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
loxfft
Cerca negli script per "curve"
Rate Of Change [Hyperbolic]Rate Of Change just got fixed!
Do note that you have to activate the "exotic calculations" inside the ROC-H settings.
A hyperbolic curve now transforms price. No more infinities on your indicators!
You may use the "exotic" function, that is embedded in my script in your own scripts.
This formula basically transforms the input (which may be zero or negative) into a strictly positive one.
While the mathematicians out there would opt for alternative formulae (like the exponential for negative numbers), I used the hyperbolic curve for continuity purposes. Feel free to build upon my calculations, and make them even better!
Tread lightly, for this is hallowed ground.
-Father Grigori
P.S. I cannot lock the source code. Science and knowledge belongs to humanity. Knowledge must not be up for sale.
Channel Lookback: Average Moving Price (CLAMP)How it works
This is a confirmation indicator based on moving averages. It compares the current price to a previous candle N periods ago, then smooths the result.
What makes this indicator novel is that it takes the smoothed curve and compares it to the previous value to see whether the slope is increasing or decreasing. Combined with a zero-cross baseline channel, we can compare the relative position of the curve, slope, or closing price to create entry signals. There are several hardcoded conditions that it checks, but this is easily changed.
Markets
The default values are best used on the SPY daily chart. With backtesting, it seemed to perform fairly well during the last year. It seems to be more accurate during choppy/bear markets, and very inaccurate during a trending market.
Eg, if you look at the period of growth that occurred during 2020, it basically said to keep shorting for months at a time. Not good. If you look at other markets (such as gold or uranium), it worked, but only if you inversed what the signal told you to do (eg going long when it says to go short). This is something you will have to test yourself, since every system and market is different. Please don't use this indicator by itself.
How to use
When combined with other indicators, this tells you whether to go long (green), go short (red), or no trade (gray). It is meant to be used as a confirmation indicator, so it will help verify other trade signals.
You can sometimes ignore the first grey circle and reuse the previous colored signal. There are a few markets (such as gold) I noticed this was helpful on; this will depend on your own trade rules and indicator system.
If you enable "simple mode" on the settings, it will draw only the final signal (long, short, no signal). I included this because it helps to reduce visual clutter.
CMO with ATR and LagF Filtering - RevNR - 12-27-22Rev NR of the CMO ATR, with LagF Filtering - Released 12-27-22 by @Hockeydude84
This code takes Chande Momentum Oscillator (CMO), adds a coded ATR option and then filters the result through a Laguerre Filter (LagF) to reduce erroneous signals.
This code also has an option for self adjusting alpha on the Lag, via a lookback table and monitoring the price rate of change (ROC) in the lookback length.
Faster ROC will allow the LagF to move faster, slower price action will slow down LagF reaction. Pausing of signals is also present based on Rate of Change of the LagF Curve
Aggressive signals and Base signaling is allowed - aggressive bases signals on increase/decrease of previous LagF curve value point, Base is greater or less than 0
Original Code credits; Lost some of this due to time and multiple script manipulations, I believe the CMO origin code is from @TradingView House Code, and the LagF from @KıvançÖzbilgiç
[blackcat] L3 Bull Bear GameLevel 3
Background
A bull bear banker fund game trajectories.
Function
This technical indicator draws a track diagram of the long-short power comparison through a custom trend line. The red curve represents the long line, and the green curve represents the short line. When the red line crosses the green line, it means that the upward momentum is sufficient, the whale is controlling the market, and the rise is imminent, which is a buy signal. When the red line enters the strong zone, it means that the whale is in control of the stock, and the stock is about to enter the stage of pulling up. On the contrary, if the green line turns upwards, it means that the whale is washing or retreating, and we must quickly reduce or clear the position. Sometimes when the indicator sends out a long entry signal, because the whale still has to go through a round of washing, I introduced a golden pit inflection point filter scheme, which can filter out these signals, so only when the signals appear at the same time is a long entry point signal.
Remarks
Feedbacks are appreciated.
VIDYA Trend StrategyOne of the most common messages I get is people reaching out asking for quantitative strategies that trade cryptocurrency. This has compelled me to write this script and article, to help provide a quantitative/technical perspective on why I believe most strategies people write for crypto fail catastrophically, and how one might build measures within their strategies that help reduce the risk of that happening. For those that don't trade crypto, know that these approaches are applicable to any market.
I will start off by qualifying up that I mainly trade stocks and ETFs, and I believe that if you trade crypto, you should only be playing with money you are okay with losing. Most published crypto strategies I have seen "work" when the market is going up, and fail catastrophically when it is not. There are far more people trying to sell you a strategy than there are people providing 5-10+ year backtest results on their strategies, with slippage and commissions included, showing how they generated alpha and beat buy/hold. I understand that this community has some really talented people that can create some really awesome things, but I am saying that the vast majority of what you find on the internet will not be strategies that create alpha over the long term.
So, why do so many of these strategies fail?
There is an assumption many people make that cryptocurrency will act just like stocks and ETFs, and it does not. ETF returns have more of a Gaussian probability distribution. Because of this, ETFs have a short term mean reverting behavior that can be capitalized on consistently. Many technical indicators are built to take advantage of this on the equities market. Many people apply them to crypto. Many of those people are drawn down 60-70% right now while there are mean reversion strategies up YTD on equities, even though the equities market is down. Crypto has many more "tail events" that occur 3-4+ standard deviations from the mean.
There is a correlation in many equities and ETF markets for how long an asset continues to do well when it is currently doing well. This is known as momentum, and that correlation and time-horizon is different for different assets. Many technical indicators are built based on this behavior, and then people apply them to cryptocurrency with little risk management assuming they behave the same and and on the same time horizon, without pulling in the statistics to verify if that is actually the case. They do not.
People do not take into account the brokerage commissions and slippage. Brokerage commissions are particularly high with cryptocurrency. The irony here isn't lost to me. When you factor in trading costs, it blows up most short-term trading strategies that might otherwise look profitable.
There is an assumption that it will "always come back" and that you "HODL" through the crash and "buy more." This is why Three Arrows Capital, a $10 billion dollar crypto hedge fund is now in bankruptcy, and no one can find the owners. This is also why many that trade crypto are drawn down 60-70% right now. There are bad risk practices in place, like thinking the martingale gambling strategy is the same as dollar cost averaging while also using those terms interchangeably. They are not the same. The 1st will blow up your trade account, and the 2nd will reduce timing risk. Many people are systematically blowing up their trade accounts/strategies by using martingale and calling it dollar cost averaging. The more risk you are exposing yourself too, the more important your risk management strategy is.
There is an odd assumption some have that you can buy anything and win with technical/quantitative analysis. Technical analysis does not tell you what you should buy, it just tells you when. If you are running a strategy that is going long on an asset that lost 80% of its value in the last year, then your strategy is probably down. That same strategy might be up on a different asset. One might consider a different methodology on choosing assets to trade.
