Python量化交易——股票技术分析TA-lib常用函数的python实现版
量化交易常用技术分析函数
量化交易常用技术分析函数
做量化交易的同学们都常常会用到技术分析，通过对历史价格的技术分析，对未来的股票走向做出预测。在这个领域，ta-lib是一个很著名的技术分析函数包，安装这个包之后，可以直接使用很多技术分析函数。
不过，ta-lib不太容易安装，想要知道每个技术参数具体是怎么计算出来的，也不太容易，因此，我整理了股票技术分析常用函数的python实现版，分享给大家，感谢代码原作者的分享
代码原作者：
@author: Bruno Franca
@author: Peter Bakker
下面的代码用于计算不同的技术指标，所有技术指标都基于一组历史数据，该历史数据包含一只股票在过去200个交易日的K线数据（即open, high, low, close, vol 开盘价，最高价、最低价、收盘价、交易量数据），这组数据已经保存在了一个test_data.csv文件中，可以点击这里下载。
数据准备
使用pandas可以很容易地导入csv文件中的数据
import numpy
import pandas as pd
import math as m
# 导入历史数据
data = pd.read_csv('test_data.csv')
# 整理历史数据
data.index = data.set_index('date')
data.index = pd.to_datetime(data.index)
技术指标
移动平均价MA
#Moving Average
def MA(df, n):
MA = pd.Series(pd.rolling_mean(df['Close'], n), name = 'MA_' + str(n))
df = df.join(MA)
return df
#Exponential Moving Average
def EMA(df, n):
EMA = pd.Series(pd.ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))
df = df.join(EMA)
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 < 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), 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, EMA smoothing, nS = slowing (1 if no slowing)
def STO(df, nK, nD, nS=1):
SOk = pd.Series((df['Close'] - df['Low'].rolling(nK).min()) / (df['High'].rolling(nK).max() - df['Low'].rolling(nK).min()), name = 'SO%k'+str(nK))
SOd = pd.Series(SOk.ewm(ignore_na=False, span=nD, min_periods=nD-1, adjust=True).mean(), name = 'SO%d'+str(nD))
SOk = SOk.ewm(ignore_na=False, span=nS, min_periods=nS-1, adjust=True).mean()
SOd = SOd.ewm(ignore_na=False, span=nS, min_periods=nS-1, adjust=True).mean()
df = df.join(SOk)
df = df.join(SOd)
return df
# Stochastic Oscillator, SMA smoothing, nS = slowing (1 if no slowing)
def STO(df, nK, nD, nS=1):
SOk = pd.Series((df['Close'] - df['Low'].rolling(nK).min()) / (df['High'].rolling(nK).max() - df['Low'].rolling(nK).min()), name = 'SO%k'+str(nK))
SOd = pd.Series(SOk.rolling(window=nD, center=False).mean(), name = 'SO%d'+str(nD))
SOk = SOk.rolling(window=nS, center=False).mean()
SOd = SOd.rolling(window=nS, center=False).mean()
df = df.join(SOk)
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
#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
#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]
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 = pd.Series(pd.rolling_sum(pd.Series(VM), n) / pd.rolling_sum(pd.Series(TR), n), name = 'Vortex_' + str(n))
df = df.join(VI)
return df
#KST Oscillator
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 = pd.Series(pd.rolling_sum(ROC1, n1) + pd.rolling_sum(ROC2, n2) * 2 + pd.rolling_sum(ROC3, n3) * 3 + pd.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
#Relative Strength Index
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 = 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 / (PosDI + NegDI), name = 'RSI_' + str(n))
df = df.join(RSI)
return df
#True Strength Index
def TSI(df, r, s):
M = pd.Series(df['Close'].diff(1))
aM = abs(M)
EMA1 = pd.Series(pd.ewma(M, span = r, min_periods = r - 1))
aEMA1 = pd.Series(pd.ewma(aM, span = r, min_periods = r - 1))
