Momentum-Based Buy/Sell SignalsBuy Signal:
Triggered when ROC > threshold and the MACD line crosses above the Signal line.
Sell Signal:
Triggered when ROC < threshold and the MACD line crosses below the Signal line.
Visual Elements:
Green labels with "Buy" are displayed below the bars for buy signals.
Red labels with "Sell" are displayed above the bars for sell signals.
The background turns green during a buy signal and red during a sell signal for better visual clarity.
Cerca negli script per "momentum"
Momentum Probability Oscillator [SS]This is the momentum based probability indicator.
What it does?
This takes the average of MFI, Stochastics and RSI and plots it out as an independent oscillator.
It then tracks bullish vs bearish instances. Bullish is defined as a greater move from open to high than open to low and inverse for bearish.
It stores this data and these averages and plots these levels as a graph.
The graph depicts the max bullish values at the top, the min bearish values at the bottom and the averages in between:
It will plot the average "threshold" value in yellow:
The threshold value is key. A ticker trading above the threshold is generally bullish. Below is bearish.
The threshold value frequently acts as support and resistance levels (see below):
Resistance:
Support:
The indicator also shows you the amount of time a ticker has spent in each region, over a defined lookback period (defaulted to 500):
When you see that cumulatively, more time has been spent in a bullish range or a bearish range, it can help you ascertain the prevailing sentiment at that time.
The indicator will also calculate the average price range based on the underlying oscillator value. It does this through use of ATR based techniques, as its not usually possible to calculate a price from an oscillator:
This is intended as a general reference and not a precise target, as it is using ATR as opposed to the actual technical value itself.
As this is an oscillator, you can use it to look for divergences as well. The advantage to having it formulated in this way is:
a) You get the power of all 3 indicators (stochastics, MFI and RSI) in one and
b) You are adding context to the underlying technical reading. The indicator is plotting out the average, max and min ranges for the selected ticker and performing assessments based on these ranges that add context to the current PA.
You also have the ability to see the specific technical levels associated with each specific technical indicator. If you open up the settings menu and select "Show Table", this will appear:
This will show you the exact values of each of the technicals the indicator is using in its range assessment.
And that is basically the bulk of the indicator!
I use this predominately on the smaller timeframes, especially when there is a lot of chop, to ascertain the overall sentiment.
I also will reference it on the 1 hour to see what the prevailing sentiment is and whether the stock is at an area of technical resistance or support. For example, here is what I referenced on SPY today:
QUICK NOTE:
It works best with RTH (regular trading hours) turned on and ETH (extended trading hours) turned off!
That's it!
Hopefully you like it and leave your comments and suggestions below!
Multi SMI Ergodic OscillatorThe Multi SMI Ergodic Oscillator (Multi SMIEO) indicator can be used to identify potential buy and sell signals based on the relationship between the TSI and EMA lines.
The script is creating an indicator that plots multiple (3) sets of Time Series Indicator (TSI-Indicator) and Exponential Moving Average (EMA-Signal) lines as a single indicator.
The TSI is a momentum oscillator that helps identify overbought and oversold conditions. It is calculated using the close prices of an asset, a short-term moving average, and a long-term moving average. The script uses three different pairs of input values for the short-term and long-term periods, which can be adjusted by the user.
The EMA is a type of moving average that gives more weight to recent prices. It is calculated by applying a weighting factor to the most recent price, and then adding that weighted value to the previous EMA value. The script uses three different input values for the length of the EMA, which can also be adjusted by the user.
After calculating the TSI and EMA for each set, the script plots them on the same graph, with different colors and widths to differentiate them. The three sets of TSI and EMA lines are plotted to allow the user to compare the results of different periods. The script also plots a horizontal line at zero, which is used as a reference point for the oscillations of the indicator lines.
One way to use this indicator is to look for crossovers between the TSI and the EMA lines. A bullish crossover occurs when the TSI crosses above the EMA. This suggests that the buying pressure is increasing and a potential buy signal is generated. A bearish crossover occurs when the TSI crosses below the EMA. This suggests that the selling pressure is increasing and a potential sell signal is generated.
Some other ways that the indicator can be used include:
1. Identifying trends: The TSI and EMA lines can be used to identify the direction of the trend. An uptrend is present when the TSI and EMA lines are both trending upwards, while a downtrend is present when the TSI and EMA lines are both trending downwards.
2. Overbought and oversold conditions: The TSI can be used to identify overbought and oversold conditions. When the TSI is above the upper limit of the range, the asset is considered overbought and may be due for a price correction. Conversely, when the TSI is below the lower limit of the range, the asset is considered oversold and may be due for a price rebound.
