Guppy Wave [UkutaLabs]█ OVERVIEW
The Guppy Wave Indicator is a collection of Moving Averages that provide insight on current market strength. This is done by plotting a series of 12 Moving Averages and analysing where each one is positioned relative to the others.
In doing this, this script is able to identify short-term moves and give an idea of the current strength and direction of the market.
The aim of this script is to simplify the trading experience of users by automatically displaying a series of useful Moving Averages to provide insight into short-term market strength.
█ USAGE
The Guppy Wave is generated using a series of 12 total Moving Averages composed of 6 Small-Period Moving Averages and 6 Large Period Moving Averages. By measuring the position of each moving average relative to the others, this script provides unique insight into the current strength of the market.
Rather than simply plotting 12 Moving Averages, a color gradient is instead drawn between the Moving Averages to make it easier to visualise the distribution of the Guppy Wave. The color of this gradient changes depending on whether the Small-Period Averages are above or below the Large-Period Averages, allowing traders to see current short-term market strength at a glance.
When the gradient fans out, this indicates a rapid short-term move. When the gradient is thin, this indicates that there is no dominant power in the market.
█ SETTINGS
• Moving Average Type: Determines the type of Moving Average that get plotted (EMA, SMA, WMA, VWMA, HMA, RMA)
• Moving Average Source: Determines the source price used to calculate Moving Averages (open, high, low, close, hl2, hlc3, ohlc4, hlcc4)
• Bearish Color: Determines the color of the gradient when Small-Period MAs are above Large-Period MAs.
• Bullish Color: Determines the color of the gradient when Small-Period MAs are below Large-Period MAs.
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Johnny's Adjusted BB Buy/Sell Signal"Johnny's Adjusted BB Buy/Sell Signal" leverages Bollinger Bands and moving averages to provide dynamic buy and sell signals based on market conditions. This indicator is particularly useful for traders looking to identify strategic entry and exit points based on volatility and trend analysis.
How It Works
Bollinger Bands Setup: The indicator calculates Bollinger Bands using a specified length and multiplier. These bands serve to identify potential overbought (upper band) or oversold (lower band) conditions.
Moving Averages: Two moving averages are calculated — a trend moving average (trendMA) and a long-term moving average (longTermMA) — to gauge the market's direction over different time frames.
Market Phase Determination: The script classifies the market into bullish or bearish phases based on the relationship of the closing price to the long-term moving average.
Strong Buy and Sell Signals: Enhanced signals are generated based on how significantly the price deviates from the Bollinger Bands, coupled with the average candle size over a specified lookback period. The signals are adjusted based on whether the market is bullish or bearish:
In bullish markets, a strong buy signal is triggered if the price significantly drops below the lower Bollinger Band. Conversely, a strong sell signal is activated when the price rises well above the upper band.
In bearish markets, these signals are modified to be more conservative, adjusting the thresholds for triggering strong buy and sell signals.
Features:
Flexibility: Users can adjust the length of the Bollinger Bands and moving averages, as well as the multipliers and factors that determine the strength of buy and sell signals, making it highly customizable to different trading styles and market conditions.
Visual Aids: The script vividly plots the Bollinger Bands and moving averages, and signals are visually represented on the chart, allowing traders to quickly assess trading opportunities:
Regular buy and sell signals are indicated by simple shapes below or above price bars.
Strong buy and sell signals are highlighted with distinctive colors and placed prominently to catch the trader's attention.
Background Coloring: The background color changes based on the market phase, providing an immediate visual cue of the market's overall sentiment.
Usage:
This indicator is ideal for traders who rely on technical analysis to guide their trading decisions. By integrating both Bollinger Bands and moving averages, it provides a multi-faceted view of market trends and volatility, making it suitable for identifying potential reversals and continuation patterns. Traders can use this tool to enhance their understanding of market dynamics and refine their trading strategies accordingly.
Danger Signals from The Trading MindwheelThe " Danger Signals " indicator, a collaborative creation from the minds at Amphibian Trading and MARA Wealth, serves as your vigilant lookout in the volatile world of stock trading. Drawing from the wisdom encapsulated in "The Trading Mindwheel" and the successful methodologies of legends like William O'Neil and Mark Minervini, this tool is engineered to safeguard your trading journey.
Core Features:
Real-Time Alerts: Identify critical danger signals as they emerge in the market. Whether it's a single day of heightened risk or a pattern forming, stay informed with specific danger signals and a tally of signals for comprehensive decision-making support. The indicator looks for over 30 different signals ranging from simple closing ranges to more complex signals like blow off action.
Tailored Insights with Portfolio Heat Integration: Pair with the "Portfolio Heat" indicator to customize danger signals based on your current positions, entry points, and stops. This personalized approach ensures that the insights are directly relevant to your trading strategy. Certain signals can have different meanings based on where your trade is at in its lifecycle. Blow off action at the beginning of a trend can be viewed as strength, while after an extended run could signal an opportunity to lock in profits.
Forward-Looking Analysis: Leverage the 'Potential Danger Signals' feature to assess future risks. Enter hypothetical price levels to understand potential market reactions before they unfold, enabling proactive trade management.
The indicator offers two different modes of 'Potential Danger Signals', Worst Case or Immediate. Worst Case allows the user to input any price and see what signals would fire based on price reaching that level, while the Immediate mode looks for potential Danger Signals that could happen on the next bar.
This is achieved by adding and subtracting the average daily range to the current bars close while also forecasting the next values of moving averages, vwaps, risk multiples and the relative strength line to see if a Danger Signal would trigger.
User Customization: Flexibility is at your fingertips with toggle options for each danger signal. Tailor the indicator to match your unique trading style and risk tolerance. No two traders are the same, that is why each signal is able to be turned on or off to match your trading personality.
Versatile Application: Ideal for growth stock traders, momentum swing traders, and adherents of the CANSLIM methodology. Whether you're a novice or a seasoned investor, this tool aligns with strategies influenced by trading giants.
Validation and Utility:
Inspired by the trade management principles of Michael Lamothe, the " Danger Signals " indicator is more than just a tool; it's a reflection of tested strategies that highlight the importance of risk management. Through rigorous validation, including the insights from "The Trading Mindwheel," this indicator helps traders navigate the complexities of the market with an informed, strategic approach.
Whether you're contemplating a new position or evaluating an existing one, the " Danger Signals " indicator is designed to provide the clarity needed to avoid potential pitfalls and capitalize on opportunities with confidence. Embrace a smarter way to trade, where awareness and preparation open the door to success.
Let's dive into each of the components of this indicator.
Volume: Volume refers to the number of shares or contracts traded in a security or an entire market during a given period. It is a measure of the total trading activity and liquidity, indicating the overall interest in a stock or market.
Price Action: the analysis of historical prices to inform trading decisions, without the use of technical indicators. It focuses on the movement of prices to identify patterns, trends, and potential reversal points in the market.
Relative Strength Line: The RS line is a popular tool used to compare the performance of a stock, typically calculated as the ratio of the stock's price to a benchmark index's price. It helps identify outperformers and underperformers relative to the market or a specific sector. The RS value is calculated by dividing the close price of the chosen stock by the close price of the comparative symbol (SPX by default).
Average True Range (ATR): ATR is a market volatility indicator used to show the average range prices swing over a specified period. It is calculated by taking the moving average of the true ranges of a stock for a specific period. The true range for a period is the greatest of the following three values:
The difference between the current high and the current low.
The absolute value of the current high minus the previous close.
The absolute value of the current low minus the previous close.
Average Daily Range (ADR): ADR is a measure used in trading to capture the average range between the high and low prices of an asset over a specified number of past trading days. Unlike the Average True Range (ATR), which accounts for gaps in the price from one day to the next, the Average Daily Range focuses solely on the trading range within each day and averages it out.
Anchored VWAP: AVWAP gives the average price of an asset, weighted by volume, starting from a specific anchor point. This provides traders with a dynamic average price considering both price and volume from a specific start point, offering insights into the market's direction and potential support or resistance levels.
Moving Averages: Moving Averages smooth out price data by creating a constantly updated average price over a specific period of time. It helps traders identify trends by flattening out the fluctuations in price data.
Stochastic: A stochastic oscillator is a momentum indicator used in technical analysis that compares a particular closing price of an asset to a range of its prices over a certain period of time. The theory behind the stochastic oscillator is that in a market trending upwards, prices will tend to close near their high, and in a market trending downwards, prices close near their low.