EMA2 = pd.Series(pd.ewma(EMA1, span = s, min_periods = s - 1))
aEMA2 = pd.Series(pd.ewma(aEMA1, span = s, min_periods = s - 1))
TSI = pd.Series(EMA2 / aEMA2, name = 'TSI_' + str(r) + '_' + str(s))
df = df.join(TSI)
return df
#Accumulation/Distribution
def ACCDIST(df, n):
ad = (2 * df['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 = pd.Series(ROC, name = 'Acc/Dist_ROC_' + str(n))
df = df.join(AD)
return df
#Chaikin Oscillator
def Chaikin(df):
ad = (2 * df['Close'] - df['High'] - df['Low']) / (df['High'] - df['Low']) * df['Volume']
Chaikin = pd.Series(pd.ewma(ad, span = 3, min_periods = 2) - pd.ewma(ad, span = 10, min_periods = 9), name = 'Chaikin')
df = df.join(Chaikin)
return df
#Money Flow Index and Ratio
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 = pd.Series(PosMF)
TotMF = PP * df['Volume']
MFR = pd.Series(PosMF / TotMF)
MFI = pd.Series(pd.rolling_mean(MFR, n), name = 'MFI_' + str(n))
df = df.join(MFI)
return df
#On-balance Volume
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 = pd.Series(OBV)
OBV_ma = pd.Series(pd.rolling_mean(OBV, n), name = 'OBV_' + str(n))
df = df.join(OBV_ma)
return df
#Force Index
def FORCE(df, n):
F = pd.Series(df['Close'].diff(n) * df['Volume'].diff(n), name = 'Force_' + str(n))
df = df.join(F)
return df
#Ease of Movement
def EOM(df, n):
EoM = (df['High'].diff(1) + df['Low'].diff(1)) * (df['High'] - df['Low']) / (2 * df['Volume'])
Eom_ma = pd.Series(pd.rolling_mean(EoM, n), name = 'EoM_' + str(n))
df = df.join(Eom_ma)
return df
#Commodity Channel Index
def CCI(df, n):
PP = (df['High'] + df['Low'] + df['Close']) / 3
CCI = pd.Series((PP - pd.rolling_mean(PP, n)) / pd.rolling_std(PP, n), name = 'CCI_' + str(n))
df = df.join(CCI)
return df
#Coppock Curve
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 = pd.Series(pd.ewma(ROC1 + ROC2, span = n, min_periods = n), name = 'Copp_' + str(n))
df = df.join(Copp)
return df
#Keltner Channel
def KELCH(df, n):
KelChM = pd.Series(pd.rolling_mean((df['High'] + df['Low'] + df['Close']) / 3, n), name = 'KelChM_' + str(n))
KelChU = pd.Series(pd.rolling_mean((4 * df['High'] - 2 * df['Low'] + df['Close']) / 3, n), name = 'KelChU_' + str(n))
KelChD = pd.Series(pd.rolling_mean((-2 * df['High'] + 4 * df['Low'] + df['Close']) / 3, n), name = 'KelChD_' + str(n))
df = df.join(KelChM)
df = df.join(KelChU)
df = df.join(KelChD)
return df
#Ultimate Oscillator
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 = pd.Series((4 * pd.rolling_sum(pd.Series(BP_l), 7) / pd.rolling_sum(pd.Series(TR_l), 7)) + (2 * pd.rolling_sum(pd.Series(BP_l), 14) / pd.rolling_sum(pd.Series(TR_l), 14)) + (pd.rolling_sum(pd.Series(BP_l), 28) / pd.rolling_sum(pd.Series(TR_l), 28)), name = 'Ultimate_Osc')
df = df.join(UltO)
return df
#Donchian Channel
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 = pd.Series(DC_l, name = 'Donchian_' + str(n))
DonCh = DonCh.shift(n - 1)
df = df.join(DonCh)
return df
#Standard Deviation
def STDDEV(df, n):
df = df.join(pd.Series(pd.rolling_std(df['Close'], n), name = 'STD_' + str(n)))
return df
var_code = e516ddff-ca78-4195-93f3-dcb8063b2839
