o ?Bi@stdZddlZddlZddlmZmZmZm Z GdddeZ GdddeZ Gdd d eZ Gd d d eZ Gd d d eZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGd d!d!eZdld$d%Zdld&d'Zdmd)d*Zdnd,d-Zdod.d/Zdod0d1Zdpd3d4Zdpd5d6Z dpd7d8Z!dpd9d:Z"dpd;d<Z#dqd>d?Z$drdAdBZ%dsdEdFZ&dtdGdHZ' I = C J I I I = #dudKdLZ( #dvdPdQZ) I = C J I I I = ( #dwdRdSZ* #dxdTej+fdUdVZ, #dxdTej+fdWdXZ-dxdYdZZ.dyd\d]Z/dzd^d_Z0dzd`daZ1d{dddeZ2d{dfdgZ3d{dhdiZ4d{djdkZ5dS)|zg .. module:: trend :synopsis: Trend Indicators. .. moduleauthor:: Dario Lopez Padial (Bukosabino) N)IndicatorMixin_ema _get_min_max_smac @sjeZdZdZ ddejdejdedefdd Zd d Z d ejfd dZ d ejfddZ d ejfddZ dS)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. FhighlowwindowfillnacCs$||_||_||_||_|dSN)_high_low_window_fillna_run)selfrr r r rH/var/www/html/Trade-python/venv/lib/python3.10/site-packages/ta/trend.py__init__ s  zAroonIndicator.__init__cstjrdnjd}jjjd|d}|jfdddd_jjjd|d}|jfdddd_dS)N min_periodsctt|jdSNd)floatnpargmaxrxrrr/z%AroonIndicator._run..Trawcrr)rrargminrrr!rrr"4r#)rrr rollingapply _aroon_upr _aroon_down)rr rolling_high rolling_lowrr!rr)s   zAroonIndicator._runreturncC&|j|jdd}tj|d|jdS)z]Aroon Up Channel Returns: pandas.Series: New feature generated. rvalue aroon_up_name) _check_fillnar)pdSeriesr)raroon_up_seriesrrraroon_up7zAroonIndicator.aroon_upcCr.)z_Aroon Down Channel Returns: pandas.Series: New feature generated. rr/ aroon_down_r2)r4r*r5r6r)raroon_down_seriesrrr aroon_down@r9zAroonIndicator.aroon_downcCs0|j|j}|j|dd}tj|d|jdS)z\Aroon Indicator Returns: pandas.Series: New feature generated. rr/ aroon_ind_r2)r)r*r4r5r6r)r aroon_diffrrraroon_indicatorIs zAroonIndicator.aroon_indicatorNrF) __name__ __module__ __qualname____doc__r5r6intboolrrr8r<r?rrrrrs     rc @sreZdZdZ    ddejdeded ed ef d d Zd dZ dejfddZ dejfddZ dejfddZ dS)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.  Fclose window_slow window_fast window_signr cC*||_||_||_||_||_|dSr )_close _window_slow _window_fast _window_signrr)rrKrLrMrNr rrrrd  z MACD.__init__cCs\t|j|j|j|_t|j|j|j|_|j|j|_t|j|j|j|_ |j|j |_ dSr ) rrPrRr_emafastrQ_emaslow_macdrS _macd_signal _macd_diffr!rrrrss z MACD._runr-cC.|j|jdd}tj|d|jd|jdS)zVMACD Line Returns: pandas.Series: New feature generated. rr/MACD__r2)r4rWr5r6rRrQ)r macd_seriesrrrmacdzz MACD.macdcCrZ)zXSignal Line Returns: pandas.Series: New feature generated. rr/ MACD_sign_r\r2)r4rXr5r6rRrQ)rmacd_signal_seriesrrr macd_signals zMACD.macd_signalcCrZ)z[MACD Histogram Returns: pandas.Series: New feature generated. rr/ MACD_diff_r\r2)r4rYr5r6rRrQ)rmacd_diff_seriesrrr macd_diffr_zMACD.macd_diffNrHrIrJF) rArBrCrDr5r6rErFrrr^rbrerrrrrGTs*   rGc@s:eZdZdZd dejdedefddZd ejfd d Z d S) EMAIndicatorzEMA - Exponential Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. FrKr r cC||_||_||_dSr rPrrrrKr r rrrr zEMAIndicator.