Aroon Oscillator of Adaptive RSI [Loxx]Aroon Oscillator of Adaptive RSI uses RSI to calculate AROON in attempt to capture more trend and momentum quicker than Aroon or RSI alone. Aroon Oscillator of Adaptive RSI has three different types of RSI calculations and the choice of either fixed, VHF Adaptive, or Band-pass Adaptive cycle measures to calculate RSI.
Arron Oscillator:
The Aroon Oscillator was developed by Tushar Chande in 1995 as part of the Aroon Indicator system. Chande’s intention for the system was to highlight short-term trend changes. The name Aroon is derived from the Sanskrit language and roughly translates to “dawn’s early light.”
The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend and the likelihood that it will continue.
Aroon oscillator readings above zero indicate that an uptrend is present, while readings below zero indicate that a downtrend is present. Traders watch for zero line crossovers to signal potential trend changes. They also watch for big moves, above 50 or below -50 to signal strong price moves.
Wilders' RSI:
The Relative Strength Index (RSI) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements. Essentially RSI, when graphed, provides a visual mean to monitor both the current, as well as historical, strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period creating a reliable metric of price and momentum changes. Given the popularity of cash settled instruments (stock indexes) and leveraged financial products (the entire field of derivatives); RSI has proven to be a viable indicator of price movements.
RSX RSI:
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI, but with one important exception: the noise is gone with no added lag.
Rapid RSI:
Rapid RSI Indicator, from Ian Copsey's article in the October 2006 issue of Stocks & Commodities magazine.
RapidRSI resembles Wilder's RSI, but uses a SMA instead of a WilderMA for internal smoothing of price change accumulators.
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.
Band-pass Adaptive Cycle
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal ; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth.
Included:
-Toggle on/off bar coloring
-Customize RSI signal using fixed, VHF Adaptive, and Band-pass Adaptive calculations
-Choose from three different RSI types
Happy trading! Indicateur
Adaptive EnvelopeI bring to your attention a dynamic indicator Adaptive Envelope .
The main qualitative characteristic of the technical indicator is adaptability. This means that it does not need to be adjusted for each tool. The adaptive envelope itself dynamically adjusts to the volatility of each individual instrument, or even timeframe.
And thanks to a wide range of settings, the indicator can be adjusted to your needs. Let's consider an example of the use of the indicator in trading.
Option #1. The envelope shows the "stretch" of the market - that is, the price of the asset beyond normal volatility. And it is at such moments that the probability of returning to the average is highest. That is, for such a signal, we wait for the exit to the moving average, and when returning with a stop order, we enter the averaging direction.
Option #2. Another option for trading is to buy at the lower level, as well as additional purchases along the lines of the envelope. Exit - on the middle line of the envelope (for shorts on the contrary) - so we have a full adaptability of the strategy. I repeat that due to adaptability, there will be no need to reconfigure when changing market characteristics.
Thank you for attention. Sincerely, Oleksandr Yanchak. Capitalizator.UA
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action zone - ATR stop reverse order strategy v0.1 by 9nckACTION ZONE-ATR MOD v0.1 DOCUMENTATION
Overview
This tradingview pine script strategy is mainly created to enrich my coding skill. It is a combination of “CDC-ACTIONZONE” and my personal studies of trading techniques in various sources e.g.book, course or blog. This strategy purposefully built to connect with my automatic trading bot. However, It will be very useful to aid your trading routine by diminishing mental distraction which possibly leads to bad trades.
How does it work?
This strategy will do a basic simple thing that most traders do by creating entry signals on both sides long/short and also set the stop loss. Furthermore, It will also reverse the order (from long to short and vice versa (if long/short conditions are met). Finally, it will recalculate the stop loss/take profit price in every complete bar to increase the chance of winning and limit our loss.
Entry rules(Long/Short)
If you have no open order, an order will be created when a fast EMA crosses(up(long)/down(short) the slow EMA(It’s as simple as that).
If you have an open order, the current order will be (sold if long, covered if short) and the opposite side order will be created.
Exit and Reverse rules(Long/Short)
If fast EMA cross (DOWN(long), UP(short)), the current order will be closed, THE OPPOSITE SIDE ORDER WILL ALSO BE CREATED.
Risk management
FLEX STOP PRICE : initial value will be set at the bar which order created. It is a fast ema (+/-) MIDDLE ATR value.
If MIDDLE ATR value rises, it will be our new stop price.
If MIDDLE ATR value falls, stop price unchanged
If Price OVERBOUGHT(long)/SOLD(short), LOW of that bar will be a new stop price.
Minimum position hold period
In order to eliminate risk of repeatedly open, close orders in sideway trends. Minimum hold period must be passed to start exit our position. However, It always respects stop loss prices. The value refers to the number of bars.
MUST READ!!!
This strategy uses only MARKET ORDER. If you trade with a bot, make sure you choose only enormous market cap tokens.
This strategy is bi-direction strategy. It will work best in the DERIVATIVE market.
It was initially designed to compete in the cryptocurrency market which has very high volume and volatility.
I only use this strategy in 1HR (acceptable change rate, optimum trade frequency)
How (should) we use it?
Choose crypto future pairs (recommend only top 10-15 market volume pairs in Binance, let’s say 1000M+ trade value)
Choose your time frame (1H is strongly recommended)
Setup your portfolio profile (Setting->Properties) such as Initial cap, order size, commission. DO NOT USE CAL ON EVERY TICK IT WILL CAUSE REPAINTING AND YOUR CAPITAL IS BLEEDING !!!