Lastly, most strategies are over-fit, or curve-fit. The more complicated and more parameters/settings you have in your model, the more likely it is just fit to historical data and will not perform similar in live trading. This is one of the reasons why I like simple models with few parameters. They are less likely to be over-fit to historical data. If the strategy only works with 1 set of parameters, and there isn't a range of parameters around it that create alpha, then your strategy is over-fit and is probably not suitable for live trading.
So, what can I do about all of this!?
I created the VIDYA Trend Strategy to provide an example of how one might create a basic model with a basic risk management strategy that might generate long term alpha on a volatile asset, like cryptocurrency. This is one (of many) risk management strategies that can reduce the volatility of your returns when trading any asset. I chose the Variable Index Dynamic Average (VIDYA) for this example because it's calculation filters out some market noise by taking into account the volatility of the underlying asset. I chose a trend following strategy because regressions are capturing behaviors that are not just specific to the equities market.
The more volatile an asset, the more you have to back-off the short term price movement to effectively trend-follow it. Otherwise, you are constantly buying into short term trends that don't represent the trend of the asset, then they reverse and loose money. This is why I am applying a trend following strategy to a 4 hour chart and not a 4 minute chart. It is also important to note that following these long term trends on a volatile asset exposes you to additional risk. So, how might one mitigate some of that risk?
One of the ways of reducing timing risk is scaling into a trade. This is different from "doubling down" or "trippling down." It is really a basic application of dollar cost averaging to reduce timing risk, although DCA would typically happen over a longer time period. If it is really a trend you are following, it will probably still be a trend tomorrow. Trend following strategies have lower win rates because the beginning of a trend often reverses. The more volatile the asset, the more likely that is to happen. However, we can reduce risk of buying into a reversal by slowly scaling into the trend with a small % of equity per trade.
Our example "VIDYA Trend Strategy" executes this by looking at a medium-term, volatility adjusted trend on a 4 hour chart. The script scales into it with 4% of the account equity every 4-hours that the trend is still up. This means you become fully invested after 25 trades/bars. It also means that early in the trade, when you might be more likely to experience a reversal, most of your account equity is not invested and those losses are much smaller. The script sells 100% of the position when it detects a trend reversal. The slower you scale into a trade, the less volatile your equity curve will be. This model also includes slippage and commissions that you can adjust under the "settings" menu.
This fundamental concept of reducing timing risk by scaling into a trade can be applied to any market.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Relative Aggregate Strength OscillatorCredits to
@wolneyyy - "Mean Deviation Detector - Throw Out All Other Indicators"
And
@algomojo - "Responsive Coppock Curve"
And the default Relative Strength Index
The candles are the average of the MFI ,CCI ,MOM and RSI values presented as candles, they seemed similar enough in style to me so I created candles out of each and the took the sum of all the candle's OHLC values and divided by 4 to get an average.
In the Background we have @wolneyyy's - "Mean Deviation Detector - Throw Out All Other Indicators" in blue
along with @algomojo's - "Responsive Coppock Curve" in red and green.
Moving_AveragesLibrary "Moving_Averages"
This library contains majority important moving average functions with int series support. Which means that they can be used with variable length input. For conventional use, please use tradingview built-in ta functions for moving averages as they are more precise. I'll use functions in this library for my other scripts with dynamic length inputs.
ema(src, len)
Exponential Moving Average (EMA)
Parameters:
src : Source
len : Period
Returns: Exponential Moving Average with Series Int Support (EMA)
alma(src, len, a_offset, a_sigma)
Arnaud Legoux Moving Average (ALMA)
Parameters:
src : Source
len : Period
a_offset : Arnaud Legoux offset
a_sigma : Arnaud Legoux sigma
Returns: Arnaud Legoux Moving Average (ALMA)
covwema(src, len)
Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)
Parameters:
src : Source
len : Period
Returns: Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)
covwma(src, len)
Coefficient of Variation Weighted Moving Average (COVWMA)
Parameters:
src : Source
len : Period
Returns: Coefficient of Variation Weighted Moving Average (COVWMA)
dema(src, len)
DEMA - Double Exponential Moving Average
Parameters:
src : Source
len : Period
Returns: DEMA - Double Exponential Moving Average
edsma(src, len, ssfLength, ssfPoles)
EDSMA - Ehlers Deviation Scaled Moving Average
Parameters:
src : Source
len : Period
ssfLength : EDSMA - Super Smoother Filter Length
ssfPoles : EDSMA - Super Smoother Filter Poles
Returns: Ehlers Deviation Scaled Moving Average (EDSMA)
eframa(src, len, FC, SC)
Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)
Parameters:
src : Source
len : Period
FC : Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average
SC : Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average
Returns: Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)
ehma(src, len)
EHMA - Exponential Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Exponential Hull Moving Average (EHMA)
etma(src, len)
Exponential Triangular Moving Average (ETMA)
Parameters:
src : Source
len : Period
Returns: Exponential Triangular Moving Average (ETMA)
frama(src, len)
Fractal Adaptive Moving Average (FRAMA)
Parameters:
src : Source
len : Period
Returns: Fractal Adaptive Moving Average (FRAMA)
hma(src, len)
HMA - Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Hull Moving Average (HMA)
jma(src, len, jurik_phase, jurik_power)
Jurik Moving Average - JMA
Parameters:
src : Source
len : Period
jurik_phase : Jurik (JMA) Only - Phase
jurik_power : Jurik (JMA) Only - Power
Returns: Jurik Moving Average (JMA)
kama(src, len, k_fastLength, k_slowLength)
Kaufman's Adaptive Moving Average (KAMA)
Parameters:
src : Source
len : Period
k_fastLength : Number of periods for the fastest exponential moving average
k_slowLength : Number of periods for the slowest exponential moving average
Returns: Kaufman's Adaptive Moving Average (KAMA)
