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Dynamic Jurik RSX w/ Fisher Transform█ Introduction The Dynamic Jurik RSX with Fisher Transform is a powerful and adaptive momentum indicator designed for traders who seek a non-laggy view of price movements. This script is based on the classic Jurik RSX (Relative Strength Index). It also includes features such as the dynamic overbought and oversold limits, the Inverse Fisher Transform, trend display, slope calculations, and the ability to color extremes for better clarity. █ Key Features:  • RSX: The Relative Strength Index (RSX) in this script is based on Jurik’s RSX, which is smoother than the traditional RSI and aims to reduce noise and lag. This script calculates the RSX using an exponential smoothing technique and adaptive adjustments.  • Inverse Fisher Transform: This script can optionally apply the Inverse Fisher Transform to the RSX, which helps to normalize the RSX values, compressing them between -1 and 1. The inverse transformation makes it easier to spot extreme values (overbought and oversold conditions) by enhancing the visual clarity of those extremes. It also smooths the curve over a user-defined period in hopes of providing a more consistent signal.  • Dynamic Limits: The dynamic overbought and oversold limits are calculated based on the RSX's recent high and low values. The limits adjust dynamically depending on market conditions, making them more relevant to current price action.  • Slope Display: The slope of the RSX is calculated as the rate of change between the current and previous RSX value. The slope is displayed as dots when the slope exceeds the threshold designated by the user, providing visual cues for momentum shifts.  • Trend Coloring: Optionally, the user can also enable a trend-based display. It is simply based on current value of RSX versus the previous one. If RSX is rising then the trend is bullish, if not, then the trend is bearish.  • Coloring Extremes: Users can configure the RSX to color the chart when prices enter extreme conditions, such as overbought or oversold zones, providing visual cues for market reversals. █ Attached Chart Notes:  • Top Panel: Enabled dynamic limits, Trend display, standard Jurik RSX with 20 lookback period, and Slope display.  • Middle Panel: Enabled dynamic limits, Extremes display, and standard Jurik RSX with 20 lookback period.  • Bottom Panel: Enabled dynamic limits, Trend display, Inverse Fisher Transform with 14 lookback period and 9 smoothing period. and Slope display. █ Credits: Special thanks to Everget for providing the original script. The script was also slightly modified based on updates from outside sources. █ Disclaimer: This script is for educational purposes only and should not be considered financial advice. Always conduct your own research and consult a professional before making any trading decisions.
Wskaźnik Pine Script™
od x_SharpVisionTraders_x
Gann + Laplace Smoothed Hybrid Volume Spread Analysis Indicator This Indicator stands apart by integrating the principles of the upgraded Discrete Fourier Transform (DFT), the Laplace Stieltjes Transform and volume spread analysis, enhanced with a layer of Fourier smoothing to distill market noise and highlight trend directions with unprecedented clarity. The length of EMA and Strategy Entries are modified with the Gann swings. This smoothing process allows traders to discern the true underlying patterns in volume and price action, stripped of the distractions of short-term fluctuations and noise. The core functionality of the GannLSHVSA revolves around the innovative combination of volume change analysis, spread determination (calculated from the open and close price difference), and the strategic use of the EMA (default 10) to fine-tune the analysis of spread by incorporating volume changes. Trend direction is validated through a moving average (MA) of the histogram, which acts analogously to the Volume MA found in traditional volume indicators. This MA serves as a pivotal reference point, enabling traders to confidently engage with the market when the histogram's movement concurs with the trend direction, particularly when it crosses the Trend MA line, signalling optimal entry points. It returns 0 when MA of the histogram and EMA of the Price Spread are not align. WHAT IS GannLSHVSA INDICATOR: The GannLSHVSA plots a positive trend when a positive Volume smoothed Spread and EMA of Volume smoothed price is above 0, and a negative when negative Volume smoothed Spread and EMA of Volume smoothed price is below 0. When this conditions are not met it plots 0. ORIGINALITY & USEFULNESS: The GannLSHVSA Strategy is unique because it applies upgraded DFT, the Laplace Stieltjes Transform for data smoothing, effectively filtering out the minor fluctuations and leaving traders with a clear picture of the market's true movements. The DFT's ability to break down market signals into constituent frequencies offers a granular view of market dynamics, highlighting the amplitude and phase of each frequency component. This, combined with the strategic application of Ehler's Universal Oscillator principles via a histogram, furnishes traders with a nuanced understanding of market volatility and noise levels, thereby facilitating more informed trading