Triple Confirmation Kernel Regression Base [QuantraSystems]Kernel Regression Oscillator - BASE
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator. The additional Chart Overlay Indicator adds confidence to the signal.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart - This Indicator.
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
Regressions
FX Forecasting Model [TrendX_]FX Forecasting Model indicator is a forecasting tool that takes advantages of macroeconomic analysis and market surveillance to predict Exchange rate movement.
*** Customize the macro data for home country (base currency) and foreign country
USAGE
This consists of 4 editable options align with 4 Forecasting Models
TrendX Model)
TrendX Model is a type of multiple linear regression, which is a statistical method that estimates the relationship between the currency exchange rate and various macroeconomic indicators.
*** Remember the 1st thing to do is to customize the macro data for home country (base currency) and foreign country, before take any further steps.
Purchasing Power Parity (PPP Model)
The PPP model is a conceptual model of currency exchange. The model illustrates how the exchange rate between two countries’ currencies is influenced by the variations in the prices of goods and services in those countries, which depend on the inflation rate. The activity of buying and selling goods and services internationally will shift the exchange rate to balance the prices in both countries.
Interest Rate Parity (IRP Model)
Interest Rate Parity (IRP) model is a theoretical model that relates the interest rates and the exchange rates of two countries. According to IRP, the difference between the forward and spot exchange rates of two currencies should be equal to the difference between their interest rates. IRP helps traders to determine the fair value of a currency pair and compare it with the market value. If the market value deviates from the fair value, then there is a potential for arbitrage or hedging.
Combined Forecast Model (Mixed Model)
Since each model has its own advantages, many people are interested in the concept of using a mix of forecasts to get better results than any single forecast. Mix Model is a method that uses different proportions of the forecasts from three models: TrendX, PPP and IRP models. The default proportion is 0.2 for TrendX, and 0.4 for both PPP and IRP. You can change these proportions according to your liking.
CONCLUSION
FX Forecasting Model Indicator is very practical for FOREX traders who wants to make informed and rational decisions based on Macroeconomic Analysis. It can help find arbitrage opportunity in currency exchange market. Accordingly, it can also be helpful for traders to use alongside other forms of Technical Analysis.
DISCLAIMER
The results achieved in the past are not all reliable sources of what will happen in the future. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur.
Therefore, you should always exercise caution and judgment when making decisions based on past performance.
Chaos CypherOverview
Technically a smooth linear rate transformation, the "Chaos Cypher" drew some inspiration from the principles of Markov and chaos. Aside from price action, this combination provides a different lens through which to observe and interpret market movements. Markov models are based on the principle that future states depend only on the current state, not on the sequence of events that preceded it. Chaos theory deals with systems that are highly sensitive to initial conditions, a concept popularly referred to as the butterfly effect.
Efficient with Minimal Data: Designed to perform efficiently, the CC indicator is particularly useful in situations regardless of extensive historical data, except for obvious back testing, while still providing strength at identifying potential overbought/oversold zones and critical divergences.
Simplified Momentum Analysis: With further inspiration from the triple smoothed exponential rate, the CC actually uses linear regression for its calculations. This approach allows for a clear and more straightforward identification of deviations in momentum. The smoothing helps allow it to provide details while still operating at a fast pace due to the regression speed.
Adaptable to Various Timeframes: The transformation calculation then employed effectively narrows its scope in relation to the pace, enhancing its applicability across multiple timeframes and periods. This flexibility makes it a versatile tool suitable for various strategies and market conditions.
Fisher Transform Style Presentation: The indicator is presented in a style reminiscent of the Fisher Transform. However, this method of the script recalculates based on every individual dataset. To maintain efficiency, the adjustable length only applies to the regression rate.
The Chaos Cypher when compared to the Fisher Transform
Inversion Option for Leads: Lastly, an intriguing find when testing this script is the potential of the inversion option. This aspect proved particularly useful when searching for pullbacks on a trending market.
Conclusion
This indicator is designed to be forward-thinking and attempts to combine theoretical concepts with practicality. It has the ability to work with minimal data, adapt to various timeframes, and provide clear views of market movements. It back tested very well even when unrealistically used as a sole instrument.
"Two states differing by imperceptible amounts may eventually evolve into two considerably different states ... If, then, there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible....In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent." -Edward Norton Lorenz
Kernel Regression RibbonKernel Regression Ribbon is a flexible, visually pleasing trend identification tool. Plotting 8 different kernel regressions of different types and parameters allows the user to see where levels of support and resistance are being tested, retested and broken.
What’s Kernel Regression?
A statistical method for estimating the best fitting curve for a dataset, in this case, a time/price chart.
How’s Kernel Regression different from a Moving Average?
A Moving Average is basically a simple form of Kernel Regression, in that it uses a fixed (Retangular) Kernel function. In an MA, all data points are weighted equally over its length. However, a Kernel function reacts more to data points that are closer to the current point. This means it will adapt more quickly to changes in data than an MA. Due to this adaptability, Kernel functions often form part of Machine Learning.
Using this indicator:
Explore the default Regular mode first to get a feel for the inputs, which are more numerous than for MAs. Try out different settings, filters and intervals to get the best out of each kernel. Not all parameters are available for each KR. There are info tips to explain this in the menu, but I’ve also included handy, optional labels on the chart for each KR as a more accessible guide.
Once you know your way round the Regular mode, check out the Presets and start changing the parameters of each kernel to your liking in the “User KR1, KR2, … “ mode. Each kernel type has its strong and weak points. Blending different kernels is where this indicator comes into its own. Give your charts a funky shine!
This indicator does NOT repaint.
This script acknowledges, and hopefully showcases, the great work of @veryfid Kernel Regression Toolkit.
COSTAR Strategy [SS]A little late posting this but here it is, as promised!
This is the companion to the COSTAR indicator.
What it does:
It creates a co-integration paired relationship with a separate, cointegrated ticker. It then plots out the expected range based on the value of the cointegrated pair. When the current ticker is below the value of its co-integrated partner, it becomes a "Buy" and should be longed. When it becomes overvalued in comparison, it becomes a "Sell" and should be shorted.
The example above is with BA and USO, which have a strong inverse relationship.
How it works:
I made the strategy version a bit more intuitive. Instead of you selecting the parameters for your model, it will autoselect the ideal parameters based on your desired co-integrated pair. You simply enter the ticker you want to compare against, and it will sort through the values at various lags to find significance and stationarity. It will then create a model and plot the model out for you on your chart, as you can see above.
The premise of the strategy:
The premise of the strategy is as stated before. You long when the ticker is undervalued in comparison to its co-integrated pair, and short when it is overvalued. The conditions for entry are simply a co-integrated pair being over the expected range (short) or below the expected range (long).
The condition to exit is a "re-integration", or a crossover of the expected value of the ticker (the centreline).
What if it can't find a relationship?
In some instances, the indicator will not be able to determine a co-integrated relationship, owning to a lack of stationarity between the data. When this happens, you will get the following error:
The indicator provides you with prompts, such as switching the timeframe or trying an alternative ticker. In the case displayed above, if we simply switch to the 1 hour timeframe, we have a viable model with great backtest results:
You can toggle in the settings menu the various parameters, such as timeframe, fills and displays.
