scalping with market facilitationThis strategy is for scalping low timeframes for 10 pips. I have yet to see a strategy with this unique combo of indicators.
First we have volume indicator market facilitation, where we are looking for volume and mfi to be up, then we want the adx 5 to be above level 30 and above its ema period 3, then if these conditions are good we take shorts when ema 8 is below ema 100 and longs when ema8 is above ema 100 with parabolic sar in its propet place, also to verify trend we have obv over or under its ema of 55 and macd line over its signal line.
I have heikenashi bars on with the regular priceline showing so j see actual price levels, when i get a buy signal i set a buystop above the high of that bar and have a stoploss of 7.5 pips and a take profit of 10 pips, reverse for sells, i have to use metatrader to trade so i use this as my signals to trade.
Note this is not advice trade at your own risk no guarantees in anything in life, but i wanted to share this for it is helping me with my trades to be more strict and semi mechanical. I use it for forex time frames 1 3 5 15 mjn
Wyszukaj w skryptach "adx"
Henry's Vwap-VolumeThis Indicator is meant to provide Futures Volume and Vwap Signal in spot charts of Nifty and Banknifty Traders.
Concepts and Features of this indicators are as follows :
1) Now u don't have to select and change to futures scrip often or have both spot and futures chart in same window to watch the Futures Volume and Vwap.
2) U get Both Volume and Vwap signal as a indicator in single pane.
3) Its for Nifty and Banknifty Traders specially.
4)Volume with moving average is from the futures chart of banknifty or nifty,also may select any other futures script as per ur need.
(MOVING AVERAGE of VOLUME is plotted in Blue columns over the Volume.)
5)Vwap signal is also derived from the futures chart of banknifty or nifty,also may select any other futures script as per ur need.
(VWAP SIGNAL is plotted in GREEN or RED as background.If futures price higher than Vwap then Green , opposite for Red. )
6)The idea of this script is to give extra confirmation of a clear down or uptrend while u are in the spot chart.(nifty and banknifty)
7) U can select and change any scrip u like.But I urge to use futures chart of banknifty or nifty.
I hope this indicator will help a lot of retail investor save their hard earned money in the stock market and benefit from Mr. NK's strategy.
How to Use :
Go Long - when background is Green.
Go Short -when background is Red.
(Also take confirmation from the blue columns -moving average of volumes.volume higher or less than it.)
Limitations :
U can only use it for intraday,less than 1D timeframe.
Will not work in sideways market.
Take help of other indicators also like Rsi,adx,etc.
Best of Luck,
Henry
DMI StrategyThis strategy is based on DMI indicator. It helps me to identify base or top of the script. I mostly use this script to trade in Nifty bank options, even when the signal comes in nifty. It can be used to trade in other scripts as well. Pivot points can also be used to take entry. Long entry is taken when DI+(11) goes below 10 and DI-(11) goes above 40, whereas short entry is taken when DI-(11) goes below 10 and DI+(11) goes above 40.
For bank nifty, I take the trade in the strike price for which the current premium is nearby 300, with the SL of 20%. If premium goes below 10% I buy one more lot to average, but exit if the premium goes below 20% of the first entry. If the trade moves in the correct direction, we need to start trailing our stoploss or exit at the pre-defined target.
Please have a look at strategy tester to back test.
VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones [Loxx]VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones is an RSI indicator with adaptive inputs, Digital Kahler filtering, and Dynamic Zones. This indicator uses a Vertical Horizontal Filter for calculating the adaptive period inputs and allows the user to select from 7 different types of RSI.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
What is Digital Kahler?
From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic"
Digital Indicators
Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible.
How Is It Done?
The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI .
First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
4 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Loxx's Variety RSI
Loxx's Dynamic Zones
R19 STRATEGYHello again.
Let me introduce you R19 Strategy I wrote for mostly BTC long/short signals
This is an upgrated version of STRATEGY R18 F BTC strategy.
I checked this strategy on different timeframes and different assest and found it very usefull for BTC 1 Hour and 5 minutes chart.
Strategy is basically takes BTC/USDT as a main indicator, so you can apply this strategy to all cryptocurrencies as they mostly acts accordingly with BTC itself (Of course you can change main indicator to different assets if you think that there is a positive corelation with. i.e. for BTC signals you can sellect DXY index for main indicator to act for BTC long/short signals)
Default variables of the inticator is calibrated to BTC/USDT 5 minute chart. I gained above %77 success.
