Retail & Banker Net PositionsIn any market there are two major sets of participants, Retail traders (like you & I) who command relatively small amounts of capital and typically enter and exits positions quickly, and then Institutional Traders (sometimes referred to as whales) who command large amounts of capital and dictate the overall trend of the market but enter and exit positions slowly.
In this indicator we look at the distinct volume of these two sets of traders and use the net positions of this volume to determine if they are net long (Buying) in the market or net short (Selling).
When each set of traders are on opposite sides of the market (Retail are selling & Institutions are buying for example) it usually results in a battle and choppy price action... the majority of these battles are won by the Institutions as their large sums of money dictate the overall direction markets move.
Some of the best opportunities are when both sets of traders are on the same side of the market & this is where we see real momentum enter the market with quick price moves.
Happy trading =)
Cari dalam skrip untuk "momentum"
T3 Velocity Candles [Loxx]T3 Velocity Candles is a candle coloring overlay that calculates its gradient coloring using T3 velocity.
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.
Stacked EMAsStacked Daily & Weekly EMAs + Labels
Pretty much self-explanatory indicator that shows the current momentum based on the key exponential moving averages.
Three stages of the EMAs:
1. Stacked Positively (Bullish) - EMAs are stacked on top of each other which represents a healthy bullish uptrend (green Label).
2. Stacked Negatively (Bearish) - EMAs are stacked below each other meaning the trend is bearish (red label).
3. Stacked Neutral (Neutral) - EMAs are crossing each other without any clear direction = chop (yellow label).
Hope it helps.
TMA-LegacyThis is a script based on the original TMA- RSI Divergence indicator by PhoenixBinary.
The Phoenix Binary community and the TMA community built this version to be public code for the community for further use and revision after the reported passing of Phoenix Binary (The community extends our condolences to Phoenix's family.
The intended uses are the same as the original but some calculations are different and may not act or signal the same as the original.
Description of the indicator from original posting.
This indicator was inspired by Arty and Christy .
█ COMPONENTS
Here is a brief overview of the indicator from the original posting:
1 — RSI Divergence
Arty uses the RSI divergence as a tool to find entry points and possible reversals. He doesn't use the traditional overbought/oversold. He uses a 50 line. This indicator includes a 50 line and a floating 50 line.
The floating 50 line is a multi-timeframe smoothed moving average . Price is not linear, therefore, your 50 line shouldn't be either.
The RSI line is using a dynamic color algo that shows current control of the market as well as possible turning points in the market.
2 — Smoothed RSI Divergence
The Smoothed RSI Divergence is a slower RSI with different calculations to smooth out the RSI line. This gives a different perspective of price action and more of a long term perspective of the trend. When crosses of the floating 50 line up with the traditional RSI crossing floating 50.
3 — Momentum Divergence
This one will take a little bit of time to master. But, once you master this, and combined with the other two, damn these entries get downright lethal!
Trend Strength Directional IndicatorThis study was inspired by two famous Trading View contributors. Shout out to Lazy Bear and Crypto Face!
In this study you have a live view of the strength of direction the market is heading. The indicator that looks like a black wave is showing us the momentum of price action. When a green dot appears under the lower level it is a indication that we should consider buying, and if the red dot appears over the upper level we should sell. The custom MFI indicator determines how much money is flowing into the market. If it is green that means money is flowing into the market and if it shows red it means that money is flowing out of the market.
VuManChu MorpheusThis is our newest momentum based indicator. With this indicator, we have combined several oscillators including RSI and Stochastic to provide the user with best entries for your trading.
When the oscillator is above the over bought bands (white line at the top) and crosses down the signal (dotted lines), it is usually a good SELL signal. When the oscillator crosses above the signal when below the oversold band (white line at the bottom), it is a good BUY signal.
After forming the larger wave, the “anchor wave” can be used for a secondary confirmation to enter a Sell or a BUY
The yellow and red line in the middle is the dynamic VWAP
Rocket ships are Divergences
Grey rocket ships are hidden divergences
Blood drops are divergences
Grey blood drops are hidden divergences
Trend Indicator A-V2 (Smoothed Heikin Ashi Cloud)"Trend Indicator A-V2" and "Trend Indicator B-V2" are updated and improved versions of my initial trend indicators. Totally rethinking the code, adding highs and lows in the calculations, including some more customisation through colour schemes.
In practice, this indicator uses EMAs and Heikin Ashi to provide an overall idea of the trend.
The "Trend Indicator A-V2" is an overlay showing “Smoothed Heikin Ashi” .
The "Trend Indicator B-V2" uses the same values in a different way to measure the momentum of the trend and identify potential trend rejections.
Please, take into account that it is a lagging indicator.
Momentum Rotation Indicator [CC]I have developed this custom indicator very loosely based on the Sector Rotation Model (Giorgos E. Siligardos. Technical Analysis of Stocks & Commodities, August 2012) and I called it the MRI because this is essentially a brain scan of any particular stock. This will not only tell you when a stock is breaking out over the market at large but also how the stock is doing compared to its own history. Buy when the line turns green and sell when the line turns red.
