"""
Indicators as shown by Peter Bakker at:
https://www.quantopian.com/posts/technical-analysis-indicators-without-talib-code
"""
"""
25-Mar-2018: Fixed syntax to support the newest version of Pandas. Warnings should no longer appear.
Fixed some bugs regarding min_periods and NaN.
If you find any bugs, please report to github.com/palmbook
"""
# Import Built-Ins
import logging
# Import Third-Party
import pandas as pd
import numpy as np
# Import Homebrew
# Init Logging Facilities
log = logging.getLogger(__name__)
def moving_average(df, n):
"""Calculate the moving average for the given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean(), name='MA_' + str(n))
df = df.join(MA)
return df
def exponential_moving_average(df, n):
"""
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
EMA = pd.Series(df['Close'].ewm(span=n, min_periods=n).mean(), name='EMA_' + str(n))
df = df.join(EMA)
return df
def momentum(df, n):
"""
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
M = pd.Series(df['Close'].diff(n), name='Momentum_' + str(n))
df = df.join(M)
return df
def rate_of_change(df, n):
"""
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
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
def average_true_range(df, n):
"""
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
i = 0
TR_l = [0]
while i < df.index[-1]:
TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean(), name='ATR_' + str(n))
df = df.join(ATR)
return df
def bollinger_bands(df, n):
"""
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean())
MSD = pd.Series(df['Close'].rolling(n, min_periods=n).std())
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
def ppsr(df):
"""Calculate Pivot Points, Supports and Resistances for given data
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
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
def stochastic_oscillator_k(df):
"""Calculate stochastic oscillator %K for given data.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name='SO%k')
df = df.join(SOk)
return df
def stochastic_oscillator_d(df, n):
"""Calculate stochastic oscillator %D for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name='SO%k')
SOd = pd.Series(SOk.ewm(span=n, min_periods=n).mean(), name='SO%d_' + str(n))
df = df.join(SOd)
return df
def trix(df, n):
"""Calculate TRIX for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
EX1 = df['Close'].ewm(span=n, min_periods=n).mean()
EX2 = EX1.ewm(span=n, min_periods=n).mean()
EX3 = EX2.ewm(span=n, min_periods=n).mean()
i = 0
ROC_l = [np.nan]
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
def average_directional_movement_index(df, n, n_ADX):
"""Calculate the Average Directional Movement Index for given data.
:param df: pandas.DataFrame
:param n:
:param n_ADX:
:return: pandas.DataFrame
"""
i = 0
UpI = []
DoI = []
while i + 1 <= df.index[-1]:
UpMove = df.loc[i + 1, 'High'] - df.loc[i, 'High']
DoMove = df.loc[i, 'Low'] - df.loc[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.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean())
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean() / ATR)
NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean() / ATR)
ADX = pd.Series((abs(PosDI - NegDI) / (PosDI + NegDI)).ewm(span=n_ADX, min_periods=n_ADX).mean(),
name='ADX_' + str(n) + '_' + str(n_ADX))
df = df.join(ADX)
return df
def macd(df, n_fast, n_slow):
"""Calculate MACD, MACD Signal and MACD difference
:param df: pandas.DataFrame
:param n_fast:
:param n_slow:
:return: pandas.DataFrame
"""
EMAfast = pd.Series(df['Close'].ewm(span=n_fast, min_periods=n_slow).mean())
EMAslow = pd.Series(df['Close'].ewm(span=n_slow, min_periods=n_slow).mean())
MACD = pd.Series(EMAfast - EMAslow, name='MACD_' + str(n_fast) + '_' + str(n_slow))
MACDsign = pd.Series(MACD.ewm(span=9, min_periods=9).mean(), 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
def mass_index(df):
"""Calculate the Mass Index for given data.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
Range = df['High'] - df['Low']
EX1 = Range.ewm(span=9, min_periods=9).mean()
EX2 = EX1.ewm(span=9, min_periods=9).mean()
Mass = EX1 / EX2
MassI = pd.Series(Mass.rolling(25).sum(), name='Mass Index')
df = df.join(MassI)
return df
def vortex_indicator(df, n):
"""Calculate the Vortex Indicator for given data.
Vortex Indicator described here:
http://www.vortexindicator.com/VFX_VORTEX.PDF
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
i = 0
TR = [0]
while i < df.index[-1]:
Range = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR.append(Range)
i = i + 1
i = 0
VM = [0]
while i < df.index[-1]:
Range = abs(df.loc[i + 1, 'High'] - df.loc[i, 'Low']) - abs(df.loc[i + 1, 'Low'] - df.loc[i, 'High'])
VM.append(Range)
i = i + 1
VI = pd.Series(pd.Series(VM).rolling(n).sum() / pd.Series(TR).rolling(n).sum(), name='Vortex_' + str(n))
df = df.join(VI)
return df
def kst_oscillator(df, r1, r2, r3, r4, n1, n2, n3, n4):
"""Calculate KST Oscillator for given data.
:param df: pandas.DataFrame
:param r1:
:param r2:
:param r3:
:param r4:
:param n1:
:param n2:
:param n3:
:param n4:
:return: pandas.DataFrame
"""
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(
ROC1.rolling(n1).sum() + ROC2.rolling(n2).sum() * 2 + ROC3.rolling(n3).sum() * 3 + ROC4.rolling(n4).sum() * 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
def relative_strength_index(df, n):
"""Calculate Relative Strength Index(RSI) for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
i = 0
UpI = [0]
DoI = [0]
while i + 1 <= df.index[-1]:
UpMove = df.loc[i + 1, 'High'] - df.loc[i, 'High']
DoMove = df.loc[i, 'Low'] - df.loc[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(UpI.ewm(span=n, min_periods=n).mean())
NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean())
RSI = pd.Series(PosDI / (PosDI + NegDI), name='RSI_' + str(n))
df = df.join(RSI)
return df
def true_strength_index(df, r, s):
"""Calculate True Strength Index (TSI) for given data.
