Advanced Options Trading Indicator: Buy & Sell Signal Generator This powerful custom indicator combines the Relative Strength Index (RSI) and Moving Average (MA) to help traders identify optimal entry and exit points in the options market. The indicator generates real-time buy and sell signals based on RSI crossovers and price positioning relative to the moving average, providing actionable insights for traders seeking to make informed decisions. Additionally, it calculates potential call and put option strike prices with a buffer for added flexibility and precision, ensuring a well-rounded approach to options trading.
Statistics
Machine Learning Price Target Prediction Signals [AlgoAlpha]Introducing the Machine Learning Price Target Predictions, a cutting-edge trading tool that leverages kernel regression to provide accurate price targets and enhance your trading strategy. This indicator combines trend-based signals with advanced machine learning techniques, offering predictive insights into potential price movements. Perfect for traders looking to make data-driven decisions with confidence.
What is Kernel Regression and How It Works
Kernel regression is a non-parametric machine learning technique that estimates the relationship between variables by weighting data points based on their similarity to a given input. The similarity is determined using a kernel function, such as the Gaussian (RBF) kernel, which assigns higher weights to closer data points and progressively lower weights to farther ones. This allows the model to make smooth and adaptive predictions, balancing recent data and historical trends.
Key Features
🎯 Predictive Price Targets : Uses kernel regression to estimate the magnitude of price movements.
📈 Dynamic Trend Analysis : Multiple trend detection methods, including EMA crossovers, Hull Moving Average, and SuperTrend.
🔧 Customizable Settings : Adjust bandwidth for kernel regression and tweak trend indicator parameters to suit your strategy.
📊 Visual Trade Levels : Displays take-profit and stop-loss levels directly on the chart with customizable colors.
📋 Performance Metrics : Real-time win rate, recommended risk-reward ratio, and training data size displayed in an on-chart table.
🔔 Alerts : Get notified for new trends, take-profit hits, and stop-loss triggers.
How to Use
🛠 Add the Indicator : Add it to your favorites and apply it to your chart. Configure the trend detection method (SuperTrend, HMA, or EMA crossover) and other parameters based on your preferences.
📊 Analyze Predictions : Observe the predicted move size, recommended risk-reward ratio, and trend direction. Use the displayed levels for trade planning.
🔔 Set Alerts : Enable alerts for trend signals, take-profit hits, or stop-loss triggers to stay informed without constant monitoring.
How It Works
The indicator calculates features such as price volatility, relative strength, and trend signals, which are stored during training periods. When a trend change is detected, the kernel regression model predicts the likely price move based on these features. Predictions are smoothed using the specified bandwidth to avoid overfitting while ensuring timely responses to feature changes. Visualized take-profit and stop-loss levels help traders optimize risk management. Real-time metrics like win rate and recommended risk-reward ratios provide actionable insights for decision-making.
Enhanced Price Z-Score OscillatorThe Enhanced Price Z-Score Oscillator by tkarolak is a powerful tool that transforms raw price data into an easy-to-understand statistical visualization using Z-Score-derived candlesticks. Simply put, it shows how far prices stray from their average in terms of standard deviations (Z-Scores), helping traders identify when prices are unusually high (overbought) or unusually low (oversold).
The indicator’s default feature displays Z-Score Candlesticks, where each candle reflects the statistical “distance” of the open, high, low, and close prices from their average. This creates a visual map of market extremes and potential reversal points. For added flexibility, you can also switch to Z-Score line plots based on either Close prices or OHLC4 averages.
With clear threshold lines (±2σ and ±3σ) marking moderate and extreme price deviations, and color-coded zones to highlight overbought and oversold areas, the oscillator simplifies complex statistical concepts into actionable trading insights.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
momentum indicatorThe Rational Quadratic Smoother uses the Rational Quadratic Kernel to create a non-repainting, adaptive smoothing of price data. This method provides a balance between long-term trends and short-term movements by adjusting the weight of distant data points using a kernel function. Traders can use this indicator to:
Smooth price data for better trend identification.
Filter out noise without introducing lag.
Combine it with other indicators for advanced strategies.
Key Features:
Adjustable Lookback Period: Controls the range of data points considered.