While each of these components offer unique insights into market behavior, providing sell signals under specific conditions, the power of combining these different signals lies in their ability to confirm each other's signals. This in turn reduces false positives and provides a more reliable basis for trading decisions
These signals can be recognized at any time, however the indicators power is in it's ability to take into account where a trade is in terms of your entry price and stop.
If a trade just started, it hasn’t earned much leeway. Kind of like a new employee that shows up late on the first day of work. It’s less forgivable than say the person who has been there for a while, has done well, is on time, and then one day comes in late.
Contextual Sensitivity:
For instance, a high volume sell-off coupled with a bearish price action pattern significantly strengthens the sell signal. When the price closes below an Anchored VWAP or a critical moving average in this context, it reaffirms the bearish sentiment, suggesting that the momentum is likely to continue downwards.
By considering the relative strength line (RS) alongside volume and price action, the indicator can differentiate between a normal retracement in a strong uptrend and a when a stock starts to become a laggard.
The integration of ATR and ADR provides a dynamic framework that adjusts to the market's volatility. A sudden increase in ATR or a character change detected through comparing short-term and long-term ADR can alert traders to emerging trends or reversals.
The "Danger Signals" indicator exemplifies the power of integrating diverse technical indicators to create a more sophisticated, responsive, and adaptable trading tool. This approach not only amplifies the individual strengths of each indicator but also mitigates their weaknesses.
Portfolio Heat Indicator can be found by clicking on the image below
Danger Signals Included
Price Closes Near Low - Daily Closing Range of 30% or Less
Price Closes Near Weekly Low - Weekly Closing Range of 30% or Less
Price Closes Near Daily Low on Heavy Volume - Daily Closing Range of 30% or Less on Heaviest Volume of the Last 5 Days
Price Closes Near Weekly Low on Heavy Volume - Weekly Closing Range of 30% or Less on Heaviest Volume of the Last 5 Weeks
Price Closes Below Moving Average - Price Closes Below One of 5 Selected Moving Averages
Price Closes Below Swing Low - Price Closes Below Most Recent Swing Low
Price Closes Below 1.5 ATR - Price Closes Below Trailing ATR Stop Based on Highest High of Last 10 Days
Price Closes Below AVWAP - Price Closes Below Selected Anchored VWAP (Anchors include: High of base, Low of base, Highest volume of base, Custom date)
Price Shows Aggressive Selling - Current Bars High is Greater Than Previous Day's High and Closes Near the Lows on Heaviest Volume of the Last 5 Days
Outside Reversal Bar - Price Makes a New High and Closes Near the Lows, Lower Than the Previous Bar's Low
Price Shows Signs of Stalling - Heavy Volume with a Close of Less than 1%
3 Consecutive Days of Lower Lows - 3 Days of Lower Lows
Close Lower than 3 Previous Lows - Close is Less than 3 Previous Lows
Character Change - ADR of Last Shorter Length is Larger than ADR of Longer Length
Fast Stochastic Crosses Below Slow Stochastic - Fast Stochastic Crosses Below Slow Stochastic
Fast & Slow Stochastic Curved Down - Both Stochastic Lines Close Lower than Previous Day for 2 Consecutive Days
Lower Lows & Lower Highs Intraday - Lower High and Lower Low on 30 Minute Timeframe
Moving Average Crossunder - Selected MA Crosses Below Other Selected MA
RS Starts Curving Down - Relative Strength Line Closes Lower than Previous Day for 2 Consecutive Days
RS Turns Negative Short Term - RS Closes Below RS of 7 Days Ago
RS Underperforms Price - Relative Strength Line Not at Highs, While Price Is
Moving Average Begins to Flatten Out - First Day MA Doesn't Close Higher
Price Moves Higher on Lighter Volume - Price Makes a New High on Light Volume and 15 Day Average Volume is Less than 50 Day Average
Price Hits % Target - Price Moves Set % Higher from Entry Price
Price Hits R Multiple - Price hits (Entry - Stop Multiplied by Setting) and Added to Entry
Price Hits Overhead Resistance - Price Crosses a Swing High from a Monthly Timeframe Chart from at Least 1 Year Ago
Price Hits Fib Level - Price Crosses a Fib Extension Drawn From Base High to Low
Price Hits a Psychological Level - Price Crosses a Multiple of 0 or 5
Heavy Volume After Significant Move - Above Average and Heaviest Volume of the Last 5 Days 35 Bars or More from Breakout
Moving Averages Begin to Slope Downward - Moving Averages Fall for 2 Consecutive Days
Blow Off Action - Highest Volume, Largest Spread, Multiple Gaps in a Row 35 Bars or More Post Breakout
Late Buying Frenzy - ANTS 35 Bars or More Post Breakout
Exhaustion Gap - Gap Up 5% or Higher with Price 125% or More Above 200sma
Moving Average Compendium RefurbishedThis is my effort to bring together in a single script the widest range of moving averages possible.
I aggregated the calculation of averages within a library.
For more information about the library follow the link:
Basically this indicator is the visual result of this library.
You can choose the moving average and the script updates the chart as per the type.
The unique parameters of certain moving averages remain at their default values.
To have a rainbow of moving averages I also made an indicator:
Available moving averages:
AARMA = 'Adaptive Autonomous Recursive Moving Average'
ADEMA = '* Alpha-Decreasing Exponential Moving Average'
AHMA = 'Ahrens Moving Average'
ALMA = 'Arnaud Legoux Moving Average'
ALSMA = 'Adaptive Least Squares'
AUTOL = 'Auto-Line'
CMA = 'Corrective Moving average'
CORMA = 'Correlation Moving Average Price'
COVWEMA = 'Coefficient of Variation Weighted Exponential Moving Average'
COVWMA = 'Coefficient of Variation Weighted Moving Average'
DEMA = 'Double Exponential Moving Average'
DONCHIAN = 'Donchian Middle Channel'
EDMA = 'Exponentially Deviating Moving Average'
EDSMA = 'Ehlers Dynamic Smoothed Moving Average'
EFRAMA = '* Ehlrs Modified Fractal Adaptive Moving Average'
EHMA = 'Exponential Hull Moving Average'
EMA = 'Exponential Moving Average'
EPMA = 'End Point Moving Average'
ETMA = 'Exponential Triangular Moving Average'
EVWMA = 'Elastic Volume Weighted Moving Average'
FAMA = 'Following Adaptive Moving Average'
FIBOWMA = 'Fibonacci Weighted Moving Average'
FISHLSMA = 'Fisher Least Squares Moving Average'
FRAMA = 'Fractal Adaptive Moving Average'
GMA = 'Geometric Moving Average'
HKAMA = 'Hilbert based Kaufman\'s Adaptive Moving Average'
HMA = 'Hull Moving Average'
JURIK = 'Jurik Moving Average'
KAMA = 'Kaufman\'s Adaptive Moving Average'
LC_LSMA = '1LC-LSMA (1 line code lsma with 3 functions)'
LEOMA = 'Leo Moving Average'
LINWMA = 'Linear Weighted Moving Average'
LSMA = 'Least Squares Moving Average'
MAMA = 'MESA Adaptive Moving Average'
MCMA = 'McNicholl Moving Average'
MEDIAN = 'Median'
REGMA = 'Regularized Exponential Moving Average'
REMA = 'Range EMA'
REPMA = 'Repulsion Moving Average'
RMA = 'Relative Moving Average'
RSIMA = 'RSI Moving average'
RVWAP = '* Rolling VWAP'
SMA = 'Simple Moving Average'
SMMA = 'Smoothed Moving Average'
SRWMA = 'Square Root Weighted Moving Average'
SW_MA = 'Sine-Weighted Moving Average'
SWMA = '* Symmetrically Weighted Moving Average'
TEMA = 'Triple Exponential Moving Average'
THMA = 'Triple Hull Moving Average'
TREMA = 'Triangular Exponential Moving Average'
TRSMA = 'Triangular Simple Moving Average'
TT3 = 'Tillson T3'
VAMA = 'Volatility Adjusted Moving Average'
VIDYA = 'Variable Index Dynamic Average'
VWAP = '* VWAP'
VWMA = 'Volume-weighted Moving Average'
WMA = 'Weighted Moving Average'
WWMA = 'Welles Wilder Moving Average'
XEMA = 'Optimized Exponential Moving Average'
ZEMA = 'Zero-Lag Exponential Moving Average'
ZSMA = 'Zero-Lag Simple Moving Average'
T3 PPO [Loxx]T3 PPO is a percentage price oscillator indicator using T3 moving average. This indicator is used to spot reversals. Dark red is upward price exhaustion, dark green is downward price exhaustion.
