Advanced Awesome Oscillator [CryptoSea]Advanced AO Analysis Indicator
The Advanced AO Analysis indicator is a sophisticated tool designed to evaluate the Awesome Oscillator (AO) in search of regular and hidden divergences that signal potential price reversals. By tracking the intensity and duration of the AO's movements, this indicator aids traders in pinpointing critical points in price action.
Key Features
Divergence Detection: Identifies both regular and hidden bullish and bearish divergences, providing early signs of potential market reversals.
Customizable Lookback Periods: Allows users to set specific lookback windows to define the strength and relevance of detected divergences.
Adaptive Oscillator Display: Features customizable display options for the AO, enabling users to view data in different modes suited to their analysis needs.
Alert System: Includes configurable alerts to notify users of potential divergence formations, helping traders respond promptly.
How it Works
AO Calculation: Computes the AO as the difference between short-term and long-term moving averages of the midpoints of bars, highlighting momentum shifts.
Pivot Point Analysis: Utilizes advanced algorithms to find low and high pivot points based on the oscillator values, crucial for spotting trend reversals.
Range Validation: Verifies that divergences occur within a predefined range from pivot points, ensuring their validity and strength.
Visualisation: Plots AO values and potential divergences directly on the chart, aiding in quick visual analysis.
Application
Strategic Decision-Making: Assists traders in making informed decisions by providing detailed analysis of AO movements and divergence.
Trend Confirmation: Reinforces trading strategies by confirming potential reversals with pivot point detection and divergence analysis.
Behavioural Insight: Offers insights into market dynamics and sentiment by analyzing the depth and duration of AO cycles above and below zero.
The Advanced AO Analysis indicator equips traders with a powerful analytical tool for studying the Awesome Oscillator in-depth, enhancing their ability to spot and act on divergence-based trading opportunities in the cryptocurrency markets.
Ao
Divergence Signal [TradingFinder] RSI & MACD Reversal On Swing🔵 Introduction
Sometimes in analyzing price charts using indicators, you may observe a discrepancy. For instance, while the price of stocks, currencies, or commodities is increasing, the indicator shows a decrease. Such a phenomenon in technical analysis is termed "divergence." Divergences are categorized into three types based on their formation and the prediction they make about the continuation of the price trend: "Regular Divergence," "Hidden Divergence," and "Time Divergence."
🟣 Important :
• This indicator exclusively identifies regular divergences since its primary function is to detect reversal points.
• This indicator identifies divergences using three indicators: "Moving Average Convergence Divergence" (MACD), "Relative Strength Index" (RSI), and "Awesome Oscillator" (AO). The user can choose each of these indicators in the settings using the "Divergence Detection Method" dropdown menu for identifying divergences. These settings are by default set to the MACD mode.
🔵Types of Divergence
Divergences, as mentioned, offer different predictions about the continuation of price trends. Hence, they have various types. We will focus on explaining regular divergences based on this indicator.
🟣 Regular Divergence(RD) :
Regular divergence is a situation arising from contradictory behavior between the indicator and the price chart at the end of a trend. By identifying regular divergences, we anticipate a change in trend direction resembling a reversal pattern.
Regular divergence has two types based on the trend and prediction:
Negative Regular Divergence (RD-) :
This type occurs between two price peaks at the end of an uptrend. Despite forming a new high, the indicator fails to recognize it, indicating a negative regular divergence. The likelihood of a subsequent downtrend is high. Negative divergence suggests strong selling pressure and weak buying power, portraying an unfavorable future for the stock.
Positive Regular Divergence (RD+) :
In contrast, positive regular divergence happens at the end of a downtrend and between two price troughs. As depicted in the chart, although the price forms a new low, the indicator doesn't acknowledge it. Positive regular divergence indicates robust buying pressure and weak selling power. Upon identifying positive divergence in the chart, we expect a price increase for the stock under review
🔵 How to Use
Information from the indicator is displayed in two ways: Table and Label.