Relative Weighting: Fine-tunes the influence of long-term vs. short-term data.
Customizable smoothing to fit different trading styles (scalping, swing trading, etc.).
STRX - Correlation DominationThis indicator displays the correlation among three selected assets (for example, Gold, Dollar Index, and Nasdaq) on a custom timeframe. A table positioned at the top-right corner of the chart lets you quickly see the correlation between:
Asset 1 vs Asset 2
Asset 1 vs Asset 3
Asset 2 vs Asset 3
Correlations are calculated using the Pearson correlation function (ta.correlation). If the correlation is greater than or equal to 0.4, the value appears in green (strong positive correlation). If it is less than or equal to -0.4, it appears in red (strong negative correlation). Otherwise, it is displayed in yellow (weak correlation).
Multi-asset and multi-timeframe: Compare up to three instruments at once on your chosen timeframe.
Customizable period: Use the “Correlation Period” setting to adjust the correlation calculation window.
Clear table format: The results are immediately visible in an easy-to-read table.
Disclaimer: This script is provided solely for educational and informational purposes. It does not constitute a recommendation or an invitation to invest. Use it as an additional resource and always conduct thorough market analysis before opening any trading positions. Past performance does not guarantee future results.
Fibonacci Trend [ChartPrime]Fibonacci Trend Indicator
This powerful indicator leverages supertrend analysis to detect market direction while overlaying dynamic Fibonacci levels to highlight potential support, resistance, and optimal trend entry zones. With its straightforward design, it is perfect for traders looking to simplify their workflow and enhance decision-making.
⯁ KEY FEATURES AND HOW TO USE
⯌ Supertrend Trend Identification :
The indicator uses a supertrend algorithm to identify market direction. It displays purple for downtrends and green for uptrends, ensuring quick and clear trend analysis.
⯌ Fibonacci Levels for Current Swings :
Automatically calculates Fibonacci retracement levels (0.236, 0.382, 0.618, 0.786) for the current swing leg.
- These levels act as key zones for potential support, resistance, and trend continuation.
- The high and low swing points are labeled with exact prices, ensuring clarity.
- If the swing range is insufficient (less than five times ATR), Fibonacci levels are not displayed, avoiding irrelevant data.
⯌ Extended Fibonacci Levels :
User-defined extensions project Fibonacci levels into the future, aiding traders in planning price targets or projecting key zones.
⯌ Optimal Trend Entry Zone :
A filled area between 0.618 and 0.786 levels visually highlights the optimal entry zone for trend continuation. This allows traders to refine their entry points during pullbacks.
⯌ Diagonal Trend Line :
A dashed diagonal line connects the swing high and low, visually confirming the range and trend strength of the current swing.
⯌ Visual Labels for Fibonacci Levels :
Each Fibonacci level is marked with a label displaying its value for quick reference.
⯁ HOW TRADERS CAN POTENTIALLY USE THIS TOOL
Fibonacci Retracements:
Use the Fibonacci retracement levels to find key support or resistance zones where the price may pull back before continuing its trend.
Example: Enter long trades when the price retraces to 0.618–0.786 levels in an uptrend.
Fibonacci Extensions:
Use Fibonacci extensions to project future price targets based on the current trend's swing leg. Levels like 127.2% and 161.8% are commonly used as profit-taking zones.
Reversal Identification:
Spot potential reversals by monitoring price reactions at key Fibonacci retracement levels (e.g., 0.236 or 0.382) or the swing high/low.
Optimal Trend Entries:
The filled zone between 0.618 and 0.786 is a statistically strong area for entering a position in the direction of the trend.
Example: Enter long positions during retracements to this range in an uptrend.
Risk Management:
Set stop-losses below key Fibonacci levels or the swing low/high, and take profits at extension levels, enhancing your trade management strategies.
⯁ CONCLUSION
The Fibonacci Trend Indicator is a straightforward yet effective tool for identifying trends and key Fibonacci levels. It simplifies analysis by integrating supertrend-based trend identification with Fibonacci retracements, extensions, and optimal entry zones. Whether you're a beginner or experienced trader, this indicator is an essential addition to your toolkit for trend trading, reversal spotting, and risk management.
