Performance Summary and Shading (Offset Version)Modified "Recession and Crisis Shading" Indicator by @haribotagada (Original Link: )
The updated indicator accepts a days offset (positive or negative) to calculate performance between the offset date and the input date.
Potential uses include identifying performance one week after company earnings or an FOMC meeting.
This feature simplifies input by enabling standardized offset dates, while still allowing flexibility to adjust ranges by overriding inputs as needed.
Summary of added features and indicator notes:
Inputs both positive and negative offset.
By default, the script calculates performance from the close of the input date to the close of the date at (input date + offset) for positive offsets, and from the close of (input date - offset) to the close of the input date for negative offsets. For example, with an input date of November 1, 2024, an offset of 7 calculates performance from the close on November 1 to the close on November 8, while an offset of -7 calculates from the close on October 25 to the close on November 1.
Allows user to perform the calculation using the open price on the input date instead of close price
The input format has been modified to allow overrides for the default duration, while retaining the original capabilities of the indicator.
The calculation shows both the average change and the average annualized change. For bar-wise calculations, annualization assumes 252 trading days per year. For date-wise calculations, it assumes 365 days for annualization.
Carries over all previous inputs to retain functionality of the previous script. Changes a few small settings:
Calculates start to end date performance by default instead of peak to trough performance.
Updates visuals of label text to make it easier to read and less transparent.
Changed stat box color scheme to make the text easier to read
Updated default input data to new format of input with offsets
Changed default duration statistic to number of days instead of number of bars with an option to select number of bars.
Potential Features to Add:
Import dataset from CSV files or by plugging into TradingView calendar
Example Input Datasets:
Recessions:
2020-02-01,COVID-19,59
2007-12-01,Subprime mortgages,547
2001-03-01,Dot-com,243
1990-07-01,Oil shock,243
1981-07-01,US unemployment,788
1980-01-01,Volker,182
1973-11-01,OPEC,485
Japan Revolving Door Elections
2006-09-26, Shinzo Abe
2007-09-26, Yasuo Fukuda
2008-09-24, Taro Aso
2009-09-16, Yukio Hatoyama
2010-07-08, Naoto Kan
2011-09-02, Yoshihiko Noda
Hope you find the modified indicator useful and let me know if you would like any features to be added!
Komut dosyalarını "2020年+美元兑日元汇率" için ara
STANDARD DEVIATION INDICATOR BY WISE TRADERWISE TRADER STANDARD DEVIATION SETUP: The Ultimate Volatility and Trend Analysis Tool
Unlock the power of STANDARD DEVIATIONS like never before with the this indicator, a versatile and comprehensive tool designed for traders who seek deeper insights into market volatility, trend strength, and price action. This advanced indicator simultaneously plots three sets of customizable Deviations, each with unique settings for moving average types, standard deviations, and periods. Whether you’re a swing trader, day trader, or long-term investor, the STANDARD DEVIATION indicator provides a dynamic way to spot potential reversals, breakouts, and trend-following opportunities.
Key Features:
STANDARD DEVIATIONS Configuration : Monitor three different Bollinger Bands at the same time, allowing for multi-timeframe analysis within a single chart.
Customizable Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, and VWMA to calculate the basis of each band according to your preferred method.
Dynamic Standard Deviations: Set different standard deviation multipliers for each band to fine-tune sensitivity for various market conditions.
Visual Clarity: Color-coded bands with adjustable thicknesses provide a clear view of upper and lower boundaries, along with fill backgrounds to highlight price ranges effectively.
Enhanced Trend Detection: Identify potential trend continuation, consolidation, or reversal zones based on the position and interaction of price with the three bands.
Offset Adjustment: Shift the bands forward or backward to analyze future or past price movements more effectively.
Why Use Triple STANDARD DEVIATIONS ?
STANDARD DEVIATIONS are a popular choice among traders for measuring volatility and anticipating potential price movements. This indicator takes STANDARD DEVIATIONS to the next level by allowing you to customize and analyze three distinct bands simultaneously, providing an unparalleled view of market dynamics. Use it to:
Spot Volatility Expansion and Contraction: Track periods of high and low volatility as prices move toward or away from the bands.
Identify Overbought or Oversold Conditions: Monitor when prices reach extreme levels compared to historical volatility to gauge potential reversal points.
Validate Breakouts: Confirm the strength of a breakout when prices move beyond the outer bands.
Optimize Risk Management: Enhance your strategy's risk-reward ratio by dynamically adjusting stop-loss and take-profit levels based on band positions.
Ideal For:
Forex, Stocks, Cryptocurrencies, and Commodities Traders looking to enhance their technical analysis.
Scalpers and Day Traders who need rapid insights into market conditions.
Swing Traders and Long-Term Investors seeking to confirm entry and exit points.
Trend Followers and Mean Reversion Traders interested in combining both strategies for maximum profitability.
Harness the full potential of STANDARD DEVIATIONS with this multi-dimensional approach. The "STANDARD DEVIATIONS " indicator by WISE TRADER will become an essential part of your trading arsenal, helping you make more informed decisions, reduce risks, and seize profitable opportunities.
Who is WISE TRADER ?