🟣 Table : The table displays information about the latest divergence. This includes the type of divergence, existence or absence of divergence, consecutive divergences, divergence quality, and change in indicator phase.
Type Divergence : Indicates the type of divergence, which can be either "Bullish Divergence" or "Bearish Divergence."
Exist : Indicates the presence of divergence with a "+" sign and absence with a "-" sign. A green color is used for bullish divergence and red for bearish divergence.
Consecutive : Shows the number of consecutive divergences. For example, if there are 3 consecutive divergences, the number 3 is displayed.
Divergence Quality : Displays the quality of the divergence based on the number of consecutive divergences. If there is 1 divergence, the quality is "Normal"; for 2 divergences, it's "Good"; and for 3 or more divergences, it's "Strong."
Change Phase Indicator : Indicates whether a phase change in the indicator has occurred with "+" for yes and "-" for no.
🟣 Label : Unlike the table, which only shows information about the latest divergence, labels display information about each divergence at the point where it occurs. The information includes the type of divergence, detection method, divergence quality, consecutive divergences, and change in phase indicator. The selected method of detection is also displayed. For example, if the chosen method is the "AO" indicator, the label will show "Method: AO."
🔵 Settings
Fractal Period : Determines the period of swings. The minimum and default value is 2.
Divergence Detect Method : Selects the indicator (MACD, RSI, or AO) used for detecting divergences. The default indicator is MACD.
Show Fractal : Chooses whether to display fractals or not. The default is "No."
Show Table : Determines whether to display the table or not. The default is "Yes."
Show Label : Chooses whether to display labels or not. The default is "Yes."
Label Size : Adjusts the size of the labels from "Tiny" to "Large."
TTM Waves ABC ATR AO MOM SQZ//All code picked from many indicators, if you recognize your code, pls comment so people can see your awesome work! I only edited and added them all together so people don't use all their indicator slots. Hope this indicator helps as many people as it can. LFG!!!
AO (Awesome Oscillator) Useful to find potential reversals in trend.
MOM (Momentum) An oscillator that measures momentum.
ATR (Average True Range) Measures the upside and downside from the average price movement occuring. 1 ATR is the general measurement. Many traders use 2ATR to set a stop and 4ATR to set take profit from their entry based on current reading from the ATR.
SQZ ( TTM Squeeze) Measures when bollinger bands have left the interior of the Keltner Channel in an attempt to predict volatility thats about to happen to either side. Green = Move is probably about to happen.
TTM Waves ( Waves A, B, and C) Measure the previous candles to determine chop, positive or negative trends. C measures the previous 30 candles or so, B the last 15 or so, and A measures the last 8 or so. You can use all three or just one. You can sneak in a move if the 2 fastest ones have moved into your preferred area. (Positive or Negative) If the wave is not fully positve or negative then that is probably chop.
-Penguincryptic
Any Oscillator Underlay [TTF]We are proud to release a new indicator that has been a while in the making - the Any Oscillator Underlay (AOU) !
Note: There is a lot to discuss regarding this indicator, including its intent and some of how it operates, so please be sure to read this entire description before using this indicator to help ensure you understand both the intent and some limitations with this tool.
Our intent for building this indicator was to accomplish the following:
Combine all of the oscillators that we like to use into a single indicator
Take up a bit less screen space for the underlay indicators for strategies that utilize multiple oscillators
Provide a tool for newer traders to be able to leverage multiple oscillators in a single indicator
Features:
Includes 8 separate, fully-functional indicators combined into one
Ability to easily enable/disable and configure each included indicator independently
Clearly named plots to support user customization of color and styling, as well as manual creation of alerts
Ability to customize sub-indicator title position and color
Ability to customize sub-indicator divider lines style and color
Indicators that are included in this initial release:
TSI
2x RSIs (dubbed the Twin RSI )
Stochastic RSI
Stochastic
Ultimate Oscillator
Awesome Oscillator
MACD
Outback RSI (Color-coding only)
Quick note on OB/OS:
Before we get into covering each included indicator, we first need to cover a core concept for how we're defining OB and OS levels. To help illustrate this, we will use the TSI as an example.
