Intermediate Williams %R w/ Discontinued Signal Lines [Loxx]Intermediate Williams %R w/ Discontinued Signal Lines is a Williams %R indicator with advanced options:
-Williams %R smoothing, 30+ smoothing algos found here:
-Williams %R signal, 30+ smoothing algos found here:
-DSL lines with smoothing or fixed overbought/oversold boundaries, smoothing algos are EMA and FEMA
-33 Expanded Source Type inputs including Heiken-Ashi and Heiken-Ashi Better, found here:
What is Williams %R?
Williams %R, also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods.
Included:
-Toggle on/off bar coloring
-Toggle on/off signal line
"algo"に関するスクリプトを検索
OrdinaryLeastSquaresLibrary "OrdinaryLeastSquares"
One of the most common ways to estimate the coefficients for a linear regression is to use the Ordinary Least Squares (OLS) method.
This library implements OLS in pine. This implementation can be used to fit a linear regression of multiple independent variables onto one dependent variable,
as long as the assumptions behind OLS hold.
solve_xtx_inv(x, y) Solve a linear system of equations using the Ordinary Least Squares method.
This function returns both the estimated OLS solution and a matrix that essentially measures the model stability (linear dependence between the columns of 'x').
NOTE: The latter is an intermediate step when estimating the OLS solution but is useful when calculating the covariance matrix and is returned here to save computation time
so that this step doesn't have to be calculated again when things like standard errors should be calculated.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
Returns: Returns both the estimated OLS solution and a matrix that essentially measures the model stability (xtx_inv is equal to (X'X)^-1).
solve(x, y) Solve a linear system of equations using the Ordinary Least Squares method.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
Returns: Returns the estimated OLS solution.
standard_errors(x, y, beta_hat, xtx_inv) Calculate the standard errors.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.
beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().
xtx_inv : This is (X'X)^-1, which means we take the transpose of the X matrix, multiply that the X matrix and then take the inverse of the result.
This essentially measures the linear dependence between the columns of the X matrix.
Returns: The standard errors.
estimate(x, beta_hat) Estimate the next step of a linear model.
Parameters:
x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.
beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().
Returns: Returns the new estimate of Y based on the linear model.
NormalizedOscillatorsLibrary "NormalizedOscillators"
Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome.
rsi2(upper, lower) RSI - second step
Parameters:
upper : Upwards momentum
lower : Downwards momentum
Returns: Oscillator value
Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1)
Originally: 100.0 - (100.0 / (1.0 + upper / lower))
Ignoring the 100 scale factor, we get: upper / (upper + lower)
Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower)
rms(src, len) Root mean square (RMS)
Parameters:
src : Source series
len : Lookback period
Based on by John F. Ehlers implementation
ift(src) Inverse Fisher Transform
Parameters:
src : Source series
Returns: Normalized series
Based on by John F. Ehlers implementation
The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression
The inputs may be further modified, if needed
stoch(src, len) Stochastic
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the January 2014 issue of Stocks and Commodities
This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it)
This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1
I do not know, if this the right way to fix this issue, but it works for now
netKendall(src, len) Noise Elimination Technology by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the December 2020 issue of Stocks and Commodities
Uses simplified Kendall correlation algorithm
Implementation by @QuantTherapy:
rsi(src, len, smooth) RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
vrsi(src, len, smooth) Volume-scaled RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
This is my own version of RSI. It scales price movements by the proportion of RMS of volume
mrsi(src, len, smooth) Momentum RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
rrsi(src, len, smooth) Rocket RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
Does not include Fisher Transform of the original implementation, as the output must be normalized
Does not include momentum smoothing length configuration, so always assumes half the lookback length
mfi(src, len, smooth) Money Flow Index
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers
Parameters:
src : Source series
in_gamma : Damping factor (default is -1 to generate from len)
len : Lookback period (alternatively, if gamma is not set)
Returns: Oscillator series
The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length,
an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better?
fe(len) Choppiness Index or Fractal Energy
Parameters:
len : Lookback period
Returns: Oscillator series
The Choppiness Index (CHOP) was created by E. W. Dreiss
This indicator is sometimes called Fractal Energy
er(src, len) Efficiency ratio
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Based on Kaufman Adaptive Moving Average calculation
This is the correct Efficiency ratio calculation, and most other implementations are wrong:
the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range!
For reference, see Stocks and Commodities June 1995
dmi(len, smooth) Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Based on the original Tradingview algorithm
Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!)
Only ADX is returned
Rescaled to fit -1 to +1
Unlike most oscillators, there is no src parameter as DMI works directly with high and low values
fdmi(len, smooth) Fast Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured
doOsc(type, src, len, smooth) Execute a particular Oscillator from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations
LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com
doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
Returns: Oscillator series
Average Down [Zeiierman]AVERAGING DOWN
Averaging down is an investment strategy that involves buying additional contracts of an asset when the price drops. This way, the investor increases the size of their position at discounted prices. The averaging down strategy is highly debated among traders and investors because it can either lead to huge losses or great returns. Nevertheless, averaging down is often used and favored by long-term investors and contrarian traders. With careful/proper risk management, averaging down can cover losses and magnify the returns when the asset rebounds. However, the main concern for a trader is that it can be hard to identify the difference between a pullback or the start of a new trend.
HOW DOES IT WORK
Averaging down is a method to lower the average price at which the investor buys an asset. A lower average price can help investors come back to break even quicker and, if the price continues to rise, get an even bigger upside and thus increase the total profit from the trade. For example, We buy 100 shares at $60 per share, a total investment of $6000, and then the asset drops to $40 per share; in order to come back to break even, the price has to go up 50%. (($60/$40) - 1)*100 = 50%.
The power of Averaging down comes into play if the investor buys additional shares at a lower price, like another 100 shares at $40 per share; the total investment is ($6000+$4000 = $10000). The average price for the investment is now $50. (($60 x 100) + ($40 x 100))/200; in order to get back to break even, the price has to rise 25% ($50/$40)-1)*100 = 25%, and if the price continues up to $60 per share, the investor can secure a profit at 16%. So by averaging down, investors and traders can cover the losses easier and potentially have more profit to secure at the end.
THE AVERAGE DOWN TRADINGVIEW TOOL
This script/indicator/trading tool helps traders and investors to get the average price of their position. The tool works for Long and Short and displays the entry price, average price, and the PnL in points.
HOW TO USE
Use the tool to calculate the average price of your long or short position in any market and timeframe.
Get the current PnL for the investment and keep track of your entry prices.
APPLY TO CHART
When you apply the tool on the chart, you have to select five entry points, and within the setting panel, you can choose how many of these five entry points are active and how many contracts each entry has. Then, the tool will display your average price based on the entries and the number of contracts used at each price level.
LONG
Set your entries and the number of contracts at each price level. The indicator will then display all your long entries and at what price you will break even. The entry line changes color based on if the entry is in profit or loss.
SHORT
Set your entries and the number of contracts at each price level. The indicator will then display all your short entries and at what price you will break even. The entry line changes color based on if the entry is in profit or loss.
-----------------
Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Example: Monte Carlo SimulationExperimental:
Example execution of Monte Carlo Simulation applied to the markets(this is my interpretation of the algo so inconsistencys may appear).
note:
the algorithm is very demanding so performance is limited.
RAT Moving Average Crossover StrategyThis is based on general moving average crossovers but some modifications made to generate buy sell signals.