What is Percentage Price Oscillator (PPO)?
The percentage price oscillator (PPO) is a technical momentum indicator that shows the relationship between two moving averages in percentage terms. The moving averages are a 26-period and 12-period exponential moving average (EMA).
The PPO is used to compare asset performance and volatility, spot divergence that could lead to price reversals, generate trade signals, and help confirm trend direction.
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.
Consensio V2 - Relativity IndicatorThis indicator is based on Consensio Trading System by Tyler Jenks.
It is showing you in real-time when Relativity is changing. It will help you understand when you should probably lower your position, and when to strengthen your position, when to enter a market, and when to exit a market.
What is Relativity?
According to this trading system, you start by laying 3 Simple Moving Averages:
A Long-Term Moving Average (LTMA).
A Short-Term Moving Average (STMA).
A Price Moving Average (Price).
*The "Price" should be A relatively short Moving Average in order to reflect the current price.
When laying out those 3 Moving averages on top of each other, you discover 13 unique types of relationships:
Relativity A: Price > STMA, Price > LTMA, STMA > LTMA
Relativity B: Price = STMA, Price > LTMA, STMA > LTMA
Relativity C: Price < STMA, Price > LTMA, STMA > LTMA
Relativity D: Price < STMA, Price = LTMA, STMA > LTMA
Relativity E: Price < STMA, Price < LTMA, STMA > LTMA
Relativity F: Price < STMA, Price < LTMA, STMA = LTMA
Relativity G: Price < STMA, Price < LTMA, STMA < LTMA
Relativity H: Price = STMA, Price < LTMA, STMA < LTMA
Relativity I: Price > STMA, Price < LTMA, STMA < LTMA
Relativity J: Price > STMA, Price = LTMA, STMA < LTMA
Relativity K: Price > STMA, Price > LTMA, STMA < LTMA
Relativity L: Price > STMA, Price > LTMA, STMA = LTMA
Relativity M: Price = STMA, Price = LTMA, STMA = LTMA
So what's the big deal, you may ask?
For the market to go from Bullish State (type A) to Bearish state (type G), the Market must pass through Relativity B, C, D, E, F.
For the market to go from Bearish State (type G) to Bullish state (type A), the Market must pass through Relativity H, I, J, K, L.
Knowing This principle helps you better plan when to enter a market, and when to exit a market, when to Lower your position and when to strengthen your position.
Recommendations
Different Moving Averages may suit you better when trading different assets on different time periods. You can go into the indicator settings and change the Moving Averages values if needed.
When Moving Averages are consolidating, the Relativity can change direction more often. When this happens, it is better to wait for a stronger signal than to trade on every signal.
you should also use the "Consensio Directionality Indicator" to predict the directionality of the market. While using both of my Consensio indicators together, please make sure that the Moving Averages on both of them are set to the same values
Consensio Trading System encourages you to make decisions based on Moving Averages only. I highly recommend disabling "candle view" by switching to "line view" and changing the opacity of the line to 0.
Moving Average Compilation by CryptonerdsThis script contains all commonly used types of moving averages in a single script. To our surprise, it turned out that there was no script available yet that contains multiple types of moving averages.
The following types of moving averages are included:
Simple Moving Averages (SMA)
Exponential Moving Averages (EMA)
Double Exponential Moving Averages (DEMA)
Display Triple Exponential Moving Averages (TEMA)
Display Weighted Moving Averages (WMA)
Display Hull Moving Averages (HMA)
Wilder's exponential moving averages (RMA)
Volume-Weighted Moving Averages (VWMA)
The user can configure what type of moving averages are displayed, including the length and up to five multiple moving averages per type. If you have any other request related to adding moving averages, please leave a comment in the section below.
If you've learned something new and found value, leave us a message to show your support!
Trade System Crypto InvestidorTrade System created to facilitate the visualization of crossing and extensions of the movements with Bollinger bands.
Composed by:
Moving Averages of 21, 50, 100 and 200.
Exponential Moving Averages: 17,34,72,144, 200 and 610.
Bollinger bands with standard deviation 2 and 3.
How it works?
The indicators work together, however there are some important cross-averages that need to be identified.
- Crossing the MA21 with 50, 100 and 200 up or down will dictate an up or down trend.
- MA200 and EMA200 are excellent indicators of resistance and support zone, if the price is above these averages it will be a great support, if the price is below these averages it will indicate strong resistance.
- Another important crossover refers to exponential moving averages of 17 to 72 indicates a possible start of a trend
- The crossing of the exponential moving average of 34 with 144 will confirm the crossing mentioned above.
- In addition, the exponential moving average of 610 used by Bo Williams is an excellent reference for dictating an upward or downward trend, if the price is above it it will possibly confirm an upward trend and the downside.
- To conclude we have bollinger bands with standard deviation 2 and 3, they help to identify the maximum movements.
Trend TraderDescription and Usage of the "Trend Trader" Indicator
The "Trend Trader" indicator, created by Gerardo Mercado as a legacy project, is a versatile trading tool designed to identify potential buy and sell signals across various instruments. While it provides predefined settings for popular instruments like US30, NDX100, GER40, and GOLD, it can be seamlessly adapted to any market, including forex pairs like EUR/USD. The indicator combines moving averages, time-based filters, and MACD confirmation to enhance decision-making for traders.
How It Works
Custom Moving Averages (MAs):
The indicator uses two moving averages:
Short MA: A faster-moving average (default: 10 periods).
Long MA: A slower-moving average (default: 100 periods).
Buy signals are generated when the Short MA crosses above the Long MA.
Sell signals are triggered when the Short MA crosses below the Long MA.
Time-Based Signals:
The user can define specific trading session times (start and end in UTC) to focus on high-activity periods for their chosen market.
Signals and background coloring are only active during the allowed session times.
MACD Confirmation:
A MACD (Moving Average Convergence Divergence) calculation on a 15-minute timeframe ensures stronger confirmation for signals.
Buy signals require the MACD line to be above the signal line.
Sell signals require the MACD line to be at or below the signal line.
Target Levels:
Predefined profit targets are dynamically set based on the selected trading instrument.
While it includes settings for US30, NDX100, GER40, and GOLD, the target levels can be adjusted to fit the volatility and structure of any asset, including forex pairs like EUR/USD.
Target 1 and Target 2 levels display when these thresholds are met after an entry signal.
Adaptability to Any Market:
Although predefined options are included for specific instruments, the indicator's moving averages, time settings, and MACD logic are applicable to any tradable asset, making it suitable for forex, commodities, indices, and more.
Visual Alerts:
Labels appear on the chart to highlight "BUY" and "SELL" signals at crossover points.
Additional labels indicate when price movements reach the predefined target levels.
Bar and background coloring visually represent active signals and MACD alignment.
Purpose
The indicator aims to simplify trend-following and momentum-based trading strategies. By integrating moving averages, MACD, customizable time sessions, and dynamic targets, it offers clear entry and exit points while being adaptable to the needs of individual traders across diverse markets.
How to Use
Setup:
Add the indicator to your TradingView chart.
Configure the moving average periods, trading session times, and target levels according to your preferences.
Select the instrument for predefined target settings or customize them to fit the asset you’re trading (e.g., EUR/USD or other forex pairs).
Interpreting Signals:
Buy Signal: The Short MA crosses above the Long MA, MACD confirms the upward trend, and the session is active.
Sell Signal: The Short MA crosses below the Long MA, MACD confirms the downward trend, and the session is active.
Adapt for Any Instrument:
Adjust the predefined target levels to match the volatility and trading style for your chosen asset.
For forex pairs like EUR/USD, consider typical pip movements to set appropriate profit targets.
Targets:
Use the provided target labels (e.g., 50 or 100 points) or customize them to reflect realistic profit goals based on the asset’s volatility.
Visual Aids:
Pay attention to the background color:
Greenish: Indicates a bullish trend during the allowed session.
Redish: Indicates a bearish trend during the allowed session.
Use the "BUY" and "SELL" labels for actionable insights.
