Volume-Supported Linear Regression Trend Modified StrategyHi everyone, this will be my first published script on Tradingview, maybe more to come.
For quite some time I have been looking for a script that performs no matter if price goes up or down or sideways. I believe this strategy comes pretty close to that. Although nowhere near the so called "buy&hold equity" of BTC, it has produced consistent profits even when price goes down.
It is a strategy which seems to work best on the 1H timeframe for cryptocurrencies.
Just by testing different settings for SL and TP you can customize it for each pair.
THE STRATEGY:
Basically, I used the Volume Supported Linear Regression Trend Model that LonesomeTheBlue has created and modified a few things such as entry and exit conditions. So all credits go to him!
LONG ENTRY: When there is a bullish cross of the short term trend (the histogram/columns), while the long term trend is above 0 and rising.
SHORT ENTRY: When there is a bearish cross (green to red) of the short-term trend (the histogram/columns), while the long term trend is beneath 0 and decreasing.
LONG EXIT: Bearish crossover of short-term trend while long term trend is below 0
SHORT EXIT: Bullish crossover of short-term trend while long term trend is above 0
Combining this with e.g. a SL of 2% and a TP of 20% (as used in my backtesting), combined with pyramiding and correct risk management, it gives pretty consistent results.
Be aware, this is only for educational purpose and in no means financial advise. Past results do not guarantee future results. This strategy can lose money!
Enjoy :)
PS: It works not only on BTC of course, works even better on some other major crypto pairs. I'll leave it to you to find out which ones ;)
Wyszukaj w skryptach "N+credit最新动态"
MTF OB Supply Demand ZonesHello everyone,
This exceptional indicator provides you with visual representations of bullish and bearish order blocks or supply and demand zones across multiple timeframes. In simple terms, bullish order blocks are represented by a small red candle followed by a large red candle, while bearish order blocks are depicted as a small green candle followed by a large red candle. Supply and demand zones are drawn by using order blocks.
Features:
Display order blocks from up to three different timeframes.
Customize the maximum number of boxes shown and the colors of the zones.
Choose from three different modes: OB (Order Block), Extended OB, and Supply/Demand.
Mode Descriptions:
OB: Includes the body of the candle.
Extended OB: Encompasses the body and wick of the candle.
Supply/Demand: Covers the body, wick, and half the body of the large candle.
Usage:
Ensure that charts 2 and 3 are set to a higher timeframe. For modes 2 and 3, it’s recommended to reduce the maximum number of boxes shown. The zones or boxes are transparent, allowing for overlap. This feature aids in identifying reversal zones or confirmed zones. The more intense the color, the stronger the confirmation. If a green zone overlaps a red zone (or vice versa), it signifies a reversal zone.
Thank you for checking out this indicator!
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Additional Information:
Order blocks refer to specific price areas where large market participants, such as institutional traders, have previously placed significant buy or sell orders. These clusters of orders can impact price movement, liquidity, and market sentiment.
Order blocks are a strategic approach to identifying key levels of support and resistance based on the behavior of institutional traders. These key levels are then utilized as entry or exit points for trades.
An order block is an area where there has been a large concentration of limit orders awaiting execution. These blocks are identified on a chart by observing previous price action and pinpointing areas where the price experienced significant movement or abrupt changes in direction.
Order blocks are used in the following popular trading philosophies:
Smart Money Concepts (SMC)
Inner Circle Trading (ICT)
Price Action
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Credits to: @AGFXTRADING
Hurst Future Lines of Demarcation StrategyJ. M. Hurst introduced a concept in technical analysis known as the Future Line of Demarcation (FLD), which serves as a forward-looking tool by incorporating a simple yet profound line into future projections on a financial chart. Specifically, the FLD is constructed by offsetting the price half a cycle ahead into the future on the time axis, relative to the Hurst Cycle of interest. For instance, in the context of a 40 Day Cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart, offering an idea of future price movement anticipations.
The utility of FLDs extends into three critical areas of insight, which form the backbone of the FLD Trading Strategy:
A price crossing the FLD signifies the confirmation of either a peak or trough formation, indicating pivotal moments in price action.
Such crossings also help determine precise price targets for the upcoming peak or trough, aligned with the cycle of examination.
Additionally, the occurrence of a peak in the FLD itself signals a probable zone where the price might experience a trough, helping to anticipate of future price movements.
These insights by Hurst in his "Cycles Trading Course" during the 1970s, are instrumental for traders aiming to determine entry and exit points, and to forecast potential price movements within the market.
To use the FLD Trading Strategy, for example when focusing on the 40 Day Cycle, a trader should primarily concentrate on the interplay between three Hurst Cycles:
The 20 Day FLD (Signal) - Half the length of the Trade Cycle
The 40 Day FLD (Trade) - The Cycle you want to trade
The 80 Day FLD (Trend) - Twice the length of the Trade Cycle
Traders can gauge trend or consolidation by watching for two critical patterns:
Cascading patterns, characterized by several FLDs running parallel with a consistent separation, typically emerge during pronounced market trends, indicating strong directional momentum.