The TSI by default has a mid-point of 0 and a range of -100 to 100. As a result, a common practice is to place lines on the -30 and +30 levels to represent OS and OB zones, respectively. Most people tend to view these levels as distance from the edges/outer bounds or as absolute levels, but we feel a more way to frame the OB/OS concept is to instead define it as distance ("offset") from the mid-line. In keeping with the -30 and +30 levels in our example, the offset in this case would be "30".
Taking this a step further, let's say we decided we wanted an offset of 25. Since the mid-point is 0, we'd then calculate the OB level as 0 + 25 (+25), and the OS level as 0 - 25 (-25).
Now that we've covered the concept of how we approach defining OB and OS levels (based on offset/distance from the mid-line), and since we did apply some transformations, rescaling, and/or repositioning to all of the indicators noted above, we are going to discuss each component indicator to detail both how it was modified from the original to fit the stacked-indicator model, as well as the various major components that the indicator contains.
TSI:
This indicator contains the following major elements:
TSI and TSI Signal Line
Color-coded fill for the TSI/TSI Signal lines
Moving Average for the TSI
TSI Histogram
Mid-line and OB/OS lines
Default TSI fill color coding:
Green : TSI is above the signal line
Red : TSI is below the signal line
Note: The TSI traditionally has a range of -100 to +100 with a mid-point of 0 (range of 200). To fit into our stacking model, we first shrunk the range to 100 (-50 to +50 - cut it in half), then repositioned it to have a mid-point of 50. Since this is the "bottom" of our indicator-stack, no additional repositioning is necessary.
Twin RSI:
This indicator contains the following major elements:
Fast RSI (useful if you want to leverage 2x RSIs as it makes it easier to see the overlaps and crosses - can be disabled if desired)
Slow RSI (primary RSI)
Color-coded fill for the Fast/Slow RSI lines (if Fast RSI is enabled and configured)
Moving Average for the Slow RSI
Mid-line and OB/OS lines
Default Twin RSI fill color coding:
Dark Red : Fast RSI below Slow RSI and Slow RSI below Slow RSI MA
Light Red : Fast RSI below Slow RSI and Slow RSI above Slow RSI MA
Dark Green : Fast RSI above Slow RSI and Slow RSI below Slow RSI MA
Light Green : Fast RSI above Slow RSI and Slow RSI above Slow RSI MA
Note: The RSI naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Stochastic and Stochastic RSI:
These indicators contain the following major elements:
Configurable lengths for the RSI (for the Stochastic RSI only), K, and D values
Configurable base price source
Mid-line and OB/OS lines
Note: The Stochastic and Stochastic RSI both have a normal range of 0 to 100 with a mid-point of 50, so no rescaling or transformations are done on either of these indicators. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Ultimate Oscillator (UO):
This indicator contains the following major elements:
Configurable lengths for the Fast, Middle, and Slow BP/TR components
Mid-line and OB/OS lines
Moving Average for the UO
Color-coded fill for the UO/UO MA lines (if UO MA is enabled and configured)
Default UO fill color coding:
Green : UO is above the moving average line
Red : UO is below the moving average line
Note: The UO naturally has a range of 0 to 100 with a mid-point of 50, so no rescaling or transformation is done on this indicator. The only manipulation done is to properly position it in the indicator-stack based on which other indicators are also enabled.
