Ehlers SuperSmootherJohn F. Ehlers has provided the SuperSmoother filter in several of his works, including his book "Cyclical Analytics for Traders", Chapter 3.
The SuperSmoother filter is utilized whenever one might typically apply a moving average of any kind. The outcome is that the output signal from the SuperSmoother filter displays significantly less lag compared to an equivalent amount of smoothing from a moving average. The lag difference between a moving average and the SuperSmoother filter becomes even more pronounced when critical periods are extended.
Market data contains noise, and the purpose of smoothing filters is to mitigate this noise. In fact, there are various types of noise inherent in market data. One type of noise is systemic, originating from random trading activities. Another type is aliasing noise, which arises due to the use of discrete data. Aliasing noise dominates the data when considering shorter cycle durations.
It's tempting to perceive market data as a continuous wave, but that's a misconception. Taking the closing price as representative of a bar provides just a single data point. Whether you opt for the midpoint between the high and low instead of the closing price, you're still limited to one sample per bar. Given the discrete nature of this data, certain spectral implications must be considered. For instance, the shortest feasible analysis period (without aliasing) is a two-bar cycle. This is referred to as the Nyquist frequency, at 0.5 cycles per sample.
An ideally sampled two-beat sinusoidal cycle becomes rectified when discretized. However, peak sampling for the cycle isn't always guaranteed, and interference between the sampling rate and the data frequency results in aliasing noise. This noise decreases as the data period lengthens. For example, a four-beat cycle implies four samples per cycle. With more samples, the sampled data provides a better representation of the sinusoidal component. The replica becomes even more accurate for an eight-bar data component. The increased precision of discrete data signifies that aliasing noise decreases as cycle durations expand.
A smoothing filter should possess the selectivity to reduce the aliasing noise below systemic noise levels. Given that aliasing noise increases by 6 dB per octave above the filter's selected cutoff frequency and the SuperSmoother's attenuation rate is 12 dB per octave, the SuperSmoother filter emerges as an effective tool to virtually eliminate aliasing noise in its output signal.
There are already several SuperSmoother indicators on Tradingview, but I like to structure the code and highlight the main components as functions rather than hiding them in the code. I hope this is useful for those who are starting to learn Pine Script.
Cari dalam skrip untuk "wave"
YinYang RSI Volume Trend StrategyThere are many strategies that use RSI or Volume but very few that take advantage of how useful and important the two of them combined are. This strategy uses the Highs and Lows with Volume and RSI weighted calculations on top of them. You may be wondering how much of an impact Volume and RSI can have on the prices; the answer is a lot and we will discuss those with plenty of examples below, but first…
How does this strategy work?
It’s simple really, when the purchase source crosses above the inner low band (red) it creates a Buy or Long. This long has a Trailing Stop Loss band (the outer low band that's also red) that can be adjusted in the Settings. The Stop Loss is based on a % of the inner low band’s price and by default it is 0.1% lower than the inner band’s price. This Stop Loss is not only a stop loss but it can also act as a Purchase Available location.
You can get back into a trade after a stop loss / take profit has been hit when your Reset Purchase Availability After condition has been met. This can either be at Stop Loss, Entry or None.
It is advised to allow it to reset in case the stop loss was a fake out but the call was right. Sometimes it may trigger stop loss multiple times in a row, but you don’t lose much on stop loss and you gain lots when the call is right.
The Take Profit location is the basis line (white). Take Profit occurs when the Exit Source (close, open, high, low or other) crosses the basis line and then on a different bar the Exit Source crosses back over the basis line. For example, if it was a Long and the bar’s Exit Source closed above the basis line, and then 2 bars later its Exit Source closed below the basis line, Take Profit would occur. You can disable Take Profit in Settings, but it is very useful as many times the price will cross the Basis and then correct back rather than making it all the way to the opposing zone.
Longs:
If for instance your Long doesn’t need to Take Profit and instead reaches the top zone, it will close the position when it crosses above the inner top line (green).
Please note you can change the Exit Source too which is what source (close, open, high, low) it uses to end the trades.
The Shorts work the same way as the Long but just opposite, they start when the purchase source crosses under the inner upper band (green).
Shorts:
Shorts take profit when it crosses under the basis line and then crosses back.
Shorts will Stop loss when their outer upper band (green) is crossed with the Exit Source.
Short trades are completed and closed when its Exit Source crosses under the inner low red band.
So, now that you understand how the strategy works, let’s discuss why this strategy works and how it is profitable.
First we will discuss Volume as we deem it plays a much bigger role overall and in our strategy:
As I’m sure many of you know, Volume plays a huge factor in how much something moves, but it also plays a role in the strength of the movement. For instance, let’s look at two scenarios:
Bitcoin’s price goes up $1000 in 1 Day but the Volume was only 10 million
Bitcoin’s price goes up $200 in 1 Day but the Volume was 40 million
If you were to only look at the price, you’d say #1 was more important because the price moved x5 the amount as #2, but once you factor in the volume, you know this is not true. The reason why Volume plays such a huge role in Price movement is because it shows there is a large Limit Order battle going on. It means that both Bears and Bulls believe that price is a good time to Buy and Sell. This creates a strong Support and Resistance price point in this location. If we look at scenario #2, when there is high volume, especially if it is drastically larger than the average volume Bitcoin was displaying recently, what can we decipher from this? Well, the biggest take away is that the Bull’s won the battle, and that likely when that happens we will see bullish movement continuing to happen as most of the Bears Limit Orders have been fulfilled. Whereas with #2, when large price movement happens and Bitcoin goes up $1000 with low volume what can we deduce? The main takeaway is that Bull’s pressured the price up with Market Orders where they purchased the best available price, also what this means is there were very few people who were wanting to sell. This generally dictates that Whale Limit orders for Sells/Shorts are much higher up and theres room for movement, but it also means there is likely a whale that is ready to dump and crash it back down.
You may be wondering, what did this example have to do with YinYang RSI Volume Trend Strategy? Well the reason we’ve discussed this is because we use Volume multiple times to apply multiplications in our calculations to add large weight to the price when there is lots of volume (this is applied both positively and negatively). For instance, if the price drops a little and there is high volume, our strategy will move its bounds MUCH lower than the price actually dropped, and if there was low volume but the price dropped A LOT, our strategy will only move its bounds a little. We believe this reflects higher levels of price accuracy than just price alone based on the examples described above.
Don’t believe us?
Here is with Volume NOT factored in (VWMA = SMA and we remove our Volume Filter calculation):
Which produced -$2880 Profit
Here is with our Volume factored in:
Which produced $553,000 (55.3%)
As you can see, we wen’t from $-2800 profit with volume not factored to $553,000 with volume factored. That's quite a big difference! (Please note previous success does not predict future success we are simply displaying the $ amounts as example).
Now how about RSI and why does it matter in this strategy?
As I’m sure most of you are aware, RSI is one of the leading indicators used in trading. For this reason we figured it would only make sense to incorporate it into our calculations. We fiddled with RSI for quite awhile and sometimes what logically seems to be the right way to use it isn’t. Now, because of this, our RSI calculation is a little odd, but basically what we’re doing is we calculate the RSI, then turn it into a percentage (between 0-1) that can easily be multiplied to the price point we need. The price point we use is the difference between our high purchase zone and our low purchase zone. This allows us to see how much price movement there is between zones. We multiply our zone size with our RSI multiplication and we get the amount we will add +/- to our basis line (white line). This officially creates the NEW high and low purchase zones that we are actually using and displaying in our trades.
If you found that confusing, here are some examples to why it is an important calculation for this strategy:
Before RSI factored in:
Which produced 27.8% Profit
After RSI factored in:
Which produced 553% Profit
As you can see, the RSI makes not only the purchase zones more accurate, but it also greatly increases the profit the strategy is able to make. It also helps ensure an relatively linear profit slope so you know it is reliable with its trades.
This strategy can work on pretty much anything, but you should tweak the values a bit for each pair you are trading it with for best results.
We hope you can find some use out of this simple but effective strategy, if you have any questions, comments or concerns please let us know.
HAPPY TRADING!
[MAD] MindreaderHi,
This is a multiband indicator that shows you liquid support and resistance ranges based on growing offsets and growing ATR channels.
In the end, when setup well, you can make, based on historical observations, estimates of how traders will react, maybe identical again.
How to use:
Setup:
Activate the two checkboxes for centerline and All_Lines
Start with the middle line to establish the general direction of the asset.
With the 6 following options, you try to match the trends in the outer bands as good as possible.
Small changes can be made by till you have best fitting overall bands. I tried to make small steppingsize to visual setup very easy. Change a bit... wait look,... change a bit, wait look...
Deactivate the two setup boxes and continue with setting up the colors.
Have fun figuring out the perfect wave !!
Random Market «NoaTrader»This is a simple script for generating random data shown as candles. The purpose of it is the following:
1- To see what works here. If everything is random and something is working, is there really any reason behind it?
2- To see what NOT works here! this is probably the most interesting part. Human behaviors are more likely to generate bubbles so theories like Elliot waves don't work here but do work on real charts! that is an interesting thing!
3- To find out the exact parameters defining a market which is a bit more complicated and deeper. If you look closely to candles you can say that it is not natural like other candle charts. If you have watched different timeframes enough, you have a sense of the difference between them. Why? What is natural? The volume? The wicks? The seasonality? The amount of randomness? The cycle of momentum change? ... If you can generate candles more similar to real ones it means you know the details of market much better!