Consolidation patterns, on the other hand, occur when multiple FLDs intersect and navigate within the same price bandwidth, often reversing direction to traverse this range multiple times. This tangled scenario results in the formation of Pause Zones, areas where price momentum is likely to temporarily stall or where the emergence of a significant trend might be delayed.
This simple FLD indicator provides 3 FLDs with optional source input and smoothing, A-through-H FLD interaction background, adjustable “Close the Trade” triggers, and a simple strategy for backtesting it all.
The A-through-H FLD interactions are a framework designed to classify the different types of price movements as they intersect with or diverge from the Future Line of Demarcation (FLD). Each interaction (designated A through H by color) represents a specific phase or characteristic within the cycle, and understanding these can help traders anticipate future price movements and make informed decisions.
The adjustable “Close the Trade” triggers are for setting the crossover/under that determines the trade exits. The options include: Price, Signal FLD, Trade FLD, or Trend FLD. For example, a trader may want to exit trades only when price finally crosses the Trade FLD line.
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 @Hpotter
👏 @parisboy
[TTI] High Volume Close (HVC) Setup📜 ––––HISTORY & CREDITS––––
The High Volume Close (HVC) Setup is a specialised indicator designed for the TradingView platform used to identify specific bar. This tool was developed with the objective of identifying a technical pattern that trades have claimed is significant trading opportunities through a unique blend of volume analysis and price action strategies. It is based on the premise that high-volume bars, when combined with specific price action criteria, can signal key market movements.
The HVC is applicable both for swing and longer term trading and as a technical tool it can be used by traders of any asset type (stocks, ETF, crypto, forex etc).
🦄 –––UNIQUENESS–––
The uniqueness of the HVC Setup lies in its flexibility to determine an important price level based on historically important bar. The idea is to identify significant bars (e.g. those who have created the HIGHEST VOLUME: Ever, Yearly, Quarterly and meet additional criteria from the settings) and plot on the chart the close on that day as a significant level as well as theoretical stop loss and target levels. This approach allows traders to discern high volume bars that are contextually significant — a method not commonly found in standard trading tools.
🎯 ––––WHAT IT DOES––––
The HVC Setup indicator performs a series of calculations to identify high volume close bars/bar (HVC bars) based on the user requirements.
These bars are determined based on the highest volume recorded within a user-inputs:
👉 Period (Ever, Yearly, Quarterly) and must meet additional criteria such as:
👉 a minimum percentage Price Change (change is calculated based on a close/close) and
👉 specific Closing Range requirements for the HVC da.
The theory is that this is a significant bar that is important to know where it is on the chart.
The script includes a comparative analysis of the HVC bar's price against historical price highs (all-time, yearly, quarterly), which provides further context and significance to the identified bars. All of these USER input requirement are then taken into account as a condition to identity the High Volume Close Bar (HVC).
The visual representation includes color-coded bar (default is yellow) and lines to delineate these key trading signals. It then draws a blue line for the place where the close ofthe bar is, a red line that would signify a stop loss and 2 target profit levels equal to 2R and 3R of the risked level (close-stop loss). Additional lines can be turned on/off with their coresponding checkboxes in the settings.
If the user chooses "Ever" for Period - the script will look at the first available bar ever in Tradingview - this is generally the IPO bar;
If the users chooses "Yearly" - the script would look at the highest available bar for a completed year;
If the users chooses "Quarterly" - it would do the same for the quarter. (works on daily timeframe only);
While we have not backtested the performance of the script, this methodology has been widely publicised.
🛠️ ––––HOW TO USE IT––––
To utilize the HVC Setup effectively:
👉Customize Input Settings: Choose the HVC period, percentage change threshold, closing range, stop loss distance, and target multiples according to your trading strategy. Use the tick boxes to enable and disable if a given condition is used within the calculation.
👉Identify HVC Bars: The script highlights HVC bars, indicating potential opportunities based on volume and price action analysis.
👉Interpret Targets and Stop Losses: Use the color-coded lines (green for targets, red for stop losses) to guide your trade entries and exits.
👉Contextual Analysis: Always consider the HVC bar signals in conjunction with overall market trends and additional technical indicators for comprehensive trading decisions.
This script is designed to assist traders in identifying high-potential trading setups by using a combination of volume and price analysis, enhancing traditional methods with a unique, algorithmically driven approach.
Fractional Differentiation█ Description
This Pine Script indicator implements fractional differentiation, a mathematical operation that extends the concept of differentiation to non-integer orders. Fractional differentiation is particularly significant in financial analysis, as it enables analysts to uncover underlying patterns in price series that are not evident with traditional integer-order differentiation. The motivation behind fractional differencing lies in its ability to balance the trade-off between retaining data/feature memory and ensuring stationarity.
█ Significance
Fractional differentiation offers a nuanced view of market data, allowing for the adjustment of the differentiation order to balance between signal clarity and noise reduction. This is especially useful in financial markets, where the choice of differentiation order can highlight long-term trends or short-term price movements without completely smoothing out the valuable market noise.