Let me know if there are any other indicators you would like to see me publish!
Momentum PBless"A mean is a moving average of price. And if we measure price action in relation to its own moving average, the nominal price is no longer our primary concern. We’re looking at a market’s relationship to its own moving averages and the structures it forms around them. That way, we have a unit of measure that’s primarily a function of a market’s action, not so much the constant distortions in the money metric. When we oscillate the price of a market around a mean, previously unseen trends and structures emerge." - www.olivermsa.com
The momentum is calculated with the price ratio to its SMA. On the short term it is used as a variation of the price to the SMA 15 days (equivalent 3 weeks). The middle term it is used as the weekly price vs the SMA 12 weeks (equivalent 3 months) and on the long term as the monthly price vs the SMA 36 months (equivalent 3 years).
Momentum Cloud HashesYellow Cloud Showing Uptrend Momentum cloud based on Upper half of Upper Bollinger Band (Std Deviation 1 to Std Deviation 2).
Include :
Upper Keltner Channel line - price need to be above this to be uptrend
EMA 5 and EMA 10
Use VWMA 10 - immediate support for an uptrend line
Black Traingle - Price Closed under VWMA 10
Red Diamond - EMA 5 closed under Std Deviation 1
Edit it as you wish.
TMO with TTM SqueezeApplication of the TTM squeeze and the short-term momentum TTM Wave A in action. This is an example where the short-term wave will react faster than the TTM to give you a signal to start building your positions.
This indicator needs to be combined with "TTM Wave A" (add to existing pane).
The TTM Squeeze works like a better MACD. There is a zeroline and histogram bars above / below represent positive and negative momo. As the height of the bar decreases when above the zeroline, that is called decreasingly positive momo and as the height of the bar decreases when below the zeroline, that is called decreasingly negative momo. The dots on the TTM Squeeze: Red dots represent consolidation where Bollingers are inside the Keltner Channels and green dots represent a move out of consolidation or "squeeze fire". As price action comes out of consolidation there is a bigger move up/down depending on where momo is heading and where prices are (key support/resistance levels, fib areas). You want to use the TTM Squeeze and A wave TOGETHER - TTM Squeeze is your main momo and your A wave is a short-term momo wave that reacts faster and works as a leading gauge. You need to use them TOGETHER to gauge where price action may be heading. When the TTM Squeeze and A wave move lockstep together, let's say both are decreasingly positive, there is a good probability it continues to move in that direction to the next support levels. TWO bars on the TTM Squeeze of different heights is confirmation that in most cases means it will move in the direction of those bars. So if decreasingly positive, you'll see two darker bars. By the time you get your 2nd bar on the TTM Squeeze, it is often too late or you're losing profit. Way to counter that is after you get one darker bar in the opposite direction of current trend, use A wave to "predict" the next wave, the more A wave histogram bars going towards the other direction, the higher the certainty it will hit. Lastly, using these waves together works best when you look at it on MULTIPLE TIME FRAMES. (Credit for this details goes to Brady from Atlas).
TTM Wave AApplication of the TTM squeeze and the short-term momentum TTM Wave A in action. This is an example where the short-term wave will react faster than the TTM to give you a signal to start building your positions.
This indicator needs to be combined with "TMO with TTM Squeeze" (add to existing pane).
The TTM Squeeze works like a better MACD. There is a zeroline and histogram bars above / below represent positive and negative momo. As the height of the bar decreases when above the zeroline, that is called decreasingly positive momo and as the height of the bar decreases when below the zeroline, that is called decreasingly negative momo. The dots on the TTM Squeeze: Red dots represent consolidation where Bollingers are inside the Keltner Channels and green dots represent a move out of consolidation or "squeeze fire". As price action comes out of consolidation there is a bigger move up/down depending on where momo is heading and where prices are (key support/resistance levels, fib areas). You want to use the TTM Squeeze and A wave TOGETHER - TTM Squeeze is your main momo and your A wave is a short-term momo wave that reacts faster and works as a leading gauge. You need to use them TOGETHER to gauge where price action may be heading. When the TTM Squeeze and A wave move lockstep together, let's say both are decreasingly positive, there is a good probability it continues to move in that direction to the next support levels. TWO bars on the TTM Squeeze of different heights is confirmation that in most cases means it will move in the direction of those bars. So if decreasingly positive, you'll see two darker bars. By the time you get your 2nd bar on the TTM Squeeze, it is often too late or you're losing profit. Way to counter that is after you get one darker bar in the opposite direction of current trend, use A wave to "predict" the next wave, the more A wave histogram bars going towards the other direction, the higher the certainty it will hit. Lastly, using these waves together works best when you look at it on MULTIPLE TIME FRAMES. (Credit for this details goes to Brady from Atlas).
Momentum Adjusted EMA TrendThe script draws a moving average which responds to trend changes extraordinary fast!