P.S: the random function of trading view works differently on different symbols and timeframes..
Price Depth Analysis to the MAHello Traders! Today, I bring you an indicator that can greatly assist you in your trading. This indicator aims to analyze the Expansion and Contraction process of the price in relation to a moving average. We refer to "Expansion" when the price moves away from the moving average; a significant expansion could signal that the asset is in a strong trend. On the other hand, when we refer to "Contraction", it's when the price approaches or returns to the moving average. A contraction could signal that the asset is losing momentum and might be preparing for a trend change or consolidation.
To use the indicator, the first thing you need to do is define the type of analysis you want to perform (from the indicator settings) whether you want to evaluate prices above the moving average or below. You should also select the type of moving average and its period.
The indicator will search for the maximum distance in all the chart bars, which will be represented with a yellow label.
From that value, the indicator will generate a certain number of proportional levels (configurable up to 20) and will count all the bars that reached each level. This will be represented in a table showing both the number of bars that reached each range and the percentage in relation to the total bars of all ranges.
Additionally, there's the possibility to view the ranges directly for the current price, providing a good reference.
>> Alerts:
The indicator comes with alerts that notify traders about specific price movements in relation to a moving average (MA). These alerts are triggered when the price enters different ranges, either above or below the MA.
>> Settings:
- Type of Analysis: Users can choose to analyze the price either above or below the MA.
- Length of the moving average: Length of the MA.
- Source of the moving average: Source to calculate the MA (e.g., close, open).
- Type of moving average: Type of MA (SMA, EMA, WMA, VWMA, HMA).
- Show Moving Average: Option to display or hide the MA on the chart.
- Number of levels: Number of levels or ranges to categorize the distance between the price and the MA.
- Number of decimals: Number of decimals to display in labels and tables.
- Show Ranges: Option to display or hide the ranges on the chart.
- Extend Range: Extension of the ranges into future bars.
- Range Fill Transparency: Transparency of the range fill.
>> Potential Utility of the Indicator:
- Entry and Exit Optimization:
By understanding the percentages of each range, traders can identify optimal levels to enter or exit a trade, maximizing profits and minimizing losses.
- Risk Management:
Range percentages can help determine market volatility. A range with a high percentage indicates greater volatility, which can be useful for setting wider stop losses or adjusting position size.
- Overbought and Oversold Zone Identification:
If a price is at the upper or lower extreme of its percentage range, it may indicate overbought or oversold conditions, respectively. These zones can be opportunities for counter-trend trades.
- Momentum Assessment:
A rapid change in range percentages can indicate strong momentum in a particular direction. Traders can use this information to ride the momentum wave or prepare for a potential reversal.
- False Signal Filtering:
By combining range percentage knowledge with other indicators, traders can filter out signals that might be less reliable, thus improving trade accuracy.
- Strategic Planning:
Knowing range percentages allows traders to adapt their strategies according to market conditions. For instance, in a market with narrow ranges and low percentages, they might opt for range strategies. In markets with wide ranges and high percentages, they might look for trend strategies.
- Trend Strength Evaluation:
If range percentages show that the price consistently stays at one end of the range, this may signal a strong and sustained trend.
- Improved Trading Discipline:
By basing trading decisions on quantitative data like range percentages, traders can trade more objectively and disciplined, avoiding impulsive or emotion-based decisions.
>> Future Indicator Update:
- In future versions, we plan to incorporate a detailed analysis based on the historical behavior of candles after the price enters a specific range. For instance, if after an upward movement the price enters a certain range and historically, the next candle tends to be bearish in a high percentage of occasions, this information will be highlighted and presented clearly to the user. The idea behind this addition is to provide traders with a statistical edge, allowing them to anticipate potential market movements with greater accuracy. Moreover, this information could be used to seek trading opportunities in smaller timeframes, aligning the trade direction based on the probability of this mentioned candle.
>> Conclusions:
- In summary, a detailed understanding of each range's percentages in an indicator provides traders with a valuable tool to analyze the market, make informed decisions, and enhance their trading. By grasping the significance of these percentages, traders can adapt their strategies and techniques to fully leverage the opportunities the market presents.
Indicator Based Market Exposure (IBME)The Indicator Based Market Exposure (IBME) system was created by Big Wave Chartist as a way to navigate the markets using a confluence of three different signals to determine when the "internals" of the market are in your favor and how heavily invested to be at any point. The idea of the system is also to flash warning signs when the market internals are beginning to deteriorate so as to take a defensive stance. Of course this system can be strictly adhered to, or it can be incorporated into a more discretionary style of trading, and be combined with progressive exposure into (and out of) the market as positions gain (or lose) traction.
The IBME displays a straightforward action signal based on the combination of the 3 separate signals:
Green 🟢 Full size-longs permitted
Yellow 🟡 Pilot positions permitted
Red 🔴 No longs allowed
So let's get into the signals used:
McClellan Summation Index
Net New Highs/Lows
Net New Highs Crossover
McClellan Summation Index (MSI)
The McClellan Summation Index is a long-term version of the McClellan Oscillator, which is a market breadth indicator based on stock advances and declines. Interpretation is similar to that of the McClellan Oscillator, except that it is more suited to intermediate to major trends and related reversals. The McClellan Summation Index can be calculated as the sum of all the daily values of the McClellan Oscillator. This is used along with the 10-sma to watch for a crossover indicating an uptrend or downtrend beginning.
Net New Highs/Lows
This is the net number of stocks making 52-week highs or lows. For instance, if there are 60 new 52-week highs and 20 new 52-week lows, the net number will be 40 net new 52 week highs. This signal is particularly useful in gauging breadth.
Net New Highs Crossover
This is the description of NNHC from the original separate version of this indicator created by HikoStory: "Net New Highs can guide you to increase or decrease your exposure based on the current market health. They are calculated by subtracting the new highs from the new lows, based on all stocks of the...NASDAQ. A positive value shows that the market is doing good, since more stocks are making new highs compared to new lows. A negative value shows that the market is doing bad, since more stocks are making new lows compared to new highs. Combined with a moving average you can see crossovers that can warn you early when there is a change in the current market health."
The default index for the IBME is the Nasdaq.
The IBME is meant to be used on a daily time frame chart, therefore the signal will only show on a daily time frame chart.
Display options include:
Show/hide individual signals
Table background/font color
Table size/placement
Recursive Micro Zigzag🎲 Overview
Zigzag is basic building block for any pattern recognition algorithm. This indicator is a research-oriented tool that combines the concepts of Micro Zigzag and Recursive Zigzag to facilitate a comprehensive analysis of price patterns. This indicator focuses on deriving zigzag on multiple levels in more efficient and enhanced manner in order to support enhanced pattern recognition.
The Recursive Micro Zigzag Indicator utilises the Micro Zigzag as the foundation and applies the Recursive Zigzag technique to derive higher-level zigzags. By integrating these techniques, this indicator enables researchers to analyse price patterns at multiple levels and gain a deeper understanding of market behaviour.
🎲 Concept:
Micro Zigzag Base : The indicator utilises the Micro Zigzag concept to capture detailed price movements within each candle. It allows for the visualisation of the sequential price action within the candle, aiding in pattern recognition at a micro level.
Basic implementation of micro zigzag can be found in this link - Micro-Zigzag
Recursive Zigzag Expansion : Building upon the Micro Zigzag base, the indicator applies the Recursive Zigzag concept to derive higher-level zigzags. Through recursive analysis of the Micro Zigzag's pivots, the indicator uncovers intricate patterns and trends that may not be evident in single-level zigzags.
Earlier implementations of recursive zigzag can be found here:
Recursive Zigzag
Recursive Zigzag - Trendoscope
And the libraries
rZigzag
ZigzagMethods
The major differences in this implementation are
Micro Zigzag Base - Earlier implementation made use of standard zigzag as base whereas this implementation uses Micro Zigzag as base
Not cap on Pivot depth - Earlier implementation was limited by the depth of level 0 zigzag. In this implementation, we are trying to build the recursive algorithm progressively so that there is no cap on the depth of level 0 zigzag. But, if we go for higher levels, there is chance of program timing out due to pine limitations.
These algorithms are useful in automatically spotting patterns on the chart including Harmonic Patterns, Chart Patterns, Elliot Waves and many more.
@tk · fractal rsi levels█ OVERVIEW
This script is an indicator that helps traders to identify the RSI Levels for multiple fractals wherever the current timeframe is. This script was based on RSI Levels, 20-30 & 70-80 by abdomi indicator, that calculates the Relative Strenght Index levels based on the asset's price and plots it into the chart, creating a "wave" style indicator. The core feature of this indicator is the fractal rays, so trader can visualize each of the oversold and overbought levels of multiple timeframe on the current timeframe that he is on. The indicator will plots multiple rays after the chart bars. indicating where is the oversold and overbought levels for others fractals.
█ MOTIVATION
Since the RSI Levels, 20-30 & 70-80 by abdomi indicator helps a lot to identify the possible price levels when the asset is oversold or overbought, I saw myself drawing multiple horizontal lines on these levels in lower timeframes so, in an uptrend or downtrend, I can try to get a pullback of these trends when the asset reaches oversold or overboght levels. So, I get the idea to make those lines visible in multiple timeframes so I don't need to draw it myself manually anymore.
