Sessions [TradingFinder] New York, London, Tokyo & Sydney ForexTiming is one of the influential factors in a trader's position. This indicator categorizes transactions into three sessions (Asia, Europe, and America). Five significant trading cities (New York, London, Frankfurt, Tokyo, and Sydney) are selectable.
I recommend using the tool on a 5-minute time frame, but it is usable on all time frames.
Settings:
• Trading sessions: Display or hide each trading session as needed.
• Color: Change the color of each box.
• Session time intervals: The default is based on the main working hours for each time interval and can be adjusted.
• Information table: Delete or display additional information table.
Information Table:
• Trading sessions
• Opening and closing times of each trading session
How to Use:
Initiating trading sessions involves entering with increased liquidity, and the market usually experiences significant movements. Many trading strategies are based on "time" and "session openings." This tool empowers traders to focus intensely on each time interval.
These trading sessions are crucial for all Forex, stock, and index traders:
The total price ceiling and floor in the Asia session (Tokyo and Sydney) are crucial for traders in the European session.
The European session starts with Frankfurt, and an hour later, London begins, collectively forming the European session.
The dashboard provides additional information, displaying hours based on UTC.
Customization options are considered in all sections so that everyone can apply their own settings.
Important: Default times are the most accurate for each region, and in most indicators, this time is not correctly selected. Therefore, the level of influence and time intervals are specified at the beginning of each session. If you are using another indicator, match its default time to the announced time and share the results with me in the comments.
Statistics
Kendall's Tau Correlation Regimes [NariCapitalTrading]The "Kendall's Tau Correlation Regimes" indicator is designed to analyze price data and determine market regimes based on Kendall's Tau correlation coefficient. It provides insight into the strength and direction of the correlation between two data series: close price and a selected moving average.
User Inputs:
Period: Defines the lookback period for calculating Kendall's Tau correlation. It can be adjusted using the input slider, with a minimum value of 1.
Threshold: Sets the threshold for identifying bullish and bearish market regimes. The user can adjust this value within the range of 0.1 to 1.0 with step increments of 0.1.
MA Type: Allows users to select the type of moving average to be used in the correlation calculation. Options include Simple Moving Average (SMA), Exponential Moving Average (EMA), and Hull Moving Average (HMA).
Kendall's Tau Correlation Calculation:
Calculates Kendall's Tau correlation coefficient between the closing price and the selected moving average.
Kendall's Tau measures the strength and direction of the ordinal association between two data series. It assesses whether the data pairs are in the same order or not.
The calculation involves counting concordant and discordant pairs of data points and then computing the coefficient.
Market Regime Identification:
Based on the threshold defined by the user, the indicator identifies two market regimes: bullish and bearish.
A regime is considered bullish when the Kendall's Tau correlation coefficient is greater than the threshold.
A regime is considered bearish when the Kendall's Tau correlation coefficient is less than the negative of the threshold.
Plotting:
The indicator plots the calculated Kendall's Tau correlation coefficient as a blue line on a separate indicator pane.
It also highlights bullish regimes with a green background and bearish regimes with a red background.
Conclusion:
The "Kendall's Tau Correlation Regimes" indicator provides traders with a visual aid for assessing market regimes based on the strength of correlation between price and a selected moving average.
Disclaimer: This indicator is for educational and informational purposes only.
Least Median of Squares Regression | ymxbThe Least Median of Squares (LMedS) is a robust statistical method predominantly used in the context of regression analysis. This technique is designed to fit a model to a dataset in a way that is resistant to outliers. Developed as an alternative to more traditional methods like Ordinary Least Squares (OLS) regression, LMedS is distinguished by its focus on minimizing the median of the squares of the residuals rather than their mean. Residuals are the differences between observed and predicted values.
The key advantage of LMedS is its robustness against outliers. In contrast to methods that minimize the mean squared residuals, the median is less influenced by extreme values, making LMedS more reliable in datasets where outliers are present. This is particularly useful in linear regression, where it identifies the line that minimizes the median of the squared residuals, ensuring that the line is not overly influenced by anomalies.
STATISTICAL PROPERTIES
A critical feature of the LMedS method is its robustness, particularly its resilience to outliers. The method boasts a high breakdown point, which is a measure of an estimator's capacity to handle outliers. In the context of LMedS, this breakdown point is approximately 50%, indicating that it can tolerate corruption of up to half of the input data points without a significant degradation in accuracy. This robustness makes LMedS particularly valuable in real-world data analysis scenarios, where outliers are common and can severely skew the results of less robust methods.
Rousseeuw, Peter J.. “Least Median of Squares Regression.” Journal of the American Statistical Association 79 (1984): 871-880.
The LMedS estimator is also characterized by its equivariance under linear transformations of the response variable. This means that whether you transform the data first and then apply LMedS, or apply LMedS first and then transform the data, the end result remains consistent. However, it's important to note that LMedS is not equivariant under affine transformations of both the predictor and response variables.
ALGORITHM
The algorithm randomly selects pairs of points, calculates the slope (m) and intercept (b) of the line, and then evaluates the median squared deviation (mr2) from this line. The line minimizing this median squared deviation is considered the best fit.
DISCLAIMER
In the LMedS approach, a subset of the data is randomly selected to compute potential models (e.g., lines in linear regression). The method then evaluates these models based on the median of the squared residuals. Since the selection of data points is random, different runs may select different subsets, leading to variability in the computed models.
Autocorrelation Candles [SS]Hey everyone, this is the Autocorrelation Candles indicator!
