Chan Theory - CHANLUN | CZSCChan Theory (CHANLUN) is a technical analysis theory created by Chinese analyst CZSC, primarily applied in the analysis and decision-making of financial markets such as stocks, futures, forex, and crypto.
It is a technical analysis method based on price and time, including candlestick patterns, fractal theory, box theory, trend theory, divergence theory, multiple time frame analysis, and more.
"Chan" means zen, indicating that the fluctuations in the market are rooted in human nature, such as greed, anger, ignorance, slowness, and suspicion.
"Chan" is also the pinyin of the Chinese character '缠', which means entanglement or entwining. as the fluctuations in the stock market were intertwined like a spiral.
Concepts
Fractal - fractal is formed by three candlesticks, with the middle one being the highest for a top fractal and the lowest for a bottom fractal. In Chan Theory, the first step is to traverse all candlesticks to find all valid fractals.
Stroke - stroke is usually composed of multiple fractals, with a top fractal and a bottom fractal at both ends, and the connection between them forms a stroke with clear high and low points. This is the smallest unit of composition in Chan Theory, similar to the zigzag algorithm.
Segment - segment is generated from strokes based on the feature sequence algorithm, and a segment contains at least three strokes. a segment is a higher level of period, indicating the trend of the market at a higher level，similar to period 5M to period 30M.
Box - box is the overlapping area of multiple segments, and a box contains at least three segments. A box represents a densely traded area and a temporary consensus price range，the bull-bear battle has not produced a clear outcome, it means that the market is in a state of uncertainty and that the direction of the trend is unclear.
Trend - In Chan Theory, two or more boxes in the same direction form a trend，If the box position are gradually rising, it is defined as an uptrend，conversely, it is a downtrend.
Differences with ZigZag
Both the Chan Theory Stroke and the ZigZag are formed by connecting the high and low points to create a line. But in Chan Theory, there are strict additional requirements:
There must be at least five candlesticks between the high and low points, Otherwise it does not form a Stroke.
The high and low fractal cannot share the same candlestick，Otherwise it does not form a Stroke.
There must be at least three candlesticks between the high and low fractal，these three candlesticks must move in the same direction.
There may be complex situations where there are multiple top or bottom patterns in a single Stroke, requiring special handling to determine the connection rules for the lines.
Chan Theory is a complex theory that includes not only Stroke, but also other theories such as Box、Recursion and Divergence.
Recursion
The processing flow of the Chan Theory is similar to a ternary algorithm, It organizes chaotic candlestick into an orderly system (Fractal -> Stroke -> Segment -> Box -> Trend)，levels gradually increase from small to large. We can let the levels develop continuously to obtain the appropriate level for analysis and trading, In Chan Theory, it is called "recursion". This method allows us to observe the structure of smaller levels to make trading decisions at the current level，and it allows us to combine multiple levels to determine specific trading points.
Divergence
Chan Theory uses MACD to infer the strength of the trend as momentum analysis. Chan Theory calculates the MACD area of the K-line to quantify the strength of a trend, and compares the areas of the front and back two sections of the same level box to determine whether the trend is exhausted，it is called "divergence". this is one of the important part to determine trading points.
缠论是一种技术分析理论，由中国分析师 "缠中说禅"所创立，主要应用于股票、期货、外汇、加密货币等金融市场的分析和决策。
市场哲学和禅
以股市为基础。缠者，价格重叠区间也，买卖双方阵地战之区域也；禅者，破解之道也。以阵地战为
中心，比较前后两段之力度大小，大者，留之，小者，去之。
以现实存在为基础。缠者，人性之纠结，贪嗔疾慢疑也；禅者，觉悟、超脱者也。以禅破缠，上善若
水，尤如空筒，随波而走，方入空门。
技术分析简解
以走势中枢为中间点的力度比较，尤如拔河，力大者，持有原仓位，力小者，反向操作。
把走势全部同级别分解，关注新的走势之形成，以前一走势段为中间点与再前一走势段比大小，大者，
留之，小者，去之。
进行多重赋格性的同级别分解操作，尤如行船、尤如开车，以不同档位适应不同情况
技术分析量化组件
形态学 - 笔、线段、走势中枢、走势类型
动力学 - 背驰、走势中枢、走势的能量结构
壹缠脚本是以缠论为核心理论，实现的技术分析指标系统
功能说明
基于缠论分析 实时笔段走势画线、自动中枢标识、多级别K线递归走势、实时标注缠论三类买卖点
支持配置多种笔、段、走势规则 满足交易者的笔段习惯和风格
支持TradingView警报机制 实时推送各级别买卖点通知到邮箱或Webhook
脚本图例说明
笔段走势 - 蓝线为当前级别K线构成的笔，紫色线为基于笔级别特征序列处理生成的段，紫线为基于当前级别段生成的走势
中枢级别 - 各级别画线、中枢、买卖点提示信息采用同一颜色。即笔级别中枢同为浅蓝色、段级别中枢为橙色。
MACD面积 - 笔段走势的末端数字为对应笔段的MACD面积, 蓝色为笔MACD面积，橙色为段MACD面积，紫色为走势MACD面积。
在腳本中搜尋"algo"
WaveTrend 3D█ OVERVIEW
WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm.
█ BACKGROUND
The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first ported to PineScript in 2014 by the user @LazyBear, and since then, it has ascended to become one of the Top 5 most popular scripts on TradingView.
The WT algorithm appears to have origins in a lesser-known proprietary algorithm called Trading Channel Index (TCI), created by AIQ Systems in 1986 as an integral part of their commercial software suite, TradingExpert Pro. The software’s reference manual states that “TCI identifies changes in price direction” and is “an adaptation of Donald R. Lambert’s Commodity Channel Index (CCI)”, which was introduced to the world six years earlier in 1980. Interestingly, a vestige of this early beginning can still be seen in the source code of LazyBear’s script, where the final EMA calculation is stored in an intermediate variable called “tci” in the code.