Wise Trader is a highly skilled trader who launched his channel in 2020 during the COVID-19 pandemic, quickly building a loyal following. With thousands of paid subscribed members and over 70,000 YouTube subscribers, Wise Trader has become a trusted authority in the trading world. He is known for his ability to navigate significant events, such as the Indian elections and stock market crashes, providing his audience with valuable insights into market movements and volatility. With a deep understanding of macroeconomics and its correlation to global stock markets, Wise Trader shares informed strategies that help traders make better decisions. His content covers technical analysis, trading setups, economic indicators, and market trends, offering a comprehensive approach to understanding financial markets. The channel serves as a go-to resource for traders who want to enhance their skills and stay informed about key market developments.
Bitcoin Logarithmic Growth Curve 2024The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements.
The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity.
Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences.
The Bitcoin logarithmic growth curve has the following function:
y = 10^(a * log10(x) - b)
In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart.
How is it made (You can skip this section if you’re not a fan of math):
To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows:
113, 18.55
240, 1004.42
451, 19128.27
655, 65502.47
The same process was applied to the bear market low values:
103, 2.48
267, 211.03
471, 3192.87
676, 16255.15
Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values.
For the cycle peak (x,y) values:
2.053, 1.268
2.380, 3.002
2.654, 4.282
2.816, 4.816
And for the bear market low (x,y) values:
2.013, 0.394
2.427, 2.324
2.673, 3.504
2.830, 4.211
Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary).
Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship.
The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value.
Below are the results:
It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression.
These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063).
The final bull cycle function is:
y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324)
The final bear cycle function is:
y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063)
The main Criticisms of growth curve models:
The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak.
This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band.
Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post:
This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns.
Our recommendations:
Drawing on the concept of diminishing returns, we propose alternative settings for this model that we believe provide a more realistic forecast aligned with this theory. The adjusted parameters apply only to the top band: a-value: 3.637 ± 0.2343 and b-parameter: -5.369 ± 0.6264. However, please note that these values are highly subjective, and you should be aware of the model's limitations.
Conservative bull cycle model:
y = 10^(3.637 ± 0.2343 * log10(x) - 5.369 ± 0.6264)
Season ChartThis overlay is built on the idea of seasonal charts.
It is constructed by taking the percentage change from each close and recording that change for every trading day of any year that is within the sample. We then take the average for each day of all the years.
These averages are then cumulated to create the chart as per traditional seasonal chart construction.
I have also taken a trimmed mean of the averages to try and dampen the impact one off moves that may have a dramatic effect on the daily averages (for example the crash to $0 in oil in April 2020) however, even removing 10% may not guarantee one off moves won’t affect the average.
The construction of the chart is completely dependent on the data provided by TradingView and so it is recommended that if longer sample sizes are used, the user go back to check that the years contained within the sample have a full history. Some data may have large gaps in their history and this can distort the seasonality readings.
I have attempted to align the chart with the first trading day of the year, but the start of some months may be out by a day or two as it becomes difficult to track all weeks with differing market holidays closures each year and this in turn varies the total amount of actual trading days in each year as well as leap years.
This overlay is designed for the Daily time frame only and will not work on Crypto or any other instrument that trades outside of usual business weekdays. Future updates may include the ability to adapt to Crypto instruments.
All feedback and comments welcome!
Wedge Pop & Drop [QuantVue]A "Wedge Pop" is a trading pattern popularized by Oliver Kell, a notable trader who won the 2020 US Investing Championship with a remarkable return of 941%. This pattern, often referred to as "The Money Pattern" in his trading strategy, serves as a critical signal indicating the beginning of a new uptrend in a stock.
A Wedge Pop occurs when a stock first trades up through the moving averages after reaching a downside extension. Conversely, a Wedge Drop refers to the first time a stock trades down through the moving averages after reaching an upside extension.
How the Indicator Works:
The indicator uses the Average True Range (ATR) and the 10-period Exponential Moving Average (10 EMA) to identify upside and downside extensions. An upside extension occurs when the low of the current bar is greater than 1.5 (default) times the ATR above the moving average. A downside extension occurs when the high of the current bar is less than 1.5 times the ATR below the moving average.
Once an extension has been reached, the first time the security trades back through the moving averages, it triggers a Wedge Pop/Drop.
Give this indicator a BOOST and COMMENT your thoughts below!
We hope you enjoy.
Cheers!
Intellect_city - Halvings Bitcoin CycleWhat is halving?
The halving timer shows when the next Bitcoin halving will occur, as well as the dates of past halvings. This event occurs every 210,000 blocks, which is approximately every 4 years. Halving reduces the emission reward by half. The original Bitcoin reward was 50 BTC per block found.
Why is halving necessary?
Halving allows you to maintain an algorithmically specified emission level. Anyone can verify that no more than 21 million bitcoins can be issued using this algorithm. Moreover, everyone can see how much was issued earlier, at what speed the emission is happening now, and how many bitcoins remain to be mined in the future. Even a sharp increase or decrease in mining capacity will not significantly affect this process. In this case, during the next difficulty recalculation, which occurs every 2014 blocks, the mining difficulty will be recalculated so that blocks are still found approximately once every ten minutes.