I have formulated it in a way that is similar to the TD 9 candle counting indicators, only instead of TD, its using a lagged autocorrelation of previous candle over a 14 period look back.
It operates similar to trend correlations (for example, my Trend Correlation Oscillator Indicator), however instead of correlating to time, it correlates to itself (autoregression). The theory being, as the autoregression correlation increases and the market becomes too "trendy", we are due for a reversal.
The indicator will display the current lagged correlation of each candle below it. When we approach a period of previous reversal, it will change the colour to orange. When we reach a very high autocorrelation (0.94 or greater), it will turn red and signal a potential reversal to the upside or downside:
Uses:
I will reference this on the larger timeframes (Daily, weekly and 4 hour) about a couple times a week or after a major trend day to see where we are.
You can use this on the smaller timeframes as well, it will work just fine.
Customizations:
I have been listening and learning my lesson, I have made the ability to customize the base text colour to black or white depending on your theme use! SO if you have white theme, you can change to black and vice versa.
As well, if you don't want labels on every candle, in the settings menu there is an option to limit the labels to a desired amount. You select the max amount and it will adjust.
You can also adjust the size of the labels between tiny, medium, and large.
Conclusion
And that's the indicator! Despite being fairly simple in concept, I have been working away on it for a bit with some logistic issues that I finally got sorted.
Hopefully, you enjoy,
Leave your questions below!
Safe trades everyone!
Advanced Volume Analytics and Distribution IndicatorThe Advanced Volume Analytics and Distribution Indicator is a sophisticated tool designed for financial analysts and traders who seek in-depth insights into market volume dynamics. This Pine Script-based indicator is a comprehensive solution, offering a rich set of features that analyze volume data using various statistical methods and theories. It's tailored for those who require a deeper understanding of market movements and volume distribution.
Key Features:
Volume Distribution Analysis: Utilizes standard deviation and mean calculations to analyze the distribution of trading volume. Employs z-scores to measure the standard deviations of volume from its mean, offering insights into volume anomalies.
Bell Curve Modeling: Constructs a bell curve (normal distribution) based on volume data, enabling users to visualize and assess the distribution of volume in a standard statistical format.
Provides a z-score based bell curve, offering a normalized view of volume deviations.
Exponential Smoothing: Applies exponential smoothing to volume data, giving more weight to recent observations. This feature is crucial for analyzing trending behaviors in volume data.
Stress Metric Calculation: Introduces a unique 'stress' metric, calculated using a custom formula. This metric is designed to evaluate the volatility or variability in the volume data over a specified period.
Central Limit Theorem (CLT) Mean Estimation: Implements CLT for estimating the mean of volume data. The CLT states that the distribution of sample means approximates a normal distribution as the sample size becomes larger.
Variance Point Estimation: Calculates the variance of volume data, providing insights into its variability and consistency over time.
Chi-Squared Test (Commented): Although not active in the initial release, the script includes a framework for a Chi-Squared Test to compare observed and expected volume frequencies, offering potential for future statistical comparisons.
Percentile Calculations and Convolution: Performs percentile calculations on volume data and employs convolution to these percentiles, enabling a more nuanced analysis of volume distribution.
Customizability: Users can input various parameters like anchor period, degrees of freedom, and smoothing preferences, making the tool adaptable to different analysis needs.
Visualization and Plotting: Features multiple plots for easy visualization of volume metrics, including stress, bell curves, point estimators, and smoothed data.
Theoretical Foundations:
This indicator is grounded in established statistical theories and methods, including the Central Limit Theorem, Chi-Squared Test (for future implementations), and convolution techniques. These foundations ensure that the indicator not only provides practical insights but also maintains a high standard of statistical rigor.
Intended Users:
This indicator is ideal for technical analysts, traders, and financial professionals who require a deep and statistically sound understanding of market volume behavior.
Release Notes:
This tool is designed a theoretical test of established statistical models and requires familiarity with Pine Script for customization. Future updates may include activation and expansion of the Chi-Squared Test functionality and additional statistical modules based on user feedback. It should be noted that it is advisable to use a logarithmic-inverted scale; when combined, these scales can provide a unique perspective that neither could offer alone. This combination might be particularly useful in highlighting exponential growth or decay trends, or in cases where the most significant data points are in the lower range of the dataset.
Notes of Stress Calculations:
The "stress metric" in the script is a custom-designed feature intended to measure the level of variability or volatility in the volume data over a given time period. This metric is calculated using a novel approach with concepts similar to those used in the field of engineering , particularly in stress analysis and finite element analysis (FEA).
Segmentation of Time Frame:
The script divides the given time frame (timeFrame) into smaller segments based on a specified number of units (units). This segmentation essentially breaks down the entire period into smaller, more manageable intervals for analysis. For each segment, the script calculates a 'stress' value. This involves iterating through each segment and performing calculations based on the source data (src), the default src is the volume data.
Calculation per Segment:
For each segment, the script identifies two points: the starting point (x1) and the ending point (x2). It then retrieves the corresponding values of the source data at these points (y1 and y2).
It calculates the difference in the x-axis (delta_x, the length of the segment) and the difference in the y-axis (delta_y, the change in volume over that segment).
Stress Calculation:
The script then calculates the 'stress' for each segment as the ratio of delta_y to delta_x. This ratio gives a measure of how much the volume has changed per unit of time within each segment. The stress values for each segment are then summed up to provide a cumulative measure of stress over the entire time frame.