Trend Reversal Probability [Algoalpha]Introducing Trend Reversal Probability by AlgoAlpha – a powerful indicator that estimates the likelihood of trend reversals based on an advanced custom oscillator and duration-based statistics. Designed for traders who want to stay ahead of potential market shifts, this indicator provides actionable insights into trend momentum and reversal probabilities.
Key Features :
🔧 Custom Oscillator Calculation: Combines a dual SMA strategy with a proprietary RSI-like calculation to detect market direction and strength.
📊 Probability Levels & Visualization: Plots average signal durations and their statistical deviations (±1, ±2, ±3 SD) on the chart for clear visual guidance.
🎨 Dynamic Color Customization: Choose your preferred colors for upward and downward trends, ensuring a personalized chart view.
📈 Signal Duration Metrics: Tracks and displays signal durations with columns representing key percentages (80%, 60%, 40%, and 20%).
🔔 Alerts for High Probability Events: Set alerts for significant reversal probabilities (above 84% and 98% or below 14%) to capture key trading moments.
How to Use :
Add the Indicator: Add Trend Reversal Probability to your favorites by clicking the star icon.
Market Analysis: Use the plotted probability levels (average duration and ±SD bands) to identify overextended trends and potential reversals. Use the color of the duration counter to identify the current trend.
Leverage Alerts: Enable alerts to stay informed of high or extreme reversal probabilities without constant chart monitoring.
How It Works :
The indicator begins by calculating a custom oscillator using short and long simple moving averages (SMA) of the midpoint price. A proprietary RSI-like formula then transforms these values to estimate trend direction and momentum. The duration between trend reversals is tracked and averaged, with standard deviations plotted to provide probabilistic guidance on trend longevity. Additionally, the indicator incorporates a cumulative probability function to estimate the likelihood of a trend reversal, displaying the result in a data table for easy reference. When probability levels cross key thresholds, alerts are triggered, helping traders take timely action.
Stop Loss & TargetHow to Use the SL/TP Indicator
The SL/TP indicator is a versatile tool designed for traders to easily visualize entry, stop-loss (SL), and take-profit (TP) levels on their charts. This guide will walk you through the steps to configure and use the indicator effectively.
Features:
Configure Long Trades and Short Trades independently.
Define Entry Price, Stop Loss, and up to three Take Profit levels for each trade.
Customize line colors for better visualization.
Works for both risk-reward and target-based trading.
Adding the Indicator:
Open the TradingView platform.
Search for the indicator name: SL/TP.
Click the Add to Chart button to apply it.
Configuration:
1. Long Trade Settings
Enable Long Trade: Check this option to activate long trade lines on the chart.
Long Entry Price: Input the price at which you plan to enter the long trade.
Long Stop Loss: Input your stop-loss level for the long trade.
Line Colors: You can customize the colors for the Entry, SL, and TP lines in the Long Trade settings group.
Take Profit Levels (Calculated Automatically):
TP1: 1:1 Risk-Reward ratio (difference between Entry and SL added to Entry).
TP2: 1:2 Risk-Reward ratio.
TP3: 1:3 Risk-Reward ratio.
2. Short Trade Settings
Enable Short Trade: Check this option to activate short trade lines on the chart.
Short Entry Price: Input the price at which you plan to enter the short trade.
Short Stop Loss: Input your stop-loss level for the short trade.
Line Colors: You can customize the colors for the Entry, SL, and TP lines in the Short Trade settings group.
Take Profit Levels (Calculated Automatically):
TP1: 1:1 Risk-Reward ratio (difference between Entry and SL subtracted from Entry).
TP2: 1:2 Risk-Reward ratio.
TP3: 1:3 Risk-Reward ratio.
Visualizing on the Chart:
Once you configure the settings and enable the trade, the indicator will draw horizontal lines on the chart for:
Entry Price
Stop Loss
Take Profit Levels (TP1, TP2, TP3)
Each line will extend to three bars ahead of the current bar index.
Customization:
Adjust colors for better visibility depending on your chart theme.
The width and style of lines can also be modified in the source code if needed.
Example Usage:
Long Trade Example:
Enable Long Trade: Check the box.
Set Entry Price: 100.
Set Stop Loss: 95.