And that is the strategy in a nutshell, be sure to check out its partner indicator, COSTAR, for more information on the premise of using co-integrated models for trading. And let me know your questions below!
Safe trades everyone!
Adaptiv Trend Projection with Dynamic Length RegressionThe Adaptive Trend Projection indicator is a robust tool designed to provide an optimal trend projection calculated in a highly sophisticated manner. By utilizing linear regression lengths ranging from 20 to 200, this indicator estimates the duration of the trend by dynamically adjusting the projection length based on the calculated trend's strength.
Key Features:
1. Dynamic Length Adjustment: The indicator intelligently adapts the projection length between 20 and 200 using linear regression, ensuring adaptability to market conditions.
2. Trend Strength Calculation: Through linear regression analysis, the indicator calculates the slope, average, and intercept for each selected length, providing insights into the strength and direction of the trend.
3. Deviation Analysis: Beyond traditional trend analysis, the indicator calculates standard deviation, Pearson's correlation coefficient, and deviation values, offering a comprehensive view of market dynamics.
4. Confidence Levels: A unique feature of the Adaptive Trend Projection is its ability to determine confidence levels based on the highest Pearson's R value. Reliability is categorized into levels such as Neutral, Moderate, High, Very High, and Ultra High, providing users with a quick assessment of the projection's robustness.
5. Dynamic Forecasting: The indicator not only analyzes historical data but extends its functionality by dynamically forecasting future trend points. The projection adjusts in length based on the strength of the trend, allowing for more accurate predictions.
6. Visual Clarity: Enhancing visual clarity, the Adaptive Trend Projection indicator uses different line styles, widths, and colors to highlight crucial points, making it easier for traders to interpret and act upon the information.
In conclusion, the Adaptive Trend Projection indicator offers a nuanced understanding of market trends by combining advanced linear regression techniques, deviation analysis, and confidence level assessments. This enables traders to make informed decisions.
KNN ATR Dual Range Predictions [SS]Excited to release this indicator!
I wanted to do a machine learning, ATR based indicator for a while, but I first had to learn about machine learning algos haha.
Now that I have created a KNN based regression methodology (shared in a previous indicator), I can finally do it!
So this is a Nearest Known Neighbor or KNN regression based indicator that uses ATR (average ranges) to predict future ranges.
It operates by calculating the move from High to Open and Open to Low and performing KNN regression to look for other, similar instances of similar movements and what followed those movements.
It provides for 2 methods of KNN regression, the traditional Cluster method (where it identifies a number of clusters within a tolerance range and averages them out), or the method of last instance (where it finds the most recent identical instance and plots the result from that).
You can toggle the parameters as you wish, including the:
a) Type of Regression
b) Number of Clusters
c) Tolerance for Clusters
Others functions:
The indicator provides for the ability to view 2 different timeframe targets. The default calculation is the current timeframe you are on. So if you are on the 1 minute, 5 minute or 1 hour, it will automatically default the primary range to this timeframe. This cannot be changed.
But it permits for a second prediction to be calculated for a timeframe you can specify. The example in the chart above is the 1 hour overlaid on the 5 minute chart.
You can see how the model is performing in the statistics table. The statistics table can be removed as well if you don't want it overlaid on your chart.
You can also toggle off and on the various ranges. IF you only want to visualize 1 hour levels on a 5 minute chart, you can toggle off the bands and just view the higher tf data. Inversely, if you only want the current timeframe data and not the higher tf data, you can toggle the higher tf data off as well.
General Use Tips:
Some general use tips include:
🎯The default settings are appropriate for most common tickers. Because this is performing an autoregression on itself, the parameters tend to be more tight vs. performing dual correlation between two separate tickers which are sizably different in scale (which would require a higher tolerance).
Here is an example of YM1!, which is a sizably larger ticker, however it is performing well with the current settings.
🎯 If you get not great results from your ranges or an error in the correlation table, something like this:
It means the parameters are too tight for what you want to do and it is having trouble identifying other, similar cases (in this case, the lookback length was significantly shortened). The first step is to:
a) Expand your lookback range (up to 500 is usually sufficient). This should resolve most issues in most cases. If not:
b) If you are using the Cluster method, try broadening your cluster tolerance by 0.5 increments.
Between those two implementations, you should get a functional model. And it actually honestly hasn't happened to me in general use, I had to force that example by significantly shortening the lookback period.
Concluding Remarks
And that's pretty much the indicator.
I hope you enjoy it! I was really excited to be finally able to do it, like I said I attempted to do this for a while but needed to research the whole KNN process and how its performed.
Enjoy and leave your comments and questions below!
KNN Regression [SS]Another indicator release, I know.
But note, this isn't intended to be a stand-alone indicator, this is just a functional addition for those who program Machine Learning algorithms in Pinescript! There isn't enough content here to merit creating a library for (it's only 1 function), but it's a really useful function for those who like machine learning and Nearest Known Neighbour Algos (or KNN).
About the indicator:
This indicator creates a function to perform KNN-based regression.
In contrast to traditional linear regression, KNN-based regression has the following advantages over linear regression:
Advantages of KNN Regression vs. Linear Regression:
🎯 Non-linearity: KNN is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. This allows it to capture non-linear relationships between features and the target variable.
🎯Simple Implementation: KNN is conceptually simple and easy to understand. It doesn't require the estimation of parameters, making it straightforward to implement.
🎯Robust to Outliers: KNN is less sensitive to outliers compared to linear regression. Outliers can have a significant impact on linear regression models, but KNN tends to be less affected.
Disadvantages of KNN Regression vs. Linear Regression:
🎯 Resource Intensive for Computation: Because KNN operates on identifying the nearest neighbors in a dataset, each new instance has to be searched for and identified within the dataset, vs. linear regression which can create a coefficient-based model and draw from the coefficient for each new data point.
🎯Curse of Dimensionality: KNN performance can degrade with an increasing number of features, leading to a "curse of dimensionality." This is because, in high-dimensional spaces, the concept of proximity becomes less meaningful.
🎯Sensitive to Noise: KNN can be sensitive to noisy data, as it relies on the local neighborhood for predictions. Noisy or irrelevant features may affect its performance.
Which is better?
I am very biased, coming from a statistics background. I will always love linear regression and will always prefer it over KNN. But depending on what you want to accomplish, KNN makes sense. If you are using highly skewed data or data that you cannot identify linearity in, KNN is probably preferable.
However, if you require precise estimations of ranges and outliers, such as creating co-integration models, I would advise sticking with linear regression. However, out of curiosity, I exported the function into a separate dummy indicator and pulled in data from QQQ to predict SPY close, and the results are actually very admirable:
And plotted with showing the standard error variance:
Pretty impressive, I must say I was a little shocked, it's really giving linear regression a run for its money. In school I was taught LinReg is the gold standard for modeling, nothing else compares. So as with most things in trading, this is challenging some biases of mine ;).