This indicator is a flexible and powerful tool, suitable for traders across all markets. Its adaptability ensures that it can enhance your strategy, whether you’re trading forex, commodities, indices, or other assets. By offering actionable alerts and customizable settings, the "Trend Trader" serves as a valuable addition to any trader’s toolkit. FX:EURUSD
DECODE Moving Average ToolkitDECODE Moving Average Toolkit: Your All-in-One MA Analysis Powerhouse!
This versatile indicator is designed to be your go-to solution for analysing trends, identifying potential entry/exit points, and staying ahead of market movements using the power of Moving Averages (MAs).
Whether you're a seasoned trader or just starting out, the Decode MAT offers a comprehensive suite of features in a user-friendly package.
Key Features:
Multiple Moving Averages: Visualize up to 10 Moving Averages simultaneously on your chart.
Includes 5 Exponential Moving Averages (EMAs) and 5 Simple Moving Averages (SMAs).
Easily toggle the visibility of each MA and customize its length to suit your trading style and the asset you're analyzing.
Dynamic MA Ribbons: Gain a clearer perspective on trend direction and strength with 5 configurable MA Ribbons.
Each ribbon is formed between a corresponding EMA and SMA (e.g., EMA 20 / SMA 20).
The ribbon color changes to indicate bullish (e.g., green) or bearish (e.g., red) sentiment, providing an intuitive visual cue.
Toggle ribbon visibility with a single click.
Powerful Crossover Alerts: Never miss a potential trading opportunity with up to 5 customizable MA Crossover Alerts.
Define your own fast and slow MAs for each alert from any of the 10 available MAs.
Receive notifications directly through TradingView when your specified MAs cross over or cross under.
Optionally display visual symbols (e.g., triangles ▲▼) directly on your chart at the exact crossover points for quick identification.
Highly Customizable:
Adjust the source price (close, open, etc.) for all MA calculations.
Fine-tune the appearance (colors, line thickness) of every MA line, ribbon, and alert symbol to match your charting preferences.
User-Friendly Interface: All settings are neatly organized in the indicator's input menu, making configuration straightforward and intuitive.
How Can You Use the Decode MAT in Your Trading?
This toolkit is incredibly versatile and can be adapted to various trading strategies:
Trend Identification:
Use longer-term MAs (e.g., 50, 100, 200 period) to identify the prevailing market trend. When prices are consistently above these MAs, it suggests an uptrend, and vice-versa.
Observe the MA ribbons: A consistently green ribbon can indicate a strong uptrend, while a red ribbon can signal a downtrend. The widening or narrowing of the ribbon can also suggest changes in trend momentum.
Dynamic Support & Resistance:
Shorter-term MAs (e.g., 10, 20 period EMAs) can act as dynamic levels of support in an uptrend or resistance in a downtrend. Look for price pullbacks to these MAs as potential entry opportunities.
Crossover Signals (Entries & Exits):
Golden Cross / Death Cross: Configure alerts for classic crossover signals. For example, a 50-period MA crossing above a 200-period MA (Golden Cross) is often seen as a long-term bullish signal. Conversely, a 50-period MA crossing below a 200-period MA (Death Cross) can be a bearish signal.
Shorter-Term Signals: Use crossovers of shorter-term MAs (e.g., EMA 10 crossing EMA 20) for more frequent, shorter-term trading signals. A fast MA crossing above a slow MA can signal a buy, while a cross below can signal a sell.
Use the on-chart symbols for quick visual confirmation of these crossover events.
Confirmation Tool:
Combine the Decode MAT with other indicators (like RSI, MACD, or volume analysis) to confirm signals and increase the probability of successful trades. For instance, a bullish MA crossover combined with an oversold RSI reading could strengthen a buy signal.
Multi-Timeframe Analysis:
Apply the toolkit across different timeframes to get a broader market perspective. A long-term uptrend on the daily chart, confirmed by a short-term bullish crossover on the 1-hour chart, can provide a higher-confidence entry.
The DECODE Moving Average Toolkit empowers you to tailor your MA analysis precisely to your needs.
[blackcat] L3 Adaptive Trend SeekerOVERVIEW
The indicator is designed to help traders identify dynamic trends in various markets efficiently. It employs advanced calculations including Dynamic Moving Averages (DMAs) and multiple moving averages to filter out noise and provide clear buy/sell signals 📈✨. By utilizing innovative algorithms that adapt to changing market conditions, this tool enables users to make informed decisions across different timeframes and asset classes.
This versatile indicator serves both novice and experienced traders seeking reliable ways to navigate volatile environments. Its primary objective is to simplify complex trend analysis into actionable insights, making it an indispensable addition to any trader’s arsenal ⚙️🎯.
FEATURES
Customizable Dynamic Moving Average: Calculates an adaptive moving average tailored to specific needs using customizable coefficients.
Trend Identification: Utilizes multi-period moving averages (e.g., short-term, medium-term, long-term) to discern prevailing trends accurately.
Crossover Alerts: Provides visual cues via labels when significant crossover events occur between key indicators.
Adjusted MA Plots: Displays steplines colored according to the current trend direction (green for bullish, red for bearish).
Historical Price Analysis: Analyzes historical highs and lows over specified periods, ensuring robust trend identification.
Conditional Signals: Generates bullish/bearish conditions based on predefined rules enhancing decision-making efficiency.
HOW TO USE
Script Installation:
Copy the provided code and add it under Indicators > Add Custom Indicator within TradingView.
Choose an appropriate name and enable it on your desired charts.
Parameter Configuration:
Adjust the is_trend_seeker_active flag to activate/deactivate the core functionality as needed.
Modify other parameters such as smoothing factors if more customized behavior is required.
Interpreting Trends:
Observe the steppled lines representing the long-term/trend-adjusted moving averages:
Green indicates a bullish trend where prices are above the dynamically calculated threshold.
Red signifies a bearish environment with prices below respective levels.
Pay attention to labels marked "B" (for Bullish Crossover) and "S" (for Bearish Crossover).
Signal Integration:
Incorporate these generated signals within broader strategies involving support/resistance zones, volume data, and complementary indicators for stronger validity.
Use crossover alerts responsibly by validating them against recent market movements before execution.
Setting Up Alerts:
Configure alert notifications through TradingView’s interface corresponding to crucial crossover events ensuring timely responses.
Backtesting & Optimization:
Conduct extensive backtests applying diverse datasets spanning varied assets/types verifying robustness amidst differing conditions.
Refine parameters iteratively improving overall effectiveness and minimizing false positives/negatives.
EXAMPLE SCENARIOS
Swing Trading: Employ the stepline crossovers coupled with momentum oscillators like RSI to capitalize on intermediate trend reversals.
Day Trading: Leverage rapid adjustments offered by short-medium term MAs aligning entries/exits alongside intraday volatility metrics.
LIMITATIONS
The performance hinges upon accurate inputs; hence regular recalibration aligning shifting dynamics proves essential.
Excessive reliance solely on this indicator might lead to missed opportunities especially during sideways/choppy phases necessitating additional filters.
Always consider combining outputs with fundamental analyses ensuring holistic perspectives while managing risks effectively.
NOTES
Educational Resources: Delve deeper into principles behind dynamic moving averages and their significance in technical analysis bolstering comprehension.
Risk Management: Maintain stringent risk management protocols integrating stop-loss/profit targets safeguarding capital preservation.
Continuous Learning: Stay updated exploring evolving financial landscapes incorporating new methodologies enhancing script utility and relevance.
THANKS
Thanks to all contributors who have played vital roles refining and optimizing this script. Your valuable feedback drives continual enhancements paving way towards superior trading experiences!
Happy charting, and here's wishing you successful ventures ahead! 🌐💰!
Moving average with different timeThis script allowing you to plot up to 6 different types of moving averages (MAs) on the chart, each with customizable parameters such as type, length, source, color, and timeframe. It also allows you to set different timeframes for each moving average.
Key Features:
Multiple Moving Averages: You can add up to 6 different moving averages to your chart.
Each MA can be one of the following types: SMA, EMA, SMMA (RMA), WMA, or VWMA.
Custom Timeframes: Each moving average can be applied to a specific timeframe, giving you flexibility to compare different periods (e.g., a 50-period moving average on the 1-hour chart and a 200-period moving average on the 4-hour chart).