█ CONCEPT
The trading concept to use this indicator is the concept to make entries on uptrend or downtrend pullbacks when the asset price reaches oversold or overbought levels. But this strategy don't works alone. It needs to be aligned together with others indicators like Exponential Moving Averages, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis.
█ CUSTOMIZATION
This indicator allows the trader to customize the following settings:
GENERAL
Text size
Changes the font size of the labels to improve accessibility.
Type: string
Options: `tiny`, `small`, `normal`, `large`.
Default: `small`
RSI LEVELS · SETTINGS
Pre-oversold Level
Changes the RSI Level to calculate the "pre-oversold" price level on the chart.
Type: int
Min: 1
Max: 49
Default: 33
Pre-overbought Level
Changes the RSI Level to calculate the "pre-overbought" price level on the chart.
Type: int
Min: 51
Max: 100
Default: 67
Show "Pre-over" Levels
Enables / Disables the pre-oversold and pre-overbought levels on the chart.
Type: bool
Default: true
FRACTAL RAYS · SETTINGS
Length
Changes the base length for the RSI calculation.
Type: int
Min: 1
Default: 14
Source
Changes the base source for the RSI calculation.
Type: float
Default: close
FRACTAL RAYS · STYLE
Ray Color
Changes the color of all fractal rays and its label.
Type: color
Default: color.rgb(187, 74, 207)
Ray Style
Changes the style of all fractal rays.
Type: string
Options: `line.style_solid`, `line.style_dashed`, `line.style_dotted`
Default: line.style_dotted
Ray Length
Changes the length of all fractal rays.
Type: int
Default: 15
FRACTAL RAYS · OVERSOLD
Oversold Level
Changes the base RSI Level for fractal rays calculation.
Type: int
Min: 1
Default: 30
Oversold Prefix
Customizes the fractal ray label with a prefix text.
Type: string
Default: 🚀
Oversold Suffix
Customizes the fractal ray label with a suffix text.
Type: string
Default: (empty)
FRACTAL RAYS · OVERBOUGHT
Overbought Level
Changes the base RSI Level for fractal rays calculation.
Type: int
Min: 1
Default: 70
Overbought Prefix
Customizes the fractal ray label with a prefix text.
Type: string
Default: 🐻
Overbought Suffix
Customizes the fractal ray label with a suffix text.
Type: string
Default: (empty)
FRACTAL RAYS · VISIBILITY RULES
These rules are applied for each of fractal rays so, the traders can choose what timeframes they wants to show the fractal rays for each of it. The rule will be applied as the following condition: `if timeframe != CURRENT_TIMEFRAME and timeframe <= CHOSEN_OPTION`. Actually, the fractal rays are on the chart but, isn't visible because it was applied a transparent color, so it is visually not on the chart to prevent chart's over polution.
LABELS
Show Labels on Price Scale
Shows labels on price scale.
Type: bool
Default: false
Show Price on Fractal Rays
Shows the RSI Level price on each of fractal rays respectively.
Type: bool
Default: false
█ EXTERNAL LIBRARIES
This script uses the `tk` library to calculate RSI Levels. It is a library that contains various functions that helps pine script developers to calculate RSI Levels.
█ FUNCTIONS
The library contains the following functions:
fn_fractalVisibilityRule(string visibilityRule)
Converts the fractal rays timeframe visibility rule label to timestamp int.
Parameters:
visibilityRule: (string) Fractal ray visibility rule label.
Returns: (int) Fractal ray visibility rule timestamp.
fn_requestFractal(string period, expression)
Converts the fractal rays timeframe visibility rule label to timestamp int.
Parameters:
period: (string) Timeframe period for the desired fractal.
expression: (mixed) Security expression that will be applied for calculation.
Returns: (mixed) A result determined by expression.
fn_plotRay(float y, string label, color color, int length)
Plots ray after chart bars for the current time.
Parameters:
period: (string) Timeframe period for the desired fractal.
expression: (mixed) Security expression that will be applied for calculation.
Returns: (void) This function only plots the elements into the chart
fn_plotRsiLevelRay(simple string period, simple int level, color color)
Plots RSI Levels ray after chart bars for the current time.
Parameters:
period: (simple string) Timeframe period.
level: (simple int) Relative Strength Index level.
color: (color) The color of both, ray and label text.
Returns: (void) This function only plots the elements into the chart
Market Time Cycle (Expo)█ Time Cycles Overview
Time cycles are a fascinating and powerful concept in the world of trading and investing. They are all about understanding and predicting the timing of market moves based on the premise that market events and price movements are not random, but instead occur in repeatable, cyclical patterns.
The Concept of Time Cycles: The foundation of time cycles lies in the belief that historical market patterns tend to repeat themselves over specific periods. These periods or cycles could be influenced by a myriad of factors like economic data releases, earnings reports, geopolitical events, or even natural human behavior. For example, some traders observe increased market activity around the start and end of a trading day, which is a form of intraday time cycle.
Understanding time cycles can provide traders with a roadmap, helping them anticipate potential trend shifts and make more informed decisions about when to buy or sell.
█ Indicator Overview
The Market Time Cycle (Expo) is designed to help traders track and analyze market cycles and generate signals for potential trading opportunities. It uses mathematical techniques to analyze market cycles and detect possible turning points. It does this by projecting the estimated cycle timeline and providing visual indications of cyclical phases through the use of color-coded lines and sine wave cycles.
Time cycles offer a compelling way to forecast market trends and time your trades better. By adding time cycles to your trading toolbox, you could potentially gain a new perspective on market movements and refine your trading strategy further. The indicator generates trading signals based on the sine wave's behavior. When the sine wave crosses certain thresholds, the indicator generates a signal suggesting a potential trading opportunity based on cycle behavior.
█ How to use
This indicator can be a valuable tool to help traders understand and predict market trends and time their trades more accurately. By visualizing the cyclic nature of markets, traders can better anticipate potential turning points and adjust their trading strategies accordingly. It helps traders to spot ideal entry and exit points based on the cyclical nature of financial markets.
█ Settings
You can customize the number of bars (NumbOfBars) that are taken into consideration for the cycle. Including a higher number of bars will provide more data, which can be helpful for analyzing long-term trends.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
BankNifty targets using VIX Version 2Original Idea Credit: Verified Market Waves
Hi,
After watching different videos online on how to get targets of BankNifty & Nifty decided to write this small script using VIX.
Nothing great but I really like the concept of getting high and low targets for the day or weekly or monthly or yearly.
What does the script do
1. We get closing of India Vix & BankNifty and Nifty
2. We get square root of Daily (365 days) | Weekly (52) | Monthly (12) & Yearly (1)
3. We divide India Vix closing with different square root to get a decimal value.
4. We use the derived value from step 3 which is used as % to calculate high and low values on BankNifty close price.
Small explanation via below screen shot to understand how to use it.
As always it comes with source code so you can modify as per your requirement.
Hope it helps 👍
Quinn-Fernandes Fourier Transform of Filtered Price [Loxx]Down the Rabbit Hole We Go: A Deep Dive into the Mysteries of Quinn-Fernandes Fast Fourier Transform and Hodrick-Prescott Filtering
In the ever-evolving landscape of financial markets, the ability to accurately identify and exploit underlying market patterns is of paramount importance. As market participants continuously search for innovative tools to gain an edge in their trading and investment strategies, advanced mathematical techniques, such as the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter, have emerged as powerful analytical tools. This comprehensive analysis aims to delve into the rich history and theoretical foundations of these techniques, exploring their applications in financial time series analysis, particularly in the context of a sophisticated trading indicator. Furthermore, we will critically assess the limitations and challenges associated with these transformative tools, while offering practical insights and recommendations for overcoming these hurdles to maximize their potential in the financial domain.
Our investigation will begin with a comprehensive examination of the origins and development of both the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter. We will trace their roots from classical Fourier analysis and time series smoothing to their modern-day adaptive iterations. We will elucidate the key concepts and mathematical underpinnings of these techniques and demonstrate how they are synergistically used in the context of the trading indicator under study.
As we progress, we will carefully consider the potential drawbacks and challenges associated with using the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter as integral components of a trading indicator. By providing a critical evaluation of their computational complexity, sensitivity to input parameters, assumptions about data stationarity, performance in noisy environments, and their nature as lagging indicators, we aim to offer a balanced and comprehensive understanding of these powerful analytical tools.
In conclusion, this in-depth analysis of the Quinn-Fernandes Fourier Transform and the Hodrick-Prescott Filter aims to provide a solid foundation for financial market participants seeking to harness the potential of these advanced techniques in their trading and investment strategies. By shedding light on their history, applications, and limitations, we hope to equip traders and investors with the knowledge and insights necessary to make informed decisions and, ultimately, achieve greater success in the highly competitive world of finance.
█ Fourier Transform and Hodrick-Prescott Filter in Financial Time Series Analysis
Financial time series analysis plays a crucial role in making informed decisions about investments and trading strategies. Among the various methods used in this domain, the Fourier Transform and the Hodrick-Prescott (HP) Filter have emerged as powerful techniques for processing and analyzing financial data. This section aims to provide a comprehensive understanding of these two methodologies, their significance in financial time series analysis, and their combined application to enhance trading strategies.