Weis pip zigzag jayyWhat you see here is the Weis pip zigzag wave plotted directly on the price chart. This script is the companion to the Weis pip wave ( ) which is plotted in the lower panel of the displayed chart and can be used as an alternate way of plotting the same results. The Weis pip zigzag wave shows how far in terms of price a Weis wave has traveled through the duration of a Weis wave. The Weis pip zigzag wave is used in combination with the Weis cumulative volume wave. The two waves must be set to the same "wave size".
To use this script you must set the wave size. Using the traditional Weis method simply enter the desired wave size in the box "Select Weis Wave Size" In this example, it is set to 5. Each wave for each security and each timeframe requires its own wave size. Although not the traditional method a more automatic way to set wave size would be to use ATR. This is not the true Weis method but it does give you similar waves and, importantly, without the hassle described above. Once the Weis wave size is set then the pip wave will be shown.
I have put a pip zigzag of a 5 point Weis wave on the bar chart - that is a different script. I have added it to allow your eye to see what a Weis wave looks like. You will notice that the wave is not in straight lines connecting wave tops to bottoms this is a function of the limitations of Pinescript version 1. This script would need to be in version 4 to allow straight lines. There are too many calculations within this script to allow conversion to Pinescript version 4 or even Version 3. I am in the process of rewriting this script to reduce the number of calculations and streamline the algorithm.
The numbers plotted on the chart are calculated to be relative numbers. The script is limited to showing only three numbers vertically. Only the highest three values of a number are shown. For example, if the highest recent pip value is 12,345 only the first 3 numerals would be displayed ie 123. But suppose there is a recent value of 691. It would not be helpful to display 691 if the other wave size is shown as 123. To give the appropriate relative value the script will show a value of 7 instead of 691. This informs you of the relative magnitude of the values. This is done automatically within the script. There is likely no need to manually override the automatically calculated value. I will create a video that demonstrates the manual override method.
What is a Weis wave? David Weis has been recognized as a Wyckoff method analyst he has written two books one of which, Trades About to Happen, describes the evolution of the now popular Weis wave. The method employed by Weis is to identify waves of price action and to compare the strength of the waves on characteristics of wave strength. Chief among the characteristics of strength is the cumulative volume of the wave. There are other markers that Weis uses as well for example how the actual price difference between the start of the Weis wave from start to finish. Weis also uses time, particularly when using a Renko chart. Weis specifically uses candle or bar closes to define all wave action ie a line chart.
David Weis did a futures io video which is a popular source of information about his method.
This is the identical script with the identical settings but without the offending links. If you want to see the pip Weis method in practice then search Weis pip wave. If you want to see Weis chart in pdf then message me and I will give a link or the Weis pdf. Why would you want to see the Weis chart for May 27, 2020? Merely to confirm the veracity of my algorithm. You could compare my Weis chart here () from the same period to the David Weis chart from May 27. Both waves are for the ES!1 4 hour chart and both for a wave size of 5.
Price Action and 3 EMAs Momentum plus Sessions FilterThis indicator plots on the chart the parameters and signals of the Price Action and 3 EMAs Momentum plus Sessions Filter Algorithmic Strategy. The strategy trades based on time-series (absolute) and relative momentum of price close, highs, lows and 3 EMAs.
I am still learning PS and therefore I have only been able to write the indicator up to the Signal generation. I plan to expand the indicator to Entry Signals as well as the full Strategy.
The strategy works best on EURUSD in the 15 minutes TF during London and New York sessions with 1 to 1 TP and SL of 30 pips with lots resulting in 3% risk of the account per trade. I have already written the full strategy in another language and platform and back tested it for ten years and it was profitable for 7 of the 10 years with average profit of 15% p.a which can be easily increased by increasing risk per trade. I have been trading it live in that platform for over two years and it is profitable.
Contributions from experienced PS coders in completing the Indicator as well as writing the Strategy and back testing it on Trading View will be appreciated.
STRATEGY AND INDICATOR PARAMETERS
Three periods of 12, 48 and 96 in the 15 min TF which are equivalent to 3, 12 and 24 hours i.e (15 min * period / 60 min) are the foundational inputs for all the parameters of the PA & 3 EMAs Momentum + SF Algo Strategy and its Indicator.
3 EMAs momentum parameters and conditions
• FastEMA = ema of 12 periods
• MedEMA = ema of 48 periods
• SlowEMA = ema of 96 periods
• All the EMAs analyse price close for up to 96 (15 min periods) equivalent to 24 hours
• There’s Upward EMA momentum if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA
• There’s Downward EMA momentum if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA
PA momentum parameters and conditions
• HH = Highest High of 48 periods from 1st closed bar before current bar
• LL = Lowest Low of 48 periods from 1st closed bar from current bar
• Previous HH = Highest High of 84 periods from 12th closed bar before current bar
• Previous LL = Lowest Low of 84 periods from 12th closed bar before current bar
• All the HH & LL and prevHH & prevLL are within the 96 periods from the 1st closed bar before current bar and therefore indicative of momentum during the past 24 hours
• There’s Upward PA momentum if price close > HH and HH > prevHH and LL > prevLL
• There’s Downward PA momentum if price close < LL and LL < prevLL and HH < prevHH
Signal conditions and Status (BuySignal, SellSignal or Neutral)
• The strategy generates Buy or Sell Signals if both 3 EMAs and PA momentum conditions are met for each direction and these occur during the London and New York sessions
• BuySignal if price close > FastEMA and FastEMA > MedEMA and MedEMA > SlowEMA and price close > HH and HH > prevHH and LL > prevLL and timeinrange (LDN&NY) else Neutral
• SellSignal if price close < FastEMA and FastEMA < MedEMA and MedEMA < SlowEMA and price close < LL and LL < prevLL and HH < prevHH and timeinrange (LDN&NY) else Neutral
Entry conditions and Status (EnterBuy, EnterSell or Neutral)(NOT CODED YET)
• ENTRY IS NOT AT THE SIGNAL BAR but at the current bar tick price retracement to FastEMA after the signal
• EnterBuy if current bar tick price <= FastEMA and current bar tick price > prevHH at the time of the Buy Signal
• EnterSell if current bar tick price >= FastEMA and current bar tick price > prevLL at the time of the Sell Signal
NAND PerceptronExperimental NAND Perceptron based upon Python template that aims to predict NAND Gate Outputs. A Perceptron is one of the foundational building blocks of nearly all advanced Neural Network layers and models for Algo trading and Machine Learning.
The goal behind this script was threefold:
To prove and demonstrate that an ACTUAL working neural net can be implemented in Pine, even if incomplete.
To pave the way for other traders and coders to iterate on this script and push the boundaries of Tradingview strategies and indicators.
To see if a self-contained neural network component for parameter optimization within Pinescript was hypothetically possible.
NOTE: This is a highly experimental proof of concept - this is NOT a ready-made template to include or integrate into existing strategies and indicators, yet (emphasis YET - neural networks have a lot of potential utility and potential when utilized and implemented properly).
Hardcoded NAND Gate outputs with Bias column (X0):
// NAND Gate + X0 Bias and Y-true
// X0 // X1 // X2 // Y
// 1 // 0 // 0 // 1
// 1 // 0 // 1 // 1
// 1 // 1 // 0 // 1
// 1 // 1 // 1 // 0
Column X0 is bias feature/input
Column X1 and X2 are the NAND Gate
Column Y is the y-true values for the NAND gate
yhat is the prediction at that timestep
F0,F1,F2,F3 are the Dot products of the Weights (W0,W1,W2) and the input features (X0,X1,X2)
Learning rate and activation function threshold are enabled by default as input parameters
Uncomment sections for more training iterations/epochs:
Loop optimizations would be amazing to have for a selectable length for training iterations/epochs but I'm not sure if it's possible in Pine with how this script is structured.