kijun(_high, _low, len, kidiv)
Kijun v2
Parameters:
_high : High value of bar
_low : Low value of bar
len : Period
kidiv : Kijun MOD Divider
Returns: Kijun v2
lsma(src, len, offset)
LSMA/LRC - Least Squares Moving Average / Linear Regression Curve
Parameters:
src : Source
len : Period
offset : Offset
Returns: Least Squares Moving Average (LSMA)/ Linear Regression Curve (LRC)
mf(src, len, beta, feedback, z)
MF - Modular Filter
Parameters:
src : Source
len : Period
beta : Modular Filter, General Filter Only - Beta
feedback : Modular Filter Only - Feedback
z : Modular Filter Only - Feedback Weighting
Returns: Modular Filter (MF)
rma(src, len)
RMA - RSI Moving average
Parameters:
src : Source
len : Period
Returns: RSI Moving average (RMA)
sma(src, len)
SMA - Simple Moving Average
Parameters:
src : Source
len : Period
Returns: Simple Moving Average (SMA)
smma(src, len)
Smoothed Moving Average (SMMA)
Parameters:
src : Source
len : Period
Returns: Smoothed Moving Average (SMMA)
stma(src, len)
Simple Triangular Moving Average (STMA)
Parameters:
src : Source
len : Period
Returns: Simple Triangular Moving Average (STMA)
tema(src, len)
TEMA - Triple Exponential Moving Average
Parameters:
src : Source
len : Period
Returns: Triple Exponential Moving Average (TEMA)
thma(src, len)
THMA - Triple Hull Moving Average
Parameters:
src : Source
len : Period
Returns: Triple Hull Moving Average (THMA)
vama(src, len, volatility_lookback)
VAMA - Volatility Adjusted Moving Average
Parameters:
src : Source
len : Period
volatility_lookback : Volatility lookback length
Returns: Volatility Adjusted Moving Average (VAMA)
vidya(src, len)
Variable Index Dynamic Average (VIDYA)
Parameters:
src : Source
len : Period
Returns: Variable Index Dynamic Average (VIDYA)
vwma(src, len)
Volume-Weighted Moving Average (VWMA)
Parameters:
src : Source
len : Period
Returns: Volume-Weighted Moving Average (VWMA)
wma(src, len)
WMA - Weighted Moving Average
Parameters:
src : Source
len : Period
Returns: Weighted Moving Average (WMA)
zema(src, len)
Zero-Lag Exponential Moving Average (ZEMA)
Parameters:
src : Source
len : Period
Returns: Zero-Lag Exponential Moving Average (ZEMA)
zsma(src, len)
Zero-Lag Simple Moving Average (ZSMA)
Parameters:
src : Source
len : Period
Returns: Zero-Lag Simple Moving Average (ZSMA)
evwma(src, len)
EVWMA - Elastic Volume Weighted Moving Average
Parameters:
src : Source
len : Period
Returns: Elastic Volume Weighted Moving Average (EVWMA)
tt3(src, len, a1_t3)
Tillson T3
Parameters:
src : Source
len : Period
a1_t3 : Tillson T3 Volume Factor
Returns: Tillson T3
gma(src, len)
GMA - Geometric Moving Average
Parameters:
src : Source
len : Period
Returns: Geometric Moving Average (GMA)
wwma(src, len)
WWMA - Welles Wilder Moving Average
Parameters:
src : Source
len : Period
Returns: Welles Wilder Moving Average (WWMA)
ama(src, _high, _low, len, ama_f_length, ama_s_length)
AMA - Adjusted Moving Average
Parameters:
src : Source
_high : High value of bar
_low : Low value of bar
len : Period
ama_f_length : Fast EMA Length
ama_s_length : Slow EMA Length
Returns: Adjusted Moving Average (AMA)
cma(src, len)
Corrective Moving average (CMA)
Parameters:
src : Source
len : Period
Returns: Corrective Moving average (CMA)
gmma(src, len)
Geometric Mean Moving Average (GMMA)
Parameters:
src : Source
len : Period
Returns: Geometric Mean Moving Average (GMMA)
ealf(src, len, LAPercLen_, FPerc_)
Ehler's Adaptive Laguerre filter (EALF)
Parameters:
src : Source
len : Period
LAPercLen_ : Median Length
FPerc_ : Median Percentage
Returns: Ehler's Adaptive Laguerre filter (EALF)
elf(src, len, LAPercLen_, FPerc_)
ELF - Ehler's Laguerre filter
Parameters:
src : Source
len : Period
LAPercLen_ : Median Length
FPerc_ : Median Percentage
Returns: Ehler's Laguerre Filter (ELF)
edma(src, len)
Exponentially Deviating Moving Average (MZ EDMA)
Parameters:
src : Source
len : Period
Returns: Exponentially Deviating Moving Average (MZ EDMA)
pnr(src, len, rank_inter_Perc_)
PNR - percentile nearest rank
Parameters:
src : Source
len : Period
rank_inter_Perc_ : Rank and Interpolation Percentage
Returns: Percentile Nearest Rank (PNR)
pli(src, len, rank_inter_Perc_)
PLI - Percentile Linear Interpolation
Parameters:
src : Source
len : Period
rank_inter_Perc_ : Rank and Interpolation Percentage
Returns: Percentile Linear Interpolation (PLI)
rema(src, len)
Range EMA (REMA)
Parameters:
src : Source
len : Period
Returns: Range EMA (REMA)
sw_ma(src, len)
Sine-Weighted Moving Average (SW-MA)
Parameters:
src : Source
len : Period
Returns: Sine-Weighted Moving Average (SW-MA)
vwap(src, len)
Volume Weighted Average Price (VWAP)
Parameters:
src : Source
len : Period
Returns: Volume Weighted Average Price (VWAP)
mama(src, len)
MAMA - MESA Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: MESA Adaptive Moving Average (MAMA)
fama(src, len)
FAMA - Following Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: Following Adaptive Moving Average (FAMA)
hkama(src, len)
HKAMA - Hilbert based Kaufman's Adaptive Moving Average
Parameters:
src : Source
len : Period
Returns: Hilbert based Kaufman's Adaptive Moving Average (HKAMA)
Laguerre FilterThis is the Laguerre Filter by John F. Ehlers. He published this filter design in the article Time Wrap - Without Space Travel.
This study open gamma and weight of each filter for to customize the filter design.
For example, setting gamma to 0.2, 0.7, 0.8,0.8 and weight 1,2,2,1, the curve is a very nice simulation to ALMZ(50),
setting gamma to 0.3, 0.3, 0.8, 0.9 and weight of 1,3,3,1 will produce a curve close to EMA(20),
basically, the Laguerre filter can simulate various type of moving averages.
The beauty of this Filter is it use only one data point and 3 filters output to reproduce various moving average, it is
an innovative approach to develop smoothing lines, and to avoid whipsaws on the price.
Each filter can be plotted for evaluation too.
Data of the first filter allows to input any bar, eg. 0 for current bar, 1 for one previous, for the purpose to evaluate forecasting idea.
Follows Ehlers' paper, this study allows to plot the weighted average line with all filter off.
MathStatisticsKernelDensityEstimationLibrary "MathStatisticsKernelDensityEstimation"
(KDE) Method for Kernel Density Estimation
kde(observations, kernel, bandwidth, nsteps)
Parameters:
observations : float array, sample data.
kernel : string, the kernel to use, default='gaussian', options='uniform', 'triangle', 'epanechnikov', 'quartic', 'triweight', 'gaussian', 'cosine', 'logistic', 'sigmoid'.
bandwidth : float, bandwidth to use in kernel, default=0.5, range=(0, +inf), less will smooth the data.
nsteps : int, number of steps in range of distribution, default=20, this value is connected to how many line objects you can display per script.