█ The Quinn-Fernandes Fourier Transform: History, Applications, and Use in Financial Time Series Analysis
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique developed by John J. Quinn and Mauricio A. Fernandes in the early 1990s. It builds upon the classical Fourier Transform by introducing an adaptive approach that improves the identification of dominant frequencies in noisy signals. This section will explore the history of the Quinn-Fernandes Fourier Transform, its applications in various domains, and its specific use in financial time series analysis.
History of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform was introduced in a 1993 paper titled "The Application of Adaptive Estimation to the Interpolation of Missing Values in Noisy Signals." In this paper, Quinn and Fernandes developed an adaptive spectral estimation algorithm to address the limitations of the classical Fourier Transform when analyzing noisy signals.
The classical Fourier Transform is a powerful mathematical tool that decomposes a function or a time series into a sum of sinusoids, making it easier to identify underlying patterns and trends. However, its performance can be negatively impacted by noise and missing data points, leading to inaccurate frequency identification.
Quinn and Fernandes sought to address these issues by developing an adaptive algorithm that could more accurately identify the dominant frequencies in a noisy signal, even when data points were missing. This adaptive algorithm, now known as the Quinn-Fernandes Fourier Transform, employs an iterative approach to refine the frequency estimates, ultimately resulting in improved spectral estimation.
Applications of the Quinn-Fernandes Fourier Transform
The Quinn-Fernandes Fourier Transform has found applications in various fields, including signal processing, telecommunications, geophysics, and biomedical engineering. Its ability to accurately identify dominant frequencies in noisy signals makes it a valuable tool for analyzing and interpreting data in these domains.
For example, in telecommunications, the Quinn-Fernandes Fourier Transform can be used to analyze the performance of communication systems and identify interference patterns. In geophysics, it can help detect and analyze seismic signals and vibrations, leading to improved understanding of geological processes. In biomedical engineering, the technique can be employed to analyze physiological signals, such as electrocardiograms, leading to more accurate diagnoses and better patient care.
Use of the Quinn-Fernandes Fourier Transform in Financial Time Series Analysis
In financial time series analysis, the Quinn-Fernandes Fourier Transform can be a powerful tool for isolating the dominant cycles and frequencies in asset price data. By more accurately identifying these critical cycles, traders can better understand the underlying dynamics of financial markets and develop more effective trading strategies.
The Quinn-Fernandes Fourier Transform is used in conjunction with the Hodrick-Prescott Filter, a technique that separates the underlying trend from the cyclical component in a time series. By first applying the Hodrick-Prescott Filter to the financial data, short-term fluctuations and noise are removed, resulting in a smoothed representation of the underlying trend. This smoothed data is then subjected to the Quinn-Fernandes Fourier Transform, allowing for more accurate identification of the dominant cycles and frequencies in the asset price data.
By employing the Quinn-Fernandes Fourier Transform in this manner, traders can gain a deeper understanding of the underlying dynamics of financial time series and develop more effective trading strategies. The enhanced knowledge of market cycles and frequencies can lead to improved risk management and ultimately, better investment performance.
The Quinn-Fernandes Fourier Transform is an advanced spectral estimation technique that has proven valuable in various domains, including financial time series analysis. Its adaptive approach to frequency identification addresses the limitations of the classical Fourier Transform when analyzing noisy signals, leading to more accurate and reliable analysis. By employing the Quinn-Fernandes Fourier Transform in financial time series analysis, traders can gain a deeper understanding of the underlying financial instrument.
Drawbacks to the Quinn-Fernandes algorithm
While the Quinn-Fernandes Fourier Transform is an effective tool for identifying dominant cycles and frequencies in financial time series, it is not without its drawbacks. Some of the limitations and challenges associated with this indicator include:
1. Computational complexity: The adaptive nature of the Quinn-Fernandes Fourier Transform requires iterative calculations, which can lead to increased computational complexity. This can be particularly challenging when analyzing large datasets or when the indicator is used in real-time trading environments.
2. Sensitivity to input parameters: The performance of the Quinn-Fernandes Fourier Transform is dependent on the choice of input parameters, such as the number of harmonic periods, frequency tolerance, and Hodrick-Prescott filter settings. Choosing inappropriate parameter values can lead to inaccurate frequency identification or reduced performance. Finding the optimal parameter settings can be challenging, and may require trial and error or a more sophisticated optimization process.
3. Assumption of stationary data: The Quinn-Fernandes Fourier Transform assumes that the underlying data is stationary, meaning that its statistical properties do not change over time. However, financial time series data is often non-stationary, with changing trends and volatility. This can limit the effectiveness of the indicator and may require additional preprocessing steps, such as detrending or differencing, to ensure the data meets the assumptions of the algorithm.
4. Limitations in noisy environments: Although the Quinn-Fernandes Fourier Transform is designed to handle noisy signals, its performance may still be negatively impacted by significant noise levels. In such cases, the identification of dominant frequencies may become less reliable, leading to suboptimal trading signals or strategies.
5. Lagging indicator: As with many technical analysis tools, the Quinn-Fernandes Fourier Transform is a lagging indicator, meaning that it is based on past data. While it can provide valuable insights into historical market dynamics, its ability to predict future price movements may be limited. This can result in false signals or late entries and exits, potentially reducing the effectiveness of trading strategies based on this indicator.
Despite these drawbacks, the Quinn-Fernandes Fourier Transform remains a valuable tool for financial time series analysis when used appropriately. By being aware of its limitations and adjusting input parameters or preprocessing steps as needed, traders can still benefit from its ability to identify dominant cycles and frequencies in financial data, and use this information to inform their trading strategies.
█ Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
Another significant advantage of the HP Filter is its ability to adapt to changes in the underlying trend. This feature makes it particularly well-suited for analyzing financial time series, which often exhibit non-stationary behavior. By employing the HP Filter to smooth financial data, traders can more accurately identify and analyze the long-term trends that drive asset prices, ultimately leading to better-informed investment decisions.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
█ Combined Application of Fourier Transform and Hodrick-Prescott Filter
The integration of the Fourier Transform and the Hodrick-Prescott Filter in financial time series analysis can offer several benefits. By first applying the HP Filter to the financial data, traders can remove short-term fluctuations and noise, effectively isolating the underlying trend. This smoothed data can then be subjected to the Fourier Transform, allowing for the identification of dominant cycles and frequencies with greater precision.
By combining these two powerful techniques, traders can gain a more comprehensive understanding of the underlying dynamics of financial time series. This enhanced knowledge can lead to the development of more effective trading strategies, better risk management, and ultimately, improved investment performance.
The Fourier Transform and the Hodrick-Prescott Filter are powerful tools for financial time series analysis. Each technique offers unique benefits, with the Fourier Transform being adept at identifying dominant cycles and frequencies, and the HP Filter excelling at isolating long-term trends from short-term noise. By combining these methodologies, traders can develop a deeper understanding of the underlying dynamics of financial time series, leading to more informed investment decisions and improved trading strategies. As the financial markets continue to evolve, the combined application of these techniques will undoubtedly remain an essential aspect of modern financial analysis.
█ Features
Endpointed and Non-repainting
This is an endpointed and non-repainting indicator. These are crucial factors that contribute to its usefulness and reliability in trading and investment strategies. Let us break down these concepts and discuss why they matter in the context of a financial indicator.
1. Endpoint nature: An endpoint indicator uses the most recent data points to calculate its values, ensuring that the output is timely and reflective of the current market conditions. This is in contrast to non-endpoint indicators, which may use earlier data points in their calculations, potentially leading to less timely or less relevant results. By utilizing the most recent data available, the endpoint nature of this indicator ensures that it remains up-to-date and relevant, providing traders and investors with valuable and actionable insights into the market dynamics.
2. Non-repainting characteristic: A non-repainting indicator is one that does not change its values or signals after they have been generated. This means that once a signal or a value has been plotted on the chart, it will remain there, and future data will not affect it. This is crucial for traders and investors, as it offers a sense of consistency and certainty when making decisions based on the indicator's output.
Repainting indicators, on the other hand, can change their values or signals as new data comes in, effectively "repainting" the past. This can be problematic for several reasons:
a. Misleading results: Repainting indicators can create the illusion of a highly accurate or successful trading system when backtesting, as the indicator may adapt its past signals to fit the historical price data. This can lead to overly optimistic performance results that may not hold up in real-time trading.
b. Decision-making uncertainty: When an indicator repaints, it becomes challenging for traders and investors to trust its signals, as the signal that prompted a trade may change or disappear after the fact. This can create confusion and indecision, making it difficult to execute a consistent trading strategy.
The endpoint and non-repainting characteristics of this indicator contribute to its overall reliability and effectiveness as a tool for trading and investment decision-making. By providing timely and consistent information, this indicator helps traders and investors make well-informed decisions that are less likely to be influenced by misleading or shifting data.
Inputs
Source: This input determines the source of the price data to be used for the calculations. Users can select from options like closing price, opening price, high, low, etc., based on their preferences. Changing the source of the price data (e.g., from closing price to opening price) will alter the base data used for calculations, which may lead to different patterns and cycles being identified.
Calculation Bars: This input represents the number of past bars used for the calculation. A higher value will use more historical data for the analysis, while a lower value will focus on more recent price data. Increasing the number of past bars used for calculation will incorporate more historical data into the analysis. This may lead to a more comprehensive understanding of long-term trends but could also result in a slower response to recent price changes. Decreasing this value will focus more on recent data, potentially making the indicator more responsive to short-term fluctuations.