Awesome Oscillator (AO):
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the AO calculation
Configurable price source for the moving averages used in the AO calculation
Mid-line
Option to display the AO as a line or pseudo-histogram
Moving Average for the AO
Color-coded fill for the AO/AO MA lines (if AO MA is enabled and configured)
Default AO fill color coding (Note: Fill was disabled in the image above to improve clarity):
Green : AO is above the moving average line
Red : AO is below the moving average line
Note: The AO is technically has an infinite (unbound) range - -∞ to ∞ - and the effective range is bound to the underlying security price (e.g. BTC will have a wider range than SP500, and SP500 will have a wider range than EUR/USD). We employed some special techniques to rescale this indicator into our desired range of 100 (-50 to 50), and then repositioned it to have a midpoint of 50 (range of 0 to 100) to meet the constraints of our stacking model. We then do one final repositioning to place it in the correct position the indicator-stack based on which other indicators are also enabled. For more details on how we accomplished this, read our section "Binding Infinity" below.
MACD:
This indicator contains the following major elements:
Configurable lengths for the Fast and Slow moving averages used in the MACD calculation
Configurable price source for the moving averages used in the MACD calculation
Configurable length and calculation method for the MACD Signal Line calculation
Mid-line
Note: Like the AO, the MACD also technically has an infinite (unbound) range. We employed the same principles here as we did with the AO to rescale and reposition this indicator as well. For more details on how we accomplished this, read our section "Binding Infinity" below.
Outback RSI (ORSI):
This is a stripped-down version of the Outback RSI indicator (linked above) that only includes the color-coding background (suffice it to say that it was not technically feasible to attempt to rescale the other components in a way that could consistently be clearly seen on-chart). As this component is a bit of a niche/special-purpose sub-indicator, it is disabled by default, and we suggest it remain disabled unless you have some pre-defined strategy that leverages the color-coding element of the Outback RSI that you wish to use.
Binding Infinity - How We Incorporated the AO and MACD (Warning - Math Talk Ahead!)
Note: This applies only to the AO and MACD at time of original publication. If any other indicators are added in the future that also fall into the category of "binding an infinite-range oscillator", we will make that clear in the release notes when that new addition is published.
To help set the stage for this discussion, it's important to note that the broader challenge of "equalizing inputs" is nothing new. In fact, it's a key element in many of the most popular fields of data science, such as AI and Machine Learning. They need to take a diverse set of inputs with a wide variety of ranges and seemingly-random inputs (referred to as "features"), and build a mathematical or computational model in order to work. But, when the raw inputs can vary significantly from one another, there is an inherent need to do some pre-processing to those inputs so that one doesn't overwhelm another simply due to the difference in raw values between them. This is where feature scaling comes into play.
With this in mind, we implemented 2 of the most common methods of Feature Scaling - Min-Max Normalization (which we call "Normalization" in our settings), and Z-Score Normalization (which we call "Standardization" in our settings). Let's take a look at each of those methods as they have been implemented in this script.
Min-Max Normalization (Normalization)
This is one of the most common - and most basic - methods of feature scaling. The basic formula is: y = (x - min)/(max - min) - where x is the current data sample, min is the lowest value in the dataset, and max is the highest value in the dataset. In this transformation, the max would evaluate to 1, and the min would evaluate to 0, and any value in between the min and the max would evaluate somewhere between 0 and 1.
The key benefits of this method are:
It can be used to transform datasets of any range into a new dataset with a consistent and known range (0 to 1).
It has no dependency on the "shape" of the raw input dataset (i.e. does not assume the input dataset can be approximated to a normal distribution).
But there are a couple of "gotchas" with this technique...
First, it assumes the input dataset is complete, or an accurate representation of the population via random sampling. While in most situations this is a valid assumption, in trading indicators we don't really have that luxury as we're often limited in what sample data we can access (i.e. number of historical bars available).
Second, this method is highly sensitive to outliers. Since the crux of this transformation is based on the max-min to define the initial range, a single significant outlier can result in skewing the post-transformation dataset (i.e. major price movement as a reaction to a significant news event).
