V.jdZddlZddlZddlmZmZmZm Z GddeZ GddeZ Gdd eZ Gd d eZ Gd d eZGddeZGddeZGddeZGddeZGddeZGddeZGddeZGddeZGddeZGd d!eZd?d"Zd?d#Zd@d$ZdAd%ZdBd&ZdBd'ZdCd(ZdCd)Z dCd*Z!dCd+Z"dCd,Z#dDd-Z$dEd.Z%dFd/Z&dGd0Z' dHd1Z( dId2Z) dJd3Z* dKd4ejVfd5Z, dKd4ejVfd6Z-dKd7Z.dLd8Z/dMd9Z0dMd:Z1dNd;Z2dNd<Z3dNd=Z4dNd>Z5y)Ozg .. module:: trend :synopsis: Trend Indicators. .. moduleauthor:: Dario Lopez Padial (Bukosabino) N)IndicatorMixin_ema _get_min_max_smac eZdZdZ d dej dej dedefdZdZ dej fd Z dej fd Z dej fd Z y )AroonIndicatoraAroon Indicator Identify when trends are likely to change direction. Aroon Up = ((N - Days Since N-day High) / N) x 100 Aroon Down = ((N - Days Since N-day Low) / N) x 100 Aroon Indicator = Aroon Up - Aroon Down https://www.investopedia.com/terms/a/aroon.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window(int): n period. fillna(bool): if True, fill nan values. highlowwindowfillnac\||_||_||_||_|j yN)_high_low_window_fillna_run)selfr r r r s JC:\xampp\htdocs\tradingbinance\backend\.venv\Lib\site-packages\ta/trend.py__init__zAroonIndicator.__init__ s)     cTjrdnjdz}jjjdz|}|j fdd_j jjdz|}|j fdd_y)N min_periodsc`ttj|jz dzSNd)floatnpargmaxrxrs rz%AroonIndicator._run../ eBIIaL)DLL83>rTrawc`ttj|jz dzSr)rr argminrr"s rr$z%AroonIndicator._run..4r%r)rrrrollingapply _aroon_upr _aroon_down)rr rolling_high rolling_lows` rrzAroonIndicator._run)s<D, ii'' q(8k'R &,, >D- rreturnc|j|jd}tj|d|jS)z]Aroon Up Channel Returns: pandas.Series: New feature generated. rvalue aroon_up_name) _check_fillnar,pdSeriesr)raroon_up_seriess raroon_upzAroonIndicator.aroon_up7s9 ,,T^^1,Eyy4<<./IJJrc|j|jd}tj|d|jS)z_Aroon Down Channel Returns: pandas.Series: New feature generated. rr2 aroon_down_r5)r7r-r8r9r)raroon_down_seriess r aroon_downzAroonIndicator.aroon_down@s= !..t/?/?q.Iyy*;t||n1MNNrc|j|jz }|j|d}tj|d|j S)z\Aroon Indicator Returns: pandas.Series: New feature generated. rr2 aroon_ind_r5)r,r-r7r8r9r)r aroon_diffs raroon_indicatorzAroonIndicator.aroon_indicatorIsI ^^d&6&66 '' !'< yyJt||n*EFFrNF) __name__ __module__ __qualname____doc__r8r9intboolrrr;r?rCrrrrsv$QVII$&II7:IM  K"))KOBIIOGGrrc eZdZdZ ddej dedededef dZdZ d ej fd Z d ej fd Z d ej fd Z y )MACDaMoving Average Convergence Divergence (MACD) Is a trend-following momentum indicator that shows the relationship between two moving averages of prices. https://school.stockcharts.com/doku.php?id=technical_indicators:moving_average_convergence_divergence_macd Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. close window_slow window_fast window_signr cj||_||_||_||_||_|j yr)_close _window_slow _window_fast _window_signrr)rrOrPrQrRr s rrz MACD.__init__ds3 '''  rct|j|j|j|_t|j|j |j|_|j|j z |_t|j|j|j|_ |j|jz |_ yr) rrTrVr_emafastrU_emaslow_macdrW _macd_signal _macd_diff)rs rrz MACD._runssT[[$*;*;T\\J T[[$*;*;T\\J ]]T]]2  T->-> M**t'8'88rr0c|j|jd}tj|d|jd|j S)zVMACD Line Returns: pandas.Series: New feature generated. rr2MACD__r5)r7r[r8r9rVrU)r macd_seriess rmacdz MACD.macdzsN ((1(= yy d&7&7%8$:K:K9LM  rc|j|jd}tj|d|jd|j S)zXSignal Line Returns: pandas.Series: New feature generated. rr2 MACD_sign_r`r5)r7r\r8r9rVrU)rmacd_signal_seriess r macd_signalzMACD.macd_signalsR"//0A0A/Kyy d//0$2C2C1DE  rc|j|jd}tj|d|jd|j S)z[MACD Histogram Returns: pandas.Series: New feature generated. rr2 MACD_diff_r`r5)r7r]r8r9rVrU)rmacd_diff_seriess r macd_diffzMACD.macd_diffsO  --dooQ-Gyy Z0A0A/B!DDUDUCV#W  rN F) rFrGrHrIr8r9rJrKrrrbrfrjrLrrrNrNTs $ yy       9  bii    RYY    299  rrNcZeZdZdZd dej dedefdZdej fdZ y) EMAIndicatorzEMA - Exponential Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rOr r c.