Functionality of the function
I have permitted 3 types of KNN regression. Traditional KNN regression, as I understand it, revolves around clustering. ( Clustering refers to identifying a cluster, normally 3, of identical cases and averaging out the Dependent variable in each of those cases) . Clustering is great, but when you are working with a finite dataset, identifying exact matches for 2 or 3 clusters can be challenging when you are only looking back at 500 candles or 1000 candles, etc.
So to accommodate this, I have added a functionality to clustering called "Tolerance". And it allows you to set a tolerance level for your Euclidean distance parameters. As a default, I have tested this with a default of 0.5 and it has worked great and no need to change even when working with large numbers such as NQ and ES1!.
However, I have added 2 additional regression types that can be done with KNN.
#1 One is a regression by the last IDENTICAL instance, which will find the most recent instance of a similar Independent variable and pull the Dependent variable from that instance. Or
#2 Average from all IDENTICAL instances.
Using the function
The code has the instructions for integrating the function into your own code, the parameters, and such, so I won't exhaust you with the boring details about that here.
But essentially, it exports 3, float variables, the Result, the Correlation, and the simplified R2.
As this is KNN regression, there are no coefficients, slopes, or intercepts and you do not need to test for linearity before applying it.
Also, the output can be a bit choppy, so I tend to like to throw in a bit of smoothing using the ta.sma function at a deault of 14.
For example, here is SPY from QQQ smoothed as a 14 SMA:
And it is unsmoothed:
It seems relatively similar but it does make a bit of an aesthetic difference. And if you are doing it over 14, there is no data loss and it is still quite reactive to changes in data.
And that's it! Hopefully you enjoy and find some interesting uses for this function in your own scripts :-).
Safe trades everyone!
Predictive Candles Variety Pack [SS]This indicator provides you with the ability to select from a variety of candle prediction methods.
It permits for:
👉 Traditional Linear Regression Candle Predictions
👉 Candle Predictions based on the underlying Stochastics
👉 Candle Predictions based on the underlying RSI
👉 Candle Predictions based on the underlying MFI
👉 Candle Predictions based on the EMA 9
👉 Candle Predictions based on ARIMA modelling
Which is best?
Each method serves its unique purpose.
Here are some general tips of which candles are better suited for what:
🎯Trend Following🎯
For Trend following, the EMA 9 would be an appropriate choice of candle as it helps you to identify the current trend and potential early pullbacks/reversals.
🎯Momentum Following🎯
Momentum following is best carried out with the Stochastics Candles.
🎯Pullback Determination🎯
Pullback Determination is best accomplished through the RSI candles, as the ranges compress or expand based on the current state of oversold/overboughtness.
🎯Detrended Range🎯
To see the detrended range of where the ticker should be falling, absent the trendy noise, it's best to use the ARIMA candles.
Other Features
👉 Other features include a Backtest option that can be toggled on or off and will backtest over the length of the assessment. I don't recommend leaving it on as it can be resource-heavy on Pinescript though.
👉 The ability to adjust the transparency of the candles if you want them to be more or less visible.
Troubleshooting Note
The ARIMA modeling version is extremely resource-heavy, as it has to fully develop an ARIMA model. I have tried to optimize it by reducing the lagged assessment to just 2 lags. If you are using a free or non-premium membership, you may need to reduce the length of the assessment.
And that's it! Pretty straightforward indicator.
Hope you enjoy it!
MacroTrend VisionThe "MacroTrend Vision" indicator is crafted with a singular goal – to provide traders with a quick and insightful snapshot of a country's global index. Seamlessly combining macroeconomic and technical perspectives, this tool is designed for those seeking a straightforward yet comprehensive overview. Let's explore the key features that make the "MacroTrend Vision" a valuable asset for traders looking to grasp both the big-picture economic context and technical nuances.
1. Long-Term Vision with Weekly Periods:
Gain a genuine long-term perspective with the ability to process 2500 weekly periods. This feature ensures a holistic understanding of global indices from both macroeconomic and technical viewpoints.
2. Composite Leading Indicator (CLI) Conditions:
Integrate both macroeconomic trends and technical signals through Composite Leading Indicator (CLI) conditions derived from the Relative Strength Index (RSI), offering a comprehensive outlook for informed decision-making.
3. Deviation Bands for Volatility Analysis:
Refine market analysis with strategically integrated deviation bands (0.2 and 0.4) based on smoothed linear regression. Anticipate volatility and potential trend shifts, aligning macro and technical insights.
4. Logarithmic Scale Transformation:
Enhance precision in understanding price movements with a logarithmic scale transformation, especially beneficial for assets with exponential growth patterns.
5. Separated Window for Easy Navigation:
Streamline your analysis with a user-friendly design – a separated window allowing easy navigation through different symbols without altering indicator settings.
6. Alert System for CLI Conditions:
Stay informed about critical shifts with an alert system for both long and close conditions based on the RSI of the CLI. Even during periods of limited chart monitoring, this feature keeps you connected to macroeconomic and technical changes.
In essence, the "MacroTrend Vision" is your go-to tool for a balanced view, simplifying the complexities of global indices with a blend of macroeconomic insights and technical clarity.
COSTAR [SS]This idea came to me after I wrote the post about Co-Integration and pair trading. I wondered if you could use pair trading principles as a way to determine overbought and oversold conditions in a more neutral way than RSI or Stochastics.
The results were promising and this indicator resulted :-)!
About:
COSTAR provides another, more neutral way to determine whether an equity is overbought or oversold.
Instead of relying on the traditional oscillator based ways, such as using RSI, Stochastics and MFI, which can be somewhat biased and narrow sided, COSTAR attempts to take a neutral, unbiased approached to determine overbought and oversold conditions. It does this through using a co-integrated partner, or "pair" that is closely linked to the underlying equity and succeeds on both having a high correlation and a high t-statistic on the ADF test. It then references this underlying, co-integrated partner as the "benchmark" for the co-integration relationship.
How this succeeds as being "unbiased" and "neutral" is because it is responsive to underlying drivers. If there is a market catalyst or just general bullish or bearish momentum in the market, the indicator will be referencing the integrated relationship between the two pairs and referencing that as a baseline. If there is a sustained rally on the integrated partner of the underlying ticker that is holding, but the other ticker is lagging, it will indicate that the other ticker is likely to be under-valued and thus "oversold" because it is underperforming its benchmark partner.
This is in contrast to traditional approaches to determining overbought and oversold conditions, which rely completely on a single ticker, with no external reference to other tickers and no control over whether the move could potentially be a fundamental move based on an industry or sector, or whether it is a fluke or a squeeze.
The control for this giving "false" signals comes from its extent of modelling and assessment of the degree of integration of the relationship. The parameters are set by default to assess over a 1 year period, both the correlation and the integration. Anything that passes this degree of integration is likely to have a solid, co-integrated state and not likely to be a "fluke". Thus, the reliability of the assessment is augmented by the degree of statistical significance found within the relationship. The indicator is not going to prompt you to rely on a relationship that is statistically weak, and will warn you of such.
The indicator will show you all the information you require regarding the relationship and whether it is reliable or not, so you do not need to worry!