Customizable Inputs:
Type: Choose between SMA, EMA, SMMA, WMA, or VWMA for each MA.
Source: You can select the price data source (e.g., close, open, high, low).
Length: Set the number of periods (length) for each moving average.
Color: Each moving average can be assigned a specific color.
Timeframe: Customize the timeframe for each moving average individually (e.g., MA1 on 15-minute, MA2 on 1-hour).
User Interface:
The script includes a data window display for each moving average, allowing you to control whether to show each MA and configure its settings directly from the settings menu.
Flexible Use:
Toggle individual moving averages on and off with the show checkbox for each MA.
Customize each MA's parameters without affecting others.
Parameters:
MA Type: You can choose between different moving averages (SMA, EMA, etc.).
Source: Price data used for calculating the moving average (e.g., close, open, etc.).
Length: Defines the period (number of bars) for each moving average.
Color: Change the line color for each moving average for better visualization.
Timeframe: Set a different timeframe for each moving average (e.g., 1-day MA vs. 1-week MA).
Example Use Case:
You might use this indicator to track short-term, medium-term, and long-term trends by adding multiple MAs with different lengths and timeframes. For example:
MA1 (20-period) might be an SMA on a 1-hour chart.
MA2 (50-period) might be an EMA on a 4-hour chart.
MA3 (100-period) might be a WMA on a daily chart.
This setup allows you to visually track the market's behavior across different timeframes and better identify trends, crossovers, and other patterns.
How to Customize:
Show/Hide MAs: Enable or disable each moving average from the input menu.
Modify Parameters: Change the MA type, source, length, and color for each individual moving average.
Timeframes: Set different timeframes for each moving average for more detailed analysis.
With this Moving Average Ribbon, you get a versatile and visually rich tool to aid in technical analysis.
Position resetThe "Position Reset" indicator
The Position Reset indicator is a sophisticated technical analysis tool designed to identify possible entry points into short positions based on an analysis of market volatility and the behavior of various groups of bidders. The main purpose of this indicator is to provide traders with information about the current state of the market and help them decide whether to open short positions depending on the level of volatility and the mood of the main players.
The main components of the indicator:
1. Parameters for the RSI (Relative Strength Index):
The indicator uses two sets of parameters to calculate the RSI: one for bankers ("Banker"), the other for hot money ("Hot Money").
RSI for Bankers:
RSIBaseBanker: The baseline for calculating bankers' RSI. The default value is 50.
RSIPeriodBanker: The period for calculating the RSI for bankers. The default period is 14.
RSI for hot money:
RSIBaseHotMoney: The baseline for calculating the RSI of hot money. The default value is 30.
RSIPeriodHotMoney: The period for calculating the RSI for hot money. The default period is 21.
These parameters allow you to adjust the sensitivity of the indicator to the actions of different groups of market participants.
2. Sensitivity:
Sensitivity determines how strongly changes in the RSI will affect the final result of calculations. It is configured separately for bankers and hot money:
SensitivityBanker: Sensitivity for bankers' RSI. It is set to 2.0 by default.
SensitivityHotMoney: Sensitivity for hot money RSI. It is set to 1.0 by default.
Changing these parameters allows you to adapt the indicator to different market conditions and trader preferences.
3. Volatility Analysis:
Volatility is measured based on the length of the period, which is set by the volLength parameter. The default length is 30 candles. The indicator calculates the difference between the highest and lowest value for the specified period and divides this difference by the lowest value, thus obtaining the volatility coefficient.
Based on this coefficient, four levels of volatility are distinguished.:
Extreme volatility: The coefficient is greater than or equal to 0.25.
High volatility: The coefficient ranges from 0.125 to 0.2499.
Normal volatility: The coefficient ranges from 0.05 to 0.1249.
Low volatility: The coefficient is less than 0.0499.
Each level of volatility has its own significance for making decisions about entering a position.
4. Calculation functions:
The indicator uses several functions to process the RSI and volatility data.:
rsi_function: This function applies to every type of RSI (bankers and hot money). It adjusts the RSI value according to the set sensitivity and baseline, limiting the range of values from 0 to 20.
Moving Averages: Simple moving averages (SMA), exponential moving averages (EMA), and weighted moving averages (RMA) are used to smooth fluctuations. They are applied to different time intervals to obtain the average values of the RSI.
Thus, the indicator creates a comprehensive picture of market behavior, taking into account both short-term and long-term dynamics.
5. Bearish signals:
Bearish signals are considered situations when the RSI crosses certain levels simultaneously with a drop in indicators for both types of market participants (bankers and hot money).:
The bankers' RSI crossing is below the level of 8.5.
The current hot money RSI is less than 18.
The moving averages for banks and hot money are below their signal lines.
The RSI values for bankers are less than 5.
These conditions indicate a possible beginning of a downtrend.
6. Signal generation:
Depending on the current level of volatility and the presence of bearish signals, the indicator generates three types of signals:
Orange circle: Extremely high volatility and the presence of a bearish signal.
Yellow circle: High volatility and the presence of a bearish signal.
Green circle: Low volatility and the presence of a bearish signal.
These visual markers help the trader to quickly understand what level of risk accompanies each specific signal.
7. Notifications:
The indicator supports the function of sending notifications when one of the three types of signals occurs. The notification contains a brief description of the conditions under which the signal was generated, which allows the trader to respond promptly to a change in the market situation.
Advantages of using the "Position Reset" indicator:
Multi-level analysis: The indicator combines technical analysis (RSI) and volatility assessment, providing a comprehensive view of the current market situation.
Flexibility of settings: The ability to adjust the sensitivity parameters and the RSI baselines allows you to adapt the indicator to any market conditions and personal preferences of the trader.
Clear visualization: The use of colored labels on the chart simplifies the perception of information and helps to quickly identify key points for entering a trade.
Notification support: The notification sending feature makes it much easier to monitor the market, allowing you to respond to important events in time.
Waldo's RSI Color Trend Candles
TradingView Description for Waldo's RSI Color Trend Candles
Title: Waldo's RSI Color Trend Candles
Short Title: Waldo RSI CTC
Overview:
Waldo's RSI Color Trend Candles is a visually intuitive indicator designed to enhance your trading experience by color-coding candlesticks based on the integration of Relative Strength Index (RSI) momentum and moving average trend analysis. This innovative tool overlays directly on your price chart, providing a clear, color-based representation of market sentiment and trend direction.
What is it?
This indicator combines the power of RSI with the simplicity of moving averages to offer traders a unique way to visualize market conditions:
RSI Integration: The RSI is computed with customizable parameters, allowing traders to adjust how momentum is interpreted. The RSI values influence the primary color of the candles, indicating overbought or oversold market states.
Moving Averages: Utilizing two Simple Moving Averages (SMAs) with user-defined lengths, the indicator helps in identifying trend directions through their crossovers. The fast MA and slow MA can be toggled on/off for visual clarity.
Color Trend Candles: The 'Color Trend Candles' feature uses a dynamic color scheme to reflect different market conditions:
Purple for overbought conditions when RSI exceeds the set threshold (default 70).
Blue for oversold conditions when RSI falls below the set threshold (default 44).
Green indicates a bullish trend, confirmed by both price action and RSI being bullish (fast MA crossing above slow MA, with price above the slow MA).
Red signals a bearish trend, when both price and RSI are bearish (fast MA crossing below slow MA, with price below the slow MA).
Gray for neutral or mixed market sentiment, where signals are less clear or contradictory.
How to Use It:
Waldo's RSI Color Trend Candles is tailored for traders who appreciate visual cues in their trading strategy:
Trend and Momentum Insight: The color of each candle gives an immediate visual representation of both the trend (via MA crossovers) and momentum (via RSI). Green and red candles align with bullish or bearish trends, respectively, providing a quick reference for market direction.
Identifying Extreme Conditions: Purple and blue candles highlight potential reversal zones or areas where the market might be overstretched, offering opportunities for contrarian trades or to anticipate market corrections.
Customization: Users can adjust the RSI length, overbought/oversold levels, and the lengths of the moving averages to align with their trading style or the specific characteristics of the asset they're trading.
This customization ensures the indicator can be tailored to various market conditions.
Simplified Decision Making: Designed for traders who prefer a visual approach, this indicator simplifies the decision-making process by encoding complex market data into an easy-to-understand color system.
However, for a robust trading strategy, it's recommended to use it alongside other analytical tools.