█ IMPLEMENTATION DETAILS
WaveTrend 3D is an alternative implementation of WaveTrend that directly addresses some of the known shortcomings of the indicator, including its unbounded extremes, susceptibility to whipsaw, and lack of insight into other timeframes.
In the canonical WT approach, an exponential moving average (EMA) for a given lookback window is used to assess the variability between price and two other EMAs relative to a second lookback window. Since the difference between the average price and its associated EMA is essentially unbounded, an arbitrary scaling factor of 0.015 is typically applied as a crude form of rescaling but still fails to capture 20-30% of values between the range of -100 to 100. Additionally, the trigger signal for the final EMA (i.e., TCI) crossover-based oscillator is a four-bar simple moving average (SMA), which further contributes to the net lag accumulated by the consecutive EMA calculations in the previous steps.
The core idea behind WT3D is to replace the EMA-based crossover system with modern Digital Signal Processing techniques. By assuming that price action adheres approximately to a Gaussian distribution, it is possible to sidestep the scaling nightmare associated with unbounded price differentials of the original WaveTrend method by focusing instead on the alteration of the underlying Probability Distribution Function (PDF) of the input series. Furthermore, using a signal processing filter such as a Butterworth Filter, we can eliminate the need for consecutive exponential moving averages along with the associated lag they bring.
Ideally, it is convenient to have the resulting probability distribution oscillate between the values of -1 and 1, with the zero line serving as a median. With this objective in mind, it is possible to borrow a common technique from the field of Machine Learning that uses a sigmoid-like activation function to transform our data set of interest. One such function is the hyperbolic tangent function (tanh), which is often used as an activation function in the hidden layers of neural networks due to its unique property of ensuring the values stay between -1 and 1. By taking the first-order derivative of our input series and normalizing it using the quadratic mean, the tanh function performs a high-quality redistribution of the input signal into the desired range of -1 to 1. Finally, using a dual-pole filter such as the Butterworth Filter popularized by John Ehlers, excessive market noise can be filtered out, leaving behind a crisp moving average with minimal lag.
Furthermore, WT3D expands upon the original functionality of WT by providing:
First-class support for multi-timeframe (MTF) analysis
Kernel-based regression for trend reversal confirmation
Various options for signal smoothing and transformation
A unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
█ SETTINGS
This is a summary of the settings used in the script listed in roughly the order in which they appear. By default, all default colors are from Google's TensorFlow framework and are considered to be colorblind safe.
Source: The input series. Usually, it is the close or average price, but it can be any series.
Use Mirror: Whether to display a mirror image of the source series; for visualizing the series as a 3D waveform similar to a soundwave.
Use EMA: Whether to use an exponential moving average of the input series.
EMA Length: The length of the exponential moving average.
Use COG: Whether to use the center of gravity of the input series.
COG Length: The length of the center of gravity.
Speed to Emphasize: The target speed to emphasize.
Width: The width of the emphasized line.
Display Kernel Moving Average: Whether to display the kernel moving average of the signal. Like PCA, an unsupervised Machine Learning technique whereby neighboring vectors are projected onto the Principal Component.
Display Kernel Signal: Whether to display the kernel estimator for the emphasized line. Like the Kernel MA, it can show underlying shifts in bias within a more significant trend by the colors reflected on the ribbon itself.
Show Oscillator Lines: Whether to show the oscillator lines.
Offset: The offset of the emphasized oscillator plots.
Fast Length: The length scale factor for the fast oscillator.
Fast Smoothing: The smoothing scale factor for the fast oscillator.
Normal Length: The length scale factor for the normal oscillator.
Normal Smoothing: The smoothing scale factor for the normal frequency.
Slow Length: The length scale factor for the slow oscillator.
Slow Smoothing: The smoothing scale factor for the slow frequency.
Divergence Threshold: The number of bars for the divergence to be considered significant.
Trigger Wave Percent Size: How big the current wave should be relative to the previous wave.
Background Area Transparency Factor: Transparency factor for the background area.
Foreground Area Transparency Factor: Transparency factor for the foreground area.
Background Line Transparency Factor: Transparency factor for the background line.
Foreground Line Transparency Factor: Transparency factor for the foreground line.
Custom Transparency: Transparency of the custom colors.
Total Gradient Steps: The maximum amount of steps supported for a gradient calculation is 256.
Fast Bullish Color: The color of the fast bullish line.
Normal Bullish Color: The color of the normal bullish line.
Slow Bullish Color: The color of the slow bullish line.
Fast Bearish Color: The color of the fast bearish line.
Normal Bearish Color: The color of the normal bearish line.
Slow Bearish Color: The color of the slow bearish line.
Bullish Divergence Signals: The color of the bullish divergence signals.
Bearish Divergence Signals: The color of the bearish divergence signals.
█ ACKNOWLEDGEMENTS
@LazyBear - For authoring the original WaveTrend port on TradingView
@PineCoders - For the beautiful color gradient framework used in this indicator
@veryfid - For the inspiration of using mirrored signals for cycle analysis and using multiple lookback windows as proxies for other timeframes
Improved Chaikin Money FlowChaikin Money Flow is a well-known Indicator for gauging buying/selling pressure. Marc Chaikin intended this to be used on the daily timeframe to capture the behavior of price action at or near the daily close when larger-scale actors influence the market. The calculation is straight forward as described within the built-in TradingView "CMF" indicator:
1. Period Money Flow Multiplier = ((Close - Low) - (High - Close)) /(High - Low)
2. Period Money Flow Volume = Period Money Flow Multiplier x Volume for the Period
3. Chaikin Money Flow = 21 Period Sum of Money Flow Volume / 21 Period Sum of Volume
There is, however, a problem with this algorithm: it does not account for daily gaps in price action. This leads to the indicator sometimes moving out-of-sync with price action and/or an under-emphasis of the magnitude change of the indicator relative to the change in price action. This is a significant problem for someone trying to read divergences against an underlying.
