Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
Komut dosyalarını "curve" için ara
FX Index Curve Oscillator (FICO)We can approximate the TVC:DXY with simple multiplication, rather than using geometric weighted averages; the values will be different, but the charts will look almost the same. Because we can make a "good enough" version of DXY, we can also extend this concept to the other major currencies:
AUD - Yellow
CAD - Red
CHF - Orange
EUR - Purple
GBP - Green
JPY - White
NZD - Lime green
USD - Blue
This indicator works by constructing an "index" for each currency, performing a lookback to figure out the rate of change, and then smoothing the values. These values are fed through an oscillator to normalize them between -1.00 and +1.00, before finally being smoothed again. Interestingly, using HMA to smooth them the second time will cause the values to leak past 1.00, which we can also use as a signal.
If you want to change the values, I find that the biggest difference comes from the lookback and oscillator settings; the MA/smoothing is probably good enough. The default settings are for doing forex trades on the daily chart. Other timeframes are possible, but I could not find any settings that work. It might also be possible to use a similar approach on other assets (crypto, metals, indexes, etc) but I have not tried yet.
In my own testing, what I found to be a good approach is to look for a currency to be above +1 and another to be below -1, and then look for color changes; ideally this will happen on the same bar/candle.
You can also consider two line crosses, breaking above or below 1, etc as other entry signals. I find that price will either move immediately, or take a candle or two to retrace and then start moving.
Happy trading!
Unfortunately, the indicator pane can get quite crowded; if you're testing for a single currency pair, you may want to disable some of the plotted lines:
MarketcapDefinition
This indicator was designed to reveal the relationship between the price of the product and its market value. The red average marketcap line that appears on the chart is the line. And the further up this line moves from the chart, the more it shows that there is a mismatch between the price and the market value. So what does this incompatibility mean? There are purchases of the product, but since the supply of the product into circulation is constantly increasing, it means that these purchases are not reflected in the price, which means there is inflation.
The main purpose of our indicator is to calculate inflation of the product. It is the understanding of whether or not the amount of supply put into circulation in response to the investment is reflected in the product price while increasing the market value.
Attention: Transactions are made based on the data received via CRYPTOCAP. In cases where this data cannot be received, the "UNSUPPORTED SOURCE" warning is displayed. You can use Settings to change the source from which data can be retrieved.
Labels
The labels are explained one by one below.
MARKETCAP: Shows the current market value.
ATH MARKETCAP: Shows the highest market value of all time.
MARKETCAP RATIO: It gives the ratio between the highest level and the lowest level of the market value.
PRICE RATIO: Gives the ratio between the highest level and the lowest level of the crypto price.
ALL INFLATION PERCENT: It refers to the percentage of all inflation that has developed so far. It is also the percentage difference between market value and price.
MONTHLY INFLATION PERCENT: It refers to the monthly estimated inflation percentage.
CIRCULATING SUPPLY: It refers to the estimated circulation supply of the product.
Best Use
It should bring to mind the idea that the further the indicator curve moves away from the price, the higher the inflation will be. In order for a product to reach its previous peak, its market value must normally increase by the "MARKETCAP RATIO" value and the "PRICE RATIO" value. This should make you think that this product needs more investment to reach its former peak. And it is necessary to be careful when purchasing such products.
Disclaimer
This indicator is for informational purposes only and should be used for educational purposes only. You may lose money if you rely on this to trade without additional information. Use at your own risk.
Version
v1.0
MathTransformLibrary "MathTransform"
Auxiliary functions for transforming data using mathematical and statistical methods
scaler_zscore(x, lookback_window)
Calculates Z-Score normalization of a series.
Parameters:
x (float) : : floating point series to normalize
lookback_window (int) : : lookback period for calculating mean and standard deviation
Returns: Z-Score normalized series
scaler_min_max(x, lookback_window, min_val, max_val, empiric_min, empiric_max, empiric_mid)
Performs Min-Max scaling of a series within a given window, user-defined bounds, and optional midpoint
Parameters:
x (float) : : floating point series to transform
lookback_window (int) : : int : optional lookback window size to consider for scaling.
min_val (float) : : float : minimum value of the scaled range. Default is 0.0.
max_val (float) : : float : maximum value of the scaled range. Default is 1.0.
empiric_min (float) : : float : user-defined minimum value of the input data. This means that the output could exceed the `min_val` bound if there is data in `x` lesser than `empiric_min`. If na, it's calculated from `x` and `lookback_window`.
empiric_max (float) : : float : user-defined maximum value of the input data. This means that the output could exceed the `max_val` bound if there is data in `x` greater than `empiric_max`. If na, it's calculated from `x` and `lookback_window`.
empiric_mid (float) : : float : user-defined midpoint value of the input data. If na, it's calculated from `empiric_min` and `empiric_max`.
Returns: rescaled series
log(x, base)
Applies logarithmic transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : logarithmic base, must be greater than 0
Returns: logarithm of the value to the given base, if x <= 0, returns logarithm of 1 to the given base
exp(x, base)
Applies exponential transformation to a value, base can be user-defined.
Parameters:
x (float) : : floating point value to transform
base (float) : : base of the exponentiation, must be greater than 0
Returns: the result of raising the base to the power of the value
power(x, exponent)
Applies power transformation to a value, exponent can be user-defined.
Parameters:
x (float) : : floating point value to transform
exponent (float) : : exponent for the transformation
Returns: the value raised to the given exponent, preserving the sign of the original value
tanh(x, scale)
The hyperbolic tangent is the ratio of the hyperbolic sine and hyperbolic cosine. It limits an output to a range of −1 to 1.
Parameters:
x (float) : : floating point series
scale (float)
sigmoid(x, scale, offset)
Applies the sigmoid function to a series.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the sigmoid function
sigmoid_double(x, scale, offset)
Applies a double sigmoid function to a series, handling positive and negative values differently.
Parameters:
x (float) : : floating point series to transform
scale (float) : : scaling factor for the sigmoid function
offset (float) : : offset for the sigmoid function
Returns: transformed series using the double sigmoid function
logistic_decay(a, b, c, t)
Calculates logistic decay based on given parameters.
Parameters:
a (float) : : parameter affecting the steepness of the curve
b (float) : : parameter affecting the direction of the decay
c (float) : : the upper bound of the function's output
t (float) : : time variable
Returns: value of the logistic decay function at time t
Linear Regression Channel Slow And Fast (Multi time frame)Linear Regression Channels are useful measure for technical and quantitative analysis in financial markets that help identifying trends and trend direction. The use of standard deviation gives traders ideas as to when prices are becoming overbought or oversold relative to the long term trend
The basis of a linear regression channel
Linear Regression Line – is a line drawn according to the least-squares statistical technique which produces a best-fit line that cuts through the middle of price action, a line that best fits all the data points of interest. The resulting fitted model can be used to summarize the data, to predict unobserved values from the same system. Linear Regression Line then present basis for the channel calculations
The linear regression channel
2. Upper Channel Line – A line that runs parallel to the Linear Regression Line and is usually one to two standard deviations above the Linear Regression Line.
