Reverse Ehler Instantaneous Trendline - TraderHalaiThis script uses a reverse function of the famous Ehler Instantaneous Trendline to calculate the source price required in order to change from Bullish to bearish
From my analysis, the reverse price does appear to be rather choppy, though it is 100% accurate. This is because Ehler's Instantaneous Trendline tends to remain trending for longer periods of time with above average hold periods.
The main suitability for this would be higher level timeframes, such as Weekly, 5 daily, 3 daily. From my findings Smoothed Heikin Ashi Trend, tends to provide better risk-adjusted returns across most timeframes (Higher return to drawdown ratio)
As I have spent a bit of time getting the reverse function mathematics to work, I decided to publish this as open source for the benefit, scrutiny and for further development by the TradingView community anyways.
Enjoy!
在脚本中搜索"Heikin Ashi"
Hodrick-Prescott MACD [Loxx]Hodrick-Prescott MACD is a MACD indicator using a Hodrick-Prescott Filter.
What is Hodrick–Prescott filter?
The Hodrick–Prescott filter (also known as Hodrick–Prescott decomposition) is a mathematical tool used in macroeconomics, especially in real business cycle theory, to remove the cyclical component of a time series from raw data. It is used to obtain a smoothed-curve representation of a time series, one that is more sensitive to long-term than to short-term fluctuations. The adjustment of the sensitivity of the trend to short-term fluctuations is achieved by modifying a multiplier Lambda.
The filter was popularized in the field of economics in the 1990s by economists Robert J. Hodrick and Nobel Memorial Prize winner Edward C. Prescott, though it was first proposed much earlier by E. T. Whittaker in 1923.
There are some drawbacks to use the HP filter than you can read here: en.wikipedia.org
Included
Bar coloring
3 types of signals
Alerts
Loxx's Expanded Source Types
HTF CandlesThis Indicator overlay candles from a timeframe input.
Use it to see Higher Time candles on your current chart
Heikin ashi optionnal
Stepped Moving Average of CCI [Loxx]Stepped Moving Average of CCI is a CCI that applies a stepping algorithm to smooth CCI. This allows for noice reduction and better identification of breakouts/breakdowns/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.
Included:
Bar coloring
4 signal variations w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
T3 Velocity [Loxx]T3 Velocity is a simple velocity indicator using T3 moving average that uses gradient colors to better identify trends.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
PPO w/ Discontinued Signal Lines [Loxx]PPO w/ Discontinued Signal Lines is a Percentage Price Oscillator with some upgrades. This indicator has 33 source types and 35+ moving average types as well as Discontinued Signal Lines and divergences. These additions reduce noise and increase hit rate.
What is the Price Percentage Oscillator?
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.
Included:
Bar coloring
3 signal variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Chervolinos_Rob Hoffman_Inventory Retracement Bar_and_OverlayHere is something like a combo from the well known Rob Hoffman (Overlay) Indicator and the Inventory Retracement Bar without any ballast
This really smart strategy with a low risk and a quick profit. I combine this two Indicators to save space.
The first condition is that the orange line and the lime line must be parallel and there is no other line between them because this condition is moving under 45 angle.
The second condition is that the target candles must be below the orange line in the case of the downtrend as we see.
As we see it here in the case of an uptrend should be candles above the orange line and this is logical as we see here.
Sometimes we noticed the appearance of the signal onto the candle but the conditions were not applicable because there is an orange line between the green line and the orange line and this means that the signal is fake.
This candle is also good for entry and we can place a buy order above it but is it beginner, so you must respect the conditions in order to be able to master it very well.
Enter with Confidence all conditions are present a red arrow above the candle and the candle is above the orange line and there are no lines between the lime and
orange line. Yes this is our target the entry-point will be a little above the wicked the candle, that is you will not buy now but it's a price exceeds the weight limit
even slightly, we will buy directly it is hoffman's method. Expected if the price in which resistance occurred which is the resistance represented
by the candlewick will be broken the price for rise up and strongly and if it does not happen you will not lose anything anyway to stop loss and take profit. Try the ratio by 1,5.
This part of this strategy is one of the best trading strategies with a low risk rate and can be used as an initial guide to know the market movement and to enter successful trades.
Let's start correctly. This strategy can be used on any time frame from one minute to one day or even more, but I recommend using it on a 10-minute frame one hour or 30 minutes frame. Here I use the 30-Minute frame.