You can potentially mitigate those 2 "gotchas" by using a mechanism or technique to find and discard outliers (e.g. calculate the mean and standard deviation of the input dataset and discard any raw values more than 5 standard deviations from the mean), but if your most recent datapoint is an "outlier" as defined by that algorithm, processing it using the "scrubbed" dataset would result in that new datapoint being outside the intended range of 0 to 1 (e.g. if the new datapoint is greater than the "scrubbed" max, it's post-transformation value would be greater than 1). Even though this is a bit of an edge-case scenario, it is still sure to happen in live markets processing live data, so it's not an ideal solution in our opinion (which is why we chose not to attempt to discard outliers in this manner).
Z-Score Normalization (Standardization)
This method of rescaling is a bit more complex than the Min-Max Normalization method noted above, but it is also a widely used process. The basic formula is: y = (x – μ) / σ - where x is the current data sample, μ is the mean (average) of the input dataset, and σ is the standard deviation of the input dataset. While this transformation still results in a technically-infinite possible range, the output of this transformation has a 2 very significant properties - the mean (average) of the output dataset has a mean (μ) of 0 and a standard deviation (σ) of 1.
The key benefits of this method are:
As it's based on normalizing the mean and standard deviation of the input dataset instead of a linear range conversion, it is far less susceptible to outliers significantly affecting the result (and in fact has the effect of "squishing" outliers).
It can be used to accurately transform disparate sets of data into a similar range regardless of the original dataset's raw/actual range.
But there are a couple of "gotchas" with this technique as well...
First, it still technically does not do any form of range-binding, so it is still technically unbounded (range -∞ to ∞ with a mid-point of 0).
Second, it implicitly assumes that the raw input dataset to be transformed is normally distributed, which won't always be the case in financial markets.
The first "gotcha" is a bit of an annoyance, but isn't a huge issue as we can apply principles of normal distribution to conceptually limit the range by defining a fixed number of standard deviations from the mean. While this doesn't totally solve the "infinite range" problem (a strong enough sudden move can still break out of our "conceptual range" boundaries), the amount of movement needed to achieve that kind of impact will generally be pretty rare.
The bigger challenge is how to deal with the assumption of the input dataset being normally distributed. While most financial markets (and indicators) do tend towards a normal distribution, they are almost never going to match that distribution exactly. So let's dig a bit deeper into distributions are defined and how things like trending markets can affect them.
Skew (skewness): This is a measure of asymmetry of the bell curve, or put another way, how and in what way the bell curve is disfigured when comparing the 2 halves. The easiest way to visualize this is to draw an imaginary vertical line through the apex of the bell curve, then fold the curve in half along that line. If both halves are exactly the same, the skew is 0 (no skew/perfectly symmetrical) - which is what a normal distribution has (skew = 0). Most financial markets tend to have short, medium, and long-term trends, and these trends will cause the distribution curve to skew in one direction or another. Bullish markets tend to skew to the right (positive), and bearish markets to the left (negative).
Kurtosis: This is a measure of the "tail size" of the bell curve. Another way to state this could be how "flat" or "steep" the bell-shape is. If the bell is steep with a strong drop from the apex (like a steep cliff), it has low kurtosis. If the bell has a shallow, more sweeping drop from the apex (like a tall hill), is has high kurtosis. Translating this to financial markets, kurtosis is generally a metric of volatility as the bell shape is largely defined by the strength and frequency of outliers. This is effectively a measure of volatility - volatile markets tend to have a high level of kurtosis (>3), and stable/consolidating markets tend to have a low level of kurtosis (<3). A normal distribution (our reference), has a kurtosis value of 3.
So to try and bring all that back together, here's a quick recap of the Standardization rescaling method:
The Standardization method has an assumption of a normal distribution of input data by using the mean (average) and standard deviation to handle the transformation
Most financial markets do NOT have a normal distribution (as discussed above), and will have varying degrees of skew and kurtosis
Q: Why are we still favoring the Standardization method over the Normalization method, and how are we accounting for the innate skew and/or kurtosis inherent in most financial markets?