||_||_||_yrrTrrrrOr r s rrzEMAIndicator.__init__   rr0ct|j|j|j}t j |d|jS)zmExponential Moving Average (EMA) Returns: pandas.Series: New feature generated. ema_r5)rrTrrr8r9)rrvs r ema_indicatorzEMAIndicator.ema_indicator: DKKt||<yyd4<<.$9::rNF) rFrGrHrIr8r9rJrKrrwrLrrrprps5bii4 ;ryy;rrpcZeZdZdZd dej dedefdZdej fdZ y) SMAIndicatorzSMA - Simple Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rOr r c.||_||_||_yrrrrss rrzSMAIndicator.__init__rtrr0ct|j|j|j}t j |d|jS)zhSimple Moving Average (SMA) Returns: pandas.Series: New feature generated. sma_r5)rrTrrr8r9)rrs r sma_indicatorzSMAIndicator.sma_indicatorrxrN)F) rFrGrHrIr8r9rJrKrrrLrrr|r|s5biid ;ryy;rr|c`eZdZdZd dej dedefdZdZ dej fdZ y ) WMAIndicatorzWMA - Weighted Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rOr r cN||_||_||_|jyrrTrrrrss rrzWMAIndicator.__init__     rc Jtjtd|jdzDcgc]$}|dz|j|jdzzz &c}}d}|jj |jj ||d|_ycc}w)Nrcfd}|S)Nc*|zjSr)sum)r#weights r_weighted_averagezFWMAIndicator._run..weighted_average.._weighted_averages ''))rrL)rrs` rweighted_averagez+WMAIndicator._run..weighted_averages *% $rTr&)r8r9rangerrTr*r+_wma)ri_weightrs rrzWMAIndicator._runs))q$,,"23 3AA)9:;3   % KK'' 5;; W %4<   s)B r0c|j|jd}tj|d|jS)zjWeighted Moving Average (WMA) Returns: pandas.Series: New feature generated. rr2wma_r5)r7rr8r9r)rwmas rrzWMAIndicator.wmas9   ! 4yyT$,,#899rNrnF) rFrGrHrIr8r9rJrKrrrrLrrrrs:bii$  $:RYY:rrc`eZdZdZd dej dedefdZdZ dej fdZ y ) TRIXIndicatoraSTrix (TRIX) Shows the percent rate of change of a triple exponentially smoothed moving average. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rOr r cN||_||_||_|jyrrrss rrzTRIXIndicator.__init__rrct|j|j|j}t||j|j}t||j|j}||j d|j z |j d|j z |_|xj dzc_y)Nr fill_valuer)rrTrrshiftmean_trix)rema1ema2ema3s rrzTRIXIndicator._run sDKKt||<D$,, 5D$,, 5TZZdiikZBBdjj $))+GQG    c rr0c|j|jd}tj|d|jS)zXTrix (TRIX) Returns: pandas.Series: New feature generated. rr2trix_r5)r7rr8r9r)r trix_seriess rtrixzTRIXIndicator.trixs9 ((1(= yyU4<<.+ABBrNF) rFrGrHrIr8r9rJrKrrrrLrrrrs< bii4 CbiiCrrc eZdZdZ d dej dej dededef dZdZ d ej fd Z y ) MassIndexaMass Index (MI) It uses the high-low range to identify trend reversals based on range expansions. It identifies range bulges that can foreshadow a reversal of the current trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window_fast(int): fast period value. window_slow(int): slow period value. fillna(bool): if True, fill nan values. r r rQrPr cj||_||_||_||_||_|j yr)rrrVrUrr)rr r rQrPr s rrzMassIndex.__init__1s2  ''  rcX|jrdn |j}|j|jz }t ||j |j}t ||j |j}||z }|j |j|j|_y)Nrr) rrUrrrrVr*r_mass)rr amplituderrmasss rrzMassIndex._run@s<-> JJ* It00$,,?D$++T\\:d{\\$"3"3\MQQS rr0c|j|jd}tj|d|jd|j S)z\Mass Index (MI) Returns: pandas.Series: New feature generated. rr2 mass_index_r`r5)r7rr8r9rVrU)rrs r mass_indexzMassIndex.mass_indexHsN !!