[SGM Volatility Lvl]Choppiness Index (CI)
The Choppiness Index is a technical analysis tool used to determine whether a market is trending or consolidating. CI values range between 0 and 100:
- Higher values (close to 100) indicate a choppy market (i.e., the market is consolidating and not trending strongly).
- Lower values (close to 0) signify a trending market (either up or down).
In this script:
- CI values above 62 are considered to represent high volatility.
- CI values below 28 are viewed as representing lower volatility or consolidation.
How the Indicator Works
Choppiness Index Calculation
The CI is calculated using the average true range (ATR) and the high-low range over the specified length:
ci = 100 * math.log10(math.sum(ta.atr(1), length_line) / (ta.highest(length_line) - ta.lowest(length_line))) / math.log10(length_line)
Volatility Determination
The script determines the market's volatility state based on CI:
if ci >= 62
ischarge := 2
if ci <= 28
ischarge := 0
- ischarge = 2 indicates high volatility.
- ischarge = 0 indicates consolidation.
Line Setup
Lines are set on the chart based on the market's volatility:
- If CI increases and indicates high volatility, a line (colored with `volcolor`) is drawn at the close price of the bar.
- If CI decreases and indicates consolidation, a line (colored with `conColor`) is drawn at the close price of the bar.
Line Extension
The lines are automatically extended to the next indicator update or bar:
for i = 0 to array.size(ray) - 1
if i < array.size(ray) - 1
current_line = array.get(ray, i)
next_line = array.get(ray, i + 1)
if not na(current_line) and not na(next_line)
line.set_x2(current_line, line.get_x1(next_line))
else
line.set_x2(current_line, bar_index)
Relevance
Identifying Key Levels
The indicator helps traders identify key levels as follows:
- High Volatility : Lines indicating high volatility suggest strong trending movements. These levels can signify breakout points or areas where the price has made significant moves.
- Consolidation : Lines indicating consolidation suggest the market is ranging. These levels can be used to identify sideways movements, areas of accumulation or distribution, and potential breakout zones.
Potential Future Points of Interest
- High Volatility Lines: Can serve as resistance or support levels if the market revisits these areas.
- Consolidation Lines: Highlight potential zones for price breakouts or reversals when the market transitions from consolidation to a trending phase.
In summary, this indicator can be particularly useful for traders looking to identify periods of high volatility and consolidation. By marking such periods on the chart, traders can better understand market behavior and spot potential trading opportunities.
Cari dalam skrip untuk "breakout"
Bollinger Bands with Squeeze and SMA Indicator Description: BB+SMA
Overview:
Bollinger Bands (BB): Computes and plots three bands based on a selected moving average type (SMA, EMA, SMMA (RMA), WMA, VWMA) and standard deviation multiplier. The bands indicate potential support and resistance levels relative to price volatility.
Squeeze Condition: Detects periods of low volatility (squeeze) when the distance between the upper and lower Bollinger Bands narrows significantly. This condition can signal potential price breakouts.
Simple Moving Average (SMA): Calculates and plots a simple moving average based on user-defined length. It smooths price data to highlight trends and potential reversals.
Smoothing Line: Further enhances the SMA by applying different smoothing methods (SMA, EMA, SMMA (RMA), WMA, VWMA) over a specified smoothing length. It helps in identifying smoother trends and changes in direction.
Key Components:
Inputs: Users can adjust parameters such as Bollinger Bands length, type of moving average, standard deviation multiplier, squeeze condition length, squeeze threshold percentage, SMA length, smoothing method, and smoothing length.
Plotting: Displays the Bollinger Bands (basis, upper, lower), SMA, squeeze condition bands (basis, upper, lower), and a smoothing line on the chart.
Visualization: Utilizes different colors and line styles for clarity in visualizing each component's plot on the chart.
Purpose:
Helps traders identify potential price volatility, trend reversals, and breakout opportunities using Bollinger Bands, SMA, squeeze conditions, and smoothed moving averages.
Enhances technical analysis by providing clear visual cues for trend strength and potential entry/exit points based on the specified parameters.
Conclusion:
The "BB+SMA" indicator integrates multiple technical analysis tools into a single script, offering traders a comprehensive approach to analyzing price movements and making informed trading decisions directly on TradingView charts.
