requires at least 120 cycles to be accurate
K = pd.Series(S).rolling(N).mean() #Find the average value first (please let us know if there is a way to improve the performance without cycling)
for i in range(N+1, len(S)): K[i] = (M * S[i] + (N -M) * K[i-1]) / N # Because we want to take K[i-1], range(N+1, len(S))
return K
def AVEDEV(S,N): #Mean absolute deviation (the average of the absolute difference between the sequence and its mean)
avedev=pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean())
return avedev.values
def SLOPE(S,N,RS=False): #Return the slope of linear regression for N cycles of S sequence (only the slope is returned by default, not the whole linear sequence)
M=pd.Series(S[-N:]); poly = np.polyfit(M.index, M.values,deg=1); Y=np.polyval(poly, M.index);
if RS: return Y[1]-Y[0],Y
return Y[1]-Y[0]
The realized technical indicators basically cover the vast majority of commonly used indicators
#------------------Level 2: technical index function (all realized through level 0 and level 1 functions)------------------------------
def MACD(CLOSE,SHORT=12,LONG=26,M=9): # For the relationship between EMA, S takes 120 days, which is the same as the two decimal places of snowball
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 index
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):
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 indicators
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 deviation rate
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 index, brin zone
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): #Daily average of real fluctuation N
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 long and short index
return (MA(CLOSE,M1)+MA(CLOSE,M2)+MA(CLOSE,M3)+MA(CLOSE,M4))/4
def DMI(CLOSE,HIGH,LOW,M1=14,M2=6): #Trend indicators: the results are completely consistent with flush and tongdaxin
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): #Tang Anqi channel (turtle) trading index, the avenue to Jane, can cross cattle and bears
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): #Kentner trading channel, N chooses 20 days and ATR chooses 10 days
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): #Triple exponential smoothing average
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 capacity ratio
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): #Simple fluctuation index
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): #Interval Oscillation line
DPO = CLOSE - REF(MA(CLOSE, M1), M2); MADPO = MA(DPO, M3)
return DPO, MADPO
def BRAR(OPEN,CLOSE,HIGH,LOW,M1=26): #BRAR-ARBR emotional indicators
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 DMA(CLOSE,N1=10,N2=50,M=10): #Parallel line difference index
DIF=MA(CLOSE,N1)-MA(CLOSE,N2); DIFMA=MA(DIF,M)
return DIF,DIFMA
def MTM(CLOSE,N=12,M=6): #Momentum index
MTM=CLOSE-REF(CLOSE,N); MTMMA=MA(MTM,M)
return MTM,MTMMA
def MASS(HIGH,LOW,N1=9,N2=25,M=6): # Mace line
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): #Rate of change index
ROC=100*(CLOSE-REF(CLOSE,N))/REF(CLOSE,N); MAROC=MA(ROC,M)
return ROC,MAROC
def EXPMA(CLOSE,N1=12,N2=50): #EMA index average index
return EMA(CLOSE,N1),EMA(CLOSE,N2);
def OBV(CLOSE,VOL): #Energy tide index
return SUM(IF(CLOSE>REF(CLOSE,1),VOL,IF(CLOSE<REF(CLOSE,1),-VOL,0)),0)/10000
def ASI(OPEN,CLOSE,HIGH,LOW,M1=26,M2=10): #Vibration lifting index
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
#I hope you can submit more indicators and functions https://github.com/mpquant/MyTT
Bol with index data acquisition and drawing demonstration (Shanghai Composite Index)
up,mid,lower=BOLL(CLOSE) #Get brin band data plt.figure(figsize=(15,8)) plt.plot(CLOSE,label='Shanghai '); plt.plot(up,label='up'); #Drawing display plt.plot(mid,label='mid'); plt.plot(lower,label='lower');
Third party libraries need to be installed (TA lib libraries are not required, and pandas can be installed for all indicators)
- pandas