$**A!6yy T%6%6$7q9J9J8KL  rNrnrEF) rFrGrHrIr8r9rJrKrrrrLrrrr se( ii YY      T  BII  rrceZdZdZ ddej dej dededededefd Zd Z d ej fd Z d ej fd Z d ej fdZ d ej fdZ y)IchimokuIndicatoruIchimoku Kinkō Hyō (Ichimoku) http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): n1 low period. window2(int): n2 medium period. window3(int): n3 high period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. r r window1window2window3visualr c||_||_||_||_||_||_||_|jyr)rr_window1_window2_window3_visualrr)rr r rrrrr s rrzIchimokuIndicator.__init__cs>        rc8|jrdn |j}|jrdn |j}d|jj |j|j |j j |j|jzz|_d|jj |j|j |j j |j|jzz|_ y)Nr?r) rrrrr*maxrmin_conv_base)rmin_periods_n1min_periods_n2s rrzIchimokuIndicator._runvs"ll "ll  JJ  t}}.  I M M Oii >JNNP Q   JJ  t}}.  I M M Oii >JNNP Q  rr0c|j|jd}tj|d|jd|j S)ziTenkan-sen (Conversion Line) Returns: pandas.Series: New feature generated. r2ichimoku_conv_r`r5)r7rr8r9rr)r conversions richimoku_conversion_linez*IchimokuIndicator.ichimoku_conversion_linesH '' "'= yy ~dmm_Admm_M  rc|j|jd}tj|d|jd|j S)zbKijun-sen (Base Line) Returns: pandas.Series: New feature generated. rr2ichimoku_base_r`r5)r7rr8r9rr)rbases richimoku_base_linez$IchimokuIndicator.ichimoku_base_linesC !!$**B!7yynT]]O1T]]O$TUUrc4d|j|jzz}|jr+|j|j|j n|}|j |d}tj|d|jd|jS)zkSenkou Span A (Leading Span A) Returns: pandas.Series: New feature generated. rrrr2 ichimoku_a_r`r5) rrrrrrr7r8r9r)rspanas r ichimoku_azIchimokuIndicator.ichimoku_as tzzDJJ./|| KK %**,K ?  ""5"3yy{4==/4==/%RSSrcd|jj|jdj|jj|jdj zz}|j r+|j|j|jn|}|j|d}tj|d|jd|j S) zkSenkou Span B (Leading Span B) Returns: pandas.Series: New feature generated. rrrrrr2 ichimoku_b_r`r5)rr*rrrrrrrrr7r8r9r)rspanbs r ichimoku_bzIchimokuIndicator.ichimoku_bs  JJ  t}}!  < @ @ Bii 1=AAC D  || KK %**,K ?  ""5"3yy{4==/4==/%RSSrN)rnrl4FF)rFrGrHrIr8r9rJrKrrrrrrrLrrrrTs $iiYY     &    "))  VBIIV TBII TTBIITrrceZdZdZ ddej dedededededed ed ed ed efd ZdZ dej fdZ dej fdZ dej fdZ y) KSTIndicatora@KST Oscillator (KST Signal) It is useful to identify major stock market cycle junctures because its formula is weighed to be more greatly influenced by the longer and more dominant time spans, in order to better reflect the primary swings of stock market cycle. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst Args: close(pandas.Series): dataset 'Close' column. roc1(int): roc1 period. roc2(int): roc2 period. roc3(int): roc3 period. roc4(int): roc4 period. window1(int): n1 smoothed period. window2(int): n2 smoothed period. window3(int): n3 smoothed period. window4(int): n4 smoothed period. nsig(int): n period to signal. fillna(bool): if True, fill nan values. rOroc1roc2roc3roc4rrrwindow4nsigr c ||_||_||_||_||_||_||_||_| |_| |_ | |_ |jyr) rT_r1_r2_r3_r4rrr_window4_nsigrr) rrOrrrrrrrrrr s rrzKSTIndicator.__init__sZ        rc(|jrdn |j}|jrdn |j}|jrdn |j}|jrdn |j}|j |j j |j|j jz |j j |j|j jz j|j|j}|j |j j |j|j jz |j j |j|j jz j|j|j}|j |j j |j|j jz |j j |j|j jz j|j|j}|j |j j |j|j jz |j j |j|j jz j|j|j}d|d|zzd|zzd|zzz|_ |jj|jdj|_y)Nrrrrr)rrrrrrTrrrr*rrr_kstr_kst_sig) rrrmin_periods_n3min_periods_n4rocma1rocma2rocma3rocma4s rrzKSTIndicator._runs"ll "ll "ll "ll KKkk''T[[=M=M=O'PQ++##DHH9I9I9K#L M WT]]W ? TV KKkk''T[[=M=M=O'PQ++##DHH9I9I9K#L M WT]]W ? TV KKkk''T[[=M=M=O'PQ++##DHH9I9I9K#L M WT]]W ? TV KKkk''T[[=M=M=O'PQ++##DHH9I9I9K#L M WT]]W ? TV 6AJ.V;a&jHI  ))$**!)DIIK rr0cj|j|jd}tj|dS)zbKnow Sure Thing (KST) Returns: pandas.Series: New feature generated. rr2kstr5)r7rr8r9)r kst_seriess rrzKSTIndicator.kst. '' '; yy%00rcj|j|jd}tj|dS)zSignal Line Know Sure Thing (KST) nsig-period SMA of KST Returns: pandas.Series: New feature generated. rr2kst_sigr5)r7rr8r9)rkst_sig_seriess rrzKSTIndicator.kst_sig(s.