Note: I have never seen a published attempt to improve this indicator which is why I decided that there had to be a way to do it.
In order to mitigate this issue, I have taken the basic script provided by TradingView and made a key modification. If the open of a candle is outside the range of the previous candle, then the close of the previous candle is used as the "high" for the current candle (in the case of a gap down) or the "low" for the current candle (in the case of a gap up). However, if the close of the current candle exceeds the previous close, highs and lows for the current candle are calculated as normal. I believe this accounts for gaps in price action without significantly altering the original intent of the indicator.
I have made four other minor tweaks:
1. Default style is color coded area above and below the Zero Line
2. Range scaled to +/-100 instead of +/-1 (displays better on graph)
3. Set timeframe to Daily (as that is the timeframe for which this indicator was intended by Chaikin)
4. Length defaults to 21 (which is what Chaikin uses)
Extreme Trend Reversal Points [HeWhoMustNotBeNamed]Using moving average crossover for identifying the change in trend is very common. However, this method can give lots of false signals during the ranging markets. In this algorithm, we try to find the extreme trend by looking at fully aligned multi-level moving averages and only look at moving average crossover when market is in the extreme trend - either bullish or bearish. These points can mean long term downtrend or can also cause a small pullback before trend continuation. In this discussion, we will also check how to handle different scenarios.
🎲 Components
🎯 Recursive Multi Level Moving Averages
Multi level moving average here refers to applying moving average on top of base moving average on multiple levels. For example,
Level 1 SMA = SMA(source, length)
Level 2 SMA = SMA(Level 1 SMA, length)
Level 3 SMA = SMA(Level 2 SMA, length)
..
..
..
Level n SMA = SMA(Level (n-1) SMA, length)
In this script, user can select how many levels of moving averages need to be calculated. This is achieved through " recursive moving average " algorithm. Requirement for building such algorithm was initially raised by @loxx
While I was able to develop them in minimal code with the help of some of the existing libraries built on arrays and matrix , I also thought why not extend this to find something interesting.
Note that since we are using variable levels - we will not be able to plot all the levels of moving average. (This is because plotting cannot be done in the loop). Hence, we are using lines to display the latest moving average levels in front of the last candle. Lines are color coded in such a way that least numbered levels are greener and higher levels are redder.
🎯 Finding the trend and range
Strength of fully aligned moving average is calculated based on position of each level with respect to other levels.
For example, in a complete uptrend, we can find
source > L(1)MA > L(2)MA > L(3)MA ...... > L(n-1)MA > L(n)MA
Similarly in a complete downtrend, we can find
source < L(1)MA < L(2)MA < L(3)MA ...... < L(n-1)MA < L(n)MA
Hence, the strength of trend here is calculated based on relative positions of each levels. Due to this, value of strength can range from 0 to Level*(Level-1)/2
0 represents the complete downtrend
Level*(Level-1)/2 represents the complete uptrend.
Range and Extreme Range are calculated based on the percentile from median. The brackets are defined as per input parameters - Range Percentile and Extreme Range Percentile by using Percentile History as reference length.
Moving average plot is color coded to display the trend strength.
Green - Extreme Bullish
Lime - Bullish
Silver - range
Orange - Bearish
Red - Extreme Bearish
🎯 Finding the trend reversal
Possible trend reversals are when price crosses the moving average while in complete trend with all the moving averages fully aligned. Triangle marks are placed in such locations which can help observe the probable trend reversal points. But, there are possibilities of trend overriding these levels. An example of such thing, we can see here:
In order to overcome this problem, we can employ few techniques.
1. After the signal, wait for trend reversal (moving average plot color to turn silver) before placing your order.
2. Place stop orders on immediate pivot levels or support resistance points instead of opening market order. This way, we can also place an order in the direction of trend. Whichever side the price breaks out, will be the direction to trade.
3. Look for other confirmations such as extremely bullish and bearish candles before placing the orders.
🎯 An example of using stop orders
Let us take this scenario where there is a signal on possible reversal from complete uptrend.
Create a box joining high and low pivots at reasonable distance. You can also chose to add 1 ATR additional distance from pivots.
Use the top of the box as stop-entry for long and bottom as stop-entry for short. The other ends of the box can become stop-losses for each side.
After few bars, we can see that few more signals are plotted but, the price is still within the box. There are some candles which touched the top of the box. But, the candlestick patterns did not represent bullishness on those instances. If you have placed stop orders, these orders would have already filled in. In that case, just wait for position to hit either stop or target.
For bullish side, targets can be placed at certain risk reward levels. In this case, we just use 1:1 for bullish (trend side) and 1:1.5 for bearish side (reversal side)
In this case, price hit the target without any issue:
Wait for next reversal signal to appear before placing another order :)
American Approximation Bjerksund & Stensland 2002 [Loxx]American Approximation Bjerksund & Stensland 2002 is an American Options pricing model. This indicator also includes numerical greeks. You can compare the output of the American Approximation to the Black-Scholes-Merton value on the output of the options panel.
The Bjerksund & Stensland (2002) Approximation
The Bjerksund and Stensland (2002) approximation divides the time to maturity into two parts, each with a separate flat exercise boundary. It is thus a straightforward generalization of the Bjerksund-Stensland 1993 algorithm. The method is fast and efficient and should be more accurate than the Barone-Adesi and Whaley (1987) and the Bjerksund and Stensland (1993b) approximations. The algorithm requires an accurate cumulative bivariate normal approximation. Several approximations that are described in the literature are not sufficiently accurate, but the Genze algorithm works.