Control Over Display: The option to show or hide moving averages and to enable or disable the color-coding of candles provides users with control over how information is presented, allowing for a cleaner chart or more detailed analysis as preferred.
Conclusion:
Waldo's RSI Color Trend Candles offers a fresh, visually appealing method to interpret market trends and momentum through the color of candlesticks. It's ideal for traders looking for a straightforward way to gauge market sentiment at a glance. While this indicator can significantly enhance your trading setup, remember to incorporate it within a broader strategy, using additional confirmation from other indicators or analysis methods to manage risk and validate trading decisions. Dive into the colorful world of trading with Waldo's RSI Color Trend Candles and let the market's mood guide your trades with clarity and ease.
Dashboard MTF profile volume Indicator Description
This indicator, titled "Swing Points and Liquidity & Profile Volume," combines multiple features to provide a comprehensive market analysis:
Volume Profile: Displays buy and sell volumes across multiple timeframes (1 minute, 5 minutes, 15 minutes, 1 hour, 4 hours, 1 day).
Volume Moving Averages: Plots two moving averages (short and long) to analyze volume trends.
Dashboard: A summary dashboard shows buy and sell volumes for each timeframe, with distinct colors for better visualization.
Swing Points: Identifies liquidity levels and swing points to help pinpoint key entry and exit zones.
How to Use
1. Indicator Installation
Go to TradingView.
Open the Pine Script Editor.
Copy and paste the provided code.
Click on "Add to Chart."
2. Indicator Settings
The indicator offers several customizable parameters:
Display Volume (1 minute, 5 minutes, 15 minutes, 1 hour, 4 hours, 1 day): Enable or disable volume display for each timeframe.
Short Moving Average Length (MA): Set the short moving average period (default: 5).
Long Moving Average Length (MA): Set the long moving average period (default: 14).
Dashboard Position: Choose where to display the dashboard (bottom-right, bottom-left, top-right, top-left).
Text Color: Customize the text color in the dashboard.
Text Size: Choose text size (small, normal, large).
3. Using the Indicator
Volume Analysis
The dashboard displays buy (Buy Volume) and sell (Sell Volume) volumes for each timeframe.
Buy Volume: Volume of trades where the closing price is higher than the opening price (aggressive buying).
Sell Volume: Volume of trades where the closing price is equal to or lower than the opening price (aggressive selling).
Volumes are displayed in real-time and update with each new candle.
Volume Moving Averages
Two moving averages are plotted on the chart:
MA Volume (Short): Short moving average (blue) to identify short-term volume trends.
MA Volume (Long): Long moving average (red) to identify long-term volume trends.
Use these moving averages to spot accumulation or distribution periods.
Swing Points and Liquidity
Swing points are identified based on price levels where volumes are highest.
These levels can act as support/resistance zones or liquidity areas to plan entries and exits.
Usage Guidelines
1. Entering a Position
Buy (Long):
When Buy Volume is significantly higher than Sell Volume across multiple timeframes.
When the short moving average (blue) crosses above the long moving average (red).
Sell (Short):
When Sell Volume is significantly higher than Buy Volume across multiple timeframes.
When the short moving average (blue) crosses below the long moving average (red).
2. Exiting a Position
Use liquidity levels (swing points) to set profit targets or stop-loss levels.
Monitor volume changes to anticipate trend reversals.
3. Risk Management
Use stop-loss orders to limit losses.
Avoid trading during low-volume periods to reduce false signals.
Compliance with Trading View Guidelines
Intellectual Property:
The code is provided for educational and personal use. You may modify and use it but cannot resell or distribute it as your own work.
Responsible Use:
Trading View encourages responsible use of indicators. Test the indicator on a demo account before using it in live trading.
Transparency:
The code is fully transparent and can be reviewed in the Pine Script Editor. You may modify it to suit your needs.
Practical Examples
Scenario 1: Bullish Trend
Buy Volume is high on 1-hour and 4-hour time frames.
The short moving average (blue) is above the long moving average (red).
Action: Open a long position (Buy) and set a stop-loss below the last swing low.
Scenario 2: Bearish Trend
Sell Volume is high on 1-hour and 4-hour time frames.
The short moving average (blue) is below the long moving average (red).
Action: Open a short position (Sell) and set a stop-loss above the last swing high.
MVSF 6.0[ELPANO]The "MVSF 6.0 " indicator, which stands for Multi-Variable Strategy Framework, overlays on price charts to aid in trading decisions. It combines various moving averages and volume data to generate buy and sell signals based on predefined conditions.
Key features of the indicator include:
Moving Averages: It uses three exponential moving averages (EMAs) with lengths of 200, 100, and 50, and two simple moving averages (SMAs) with lengths of 14 and 9. These averages are combined into a single average line to detect trends.
Volume Analysis: The volume is assessed over a specified period (default is 2 bars) to determine its trend relative to its average, influencing the color and interpretation of signals.
Price Source and VWAP: Users can select the price (close, low, or high) used for calculations. The volume-weighted average price (VWAP) serves as a potential benchmark or condition in signal generation.
Signal Generation: Buy and sell signals are based on the relationship of the price to the average line and VWAP, the direction of the last candle, and the trend direction of the average line. These signals are visually represented on the chart.
Customization: Traders can toggle the visibility of signals, entry points, the average line, and even use these elements as conditions for filtering signals.
This script is designed to be flexible, allowing traders to modify settings according to their strategy needs. The description and implementation aim to provide clarity on how each component works together to assist in trading decisions, adhering to best practices for creating and publishing trading scripts.
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Der Indikator "MVSF 6.0 ", der für Multi-Variable Strategy Framework steht, wird über Preisdiagramme gelegt, um bei Handelsentscheidungen zu helfen. Er kombiniert verschiedene gleitende Durchschnitte und Volumendaten, um Kauf- und Verkaufssignale basierend auf vordefinierten Bedingungen zu generieren.
Wesentliche Merkmale des Indikators umfassen:
Gleitende Durchschnitte: Es werden drei exponentielle gleitende Durchschnitte (EMAs) mit Längen von 200, 100 und 50 sowie zwei einfache gleitende Durchschnitte (SMAs) mit Längen von 14 und 9 verwendet. Diese Durchschnitte werden zu einer einzelnen Durchschnittslinie kombiniert, um Trends zu erkennen.
Volumenanalyse: Das Volumen wird über einen festgelegten Zeitraum (standardmäßig 2 Balken) bewertet, um seinen Trend im Vergleich zum Durchschnitt zu bestimmen, was die Farbe und Interpretation der Signale beeinflusst.
Preisquelle und VWAP: Benutzer können den für Berechnungen verwendeten Preis (Schluss-, Tief- oder Hochkurs) auswählen. Der volumengewichtete Durchschnittspreis (VWAP) dient als mögliche Benchmark oder Bedingung bei der Generierung von Signalen.
Signalgenerierung: Kauf- und Verkaufssignale basieren auf dem Verhältnis des Preises zur Durchschnittslinie und zum VWAP, der Richtung der letzten Kerze und der Trendrichtung der Durchschnittslinie. Diese Signale werden visuell auf dem Diagramm dargestellt.
Anpassung: Händler können die Sichtbarkeit von Signalen, Einstiegspunkten, der Durchschnittslinie und sogar deren Verwendung als Bedingungen für die Filterung von Signalen ein- und ausschalten.
Dieses Skript ist so konzipiert, dass es flexibel ist und Händlern erlaubt, die Einstellungen gemäß ihren Strategiebedürfnissen zu modifizieren. Die Beschreibung und Implementierung zielen darauf ab, Klarheit darüber zu schaffen, wie jede Komponente zusammenarbeitet, um bei Handelsentscheidungen zu helfen, und halten sich an die besten Praktiken für die Erstellung und Veröffentlichung von Handelsskripten.
Trend DetectorThe Trend Detector indicator is a powerful tool to help traders identify and visualize market trends with ease. This indicator uses multiple moving averages (MAs) of different timeframes to provide a comprehensive view of market trends, making it suitable for traders of all experience levels.
█ USAGE
This indicator will automatically plot the chosen moving averages (MAs) on your chart, allowing you to visually assess the trend direction. Additionally, a table displaying the trend data for each selected MA timeframe is included to provide a quick overview.