Error metrics and loss have not been implemented due to difficulty with script length and iterations vs epochs - I haven't been able to configure the input parameters to successfully predict the right values for all four y-true values for the NAND gate (only been able to get 3/4; If you're able to get all four predictions to be correct, let me know, please).
// //---- REFERENCE for final output
// A3 := 1, y0 true
// B3 := 1, y1 true
// C3 := 1, y2 true
// D3 := 0, y3 true
PLEASE READ: Source article/template and main code reference:
towardsdatascience.com
towardsdatascience.com
towardsdatascience.com
Baseline-C [ID: AC-P]The "AC-P" version of jiehonglim's NNFX Baseline script is my personal customized version of the NNFX Baseline concept as part of the NNFX Algorithm stack/structure for 1D Trend Trading for Forex. Everget's JMA implementation is used for the baseline smoothing method, with optional ATR bands at 1.0x and 1.5x from the baseline.
NNFX = No Nonsense Forex
Baseline = Component of the NNFX Algorithm that consists of a single moving average
Baseline ---> Meant to be used in conjunction with ATR/C1/C2/Vol Indicator/Exit Indicator as per NNFX Algorithm setup/structure. C1 is 1st Confirmation Indicator, C2 is 2nd Confirmation Indicator.
JMA (Jurik Moving Average) is used for the baseline and slow baseline.
A slow baseline option is included, but disabled by default.
The faint orange/purple lines are 1.0x/1.5x ATR from the Baseline, and are what I use as potential TP/SL targets or to evaluate when to stay out of a trade (chop/missed entry/exit/other/ATR breach), depending on the trade setup (in conjunction with C1/C2/Vol Indicator/Exit Indicator)
This script is heavily based upon jiehonglim's NNFX Baseline script for signaling, barcoloring, and ATR.
SSL Channel option included but disabled by default (Erwinbeckers SSL component)
POC (Point of Control) from Volume Profile is included/enabled by default for both the current timeframe and 12HR timeframe
03.freeman's InfoPanel Divergence Indicator was used a reference to replace the current/previous ATR information infopanel/info draw from jiehonglim's script. I'm not sure whether I like the previous way ATR info was displayed vs how I have it currently, but it's something that is completely optional:
Specifically: I am tuning this baseline/indicator for 1D trading as part of the NNFX system, for Forex.
DO NOT USE THIS INDICATOR WITHOUT PROPER TUNING/ADJUSTMENT for your timeframe and asset class.
Note about lack of alerts:
Alerts for baseline crosses (and other crosses) have been purposefully omitted for this version upon initial publication. While getting alerts for baseline crosses under certain conditions/filtered conditions that eliminate low-importance signals and crossover whipsaw would be great, it's something I'm still looking into.
SPECIFICALLY: There are entry, exit, take profit, and continuation signal components in relation to the Baseline to the rest of the NNFX Algorithm stack (ATR/C1/C2/Vol Indicator/Exit Indicator), including but limited to the "1 candle rule" and the "7 candle rule" as per NNFX.
Implementing alerts that are significant that also factor in these rules while reducing alert spam/false signals would be ideal, but it's also the HTF/Daily chart - visually, entry/exit/continuation signal alignment is easy to spot when trading 1D - alerts may be redundant/a pursuit in diminishing returns (for now).
//-------------------------------------------------------------------
// Acknowledgements/Reference:
// jiehonglim, NNFX Baseline Script - Moving Averages
//
// Fractured, Many Moving Averages
//
// everget, Jurik Moving Average/JMA
//
// 03.freeman, InfoPanel Divergence Indicator
//
// Ggqmna Volume stops
//
// Libertus RSI Divs
//
// ChrisMoody, CM_Price-Action-Bars-Price Patterns That Work
//
// Erwinbeckers SSL Channel
//
RSI Apex: Breakout & DivergenceRSI Apex: Breakout & Divergence System
RSI Apex：突破与背离交易系统
🇬🇧 English Description
RSI Apex is a comprehensive trading system designed to capture both Trend Breakouts and Market Reversals. Unlike traditional RSI indicators that rely solely on fixed levels (70/30), RSI Apex integrates Donchian Channels, Volatility Squeeze, and the Libertus Divergence Algorithm to provide high-probability signals.
🚀 Key Features
Trend Push System (Donchian Breakout):
Detects when RSI momentum is strong enough to push the upper/lower Donchian Channel bands.
Signal: Displays ▲ (Bull) or ▼ (Bear) at levels 20/80.
Libertus Divergence (No-Lag):
Uses a real-time pivot tracking algorithm to identify divergences between Price and RSI without the lag of traditional pivot points.
Signal: Displays "Div" labels at levels 30/70.
Smart Coloring (Extreme Highlight):
Green/Red: Normal Trend.
White (Extreme): When RSI breaches 70 (Overbought) or 30 (Oversold), the line turns bright White. This highlights the most volatile zones where reversals or strong continuations occur.
Volatility Squeeze Filter:
Monitors market volatility. When the Donchian Channel compresses significantly (below historical average), the background turns Purple.
Meaning: "Calm before the storm"—expect a major move soon.
🛠 How to Use
Trend Following: Enter when you see Green/Red RSI lines accompanied by ▲ / ▼ signals. This indicates a "Trend Push."
Reversal Trading: Look for "Div" signals when the RSI line is White (Extreme). This suggests momentum is fading despite price action.
Exit/Take Profit: Watch for the "Weak" label, which appears when RSI falls back into the neutral zone.
Dashboard: Monitor real-time RSI Value, Market State (Bullish/Bearish/Extreme), and Volatility (Squeeze/Expanding) in the bottom-right table.
🇨🇳 中文简介
RSI Apex 是一套旨在捕捉趋势爆发 (Breakout) 和 市场反转 (Reversal) 的综合交易系统。与仅依赖固定 70/30 线的传统 RSI 不同，本指标融合了 唐奇安通道 (Donchian Channels)、波动率挤压 (Squeeze) 以及 Libertus 无滞后背离算法，以提供高胜率的交易信号。
🚀 核心功能
强趋势推动系统 (唐奇安突破):
检测 RSI 动能是否强劲到足以推动唐奇安通道的上轨或下轨扩张。
信号: 在 20/80 轴位置显示 ▲ (多头推动) 或 ▼ (空头推动)。
Libertus 智能背离 (无滞后):
采用实时 Pivot 追踪算法，精准识别价格与 RSI 之间的背离，解决了传统背离指标的滞后问题。
信号: 在 30/70 轴位置显示 "Div" 标签。
智能变色 (极端行情高亮):
绿色/红色: 正常趋势状态。
白色 (White): 极端区域。当 RSI 突破 70 (超买) 或跌破 30 (超卖) 时，线条会强制变为醒目的亮白色，提示此处为变盘/背离高发区。
波动率挤压 (Squeeze) 过滤器:
实时监控市场波动率。当通道宽度显著收窄（低于历史平均水平）时，背景会填充为半透明紫色。
含义: “暴风雨前的宁静”——预示着大行情即将爆发，此时应空仓等待突破方向。
🛠 使用策略
顺势交易 (Trend): 当 RSI 呈现 绿色/红色 并伴随 ▲ / ▼ 信号时进场。这代表动能极强，处于主升/主跌浪。
左侧反转 (Reversal): 重点关注 RSI 线条变为 白色 (Extreme) 时出现的 "Div" 背离信号。这通常意味着价格虽创新高，但动能已耗尽。
止盈/离场: 留意 "Weak" (衰竭) 标签，它出现在 RSI 掉回中间震荡区时。
仪表盘: 右下角面板实时显示 RSI 数值、市场状态 (极值/背离/趋势) 以及波动率状态 (挤压/扩张)。
Planetary Retrograde Periods█ PLANETARY RETROGRADE PERIODS
Visualize when planets appear to move backward through the zodiac. This indicator detects and displays retrograde motion for all 8 planets that exhibit apparent retrograde motion from Earth's perspective: Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto.
