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Update technical_indicators.py #13

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60 changes: 59 additions & 1 deletion technical_indicators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,8 @@ def moving_average(df, n):
"""
MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean(), name='MA_' + str(n))
df = df.join(MA)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -44,6 +46,8 @@ def exponential_moving_average(df, n):
"""
EMA = pd.Series(df['Close'].ewm(span=n, min_periods=n).mean(), name='EMA_' + str(n))
df = df.join(EMA)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -56,6 +60,8 @@ def momentum(df, n):
"""
M = pd.Series(df['Close'].diff(n), name='Momentum_' + str(n))
df = df.join(M)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -70,6 +76,8 @@ def rate_of_change(df, n):
N = df['Close'].shift(n - 1)
ROC = pd.Series(M / N, name='ROC_' + str(n))
df = df.join(ROC)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -89,6 +97,8 @@ def average_true_range(df, n):
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)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -107,6 +117,8 @@ def bollinger_bands(df, n):
b2 = (df['Close'] - MA + 2 * MSD) / (4 * MSD)
B2 = pd.Series(b2, name='Bollinger%b_' + str(n))
df = df.join(B2)
#Rename to reflect period
df.rename(columns = {'MA' : 'MA{}'.format(n)}, inplace=True)
return df


Expand All @@ -126,6 +138,7 @@ def ppsr(df):
psr = {'PP': PP, 'R1': R1, 'S1': S1, 'R2': R2, 'S2': S2, 'R3': R3, 'S3': S3}
PSR = pd.DataFrame(psr)
df = df.join(PSR)
#Rename to reflect period
return df


Expand All @@ -137,6 +150,7 @@ def stochastic_oscillator_k(df):
"""
SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name='SO%k')
df = df.join(SOk)
#Rename to reflect period
return df


Expand All @@ -149,6 +163,8 @@ def stochastic_oscillator_d(df, n):
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)
#Rename to reflect period
df.rename(columns = {'SOd' : 'SOd{}'.format(n)}, inplace=True)
return df


Expand All @@ -170,6 +186,8 @@ def trix(df, n):
i = i + 1
Trix = pd.Series(ROC_l, name='Trix_' + str(n))
df = df.join(Trix)
#Rename to reflect period
df.rename(columns = {'Trix' : 'Trix{}'.format(n)}, inplace=True)
return df


Expand Down Expand Up @@ -213,6 +231,8 @@ def average_directional_movement_index(df, n, n_ADX):
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)
#Rename to reflect period
df.rename(columns = {'ADX' : 'ADX{}+{}'.format(n, n_ADX)}, inplace=True)
return df


Expand All @@ -232,6 +252,12 @@ def macd(df, n_fast, n_slow):
df = df.join(MACD)
df = df.join(MACDsign)
df = df.join(MACDdiff)
#Rename to reflect period
df.rename(columns = {'MACD' : 'MACD{}_{}'.format(n_fast,n_slow)}, inplace=True)
#Rename to reflect period
df.rename(columns = {'MACDsign' : 'MACDsign{}_{}'.format(n_fast,n_slow)}, inplace=True)
#Rename to reflect period
df.rename(columns = {'MACDdiff' : 'MACDdiff{}_{}'.format(n_fast, n_slow)}, inplace=True)
return df


Expand All @@ -247,6 +273,7 @@ def mass_index(df):
Mass = EX1 / EX2
MassI = pd.Series(Mass.rolling(25).sum(), name='Mass Index')
df = df.join(MassI)
#Rename to reflect period
return df


Expand All @@ -273,6 +300,8 @@ def vortex_indicator(df, n):
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)
#Rename to reflect period
df.rename(columns = {'VI' : 'VI{}'.format(n)}, inplace=True)
return df


Expand Down Expand Up @@ -307,6 +336,8 @@ def kst_oscillator(df, r1, r2, r3, r4, n1, n2, n3, n4):
name='KST_' + str(r1) + '_' + str(r2) + '_' + str(r3) + '_' + str(r4) + '_' + str(n1) + '_' + str(
n2) + '_' + str(n3) + '_' + str(n4))
df = df.join(KST)
#Rename to reflect period
df.rename(columns = {'KSI' : 'KSI{}_{}_{}_{}_{}_{}_{}_{}'.format(r1,r2,r3,r4,n1,n2,n3,n4)}, inplace=True)
return df