++DMM+Cyyi88rc|j|jz }|j|d}tj|dS)zDiff Know Sure Thing (KST) KST - Signal_KST Returns: pandas.Series: New feature generated. rr2kst_diffr5)rrr7r8r9)rrs rrzKSTIndicator.kst_diff3s<99t}},%%ha%8yy 33rN rrrrrrnF) rFrGrHrIr8r9rJrKrrrrrrLrrrrs4yy     62Lh1RYY1 9 9 4")) 4rrc`eZdZdZd dej dedefdZdZ dej fdZ y ) DPOIndicatoraDetrended Price Oscillator (DPO) Is an indicator designed to remove trend from price and make it easier to identify cycles. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rOr r cN||_||_||_|jyrrrss rrzDPOIndicator.__init__NrrcJ|jrdn |j}|jjt d|jzdz|jj |jj |j|j z |_y)Nrrrrr)rrrTrrJrr*_dpo)rrs rrzDPOIndicator._runTs<._mads&77266!bggaj.12 2rrg@rT) rrrrrTr*rrr+_cci)rrr typical_prices rrzCCIIndicator._runs 3 <>q4<<PTTV ! $=$I$It$I$T!q#dii.1,-A !a% 99QU#eDLL&99;+DLL1,<=> IIaL.**tzz//22IIOOA&2 Gi'GaK8GCDI'IM:iGHHHS-1ABC zz|((T\\:>>@ ! oo4o(q#dii.1,-A !a% 99QU#eDLL&99;dllQ&'( IIaL.HHS-1ABC zz|((T\\:>>@ ! oo4o(q#dii.1,-A !a% 99QU#eDLL&99;dllQ&'( IIaL.rr0ctjt|j}t |jD](\}}|dk7rd|j ||z z||<$d||<*tjt|j}t |jD](\}}|dk7rd|j ||z z||<$d||<*tjt|j}tt|jD]F}||||zdk7r1dtj||||z ||||zz z||<Bd||<Htjt|j}|d|jj||j<t|jdzt|D]<}||dz |jdz z||dz zt|jz ||<>tj|j|fd}tj||j j"}|j%|d}tj|d S) znAverage Directional Index (ADX) Returns: pandas.Series: New feature generated.tr rrr)axis)dataindexrr2adxr5)r r"r$r% enumerater)r*rrrrr concatenater#r8r9rTr6r7)rdipidxr3dindirectional_index adx_seriesrs rr7zADXIndicator.adxs? hhs499~&#DII.JCz$))C.5"89CC /hhs499~&#DII.JCz$))C.5"89CC /HHS^4TYY(C3x#c("a'),rvvXC(SXC-@A0*!#& *+!#&)XXc$))n- #4Q#F#K#K#M 4<< t||a'Z9AAE"dllQ&67;LQQRU;SSdll#$JqM: ^^T%6%6 $C!L YYJdkk6G6GH '' "'= yy%00rctjt|j}t dt|j dz D]Y}|j |dk7r3d|j ||j |z z|||jz<Hd|||jz<[|jtj||jjd}tj|dS) zmPlus Directional Indicator (+DI) Returns: pandas.Series: New feature generated. rrrr6rr2adx_posr5) r r"r$rTrr%r)rr7r8r9r6)rr:radx_pos_seriess rrAzADXIndicator.adx_pos4 hhs4;;'(q#dii.1,-Ayy|q (+tyy|diil/J(KA $%)*A $% .++ IIc!2!2 32, yyi88rctjt|j}t dt|j dz D]Y}|j |dk7r3d|j ||j |z z|||jz<Hd|||jz<[|jtj||jjd}tj|dS) znMinus Directional Indicator (-DI) Returns: pandas.Series: New feature generated. rrrr@rr2adx_negr5) r r"r$rTrr%r*rr7r8r9r6)rr<radx_neg_seriess rrEzADXIndicator.adx_negIrCrNry) rFrGrHrIr8r9rJrKrrr7rArErLrrrrs: ii YY yy     3j/1RYY/1b99*99rrc eZdZdZ d dej dej dej dedef dZdZ d Z d Z d Z y )VortexIndicatoraHVortex Indicator (VI) It consists of two oscillators that capture positive and negative trend movement. A bullish signal triggers when the positive trend indicator crosses above the negative trend indicator or a key level. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. r r rOr r cj||_||_||_||_||_|j yrrrs rrzVortexIndicator.__init__prrc|jjd|jj}|j|j|j |}|j rdn |j}|j|j|j}tj|j|j jdz }tj|j |jjdz }|j|j|j|z |_ |j|j|j|z |_ y)Nrrrr)rTrr _true_rangerrrrr*rr r_vip_vin)rr+ true_rangertrnvmpvmms rrzVortexIndicator._runs kk''dkk6F6F6H'I %%djj$))[I <>!a%0D99>>!a%0Ddjjooa00-1 *  22-1 *%)ZZ__QU%;'4::??1q5+AN+RS& "djjooa00#H)7DJJOOA&$,M*.