o ?Bi @s2dZddlZddlZddlmZmZGdddeZGdddeZ Gdd d eZ Gd d d eZ Gd d d eZ GdddeZ GdddeZGdddeZGdddeZGdddeZGdddeZdZdejfddZd[dejfd!d"Z #  $ % & ' d\dejfd(d)Zd]dejfd+d,Z d]dejfd-d.ZdZdejfd/d0Zd^dejfd3d4Zd_dejfd8d9Zd`d;ejdd?Z  * * dad;ejdr?F) r7r8r9r:r"r#r;r<rrrMrrrrr=?s   r=c@speZdZdZ       dd ejd ejd ejd ed edededededefddZ ddZ dejfddZ dS)UltimateOscillatoraTUltimate Oscillator Larry Williams' (1976) signal, a momentum oscillator designed to capture momentum across three different timeframes. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator BP = Close - Minimum(Low or Prior Close). TR = Maximum(High or Prior Close) - Minimum(Low or Prior Close) Average7 = (7-period BP Sum) / (7-period TR Sum) Average14 = (14-period BP Sum) / (14-period TR Sum) Average28 = (28-period BP Sum) / (28-period TR Sum) UO = 100 x [(4 x Average7)+(2 x Average14)+Average28]/(4+2+1) Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window1(int): short period. window2(int): medium period. window3(int): long period. weight1(float): weight of short BP average for UO. weight2(float): weight of medium BP average for UO. weight3(float): weight of long BP average for UO. fillna(bool): if True, fill nan values with 50. r@@?Fhighlowrwindow1window2window3weight1weight2weight3r c CsH||_||_||_||_||_||_||_||_| |_| |_ | dSr ) _high_lowr _window1_window2_window3_weight1_weight2_weight3rr) rrVrWrrXrYrZr[r\r]r rrrrs  zUltimateOscillator.__init__c Cs"|jd}||j|j|}|jt|j|djddd}|jr%dn|j }|jr-dn|j }|jr5dn|j }|j |j |d |j |j |d }|j |j |d |j |j |d }|j |j |d |j |j |d } d|j||j||j| |j|j|j|_dS)Nr)rWrF)axisskipnarrgY@)r rE _true_ranger^r_r" DataFrameminrr`rarbrollingsumrcrdre_uo) r close_shift true_rangebuying_pressurerJ min_periods_mmin_periods_lenavg_savg_mavg_lrrrrs@  zUltimateOscillator._runr+cCr,)z`Ultimate Oscillator Returns: pandas.Series: New feature generated. r-r.uor1)r3rnr"r#)r ultimate_oscrrrultimate_oscillatorr5z&UltimateOscillator.ultimate_oscillatorNrQrrRrSrTrUF) r7r8r9r:r"r#r;floatr<rrryrrrrrPzs@!   rPc @sheZdZdZ   ddejdejdejded ed ef d d Zd dZ dejfddZ dejfddZ dS)StochasticOscillatoraStochastic Oscillator Developed in the late 1950s by George Lane. The stochastic oscillator presents the location of the closing price of a stock in relation to the high and low range of the price of a stock over a period of time, typically a 14-day period. https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full Args: close(pandas.Series): dataset 'Close' column. high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window(int): n period. smooth_window(int): sma period over stoch_k. fillna(bool): if True, fill nan values. rFrVrWrr smooth_windowr cCs0||_||_||_||_||_||_|dSr )r r^r_r_smooth_windowrr)rrVrWrrr~r rrrrs  zStochasticOscillator.__init__cCsX|jrdn|j}|jj|j|d}|jj|j|d}d|j||||_dS)Nrrhr) rrr_rlrkr^maxr _stoch_k)rrsminsmaxrrrrszStochasticOscillator._runr+cCr,)zbStochastic Oscillator Returns: pandas.Series: New feature generated. r-r.stoch_kr1)r3rr"r#)rrrrrstochr5zStochasticOscillator.stochcCsB|jrdn|j}|jj|j|d}|j|dd}tj|ddS)ziSignal Stochastic Oscillator Returns: pandas.Series: New feature generated. rrhr-r.stoch_k_signalr1)rrrrlr!r3r"r#)rrstoch_drrr stoch_signal sz!StochasticOscillator.stoch_signalNrr}F) r7r8r9r:r"r#r;r<rrrrrrrrr|s*  r|c @sReZdZdZ    ddejdeded ed ef d d Zd dZ dejfddZ dS) KAMAIndicatoraKaufman's Adaptive Moving Average (KAMA) Moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements. https://www.tradingview.com/ideas/kama/ Args: close(pandas.Series): dataset 'Close' column. window(int): n period. pow1(int): number of periods for the fastest EMA constant. pow2(int): number of periods for the slowest EMA constant. fillna(bool): if True, fill nan values. Frrpow1pow2r cC*||_||_||_||_||_|dSr )r r_pow1_pow2rr)rrrrrr rrrr.  zKAMAIndicator.