C = alpha2*S^B - alpha2*phi(S, t1, B, I2, I2)
+ phi(S, t1, I2, I2) - phi(S, t1, I, I1, I2)
- X*phi(S, t1, 0, I2, I2) + X*phi(S, t1, 0, I1, I2)
+ alpha1*phi(X, t1, B, I1, I2) - alpha1*psi*St, T, B, I1, I2, I1, t1)
+ psi(S, T, 1, I1, I2, I1, t1) - psi(S, T, 1, X, I2, I1, t1)
- X*psi(S, T, 0, I1, I2, I1, t1) + psi(S, T, 0 ,X, I2, I1, t1)
where
alpha1 = (I1 - X)*I1^-B
alpha2 = (I2 - X)*I2^-B
B = (1/2 - b/v^2) + ((b/v^2 - 1/2)^2 + 2*(r/v^2))^0.5
The function psi(S, T, y, H, I) is given by
psi(S, T, gamma, H, I) = e^lambda * S^gamma * (N(-d) - (I/S)^k * N(-d2))
d = (log(S/H) + (b + (gamma - 1/2) * v^2) * T) / (v * T^0.5)
d2 = (log(I^2/(S*H)) + (b + (gamma - 1/2) * v^2) * T) / (v * T^0.5)
lambda = -r + gamma * b + 1/2 * gamma * (gamma - 1) * v^2
k = 2*b/v^2 + (2 * gamma - 1)
and the trigger price I is defined as
I1 = B0 + (B(+infi) - B0) * (1 - e^h1)
I2 = B0 + (B(+infi) - B0) * (1 - e^h2)
h1 = -(b*t1 + 2*v*t1^0.5) * (X^2 / ((B(+infi) - B0))*B0)
h2 = -(b*T + 2*v*T^0.5) * (X^2 / ((B(+infi) - B0))*B0)
t1 = 1/2 * (5^0.5 - 1) * T
B(+infi) = (B / (B - 1)) * X
B0 = max(X, (r / (r - b)) * X)
Moreover, the function psi(S, T, gamma, H, I2, I1, t1) is given by
psi(S, T, gamma, H, I2, I1, t1, r, b, v) = e^(lambda * T) * S^gamma * (M(-e1, -f1, rho) - (I2/S)^k * M(-e2, -f2, rho)
- (I1/S)^k * M(-e3, -f3, -rho) + (I1/I2)^k * M(-e4, -f4, -rho))
where (see screenshot for e and f values)
b=r options on non-dividend paying stock
b=r-q options on stock or index paying a dividend yield of q
b=0 options on futures
b=r-rf currency options (where rf is the rate in the second currency)
Inputs
S = Stock price.
K = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
c = Cost of Carry
V = Variance of the underlying asset price
cnd1(x) = Cumulative Normal Distribution
cbnd3(x) = Cumulative Bivariate Normal Distribution
nd(x) = Standard Normal Density Function
convertingToCCRate(r, cmp ) = Rate compounder
Numerical Greeks or Greeks by Finite Difference
Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs)
Things to know
Only works on the daily timeframe and for the current source price.
You can adjust the text size to fit the screen
Nadaraya-Watson CombineThis is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue.
In addition, I've added a plot of the actual values of the current barstate of
the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at
each time update. It is interesting to compare the trajectory of the end points of the Estimator and
Envelope to the smoothing function at each time update. Due to the kernel smoothing at each update the
history is lost at each update (repaint).
I've added a feature to allow adjustment to the kernel smoothing algorithm as suggested by thomsonraja (line 59).
The settings and usage are repeated from Lux Algo below.
Settings
Window Size: Determines the number of recent price observations to be used to fit the Nadaraya-Watson Estimator.
Bandwidth: Controls the degree of smoothness of the envelopes , with higher values returning smoother results.
Mult: Controls the envelope width.
Src: Input source of the indicator.
Kernel power: See line 59, adjusts the exponential power (powh) as suggested by thomsonraja
Kernel denominator: See line 59, adjusts the denominator (den) as suggested by thomsonraja
Usage
This tool outlines extremes made by the prices within the selected window size.
This is achieved by estimating the underlying trend in the price using kernel smoothing,
calculating the mean absolute deviations from it, and adding/subtracting it
from the estimated underlying trend.
I repeat Lux Algo's caution: 'we do not recommend this tool to be used alone
or solely for real time applications.'
End-Pointed SSA of Normalized Price Corridor [Loxx]End-Pointed SSA of Normalized Price Corridor is an end-pointed SSA of normalized input price to output a smoothed normalized oscillator of price. Corridors are added in attempt to decipher larger trend direction of price. These corridor trend lines are based on highs and lows of price. Due to the SSA algorithm, this indicator takes some time load on the chat, so be patient. You can adjust the lag parameter downward to speed up the indicator load time but this will also degrade the signal. There are many different ways to use this indicator. It is also Renko chart friendly.
An example of emerging trends (these do not repaint)
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
End-pointed SSA of Williams %R [Loxx]End-pointed SSA of Williams %R is an indicator that runes Williams %R SSA calculation through a Singular Spectrum Analysis (SSA) algorithm to derive a smoother final output. The reduction in noise from the traditional Williams %R is significant.
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.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included:
Bar coloring
[*Alerts
[*Signals
[*Loxx's Expanded Source Types
Related Williams %R Indicators
Williams %R on Chart w/ Dynamic Zones
Williams %R w/ Bollinger Bands
Intermediate Williams %R w/ Discontinued Signal Lines
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Normalized Price Oscillator
SSA of Price [Loxx]SSA of Price ris an indicator that runs an SSA calculation on price to derive final output. This indicator also serves to introduce the concept of SSA to the Pine Coder community. The data returned from this algorithm is an array of modeled values on past X bars. Unlike the end-pointed SSA posted previously, this version pulls the modeled data from the output array and draws a line backward from the current bar. This indicator recalculates so past observations aren't very useful, but the current observation is since the current bar is index 0 of the output array which means it's the endpointed value.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
End-pointed SSA of Normalized Price Oscillator [Loxx]End-pointed SSA of Normalized Price Oscillator is an indicator that converts source price into a normalized oscillator and runs an SSA calculation to derived a smoother final output. This indicator also serves to introduce the concept of SSA to the Pine Coder community. The data returned from this algorithm is an array of modeled values on past X bars. We could use this data but it's not useful, so instead we use the end-pointed value which is the first value of the array at index 0.
