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乐学偶得（http://lexueoude.com) 公众号： 乐学Fintech
用代码理解分析解决金融问题
之前跟大家分享过用Python调用talib实现技术指标分析，但是许多小伙伴有更高的需求：比如需要指标自定义，或者想明白技术分析背后的原理。所以，这一期我们跟大家分享一下通过纯Python+Pandas+Numpy+Math实现talib中的常见指标，是学习底层算法与自定义交易指标，提升内功的非常好的材料。
我们需要的库非常简单，只需要这三个：
import numpy
from pandas import *
from math import *
关于pandas版本的问题，我们视频中跟大家分享过，注意以下用法需要pandas的版本为0.21，若报ewma无法调用的错误，可以指定安装此版本的pandas，或者通过
pandas.DataFrame(ts).ewm(span=12).mean()
这样调用的方法解决。
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 + 2 * MSD) / (4 * MSD)
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 = (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 = 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 = (2 * df['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']) / (2 * df['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((4 * df['High'] – 2 * df['Low'] + df['Close']) / 3, n), name = 'KelChU_' + str(n))
KelChD = Series(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
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((4 * rolling_sum(Series(BP_l), 7) / rolling_sum(Series(TR_l), 7)) + (2 * rolling_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
参考资料：
乐学偶得《Python零基础入门到6大方向热门应用》、《零基础Python玩Fintech金融量化》、《Python股票量化投资策略与交易》等笔记
开源项目：ultrafinance
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公众号： 乐学Fintech
用代码理解分析解决金融问题
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