How to Use
The first step to use COSTAR is identifying which ticker has a strong relationship with the current ticker. In the main chart, you will see that SPY is overlaid with VIX. There is a strong, negative correlation between the VIX and SPY. When VIX is entered as the paired ticker, the indicator returns the data as stationary, indicating a compatible match.
Now you have 3 ways of viewing this relationship, 2 of which are going to be directly applicable to trading.
You can view them as
Price to Price Ratio (Not very useful for trading, but if you are curious)
Z-Score: Helpful for trading
Co-integration: Helpful for trading
Here is an example of all three:
Example of Z-Score Chart:
Example of Price Ratio:
Example of Co-Integration Pair:
Using for Trading
As stated above, the two best ways to use this for trading is to either use the Z-Score Chart or the Co-Integrated Pair chart.
The Z-Score chart is based off of the price ratio data and provides an assessment of both the independent and dependent data.
The co-integration shows the dependent (the ticker you are trading) in yellow and the independent (the ticker you are referencing) in teal. When teal is above yellow, you will see it is green. This means, based on your benchmark pair, there is still more up room and the ticker you are trading is actually lagging behind.
When the yellow crosses up, it will turn red. This means that your ticker is out-performing the benchmark pair and you likely will see pullback and a "regression to the mean" through re-integration.
The indicator is capable of plotting out entries and exits, which are guided by the z-score:
How Effective is it?
I created a basic strategy in Pinescript, and the back-test results vary. Trading ES1! using NQ1! as the co-integrated pair, results were around 78% effective.
With VIX, results were around 50% effective, but with a net profit.
Generally, the efficacy surpassed that of both stochastics and RSI.
I will be releasing the strategy version of this in the coming days, still just cleaning up that code and making it more "public use" friendly.
Other Applications
If you are a pair trader, you can technically use this for pair trading as well. That's essentially all this is doing :-).
Tips
If you are trading a ticker such as MSFT, AMD, KO etc., it's best to try to find an ETF or index that has that particular ticker as a large holding and use that as your benchmark. You will see on the indicator whether there is a high correlation and whether the data is indeed stationary.
If the indicator returns "Non-stationary", you can attempt to extend your regression range from 252 to 500. If this fixes the issue, ensure that the correlation is still >= 0.5 or <= -0.5. If this does not work still, you will need to find another pair, as its likely the result of incompatibility and an insignificant relationship.
To help you identify tickers with strong relationships, consider using a correlation heatmap indicator. I have one available and I think there are a couple of other similar ish ones out there. You want to make sure the relationship is stable over time (a correlation of >= 0.50 or <= -0.5 over the past 252 to 500 days).
IMPORTANT: The long and short exits delete the signal after one is signaled. Therefore, when you look back in the chart you will notice there are no signals to exit long or short. That is because they signal as they happen. This is to keep the chart clean.
'Tis all my friends!
Hope you enjoy and let me know your questions and suggestions below!
Side note:
COSTAR stands for Co-integration Statistical Analysis and Regression. ;)
Linear Regression Forecast Tool [Daveatt]Hello traders,
Navigating through the financial markets requires a blend of analysis, insight, and a touch of foresight.
My Linear Regression Forecast Tool is here to add that touch of foresight to your analysis toolkit on TradingView!
Linear Regression is the heart of this tool, a statistical method that explores the relationship between a dependent variable and one (or more) independent variable(s).
In simpler terms, it finds a straight line that best fits a set of data points.
This "line of best fit" then becomes a visual representation of the relationship in the data, providing a basis for making predictions.
Here's what the Linear Regression Forecast Tool brings to your trading table:
Multiple Indicator Choices: Select from various market indicators like Simple Moving Averages, Bollinger Bands, or the Volume Weighted Average Price as the basis for your linear regression analysis.
Customizable Forecast Periods: Define how many periods ahead you want to forecast, adjusting to your analysis needs, whether that's looking 5, 7, or 10 periods into the future.
On-Chart Forecast Points: The tool plots the forecasted points on your chart, providing a straightforward visual representation of potential future values based on past data.
In this script:
1. We first calculate the indicator using the specified period.
2. We then use the ta.linreg function to calculate a linear regression curve fitted to the indicator over the last Period bars.
3. We calculate the slope of the linear regression curve using the last two points on the curve.
We use this slope to extrapolate the linear regression curve to forecast the next X points of the indicator.
4/ Finally, we use the plot function to plot the original indicator and the forecasted points on the chart, using the offset parameter to shift the forecasted points to the right (into the future).
This method assumes that the trend represented by the linear regression curve will continue, which may not always be the case, especially in volatile or changing market conditions.
Examples:
Works with a moving average
Works with a Bollinger band
The code can be adapted to work with any other indicator (imagine RSI, MACD, other Moving Average Type, PSAR, Supertrend, etc...)
Conclusion
The Linear Regression Forecast Tool doesn't promise to tell the future but provides a structured way to visualize possible future price trends based on historical data. I
Remember, no tool can predict market conditions with certainty.
It's always advisable to corroborate findings with other analysis methods and stay updated with market news and events.
Happy trading!
Quadratic & Linear Time Series Regression [SS]Hey everyone,
Releasing the Quadratic/Linear Time Series regression indicator.
About the indicator:
Most of you will be familiar with the conventional linear regression trend boxes (see below):
This is an awesome feature in Tradingview and there are quite a few indicators that follow this same principle.
However, because of the exponential and cyclical nature of stocks, linear regression tends to not be the best fit for stock time series data. From my experience, stocks tend to fit better with quadratic (or curvlinear) regression, which there really isn't a lot of resources for.
To put it into perspective, let's take SPX on the 1 month timeframe and plot a linear regression trend from 1930 till now:
You can see that its not really a great fit because of the exponential growth that SPX has endured since the 1930s. However, if we take a quadratic approach to the time series data, this is what we get:
This is a quadratic time series version, extended by up to 3 standard deviations. You can see that it is a bit more fitting.
Quadratic regression can also be helpful for looking at cycle patterns. For example, if we wanted to plot out how the S&P has performed from its COVID crash till now, this is how it would look using a linear regression approach:
But this is how it would look using the quadratic approach:
So which is better?
Both linear regression and quadratic regression are pivotal and important tools for traders. Sometimes, linear regression is more appropriate and others quadratic regression is more appropriate.
In general, if you are long dating your analysis and you want to see the trajectory of a ticker further back (over the course of say, 10 or 15 years), quadratic regression is likely going to be better for most stocks.
If you are looking for short term trades and short term trend assessments, linear regression is going to be the most appropriate.
The indicator will do both and it will fit the linear regression model to the data, which is different from other linreg indicators. Most will only find the start of the strongest trend and draw from there, this will fit the model to whatever period of time you wish, it just may not be that significant.
But, to keep it easy, the indicator will actually tell you which model will work better for the data you are selecting. You can see it in the example in the main chart, and here:
Here we see that the indicator indicates a better fit on the quadratic model.
And SPY during its recent uptrend:
For that, let's take a look at the Quadratic Vs the Linear, to see how they compare:
Quadratic:
Linear:
Functions:
You will see that you have 2 optional tables. The statistics table which shows you:
The R Squared to assess for Variance.