Harmonic Period: This input represents the harmonic period, which is the number of harmonics used in the Fourier Transform. A higher value will result in more harmonics being used, potentially capturing more complex cycles in the price data. Increasing the harmonic period will include more harmonics in the Fourier Transform, potentially capturing more complex cycles in the price data. However, this may also introduce more noise and make it harder to identify clear patterns. Decreasing this value will focus on simpler cycles and may make the analysis clearer, but it might miss out on more complex patterns.
Frequency Tolerance: This input represents the frequency tolerance, which determines how close the frequencies of the harmonics must be to be considered part of the same cycle. A higher value will allow for more variation between harmonics, while a lower value will require the frequencies to be more similar. Increasing the frequency tolerance will allow for more variation between harmonics, potentially capturing a broader range of cycles. However, this may also introduce noise and make it more difficult to identify clear patterns. Decreasing this value will require the frequencies to be more similar, potentially making the analysis clearer, but it might miss out on some cycles.
Number of Bars to Render: This input determines the number of bars to render on the chart. A higher value will result in more historical data being displayed, but it may also slow down the computation due to the increased amount of data being processed. Increasing the number of bars to render on the chart will display more historical data, providing a broader context for the analysis. However, this may also slow down the computation due to the increased amount of data being processed. Decreasing this value will speed up the computation, but it will provide less historical context for the analysis.
Smoothing Mode: This input allows the user to choose between two smoothing modes for the source price data: no smoothing or Hodrick-Prescott (HP) smoothing. The choice depends on the user's preference for how the price data should be processed before the Fourier Transform is applied. Choosing between no smoothing and Hodrick-Prescott (HP) smoothing will affect the preprocessing of the price data. Using HP smoothing will remove some of the short-term fluctuations from the data, potentially making the analysis clearer and more focused on longer-term trends. Not using smoothing will retain the original price fluctuations, which may provide more detail but also introduce noise into the analysis.
Hodrick-Prescott Filter Period: This input represents the Hodrick-Prescott filter period, which is used if the user chooses to apply HP smoothing to the price data. A higher value will result in a smoother curve, while a lower value will retain more of the original price fluctuations. Increasing the Hodrick-Prescott filter period will result in a smoother curve for the price data, emphasizing longer-term trends and minimizing short-term fluctuations. Decreasing this value will retain more of the original price fluctuations, potentially providing more detail but also introducing noise into the analysis.
Alets and signals
This indicator featues alerts, signals and bar coloring. You have to option to turn these on/off in the settings menu.
Maximum Bars Restriction
This indicator requires a large amount of processing power to render on the chart. To reduce overhead, the setting "Number of Bars to Render" is set to 500 bars. You can adjust this to you liking.
█ Related Indicators and Libraries
Goertzel Cycle Composite Wave
Goertzel Browser
Fourier Spectrometer of Price w/ Extrapolation Forecast
Fourier Extrapolator of 'Caterpillar' SSA of Price
Normalized, Variety, Fast Fourier Transform Explorer
Real-Fast Fourier Transform of Price Oscillator
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
STD-Stepped Fast Cosine Transform Moving Average
Variety RSI of Fast Discrete Cosine Transform
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Breaker Blocks with Signals [LuxAlgo]The Breaker Blocks with Signals indicator aims to highlight a complete methodology based on breaker blocks. Breakout signals between the price and breaker blocks are highlighted and premium/discount swing levels are included to provide potential take profit/stop loss levels.
This script also includes alerts for each signal highlighted.
🔶 SETTINGS
🔹 Breaker Blocks
Length: Sensitivity of the detected swings used to construct breaker blocks. Higher values will return longer term breaker blocks.
Use only candle body: Only use the candle body when determining the maximum/minimum extremities of the order blocks.
Use 2 candles instead of 1: Use two candles to confirm the occurrence of a breaker block.
Stop at first break of center line: Do not highlight breakout signals after invalidation until reset.
🔹 PD Array
Only when E is in premium/discount zone: Only set breaker block if point E of wave ABCDE is within the corresponding zone.
Show premium discount zone: Show premium/discount zone.
Highlight Swing Break: Highlight occurrences of price breaking a previous swing level.
Show Swings/PD Arrays: Show swing levels/labels and pd areas.
🔶 USAGE
The Breaker Blocks with Signals indicator aims to provide users with a minimalistic display alongside optimal signals to be aware of for finding trade setups as shown below.
Here we can see a MSS occurred allowing the indicator to detect a Breaker Block (-BB) & display a red arrow to confirm this signal.
The signal(s) that can be used for potential entries are only during retests of the breaker blocks.
A potential strategy traders could use with this indicator is to target the corresponding Discount PD Arrays detected (for a short position) and Premium PD Arrays (for a long position).
In the image above we can see price generated the potential entry signals in orange & fell to the Discount PD Arrays as a logical setup to look for with this indicator.
As we can see in the image above, signals can be considered invalid when price closes above the 50% level in which it would be suggested to wait for another setup.
Users still looking for more potential setups based on the same breaker block can disable the "Stop at first break of center line" setting within the settings menu.
In the image above we can see a bullish example whereas price confirmed a bullish breaker block (+BB), had a quick pullback into it that was confirmed by the green arrow, and then reached the Premium PD Arrays.
While retests of breaker blocks can still function well if they occur later in the price action, it's most preferable for users to look for entry signals that are near confirmed breaker blocks (5-10 bars) opposed to waiting 20+ bars.
Additional take profits based on the occurence of the breaker blocks are given in order to provide targets after the occurence of a breaker block breakout.
🔶 DETAILS
Breaker blocks are formed after a mitigated order block, these can provide change of polarity opportunities, thus playing a role as a potential support/resistance. It is the re-test/retrace of price to a breaker block that will set the conditions to provide signals.
The above chart describes the creation of a breaker block.
The signal generation logic makes use of various rules described below:
Bullish Breaker Blocks:
opening price is within the breaker block, while the closing price is above the upper extremity of the breaker block.
Price did not cross the breaker block average in the interval since the previous breakout.
Bearish Breaker Blocks:
opening price is within the breaker block, while the closing price is below the lower extremity of the breaker block.
Price did not cross the breaker block average in the interval since the previous breakout.
When a new pattern is formed, all previous drawings are removed.
🔶 RELATED SCRIPTS
Crypto Uptrend Script + Pullback//Volume CandlesDescription: his is an adaption of my Pullback candle - This works on all timeframes and Markets (Forex//Stocks//)
Crypto Uptrend Script with Pullback Candle allows traders to get into a trend when the price is at end of a pullback and entering a balance phase in the market (works on all markets). The use of Moving averages to help identify a Trends and the use of Key levels to help traders be aware of where strong areas are in the market.
This script can work really well in Crypto Bull Runs when used on HTF and with confluences
The script has key support and resistance zones which are made up of quarterly data. Price reacts to these areas but patience is required as price will take time to come into these areas
I have updated the Pullback Candle with the use of Volume to filter out the weak Pullback Candles -
There are new candles to the script.
The First candle is the Bullish Volume Candle - This candle is set to a multiplier of 2x with a crossover of 50/100 on Volume - this then will paint a purple candle.
Uses of the Bullish Volume Candle:
Breakthrough of key areas // special chart patterns
Rejection of key areas
End of a impulse wave (Profit Takers)
The second candle is a Hammer - I prefer using the Hammers on Higher Timeframes however they do work on all timeframes. .
The third candle is a Exhaustion of impulse downward move.
Uses of this candle - can denote a new trend but has to be with confluence to a demand area // support area or with any use of technical analysis - using this alone is not advised
The fourth candle is a indecision candle in the shape of a Doji - this candle can help identify if the trend is in a continuation or a reversal
This script can work really well in Crypto Bull Runs
Disclaimer: There will be Pullbacks with High Volume (Breakouts) and not go the way as intended but this script is to allow traders to get into trends at good price levels. The script can paint signals in areas where price is too expensive so please do your own due diligence on the markets as this script is to help get into good areas of price
Please leave a thumbs up if you like this script and message me for information on how to use the script.
Parallel Projections [theEccentricTrader]█ OVERVIEW
This indicator automatically projects parallel trendlines or channels, from a single point of origin. In the example above I have applied the indicator twice to the 1D SPXUSD. The five upper lines (green) are projected at an angle of -5 from the 1-month swing high anchor point with a projection ratio of -72. And the seven lower lines (blue) are projected at an angle of 10 with a projection ratio of 36 from the 1-week swing low anchor point.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Trendlines
Trendlines are straight lines that are drawn between two or more points on a price chart. These lines are used as dynamic support and resistance levels for making strategic decisions and predictions about future price movements. For example traders will look for price movements along, and reactions to, trendlines in the form of rejections or breakouts/downs.
█ FEATURES
Inputs
• Anchor Point Type
• Swing High/Low Occurrence
• HTF Resolution
• Highest High/Lowest Low Lookback
• Angle Degree
• Projection Ratio
• Number Lines
• Line Color
Anchor Point Types
• Swing High
• Swing Low
• Swing High (HTF)
• Swing Low (HTF)
• Highest High
• Lowest Low
• Intraday Highest High (intraday charts only)
• Intraday Lowest Low (intraday charts only)
Swing High/Swing Low Occurrence
This input is used to determine which historic peak or trough to reference for swing high or swing low anchor point types.