3. Lower Channel Line – This line runs parallel to the Linear Regression Line and is usually one to two standard deviations below the Linear Regression Line.
Unlike Fibonacci Channels and Andrew’s Pitchfork, Linear Regression Channels are calculated using statistical methods, both for the regression line (as expressed above) and deviation channels. Upper and Lower channel lines are presenting the idea of bell curve method, also known as a normal distribution and are calculated using standard deviation function.
A standard deviation include 68% of the data points, two standard deviations include approximately 95% of the data points and any data point that appears outside two standard deviations is very rare.
It is often assumed that the data points will move back toward the average, or regress and channels would allow us to see when a security is overbought or oversold and ready to revert to the mean
please note : Over time, the price will move up and down, and the linear regression channel will experience changes as old prices fall off and new prices appear
Papercuts Recency CandlesPapercuts Recency Candles
V0.8 by Joel Eckert @PapercutsTrading
***This is currently an experimental visual exploratory concept.***
*** Experimental tools should only be explored by fellow coders and experienced traders.***
DESCRIPTION:
As coders, how can we seamlessly transition between actual and smoothed price data sets as data ages?
This is a visual experiment to see if and how data can be smoothly transitioned from one value to another over a set number of candles. If we visualize a chart in 3 zones, a head, a body, and a tail we can start to understand how this could work. The head zone would represent the first data set of actual asset prices. The body zone would represent the transition period from the first to the to the second data set. Last, the tail zone would represent the second data set made of a Hull Moving Average of the asset.
CONCEPT:
It is conceived that data and position precision constantly shift as they decay or age, therefore making older price levels act more like price regions or zones vs exact price points. This is what I am calling Recency.
This indicator utilizes the concept of "Recency" to explore the possibility of a new style of candle. It aims to maintain accurately on recent prices action but loosen up accuracy on older price action. The very nature of this requires ALTERING HISTORICAL DATA within the body zone or transition candles to achieve the effect. It is similar to trying to merge a line chart type with a candle chart type.
This experiment of using recency for candles was to create candles that stay more accurate near current price but fade away into a simple line as they age out, resulting in a simplified view of the big picture which consists of older price action.
This experimental design theoretically will help you stay focused only on what is currently unfolding and to minimize distractions from older price nuances.
USAGE:
WHO:
This is not recommended for new traders or novices that are unfamiliar with standard tools. Standardized tools should always be used to get grounded and build a foundation.
Active traders who are familiar with trading comfortably should experiment with this to see if they find it interesting or usable.
Pine coders may find this concept interesting enough, and may adapt the idea to other elements of their own scripts if they find it interesting… I just ask they give credit where credit is due.
HOW:
The best way to visualize how this works is to do the following:
Load it on a chart.
Turn off Standard candles in Chart Setting of the current window. I actually just turn off the bodies and borders, and dim the old wicks as I like the way the old wicks look when left alone with these new candles.
Enable chart replay at a faster speed, like 3x, and play back the chart to watch the behavior of the candles.
You’ll be able to see how the head of the candle type preserves OHLC, and indicates direction but as the candle starts to age it progressively flowers into the HMA
While it plays back try adjusting settings to see how they affect behavior.
You can see the data average in real-time which often reveals how unstable actual price noise really is.
The head candle diagonals indicate the candle body direction.
SETTINGS:
Coloring: You can choose your own bullish or bearish colors to match your scheme.
Price Line: The price line is colored according to the trend and
Head Length: These candles are true to the source high and low. They remain slightly brighter than transition candles. We have a max of 50 to keep things responsive.
Time Decay Length: This is the amount of candles it takes to transition to the tail. Max is 300 to keep things responsive.
Decay Continuity: This forces transition candles to complete the HMA curve instead of creating gaps when conforming to it. The best way to visualize this feature is to run a 3x replay of an asset, and toggle the result on and off. On is preferred.
Tail HMA Length: This is the smoothing amount for the resulting HMA stepline that calculates every close, but has a delayed draw until after the transition candles. You can optionally turn off the delayed visibility to help with comprehension.
Tail HMA Weight: This is simply an option to make the tail thicker or thinner. This also adjusts the border on the head candles to help them stand out.
Show Side Bias Dots: Default true: Draws a dot when bias to one side changes to help keep you on the right side of trade. Side bias is simply the alignment of 3 moving averages in one direction.
IMPORTANT NOTES:
You'll have to turn off or dim the standard candles in your view "Chart Settings" to see this properly.
Be aware that since the candles are based on boxes and utilize the “recency concept”, which means their data decays and changes as it ages. This results in a cleaner chart overall, but exact highs and lows will be averaged out as the data decays, forming a Hull Moving Average stepline of your defined length once decay has finished.
SUMMARY OF HOW IT WORKS:
First it takes candle information and creates unique boxes that represent each candle based on the high and low. It utilizes boxes because standard candles once written, cannot be later altered or removed… which is a key element for this effect to work.
Next it creates a second box and line from open to close for the body of the Head candles. This indicates direction at a glance.
As candles age beyond the defined distance of the “Head” they enter the "Body" aka "Time Decay" zone. Here the accuracy of the high and low will be averaged down using an incremental factor of the HMA, defined by "Time Decay Length" amount of candles.
The resulting tail is an HMA of Tail HMA Length. This tail is always calculate at close, but is not drawn instantly. The draw is delayed so that there is not overlapping data, and this makes the effect look more elegant.
There are also two EMAs within the script that do nothing but help candle coloring and help provide a trade side bias. When both EMA's and the HMA align, a side bias is defined. Only when the side bias changes will a new dot is formed.
Head candles have been simplified from previous versions to be easier to read at a a glance.
Turn of the Month Strategy [Honestcowboy]The end of month effect is a well known trading strategy in the stock market. Quite simply, most stocks go up at the end of the month. What's even better is that this effect spills over to the next phew days of the next month.
In this script we backtest this theory which should work especially well on SP500 pair.
By default the strategy buys 2 days before the end of each month and exits the position 3 days into the next month.
The strategy is a long only strategy and is extremely simple. The SP500 is one of the #1 assets people use for long term investing due to it's "9.8%" annualised return. However as a trader you want the best deal possible. This strategy is only inside the market for about 25% of the time while delivering a similar return per exposure with a lower drawdown.
Here are some hypothesis why turn of the month effect happens in the stock markets:
Increased inflow from savings accounts to stocks at end of month
Rebalancing of portfolios by fund managers at end of month
The timing of monthly cash flows received by pension funds, which are reinvested in the stock market.
The script also has some inputs to define how many days before end of the month you want to buy the asset and how long you want to hold it into the next month.
It is not possible to buy the asset exactly on this day every month as the market closes on the weekend. I've added some logic where it will check if that day is a friday, saturdady or sunday. If that is the case it will send the buy signal on the end of thursday, this way we enter on the friday and don't lose that months trading opportunity.
The backtest below uses 4% exposure per trade as to show the equity curve more clearly and because of publishing rules. However, most fund managers and investors use 100% exposure. This way you actually risk money to earn money. Feel free to adjust the settings to your risk profile to get a clearer picture of risks and rewards before implementing in your portfolio.