3. Confirming price action: The Multi SMIEO indicator can be used to confirm price action. If a bullish divergence is present, it confirms a potential bullish reversal. If a bearish divergence is present, it confirms a potential bearish reversal.
4. Multiple time frame analysis: By using different periods for the TSI and EMA lines, the indicator can be used to analyze the asset on multiple time frames. It can be useful to compare the results of different periods to get a better understanding of the asset's price movements.
5. Risk management: This indicator can be used as an element of risk management strategy, it can help traders to identify overbought and oversold conditions to set stop loss or take profit levels.
The Multi SMI Ergodic Oscillator (Multi SMIEO) is a versatile indicator that can be used in a number of ways to analyze the price movements of an asset. It can be used to identify potential buy and sell signals, trends, overbought and oversold conditions, and to confirm price action. By using different periods for the TSI and EMA lines, the indicator can also be used to analyze the asset on multiple time frames. However, it is important to remember that indicators are based on historical data, and past performance does not guarantee future results.
It is important to use the indicator as part of a comprehensive trading strategy that includes risk management and other analysis techniques, such as fundamental and technical analysis. It is also important to keep in mind that indicators are not a standalone solution for trading, they should be used in conjunction with other market analysis and research techniques to generate better results.
Lastly, it is important to keep in mind that trading in financial markets comes with a certain level of risk and it is crucial to always have a proper risk management plan in place. Never invest more than you can afford to lose.
Point Of ControlStrategy and indicators are explained on the Chart.
Here's how i read the chart.
Entry:
1. Let the price close above the Ichimoku cloud
2. Price is above Volume Support zone
2. Make sure that momentum indicated with Green Triangles for Long Position
Exit:
1. Orange cross at the bottom of the candle indicates price is about to weaken
2. Best time to exit is Volume Resistance + Bearish(Hammer or Engulf )
PS: Use it along with R-Smart for better results
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.
Crypto Squeeze StrategyThis strategy was inspired by two famous Trading View contributors. Shout out to Lazy Bear and Crypto Face!
The strategy includes a similar replication of the blue wave, and MFI indicator. The point of the strategy is to buy when the blue wave crosses up the zero value, and the MFI is greater than zero value. This indicates that there is strong bullish momentum and money flowing into the market.
Momentum Trading By Mahfuz AzimA following indicator is Momentum Trading that uses fast QQE crosses with Moving Averages
Use for trend direction filtering. QQE or Qualitative Quantitative Estimation is based
Relative strength index (RSI), but uses a smoothing technique as an additional transformation. Three crosses can be selected (all selected by default)
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 StrategyThis strategy uses momentum to determine when to enter and exit positions. The default settings are set to look for a new 63 day high (~1 trading quarter) and a new 40 day relative high. If the stock is trending above the 50 day moving average it is a candidate to be bought. Stops are triggered when price closes below the 20 day or 50 day EMAs depending on how well the stock is trending. A stop could also be triggered even if price continues to move up, but is breaking down on a relative basis to a benchmark either SPX or BTCUSD . The goal is to hold on to our winners for as long as possible and cut the losers as soon as possible. This will alow us to capture the majority of major trends while avoiding many large drawdown and relative losers.
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 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.
Momentum Strategy (BTC/USDT; 30m) - STOCH RSI (with source code)Here's a strategy for low time frames (30min suggested) for BTC , based on momentum Analysis using Stochastic RSI
By default the strategy will use the 50% of the specified capital for each trade; if "Gamble Sizing" is enabled, it will add the specified amount of capital (25% by default, until reaching the 100% limit or lower) for the next trade after having detected a loss in the previous trade; if the next trade is successful, the size for the next trade comes back to 50%
• Trend Filter LONG: If the fast exponential moving average is UNDER the slow exponential moving average , it won't open LONG positions
• Trend Filter SHORT: If the fast exponential moving average is ABOVE the slow exponential moving average , it won't open SHORT positions
• Bars delay: the strategy will wait the specified amount of bars before closing the current position; the counter is triggered as soon as the closing trade condition is verified
BY MAKING USE OF THIS STRATEGY, YOU ACKNOWLEDGE AND AGREE THAT: (1) YOU ARE AWARE OF THE RISKS ASSOCIATED WITH TRANSACTIONS OF DIGITAL CURRENCIES AND THEIR DERIVATIVES; (2) YOU SHALL ASSUME ALL RISKS RELATED TO THE USE OF THIS STRATEGY AND TRANSACTIONS OF DIGITAL CURRENCIES AND THEIR DERIVATIVES; AND (3) I SHALL NOT BE LIABLE FOR ANY SUCH RISKS OR ADVERSE OUTCOMES.
SOURCE CODE BELOW
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.
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.
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 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