Returns: tuple with signature: (float array, float array)
draw_horizontal(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a horizontal distribution at current location on chart.
Parameters:
distribution_x : float array, distribution points x value.
distribution_y : float array, distribution points y value.
distribution_lines : line array, array to append the distribution curve lines.
graph_lines : line array, array to append the graph lines.
graph_labels : label array, array to append the graph labels.
Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels.
draw_vertical(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a vertical distribution at current location on chart.
Parameters:
distribution_x : float array, distribution points x value.
distribution_y : float array, distribution points y value.
distribution_lines : line array, array to append the distribution curve lines.
graph_lines : line array, array to append the graph lines.
graph_labels : label array, array to append the graph labels.
Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels.
style_distribution(lines, horizontal, to_histogram, line_color, line_style, linewidth) Style the distribution lines.
Parameters:
lines : line array, distribution lines to style.
horizontal : bool, default=true, if the display is horizontal(true) or vertical(false).
to_histogram : bool, default=false, if graph style should be switched to histogram.
line_color : color, default=na, if defined will change the color of the lines.
line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both)
linewidth : int, default=na, if defined will change the line width.
Returns: void.
style_graph(lines, lines, horizontal, line_color, line_style, linewidth) Style the graph lines and labels
Parameters:
lines : line array, graph lines to style.
lines : labels array, graph labels to style.
horizontal : bool, default=true, if the display is horizontal(true) or vertical(false).
line_color : color, default=na, if defined will change the color of the lines.
line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both)
linewidth : int, default=na, if defined will change the line width.
Returns: void.
Vigia blai5VIGÍA is the latest and current version of this weighted indicator that collects, combines and harmonizes the values of four other classic indicators: RSI, MFI, Bollinger Bands and Stochastic.
It is a 2nd Generation indicator, as it does not base its algorithm on pure price data, but on its evolution (volatility, volume differences, power variations, cycle phase ...) working from first generation indicators included and mixed in the algorithm.
With the RSI we detect current power or depletion; the MFI adds the harmonization between price and volume; Bollinger Bands warn us of positions in areas close to support and resistance, and Stochastic informs us of the favorable and unfavorable phases of its cycle. VIGÍA tries to gather all this information in a single value and signal. This is how the curve of this indicator emerges.
The layout of this curve is its own and different from that of the other four separately. But the key idea of this complex indicator is to harmonize the signals.
By "harmonizing" we mean that an exaggerated value of one of the individual indicators, being part of a set, is nuanced. On the other hand, a simultaneous good look in two or more, enhances the resulting signal making it more visible and clear for trading.
One of the main effects that I have tried to enhance in the various versions of VIGÍA is its geometry, so one of the best ways to operate the indicator is divergences, which are generally quite reliable.
But, unlike so many conventional indicators, VIGÍA allows us a relatively large number of operations, which can satisfy both lovers of the most daring techniques and those who are more prudent in their trading.
In the first place, the black line is properly the Watch Signal (SV), the soul and central element of this entire invention.
On it you will see that a red line is oscillating. It is an Exponential Average of the indicator itself (by default, value 20). It is of enormous interest for trading since the SV cuts on its Average can be taken as entry and exit signals. (To check it, you just have to check it on the history of any value or index).
But there are more elements. An important change is the transformation of fixed levels into variable trading bands. This system allows the environment to adapt to changes in the asset price, recognizing and transforming itself according to the trend or laterality phases through which it runs. The signal moves above and below a central zero value and (as always) with no extreme limits, because it is important to remember that VIGÍA is not an oscillator and that prevents it from reaching a predefined extreme and being 'keyed in'.
On the upper variable band, we enter the overload zone, in Vigía's own jargon, while under the lower variable band, the situation of the indicator is on discharge. It is interesting to observe how, precisely the crossing of these variable bands by Vigía coincides on many occasions with the fastest and most productive phase of the entire price shift, far from concepts that in this phase we should already abandon as outdated and unreliable such as "overbought" or "oversold."
The last two elements remain to be described: a timid blue dashed line and that flickering central area of color called the Astro.
The blue dashed line is named Filter. It is a much more useful element than its smooth and modest journey appears. The Filter has some really fascinating features. Notice, for example, that it is the only line that I keep in visible numerical value, to know exactly when it has a positive and negative value. In periods of laterality, it is a good ally to help us make decisions. It does more things, but that is a prize reserved for whoever pays some attention to it… :-))
We will finish by Astro. Astro is an indicator with its own personality that I designed separately, it is available independently, but I ended up incorporating it into Watcher, which also happens with the Medium Proportional Volume (MPV). Both can be presented or hidden, according to the tastes or needs of the user.
Astro is an adjustable trend indicator, a very useful little tool that will help us identify the critical points where we must consider entries or changes in position. Its default value is 8 cycles, which is a good fit for daily stocks, but I have left open the possibility of modifying its period to be able to take advantage of all its power in intraday temporalities. Once again, I invite you to DO NOT believe me, but to launch the indicator on any asset and evaluate the signals that Astro has offered on its history.
Exponential RegressionIn Tradingview it is not possible to actually display arbitrary non-linear functions retrospectively.
Series objects can only depend on the current or past bars
Thus, while regression is possible, display of a non-linear curve into the past is not possible
This script is a workaround to be able to still display an exponential fit of the last n bars.
It is based on a linear regression of the log(close). The parameters of this regression are printed in the label.
To create the correct plot, these parameters have to be written into the properties of the indicator.
The functions displayed follow the expression exp(A)* exp(pot*t+d)
where d =0 for the center line, and d = +-std * upperMult for the upper and lower line respectiveley.
The parameters of the function are:
amplitude in log scale A
exponent of the exponential function pot
standard deviation of the linear regression std
number of bars of the current chart bindex
multiplicator of the std of the upper and lower exponential line upperMult and lowerMult +
This code is a version of the built-in "linear regression" script of Tradingview alztered by Forza so it can be plotted correctly on logarithmic charts
The code of Forza was further adjusted by altustro to be able to plot the full exponential curve also in regular scale
Penrose DiagramGreetings Traders! This is my measly 2 dimensional non-curved take on a Penrose Diagram .
What is a Penrose Diagram?
jila.colorado.edu
"A Penrose diagram is a kind of spacetime diagram arranged to make clear the complete causal structure of any given geometry. They are an indispensable map for navigating inside a black hole. Roger Penrose, who invented this kind of diagram in the early 1950s, himself calls them conformal diagrams." - Source: jila.colorado.edu
Penrose Diagrams allow an understanding of an infinite surface by projecting 4D Spheres on 2 Dimension Squares. The 4D shape within the 2D Square has the ability to reference a definable edge on an infinite boundary.