__init__c CsD|jj}tt|jt|jd}|jrdn|j}t|t||j}|j |j|d }tj ||t ||dkd}|d|j dd|jdd|jddj}t|j|_t|j}d} t|D]5} t|| rztj|j| <qj| r|| |j| <d } qj|j| d|| || |j| d|j| <qjdS) Nrrrh)outrrTrUrTF)r valuesr"r#rFr$rollrrrlrmdivide zeros_likerrzerossize_kamalenrangeisnannan) r close_valuesvolrer_numer_denefficiency_ratiosmoothing_constantlen_kama first_valueirrrr=s8   zKAMAIndicator._runr+cCs2tj|j|jjd}|j||jd}tj|ddS)zuKaufman's Adaptive Moving Average (KAMA) Returns: pandas.Series: New feature generated. rr.kamar1)r"r#rr rr3)r kama_seriesrrrr_szKAMAIndicator.kamaNrrrF) r7r8r9r:r"r#r;r<rrrrrrrrs& "rc@r) ROCIndicatoruRate of Change (ROC) The Rate-of-Change (ROC) indicator, which is also referred to as simply Momentum, is a pure momentum oscillator that measures the percent change in price from one period to the next. The ROC calculation compares the current price with the price “n” periods ago. The plot forms an oscillator that fluctuates above and below the zero line as the Rate-of-Change moves from positive to negative. As a momentum oscillator, ROC signals include centerline crossovers, divergences and overbought-oversold readings. Divergences fail to foreshadow reversals more often than not, so this article will forgo a detailed discussion on them. Even though centerline crossovers are prone to whipsaw, especially short-term, these crossovers can be used to identify the overall trend. Identifying overbought or oversold extremes comes naturally to the Rate-of-Change oscillator. https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. Frrr cCr r r rrrrrrzROCIndicator.__init__cCs,|j|j|j|j|jd|_dSNr)r rEr_roc)rrrrrs   zROCIndicator._runr+cC||j}tj|ddS)zaRate of Change (ROC) Returns: pandas.Series: New feature generated. rocr1)r3rr"r#)r roc_seriesrrrr zROCIndicator.rocNrF) r7r8r9r:r"r#r;r<rrrrrrrrjs rc @sReZdZdZ   ddejdejdeded ef d d Zd d Z dejfddZ dS)AwesomeOscillatorIndicatorupAwesome Oscillator From: https://www.tradingview.com/wiki/Awesome_Oscillator_(AO) The Awesome Oscillator is an indicator used to measure market momentum. AO calculates the difference of a 34 Period and 5 Period Simple Moving Averages. The Simple Moving Averages that are used are not calculated using closing price but rather each bar's midpoints. AO is generally used to affirm trends or to anticipate possible reversals. From: https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator Awesome Oscillator is a 34-period simple moving average, plotted through the central points of the bars (H+L)/2, and subtracted from the 5-period simple moving average, graphed across the central points of the bars (H+L)/2. MEDIAN PRICE = (HIGH+LOW)/2 AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34) where SMA — Simple Moving Average. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): short period. window2(int): long period. fillna(bool): if True, fill nan values with -50. "FrVrWrXrYr cCrr )r^r_r`rarr)rrVrWrXrYr rrrrrz#AwesomeOscillatorIndicator.__init__cCs^d|j|j}|jr dn|j}|jrdn|j}|j|j|d|j|j|d|_dS)Ng?rrh)r^r_rr`rarlr!_ao)r median_pricerJrsrrrrszAwesomeOscillatorIndicator._runr+cCr,)z_Awesome Oscillator Returns: pandas.Series: New feature generated. rr.aor1)r3rr"r#)r ao_seriesrrrawesome_oscillatorr5z-AwesomeOscillatorIndicator.awesome_oscillatorNrrF) r7r8r9r:r"r#r;r<rrrrrrrrs$%  rc @sReZdZdZ  ddejdejdejdedef d d Zd d Z d ejfddZ dS)WilliamsRIndicatorauWilliams %R Developed by Larry Williams, Williams %R is a momentum indicator that is the inverse of the Fast Stochastic Oscillator. Also referred to as %R, Williams %R reflects the level of the close relative to the highest high for the look-back period. In contrast, the Stochastic Oscillator reflects the level of the close relative to the lowest low. %R corrects for the inversion by multiplying the raw value by -100. As a result, the Fast Stochastic Oscillator and Williams %R produce the exact same lines, only the scaling is different. Williams %R oscillates from 0 to -100. Readings from 0 to -20 are considered overbought. Readings from -80 to -100 are considered oversold. Unsurprisingly, signals derived from the Stochastic Oscillator are also applicable to Williams %R. %R = (Highest High - Close)/(Highest High - Lowest Low) * -100 Lowest Low = lowest low for the look-back period Highest High = highest high for the look-back period %R is multiplied by -100 correct the inversion and move the decimal. https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r The Williams %R oscillates from 0 to -100. When the indicator produces readings from 0 to -20, this indicates overbought market conditions. When readings are -80 to -100, it indicates oversold market conditions. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. lbp(int): lookback period. fillna(bool): if True, fill nan values with -50. rFrVrWrlbpr cCrr )r^r_r _lbprr)rrVrWrrr rrrrrzWilliamsRIndicator.__init__cCsX|jrdn|j}|jj|j|d}|jj|j|d}d||j|||_dS)Nrrhi) rrr^rlrr_rkr _wr)rr highest_high lowest_lowrrrrszWilliamsRIndicator._runr+cCr,)zXWilliams %R Returns: pandas.Series: New feature generated. ir.wrr1)r3rr"r#)r wr_seriesrrr williams_rr5zWilliamsRIndicator.williams_rNr6) r7r8r9r:r"r#r;r<rrrrrrrrs"*  rc @sZeZdZdZ    ddejdededed ef d d Zd d Z ddZ ddZ ddZ dS)StochRSIIndicatoraStochastic RSI The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change. https://school.stockcharts.com/doku.php?id=technical_indicators:stochrsi https://www.investopedia.com/terms/s/stochrsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period smooth1(int): moving average of Stochastic RSI smooth2(int): moving average of %K fillna(bool): if True, fill nan values. rr}Frrsmooth1smooth2r cCrr )r r_smooth1_smooth2rr)rrrrrr rrrr8rzStochRSIIndicator.__init__cCsft|j|j|jd|_|j|j}|j||j|j||_ |j |j  |_ dS)Nrrr ) rr rrr0r%rlrkr _stochrsirr! _stochrsi_k)rlowest_low_rsirrrrGs zStochRSIIndicator._runcCr)z[Stochastic RSI Returns: pandas.Series: New feature generated. stochrsir1)r3rr"r#)rstochrsi_seriesrrrrQrzStochRSIIndicator.stochrsicCr)z^Stochastic RSI %k Returns: pandas.Series: New feature generated. stochrsi_kr1)r3rr"r#)rstochrsi_k_seriesrrrrZrzStochRSIIndicator.stochrsi_kcCs*|j|j}||}tj|ddS)z^Stochastic RSI %d Returns: pandas.Series: New feature generated. stochrsi_dr1)rrlrr!r3r"r#)rstochrsi_d_seriesrrrrcs zStochRSIIndicator.stochrsi_dNrr}r}F) r7r8r9r:r"r#r;r<rrrrrrrrrr%s*   rc @sZeZdZdZ    ddejdeded ed ef d d Zd dZ ddZ ddZ ddZ dS)PercentagePriceOscillatora  The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo Args: close(pandas.Series): dataset 'Price' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. r Frr@rA window_signr cCrr )r rBrC _window_signrr)rrr@rArr rrrr}rz"PercentagePriceOscillator.__init__cC\t|j|j|j}t|j|j|j}|||d|_t|j|j|j|_|j|j|_dSr) rr rCrrB_ppor _ppo_signal _ppo_histr_emafast_emaslowrrrr zPercentagePriceOscillator._runcC.|j|jdd}tj|d|jd|jdS)zmPercentage Price Oscillator Line Returns: pandas.Series: New feature generated. rr.PPO__r1)r3rr"r#rCrB)r ppo_seriesrrrppozPercentagePriceOscillator.ppocCr)ztPercentage Price Oscillator Signal Line Returns: pandas.Series: New feature generated. rr. PPO_sign_rr1)r3rr"r#rCrB)rppo_signal_seriesrrr ppo_signalz$PercentagePriceOscillator.ppo_signalcCr)zrPercentage Price Oscillator Histogram Returns: pandas.Series: New feature generated. rr. PPO_hist_rr1)r3rr"r#rCrB)rppo_hist_seriesrrrppo_histrz"PercentagePriceOscillator.ppo_histNrrrF) r7r8r9r:r"r#r;r<rrrrrrrrrrns*  rc @sreZdZdZ    ddejdeded ed ef d d Zd dZ dejfddZ dejfddZ dejfddZ dS)PercentageVolumeOscillatora8 The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo Args: volume(pandas.Series): dataset 'Volume' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. rrrFvolumer@rArr cCrr )_volumerBrCrrr)rrr@rArr rrrrrz#PercentageVolumeOscillator.__init__cCrr) rrrCrrB_pvor _pvo_signal _pvo_histrrrrrrzPercentageVolumeOscillator._runr+cCr)zUPVO Line Returns: pandas.Series: New feature generated. rr.PVO_rr1)r3rr"r#rCrB)r pvo_seriesrrrpvorzPercentageVolumeOscillator.pvocCr)zXSignal Line Returns: pandas.Series: New feature generated. rr. PVO_sign_rr1)r3rr"r#rCrB)rpvo_signal_seriesrrr pvo_signalrz%PercentageVolumeOscillator.pvo_signalcCr)zUHistgram Returns: pandas.Series: New feature generated. rr. PVO_hist_rr1)r3rr"r#rCrB)rpvo_hist_seriesrrrpvo_histrz#PercentageVolumeOscillator.pvo_histNr) r7r8r9r:r"r#r;r<rrrrrrrrrrs*   rrFr+cCt|||dS)a(Relative Strength Index (RSI) Compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset. https://www.investopedia.com/terms/r/rsi.asp 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)rr0rrrrr0sr0r>r?cCst||||dS)aTrue strength index (TSI) Shows both trend direction and overbought/oversold conditions. https://en.wikipedia.org/wiki/True_strength_index Args: close(pandas.Series): dataset 'Close' column. window_slow(int): high period. window_fast(int): low period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rr@rAr )r=rMrrrrrMs rMrQrRrSrTrUc Cs t|||||||||| d S)aUltimate Oscillator Larry Williams' (1976) signal, a momentum oscillator designed to capture momentum across three different timeframes. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator BP = Close - Minimum(Low or Prior Close). TR = Maximum(High or Prior Close) - Minimum(Low or Prior Close) Average7 = (7-period BP Sum) / (7-period TR Sum) Average14 = (14-period BP Sum) / (14-period TR Sum) Average28 = (28-period BP Sum) / (28-period TR Sum) UO = 100 x [(4 x Average7)+(2 x Average14)+Average28]/(4+2+1) Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window1(int): short period. window2(int): medium period. window3(int): long period. weight1(float): weight of short BP average for UO. weight2(float): weight of medium BP average for UO. weight3(float): weight of long BP average for UO. fillna(bool): if True, fill nan values with 50. Returns: pandas.Series: New feature generated. rVrWrrXrYrZr[r\r]r )rPryrrrrry+s+ ryr}cCt||||||dS)aStochastic Oscillator Developed in the late 1950s by George Lane. The stochastic oscillator presents the location of the closing price of a stock in relation to the high and low range of the price of a stock over a period of time, typically a 14-day period. https://www.investopedia.com/terms/s/stochasticoscillator.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. smooth_window(int): sma period over stoch_k fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rVrWrrr~r )r|rrrrrrdsrcCr)a Stochastic Oscillator Signal Shows SMA of Stochastic Oscillator. Typically a 3 day SMA. https://www.investopedia.com/terms/s/stochasticoscillator.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. smooth_window(int): sma period over stoch_k fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)r|rrrrrrsrcCt|||||dS)aWilliams %R From: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:williams_r Developed by Larry Williams, Williams %R is a momentum indicator that is the inverse of the Fast Stochastic Oscillator. Also referred to as %R, Williams %R reflects the level of the close relative to the highest high for the look-back period. In contrast, the Stochastic Oscillator reflects the level of the close relative to the lowest low. %R corrects for the inversion by multiplying the raw value by -100. As a result, the Fast Stochastic Oscillator and Williams %R produce the exact same lines, only the scaling is different. Williams %R oscillates from 0 to -100. Readings from 0 to -20 are considered overbought. Readings from -80 to -100 are considered oversold. Unsurprisingly, signals derived from the Stochastic Oscillator are also applicable to Williams %R. %R = (Highest High - Close)/(Highest High - Lowest Low) * -100 Lowest Low = lowest low for the look-back period Highest High = highest high for the look-back period %R is multiplied by -100 correct the inversion and move the decimal. From: https://www.investopedia.com/terms/w/williamsr.asp The Williams %R oscillates from 0 to -100. When the indicator produces readings from 0 to -20, this indicates overbought market conditions. When readings are -80 to -100, it indicates oversold market conditions. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. lbp(int): lookback period. fillna(bool): if True, fill nan values with -50. Returns: pandas.Series: New feature generated. rVrWrrr )rrrrrrrs ) rrrcCr)uAwesome Oscillator From: https://www.tradingview.com/wiki/Awesome_Oscillator_(AO) The Awesome Oscillator is an indicator used to measure market momentum. AO calculates the difference of a 34 Period and 5 Period Simple Moving Averages. The Simple Moving Averages that are used are not calculated using closing price but rather each bar's midpoints. AO is generally used to affirm trends or to anticipate possible reversals. From: https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator Awesome Oscillator is a 34-period simple moving average, plotted through the central points of the bars (H+L)/2, and subtracted from the 5-period simple moving average, graphed across the central points of the bars (H+L)/2. MEDIAN PRICE = (HIGH+LOW)/2 AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34) where SMA — Simple Moving Average. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): short period. window2(int): long period. fillna(bool): if True, fill nan values with -50. Returns: pandas.Series: New feature generated. rVrWrXrYr )rrrrrrrs $ rrrrcCr)a_Kaufman's Adaptive Moving Average (KAMA) Moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements. https://www.tradingview.com/ideas/kama/ Args: close(pandas.Series): dataset 'Close' column. window(int): n number of periods for the efficiency ratio. pow1(int): number of periods for the fastest EMA constant. pow2(int): number of periods for the slowest EMA constant. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rrrrr )rrr rrrrs  rrrrr cCr)uRate of Change (ROC) The Rate-of-Change (ROC) indicator, which is also referred to as simply Momentum, is a pure momentum oscillator that measures the percent change in price from one period to the next. The ROC calculation compares the current price with the price “n” periods ago. The plot forms an oscillator that fluctuates above and below the zero line as the Rate-of-Change moves from positive to negative. As a momentum oscillator, ROC signals include centerline crossovers, divergences and overbought-oversold readings. Divergences fail to foreshadow reversals more often than not, so this article will forgo a detailed discussion on them. Even though centerline crossovers are prone to whipsaw, especially short-term, these crossovers can be used to identify the overall trend. Identifying overbought or oversold extremes comes naturally to the Rate-of-Change oscillator. https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum Args: close(pandas.Series): dataset 'Close' column. window(int): n periods. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r)rrrrrrrsrrrcCr)aStochastic RSI The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change. https://www.investopedia.com/terms/s/stochrsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period smooth1(int): moving average of Stochastic RSI smooth2(int): moving average of %K fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rrrrr )rrr rrrr2  rcCr)aStochastic RSI %k The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change. https://www.investopedia.com/terms/s/stochrsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period smooth1(int): moving average of Stochastic RSI smooth2(int): moving average of %K fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r )rrr rrrrPr rcCr)aStochastic RSI %d The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change. https://www.investopedia.com/terms/s/stochrsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period smooth1(int): moving average of Stochastic RSI smooth2(int): moving average of %K fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. r )rrr rrrrnr rrrr@rArcCraE The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo Args: close(pandas.Series): dataset 'Price' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. rr@rArr )rrr rrrrrcCrr )rrr rrrrsrcCrr )rrr rrrrrrrcCt|||||d}|Sas The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo Args: volume(pandas.Series): dataset 'Volume' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. )rr@rArr )rrrr@rArr indicatorrrrrrcCrr)rrrrrrrrrcCrr)rrrrrrr)rrr6rOrzrrrrrr)(r:numpyr$pandasr"ta.utilsrrrr=rPr|rrrrrrrr#r0rMryrrrrrr;r<rrrrrrrrrrrrrrsd1;_AP.DIIIJ 9! .)            ! $ $