█ Approximations Used
The implementation relies on the Gamma function for the computation of coefficients in the fractional differentiation formula. Given the complexity of the Gamma function, this script uses an approximation method based on the Lanczos approximation for the logarithm of the Gamma function, as detailed in "An Analysis Of The Lanczos Gamma Approximation" by Glendon Ralph Pugh (2004). This approximation strikes a balance between computational efficiency and accuracy, making it suitable for real-time market analysis in Pine Script.
█ Limitations
While this script opens new avenues for market analysis, it comes with inherent limitations:
- The approximation of the Gamma function, although accurate, is not exact. The precision of the fractional differentiation result may vary slightly, especially for higher-order differentiations.
- The script's performance is subject to Pine Script's execution environment, with a default loop limit set to 100 iterations for practicality. Users might need to adjust this limit based on their specific use case, balancing between computational load and the desired depth of historical data analysis.
█ Credits
This script makes use of the `MathSpecialFunctionsGamma` library, authored by Ricardo Santos . This library provides essential mathematical functions, including an approximation of the Gamma function, which is crucial for the fractional differentiation calculation.
I also extend my sincere gratitude to
Dr. Marcos López de Prado for his seminal work, Advances in Financial Machine Learning (2018). Dr. López de Prado's insights have significantly influenced our approach to developing sophisticated analytical tools.
Dr. Ernie Chan for his freely and generously sharing valuable insights via discourse on quantitative trading strategies through his talks and publications.
Blockunity US Market Liquidity (BML)Get a clear view of US market liquidity and monitor its status at a glance to anticipate movements on risky assets.
The Idea
The BML aggregates and analyzes total USD market liquidity in trillions of dollars. It is used to monitor the liquidity of the USD market. When liquidity is good, all is well. If liquidity is low, the US will maneuver and sell treasury bills (debt) to replenish its treasury, which can lead to bearish pressure on markets, particularly those considered risky, such as Bitcoin.
How to Use
The indicator is very easy to use, there's nothing special about it. This tool is mainly intended to be used as fundamental information, and not for active trading.
Elements
The US Market Liquidity has several distinct components:
FED Balance Sheet
The Fed credits member banks’ Fed accounts with money, and in return, banks sell the Fed US Treasuries and/or US Mortgage-Backed Securities. This is how the Fed “prints” money to juice the financial system.
US Treasury General Account
The US Treasury General Account (TGA) balances with the NY Fed. When it decreases, it means the US Treasury is injecting money into the economy directly and creating activity. When it increases, it means the US Treasury is saving money and not stimulating economic activity. The TGA also increases when the Treasury sells bonds. This action removes liquidity from the market as buyers must pay for their bonds with dollars.
Overnight Reverse Repurchase Agreements
A reverse repurchase agreement (known as Reverse Repo or RRP) is a transaction in which the New York Fed under the authorization and direction of the Federal Open Market Committee sells a security to an eligible counterparty with an agreement to repurchase that same security at a specified price at a specific time in the future.
Earnings Remittances Due to the Treasury
The Federal Reserve Banks remit residual net earnings to the US Treasury after providing for the costs of operations, payment of dividends, and the amount necessary to maintain each Federal Reserve Bank’s allotted surplus cap. Positive amounts represent the estimated weekly remittances due to the US Treasury. Negative amounts represent the cumulative deferred asset position, which is incurred during a period when earnings are not sufficient to provide for the cost of operations, payment of dividends, and maintaining surplus.
Settings
Several parameters can be defined in the indicator configuration. You can:
Choose the smoothing and timeframe to be used in the plot.
Set the EMA lookback period and display it or not. This affects the color of the main plot.
Set the period to be taken into account when calculating the variation rate in the table.
Select the data to be taken into account in the calculation.
Activate or not the barcolor.
Lastly, you can modify all table parameters.
MTF HalfTrendIntroduction
A half-trend indicator is a technical analysis tool that uses moving averages and price data to find potential trend reversal and entry points in the form of graphical arrows showing market turning points.
The salient features of this indicator are:
- It uses the phenomenon of moving averages.
- It is a momentum indicator.
- It can indicate a trend change.
- It is capable of detecting a bullish or bearish trend reversal.
- It can signal to sell/buy.
- It is a real-time indicator.
Multi-Timeframe Application
A standout feature is its flexibility across timeframes. Traders have the liberty to choose any timeframe on the chart, enhancing the tool's versatility and making it suitable for both short-term and long-term analyses.
Principle of the Half Trend indicator
This indicator is based on the moving averages. The moving average is the average of the fluctuation or change in the price of an asset. These averages are taken for a time interval.
So, a half-trend indicator takes the moving averages phenomenon as its principle for working. The most commonly used moving averages in a half trend indicator are:
- Relative strength index (RSI)
- EMA (estimated moving average)
Components of a Half Trend indicator
There are two main components of a half trend indicator:
- Half trend line
- Arrows
- ATR lines
Half trend line
Half trend line represents this indicator on a candlestick chart. This line shows the trend of a chart in real-time. A half-trend line is based on the moving averages.