The stress metric is essentially a measure of the volatility or variability in volume data. High stress values indicate larger changes in volume over shorter periods, suggesting more volatile market conditions. For traders and analysts, understanding the level of volatility is crucial. It can inform decision-making processes, risk management strategies, and provide insights into market sentiment. By comparing stress levels across different time frames or different securities, analysts can gain insights into relative market dynamics.
Blockunity Drawdown Visualizer (BDV)Monitor the drawdown (value of the drop between the highest and lowest points) of assets and act accordingly to reduce your risk.
Introducing BDV, the incredibly intuitive metric that visualizes asset drawdowns in the most visually appealing manner. With its color gradient display, BDV allows you to instantly grasp the state of retracement from the asset’s highest price level. But that’s not all – you have the option to display the oscillator’s colorization directly on your chart, enhancing your analysis even further.
The Idea
The goal is to provide the community with the best and most complete tool for visualizing the Drawdown of any asset.
How to Use
Very simple to use, the indicator takes the form of an oscillator, with colors ranging from red to green depending on the Drawdown level. A table summarizes several key data points.
Elements
On the oscillator, you'll find a line with a color gradient showing the asset's Drawdown. The flatter line represents the Max Drawdown (the lowest value reached).
In addition, the table summarizes several data:
The asset's All Time High (ATH).
Current Drawdown.
The Max Drawdown that has been reached.
Settings
First of all, you can activate a "Bar Color" in the settings (You must also uncheck "Borders" and "Wick" in your Chart Settings):
You can display Fibonacci levels on the oscillator. You'll see that levels can be relevant to drawdown. The color of the levels is also configurable.
In the calculation parameters, you can first choose between taking the High of the candles or the Close. By default this is Close, but if you change the parameter to High, the indication next to ATH in the table will change, and you'll see that the values in the table will be affected.
The second calculation parameter (Start Date) lets you modify the effective start date of the ATH, which will affect the drawdown level. Here's an example:
How it Works
First, we calculate the ATH:
var bdv_top = bdv_source
bdv_top := na(bdv_top ) ? bdv_source : math.max(bdv_source, bdv_top )
Then the drawdown is calculated as follows:
bdv = ((bdv_source / bdv_top) * 100) - 100
Then the max drawdown :
bdv_max = bdv
bdv_max := na(bdv_max ) ? bdv : math.min(bdv, bdv_max )
Hodl Calculation v1.0I have developed an indicator that calculates the value of our currency if we had periodically bought any stock or cryptocurrency on any exchange. I believe many individuals would be interested in computing such values.
You can customize the start and end times, choose the amount of currency to be used for each deal, and select from two frequency options.
The first option involves specific intervals, such as hourly, every three days, or bi-weekly.
The second option allows purchases at specific dates or times, like every 15th of the month at 12:00 PM, every Monday at 11:00 AM, or every day at 6:00 AM.
After selecting the frequency, the indicator performs calculations and presents statistical information in a table.
The summarized data includes frequency value, total selected period duration, number of deals, total quantity, total cost, current value, and profit/loss status.
IBIT Premium to CoinbaseThe BTC ETF premium indicator for TradingView is a specialized tool designed to measure and visualize the premium or discount of the iShares Bitcoin Trust (IBIT), an investment vehicle that holds Bitcoin, relative to the actual price of Bitcoin on the Coinbase exchange. This indicator can be particularly insightful for traders interested in the BTC securities market and those analyzing the demand for Bitcoin as reflected by institutional investment products.
#### Description:
The BTC ETF premium indicator in TradingView leverages an advanced Pine Script algorithm to calculate the premium (or discount) percentage of IBIT compared to the spot price of Bitcoin (BTC/USD) on Coinbase. The premium is a critical insight that reflects market sentiment and potentially arbitrage opportunities between the trust's share price and the underlying cryptocurrency asset.
Here's how the indicator works:
1. **Calculation Methodology:**
- **Implied Bitcoin Price of IBIT:** We determine the implied price of Bitcoin within IBIT by dividing the IBIT closing price by the known ratio of Bitcoin per share.
- **IBIT Premium to Coinbase:** The percentage premium is then calculated as:
$$\text{IBIT Premium} = \frac{(\text{Implied Bitcoin Price of IBIT } - \text{Actual Bitcoin Price on Coinbase})}{\text{Actual Bitcoin Price on Coinbase}} \times 100$$
- This calculation is performed using the closing prices on a per-minute basis to ensure timely and accurate analysis.
2. **Visualization:** The indicator plots the premium as a step line chart, making it easy to visualize changes over time. A dynamic label accompanies the plot, displaying the implied Bitcoin price, the actual percentage premium or discount, and whether the premium is trending up or down compared to the previous day's value.
3. **Usage Scenario:** Traders can use this indicator to monitor the live premium 24/7 and analyze how it behaves during different market conditions, including when the equity market, where IBIT is traded, is closed.
#### Additional Features:
- **Color-Coding:** The premium is color-coded in green when positive (premium) and in red when negative (discount), aiding quick visual assessment.
- **Zero-Line Reference:** A horizontal line is drawn at zero to easily identify when IBIT is trading at par with the spot price of Bitcoin.
- **Real-Time Label Updates:** The label updates in real time with the latest premium/discount information and includes an arrow to signify the trend direction.
#### Access and Usage:
The indicator can be favorited or added to your TradingView charts. You are also welcome to use the source code as a foundation for further customization to suit your trading strategies.
#### Notes:
Please consider that the IBIT has specific trading hours, and the indicator can show live changes even when its market is closed, which might lead to discrepancies from official static data. For best performance, use this indicator alongside the IBIT candlestick chart on TradingView.