decisions. The Gann swing strategy is developed by meomeo105, this Gann high and low algorithm forms the basis of the EMA modification. DETAILED DESCRIPTION: My detailed description of the indicator and use cases which I find very valuable. What is the meaning of price spread? In finance, a spread refers to the difference between two prices, rates, or yields. One of the most common types is the bid-ask spread, which refers to the gap between the bid (from buyers) and the ask (from sellers) prices of a security or asset. We are going to use Open-Close spread. What is Volume spread analysis? Volume spread analysis (VSA) is a method of technical analysis that compares the volume per candle, range spread, and closing price to determine price direction. What does this mean? We need to have a positive Volume Price Spread and a positive Moving average of Volume price spread for a positive trend. OR via versa a negative Volume Price Spread and a negative Moving average of Volume price spread for a negative trend. What if we have a positive Volume Price Spread and a negative Moving average of Volume Price Spread? It results in a neutral, not trending price action. Thus the Indicator/Strategy returns 0 and Closes all long and short positions. I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT. 6 days ago Release Notes
Wskaźnik Pine Script™
od AutomatedTradingAlgorithms
1
mathLibrary "math" It's a library of discrete aproximations of a price or Series float it uses Fourier Discrete transform, Laplace Discrete Original and Modified transform and Euler's Theoreum for Homogenus White noice operations. Calling functions without source value it automatically take close as the default source value. Here is a picture of Laplace and Fourier approximated close prices from this library: Copy this indicator and try it yourself: import AutomatedTradingAlgorithms/math/1 as math //@version=5 indicator("Close Price with Aproximations", shorttitle="Close and Aproximations", overlay=false) // Sample input data (replace this with your own data) inputData = close // Plot Close Price plot(inputData, color=color.blue, title="Close Price") ltf32_result = math.LTF32(a=0.01) plot(ltf32_result, color=color.green, title="LTF32 Aproximation") fft_result = math.FFT() plot(fft_result, color=color.red, title="Fourier Aproximation") wavelet_result = math.Wavelet() plot(wavelet_result, color=color.orange, title="Wavelet Aproximation") wavelet_std_result = math.Wavelet_std() plot(wavelet_std_result, color=color.yellow, title="Wavelet_std Aproximation") DFT3(xval, _dir) Discrete Fourier Transform with last 3 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter   Returns: Aproxiated source value DFT2(xval, _dir) Discrete Fourier Transform with last 2 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter   Returns: Aproxiated source value FFT(xval) Fast Fourier Transform once. It aproximates usig last 3 points.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value DFT32(xval) Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value DTF32(xval) Combined Discrete Fourier Transforms of DFT3 and DTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series   Returns: Aproxiated source value LFT3(xval, _dir, a) Discrete Laplace Transform with last 3 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter      a (float) : laplace coeficient   Returns: Aproxiated source value LFT2(xval, _dir, a) Discrete Laplace Transform with last 2 points   Parameters:      xval (float) : Source series      _dir (int) : Direction parameter      a (float) : laplace coeficient   Returns: Aproxiated source value LFT(xval, a) Fast Laplace Transform once. It aproximates usig last 3 points.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value LFT32(xval, a) Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value LTF32(xval, a) Combined Discrete Laplace Transforms of LFT3 and LTF2 it aproximates last point by first aproximating last 3 ponts and than using last 2 points of the previus.   Parameters:      xval (float) : Source series      a (float) : laplace coeficient   Returns: Aproxiated source value whitenoise(indic_, _devided, minEmaLength, maxEmaLength, src) Ehler's Universal Oscillator with White Noise, without extra aproximated src. It uses dinamic EMA to aproximate indicator and thus reducing noise.   Parameters:      indic_ (float) : Input series for the indicator values to be smoothed      _devided (int) : Divisor for oscillator calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed indicator value whitenoise(indic_, dft1, _devided, minEmaLength, maxEmaLength, src) Ehler's Universal Oscillator with White Noise and DFT1. It uses src and sproxiated src (dft1) to clearly define white noice. It uses dinamic EMA to aproximate indicator and thus reducing noise.   Parameters:      indic_ (float) : Input series for the indicator values to be smoothed      dft1 (float) : Aproximated src value for white noice calculation      _devided (int) : Divisor for oscillator calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed indicator value smooth(dft1, indic__, _devided, minEmaLength, maxEmaLength, src) Smoothing source value with help of indicator series and aproximated source value It uses src and sproxiated src (dft1) to clearly define white noice. It uses dinamic EMA to aproximate src and thus reducing noise.   