__init__r-cC(t|j|j|j}tj|d|jdS)zmExponential Moving Average (EMA) Returns: pandas.Series: New feature generated. ema_r2)rrPrrr5r6)rrnrrr ema_indicatorzEMAIndicator.ema_indicatorNrhF) rArBrCrDr5r6rErFrrorrrrrgrgc@s:eZdZdZd dejdedefddZdejfd d Z d S) SMAIndicatorzSMA - Simple Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. FrKr r cCrir rjrkrrrrrlzSMAIndicator.__init__r-cCrm)zhSimple Moving Average (SMA) Returns: pandas.Series: New feature generated. sma_r2)rrPrrr5r6)rrtrrr sma_indicatorrpzSMAIndicator.sma_indicatorN)F) rArBrCrDr5r6rErFrrurrrrrsrrrsc@BeZdZdZddejdedefddZd d Z d ejfd d Z dS) WMAIndicatorzWMA - Weighted Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. rJFrKr r cC||_||_||_|dSr rPrrrrkrrrr zWMAIndicator.__init__csNtfddtdjdD}dd}jjj||dd_dS)Ncs$g|]}|djjdqS)r)r).0ir!rr sz%WMAIndicator._run..rcsfdd}|S)Ncs |Sr )sumrweightrr_weighted_averages zFWMAIndicator._run..weighted_average.._weighted_averager)rrrrrweighted_averages z+WMAIndicator._run..weighted_averageTr$)r5r6rangerrPr'r(_wma)r_weightrrr!rrs  zWMAIndicator._runr-cCr.)zjWeighted Moving Average (WMA) Returns: pandas.Series: New feature generated. rr/wma_r2)r4rr5r6r)rwmarrrrr9zWMAIndicator.wmaNrJF) rArBrCrDr5r6rErFrrrrrrrrws rwc@rv) TRIXIndicatoraSTrix (TRIX) Shows the percent rate of change of a triple exponentially smoothed moving average. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. FrKr r cCrxr ryrkrrrrrzzTRIXIndicator.__init__cCsnt|j|j|j}t||j|j}t||j|j}||jd|d|jd|d|_|jd9_dS)Nr fill_valuer)rrPrrshiftmean_trix)rema1ema2ema3rrrr s zTRIXIndicator._runr-cCr.)zXTrix (TRIX) Returns: pandas.Series: New feature generated. rr/trix_r2)r4rr5r6r)r trix_seriesrrrtrixr9zTRIXIndicator.trixNrF) rArBrCrDr5r6rErFrrrrrrrr   rc @sReZdZdZ   ddejdejdeded ef d d Zd d Z dejfddZ dS) MassIndexaMass Index (MI) It uses the high-low range to identify trend reversals based on range expansions. It identifies range bulges that can foreshadow a reversal of the current trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window_fast(int): fast period value. window_slow(int): slow period value. fillna(bool): if True, fill nan values. rJrFrr rMrLr cCrOr )r rrRrQrr)rrr rMrLr rrrr1rTzMassIndex.__init__cCs^|jrdn|j}|j|j}t||j|j}t||j|j}||}|j|j|d|_dS)Nrr) rrQr rrrRr'r_mass)rr amplituderrmassrrrr@s  zMassIndex._runr-cCrZ)z\Mass Index (MI) Returns: pandas.Series: New feature generated. rr/ mass_index_r\r2)r4rr5r6rRrQ)rrrrr mass_indexHr_zMassIndex.mass_indexNrJrF) rArBrCrDr5r6rErFrrrrrrrr s$ rc@seZdZdZ     ddejdejded ed ed ed efd dZddZ dejfddZ dejfddZ dejfddZ dejfddZ dS)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. rJrH4Frr window1window2window3visualr cC6||_||_||_||_||_||_||_|dSr )r r_window1_window2_window3_visualrr)rrr rrrrr rrrrc  zIchimokuIndicator.