GBTC Premium to CoinbaseThe BTC ETF premium indicator for TradingView is a specialized tool designed to measure and visualize the premium or discount of the Grayscale Bitcoin Trust (GBTC), an investment vehicle that holds Bitcoin, relative to the actual price of Bitcoin on the Coinbase exchange. This indicator can be particularly insightful for traders interested in the BTC securities market and those analyzing the demand for Bitcoin as reflected by institutional investment products.
#### Description:
The BTC ETF premium indicator in TradingView leverages an advanced Pine Script algorithm to calculate the premium (or discount) percentage of GBTC compared to the spot price of Bitcoin (BTC/USD) on Coinbase. The premium is a critical insight that reflects market sentiment and potentially arbitrage opportunities between the trust's share price and the underlying cryptocurrency asset.
Here's how the indicator works:
1. **Calculation Methodology:**
- **Implied Bitcoin Price of GBTC:** We determine the implied price of Bitcoin within GBTC by dividing the GBTC closing price by the known ratio of Bitcoin per share.
- **GBTC Premium to Coinbase:** The percentage premium is then calculated as:
$$\text{GBTC Premium} = \frac{(\text{Implied Bitcoin Price of GBTC} - \text{Actual Bitcoin Price on Coinbase})}{\text{Actual Bitcoin Price on Coinbase}} \times 100$$
- This calculation is performed using the closing prices on a per-minute basis to ensure timely and accurate analysis.
2. **Visualization:** The indicator plots the premium as a step line chart, making it easy to visualize changes over time. A dynamic label accompanies the plot, displaying the implied Bitcoin price, the actual percentage premium or discount, and whether the premium is trending up or down compared to the previous day's value.
3. **Usage Scenario:** Traders can use this indicator to monitor the live premium 24/7 and analyze how it behaves during different market conditions, including when the equity market, where GBTC is traded, is closed.
#### Additional Features:
- **Color-Coding:** The premium is color-coded in green when positive (premium) and in red when negative (discount), aiding quick visual assessment.
- **Zero-Line Reference:** A horizontal line is drawn at zero to easily identify when GBTC is trading at par with the spot price of Bitcoin.
- **Real-Time Label Updates:** The label updates in real time with the latest premium/discount information and includes an arrow to signify the trend direction.
#### Access and Usage:
The indicator can be favorited or added to your TradingView charts. You are also welcome to use the source code as a foundation for further customization to suit your trading strategies.
#### Notes:
Please consider that the GBTC has specific trading hours, and the indicator can show live changes even when its market is closed, which might lead to discrepancies from official static data. For best performance, use this indicator alongside the GBTC candlestick chart on TradingView.
TabulateTabulate statistics from up to 10 symbols and display in a table format
you can choose to display up to 20 periods data points back in time
Symbol and description
Select a symbol and provide a description for it, this would be used as the label on the left column
Highlighting options
Color based on value increase/decrease
Colors the value green if it has increased from previous value, or red if it has decreased
Color based on positive/negative value
Stat will be colored accordingly green if positive, red if negative
Table value formatting
Allows you to change how the values are formatted for easy viewing
if 0.000% is selected, the source will be replaced by the following formula (close - open)/open
table text color should be changed to "Color based on positive/negative value"
NOTE: symbols that hold only monthly values would need to be placed in a chart with real time data to show the latest period.
Trended CVD [Mxwll]Hey!
This indicator "Trended CVD" categorizes price movement by trend (using zig zag) and calculates cumulative volume delta for the entirety of the price move.
Features
CVD calculated for the trend
CVD divergences are distinguished (uptrend and falling CVD / downtrend and rising CVD)
CVD output normalized to scale with chart, and is plotted alongside the trend
Can be used for trend confirmation (CVD trend correlating with price trend)
All regular zig-zag features available
What constitutes a trend is customizable. Can locate small, medium, large price trends with detailed user-input settings.
How-To Use Trended CVD
The image above shows one of two primary uses for the indicator.
In the left-half of the image, price is downtrending simultaneously with CVD; thereby, CVD is confirming the downtrend.
The right-half of the image shows price uptrending simultaneously with CVD; CVD is confirming the uptrend.
This information can be used to classify the "strength" of the price move, and decide to trade with it or against it.
The image above shows the second primary use for the indicator.
A slight price decrease transpires while CVD increases - CVD diverging upwards from the price trend.
This information can be used to classify the strength of the downtrend, and decide to trade against it, or abstain from trading with it.
The image above shows, subsequent to divergence, price failed to sustain "meaningful" downwards movement.
Labels oriented at the final pivot of a trend show the cumulative volume delta for the entirety of the price move (distinguishable by the superimposed zig zag line).
That's really it! A more complex concept integrated with a simple output.
Thank you!
BTC ETF VolumesVolume
This script plots the trading volume of all BTC spot ETFs as well as the aggregate volume. Works on any chart and any timeframe.
Indicators
The volume of every ETF is plotted in a different color, with the total column adding up to the aggregate volume.
If you have price and indicator labels enabled you will also see individual ETF volume on your price scale on the right hand side.
If more BTC ETFs get launched I will add them.
Bitcoin ETF Tracker (BET)Get all the information you need about all the different Bitcoin ETFs.
With the Bitcoin ETF Tracker, you can observe all possible Bitcoin ETF data:
The ETF name.
The ticker.
The price.
The volume.
The share of total ETF volume.
The ETF fees.
The exchange and custodian.
At the bottom of the table, you'll find the day's total volume.