Powered by the BlueprintResearch lib_ephemeris library.
█ FEATURES
• 8 Planets Supported — Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto
• Two-Phase Visualization — Distinguishes first half (speed increasing in retrograde direction) from second half (speed decreasing toward direct motion) with different transparency levels
• Future Projections — Projects upcoming retrograde periods up to 500 bars ahead on any timeframe
• Station Markers — Clear labels for Station Retrograde (℞), Midpoint (½), and Station Direct (D)
• Timezone-Aware Labels — Future date/time labels display in your selected timezone
• Alert Conditions — Set alerts for station retrograde, station direct, or any station point
• Per-Planet Colors — Customize colors for each planet individually
• Speed-Based Detection — More accurate than longitude-based methods
█ HOW TO USE
1. Select a Planet — Choose which planet to track from the dropdown (Mercury through Pluto)
2. Enable Two-Phase Display — Toggle "Show Retrograde Halves" to see first half vs. second half shading
3. Configure Future Projections — Set how many bars ahead to scan (1-500) and enable/disable date labels
4. Set Your Timezone — Choose your timezone for accurate future date/time display
5. Customize Colors — Adjust planet colors, transparency levels, and label text color to match your chart theme
6. Create Alerts — Use TradingView's alert system with the built-in conditions for station points
█ UNDERSTANDING THE DISPLAY
Background Colors:
• First Half of the Planet’s retrograde (lighter shade)
• Second Half of the Planet’s retrograde period (darker shade)
Future Projection Lines:
• ℞ (Station Retrograde) — Yellow dotted line marking when the planet will station retrograde
• ½ (Midpoint) — Shorter line in planet color marking the halfway point of the retrograde period
• D (Station Direct) — Green dotted line marking when the planet will station direct
Labels:
• Top label shows planet symbol and station type
• Bottom label shows projected date and time (optional)
█ ACCURACY
This indicator uses speed-based detection
Timing Accuracy:
• All planets (Mercury through Pluto): Within hours to ±1 day
• Future projections maintain accuracy up to 500 bars on any timeframe
• Spot tested on Daily and Weekly charts with excellent results
For Critical Applications:
Cross-reference with professional ephemeris tools such as JPL Horizons or Swiss Ephemeris for mission-critical timing.
█ TECHNICAL DETAILS
Theory: VSOP87 (Mercury through Neptune), Meeus algorithms (Pluto)
█ REFERENCES
• Meeus, Jean. "Astronomical Algorithms" (2nd Edition, 1998)
• Bretagnon & Francou. "VSOP87 Solutions" — Astronomy and Astrophysics 202 (1988)
Auction Session Ranges (AMT Edition) [ Alerts] Auction Session Ranges (AMT Edition)
► Overview
The Session Ranges ( AMT Edition) is a session-based market structure and auction analysis tool designed to visually reveal acceptance, rejection, imbalance, and continuation across the Asia, London, and New York CME trading sessions.
Unlike typical indicators, this script is grounded in Auction Market Theory (AMT) and session-based structure, focusing on how price behaves at session extremes rather than relying on lagging calculations, oscillators, or predictive algorithms. Its purpose is to highlight areas where the market has earned the right to be traded, providing traders with a clear, rules-based framework for high-probability directional trades.
Important for backtesting: To properly backtest session extremes, Interaction Lines, and Closest Opposite Extreme Lines, you must use TradingView’s replay mode, as real-time bar-by-bar progression is required to observe how the market interacts with session extremes over time.
► Key Innovations
This is not a conventional session high/low indicator. Its originality comes from several unique design elements:
Differentiates interaction from true acceptance: Price touching an extreme does not automatically indicate directional intent.
Separates directional confirmation from range-bound indecision: Only confirmed crossings beyond the Interaction Line signal actionable bias.
Tracks failed auctions and partial acceptance: No volume profile or order book data required.
Visual, rule-based trade permission: Signals are objective, minimizing subjective interpretation.
Interaction & Closest Opposite Extreme Lines: Together, these lines map how far an auction progresses after an extreme is tested, highlighting continuation, partial acceptance, or failed auctions.
► Core Concepts Explained
1. Session Highs & Lows (Solid Lines)
Plotted continuously for each CME session (Asia, London, New York).
Represent the current auction boundaries for that session.
2. True Interaction Lines (Thick Dotted Lines)
Drawn when price touches or breaks a session extreme:
Touching session high → dotted line at the low of that candle
Touching session low → dotted line at the high of that candle
Auction context:
Touching alone ≠ acceptance
Acceptance occurs only when price moves beyond the Interaction Line and holds
Trading principle:
Price has not crossed → no directional bias → do not trade
Price crosses and holds → directional bias established
3. Acceptance vs Rejection
Accepted direction: Price crosses and holds beyond the Interaction Line
Rejected direction: Price crosses the line but immediately reverses
Neutral / No-Trade: Price trapped between extreme and Interaction Line
Important: Acceptance is conditional and dynamic. Each time price crosses back over the Interaction Line, acceptance is lost.
4. New Extremes = Continuation
Once an Interaction Line is crossed, each new session extreme in that direction reinforces the trend.
Traders should only look for continuation setups along the established directional bias.
AMT interpretation:
Repeated new extremes → directional imbalance
Failure to make new extremes → potential balance or rotation
5. Closest Opposite Extreme Lines (Thin Dotted Lines)
After acceptance, the script tracks price progress toward the opposite session extreme.
Plotted only if price reaches a user-defined percentage of the session range.
Helps identify:
Full acceptance (price reaches opposite extreme)
Partial acceptance (price stalls)
Failed auctions (price cannot progress meaningfully)
Trading guidance once Closest Lines appear:
Partial acceptance: Price stalls near the Closest Line but does not fully reach the opposite extreme → bias remains valid, but the move may be weakening; consider scaling out or tightening stops.
Full acceptance: Price reaches the opposite extreme → directional auction fully confirmed; bias continues, but expect potential rotation or balance afterward.
Failed auction (cannot progress meaningfully): Price reverses before reaching the Closest Line → signals exhaustion; avoid chasing the move and treat as potential trend failure.
Note: Only relevant after Interaction Line is crossed; if price never crosses the Interaction Line, Closest Lines have no trading significance.
► Step-by-Step Usage
Wait for a session extreme
Let price interact with the session high or low.
Observe the Interaction Line
No cross → do not trade
Cross and hold → directional bias established
Trade in the direction of new extremes only
Ignore counter-trend trades unless the Interaction Line is lost
Manage risk using structure
Interaction Line acts as a dynamic invalidation level
Use Closest Lines for context
Partial acceptance → bias valid, watch for weakening
Full acceptance → bias strong, continuation likely
Failed attempt → potential exhaustion, do not chase
Useful for trade management, scaling, and expectation setting
► Price Retests & Pullbacks
Scenario:
Price crosses above the Interaction Line (e.g., from a low interaction).
Over the next 3–4 15-minute bars, price dips back toward the Interaction Line, with wicks touching it but no decisive close below.