**'/)0.1/$** yy622rcj|j|jd}tj|dS)z`PSAR up trend value Returns: pandas.Series: New feature generated. rr2psarupr5)r7rhr8r9)rpsar_up_seriess rpsar_upzPSARIndicator.psar_ups. ++DMM+Dyyh77rcj|j|jd}tj|dS)zbPSAR down trend value Returns: pandas.Series: New feature generated. rr2psardownr5)r7rir8r9)rpsar_down_seriess r psar_downzPSARIndicator.psar_down#s0  --dooR-Hyy) ;;rc@|jj|jj|jjdj zd}|j|dk(d}t j ||jjdS)zjPSAR up trend value indicator Returns: pandas.Series: New feature generated. rrpsariupr6r6) rhwherenotnullrisnullr8r9rTr6r indicators rpsar_up_indicatorzPSARIndicator.psar_up_indicator,sz MM'' MM ! ! #dmm&9&9!&<&C&C&E Eq OOINA6 yy$++*;*;)LLrc@|jj|jj|jj dj zd}|j|dk(d}t j||jjdS)zlPSAR down trend value indicator Returns: pandas.Series: New feature generated. rr psaridownr) rhrrirrrr8r9rTr6rs rpsar_down_indicatorz!PSARIndicator.psar_down_indicator8sz MM'' OO # # %(=(=a(@(G(G(I I1 OOINA6 yy$++*;*;+NNrNg{Gz?g?F)rFrGrHrIr8r9rrKrrrvr{rrrrLrrr]r]s0iiYYyy     "D=L3bii388<299< M299 M ORYY Orr]cbeZdZdZ d dej dedededededefd Zd Z d Z y ) STCIndicatora:Schaff Trend Cycle (STC) The Schaff Trend Cycle (STC) is a charting indicator that is commonly used to identify market trends and provide buy and sell signals to traders. Developed in 1999 by noted currency trader Doug Schaff, STC is a type of oscillator and is based on the assumption that, regardless of time frame, currency trends accelerate and decelerate in cyclical patterns. https://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. cycle(int): cycle size smooth1(int): ema period over stoch_k smooth2(int): ema period over stoch_kd fillna(bool): if True, fill nan values. rOrPrQcyclesmooth1smooth2r c||_||_||_||_||_||_||_|jyr)rTrUrV_cycle_smooth1_smooth2rr)rrOrPrQrrrr s rrzSTCIndicator.__init__[s@ ''     rct|j|j|j}t|j|j|j}||z }|j |j j}|j |j j}d||z z||z z }t||j|j}|j |j j}|j |j j} d||z z| |z z } t| |j|j|_ y)N)r r) rrTrVrrUr*rrrrr_stc) rrYrZr[_macdmin_macdmax_stoch_k_stoch_d _stoch_d_min _stoch_d_max _stoch_kds rrzSTCIndicator._runns T%6%6 E T%6%6 E8#== =488:== =488:%(*+x(/BC$-->''t{{';??A ''t{{';??A 8l23|l7RS DMM4<<@ rcf|j|j}tj|dS)z_Schaff Trend Cycle Returns: pandas.Series: New feature generated. stcr5)r7rr8r9)r stc_seriess rrzSTCIndicator.stc}s) '' 2 yy%00rN2rrrF) rFrGrHrIr8r9rJrKrrrrLrrrrEss0yy     & A1rrc:t|||jS)zaExponential Moving Average (EMA) Returns: pandas.Series: New feature generated. rOr r )rprwrs rrwrw eF6 B P P RRrc:t|||jS)z\Simple Moving Average (SMA) Returns: pandas.Series: New feature generated. r)r|rrs rrrrrc:t|||jS)z^Weighted Moving Average (WMA) Returns: pandas.Series: New feature generated. r)rrrs r wma_indicatorrs eF6 B F F HHrc>t|||d|jS)aMoving Average Convergence Divergence (MACD) Is a trend-following momentum indicator that shows the relationship between two moving averages of prices. https://en.wikipedia.org/wiki/MACD Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rnrOrPrQrRr )rNrb)rOrPrQr s rrbrbs)"     df  rc>t|||||jS)aMoving Average Convergence Divergence (MACD Signal) Shows EMA of MACD. https://en.wikipedia.org/wiki/MACD Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rNrfrs rrfrfs)"    km rc>t|||||jS)aMoving Average Convergence Divergence (MACD Diff) Shows the relationship between MACD and MACD Signal. https://en.wikipedia.org/wiki/MACD Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rNrjrs rrjrjs)"    