How does halving work in Bitcoin blocks?
The miner who collects the block adds a so-called coinbase transaction. This transaction has no entry, only exit with the receipt of emission coins to your address. If the miner's block wins, then the entire network will consider these coins to have been obtained through legitimate means. The maximum reward size is determined by the algorithm; the miner can specify the maximum reward size for the current period or less. If he puts the reward higher than possible, the network will reject such a block and the miner will not receive anything. After each halving, miners have to halve the reward they assign to themselves, otherwise their blocks will be rejected and will not make it to the main branch of the blockchain.
The impact of halving on the price of Bitcoin
It is believed that with constant demand, a halving of supply should double the value of the asset. In practice, the market knows when the halving will occur and prepares for this event in advance. Typically, the Bitcoin rate begins to rise about six months before the halving, and during the halving itself it does not change much. On average for past periods, the upper peak of the rate can be observed more than a year after the halving. It is almost impossible to predict future periods because, in addition to the reduction in emissions, many other factors influence the exchange rate. For example, major hacks or bankruptcies of crypto companies, the situation on the stock market, manipulation of “whales,” or changes in legislative regulation.
---------------------------------------------
Table - Past and future Bitcoin halvings:
---------------------------------------------
Date: Number of blocks: Award:
0 - 03-01-2009 - 0 block - 50 BTC
1 - 28-11-2012 - 210000 block - 25 BTC
2 - 09-07-2016 - 420000 block - 12.5 BTC
3 - 11-05-2020 - 630000 block - 6.25 BTC
4 - 20-04-2024 - 840000 block - 3.125 BTC
5 - 24-03-2028 - 1050000 block - 1.5625 BTC
6 - 26-02-2032 - 1260000 block - 0.78125 BTC
7 - 30-01-2036 - 1470000 block - 0.390625 BTC
8 - 03-01-2040 - 1680000 block - 0.1953125 BTC
9 - 07-12-2043 - 1890000 block - 0.09765625 BTC
10 - 10-11-2047 - 2100000 block - 0.04882813 BTC
11 - 14-10-2051 - 2310000 block - 0.02441406 BTC
12 - 17-09-2055 - 2520000 block - 0.01220703 BTC
13 - 21-08-2059 - 2730000 block - 0.00610352 BTC
14 - 25-07-2063 - 2940000 block - 0.00305176 BTC
15 - 28-06-2067 - 3150000 block - 0.00152588 BTC
16 - 01-06-2071 - 3360000 block - 0.00076294 BTC
17 - 05-05-2075 - 3570000 block - 0.00038147 BTC
18 - 08-04-2079 - 3780000 block - 0.00019073 BTC
19 - 12-03-2083 - 3990000 block - 0.00009537 BTC
20 - 13-02-2087 - 4200000 block - 0.00004768 BTC
21 - 17-01-2091 - 4410000 block - 0.00002384 BTC
22 - 21-12-2094 - 4620000 block - 0.00001192 BTC
23 - 24-11-2098 - 4830000 block - 0.00000596 BTC
24 - 29-10-2102 - 5040000 block - 0.00000298 BTC
25 - 02-10-2106 - 5250000 block - 0.00000149 BTC
26 - 05-09-2110 - 5460000 block - 0.00000075 BTC
27 - 09-08-2114 - 5670000 block - 0.00000037 BTC
28 - 13-07-2118 - 5880000 block - 0.00000019 BTC
29 - 16-06-2122 - 6090000 block - 0.00000009 BTC
30 - 20-05-2126 - 6300000 block - 0.00000005 BTC
31 - 23-04-2130 - 6510000 block - 0.00000002 BTC
32 - 27-03-2134 - 6720000 block - 0.00000001 BTC
FiboSequFiboSequ: Fibonacci Sequence Marking
Leonardo Fibonacci was an Italian mathematician who lived in the 12th century. His real name was Leonardo of Pisa, but he is commonly known as "Fibonacci." Fibonacci is famous for introducing the Hindu-Arabic numeral system to the Western world. This system is the basis of the modern decimal number system we use today.
Fibonacci Sequence
The Fibonacci sequence is a series of numbers that frequently appears in mathematics and nature. The first two numbers in the sequence are 0 and 1, and each subsequent number is the sum of the two preceding numbers.
The sequence is as follows:
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, ...
Fibonacci Time Zones:
Fibonacci time zones are used to identify potential turning points in the market at specific time intervals. These time zones correspond to the Fibonacci sequence in terms of consecutive days or weeks.
The Fibonacci sequence has a wide range of applications in both mathematics and nature. Leonardo Fibonacci's work has had a significant impact on the development of modern mathematics and numeral systems. In financial markets, the Fibonacci sequence and ratios are frequently used by technical analysts to predict and analyze market movements.
Description:
Overview:
The FiboSequ indicator marks significant days on a price chart based on the Fibonacci sequence. This can help traders identify potential turning points or areas of interest in the market. The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, often found in nature and financial markets.
Fibonacci Sequence:
The sequence used in this indicator includes: 1, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, and 2584.
These numbers represent the days to be marked on the chart, highlighting possible significant market movements.
