__init__.py | searchcode

/ultrafinance/pyTaLib/init.py

http://ultra-finance.googlecode.com/
Python | 359 lines | 296 code | 29 blank | 34 comment | 29 complexity | f85867b6264cb170bebcc47be811141a MD5 | raw file

'''

Created on April 15, 2012



@author: Bruno Franca

'''

from pandas import *

from math import *



#Moving Average

def MA(df, n):

    MA = Series(rolling_mean(df['Close'], n), name = 'MA_' + str(n))

    df = df.join(MA)

    return df



#Exponential Moving Average

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



#Momentum

def MOM(df, n):

    M = 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 = 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 = Series(TR_l)

    ATR = Series(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 = 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



#Pivot Points, Supports and Resistances

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



#Stochastic oscillator %K

def STOK(df):

    SOk = 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 = 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



#Trix

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



#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 = 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



#MACD, MACD Signal and MACD difference

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



#Mass Index

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



#Vortex Indicator

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



#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 = 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



#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 = 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



#True Strength Index

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



#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 = 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 = Series(ewma(ad, span = 3, min_periods = 2) - 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 = 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



#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 = Series(OBV)

    OBV_ma = Series(rolling_mean(OBV, n), name = 'OBV_' + str(n))

    df = df.join(OBV_ma)

    return df



#Force Index

def FORCE(df, n):

    F = 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 = Series(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 = Series((PP - rolling_mean(PP, n)) / 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 = Series(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 = 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



#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 = 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



#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 = 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(Series(rolling_std(df['Close'], n), name = 'STD_' + str(n)))

    return df
/ultrafinance/pyTaLib/__init__.py

/ultrafinance/pyTaLib/init.py