Strategy simply uses, ADX, MACD, SMA, Fibo, RSI combination and opens positions accordingly. Timeframe variable is very important that, strategy decides according the timeframe you've sellected but acts within the timeframe in the chart. For example, if you're on the 5 minutes chart, but you've selected 1 hour for the time frame variable, strategy looks for 1 hour MACD crossover for opening a position, but this happens in 5 minutes candle, It acts quickly and opens the position.
Strategy also uses a trailing stop loss feature. You can determine max stoploss, at which point trailing starts and at which distance trailing follows. The green and red lines will show your stoploss levels according to the position strategy enters (green for long, red for short stop loss levels). When price exceeds to the certaing levels of success, stop loss goes with the profitable price (this means, when strategy opens a position, you can put your stop loss to the green/red line in actual trading)
You can fine tune strategy to all assets.
Please write down your comments if you get more successfull about different time zones and different assets. And please tell me your fine tuning levels of this strategy as well.
See you all.
VHF-Adaptive T3 iTrend [Loxx]VHF-Adaptive T3 iTrend is an iTrend indicator with T3 smoothing and Vertical Horizontal Filter Adaptive period input. iTrend is used to determine where the trend starts and ends. You'll notice that the noise filter on this one is extreme. Adjust the period inputs accordingly to suit your take and your backtest requirements. This is also useful for scalping lower timeframes. Enjoy!
What is VHF Adaptive Period?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
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
Alerts
Signals
Loxx's Expanded Source Types
VHF-Adaptive CCI [Loxx]VHF-Adaptive CCI is a CCI indicator with adaptive period inputs using vertical horizontal filtering.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Included
Bar coloring
Signals
Alerts
VHF-Adaptive T3 w/ Expanded Source Types [Loxx]VHF-Adaptive T3 w/ Expanded Source Types is a T3 moving average with expanded source types and adaptive period inputs using a vertical horizontal filter
What is T3?
Developed by Tim Tillson, the T3 Moving Average is considered superior to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Included
Bar coloring
Alerts
Loxx's Expanded Source Types
VHF Adaptive Fisher Transform [Loxx]VHF Adaptive Fisher Transform is an adaptive cycle Fisher Transform using a Vertical Horizontal Filter to calculate the volatility adjusted period.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
Included:
Zero-line and signal cross options for bar coloring
Customizable overbought/oversold thresh-holds
Alerts
Signals
Ultimate RSI With Some Spices★彡 𝓤𝓵𝓽𝓲𝓶𝓪𝓽𝓮 𝓡𝓢𝓘 𝓦𝓲𝓽𝓱 𝓢𝓸𝓶𝓮 𝓢𝓹𝓲𝓬𝓮𝓼 彡★
* Hi everybody here's the ★彡 𝓤𝓵𝓽𝓲𝓶𝓪𝓽𝓮 𝓡𝓢𝓘 𝓦𝓲𝓽𝓱 𝓢𝓸𝓶𝓮 𝓢𝓹𝓲𝓬𝓮𝓼 彡★ indicator and how to use it :
彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡彡
First we have that red : green {RSI EMA Line}line in the indicator which show the current symbol situation \
𝐒𝐢𝐦𝐩𝐥𝐲 : 𝐢𝐟 𝐭𝐡𝐞 𝐥𝐢𝐧𝐞 𝐜𝐥𝐨𝐬𝐞 𝐰𝐢𝐭𝐡 𝐠𝐫𝐞𝐞𝐧 𝐜𝐨𝐥𝐨𝐫 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐨𝐩𝐞𝐧 𝐚 𝐥𝐨𝐧𝐠 𝐭𝐫𝐚𝐝𝐞 𝐚𝐧𝐝 𝐞𝐱𝐢𝐭 𝐰𝐡𝐞𝐧 𝐭𝐡𝐞 𝐫𝐞𝐝 𝐜𝐨𝐥𝐨𝐫 𝐚𝐩𝐩𝐞𝐚𝐫𝐬
I𝐧 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐭𝐢𝐦𝐞 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐨𝐭𝐡𝐞𝐫 𝐭𝐡𝐢𝐧𝐠𝐬 𝐭𝐨 𝐮𝐬𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐚𝐭 𝐰𝐨𝐮𝐥𝐝 𝐡𝐞𝐥𝐩 𝐮𝐬 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐚 𝐠𝐨𝐨𝐝 𝐨𝐫𝐝𝐞𝐫
Like The 𝐂𝐲𝐜𝐥𝐞𝐫 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 𝐥𝐞𝐯𝐞𝐥𝐬
We have the 𝐧𝐞𝐱𝐭 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 𝐥𝐞𝐯𝐞𝐥𝐬 𝟐𝟎 ,𝟑𝟎 ,𝟓𝟎 ,𝟔𝟏.𝟖 ,𝟖𝟎 { 61.8 𝐢𝐬 𝐭𝐡𝐞 𝐭𝐡𝐞 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 𝐠𝐨𝐥𝐝𝐞𝐧 𝐫𝐚𝐭𝐢𝐨 }
About The 𝐂𝐲𝐜𝐥𝐞𝐫 you can use it to know where is the current symbol go { UP : Green ,| Down : Red ,| White : Where the symbol movement is in a slight fluctuation without any significant up or down }
░▒▓█ 𝐍𝐨𝐭𝐞 : 𝐓𝐡𝐞 𝐂𝐲𝐜𝐥𝐞𝐫 𝐥𝐢𝐧𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 𝐥𝐞𝐯𝐞𝐥 ' 𝐫𝐚𝐭𝐢𝐨 𝟓𝟎 ' █▓▒░
𝐡𝐞𝐫𝐞'𝐬 𝐚 𝐟𝐚𝐬𝐭 𝐩𝐡𝐨𝐭𝐨 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐰 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠
When the RSI EMA Line reach the purple 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 level its a very good entry point where the RSI is over sold and ready to g oup again