Expand Down Expand Up @@ -340,6 +371,8 @@ def relative_strength_index(df, n):
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)
#Rename to reflect period
df.rename(columns = {'RSI' : 'RSI{}'.format(n)}, inplace=True)
return df


Expand All @@ -359,6 +392,8 @@ def true_strength_index(df, r, s):
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)
#Rename to reflect period
df.rename(columns = {'TSI' : 'TSI{}_{}'.format(r, s)}, inplace=True)
return df


Expand All @@ -375,6 +410,8 @@ def accumulation_distribution(df, n):
ROC = M / N
AD = pd.Series(ROC, name='Acc/Dist_ROC_' + str(n))
df = df.join(AD)
#Rename to reflect period
df.rename(columns = {'AD' : 'AD{}'.format(n)}, inplace=True)
return df


Expand All @@ -387,6 +424,7 @@ def chaikin_oscillator(df):
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


Expand All @@ -411,6 +449,8 @@ def money_flow_index(df, n):
MFR = pd.Series(PosMF / TotMF)
MFI = pd.Series(MFR.rolling(n, min_periods=n).mean(), name='MFI_' + str(n))
df = df.join(MFI)
#Rename to reflect period
df.rename(columns = {'MFI' : 'MFI{}'.format(n)}, inplace=True)
return df


Expand All @@ -434,6 +474,8 @@ def on_balance_volume(df, n):
OBV = pd.Series(OBV)
OBV_ma = pd.Series(OBV.rolling(n, min_periods=n).mean(), name='OBV_' + str(n))
df = df.join(OBV_ma)
#Rename to reflect period
df.rename(columns = {'OBC_ma' : 'OBV_ma{}'.format(n)}, inplace=True)
return df


Expand All @@ -446,6 +488,8 @@ def force_index(df, n):
"""
F = pd.Series(df['Close'].diff(n) * df['Volume'].diff(n), name='Force_' + str(n))
df = df.join(F)
#Rename to reflect period
df.rename(columns = {'F' : 'F{}'.format(n)}, inplace=True)
return df


Expand All @@ -459,6 +503,8 @@ def ease_of_movement(df, n):
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)
#Rename to reflect period
df.rename(columns = {'Eom_ma' : 'Eom_ma{}'.format(n)}, inplace=True)
return df


Expand All @@ -473,6 +519,8 @@ def commodity_channel_index(df, n):
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)
#Rename to reflect period
df.rename(columns = {'CCI' : 'CCI{}'.format(n)}, inplace=True)
return df


Expand All @@ -491,6 +539,8 @@ def coppock_curve(df, n):
ROC2 = M / N
Copp = pd.Series((ROC1 + ROC2).ewm(span=n, min_periods=n).mean(), name='Copp_' + str(n))
df = df.join(Copp)
#Rename to reflect period
df.rename(columns = {'Copp' : 'Copp{}'.format(n)}, inplace=True)
return df


Expand All @@ -510,6 +560,8 @@ def keltner_channel(df, n):
df = df.join(KelChM)
df = df.join(KelChU)
df = df.join(KelChD)
#Rename to reflect period
df.rename(columns = {'KelChU' : 'KelChU{}'.format(n),'KelChD' : 'KelChD{}'.format(n),'KelChM' : 'KelChM{}'.format(n)}, inplace=True)
return df


Expand All @@ -533,6 +585,8 @@ def ultimate_oscillator(df):
pd.Series(BP_l).rolling(28).sum() / pd.Series(TR_l).rolling(28).sum()),
name='Ultimate_Osc')
df = df.join(UltO)
#reflect perion
df.rename(columns = {'UltO' : 'UltO{}'.format(n)}, inplace=True)
return df


Expand All @@ -556,7 +610,10 @@ def donchian_channel(df, n):

donchian_chan = pd.Series(dc_l, name='Donchian_' + str(n))
donchian_chan = donchian_chan.shift(n - 1)
return df.join(donchian_chan)
df.join(donchian_chan)
#reflect perion
df.rename(columns = {'donchian_chan' : 'donchian_chan{}'.format(n)}, inplace=True)
return df


def standard_deviation(df, n):
Expand All @@ -567,4 +624,5 @@ def standard_deviation(df, n):
:return: pandas.DataFrame
"""
df = df.join(pd.Series(df['Close'].rolling(n, min_periods=n).std(), name='STD_' + str(n)))
#reflect perion
return df