Crypto Realized Profits/Losses Extremes [AlgoAlpha]🌟🚀 Introducing the Crypto Realized Profits/Losses Extremes Indicator by AlgoAlpha 🚀🌟
Unlock the potential of cryptocurrency markets with our cutting-edge On-Chain Pine Script™ indicator, designed to highlight extreme realized profit and loss zones! 🎯📈
Key Features:
✨ Realized Profits/Losses Calculation: Uses real-time data from the blockchain to monitor profit and loss realization events.
📊 Multi-Crypto Compatibility: The Indicator is compatible on other Crypto tickers besides Bitcoin.
⚙️ Customizable Sensitivity: Adjust the look-back period, normalization period, and deviation thresholds to tailor the indicator to your trading style.
🎨 Visual Enhancements: Choose from a variety of colors for up and down trends, and toggle extreme profit/loss overlay for easy viewing.
🔔 Integrated Alerts: Set up alerts for high and extreme profit or loss conditions, helping you stay ahead of significant market movements.
🔍 How to Use:
🛠 Add the Indicator: Add the indicator to favorites. Customize settings like period lengths and deviation thresholds according to your needs.
📊 Market Analysis: Monitor the main oscillator and the bands to understand current profit and loss extremes in the market. When the oscillator is at the upper band, this means that the market is doing really well and traders/investors will be likely to take profit and cause a reversal. The opposite is true when the oscillator reaches the lower band. The main oscillator can also be used for trend analysis.
🔔 Set Alerts: Configure alerts to notify you when the market enters a zone of high profit or loss, or during trend changes, enabling timely decisions without constant monitoring.
How It Works:
The indicator calculates a normalized area under the RSI curve applied on on-chain data regarding the number of wallets in profit. It employs a custom "src" variable that aggregates data from the blockchain about profit and loss addresses, adapting to intraday or longer timeframes as needed. The main oscillator plots this normalized area, while the upper and lower bands are plotted based on a deviation metric to identify extreme conditions. Colored fills between these bands visually denote these zones. For interaction, the indicator plots bubbles for extreme profits or losses and provides optional bar coloring to reflect the current market trend.
🚀💹 Enjoy a comprehensive, customizable, and visually engaging tool that helps you stay ahead in the fast-paced crypto market!
price action reversion bands - [SigmaStreet]█ OVERVIEW
The "Price Action Reversion Bands" is designed to help traders identify potential reversal zones through the integration of polynomial regression, fractal analysis, and pinbar detection. This tool overlays directly onto the price chart, providing dynamic visual cues and signals for market reversals. Its unique synthesis of these methodologies offers traders a powerful, multifaceted approach to market analysis.
█ CONCEPTS
Polynomial Regression Bands:
What It Does:
Models the main trend using a polynomial equation to create a middle trend line with dynamic support and resistance bands.
How It Works:
Calculates polynomial coefficients to plot a regression line and adjusts the bands according to market volatility and conditions.
Fibonacci Retracement Levels:
What It Does:
Provides additional lines inside the regression bands at key Fibonacci ratios to identify potential support and resistance areas.
How It Works:
Calculates retracement levels by identifying high and low points over the same period used to calculate the regression bands, applying Fibonacci ratios to these points.
Fractal Analysis:
What It Does: Identifies natural resistance and support levels, indicating potential reversal zones.
How It Works: Detects fractals based on a specific pattern of price action, using Williams Fractal methodology.
Pinbar Detection:
What It Does: Signals potential price reversals through pinbar candlestick patterns.
How It Works: Analyzes
candlesticks to identify pinbars which show a rejection of prices, suggesting possible reversals.
█ ORIGINALITY AND USEFULNESS
The price action reversion bands distinguishes itself through its innovative integration of several advanced analytical methods, providing traders with a holistic view of potential market reversals:
Unique Combination:
While many tools use these techniques in isolation, this indicator synergistically combines polynomial regression, Fibonacci retracement levels, fractal analysis, and pinbar detection. This multi-faceted approach allows traders to assess strength, potential reversal zones, and price rejection more effectively than using traditional single-method indicators.
Advanced Polynomial Regression Application:
Unlike standard regression tools that offer static insights, this indicator dynamically adjusts its regression bands based on real-time market volatility, providing a more accurate reflection of market conditions.
Enhanced Signal Reliability:
By using fractals and pinbars in conjunction to validate each other, the indicator significantly increases the reliability of its reversal signals. This dual-validation method filters out less probable signals, focusing on high-probability trading opportunities.
Customization and Flexibility:
It offers unprecedented customization options, allowing traders to fine-tune the tool according to their trading style and market conditions. Traders can adjust the polynomial degree, the sensitivity of the Fibonacci retracements, and even the definition of what constitutes a significant pinbar, making it highly adaptable to various trading scenarios.
Educational Value:
The indicator not only aids in trading but also serves as an educational tool that helps traders understand the interaction between different types of market analysis techniques. This contributes to a deeper knowledge base and better trading decisions over time.
These distinctive features make the "Price Action Reversion Bands - " not just another indicator but a comprehensive trading tool that enhances decision-making through a well-rounded analysis of market dynamics.
█ HOW TO USE
Installation and Setup:
Apply the indicator to your TradingView chart from the "Indicators" menu.
Select either polynomial regression or Fibonacci retracement as the basis for the bands through the indicator settings.
Reading the Indicator:
Monitor the approach of price to the upper and lower bands which indicate potential reversal zones.
Look for fractal and pinbar formations near these bands for additional signal confirmation.
Customization:
Adjust settings such as the polynomial degree, data window length, and engagement zones to tailor the bands to your trading style.
Modify visual aspects like color and line type for better clarity and personal preference.
█ FEATURES
Dynamic Adjustment:
Bands adjust in real-time based on incoming price data and selected settings.
Multiple Analysis Techniques: Combines several analytical techniques to provide a comprehensive view of potential market movements. The integration of polynomial regression with Fibonacci levels, supplemented by fractal and pinbar analysis, marks this tool as particularly innovative, offering a level of synthesis that enhances predictive accuracy and usability.
User-Friendly Customization: Allows for extensive customization to suit individual trading strategies and preferences.
█ LIMITATIONS
Market Dependency:
Performance may vary significantly across different markets and conditions.
Parameter Sensitivity: Requires fine-tuning of parameters to ensure optimal performance, which might demand a steep learning curve for new users.
█ NOTES
For best results, combine this tool with other forms of analysis, such as fundamental analysis and other technical indicators, to confirm signals and enhance decision-making.
█ THANKS
Special thanks to the PineCoders community the Pine Coders themselves for their foundational contributions to the concepts used in this script. Their pioneering work in the fields of technical analysis and Pine Script development has been invaluable. This script is a testament to the collaborative spirit of the TradingView developer community, integrating analytical techniques with innovative approaches to offer a tool that is both modern and cutting-edge.
Yield Curve SpaghettiDisplays the difference in yield between multiple bond pairs for a given country.