Parameters:      dft1 (float) : Value to be smoothed.      indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)      _devided (int) : Divisor for smoothing calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed source (src) series smooth(indic__, _devided, minEmaLength, maxEmaLength, src) Smoothing source value with help of indicator series It uses dinamic EMA to aproximate src and thus reducing noise.   Parameters:      indic__ (float) : Optional input for indicator to help smooth dft1 (default is FFT)      _devided (int) : Divisor for smoothing calculations      minEmaLength (int) : Minimum EMA length      maxEmaLength (int) : Maximum EMA length      src (float) : Source series   Returns: Smoothed src series vzo_ema(src, len) Volume Zone Oscillator with EMA smoothing   Parameters:      src (float) : Source series      len (simple int) : Length parameter for EMA   Returns: VZO value vzo_sma(src, len) Volume Zone Oscillator with SMA smoothing   Parameters:      src (float) : Source series      len (int) : Length parameter for SMA   Returns: VZO value vzo_wma(src, len) Volume Zone Oscillator with WMA smoothing   Parameters:      src (float) : Source series      len (int) : Length parameter for WMA   Returns: VZO value alma2(series, windowsize, offset, sigma) Arnaud Legoux Moving Average 2 accepts sigma as series float   Parameters:      series (float) : Input series      windowsize (int) : Size of the moving average window      offset (float) : Offset parameter      sigma (float) : Sigma parameter   Returns: ALMA value Wavelet(src, len, offset, sigma) Aproxiates srt using Discrete wavelet transform.   Parameters:      src (float) : Source series      len (int) : Length parameter for ALMA      offset (simple float)      sigma (simple float)   Returns: Wavelet-transformed series Wavelet_std(src, len, offset, mag) Aproxiates srt using Discrete wavelet transform with standard deviation as a magnitude.   Parameters:      src (float) : Source series      len (int) : Length parameter for ALMA      offset (float) : Offset parameter for ALMA      mag (int) : Magnitude parameter for standard deviation   Returns: Wavelet-transformed series LaplaceTransform(xval, N, a) Original Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient   Returns: Aproxiated source value NLaplaceTransform(xval, N, a, repeat) Y repetirions on Original Laplace Transform over N set of close prices, each time N-k set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformsum(xval, N, a, b) Sum of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value NLaplaceTransformdiff(xval, N, a, b, repeat) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value N_divLaplaceTransformdiff(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, with dynamic rotation   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformdiff(xval, N, a, b) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value NLaplaceTransformdiffFrom2(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value N_divLaplaceTransformdiffFrom2(xval, N, a, b, repeat) N repetitions of Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor, dynamic rotation   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient      repeat (int) : number of repetitions   Returns: Aproxiated source value LaplaceTransformdiffFrom2(xval, N, a, b) Difference of 2 exponent coeficient of Laplace Transform over N set of close prices, second element has for 1 higher exponent factor   Parameters:      xval (float) : series to aproximate      N (int) : number of close prices in calculations      a (float) : laplace coeficient      b (float) : second laplace coeficient   Returns: Aproxiated source value
Biblioteka Pine Script™
od AutomatedTradingAlgorithms
1
Fine-tune Inputs: Fourier Smoothed Volume zone oscillator WFSVZ0Use this Strategy to Fine-tune inputs for the (W&)FSVZ0 Indicator. Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data. I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame. MEANINGFUL DESCRIPTION: The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction. The Wavelet & Fourier Smoothed Volume Zone Oscillator (W&)FSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the Discrete Fourier Transform . Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends. By combining the Wavalet and Fourier aproximation with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions. HOW TO USE THE INDICATOR: The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well) The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend. HOW TO USE THE STRATEGY: Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M. When I ndicator/Strategy returns 0 or natural trend , Strategy Closes All it's positions. ORIGINALITY & USFULLNESS: Personal combination of Fourier and Wavalet aproximation of a price which results in less noise Volume Zone Oscillator. The Wavelet Transform is a powerful mathematical tool for signal analysis, particularly effective in analyzing signals with varying frequency or non-stationary characteristics. It dissects a signal into wavelets, small waves with varying frequency and limited duration, providing a multi-resolution analysis. This approach captures both frequency and location information, making it especially useful for detecting changes or anomalies in complex signals. The Discrete Fourier Transform (DFT) is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element. This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding. DETAILED DESCRIPTION: My detailed description of the indicator and use cases which I find very valuable. What is oscillator? Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets. What is volume zone oscillator? Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price. Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average. What does this mean? If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely. If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely. Rising means that VZO on close is higher than the previous day. Falling means that VZO on close is lower than the previous day. What if VZO is falling above 0 line? It means we have a high probability of a bearish trend. Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend. In the next Image you can see that trend is negative on 4h, negative on 12h and positive on 1D. That means trend is negative. I am sorry, the chart is a bit messy. The idea is to use the indicator over more than 1 Timeframe. What is approximation and smoothing? They are mathematical concepts for making a discrete set of numbers a continuous curved line. Fourier and Wavelet approximation of a close price are taken from aprox library. Key Features: You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length. Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification. Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics. Choose between the traditional Fast Fourier Transform (FFT) , the innovative Double Discrete Fourier Transform (DTF32) and Wavelet soothed Fourier soothed price series to suit your analytical needs. Image of Wavelet transform with FAST settings, Double Fourier transform with FAST settings. Improved noice reduction with SLOW settings, and standard FSVZO with SLOW settings: Fast setting are setting by default: VZO length = 2 NoiceR max Length = 2 Slow settings are: VZO length = 5 or 7 NoiceR max Length = 8 As you can see fast setting are more volatile. I suggest averaging fast setting on 4h 12h 1d 2d 3d 4d W and M Timeframe to get a clear view on market trend. What if I want long only when VZO is rising and above 15 not 0? You have set Setting VzoDifference to 15. That reduces the number of trend changes. Example of W&FSVZO with VzoDifference 15 than 0: VZO crossed 0 line but not 15 line and that's why Indicator returns 0 in one case an 1 in another. What is Smooth length setting? A way of calculating Bullish or Bearish (W&)FSVZO . If smooth length is 2 the trend is rising if: rising = VZO > ta.ema(VZO, 2) Meaning that we check if VZO is higher that exponential average of the last 2 elements. If smooth length is 1 the trend is rising if: rising = VZO_ > VZO_ Use this Strategy to fine-tune inputs for the (W&)FSVZO Indicator. (Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data) I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame . When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame . I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
Strategia Pine Script™
od AutomatedTradingAlgorithms
FunctionDiscreteCosineTransformLibrary "FunctionDiscreteCosineTransform" Discrete Cosine Transform (DCT) The Discrete Cosine Transform (DCT) is a mathematical algorithm that converts a series of samples of a signal, typically in the time domain, into another domain called the frequency or spectral domain. It's commonly used for data compression and image/video coding applications such as JPEG and MPEG standards. The DCT works by multiplying the input sequence with specific cosine functions that are pre-defined and then summing up these products to obtain a new series of values, which represent the frequency components of the original signal. The main advantage of the DCT over other transforms like Fourier Transform is its ability to handle non-stationary signals (i.e., signals with varying statistical properties) more effectively due to its localized basis functions. In simple terms, the DCT can be thought of as a way to break down an image or video into different frequency components and then compress them without losing too much information. This compression technique is essential for efficient transmission and storage of digital media files over the internet or on devices with limited memory capacity. ~Mixtral4x7b ___ Reference: lcamtuf.substack.com dct(data, len)   Discrete Cosine Transform.   Parameters:      data (array) : Data source.      len (int) : Length of the sampling window.   Returns: List with frequency domain transformed information. dct(data, len)   Discrete Cosine Transform.   Parameters:      data (float) : Data source.      len (int) : Length of the sampling window.   Returns: List with frequency domain transformed information. idct(data, len)   Inverse Discrete Cosine Transform.   Parameters:      data (array) : Data source.      len (int) : Length of the sampling window.   Returns: List with time domain transformed information. idct(data, len)   Inverse Discrete Cosine Transform.   Parameters:      data (float) : Data source.      len (int) : Length of the sampling window.   Returns: List with time domain transformed information.