What is Singular Spectrum Analysis (SSA)?
Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition (SVD) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA. This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA. The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA, one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA, the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Itakura-Saito Autoregressive Extrapolation of Price [Loxx]Itakura-Saito Autoregressive Extrapolation of Price is an indicator that uses an autoregressive analysis to predict future prices. This is a linear technique that was originally derived or speech analysis algorithms.
What is Itakura-Saito Autoregressive Analysis?
The technique of linear prediction has been available for speech analysis since the late 1960s (Itakura & Saito, 1973a, 1970; Atal & Hanauer, 1971), although the basic principles were established long before this by Wiener (1947). Linear predictive coding, which is also known as autoregressive analysis, is a time-series algorithm that has applications in many fields other than speech analysis (see, e.g., Chatfield, 1989).
Itakura and Saito developed a formulation for linear prediction analysis using a lattice form for the inverse filter. The Itakura–Saito distance (or Itakura–Saito divergence) is a measure of the difference between an original spectrum and an approximation of that spectrum. Although it is not a perceptual measure it is intended to reflect perceptual (dis)similarity. It was proposed by Fumitada Itakura and Shuzo Saito in the 1960s while they were with NTT. The distance is defined as: The Itakura–Saito distance is a Bregman divergence, but is not a true metric since it is not symmetric and it does not fulfil triangle inequality.
read more: Selected Methods for Improving Synthesis Speech Quality Using Linear Predictive Coding: System Description, Coefficient Smoothing and Streak
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Related Indicators (linear extrapolation of price)
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
APA Adaptive Fisher Transform [Loxx]APA Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Ehlers Autocorrelation Periodogram Algorithm to calculate the dominant cycle period.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
Included:
Zero-line and signal cross options for bar coloring
Customizable overbought/oversold thresh-holds
Alerts
Signals
Jurik Filtered, Composite Fractal Behavior (CFB) Channels [Loxx]Double Jurik-Filtered Composite Fractal Behavior (CFB) Channels is a channel indicator that acts as both a baseline, similar to Donchian, and as support and resistance levels. This indicator is price time adaptive meaning it flexes to price volatility waves. The indicators adaptive nature is calculated using the Composite Fractal Behavior (CFB) algorithm. The result of this adaptive calculation is then smoothed using Jurik Filtering, and then it's normalized to conform to a range of values. This helps better identify trends.
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]Adaptive, Double Jurik Filter Moving Average (AJFMA) is moving average like Jurik Moving Average but with the addition of double smoothing and adaptive length (Autocorrelation Periodogram Algorithm) and power/volatility {Juirk Volty) inputs to further reduce noise and identify trends.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
- Double calculation of AJFMA for even smoother results
Ehlers Adaptive Relative Strength Index (RSI) [Loxx]Ehlers Adaptive Relative Strength Index (RSI) is an implementation of RSI using Ehlers Autocorrelation Periodogram Algorithm to derive the length input for RSI. Other implementations of Ehers Adaptive RSI rely on the inferior Hilbert Transformer derive the dominant cycle.
In his book "Cycle Analytics for Traders Advanced Technical Trading Concepts", John F. Ehlers describes an implementation for Adaptive Relative Strength Index in order to solve for varying length inputs into the classic RSI equation.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the autocorrelation periodogram algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Adaptive RSI?
From his Ehlers' book mentioned above, page 137:
"The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. Since the objective is to use only those frequency components passed by the roofing filter, the variable "filt" is used as a data input rather than closing prices. Rather than independently taking the averages of the numerator and denominator, I chose to perform smoothing on the ratio using the SuperSmoother filter. The coefficients for the SuperSmoother filters have previously been computed in the dominant cycle measurement part of the code."
Happy trading!
Special Long Short ConditionsIt generates buy and sell signals using a special algorithm when various moving averages approach and diverge from the price. It aims to stay in the trend as long as possible. Please control the risks of your investments well. It is not investment advice.
It calculates using fast, slow period and distance and near distance units. You can adapt it by changing the parameters, but too much optimization is not recommended.
Cesitli hareketli ortalamalarin fiyata yakinlasmasi ve uzaklasmasi durumunda ozel bir algoritma kullanarak al ve sat sinyalleri uretir. Trendde mumkun olabildigince kalabilmeyi amaclar. Lutfen yatirimlarizda risklerini iyi kontrol edin. Yatirim tavsiyesi degildir.
Hizli,yavas periyot ve uzak, yakin uzaklik birimlerini kullanarak hesaplama yapar. Parametreleri degistirerek kendinize uyarlayabilirsiniz fakat cok fazla optimizasyon tavsiye edilemez.
Auto Phivots S/R [DM]Greetings colleagues
Today I share the classic pivot points indicator
Added options:
Standard levels
Fibonacci levels "up to 261'8"
Logarithmic scale option
//Pivot Points Standard
//Pivot Points Standard — is a technical indicator that is used to determine the levels
//at which price may face support or resistance. The Pivot Points indicator consists of
//a pivot point (PP) level and several support (S) and resistance (R) levels.