Liquidity Hour by Ibramiho v2Liquidity Hour by Ibramiho (Version 2) - Identify High-Potential Reversal Zones
Understanding the pre-New York session hour is crucial for institutional traders. This period is often characterized by increased liquidity and price volatility as major financial players prepare for the upcoming trading day. The Liquidity Hour indicator capitalizes on this phenomenon, automatically pinpointing the candle (by default, in orange) immediately before the New York session opens.
Why Focus on This Candle?
Liquidity Magnet: Institutional traders often use this hour to establish or adjust positions, creating pockets of liquidity.
Breakout and Retracement Potential: The indicator helps you spot potential areas where price might retrace after a breakout, offering high-probability trading opportunities.
Visual Clarity: The highlighted candle acts as a visual anchor, making it easy to identify these key levels on your chart.
How It Works
1. Automatic Detection: The indicator intelligently detects the pre-New York session candle, regardless of your chart's timeframe.
2. Colour Coding: The candle is highlighted in orange (customizable), instantly drawing your attention.
3. Trade Insights: Watch for price breakouts above or below the highlighted candle. When price retraces back to this level, it signals a potential entry or exit point.
Key Features
Customizable Colour: Change the highlight colour to suit your chart preferences.
Working Timeframes: Works on timeframes, from minutes up to 2 hours timeframe.
Versatile Trading: Suitable for both intraday and swing trading strategies.
Unlock the Power of Institutional Liquidity
Don't miss out on the opportunities that arise in the hour before the New York session. With the Liquidity Hour indicator, you'll gain a valuable edge by identifying key levels where price action is most likely to reverse.
Futures Auto Levels [NariCapitalTrading]Futures Auto Levels Indicator
Introduction
The "Futures Auto Levels" (FAL) indicator shows the previous day's levels, weekly open, high, low, and the Initial Balance Range (IBR).
Indicator Components
The FAL indicator comprises the following components:
Previous Day's Levels: These include the open, high, low, and close of the previous trading day. They are represented on the chart by lines and labels, helping to identify significant price levels from the prior session.
Weekly Open, High, Low: These levels represent the open, high, and low prices of the current trading week.
Initial Balance Range (IBR): The IBR is calculated based on the price range during the first 60 minutes of the trading day. It helps identify initial trading range and potential breakout levels.
How to Use the Indicator
1. Previous Day's Levels:
Monitor the previous day's open, high, low, and close to identify key support and resistance levels.
Use these levels to gauge market sentiment and potential price reversals.
2. Weekly Open, High, Low:
Pay attention to the weekly open, high, and low to understand the market's behavior within the weekly timeframe.
These levels can act as reference points for setting profit targets and stop-loss orders.
3. Initial Balance Range (IBR):
Watch for price movements within the IBR to identify potential trading opportunities.
Breakouts above or below the IBR may signal the beginning of a new trend or continuation of the current trend.
Suggested/Potential Strategies
Reversal Trading: Look for price reversals around previous day's levels, especially when they coincide with other technical indicators or significant support/resistance zones.
Trend Following: Follow the trend by trading breakouts above/below the IBR or weekly high/low levels. Use trailing stops to capture profits while the trend remains intact.
Range Trading: Trade within the IBR when the market is consolidating. Buy near the IBR low and sell near the IBR high, with tight stop-loss orders to manage risk.
Conclusion
The Futures Auto Levels indicator is designed to help incorporate levels into trading analysis and trading strategies to improve profitability and consistency.
XXPivotsBreakoutsLibrary "XXPivotsBreakouts"
Utilizes k-NN machine learning to predict breakout zones from pivot points, aiding traders in identifying potential bullish and bearish market movements. Ideal for trend-following and breakout strategies.
breakouts(pivotBars, numNeighbors, maxData, predictionSmoothing)
Detects and predicts breakout points from pivot data.
Parameters:
pivotBars (int) : int: Number of bars for pivot point detection.
numNeighbors (int) : int: Neighbors count for k-NN prediction.
maxData (int) : int: Maximum pivot data points for analysis.
predictionSmoothing (int) : int: Smoothing period for predictions.
Returns: : Lower and higher prediction bands plus pivot signal, 1 for ph and -1 for pl.