It's calculated using Momentum, Acceleration and Probability (Psychological Effect) by interfering the Golden Ratio!
I got the idea thanks to Tradingview user DGT (dgtrd) and his/her excellent descriptions.
The indicator is simplified for users and the default settings work great, so use it as you like specially as a trend indicator.
MAPS - LongShortThis script analyzes volume and momentum for different timeframes to spot opportunities for Longs or Shorts.
Please see below for access to indicators.
13612WThis script is a 13612W momentum filter used in the Vigilant Asset Allocation (VAA) and Defensive Asset Allocation (DAA) created by Wouter J. Keller and Jan Willem Keuning.
This asset allocation strategy was uploaded to SSRN in 2017 and 2018.
13612W Calculation Method
(Profitability in Last 1 months * 12 +
Profitability in Last 3 months * 4 +
Profitability in Last 6 months * 2 +
Profitability in Last 12 months)/4
Let me briefly explain one of the VAAs, VAA-G4.
The VAA-G4 has an annualized return of 17.7%, a Sharpe ratio of 1.07% and Max Drawdown of 16.1%.
(It's too long and complicated to describe all VAA, DAA strategies. Above all, the translator performance is not good.)
VAA Global 4 Universe: SPY, EFA, EEM, AGG
Cash Universe: SHY , IEF, LQD
If 13612W of VAA Global 4 Universe is negative at least one
>> 100% of assets hold one of the highest 13612W of Cash Universe
If all 13612W of VAA Global 4 Universe are positive
>> 100% of assets hold one of the highest 13612W of VAA Global 4 Universe
Rebalancing is done every month according to this method.
Momentum FactorThis indicator provides alerts for trading opportunities the idea is based of momentum shifting in the market.
We combine this signal indicator with a system of rules to increase the probability of having a winning trade.
when combined with renko charts this signal can help you catch market breakouts
Use the link below to obtain access to this indicator.
Ehlers Restoring Pull Indicator [CC]The Restoring Pull Indicator was created by John Ehlers (Stocks & Commodities V.11:10 (395-400)) and this was one of his earliest indicators that functions as a momentum indicator . Buy when the indicator line is green and sell when it is red.
Let me know if you would like to see me publish more scripts or if you want something custom done!
Note: I'm republishing this because the original script couldn't be found in searches so this will fix that.
Momentum Squeezing Oscillator Just simple momentum oscillator modeled after the TMI squeeze from john Carter available on TD Ameritrade
Just an added tool to the tool belt
Momentum Log logarithmic momentum oscillator
you can buy if cross zero up , sell cross zero down or if cross up or down the signal (buy or sell)
nice to see trends of larger picture
Hendo's Momentum Fader AdvancedMomentum fader with volatillity index, directional bias, volatillity range and multiple settings.
How to use:
Pick a chart, mainly developed on XBT but supports every pair/index.
Pick a time frame:
1M, 5M, 15M, 30M, 1HR, 4HR. Other time frames are not yet supported.
Set your "volatillity setting"
Higher means bigger stop losses (and less risk/reward:more accuracy)
Last but not least:
Pick a setting for the algo to calculate with, every timeframe has multiple settings to work with.
Exits must be done manually, it's not a pivot to pivot indicator.
Interpretation:
Lime green bars signal bullish pivots
Maroon red bars signal bearish pivots
The upper&lower boundaries of the "bands" are logical places to put stop losses outside of volatillity
The colouring of said bounderies indicate trend, likelihood of continuation.
Yellow: Pivot , Green: Bullish , Red: Bearish . Transitions from Yellow/Green to Red and the other way around are the most consistent.
Entries are also signalled in blue (long) or red (short) they are determined by the algo itself. 90% of them run in profit (some short term), keep in mind that exits must be done manually.
Access is ONLY for members.
Don't request the code, I'm not letting people "help" with developments nor am I selling the code.
Stay tuned for more!
IPR Momentum Fader MSCMCMMomentum fader with volatillity index, directional bias, comfortable range and multiple settings.
How to use:
Pick a chart, mainly developed on XBT but supports every paid/index.
Supported Time Frames:
1M, 5M, 15M, 30M, 1HR, 4HR. Anything outside of these time frames will throw an error because those are not yet supported.
After you've picked a time frame select the stop loss size (is not calculated in pips but in volatillity index)
Then pick a setting for the algo to do it's calculations with. Every time frame comes with it's own algo to determine entries.
Exits must be done manually, it's not a pivot to pivot indicator.
Lime green bars signal bullish pivots
Maroon red bars signal bearish pivots
The upper boundaries of the "bands" are logical places to put stop losses outside of volatillity
The colouring of said bounderies indicate trend, likelihood of continuation.
Yellow: Pivot, Green: Bullish, Red: Bearish. Transitions from Yellow/Green to Red and the other way around are the most consistent.
Entries are signalled in blue (long) or red (short) and are fully calculated by the algo. About 95% run in profit short term: The only manual input the user has to give is the exit.
Don't request the code, I'm not selling it, it's not for unlicensed use.
