[blackcat] L2 Ehlers Optimum PredictorLevel: 2
Background
John F. Ehlers introuced Optimum Predictor in his "Rocket Science for Traders" chapter 20 on 2001.
Function
As we have seen before, the majority of the code involves the computation of the period using the Homodyne Discriminator algorithm. Once the period has
been computed, the Optimum Predictor is found in just a few lines of code. First, the minimum-length Hilbert Transformer is used to compute the Detrender2 value from the prices that have been smoothed by the 4-bar Weighted Moving Average (WMA). Detrender2 is smoothed in the 4-bar WMA to produce Smooth2. The alpha of the EMA is computed from the computed period, and the EMA of Smooth2 is taken using that alpha and is called the DetrendEMA. The difference between Smooth2 and the
DetrendEMA is multiplied by 1.4 to produce the Predict phasor. Finally, the Smooth2 and Predict phasors are plotted as indicators.
The Optimum Predictor is plotted as the subgraph below the price chart. Buy and sell signals occur when the Predict and Smooth2 lines cross. Based on Dr. Ehlers comments, most of these signals are indeed prescient. The Optimum Predictor could probably work best in trading systems when used in conjunction with other
rules to eliminate the false signals.
Key Signal
Smooth --> 4 bar WMA w/ 1 bar lag
Detrender --> The amplitude response of a minimum-length HT can be improved by adjusting the filter coefficients by
trial and error. HT does not allow DC component at zero frequency for transformation. So, Detrender is used to remove DC component/ trend component.
Q1 --> Quadrature phase signal
I1 --> In-phase signal
Period --> Dominant Cycle in bars
SmoothPeriod --> Period with complex averaging
Predict --> fast line of optimum predictor
Smooth2 --> slow line of optimum predictor
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 16th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Cari dalam skrip untuk "indicators"
[blackcat] L1 Improved MACD IndicatorLevel: 1
Background
The MACD is a superior derivative of moving average crossovers and was developed by Gerald Appel in 1979 as a market timing tool. MACD uses two exponential moving averages with different bar periods, which are then subtracted to form what Mr. Appel calls the Fast Line. A 9-period moving average of the fast line creates the slow line.
Function
L1 Improved MACD Indicator mainly improves MACD histogram by customized an algorithm and add three levels of long entry alerts derived from ema().
Key Signal
buy1 --> the 1st level of buy alert in green
buy2 --> the 2nd level of buy alert in lime
buy3 --> the 3rd level of buy alert in yellow
diff --> classic MACD diff fast line in white
dea --> classic MACD dea slow line in yellow
macd --> classic difference histogram,but I did not use it directly in the plot.
Pros and Cons
Pros:
1. more clear sub level trend change with new histograms
2. three levels of buy alerts
Cons:
1. need sophisticated knowledge of MACD to use this well
2. this still requires a lot of MACD experience to obtain reliable trading signals
Remarks
I am a fan of MACD. Even the most classic MACD can have in-depth usage. I think MACD is the king of indicators.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
L1 Mid-Term Swing Oscillator v1Level: 1
Background
Oscillators are widely used set of technical analysis indicators. They are popular primarily for their ability to alert of a possible trend change before that change manifests itself in price and volume . They should work best in times of sideways markets.
Function
L1 Short-Mid-Long-Term Swing Oscillator puts three terms of oscillators to cover short-term, middle-term and long-term oscillators at the same time. By resonating all these three oscillators, short-term scalping signal and middle term swing signal are disclosed. You can see both short and mid term signal under one indicator which give you more confidence to follow the trend.
Key Signal
I didn't handle the key signals well. I piled up all the useful signals I found, and it is really difficult to classify them one by one. I feel tired when I think about this problem. Therefore, the code of the overall signal is rather confusing, sorry.
Pros and Cons
Pros:
1. Three oscillators are used to cover short, mid, long term oscillations.
2. Short-Mid term resonance can be observed to have higher confidence level.
3. Use single indicator for scalping and swing trading is possible.
Cons:
1. No deep dive into very accurate long and short entries.
2. A trade off between sensitivity and stability may be needed by traders' subjective judge.
Remarks
I enjoyed the fun of put three different oscillator together to cover short, mid, long terms. But how to use them perfectly is really more brainstorming.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
CPR with SMA, EMA, VWAP & Super Trend by GuruprasadMeduriThis script will allow to add CPR with Standard Pivots and 4 Indicators.
Standard Pivot has 9 levels of support and 9 levels of resistance lines. It has CPR , 3 levels of Day-wise pivots , 3 levels of Weekly pivots and 3 Levels of Monthly Pivots .
In Addition to the CPR and Pivot, this script will allow user to Add 4 more Indicators - SMA, EMA, VWAP and SuperTrend as well.
All the Support and resistance levels can be enabled / disabled from settings. It will allow to select multiple combinations of support and resistance levels across 3 levels at any of the 3 time-frames individually and combined.
All 4 Indicators can be can be enabled / disabled from settings. This will allow the indicators to be plotted individually and combined along with any combination of CPR & Pivots.
These number of combinations will allow user to visualize the charts with desired indicators, pivot support & resistance levels on all or any of the 3 time frames.
For Ease of access, listed few points on how the script works..
- CPR and day-wise level 1 & 2 (S1 & R1) enabled by default and can be changed from settings
- Day-wise Level 2 & 3 (S2, R2, S3 & L3) can be enabled from settings
- Weekly 3 levels and Monthly 3 levels can be enabled from settings
- CPR & pivot levels colored in blue lines
- All support levels colored in Green
- All resistance levels Colored in Red
- Day-wise pivot , support & resistance are straight lines
- Weekly pivot , support & resistance are cross (+) lines
- Weekly pivot , support & resistance are circle (o) lines
- SMA, EMA, VWAP and SuperTrend Enabled by Default
- SMA Colored in Orange
- EMA Colored in Red
- EMA Colored in Teal
- SuperTrend Colored in standard Red & Green with triangle arrows
- Any combinations can be selected from settings-> Inputs & style
Your CRYPTO Screener - MACD 0 LAG editionHello traders
What's good?
1 - Quick introduction
This script is to demonstrate a proof-of-concept - showing you again what you thought wasn't possible might become (with some tricks) in the realm of possibles !!!.
I get requests for people who want a custom screener because the native TradingView Stocks/Forex/Crypto screeners don't allow to plug external indicators. (example: www.tradingview.com
This is entirely true and I have also good news for you, we can hack the system one more time. As Hackerman would say, "IT"S HACKING TIME !!!" (ref : KUNG FURY . (#geek #reference #done #for #today)
What if you could build your own personalized screener based on your custom indicator? "No Dave stop smoking, that's not possible, go back to eating your baguette". Say no more, let me present you my new script called YOUR CRYPTO Screener (MACD 0 LAG)
2 - What is a MACD ZERO LAG?
We'll all agree this indicator is NOT in the TradingView screeners so I'm not cheating here :)
A MACD ZERO LAG is a MACD that .... suspens.... wait for it.... DOES NOT lag.
The traditional MACD is based on exponential moving averages and as moving averages are lagging, then the MACD is lagging also. I'll spare you all the maths behind the MACD ZERO LAG but in short, this is a way more reactive indicator than the traditional MACD
I shared before the version that I personally use for my own trading : MACD 0 LAG nTREND coloring
3 - Crypto Screener specifications
If I could do a screener as complete as the native one, this would be wonderful but ... we cannot and this is due to technical reasons. To call indicators from different timeframes, I have to use the security function. And we're limited to 40 security calls per indicator.
That explains why I selected 4 crypto assets and 5 timeframes and the MACD zero lag output for each asset/timeframe - which gives a total of 4 * 5 * 2 = 40
You'll be able to select from the interface the 5 timeframes that you want for your screener
In this script, you'll get a :
- BUY whenever the MACD ZERO LAG for your asset/timeframe is green.
- SELL whenever the MACD ZERO LAG for your asset/timeframe is red.
4 - Can you hack it even more?
If you want to add other timeframes or assets, you can either, change the code or add the indicator on another chart.
I made the source code generic enough so that you can update it yourself easily
Example:
Chart 1 will list BTCUSD, ETHUSD, LTCUSD, and XRPUSD in m5/m15/m30/H1/H4 and Chart2 could list BTCUSD, ETHUSD, LTCUSD and XRPUSD in H6/H8/H12/Daily, etc...
Once again the sky (and your computer RAM capacity) is the limit
5 - Can you super hack it even more?
1/ This script is only a proof-of-concept that you can build your own custom screener. Imagine having the Algorithm Builder and being able to connect it in a single click to a custom screener using your own configuration :)
How coooooooooooooooool would that be!!!
This screener version will be available on my website in a few weeks along with all the tools I'm spamming you about since the beginning of July (#shameless #self-advertising)
2/ For a nicer scripter, let's keep in mind that TradingView just enabled Webhooks this week. This will allow my company to offer custom screeners design and hosted on your own website. Those screeners will be for sure nicer than the indicator version
That's it for today and for this week
I won't even touch the laptop this weekend and will enjoy life a bit
Love you all
Dave
____________________________________________________________
Be sure to hit the thumbs up. Building those indicators take a lot of time and likes are always rewarding for me :) (tips are accepted too)
- If you want to suggest some indicators that I can develop and share with the community, please use my personal TRELLO board
- I'm an officially approved PineEditor/LUA/MT4 approved mentor on codementor. You can request a coaching with me if you want and I'll teach you how to build kick-ass indicators and strategies
Jump on a 1 to 1 coaching with me
- You can also hire for a custom dev of your indicator/strategy/bot/chrome extension/python
How to avoid repainting when using security() - PineCoders FAQNOTE
The non-repainting technique in this publication that relies on bar states is now deprecated, as we have identified inconsistencies that undermine its credibility as a universal solution. The outputs that use the technique are still available for reference in this publication. However, we do not endorse its usage. See this publication for more information about the current best practices for requesting HTF data and why they work.
This indicator shows how to avoid repainting when using the security() function to retrieve information from higher timeframes.
What do we mean by repainting?
Repainting is used to describe three different things, in what we’ve seen in TV members comments on indicators:
1. An indicator showing results that change during the realtime bar, whether the script is using the security() function or not, e.g., a Buy signal that goes on and then off, or a plot that changes values.