I thought this concept interesting, as I tend to hold a viewpoint of price being infinite in its own nature, and tried to combine the concepts as best I could.
In a true Penrose Diagram, you'd see curvature as you reached the edge of each Diagram Square to represent the curvature of space-time and the distorted boundary. However due to limitations of Pine and perhaps my own abilities, I decided to go the cheap route and opt for straight lines, as well as beginning each new Penrose Diagram on a new Session Start.
Also in this version, you will notice on some instruments that, as an example, two triangles overlap each other when you Extend the Penrose Diagram. The result of this is intentional and is due to the calculation of the first Triangle for the Penrose Diagram. The data points for each triangle are variable upon the average highest/lowest point from Open Price and the Max/Min highest/lowest point from Open Price over a variable amount of days. For typical instruments such as Futures products and Forex, it is common to see differences between the Highest High Breakout & Lowest Low Breakout, and therefore a difference between the true center of the Triangle and the corresponding angles. I prefer to use a 20-Day Average Period for Forex and Futures Products, for Crypto a 30 Day Average Period, all adjustable from the options menu.
Due to the differences between the data points(particularly the difference from the Session Open to the Highest High/ Lowest Low), the initial values for the extended Penrose Diagram will sometimes overlap with the initial triangle for Penrose Diagram creation. However keep in mind, all triangles are exactly the same, just rotated 180°and translated above and below the current Session Open Price. You can confirm this yourself using the handy Triangle Tool in your Drawing Tools directly from your TradingView Chart.
Haven't managed to play around on all instruments yet, so give it a whirl, and any feedback is greatly appreciated. Hope you enjoy!
-@DayTradingOil
Auto PitchFan, Fib Extension/Retracement and ZigZag by DGT Aᴜᴛᴏ PɪᴛᴄʜFᴀɴ, Fɪʙ Exᴛᴇɴꜱɪᴏɴ/Rᴇᴛʀᴀᴄᴇᴍᴇɴᴛ ᴀɴᴅ ZɪɢZᴀɢ
This study aim to automate PitchFan drawing tool and combines it with Fibonacci Extentions/Retracements and ZigZag.
Common setting options, applied to all components of the study
Deviation , is a multiplier that affects how much the price should deviate from the previous pivot in order for the bar to become a new pivot. Increasing its value is one way to get higher timeframe Levels
Depth , affects the minimum number of bars that will be taken into account when building
Historical PitchFan / Fibonacci Levels option will allow plotting of PitchFan / Fibonacci Levels on previous Pivot Highs/Lows
█ PɪᴛᴄʜFᴀɴ — is a set of rays spreading out of the point of a trend's beginning. These rays inclined with the coefficients formed by a Fibonacci number sequence. It is recommended to adjust the Pitchfan plottings to fit after the first wave of the trend has passed and the correction has clearly begun.
PitchFan rays corresponding to Fibonacci levels appear on a chart and represent inclined lines of support and resistance. Price areas near these rays are estimated areas from which the price can turn around or make a significant rebound. The whole logic of working with a pitchfan is based on one simple idea - if the price has bounced off the level, then the correction is likely to end, and the trend will continue. If the price has broken the first resistance, you should wait for the next level test
- Automatically plots PitchFan Rays, based on Pivot Lows/Highs.
- Ability to set ALERTs for each PitchFan Ray Level
- PitchFan Labels displays the price of the line at the last bar, the price value will be recalculated in each new bar
Please check the link provided below with the educational post of how to apply pitchfork, application of pitchfan is same keeping in mind the recommendation stated above
█ Fɪʙ Rᴇᴛʀᴀᴄᴇᴍᴇɴᴛs / Exᴛᴇɴꜱɪᴏɴꜱ
Fibonacci retracements is a popular instrument used by technical analysts to determine support and resistance areas. In technical analysis, this tool is created by taking two extreme points (usually a peak and a trough) on the chart and dividing the vertical distance by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%.
Fibonacci extensions are a tool that traders can use to establish profit targets or estimate how far a price may travel AFTER a retracement/pullback is finished. Extension levels are also possible areas where the price may reverse.
IMPORTANT NOTE: Fibonacci extensions option may require to do further adjustment of the study parameters for proper usage. Extensions are aimed to be used when a trend is present and they aim to measure how far a price may travel AFTER a retracement/pullback. I will strongly suggest users of this study to check the education post for further details where to use extensions and where to use retracements
- Automatically plots possible Support and Resistance levels, based on Pivot Lows/Highs.
- Ability to set ALERTs for each Fibonacci Extension/Retracement Level
- Labels displays the level and the level price
█ Zɪɢ Zᴀɢ — The Zig Zag indicator is used to help identify price trends and changes in price trends. The Zig Zag indicator plots points on a chart whenever prices reverse by a percentage greater than a Deviation variable. Straight lines are then drawn, connecting these points. The Zig Zag indicator serves base for PitchFan and Fibonacci Retracements / Extensions
█ OTHER
PitchFan is often used in combination with the other indicators and/or drawing tools such as Fibonacci Retracement, Fibonacci Channels, Fibonacci Time Zone and others. It allows identify the most powerful areas from which price can turn and to get more accurate trading signal
Andrews’ Pitchfork, how to apply pitchfork and automated pitchfork study
Fibonacci Fans, how to apply fibonacci fans and automated fibonacci speed and resistance fans study
Fibonacci Extension / Retracement, where to use extensions and where to use retracements and automated fibonacci extension / retracement / pivot points study
Others
Fibonacci Channels, how to apply fibonacci channels and automated fibonacci channels study
Linear Regression Channels, , what linear regression channels are? and linear regression channel/curve/slope study
Disclaimer :
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Bitcoin Bulls and Bears by @dbtrBitcoin 🔥 Bulls & Bears 🔥
v1.0
This free-of-charge BTC market analysis indicator helps you better understand what's going with Bitcoin from a high-level perspective. At a glance, it will give you an immediate understanding of Bitcoin’s historic price channel dating back to 2011, past and current market cycles, as well as current key support levels.
Usage
Use this indicator with any BTCUSD pairs , ideally with a long price history (such as BNC:BLX )
We recommend to use this indicator in log mode, combined with Weekly or Monthly timeframe.