MLPivotsBreakoutsLibrary "MLPivotsBreakouts"
Utilizes k-NN machine learning to predict breakout zones from pivot points, aiding traders in identifying potential bullish and bearish market movements. Ideal for trend-following and breakout strategies.
breakouts(source, pivotBars, numNeighbors, maxData, predictionSmoothing)
Parameters:
source (float) : series float: Price data for analysis.
pivotBars (int) : int: Number of bars for pivot point detection.
numNeighbors (int) : int: Neighbors count for k-NN prediction.
maxData (int) : int: Maximum pivot data points for analysis.
predictionSmoothing (int) : int: Smoothing period for predictions.
@return : Lower and higher prediction bands plus pivot signal, 1 for ph and -1 for pl.
MA Slope [EMA Magic]█ Overview:
The MA Slope calculates the slope based on a given moving average.
The Moving Average Slope indicator allows you to identify the direction and the strength of a trend.
It calculates the rate of change in percentage based on the user-defined moving average.
█ Calculation: This indicator calculates the slope based on the changes of moving average and normalizes it with Average True Range(ATR).
The default value of ATR is 7.I recommend not changing it unless you know exactly what are you doing.
█ Input Settings:
The settings are divided into three sections:
The first section is for time frame adjustments. Modify it separately from the chart, Allows you to use moving averages from different time frames.
In the second section, you can configure the base calculation,including Moving Average and Average True Range(ATR) settings.
In the third section, you can detect breakout and sudden change signals, which are highlighted in the background of the indicator.
Note that When you change the breakout limit value, it also affects the band limit indicator on your chart.
To avoid signal confusion, use only one at a time.
Here is the example the breakout signals:
█ Usage:
When the slope is increasing, it indicates an uptrend.
When the slope is decreasing, it indicates a downtrend.
When the slope is moving around zero and choppy, it indicates no specific trend or price is in a range zone.
Uptrend and Range Zone example:
Downtrend example:
Slope peaks on extreme levels can signal a potential trend reversal point.
Breakout of the upper or lower bands can be translated into a trading signal.Indicating that price will probably continue to move in the direction of the breakout.
Favor long setups when the slope is increasing or it is positive and favor short setups when the slope is decreasing or it is negative.
Fits with any moving average you use, e.g., EMA, WMA, MA Ribbon, and more.
█ Alert
Alerts are available for both signal conditions.
█ Recap
Take the time to study price movements alongside this indicator for a deeper understanding.Whether you're a novice or experienced trader, this indicator can come helpful
Contraction Box & Doji LinesContraction & Doji Lines indicator is designed to identify and visualize potential support and resistance levels on a price chart. It does this by detecting doji candlestick patterns and drawing horizontal lines from the middle of the doji bodies to the right. Additionally, it also highlights price contraction zones with colored boxes.
The indicator first identifies doji candlestick patterns that it suggests indecision in the market, a horizontal line and these horizontal lines can act as potential support or resistance levels. Traders can observe price reactions around these lines. If the price approaches a line and bounces off it, it may indicate a significant level in the market.
In addition to doji lines, this indicator also highlights price contraction zones. When a contraction zone is detected, a colored box is drawn to highlight this zone. The box extends from the fifth bar ago (left side) to the current bar (right side), with the highest high and lowest low of the identified zone. The color and width of this box can be customized using the "Box Line Border Color," "Box Background Color," and "Box Width" parameters.
A possible strategy could be can use the doji lines as potential support and resistance levels to make trading decisions. For example, if the price breaks above a doji line and holds, it may indicate a bullish signal.
The colored boxes highlight areas of price contraction, which often precede significant price movements. Traders can use these zones to anticipate potential breakouts or breakdowns.
For example, you might enter a long (buy) position if it anticipate a breakout from a contraction zone with a target price set above the breakout level. Conversely, you might enter a short (sell) position if they anticipate a breakdown from a contraction zone with a target price set below the breakdown level.
Retest Support Resistance Signals [ChartPrime]The Retest Support Resistance Signals Indicator is a powerful tool designed to assist traders in identifying key support and resistance levels within the market. Most importantly and uniquely it identifies retests of these structures and displays them on the trader's chart. By utilizing a combination of pivot points and price action analysis, this indicator offers valuable insights for both signal-based and support/resistance trading strategies.