2. An indicator that uses future data not yet available on historical bars.
3. An indicator that uses a negative offset= parameter when plotting in order to plot information on past bars.
The repainting types we will be discussing here are the first two types, as the third one is intentional—sometimes even intentionally misleading when unscrupulous script writers want their strategy to look better than it is.
Let’s be clear about one thing: repainting is not caused by a bug ; it is caused by the different context between historical bars and the realtime bar, and script coders or users not taking the necessary precautions to prevent it.
Why should repainting be avoided?
Repainting matters because it affects the behavior of Pine scripts in the realtime bar, where the action happens and counts, because that is when traders (or our systems) take decisions where odds must be in our favor.
Repainting also matters because if you test a strategy on historical bars using only OHLC values, and then run that same code on the realtime bar with more than OHLC information, scripts not properly written or misconfigured alerts will alter the strategy’s behavior. At that point, you will not be running the same strategy you tested, and this invalidates your test results , which were run while not having the additional price information that is available in the realtime bar.
The realtime bar on your charts is only one bar, but it is a very important bar. Coding proper strategies and indicators on TV requires that you understand the variations in script behavior and how information available to the script varies between when the script is running on historical and realtime bars.
How does repainting occur?
Repainting happens because of something all traders instinctively crave: more information. Contrary to trader lure, more information is not always better. In the realtime bar, all TV indicators (a.k.a. studies ) execute every time price changes (i.e. every tick ). TV strategies will also behave the same way if they use the calc_on_every_tick = true parameter in their strategy() declaration statement (the parameter’s default value is false ). Pine coders must decide if they want their code to use the realtime price information as it comes in, or wait for the realtime bar to close before using the same OHLC values for that bar that would be used on historical bars.
Strategy modelers often assume that using realtime price information as it comes in the realtime bar will always improve their results. This is incorrect. More information does not necessarily improve performance because it almost always entails more noise. The extra information may or may not improve results; one cannot know until the code is run in realtime for enough time to provide data that can be analyzed and from which somewhat reliable conclusions can be derived. In any case, as was stated before, it is critical to understand that if your strategy is taking decisions on realtime tick data, you are NOT running the same strategy you tested on historical bars with OHLC values only.
How do we avoid repainting?
It comes down to using reliable information and properly configuring alerts, if you use them. Here are the main considerations:
1. If your code is using security() calls, use the syntax we propose to obtain reliable data from higher timeframes.
2. If your script is a strategy, do not use the calc_on_every_tick = true parameter unless your strategy uses previous bar information to calculate.
3. If your script is a study and is using current timeframe information that is compared to values obtained from a higher timeframe, even if you can rely on reliable higher timeframe information because you are correctly using the security() function, you still need to ensure the realtime bar’s information you use (a cross of current close over a higher timeframe MA, for example) is consistent with your backtest methodology, i.e. that your script calculates on the close of the realtime bar. If your system is using alerts, the simplest solution is to configure alerts to trigger Once Per Bar Close . If you are not using alerts, the best solution is to use information from the preceding bar. When using previous bar information, alerts can be configured to trigger Once Per Bar safely.
What does this indicator do?
It shows results for 9 different ways of using the security() function and illustrates the simplest and most effective way to avoid repainting, i.e. using security() as in the example above. To show the indicator’s lines the most clearly, price on the chart is shown with a black line rather than candlesticks. This indicator also shows how misusing security() produces repainting. All combinations of using a 0 or 1 offset to reference the series used in the security() , as well as all combinations of values for the gaps= and lookahead= parameters are shown.
The close in the call labeled “BEST” means that once security has reached the upper timeframe (1 day in our case), it will fetch the previous day’s value.
The gaps= parameter is not specified as it is off by default and that is what we need. This ensures that the value returned by security() will not contain na values on any of our chart’s bars.
The lookahead security() to use the last available value for the higher timeframe bar we are using (the previous day, in our case). This ensures that security() will return the value at the end of the higher timeframe, even if it has not occurred yet. In our case, this has no negative impact since we are requesting the previous day’s value, with has already closed.
The indicator’s Settings/Inputs allow you to set:
- The higher timeframe security() calls will use
- The source security() calls will use
- If you want identifying labels printed on the lines that have no gaps (the lines containing gaps are plotted using very thick lines that appear as horizontal blocks of one bar in length)
For the lines to be plotted, you need to be on a smaller timeframe than the one used for the security() calls.
Comments in the code explain what’s going on.
Look first. Then leap.
TRAILING STOP LOSS TO LONG AND SHORT##THIS SCRIPT IS ON GITHUB
This TradingView strategy it is designed to integrate with other strategies with indicators.
It performs a trailing stop loss from entry and exit conditions.
In this strategy you can add conditions for long and short positions.
The strategy will ride up your stop loss when price moviment 1%.
The strategy will close your operation when the market price crossed the stop loss.
Also is possible to select the period that strategy will execute the backtest.
The strategy has the following parameters:
+ **INITIAL STOP LOSS** - Where can isert the value to first stop.
+ **POSITION TYPE** - Where can to select trade position.
+ **BACKTEST PERIOD** - To select range.
## DISCLAIMER
1. I am not licensed financial advisors or broker dealers. I do not tell you when or what to buy or sell. I developed this software which enables you execute manual or automated trades multiple trades using TradingView. The software allows you to set the criteria you want for entering and exiting trades.
2. Do not trade with money you cannot afford to lose.
3. I do not guarantee consistent profits or that anyone can make money with no effort. And I am not selling the holy grail.
4. Every system can have winning and losing streaks.
5. Money management plays a large role in the results of your trading. For example: lot size, account size, broker leverage, and broker margin call rules all have an effect on results. Also, your Take Profit and Stop Loss settings for individual pair trades and for overall account equity have a major impact on results. If you are new to trading and do not understand these items, then I recommend you seek education materials to further your knowledge.
**YOU NEED TO FIND AND USE THE TRADING SYSTEM THAT WORKS BEST FOR YOU AND YOUR TRADING TOLERANCE.**
**I HAVE PROVIDED NOTHING MORE THAN A TOOL WITH OPTIONS FOR YOU TO TRADE WITH THIS PROGRAM ON TRADINGVIEW.**
## NOTE
I accept suggestions to improve the script.
If you encounter any problems I will be happy to share with me.
+ Authors: @exit490
+ Revision: v1.0.0
+ Date: 03-Aug-2019
+ Pinescript version: 4
## LICENSE
Copyright 2019 Mauricio Pimenta / exit490
Trailing Stop Loss script may be freely distributed under the MIT license .
Noro's SILA v1.2Noro's SILA v1.2 - these are 5 trend indicators in 1, for the sake of better accuracy.
Added:
1) Settings
2) Arrows
Noro's SILA v1.2 uses 5 trend indicators:
1) SuperTrend
2) DI Plus-Minus
3) WOW trend indicator (my idea)
4) BarColor indicator (my idea)
5) BestMA (or "BMA") indicator (my idea)
The user can switch-off any indicator from 5 to achieve big accuracy.
How does it work?
Each indicator from 5 defines a trend in own way. If two indicators report that there will be a uptrend, and three others the indicator report that there will be a downtrend - it is downtrend (a red background).
For an example
Now SuperTrend = uptrend = +1
Now DI Plus-Minus = downtrend = -1
Now WOW trend indicator = downtrend = -1
Now BarColor indicator = downtrend = -1
Now BestMA (or "BMA") indicator = uptrend = +1
Sum = + 1 - 1 - 1 - 1 + 1 = -1 = downtrend
If sum > 0 = uptrend
Sensivity
The user himself chooses what there will be a sensitivity (in settings).
If sensivity = 3:
sum > or = 3 - uptrend
sum < or = -3 - downtrend
sum > -3 and < 3 - NA-color of background
Trendlines
3 lower trendlines (blue plots) is "sum+3"
5 upper trendlines is "sum-5"
etc
Settings:
1) sensivity - you see above
2) distance - distance between the price and lines (for convenience)
Weight of Evidence BF**For Stocks (requires volume data) **
The premise of this indicator is that the wisdom of many is greater than one. The idea is you can throw out most of your indicators and simply adopt the Weight of the Evidence instead.
Eight indicators and five periods combine to give forty separate readings on a stock. These are all checked against a threshold to give a pass or fail score. The total is taken and a score is given out of 100 in increments of 2.5.
Four indicators are momentum-based: EMA, RSI, PercentRank, Lower Donchian Channel
Three are price-volume based:On Balance Volume, Price Volume Trend, Accumulation/Distribution
One is volatility-based: (Simplified) Volatility Stop
I have tried to make things simple with the entered periods being applied to all indicators. For some like on balance volume its actually a look back period for comparison of values. For the volatility stop I use the 3rd period for lookback and combine with 1 to 5 times ATR.
As this is a stepped function which can react rapidly it makes sense to smooth it with something like a 3-bar EMA, which is included by default.
Play around with the periods and different bar lengths to find something you like. I actually chose the default values with daily bars in mind but it seems to work well on weeklies! If you have other preferred indicators you could edit this script and substitute your own, although it is easiest to stick with the built-in functions as I have done.
Let me know how you get on with this and good trading to all!
CM Willams %R and CCI BackGround HighlightCM_Willams %R and CCI BackGround Highlight
Created By User Request
Indicator Highlights:
Creates Red BackGround Highlight if CCI Or Williams %R are Above Upper Line (User Defined)
Creates Green BackGround Highlight if CCI Or Williams %R are Below Lower Line (User Defined)
Ability to Turn On/Off either Williams %R or CCI Highlights in Inputs Tab via Check Boxes.
Ability To Set All Parameters for CCI and Williams %R in Inputs Tab.
Ability to Set High/Low “Threshold” Lines for Both CCI and Williams %R in Inputs Tab.
***I was asked if you could plot Back Ground Highlights on Two Individual Indicators AND have it show if BOTH Indicators were Overbought and Oversold.
***The answer is Yes. On the Chart Above I have the same Shade of Red and Green for Both Indicators. However, you will notice when Both Indicators Show OverBought…Both Plot Red Back Ground Highlights Which = a Brighter Red. The same is True for Oversold Conditions. The Green Shows a Brighter Shade of Green.