How It Works:
User Input:
Users can input the starting date (Year, Month, and Day) from which the Fibonacci sequence will begin to be calculated.
This allows flexibility and customization based on the trader's analysis needs.
Calculation:
The starting date is converted into a timestamp in seconds.
For each bar on the chart, the number of days since the starting date is calculated.
The indicator checks if the current day matches any of the Fibonacci sequence days, the previous day, or the next day.
In this indicator, Fibonacci numbers can be displayed on the chart as plus and minus 2 days. For example, for the 145th day, signals start to appear as 143,144 and 145. This is due to dates that sometimes coincide with weekends and public holidays.
Marking the Chart:
When a match is found, a label is placed above the bar indicating the day number from the Fibonacci sequence.
These labels are colored blue with white text for easy visibility.
Usage:
This indicator can be used on any timeframe and market to help identify potential areas where price might react.
It is especially useful for those who employ Fibonacci analysis in their trading strategy.
Example:
If the starting date is January 1, 2020, the indicator will mark significant Fibonacci days (e.g., 1, 3, 5, 8 days, etc.) on the chart from this date onward.
Community Guidelines Compliance:
This indicator adheres to TradingView's Pine Script community guidelines.
It provides customizable user inputs and does not violate any terms of use.
By using the FiboSequ indicator, traders can enhance their technical analysis by incorporating time-based Fibonacci levels, potentially leading to better market timing and decision-making.
Frequently Asked Questions (FAQ)
Q: What is the FiboSequ indicator?
A: The FiboSequ indicator is a technical analysis tool that marks significant days on a price chart based on the Fibonacci sequence. This indicator helps traders identify potential turning points or areas of interest in the market.
Q: What is the Fibonacci sequence and why is it important?
A: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones. The first two numbers are 0 and 1. This sequence frequently appears in nature and financial markets and is used in technical analysis to identify important support and resistance levels.
Q: How do the Fibonacci time zones in the indicator work?
A: Fibonacci time zones are used to identify potential market turning points at specific time intervals. The indicator calculates days based on the Fibonacci sequence (e.g., 1, 3, 5, 8 days, etc.) from the starting date and marks them on the chart.
Q: How can users set the starting date?
A: Users can input the starting date by specifying the year, month, and day. This sets the date from which the indicator begins its calculations, providing flexibility for user analysis.
Q: What do the labels in the indicator represent?
A: The labels mark specific days in the Fibonacci sequence. For example, 1st day, 3rd day, 5th day, etc. These labels are displayed in blue with white text for easy visibility.
Q: Which timeframes can I use the FiboSequ indicator on?
A: The FiboSequ indicator can be used on any timeframe. This includes daily, weekly, or monthly charts, as well as shorter timeframes.
Q: Which markets can the FiboSequ indicator be used in?
A: The FiboSequ indicator can be used in various financial markets, including stocks, forex, cryptocurrencies, commodities, and more.
Q: How can I achieve better market timing with the FiboSequ indicator?
A: The FiboSequ indicator helps identify potential market turning points using time-based Fibonacci levels. This can lead to better market timing and more informed trading decisions for traders.
-Please feel free to write your valuable comments and opinions. I attach importance to your valuable opinions so that I can improve myself.
Bitcoin Regression Price BoundariesTLDR
DCA into BTC at or below the blue line. DCA out of BTC when price approaches the red line. There's a setting to toggle the future extrapolation off/on.
INTRODUCTION
Regression analysis is a fundamental and powerful data science tool, when applied CORRECTLY . All Bitcoin regressions I've seen (Rainbow Log, Stock-to-flow, and non-linear models), have glaring flaws ... Namely, that they have huge drift from one cycle to the next.
Presented here, is a canonical application of this statistical tool. "Canonical" meaning that any trained analyst applying the established methodology, would arrive at the same result. We model 3 lines:
Upper price boundary (red) - Predicted the April 2021 top to within 1%
Lower price boundary (green)- Predicted the Dec 2022 bottom within 10%
Non-bubble best fit line (blue) - Last update was performed on Feb 28 2024.
NOTE: The red/green lines were calculated using solely data from BEFORE 2021.
"I'M INTRUIGED, BUT WHAT EXACTLY IS REGRESSION ANALYSIS?"
Quite simply, it attempts to draw a best-fit line over some set of data. As you can imagine, there are endless forms of equations that we might try. So we need objective means of determining which equations are better than others. This is where statistical rigor is crucial.
We check p-values to ensure that a proposed model is better than chance. When comparing two different equations, we check R-squared and Residual Standard Error, to determine which equation is modeling the data better. We check residuals to ensure the equation is sufficiently complex to model all the available signal. We check adjusted R-squared to ensure the equation is not *overly* complex and merely modeling random noise.
While most people probably won't entirely understand the above paragraph, there's enough key terminology in for the intellectually curious to research.
DIVING DEEPER INTO THE 3 REGRESSION LINES ABOVE
WARNING! THIS IS TECHNICAL, AND VERY ABBREVIATED
We prefer a linear regression, as the statistical checks it allows are convenient and powerful. However, the BTCUSD dataset is decidedly non-linear. Thus, we must log transform both the x-axis and y-axis. At the end of this process, we'll use e^ to transform back to natural scale.