__init__cCs|jrdn|j}|jr dn|j}d|jj|j|d|jj|j|d|_d|jj|j|d|jj|j|d|_ dS)Nr?r) rrrr r'maxrmin_conv_base)rmin_periods_n1min_periods_n2rrrrvs zIchimokuIndicator._runr-cCrZ)ziTenkan-sen (Conversion Line) Returns: pandas.Series: New feature generated. r/ichimoku_conv_r\r2)r4rr5r6rr)r conversionrrrichimoku_conversion_liner_z*IchimokuIndicator.ichimoku_conversion_linecCrZ)zbKijun-sen (Base Line) Returns: pandas.Series: New feature generated. rr/ichimoku_base_r\r2)r4rr5r6rr)rbaserrrichimoku_base_linesz$IchimokuIndicator.ichimoku_base_linecCsZd|j|j}|jr|j|j|dn|}|j|dd}tj|d|j d|jdS)zkSenkou Span A (Leading Span A) Returns: pandas.Series: New feature generated. rrrr/ ichimoku_a_r\r2) rrrrrrr4r5r6r)rspanarrr ichimoku_aszIchimokuIndicator.ichimoku_acCszd|jj|jdd|jj|jdd}|jr%|j|j| dn|}|j |dd}t j |d|j d|jd S) zkSenkou Span B (Leading Span B) Returns: pandas.Series: New feature generated. rrrrrr/ ichimoku_b_r\r2)r r'rrrrrrrrr4r5r6r)rspanbrrr ichimoku_bszIchimokuIndicator.ichimoku_bN)rJrHrFF)rArBrCrDr5r6rErFrrrrrrrrrrrTs6    rc@seZdZdZ          ddejd ed ed ed ed edededededefddZddZ dejfddZ dejfddZ dejfddZ dS) 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. rrJFrKroc1roc2roc3roc4rrrwindow4nsigr c CsN||_||_||_||_||_||_||_||_| |_| |_ | |_ | dSr ) rP_r1_r2_r3_r4rrr_window4_nsigrr) rrKrrrrrrrrrr rrrrs zKSTIndicator.__init__c Cs|jrdn|j}|jr dn|j}|jrdn|j}|jrdn|j}|j|jj|j|jd|jj|j|jdj |j|d}|j|jj|j |jd|jj|j |jdj |j|d}|j|jj|j |jd|jj|j |jdj |j|d}|j|jj|j |jd|jj|j |jdj |j|d}d|d|d|d||_ |j j |jdd|_dS)Nrrrrr{)rrrrrrPrrrr'rrr_kstr_kst_sig) rrrmin_periods_n3min_periods_n4rocma1rocma2rocma3rocma4rrrrsT" zKSTIndicator._runr-cC|j|jdd}tj|ddS)zbKnow Sure Thing (KST) Returns: pandas.Series: New feature generated. rr/kstr2)r4rr5r6)r kst_seriesrrrrzKSTIndicator.kstcCr)zSignal Line Know Sure Thing (KST) nsig-period SMA of KST Returns: pandas.Series: New feature generated. rr/kst_sigr2)r4rr5r6)rkst_sig_seriesrrrr(szKSTIndicator.kst_sigcC(|j|j}|j|dd}tj|ddS)zDiff Know Sure Thing (KST) KST - Signal_KST Returns: pandas.Series: New feature generated. rr/kst_diffr2)rrr4r5r6)rrrrrr3s zKSTIndicator.kst_diffN rrrrrrrrrJF) rArBrCrDr5r6rErFrrrrrrrrrrsN    4  rc@rv) 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. rFrKr r cCrxr ryrkrrrrNrzzDPOIndicator.__init__cCsP|jrdn|j}|jjtd|jd|jd|jj|j|d|_dS)Nrrrrr)rrrPrrErr'_dpo)rrrrrrTszDPOIndicator._runr-cCs(|j|jdd}tj|dt|jdS)zmDetrended Price Oscillator (DPO) Returns: pandas.Series: New feature generated. rr/dpo_r2)r4rr5r6strr)r dpo_seriesrrrdpo]szDPOIndicator.dpoNrF) rArBrCrDr5r6rErFrrrrrrrr@rrc @sXeZdZdZ   ddejdejdejded ed ef d d Z d dZ dejfddZ dS) CCIIndicatoraCommodity 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 period. constant(int): constant. fillna(bool): if True, fill nan values. rQ?Frr rKr constantr cC0||_||_||_||_||_||_|dSr )r rrPr _constantrr)rrr rKr rr rrrr{  zCCIIndicator.