A: Well, since we're only trying to rescale oscillators that by-definition have a midpoint of 0, kurtosis isn't a major concern beyond the affect it has on the post-transformation scaling (specifically, the number of standard deviations from the mean we need to include in our "artificially-bound" range definition).
Q: So that answers the question about kurtosis, but what about skew?
A: So - for skew, the answer is in the formula - specifically the mean (average) element. The standard mean calculation assumes a complete dataset and therefore uses a standard (i.e. simple) average, but we're limited by the data history available to us. So we adapted the transformation formula to leverage a moving average that included a weighting element to it so that it favored recent datapoints more heavily than older ones. By making the average component more adaptive, we gained the effect of reducing the skew element by having the average itself be more responsive to recent movements, which significantly reduces the effect historical outliers have on the dataset as a whole. While this is certainly not a perfect solution, we've found that it serves the purpose of rescaling the MACD and AO to a far more well-defined range while still preserving the oscillator behavior and mid-line exceptionally well.
The most difficult parts to compensate for are periods where markets have low volatility for an extended period of time - to the point where the oscillators are hovering around the 0/midline (in the case of the AO), or when the oscillator and signal lines converge and remain close to each other (in the case of the MACD). It's during these periods where even our best attempt at ensuring accurate mirrored-behavior when compared to the original can still occasionally lead or lag by a candle.
Note: If this is a make-or-break situation for you or your strategy, then we recommend you do not use any of the included indicators that leverage this kind of bounding technique (the AO and MACD at time of publication) and instead use the Trandingview built-in versions!
We know this is a lot to read and digest, so please take your time and feel free to ask questions - we will do our best to answer! And as always, constructive feedback is always welcome!
PDFMA Awesome Oscillator [Loxx]Theory:
Bill Williams's Awesome Oscillator Technical Indicator (AO) is a 34-period simple moving average, plotted through the bars midpoints (H+L)/2, which is subtracted from the 5-period simple moving average, built across the bars midpoints (H+L)/2. It shows us quite clearly what’s happening to the market driving force at the present moment.
This version uses PdfMA (Probability Density Function weighted Moving Average) instead of SMA (Simple Moving Average). This is a deviation from the original AO since in the AO since there is no parameter that you can change, but with this version, you can change the variance part of the PdfMA calculation. That way you can get different values for the AO even without changing periods of calculation (the general rule of thumb is: the greater the variance, the smoother the result)
Usage:
You can use color changes (mainly on zero cross) for trend change signals
Bogdan Ciocoiu - Code runnerDescription
The Code Runner is a hybrid indicator that leverages other pre-configured, integrated open-source algorithms to help traders spot regular and continuation divergences.
The Code Runner specialises in integrating some of the most popular oscillators well known for their accuracy when scalping using divergence strategies.
Uniqueness
The Code Runner stands out as a one-stop-shop pack of oscillator algorithms that traders can further customise to spot divergences.
The indicator's uniqueness stands from its capability to recast each algorithm to apply to the same scale. This feature is achieved by manually adjusting the outputs of each algorithm to fit on a scale between +100 and -100.
Another benefit of the Code Runner comes from its standardisation of outputs, mainly consisting of lines. Showing lines enables traders to draw potential regular and continuation divergences quickly.
The indicator has been pre-configured to support scalping at 1-5 minutes.
Open-source
The Code Runner uses the following open-source scripts and algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
These algorithms are available in the public domain either in TradingView space or outside (given their popularity in the financial markets industry).
Bogdan Ciocoiu - MoonshotDescription
Moonshot is an indicator that encapsulates the value delivered by the TSI, MACD, Awesome Oscillator and CCI algorithms to produce signals to enable users to enter positions in ideal market conditions. Moonshot integrates the value delivered by the above four algorithms into one script.
This indicator is particularly useful when trading continuation/reversal divergence strategies.
Uniqueness
The Moonshot's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for 1-3 minute scalping techniques.