Plotting the log transformed data reveals a crucial visual insight. The best fit line for the blowoff tops is different than for the lower price boundary. This is why other models have failed. They attempt to model ALL the data with just one equation. This causes drift in both the upper and lower boundaries. Here we calculate these boundaries as separate equations.
Upper Boundary (in red) = e^(3.24*ln(x)-15.8)
Lower Boundary (green) = e^(0.602*ln^2(x) - 4.78*ln(x) + 7.17)
Non-Bubble best fit (blue) = e^(0.633*ln^2(x) - 5.09*ln(x) +8.12)
* (x) = The number of days since July 18 2010
Anyone familiar with Bitcoin, knows it goes in cycles where price goes stratospheric, typically measured in months; and then a lengthy cool-off period measured in years. The non-bubble best fit line methodically removes the extreme upward deviations until the residuals have the closest statistical semblance to normal data (bell curve shaped data).
Whereas the upper/lower boundary only gets re-calculated in hindsight (well after a blowoff or capitulation occur), the Non-Bubble line changes ever so slightly with each new datapoint. The last update to this line was made on Feb 28, 2024.
ENOUGH NERD TALK! HOW CAN I APPLY THIS?
In the simplest terms, anything below the blue line is a statistical buying opportunity. The closer you approach the green line (the lower boundary) the more statistically strong that opportunity is. As price approaches the red line, is a growing statistical likelyhood/danger of an imminent blowoff top.
So a wise trader would DCA (dollar cost average) into Bitcoin below the blue line; and would DCA out of Bitcoin as it approaches the red line. Historically, you may or may not have a large time-window during points of maximum opportunity. So be vigilant! Anything within 10-20% of the boundary should be regarded as extreme opportunity.
Note: You can toggle the future extrapolation of these lines in the settings (default on).
CLOSING REMARKS
Keep in mind this is a pure statistical analysis. It's likely that this model is probing a complex, real economic process underlying the Bitcoin price. Statistical models like this are most accurate during steady state conditions, where the prevailing fundamentals are stable. (The astute observer will note, that the regression boundaries held despite the economic disruption of 2020).
Thus, it cannot be understated: Should some drastic fundamental change occur in the underlying economic landscape of cryptocurrency, Bitcoin itself, or the broader economy, this model could drastically deviate, and become significantly less accurate.
Furthermore, the upper/lower boundaries cross in the year 2037. THIS MODEL WILL EVENTUALLY BREAK DOWN. But for now, given that Bitcoin price moves on the order of 2000% from bottom to top, it's truly remarkable that, using SOLELY pre-2021 data, this model was able to nail the top/bottom within 10%.
MicroStrategy / Bitcoin Market Cap RatioThis indicator offers a unique analytical perspective by comparing the market capitalization of MicroStrategy (MSTR) with that of Bitcoin (BTC) . Designed for investors and analysts interested in the correlation between MicroStrategy's financial performance and the Bitcoin market, the script calculates and visualizes the ratio of MSTR's market capitalization to Bitcoin's market capitalization.
Key Features:
Start Date: The script considers data starting from July 28, 2020, aligning with MicroStrategy's initial announcement to invest in Bitcoin.
Data Sources: It retrieves real-time data for MSTR's total shares outstanding, MSTR's stock price, and BTC's market capitalization.
Market Cap Calculations: The script calculates MicroStrategy's market cap by multiplying its stock price with the total shares outstanding. It then forms a ratio of MSTR's market cap to BTC's market cap.
Bollinger Bands: To add a layer of analysis, the script includes Bollinger Bands around the ratio, with customizable parameters for length and multiplier. These bands can help identify overbought or oversold conditions in the relationship between MSTR's and BTC's market values.
The indicator plots the MSTR/BTC market cap ratio and the Bollinger Bands, providing a clear visual representation of the relationship between these two market values over time.
This indicator is ideal for users who are tracking the impact of Bitcoin's market movements on MicroStrategy's valuation or vice versa. It provides a novel way to visualize and analyze the interconnectedness of a leading cryptocurrency asset and a major corporate investor in the space.
hamster-bot MRS 2 (simplified version) MRS - Mean Reversion Strategy (Countertrend) (Envelope strategy)
This script does not claim to be unique and does not mislead anyone. Even the unattractive backtest result is attached. The source code is open. The idea has been described many times in various sources. But at the same time, their collection in one place provides unique opportunities.
Published by popular demand and for ease of use. so that users can track the development of the script and can offer their ideas in the comments. Otherwise, you have to communicate in several telegram chats.
Representative of the family of counter-trend strategies. The basis of the strategy is Mean reversion . You can also read about the Envelope strategy .
Mean reversion , or reversion to the mean, is a theory used in finance that suggests that asset price volatility and historical returns eventually will revert to the long-run mean or average level of the entire dataset.
The strategy is very simple. Has very few settings. Good for beginners to get acquainted with algorithmic trading. A simple adjustment will help avoid overfitting. There are many variations of this strategy, but for understanding it is better to start with this implementation.
Principle of operation.
1)
A conventional MA is being built. (fuchsia line). A limit order is placed on this line to close the position.