Interpretation:
Initial Acceptance Confirmed: Bias remains valid while price holds above/below the line.
Temporary Pullback / Retest: Market is re-evaluating the auction; testing participant agreement.
Wicks Touching the Line: Partial probing or liquidity sweep; market still respects original acceptance.
Trading Implication:
Continuation bias remains intact.
Pullbacks near the Interaction Line offer lower-risk entries.
Decisive close below → acceptance lost, signaling trend failure or invalidation.
Market Psychology:
Healthy auction behavior: extreme tested → acceptance confirmed → boundary retested for liquidity → continuation.
Failure to hold above signals weak acceptance or exhaustion.
✅ Key Takeaways:
Holding above Interaction Line → bias intact, pullback = opportunity
Closing below Interaction Line → acceptance lost, bias invalidated
Wicks touching only → normal retest, still valid
► No-Trade Conditions
Avoid trading when:
Price never crosses the Interaction Line
Price remains trapped between the extreme and the Interaction Line
Market rotates without forming new extremes
These indicate balance, not directional opportunity.
► Alerts
Optional alerts trigger when price crosses an Interaction Line for:
Asia session
London session
New York session
Alerts signal possible acceptance, not automatic trade entries.
► Who This Script Is For
Best suited for traders who:
Trade session structure in futures, indices, or FX
Follow Auction Market Theory principles
Prefer objective, rules-based confirmation
Want fewer but higher-quality trade opportunities
Not intended for:
Indicator stacking
Predictive trading
High-frequency scalping without structure
► Final Notes
This script does not tell you when to buy or sell.
It shows where the market has earned the right to be traded.
Use it as a decision filter, not a prediction engine.
TAN Omni-Dash v50: Dividend Payout for Jan 2026 TradableJust a simple Momentum swing algo. It's mainly for keeping an eye out for Jan 2026 ex-divedent payouts list. This code contains top 100 most profitable payouts.
Webhook Candle Sender (OHLCV)This indicator sends OHLCV (Open, High, Low, Close, Volume) candle data via webhook on every confirmed bar close.
It is designed to integrate TradingView with an external trading or analytics system (e.g. a local Flask server, paper trading engine, or algorithmic agent).
Features:
• Sends data only on bar close (no repainting)
• Works on any symbol (stocks, crypto, forex)
• Works on any timeframe
• Outputs structured JSON suitable for APIs and bots
• Uses TradingView alert() function for webhook delivery
Typical use cases:
• Algorithmic trading research
• Paper trading systems
• Backtesting external strategies
• Educational and learning purposes
This script does NOT place trades, manage risk, or provide trading signals.
It only transmits candle data.
No financial advice is provided.
TGS By ShadTGS Levels — Tesla–W.D. Gann Strategy
TGS Levels is a price-geometry indicator designed to map algorithmic decision zones on the chart using principles inspired by W.D. Gann price geometry and Tesla 3-6-9 harmonic structure.
This indicator is not a signal generator.
It provides a structured price map to help traders understand where reactions or breakouts are statistically more likely to occur.
🔹 Core Concept
Markets do not move randomly.
They rotate and expand around harmonic price cycles.
TGS Levels automatically plots a 100-unit price cycle framework (ideal for XAUUSD / Gold) and divides each cycle into hierarchical angles used by institutional and algorithmic trading models.
🔹 Level Hierarchy (Very Important)
TGS uses four types of levels, each with a different purpose:
🔴 SUPER ANGLE (+45)
Primary decision level
Price often shows strong rejection or explosive breakout
Highest importance level
🟥 MAIN ANGLES (+27, +63, +81)
High-probability reaction zones
Used for structured pullbacks, rejections, or continuation confirmation
🟠 SECONDARY ANGLES (+18, +36, +54, +72, +90)
Context & management levels
Expect hesitation, partial profit zones, or stop-tightening areas
Not recommended for direct entries
🟡 MICRO LEVELS (+3, +6, +9)
Liquidity & compression map
Help visualize absorption, stop hunts, and consolidation
For structure awareness only
🔹 What This Indicator Is Used For
✔ Identifying where price is likely to react
✔ Understanding market structure and rotation
✔ Distinguishing rejection vs breakout zones
✔ Improving trade timing when combined with:
Volatility (ATR)
Market structure (HL / LH / BOS)
Session timing (London / New York)
🔹 What This Indicator Is NOT
❌ Not a buy/sell signal
❌ Not a prediction tool
❌ Not based on indicators like RSI or MACD
TGS Levels is a price-first framework, designed to be used with price action, volatility, and structure.
🔹 Best Use Case
Asset: XAUUSD (Gold)
Execution Timeframe: M5
Sessions: London & New York
Style: Scalping / Intraday structure trading
The same logic can be adapted to other instruments by adjusting the cycle size.
🔹 How to Trade With TGS (High-Level)
When volatility is low or falling → expect rejections at main/super angles
When volatility is expanding → expect breakouts through angles
Use oscillators (like Stochastic) only for timing, not direction
Always confirm with price behavior at the level
🔹 Final Note
TGS Levels provides a clean, non-repainting price map that stays aligned when zooming or scrolling the chart.
All levels are calculated automatically and update dynamically with price.
Levels explain behavior — reactions create opportunity.
TGS By ShadTGS Levels — Tesla–W.D. Gann Strategy
TGS Levels is a price-geometry indicator designed to map algorithmic decision zones on the chart using principles inspired by W.D. Gann price geometry and Tesla 3-6-9 harmonic structure.
This indicator is not a signal generator.
It provides a structured price map to help traders understand where reactions or breakouts are statistically more likely to occur.
🔹 Core Concept
Markets do not move randomly.
They rotate and expand around harmonic price cycles.
TGS Levels automatically plots a 100-unit price cycle framework (ideal for XAUUSD / Gold) and divides each cycle into hierarchical angles used by institutional and algorithmic trading models.
🔹 Level Hierarchy (Very Important)
TGS uses four types of levels, each with a different purpose:
🔴 SUPER ANGLE (+45)
Primary decision level
Price often shows strong rejection or explosive breakout
Highest importance level
🟥 MAIN ANGLES (+27, +63, +81)
High-probability reaction zones
Used for structured pullbacks, rejections, or continuation confirmation
🟠 SECONDARY ANGLES (+18, +36, +54, +72, +90)
Context & management levels
Expect hesitation, partial profit zones, or stop-tightening areas
Not recommended for direct entries
🟡 MICRO LEVELS (+3, +6, +9)
Liquidity & compression map
Help visualize absorption, stop hunts, and consolidation
For structure awareness only
🔹 What This Indicator Is Used For
✔ Identifying where price is likely to react
✔ Understanding market structure and rotation
✔ Distinguishing rejection vs breakout zones
✔ Improving trade timing when combined with:
Volatility (ATR)
Market structure (HL / LH / BOS)
Session timing (London / New York)
🔹 What This Indicator Is NOT
❌ Not a buy/sell signal
❌ Not a prediction tool
❌ Not based on indicators like RSI or MACD
TGS Levels is a price-first framework, designed to be used with price action, volatility, and structure.
🔹 Best Use Case
Asset: XAUUSD (Gold)
Execution Timeframe: M5
Sessions: London & New York
Style: Scalping / Intraday structure trading
The same logic can be adapted to other instruments by adjusting the cycle size.
🔹 How to Trade With TGS (High-Level)
When volatility is low or falling → expect rejections at main/super angles
When volatility is expanding → expect breakouts through angles
Use oscillators (like Stochastic) only for timing, not direction
Always confirm with price behavior at the level
🔹 Final Note
TGS Levels provides a clean, non-repainting price map that stays aligned when zooming or scrolling the chart.