HTF Resolution
This input is used to determine which higher timeframe to reference for swing high (HTF) or swing low (HTF) anchor point types.
Highest High/Lowest Low Lookback
This input is used to determine the lookback length for highest high or lowest low anchor point types.
Intraday Highest High/Lowest Low Lookback
When using intraday highest high or lowest low anchor point types, the lookback length is calculated automatically based on number of bars since the daily candle opened.
Angle Degree
This input is used to determine the angle of the trendlines. The output is expressed in terms of point or pips, depending on the symbol type, which is then passed through the built in math.todegrees() function. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
It is also worth mentioning that as more lines are added the gaps between the lines, that are closest to the anchor point, will get tighter as they make their way up the y-axis. Although the gaps between the lines will stay constant at the x2 plot, i.e. a distance of 10 points between them, they will gradually get tighter and tighter at the point of origin as the slope of the lines get steeper.
Projection Ratio
This input is used to determine the distance between the parallels, expressed in terms of point or pips. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
Number Lines
This input is used to determine the number of lines to be drawn on the chart, maximum is 500.
█ LIMITATIONS
All green and red candle calculations are based on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. This may cause some unexpected behaviour on some markets and timeframes. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with.
If the lines do not draw or you see a study error saying that the script references too many candles in history, this is most likely because the higher timeframe anchor point is not present on the current timeframe. This problem usually occurs when referencing a higher timeframe, such as the 1-month, from a much lower timeframe, such as the 1-minute. How far you can lookback for higher timeframe anchor points on the current timeframe will also be limited by your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000.
█ RAMBLINGS
It is my current thesis that the indicator will work best when used in conjunction with my Wavemeter indicator, which can be used to set the angle and projection ratio. For example, the average wave height or amplitude could be used as the value for the angle and projection ratio inputs. Or some factor or multiple of such an average. I think this makes sense as it allows for objectivity when applying the indicator across different markets and timeframes with different energies and vibrations.
“If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.”
― Nikola Tesla
Fan Projections [theEccentricTrader]█ OVERVIEW
This indicator automatically projects trendlines in the shape of a fan, from a single point of origin. In the example above I have applied the indicator twice to the 1D SPXUSD. The seven upper lines (green) are projected at an angle of -5 from the 1-month swing high anchor point. And the five lower lines (blue) are projected at an angle of 10 from the 1-week swing low anchor point.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Trendlines
Trendlines are straight lines that are drawn between two or more points on a price chart. These lines are used as dynamic support and resistance levels for making strategic decisions and predictions about future price movements. For example traders will look for price movements along, and reactions to, trendlines in the form of rejections or breakouts/downs.
█ FEATURES
Inputs
• Anchor Point Type
• Swing High/Low Occurrence
• HTF Resolution
• Highest High/Lowest Low Lookback
• Angle Degree
• Number Lines
• Line Color
Anchor Point Types
• Swing High
• Swing Low
• Swing High (HTF)
• Swing Low (HTF)
• Highest High
• Lowest Low
• Intraday Highest High (intraday charts only)
• Intraday Lowest Low (intraday charts only)
Swing High/Swing Low Occurrence
This input is used to determine which historic peak or trough to reference for swing high or swing low anchor point types.
HTF Resolution
This input is used to determine which higher timeframe to reference for swing high (HTF) or swing low (HTF) anchor point types.
Highest High/Lowest Low Lookback
This input is used to determine the lookback length for highest high or lowest low anchor point types.
Intraday Highest High/Lowest Low Lookback
When using intraday highest high or lowest low anchor point types, the lookback length is calculated automatically based on number of bars since the daily candle opened.
Angle Degree
This input is used to determine the angle of the trendlines. The output is expressed in terms of point or pips, depending on the symbol type, which is then passed through the built in math.todegrees() function. Positive numbers will project the lines upwards while negative numbers will project the lines downwards. Depending on the market and timeframe, the impact input values will have on the visible gaps between the lines will vary greatly. For example, an input of 10 will have a far greater impact on the gaps between the lines when viewed from the 1-minute timeframe than it would on the 1-day timeframe. The input is a float and as such the value passed through can go into as many decimal places as the user requires.
It is also worth mentioning that as more lines are added the gaps between the lines, that are closest to the anchor point, will get tighter as they make their way up the y-axis. Although the gaps between the lines will stay constant at the x2 plot, i.e. a distance of 10 points between them, they will gradually get tighter and tighter at the point of origin as the slope of the lines get steeper.
Number Lines
This input is used to determine the number of lines to be drawn on the chart, maximum is 500.
█ LIMITATIONS
All green and red candle calculations are based on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. This may cause some unexpected behaviour on some markets and timeframes. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with.
If the lines do not draw or you see a study error saying that the script references too many candles in history, this is most likely because the higher timeframe anchor point is not present on the current timeframe. This problem usually occurs when referencing a higher timeframe, such as the 1-month, from a much lower timeframe, such as the 1-minute. How far you can lookback for higher timeframe anchor points on the current timeframe will also be limited by your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000.
█ RAMBLINGS
It is my current thesis that the indicator will work best when used in conjunction with my Wavemeter indicator, which can be used to set the angle. For example, the average wave height or amplitude could be used as the value for the angle input. Or some factor or multiple of such an average. I think this makes sense as it allows for objectivity when applying the indicator across different markets and timeframes with different energies and vibrations.
“If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.”
― Nikola Tesla
Swing Counter [theEccentricTrader]█ OVERVIEW
This indicator counts the number of confirmed swing high and swing low scenarios on any given candlestick chart and displays the statistics in a table, which can be repositioned and resized at the user's discretion.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Peak and Trough Prices (Advanced)
• The advanced peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the highest preceding green candle high price, depending on which is higher.
• The advanced trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the lowest preceding red candle low price, depending on which is lower.
Green and Red Peaks and Troughs
• A green peak is one that derives its price from the green candle/s that constitute the swing high.
• A red peak is one that derives its price from the red candle that completes the swing high.
• A green trough is one that derives its price from the green candle that completes the swing low.
• A red trough is one that derives its price from the red candle/s that constitute the swing low.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
█ FEATURES
Inputs
• Start Date
• End Date
• Position
• Text Size
• Show Sample Period
• Show Plots
• Show Lines
Table
The table is colour coded, consists of three columns and nine rows. Blue cells denote neutral scenarios, green cells denote return line uptrend and uptrend scenarios, and red cells denote downtrend and return line downtrend scenarios.
The swing scenarios are listed in the first column with their corresponding total counts to the right, in the second column. The last row in column one, row nine, displays the sample period which can be adjusted or hidden via indicator settings.
Rows three and four in the third column of the table display the total higher peaks and higher troughs as percentages of total peaks and troughs, respectively. Rows five and six in the third column display the total lower peaks and lower troughs as percentages of total peaks and troughs, respectively. And rows seven and eight display the total double-top peaks and double-bottom troughs as percentages of total peaks and troughs, respectively.
Plots
I have added plots as a visual aid to the swing scenarios listed in the table. Green up-arrows with ‘HP’ denote higher peaks, while green up-arrows with ‘HT’ denote higher troughs. Red down-arrows with ‘LP’ denote higher peaks, while red down-arrows with ‘LT’ denote lower troughs. Similarly, blue diamonds with ‘DT’ denote double-top peaks and blue diamonds with ‘DB’ denote double-bottom troughs. These plots can be hidden via indicator settings.
Lines
I have also added green and red trendlines as a further visual aid to the swing scenarios listed in the table. Green lines denote return line uptrends (higher peaks) and uptrends (higher troughs), while red lines denote downtrends (lower peaks) and return line downtrends (lower troughs). These lines can be hidden via indicator settings.
█ HOW TO USE
This indicator is intended for research purposes and strategy development. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe. It can, for example, give you an idea of any inherent biases such as a greater proportion of higher peaks to lower peaks. Or a greater proportion of higher troughs to lower troughs. Such information can be very useful when conducting top down analysis across multiple timeframes, or considering entry and exit methods.
What I find most fascinating about this logic, is that the number of swing highs and swing lows will always find equilibrium on each new complete wave cycle. If for example the chart begins with a swing high and ends with a swing low there will be an equal number of swing highs to swing lows. If the chart starts with a swing high and ends with a swing high there will be a difference of one between the two total values until another swing low is formed to complete the wave cycle sequence that began at start of the chart. Almost as if it was a fundamental truth of price action, although quite common sensical in many respects. As they say, what goes up must come down.
The objective logic for swing highs and swing lows I hope will form somewhat of a foundational building block for traders, researchers and developers alike. Not only does it facilitate the objective study of swing highs and swing lows it also facilitates that of ranges, trends, double trends, multi-part trends and patterns. The logic can also be used for objective anchor points. Concepts I will introduce and develop further in future publications.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge.
The sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
█ NOTES
I feel it important to address the mention of advanced peak and trough price logic. While I have introduced the concept, I have not included the logic in my script for a number of reasons. The most pertinent of which being the amount of extra work I would have to do to include it in a public release versus the actual difference it would make to the statistics. Based on my experience, there are actually only a small number of cases where the advanced peak and trough prices are different from the basic peak and trough prices. And with adequate multi-timeframe analysis any high or low prices that are not captured using basic peak and trough price logic on any given time frame, will no doubt be captured on a higher timeframe. See the example below on the 1H FOREXCOM:USDJPY chart (Figure 1), where the basic peak price logic denoted by the indicator plot does not capture what would be the advanced peak price, but on the 2H FOREXCOM:USDJPY chart (Figure 2), the basic peak logic does capture the advanced peak price from the 1H timeframe.