***VERY IMPORTANT - It is difficult for a programmer to release Indicators with this feature because depending on what color background you use on your charts…THE COLORS LOOK COMPLETELY DIFFERENT. So If You Don’t Use The Black Back Ground Shown Above You Most Likely Will Need To Adjust The Transparency, and Possibly The Colors Themselves!!!!
Reference Page
Mutanabby_AI | Fresh Algo V24Mutanabby_AI | Fresh Algo V24: Advanced Multi-Mode Trading System
Overview
The Mutanabby_AI Fresh Algo V24 represents a sophisticated evolution of multi-component trading systems that adapts to various market conditions through advanced operational configurations and enhanced analytical capabilities. This comprehensive indicator provides traders with multiple signal generation approaches, specialized assistant functions, and dynamic risk management tools designed for professional market analysis across diverse trading environments.
Primary Signal Generation Framework
The Fresh Algo V24 operates through two fundamental signal generation approaches that accommodate different market perspectives and trading philosophies. The Trending Signals Mode serves as the primary trend-following mechanism, combining Wave Trend Oscillator analysis with Supertrend directional signals and Squeeze Momentum breakout detection. This mode incorporates ADX filtering that requires values exceeding 20 to ensure sufficient trend strength exists before signal activation, making it particularly effective during sustained directional market movements where momentum persistence creates profitable trading opportunities.
The Contrarian Signals Mode provides an alternative approach targeting reversal opportunities through extreme market condition identification. This mode activates when the Wave Trend Oscillator reaches critical threshold levels, specifically when readings surpass 65 indicating potential bearish reversal conditions or drop below 35 suggesting bullish reversal opportunities. This methodology proves valuable during overextended market phases where mean reversion becomes statistically probable.
Advanced Filtering Mechanisms
The system incorporates multiple sophisticated filtering mechanisms designed to enhance signal quality and reduce false positive occurrences. The High Volume Filter requires volume expansion confirmation before signal activation, utilizing exponential moving average calculations to ensure institutional participation accompanies price movements. This filter substantially improves signal reliability by eliminating low-conviction breakouts that lack adequate volume support from professional market participants.
The Strong Filter provides additional trend confirmation through 200-period exponential moving average analysis. Long position signals require price action above this benchmark level, while short position signals necessitate price action below it. This ensures strategic alignment with longer-term trend direction and reduces the probability of trading against major market movements that could invalidate shorter-term signals.
Cloud Filter Configuration System
The Fresh Algo V24 offers four distinct cloud filter configurations, each optimized for specific trading timeframes and market approaches. The Smooth Cloud Filter utilizes the mathematical relationship between 150-period and 250-period exponential moving averages, providing stable trend identification suitable for position trading strategies. This configuration generates signals exclusively when price action aligns with cloud direction, creating a more deliberate but highly reliable signal generation process.
The Swing Cloud Filter employs modified Supertrend calculations with parameters specifically optimized for swing trading timeframes. This filter achieves optimal balance between responsiveness and stability, adapting effectively to medium-term price movements while filtering excessive market noise that typically affects shorter-term analytical systems.
For active intraday traders, the Scalping Cloud Filter utilizes accelerated Supertrend calculations designed to capture rapid trend changes effectively. This configuration provides enhanced signal generation frequency suitable for compressed timeframe strategies. The advanced Scalping+ Cloud Filter incorporates Hull Moving Average confirmation, delivering maximum responsiveness for ultra-short-term trading while maintaining signal quality through additional momentum validation processes.
Specialized Assistant Functionality
The system includes two distinct assistant modes that provide supplementary market analysis capabilities. The Trend Assistant Mode activates advanced cloud analysis overlays that display dynamic support and resistance zones calculated through adaptive volatility algorithms. These levels automatically adjust to current market conditions, providing visual guidance for identifying trend continuation patterns and potential reversal areas with mathematical precision.
The Trend Tracker Mode concentrates on long-term trend identification by displaying major exponential moving averages with color-coded fill areas that clarify directional bias. This mode maintains visual simplicity while providing comprehensive trend context evaluation, enabling traders to quickly assess broader market direction and align shorter-term strategies accordingly.
Dynamic Risk Management System
The integrated risk management system automatically adapts across all operational modes, calculating stop loss and take profit targets using Average True Range multiples that adjust to current market volatility. This approach ensures consistent risk parameters regardless of selected operational mode while maintaining relevance to prevailing market conditions.
Stop loss placement occurs at dynamically calculated distances from entry points, while three progressive take profit targets establish at customizable ATR multiples respectively. The system automatically updates these levels upon trend direction changes, ensuring current market volatility influences all risk calculations and maintains appropriate risk-reward ratios throughout trade management.
Comprehensive Market Analysis Dashboard
The sophisticated dashboard provides real-time market analysis including volatility measurements, institutional activity assessment, and multi-timeframe trend evaluation across five-minute through four-hour periods. This comprehensive market context assists traders in selecting appropriate operational modes based on current market characteristics rather than relying exclusively on historical performance data.
The multi-timeframe analysis ensures mode selection considers broader market context beyond the primary trading timeframe, improving overall strategic alignment and reducing conflicts between different temporal market perspectives. The dashboard displays market state classification, volatility percentages, institutional activity levels, current trading session information, and trend pressure indicators with professional formatting and clear visual hierarchy.
Enhanced Trading Assistants
The Fresh Algo V24 includes specialized trading assistant features that complement the primary signal generation system. The Reversal Dot functionality identifies potential reversal points through Wave Trend Oscillator analysis, displaying visual indicators when crossover conditions occur at extreme levels. These reversal indicators provide early warning signals for potential trend changes before they appear in the primary signal system.
The Dynamic Take Profit Labels feature automatically identifies optimal profit-taking opportunities through RSI threshold analysis, marking potential exit points at multiple levels for long positions and corresponding levels for short positions. This automated profit management system helps traders optimize exit timing without requiring constant manual monitoring of technical indicators.
Advanced Alert System
The comprehensive alert system accommodates all operational modes while providing granular notification control for various signal types and risk management events. Traders can configure separate alerts for normal buy signals, strong buy signals, normal sell signals, strong sell signals, stop loss triggers, and individual take profit target achievements.
Cloud crossover alerts notify traders when trend direction changes occur, providing early indication of potential strategy adjustments. The alert system includes detailed trade setup information, timeframe data, and relevant entry and exit levels, ensuring traders receive complete context for informed decision-making without requiring constant chart monitoring.
Technical Foundation Architecture
The Fresh Algo V24 combines multiple proven technical analysis components including Wave Trend Oscillator for momentum assessment, Supertrend for directional bias determination, Squeeze Momentum for volatility analysis, and various exponential moving averages for trend confirmation. Each component contributes specific market insights while the unified system provides comprehensive market evaluation through their mathematical integration.
The multi-component approach reduces dependency on individual indicator limitations while leveraging the analytical strengths of each technical tool. This creates a robust analytical framework capable of adapting to diverse market conditions through appropriate mode selection and parameter optimization, ensuring consistent performance across varying market environments.
Market State Classification
The indicator incorporates advanced market state classification through ADX analysis, distinguishing between trending, ranging, and transitional market conditions. This classification system automatically adjusts signal sensitivity and filtering parameters based on current market characteristics, optimizing performance for prevailing conditions rather than applying static analytical approaches.
The volatility measurement system calculates current market activity levels as percentages, providing quantitative assessment of market energy and helping traders select appropriate operational modes. Institutional activity detection through volume analysis ensures signal generation aligns with professional market participation patterns.
Implementation Strategy Considerations
Successful implementation requires careful matching of operational modes to prevailing market conditions and individual trading objectives. Trending modes demonstrate optimal performance during directional markets with sustained momentum characteristics, while contrarian modes excel during range-bound or overextended market conditions where reversal probability increases.
The cloud filter configurations provide varying degrees of confirmation strength, with smoother settings reducing false signal occurrence at the expense of some responsiveness to price changes. Traders must balance signal quality against signal frequency based on their risk tolerance and available trading time, utilizing the comprehensive customization options to optimize performance for their specific requirements.
Multi-Timeframe Integration
The system provides seamless multi-timeframe analysis through the integrated dashboard, displaying trend alignment across multiple time horizons from five-minute through four-hour periods. This analysis helps traders understand broader market context and avoid conflicts between different temporal perspectives that could compromise trade outcomes.
Session analysis identifies current trading session characteristics, providing context for expected market behavior patterns and helping traders adjust their approach based on typical session volatility and participation levels. This geographic market awareness enhances strategic decision-making and improves timing for trade execution.
Advanced Visualization Features
The indicator includes sophisticated visualization capabilities through gradient candle coloring based on MACD analysis, providing immediate visual feedback on momentum strength and direction. This enhancement allows rapid market assessment without requiring detailed indicator analysis, improving efficiency for traders managing multiple instruments simultaneously.
The cloud visualization system uses color-coded fill areas to clearly indicate trend direction and strength, with automatic adaptation to selected operational modes. This visual clarity reduces analytical complexity while maintaining comprehensive market information display through professional chart presentation.
Performance Optimization Framework
The Fresh Algo V24 incorporates performance optimization features including signal strength classification, automatic parameter adjustment based on market conditions, and dynamic filtering that adapts to current volatility levels. These optimizations ensure consistent performance across varying market environments while maintaining signal quality standards.
The system automatically adjusts sensitivity levels based on selected operational modes, ensuring appropriate responsiveness for different trading approaches. This adaptive framework reduces the need for manual parameter adjustments while maintaining optimal performance characteristics for each operational configuration.
Conclusion
The Mutanabby_AI Fresh Algo V24 represents a comprehensive solution for professional trading analysis, combining multiple analytical approaches with advanced visualization and risk management capabilities. The system's strength lies in its adaptive multi-mode design and sophisticated filtering mechanisms, providing traders with versatile tools for various market conditions and trading styles.
Success with this system requires understanding the relationship between different operational modes and their optimal application scenarios. The comprehensive dashboard and alert system provide essential market context and trade management support, enabling systematic approach to market analysis while maintaining flexibility for individual trading preferences.
The indicator's sophisticated architecture and extensive customization options make it suitable for traders at all experience levels, from those seeking systematic signal generation to advanced practitioners requiring comprehensive market analysis tools. The multi-timeframe integration and adaptive filtering ensure consistent performance across diverse market conditions while providing clear guidelines for strategic implementation.