When the RSI EMA Line reach the Golden 𝐟𝐢𝐛𝐨𝐧𝐚𝐜𝐜𝐢 level its a very important area in the line crossover it then it's a very amazing entry time but if the RSI EMA line crossunder this line then the price w'll drop down
❤❤❤❤ 𝐟𝐢𝐧𝐚𝐥𝐥𝐲 𝐇𝐚𝐯𝐞 𝐚 𝐠𝐨𝐨𝐝 𝐭𝐢𝐦𝐞 ❤❤❤❤
Genesis Matrix [Loxx]Over a decade ago, the Genesis Matrix system was one of best strategies for new traders looking to learn how to really trade trends. Fast forward to 2022, a new version of Genesis Matrix has emerged using TVI, CCI, HL Channel & T3
What is T3?
The T3 moving average is an indicator of an indicator since it includes several EMAs of another EMA. Unlike any other moving average, it adds the so-called volume factor, a value between 0 and 1. Like the SMA, traders typically use this indicator to spot trends and trend reversals.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
Genesis matrix uses Jurik-Smoothed CCI w/ MA Deviation--a spin on regular CCI .Usually CCI is calculated as using average ( Simple Moving Average ) and mean deviation. In this version, average is replaced with well known JMA (Jurik Moving Average) instead for the smoothing phase and the deviation is replaced with variety moving average deviation. The result in this one is responsive and fast (as expected) and also it is smoother than the original CCI (as expected).
What is SSL?
Known as the SSL, the Semaphore Signal Level channel chart alert is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. For our purposes here, SSL has been modified to allow for different moving average selection and different closing price look back periods.
What is William Blau Ergodic Tick Volume?
This is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence" (1995). If you like to learn more, we advise you to read this book. His book focuses on three key aspects of trading: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum in step-by-step examples. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods.
William Blau's definition of TVI ergodicity is that the indictor is ergodic when periods are set to 32, 5, 1, and the signal is set to 5. Other combinations are not ergodic, according to Blau.
How to use
Long signal: All 4 indicators turn green
Short signal: All 4 indicators turn red
Included
Bar coloring
William Blau Ergodic Tick Volume Indicator (TVI) [Loxx]William Blau Ergodic Tick Volume Indicator (TVI) is a volume/volatility indicator that is used for finding reversals in price movement
What is William Blau Ergodic Tick Volume?
This is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence" (1995). If you like to learn more, we advise you to read this book. His book focuses on three key aspects of trading: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum in step-by-step examples. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods.
William Blau's definition of TVI ergodicity is that the indictor is ergodic when periods are set to 32, 5, 1, and the signal is set to 5. Other combinations are not ergodic, according to Blau.
How to use TVI
TVI bar color change is a signal to enter the market. When the TVI changes from yellow to red, it is a signal to buy and if the TVI bar changes from blue to green, it is a signal to sell.
Just like the MACD and TRIX, the zero line on the indicator determines market sentiment and trend. If the TVI bars are above the zero line it's bullish and if the TVI bars are below the zero line the trend is bearish. Zero line crosses can be used to determine continuation and trend entries as well.
Included
Bar coloring
35+ moving averages for both TVI and the signal
3ngine Global BoilerplateABOUT THE BOILERPLATE
This strategy is designed to bring consistency to your strategies. It includes a macro EMA filter for filtering out countertrend trades,
an ADX filter to help filter out chop, a session filter to filter out trades outside of desired timeframe, alert messages setup for automation,
laddering in/out of trades (up to 6 rungs), trailing take profit , and beautiful visuals for each entry. There are comments throughout the
strategy that provide further instructions on how to use the boilerplate strategy. This strategy uses `threengine_global_automation_library`
throughout and must be included at the top of the strategy using `import as bot`. This allows you to use dot notation
to access functions in the library - EX: `bot.orderCurrentlyExists(orderID)`.
HOW TO USE THIS STRATEGY
1. Add your inputs
There is a section dedicated for adding your own inputs near the top of the strategy, just above the boilerplate inputs
2. Add your calculations
If your strategy requires calculations, place them in the `Strategy Specific Calculations` section
3. Add your entry criteria
Add your criteria to strategySpecificLongConditions (this gets combined with boilerplate conditions in longConditionsMet)
Add your criteria to strategySpecificShortConditions (this gets combined with boilerplate conditions in shortConditionsMet)
Set your desired entry price (calculated on every bar unless stored as a static variable) to longEntryPrice and shortEntryPrice. ( This will be the FIRST ladder if using laddering capabilities. If you pick 1 for "Ladder In Rungs" this will be the only entry. )
4. Plot anything you want to overlay on the chart in addition to the boilerplate plots and labels. Included in boilerplate:
Average entry price
Stop loss
Trailing stop
Profit target
Ladder rungs
threengine_global_automation_libraryLibrary "threengine_global_automation_library"
A collection of functions used for trade automation
getBaseCurrency()
Gets the base currency for the chart's ticker. Supported trade pairs are USD, USDT, USDC, BTC, and PERP.
Returns: Base currency as a string
getChartSymbol()
Get the current chart's symbol without the base currency appended to it. Supported trade paris are USD, USDT, USDC, BTC, and PERP.
Returns: Ssymbol and base currency
getDecimals()
Calculates how many decimals are on the quote price of the current market
Returns: The current deimal places on the market quote price
checkVar()
Plot a string as a label on the chart to test variable value. Use str.tostring() for any variable that isn't a string.
Returns: Label with stringified variable
getStrategyAlertMessage()
Generates stringified JSON for a limit order that can be passed to the strategy alert_message for a long entry.
Returns: Stringifed JSON for a long entry
taGetAdx()
Calculates the Average Directional Index
Returns: The value of ADX as a float
taGetEma()
Calculates the EMA based on a type, source, and length. Supported types are EMA, SMA, RMA, and WMA.
Returns: The value of the selected EMA
isBetweenTwoTimes()
Checks to see if within a rage based on two times
@retunrs true/false boolean
getAllTradeIDs()
This gets all closed trades and open trades
@retunrs an array of all open and closed trade ID's
getOpenTradeIDs()
This gets all open trades
@retunrs an array of all open trade ID's
orderAlreadyExists()
This checks to see if a provided order id uses the getAllTradeIDs() function to check
@retunrs an array of all open and closed trade ID's
orderCurrentlyExists()
This checks to see if a provided order id uses the getAllTradeIDs() function to check
Returns: an array of all open and closed trade ID's
getContractCount()
calulates the number of contracts you can buy with a set amount of capital and a limit price
Returns: number of contracts you can buy based on amount of capital you want to use and a price
getLadderSteps()
Returns: array of ladder entry prices and amounts based on total amount you want to invest across all ladder rungs and either a range between ladderStart and LadderStop based on specificed number of ladderRungs OR ladderStart, ladderRungs, and LadderSpacingPercent
TSI - ADX HISTOGRAM MTFThis is my hybrid indicator to recognize overbought-oversold ranges, the strong of the movement and the direction.
Thanks to Bjorgum and Rave 444 for develop this two indicators.
Pips Stepped VHF-Adaptive VMA w/ Expanded Source Types [Loxx]Pips Stepped VHF-Adaptive VMA w/ Expanded Source Types is a volatility adaptive Variable Moving Average (VMA) with stepping by pips.
What is Variable Moving Average (VMA)?
VMA (Variable Moving Average) is often mistakenly confused with the VIDYA (Volatility Index Dynamic Average) which is not strange since Tushar Chande took part in developing both. But the VMA was preceding the VIDYA and should not be mistaken for it.
What is Vertical Horizontal Filter (VHF)?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX in the Directional Movement System. Trend indicators can then be employed in trending markets and momentum indicators in ranging markets.
VMA, as is, is a "good candidate" for this type of filtering since it tends to produce prolonged periods of nearly horizontal values when the volatility of the market is low, so, when the step filtering is applied to it, the small slope changes that are happening as a results of the semi EMA calculation are filtered out, and signals are becoming more usable.
Included:
-Color bars
-Show signals
-Long/short alerts