The Correlation to assess for the strength of the trend.
The Confidence interval which is set at a default of 1.96 but can be toggled to adjust for the confidence reading in the settings menu. (The confidence interval gives us a range of values that is likely to contain the true value of the coefficient with a certain level of confidence).
The strongest relationship (quadratic or linear).
Then there is the range table, which shows you the anticipated price ranges based on the distance in standard deviations from the mean.
The range table will also display to you how often a ticker has spent in each corresponding range, whether that be within the anticipated range, within 1 SD, 2 SD or 3 SD.
You can select up to 3 additional standard deviations to plot on the chart and you can manually select the 3 standard deviations you want to plot. Whether that be 1, 2, 3, or 1.5, 2.5 or 3.5, or any combination, you just enter the standard deviations in the settings menu and the indicator will adjust the price targets and plotted bands according to your preferences. It will also count the amount of time the ticker spent in that range based on your own selected standard deviation inputs.
Tips on Use:
This works best on the larger timeframes (1 hour and up), with RTH enabled.
The max lookback is 5,000 candles.
If you want to ascertain a longer term trend (over years to months), its best to adjust your chart timeframe to the weekly and/or monthly perspective.
And that's the indicator! Hopefully you all find it helpful.
Let me know your questions and suggestions below!
Safe trades to all!
Relational Quadratic Kernel Channel [Vin]The Relational Quadratic Kernel Channel (RQK-Channel-V) is designed to provide more valuable potential price extremes or continuation points in the price trend.
Example:
Usage:
Lookback Window: Adjust the "Lookback Window" parameter to control the number of previous bars considered when calculating the Rational Quadratic Estimate. Longer windows capture longer-term trends, while shorter windows respond more quickly to price changes.
Relative Weight: The "Relative Weight" parameter allows you to control the importance of each data point in the calculation. Higher values emphasize recent data, while lower values give more weight to historical data.
Source: Choose the data source (e.g., close price) that you want to use for the kernel estimate.
ATR Length: Set the length of the Average True Range (ATR) used for channel width calculation. A longer ATR length results in wider channels, while a shorter length leads to narrower channels.
Channel Multipliers: Adjust the "Channel Multiplier" parameters to control the width of the channels. Higher multipliers result in wider channels, while lower multipliers produce narrower channels. The indicator provides three sets of channels, each with its own multiplier for flexibility.
Details:
Rational Quadratic Kernel Function:
The Rational Quadratic Kernel Function is a type of smoothing function used to estimate a continuous curve or line from discrete data points. It is often used in time series analysis to reduce noise and emphasize trends or patterns in the data.
The formula for the Rational Quadratic Kernel Function is generally defined as:
K(x) = (1 + (x^2) / (2 * α * β))^(-α)
Where:
x represents the distance or difference between data points.
α and β are parameters that control the shape of the kernel. These parameters can be adjusted to control the smoothness or flexibility of the kernel function.
In the context of this indicator, the Rational Quadratic Kernel Function is applied to a specified source (e.g., close prices) over a defined lookback window. It calculates a smoothed estimate of the source data, which is then used to determine the central value of the channels. The kernel function allows the indicator to adapt to different market conditions and reduce noise in the data.
The specific parameters (length and relativeWeight) in your indicator allows to fine-tune how the Rational Quadratic Kernel Function is applied, providing flexibility in capturing both short-term and long-term trends in the data.
To know more about unsupervised ML implementations, I highly recommend to follow the users, @jdehorty and @LuxAlgo
Optimizing the parameters:
Lookback Window (length): The lookback window determines how many previous bars are considered when calculating the kernel estimate.
For shorter-term trading strategies, you may want to use a shorter lookback window (e.g., 5-10).
For longer-term trading or investing, consider a longer lookback window (e.g., 20-50).
Relative Weight (relativeWeight): This parameter controls the importance of each data point in the calculation.
A higher relative weight (e.g., 2 or 3) emphasizes recent data, which can be suitable for trend-following strategies.
A lower relative weight (e.g., 1) gives more equal importance to historical and recent data, which may be useful for strategies that aim to capture both short-term and long-term trends.
ATR Length (atrLength): The length of the Average True Range (ATR) affects the width of the channels.
Longer ATR lengths result in wider channels, which may be suitable for capturing broader price movements.
Shorter ATR lengths result in narrower channels, which can be helpful for identifying smaller price swings.
Channel Multipliers (channelMultiplier1, channelMultiplier2, channelMultiplier3): These parameters determine the width of the channels relative to the ATR.
Adjust these multipliers based on your risk tolerance and desired channel width.
Higher multipliers result in wider channels, which may lead to fewer signals but potentially larger price movements.
Lower multipliers create narrower channels, which can result in more frequent signals but potentially smaller price movements.
Regression Line (Log)This indicator is based on the "Linear Regression Channel (Log)," which, in turn, is derived from TradingView's "Linear Regression Channel."
The "Regression Line (Log)" indicator is a valuable tool for traders and investors seeking to gain insights into long-term market trends. This indicator is personally favored for its ability to provide a comprehensive view of price movements over extended periods. It offers a unique perspective compared to traditional linear regression lines and moving averages, making it a valuable addition to the toolkit of experienced traders and investors.
Indicator Parameters:
Before delving into the details, it's worth noting that the chosen number of periods (2870) is a personal preference. This specific value is utilized for the S&P 500 index due to its alignment with various theories regarding the beginning of the modern economic era in the stock market. Different analysts propose different starting points, such as the 1950s, 1970s, or 1980s. However, users are encouraged to adjust this parameter to suit their specific needs and trading strategies.
How It Works:
The "Regression Line (Log)" indicator operates by transforming the closing price data into a logarithmic scale. This transformation can make the linear regression more suitable for data with exponential trends or rapid growth. Here's a breakdown of its functioning and why it can be advantageous for long-term trend analysis:
1. Logarithmic Transformation : The indicator begins by applying a logarithmic transformation to the closing price. This transformation helps capture price movements proportionally, making it especially useful for assets that exhibit exponential or rapid growth. This transformation can render linear regression more suitable for data with exponential or fast-paced trends.
2. Linear Regression on Log Scale : After the logarithmic transformation, the indicator calculates a linear regression line (lrc) on this log-transformed data. This step provides a smoother representation of long-term trends compared to a linear regression line on a linear scale.
3. Exponential Reversion : To present the results in a more familiar format, the indicator reverts the log-transformed regression line back to a linear scale using the math.exp function. This final output is the "Linear Regression Curve," which can be easily interpreted on standard price charts.
Advantages:
- Long-Term Trend Clarity : The logarithmic scale better highlights long-term trends and exponential price movements, making it a valuable tool for investors seeking to identify extended trends.
- Smoothing Effect : The logarithmic transformation and linear regression on a log scale smooth out price data, reducing noise and providing a clearer view of underlying trends.
- Adaptability : The indicator allows traders and investors to customize the number of periods (length) to align with their preferred historical perspective or trading strategy.
- Complementary to Other Tools : While not meant to replace other technical indicators, the "Regression Line (Log)" indicator complements traditional linear regression lines and moving averages, offering an alternative perspective for more comprehensive analysis.