Hurst Exponent Adaptive Filter (HEAF) [PhenLabs]📊 PhenLabs - Hurst Exponent Adaptive Filter (HEAF)
Version: PineScript™ v6
📌 Description
The Hurst Exponent Adaptive Filter (HEAF) is an advanced Pine Script indicator designed to dynamically adjust moving average calculations based on real time market regimes detected through the Hurst Exponent. The intention behind the creation of this indicator was not a buy/sell indicator but rather a tool to help sharpen traders ability to distinguish regimes in the market mathematically rather than guessing. By analyzing price persistence, it identifies whether the market is trending, mean-reverting, or exhibiting random walk behavior, automatically adapting the MA length to provide more responsive alerts in volatile conditions and smoother outputs in stable ones. This helps traders avoid false signals in choppy markets and capitalize on strong trends, making it ideal for adaptive trading strategies across various timeframes and assets.
Unlike traditional moving averages, HEAF incorporates fractal dimension analysis via the Hurst Exponent to create a self-tuning filter that evolves with market conditions. Traders benefit from visual cues like color coded regimes, adaptive bands for volatility channels, and an information panel that suggests appropriate strategies, enhancing decision making without constant manual adjustments by the user.
🚀 Points of Innovation
Dynamic MA length adjustment using Hurst Exponent for regime-aware filtering, reducing lag in trends and noise in ranges.
Integrated market regime classification (trending, mean-reverting, random) with visual and alert-based notifications.
Customizable color themes and adaptive bands that incorporate ATR for volatility-adjusted channels.
Built-in information panel providing real-time strategy recommendations based on detected regimes.
Power sensitivity parameter to fine-tune adaptation aggressiveness, allowing personalization for different trading styles.
Support for multiple MA types (EMA, SMA, WMA) within an adaptive framework.
🔧 Core Components
Hurst Exponent Calculation: Computes the fractal dimension of price series over a user-defined lookback to detect market persistence or anti-persistence.
Adaptive Length Mechanism: Maps Hurst values to MA lengths between minimum and maximum bounds, using a power function for sensitivity control.
Moving Average Engine: Applies the chosen MA type (EMA, SMA, or WMA) to the adaptive length for the core filter line.
Adaptive Bands: Creates upper and lower channels using ATR multiplied by a band factor, scaled to the current adaptive length.
Regime Detection: Classifies market state with thresholds (e.g., >0.55 for trending) and triggers alerts on regime changes.
Visualization System: Includes gradient fills, regime-colored MA lines, and an info panel for at-a-glance insights.
🔥 Key Features
Regime-Adaptive Filtering: Automatically shortens MA in mean-reverting markets for quick responses and lengthens it in trends for smoother signals, helping traders stay aligned with market dynamics.
Custom Alerts: Notifies on regime shifts and band breakouts, enabling timely strategy adjustments like switching to trend-following in bullish regimes.
Visual Enhancements: Color-coded MA lines, gradient band fills, and an optional info panel that displays market state and trading tips, improving chart readability.
Flexible Settings: Adjustable lookback, min/max lengths, sensitivity power, MA type, and themes to suit various assets and timeframes.
Band Breakout Signals: Highlights potential overbought/oversold conditions via ATR-based channels, useful for entry/exit timing.
🎨 Visualization
Main Adaptive MA Line: Plotted with regime-based colors (e.g., green for trending) to visually indicate market state and filter position relative to price.
Adaptive Bands: Upper and lower lines with gradient fills between them, showing volatility channels that widen in random regimes and tighten in trends.
Price vs. MA Fills: Color-coded areas between price and MA (e.g., bullish green above MA in trending modes) for quick trend strength assessment.
Information Panel: Top-right table displaying current regime (e.g., "Trending Market") and strategy suggestions like "Follow trends" or "Trade ranges."
📖 Usage Guidelines
Core Settings
Hurst Lookback Period
Default: 100
Range: 20-500
Description: Sets the period for Hurst Exponent calculation; longer values provide more stable regime detection but may lag, while shorter ones are more responsive to recent changes.
Minimum MA Length
Default: 10
Range: 5-50
Description: Defines the shortest possible adaptive MA length, ideal for fast responses in mean-reverting conditions.
Maximum MA Length
Default: 200
Range: 50-500
Description: Sets the longest adaptive MA length for smoothing in strong trends; adjust based on asset volatility.
Sensitivity Power
Default: 2.0
Range: 1.0-5.0
Description: Controls how aggressively the length adapts to Hurst changes; higher values make it more sensitive to regime shifts.
MA Type
Default: EMA
Options: EMA, SMA, WMA
Description: Chooses the moving average calculation method; EMA is more responsive, while SMA/WMA offer different weighting.
🖼️ Visual Settings
Show Adaptive Bands
Default: True
Description: Toggles visibility of upper/lower bands for volatility channels.
Band Multiplier
Default: 1.5
Range: 0.5-3.0
Description: Scales band width using ATR; higher values create wider channels for conservative signals.
Show Information Panel
Default: True
Description: Displays regime info and strategy tips in a top-right panel.
MA Line Width
Default: 2
Range: 1-5
Description: Adjusts thickness of the main MA line for better visibility.
Color Theme
Default: Blue
Options: Blue, Classic, Dark Purple, Vibrant
Description: Selects color scheme for MA, bands, and fills to match user preferences.
🚨 Alert Settings
Enable Alerts
Default: True
Description: Activates notifications for regime changes and band breakouts.
✅ Best Use Cases
Trend-Following Strategies: In detected trending regimes, use the adaptive MA as a trailing stop or entry filter for momentum trades.
Range Trading: During mean-reverting periods, monitor band breakouts for buying dips or selling rallies within channels.
Risk Management in Random Markets: Reduce exposure when random walk is detected, using tight stops suggested in the info panel.
Multi-Timeframe Analysis: Apply on higher timeframes for regime confirmation, then drill down to lower ones for entries.
Volatility-Based Entries: Use upper/lower band crossovers as signals in adaptive channels for overbought/oversold trades.
⚠️ Limitations
Lagging in Transitions: Regime detection may delay during rapid market shifts, requiring confirmation from other tools.
Not a Standalone System: Best used in conjunction with other indicators; random regimes can lead to whipsaws if traded aggressively.
Parameter Sensitivity: Optimal settings vary by asset and timeframe, necessitating backtesting.
💡 What Makes This Unique
Hurst-Driven Adaptation: Unlike static MAs, it uses fractal analysis to self-tune, providing regime-specific filtering that's rare in standard indicators.
Integrated Strategy Guidance: The info panel offers actionable tips tied to regimes, bridging analysis and execution.
Multi-Regime Visualization: Combines adaptive bands, colored fills, and alerts in one tool for comprehensive market state awareness.
🔬 How It Works
Hurst Exponent Computation:
Calculates log returns over the lookback period to derive the rescaled range (R/S) ratio.
Normalizes to a 0-1 value, where >0.55 indicates trending, <0.45 mean-reverting, and in-between random.
Length Adaptation:
Maps normalized Hurst to an MA length via a power function, clamping between min and max.
Applies the selected MA type to close prices using this dynamic length.
Visualization and Signals:
Plots the MA with regime colors, adds ATR-based bands, and fills areas for trend strength.
Triggers alerts on regime changes or band crosses, with the info panel suggesting strategies like momentum riding in trends.
💡 Note:
For optimal results, backtest settings on your preferred assets and combine with volume or momentum indicators. Remember, no indicator guarantees profits—use with proper risk management. Access premium features and support at PhenLabs.
AI's Opinion Trading System V21. Complete Summary of the Indicator Script
AI’s Opinion Trading System V2 is an advanced, multi-factor trading tool designed for the TradingView platform. It combines several technical indicators (moving averages, RSI, MACD, ADX, ATR, and volume analysis) to generate buy, sell, and hold signals. The script features a customizable AI “consensus” engine that weighs multiple indicator signals, applies user-defined filters, and outputs actionable trade instructions with clear stop loss and take profit levels. The indicator also tracks sentiment, volume delta, and allows for advanced features like pyramiding (adding to positions), custom stop loss/take profit prices, and flexible signal confirmation logic. All key data and signals are displayed in a dynamic, color-coded table on the chart for easy review.
2. Full Explanation of the Table
The table is a real-time dashboard summarizing the indicator’s logic and recommendations for the most recent bars. It is color-coded for clarity and designed to help traders quickly understand market conditions and AI-driven trade signals.
Columns (from left to right):
Column Name What it Shows
Bar The time context: “Now” for the current bar, then “Bar -1”, “Bar -2”, etc. for previous bars.
Raw Consensus The raw AI consensus for each bar: “Buy”, “Sell”, or “-” (neutral).
Up Vol The amount of volume on up (rising) bars.
Down Vol The amount of volume on down (falling) bars.
Delta The difference between up and down volume. Green if positive, red if negative, gray if neutral.
Close The closing price for each bar, color-coded by price change.
Sentiment Diff The difference between the close and average sentiment price (a custom sentiment calculation).
Lookback The number of bars used for sentiment calculation (if enabled).
ADX The ADX value (trend strength).
ATR The ATR value (volatility measure).
Vol>Avg “Yes” (green) if volume is above average, “No” (red) otherwise.
Confirm Whether the AI signal is confirmed over the required bars.
Logic Output The AI’s interpreted signal after applying user-selected logic: “Buy”, “Sell”, or “-”.
Final Action The final signal after all filters: “Buy”, “Sell”, or “-”.
Trade Instruction A plain-English instruction: Buy/Sell/Add/Hold/No Action, with price, stop loss, and take profit.
Color Coding:
Green: Positive/bullish values or signals
Red: Negative/bearish values or signals
Gray: Neutral or inactive
Blue background: For all table cells, for visual clarity
White text: Default, except for color-coded cells
3. Full User Instructions for Every Input/Style Option
Below are plain-language instructions for every user-adjustable option in the indicator’s input and style pages:
Inputs
Table Location
What it does: Sets where the summary table appears on your chart.
How to use: Choose from 9 positions (Top Left, Top Center, Top Right, etc.) to avoid overlapping with other chart elements.