There are two further components of a half-trend line:
- Redline
- Blue line
A red line represents a bearish trend. When the half-trend line turns red, a trend is facing a dip. It is time for the bears to take control of the market. A bearish control of the market represents the domination of sellers in the market.
On the other hand, the blue line represents the bullish nature of the market. It tells a trader that the bullish sentiment of the market is prevailing. A bullish market means the number of buyers is significantly greater than the number of sellers.
Moreover, a trader can change these colors to his choice by customization.
Arrows
There are two types of arrows in this indicator which help a trader with the entry and exit points. These arrows are,
- Blue arrow
- Red arrow
A blue arrow signals a buying trade; on the other hand, a red arrow tells a trader about the selling of the assets. These arrows work with the moving average line to formulate a trading strategy.
The color of these arrows is changed if a trader desires so.
ATR lines
The ATR blue and red lines represent the Average True Range of the Half trend line. They may be used as stop loss or take profit levels.
Pros and Cons
Pros
- It is a very easy to eyes indicator.
- This is a very useful friendly indicator.
- It provides sufficient information to beginner traders.
- It provides sufficient information for entry points in a trade.
- A half-trend indicator provides a good exit strategy for a trader.
- It provides information about market reversals.
- It helps a trader to find a bullish and bearish sentiment in the market.
Cons
- It is a real-time indicator. So, it can lag.
- The lagging of this indicator can lead to miss opportunities.
- The most advanced and professional traders may not rely on this indicator for crucial trading decisions.
- The lagging of this indicator can predict false reversals of the market.
- It can create false signals.
- It requires the confluence of the other technical tools for a better success ratio.
Settings for Half Trend indicator
The default settings for half trend indicator are:
Amplitude = 2
Channel deviation = 2
Different markets or financial instruments may require different settings for optimal execution.
Amplitude: The degree that the Half trend line takes the internal variables into consideration. The higher the number, the fewer trades. The default value is 2.
Channel deviation: The ATR value calculation from the Half trend line. The default value is 2.
Trading strategy
It is an effective indicator in terms of strategy formation for a trading setup. The new and beginner trades can take benefit from this indicator for the formulation of a good trading setup. This indicator also helps seasoned and professional traders formulate a good trading setup with other technical tools.
The trading strategy involving a half-trend indicator is divided into three parts:
- Entry and exit
- Risk management
- Take profit
Entry and exit
It is an effective indicator that provides sufficient information about the entry and exit points in a trading setup. The profit of a trader is directly proportional to the appropriate entry and exit points. So, it is a crucial step in any trading setup.
The blue and red arrows provide information about the entry and exit points in a trading setup. Furthermore, the entry and exit for the bullish and bearish setups are as follows.
Entry and exit for a bullish setup
If a blue arrow appears under the half-trend line, it means the bullish sentiment of the market is getting stronger in the future. So, it is a signal for entry in a bullish setup.
As the red arrow appears on the chart, it is a signal to exit your trade. The red arrow represents a reversal in the market, so it is a good opportunity to close your trade in a bullish setup.
Entry and exit for a bearish setup
Suppose a red arrow appears above the red moving average line. It is a good opportunity to enter a trade in a bearish setup. The red line represents that sooner the sellers are going to take control and the value of the asset is about to face a dip. So it is the best time to make your move.
As the opposite arrow appears in the chart, it is time to exit from a bearish trade setup.
Re-entering a position
Bullish setup
- The half-trend line is blue.
- At least one candle closes below the blue half-trend line.
- Enter on the candle that closes above the blue half-trend line.
Bearish setup
- The half-trend line is red.
- At least one candle closes above the red half-trend line.
- Enter on the candle that closes below the red half-trend line.
Risk management
Risk management is an integral part of a trading setup. It is an important step to protect your potential profits and losses.
When trading in a bullish market, place the stop loss at the prior swing low. It will help you to cut your losses in case the prices move to the lower end.
In the case of a bearish market, place your stop loss above the prior swing high.
A trader may trail the stop loss using the ATR lines.
The new trader often makes mistakes in the placement of the stop loss. If you don’t place the stop loss at an appropriate point. It can drain your bank account and ruin your trading experience. Is is recommended not to risk more than 2% of your trading account, per trade.
Take profit
The blue ATR line may be used as one take profit level on a bullish setup followed by the previous swing high. The signal reversal would indicate the final take profit and closing of any position.
The red ATR line may be used as one take profit level on a bearish setup followed by the previous swing low. The signal reversal would indicate the final take profit and closing of any position.
Conclusion
A half trend indicator is a decent indicator that can transform your trading experience. It is a dual indicator that is based on the moving averages as well as helps you to form a trading strategy. If you are a new trader, this indicator can help you to learn and flourish in the trading universe. If you are a seasoned trader, I recommend you use this indicator with other technical analysis tools to enhance your success ratio.
All credits go to:
- @everget the original creator of this indicator (I just added the MTF capability).
- Ali Muhammad original author of much of the description used.
Leading T3Hello Fellas,
Here, I applied a special technique of John F. Ehlers to make lagging indicators leading. The T3 itself is usually not realling the classic lagging indicator, so it is not really needed, but I still publish this indicator to demonstrate this technique of Ehlers applied on a simple indicator.