█ FEATURES
1. Customizable Moving Averages: The indicator supports various types of moving averages, including Simple (SMA) , Exponential (EMA) , Smoothed (RMA) , Weighted (WMA) , and Volume-Weighted (VWMA) . You can select the type and length for each MA.
2. Multiple Timeframes: Plot moving averages for different timeframes on a single chart, including fast (short-term) , mid (medium-term) , and slow (long-term) MAs.
3. Trend Detector Table: A customizable table displays the trend direction (Up or Down) for each selected MA timeframe, providing a quick and easy way to assess the market's overall trend.
4. Customizable Appearance: Adjust the colors, frame, border, and text of the Trend Detector Table to match your chart's style and preferences.
5. Wait for Timeframe Close: Option to wait until the selected timeframe closes to plot the MA, which will remove the gaps.
█ CONCLUSION
The Trend Detector indicator is a versatile and user-friendly tool designed to enhance your trading strategy. By providing a clear visualization of market trends across multiple timeframes, this indicator helps you make informed trading decisions with confidence and trade with the market trend. Whether you're a day trader or a long-term investor, this indicator is an essential addition to your trading toolkit.
█ IMPORTANT
This indicator is a tool to aid in your analysis and should not be used as the sole basis for trading decisions. It is recommended to use this indicator in conjunction with other tools and perform comprehensive market analysis before making any trades.
Happy trading!
Uptrick: MultiMA_VolumePurpose:
The "Uptrick: MultiMA_Volume" indicator, identified by its abbreviated title 'MMAV,' is meticulously designed to provide traders with a comprehensive view of market dynamics by incorporating multiple moving averages (MAs) and volume analysis. With adjustable inputs and customizable visibility options, traders can tailor the indicator to their specific trading preferences and strategies, thereby enhancing its utility and usability.
Explanation:
Input Variables and Customization:
Traders have the flexibility to adjust various parameters, including the lengths of different moving averages (SMA, EMA, WMA, HMA, and KAMA), as well as the option to show or hide each moving average and volume-related components.
Customization options empower traders to fine-tune the indicator according to their trading styles and market preferences, enhancing its adaptability across different market conditions.
Moving Averages and Trend Identification:
The script computes multiple types of moving averages, including Simple (SMA), Exponential (EMA), Weighted (WMA), Hull (HMA), and Kaufman's Adaptive (KAMA), allowing traders to assess trend directionality and strength from various perspectives.
Traders can determine potential price movements by observing the relationship between the current price and the plotted moving averages. For example, prices above the moving averages may suggest bullish sentiment, while prices below could indicate bearish sentiment.
Volume Analysis:
Volume analysis is integrated into the indicator, enabling traders to evaluate volume dynamics alongside trend analysis.
Traders can identify significant volume spikes using a customizable threshold, with bars exceeding the threshold highlighted to signify potential shifts in market activity and liquidity.
Determining Potential Price Movements:
By analyzing the relationship between price and the plotted moving averages, traders can infer potential price movements.
Bullish biases may be suggested when prices are above the moving averages, accompanied by rising volume, while bearish biases may be indicated when prices are below the moving averages, with declining volume reinforcing the potential for downward price movements.
Utility and Potential Usage:
The "Uptrick: MultiMA_Volume" indicator serves as a comprehensive tool for traders, offering insights into trend directionality, strength, and volume dynamics.
Traders can utilize the indicator to identify potential trading opportunities, confirm trend signals, and manage risk effectively.
By consolidating multiple indicators into a single chart, the indicator streamlines the analytical process, providing traders with a concise overview of market conditions and facilitating informed decision-making.
Through its customizable features and comprehensive analysis, the "Uptrick: MultiMA_Volume" indicator equips traders with actionable insights into market trends and volume dynamics. By integrating trend analysis and volume assessment into their trading strategies, traders can navigate the markets with confidence and precision, thereby enhancing their trading outcomes.
VCC SmtmWorks better for Cryptos (1W and greater than) timeframes.
This strategy incorporates multiple indicators to make informed trading signals. It leverages the Stochastic indicator to assess price momentum, utilizes the Bollinger Band to identify potential oversold and overbought conditions, and closely monitors Moving Averages to gauge the trend's bullish or bearish nature.
A long signal will be displayed if the following conditions are met:
The Stochastic D and Stochastic K both indicate an oversold condition, with Stochastic K being lower than Stochastic D.
The current Price Low is below the Bollinger Lower Band.
The Price Close is currently below all Moving Averages.
A Death Cross pattern has formed among the Moving Averages.
A short signal will be displayed if the opposite of the long conditions are true:
The Stochastic D and Stochastic K both indicate an overbought condition, with Stochastic K being higher than Stochastic D.
The current Price High is above the Bollinger Upper Band.
The Price Close is currently above all Moving Averages.
A Golden Cross pattern has formed among the Moving Averages.
T3 JMA KAMA VWMAEnhancing Trading Performance with T3 JMA KAMA VWMA Indicator
Introduction
In the dynamic world of trading, staying ahead of market trends and capitalizing on volume-driven opportunities can greatly influence trading performance. To address this, we have developed the T3 JMA KAMA VWMA Indicator, an innovative tool that modifies the traditional Volume Weighted Moving Average (VWMA) formula to increase responsiveness and exploit high-volume market conditions for optimal position entry. This article delves into the idea behind this modification and how it can benefit traders seeking to gain an edge in the market.
The Idea Behind the Modification
The core concept behind modifying the VWMA formula is to leverage more responsive moving averages (MAs) that align with high-volume market activity. Traditional VWMA utilizes the Simple Moving Average (SMA) as the basis for calculating the weighted average. While the SMA is effective in providing a smoothed perspective of price movements, it may lack the desired responsiveness to capitalize on short-term volume-driven opportunities.
To address this limitation, our T3 JMA KAMA VWMA Indicator incorporates three advanced moving averages: T3, JMA, and KAMA. These MAs offer enhanced responsiveness, allowing traders to react swiftly to changing market conditions influenced by volume.
T3 (T3 New and T3 Normal):
The T3 moving average, one of the components of our indicator, applies a proprietary algorithm that provides smoother and more responsive trend signals. By utilizing T3, we ensure that the VWMA calculation aligns with the dynamic nature of high-volume markets, enabling traders to capture price movements accurately.
JMA (Jurik Moving Average):
The JMA component further enhances the indicator's responsiveness by incorporating phase shifting and power adjustment. This adaptive approach ensures that the moving average remains sensitive to changes in volume and price dynamics. As a result, traders can identify turning points and anticipate potential trend reversals, precisely timing their position entries.
KAMA (Kaufman's Adaptive Moving Average):
KAMA is an adaptive moving average designed to dynamically adjust its sensitivity based on market conditions. By incorporating KAMA into our VWMA modification, we ensure that the moving average adapts to varying volume levels and captures the essence of volume-driven price movements. Traders can confidently enter positions during periods of high trading volume, aligning their strategies with market activity.
Benefits and Usage
The modified T3 JMA KAMA VWMA Indicator offers several advantages to traders looking to exploit high-volume market conditions for position entry:
Increased Responsiveness: By incorporating more responsive moving averages, the indicator enables traders to react quickly to changes in volume and capture short-term opportunities more effectively.
Enhanced Entry Timing: The modified VWMA aligns with high-volume periods, allowing traders to enter positions precisely during price movements influenced by significant trading activity.
Improved Accuracy: The combination of T3, JMA, and KAMA within the VWMA formula enhances the accuracy of trend identification, reversals, and overall market analysis.
Comprehensive Market Insights: The T3 JMA KAMA VWMA Indicator provides a holistic view of market conditions by considering both price and volume dynamics. This comprehensive perspective helps traders make informed decisions.
Analysis and Interpretation
The modified VWMA formula with T3, JMA, and KAMA offers traders a valuable tool for analyzing volume-driven market conditions. By incorporating these advanced moving averages into the VWMA calculation, the indicator becomes more responsive to changes in volume, potentially providing deeper insights into price movements.
When analyzing the modified VWMA, it is essential to consider the following points:
Identifying High-Volume Periods:
The modified VWMA is designed to capture price movements during high-volume periods. Traders can use this indicator to identify potential market trends and determine whether significant trading activity is driving price action. By focusing on these periods, traders may gain a better understanding of the market sentiment and adjust their strategies accordingly.