ik rc>t|||||jS)a7Average Directional Movement Index (ADX) The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) are derived from smoothed averages of these differences, and measure trend direction over time. These two indicators are often referred to collectively as the Directional Movement Indicator (DMI). The Average Directional Index (ADX) is in turn derived from the smoothed averages of the difference between +DI and -DI, and measures the strength of the trend (regardless of direction) over time. Using these three indicators together, chartists can determine both the direction and strength of the trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r r rOr r )rr7rs rr7r7s#6  s%v  ce rc>t|||||jS)a@Average Directional Movement Index Positive (ADX) The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) are derived from smoothed averages of these differences, and measure trend direction over time. These two indicators are often referred to collectively as the Directional Movement Indicator (DMI). The Average Directional Index (ADX) is in turn derived from the smoothed averages of the difference between +DI and -DI, and measures the strength of the trend (regardless of direction) over time. Using these three indicators together, chartists can determine both the direction and strength of the trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrArs rrArA#6  s%v  girc>t|||||jS)a@Average Directional Movement Index Negative (ADX) The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) are derived from smoothed averages of these differences, and measure trend direction over time. These two indicators are often referred to collectively as the Directional Movement Indicator (DMI). The Average Directional Index (ADX) is in turn derived from the smoothed averages of the difference between +DI and -DI, and measures the strength of the trend (regardless of direction) over time. Using these three indicators together, chartists can determine both the direction and strength of the trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrErs rrErE0rrc>t|||||jS)aVortex Indicator (VI) It consists of two oscillators that capture positive and negative trend movement. A bullish signal triggers when the positive trend indicator crosses above the negative trend indicator or a key level. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rHrTrs rrTrTP%&  s%v rc>t|||||jS)aVortex Indicator (VI) It consists of two oscillators that capture positive and negative trend movement. A bearish signal triggers when the negative trend indicator crosses above the positive trend indicator or a key level. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rHrXrs rrXrXhrrc:t|||jS)aTrix (TRIX) Shows the percent rate of change of a triple exponentially smoothed moving average. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrrs rrrs uVF C H H JJrc>t|||||jS)aWMass Index (MI) It uses the high-low range to identify trend reversals based on range expansions. It identifies range bulges that can foreshadow a reversal of the current trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window_fast(int): fast window value. window_slow(int): slow window value. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r r rQrPr )rrrs rrrs)(      jl rc@t||||||jS)a"Commodity Channel Index (CCI) CCI measures the difference between a security's price change and its average price change. High positive readings indicate that prices are well above their average, which is a show of strength. Low negative readings indicate that prices are well below their average, which is a show of weakness. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n periods. constant(int): constant. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r r rOr r r )r rrs rrrs&.  s%RX  ce rc:t|||jS)aDetrended Price Oscillator (DPO) Is an indicator designed to remove trend from price and make it easier to identify cycles. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrrs rrrs eF6 B F F HHrc Jt|||||||||d|  jS)aKST Oscillator (KST) It is useful to identify major stock market cycle junctures because its formula is weighed to be more greatly influenced by the longer and more dominant time spans, in order to better reflect the primary swings of stock market cycle. https://en.wikipedia.org/wiki/KST_oscillator Args: close(pandas.Series): dataset 'Close' column. roc1(int): r1 period. roc2(int): r2 period. roc3(int): r3 period. roc4(int): r4 period. window1(int): n1 smoothed period. window2(int): n2 smoothed period. window3(int): n3 smoothed period. window4(int): n4 smoothed period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rn rOrrrrrrrrrr )rr) rOrrrrrrrrr s rrrs<H        ce  rc Bt|||||||jS)atSchaff Trend Cycle (STC) The Schaff Trend Cycle (STC) is a charting indicator that is commonly used to identify market trends and provide buy and sell signals to traders. Developed in 1999 by noted currency trader Doug Schaff, STC is a type of oscillator and is based on the assumption that, regardless of time frame, currency trends accelerate and decelerate in cyclical patterns. https://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. cycle(int): n period smooth1(int): ema period over stoch_k smooth2(int): ema period over stoch_kd fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rOrPrQrrrr )rrrs rrrs/4   ce rc Jt|||||||||| |  jS)a|KST Oscillator (KST Signal) It is useful to identify major stock market cycle junctures because its formula is weighed to be more greatly influenced by the longer and more dominant time spans, in order to better reflect the primary swings of stock market cycle. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst Args: close(pandas.Series): dataset 'Close' column. roc1(int): roc1 period. roc2(int): roc2 period. roc3(int): roc3 period. roc4(int): roc4 period. window1(int): n1 smoothed period. window2(int): n2 smoothed period. window3(int): n3 smoothed period. window4(int): n4 smoothed period. nsig(int): n period to signal. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrrs rrr7s<L       gi rr0c Bt||||d||jS)a*Tenkan-sen (Conversion Line) It identifies the trend and look for potential signals within that trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): n1 low period. window2(int): n2 medium period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rr r rrrrr )rrr r rrrr s rrrls1(     !rc Bt||||d||jS)a#Kijun-sen (Base Line) It identifies the trend and look for potential signals within that trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): n1 