Figure 1.
Figure 2.
█ RAMBLINGS
“Never was there an age that placed economic interests higher than does our own. Never was the need of a scientific foundation for economic affairs felt more generally or more acutely. And never was the ability of practical men to utilize the achievements of science, in all fields of human activity, greater than in our day. If practical men, therefore, rely wholly on their own experience, and disregard our science in its present state of development, it cannot be due to a lack of serious interest or ability on their part. Nor can their disregard be the result of a haughty rejection of the deeper insight a true science would give into the circumstances and relationships determining the outcome of their activity. The cause of such remarkable indifference must not be sought elsewhere than in the present state of our science itself, in the sterility of all past endeavours to find its empirical foundations.” (Menger, 1871, p.45).
█ BIBLIOGRAPHY
Menger, C. (1871) Principles of Economics. Reprint, Auburn, Alabama: Ludwig Von Mises Institute: 2007.
GRIDBOT Scalper by nnamWhat is this Indicator used for?
Made specifically for GRID Bots
note: before continuing... this indicator works on any timeframe, but it WORKS BEST ON THE 15 MINUTE TIMEFRAME
Straters and Forex Master Pattern Value Line Traders use this to help determine when the price could reverse.
This indicator is a scalping indicator that produces signals when a "potential" reversal in price is indicated. When the price moves UP and a Potential Bearish Reversal Signal occurs, traders can use this signal as a potential SHORT entry signal for their Short Grid Bot. The process is the same in reverse. After a sustained move down, a Potential Bullish Signal can be used by the trader as a potential LONG entry signal for their GridBot.
As shown in the screenshot below, lines develop on the chart (either RED or GREEN) indicating that a sustained move in one direction is currently occurring; however, there is no potential reversal signal plotted (this means that price action is currently moving in one direction only).
As shown in the screenshot below, lines can be used as a stop-loss after entering the GRIDbot. (usually, by this time, the Grid Bot is in Profit as it usually moves in the opposite direction first)
What this Indicator Does
The GRIDBOT Scalper provides information regarding potential reversals in the market after a sustained movement in one direction (either Bullish or Bearish).
The indicator is based on PRICE-ACTION ONLY and does not take into account the current state of the market (Bullish or Bearish).
Once the price moves in a particular direction for at least 14 bars , a line appears as shown in a previous screenshot. Once the price stops moving in that direction and begins moving in the opposite direction - and after a sustained run - a "signal" appears alerting the trader that a "potential" reversal could be on the horizon soon.
If price moves in one direction and plots both a line and a signal and then begins moving back in the other direction in a sustained manner, the original signal will remain even when a NEW line begins forming (the original line will disappear). (see below) This line will continue to move as the price continues to move. Not until a signal plots on the chart is the potential reversal forming. THE LINE DOES NOT SIGNAL A REVERSAL . Some traders, however, use this information to "ride the wave UP or DOWN" and exit their positions once the signal prints.
As shown below, optional input settings allow the trader to set the line at CLOSE or HIGH/LOW of the candle preceding the potential reversal.
It is suggested to use Close instead of High or Low but the setting allows one to use either.
As shown in the screenshot below, it is typical on LOWER TIME FRAMES to see the price pass the signal line. The Indicator works best on the 15 minute timeframe, as it gives the trader time to make the decisions required as the volatility is less on the 15 minute chart vs the 1 minute or 5 minute charts.
If you have any questions or suggestions for this indicator, please join our Discord. We offer free training on this Indicator on our Discord Server.
True Trend Average BandsThis is the indicator I am most proud of. After reading Glenn Neely's book "Mastering Eliott Waves" / "Neowave" and chatting with @timwest who got acknowledged by Neely, we came up with the idea of an moving average which does calculate the real average price since a trend started. Addionally I adapted a method from Neely Neowave and Tim Wests TimeAtMode to not force a timeframe on a chart but instead let the charts data decide which timeframe to use, to then calculate the real average price since the trend started.
It took me a while to get this right and coded, so take a moment and dive deeper and you might learn something new.
We assume that the price is in multiple trends on multiple timeframes, this is caused by short term traders, long term traders and investors who trade on different timeframes. To find out in which timeframe the important trends are, we have to look out for significant lows and highs. Then we change the timeframe in the chart to a value so that we have 10 to 20 bars since the significant low/high. While new bars are printed, and we reach more than 20 bars, we have to switch to a higher timeframe so we have 10 to 20 bars again. In the chart you see two significant trends: a downtrend on the 3 week timeframe and an uptrend from the 2 month timeframe. Based on the logic I have described, these are the two important timeframes to watch right now for the spx (there is another uptrend in the yearly chart, which is not shown here).
Now that we understand how to find the important timeframes, let's look what the magic in this script is that tells us the real average price since a trend started.
I developed a new type of moving average, which includes only the prices since a trend started. The difference to the regular sma is that it will not include prices which happened before the significant low or high happened. For example, if a top happened in a market 10 days ago, the regular sma20 would be calculated by 10 bars which happened before the top and 10 bars which happened after the top. If we want to know the average price of the last 10 bars we manually have to change the ma20 to the ma10 which is annoying manual work, additionally even if we use the ma10 in this case, and we look at yesterday's bar the ma10 will include 9 bars from after the top and one bar before the top, so the ma10 would only show the real average price for the current bar which is not what we want.
To come up with a solution to this problem, the True Trend Average searches for the lowest/highest bar in a given period (20 bars). Then starts to calculate the average value since the low/high. For example: if the price reaches a new 20 day high and then trades below it, the day of the high will be the sma1, the day after it's the sma2, ... up to the maximum look back length.
This way, we always know what the average price would have been if someone sold/bought a little bit every bar of his investment since the high/low.
Why is this even important? Let's assume we missed selling the top or buying the low, and think it would have been at least better to buy/sell a little bit since the new trend started. Once the price reaches the true trend average again, we can buy/sell, and it would be as good as selling/buying a little bit every day. We find prices to buy the dip and sell the bounce, which are as good as scaling in/out.
There is a lot more we can learn from these price levels but I think it is better to let you figure out yourself what you can learn from the information given by this indicator. Think about how market participants who accumulate or distribute feel when prices are above or below certain levels.
Now that we understand this new type of moving average, let's look into the lines we see in the chart:
The upper red band line shows the true trend average high price since the last significant top within 20 bars.
The lower red band line shows the true trend average hl2 price since the last significant top within 20 bars.
The lower green band line shows the true trend average low price since the last significant low within 20 bars.
The upper green band line shows the true trend average hl2 price since the last significant low within 20 bars.
The centerline is the average between the upper red band and the lower green band.
The teal lines show 1 standard deviation from the outer bands.
Before today only a few people had access to this indicator, now that it is public and open source, I am curious if you will find it useful and what you will do with it. Please share your findings.
/edit: The chart only shows the 3week timeframe so here are the other two trends from the 2month and 1year timeframe
Sinusoidal High Pass Filter (SHPF)Sinusoidal High Pass Filter
This script implements a sinusoidal high pass filter, which is a type of digital filter that is used to remove low frequency components from a signal. The filter is defined by a series of weights that are applied to the input data, with the weights being determined by a sinusoidal function. The resulting filtered signal is then plotted on a chart, allowing the user to visualize the effect of the filter on the original signal.
The script begins by defining the sinusoidal_hpf function, which takes three arguments: _series, _period, and _phase_shift. The _series argument is the input data series that will be filtered, and the _period argument determines the length of the filter. The _phase_shift argument is an optional parameter that allows the user to adjust the phase of the sinusoidal function that is used to calculate the filter weights.
The function then initializes a variable ma to 0.0, and loops through each data point in the input series, starting from the most recent and going back in time for the specified _period number of points. For each data point, the function calculates a weight using a sinusoidal function, and adds the weighted data point to the ma variable. Finally, the function returns the average of the weighted data points by dividing ma by the _period.
The script also includes user input fields for the Length and Phase Shift parameters, which allows the user to customize the filter according to their specific needs. The filtered signal is then plotted on a chart, along with a reference line at 0.
Overall, this script provides a useful tool for analyzing and processing financial data, and can be easily customized to fit the needs of the user.
Musashi_Fractal_Dimension === Musashi-Fractal-Dimension ===
This tool is part of my research on the fractal nature of the markets and understanding the relation between fractal dimension and chaos theory.
To take full advantage of this indicator, you need to incorporate some principles and concepts:
- Traditional Technical Analysis is linear and Euclidean, which makes very difficult its modeling.
- Linear techniques cannot quantify non-linear behavior
- Is it possible to measure accurately a wave or the surface of a mountain with a simple ruler?
- Fractals quantify what Euclidean Geometry can’t, they measure chaos, as they identify order in apparent randomness.
- Remember: Chaos is order disguised as randomness.
- Chaos is the study of unstable aperiodic behavior in deterministic non-linear dynamic systems
- Order and randomness can coexist, allowing predictability.
- There is a reason why Fractal Dimension was invented, we had no way of measuring fractal-based structures.
- Benoit Mandelbrot used to explain it by asking: How do we measure the coast of Great Britain?