Features
🕵🏻♂️ Historic price channel curve since 2011
🚨 Bull & bear market cycles (dynamic)
🔥 All-time highs (dynamic)
🌟 Weekly support (dynamic, based on 20 SMA )
💪 Long-term support (channel bottom)
🔝 Potential future price targets (dynamic)
❎ Overbought RSI coloring
📏 Log/non-log support
🌚 Dark mode support
Remarks
With exception of the price channel curve, anything in this indicator is calculated dynamically , including bull/bear market cycles (based on a tweaked 20SMA), ATHs, and so on. As a result, historic market cycles may not be 100% accurately reflected and may also differ slightly in between various time-frames (closest result: Monthly). The indicator may even consider periods of heavy ups/downs as their own market cycles, even though they weren’t. Due to its dynamic nature, this indicator can however adapt to the future and helps you quickly identify potential changes in market structure, even if the indicator is no longer updated.
On top of that bullmarket cycles (colored in green) feature an ingrained RSI: the darker the green color, the more the RSI is overbought and close to a correction (darkest color in the chart = 90 Weekly RSI). In comparison with past bull cycles, it helps you easily spot potential reversal zones.
Thanks
Thanks to @quantadelic and @mabonyi which both have worked on the BTC "growth zones" indicator including the price channel, of which I have used parts of the code as well as the actual price channel data.
Follow me
Follow me here on TradingView to be notified as soon as new free and premium indicators and trading strategies are published. Inquire me for any other requests.
Enjoy & happy trading!
Moving RegressionMoving Regression is a generalization of moving average and polynomial regression.
The procedure approximates a specified number of prior data points with a polynomial function of a user-defined degree. Then, polynomial interpolation of the last data point is used to construct a Moving Regression time series.
Application:
Moving Regression allows one to smooth noise on the analyzed chart, assess momentum, confirm trends, and establish areas of support and resistance.
In addition, it can be used as a simple stand-alone forecasting method to identify trend direction and trend reversal points. When the local polynomial is predicted to move up in the next time step, the color of the Moving Regression curve will be green. Otherwise, the color of the curve is red. This function is (de)activated using the Predict Trend Direction flag.
Selecting the model parameters:
The effects of the moving window Length and the Local Polynomial Degree are confounded. This allows for finding the optimal trade-off between noise (variance) and lag (bias). Higher Length and lower Polynomial Degree (such as 1, i.e. linear), will result in "smoother" time series but at the cost of greater lag. Increasing the Polynomial Degree to, for example, 2 (squared) while maintaining the Length will diminish the lag and thus compromise the noise-lag tradeoff.
Relation to other methods:
When the degree of the local polynomial is set to 0 (i.e., fitting data to a constant level), the Moving Regression time series exactly matches the Simple Moving Average of the same length.
ALL TIME HIGHHH BTC!
Ola Amigos,
Weekly indicator.
This indicator, more commonly called "the Pi indicator", is based on the use of two moving averages: mm50 and mm16.
By dividing a mm50 in order to try to get as close as possible to PI, we fall back on mm16.
50/16 = 3.13
When the white curve passes above the blue curve, we are on historic highs.
It is very easy to test on the BLX, being the graph with the most history.
We can say thank you to JDC for this nice discovery which remains very useful for the rest of the BTC run.
Love!
Ola Amigos,
Weekly indicator.
Cet indicateur, plus communément appelé "the Pi indicator" est basé sur l'utilisation de deux moyennes mobiles : mm50 et mm16.
En divisant une mm50 afin de chercher a essayer de se rapprocher le plus pres de PI, on retombe sur la mm16.
50/16= 3.13
Lorsque la courbe blanche passe au dessus de la courbe bleue, on se trouve sur des plus hauts historiques.
Il est très facile a tester sur le BLX , étant le graph avec le plus d'historique.
On peut dire merci a JDC pour cette jolie découverte qui reste tres utile pour la suite du run BTC .
Love!
EMA21 Speed & AccelThe script calculates an plots first and second derivatives of a EMA of "length" periods, with a default value of 21 periods.
- Blue curve is the first derivative of the EMA, which can be interpreted as the "speed" , "slope", or percentage of gains (or loses) walking over the EMA, measured in % per period. If timeframe is Days, it will show a %/day on the scale @ the right of graphic.
- Fuchsia curve is the second derivative of the EMA, and can be assumed to be the "acceleration" or driving force that could augment or diminish the EMA Speed.
When Speed & Acceleration ar both >=0, EMA is in positive rally, and becoming stepper, so the bacground is colored green.
First and second derivatoves are performed using "basis functions", as are applied in FEM implementation.
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El Script calcula y plotea la primera y segunga derivada de una EMA de "length" períodos, con un valor por defecto de 21.
- La curva azul es la primera derivada de la EMA; que puede ser interpretada conmo su "velocidad", "pendiente" o % de ganancia o pérdida que se tendría sobre la EMA. Cuando la unidad de tiempo del gráfico es Días, permite visualizar en la escala de la derecha el % de ganancia o pérdida por día.
- La curva Fucsia muestra la segunda derivada, o "Aceleración", que se puede interpretar como la fuerza que puede aumentar o dismu¿inuir la pendiente de la EMA.
Cuando la Velocidad y Aceleración son mayores que cero, las ganancias aumentan cada período, y el findo de colorea de verde.
Las derivadas primera y segunda se calculan usando técnicas de funciones de forma, como las aplicadas en el MEF.
VAMA Volume Adjusted Moving AverageRichard Arms' Volume Adjusted Moving Average
Settings:
• Inp Avg Vol: Input - Purist method but not intended for live analysis, to retroactively alter MA curve enter Avg Vol from value shown on label into Use Avg Vol field.
• Inp Avg Vol: Current - Live method using current volume , to retroactively alter past MA curve toggle any setting back and forth to force recalculation.
• Inp Avg Vol: Subset - Similar to Current, but uses a subset rather than all bars for avg vol.
• Use Avg Vol - Used for Inp Avg Vol: Input mode. Enter volume from Avg Vol label here after each new bar closes, label will turn green, else red.
• Subset Data - Lookback length used for Inp Avg Vol: Subset mode.
• VAMA Length - Specified number of volume ratio buckets to be reached.
• Volume Incr - Size of volume ratio buckets.
• VAMA Source - Price used for volume weighted calculations.
• VAMA Strict - Must meet desired volume requirements, even if N bars has to exceed VAMA Length to do it.
• Show Avg Vol Label - Displays label on chart of total chart volume.
Notes: VAMA was created by Richard Arms. It utilizes a period length that is based on volume increments rather than time. It is an unusual indicator in that it cannot be used in some platforms in realtime mode as Arms had originally intended. VAMA requires that the average volume first be calculated for the entire chart duration, then that average volume is used to derive the variable adaptive length of the moving average. The consequence of this is that with each new bar, the new average volume alters the moving average period for the entire history. Since Pine scripts evaluate all historical bars only once upon initial script execution, there is no way to automatically shift the previous moving average values retroactively once a new bar has formed. Thus the historical plot of the moving average cannot be updated in realtime, but instead can only plot through previous bar that existed upon load or reinitialization through changing some setting.