2)
(green line) A limit order is placed on this line to open a long position
3)
(red line) A limit order is placed on this line to open a short position
Attention!
Please note that a limit order is used. Conclude that the strategy has a limited capacity. And the results obtained on low-liquid instruments will be too high in the tester. On real auctions there will be a different result.
Note for testing the strategy in the spot market:
When testing in the spot market, do not include both long and short at the same time. It is recommended to test only the long mode on the spot. Short mode for more advanced users.
Settings:
Available types of moving averages:
SMA
EMA
TEMA - triple exponential moving average
DEMA - Double Exponential Moving Average
ZLEMA - Zero lag exponential moving average
WMA - weighted moving average
Hma - Hull Moving Average
Thma - Triple Exponential Hull Moving Average
Ehma - Exponential Hull Moving Average
H - MA built based on highs for n candles | ta.highest(len)
L - MA built based on lows for n candles | ta.lowest(len)
DMA - Donchian Moving Average
A Kalman filter can be applied to all MA
The peculiarity of the strategy is a large selection of MA and the possibility of shifting lines. You can set up a reverse trending strategy on the Donchian channel for example.
Use Long - enable/disable opening a Long position
Use Short - enable/disable opening a Short position
Lot Long, % - % allocated from the deposit for opening a Long position. In the spot market, do not use % greater than 100%
Lot Short, % - allocated % of the deposit for opening a Short position
Start date - the beginning of the testing period
End date - the end of the testing period (Example: only August 2020 can be tested)
Mul - multiplier. Used to offset lines. Example:
Mul = 0.99 is shift -1%
Mul = 1.01 is shift +1%
Non-strict recommendations:
1) Test the SPOT market on crypto exchanges. (The countertrend strategy has liquidation risk on futures)
2) Symbols altcoin/bitcoin or altcoin/altcoin. Example: ETH/BTC or DOGE/ETH
3) Timeframe is usually 1 hour
If the script passes moderation, I will supplement it by adding separate settings for closing long and short positions according to their MA
Bankruptcy Risk: Altman Z-Score, Zmijewski Score, Grover GThis custom indicator calculates three common bankruptcy risk scores:
Altman Z-Score
Zmijewski Score
Grover G-Score
Altman Z-Score
Companies are in healthy condition if the Z-Score > 2.6.
Companies are in vulnerable conditions and need improvement (grey area) if the score is between 1.1 - 2.6.
Companies have the potential to lead to serious bankruptcy if the Z-Score < 1.1.
Zmijewski Score
The company has the potential to go bankrupt if the value of X Score > 0.
The company is healthy if the value of X Score < 0.
Grover Model: G-Score
The company has the potential to go bankrupt if the G Score ≤ -0.02.
The company is in good health if the value of G Score ≥ 0.001.
The indicator pulls key financial metrics and calculates each score, then displays the results in a table with color-coding based on the level of bankruptcy risk.
Users can toggle between FQ and FY periods and view details on the underlying metrics. This provides an easy way to visualize bankruptcy risk for a company and compare across different scoring models.
Useful for fundamental analysis and assessing financial health.
The financial ratios and methodology are based on research described in
"Analysis of Bankruptcy Prediction Models in Determining Bankruptcy of Consumer Goods Companies in Indonesia" (Thomas et al., 2020).
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical.
The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators.
Tutorial:
There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them.
In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them.
We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel.
If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful.
In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible.
As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look.
We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on.
Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on.
When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are:
Machine Learning Length: 50
KNN Length:5
Fast ML Data Length: 5
Slow ML Data Length: 30
For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50.
As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines.
We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical.
Settings:
Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics.
Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional.
Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN.
Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results.
K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results.
Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast.
Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Triple Ehlers Market StateClear trend identification is an important aspect of finding the right side to trade, another is getting the best buying/selling price on a pullback, retracement or reversal. Triple Ehlers Market State can do both.
Three is always better
Ehlers’ original formulation produces bullish, bearish and trendless signals. The indicator presented here gate stages three correlation cycles of adjustable lengths and degree thresholds, displaying a more refined view of bullish, bearish and trendless markets, in a compact and novel way.
Stick with the default settings, or experiment with the cycle period and threshold angle of each cycle, then choose whether ‘Recent trend weighting’ is included in candle colouring.
John Ehlers is a highly respected trading maths head who may need no introduction here. His idea for Market State was published in TASC June 2020 Traders Tips. The awesome interpretation of Ehlers’ work on which Triple Ehlers Market State’s correlation cycle calculations are based can be found at:
DISCLAIMER: None of this is financial advice.
Cycles: 4x dual inputs: Swing / Time Cycles projected forward//Purpose/Premise:
To project forward vertical 'cycle' lines based on user-input anchor points, and to search for confluence.
The idea being that if several well-anchored cycles agree (i.e. we see multiple bunched vertical line confluence in the future), then this may add support to an already existing trade idea, or may indicate an increased likelihood of a shift in direction.
//Usage & notes:
~In the above chart I've anchored to obvious swing lows and swing highs in Btc/Usd from 2020-2022. You could also use fixed time-based cycles from a favored start anchor point. Bars per cycle are printed at the top of each cycle box if your're interested in time cycles. I.e. for 1, 2, 3 month cycles: for BTC you could use 30, 60, 90 bars on daily; for S&P you could use 20, 40, 60 bars on daily.