//
//Calculation
//PP, resistance and support values are calculated in different ways, depending on
//the type of the indicator, specified by the Type field in indicator inputs. To
//calculate PP and support/resistance levels, the values OPENcurr, OPENprev, HIGHprev,
//LOWprev, CLOSEprev are used, which are the values of the current open and previous
//open, high, low and close, respectively, on the indicator resolution. The indicator
//resolution is set by the input of the Pivots Timeframe. If the Pivots Timeframe is set
//to AUTO (the default value), then the increased resolution is determined by the
//following algorithm:
//
//for intraday resolutions up to and including 15 min, DAY (1D) is used
//for intraday resolutions more than 15 min, WEEK (1W) is used
//for daily resolutions MONTH is used (1M)
//for weekly and monthly resolutions, 12-MONTH (12M) is used
iBagging Multi-IndicatorHello traders!
You know, machine learning is a very popular theme nowadays. The best tricks and methods were borrowed from Math and Computer Science to improve and create ML algorithms. As you know, one of our analysts is a great fan of ML, thus he decided to borrow on very powerful method from ML.
We have taken 5 indicators, tuned them a bit and make them to vote. If the number of voters is more than the threshold the the bullish/bearish signal shows. It's called Bagging, when some algorithms are voting to classify or to regress. We use EMA Cross with NATR filter, BB Width, divergency detector and bull bear power. This bundle in my opinion is one of the best to define entries. Check it up in you daily trading staff. You shouldn't forget about tuning the parameters on different coins and timeframes. We checked it on 1H BTCUSDT and default parameters are for this combination. I hope you'll enjoy my masterpiece.
How to use it?
Just add it to the chart and check up signals.
SQZMOM + ADX
ENGLISH:
SQUEEZE MOMENTUM INDICATOR
The Squeeze Momentum Indicator is a momentum oscillator that indicates the explosiveness with which the price is going to move. Its first known version was called "TTM Squeeze" by John Carter explained in his book "Mastering the Trade" (chapter 11). and popularized on TradingView by a developer named LazyBear.
The black crosses in the middle line show that the market has just entered a consolidation. This means low volatility, the market is preparing for an explosive move (up or down). The gray crosses signify the "Squeeze". Carter suggests waiting until the first gray after a black cross and taking a position in the direction of the oscillator. For its part, LazyBear recommends using an additional indicator such as ADX to enhance the effectiveness of the points of entry and closing of positions.
IMPROVEMENTS IN UNDERSTANDING THE OSCILLATOR:
During the indicator creation process we were able to understand the oscillator logic in greater depth, and based on this knowledge we implemented improvements.
IMPROVEMENTS IN THE DEVELOPMENT OF THE OSCILLATOR:
SIDE PANEL:
• The ADX algorithm was incorporated, which is displayed numerically in the right panel of the indicator, it shows the value of the ADX and its directionality.
• An arrow type pointer was added to indicate the Directionality of the Oscillator.
• Two Exponential Moving Averages of 11 and 55 periods were added to the right panel, this will mark if the trend is bullish or bearish depending on the crossing of the EMAs.
• An indicator of the Squeeze of the indicator was also included, which marks the periods of consolidation of the price (OFF) and the periods where the price should react explosively.
• Added a function that allows the automatic color change of the panels based on the color of the oscillator and the ADX.
o ADX: Dark Green (Bullish Force).
o ADX: Light Green (Loss of bullish strength).
o ADX: Dark Red (Bearish Force).
o ADX: Light Red (Loss of bearish strength).
o ADX: Orange (Lost strength, Disinterest and low volume).
SIGNALS:
A very famous strategy that we have learned is that of the trading expert Jaime Merino, who by combining the Squeeze Momentum Indicator and a common ADX, managed to efficiently link the weakness of the ADX with the beginning of a bullish or bearish momentum. The parameterization of its strategy was signaled in buy and sell alerts, which are represented as follows:
B (Buy): It is activated when a bearish movement marked by the ADX (Negative Slope) ends and the oscillator takes bullish directionality (Bullish impulse).
S (Sell): It is activated when an upward movement marked by the ADX (Negative Slope) ends and the oscillator takes bearish directionality. (Bearish momentum).
FILTER:
In order to prevent any trader from trading against the trend, a filter was added that limits the alerts for bearish entries when the trend is up and vice versa, that is, when the EMA 10 is above the EMA55, it is understood that the trend is bullish in that time frame, therefore the bearish entry alerts will not be activated. It will be the decision of each trader whether to activate or deactivate this function.
ALERTS:
This is undoubtedly the most anticipated function by all Latin American traders, (Just kidding), but being aware, I am very proud of the implementation of alerts for each improvement made to this indicator, if you decide to use the Squeeze Momentum Indicator you can automate alerts for the following actions:
• Buy and Sell alerts.
• Alerts for activating the Squeeze to (ON).
• Oscillator quadrant change alerts
o Bullish momentum.
o Bearish momentum.
o Bullish force.
o Bearish force.
RECOMMENDATIONS:
One of the things that became clearer to us in the development of this indicator is the coloring of the quadrants, which is why we recommend the use of four colors, one for each oscillator grid.
ESPAÑOL:
El Squeeze Momentum Indicator es oscilador de momentum que nos indica la explosividad con que el precio se va a mover. Su primera versión conocida se llamo “TTM Squeeze” de John Carter explica en su libro "Mastering the Trade" (capítulo 11). y popularizada en TradingView por un desarrollador llamado LazyBear.
Las cruces negras en la línea media muestran que el mercado acaba de entrar en una consolidación. Esto significa baja volatilidad, el mercado se prepara para un movimiento explosivo (hacia arriba o hacia abajo). Las cruces grises significan el "Squeeze“. Carter sugiere esperar hasta el primer gris después de una cruz negra y tomar una posición en la dirección del oscilador. Por su parte LazyBear recomienda usar un indicador adicional como ADX para potenciar la efectividad de los puntos de entrada y cierre de las posiciones.