Currently supports US, DE, and GB bonds
[Wiseplat Sideways] v.04The Sideway indicator for TradingView is a powerful tool designed to identify periods of sideways or ranging price action in the market. With its intuitive interface and customizable parameters, traders can easily spot when an asset is consolidating, providing valuable insights for both trend-following and range-bound strategies.
This indicator utilizes really simple algorithm to analyze price movement and volatility, effectively filtering out noise and false signals. By plotting clear visual cues on the chart.
Traders can adjust the sensitivity parameters to tailor the indicator to their specific trading style and preferences. Whether used in isolation or in conjunction with other technical analysis tools, the Sideway indicator empowers traders to make informed decisions in dynamic market conditions.
Its user-friendly design and simple settings of parameters makes it accessible to traders of all levels, from beginners seeking clarity in choppy markets to seasoned professionals looking for confirmation of their analysis. With the Sideway indicator, traders can confidently navigate sideways price action and stay ahead of the curve in their trading endeavors.
Developer: Oleg Shpagin
Bitcoin Regression Price BoundariesTLDR
DCA into BTC at or below the blue line. DCA out of BTC when price approaches the red line. There's a setting to toggle the future extrapolation off/on.
INTRODUCTION
Regression analysis is a fundamental and powerful data science tool, when applied CORRECTLY . All Bitcoin regressions I've seen (Rainbow Log, Stock-to-flow, and non-linear models), have glaring flaws ... Namely, that they have huge drift from one cycle to the next.
Presented here, is a canonical application of this statistical tool. "Canonical" meaning that any trained analyst applying the established methodology, would arrive at the same result. We model 3 lines:
Upper price boundary (red) - Predicted the April 2021 top to within 1%
Lower price boundary (green)- Predicted the Dec 2022 bottom within 10%
Non-bubble best fit line (blue) - Last update was performed on Feb 28 2024.
NOTE: The red/green lines were calculated using solely data from BEFORE 2021.
"I'M INTRUIGED, BUT WHAT EXACTLY IS REGRESSION ANALYSIS?"
Quite simply, it attempts to draw a best-fit line over some set of data. As you can imagine, there are endless forms of equations that we might try. So we need objective means of determining which equations are better than others. This is where statistical rigor is crucial.
We check p-values to ensure that a proposed model is better than chance. When comparing two different equations, we check R-squared and Residual Standard Error, to determine which equation is modeling the data better. We check residuals to ensure the equation is sufficiently complex to model all the available signal. We check adjusted R-squared to ensure the equation is not *overly* complex and merely modeling random noise.
While most people probably won't entirely understand the above paragraph, there's enough key terminology in for the intellectually curious to research.
DIVING DEEPER INTO THE 3 REGRESSION LINES ABOVE
WARNING! THIS IS TECHNICAL, AND VERY ABBREVIATED
We prefer a linear regression, as the statistical checks it allows are convenient and powerful. However, the BTCUSD dataset is decidedly non-linear. Thus, we must log transform both the x-axis and y-axis. At the end of this process, we'll use e^ to transform back to natural scale.
Plotting the log transformed data reveals a crucial visual insight. The best fit line for the blowoff tops is different than for the lower price boundary. This is why other models have failed. They attempt to model ALL the data with just one equation. This causes drift in both the upper and lower boundaries. Here we calculate these boundaries as separate equations.
Upper Boundary (in red) = e^(3.24*ln(x)-15.8)
Lower Boundary (green) = e^(0.602*ln^2(x) - 4.78*ln(x) + 7.17)
Non-Bubble best fit (blue) = e^(0.633*ln^2(x) - 5.09*ln(x) +8.12)
* (x) = The number of days since July 18 2010
Anyone familiar with Bitcoin, knows it goes in cycles where price goes stratospheric, typically measured in months; and then a lengthy cool-off period measured in years. The non-bubble best fit line methodically removes the extreme upward deviations until the residuals have the closest statistical semblance to normal data (bell curve shaped data).
Whereas the upper/lower boundary only gets re-calculated in hindsight (well after a blowoff or capitulation occur), the Non-Bubble line changes ever so slightly with each new datapoint. The last update to this line was made on Feb 28, 2024.
ENOUGH NERD TALK! HOW CAN I APPLY THIS?
In the simplest terms, anything below the blue line is a statistical buying opportunity. The closer you approach the green line (the lower boundary) the more statistically strong that opportunity is. As price approaches the red line, is a growing statistical likelyhood/danger of an imminent blowoff top.
So a wise trader would DCA (dollar cost average) into Bitcoin below the blue line; and would DCA out of Bitcoin as it approaches the red line. Historically, you may or may not have a large time-window during points of maximum opportunity. So be vigilant! Anything within 10-20% of the boundary should be regarded as extreme opportunity.
Note: You can toggle the future extrapolation of these lines in the settings (default on).
CLOSING REMARKS
Keep in mind this is a pure statistical analysis. It's likely that this model is probing a complex, real economic process underlying the Bitcoin price. Statistical models like this are most accurate during steady state conditions, where the prevailing fundamentals are stable. (The astute observer will note, that the regression boundaries held despite the economic disruption of 2020).
Thus, it cannot be understated: Should some drastic fundamental change occur in the underlying economic landscape of cryptocurrency, Bitcoin itself, or the broader economy, this model could drastically deviate, and become significantly less accurate.
Furthermore, the upper/lower boundaries cross in the year 2037. THIS MODEL WILL EVENTUALLY BREAK DOWN. But for now, given that Bitcoin price moves on the order of 2000% from bottom to top, it's truly remarkable that, using SOLELY pre-2021 data, this model was able to nail the top/bottom within 10%.
HSI - Halving Seasonality Index for Bitcoin (BTC) [Logue]Halving Seasonality Index (HSI) for Bitcoin (BTC) - The HSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops around 538 days after each halving. Past cycles have formed macro bottoms every 948 days after each halving. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are set at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the HSI.
CSI - Calendar Seasonality Index for Bitcoin (BTC) [Logue]Calendar Seasonality Index (CSI) for Bitcoin (BTC) - The CSI takes advantage of the consistency of BTC cycles. Past cycles have formed macro tops every four years near November 21st, starting from in 2013. Past cycles have formed macro bottoms every four years near January 15th, starting from 2011. Therefore, a linear "risk" curve can be created between the bottom and top dates to measure how close BTC might be to a bottom or a top. The default triggers are at 98% risk for tops and 5% risk for bottoms. Extensions are also added as defaults to allow easy identification of the dates of the next top or bottom according to the CSI.
Greenblatts Magic Formula - A multiple approachThis indicator is supposed to help find undervalued stocks. Inspired by Joel Greenblatt's strategy where he ranks stocks with the lowest EV/EBIT and the highest ROC. Inspired by the ERP5 strategy I have added Earnings Yield together with ROC.
My approach and how I use the indicator is to see Magic Formula score as a multiple, rather than ranking the numbers between different stocks. Like P/E for comparison. Different kinds of companies trades at different multiples so you have to compare the current MF Score in relation to historical MF Score to get an idea if it truly is undervalued. You also want to see that price actually reacts to a low MF Score.