All levels are calculated automatically and update dynamically with price.
Levels explain behavior — reactions create opportunity.
TGS by Shad TGS Levels — Tesla–W.D. Gann Strategy
TGS Levels is a price-geometry indicator designed to map algorithmic decision zones on the chart using principles inspired by W.D. Gann price geometry and Tesla 3-6-9 harmonic structure.
This indicator is not a signal generator.
It provides a structured price map to help traders understand where reactions or breakouts are statistically more likely to occur.
🔹 Core Concept
Markets do not move randomly.
They rotate and expand around harmonic price cycles.
TGS Levels automatically plots a 100-unit price cycle framework (ideal for XAUUSD / Gold) and divides each cycle into hierarchical angles used by institutional and algorithmic trading models.
🔹 Level Hierarchy (Very Important)
TGS uses four types of levels, each with a different purpose:
🔴 SUPER ANGLE (+45)
Primary decision level
Price often shows strong rejection or explosive breakout
Highest importance level
🟥 MAIN ANGLES (+27, +63, +81)
High-probability reaction zones
Used for structured pullbacks, rejections, or continuation confirmation
🟠 SECONDARY ANGLES (+18, +36, +54, +72, +90)
Context & management levels
Expect hesitation, partial profit zones, or stop-tightening areas
Not recommended for direct entries
🟡 MICRO LEVELS (+3, +6, +9)
Liquidity & compression map
Help visualize absorption, stop hunts, and consolidation
For structure awareness only
🔹 What This Indicator Is Used For
✔ Identifying where price is likely to react
✔ Understanding market structure and rotation
✔ Distinguishing rejection vs breakout zones
✔ Improving trade timing when combined with:
Volatility (ATR)
Market structure (HL / LH / BOS)
Session timing (London / New York)
🔹 What This Indicator Is NOT
❌ Not a buy/sell signal
❌ Not a prediction tool
❌ Not based on indicators like RSI or MACD
TGS Levels is a price-first framework, designed to be used with price action, volatility, and structure.
🔹 Best Use Case
Asset: XAUUSD (Gold)
Execution Timeframe: M5
Sessions: London & New York
Style: Scalping / Intraday structure trading
The same logic can be adapted to other instruments by adjusting the cycle size.
🔹 How to Trade With TGS (High-Level)
When volatility is low or falling → expect rejections at main/super angles
When volatility is expanding → expect breakouts through angles
Use oscillators (like Stochastic) only for timing, not direction
Always confirm with price behavior at the level
🔹 Final Note
TGS Levels provides a clean, non-repainting price map that stays aligned when zooming or scrolling the chart.
All levels are calculated automatically and update dynamically with price.
Levels explain behavior — reactions create opportunity.
Quant-Action Pro: Triple Confluence EngineQuant-Action Pro: Triple Confluence Engine
Systematic Framework for Structural Price Action Analysis
Quant-Action Pro is a high-performance analytical engine designed to synchronize institutional liquidity flow with market geometry. Instead of traditional "signals," this framework identifies Structural States where three independent algorithmic layers align, providing a objective roadmap for the current price action context.
1. Core Algorithmic Matrix
The engine operates by monitoring the interaction between price and three proprietary logic layers:
A. Institutional Flow Node (SP2L) —
Logic: Monitors "Passive Liquidity Absorption" at the 20-period EMA.
Function: Identifies zones where institutional buyers/sellers are defending the trend's equilibrium. This is not a simple touch; it requires a validated "Touch-and-Hold" sequence.
B. Structural Flip Scanner (BTB) —
Logic: Detects the transition from old supply to new demand (S/R Flip).
Function: Uses a 3-phase Break-Test-Break verification to confirm that a structural breakout is backed by volume, reducing the risk of "Fake-outs."
C. Liquidity Compression Monitor (Micro Map) —
Logic: Statistical range-contraction analysis (Volatility Squeeze).
Function: Signals a High-Density State where price is coiling for an expansion move.
2. The Golden State: Triple Confluence Logic
The GOLD label represents the "Apex" of this engine. It is triggered only when the SP2L, BTB, and Micro Map layers synchronize on a single candle. In structural terms, this means:
Trend Defense (SP2L) is active.
Structural Breakout (BTB) is confirmed.
Volatility Expansion (MM) is imminent.
This Triple-Layer filtering ensures that Golden Signals only appear during periods of maximum market conviction.
3. Professional Implementation (Structural View)
MTF Trend Matrix: A built-in dashboard provides a 1H, 4H, and 1D diagnosis to ensure local setups align with the Macro Trend.
Smart Invalidation (Adaptive Trendlines): The engine draws dynamic geometry to define the current "Structural Floor/Ceiling." A decisive close beyond these lines acts as a clear Invalidation Point for the current thesis.
Mean Reversion: The system uses the 200-EMA as the primary directional filter, defining whether the market is in a "Bullish Expansion" or "Bearish Correction" state.
⚠️ Risk Disclaimer
Trading financial instruments involves significant risk. Quant-Action Pro is an educational tool designed for research and structural analysis. It does not provide financial advice. Past performance is not indicative of future results. Always use strict risk management.
Max Pain Options [QuantLabs] v5 (Balanced)Institutional Grade Options Analysis: Max Pain, Gamma & Pin Risk
For years, TradingView users have been flying blind without access to Options Chain data. QuantLabs: Max Pain & Gamma Exposure changes that. This is not just a support/resistance indicator—it is a sophisticated, algorithmic model that reverse-engineers the incentives of Market Makers using synthetic Black-Scholes logic.
This tool visualizes the "invisible hand" of the market: the hedging requirements of large dealers who are forced to buy or sell to keep their books neutral.
CORE FEATURES:
🔴 Max Pain Gravity Model The bright red line represents the "Max Pain" strike—the price level where the maximum amount of Options Open Interest (Calls + Puts) expires worthless.
Theory: As OpEx (Expiration) approaches, Market Makers maximize profits by pinning the price to this level.
Strategy: Use this as a mean-reversion target. If price is far away, look for a snap-back to the red line.
🟣 Gamma Exposure Profiles (The Purple Lines) These neon histograms show you the estimated "Gamma Walls."
Long Gamma: Dealers trade against the trend (stabilizing price).
Short Gamma: Dealers trade with the trend (accelerating volatility).
Visual: The larger the purple bar, the harder it will be for price to break through that level.
📦 Algorithmic "Pin Risk" Zones The dashed red box highlights the "Kill Zone." When price enters this area near expiration, volatility often dies as dealers pin the asset to kill retail premiums.
Warning: Do not expect breakouts while inside the Pin Zone.
📊 Institutional HUD A clean, non-intrusive dashboard provides real-time Greeks and risk analysis:
Pin Risk: High/Medium/Low probability of a pinned close.
Exp Mode: Detects if the market is in "Short Gamma" (Squeeze territory) or "Long Gamma" (Chop territory).
HOW IT WORKS (The Math): Since live options data is not available via Pine Script, this engine uses a proprietary Synthetic OI Distribution Model. It inputs Volume, Volatility (IV), and Time-to-Expiry into a modified Black-Scholes equation to probability-map where the heavy open interest likely sits.
SETTINGS & CUSTOMIZATION:
Responsiveness: Tuned for the "Goldilocks Zone" (Spread: 12, Decay: 22) to catch local liquidity walls without over-fitting.
Visuals: Designed for Dark Mode. High-contrast Neon aesthetics for maximum readability.