Confirmation of Trend Strength:
The modified VWMA can serve as a confirmation tool for assessing the strength of a trend. When the VWMA line aligns with the overall trend direction, it suggests that the current price movement is supported by volume. This confirmation can provide traders with additional confidence in their analysis and help them make more informed trading decisions.
Potential Entry and Exit Points:
One of the primary purposes of the modified VWMA is to assist traders in identifying potential entry and exit points. By capturing volume-driven price movements, the indicator can highlight areas where market participants are actively participating, indicating potential opportunities for opening or closing positions. Traders can use this information in conjunction with other technical analysis tools to develop comprehensive trading strategies.
Interpretation of Angle and Gradient:
The modified VWMA incorporates an angle calculation and color gradient to further enhance interpretation. The angle of the VWMA line represents the slope of the indicator, providing insights into the momentum of price movements. A steep angle indicates strong momentum, while a shallow angle suggests a slowdown. The color gradient helps visualize this angle, with green indicating bullish momentum and purple indicating bearish momentum.
Conclusion
By modifying the VWMA formula to incorporate the T3, JMA, and KAMA moving averages, the T3 JMA KAMA VWMA Indicator offers traders an innovative tool to exploit high-volume market conditions for optimal position entry. This modification enhances responsiveness, improves timing, and provides comprehensive market insights.
Enjoy checking it out!
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Credits to:
◾ @cheatcountry – Hann Window Smoothing
◾ @loxx – T3
◾ @everget – JMA
RedK K-MACD : a MACD with some more musclesMoving Averages are probably the most commonly used analysis tools, and MACD is possibly the first charting indicator a trader gets to learn about.
MACD Basic concept
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Without repeating all the tons of documentation about what MACD does, let's quickly re-visit the MACD concept from a 10-mile altitude (note we're keen on simplifying here rather than being technically accurate - so please forgive the use of any "common lingos")
- MACD goal is to represent the distance between 2 Moving Averages (MAs) - one fast and one slow, relatively - as an unrestricted zero-based oscillator.
- The value of the main MACD line is the distance, or the displacement between the 2 MA's
- usually a signal line is used (which is another MA of that distance value) to enable better visualization of the change (and rate of change, since this is all depicted on a time axis) of that displacement - this represents price momentum (price movement in the recent period versus movements for a relatively longer period).
- the difference between the main MACD line and its signal is then represented as a histogram above and below the zero line. in this case, that histogram is really redundant, since it shows a value that is already represented visually by the main line and its signal line.
How K-MACD is different
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K-MACD takes that simple concept of the classic MACD and expands around it - the idea is to use the same simple approach to representing price momentum while bringing in more insight to price moves in the short, medium and long terms, ability to represent more than 2 MA's and to enable better identification of tradeable patterns (like Volatility Contraction and others) - while still keeping things simple and visually clean.
K-MACD is an indicator that allows us to view how price moves against 3 moving averages: a fast / slow pair, and a "market" Filter or Baseline (very long) that will be used as a flag for Bear/Bull market mode. Many traders and trading literature use the 200 day (40 week) SMA as that key filter
so in total, there are 4 MA lines in K-MACD (excluding the "orange" signal line):
* Price Proxy: Which is a very fast moving average that will represent the price itself - let's use a WMA(3) or something close to that here - there will be a signal line to enable better visualization of this similar to a classic MACD - that's the orange line
* Fast & Slow MA's : Use whatever represents the "medium term" momentum for your trading - Some traders use 20 and 50, others use 10 and 20 .. if on your price chart, you keep using a pair of MA's for this, use the same settings in K-MACD - these will be represented by the 3-color Momentum Bars that fluctuate above and below the baseline
* Filter/Baseline MA: Should be your long (Bullish/Bearish Mode) MA. so 100 or 200 or any other value you consider your market to be bearish below and bullish above. on K-MACD this is actually the blue zero line - everything else is "relative" to it
Review the sample chart which explains various elements and the "price chart" setup that K-MACD represents. With K-MACD you can clean up your chart from those various Moving Averages - or use a different set than the ones you already have K-MACD represent - or other indicators (like ATR channels..etc)
Other "muscles" in the K-MACD
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- Relative vs Classic Calculation Mode
A key issue with the classic MACD is that the displacement between the 2 moving averages is represented as "absolute or direct" values - as the price of the underlying increases with time, you can't really use these values to make useful comparison between the past and now (see below example) - also you can't use them to compare 2 different instruments.
- The "Relative" calculation option in K-MACD addresses that issue by relating all "distances" to the Baseline MA as percentage (above or below) - you can see this clear when you look at the above chart the far left versus the far right and compare K-MACD with the classic MACD - the Classic option is still available
- More MA "type" options for all MA lines: choose between SMA, EMA, WMA, and RSS_WMA (which i use a lot in my trading and is my default for the Price Proxy)
- More Alerts: a total or 9 alerts (in 3 groups) are available with K-MACD (Momentum above or below baseline, Price Proxy crossing signal line, and Price Proxy crossing baseline)
- New 52 week High / Low markers: These will show as Green/red circles on the zero line in K-MACD. this will only work for 1D timeframe and above, i'm just using a simple approach and would like to keep it that way.
- i know i added some more features not covered above :) -- if you have questions about any of the settings, feel free to ask below
Closing thoughts
-------------------------
K-MACD is a combination of couple of indicators i published in the past (xMACD and Mo_Bars) - so you can go back and read about them if needed - I then added improvements to accommodate ideas from swing trading literature and common practices that i plan to focus on in future. So K-MACD is really part of my own trading setup.
I assume here that most traders are familiar with what a MACD is - so kept this post short - if you thing we should expand more about the concepts covered here let me know in the comments - i can make some separate posts with examples and more details.
I hope many fellow traders find this work useful - and feel free let me know in comments below if you do.
DIY Entry SignalsThis indicator allows you to set up entry signals based on your own conditions.
Note that this indicator DOES NOT give any information about exits. It is not intended to be a signal indicator that someone could blindly follow. It is intended for use in backtesting to help spot entry points more easily.
Also note that this indicator DOES NOT plot anything other than moving averages and entry signals. The other indicators referenced will need to be added on their own to be visible on the chart.
Credit to The_Caretaker for both BBWP and PMARP indicators. For more information on how those work, see their descriptions. Big thanks to him for making them open source, as well.
Instructions for use:
Signal Types:
This section allows you to choose whether you want long, short, or both types of signals.
Moving Averages:
Configure up to 4 moving averages to be plotted on the chart. Options include show/hide, color, length, and type.
RSI:
Choose the period and source used for the Relative Strength Index indicator, a very commonly used momentum oscillator.
Stochastic:
Choose the K, D, smoothing, and source for the Stochastic indicator, a very commonly used momentum oscillator.
BBWP:
Choose settings for the Bollinger Band Width Percentile indicator. This measures volatility based on Bollinger Bands and was created by The_Caretaker. The indicator is free and open source, so definitely check it out.
This section allows the user to choose the price source, basis type ( SMA , EMA , or VWMA ), length, and lookback. It also includes a threshold setting to determine the BBWP requirement used for entry signals.
PMARP:
Choose settings for the Price Moving Average Ratio & Percentile. This calculates the ratio between a source price and moving average over a lookback period. This was also created by The_Caretaker, and it is a free and open source indicator.
This section allows the user to choose price source, lookback, PMAR length, and moving average type.
DMI/ADX:
Choose settings for the Directional Movement Index and the Average Directional Index. This shows which direction the price is moving by comparing prior highs and lows and calculating a positive directional movement and a negative directional movement. The average of the positive and negative movements is used to plot the ADX line.
Long/Short Conditions:
Choose which indicators will be used to determine entry signals, as well as some options for each indicator that is included.
Note: A signal will only be plotted if ALL selected conditions are met.
Options in these sections include:
Faster moving averages above or below slower moving averages (implying a trend direction)
RSI thresholds (separate for long and short)
Stochastic thresholds (separate for long and short)
Whether K should be above or below D (implying trend direction of the Stochastic indicator)
Whether a signal should only be generated on the bar when the Stochastic first crosses the threshold.
BBWP on/off (The threshold for this is determined in the BBWP section of the settings)
PMARP thresholds (separate for long and short)
STD-Adaptive T3 [Loxx]STD-Adaptive T3 is a standard deviation adaptive T3 moving average filter. This indicator acts more like a trend overlay indicator with gradient coloring.
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
Loxx's Expanded Source Types