In addition, you can see the volume for the different Exchanges, as well as for the different Custodians.
If you don't want to display these lines to save space, you can uncheck "Show Additional Data" in the indicator settings.
The Idea
The goal is to provide the community with a tool for tracking all Bitcoin ETF data in a synthesized way, directly in your TradingView chart.
How to Use
Simply read the information in the table. You can hover above the Fees and Exchanges cells for more details.
The table takes space on the chart, you can remove the extra lines by unchecking "Show Additional Data" in the indicator settings or reduce text size by changing the "Table Text Size" parameter.
Upcoming Features
As soon as we have a little more history, we'll add variation rates as well as plots to observe the breakdown between the various Exchanges and Custodians.
Candle Strength AnalysisView candles differently with this new indicator designed to simply visualise and analyse price movements on your chart!
The more vibrant the colour, the stronger the conviction of its respective candle.
This simple script calculates the closing price as a percentage within the candles high/low range. A colour/strength rating is then assigned to the candle based on where this close price sits within the range.
Strong coloured candles occur when the close is very close to a high or low.
User defined percentage and colour inputs allow for quick personalisation and flexibility.
An additional wick imbalance feature identifies when a candle has a larger wick than its body, which may be used to identify a ranging market or shift in trader sentiment.
For any questions, concerns, or requests, please reach out to me in the comments below.
- The Pine Guru
ARIMA Moving Average and Forecaster [SS]Finally releasing this. This took months, over 3 months to be precise, to figure out, code and troubleshoot! I honestly was going to give up on this project, but I finally got it to actually work fairly reliably. So hopefully you like it!
This is a very basic ARIMA modeler. It can do the following:
1. Provide you with an ARIMA based Moving Average;
2. Provide you with a standard error band;
3. Auto-select a lag length for assessment based on stationarity;
3. Provide you with the option of extending the error range by a user selected amount of standard deviations; and
4. Forecasting and plotting the forecast on the chart.
I will go over each function individually, but before I do, I think its important to talk a bit about what an ARIMA Model is and does:
ARIMA stands for AutoRegressive Integrated Moving Average and is an approach to modeling and time series forecasting. In simple terms, it combines autoregressive (AR) and moving average (MA) components to capture the underlying patterns in a time series data. The "AutoRegressive" part accounts for the relationship between an observation and its previous values, while the "Moving Average" part considers the relationship between an observation and a residual error from past observations. The "Integrated" component involves differencing the time series to make it stationary, which aids in stabilizing the model. ARIMA models help predict future values based on patterns observed in historical data, making them useful for forecasting in various fields such as economics, finance, and weather prediction.
The benefits to ARIMA is it will forecast based on the current trend, but it also provides for both the up and down scenario of the trend (i.e., if we are in a downtrend, what it would look like and what values we could expect if the trend reverses and vice versa). All of this is within the scope of this indicator, believe it or not!
If you would like more information on ARIMA, you can check out my educational post about it here:
Alrighty, now for the indicator functions.
ARIMA Moving Average and Standard Error Band
The ARIMA moving average is very simple, it takes the SMA of the current trend, lags it and plots out the lagged SMA. You can toggle the auto-select lag on, or you can pick your own lag manually. The above image is an auto-selected lag, but if we manually lag it by 5, this is what it looks like:
Its simply a lagged average of the 5 SMA (that is essentially how ARIMA works, by creating a moving average and lagging the moving average).
There are some implications to selecting a lag factor when it comes to forecasting, but I will cover this in the forecasting section. But I do want to make mention, you can use the ARIMA moving average in lieu of other moving averages. The advantage to doing this is it will be able to plot out the error bands. For example, if we wanted to get an ARIMA MA of the 200 SMA, we can toggle on the error bands and this is what we get:
Or the 50 MA:
NOTE: You ABSOLUTELY SHOULD NEVER use more than a lag of 4 or 5 for Forecasting (will be discussed later).
Auto-Select Lag
The indicator pulls the ARIMA modeler framework from my Forecasting library and pulls the stationarity assessment from my SPTS library. When you are doing an ARIMA model for forecasting, we need to ensure the data is stationary. Thus, if you want to forecast out the current trend, its highly recommended you select the "Auto Determine Lag Length" to find the most appropriate lag and forecast accordingly.
You can, however, chose your own lag order (model order), but this should never be above 4 or 5.
You should never select a lag of more than 5 because you are introducing too much "trendiness" into the equation, and you will get astronomical readings. ARIMA models never generally exceed a lag of 3 or 4 at most, as they are supposed be stationary and de-trended.
Extending by Standard Deviation
There is an option to select a standard deviation extension band. This is helpful for active day trading. Here is NIO extended by 2 standard deviations:
General suggestion is to only extend by 2 standard deviations and this is sufficient for most stocks.
Forecasting
The hallmark of an ARIMA model is the ability to use it for forecasting. Thus, the forecasting feature is a large portion of this indicator. You can see it displayed in the main chart above, but let's show some other examples:
NIO on the 1 hour:
TSLA on the 4 hour:
You can also display a forecasting table:
The result row shows the most likely, conservative, price at each time increment.
The Upper Confidence and Lower Confidence show what the trend would look like if it continued up or down at the current rate and the 95% confidence intervals show the values that the true source is likely to fall between at various increments in time with a 95% confidence (i.e. 95% probability that it should fall between these levels at period xyz assuming normal distribution).