low period. window2(int): n2 medium period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rr)rrrs rrrs1(    rc Bt||||d||jS)u-Ichimoku Kinkō Hyō (Ichimoku) It identifies the trend and look for potential signals within that trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): n1 low period. window2(int): n2 medium period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rr)rrrs rrrs/$    jlrc Bt||d||||jS)u.Ichimoku Kinkō Hyō (Ichimoku) It identifies the trend and look for potential signals within that trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window2(int): n2 medium period. window3(int): n3 high period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rnr)rr)r r rrrr s rrrs/$    jlrc<t||||jS)aAroon Indicator (AI) Identify when trends are likely to change direction (uptrend). Aroon Up - ((N - Days Since N-day High) / N) x 100 https://www.investopedia.com/terms/a/aroon.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r r r r )rr;rs rr;r;s& tVF K T T VVrc<t||||jS)aAroon Indicator (AI) Identify when trends are likely to change direction (downtrend). Aroon Down - ((N - Days Since N-day Low) / N) x 100 https://www.investopedia.com/terms/a/aroon.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rr?rs rr?r?s$ tVF K V V XXrcDt||||||}|jS)anParabolic Stop and Reverse (Parabolic SAR) Returns the PSAR series with non-N/A values for upward trends https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. step(float): the Acceleration Factor used to compute the SAR. max_step(float): the maximum value allowed for the Acceleration Factor. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r r rOr^r_r )r]r{r r rOr^r_r rs rr{r{s,$ s%dXfI    rcDt||||||}|jS)apParabolic Stop and Reverse (Parabolic SAR) Returns the PSAR series with non-N/A values for downward trends https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. step(float): the Acceleration Factor used to compute the SAR. max_step(float): the maximum value allowed for the Acceleration Factor. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)r]rrs rrr's,$ s%dXfI     rcDt||||||}|jS)aParabolic Stop and Reverse (Parabolic SAR) Upward Trend Indicator Returns 1, if there is a reversal towards an upward trend. Else, returns 0. https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. step(float): the Acceleration Factor used to compute the SAR. max_step(float): the maximum value allowed for the Acceleration Factor. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)r]rrs rrr?s,$ s%dXfI  & & ((rcDt||||||}|jS)aParabolic Stop and Reverse (Parabolic SAR) Downward Trend Indicator Returns 1, if there is a reversal towards an downward trend. Else, returns 0. https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. step(float): the Acceleration Factor used to compute the SAR. max_step(float): the maximum value allowed for the Acceleration Factor. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)r]rrs rrrWs,$ s%dXfI  ( ( **r)rmFr)rlrmFrkryrrrr ) rrrrrrrrFrr)rnrlFF)rlrFFrDr)6rInumpyr pandasr8ta.utilsrrrrrrNrpr|rrrrrrr rrHr]rrwrrrbrfrjr7rArErTrXrrrrrrrr9rrrrr;r?r{rrrrLrrrs==CG^CGLG >G T;>;0;>;0(:>(:V$CN$CN1 1 haTaTHE4>E4P$F>$FN<1><1~v9>v9rD*nD*N\ON\O~?1>?1DSSI 444 @@@00K&: 8I*     0 hSX" N     2l::W,Y*0!0)0+r