- An easy way of getting the need of a dimension in between is looking at the Koch snowflake.
- Market prices tend to seek natural levels of ranges of balance. These levels can be described as attractors and are determinant.
Fractal Dimension Index ('FDI')
Determines the persistence or anti-persistence of a market.
- A persistent market follows a market trend. An anti-persistent market results in substantial volatility around the trend (with a low r2), and is more vulnerable to price reversals
- An easy way to see this is to think that fractal dimension measures what is in between mainstream dimensions. These are:
- One dimension: a line
- Two dimensions: a square
- Three dimensions: a cube.
--> This will hint you that at certain moment, if the market has a Fractal Dimension of 1.25 (which is low), the market is behaving more “line-like”, while if the market has a high Fractal Dimension, it could be interpreted as “square-like”.
- 'FDI' is trend agnostic, which means that doesn't consider trend. This makes it super useful as gives you clean information about the market without trying to include trend stuff.
Question: If we have a game where you must choose between two options.
1. a horizontal line
2. a vertical line.
Each iteration a Horizontal Line or a Square will appear as continuation of a figure. If it that iteration shows a square and you bet vertical you win, same as if it is horizontal and it is a line.
- Wouldn’t be useful to know that Fractal dimension is 1.8? This will hint square. In the markets you can use 'FD' to filter mean-reversal signals like Bollinger bands, stochastics, Regular RSI divergences, etc.
- Wouldn’t be useful to know that Fractal dimension is 1.2? This will hint Line. In the markets you can use 'FD' to confirm trend following strategies like Moving averages, MACD, Hidden RSI divergences.
Calculation method:
Fractal dimension is obtained from the ‘hurst exponent’.
'FDI' = 2 - 'Hurst Exponent'
Musashi version of the Classic 'OG' Fractal Dimension Index ('FDI')
- By default, you get 3 fast 'FDI's (11,12,13) + 1 Slow 'FDI' (21), their interaction gives useful information.
- Fast 'FDI' cross will give you gray or red dots while Slow 'FDI' cross with the slowest of the fast 'FDI's will give white and orange dots. This are great to early spot trend beginnings or trend ends.
- A baseline (purple) is also provided, this is calculated using a 21 period Bollinger bands with 1.618 'SD', once calculated, you just take midpoint, this is the 'TDI's (Traders Dynamic Index) way. The indicator will print purple dots when Slow 'FDI' and baseline crosses, I see them as Short-Term cycle changes.
- Negative slope 'FDI' means trending asset.
- Positive most of the times hints correction, but if it got overextended it might hint a rocket-shot.
TDI Ranges:
- 'FDI' between 1.0≤ 'FDI' ≤1.4 will confirm trend following continuation signals.
- 'FDI' between 1.6≥ 'FDI' ≥2.0 will confirm reversal signals.
- 'FDI' == 1.5 hints a random unpredictable market.
Fractal Attractors
- As you must know, fractals tend orbit certain spots, this are named Attractors, this happens with any fractal behavior. The market of course also shows them, in form of Support & Resistance, Supply Demand, etc. It’s obvious they are there, but now we understand that they’re not linear, as the market is fractal, so simple trendline might not be the best tool to model this.
- I’ve noticed that when the Musashi version of the 'FDI' indicator start making a cluster of multicolor dots, this end up being an attractor, I tend to draw a rectangle as that area as price tend to come back (I still researching here).
Extra useful stuff
- Momentum / speed: Included by checking RSI Study in the indicator properties. This will add two RSI’s (9 and a 7 periods) plus a baseline calculated same way as explained for 'FDI'. This gives accurate short-term trends. It also includes RSI divergences (regular and hidden), deactivate with a simple check in the RSI section of the properties.
- BBWP (Bollinger Bands with Percentile): Efficient way of visualizing volatility as the percentile of Bollinger bands expansion. This line varies color from Iced blue when low volatility and magma red when high. By default, comes with the High vols deactivated for better view of 'FDI' and RSI while all studies are included. DDWP is trend agnostic, just like 'FDI', which make it very clean at providing information.
- Ultra Slow 'FDI': I noticed that while using BBWP and RSI, the indicator gets overcrowded, so there is the possibility of adding only one 'FDI' + its baseline.
Final Note: I’ve shown you few ways of using this indicator, please backtest before using in real trading. As you know trading is more about risk and trade management than the strategy used. This still a work in progress, I really hope you find value out of it. I use it combination with a tool named “Musashi_Katana” (also found in TradingView).
Best!
Musashi
Volatility Trackerhi there, fellows.
this is a very simple and quite straightforward indicator.
so far the simplest we've built.
on what it does
in regard to current chart and timeframe it plots
a. Open - Close as a percentage of the Open (we regard open as more relevant than close, for as you can use latest estimates in current candle) in daily change coloring (so one may have an idea if there is a trend or sideways move unfolding)
b. High - Low as a percentage of the Open, so one may compare extreme moves with final ones in the period
c. Volume as a percentage distance from its WMA200 (always this one, a way better reference for normalcy). (e. g. a positive value x means Volume is x% above its WMA200)
on what it means
to the best of our imperfect and incomplete understanding, we believe that low volatility periods lead to high volatility periods, so one might want to enter the market in low volatility periods to enjoy wild rides afterwards. such a trade of course would be, for the sake of making sense, a long volatility one.
the timing for entrance could be once that the volatility waves fades to chart minimums.
we're open to critics, suggestions and comments.
best regards.
TheMas7er scalp (US equity) 5min [promuckaj]This indicator was created according to TheMas7er's trading setup, that he reveal after 18 years of working in the industry. Claims is that this setup should give you good probability to predict the price movement for US equity.
This trading setup is only for New York equity trading session from 09:30 until 4pm. The market in which you should use it are the S&P 500 , Dow Jones, and Nasdaq. Perhaps it will work on some other but for those are good according to tests. It should not used on days with high-impact news, like CPI , FOMC, NFP and so on. The model can still work there but the probability on these days is way lower.
What is the base of this indicator, it marks what is called "The Defining Range"("DR"). This defining range is from 09:30am until 10:30am New York local time, it takes those 12 candles in the 5min chart. Indicator will mark the high and low of this range, including wicks. This will help you to already know at 10:30am, with possible good probability the high or low of the day.
There is also the "Implied Defining Range"("iDR") lines inside the "DR" range, which mark the highest body and the lowest body in the "DR" range.
*The rules (it is very simple to follow):
Chart must be set in 5min timeframe.
At 10:30am you still don't know which one will be the real high or low of the day, but only one will be true.
If price is closing on 5min chart above the "DR" it should give you good probability that the low of the "DR" is the low of the day, and vice versa - if price is closing below the "DR" it should give you good probability that the high of the "DR" is the high of the day.
"iDR" gives you an early indication about what high or low of the day should be. If price is closing above "iDR" you will have an early indication that the low of the "DR" should be the low of the day, and vice versa.
Note that about closing means really closing above or below, not just wicks.
Now, after this you can realize the magnitude of possibility.
You can use any entry model you prefer to trade, it doesn't matter if you use ICT concepts, smart money concepts, volume profile , eliot waves, braking the structure concept or whatever. There are so many possibilities for trading within this rule.
Enjoy!
Sembang Kari Traders - EMA & Wave Stacked Labels + EMA 34 LinesThis script is 2 in 1 indicator.
1. Multi Timeframe EMA Labels
- This label indicator shows labels for EMA stacked up or EMA stacked down or EMA in sideway trend.
- EMA used in this script is EMA 8, EMA 21, EMA 34 and EMA 55.
- If the EMA 8 line is above EMA 21 line, and EMA 21 line is above EMA 34 line, and EMA 34 line is above EMA 55 line ( EMA STACKED UP) = the trend is BULLISH and the label will colored to GREEN on that timeframe.
- If the EMA 8 line is below EMA 21 line, and EMA 21 line is below EMA 34 line, and EMA 34 line is below EMA 55 line ( EMA STACKED DOWN) = the trend is BEARISH and the label will colored to RED on that timeframe.
- If either 1 of the EMA 8, or EMA 21, or EMA 34, or EMA 55 is NOT STACKED = the trend is SIDEWAY and the label will colored to YELLOW on that timeframe.
- Timeframe shows in label is Daily, 4 hours, 1 hour, 15 minutes and 5 minutes.
- This indicator labels will be useful to identifying trend in others timeframe without to look or open that other timeframe. Example, if u in 5 minutes timeframe chart, then u see that "D" is colored to GREEN, then straight will know that EMA 8, EMA 21, EMA 34 and EMA 55 is STACKED UP which means BULLISH without to look or open that Daily timeframe .
2. EMA 34 Lines
- This is indicator shows 3 exponential moving average line which is EMA 34 lines.
- This indicator will shows 3 lines which is GREEN, BLUE, and RED.
- The GREEN line is EMA 34 HIGH
- The BLUE line is EMA 34 CLOSE
- The RED line is EMA 34 BLUE
Trade Idea
- The idea using this indicator is we want to take an entry setup when the candle pull back to EMA 34 lines and at the same time using the EMA labels to be confirmation as label will indicates trends in multiple timeframe.
- When price moved far away from EMA 34 lines, then wait till price pullback to EMA lines and confirmed it by trend labels provided to take take a entry setup.
- this indicator can be used on all tickers