CUADRANTE DE IMPULSO: Los cuadrantes de impulso poseen el doble de potencia de los cuadrantes de fuerza, esto se debe a que su misión es cambiar la dirección del oscilador para generar las Ondas.
CUADRANTE DE FUERZA:
Los cuadrantes de fuerza poseen el menos de potencia que los cuadrantes de impulso, podríamos decir que este cuadrante representa la perdida en la fuerza del movimiento.
LOS COLORES:
Particularmente recomiendo usar la configuracion de colores que se presentan en la imagen anterior, ya que brinda mas coherencia en la realizacion del movimiento alcista y bajista.
IMPULSO ALCISTA: El oscilador se tiene pendiente POSITIVA y se encuentra por debajo del punto 0.
FUERZA ALCISTA: El oscilador se tiene pendiente POSITIVA y se encuentra por encima del punto 0.
IMPULSO BAJISTA: El oscilador se tiene pendiente NEGATIVA y se encuentra por encima del punto 0.
FUERZA BAJISTA: El oscilador se tiene pendiente NEGATIVA y se encuentra por debajo del punto 0.
MEJORAS EN LA COMPRENSIÓN DEL OSCILADOR:
Durante el proceso de creación del indicador pudimos comprender la logica del oscilador con mayor profundidad, y con base en este conocimiento implementamos mejoras
MEJORAS EN EL DESARROLLO DEL OSCILADOR:
PANEL LATERAL:
• Se incorporó el algoritmo del ADX el cual se visualiza en forma numérica en el panel derecho del indicador, el mismo muestra el valor del ADX y la direccionalidad de este.
• Se incorporó un señalador de tipo flecha que indica la Direccionalidad del Oscilador.
• Se añadió dos Medias móviles Exponenciales de 11 y 55 periodos al panel derecho, éste marcará si la tendencia es alcista o bajista en función a al cruce de las EMAs.
• También se incluyó un señalador del Squeeze del indicador, el cual marca los periodos de consolidación del precio (OFF) y los periodos donde el precio debería reaccionar de forma explosiva.
• Se añadió una función que permite el cambio de color automático de los paneles en función al color del oscilador y el ADX.
o ADX: Verde Oscuro (Fuerza alcista).
o ADX: Verde Claro (Perdida de fuerza alcista).
o ADX: Rojo Oscuro (Fuerza bajista).
o ADX: Rojo Claro (Perdida fuerza bajista).
o ADX: Naranja (Perdida fuerza, Desinterés y bajo volumen).
SEÑALES:
Una estrategia muy famosa que hemos aprendido es la del experto en trading Jaime Merino, quien combinando el Squeeze Momentum Indicator y un ADX comùn, logró relacionar de forma eficiente la debilidad del ADX con el comienzo de un impulso ALCISTA o BAJISTA. La parametrización de su estrategia fue señalizada en alertas de compra y venta, las cuales se representan de la siguiente manera:
B (Buy): Se activa cuando termina un movimiento bajista marcado por el ADX (Pendiente Negativa) y el oscilador toma Direccionalidad alcista (Impulso alcista).
S (Sell): Se activa cuando termina un movimiento alcista marcado por el ADX (Pendiente Negativa) y el oscilador toma Direccionalidad bajista. (Impulso bajista).
FILTRO:
Con la intención de evitar que ningún trader opere en contra de la tendencia, se añadió un filtro que limita las alertas entradas bajistas cuando la tendencia es alcista y viceversa, es decir, cuando la EMA 10 está por encima de la EMA55, se entiende que la tendencia es alcista en esa temporalidad, por lo cual las alertas de entradas bajistas no se activaran. Será decisión de cada trader si activa o desactiva esta función.
ALERTAS:
Esta sin duda es la función más esperada por todos los traders de Latinoamérica, (Es broma), pero siendo consientes, me siento muy orgulloso de la implementación de alertas para cada mejora realizada a este indicador, si decide usar el Squeeze Momentum Indicator podrá automatizar las alertas para las siguientes acciones:
• Alertas de Buy y Sell.
• Alertas para la activación del Squeeze a (ON).
• Alertas de cambios de cuadrantes del oscilador
o Impulso alcista.
o Impulso bajista.
o Fuerza alcista.
o Fuerza bajista.
RECOMENDACIONES:
Una de las cosas que nos quedó más claras en el desarrollo de este indicador, es la coloración de los cuadrantes, es por ellos que recomendamos el empleo de cuatro colores, uno para cada cuadricula del oscilador.
Reditum SniperReditum Sniper synchronizes multiple time frames POC's (Point of Control) to a single graph, allowing its user to find valuable area's which may act as key levels to take educated decisions. Furthermore it uses custom made formulas to find the lookback period of previously mentioned values.
Reditum Sniper vs Reditum Scanner
Reditum Sniper aproaches the markets in a similar way as the Reditum Scan, yet it counts with several updates and overall a rebrand. The Reditum Sniper includes bug fixes, weekly poc has been added, the ability to fix the max_bars_back error in any security, less memory use which is required due to errors presented in later versions of the TradingView platform, functions which simplify the code and much more!
Components of Reditum Scan:
POC's (30 min, 60 min, 120 min, 240 min, daily, weekly)
How does it work?
The algorithm allows you to identify areas of strong support and resistance , based on where most of the trading activity takes place.
What are the strategies for considering placing a trade?
1.Cluster (agglomeration of multiple POC's) to cluster (by identifying two clusters, the price could go smoothly from one Cluster to the other).
2. From cluster (agglomeration of multiple POCs, each with unique color. Now, from this cluster important movements may begin).
3. Towards a cluster (like the “from cluster” pattern, we identify areas of high interest for institutional companies, in this pattern the cluster acts like a magnet, if price is near it may be fully attracted towards the agglomeration).
If you are a member of the mastermind tribe please contact the support team within the tribe to access the indicator (do not leave personal or subscription data in the comments on this page), if not, please visit the link located in our signature.