The important levels, in my opinion, are the upper and lower confidence levels. These show you the current rate of decline or increase that the stock is expecting and what the trend would look like with a continuation or a reversal. This is ARIMA's biggest strength, as it has the ability to plot both outcomes assuming the current trend rate and time remains constant.
And that is the indicator! ARIMA is a bit of a complex process, but its a very powerful tool when used properly!
Troubleshooting:
One thing of note. Sometimes when autoselecting a length for forecasting, if there has been a heavy trend in one direction, you will not get the upper or lower confidence levels because of the lack of any up or down movement. In this case, manually select a lag of 3 to 5 to correct for this.
Let me know if you have any questions below and safe trades everyone!
GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.
G7&ECB Balance SheetThis script shows aggregated balance sheet of G7 countries and European central banks: Italy, France, Canada, Japan, United Kingdom, United States, Germany, ECB.
Balance sheets of central banks are converted from their local currencies into US Dollars to get the aggregated value.
Script works in two modes (needs to be selected from settings):
1) Shows aggregated value in USD trillions
2) Shows Rate of Change
Length parameter must be selected for Rate of Change, e.g. on a daily timeframe 365, for year over year values.
Forecast: PastFluxDelta PredictionThe theory is that time periods and the conditions during these periods repeat themselves. Especially if it is the same day of the week in the past, there is a high probability that price fluctuations will roughly repeat themselves.
Eternal return (or eternal recurrence) is a philosophical concept which states that time repeats itself in an infinite loop, and that exactly the same events will continue to occur in exactly the same way, over and over again, for eternity.
History does repeat itself.
The stock market is a manifest example.
Chief market strategist at Miller Tabak + Co. Matt Maley pointed out the strong resemblance between the stock market recently and that in the past.
Various scientific studies and articles show that there could be something to this theory
Most of the investors are ignoring the parallels between stocks today and "heady" years 1929, 1999 and 2007…
Post Labor Day sees investors returning to the S&P 500 near all-time highs and some dark economic shadows lurking …
So how should we regard these inescapable results?
Nietzsche said we should embrace them, accept them, and love them. Once they stop, expect them to start again.
But remember that the future is fundamentally uncertain and that past results are by no means a guarantee of future performance.
Based on this, this indicator uses historical trading data from a year, a week or a day ago and compares price fluctuations in the past with current conditions.
"Bars to predict" can be used to indicate how far into the future the indicator is looking.
"Amount of bars to show" determines how many bars are generally displayed. A high value allows you to see how accurate the method was in the past.
Liquidation Level ScreenerThe Liquidation Level Screener is an analytical tool designed for traders who seek a comprehensive view of potential liquidation zones in the market. This script, adaptable to almost any timeframe from 1 minute to 3 days, offers a unique perspective by mapping out key liquidation levels where significant market actions could occur.
Key Features:
Multi-Exchange Data Aggregation: Unlike many other indicators, the Liquidation Levels Indicator compiles data from multiple leading exchanges including Binance, Bitmex, Kraken, and Bitfinex. This approach ensures a more holistic and accurate representation of market sentiment, providing insights into potential liquidation points across various platforms.
Customizable Timeframes and Modes: The script is versatile, working effectively across various timeframes. It operates in two distinct modes:
Actual Levels Display: Visually represents potential liquidation levels.
Settings Mode: Showcases an open interest (OI) oscillator. When OI is exceptionally high, indicating a surge in opened positions at a specific candle, it signals traders to be vigilant about upcoming liquidation levels.
Three-Tier Liquidation System: The indicator categorizes liquidation levels into three distinct tiers based on open interest levels—1, 2, and 3—with Level 3 representing the highest concentration of open positions. This tiered approach allows traders to gauge the significance of each level and adjust their strategies accordingly.
Histogram Visualization: A novel feature of this script is the histogram on the chart's right side, representing the concentration of liquidation levels in specific market zones. This visual aid helps traders identify crucial areas that warrant close attention, enhancing decision-making.
Customizable Options:
Moving Averages: Choose from a wide range of moving average types, including VWMA, SMA, EMA, and more, to tailor the indicator to your analysis style.
Histogram Settings: Adjust the number of histograms, lookback bars, and their proximity to the latest candle, allowing for a personalized density and range of visualization.
Liquidation Level Sensitivity: Set thresholds for different liquidation levels, fine-tuning the indicator to detect varying degrees of market leverage.
Color Coding: Customize the color scheme for different leverage levels, enhancing visual clarity and ease of interpretation.
The Liquidation Level Screener offers a unique edge by highlighting potential zones where significant market movements can occur due to liquidations. By consolidating data from multiple exchanges, it provides a more rounded view of market behavior, which is essential in today’s interconnected trading environment. The tiered liquidation system and histogram feature equip traders with the ability to identify and focus on key market segments where high activity is expected. This tool is particularly valuable for traders who base their strategies on market liquidity and leverage dynamics.
Hulk Grid Algorithm V2 - The Quant ScienceIt's the latest proprietary grid algorithm developed by our team. This software represents a clearer and more comprehensive modernization of the deprecated Hulk Grid Algorithm. In this new release, we have optimized the source code architecture and investment logic, which we will describe in detail below.
Overview
Hulk Grid Algorithm V2 is designed to optimize returns in sideways market conditions. In this scenario, the algorithm divides purchases with long orders at each level of the grid. Unlike a typical grid algorithm, this version applies an anti-martingale model to mitigate volatility and optimize the average entry price. Starting from the lower level, the purchase quantity is increased at each new subsequent level until reaching the upper level. The initial quantity of the first order is fixed at 0.50% of the initial capital. With each new order, the initial quantity is multiplied by a value equal to the current grid level (where 1 is the lower level and 10 is the upper level).