The indicator will draw the following lines:
Entry Line: At 100 (customizable color).
Stop Loss Line: At 95 (customizable color).
TP1 Line: At 105 (1:1 Risk-Reward).
TP2 Line: At 110 (1:2 Risk-Reward).
TP3 Line: At 115 (1:3 Risk-Reward).
Short Trade Example:
Enable Short Trade: Check the box.
Set Entry Price: 200.
Set Stop Loss: 205.
The indicator will draw the following lines:
Entry Line: At 200 (customizable color).
Stop Loss Line: At 205 (customizable color).
TP1 Line: At 195 (1:1 Risk-Reward).
TP2 Line: At 190 (1:2 Risk-Reward).
TP3 Line: At 185 (1:3 Risk-Reward).
Notes:
Ensure that you input valid and realistic price levels for Entry and Stop Loss.
The indicator will only display lines if both the Entry Price and Stop Loss are non-zero.
Use this indicator for planning trades visually but always confirm levels with your trading strategy.
Disclaimer: This indicator is a tool to assist in trading. Use it with proper risk management and your own due diligence.
ADX (levels)This Pine Script indicator calculates and displays the Average Directional Index (ADX) along with the DI+ and DI- lines to help identify the strength and direction of a trend. The script is designed for Pine Script v6 and includes customizable settings for a more tailored analysis.
Features:
ADX Calculation:
The ADX measures the strength of a trend without indicating its direction.
It uses a smoothing method for more reliable trend strength detection.
DI+ and DI- Lines (Optional):
The DI+ (Directional Index Plus) and DI- (Directional Index Minus) help determine the direction of the trend:
DI+ indicates upward movement.
DI- indicates downward movement.
These lines are disabled by default but can be enabled via input settings.
Customizable Threshold:
A horizontal line (hline) is plotted at a user-defined threshold level (default: 20) to highlight significant ADX values that indicate a strong trend.
Slope Analysis:
The slope of the ADX is analyzed to classify the trend into:
Strong Trend: Slope is higher than a defined "medium" threshold.
Moderate Trend: Slope falls between "weak" and "medium" thresholds.
Weak Trend: Slope is positive but below the "weak" threshold.
A background color changes dynamically to reflect the strength of the trend:
Green (light or dark) indicates trend strength levels.
Custom Colors:
ADX color is customizable (default: pink #e91e63).
Background colors for trend strength can also be adjusted.
Independent Plot Window:
The indicator is displayed in a separate window below the price chart, making it easier to analyze trend strength without cluttering the main price chart.
Parameters:
ADX Period: Defines the lookback period for calculating the ADX (default: 14).
Threshold (hline): A horizontal line value to differentiate strong trends (default: 20).
Slope Thresholds: Adjustable thresholds for weak, moderate, and strong trend slopes.
Enable DI+ and DI-: Boolean options to display or hide the DI+ and DI- lines.
Colors: Customizable colors for ADX, background gradients, and other elements.
How to Use:
Identify Trend Strength:
Use the ADX value to determine the strength of a trend:
Below 20: Weak trend.
Above 20: Strong trend.
Analyze Trend Direction:
Enable DI+ and DI- to check whether the trend is upward (DI+ > DI-) or downward (DI- > DI+).
Dynamic Slope Detection:
Use the background color as a quick visual cue to assess trend strength changes.
This indicator is ideal for traders who want to measure trend strength and direction dynamically while maintaining a clean and organized chart layout.
[ADDYad] Google Search Trends - Bitcoin (2012 Jan - 2025 Jan)This Pine Script shows the Google Search Trends as an indicator for Bitcoin from January 2012 to January 2025, based on monthly data retrieved from Google Trends. It calculates and displays the relative search interest for Bitcoin over time, offering a historical perspective on its popularity mainly built for BITSTAMP:BTCUSD .
Important note: This is not a live indicator. It visualizes historical search trends based on Google Trends data.
Key Features:
Data Source : Google Trends (Last retrieved in January 10 2025).
Timeframe : The script is designed to be used on a monthly chart, with the data reflecting monthly search trends from January 2012 to January 2025. For other timeframes, the data is linearly interpolated to estimate the trends at finer resolutions.