This strategy is based on two things: Tramp Direction and the inventory retracement bar. Don't worry and don't think about it because all this will be automatic but let's understand some simple terms.
There many arrows in green and red. Please read the discription above.
Please read the following tipps:
To avoid the trend Reversal, try to add one one of the Divergence indicators to your chart.
To avoid entering in a pullback movement as much as possible.
--> Combine it with other indicators <--
Best Regards Chervolino
if there were any typographical errors, please forgive me
Note: Buy/Sell signals using non-standard chart types (Heikin Ashi, Renko, Kagi, Point & Figure, and Range) are not allowed, as they produce unrealistic results
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs. [Loxx]The full name of this indicator is R-Squared Adaptive Fisher Transform w/ Dynamic Zones and Divergences. This is an R-squared adaptive Fisher transform with adjustable dynamic zones, signals, alerts, and divergences.
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.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive an r-squared value that is then modified by a user input "factor"
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 variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
RSI Precision Trend Candles [Loxx]RSI Precision Trend Candles is a candle coloring indicator that uses an average range algorithm to determine trend direction. The precision trend algorithm can be used on any calculated output to tease out interesting trend information.
What is RSI?
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security.
The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The indicator was developed by J. Welles Wilder Jr. and introduced in his seminal 1978 book, New Concepts in Technical Trading Systems.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Variety Moving Averages w/ Dynamic Zones [Loxx]Variety Moving Averages w/ Dynamic Zones contains 33 source types and 35+ moving averages with double dynamic zones levels.
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
Alerts
Channels fill
Loxx's Expanded Source Types
35+ moving average types
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
Aarika Heikin AshiHello Traders,
This indicator is probably based for the people who use HA candlestick chart and frequently have to switch between Japanese candlestick and HA candles. The worry is over with this simple indicator.
Now you can enjoy both candlesticks at the same time.
AHA allows you to have 2 EMAs (mostly used for crossovers). Default values set at 8/21 EMA which we may indicate a trend reversal.
We strongly recommend you back-test everything that you need before you start using AHA.
Thank you.
Ichimoku Kinkō hyō 目均衡表█ OVERVIEW
Ichimoku is known to be an Indicator that completes itself, for its power but also for its complexity. This is why I decided to improve the work of
Goichi Hosoda in order to offer the maximum number of options for the most seasoned users but also beginners with options to simplify the
reading of Ichimoku (such as a panel directly giving you the status of each Ichimoku options or Supports/Resistances drawn automatically
according to the conditions chosen in the settings.
█ OPTIONS
Here is the complete list of options to implement :
- "Source" and "Alternative Source" (with lots of choices)
- Heikin Ashi volume.
- Weighted Moving Average Smoothing
- Minimum, Maximum and Adaptive Percentage Length adjustable for Tenkan-Sen, Kijun-Sen, Chikou Span and Senkou-Span)
- The Chikou has a Filter with modifiable Length (in Lookback Percentage)
- Advanced Filter Settings: Volume, Tenkan-Sen/Kijun-Sen Cross, Volatility, Tenkan-Sen Equal Kijun-Sen, Chikou Greater Than Price,
Chikou Momentum, Price Greater Than Kumo, Price Greater Than Tenkan-Sen, Chikou Trend Filter .
- Oscillator volume adjustable via drop-down menu with 5 types of oscillators available: "TFS Volume", "On Balance Volume",
"Klinger Volume", "Cumulative Volume", "Volume Zone".
- Relative Volume Strength Index with Length, Peak and EMA's adjustable. 3 Oscillators available: “On Balance Volume”,
“Cumulative Volume”, “Price Volume Trend”.
- Volatility adjustable with Fast and Slow Length.
- Totally customizable Support and Resistance.
- Bar Trend Color based on chosen settings.
- Fully customizable help panel.
- Alerts available for: Labels Detection, Support/Resistance Line Cross, Panel Trend Status Direction.
█ NOTES
Remember to only make a decision once you are sure of your analysis. Good trading sessions to everyone and don't forget,
risk management remains the most important!
Ranged Volume Study - R3c0nTraderCredits:
Thank you "EvoCrypto" for granting me permission to use "Ranged Volume" to create this version of it.
What is this and What does this do?
This study shows the ranged volume, and it can be used to produce buy signals for a 3Commas bot.
What’s different about this script?