In addition, Moonshot allows swapping or furthermore configuring the above four algorithms in such a way to align signals by colour-coding or shape thickness to aid the users with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same (including the scale at which the shapes are shown) and, in doing so, enables users to plug them in/out as needed.
Open-source
The indicator leverages the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
OS AO (P-unity MACD)This is both the Awesome Oscillator (AO) as described in New Trading Dimensions ( NTD , book by Bill Williams ), and the Profitunity MACD described in the first edition of Trading Chaos.
The calculation for both is the same. In this case I added the 5 day SMA which is the blue line. The difference between the blue line and the AO is the AC which is also described in NTD and is usually plotted in a different indicator (AC).
This is the base for the following signals:
Zone Bar
2nd Wise Man
The signals are triggered and shown on the main chart screen through the use of OS Alligator . This indicator (AO) provides further insight in analyzing those signals by reading the AO, its current position and evolution directly.
Combo Backtest 123 Reversal & Awesome Oscillator (AO) This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
WARNING:
- For purpose educate only
- This script to change bars colors.
Multiple Screeners with AlertsI already published few version of my custom screeners. Unfortunately, because of TradingView's security function call limit you can't use more than 40 stocks in 1 screener.
Fortunately, you can compute multiple values in your function and screen few indicators at once.
In this script I show how you can compute 5 indicators at the same time for 40 instruments. I display then in different labels.
Every label consist of list of instruments satisfying current indicator conditions and a value for it. It can be absolute value as for RSI or -1/1 representing Bullish/Bearish event.
Also you can create 1 alert with result of all screeners inside.
In this example I took 5 indicators with following conditions:
RSI - "RSI < 30" or "RSI > 70"
TSI - "TSI < -30" or "RSI >30"
ADX - "ADX > 40"
MACD - "MACD Bullish Cross" or "MACD Bearish Cross" (1 and -1 in screener)
AO - "AO Crosses 0 UP" or "AO Crosses 0 DOWN" (1 and -1 in screener)
Params
- bars_apart - this parameter define how may bars apart you labels are on your chart. If you see labels overlapping, increase this number.
- Parameters for all used indicators
- 40 symbol inputs for instruments you want to use in this screener
Alerts
You can create an alert from it easily by selecting screener name from the list and then selecting "Any alert() function call".
No additional configuration is required, message and alert on close is generated in the code.
You should better change default name for your alert. Sometimes because of big amount of inputs you might receive an error.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting.
This post and the script don’t provide any financial advice.
Chaos 2.0This is pure chaos!
I just wanted 1 thing I can put on a chart to try to get a clearer picture of what is going on (and not take up all the indicator spaces a free user is allowed haha)
Many things going on from so many different users
honestly I'm sorry I cant shout out everyone whose code I have ever read and used in another project just for the sake of learning more about pinescript!
As a way of shouting everyone out! (and giving out my most useful and configurable system)
I give you... CHAOS
I originally got an Alligator, AO, and Fractal script from a user ChaosTrader, then realized I love using averages!
I added the MESA (lazybear?) and the McGinley Dynamic Range (sry idk) and a simple 233 SMA.
I also found about something called the www.prorealcode.com another user had created for Pinescript.
I really liked that script so I adapted it to do the same kind of signal printing for circles and squares (crosses and series)
Check it out tell me what you think and how I can make it better for everyone!
thanks all!
Snoop
Williams AC/AO ZonesPaints price bars green when AO+AC are both green, paints bars red when both are red, and paints bars grey when both are different. (IGNORE THE CHART DIDNT MEAN TO PUBLISH IT ON THIS CHART.. OPPS)
Bill Williams. Awesome Oscillator (AO) Signal Line This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
Strategy Bill Williams. Awesome Oscillator (AO) This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
Bill Williams. Awesome Oscillator (AO) Hi
Let me introduce my Bill Williams. Awesome Oscillator (AO) script.
This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