Example: Let's say we have an initial capital of $10,000. The initial capital for the first order would be $50 * 1 = $50, for the second order $50 * 2 = $100, for the third order $50 * 3 = $150, and so on until reaching the upper level.
All previously opened orders are closed using a percentage-based stop-loss and take-profit, calculated based on the extremes of the grid.
Set Up
As mentioned earlier, the user's goal is to analyze this strategy in markets with a lack of trend, also known as sideways markets. After identifying a price range within which the asset tends to move, the user can choose to create the grid by placing the starting price at the center of the range. This way, they can consider trading the asset, if the backtesting generates a return greater than the Buy & Hold return.
Grid Configuration
To create the grid, it's sufficient to choose the starting price during the launch phase. This level will be the center of the grid from which the upper and lower levels will be calculated. The grid levels are computed using an arithmetic method, adding and subtracting a configurable fixed amount from the user interface (Grid Step $).
Example: Let's imagine choosing 1000 as the starting price and 50 as the Grid Step ($). The upper levels will be 1000, 1050, 1100, 1150, 1200. The lower levels will be 950, 900, 850, 800, and 750.
Markets
This software can be used in all markets: stocks, indices, commodities, cryptocurrencies, ETFs, Forex, etc.
Application
With this backtesting software, is possible to analyze the strategy and search for markets where it can generate better performance than Buy & Hold returns. There are no alerts or automatic investment mechanisms, and currently, the strategy can only be executed manually.
Design
Is possible to modify the grid style and customize colors by accessing the Properties section of the user interface.
Hedge Coin M - Statistical Support and ResistanceHedge Coin M - Statistical Support and Resistance
Introduction
"Hedge Coin M - Statistical Support and Resistance" is a sophisticated, statistically-driven indicator designed specifically for traders in the COIN-M market on Binance. It offers a nuanced approach to identifying key market levels, focusing on the dynamics of support and resistance through advanced volatility analysis.
Foundation and Credits:
This script is an advanced adaptation of TradingView's standard code for the Bollinger Bands indicator. It extends the foundational concept of Bollinger Bands by integrating additional volatility metrics.
Calculation Method
This indicator employs Volume Weighted Moving Averages (VWMA) to create two distinct sets of Bollinger Bands, named BB-a and BB-b.
BB-a is derived from the VWMA of high prices, targeting potential resistance levels.
BB-b is based on the VWMA of low prices, aimed at identifying critical support levels.
Users can independently adjust the standard deviation (SD) multipliers for the upper and lower bands of both BB-a and BB-b, accommodating different market conditions.
Enhanced Volatility Analysis
The indicator calculates additional standard deviation lines for the upper band of BB-a and the lower band of BB-b. These lines provide deeper insights into market volatility.
Plotted Graphs
The primary plots include the upper and lower bands of BB-a and BB-b, marked in distinct colors for clarity.
Additional SD lines are plotted to indicate potential extended levels of support and resistance, offering traders a broader view of possible market movements.
Purpose and Usage
"Hedge Coin M - Statistical Support and Resistance" is designed to provide traders with a consistent, statistical method for identifying significant price levels.
It aids in scaling entry into positions, helping traders to navigate the COIN-M market with more informed decision-making.
This tool is especially useful for traders who combine long-term holding with swing trading strategies, offering a balanced approach to market engagement.
Integration and Adaptation
Easily integrate this indicator into your TradingView chart for the COIN-M market.
Use the insights provided to complement your overall trading strategy, particularly in identifying and reacting to significant market movements.
Disclaimer
Important Note: This indicator is provided for informational purposes only. It does not constitute financial advice, investment advice, trading advice, or any other sort of advice. Trading decisions should be made based on your own analysis, prudence, and judgment. Please be aware of the risks involved in trading and consult a financial advisor if necessary.
Market Health MonitorThe Market Health Monitor is a comprehensive tool designed to assess and visualize the economic health of a market, providing traders with vital insights into both current and future market conditions. This script integrates a range of critical economic indicators, including unemployment rates, inflation, Federal Reserve funds rates, consumer confidence, and housing market indices, to form a robust understanding of the overall economic landscape.
Drawing on a variety of data sources, the Market Health Monitor employs moving averages over periods of 3, 12, 36, and 120 months, corresponding to quarterly, annual, three-year, and ten-year economic cycles. This selection of timeframes is specifically chosen to capture the nuances of economic movements across different phases, providing a balanced view that is sensitive to both immediate changes and long-term trends.
Key Features:
Economic Indicators Integration: The script synthesizes crucial economic data such as unemployment rates, inflation levels, and housing market trends, offering a multi-dimensional perspective on market health.
Adaptability to Market Conditions: The inclusion of both short-term and long-term moving averages allows the Market Health Monitor to adapt to varying market conditions, making it a versatile tool for different trading strategies.
Oscillator Thresholds for Recession and Growth: The script sets specific thresholds that, when crossed, indicate either potential economic downturns (recessions) or periods of growth (expansions), allowing traders to anticipate and react to changing market conditions proactively.
Color-Coded Visualization: The Market Health Monitor employs a color-coding system for ease of interpretation:
-- A red background signals unhealthy economic conditions, cautioning traders about potential risks.
-- A bright red background indicates a confirmed recession, as declared by the NBER, signaling a critical time for traders to reassess risk exposure.
-- A green background suggests a healthy market with expected economic expansion, pointing towards growth-oriented opportunities.