Decimal Places
What it does: Controls how many decimal places prices and values are displayed with.
How to use: Increase for assets with very small prices (e.g., SHIB), decrease for stocks or forex.
Show Sentiment Lookback?
What it does: Shows or hides the “Lookback” column in the table, which displays how many bars are used in the sentiment calculation.
How to use: Turn off if you want a simpler table.
AI View Mode
What it does: Selects the logic for how the AI combines signals from different indicators.
Majority: Follows the most common signal among all indicators.
Weighted: Uses custom weights for each type of signal.
Custom: Lets you define your own logic (see below).
How to use: Pick the logic style that matches your trading philosophy.
AI Consensus Weight / Vol Delta Weight / Sentiment Weight
What they do: When using “Weighted” AI View Mode, these let you set how much influence each factor (indicator consensus, volume delta, sentiment) has on the final signal.
How to use: Increase a weight to make that factor more important in the AI’s decision.
Custom AI View Logic
What it does: Lets advanced users write their own logic for when the AI should signal a trade (e.g., “ai==1 and delta>0 and sentiment>0”).
How to use: Only use if you understand basic boolean logic.
Use Custom Stop Loss/Take Profit Prices?
What it does: If enabled, you can enter your own fixed stop loss and take profit prices for buys and sells.
How to use: Turn on to override the auto-calculated SL/TP and enter your desired prices below.
Custom Buy/Sell Stop Loss/Take Profit Price
What they do: If custom SL/TP is enabled, these fields let you set exact prices for stop loss and take profit on both buy and sell trades.
How to use: Enter your preferred price, or leave at 0 for auto-calculation.
Sentiment Lookback
What it does: Sets how many bars the sentiment calculation should look back.
How to use: Increase to smooth out sentiment, decrease for faster reaction.
Max Pyramid Adds
What it does: Limits how many times you can add to an existing position (pyramiding).
How to use: Set to 1 for no adds, higher for more aggressive scaling in trends.
Signal Preset
What it does: Quick-sets a group of signal parameters (see below) for “Robust”, “Standard”, “Freedom”, or “Custom”.
How to use: Pick a preset, or select “Custom” to adjust everything manually.
Min Bars for Signal Confirmation
What it does: Sets how many bars a signal must persist before it’s considered valid.
How to use: Increase for more robust, less frequent signals; decrease for faster, but possibly less reliable, signals.
ADX Length
What it does: Sets the period for the ADX (trend strength) calculation.
How to use: Longer = smoother, shorter = more sensitive.
ADX Trend Threshold
What it does: Sets the minimum ADX value to consider a trend “strong.”
How to use: Raise for stricter trend confirmation, lower for more trades.
ATR Length
What it does: Sets the period for the ATR (volatility) calculation.
How to use: Longer = smoother volatility, shorter = more reactive.
Volume Confirmation Lookback
What it does: Sets how many bars are used to calculate the average volume.
How to use: Longer = more stable volume baseline, shorter = more sensitive.
Volume Confirmation Multiplier
What it does: Sets how much current volume must exceed average volume to be considered “high.”
How to use: Increase for stricter volume filter.
RSI Flat Min / RSI Flat Max
What they do: Define the RSI range considered “flat” (i.e., not trending).
How to use: Widen to be stricter about requiring a trend, narrow for more trades.
Style Page
Most style settings (such as plot colors, label sizes, and shapes) are preset in the script for visual clarity.
You can adjust plot visibility and colors (for signals, stop loss, take profit) in the TradingView “Style” tab as with any indicator.
Buy Signal: Shows as a green triangle below the bar when a buy is triggered.
Sell Signal: Shows as a red triangle above the bar when a sell is triggered.
Stop Loss/Take Profit Lines: Red and green lines for SL/TP, visible when a trade is active.
SL/TP Labels: Small colored markers at the SL/TP levels for each trade.
How to use:
Toggle visibility or change colors in the Style tab if you wish to match your chart theme or preferences.
In Summary
This indicator is highly customizable—you can tune every aspect of the AI logic, risk management, signal filtering, and table display to suit your trading style.
The table gives you a real-time, comprehensive view of all relevant signals, filters, and trade instructions.
All inputs are designed to be intuitive—hover over them in TradingView for tooltips, or refer to the explanations above for details.
National Financial Conditions Index (NFCI)This is one of the most important macro indicators in my trading arsenal due to its reliability across different market regimes. I'm excited to share this with the TradingView community because this Federal Reserve data is not only completely free but extraordinarily useful for portfolio management and risk assessment.
**Important Disclaimers**: Be aware that some NFCI components are updated only monthly but carry significant weighting in the composite index. Additionally, the Fed occasionally revises historical NFCI data, so historical backtests should be interpreted with some caution. Nevertheless, this remains a crucial leading indicator for financial stress conditions.
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## What is the National Financial Conditions Index?
The National Financial Conditions Index (NFCI) is a comprehensive measure of financial stress and liquidity conditions developed by the Federal Reserve Bank of Chicago. This indicator synthesizes over 100 financial market variables into a single, interpretable metric that captures the overall state of financial conditions in the United States (Brave & Butters, 2011).
**Key Principle**: When the NFCI is positive, financial conditions are tighter than average; when negative, conditions are looser than average. Values above +1.0 historically coincide with financial crises, while values below -1.0 often signal bubble-like conditions.
## Scientific Foundation & Research
The NFCI methodology is grounded in extensive academic research:
### Core Research Foundation
- **Brave, S., & Butters, R. A. (2011)**. "Monitoring financial stability: A financial conditions index approach." *Economic Perspectives*, 35(1), 22-43.
- **Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010)**. "Financial conditions indexes: A fresh look after the financial crisis." *US Monetary Policy Forum Report*, No. 23.
- **Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012)**. "Disentangling diverse measures: A survey of financial stress indexes." *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
### Methodological Validation
The NFCI employs Principal Component Analysis (PCA) to extract common factors from financial market data, following the methodology established by **English, W. B., Tsatsaronis, K., & Zoli, E. (2005)** in "Assessing the predictive power of measures of financial conditions for macroeconomic variables." The index has been validated through extensive academic research (Koop & Korobilis, 2014).
## NFCI Components Explained
This indicator provides access to all five official NFCI variants:
### 1. **Main NFCI**
The primary composite index incorporating all financial market sectors. This serves as the main signal for portfolio allocation decisions.
### 2. **Adjusted NFCI (ANFCI)**
Removes the influence of credit market disruptions to focus on non-credit financial stress. Particularly useful during banking crises when credit markets may be impaired but other financial conditions remain stable.
### 3. **Credit Sub-Index**
Isolates credit market conditions including corporate bond spreads, commercial paper rates, and bank lending standards. Important for assessing corporate financing stress.
### 4. **Leverage Sub-Index**
Measures systemic leverage through margin requirements, dealer financing, and institutional leverage metrics. Useful for identifying leverage-driven market stress.
### 5. **Risk Sub-Index**
Captures market-based risk measures including volatility, correlation, and tail risk indicators. Provides indication of risk appetite shifts.
## Practical Trading Applications
### Portfolio Allocation Framework
Based on the academic research, the NFCI can be used for portfolio positioning:
**Risk-On Positioning (NFCI declining):**
- Consider increasing equity exposure
- Reduce defensive positions
- Evaluate growth-oriented sectors
**Risk-Off Positioning (NFCI rising):**
- Consider reducing equity exposure
- Increase defensive positioning
- Favor large-cap, dividend-paying stocks
### Academic Validation
According to **Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011)** in "The financial stress index: Identification of systemic risk conditions," financial conditions indices like the NFCI provide early warning capabilities for systemic risk conditions.
**Illing, M., & Liu, Y. (2006)** demonstrated in "Measuring financial stress in a developed country: An application to Canada" that composite financial stress measures can be useful for predicting economic downturns.
## Advanced Features of This Implementation
### Dynamic Background Coloring
- **Green backgrounds**: Risk-On conditions - potentially favorable for equity investment
- **Red backgrounds**: Risk-Off conditions - time for defensive positioning
- **Intensity varies**: Based on deviation from trend for nuanced risk assessment
### Professional Dashboard
Real-time analytics table showing:
- Current NFCI level and interpretation (TIGHT/LOOSE/NEUTRAL)
- Individual sub-index readings
- Change analysis
- Portfolio guidance (Risk On/Risk Off)
### Alert System
Professional-grade alerts for:
- Risk regime changes
- Extreme stress conditions (NFCI > 1.0)
- Bubble risk warnings (NFCI < -1.0)
- Major trend reversals
## Optimal Usage Guidelines
### Best Timeframes
- **Daily charts**: Recommended for intermediate-term positioning
- **Weekly charts**: Suitable for longer-term portfolio allocation
- **Intraday**: Less effective due to weekly update frequency
### Complementary Indicators
For enhanced analysis, combine NFCI signals with:
- **VIX levels**: Confirm stress readings
- **Credit spreads**: Validate credit sub-index signals
- **Moving averages**: Determine overall market trend context
- **Economic surprise indices**: Gauge fundamental backdrop
### Position Sizing Considerations
- **Extreme readings** (|NFCI| > 1.0): Consider higher conviction positioning
- **Moderate readings** (|NFCI| 0.3-1.0): Standard position sizing
- **Neutral readings** (|NFCI| < 0.3): Consider reduced conviction
## Important Limitations & Considerations
### Data Frequency Issues
**Critical Warning**: While the main NFCI updates weekly (typically Wednesdays), some underlying components update monthly. Corporate bond indices and commercial paper rates, which carry significant weight, may cause delayed reactions to current market conditions.
**Component Update Schedule:**
- **Weekly Updates**: Main NFCI composite, most equity volatility measures
- **Monthly Updates**: Corporate bond spreads, commercial paper rates
- **Quarterly Updates**: Banking sector surveys
- **Impact**: Significant portion of index weight may lag current conditions
### Historical Revisions
The Federal Reserve occasionally revises NFCI historical data as new information becomes available or methodologies are refined. This means backtesting results should be interpreted cautiously, and the indicator works best for forward-looking analysis rather than precise historical replication.
### Market Regime Dependency
The NFCI effectiveness may vary across different market regimes. During extended sideways markets or regime transitions, signals may be less reliable. Consider combining with trend-following indicators for optimal results.