__init__cCsjdd}|jr dn|j}|j|j|jd}||j|j|d|j|j|j|d|d|_ dS)NcSstt|t|Sr )rrabsrrrr_madszCCIIndicator._run.._madrg@rT) rrr rrPr'rrr(_cci)rrr typical_pricerrrrs zCCIIndicator._runr-cCr)zjCommodity Channel Index (CCI) Returns: pandas.Series: New feature generated. rr/ccir2)r4rr5r6)r cci_seriesrrrrrzCCIIndicator.cciNrrF) rArBrCrDr5r6rErrFrrrrrrrrgs( rc @sreZdZdZ  ddejdejdejdedef d d Zd d Z d ejfddZ d ejfddZ d ejfddZ dS) ADXIndicatoraAverage 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. rhFrr rKr r cCrOr r rrPrrrrrr rKr r rrrrrTzADXIndicator.__init__c Cs||jdkr td|jd}t|j|d}t|j|d}||}t|jd|_ tt |j|jd|_ | j d|j|j d<|jdd}tdt |j dD]}|j |d|j |dt|j||j||j |<qW|j|jd}|jd|j}t||k|dk@|}t||k|dk@|} tt |j|jd|_| j d|j|jd<|jdd}tdt |jdD]}|j|d|j|dt|j||j||j|<qtt |j|jd|_| j d|j|jd<| jdd} tdt |jdD] }|j|d|j|dt|j| |j||j|<qdS)Nrzwindow may not be 0rrrT)drop)r ValueErrorrPrrr rrzeros _trs_initiallen_trsdropnailocr reset_indexrrr_dip_din) r close_shiftpdmpdndiff_directional_movementr}diff_up diff_downposnegrrrrsT              zADXIndicator._runr-cCstt|j}t|jD]\}}|dkr!d|j||||<q d||<q tt|j}t|jD]\}}|dkrGd|j||||<q3d||<q3tt|j}tt|jD](}||||dkrdt||||||||||<q[d||<q[tt|j}|d|j  ||j <t|j dt|D]}||d|j d||dt |j ||<qtj |j |fdd}tj||jjd}|j|dd}tj|dd S) znAverage Directional Index (ADX) Returns: pandas.Series: New feature generated.tr rrr)axis)dataindexrr/adxr2)rrrr enumeraterrrrrrr concatenaterr5r6rPrr4)rdipidxr0dindirectional_index adx_seriesr}rrrrs6     zADXIndicator.adxcCtt|j}tdt|jdD]"}|j|dkr-d|j||j||||j<qd|||j<q|jt j ||jj ddd}t j |ddS) zmPlus Directional Indicator (+DI) Returns: pandas.Series: New feature generated. rrrrrr/adx_posr2) rrrrPrrrrr4r5r6r)rrr}adx_pos_seriesrrrr4$zADXIndicator.adx_poscCr) znMinus Directional Indicator (-DI) Returns: pandas.Series: New feature generated. rrrrrr/adx_negr2) rrrrPrrrrr4r5r6r)rrr}adx_neg_seriesrrrrIrzADXIndicator.adx_negNrq) rArBrCrDr5r6rErFrrrrrrrrrrs& 51rc @sZeZdZdZ  ddejdejdejdedef d d Zd d Z d dZ ddZ ddZ dS)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. rhFrr rKr r cCrOr rrrrrrprTzVortexIndicator.__init__cCs|jjd|jd}||j|j|}|jrdn|j}|j|j|d }t |j|jd}t |j|jd}|j|j|d ||_ |j|j|d ||_ dS)Nrrrr)rPrr _true_ranger rrrr'rrr_vip_vin)rr true_rangertrnvmpvmmrrrrszVortexIndicator._runcCr)zP+VI Returns: pandas.Series: New feature generated. rr/vipr2)r4r!r5r6)rr'rrrvortex_indicator_posrz$VortexIndicator.vortex_indicator_poscCr)zP-VI Returns: pandas.Series: New feature generated. rr/vinr2)r4r"r5r6)rr)rrrvortex_indicator_negrz$VortexIndicator.vortex_indicator_negcCr)zTDiff