BACKTEST FIRST!! Back test is a combination of art, math and statis(and a bit of luck). You can apply to train and test methods or whatever you are familiar with. In my opinion, your test period should include UPTREND, SIDEWAY, DOWNTREND. Fine tune fast, slow ema first(my best ema length of 1H timeframe around 7-10, 17-22). Try to eliminate fault breakout trade and use other options only necessary. Hopefully we can use automatic optimization on Pine Script soon.
Don’t forget to turn off using a specific backtest date option to start your strategy.A
THIS IS NOT A PERFECT (OR EVEN PROFITABLE) STRATEGY. USE AT YOUR OWN RISK AND TRADE RESPONSIBLY. DYOR DUDE.
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Adaptive Ehlers Deviation Scaled Moving Average (AEDSMA)AEDSMA INTRODUCTION
This indicator is a functional enhancement to “Ehlers Deviation Scaled Moving Average (EDSMA / DSMA)”. I’ve used Volume Breakout and Volatility for dynamic length adaption and further Slope too for trend evaluation.
EDSMA was originally developed by John F. Ehlers (Stocks & Commodities V. 36:8: The Deviation-Scaled Moving Average).
IDEA PLACEMENT
I’ve traded almost every kind of market with different volatility conditions using Moving Averages. It was too much of a hassle to select and use different MA length depending upon market trend. So, the journey started with adapting Moving Averages with another parameter and that’s how “MZ SAMA ” came into being where Slope was used to adapt Adaptive Moving Average with trend change. The problem was still pretty much the same as SAMA might not be effective on every market condition. Hence, I worked on Volume to adapt Moving Averages accordingly. I cane up with “MZ RVSI ” which I used in “MZ DVAMA ” to adapt dynamic length in Adaptive Moving Average and also used “MZ RVSI " alongside Slope as confirmation of trend changes.
Meanwhile, I started using DVAMA methodology on different types on Moving Averages that allow dynamic length for example Hull Moving Average, Linear Regression Curve, SMA, WMA, TMA and many more. All of my tested Mas showed too much flexibility because of volume based Adaptive length.
I came across a script of “Adaptive Hull Moving Average” which pretty much used the similar methodology as DVAMA but when I looked into its depth, its volume oscillator wasn’t working at all and only volatility based dynamic length was used. It was an interesting idea so, I decided to use Volume and Volatility alongside for better results but was nearly impossible to achieve what I wanted using only Hull Moving Average.
I had been using EDSMA in “MA MTF Cross Strategy” and “MZ SRSI Strategy V1.0” previously. It was the perfect choice when comparing to usage of slope on it. DSMA works perfectly as support and resistance as its Deviation Scaled. So, I tried using it to adapt dynamic length based on Volume and Volatility and I wasn’t disappointed. It worked like a charm when I adapted dynamic length between 50 and 255.
DYNAMIC LENGTH BENEFITS
Dynamic length adaption methodology works in a way of adapting Relatively Lower Length leading toward overfitting if trend is supported by Volume and Volatility . Similarly, adapting Relatively Higher Length leading toward underfitting if trend isn’t supported by Volume and Volatility .
Dynamic length adaption makes Moving Average to work better for both Bull and Bear-runs avoiding almost every fake break-in and breakouts. Hence, adaptive MA becomes more reliable for breakout trading.
MA would be more useful as it would adapt almost every chart based on its Volume and Volatility data.
DYNAMIC COLORS AND TREND CORRELATION
I’ve used dynamic coloring to identify trends with more detail which are as follows:
Lime Color: Strong Uptrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Fuchsia Color: Weak uptrend only supported by Slope or whatever you’ve selected.
Red Color: Strong Downtrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Grey Color: Weak Downtrend only supported by Slope or whatever you’ve selected.
Yellow Color: Possible reversal indication by Slope if enabled. Market is either sideways, consolidating or showing choppiness during that period.
SIGNALS
Green Circle: Market good for long with support of Volume and Volatility or whatever you’ve chosen from both of them.
Red Circle: Market good to short with support from Volume and Volatility or whatever you’ve chosen from both of them.
Yellow Cross: Market either touched top or bottom ATR band and can act as good TP or SL.
EDSMA EVELOPE/BANDS: I’ve included ATR based bands to the Adaptive EDSMA which act as good support/resistance despite from main Adaptive EDSMA Curve.
DEFAULT SETTINGS
I’ve set default Minimum length to 50 and Maximum length to 255 which I’ve found works best for almost all timeframes but you can change this delta to adapt your timeframe accordingly with more precision.
Dynamic length adoption is enabled based on both Volume and Volatility but only one or none of them can also be selected.
Trend signals are enabled based on Slope and Volume but Volatility can be enabled for more precise confirmations.
In “ RVSI ” settings TFS Volume Oscillator is set to default but others work good too especially Volume Zone Oscillator. For more details about Volume Breakout you can check “MZ RVSI Indicator".
ATR breakout is set to be positive if period 14 exceeds period 46 but can be changed if more adaption with volatility is required.
EDSMA super smoother filter length is set to 20 which can be increased to 50 or more for better smoothing but this will also change slope results accordingly.
EDSMA super smoother filter poles are set to 2 because found better results with 2 instead of 3.
FURTHER ENHANCEMENTS
So far, I’ve seen better results with Volume Breakout and Volatility but other parameters such as Linear Slope of Particular MA, MACD, “MZ SRSI ”, a Conditional Uptrend MA or simply KDJ can also be used for dynamic length adaption.
I haven't yet gotten used to pine script arrays so, defining and using conditional operators is pretty much lazy programming for me. Would be great redefining everything through truth matrix instead of using if-else conditions.
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