RSI Swing + VWAP + EMA + Camarilla + PDH/PDL+CPRThis script provide the follwing -
1. Daily CPR level
2. Camarilla S3/R3
3. Previous Day High/Low (PDH/PDL)
4. Dynamic VWAP
5. Dynamic EMA 20/200
6. Dynamic RSi Swing
Dec 10
Release Notes
This script provide the follwing -
1. Daily CPR level
2. Camarilla S3/R3
3. Previous Day High/Low (PDH/PDL)
4. Dynamic VWAP
5. Dynamic EMA 20/200/36
6. Dynamic RSi Swing
Which is better: 36 EMA or 36 SMA for Support/Resistance?
✔ 36 EMA (Exponential Moving Average)
Better for intraday, short-term trading, scalping, and momentum trading.
Why?
Reacts faster to price.
Captures trend shifts early.
Works great when market is trending or volatile.
Most traders use EMA for dynamic support/resistance → works better because of crowd behavior.
Ideal for:
NIFTY, BANKNIFTY, FINNIFTY intraday | Options entries | Trend continuation trades.
Why 20 EMA is Important
The 20 EMA is one of the most widely used moving averages for intraday, swing, and positional trading because it captures short-term trend strength and momentum.
📌 20 EMA Works Best For
✔ Intraday trend identification
✔ Momentum continuation entries
✔ Dynamic support/resistance
✔ Quick reversal detection
✔ Options trading (NIFTY/BNF)
✔ Breakout & pullback trades
EMA 200 – Why It’s Extremely Important
The 200 EMA represents the long-term trend and is respected by:
Institutions
Algo systems
Big traders
Swing traders
Index traders
It acts like a major wall of support or resistance.
💡 What EMA 200 Tells You
✔ Long-term trend direction
Price above 200 EMA → Long-term uptrend
Price below 200 EMA → Long-term downtrend
✔ Strong trend reversal signals
When price crosses the 200 EMA on 15m/1h/1D charts → a deeper trend change is possible.
✔ Institutional support/resistance
Very powerful bounce/rejection zones
Many markets reverse exactly at 200 EMA
What is Previous Day High (PDH)?
The highest price the market reached in the previous trading session.
Why PDH is Important?
Acts as strong resistance
Breakout level for uptrend
Sellers often defend this zone
If broken with volume → strong bullish momentum
🔴 What is Previous Day Low (PDL)?
The lowest price the market reached in the previous trading session.
Why PDL is Important?
Acts as strong support
Breakdown level for downtrend
Buyers defend this level
If broken with volume → strong bearish trend
📌 How PDH/PDL Help in Intraday Trading
1️⃣ Range Breakout Trades
If price breaks PDH → bullish breakout (Buy CE)
If price breaks PDL → bearish breakdown (Buy PE)
What is Camarilla R3?
R3 = Resistance Level 3 in the Camarilla Pivot system.
Why R3 is important?
Acts as a major intraday resistance
Price often reverses from R3
If broken with force → strong uptrend starts
Many traders use R3 as a decision zone
Typical Market Behavior at R3
Rejection from R3 → Sell/PE opportunity
Break + Retest above R3 → CE opportunity
🔴 What is Camarilla S3?
S3 = Support Level 3 in the Camarilla Pivot system.
Why S3 is important?
Acts as a major intraday support
Buyers defend this zone
Breakdown of S3 → strong fall
S3 is often a bounce zone in the morning
Typical Market Behavior at S3
Bounce from S3 → Buy/CE opportunity
Break + Retest below S3 → PE opportunity
📌 Trader Logic: R3 & S3 Zones
⭐ 1. Range Reversal Strategy (Most Popular)
At R3 → Sell/PE
At S3 → Buy/CE
What is VWAP?
VWAP = Volume Weighted Average Price
It shows the average price at which most trading has happened during the day, based on both price and volume.
It resets every day at market open.
🔥 Why VWAP Is So Powerful?
VWAP is used by:
Institutions
Algo traders
Scalpers
Intraday traders
Dec 10
Release Notes
This script provide the follwing -
1. Daily CPR level
2. Camarilla S3/R3
3. Previous Day High/Low (PDH/PDL)
4. Dynamic VWAP
5. Dynamic EMA 20/200
6. Dynamic RSi Swing
3 hours ago
Release Notes
This script provide the follwing -
1. Daily CPR level
2. Camarilla S3/R3
3. Previous Day High/Low (PDH/PDL)
4. Dynamic VWAP
5. Dynamic EMA 20/200/36
6. Dynamic RSi Swing
Which is better: 36 EMA or 36 SMA for Support/Resistance?
✔ 36 EMA (Exponential Moving Average)
Better for intraday, short-term trading, scalping, and momentum trading.
Why?
Reacts faster to price.
Captures trend shifts early.
Works great when market is trending or volatile.
Most traders use EMA for dynamic support/resistance → works better because of crowd behavior.
Ideal for:
NIFTY, BANKNIFTY, FINNIFTY intraday | Options entries | Trend continuation trades.
Why 20 EMA is Important
The 20 EMA is one of the most widely used moving averages for intraday, swing, and positional trading because it captures short-term trend strength and momentum.
📌 20 EMA Works Best For
✔ Intraday trend identification
✔ Momentum continuation entries
✔ Dynamic support/resistance
✔ Quick reversal detection
✔ Options trading (NIFTY/BNF)
✔ Breakout & pullback trades
EMA 200 – Why It’s Extremely Important
The 200 EMA represents the long-term trend and is respected by:
Institutions
Algo systems
Big traders
Swing traders
Index traders
It acts like a major wall of support or resistance.
💡 What EMA 200 Tells You
✔ Long-term trend direction
Price above 200 EMA → Long-term uptrend
Price below 200 EMA → Long-term downtrend
✔ Strong trend reversal signals
When price crosses the 200 EMA on 15m/1h/1D charts → a deeper trend change is possible.
✔ Institutional support/resistance
Very powerful bounce/rejection zones
Many markets reverse exactly at 200 EMA
What is Previous Day High (PDH)?
The highest price the market reached in the previous trading session.
Why PDH is Important?
Acts as strong resistance
Breakout level for uptrend
Sellers often defend this zone
If broken with volume → strong bullish momentum
🔴 What is Previous Day Low (PDL)?
The lowest price the market reached in the previous trading session.
Why PDL is Important?
Acts as strong support
Breakdown level for downtrend
Buyers defend this level
If broken with volume → strong bearish trend
📌 How PDH/PDL Help in Intraday Trading
1️⃣ Range Breakout Trades
If price breaks PDH → bullish breakout (Buy CE)
If price breaks PDL → bearish breakdown (Buy PE)
What is Camarilla R3?
R3 = Resistance Level 3 in the Camarilla Pivot system.
Why R3 is important?
Acts as a major intraday resistance
Price often reverses from R3
If broken with force → strong uptrend starts
Many traders use R3 as a decision zone
Typical Market Behavior at R3
Rejection from R3 → Sell/PE opportunity
Break + Retest above R3 → CE opportunity
🔴 What is Camarilla S3?
S3 = Support Level 3 in the Camarilla Pivot system.
Why S3 is important?
Acts as a major intraday support
Buyers defend this zone
Breakdown of S3 → strong fall
S3 is often a bounce zone in the morning
Typical Market Behavior at S3
Bounce from S3 → Buy/CE opportunity
Break + Retest below S3 → PE opportunity
📌 Trader Logic: R3 & S3 Zones
⭐ 1. Range Reversal Strategy (Most Popular)
At R3 → Sell/PE
At S3 → Buy/CE
What is VWAP?