VI Returns: pandas.Series: New feature generated. rr/vidr2)r!r"r4r5r6)rr+rrrvortex_indicator_diffs z%VortexIndicator.vortex_indicator_diffNrq) rArBrCrDr5r6rErFrrr(r*r,rrrrr_s&   rc @seZdZdZ   ddejdejdejded ed ef d d Zd dZ dejfddZ dejfddZ dejfddZ dejfddZ dejfddZdS) PSARIndicatora!Parabolic Stop and Reverse (Parabolic SAR) The Parabolic Stop and Reverse, more commonly known as the Parabolic SAR,is a trend-following indicator developed by J. Welles Wilder. The Parabolic SAR is displayed as a single parabolic line (or dots) underneath the price bars in an uptrend, and above the price bars in a downtrend. 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. {Gz?皙?Frr rKstepmax_stepr cCrr )r rrP_step _max_steprr)rrr rKr0r1r rrrrrzPSARIndicator.__init__c Csvd}|j}|jjd}|jjd}|j|_tj|jj dd|_ tj|jj dd|_ t dt |jD]}d}|jj|}|jj|}|r|jj|d|||jj|d|jj|<||jj|krtd}||jj|<|}|j}n||kr|}t||j|j}|jj|d} |jj|d} | |jj|kr| |jj|<nz| |jj|kr| |jj|<nk|jj|d||jj|d||jj|<||jj|krd}||jj|<|}|j}n=||kr|}t||j|j}|jj|d} |jj|d} | |jj|kr | |j|<n| |jj|kr| |jj|<||k}|r.|jj||j j|<q3|jj||j j|<q3dS)NTrfloat64)rdtyper{Fr)r2r rrrPcopy_psarr5r6r_psar_up _psar_downrrrr3) rup_trendacceleration_factor up_trend_highdown_trend_lowr}reversalmax_highmin_lowlow1low2high1high2rrrrsp              zPSARIndicator._runr-cCr)zWPSAR value Returns: pandas.Series: New feature generated. rr/psarr2)r4r7r5r6)r psar_seriesrrrrErzPSARIndicator.psarcCr)z`PSAR up trend value Returns: pandas.Series: New feature generated. rr/psarupr2)r4r8r5r6)rpsar_up_seriesrrrpsar_uprzPSARIndicator.psar_upcCr)zbPSAR down trend value Returns: pandas.Series: New feature generated. rr/psardownr2)r4r9r5r6)rpsar_down_seriesrrr psar_down#rzPSARIndicator.psar_downcCsH|j|j|jd@d}||dkd}tj||jjddS)zjPSAR up trend value indicator Returns: pandas.Series: New feature generated. rrpsariuprr3) r8wherenotnullrisnullr5r6rPrr indicatorrrrpsar_up_indicator, zPSARIndicator.psar_up_indicatorcCsH|j|j|jd@d}||dkd}tj||jj ddS)zlPSAR down trend value indicator Returns: pandas.Series: New feature generated. rr psaridownrN) r8rOr9rPrrQr5r6rPrrRrrrpsar_down_indicator8rUz!PSARIndicator.psar_down_indicatorNr.r/F)rArBrCrDr5r6rrFrrrErIrLrTrWrrrrr-s0 F    r-c@sVeZdZdZ      ddejded ed ed ed ed efddZddZ ddZ dS) 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. 2rrFrKrLrMcyclesmooth1smooth2r cCrr )rPrQrR_cycle_smooth1_smooth2rr)rrKrLrMr\r]r^r rrrr[rzSTCIndicator.