Key Features & settings:
Retest Confirmation: The indicator waits for a break above a support or resistance level and observes subsequent price action. If price retraces and forms a wick below the level, followed by a bounce, the indicator identifies it as a retest and labels it as "R" to indicate potential support or resistance confirmation.
This indicator combines the benefits of signal-based trading and support/resistance analysis, providing users with a versatile trading tool suitable for various strategies.
Retest Weaker Toggle: Users have the option to enable or disable the retest weaker feature. When enabled, the indicator considers a support or resistance level weaker if it experiences a test. When disabled, the indicator assumes that a bounce may occur from the level.
Pivot Detection Customization: Users can adjust the pivot detection method based on either wicks or bodies. This flexibility allows traders to adapt the indicator to different market conditions and preferences. The trader can also customize the number of bars used for pivot detection on both the left and right sides. This feature enables traders to fine-tune the indicator's sensitivity and responsiveness.
Users also have control over how support or resistance levels are managed on the chart. They can choose to either stop updating the levels (freeze) or completely remove them (delete) from the chart.
Breakout Threshold Setting: Traders can adjust the breakout threshold until deletion setting. This setting determines the number of successful breakouts through a support or resistance level required to remove it from the chart. This feature helps filter out weaker levels and focus on more significant ones.
Shown above we see the retest labels in action denoted with an R label
This indicator can be a useful addition to an SR trader's toolkit. Identifying when a level in the market is retested can reveal interesting information about the underlying strength of a trend. This indicator has been designed with the two major schools of thought; a level gets weaker the more it's tested vs stronger the more it's tested. We have designed this therefore to be versatile and adapt to both thought procceses. The R labels should be taken and considered as a larger part of an analysis process and not followed blindly.
Support/ResistanceUse this code to stop support and resistance
This can be used with the momentum indicators that I have to see if we are likely to breakout or get rejected
Indicator Settings:
The indicator is titled "Support/Resistance | Breaks & Bounces" and is set to overlay on the price chart.
max_lines_count is set to 500, indicating the maximum number of support/resistance lines that can be plotted.
User Input:
The script allows users to customize the pivot method, sensitivity, and line width through input variables.
point_method determines whether the pivot calculation is based on "Candle Wicks" or "Candle Body".
left_bars represents the number of bars to the left used to identify pivot highs/lows.
right_bars is set equal to left_bars.
line_width controls the width of the support/resistance lines.
Global Variables and Arrays:
The script declares several variables and arrays to store information related to support and resistance levels, breakouts, and bounces.
high_source and low_source are calculated based on the selected pivot method.
fixed_pivot_high and fixed_pivot_low store the pivot highs and lows using the chosen sensitivity.
Variables and arrays are initialized for tracking support/resistance lines, breakout triggers, and bounce triggers.
Main Operation:
The main operation occurs when barstate.isconfirmed is true, indicating that a new bar has formed and its data is final.
The script iterates through the support/resistance lines to update their end points (x2) to the current bar.
For each support/resistance line, it checks if a breakout or bounce event has occurred based on the current and previous bar's price levels.
If a breakout or bounce event is detected, the corresponding trigger variables (red_breakout_trigger, red_rejection_trigger, green_breakout_trigger, green_rejection_trigger) are set to true.
The script also checks for changes in the pivot highs and lows and updates the support/resistance lines accordingly.
If a change is detected, it clears the existing lines, breakout, and bounce arrays and adds new lines for the updated pivot levels.
Gaussian Fisher Transform Price Reversals - FTRHello Traders !
Looking for better trading results ?
"This indicator shows you how to identify price reversals in a timely manner." John F. Ehlers
Introduction :
The Gaussian Fisher Transform Price Reversals indicator, dubbed FTR for short, is a stat based price reversal detection indicator inspired by and based on the work of the electrical engineer now private trader John F. Ehlers.
The Fisher Transform :
It is a common assumption that prices have a gaussian / normal probability density function(PDF), i.e. a sample of n close prices would be normally distributed if the probability of observing a price value say at any given standard deviation range is equal to that probability in the case of the normal distribution, e.g. 68% off all samples fell within one standard deviation around the mean, which is what we would expect if the data was normal.