~On first loading the indicator you will be asked select 'start date', and 'end date' for each of 4 sessions (8x clicks on chart). After this you can easily reset points by clicking the indicator display line three dots>> reset points. Or you can simply drag the vertical box edges (purple lines) to change your cycle anchor points.
~Be sure the start anchor point is before the end anchor point or box/lines won't appear.
~When you drop down to low timeframes you might get bar_index error due to history available: you need then to click the three dots on indicator display line >> reset points >> 8x clicks on the chart.
~Vertical projected lines will match the color of the cycle box they origninate from.
~Lines will project into the future as far as is allowed by tradingview (500 bars max)
//Inputs:
~Time start and end dates for each cycle (change these as described above, or input manually)
~Show/hide each cycle (default is show all 4)
~Formatting options: color of forward projected lines, line width, line style, line / box / text color.
~Box transparancy: Set to 100 to make boxes invisible & declutter the chart. Set to 0 for maximum opacity. Default is 80.
thanks to @Sathyamurthie for his ideas on cycle confluence which caused me to write this.
SimilarityMeasuresLibrary "SimilarityMeasures"
Similarity measures are statistical methods used to quantify the distance between different data sets
or strings. There are various types of similarity measures, including those that compare:
- data points (SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl),
- strings (Edit(Levenshtein), Lee, Hamming, Jaro),
- probability distributions (Mahalanobis, Fidelity, Bhattacharyya, Hellinger),
- sets (Kumar Hassebrook, Jaccard, Sorensen, Chi Square).
---
These measures are used in various fields such as data analysis, machine learning, and pattern recognition. They
help to compare and analyze similarities and differences between different data sets or strings, which
can be useful for making predictions, classifications, and decisions.
---
References:
en.wikipedia.org
cran.r-project.org
numerics.mathdotnet.com
github.com
github.com
github.com
Encyclopedia of Distances, doi.org
ssd(p, q)
Sum of squared difference for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the squared euclidean distance.
euclidean(p, q)
Euclidean distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the straight-line (or Euclidean).
manhattan(p, q)
Manhattan distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of absolute differences between both points.
minkowski(p, q, p_value)
Minkowsky Distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
p_value (float) : `float` P value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: Measure of similarity in the normed vector space.
chebyshev(p, q)
Chebyshev distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
correlation(p, q)
Correlation distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
cosine(p, q)
Cosine distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Cosine distance between vectors `p` and `q`.
---
angiogenesis.dkfz.de
camberra(p, q)
Camberra distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Weighted measure of absolute differences between both points.
mae(p, q)
Mean absolute error is a normalized version of the sum of absolute difference (manhattan).
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean absolute error of vectors `p` and `q`.
mse(p, q)
Mean squared error is a normalized version of the sum of squared difference.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean squared error of vectors `p` and `q`.
lorentzian(p, q)
Lorentzian distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Lorentzian distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
intersection(p, q)
Intersection distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Intersection distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
penrose(p, q)
Penrose Shape distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Penrose shape distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
meehl(p, q)
Meehl distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Meehl distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
edit(x, y)
Edit (aka Levenshtein) distance for indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Number of deletions, insertions, or substitutions required to transform source string into target string.
---
generated description:
The Edit distance is a measure of similarity used to compare two strings. It is defined as the minimum number of
operations (insertions, deletions, or substitutions) required to transform one string into another. The operations
are performed on the characters of the strings, and the cost of each operation depends on the specific algorithm
used.
The Edit distance is widely used in various applications such as spell checking, text similarity, and machine
translation. It can also be used for other purposes like finding the closest match between two strings or
identifying the common prefixes or suffixes between them.
---
github.com
www.red-gate.com
planetcalc.com
lee(x, y, dsize)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
dsize (int) : `int` Dictionary size.
Returns: Distance between two strings by accounting for dictionary size.
---
www.johndcook.com
hamming(x, y)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Length of different components on both sequences.
---
en.wikipedia.org
jaro(x, y)
Distance between two indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Measure of two strings' similarity: the higher the value, the more similar the strings are.
The score is normalized such that `0` equates to no similarities and `1` is an exact match.
---
rosettacode.org
mahalanobis(p, q, VI)
Mahalanobis distance between two vectors with population inverse covariance matrix.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
VI (matrix) : `matrix` Inverse of the covariance matrix.
Returns: The mahalanobis distance between vectors `p` and `q`.
---
people.revoledu.com
stat.ethz.ch
docs.scipy.org
fidelity(p, q)
Fidelity distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya Coefficient between vectors `p` and `q`.
---
en.wikipedia.org
bhattacharyya(p, q)
Bhattacharyya distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya distance between vectors `p` and `q`.
---
en.wikipedia.org
hellinger(p, q)
Hellinger distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The hellinger distance between vectors `p` and `q`.
---
en.wikipedia.org
jamesmccaffrey.wordpress.com
kumar_hassebrook(p, q)
Kumar Hassebrook distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Kumar Hassebrook distance between vectors `p` and `q`.