Setting Use Avg Vol to Input mode the average volume through previous bar shown in label can be entered (input) into the Inp Avg Vol setting after each new bar closes. Entering this total chart volume forces the script to reevaluate historical bars which in turn allows the historical moving average to update the plot. When using Input mode the color of the label is green when Inp Avg Vol value matches current label value, the label color red signifies Inp Avg Vol value has not been entered or is stale.
Setting Use Avg Vol to Current mode allows the script to correctly calculate and plot the correct moving average upon initial load and the realtime moving average moving forward, but can not retroactively alter the plot of the past moving average unless some change is made in the script settings, such as toggling the Use Avg Vol from Current to some other choice and then back to Current .
Setting Use Avg Vol to Subset mode uses a rolling window of volume data to calculate the average volume and can be used in realtime, but should be noted it is a deviation from Richard Arms' original specification.
VAMA info: "Trading Without Fear" by Richard W Arms, Jr, www.fidelity.com
NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
Shapeshifting Moving Average - Switching From Low-Lag To SmoothThe term "shapeshifting" is more appropriate when used with something with a shape that isn't supposed to change, this is not the case of a moving average whose shape can be altered by the length setting or even by an external factor in the case of adaptive moving averages, but i'll stick with it since it describe the purpose of the proposed moving average pretty well.
In the case of moving averages based on convolution, their properties are fully described by the moving average kernel ( set of weights ), smooth moving averages tend to have a symmetrical bell shaped kernel, while low lag moving averages have negative weights. One of the few moving averages that would let the user alter the shape of its kernel is the Arnaud Legoux moving average, which convolve the input signal with a parametric gaussian function in which the center and width can be changed by the user, however this moving average is not a low-lagging one, as the weights don't include negative values.
Other moving averages where the user can change the kernel from user settings where already presented, i posted a lot of them, but they only focused on letting the user decrease or increase the lag of the moving average, and didn't included specific parameters controlling its smoothness. This is why the shapeshifting moving average is proposed, this parametric moving average will let the user switch from a smooth moving average to a low-lagging one while controlling the amount of lag of the moving average.
Settings/Kernel Interaction
Note that it could be possible to design a specific kernel function in order to provide a more efficient approach to today goal, but the original indicator was a simple low-lag moving average based on a modification of the second derivative of the arc tangent function and because i judged the indicator a bit boring i decided to include this parametric particularity.
As said the moving average "kernel", who refer to the set of weights used by the moving average, is based on a modification of the second derivative of the arc tangent function, the arc tangent function has a "S" shaped curve, "S" shaped functions are called sigmoid functions, the first derivative of a sigmoid function is bell shaped, which is extremely nice in order to design smooth moving averages, the second derivative of a sigmoid function produce a "sinusoid" like shape ( i don't have english words to describe such shape, let me know if you have an idea ) and is great to design bandpass filters.
We modify this 2nd derivative in order to have a decreasing function with negative values near the end, and we end up with:
The function is parametric, and the user can change it ( thus changing the properties of the moving average ) by using the settings, for example an higher power value would reduce the lag of the moving average while increasing overshoots. When power < 3 the moving average can act as a slow moving average in a moving average crossover system, as weights would not include negative values.
Here power = 0 and length = 50. The shapeshifting moving average can approximate a simple moving average with very low power values, as this would make the kernel approximate a rectangular function, however this is only a curiosity and not something you should do.
As A Smooth Moving Average
“So smooth, and so tranquil. It doesn't get any quieter than this”
A smooth moving average kernel should be : symmetrical, not to width and not to sharp, bell shaped curve are often appropriates, the proposed moving average kernel can be symmetrical and can return extremely smooth results. I will use the Blackman filter as comparison.
The smooth version of the moving average can be used when the "smooth" setting is selected. Here power can only be an even number, if power is odd, power will be equal to the nearest lowest even number. When power = 0, the kernel is simply a parabola:
More smoothness can be achieved by using power = 2
In red the shapeshifting moving average, in green a Blackman filter of both length = 100. Higher values of power will create lower negative values near the border of the kernel shape, this often allow to retain information about the peaks and valleys in the input signal. Power = 6 approximate the Blackman filter pretty well.
Conclusion
A moving average using a modification of the 2nd derivative of the arc tangent function as kernel has been presented, the kernel is parametric and allow the user to switch from a low-lag moving average where the lag can be increased/decreased to a really smooth moving average.
As you can see once you get familiar with a function shape, you can know what would be the characteristics of a moving average using it as kernel, this is where you start getting intimate with moving averages.
On a side note, have you noticed that the views counter in posted ideas/indicators has been removed ? This is truly a marvelous idea don't you think ?
Thanks for reading !
Grand Trend Forecasting - A Simple And Original Approach Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator".
Time series forecasting is an area dealing with the prediction of future values of a series by using a specific model, the model is the main tool that is used for forecasting, and is often an expression based on a set of predictor terms and parameters, for example the linear regression (model) is a 1st order polynomial (expression) using 2 parameters and a predictor variable ax + b . Today we won't be using the linear regression nor the LSMA.
In time series analysis we can describe the time series with a model, in the case of the closing price a simple model could be as follows :
Price = Trend + Cycles + Noise
The variables of the model are the components, such model is additive since we add the component with each others, we should be familiar with each components of the model, the trend represent a simple long term variation of high amplitude, the cycles are periodic fluctuations centered around 0 of varying period and amplitude, the noise component represent shorter term irregular variations with mean 0.
As a trader we are mostly interested by the cycles and the trend, altho the cycles are relatively more technical to trade and can constitute parasitic fluctuations (think about retracements in a trend affecting your trend indicator, causing potential false signals).
If you are curious, in signal processing combining components has a specific name, "synthesis" , here we are dealing with additive synthesis, other type of synthesis are more specific to audio processing and are relatively more complex, but could be used in technical analysis.
So what to do with our components ? If we want to trade the trend, we should estimate right ? Estimating the trend component involve removing the cycle and noise component from the price, if you have read stuff about filters you should know where i'am going, yep, we should use filters, in the case of keeping the trend we can use a simple moving average of relatively high period, and here we go.
However the lag problem, which is recurrent, come back again, we end up with information easier to interpret (here the trend, which is a simple fluctuation such as a line or other smooth curve) at the cost of decision timing, that is unfortunate but as i said the information, here the moving average output, is relatively simple, and could be easily forecasted right ? If you plot a moving average of high period it would be easier for you to forecast its future values. And thats what we aim to do today, provide an estimate of the trend that should be easy to forecast, and should fit to the price relatively well in order to produce forecast that could determine the position of future closing prices observations.
Estimating And Forecasting The Trend
The parameter of the indicator dealing with the estimation of the trend is length , with higher values of length attenuating the cycle and noise component in the price, note however that high values of length can return a really long term trend unlike a simple moving average, so a small value of length, 14 for example can still produce relatively correct estimate of trend.
here length = 14.