As i general rule for myself I stay away from companies with EV/EBIT above 13 and generally want to see MF Score below 6-7. A company trading at a negative MF Score indicates that the company may be heavily undervalued.
Red line = EV/EBIT
Green line = ROC + EY / 2
Yellow line = "MF Score" EVEBIT - (ROC+EY/2)
Blue line = The 50 EMA of MF score
The strategy is simple. Look for companies which might be undervalued. Compare the current MF score to it's history. If it's trading near a previous bottom it indicates that the company might be undervalued. You can also use the MF EMA to see a more smooth curve to interpret the multiple.
Trending RSI [ChartPrime]Trending RSI takes a new approach to RSI intended to provide all of the missing information that traditional RSI lacks. Questions such as "why does the price continue to decline even during an oversold period?" can be aided using the Trending RSI.
These types of movements are due to the market still trending and traditional RSI can not tell traders this. Trending RSI fixes this by introducing trend information back into the oscillator. By reverse engineering RSI we have been able to make a new indicator that is no longer bound between 0 and 100. Instead it provides the traditional 70 and 30 zones as bands, and 50 as a center line that still represent these zones perfectly. This transforms RSI into a centered oscillator instead of a normalized oscillator. When the market is trending our indicator represents this as the center line being below or above 0. Just like MACD the center line is colored to represent the market phases. This helps in identifying reversals more clearly by adding a layer of confluence to the already renowned RSI. We have also included a novel filtering technique that has a low lag to smoothing ratio. This is primarily used to smooth the bands by default but you can also utilize this on the RSI. Several alerts have been included to provide users with easy to configure signals.
You can use the center line as a directional filter for your trades by only picking trades in the direction of the center line. When the center line is above 0, the market is trending up. Conversely, when the center line is below 0 the market is trending down trend. Use the polarity of the center line to estimate the strength of retracements from the oversold and overbought zones. We have also included a special moving average to help you find the momentum of a move. The Binomial MA filter approximates a normal curve making it similar to a gaussian filter. We have also included standard divergences which are fully configurable in the settings. Finally, we have built this indicator to be compatible with the built in multi time frame option to allow users to freely pick the time frame they wish to use. It is worth noting that due to the limitations of the standard MTF implementation divergences will not plot as expected when using time frames outside of the charts time frame. This is standard and also affects the built in RSI.
All of the colors are fully adjustable with the option to enable or disable the glow effect. We have also designed this indicator to only display the information for plots that are enabled to reduce clutter and provide a cleaner charting experience. All alerts are built to work with the standard alert builder and do not have to be enabled or disabled inside of the indicator.
Included Alerts:
RSI Cross Over Center
RSI Cross Under Center
RSI Cross Under Upper Range
RSI Cross Over Upper Range
RSI Cross Over Lower Range
RSI Cross Under Lower Range
RSI Cross Over MA
RSI Cross Under MA
RSI Cross Over 0
RSI Cross Under 0
Center Cross Over 0
Center Cross Under 0
Center Bullish
Center Bearish
Bullish Divergence
Bearish Divergence
In wrapping up, the Trending RSI aims to enhance the conventional RSI by adding trend insights directly into the oscillator, addressing the gap that traditional RSI leaves regarding market trends. This version of RSI breaks away from the 0 to 100 range, offering bands and a center line that better represent market conditions. It includes a set of features like the Binomial MA for momentum analysis, configurable settings for divergence detection, and compatibility with multi-time frame analysis. The color customization and glow effects aim to improve visual clarity, and the inclusion of alerts is designed to streamline alert configuration. Overall, this indicator is designed to provide a more view of the markets, suitable for traders looking to incorporate trend analysis into their RSI-based strategies.
Enjoy
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.
Targets For Many Indicators [LuxAlgo]The Targets For Many Indicators is a useful utility tool able to display targets for many built-in indicators as well as external indicators. Targets can be set for specific user-set conditions between two series of values, with the script being able to display targets for two different user-set conditions.
Alerts are included for the occurrence of a new target as well as for reached targets.
🔶 USAGE
Targets can help users determine the price limit where the price might start deviating from an indication given by one or multiple indicators. In the context of trading, targets can help secure profits/reduce losses of a trade, as such this tool can be useful to evaluate/determine user take profits/stop losses.
Due to these essentially being horizontal levels, they can also serve as potential support/resistances, with breakouts potentially confirming new trends.
In the above example, we set targets 3 ATR's away from the closing price when the price crosses over the script built-in SuperTrend indicator using ATR period 10 and factor 3. Using "Long Position Target" allows setting a target above the price, disabling this setting will place targets below the price.
Users might be interested in obtaining new targets once one is reached, this can be done by enabling "New Target When Reached" in the target logic setting section, resulting in more frequent targets.
Lastly, users can restrict new target creation until current ones are reached. This can result in fewer and longer-term targets, with a higher reach rate.
🔹 Dashboard
A dashboard is displayed on the top right of the chart, displaying the amount, reach rate of targets 1/2, and total amount.
This dashboard can be useful to evaluate the selected target distances relative to the selected conditions, with a higher reach rate suggesting the distance of the targets from the price allows them to be reached.
🔶 DETAILS
🔹 Indicators
Besides 'External' sources, each source can be set at 1 of the following Build-In Indicators :
ACCDIST : Accumulation/distribution index
ATR : Average True Range
BB (Middle, Upper or Lower): Bollinger Bands
CCI : Commodity Channel Index
CMO : Chande Momentum Oscillator
COG : Center Of Gravity
DC (High, Mid or Low): Donchian Channels
DEMA : Double Exponential Moving Average
EMA : Exponentially weighted Moving Average
HMA : Hull Moving Average
III : Intraday Intensity Index
KC (Middle, Upper or Lower): Keltner Channels
LINREG : Linear regression curve
MACD (macd, signal or histogram): Moving Average Convergence/Divergence
MEDIAN : median of the series
MFI : Money Flow Index
MODE : the mode of the series
MOM : Momentum
NVI : Negative Volume Index
OBV : On Balance Volume
PVI : Positive Volume Index
PVT : Price-Volume Trend
RMA : Relative Moving Average
ROC : Rate Of Change
RSI : Relative Strength Index
SMA : Simple Moving Average
STOCH : Stochastic
Supertrend
TEMA : Triple EMA or Triple Exponential Moving Average
VWAP : Volume Weighted Average Price
VWMA : Volume-Weighted Moving Average
WAD : Williams Accumulation/Distribution
WMA : Weighted Moving Average
WVAD : Williams Variable Accumulation/Distribution
%R : Williams %R
Each indicator is provided with a link to the Reference Manual or to the Build-In Indicators page.
The latter contains more information about each indicator.
Note that when "Show Source Values" is enabled, only values that can be logically found around the price will be shown. For example, Supertrend , SMA , EMA , BB , ... will be made visible. Values like RSI , OBV , %R , ... will not be visible since they will deviate too much from the price.