The indicator does not repaint.
In the following picture you can see a comparison of normal T3 (purple) compared to a 2-bar "leading" T3 (gradient):
The range of the gradient is:
Bottom Value: the lowest slope of the last 100 bars -> green
Top Value: the highest slope of the last 100 bars -> purple
Ehlers Special Technique
John Ehlers did develop methods to make lagging indicators leading or predictive. One of these methods is the Predictive Moving Average, which he introduced in his book “Rocket Science for Traders”. The concept is to take a difference of a lagging line from the original function to produce a leading function.
The idea is to extend this concept to moving averages. If you take a 7-bar Weighted Moving Average (WMA) of prices, that average lags the prices by 2 bars. If you take a 7-bar WMA of the first average, this second average is delayed another 2 bars. If you take the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag.
T3
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
Thanks for checking this out and give a boost, if you enjoyed the content.
Best regards,
simwai
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Credits to @loxx
Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
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Credits to:
@loxx
@Bjorgum
@Greeny
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
---
Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
QQE MOD + SSL Hybrid + Waddah Attar Explosion IndicatorINDICATOR PURPOSE
This indicator is designed to complement my original QQE MOD + SSL Hybrid + Waddah Attar Explosion strategy.
Multiple users have requested that I convert the strategy to an indicator because alertconditions do not work on strategies and people want to specific set alerts for BUY, SELL, CLOSE BUY and CLOSE SELL. This can only be achieved using alertcondition().
This indicator functions in the exact same way as the strategy, but it doesn't have any backtesting functionality. I recomment that you use the original QQE MOD + SSL Hybrid + Waddah Attar Explosion strategy for parameter tuning and backtesting, then if you need more control on alerts you can use this indicator for that purpose.
Only other difference is that I have added grey exit labels on the chart since it's not obvious where the exits would happen like it was in the strategy version.
CREDITS
QQE MOD byMihkel00
SSL Hybrid by Mihkel00
Waddah Attar Explosion by shayankm
{Gunzo} Trend Sniper (Multiple MAs with coefficient)Updated GUNZO's Trend Sniper script by adding in different MA types to choose from. This can help reduce false signals and sharpen the trend reversal points.
Here's a summary of the key changes:
1. Multiple Moving Average Types: The original script was focused solely on the Weighted Moving Average (WMA) with a coefficient. The updated script introduces flexibility by allowing users to choose from a variety of Moving Average types, including WMA, VWMA (Volume Weighted Moving Average), EMA (Exponential Moving Average), SMA (Simple Moving Average), HullMA (Hull Moving Average), TEMA (Triple Exponential Moving Average), DEMA (Double Exponential Moving Average), T3, and RMA (Running Moving Average).
2. Coefficient Integration: In the original script, the coefficient was specifically designed for the WMA calculation. The updated script extends this concept to all the selected Moving Average types. This coefficient is applied differently depending on the type of MA, often affecting the length of the MA calculation.
3. Dynamic Length Calculation: For MAs that traditionally use an integer length (like SMA, EMA, etc.), the updated script calculates this length dynamically by multiplying the user-defined length by the coefficient and then rounding it to the nearest integer. This ensures compatibility with Pine Script's requirements for these functions.
All credits to GUNZO
original script:
Price Cross Time Custom Range Interactive█ OVERVIEW
This indicator was a time-based indicator and intended as educational purpose only based on pine script v5 functions for ta.cross() , ta.crossover() and ta.crossunder() .
I realised that there is some overlap price with the cross functions, hence I integrate them into Custom Range Interactive with value variance and overlap displayed into table.
This was my submission for Pinefest #1 , I decided to share this as public, I may accidentally delete this as long as i keep as private.
█ INSPIRATION
Inspired by design, code and usage of CAGR. Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
█ FEATURES
1. Custom Range Interactive
2. Label can be resize and change color.
3. Label show tooltip for price and time.
4. Label can be offset to improve readability.
5. Table can show price variance when any cross is true.
6. Table can show overlap if found crosss is overlap either with crossover and crossunder.
7. Table text color automatically change based on chart background (light / dark mode).
8. Source 2 is drawn as straight line, while Source 1 will draw as label either above line for crossover, below line for crossunder and marked 'X' if crossing with Source 2's line.
9. Cross 'X' label can be offset to improve readability.
10. Both Source 1 and Source 2 can select Open, Close, High and Low, which can be displayed into table.
█ LIMITATIONS
1. Table is limited to intraday timeframe only as time format is not accurate for daily timeframe and above. Example daily timeframe will give result less 1 day from actual date.