---
github.com
jaccard(p, q)
Jaccard distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Jaccard distance between vectors `p` and `q`.
---
github.com
sorensen(p, q)
Sorensen distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Sorensen distance between vectors `p` and `q`.
---
people.revoledu.com
chi_square(p, q, eps)
Chi Square distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Chi Square distance between vectors `p` and `q`.
---
uw.pressbooks.pub
stats.stackexchange.com
www.itl.nist.gov
kulczynsky(p, q, eps)
Kulczynsky distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Kulczynsky distance between vectors `p` and `q`.
---
github.com
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
CalendarEurLibrary "CalendarEur"
This library provides date and time data of the important events on EUR. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
EUR high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
EUR high impact news date and time from 2020 to 2023
CalendarGbpLibrary "CalendarGbp"
This library provides date and time data of the important events on GBP. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
GBP high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
GBP high impact news date and time from 2020 to 2023
CalendarUsdLibrary "CalendarUsd"
This library provides date and time data of the important events on USD. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
HighImpactNews2015To2019()
USD high impact news date and time from 2015 to 2019
HighImpactNews2020To2023()
USD high impact news date and time from 2020 to 2023
NewsEventsGbpLibrary "NewsEventsGbp"
This library provides date and time data of the high imact news events on GBP. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
gbpNews2015To2019()
GBP high imact news date and time from 2015 to 2019
gbpNews2020To2023()
GBP high imact news date and time from 2020 to 2023
NewsEventsEurLibrary "NewsEventsEur"
This library provides date and time data of the high imact news events on EUR. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
eurNews2015To2019()
EUR high imact news date and time from 2015 to 2019
eurNews2020To2023()
EUR high imact news date and time from 2020 to 2023
NewsEventsUsdLibrary "NewsEventsUsd"
This library provides date and time data of the high imact news events on USD. Data source is csv exported from www.fxstreet.com and transformed into perfered format by C# script.
usdNews2015To2019()
USD high imact news date and time from 2015 to 2019
usdNews2020To2023()
USD high imact news date and time from 2020 to 2023
BTCUSD Price prediction based on central bank liquidityIn recent months the idea that Bitcoin prices are increasingly linked to liquidity provided by central banks has gained strength. Multiple opinion leaders in the bitcoin space have shared their thoughts to explain why this is happening and why it makes sense. Some of these people I'm talking about are Preston Pysh, Dr. Jeff Ross, Steven McClurg, Lynn Alden among others.
The reality is that the correlation between market liquidity, measured as Assets held by the Federal Reserve, Bank of Japan and European Central bank, and Bitcoin prices is high. This made me wonder whether a regression between "market liquidity" and BTCUSD prices made sense in order to understand where Bitcoin prices are in relation to the liquidity in the market. After several trials I ended up fitting a polynomial regression of degree 5 between Market Liquidity and BTCUSD prices since 2013. This regression resulted in r-squared value of 90.93%. I initially visualized the results in python notebooks but then I thought it would be cool to be able to see them in real-time in tradingview.
That's where this script comes handy...
This script takes the coefficients and intercept from the polynomial regression I built and applies them to the "market_liquidity" index. In addition, it adds upper and lower bound lines to the prediction based on a 95% confidence interval. As you will see, particularly since 2020, the price of bitcoin has rarely been above or below the lines representing the 95% confidence interval. When price has actually crossed these lines it's been in moments where Bitcoin was highly overbought or oversold. Therefore this indicator could be used to understand when it's a good moment to enter or exit the market based on central bank fundamentals.
Here's the detailed step-by-step description of what the script does
1) It defines the coefficients obtained from running the regression betweeen "market liquidity" and BTCUSD. Market liquidity is defined as:
Market liquidity = FRED:WALCL + FX_IDX:JPYUSD*FRED:JPNASSETS + FX:EURUSD*FRED:ECBASSETSW - FRED:RRPONTSYD - FRED:WTREGEN
2) It defines a scale factor. The reason for this is that coefficients from the regression are very small numbers, given the huge numbers of the value of assets held by central banks. Pinescript doesn't support numbers with many decimals and rounds them to 0, so the coefficients had to be scaled up in order to be able to calculate the regression results.
3) It calculates market liquity with the formula defined above. Market liquidity is calculated in US Dollars.
4) It calculates the predicted BTCUSD price based on the coefficients and the market liquidity values.
5) It scales down the values by the same factor used to scale the coefficients up
6) It defines the standard deviation of the "potential_btcusd_price_scaled" and the actual BTCUSD prices.
7) It defines upper and lower bounds to the BTCUSD price prediction using a z-score of 1.96, which is equivalent to 95% confidence interval.
8) Lastly it plots the BTCUSD price prediction (orange) and the upper (red) and lower(green) confidence intervals.
The script can be updated as the correlation of BTCUSD to central bank assets changes (the slope values can be updated).
How to use it:
When actual BTCUSD price (blue line in the chart) crosses over the red line (upper bound) or crosses under the green line (lower bound) it should be taken as a sign that the price of BTCUSD may be overvalued or undervalued based on the value of assets held by major central banks.