🔹 Interaction with settings
This publication contains input fields, where you can enter the necessary inputs per indicator.
Some indicators need only 1 value, others 2 or 3.
When several input values are needed, you need to separate them with a comma.
You can use 0 to 4 spaces between without a problem. Even an extra comma doesn't give issues.
The red colored help text will guide you further along (Only when Target is enabled)
Some examples that work without issues:
Some examples that work with issues:
As mentioned, the errors won't be visible when the concerning target is disabled
🔶 SETTINGS
Show Target Labels: Display target labels on the chart.
Candle Coloring: Apply candle coloring based on the most recent active target.
Target 1 and Target 2 use the same settings below:
Enable Target: Display the targets on the chart.
Long Position Target: Display targets above the price a user selected condition is true. If disabled will display the targets below the price.
New Target Condition: Conditional operator used to compare "Source A" and "Source B", options include CrossOver, CrossUnder, Cross, and Equal.
🔹 Sources
Source A: Source A input series, can be an indicator or external source.
External: External source if 'External" is selected in "Source A".
Settings: Settings of the selected indicator in "Source A", entered settings of indicators requiring multiple ones must be comma separated, for example, "10, 3".
Source B: Source B input series, can be an indicator or external source.
External: External source if 'External" is selected in "Source B".
Settings: Settings of the selected indicator in "Source B", entered settings of indicators requiring multiple ones must be comma separated, for example, "10, 3".
Source B Value: User-defined numerical value if "value" is selected in "Source B".
Show Source Values: Display "Source A" and "Source B" on the chart.
🔹 Logic
Wait Until Reached: When enabled will not create a new target until an existing one is reached.
New Target When Reached: Will create a new target when an existing one is reached.
Evaluate Wicks: Will use high/low prices to determine if a target is reached. Unselecting this setting will use the closing price.
Target Distance From Price: Controls the distance of a target from the price. Can be determined in currencies/points, percentages, ATR multiples, ticks, or using multiple of external values.
External Distance Value: External distance value when "External Value" is selected in "Target Distance From Price".
[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages.
Since signing up 2 years ago, I have been collecting various Сollections.
I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet.
And now I'm sharing my collection with you.
140 Different Types of Moving Averages are waiting for you.
Specifically :
"
AARMA | Adaptive Autonomous Recursive Moving Average
ADMA | Adjusted Moving Average
ADXMA | Average Directional Moving Average
ADXVMA | Average Directional Volatility Moving Average
AHMA | Ahrens Moving Average
ALF | Ehler Adaptive Laguerre Filter
ALMA | Arnaud Legoux Moving Average
ALSMA | Adaptive Least Squares
ALXMA | Alexander Moving Average
AMA | Adaptive Moving Average
ARI | Unknown
ARSI | Adaptive RSI Moving Average
AUF | Auto Filter
AUTL | Auto-Line
BAMA | Bryant Adaptive Moving Average
BFMA | Blackman Filter Moving Average
CMA | Corrected Moving Average
CORMA | Correlation Moving Average
COVEMA | Coefficient of Variation Weighted Exponential Moving Average
COVNA | Coefficient of Variation Weighted Moving Average
CTI | Coral Trend Indicator
DEC | Ehlers Simple Decycler
DEMA | Double EMA Moving Average
DEVS | Ehlers - Deviation Scaled Moving Average
DONEMA | Donchian Extremum Moving Average
DONMA | Donchian Moving Average
DSEMA | Double Smoothed Exponential Moving Average
DSWF | Damped Sine Wave Weighted Filter
DWMA | Double Weighted Moving Average
E2PBF | Ehlers 2-Pole Butterworth Filter
E2SSF | Ehlers 2-Pole Super Smoother Filter
E3PBF | Ehlers 3-Pole Butterworth Filter
E3SSF | Ehlers 3-Pole Super Smoother Filter
EDMA | Exponentially Deviating Moving Average (MZ EDMA)
EDSMA | Ehlers Dynamic Smoothed Moving Average
EEO | Ehlers Modified Elliptic Filter Optimum
EFRAMA | Ehlers Modified Fractal Adaptive Moving Average
EHMA | Exponential Hull Moving Average
EIT | Ehlers Instantaneous Trendline
ELF | Ehler Laguerre filter
EMA | Exponential Moving Average
EMARSI | EMARSI
EPF | Edge Preserving Filter
EPMA | End Point Moving Average
EREA | Ehlers Reverse Exponential Moving Average
ESSF | Ehlers Super Smoother Filter 2-pole
ETMA | Exponential Triangular Moving Average
EVMA | Elastic Volume Weighted Moving Average
FAMA | Following Adaptive Moving Average
FEMA | Fast Exponential Moving Average
FIBWMA | Fibonacci Weighted Moving Average
FLSMA | Fisher Least Squares Moving Average
FRAMA | Ehlers - Fractal Adaptive Moving Average
FX | Fibonacci X Level
GAUS | Ehlers - Gaussian Filter
GHL | Gann High Low
GMA | Gaussian Moving Average
GMMA | Geometric Mean Moving Average
HCF | Hybrid Convolution Filter
HEMA | Holt Exponential Moving Average
HKAMA | Hilbert based Kaufman Adaptive Moving Average
HMA | Harmonic Moving Average
HSMA | Hirashima Sugita Moving Average
HULL | Hull Moving Average
HULLT | Hull Triple Moving Average
HWMA | Henderson Weighted Moving Average
IE2 | Early T3 by Tim Tilson
IIRF | Infinite Impulse Response Filter
ILRS | Integral of Linear Regression Slope
JMA | Jurik Moving Average
KA | Unknown
KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA
KIJUN | KIJUN
KIJUN2 | Kijun v2
LAG | Ehlers - Laguerre Filter
LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions)
LEMA | Leader Exponential Moving Average
LLMA | Low-Lag Moving Average
LMA | Leo Moving Average
LP | Unknown
LRL | Linear Regression Line
LSMA | Least Squares Moving Average / Linear Regression Curve
LTB | Unknown
LWMA | Linear Weighted Moving Average
MAMA | MAMA - MESA Adaptive Moving Average
MAVW | Mavilim Weighted Moving Average
MCGD | McGinley Dynamic Moving Average
MF | Modular Filter
MID | Median Moving Average / Percentile Nearest Rank
MNMA | McNicholl Moving Average
MTMA | Unknown
MVSMA | Minimum Variance SMA
NLMA | Non-lag Moving Average
NWMA | Dürschner 3rd Generation Moving Average (New WMA)
PKF | Parametric Kalman Filter
PWMA | Parabolic Weighted Moving Average
QEMA | Quadruple Exponential Moving Average
QMA | Quick Moving Average
REMA | Regularized Exponential Moving Average
REPMA | Repulsion Moving Average
RGEMA | Range Exponential Moving Average
RMA | Welles Wilders Smoothing Moving Average
RMF | Recursive Median Filter
RMTA | Recursive Moving Trend Average
RSMA | Relative Strength Moving Average - based on RSI
RSRMA | Right Sided Ricker MA
RWMA | Regressively Weighted Moving Average
SAMA | Slope Adaptive Moving Average
SFMA | Smoother Filter Moving Average
SMA | Simple Moving Average
SSB | Senkou Span B
SSF | Ehlers - Super Smoother Filter P2
SSMA | Super Smooth Moving Average
STMA | Unknown
SWMA | Self-Weighted Moving Average
SW_MA | Sine-Weighted Moving Average
TEMA | Triple Exponential Moving Average
THMA | Triple Exponential Hull Moving Average
TL | Unknown
TMA | Triangular Moving Average
TPBF | Three-pole Ehlers Butterworth
TRAMA | Trend Regularity Adaptive Moving Average
TSF | True Strength Force
TT3 | Tilson (3rd Degree) Moving Average
VAMA | Volatility Adjusted Moving Average
VAMAF | Volume Adjusted Moving Average Function
VAR | Vector Autoregression Moving Average
VBMA | Variable Moving Average
VHMA | Vertical Horizontal Moving Average
VIDYA | Variable Index Dynamic Average
VMA | Volume Moving Average
VSO | Unknown
VWMA | Volume Weighted Moving Average
WCD | Unknown
WMA | Weighted Moving Average
XEMA | Optimized Exponential Moving Average
ZEMA | Zero Lag Moving Average
ZLDEMA | Zero-Lag Double Exponential Moving Average
ZLEMA | Ehlers - Zero Lag Exponential Moving Average
ZLTEMA | Zero-Lag Triple Exponential Moving Average
ZSMA | Zero-Lag Simple Moving Average
"
Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example.