Comprehensive Market Analysis: By combining various economic indicators, the script offers a holistic view of the market, enabling traders to make well-informed decisions based on a thorough understanding of the economic environment.
Key Criteria and Parameters:
Economic Indicators:
Labor Market: The unemployment rate is a critical indicator of economic health.
High or rising unemployment indicates reduced consumer spending and economic stress.
Inflation: Key for understanding monetary policy and consumer purchasing power.
Persistent high inflation can lead to economic instability, while deflation can signal weak
demand.
Monetary Policy: Reflected by the Federal Reserve funds rate.
Changes in the rate can influence economic activity, borrowing costs, and investor
sentiment.
Consumer Confidence: A predictor of consumer spending and economic activity.
Reflects the public’s perception of the economy
Housing Market: The housing market often leads the economy into recession and recovery.
Weakness here can signal broader economic problems.
Market Data:
Stock Market Indices: Reflect overall investor sentiment and economic
expectations. No gains in a stock market could potentially indicate that economy is
slowing down.
Credit Conditions: Indicated by the tightness of bank lending, signaling risk
perception.
Commodity Insight:
Crude Oil Prices: A proxy for global economic activity.
Indicator Timeframe:
A default monthly timeframe is chosen to align with the release frequency of many economic indicators, offering a balanced view between timely data and avoiding too much noise from short-term fluctuations. Surely, it can be chosen by trader / analyst.
The Market Health Monitor is more than just a trading tool—it's a comprehensive economic guide. It's designed for traders who value an in-depth understanding of the economic climate. By offering insights into both current conditions and future trends, it encourages traders to navigate the markets with confidence, whether through turbulent times or in periods of growth. This tool doesn't just help you follow the market—it helps you understand it.
lib_fvgLibrary "lib_fvg"
further expansion of my object oriented library toolkit. This lib detects Fair Value Gaps and returns them as objects.
Drawing them is a separate step so the lib can be used with securities. It also allows for usage of current/close price to detect fill/invalidation of a gap and to adjust the fill level dynamically. FVGs can be detected while forming and extended indefinitely while they're unfilled.
method draw(this)
Namespace types: FVG
Parameters:
this (FVG)
method draw(fvgs)
Namespace types: FVG
Parameters:
fvgs (FVG )
is_fvg(mode, precondition, filter_insignificant, filter_insignificant_atr_factor, live)
Parameters:
mode (int) : switch for detection 1 for bullish FVGs, -1 for bearish FVGs
precondition (bool) : allows for other confluences to block/enable detection
filter_insignificant (bool) : allows to ignore small gaps
filter_insignificant_atr_factor (float) : allows to adjust how small (compared to a 50 period ATR)
live (bool) : allows to detect FVGs while the third bar is forming -> will cause repainting
Returns: a tuple of (bar_index of gap bar, gap top, gap bottom)
create_fvg(mode, idx, top, btm, filled_at_pc, config)
Parameters:
mode (int) : switch for detection 1 for bullish FVGs, -1 for bearish FVGs
idx (int) : the bar_index of the FVG gap bar
top (float) : the top level of the FVG
btm (float) : the bottom level of the FVG
filled_at_pc (float) : the ratio (0-1) that the fill source needs to retrace into the gap to consider it filled/invalidated/ready for removal
config (FVGConfig) : the plot configuration/styles for the FVG
Returns: a new FVG object if there was a new FVG, else na
detect_fvg(mode, filled_at_pc, precondition, filter_insignificant, filter_insignificant_atr_factor, live, config)
Parameters:
mode (int) : switch for detection 1 for bullish FVGs, -1 for bearish FVGs
filled_at_pc (float)
precondition (bool) : allows for other confluences to block/enable detection
filter_insignificant (bool) : allows to ignore small gaps
filter_insignificant_atr_factor (float) : allows to adjust how small (compared to a 50 period ATR)
live (bool) : allows to detect FVGs while the third bar is forming -> will cause repainting
config (FVGConfig)
Returns: a new FVG object if there was a new FVG, else na
method update(this, fill_src)
Namespace types: FVG
Parameters:
this (FVG)
fill_src (float) : allows for usage of different fill source series, e.g. high for bearish FVGs, low vor bullish FVGs or close for both
method update(all, fill_src)
Namespace types: FVG
Parameters:
all (FVG )
fill_src (float)
method remove_filled(unfilled_fvgs)
Namespace types: FVG
Parameters:
unfilled_fvgs (FVG )
method delete(this)
Namespace types: FVG
Parameters:
this (FVG)
method delete_filled_fvgs_buffered(filled_fvgs, max_keep)
Namespace types: FVG
Parameters:
filled_fvgs (FVG )
max_keep (int) : the number of filled, latest FVGs to retain on the chart.
FVGConfig
Fields:
box_args (|robbatt/lib_plot_objects/36;BoxArgs|#OBJ)
line_args (|robbatt/lib_plot_objects/36;LineArgs|#OBJ)
box_show (series__bool)
line_show (series__bool)
keep_filled (series__bool)
extend (series__bool)
FVG
Fields:
config (|FVGConfig|#OBJ)
startbar (series__integer)
mode (series__integer)
top (series__float)
btm (series__float)
center (series__float)
size (series__float)
fill_size (series__float)
fill_lvl_target (series__float)
fill_lvl_current (series__float)
fillbar (series__integer)
filled (series__bool)
_fvg_box (|robbatt/lib_plot_objects/36;Box|#OBJ)
_fill_line (|robbatt/lib_plot_objects/36;Line|#OBJ)