Conclusion:
In summary, the "Regression Line (Log)" indicator is a versatile tool that can enhance your ability to analyze long-term market trends. Its logarithmic transformation provides a unique perspective on price data, particularly suited for assets with exponential growth patterns. While the choice of the number of periods is a personal one, it can be adapted to fit various historical viewpoints. This indicator is best utilized as part of a well-rounded trading strategy, in conjunction with other technical tools, to aid in informed decision-making.
Ticker Correlation Matrix Table and Heatmap [SS]Hello everyone,
I am in the process of releasing some of my own utility indicators/things I use to reference and perform analyses.
I do a lot of quantitative/math based analyses, including correlation assessments that I traditionally would need to export data from Tradingview and perform in SPSS, Excel or R. I have been slowly building a repertoire of Excel/R functionality right on pinescript so I do not need to constantly export data and can perform the assessments right on Tradingview.
This is an example of such an indicator.
About the Indicator:
It is a correlation table/matrix indicator. It will allow up to 10 ticker inputs, which can be stocks, economic data, anything available on Tradingview, and it will perform a correlation assessment in a matrix / heatmap style.
The indicator will show the various correlations among all of the selected ticker inputs and will colour them based on correlation strength and type.
Strong negative correlations will appear bright red.
Strong positive correlations will appear bright green.
Complete absence of correlation (i.e. 0) will show bright orange.
The rest will show a darker shade to indicate less strength/correlation.
Calculation Functions
In addition to outputting a correlation matrix, the indicator is also able to express the relationship between tickers in a linear expression using the y = mx + b formula.
If we look at table, we can see that MSFT and AAPL have a significantly strong correlation of 0.82.
If we wanted to express this relationship mathmatically, we can ask the indicator to represent the linear relationship in our y = mx + b format. We simply toggle to our menu and select the Convert From MSFT (Ticker 2) and convert to APPL (Ticker 3):
When we select this, a new table will populate below and give you the expression as well as the amount of error associated with it:
In this case, we can see that the equation is y = 0.553x + 0.626 with a range of around 10 points in either direction.
This means that, to convert MSFT to AAPL, we would multiply the MSFT price by 0.553 and then add 0.626. So if we try it, MSFT closed at 328.41. So we substitute:
AAPL price = 0.553(328.41) + 0.626
AAPL price = 181.61 + 0.626
AAPL Price = 182.24 +/- 10
AAPL actually closed at 184.12. So pretty good. If we try another, let's do SPY to XLF:
So we substitute, SPY closed at 449.16.
XLF Price = 449.16(0.077) + 0.084
XLF price = 34.59 + 0.084
XLF price = 34.67
XLF actually closed at 34.49.
This is handy if you want to see how one stock price may affect another. If you are long on one stock and short on another, you can use this to determine what the likely outcome may be for the alternative stock. However, I recommend only performing this on tickers that have a relationship of 0.7 or higher, or a relationship of -0.7 or lower.
I always had to use SPSS to do this, so being able to do this right in Pinescript for me is a huge convenience!
Some other uses:
As I tend to post educational stuff on Tradingview and I frequently use correlation matrices, I have formatted the indicator to be more aesthetically pleasing for these purposes. Thus, you can unselect extra ticker slots that you do not need. IF I only need to display 3 tickers, I can unselect tickers 4 - 10. The end result is a cleaner table:
Essential Functions:
The assessment length is defaulted to 75 candles on the daily timeframe. Be sure to have the daily timeframe opened when you are viewing the indicator.
You can increase or decrease the assessment length as you desire.
You can also specify the source. The source is defaulted to close, but if you want to see the direct correlation of ticker's highs and/or lows, you can modify the source input in the settings menu to look at this.
Just remember to have the chart opened to whatever timeframe you are looking at.
And that's the indicator! Hopefully you find it helpful. Its more of an academic indicator, but it is performing a function that I personally use frequently in analyses, so I hope you may also benefit from it as well!
Thanks for checking it out! Safe trades everyone!
Advanced Weighted Residual Arbitrage AnalyzerThe Advanced Weighted Residual Arbitrage Analyzer is a sophisticated tool designed for traders aiming to exploit price deviations between various asset pairs. By examining the differences in normalized price relations and their weighted residuals, this indicator provides insights into potential arbitrage opportunities in the market.
Key Features:
Multiple Relation Analysis: Analyze up to five different asset relations simultaneously, offering a comprehensive view of potential arbitrage setups.
Normalization Functions: Choose from a variety of normalization techniques like SMA, EMA, WMA, and HMA to ensure accurate comparisons between different price series.
Dynamic Weighting: Residuals are weighted based on their correlation, ensuring that stronger correlations have a more pronounced impact on the analysis. Weighting can be adjusted using several functions including square, sigmoid, and logistic.
Regression Flexibility: Incorporate linear, polynomial, or robust regression to calculate residuals, tailoring the analysis to different market conditions.
Customizable Display: Decide which plots to display for clarity and focus, including normalized relations, weighted residuals, and the difference between the screen relation and the average weighted residual.
Usage Guidelines:
Configure the asset pairs you wish to analyze using the Symbol Relations group in the settings.
Adjust the normalization, volatility, regression, and weighting functions based on your preference and the specific characteristics of the asset pairs.
Monitor the weighted residuals for deviations from the mean. Larger deviations suggest stronger arbitrage opportunities.
Use the difference plot (between the screen relation and average weighted residual) as a quick visual cue for potential trade setups. When this plot deviates significantly from zero, it indicates a possible arbitrage opportunity.
Regularly update and adjust the parameters to account for changing market conditions and ensure the most accurate analysis.
In the Advanced Weighted Residual Arbitrage Analyzer , the value set in Alert Threshold plays a crucial role in delineating a normalized band. This band serves as a guide to identify significant deviations and potential trading opportunities.
When we observe the plots of the green line and the purple line, the Alert Threshold provides a boundary for these plots. The following points explain the significance:
Breach of the Band: When either the green or purple line crosses above or below the Alert Threshold , it indicates a significant deviation from the mean. This breach can be interpreted as a potential trading signal, suggesting a possible arbitrage opportunity.
Convergence to the Mean: If the green line converges with the purple line , it denotes that the price relation has reverted to its mean. This convergence typically suggests that the arbitrage opportunity has been exhausted, and the market dynamics are returning to equilibrium.
Trade Execution: A trader can consider entering a trade when the lines breach the Alert Threshold . The return of the green line to align closely with the purple line can be seen as a signal to exit the trade, capitalizing on the reversion to the mean.
By monitoring these plots in conjunction with the Alert Threshold , traders can gain insights into market imbalances and exploit potential arbitrage opportunities. The convergence and divergence of these lines, relative to the normalized band, serve as valuable visual cues for trade initiation and termination.
When you're analyzing relations between two symbols (for instance, BINANCE:SANDUSDT/BINANCE:NEARUSDT ), you're essentially looking at the price relationship between the two underlying assets. This relationship provides insights into potential imbalances between the assets, which arbitrage traders can exploit.
Breach of the Lower Band: If the purple line touches or crosses below the lower Alert Threshold , it indicates that the first symbol (in our example, SANDUSDT ) is undervalued relative to the second symbol ( NEARUSDT ). In practical terms:
Action: You would consider buying the first symbol ( SANDUSDT ) and selling the second symbol ( NEARUSDT ).
Rationale: The expectation is that the price of the first symbol will rise, or the price of the second symbol will fall, or both, thereby converging back to their historical mean relationship.
Breach of the Upper Band: Conversely, if the difference plot touches or crosses above the upper Alert Threshold , it suggests that the first symbol is overvalued compared to the second. This implies:
Action: You'd consider selling the first symbol ( SANDUSDT ) and buying the second symbol ( NEARUSDT ).
Rationale: The anticipation here is that the price of the first symbol will decrease, or the price of the second will increase, or both, bringing the relationship back to its historical average.
Convergence to the Mean: As mentioned earlier, when the green line aligns closely with the purple line, it's an indication that the assets have returned to their typical price relationship. This serves as a signal for traders to consider closing out their positions, locking in the gains from the arbitrage opportunity.
It's important to note that when you're trading based on symbol relations, you're essentially betting on the relative performance of the two assets. This strategy, often referred to as "pairs trading," seeks to capitalize on price imbalances between related financial instruments. By taking opposing positions in the two symbols, traders aim to profit from the eventual reversion of the price difference to the mean.
Machine Learning Regression Trend [LuxAlgo]The Machine Learning Regression Trend tool uses random sample consensus (RANSAC) to fit and extrapolate a linear model by discarding potential outliers, resulting in a more robust fit.
🔶 USAGE
The proposed tool can be used like a regular linear regression, providing support/resistance as well as forecasting an estimated underlying trend.
Using RANSAC allows filtering out outliers from the input data of our final fit, by outliers we are referring to values deviating from the underlying trend whose influence on a fitted model is undesired. For financial prices and under the assumptions of segmented linear trends, these outliers can be caused by volatile moves and/or periodic variations within an underlying trend.
Adjusting the "Allowed Error" numerical setting will determine how sensitive the model is to outliers, with higher values returning a more sensitive model. The blue margin displayed shows the allowed error area.
The number of outliers in the calculation window (represented by red dots) can also be indicative of the amount of noise added to an underlying linear trend in the price, with more outliers suggesting more noise.
Compared to a regular linear regression which does not discriminate against any point in the calculation window, we see that the model using RANSAC is more conservative, giving more importance to detecting a higher number of inliners.
🔶 DETAILS
RANSAC is a general approach to fitting more robust models in the presence of outliers in a dataset and as such does not limit itself to a linear regression model.
This iterative approach can be summarized as follow for the case of our script:
Step 1: Obtain a subset of our dataset by randomly selecting 2 unique samples
Step 2: Fit a linear regression to our subset
Step 3: Get the error between the value within our dataset and the fitted model at time t , if the absolute error is lower than our tolerance threshold then that value is an inlier
Step 4: If the amount of detected inliers is greater than a user-set amount save the model
Repeat steps 1 to 4 until the set number of iterations is reached and use the model that maximizes the number of inliers
🔶 SETTINGS
Length: Calculation window of the linear regression.
Width: Linear regression channel width.
Source: Input data for the linear regression calculation.
🔹 RANSAC
Minimum Inliers: Minimum number of inliers required to return an appropriate model.
Allowed Error: Determine the tolerance threshold used to detect potential inliers. "Auto" will automatically determine the tolerance threshold and will allow the user to multiply it through the numerical input setting at the side. "Fixed" will use the user-set value as the tolerance threshold.
Maximum Iterations Steps: Maximum number of allowed iterations.
AI Moving Average (Expo)█ Overview
The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels.
█ How It Works
The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors.
The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements.
This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments.
█ How to Use
Dynamic Trend Recognition
The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality.
Trend Strength
By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends.
Key SR Levels
Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes.
█ Settings
k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations.
n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data.
maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement.
length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes.
dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information
dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Linear On MACDUnlocking the Magic of Linear Regression in TradingView
In the ever-evolving world of financial markets, traders and investors seek effective tools to gauge price movements, make informed decisions, and achieve their financial goals. One such tool that has proven its worth over time is linear regression, a mathematical concept that has found its way into technical analysis and trading strategies. In this blog post, we will explore the magic behind linear regression, delve into its history, and understand how it's widely used as a technical indicator.
The Birth of Linear Regression: From Mathematics to Trading
Linear regression is a statistical method that aims to model the relationship between two variables by fitting a linear equation to observed data. The formula for a linear regression line is typically expressed as y = a + bx, where y is the dependent variable, x is the independent variable, a is the intercept, and b is the slope.
While the roots of linear regression trace back to the field of statistics, it didn't take long for traders and investors to recognize its potential in the financial world. By applying linear regression to historical price data, traders can identify trends, assess the relationship between variables, and even predict potential future price levels.
The Linear On MACD Strategy
Let's take a closer look at a powerful example of how linear regression is employed in a trading strategy right within TradingView. The "Linear On MACD" strategy harnesses the potential of linear regression in conjunction with the Moving Average Convergence Divergence (MACD) indicator. The goal of this strategy is to generate buy and sell signals based on the interactions between the predicted stock price and the MACD indicator.
Here's a breakdown of the strategy's components:
Calculation of Linear Regression: The strategy begins by calculating linear regression coefficients for the historical stock price based on volume. This helps predict potential future price levels.
Predicted Stock Price: The linear regression results are then used to plot the predicted stock price on the chart. This provides a visual representation of where the price could trend based on historical data.
Buy and Sell Signals: The strategy generates buy signals when certain conditions are met. These conditions include the predicted stock price being between the open and close prices, a rising MACD, and other factors that suggest a potential bullish trend. On the other hand, sell signals are generated based on MACD trends and predicted price levels.
Risk Management: The strategy also incorporates risk tolerance levels to determine entry and exit points. This ensures that traders take into account their risk appetite when making trading decisions.
Embracing the Magic of Linear Regression
As we explore the "Linear On MACD" strategy, we uncover the power of linear regression in aiding traders and investors. Linear regression, a mathematical marvel, seamlessly merges with technical analysis to provide insights into potential price movements. Its historical significance in statistics blends perfectly with the demands of modern financial markets.
Whether you're a seasoned trader or a curious investor, the Linear On MACD strategy exemplifies how a robust mathematical concept can be harnessed to make informed trading decisions. By embracing the magic of linear regression, you're tapping into a tool that continues to evolve alongside the financial world it empowers.
Disclaimer: The information provided in this blog post is for educational purposes only and does not constitute financial advice. Trading and investing carry risks, and it's important to conduct thorough research and consider seeking professional advice before making any trading decisions.
AI Trend Navigator [K-Neighbor]█ Overview
In the evolving landscape of trading and investment, the demand for sophisticated and reliable tools is ever-growing. The AI Trend Navigator is an indicator designed to meet this demand, providing valuable insights into market trends and potential future price movements. The AI Trend Navigator indicator is designed to predict market trends using the k-Nearest Neighbors (KNN) classifier.