I added code so that negative volume has its own color settings and lower opacity than the positive volume.
I changed the color scheme from Yellow, Red, Green, and Black to Yellow, Red, Light Blue, and Dark Blue.
How to Use
1. On the “Inputs” tab:
a. Set your “Volume Range Length” (number of bars to look back)
b. “Heikin Ashi” – Usually I leave this enabled. Make sure this matches what you have in your strategy!
c. “Show Bar Colors” – Leave disabled. Let the Strategy script color the bars in the price chart.
d. “Show Break-Out” – Leave enabled. Highlights the volume breakout in yellow and breakdowns in red.
e. “Show Range” – Leave enabled
Bogdan Ciocoiu - Sniper EntryWhat is Sniper Entry
Sniper Entry is a set indicator that encapsulates a collection of pre-configured scripts using specific variables that enable users to extract signals by interpreting market behaviour quickly, suitable for 1-3min scalping. This instrument is a tool that acts as a confluence for traders to make decisions concerning current market conditions. This indicator does not apply solely to an asset.
What Sniper Entry is not
Sniper Entry is not interpreting fundamental analysis and will also not be providing out of box market signals. Instead, it will provide a collection of integrated and significantly improved open-source subscripts designed to help traders speculate on market trends. Traders must apply their strategies and configure Sniper Entry accordingly to maximise the script's output.
Originality and usefulness
The collection of subscripts encapsulated in this tool makes it unique in the Trading View ecosystem. This indicator enables traders to consider entry positions or exit positions by comparing similar algorithms at once.
Its usefulness also emerges from the unique configurations embedded in the indicator's settings, which are different from those of the original scripts.
This indicator's originality is also reflected in how its modules are integrated, including the integration of the settings.
Open-source reuse
I used the following open-source resources, which I simplified significantly and pre-configured for short term scalping. The source codes for the below are already in the public domain, including the following links listed below.
www.tradingview.com (open source)
(open source and generic algorithm)
www.tradingview.com (open source)
(open source)
(open source)
www.tradingview.com (generic MA algorithm and open source)
(generic VWAP algorithm and open source)
The Rush
█ OVERVIEW
This script shows when buyers are in a rush to buy and when sellers are in a rush to sell
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█ CONCEPTS
Prophet Mohamed Peace be upon Him once said something similar to this "It is not advisable to trade if you do not know the
Volume".
In his book "The Day Trader's Bible - Or My Secret In Day trading Of Stocks", Richard D. Kickoff wrote in page 55
"This shows that there was only 100 shares for sale at 180 1/8, none at all at 180f^, and only 500 at 3/8. The jump from 1 to 8 to 3/8
Emphasizes both the absence of pressure and persistency on the part of the buyers. They are not content to wait patiently until they can
Secure the stock at 180^/4; they "reach" for it."
This script was inspired by these two great men.
Prophet Mohamed Peace be upon Him showed the importance of the volume and Richard D. Kickoff explained what Prophet
Mohamed Peace be upon Him meant.
So I created this script that gauge the movement of the stock and the sentiments of the traders.
═════════════════════════════════════════════════════════════════════════
• FEATURES: The script calculates The Percentage Difference of the price and The Percentage Difference of the volume between
two success bullish candles (or two success bearish candles) and then it creates a ratio between these two Percentage
Differences and in the end the ratio is compared to the previous one to see if there is an increase or a decrease.
═════════════════════════════════════════════════════════════════════════
• HOW TO USE: if you see 2 or more successive red bars that mean bears are in hurry to sell and you can expect a bearish trend soon
if the Market Maker allows it or later if the Market Maker wants to do some distribution.
if you see 2 or more successive green bars that mean bulls are in hurry to buy and you can expect a bullish trend soon if the Market
Maker allows it or later if the Market Maker wants to do some accumulation.
═════════════════════════════════════════════════════════════════════════
• LIMITATIONS:
1- Use only Heikin Ashi chart
2- Good only if volume data is correct , meaning good for a centralized Market. (You can use it for forex or
crypto but at your own risk because those markets are not centralized)
═════════════════════════════════════════════════════════════════════════
• THANKS: I pay homage to Prophet Mohamed Peace be upon Him and Richard D. Kickoff who inspired the creation of this
Script.