Thanks for taking a look!
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Reditum Sniper sincroniza múltiples POC's (Puntos de Control) de distintos marcos de tiempo a un único grafico, permitiéndole al usuario encontrar áreas de alto valor que pueden actuar como niveles clave para tomar una decisión educada en su inversión. Además utiliza formulas propias para encontrar la cantidad de data utilizada para los valores previamente mencionados.
Reditum Sniper vs Reditum Scanner
Reditum Sniper enfrenta el mercado de una manera similar al Reditum Scan, aun así cuenta con una gran cantidad de cambios y sobre todo un cambio de marca. El Reditum Sniper incluye una gran cantidad de arreglos de bugs, el poc weekly ya esta incluido por default, la habilidad de solucionar el error max_bars_back que se presentaba en ciertos marcos de tiempo, menor uso de memoria el cual se requiere debido a que se presentaba este error en nuevas versiones de la plataforma, funciones que simplifican el código y mucho más!
Componentes de Reditum Scan:
POC´s (30 min, 60 min, 120 min, 240 min, diario, semanal)
¿Cómo funciona?
El algoritmo le permite identificar áreas de fuerte soporte y resistencia, según el lugar donde se lleva a cabo la mayor parte de la actividad comercial.
¿Cuáles son las estrategias para considerar una entrada?
1.Cluster (aglomeración de múltiples POC´s) a cluster (al identificar dos cluster el precio podría ir con fluidez desde uno al otro que lo recibe).
2.Desde cluster (lo identificamos con la aglomeración de múltiples POC identificados con colores que hacen fácil la lectura. Ahora, desde este cluster podrían iniciar movimientos importantes).
3.Hacia cluster (al igual que el patrón “desde cluster” identificamos zonas de alto interés para los institucionales, en este patrón el cluster actúa como un imán para el precio, de modo que cuando el precio está lejos de él, lo podría atraer con fuerza).
Para acceder a la herramienta, si usted es miembro de la tribu mastermind por favor comunicarse con el equipo de soporte dentro de la tribu (no dejar datos personales ni de suscripción en los comentarios de esta página), si no es miembro por favor visite el enlace a continuación en nuestra Firma.
Gracias por echarle un vistazo!.
Trend Follow SystemTrend following algorithm:
We take 1- 5 Fibonacci Ema values. 21, 34, 55, 89, 144
2- We normalize the changes of these values over time between 1-100.
3- We take the ema value of 1 length so that it does not follow a horizontal course after the normalization process.
4- In order not to experience too much change, we take the value of sma with a length of 5.
5-We think that when all values are 100, the trend is up, when all values are 0, the trend is down, otherwise the trend is horizontal.
SB Master Chart w/Smart TradeThis is a follow up script to the original SB Master Chart.
SB Master Chart was created to be a simple chart that can be easily interpreted.
The prior version of SB Master Chart added additional multi time frame alerts, alerting to oversold/overbought and high volume on 5 time periods (30m,1h, 2h, 4h, and 1d).
V4 of SB Master Chart is NOT going away. It is not being REPLACED!... This is a separate script based on the same algorithm, but is significantly different enough to warrant its own script. Please review SB Master Chart v4 for more details about the algo.
This version of SB Master Chart w/Smart Trade has additional money management parameters to help you dollar cost average in and out of a trade.
It follows the following rules
Oversold with no positions - Buy and establish a cost basis.
Oversold with positions - Only buy if below cost basis and dca percentage set in options.
Sell - Only sell if above minimum take profit and crossbelow trailing stop loss.
The script assumes you are buying and selling in lots of 1 so cost basis and positions are based on that calculation. There is a position/profit/loss table at the top of the chart.
If you don't like making trading decisions based on SB Master Chart v4, this is the chart for you, it will tell you exactly when to buy and when to sell based on the options you set. When you created the alert.
Options available
Take Profit % - This lets you specify the minimum take profit you want to sell for.
Trailing Stop % - This is not your typical trailing stop. This trailing stop is a percentage of the maximum gain. If your stock went up 100% and you set it to 75%, it will stop loss you out at 75% gain based on your cost basis/open trades
DCA % - This will only open a trade if you are below your cost basis by DCA% and there is a buy signal.
Start Year/End Year - This will allow the graph to start displaying information from the date forward. This is useful for determining Max Positions so you can calculate how much exposure each positions should have.
Disable Trade History - This will disable the labels.
Pro Algo: Find the Direction and Follow the Trend
Find direction is the key to success for all traders. This script can find the direction and Opening Range. All you need to do is follow the direction and the trend generated from this script. It can be used for day trading and swing trading on all time frames. It generates short-trend, medium-trend and long-trend lines with no repaint and no lagging. It also generates Support/Resistance (S/R) area for Day-time and 24 hours time-frame.
How does it work?
* Defines all support/resistance (S/R) levels based on floor trader’s pivot points and my own S/R levels.
* Calculates the trend/reversal signals, price reactions close to all above S/R levels.
* Generates short-trend, middle-trend and long-trend line based on all combined factors and directions.
What are the signals?
* Pink line are short-trend line
* Green line are short-trend line
* Red line are short-trend
* Deep green area is S/R Opening Range for day-time, which can be used as Open Range Breakout
*
How to use?
* Find the Opening Range(green area)
* Follow the direction of Pink/Green/Red lines
* Trade in the same direction of opening range breakout and the direction of trend(Pink/Green/Red lines)
* It works on all time-frames from 1 min, 2 min, 3 min to 4 hours chart.
* The trend lines will not change, i.e trend lines will be same for all time frames
* There is no way to filter all noises even with higher time frame, all the trades must have a stop Or Reversal the direction once the trend is changed.
* These trend lines provide excellent support and resistances levels.
How to access?
* PM me (ProKingTV) to obtain access
* 10 days Free Trial is available