But remember that some MAs are not designed to work with anything other than a chart.
All MA and Code lists are sorted strictly alphabetically by short name (A-Z).
Each MA has its own number (ID) by which you can display the Moving Average you need.
Next to the ID selection there are tooltips with short names and their numbers. Use them.
The panel below will help you to read the Name of the selected MA.
Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case.
Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know.
I have deliberately simplified all MAs to input just Source and Length.
Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.).
If you need or like any MA you will still have to take it from my collection for your code.
I tried to leave the basic MA settings inside function in first strings.
I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them.
Some of the features were created from scratch or may have been slightly modified, so please be careful.
If you would like to improve this collection, please write to me in PM.
Also Credits, Likes, Awards, Loves and Thanks to :
@alexgrover
@allanster
@andre_007
@auroagwei
@blackcat1402
@bsharpe
@cheatcountry
@CrackingCryptocurrency
@Duyck
@ErwinBeckers
@everget
@glaz
@gotbeatz26107
@HPotter
@io72signals
@JacobAmos
@JoshuaMcGowan
@KivancOzbilgic
@LazyBear
@loxx
@LuxAlgo
@MightyZinger
@nemozny
@NGBaltic
@peacefulLizard50262
@RicardoSantos
@StalexBot
@ThiagoSchmitz
@TradingView
— 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 !
So just a Big Thank You to everyone who has ever and anywhere shared their codes.
Market Internals (TICK, ADD, VOLD, TRIN, VIX)OVERVIEW
This script allows you to perform data transformations on Market Internals, across exchanges, and specify signal parameters, to more easily identify sentiment extremes.
Notable transformations include:
1. Cumulative session values
2. Directional bull-bear Ratios and Percent Differences
3. Data Normalization
4. Noise Reduction
This kind of data interaction is very useful for understanding the relationship between two mutually exclusive metrics, which is the essence of Market Internals: Up vs. Down. Even so, they are not possible with symbol expressions alone. And the kind of symbol expression needed to produce baseline data that can be reliably transformed is opaque to most traders, made worse by the fact that prerequisite symbol expressions themselves are not uniform across symbols. It's very nuanced, and if this last bit was confusing … exactly.
All this to say, rather than forcing that burden onto you, I've baked the baseline symbol expressions into the indicator so: 1) the transform functions consistently ingest the baseline data in the correct format and 2) you don't have to spend time trying to figure it all out. Trading is hard. There's no need to make it harder.
INPUTS
Indicator
Allows you to specify the base Market Internal and Exchange data to use. The list of Market Internals is simplified to their fundamental representation (TICK, ADD, VOLD, TRIN, VIX, ABVD, TKCD), and the list of Exchange data is limited to the most common (NYSE, NASDAQ, All US Stocks). There are also options for basic exchange combinations (Sum or Average of NYSE & NASDAQ).
Mode
Short for "Plot Mode", this is where you specify the bars style (Candles, Bars, Line, Circles, Columns) and the source value (used for single value plots and plot color changes).
Scale
This is the first and second data transformation grouped together. The default is to show the origin data as it might appear on a chart. You can then specify if each bar should retain it's unique value (Bar Value) or be added to a running total (Cumulative). You can also specify if you would like the data to remain unaltered (Raw) or converted to a directional ratio (Ratio) or a percentage (Percent Diff). These options determine the scale of the plot.
Both Ratio and Percent Diff. convert a given symbol into a positive or negative number, where positive numbers are bullish and negative numbers are bearish.
Ratio will divide Bull values by Bear values, then further divide -1 by the quotient if it is less than 1. For example, if "0.5" was the quotient, the Ratio would be "-2".
Percent Diff. subtracts Bear values from Bull values, then divides that difference by the sum of Bull and Bear values multiplied by 100. If a Bull value was "3" and Bear value was "7", the difference would be "-4", the sum would be "10", and the Percent Diff. would be "-40", as the difference is both bearish and 40% of total.
Ratio Norm. Threshold
This is the third data transformation . While quotients can be less than 1, directional ratios are never less than 1. This can lead to barcode-like artifacts as plots transition between positive and negative values, visually suggesting the change is much larger than it actually is. Normalizing the data can resolve this artifact, but undermines the utility of ratios. If, however, only some of the data is normalized, the artifact can be resolved without jeopardizing its contextual usefulness.
The utility of ratios is how quickly they communicate proportional differences. For example, if one side is twice as big as the other, "2" communicates this efficiently. This necessarily means the numerical value of ratios is worth preserving. Also, below a certain threshold, the utility of ratios is diminished. For example, an equal distribution being represented as 0, 1, 1:1, 50/50, etc. are all equally useful. Thus, there is a threshold, above which we want values to be exact, and below which the utility of linear visual continuity is more important. This setting accounts for that threshold.
When this setting is enabled, a ratio will be normalized to 0 when 1:1, scaled linearly toward the specified threshold when greater than 1:1, and then retain its exact value when the threshold is crossed. For example, with a threshold of "2", 1:1 = 0, 1.5:1 = 1, 2:1 = 2, 3:1 = 3, etc.
With all this in mind, most traders will want to set the ratios threshold at a level where accuracy becomes more important than visual continuity. If this level is unknown, "2" is a good baseline.
Reset cumulative total with each new session
Cumulative totals can be retained indefinitely or be reset each session. When enabled, each session has its own cumulative total. When disabled, the cumulative total is maintained indefinitely.