:param df: pandas.DataFrame
:param r:
:param s:
:return: pandas.DataFrame
"""
M = pd.Series(df['Close'].diff(1))
aM = abs(M)
EMA1 = pd.Series(M.ewm(span=r, min_periods=r).mean())
aEMA1 = pd.Series(aM.ewm(span=r, min_periods=r).mean())
EMA2 = pd.Series(EMA1.ewm(span=s, min_periods=s).mean())
aEMA2 = pd.Series(aEMA1.ewm(span=s, min_periods=s).mean())
TSI = pd.Series(EMA2 / aEMA2, name='TSI_' + str(r) + '_' + str(s))
df = df.join(TSI)
return df
def accumulation_distribution(df, n):
"""Calculate Accumulation/Distribution for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
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
def chaikin_oscillator(df):
"""Calculate Chaikin Oscillator for given data.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
ad = (2 * df['Close'] - df['High'] - df['Low']) / (df['High'] - df['Low']) * df['Volume']
Chaikin = pd.Series(ad.ewm(span=3, min_periods=3).mean() - ad.ewm(span=10, min_periods=10).mean(), name='Chaikin')
df = df.join(Chaikin)
return df
def money_flow_index(df, n):
"""Calculate Money Flow Index and Ratio for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
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.loc[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(MFR.rolling(n, min_periods=n).mean(), name='MFI_' + str(n))
df = df.join(MFI)
return df
def on_balance_volume(df, n):
"""Calculate On-Balance Volume for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
i = 0
OBV = [0]
while i < df.index[-1]:
if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] > 0:
OBV.append(df.loc[i + 1, 'Volume'])
if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] == 0:
OBV.append(0)
if df.loc[i + 1, 'Close'] - df.loc[i, 'Close'] < 0:
OBV.append(-df.loc[i + 1, 'Volume'])
i = i + 1
OBV = pd.Series(OBV)
OBV_ma = pd.Series(OBV.rolling(n, min_periods=n).mean(), name='OBV_' + str(n))
df = df.join(OBV_ma)
return df
def force_index(df, n):
"""Calculate Force Index for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
F = pd.Series(df['Close'].diff(n) * df['Volume'].diff(n), name='Force_' + str(n))
df = df.join(F)
return df
def ease_of_movement(df, n):
"""Calculate Ease of Movement for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
EoM = (df['High'].diff(1) + df['Low'].diff(1)) * (df['High'] - df['Low']) / (2 * df['Volume'])
Eom_ma = pd.Series(EoM.rolling(n, min_periods=n).mean(), name='EoM_' + str(n))
df = df.join(Eom_ma)
return df
def commodity_channel_index(df, n):
"""Calculate Commodity Channel Index for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
PP = (df['High'] + df['Low'] + df['Close']) / 3
CCI = pd.Series((PP - PP.rolling(n, min_periods=n).mean()) / PP.rolling(n, min_periods=n).std(),
name='CCI_' + str(n))
df = df.join(CCI)
return df
def coppock_curve(df, n):
"""Calculate Coppock Curve for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
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((ROC1 + ROC2).ewm(span=n, min_periods=n).mean(), name='Copp_' + str(n))
df = df.join(Copp)
return df
def keltner_channel(df, n):
"""Calculate Keltner Channel for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
KelChM = pd.Series(((df['High'] + df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
name='KelChM_' + str(n))
KelChU = pd.Series(((4 * df['High'] - 2 * df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
name='KelChU_' + str(n))
KelChD = pd.Series(((-2 * df['High'] + 4 * df['Low'] + df['Close']) / 3).rolling(n, min_periods=n).mean(),
name='KelChD_' + str(n))
df = df.join(KelChM)
df = df.join(KelChU)
df = df.join(KelChD)
return df
def ultimate_oscillator(df):
"""Calculate Ultimate Oscillator for given data.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
i = 0
TR_l = [0]
BP_l = [0]
while i < df.index[-1]:
TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR_l.append(TR)
BP = df.loc[i + 1, 'Close'] - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
BP_l.append(BP)
i = i + 1
UltO = pd.Series((4 * pd.Series(BP_l).rolling(7).sum() / pd.Series(TR_l).rolling(7).sum()) + (
2 * pd.Series(BP_l).rolling(14).sum() / pd.Series(TR_l).rolling(14).sum()) + (
pd.Series(BP_l).rolling(28).sum() / pd.Series(TR_l).rolling(28).sum()),
name='Ultimate_Osc')
df = df.join(UltO)
return df
def donchian_channel(df, n):
"""Calculate donchian channel of given pandas data frame.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
i = 0
dc_l = []
while i < n - 1:
dc_l.append(0)
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 += 1
donchian_chan = pd.Series(dc_l, name='Donchian_' + str(n))
donchian_chan = donchian_chan.shift(n - 1)
return df.join(donchian_chan)
def standard_deviation(df, n):
"""Calculate Standard Deviation for given data.
:param df: pandas.DataFrame
:param n:
:return: pandas.DataFrame
"""
df = df.join(pd.Series(df['Close'].rolling(n, min_periods=n).std(), name='STD_' + str(n)))
return df