However Price Action is not normally distributed and thus can not be conventionally interpreted in this way, Formally the Fisher Transform, transforms the distribution of bounded ranging price action (were price action takes values in a range from -1 to 1) into that of a normal distribution, alternatively it may be said the Fisher Transform changes the PDF of any waveform so that the transformed output has n approximately Gaussian PDF, It does so through the following equations. taken directly from the work of John F. Ehlers - Using The Fisher Transform
By substituting price data in the above formulas, bounded ranging price actions (over a given user defined period lookback - this determines the range price ranges in, see the Intermediate formula above) distribution is transformed to that in the normal case. This means when the input, the Intermediate ,(the Midpoint - see formula above) approaches either limit within the range the outputs are greatly amplified, this amplification accentuates /puts more weight on the larger deviations or limits within the range, conversely when price action is varying round the mean of the range the output is approximately equal to unity (the input is approximately equal to the input, the intermediate)
The inputs (Intermediates) are converted to normal outputs and the nonlinear Transfer of the Fisher Transform with varying senesitivity's (gammas) can be seen in the graph / image above. Although sensitivity adjustments are not currently available in this script (I forgot to add it) the outputs may be greatly amplified as gamma (the coefficient of the Fisher Transformation - see Fish equation) approaches 1. the purple line show this graphically, as a higher gamma leads to a greater amplification than in the standard case (the red line which is the standard fisher transformation, the black plot is the Fish with a gamma of 1, which is unity sensativity)
Reversal plots and Breakouts :
- Support lines are plotted with their corresponding Fish value when there is a crossover of the Fish and Fish SMA <= a given standard deviation of Fish
- Resistance lines are plotted with their corresponding Fish value when there is a crossunder of the Fish and Fish SMA >= a given standard deviation of Fish
- Reversals are these support and resistance line plots
Breakouts and Volume bars :
Breakouts cause the reversal lines to break (when the high/low is above the resistance/support), Breakouts are more "high quality" when they occur conditional on high volume, the highlighted bars represent volume standard deviations ranging from -3 to 3. When breakouts occure on high volume this may be a sign of the continutaion of the trend (reversals would signify the start of a new trend).
Hope you enjoy, Happy Trading !
(be sure to rocket the script if you liked it, this helps me know which of my scripts are the most useful)
peacefulIndicatorsWe are delighted to present the PeacefulIndicators library, a modest yet powerful collection of custom technical indicators created to enhance your trading analysis. The library features an array of practical tools, including MACD with Dynamic Length, Stochastic RSI with ATR Stop Loss, Bollinger Bands with RSI Divergence, and more.
The PeacefulIndicators library offers the following functions:
macdDynamicLength: An adaptive version of the classic MACD indicator, which adjusts the lengths of the moving averages based on the dominant cycle period, providing a more responsive signal.
rsiDivergence: A unique implementation of RSI Divergence detection that identifies potential bullish and bearish divergences using a combination of RSI and linear regression.
trendReversalDetection: A helpful tool for detecting trend reversals using the Rate of Change (ROC) and Moving Averages, offering valuable insights into possible market shifts.
volume_flow_oscillator: A custom oscillator that combines price movement strength and volume to provide a unique perspective on market dynamics.
weighted_volatility_oscillator: Another custom oscillator that factors in price volatility and volume to deliver a comprehensive view of market fluctuations.
rvo: The Relative Volume Oscillator highlights changes in volume relative to historical averages, helping to identify potential breakouts or reversals.
acb: The Adaptive Channel Breakout indicator combines a moving average with an adjustable volatility multiplier to create dynamic channels, useful for identifying potential trend shifts.
We hope this library proves to be a valuable addition to your trading toolbox.