2. I did not include other sources such external source or any built in sources such as hl2, hlc3, ohlc4 and hlcc4.
█ CODE EXPLAINATION
I pretty much create custom function with method which returns tuple value.
method crossVariant(float price = na, chart.point ref = na) =>
cross = ta.cross( price, ref.price)
over = ta.crossover( price, ref.price)
under = ta.crossunder(price, ref.price)
Unfortunately, I unable make the labels into array which i plan to return string value by getting the text value from array label, hence i use label.all and add incremental int value as reference.
series label labelCross = na, labelCross.delete()
var int num = 0
if over
num += 1
labelCross := label.new()
if under
num += 1
labelCross := label.new()
if cross
num += 1
labelCross := label.new()
I realised cross value can be overlap with crossover and crossunder, hence I add bool to enable force overlap and add additional bools.
series label labelCross = na, labelCross.delete()
var int num = 0
if forceOverlap
if over
num += 1
labelCross := label.new()
if under
num += 1
labelCross := label.new()
if cross
num += 1
labelCross := label.new()
else
if cross and over
num += 1
labelCross := label.new()
if cross and under
num += 1
labelCross := label.new()
if cross and not over and not under
num += 1
labelCross := label.new()
█ USAGE / EXAMPLES
Triple Moving Averages (Gradient, Alarm & Multi TF)Triple Moving Averages
Features:
- 7 Different MA's (RMA, SMA, EMA, 'WMA', HMA, DEMA, EMA)
- Gradient coloring
- Multi timeframe
- Crossover alarm's and alarm delay function
- Forecasting (By removing the last bar in the MA period)
Moving Average to easely identify the trend and trend strength.
Gradient coloring and personal color preferences can be made.
Alert Delay System
When timing is essentially, this helps you get the alarm just in time.
Use it with the triggers ONLY ONCE PER BAR or ONLY ONCE. Then the alarm comes before the close, but you don't have to worry about it triggering just seconds after bar open :)
Default = 15m Recomended for 1h chart
Alarm's
Get the alarms before it's actually crossing or when it crosses
*This is not a selfmade indicator but simply merging from several indicators and added alert delay function and multi timeframe support
// Credits
- BigBitsIO Script : Scripting Tutorial 6 Triple Many Moving Averages Forecasting
- PineCoders Script : Color Gradient Framework PineCoders
NormInvTargetSeekerNormInvTargetSeeker
The NormInvTargetSeeker is a trading tool designed to aid traders in identifying and capitalizing on Distribution and Accumulation zones, highlighting specific price levels that could serve as targets for future price movements. Although the indicator itself is not multi-timeframe, an effective trading strategy might involve signal validation across multiple timeframes.
🔶 USAGE
The indicator identifies Distribution and Accumulation zones, providing potential targets for future price moves.
Traders are encouraged to use these zones as profit targets or potential reversal points.
Confluence Zones
These zones are identified as regions where various factors or levels converge, signaling an increased probability of price reaction.
They can be used to reinforce signals or identify levels where price might encounter significant resistance or support.
🔹 Trading Strategy
First, identify a signal on your primary trading timeframe.
Manually check higher timeframes to ensure the signal aligns with them.
Use the identified zones, whether Distribution or Accumulation, as target zones for your trades.
🔶 Order Blocks
The NormInvTargetSeeker identifies "Order Blocks" by examining a specified number of consecutive candles with a specific condition: the current candle must completely engulf the previous candle. This means that both the high and low of the current candle are higher and lower, respectively, than the high and low of the previous candle, signifying a dominant move in the direction of the current candle.
🔹 Trading Strategy
Target Confirmation: Order Blocks can serve to confirm target points, providing additional validation for identified levels.
Market Insight: They offer crucial insights into whether "big hands" or institutional players are positioned as buyers or sellers in the market.
Traders can use Order Blocks as a means to validate targets or key price levels, observing if the price reacts significantly upon reaching these blocks.
They can also provide insights into the general market direction or underlying market strength by identifying where the major market players are placing their orders.
🔶 SETTINGS
The indicator allows users to adjust various parameters to customize the display and logic of the tool to fit their needs.
🔹 Display Settings
Users can customize the colors and displays of various zones and labels to match their preferences.
🔶 LICENSE AND CREDITS
This work is licensed under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). More information here: creativecommons.org
This indicator utilizes a TypeScript implementation of the Normal Inverse function as a reference, which can be found here : github.com
Special thanks to the authors of the referenced code for providing a foundation upon which this indicator was built.
🔶 UPDATES
Current Version: 1.0.0
For future updates, please check the comment section.
🔶 CONTACT
For any questions or suggestions, please feel free to contact @RickSimpson on TradingView.
[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.
Zigzag Chart Points█ OVERVIEW
This indicator displays zigzag based on high and low using latest pine script version 5 , chart.point which using time, index and price as parameters.
Pretty much a strip down using latest pine script function, without any use of library .
This allow pine script user to have an idea of simplified and cleaner code for zigzag.
█ CREDITS
LonesomeTheBlue
█ FEATURES
1. Label can be show / hide including text can be resized.
2. Hover to label, can see tooltip will show price and time.
3. Tooltip will show date and time for hourly timeframe and below while show date only for day timeframe and above.
█ NOTES
1. I admit that chart.point just made the code much more cleaner and save more time. I previously using user-defined type(UDT) which quite hassle.
2. I have no plan to extend this indicator or include alert just I thinking to explore log.error() and runtime.error() , which I may probably release in other publications.
█ HOW TO USE'
Pretty much similar inside mentioned references, which previously I created.