__init__c Cst|j|j|j}t|j|j|j}||}|j|jd}|j|jd}d||||}t||j |j}|j|jd}|j|jd} d||| |} t| |j |j|_ dS)N)r r) rrPrRrrQr'r_rrr`ra_stc) rrUrVrW_macdmin_macdmax_stoch_k_stoch_d _stoch_d_min _stoch_d_max _stoch_kdrrrrnszSTCIndicator._runcCs||j}tj|ddS)z_Schaff Trend Cycle Returns: pandas.Series: New feature generated. stcr2)r4rbr5r6)r stc_seriesrrrrj}s zSTCIndicator.stcNrZr[rrrF) rArBrCrDr5r6rErFrrrjrrrrrYEs2  rYrIFcCt|||dS)zaExponential Moving Average (EMA) Returns: pandas.Series: New feature generated. rKr r )rgrornrrrrorocCrm)z\Simple Moving Average (SMA) Returns: pandas.Series: New feature generated. rn)rsrurnrrrrurorurJcCrm)z^Weighted Moving Average (WMA) Returns: pandas.Series: New feature generated. rn)rwrrnrrr wma_indicatorrorprHcCst|||d|dS)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. rJrKrLrMrNr )rGr^)rKrLrMr rrrr^r^cCt|||||dS)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. rq)rGrbrqrrrrbrrrbcCrs)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. rq)rGrerqrrrrerrrerhcCrs)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. rr rKr r )rrrtrrrr  rcCrs)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. rt)rrrtrrrrrurcCrs)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. rt)rrrtrrrr0rurcCrs)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. rt)rr(rtrrrr(P  r(cCrs)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. rt)rr*rtrrrr*hrvr*rcCrm)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. rn)rrrnrrrrrrcCrs)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. rr rMrLr )rrrxrrrrsrrrcCst||||||dS)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. rr rKr rr )rrryrrrrs  rcCrm)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. rn)rrrnrrrrrwrrrc Cs"t|||||||||d| d S)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. rJ rKrrrrrrrrrr )rr) rKrrrrrrrrr rrrrs$ rrZr[rc Cst|||||||dS)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. rKrLrMr\r]r^r )rYrjr{rrrrjsrjc Cs"t|||||||||| | d S)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. rz)rrrzrrrr7s& rr-c Ct||||d||dS)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. rrr rrrrr )rrrr rrrr rrrrlrc Cr|)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})rrr~rrrrrrc Cr|)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})rrr~rrrrrrc Cst||d||||dS)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. rJr})rr)rr rrrr rrrrrrcCt||||dS)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. rr r r )rr8rrrrr8sr8cCr)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<rrrrr<sr<r.r/cCt||||||d}|S)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. rr rKr0r1r )r-rIrr rKr0r1r rSrrrrI rIcCr)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-rLrrrrrL'rrLcCr)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-rTrrrrrT?rrTcCr)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-rWrrrrrWWrrW)rIFr)rHrIFrfrqrrrr) rrrrrrrrFrlr)rJrHFF)rHrFFr@rX)6rDnumpyrpandasr5ta.utilsrrrrrrGrgrsrwrrrrrrrrr-rYrorurpr^rbrerrrr(r*rrrrrrjrr6rrrrr8r<rIrLrTrWrrrrsFJ+'4d '?:G B         4 ' 6