Library "peacefulIndicators"
A custom library of technical indicators for trading analysis, including MACD with Dynamic Length, Stochastic RSI with ATR Stop Loss, Bollinger Bands with RSI Divergence, and more.
macdDynamicLength(src, shortLen, longLen, signalLen, dynLow, dynHigh)
Moving Average Convergence Divergence with Dynamic Length
Parameters:
src (float) : Series to use
shortLen (int) : Shorter moving average length
longLen (int) : Longer moving average length
signalLen (int) : Signal line length
dynLow (int) : Lower bound for the dynamic length
dynHigh (int) : Upper bound for the dynamic length
Returns: tuple of MACD line and Signal line
Computes MACD using lengths adapted based on the dominant cycle period
rsiDivergence(src, rsiLen, divThreshold, linRegLength)
RSI Divergence Detection
Parameters:
src (float) : Series to use
rsiLen (simple int) : Length for RSI calculation
divThreshold (float) : Divergence threshold for RSI
linRegLength (int) : Length for linear regression calculation
Returns: tuple of RSI Divergence (positive, negative)
Computes RSI Divergence detection that identifies bullish (positive) and bearish (negative) divergences
trendReversalDetection(src, rocLength, maLength, maType)
Trend Reversal Detection (TRD)
Parameters:
src (float) : Series to use
rocLength (int) : Length for Rate of Change calculation
maLength (int) : Length for Moving Average calculation
maType (string) : Type of Moving Average to use (default: "sma")
Returns: A tuple containing trend reversal direction and the reversal point
Detects trend reversals using the Rate of Change (ROC) and Moving Averages.
volume_flow_oscillator(src, length)
Volume Flow Oscillator
Parameters:
src (float) : Series to use
length (int) : Period for the calculation
Returns: Custom Oscillator value
Computes the custom oscillator based on price movement strength and volume
weighted_volatility_oscillator(src, length)
Weighted Volatility Oscillator
Parameters:
src (float) : Series to use
length (int) : Period for the calculation
Returns: Custom Oscillator value
Computes the custom oscillator based on price volatility and volume
rvo(length)
Relative Volume Oscillator
Parameters:
length (int) : Period for the calculation
Returns: Custom Oscillator value
Computes the custom oscillator based on relative volume
acb(price_series, ma_length, vol_length, multiplier)
Adaptive Channel Breakout
Parameters:
price_series (float) : Price series to use
ma_length (int) : Period for the moving average calculation
vol_length (int) : Period for the volatility calculation
multiplier (float) : Multiplier for the volatility
Returns: Tuple containing the ACB upper and lower values and the trend direction (1 for uptrend, -1 for downtrend)
Improved Scalping Consolidation and Squeeze IndicatorThe Improved Scalping Consolidation and Squeeze Indicator (Improved Scalp C&S) is a custom TradingView indicator designed for short-term trading, specifically scalping. It detects price consolidation and potential breakout scenarios using a combination of technical analysis tools, such as the Rate of Change (ROC), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and Keltner Channels. To reduce the number of false signals, this improved version introduces a "consolidation strength" parameter, which represents the minimum number of consecutive bars required for a valid consolidation or squeeze signal.
How it works:
Consolidation Detection:
The indicator identifies price consolidation when the following conditions are met:
a. RSI is between 45 and 55, indicating a lack of strong momentum.
b. The absolute value of the MACD histogram is less than 0.1% of the closing price, suggesting a lack of directional movement.
c. The Rate of Change (ROC) is less than 1.5%, indicating relatively stable prices over the specified period.
Squeeze Detection:
The indicator detects a squeeze (a potential breakout scenario) when the Bollinger Bands are within the Keltner Channels, represented by the following conditions:
a. The lower Bollinger Band is above the lower Keltner Channel.
b. The upper Bollinger Band is below the upper Keltner Channel.
Consolidation Strength:
The consolidation strength parameter filters out weaker signals by requiring a minimum number of consecutive bars for a valid consolidation or squeeze signal. By adjusting this parameter, traders can control the sensitivity of the indicator to short-term price movements and potentially reduce the number of false signals.
When the consolidation strength criteria are met, the indicator colors the price bars within the pattern yellow for consolidation and orange for a squeeze, signaling potential trading opportunities.
Trading Strategy:
The Improved Scalping Consolidation and Squeeze Indicator can be used in various ways, depending on the trader's strategy and risk appetite. Here are some suggestions:
Range trading: During consolidation (yellow bars), traders can buy at support levels and sell at resistance levels within the range, using stop-loss orders to manage risk. However, this approach might not work well in the case of a sudden breakout.
Breakout trading: When a squeeze is detected (orange bars), traders can wait for a confirmed breakout from the consolidation pattern before entering a trade. A breakout can be confirmed by a strong price move accompanied by increased volume, a significant change in momentum, or a breach of important support or resistance levels.