█ REFERENCES
1. Zigzag Array Experimental
2. Simple Zigzag UDT
3. Zig Zag Ratio Simplified
4. Cyclic RSI High Low With Noise Filter
5. Auto AB=CD 1 to 1 Ratio Experimental
Market Performance TableThe Market Performance Table displays the performance of multiple tickers (up to 5) in a table format. The tickers can be customized by selecting them through the indicator settings.
The indicator calculates various metrics for each ticker, including the 1-day change percentage, whether the price is above the 50, 20, and 10-day simple moving averages (SMA), as well as the relative strength compared to the 10/20 SMA and 20/50 SMA crossovers. It also calculates the price deviation from the 50-day SMA.
The table is displayed on the chart and can be positioned in different locations.
Credits for the idea to @Alex_PrimeTrading ;)
True Range Moving Average Deviation🔶 Overview
The True Range Moving Average Deviation Indicator (TRMAD) is a technical analysis tool that combines elements of price deviation, volatility, and overbought/oversold conditions.
🔶 Key Components
Current price (Close) : most recent closing price of the asset.
Moving Average (MA) : represents a smoothed trendline of the asset's closing prices over a specified period. By default, TRMAD uses the Simple Moving Average (SMA) with a 20-period setting.
Average True Range (ATR) : reflects the average price range between the high and low over a given time frame. By default, TRMAD uses a 14-period ATR setting with a Simple Moving Average (SMA) calculation. ATR quantifies the historical price volatility of the asset, which is crucial for normalizing the price deviation.
🔶 Calculation
(Close - MA) / ATR
🔶 Interpretation
When TRMAD is above +3 ATR , it is often considered an indication that the asset may be overbought, suggesting a potential reversal or correction to the downside.
When TRMAD is below -3 ATR , it is often considered an indication that the asset may be oversold, suggesting a potential reversal or bounce to the upside.
TRMAD values around 0 ATR may indicate a balanced market condition.
🔶 Usage
🔹 Overbought and Oversold Conditions:
TRMAD can help identify overbought and oversold conditions. When TRMAD reaches or exceeds certain user-defined thresholds (e.g., +3 ATR or -3 ATR), it can signal that the asset is in an extreme condition.
Traders can use these extreme conditions to adjust their positions or look for potential reversal opportunities.
🔹 Divergence Analysis:
Traders often analyze divergences between the TRMAD indicator and price movements. For example, if the price is making higher highs while TRMAD is making lower highs (bearish divergence), it could indicate a potential trend reversal.
🔹 Trend Confirmation:
TRMAD can be used in conjunction with other technical indicators to confirm trends. For example, if TRMAD is consistently positive during an uptrend, it can provide confirmation of the trend's strength.
Positive TRMAD : When TRMAD is positive but hasn't reached the overbought threshold (e.g., +3 ATR), it suggests that there is some bullish momentum, but traders may exercise caution and look for other confirming signals before considering a long position.
Negative TRMAD : When TRMAD is negative but hasn't reached the oversold threshold (e.g., -3 ATR), it suggests some bearish sentiment, but traders may want to seek additional confirmation before considering a short position.
🔹 Risk Management:
Traders can use TRMAD as part of their risk management strategy. For instance, if TRMAD suggests that an asset is overbought, a trader might consider tightening their stop-loss orders to manage potential downside risk.
🔶 Credits
The idea about this indicator came from Fabio Figueiredo (Vlad)
[TTI] MarketSmith & IBD Style Model Stock Quarters 📜 ––––HISTORY & CREDITS––––
The MarketSmith & IBD Style Model Stock Quarters another Utility indicator is an original creation by TintinTrading inspired by Investor's Business Daily and William O'Neil style of presenting information. While going through the Model Stocks that IBD has been publishing, I realized that I wanted to see the exam same Quarterly presentation on the time axis in order to compare William O'Neil notes better with my own notes from Tradingview. The script is simple and could help you if you study the CANSLIM methodology.
🦄 –––UNIQUENESS–––
The distinctiveness of this indicator lies in its ability to visually delineate stock quarters directly on the price chart. It serves as a handy tool for traders who adopt a quarterly review of stock performance, in line with MarketSmith and IBD's analysis frameworks.
🛠️ ––––WHAT IT DOES––––
Quarter Marking : Draws a black line at the beginning of each financial quarter (January, April, July, and October).
Quarter Labeling : Places a label at the close of the last month in a quarter, indicating the upcoming quarter with its abbreviation and the last two digits of the year.
💡 ––––HOW TO USE IT––––
👉Installation: Add the indicator to your TradingView chart by searching for " MarketSmith & IBD Style Model Stock Quarters" in the indicator library.
👉Add to New Pane and squash the Pane Length: I add the indicator to a new pane under the price and volume charts and squash the height of the pane so that it looks exactly like the MarketSmith visuals.
👉Visual Cues:
Look for the black lines marking the start of a new quarter.
Observe the labels indicating the upcoming quarter and year, positioned at the close of the last month in a quarter.
👉Interpretation: Use these quarterly markers to align your trading strategies with quarterly performance metrics or to conduct seasonal analysis.