Biblioteka Pine Script™
od RicardoSantos
4
Ehlers Fisher Stochastic Relative Vigor Index [CC]The Fisher Stochastic Relative Vigor Index was created by John Ehlers (Cybernetic Analysis For Stocks And Futures pgs 101-104) and this is a many layered indicator created from his original Relative Vigor Index turned into a stochastic and then performing a Fisher transform on the results. I have included extra smoothing to provide clearer buy and sell signals as well as normal and strong buy and sell signals. As always strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red. Let me know if there are any other scripts you would like to see me publish!
Wskaźnik Pine Script™
od cheatcountry
6
MathTransformsHartleyLibrary "MathTransformsHartley" implementation of the Fast Discrete Hartley Transform(DHT). naive(samples) Generic naive transform for the (DHT). Parameters: samples : float array, 1d data. Returns: float array. fdht(samples) Fast Discrete Hartley Transform (DHT). Parameters: samples : float array, data samples. Returns: float array. idht(samples, asymmetric_scaling) Inverse Discrete Hartley Transform (DHT). Parameters: samples : float array, data samples. asymmetric_scaling : bool, default=true, scaling option. Returns: float array.
Biblioteka Pine Script™
od RicardoSantos
Combo Backtest 123 Reversal & Fisher Transform Indicator This is combo strategies for get a cumulative signal. First strategy This System was created from the Book "How I Tripled My Money In The Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies. The strategy buys at market, if close price is higher than the previous close during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. The strategy sells at market, if close price is lower than the previous close price during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50. Second strategy Market prices do not have a Gaussian probability density function as many traders think. Their probability curve is not bell-shaped. But trader can create a nearly Gaussian PDF for prices by normalizing them or creating a normalized indicator such as the relative strength index and applying the Fisher transform. Such a transformed output creates the peak swings as relatively rare events. Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x)) The sharp turning points of these peak swings clearly and unambiguously identify price reversals in a timely manner. WARNING: - For purpose educate only - This script to change bars colors.
Strategia Pine Script™
od HPotter
Combo Strategy 123 Reversal & Fisher Transform Indicator This is combo strategies for get a cumulative signal. First strategy This System was created from the Book "How I Tripled My Money In The Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies. The strategy buys at market, if close price is higher than the previous close during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. The strategy sells at market, if close price is lower than the previous close price during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50. Second strategy Market prices do not have a Gaussian probability density function as many traders think. Their probability curve is not bell-shaped. But trader can create a nearly Gaussian PDF for prices by normalizing them or creating a normalized indicator such as the relative strength index and applying the Fisher transform. Such a transformed output creates the peak swings as relatively rare events. Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x)) The sharp turning points of these peak swings clearly and unambiguously identify price reversals in a timely manner. WARNING: - For purpose educate only - This script to change bars colors.
Wskaźnik Pine Script™
od HPotter
3
Multifactor Inverse Fisher Strategy (ps4)Best for higher time frames - 30m, 1H, 2H, 3H, 4H, D this strategy uses several factors that are pushed through an Inverse Fisher Transform (IFT). The higher the TF, the better the performance, up to 98%, but the number of deals tends to drop). Middle time frames (5m, 15m) look viable with Scaled Price (Scaled %P) and MFI factors. The factor list can be extended to include cci, stoch, rsi_stoch, emo, macd, cog, dpo, roc, accdist, cctbb, mom, awesome, tva, etc. Some of them need to be rescaled to a 0..100 interval. The IFT produces a value in the -1..1 interval (see: www.mesasoftware.com). This indicator does NOT repaint.
Strategia Pine Script™
od capissimo
4
Fisher Transform Indicator by Ehlers Market prices do not have a Gaussian probability density function as many traders think. Their probability curve is not bell-shaped. But trader can create a nearly Gaussian PDF for prices by normalizing them or creating a normalized indicator such as the relative strength index and applying the Fisher transform. Such a transformed output creates the peak swings as relatively rare events. Fisher transform formula is: y = 0.5 * ln ((1+x)/(1-x)) The sharp turning points of these peak swings clearly and unambiguously identify price reversals in a timely manner.
Wskaźnik Pine Script™
od HPotter
7