The rough estimate of the trend is t in the code, and is an IIR filter, that is, it is based on recursion. Now i'll pass on the filter design explanation but in short, weights are constants, with higher weights allocated to the previous length values of the filter, you can see on the code that the first part of t is similar to an exponential moving average with :
t(n) = 0.9t(n-length) + 0.1*Price
However while the EMA only use the precedent value for the recursion, here we use the precedent length value, this would just output a noisy and really slow output, therefore in order to create a better fit we add : 0.9*(t(n-length) - t(n-2length)) , and this create the rough trend estimate that you can see in blue. On the parameters, 0.9 is used since it gives the best estimate in my opinion, higher values would create more periodic output and lower values would just create a rougher output.
The blue line still contain a residual of the cycle/noise component, this is why it is smoothed with a simple moving average of period length. If you are curious, a filter estimating the trend but still containing noisy fluctuations is called "Notch" filter, such filter would depending on the cutoff remove/attenuate mid term cyclic fluctuations while preserving the trend and the noise, its the opposite of a bandpass filter.
In order to forecast values, we simply sum our trend estimate with the trend estimate change with period equal to the forecasting horizon period, this is a really really simple forecasting method, but it can produce decent results, it can also allows the forecast to start from the last point of the trend estimate.
Using The Indicator
We explained the length parameter in the precedent section, src is the input series which the trend is estimated, forecast determine the forecasting horizon, recommend values for forecast should be equal to length, length/2 or length*2, altho i strongly recommend length.
here length and forecast are both equal to 14 .
The corrective parameter affect the trend estimate, it reduce the overshoot and can led to a curve that might fit better to the price.
The indicator with the non corrective version above, and the corrective one below.
The source parameter determine the source of the forecast, when "Noisy" is selected the source is the blue line, and produce a noisy forecast, when "Smooth" is selected the source is the moving average of t , this create a smoother forecast.
The width interval control...the width of the intervals, they can be seen above and under the forecast plot, they are constructed by adding/subtracting the forecast with the forecast moving average absolute error with respect to the price. Prediction intervals are often associated with a probability (determining the probability of future values being between the interval) here we can't determine such probability with accuracy, this require (i think) an analysis of the forecasting distribution as well as assumptions on the distribution of the forecasting error.
Finally it is possible to see historical forecasts, that is, forecasts previously generated by checking the "Show Historical Forecasts" option.
Examples
Good forecasts mostly occur when the price is close to the trend estimate, this include the following highlighted periods on AMD 15TF with default settings :
We can see the same thing at the end of EURUSD :
However we can't always obtain suitable fits, here it is isn't sufficient on BTCUSD :
We can see wide intervals, we could change length or use the corrective option to get better results, another option is to use a log scale.
We will end the examples with the log SPX, who posses a linear trend, so for example a linear model such as a linear regression would be really adapted, lets see how the indicator perform :
Not a great fit, we could try to use an higher length value and use "Smooth" :
Most recent fits are quite decent.
Conclusions
A forecasting indicator has been presented in this post. The indicator use an original approach toward estimating the trend component in the closing price. Of course i should have given statistics related to the forecasting error, however such analysis is worth doing with better methods and in more advanced environment allowing for optimization.
But we have learned some stuff related to signal processing as well as time series analysis, seeing a time series as the sum of various components is really helpful when it comes to make sense of chaotic and noisy series and is a basic topic in time series analysis.
You can see that in this new year i work harder on the visual of my indicators without trying to fall in the label addict trap, something that i wasn't really doing before, let me know what do you think of it.
Thanks for reading !
Trend Following or Mean RevertingThe strategy checks nature of the instruments. It Buys if the close is greater than yesterday's high, reverse the position if the close is lower than yesterday's low and repeat the process.
1. If it is trend following then the equity curve will be in uptrend
2. If it is mean reverting then the equity curve will be downtrend
Thanks to Rayner Teo.
Grover Llorens Activator [alexgrover & Lucía Llorens] Trailing stops play a key role in technical analysis and are extremely popular trend following indicators. Their main strength lie in their ability to minimize whipsaws while conserving a decent reactivity, the most popular ones include the Supertrend, Parabolic SAR and Gann Hilo activator. However, and like many indicators, most trailing stops assume an infinitely long trend, which penalize their ability to provide early exit points, this isn't the case of the parabolic SAR who take this into account and thus converge toward the price at an increasing speed the longer a trend last.
Today a similar indicator is proposed. From an original idea of alexgrover & Lucía Llorens who wanted to revisit the classic parabolic SAR indicator, the Llorens activator aim to converge toward the price the longer a trend persist, thus allowing for potential early and accurate exit points. The code make use of the idea behind the price curve channel that you can find here :
I tried to make the code as concise as possible.
The Indicator
The indicator posses 2 user settings, length and mult , length control the rate of convergence of the indicator, with higher values of length making the indicator output converge more slowly toward the price. Mult is also related with the rate of convergence, basically once the price cross the trailing stop its value will become equal to the previous trailing stop value plus/minus mult*atr depending on the previous trailing stop value, therefore higher values of mult will require more time for the trailing stop to reach the closing price, use higher values of mult if you want to avoid potential whipsaws.
Above the indicator with slow convergence time (high length) and low mult.
Points with early exit points are highlighted.
Usage For Oscillators
The difference between the closing price and an overlay indicator can provide an oscillator with characteristics depending on the indicators used for differencing, Lucía Llorens stated that we should find indicators for differencing that highlight the cycles in the price, in other terms : Price - Signal , where we want to find Signal such that we maximize the visibility of the cycles, it can be demonstrated that in the case where the closing price is an additive model : Trend + Cycles + Noise , the zero lag estimation of the Trend component can allow for the conservation of the cycle and noise component, that is : Price - Estimate(Trend) , for example the difference between the price and moving average isn't optimal because of the moving average lag, instead the use of zero lag moving averages is more suitable, however the proposed indicator allow for a surprisingly good representation of the cycles when using differencing.
The normalization of this oscillator (via the RSI) allow to make the peak amplitude of the cycles more constant. Note however that such method can return an output with a sign inverse to the one of the original cycle component.
Conclusion
We proposed an indicator which share the logic of the SAR indicator, that is using convergence toward the price in order to provide early exit points detection. We have seen that this indicator can be used to highlight cycles when used for differencing and i don't exclude publishing more indicators based on this method.
Lucía Llorens has been a great person to work with, and provided enormous feedback and support while i was coding the indicator, this is why i include her in the indicator name as well as copyright notice. I hope we can make more indicators togethers in the future.
(altho i was against using buy/sells labels xD !)
Thanks for reading !