👉Settings: The indicator does not require any user-defined settings, making it straightforward to use.
[TTI] Price confirmation indicator📜 ––––HISTORY & CREDITS––––
The Price Confirmation Indicator is an innovative tool developed by TintinTrading to help his students learn to interpret Price + Volume moves. It is designed to provide traders with a visual cue for price movement confirmation based on both price direction and trading volume. I got the idea from watching Daivd Ryan, how he explains that he looks at volume first before looking at the price of a stock.
🦄 –––UNIQUENESS–––
What sets this indicator apart is its dual analysis approach and easy interpretation: it not only evaluates price movements but also takes trading volume into account. The indicator's color-coded bars are dynamically adjusted based on the volume difference from a 50-day Simple Moving Average (SMA) of the volume. This offers traders an intuitive way to gauge both the market's direction and its strength.
🛠️ ––––WHAT IT DOES––––
The Price Confirmation Indicator performs the following functions:
👉Price Movement: Determines whether each trading day is an 'Up Day' or a 'Down Day' based on the closing price.
👉Volume Analysis: Calculates the 50-day SMA of trading volume and identifies the volume difference in percentage terms.
👉Transparency Adjustment: Dynamically adjusts the transparency of colored bars based on the volume difference.
👉Bar Coloring: Colors the bars blue for 'Up Days' and purple for 'Down Days', with the transparency indicating the strength of the volume.
Transparency Tresholds:
Full color (no transparency 0%) - Volume is greater than 40% compared to the 50DSMA Volume
Strong color (little transparency 20%) - Volume is between 20% and 40% greater than the 50DSMA Volume
Noticable color (moderate transparency 40%) - Volume is between 0% and 20% greater than the 50DSMA Volume
Negligable color (strong transparency 60%) - Volume is light and is less than 50DSMA Volume with less than 20% lower.
Weak color (very strong transparency 80%) - Volume is below 50DSMA, with between 40% and 20% lower.
Very weak color (max transparency 90%) - Volume is below 50DSMA, with between -40% and -80% lower.
Alarming weak color (color is orange) - Volume is noticably light - this generally signals velocity contraction before a breakout.
💡 ––––HOW TO USE IT––––
Installation: Search for " Price Confirmation Indicator" in TradingView’s indicator library and add it to your chart.
Settings:
Price Up Color: Customize the color for 'Up Days'.
Price Down Color: Customize the color for 'Down Days'.
Interpretation:
Blue bars signify 'Up Days', and their transparency indicates the strength of the volume.
Purple bars represent 'Down Days', with transparency again indicating volume strength.
Orange bars signify extremely low volume days.
Volume Transparency: The less transparent the bar, the stronger the volume, aiding in confirming the price direction.
The indicator is a great tool for newer traders to get in the habit of reading Price & Volume together!
[TTI] Closing Range Indicator📜 ––––HISTORY & CREDITS––––
This Pine Script Utility indicator, titled " Closing Range Indicator," is designed and developed by TintinTrading but inspired by the teaching of Investor's Business Daily (IBD) and William O'Neil. It aims to help traders identify the closing range of a given timeframe, either daily or weekly.
🦄 –––UNIQUENESS–––
The unique feature of this indicator lies in its ability to simulate a functionality of Closing Range calculation based on hovering of the mouse over the close. It employs a conditional display that allows the user to set the indicator as 'invisible' without removing it from the chart and hence provides a numerical closing range value when hovering over the indicator.
🛠️ ––––WHAT IT DOES––––
The Closing Range Indicator calculates the closing range of a trading bar in terms of percentages. It computes the difference between the closing price and the low price of the bar, and then divides it by the range of the bar.
A stock that closes on the high would display 100%
A stock that closes on the low would display 0%
Generally, the higher the percentage the more bullish the close but there are exceptions to this rule.
The indicator can operate on two timeframes:
Daily : Computes the closing range based on the daily high, low, and closing prices.
Weekly : Computes the closing range based on the weekly high, low, and closing prices. If you enable the weekly it will show the weekly close on all daily timeframes. Meaning that if the week Closing range is 54.15% on Friday, it will show the value 54.15% for all days prior to Friday from the same week.
The indicator places a label at the close of each bar, with the label's tooltip showing the calculated closing range percentage. I generally hide the label and just reference the tooltip calculation with a a hoover on top of the bar.
💡 ––––HOW TO USE IT––––
Installation: Add the indicator to your TradingView chart by searching for " Closing Range Indicator" in the indicator library.
Reorder: Reorder the indicator so that it sits as the first indicator (even above the price) on the Pane. This will make sure that you always trigger the tooltip functionality.
Go to Settings:
Timeframe: Choose between daily ('D') and weekly ('W') timeframes from the settings.
Visibility: Enable the 'Make Invisible' option if you want the indicator to be hidden.
Interpretation:
A higher percentage indicates that the closing price is closer to the high of the range, signaling bullish sentiment.
A lower percentage indicates bearish sentiment.
Tooltip: Hover over the label to view the closing range in percentage terms.