Momentum-based strategies: Traders can use other momentum-based indicators (e.g., Stochastic Oscillator, On Balance Volume) in conjunction with the Improved Scalp C&S indicator to identify potential entry and exit points during consolidation or breakout scenarios.
Fine-tuning the consolidation strength: Adjust the "consolidation strength" input to find the optimal balance between the number of signals and their accuracy. A higher value will result in fewer signals, potentially reducing the number of false signals, but it may also make the indicator less sensitive to short-term price movements.
CFB-Adaptive Trend Cipher Candles [Loxx]CFB-Adaptive Trend Cipher Candles is a candle coloring indicator that shows both trend and trend exhaustion using Composite Fractal Behavior price trend analysis. To do this, we first calculate the dynamic period outputs from the CFB algorithm and then we injection those period inputs into a correlation function that correlates price input price to the candle index. The closer the correlation is to 1, the lighter the green color until the color turns yellow, sometimes, indicating upward price exhaustion. The closer the correlation is to -1, the lighter the red color until it reaches Fuchsia color indicating downward price exhaustion. Green means uptrend, red means downtrend, yellow means reversal from uptrend to downtrend, fuchsia means reversal from downtrend to uptrend.
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.
Included
Loxx's Expanded Source Types
Related indicators:
Adaptive Trend Cipher loxx]
Dynamic Zones Polychromatic Momentum Candles
RSI Precision Trend Candles
CFB-Adaptive, Jurik DMX Histogram [Loxx]Jurik DMX Histogram is the ultra-smooth, low lag version of your classic DMI indicator. This is a momentum indicator. You can use this indicator standalone or as part of a system with a moving average and a mean reversion indicator. This indicator has both composite fractal behavior adaptive inputs and fixed inputs. The default is CFB adaptive. Dark green means strong push up, dark red, strong push down. Light green means weak push up, and light red means weak push down.
What is the directional movement index?
The directional movement index (DMI) is an indicator developed by J. Welles Wilder in 1978 that identifies in which direction the price of an asset is moving. The indicator does this by comparing prior highs and lows and drawing two lines: a positive directional movement line ( +DI ) and a negative directional movement line ( -DI ). An optional third line, called the average directional index ( ADX ), can also be used to gauge the strength of the uptrend or downtrend.
When +DI is above -DI , there is more upward pressure than downward pressure in the price. Conversely, if -DI is above +DI , then there is more downward pressure on the price. This indicator may help traders assess the trend direction. Crossovers between the lines are also sometimes used as trade signals to buy or sell.
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.
Included:
Alerts
Loxx's Expanded Source Types
Signals
Bar coloring
Average Daily Range (ADR) (Multi Timeframe, Multi Period)Average Daily Range (ADR)
(Multi Timeframe, Multi Period, Extended Levels)
Tips
• Narrow Zones are an indication of breakouts. It can be a very tight range as well.
• Wider Zones can be Sideways or Volatile.
What is this Indicator?
• This is Average Daily Range (ADR) Zones or Pivots.
• This have Multi Timeframe, Multi Period (Up to 3 Levels) and Extended Target Levels.
Advantages of this Indicator
• This is a Leading indicator, not Dynamic or Repaint.
• Helps to identify the reversal points.
• The levels are more accurate and not like the old formulas.
• Can practically follow the Buy Low and Sell High principle.
• Helps to keep minimum Stop Loss.
Who to use?
• Highly beneficial for Day Traders
• It can be used for Swing and Positions as well.
What timeframe to use?
• Any timeframe.
When to use?
• Any market conditions.
How to use?
Entry
• Long entry when the Price reach at or closer to the Green Support zone.
• Long entry when the Price retrace to the Red Resistance zone.
• Short entry when the Price reach at or closer to the Red Resistance zone.
• Short entry when the Price retrace to the Green Support zone.
• Long or Short at the Pivot line.
Exit
• Use past ADR levels as targets.
• Or use the Target levels in the indicator for breakouts.
• Use the Pivot line as target.
• Use Support or Resistance Zones as targets in reversal method.
What are the Lines?
Gray Line:
• It the day Open or can be considered as Pivot.