By intelligently analyzing recent price actions and emphasizing similar values, it helps traders to navigate complex market conditions with confidence. It provides an advanced way to analyze trends, offering potentially more accurate predictions compared to simpler trend-following methods.
█ Calculations
KNN Moving Average Calculation: The core of the algorithm is a KNN Moving Average that computes the mean of the 'k' closest values to a target within a specified window size. It does this by iterating through the window, calculating the absolute differences between the target and each value, and then finding the mean of the closest values. The target and value are selected based on user preferences (e.g., using the VWAP or Volatility as a target).
KNN Classifier Function: This function applies the k-nearest neighbor algorithm to classify the price action into positive, negative, or neutral trends. It looks at the nearest 'k' bars, calculates the Euclidean distance between them, and categorizes them based on the relative movement. It then returns the prediction based on the highest count of positive, negative, or neutral categories.
█ How to use
Traders can use this indicator to identify potential trend directions in different markets.
Spotting Trends: Traders can use the KNN Moving Average to identify the underlying trend of an asset. By focusing on the k closest values, this component of the indicator offers a clearer view of the trend direction, filtering out market noise.
Trend Confirmation: The KNN Classifier component can confirm existing trends by predicting the future price direction. By aligning predictions with current trends, traders can gain more confidence in their trading decisions.
█ Settings
PriceValue: This determines the type of price input used for distance calculation in the KNN algorithm.
hl2: Uses the average of the high and low prices.
VWAP: Uses the Volume Weighted Average Price.
VWAP: Uses the Volume Weighted Average Price.
Effect: Changing this input will modify the reference values used in the KNN classification, potentially altering the predictions.
TargetValue: This sets the target variable that the KNN classification will attempt to predict.
Price Action: Uses the moving average of the closing price.
VWAP: Uses the Volume Weighted Average Price.
Volatility: Uses the Average True Range (ATR).
Effect: Selecting different targets will affect what the KNN is trying to predict, altering the nature and intent of the predictions.
Number of Closest Values: Defines how many closest values will be considered when calculating the mean for the KNN Moving Average.
Effect: Increasing this value makes the algorithm consider more nearest neighbors, smoothing the indicator and potentially making it less reactive. Decreasing this value may make the indicator more sensitive but possibly more prone to noise.
Neighbors: This sets the number of neighbors that will be considered for the KNN Classifier part of the algorithm.
Effect: Adjusting the number of neighbors affects the sensitivity and smoothness of the KNN classifier.
Smoothing Period: Defines the smoothing period for the moving average used in the KNN classifier.
Effect: Increasing this value would make the KNN Moving Average smoother, potentially reducing noise. Decreasing it would make the indicator more reactive but possibly more prone to false signals.
█ What is K-Nearest Neighbors (K-NN) algorithm?
At its core, the K-NN algorithm recognizes patterns within market data and analyzes the relationships and similarities between data points. By considering the 'K' most similar instances (or neighbors) within a dataset, it predicts future price movements based on historical trends. The K-Nearest Neighbors (K-NN) algorithm is a type of instance-based or non-generalizing learning. While K-NN is considered a relatively simple machine-learning technique, it falls under the AI umbrella.
We can classify the K-Nearest Neighbors (K-NN) algorithm as a form of artificial intelligence (AI), and here's why:
Machine Learning Component: K-NN is a type of machine learning algorithm, and machine learning is a subset of AI. Machine learning is about building algorithms that allow computers to learn from and make predictions or decisions based on data. Since K-NN falls under this category, it is aligned with the principles of AI.
Instance-Based Learning: K-NN is an instance-based learning algorithm. This means that it makes decisions based on the entire training dataset rather than deriving a discriminative function from the dataset. It looks at the 'K' most similar instances (neighbors) when making a prediction, hence adapting to new information if the dataset changes. This adaptability is a hallmark of intelligent systems.
Pattern Recognition: The core of K-NN's functionality is recognizing patterns within data. It identifies relationships and similarities between data points, something akin to human pattern recognition, a key aspect of intelligence.
Classification and Regression: K-NN can be used for both classification and regression tasks, two fundamental problems in machine learning and AI. The indicator code is used for trend classification, a predictive task that aligns with the goals of AI.
Simplicity Doesn't Exclude AI: While K-NN is often considered a simpler algorithm compared to deep learning models, simplicity does not exclude something from being AI. Many AI systems are built on simple rules and can be combined or scaled to create complex behavior.
No Explicit Model Building: Unlike traditional statistical methods, K-NN does not build an explicit model during training. Instead, it waits until a prediction is required and then looks at the 'K' nearest neighbors from the training data to make that prediction. This lazy learning approach is another aspect of machine learning, part of the broader AI field.
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Extrapolated Previous Trend [LuxAlgo]The Extrapolated Previous Trend indicator extrapolates the estimated linear trend of the prices within a previous interval to the current interval. Intervals can be user-defined.
🔶 USAGE
Returned lines can be used to provide a forecast of trends, assuming trends are persistent in sign and slope.
Using them as support/resistance can also be an effecting usage in case the trend in a new interval does not follow the characteristic of the trend in the previous interval.
The indicator includes a dashboard showing the degree of persistence between segmented trends for uptrends and downtrends. A higher value is indicative of more persistent trend signs.
A lower value could hint at an anti-persistent behavior, with uptrends over an interval often being followed by a down-trend and vice versa. We can invert candle colors to determine future trend direction in this case.
🔶 DETAILS
This indicator can be thought of as a segmented linear model ( a(n)t + b(n) ), where n is the specific interval index. Unlike a regular segmented linear regression model, this indicator is not subject to lookahead bias, coefficients of the model are obtained on previous intervals.
The quality of the fit of the model is dependent on the variability of its coefficients a(n) and b(n) . Coefficients being less subject to change over time are more indicative of trend persistence.
🔶 SETTINGS
Timeframe: Determine the frequency at which new trends are estimated.
Open Price Regression Modelnput Variables: The user can adjust the lookbackPeriod and m (multiplier) inputs. The lookbackPeriod specifies the number of previous bars used for regression calculations, and m is used to calculate the confidence interval width.
Calculate Regression Model: The code extracts open, high, low, and close prices for the current candle. It then performs regression calculations for high, low, and close prices based on the open prices.
Calculate Predicted Prices: Using the regression coefficients and intercepts, the code calculates predicted high, low, and close prices based on the current open price.
Calculate Confidence Interval: The code computes the standard errors of the regression for high, low, and close prices and multiplies them by the specified confidence level multiplier (m) to determine the width of the confidence intervals.
Plotting: The predicted high, low, and close prices are plotted with different colors. Additionally, confidence intervals are plotted around the predicted prices using lines.
Implications and Trading Advantage:
The Open Price Regression Model aims to predict future high, low, and close prices based on the current open price. Traders can use the predicted values and confidence intervals as potential price targets and volatility measures. Traders can consider taking long or short positions based on whether the current open price is below or above the predicted prices. Can be used on a daily time frame to forecast the day's high and low and use this levels are horizontal price levels on lower timeframes.