Show Signal Ranges
Because everything in this script is designed to make identifying sentiment extremes easier, an obvious inclusion would be to not only display ranges that are considered extreme for each Market Internal, but to also change the color of the plot when it is within, or beyond, that range. That is exactly what this setting does.
Override Max & Min
While the min-max signal levels have reasonable defaults for each symbol and transformation type, the Override Max and Override Min options allow you to … (wait for it) … override the max … and min … signal levels. This may be useful should you find a different level to be more suitable for your exact configuration.
Reduce Noise
This is the fourth data transformation . While the previous Ratio Norm. Threshold linearly stretches values between a threshold and 0, this setting will exponentially squash values closer to 0 if below the lower signal level.
The purpose of this is to compress data below the signal range, then amplify it as it approaches the signal level. If we are trying to identify extremes (the signal), minimizing values that are not extreme (the noise) can help us visually focus on what matters.
Always keep both signal zones visible
Some traders like to zoom in close to the bars. Others prefer to keep a wider focus. For those that like to zoom in, if both signals were always visible, the bar values can appear squashed and difficult to discern. For those that keep a wider focus, if both signals were not always visible, it's possible to lose context if a signal zone is vertically beyond the pane. This setting allows you to decide which scenario is best for you.
Plot Colors
These define the default color, within signal color, and beyond signal color for Bullish and Bearish directions.
Plot colors should be relative to zero
When enabled, the plot will inherit Bullish colors when above zero and Bearish colors when below zero. When disabled and Directional Colors are enabled (below), the plot will inherit the default Bullish color when rising, and the default Bearish color when falling. Otherwise, the plot will use the default Bullish color for all directions.
Directional colors
When the plot colors should be relative to zero (above), this changes the opacity of a bars color if moving toward zero, where "100" percent is the full value of the original color and "0" is transparent. When the plot colors are NOT relative to zero, the plot will inherit Bullish colors when rising and Bearish colors when falling.
Differentiate RTH from ETH
Market Internal data is typically only available during regular trading hours. When this setting is enabled, the background color of the indicator will change as a reminder that data is not available outside regular trading hours (RTH), if the chart is showing electronic trading hours (ETH).
Show zero line
Similar to always keeping signal zones visible (further up), some traders prefer zooming in while others prefer a wider context. This setting allows you to specify the visibility of the zero line to best suit your trading style.
Linear Regression
Polynomial regressions are great for capturing non-linear patterns in data. TradingView offers a "linear regression curve", which this script is using as a substitute. If you're unfamiliar with either term, think of this like a better moving average.
Symbol
While the Market Internal symbol will display in the status line of the indicator, the status line can be small and require more than a quick glance to read properly. Enabling this setting allows you to specify if / where / how the symbol should display on the indicator to make distinguishing between Market Internals more efficient.
Speaking of symbols, this indicator is designed for, and limited to, the following …
TICK - The TICK subtracts the total number of stocks making a downtick from the total number of stocks making an uptick.
ADD - The Advance Decline Difference subtracts the total number of stocks below yesterdays close from the total number of stocks above yesterdays close.
VOLD - The Volume Difference subtracts the total declining volume from the total advancing volume.
TRIN - The Arms Index (aka. Trading Index) divides the ratio of Advancing Stocks / Volume by the ratio of Declining Stocks / Volume. Given the inverse correlation of this index to market movement, when transforming it to a Ratio or Percent Diff., its values are inverted to preserve the bull-bear sentiment of the transformations.
VIX - The CBOE Volatility Index is derived from SPX index option prices, generating a 30-day forward projection of volatility. Given the inverse correlation of this index to market movement, when transforming it to a Ratio or Percent Diff., its values are inverted and normalized to the sessions first bar to preserve the bull-bear sentiment of the transformations. Note: If you do not have a Cboe CGIF subscription , VIX data will be delayed and plot unexpectedly.
ABVD - The Above VWAP Difference is an unofficial index measuring all stocks above VWAP as a percent difference. For the purposes of this indicator (and brevity), TradingViews PCTABOVEVWAP has has been shortened to simply be ABVD.
TKCD - The Tick Cumulative Difference is an unofficial index that subtracts the total number of market downticks from the total number of market upticks. Where "the TICK" (further up) is a measurement of stocks ticking up and down, TKCD is a measurement of the ticks themselves. For the purposes of this indicator (and brevity), TradingViews UPTKS and DNTKS symbols have been shorted to simply be TKCD.
INSPIRATION
I recently made an indicator automatically identifying / drawing daily percentage levels , based on 4 assumptions. One of these assumptions is about trend days. While trend days do not represent the majority of days, they can have big moves worth understanding, for both capitalization and risk mitigation.
To this end, I discovered:
• Article by Linda Bradford Raschke about Capturing Trend Days.
• Video of Garrett Drinon about Trend Day Trading.
• Videos of Ryan Trost about How To Use ADD and TICK.
• Article by Jason Ruchel about Overview of Key Market Internals.
• Including links to resources outside of TradingView violates the House Rules, but they're not hard to find, if interested.
These discoveries inspired me adopt the underlying symbols in my own trading. I also found myself wanting to make using them easier, the net result being this script.
While coding everything, I also discovered a few symbols I believe warrant serious consideration. Specifically the Percent Above VWAP symbols and the Up Ticks / Down Ticks symbols (referenced as ABVD and TKCD in this indicator, for brevity). I found transforming ABVD or TKCD into a Ratio or Percent Diff. to be an incredibly useful and worthy inclusion.
ABVD is a Market Breadth cousin to Brian Shannon's work, and TKCD is like the 3rd dimension of the TICKs geometry. Enjoy.
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.
TASC 2023.10 COT Commercials Indicator█ OVERVIEW
This script implements the COT Commercials Indicator introduced by Alfred François Tagher in an article featured in TASC's October 2023 edition of Traders' Tips . The indicator is designed for use in futures markets and represents a fast stochastic (%K) calculated based on the commercial open interest values of an asset derived from the weekly Commitments Of Traders (COT) report .
█ CONCEPTS
The COT report, issued by the Commodity Futures Trading Commission (CFTC) , presents a breakdown of reportable open interest positions held by various trader groups—commercial, noncommercial, and nonreportable (small traders). Open interest reflects the total number of derivative contracts entered by market participants but not yet settled. Consequently, it can serve as a measure of market activity and liquidity.
The indicator showcased here aims to analyze changes in the reported net values of open interest for commercial traders/hedgers (often referred to as 'smart money', as they deal directly in underlying commodities). The net values are positive when the commercial traders have more long positions than short ones and negative when they hold more short positions than long ones. Positive net values indicate that commercial traders hold more long positions than short ones, while negative values indicate the opposite. Thus, overbought and oversold conditions of the COT Commercials Indicator potentially suggest collective bullish and bearish sentiments, respectively.
█ CALCULATIONS
The calculations involve these steps:
1. Net open interest values are extracted from COT data using the LibraryCOT library provided by TradingView.
2. A fast stochastic indicator (%K) is then applied to normalize these net values.
The script also provides an option of calculating and plotting the indicator curve for noncommercial (speculators) open interest.
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.