VWAP = Volume Weighted Average Price
It shows the average price at which most trading has happened during the day, based on both price and volume.
It resets every day at market open.
🔥 Why VWAP Is So Powerful?
VWAP is used by:
Institutions
Algo traders
Scalpers
Intraday traders
A program written by a beginner# TXF Choppy Market Detector (Whipsaw Filter)
## Introduction
This project is a technical indicator developed in **Pine Script v5**, specifically optimized for **Taiwan Index Futures (TXF)** intraday trading.
The TXF market is known for its frequent periods of low-volatility consolidation following sharp moves, often resulting in "whipsaws" (double-loss scenarios for trend followers). This script utilizes **volatility analysis** and **trend efficiency metrics** to filter out noise and detect potential "Stop Hunting" or "Liquidity Sweep" setups within range-bound markets.
## Methodology & Algorithms
The strategy operates on the principle of **Mean Reversion**, combining two core components:
### 1. Market Regime Filter: Choppiness Index (CHOP)
We use the Choppiness Index (originally developed by E.W. Dreiss) to determine if the market is trending or consolidating based on **Fractal Dimension** theory.
* **Logic**:
The index ranges from 0 to 100. Higher values indicate low trend efficiency (consolidation), while lower values indicate strong directional trends.
* **Condition**: `CHOP > Threshold` (Default: 50).
* **Application**: When this condition is met, the background turns **gray**, signaling a "No-Trade Zone" for trend strategies and activating the Mean Reversion logic.
### 2. Whipsaw Detection: Bollinger Bands
Bollinger Bands are used to define the dynamic statistical extremities of price action.
* **Logic**:
We identify **Fakeouts** (False Breakouts) that occur specifically during the choppy regime identified above. This is often where institutional traders hunt for liquidity (stops) before reversing the price.
#### Signal Algorithms (Pseudocode)
**A. Bull Trap (Washout High)**
A false upside breakout designed to trap long traders.
```pine
Condition:
1. Is_Choppy == true (Market is sideways)
2. High > Upper_Bollinger_Band (Price pierces the upper band)
3. Close < Upper_Bollinger_Band (Price fails to hold and closes back inside)
BTC - ALSI: Altcoin Season Index (Dynamic Eras)Title: BTC - ALSI: Altcoin Season Index (Dynamic Eras)
Overview & Philosophy
The Altcoin Season Index (ALSI) is a quantitative tool designed to answer the most critical question in crypto capital rotation: "Is it time to hold Bitcoin, or is it time to take risks on Altcoins?"
Most "Altseason" indicators suffer from Survivor Bias or Obsolescence. They either track a static list of coins that includes "dead" assets from previous cycles (ghosts of 2017), or they break completely when major tokens collapse (like LUNA or FTT).
This indicator solves this by using a Time-Varying Basket. The indicator automatically adjusts its reference list of Top 20 coins based on historical eras. This ensures the index tracks the winners of the moment—capturing the DeFi summer of 2020, the NFT craze of 2021, and the AI/Meme narratives of 2024/2025.
Methodology
The indicator calculates the percentage of the Top 20 Altcoins that are outperforming Bitcoin over a rolling window (Default: 90 Days).
The "Win" Count: For every major Altcoin performing better than BTC, the index adds a point.
Dynamic Eras: The basket of coins changes depending on the date:
2020 Era (DeFi Summer): Tracks the "Blue Chips" of the DeFi revolution like UNI, LINK, DOT, and early movers like VET and FIL.
2021 Era (Layer 1 Wars): Tracks the explosion of alternative smart contract platforms, adding winners like SOL, AVAX, MATIC, and ALGO.
2022 Era (The Survivors): Filters for resilience during the Bear Market, solidifying the status of established assets like SHIB and ATOM.
2023 Era (Infrastructure & Scale): Captures the rise of "Next-Gen" tech leading into the pre-halving year, introducing TON, APT (Aptos), and ARB (Arbitrum).
2024/25 Era (AI & Speed): Tracks the current Super-Cycle leaders, focusing on the AI narrative (TAO, RNDR), High-Performance L1s (SUI), and modern Memes (PEPE).
Chart Analysis & Strategy ( The "Alpha" )
As seen in the chart above, there is a strong correlation between ALSI Peaks and local tops in TOTAL3 (The Crypto Market Cap excluding BTC & ETH).
The Entry (Rotation): When the indicator rises above the neutral 50 line, it signals that capital is beginning to rotate out of Bitcoin and into Altcoins. This has historically been a strong confirmation signal to increase exposure to high-beta assets.
The Exit (Saturation): When the indicator hits 100 (or sustains in the Red Zone > 75), it means every single Altcoin is beating Bitcoin. Historically, this extreme exuberance often marks a local top in the TOTAL3 chart. This is the zone where smart money typically sells into strength, rather than opening new positions.
How to Read the Visuals
🚀 Altcoin Season (Red Zone > 75): Strong Altcoin dominance. The market is "Risk On."
🛡️ Bitcoin Season (Blue Zone < 25): Bitcoin dominance. Alts are bleeding against BTC. Historically, this is a defensive zone to hold BTC or Stablecoins.
Data Dashboard: A status table in the bottom-right corner displays the live Index Value, current Regime, and a System Check to ensure all 20 data feeds are active.
Settings
Lookback Period: Default 90 Days. Lowering this (e.g., to 30) makes the index faster but noisier.
Thresholds: Adjustable zones for Altcoin Season (Default: 75) and Bitcoin Season (Default: 25).
Credits & Attribution
This open-source indicator is built on the shoulders of giants. I acknowledge the original creators of the concept and the pioneers of its implementation on TradingView:
Original Concept: BlockchainCenter.net. - They established the industry standard definition: 75% of the Top 50 coins outperforming Bitcoin over 90 days = Altseason..
TradingView Implementation: Adam_Nguyen - He implemented the "Dynamic Era" logic (updating the coin list annually) on TradingView. Our code structure for the time-based switching is inspired by his methodology. See also his implementation in the chart. ( Altcoin Season Index - Adam) .
Comparison: Why use ALSI | RM?
While inspired by the above, ALSI introduces three key improvements:
Open Source: Unlike other popular TradingView versions (which are closed-source), this script is fully transparent. You can see exactly which coins are triggering the signal.
Sanitized History (Anti-Fragile): Historical Top 20 snapshots are not blindly used. "Dead" coins (like LUNA and FTT) from previous eras are manually filtered out. A raw index would crash during the Terra/FTX collapses, giving a false "Bitcoin Season" signal purely due to bad actors. The curated list preserves the integrity of the market structure signal.
Narrative Relevance: The 2024/25 basket was updated to include TAO (Bittensor) and RNDR, ensuring the index captures the dominant AI narrative, rather than tracking fading assets from the previous cycle.
You can compare the ALSI indicator with other available tradingview indicators in the chart: Different indicators for the same idea are shown in the 3 Pane window below the BTC and Total3 chart, whereas ALSI is the top pane indicator.
Important Note on Coin Selection Baskets are highly curated: Dead/irrelevant coins (FTT, LUNA, BSV) are excluded for clean signals. This prevents historical breaks and ensures Era T5 captures current narratives (AI, Memes) via TAO/RNDR. See above. Users are free to adjust the source code to test their own baskets.
Disclaimer
This script is for research and educational purposes only. Past correlations between ALSI and TOTAL3 do not guarantee future results. Market regimes can change, and "Altseasons" can be cut short by macro events.
Tags
bitcoin, btc, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths