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Heikinashi Candles Biggest problem that when we use normal heikin-ashi candlestick pattern then all applied indicator values also changes
Like Pivot Point with CPR ,,, EMA,,, SMA including VWAP indicator value
So , this indicator help us plot Heikinashi candles on Normal candles
Please follow below steps:
Add this indicator to chart
Go to setting >> un-tick Body, Border and Wick color
That will hide candles on chart and Plot heikinashi candles on Normal candles without changing any indicator value
Remember use normal candle stick pattern that
Mazuuma Churn IndicatorThis indicator was specifically made to confirm a periode of sideways movement (churn) on Bitcoin. It can probably be used for other cryptocurrencies as well. I use it on the daily timeframe.
Yellow means "Unconfirmed".
Orange means "Partially Confirmed".
Red means "Confirmed"
The indicator is not perfect, so use your common sense.
Churn starts when at least 2 of the conditions below are met (use also your common sense):
1. ATR < MA 20 on ATR
2. Distance to EMA 200 must be ≤ 16% at “Open churn”
3. EMA 12 on RSI between 40 and 60
4. ADX < 25
The above are weighted. Meaning no 1 has most significance. The numbers can be tweaked.
Reversal coming
* The indicators above break out, especially the ATR
* Color shift of the Heikin Ashi candle on weekly timeframe
* Engulfing candle on weekly timeframe
Because of the offset of the EMA 200, the precision of the Churn predictor can be off after a VERY big spike up or down, e.g. dec 2017. After such a spike use your common sense.
Personally I use this for bot trading, i.e. turn off trend following bots when in sideways market and use grid bots or other means of trading instead.
Indicator Functions with Factor and HeikinAshiHello all,
This indicator returns below selected indicators values with entered parameters.
Also you can add factorization, functions candles, function HeikinAshi and more to the plot.
VERSION:
Version 1: returns series only source and Length with already defined default values
Version 2: returns series with source, Length, p1 and p2 parameters according to the indicator definition (ex: )
PARAMETERS p1 p2
for defining multi arguments (See indicators list) indicator input value usable with verison=V2 selected.. ex: for alma( src , len ,offset=0.85,sigma=6), set source=source, len=length, p1=0.85 an p2=6
FACTOR:
Add double triple, Quadruple factors to selected indicator (like converting EMA to 2-DEMA, 3-TEMA, 4-QEMA...)
1-Original
2-Double
3-Triple
4-Quadruple
LOG
Log: Use log, log10 on function entries
PLOTTING:
PType: Plotting type of the function on the screen
Original :use original values
Org. Range (-1,1): usable for indicators between range -1 and 1
Stochastic: Convert indicator values by using stochastic calculation between -1 & 1. (use AT/% length to better view)
PercentRank: Convert indicator values by using Percent Rank calculation between -1 & 1. (use AT/% length to better view)
ST/%: length for plotting Type for stochastic and Percent Rank options
Smooth: Use SWMA for smoothing the function
DISPLAY TYPES
Plot Candles: Display the selected indicator as candle by implementing values
Plot Ind: Display result of indicator with selected source
HeikinAshi: Display Selected indicator candles with Heikin Ashi calculation
INDICATOR LIST:
hide = 'DONT DISPLAY', //Dont display & calculate the indicator. (For my framework usage)
alma = 'alma( src , len ,offset=0.85,sigma=6)', // Arnaud Legoux Moving Average
ama = 'ama( src , len ,fast=14,slow=100)', //Adjusted Moving Average
acdst = 'accdist()', // Accumulation/distribution index.
cma = 'cma( src , len )', //Corrective Moving average
dema = 'dema( src , len )', // Double EMA (Same as EMA with 2 factor)
ema = 'ema( src , len )', // Exponential Moving Average
gmma = 'gmma( src , len )', //Geometric Mean Moving Average
hghst = 'highest( src , len )', //Highest value for a given number of bars back.
hl2ma = 'hl2ma( src , len )', //higest lowest moving average
hma = 'hma( src , len )', // Hull Moving Average .
lgAdt = 'lagAdapt( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter
lgAdV = 'lagAdaptV( src , len ,perclen=5,fperc=50)', //Ehler's Adaptive Laguerre filter variation
lguer = 'laguerre( src , len )', //Ehler's Laguerre filter
lsrcp = 'lesrcp( src , len )', //lowest exponential esrcpanding moving line
lexp = 'lexp( src , len )', //lowest exponential expanding moving line
linrg = 'linreg( src , len ,loffset=1)', // Linear regression
lowst = 'lowest( src , len )', //Lovest value for a given number of bars back.
pcnl = 'percntl( src , len )', //percentile nearest rank. Calculates percentile using method of Nearest Rank.
pcnli = 'percntli( src , len )', //percentile linear interpolation. Calculates percentile using method of linear interpolation between the two nearest ranks.
rema = 'rema( src , len )', //Range EMA (REMA)
rma = 'rma( src , len )', //Moving average used in RSI . It is the exponentially weighted moving average with alpha = 1 / length.
sma = 'sma( src , len )', // Smoothed Moving Average
smma = 'smma( src , len )', // Smoothed Moving Average
supr2 = 'super2( src , len )', //Ehler's super smoother, 2 pole
supr3 = 'super3( src , len )', //Ehler's super smoother, 3 pole
strnd = 'supertrend( src , len ,period=3)', //Supertrend indicator
swma = 'swma( src , len )', //Sine-Weighted Moving Average
tema = 'tema( src , len )', // Triple EMA (Same as EMA with 3 factor)
tma = 'tma( src , len )', //Triangular Moving Average
vida = 'vida( src , len )', // Variable Index Dynamic Average
vwma = 'vwma( src , len )', // Volume Weigted Moving Average
wma = 'wma( src , len )', //Weigted Moving Average
angle = 'angle( src , len )', //angle of the series (Use its Input as another indicator output)
atr = 'atr( src , len )', // average true range . RMA of true range.
bbr = 'bbr( src , len ,mult=1)', // bollinger %%
bbw = 'bbw( src , len ,mult=2)', // Bollinger Bands Width . The Bollinger Band Width is the difference between the upper and the lower Bollinger Bands divided by the middle band.
cci = 'cci( src , len )', // commodity channel index
cctbb = 'cctbbo( src , len )', // CCT Bollinger Band Oscilator
chng = 'change( src , len )', //Difference between current value and previous, source - source.
cmo = 'cmo( src , len )', // Chande Momentum Oscillator . Calculates the difference between the sum of recent gains and the sum of recent losses and then divides the result by the sum of all price movement over the same period.
cog = 'cog( src , len )', //The cog (center of gravity ) is an indicator based on statistics and the Fibonacci golden ratio.
cpcrv = 'copcurve( src , len )', // Coppock Curve. was originally developed by Edwin "Sedge" Coppock (Barron's Magazine, October 1962).
corrl = 'correl( src , len )', // Correlation coefficient . Describes the degree to which two series tend to deviate from their ta. sma values.
count = 'count( src , len )', //green avg - red avg
dev = 'dev( src , len )', //ta.dev() Measure of difference between the series and it's ta. sma
fall = 'falling( src , len )', //ta.falling() Test if the `source` series is now falling for `length` bars long. (Use its Input as another indicator output)
kcr = 'kcr( src , len ,mult=2)', // Keltner Channels Range
kcw = 'kcw( src , len ,mult=2)', //ta.kcw(). Keltner Channels Width. The Keltner Channels Width is the difference between the upper and the lower Keltner Channels divided by the middle channel.
macd = 'macd( src , len )', // macd
mfi = 'mfi( src , len )', // Money Flow Index
nvi = 'nvi()', // Negative Volume Index
obv = 'obv()', // On Balance Volume
pvi = 'pvi()', // Positive Volume Index
pvt = 'pvt()', // Price Volume Trend
rise = 'rising( src , len )', //ta.rising() Test if the `source` series is now rising for `length` bars long. (Use its Input as another indicator output)
roc = 'roc( src , len )', // Rate of Change
rsi = 'rsi( src , len )', // Relative strength Index
smosc = 'smi_osc( src , len ,fast=5, slow=34)', //smi Oscillator
smsig = 'smi_sig( src , len ,fast=5, slow=34)', //smi Signal
stdev = 'stdev( src , len )', //Standart deviation
trix = 'trix( src , len )' , //the rate of change of a triple exponentially smoothed moving average .
tsi = 'tsi( src , len )', //True Strength Index
vari = 'variance( src , len )', //ta.variance(). Variance is the expectation of the squared deviation of a series from its mean (ta. sma ), and it informally measures how far a set of numbers are spread out from their mean.
wilpc = 'willprc( src , len )', // Williams %R
wad = 'wad()', // Williams Accumulation/Distribution .
wvad = 'wvad()' //Williams Variable Accumulation/Distribution
I will update the indicator list when I will update the library
Thanks to tradingview, @RodrigoKazuma for their open source indicators
Divergence-Support/Resistance - Widget [HeWhoMustNotBeNamed]Unique live widget which provides information about support and resistance along with distance to immediate levels.
⬜ Custom Candle Source
Three options of candles available
▶ Regular OHLC candles
▶ Heikin-Ashi candles ( Taken leaf out of @Bjorgum's book - but, still could not do it as good as him :) )
▶ Moving average candles
⬜ Multiple zigzags and choice of oscillator
Like any other zigzag based indicator, I prefer using 4 zigzag levels.
And select oscillator of your choice to identify divergence. Oscillator lengths are automatically calculated based on zigzag lengths. You can also chose external oscillator.
⬜ Support and resistance widget settings
▶ Max S/R per zigzag - this is the number of last S/R levels recorded for each zigzag
▶ Max S/R for stats - even though there can be many number of S/R levels, we cannot show all of them in the widget due to space constraint. Hence, this is set to minimal value of 3. Can be changed via this input.
▶ Max S/R for Alerts - When sending stats through alerts, users can decide how many immediate S/R levels to be sent in them.
▶ Alert Choices - Can chose to get alerts when new S/R levels formed and also when existing S/R levels broken.
Personally think it is a very handy widget to have!!
BBW EMA RIBBON+BB%B+EMA BUY/SELL SignalsBINANCE:BTCUSDT
* * * TESTED ON BTCUSDT 4H CHART * * *
The indicator gives signals based on possible ENTRY/EXIT zones (calculated using an ema ribbon of the BBW) and BB%B oversold/overbought conditions, also a 200 EMA filter is used to trade following the major trend.
ENTRY/EXIT zones can be used as a confirmation on other strategies (green zones suggest a possible entry for a long in an uptrend and for a short in a downtrend, red zones suggest a possible exit for long & short)
Only LONG signals are given.
Signals are divided in 3 categories based on their strenght:
- Weak BUY/SELL (green/red circles on chart)
- normal BUY/SELL (green/red triangles with "BUY" or "SELL" text on chart)
- strong BUY/SELL (green/red labels with "STRONG BUY" or "STRONG SELL" text on chart)
You can use signals from the same category as triggers for entering or exiting a trade or you can try using signals from different categories
All indicators settings can be costumized
Possible EXTRY/EXIT zones can be turned ON/OFF
Every signal label can be turned ON/OFF
EMA that changes color based on last candle close can be turned ON/OFF
Alerts can be set for:
- General Signals (every signal will trigger it)
- Weak Signals (only weak signals will trigger it)
- Normal Signals (only normal signals will trigger it)
- Strong Signals (only strong signals will trigger it)
There are 3 BB%B Oversold & 3 BB%B Overbought value presets:
- BASIC (default values)
- LOW (slightly lower values from default)
- HIGH (slightly higher values from default)
Tips:
Use BB%B OS LOW preset to find entries at a better price
Use BB%B OS HIGH preset to find entries faster
Use BB%B OB LOW preset to find exits faster
Use BB%B OB HIGH preset to find exits at a better price
There are 3 Filters already included:
- A checkbox to toggle ON/OFF the use of Heikin Ashi candels to detect trades
- A checkbox to toggle ON/OFF the use of an ema filter for the ribbon that tries to reduce the number of false signals on over-extended price movements
- A checkbox to toggle ON/OFF the use of an ema filter that tries to reduce the number of false signals when price crosses the moving average many times consecutively
CH-I: Trend - Higher Timeframe BodyI took the script for the built-in indicator for candle bodies of a higher timeframe (www.tradingview.com) which has a fixed border width and style and added the possibility to customize both the border width and the border style or to even disable the display of any border at all, which makes the presentation of those boxes more flexible.
Juicy TrendThis script is simple.
It uses EMA and HMA crosses to find entries and exits.
There is an option for sourcing Heikin Ashi based entries/exits.
There is an option for Stop Loss percentage.
It is made for finding Long entries on the 45min chart.
You can optimize the signals by adjusting the moving average lengths.
The default settings are for BTC/USDT 45min.
Suggestion, reduce the Stop Loss percentage for smaller / more volatile assets.