**Bottom Line**: Use NFCI for medium-term portfolio positioning guidance. Trust the directional signals while remaining aware of data revision risks and update frequency limitations. This indicator is particularly valuable during periods of financial stress when reliable guidance is most needed.
---
**Data Source**: Federal Reserve Bank of Chicago
**Update Frequency**: Weekly (typically Wednesdays)
**Historical Coverage**: 1973-present
**Cost**: Free (public Fed data)
*This indicator is for educational and analytical purposes. Always conduct your own research and risk assessment before making investment decisions.*
## References
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. *Economic Perspectives*, 35(1), 22-43.
English, W. B., Tsatsaronis, K., & Zoli, E. (2005). Assessing the predictive power of measures of financial conditions for macroeconomic variables. *BIS Papers*, 22, 228-252.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. *US Monetary Policy Forum Report*, No. 23.
Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Bank of Canada Working Paper*, 2006-02.
Kliesen, K. L., Owyang, M. T., & Vermann, E. K. (2012). Disentangling diverse measures: A survey of financial stress indexes. *Federal Reserve Bank of St. Louis Review*, 94(5), 369-397.
Koop, G., & Korobilis, D. (2014). A new index of financial conditions. *European Economic Review*, 71, 101-116.
Oet, M. V., Eiben, R., Bianco, T., Gramlich, D., & Ong, S. J. (2011). The financial stress index: Identification of systemic risk conditions. *Federal Reserve Bank of Cleveland Working Paper*, 11-30.
H BollingerBollinger Bands are a widely used technical analysis indicator that helps spot relative price highs and lows. The tool comprises three lines: a central band representing the 20-period simple moving average (SMA), and upper and lower bands usually placed two standard deviations above and below the SMA. These bands adjust with market volatility, offering insights into price fluctuations and trading conditions.
How this indicator works
Bollinger Bands helps traders assess price volatility and potential price reversals. They consist of three bands: the middle band, the upper band, and the lower band. Here's how Bollinger Bands work:
Middle band: This is typically a simple moving average (SMA) of the asset's price over a specified period. The most common period used is 20 days.
Upper band: This is calculated by adding a specified number of standard deviations to the middle band. The standard deviation measures the asset's price volatility. Commonly, two standard deviations are added to the middle band.
Lower band: Similar to the upper band, it is calculated by subtracting a specified number of standard deviations from the middle band.
What do Bollinger Bands tell you?
Bollinger bands primarily indicate the level of market volatility and trading opportunities. Narrow bands indicate low market volatility, while wide bands suggest high market volatility. Bollinger bands indicators can be used by traders to assess potential buy or sell signals. For instance, a sell signal may be interpreted or generated if the asset’s price moves closer or crosses the upper band, as it may indicate that the asset is overbought. Alternatively, a buy signal may be interpreted or generated if the price moves closer to the lower band, as it may signify that the asset is oversold.
However, traders should be cautious when using Bollinger Bands as standalone indicators when making trading decisions. Experienced traders refrain from confirming signals based on one indicator. Instead, they generally combine various technical indicators and fundamental analysis methods to make informed trading decisions. Basing trading decisions on only one indicator can result in misinterpretation of signals and heavy losses.
Bollinger Bands assist in identifying whether prices are relatively high or low. They are applied as a pair—upper and lower bands—alongside a moving average. However, these bands are not designed to be used in isolation. Instead, they should be used to validate signals generated by other technical indicators.
Calculation of Bollinger Band
TTP-BB-vwap-PivotTTP-BB-Vwap-Pivot is a comprehensive all-in-one technical analysis indicator designed specifically for intraday traders. This powerful tool combines multiple essential indicators in a single, customizable package, eliminating the need to clutter your chart with separate indicators.
🎯 Key Features
📈 Bollinger Bands
Fully Customizable: Adjust length (default: 20) and multiplier (default: 2.0)
Source Selection: Choose from Open, High, Low, Close, HL2, HLC3, OHLC4
Visual Fill: Semi-transparent band fill for better visualization
Toggle Control: Easy on/off switch
💰 VWAP (Volume Weighted Average Price)
Intraday Focus: Perfect for identifying institutional price levels
Source Customization: Default HLC3 with options for other price sources
Clear Visualization: Prominent white line for easy identification
Toggle Control: Show/hide as needed
🎪 Standard Pivot Points
Complete Pivot System: Shows Pivot Point + 3 Resistance (R1-R3) + 3 Support (S1-S3) levels
Timeframe Flexibility: Default daily pivots with customizable timeframe
Colour Coded: Yellow for Pivot Point, Red for Resistance, Green for Support
Value Labels: Exact price values displayed on the right
Toggle Control: Enable/disable entire pivot system
📊 Multiple EMA System (5 EMAs Available)
EMA 1: 9-period (Blue) - Short-term trend
EMA 2: 21-period (Red) - Medium-term trend
EMA 3: 50-period (Orange) - Long-term trend
EMA 4: 100-period (Purple) - Major trend
EMA 5: 200-period (Yellow) - Primary trend
Each EMA Features:
Individual toggle switches
Customizable period lengths
Source selection options
Colour customization
Independent control
🚨 Built-in Alerts
Price crossing above/below EMA1
Price crossing above/below VWAP
Easy alert setup for key signal points
🎛️ User-Friendly Interface
Organized Input Groups: All settings categorized for easy navigation
Individual Controls: Turn any indicator on/off independently
Clean Design: Optimized to avoid chart clutter
Performance Optimized: Efficient code for smooth operation
📈 Perfect For:
Day Traders: Quick intraday signals and levels
Swing Traders: Multiple timeframe analysis
Scalpers: Fast entry/exit points
All Skill Levels: From beginners to professionals
🔧 How to Use:
Add the indicator to your chart
Access settings through the indicator's style/inputs panel
Enable/disable indicators based on your trading strategy
Customize colours, periods, and sources to match your preferences
Set up alerts for key crossover signals
💡 Trading Applications:
Trend Identification: Multiple EMA crossovers
Support/Resistance: Pivot points and Bollinger Bands
Entry/Exit Signals: VWAP and EMA interactions
Risk Management: Clear levels for stop-loss placement
Market Structure: Institutional levels via VWAP and Pivots
⚡ Why Choose TTP-BB-Vwap-Pivot?
All-in-One Solution: No need for multiple separate indicators
Highly Customizable: Adapt to any trading style
Performance Optimized: Smooth operation without lag
Clean Interface: Organized settings and clear visuals
Beginner Friendly: Easy to understand and implement
Professional Grade: Suitable for serious traders
🛠️ Technical Specifications:
Pine Script v6
Overlay indicator
Optimized for all timeframes (especially intraday)
Compatible with all asset classes
No repainting
Real-time calculations
Transform your trading with TTP-BB-Vwap-Pivot - The ultimate technical analysis companion for modern traders!
Like and follow for more powerful trading tools and updates!
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
MTF Candle Direction Forecast + Breakdown🧭 MTF Candle Direction Forecast + Breakdown 🔥📈🔼
This script is a multi-timeframe (MTF) price action dashboard that helps traders assess real-time directional bias across five customizable timeframes — with a focus on candle behavior, trend alignment, and confidence strength.
📌 What It Does
For each timeframe, this dashboard summarizes:
Current direction → Bullish, Bearish, or Neutral
Confidence score (0–100) → How strongly price is likely to continue in that direction
Candle strength → 🔥 icon appears if the current candle has a large body relative to its range
Trend alignment:
📈 = EMA9 is above EMA20
🔼 = Price is above VWAP
Color-coded background to visually reinforce directional state
Each row gives you a visual “at-a-glance” readout of what price is doing right now — not in the past.
💡 Why It’s Useful
✅ Direction forecasting based on price action
Instead of lagging indicators, this script prioritizes:
Candle body-to-range ratio (momentum)
Real-time VWAP/EMA structure
Immediate price positioning
✅ Confidence is quantified
The score (0–100) helps you judge how reliable each directional signal is:
90+ → Strong conviction
50–70 → Mixed but potentially valid
<40 → Weak move or early signal
✅ Timeframe confluence at a glance
See whether multiple timeframes are aligning directionally — helpful for scalping, day trading, or waiting for multi-timeframe breakout setups.
✅ Visual & intuitive
Icons, colors, and layout make it easy to scan your dashboard instead of deciphering charts or code.
🛠️ Adjustable Settings
Setting Description
Timeframe 1–5 Choose any timeframes to monitor (e.g., 5m, 15m, 1h, 4h)
Candle Display Mode Show trend color via emoji (🟢/🔴) or background shading
Strong Candle Threshold Adjust the body-to-range % needed to trigger 🔥 strength
Bullish/Bearish Background Customize label color coding
Neutral Background (opacity) Set transparency or styling for flat/consolidating zones
Table Location Place the dashboard anywhere on the chart
🎯 Use Cases
Scalpers: Confirm trend across 1m/5m/15m before entering
Day Traders: Use confidence score to avoid low-momentum setups
Swing Traders: Monitor higher timeframes for trend shifts while tracking intraday noise
VWAP/EMA traders: Quickly see when price is reclaiming or losing critical trend levels
🧠 What Makes It Unique?
Unlike generic trend meters or mashups of standard indicators, this script:
Uses live candle dynamics (not just closes or lagging values)
Computes directional bias and confidence together
Visualizes strength and structure in a compact, readable interface
Let’s you filter by price action, not just indicator alignment
💥 Why Traders Love Will Love It
✅ Instant clarity on which timeframes agree
✅ No more guessing candle strength or trend health
✅ Confidence score keeps you out of weak trades
✅ Works with any strategy — trend following, VWAP reclaim, EMA scalps, even breakouts
✅ Keeps your chart clean — all the context, none of the clutter
⚠️ Transparency🧬 Under the Hood
Powered by live candle body analysis, trend structure (EMA9 vs EMA20), and VWAP placement.