Red & Green ADR Zones:
• Red Zone is Resistance.
• Green Zone is Support.
• Mostly price can reverse from this Zones.
• Multiple Red and Green Lines forms a Zone.
• These lines are average levels of past days which helps to figure out the maximum and minimum price range that can be moved in that day.
• The default number of days are 5, 7 and 14. This can be customized.
Red & Green Target Lines:
• These are Target levels.
What are the Labels?
• First Number: Price of that level.
• Numbers in (): Percentage change and Change of price from LTP (Last Traded Price) to that Level.
General Tips
• It is good if Stock trend is same as that of the Index trend.
• Lots of indicators creates lots of confusion.
• Keep the chart simple and clean.
• Buy Low and Sell High.
• Master averages or 50%.
CFB-Adaptive Velocity Histogram [Loxx]CFB-Adaptive Velocity Histogram is a velocity indicator with One-More-Moving-Average Adaptive Smoothing of input source value and Jurik's Composite-Fractal-Behavior-Adaptive Price-Trend-Period input with Dynamic Zones. All Juirk smoothing allows for both single and double Jurik smoothing passes. Velocity is adjusted to pips but there is no input value for the user. This indicator is tuned for Forex but can be used on any time series data.
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.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
CFB-Adaptive, Williams %R w/ Dynamic Zones [Loxx]CFB-Adaptive, Williams %R w/ Dynamic Zones is a Jurik-Composite-Fractal-Behavior-Adaptive Williams % Range indicator with Dynamic Zones. These additions to the WPR calculation reduce noise and return a signal that is more viable than WPR alone.
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 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.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
CFB-Adaptive CCI w/ T3 Smoothing [Loxx]CFB-Adaptive CCI w/ T3 Smoothing is a CCI indicator with adaptive period inputs and T3 smoothing. Jurik's Composite Fractal Behavior is used to created dynamic period input.
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.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
CFB Adaptive Fisher Transform [Loxx]CFB Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Jurik's Composite Fractal Behavior Algorithm to calculate the price-trend cycle period.
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.
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
CFB Adaptive MOGALEF Bands [Loxx]A Pine Script adaptation from MOGALEF Bands .
What are MOGALEF Bands?
Actual MOGALEF bands code is the final result of a lot of contributors. Syllables MO-GA-LEF are the initials of three of them.
The basic idea of bands: the markets are still in range, and trends that are moving ranges. The Mogalef bands try to estimate the current range and to project its on the future if prices move. This future estimation is often of great relevance and very useful, especialy for market profile users or pivot points users.
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.
Included:
-Color bars
-Fill levels
STD-Stepped, CFB-Adaptive Jurik Filter w/ Variety Levels [Loxx]STD-Stepped, CFB-Adaptive Jurik Filter w/ Variety Levels is a Composite Fractal Behavior, single/double Jurik filter with floating boundary levels, alerts, and signals.
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.
Included:
-Color bars
-Color background
-Color trend
-Color deadzones
-Show signals
-Long/short alerts
-ATR and quantile based levels
CFB Adaptive Gann HiLo Activator Histogram [Loxx]CFB Adaptive Gann HiLo Activator Histogram is a Composite-Fractal-Behavior-adaptive Gann HiLo activator in histogram form that has been smoothed using Jurik Filtering to reduce noise and better identify trending markets. This indicator is the CFB adaptive version of Jurik-Filtered, Gann HiLo Activator .
What is Gann HiLo
The HiLo Activator study is a trend-following indicator introduced by Robert Krausz as part of the Gann Swing trading strategy. In addition to indicating the current trend direction, this can be used as both entry signal and trailing stop.
Here is how the HiLo Activator is calculated:
1. The system calculates the moving averages of the high and low prices over the last several candles. By default, the average is calculated using the last three candles.
2. If the close price falls below the average low or rises above the average high, the system plots the opposite moving average. For example, if the price crosses above the average high, the system will plot the average low. If the price crosses below the average low afterward, the system will stop plotting the average low and will start plotting the average high, and so forth .
The plot of the HiLo Activator thus consists of sections on the top and bottom of the price plot. The sections on the bottom signify bullish trending conditions. Vice versa, those on the top signify the bearish conditions.
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.
Included
-Toggle bar color on/off