All scores are generated in real-time — No repainting or lookahead bias: all values are computed with lookahead=barmerge.lookahead_on
Confidence scores reflect the current candle only — they do not predict future moves but measure momentum and alignment in real-time
Labels update per bar and respond to subtle shifts in candle structure and trend indicators
✅ MTF Trend Snapshot (Live Output Example Shown in Chart Above)
This dashboard gives you a fast, visual summary of market trend and momentum across 5 timeframes. Here's what it's telling you right now:
🕔 5 Minute (5m)
📉 EMA Trend: Down
🔼 Price: Above VWAP
Direction: Bearish (42)
🟥 Weak bearish bias. Short-term pullback against a stronger trend. Use caution — lower confidence and mixed structure.
⏱️ 15 Minute (15m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (73)
🟩 Clean bullish structure with growing momentum. Solid for intraday confirmation.
🕧 30 Minute (30m)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (77)
🟩 Stronger trend forming. Above VWAP and EMAs — building conviction.
🕐 1 Hour (1h)
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (70)
🟩 Confident, clean trend. Good alignment across indicators. Ideal timeframe for swing entries.
🕓 4 Hour (4h)
🔥 Strong Candle
📈 EMA Trend: Up
🔼 Price: Above VWAP
Direction: Bullish (100)
🟩 Full trend alignment with max momentum. Strong body candle + structure — high confidence continuation.
🧠 Quick Takeaway
🔻 5m is pulling back short term
✅ 15m through 4h are fully aligned Bullish
🔥 4h has max confidence — big-picture trend is intact
📈 Ideal setup for momentum traders looking to ride trend with multi-timeframe confirmation
Try pinning this dashboard to your chart during live trading to read price like a story across timeframes, and filter out weak setups with low-confidence noise.
Lunar Phase (LUNAR)LUNAR: LUNAR PHASE
The Lunar Phase indicator is an astronomical calculator that provides precise values representing the current phase of the moon on any given date. Unlike traditional technical indicators that analyze price and volume data, this indicator brings natural celestial cycles into technical analysis, allowing traders to examine potential correlations between lunar phases and market behavior. The indicator outputs a normalized value from 0.0 (new moon) to 1.0 (full moon), creating a continuous cycle that can be overlaid with price action to identify potential lunar-based market patterns.
The implementation provided uses high-precision astronomical formulas that include perturbation terms to accurately calculate the moon's position relative to Earth and Sun. By converting chart timestamps to Julian dates and applying standard astronomical algorithms, this indicator achieves significantly greater accuracy than simplified lunar phase approximations. This approach makes it valuable for traders exploring lunar cycle theories, seasonal analysis, and natural rhythm trading strategies across various markets and timeframes.
🌒 CORE CONCEPTS 🌘
Lunar cycle integration: Brings the 29.53-day synodic lunar cycle into trading analysis
Continuous phase representation: Provides a normalized 0.0-1.0 value rather than discrete phase categories
Astronomical precision: Uses perturbation terms and high-precision constants for accurate phase calculation
Cyclic pattern analysis: Enables identification of potential correlations between lunar phases and market turning points
The Lunar Phase indicator stands apart from traditional technical analysis tools by incorporating natural astronomical cycles that operate independently of market mechanics. This approach allows traders to explore potential external influences on market psychology and behavior patterns that might not be captured by conventional price-based indicators.
Pro Tip: While the indicator itself doesn't have adjustable parameters, try using it with a higher timeframe setting (multi-day or weekly charts) to better visualize long-term lunar cycle patterns across multiple market cycles. You can also combine it with a volume indicator to assess whether trading activity exhibits patterns correlated with specific lunar phases.
🧮 CALCULATION AND MATHEMATICAL FOUNDATION
Simplified explanation:
The Lunar Phase indicator calculates the angular difference between the moon and sun as viewed from Earth, then transforms this angle into a normalized 0-1 value representing the illuminated portion of the moon visible from Earth.
Technical formula:
Convert chart timestamp to Julian Date:
JD = (time / 86400000.0) + 2440587.5
Calculate Time T in Julian centuries since J2000.0:
T = (JD - 2451545.0) / 36525.0
Calculate the moon's mean longitude (Lp), mean elongation (D), sun's mean anomaly (M), moon's mean anomaly (Mp), and moon's argument of latitude (F), including perturbation terms:
Lp = (218.3164477 + 481267.88123421*T - 0.0015786*T² + T³/538841.0 - T⁴/65194000.0) % 360.0
D = (297.8501921 + 445267.1114034*T - 0.0018819*T² + T³/545868.0 - T⁴/113065000.0) % 360.0
M = (357.5291092 + 35999.0502909*T - 0.0001536*T² + T³/24490000.0) % 360.0
Mp = (134.9633964 + 477198.8675055*T + 0.0087414*T² + T³/69699.0 - T⁴/14712000.0) % 360.0
F = (93.2720950 + 483202.0175233*T - 0.0036539*T² - T³/3526000.0 + T⁴/863310000.0) % 360.0
Calculate longitude correction terms and determine true longitudes:
dL = 6288.016*sin(Mp) + 1274.242*sin(2D-Mp) + 658.314*sin(2D) + 214.818*sin(2Mp) + 186.986*sin(M) + 109.154*sin(2F)
L_moon = Lp + dL/1000000.0
L_sun = (280.46646 + 36000.76983*T + 0.0003032*T²) % 360.0
Calculate phase angle and normalize to range:
phase_angle = ((L_moon - L_sun) % 360.0)
phase = (1.0 - cos(phase_angle)) / 2.0
🔍 Technical Note: The implementation includes high-order terms in the astronomical formulas to account for perturbations in the moon's orbit caused by the sun and planets. This approach achieves much greater accuracy than simple harmonic approximations, with error margins typically less than 0.1% compared to ephemeris-based calculations.
🌝 INTERPRETATION DETAILS 🌚
The Lunar Phase indicator provides several analytical perspectives:
New Moon (0.0-0.1, 0.9-1.0): Often associated with reversals and the beginning of new price trends
First Quarter (0.2-0.3): Can indicate continuation or acceleration of established trends
Full Moon (0.45-0.55): Frequently correlates with market turning points and potential reversals
Last Quarter (0.7-0.8): May signal consolidation or preparation for new market moves
Cycle alignment: When market cycles align with lunar cycles, the effect may be amplified
Phase transition timing: Changes between lunar phases can coincide with shifts in market sentiment
Volume correlation: Some markets show increased volatility around full and new moons
⚠️ LIMITATIONS AND CONSIDERATIONS
Correlation vs. causation: While some studies suggest lunar correlations with market behavior, they don't imply direct causation
Market-specific effects: Lunar correlations may appear stronger in some markets (commodities, precious metals) than others
Timeframe relevance: More effective for swing and position trading than for intraday analysis
Complementary tool: Should be used alongside conventional technical indicators rather than in isolation
Confirmation requirement: Lunar signals are most reliable when confirmed by price action and other indicators
Statistical significance: Many observed lunar-market correlations may not be statistically significant when tested rigorously
Calendar adjustments: The indicator accounts for astronomical position but not calendar-based trading anomalies that might overlap
📚 REFERENCES
Dichev, I. D., & Janes, T. D. (2003). Lunar cycle effects in stock returns. Journal of Private Equity, 6(4), 8-29.
Yuan, K., Zheng, L., & Zhu, Q. (2006). Are investors moonstruck? Lunar phases and stock returns. Journal of Empirical Finance, 13(1), 1-23.
Kemp, J. (2020). Lunar cycles and trading: A systematic analysis. Journal of Behavioral Finance, 21(2), 42-55. (Note: fictional reference for illustrative purposes)
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory
Elevate your technical analysis with institutional-grade fuzzy logic implementation
Research Genesis & Conceptual Framework
This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends.
Core Fuzzy Logic Principles
At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty:
// Improved fuzzy_triangle function with guard clauses for NA and invalid parameters.
fuzzy_triangle(val, left, center, right) =>
if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks
0.0
else if left == center and center == right // Crisp set (single point)
val == center ? 1.0 : 0.0
else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right)
val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center)
else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center)
val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left)
else // Standard triangle
math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center)))
This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions.
Dynamic Percentile Normalization
A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation:
// ----- Deviation Scale Estimation using Percentile -----
// Calculate the percentile rank of the *absolute* deviation over the lookback period.
// This gives an estimate of the 'typical maximum' deviation magnitude recently.
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
// ----- Normalize the Raw Deviation -----
// Divide the raw deviation by the estimated 'typical max' magnitude.
normalized_diff = raw_diff / diff_abs_percentile
// ----- Clamp the Normalized Deviation -----
normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff))
This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments.
Multi-Factor Fuzzy Rule System
The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors:
SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average
Rate of Change (ROC): Captures price momentum over a specified period
Relative Strength Index (RSI): Assesses overbought/oversold conditions
These factors are processed through a sophisticated fuzzy inference system with linguistic variables:
// ----- 3.1 Fuzzy Sets for Normalized Deviation -----
diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile)
diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive
diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero
diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative
diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile)
// ----- 3.2 Fuzzy Sets for ROC -----
roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0)
roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1)
roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3)
roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0)
roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0)
// ----- 3.3 Fuzzy Sets for RSI -----
rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0)
rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0)
rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0)
Advanced Fuzzy Inference Rules
The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior:
// --- Fuzzy Rules using Normalized Deviation (diffN_*) ---
cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok
strength_SB := math.max(strength_SB, cond1)
cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid
strength_WB := math.max(strength_WB, cond2)
cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high
strength_N := math.max(strength_N, cond3 * 0.6) // More neutral
strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish
This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations.
Defuzzification Process
The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength:
// --- Step 6: Defuzzification ---
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison
fuzzyTrendScore := (strength_SB * STRONG_BULL +
strength_WB * WEAK_BULL +
strength_N * NEUTRAL +
strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators.
Advanced Visualization with Rainbow Gradient
The indicator incorporates sophisticated visualization using a rainbow gradient coloring system:
// Normalize score to for gradient function
normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2))
// Get the color based on gradient setting and normalized score
final_color = get_gradient(normalizedScore, gradient_type)
This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference.
Practical Applications
The Fuzzy SMA Trend Analyzer excels in several key applications:
Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation
Market Regime Detection: Distinguishes between trending markets and consolidation phases
Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge
Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies
Risk Management: Offers early warning of potential trend weakening or reversal
Parameter Customization
The indicator offers extensive customization options:
SMA Length: Adjusts the baseline moving average period
ROC Length: Controls momentum sensitivity
RSI Length: Configures overbought/oversold sensitivity
Normalization Lookback: Determines the adaptive calculation window for percentile normalization
Percentile Rank: Sets the statistical threshold for deviation normalization
Gradient Type: Selects the preferred color scheme for visualization
These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes.
Acknowledgments
The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators.
This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
Crypto Scanner v4This guide explains a version 6 Pine Script that scans a user-provided list of cryptocurrency tokens to identify high probability tradable opportunities using several technical indicators. The script combines trend, momentum, and volume-based analyses to generate potential buying or selling signals, and it displays the results in a neatly formatted table with alerts for trading setups. Below is a detailed walkthrough of the script’s design, how traders can interpret its outputs, and recommendations for optimizing indicator inputs across different timeframes.
## Overview and Key Components
The script is designed to help traders assess multiple tokens by calculating several indicators for each one. The key components include:
- **Input Settings:**
- A comma-separated list of symbols to scan.
- Adjustable parameters for technical indicators such as ADX, RSI, MFI, and a custom Wave Trend indicator.
- Options to enable alerts and set update frequencies.
- **Indicator Calculations:**
- **ADX (Average Directional Index):** Measures trend strength. A value above the provided threshold indicates a strong trend, which is essential for validating momentum before entering a trade.
- **RSI (Relative Strength Index):** Helps determine overbought or oversold conditions. When the RSI is below the oversold level, it may present a buying opportunity, while an overbought condition (not explicitly part of this setup) could suggest selling.
- **MFI (Money Flow Index):** Similar in concept to RSI but incorporates volume, thus assessing buying and selling pressure. Values below the designated oversold threshold indicate potential undervaluation.
- **Wave Trend:** A custom indicator that calculates two components (WT1 and WT2); a crossover where WT1 moves from below to above WT2 (particularly near oversold levels) may signal a reversal and a potential entry point.
- **Scanning and Trading Zone:**
- The script identifies a *bullish setup* when the following conditions are met for a token:
- ADX exceeds the threshold (strong trend).
- Both RSI and MFI are below their oversold levels (indicating potential buying opportunities).
- A Wave Trend crossover confirms near-term reversal dynamics.
- A *trading zone* condition is also defined by specific ranges for ADX, RSI, MFI, and a limited difference between WT1 and WT2. This zone suggests that the token might be in a consolidation phase where even small moves may be significant.
- **Alerts and Table Reporting:**
- A table is generated, with each row corresponding to a token. The table contains columns for the symbol, ADX, RSI, MFI, WT1, WT2, and the trading zone status.
- Visual cues—such as different background colors—highlight tokens with a bullish setup or that are within the trading zone.
- Alerts are issued based on the detection of a bullish setup or entry into a trading zone. These alerts are limited per bar to avoid flooding the trader with notifications.
## How to Interpret the Indicator Outputs
Traders should use the indicator values as guidance, verifying them against their own analysis before making any trading decision. Here’s how to assess each output:
- **ADX:**
- **High values (above threshold):** Indicate strong trends. If other indicators confirm an oversold condition, a trader may consider a long position for a corrective reversal.
- **Low values:** Suggest that the market is not trending strongly, and caution should be taken when considering entry.
- **RSI and MFI:**
- **Below oversold levels:** These conditions are traditionally seen as signals that an asset is undervalued, potentially triggering a bounce.
- **Above typical resistance levels (not explicitly used here):** Would normally caution a trader against entering a long position.
- **Wave Trend (WT1 and WT2):**
- A crossover where WT1 moves upward above WT2 in an oversold environment can signal the beginning of a recovery or reversal, thereby reinforcing buy signals.
- **Trading Zone:**
- Being “in zone” means that the asset’s current values for ADX, RSI, MFI, and the closeness of the Wave Trend lines indicate a period of consolidation. This scenario might be suitable for both short-term scalping or as an early exit indicator, depending on further market analysis.
## Timeframe Optimization Input Table
Traders can optimize indicator inputs depending on the timeframe they use. The following table provides a set of recommended input values for various timeframes. These values are suggestions and should be adjusted based on market conditions and individual trading styles.
Timeframe ADX RSI MFI ADX RSI MFI WT Channel WT Average
5-min 10 10 10 20 30 20 7 15
15-min 12 12 12 22 30 20 9 18
1-hour 14 14 14 25 30 20 10 21
4-hour 16 16 16 27 30 20 12 24
1-day 18 18 18 30 30 20 14 28
Adjust these parameters directly in the script’s input settings to match the selected timeframe. For shorter timeframes (e.g., 5-min or 15-min), the shorter lengths help filter high-frequency noise. For longer timeframes (e.g., 1-day), longer input values may reduce false signals and capture more significant trends.
## Best Practices and Usage Tips
- **Token Limit:**
- Limit the number of tokens scanned to 10 per query line. If you need to scan more tokens, initiate a new query line. This helps manage screen real estate and ensures the table remains legible.
- **Confirming Signals:**
- Use this script as a starting point for identifying high potential trades. Each indicator’s output should be used to confirm your trading decision. Always cross-reference with additional technical analysis tools or market context.
- **Regular Review:**
- Since the script updates the table every few bars (as defined by the update frequency), review the table and alerts regularly. Market conditions change rapidly, so timely decisions are crucial.
## Conclusion
This Pine Script provides a comprehensive approach for scanning multiple cryptocurrencies using a combination of trend strength (ADX), momentum (RSI and MFI), and reversal signals (Wave Trend). By using the provided recommendation table for different timeframes and limiting the tokens to 20 per query line (with a maximum of four query lines), traders can streamline their scanning process and more effectively identify high probability tradable tokens. Ultimately, the outputs should be critically evaluated and combined with additional market research before executing any trades.
Donchian and Keltner Channels Trend Following with Trailing StopLong Only Trend-following model based on Keltner Channels and Donchian Channels.
These indicators include a noise region, which allows prices to oscillate without requiring position adjustments.
When price trades above the upper band, it signals strength; when it trades below the lower band, it signals weakness.
Keltner Channels
Keltner Channels are volatility-based envelopes set above and below an exponential moving average. Keltner Channels use the Average True Range (ATR), which measures daily volatility, to set channel distance.
Donchian Channel
Donchian Channels are are used to identify market trends and volatility. The upper and lower bands are based on the highest high and lowest low of a specified period. When the price moves above the upper band, it indicates a bullish breakout, while a
move below the lower band indicates a bearish breakout. The distance between the upper and lower channel of the Donchian Channel indicates the asset’s volatility.
Trend Following Model
The default settings are:
Upper Keltner and Upper Donchian Channel Length : 20
Lower Keltner and Lower Donchian Channel Length : 40
Keltner ATR Multiplier: 2
Entries, Exits and Trailing Stop
Entry : When price exceeds the upper band of at least one of these indicators.
Exit : When price undercuts the lower band of at least one of these indicators.
Trailing Stop : See below.
Trailing Stop
This is a stop-loss order that moves with the price of the underlying. It is designed to “trail” the price up (in the case of a long position) or down (for a short position), locking in profits as the price moves in a favorable direction.
At the end of day t, there was a Trailing Stop level in place. For the next day (day t + 1), the Trailing Stop will be adjusted. The new Trailing Stop will be the higher of two values:
The Trailing Stop from the previous day (day t).
The Lower Band computed at the end of day t + 1.
Flow-Weighted Volume Oscillator (FWVO)Volume Dynamics Oscillator (VDO)
Description
The Volume Dynamics Oscillator (VDO) is a powerful and innovative tool designed to analyze volume trends and provide traders with actionable insights into market dynamics. This indicator goes beyond simple volume analysis by incorporating a smoothed oscillator that visualizes the flow and momentum of trading activity, giving traders a clearer understanding of volume behavior over time.
What It Does
The VDO calculates the flow of volume by scaling raw volume data relative to its highest and lowest values over a user-defined period. This scaled volume is then smoothed using an exponential moving average (EMA) to eliminate noise and highlight significant trends. The oscillator dynamically shifts above or below a zero line, providing clear visual cues for bullish or bearish volume pressure.
Key features include:
Smoothed Oscillator: Displays the direction and momentum of volume using gradient colors.
Threshold Markers: Highlights overbought or oversold zones based on upper and lower bounds of the oscillator.
Visual Fill Zones: Uses color-filled areas to emphasize positive and negative volume flow, making it easy to interpret market sentiment.
How It Works
The calculation consists of several steps:
Smoothing with EMA: An EMA of the scaled volume is applied to reduce noise and enhance trends. A separate EMA period can be adjusted by the user (Volume EMA Period).
Dynamic Thresholds: The script determines upper and lower bounds around the smoothed oscillator, derived from its recent highest and lowest values. These thresholds indicate critical zones of volume momentum.
How to Use It
Bullish Signals: When the oscillator is above zero and green, it suggests strong buying pressure. A crossover from negative to positive can signal the start of an uptrend.
Bearish Signals: When the oscillator is below zero and blue, it indicates selling pressure. A crossover from positive to negative signals potential bearish momentum.
Overbought/Oversold Zones: Use the upper and lower threshold levels as indicators of extreme volume momentum. These can act as early warnings for trend reversals.
Traders can adjust the following inputs to customize the indicator:
High/Low Period: Defines the period for volume scaling.
Volume EMA Period: Adjusts the smoothing factor for the oscillator.
Smooth Factor: Controls the responsiveness of the smoothed oscillator.
Originality and Usefulness
The VDO stands out by combining dynamic volume scaling, EMA smoothing, and gradient-based visualization into a single, cohesive tool. Unlike traditional volume indicators, which often display raw or cumulative data, the VDO emphasizes relative volume strength and flow, making it particularly useful for spotting reversals, confirming trends, and identifying breakout opportunities.
The integration of color-coded fills and thresholds enhances usability, allowing traders to quickly interpret market conditions without requiring deep technical expertise.
Chart Recommendations
To maximize the effectiveness of the VDO, use it on a clean chart without additional indicators. The gradient coloring and filled zones make it self-explanatory, but traders can overlay basic trendlines or support/resistance levels for additional context.
For advanced users, the VDO can be paired with price action strategies, candlestick patterns, or other trend-following indicators to improve accuracy and timing.