Purpose : This indicator helps visualize Bitcoin's search interest over the years, offering insights into public interest and sentiment during specific periods (e.g., major price movements or news events).
Data Handling : The data is interpolated for use on non-monthly timeframes, allowing you to view search trends on any chart timeframe. This makes it versatile for use in longer-term analysis or shorter timeframes, despite the raw data being available only on a monthly basis. However, it is most relevant for Monthly, Weekly, and Daily timeframes.
How It Works:
The script calculates the number of months elapsed since January 1, 2012, and uses this to interpolate Google Trends data values for any given point in time on the chart.
The linear interpolation function adjusts the monthly data to provide an approximate trend for intermediate months.
Why It's Useful:
Track Bitcoin's historic search trends to understand how interest in Bitcoin evolved over time, potentially correlating with price movements.
Correlate search trends with price action and other market indicators to analyze the effects of public sentiment and sentiment-driven market momentum.
Final Notes:
This script is unique because it shows real-world, non-financial dataset (Google Trends) to understand price action of Bitcoin correlating with public interest. Hopefully is a valuable addition to the TradingView community.
ADDYad
Simple Average Price & Target ProfitThis script is designed to help users calculate and visualize the weighted average price of an asset based on multiple entry points, along with the target price and the potential profit. The user can input specific prices for three different entries, along with the percentage of total investment allocated to each price point. The script then calculates the weighted average price based on these entries and displays it on the chart. Additionally, it calculates the potential profit at a given target price, which is plotted on the chart.
ADR Table BY @ICT_YEROADR Table BY @ICT_YERO
Created by: @ICT_YERO
This custom indicator is designed to provide the Average Daily Range (ADR) for multiple timeframes, including Daily, 4-Hour, and 1-Hour. The indicator is tailored to assist traders in understanding price volatility and making informed trading decisions.
Key Features
Multi-Timeframe ADR Calculation:
Automatically calculates and displays the ADR for Daily, 4-Hour, and 1-Hour timeframes.
Helps traders identify potential price movement ranges for different trading sessions.
Dynamic Range Visualization:
Clear visual representation of the ADR on the chart, making it easy to spot price extremes.
Real-time updates to reflect changes in price movement.
Custom Alerts:
Option to set alerts when the price approaches the ADR high or low.
Useful for identifying potential reversal zones or breakout opportunities.
User-Friendly Interface:
Simple and intuitive settings to customize colors, levels, and display preferences.
Seamlessly integrates with your existing TradingView setup.
ICT-Inspired Methodology:
Designed for traders who follow ICT concepts, focusing on precision and high-probability setups.
Applications
Range Trading: Helps determine the high and low boundaries for scalping or intraday setups.
Volatility Analysis: Understand market behavior during different times of the day or week.
Reversal Zones: Identify areas where price is likely to reverse, based on ADR extremes.
Whether you're a scalper, day trader, or swing trader, this indicator provides a comprehensive overview of price volatility across multiple timeframes, making it an essential tool for your trading arsenal.
Poisson Projection of Price Levels### **Poisson Projection of Price Levels**
**Overview:**
The *Poisson Projection of Price Levels* is a cutting-edge technical indicator designed to identify and visualize potential support and resistance levels based on historical price interactions. By leveraging the Poisson distribution, this tool dynamically adjusts the significance of each price level's past "touches" to project future interactions with varying degrees of probability. This probabilistic approach offers traders a nuanced view of where price levels may hold or react in upcoming bars, enhancing both analysis and trading strategies.
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**🔍 **Math & Methodology**
1. **Strata Levels:**
- **Definition:** Strata are horizontal lines spaced evenly around the current closing price.
- **Calculation:**
\
where \(i\) ranges from 0 to \(\text{Strata Count} - 1\).
2. **Forecast Iterations:**
- **Structure:** The indicator projects five forecast iterations into the future, each spaced by a Fibonacci sequence of bars: 2, 3, 5, 8, and 13 bars ahead. This spacing is inspired by the Fibonacci sequence, which is prevalent in financial market analysis for identifying key levels.
- **Purpose:** Each iteration represents a distinct forecast point where the price may interact with the strata, allowing for a multi-step projection of potential price levels.
3. **Touch Counting:**
- **Definition:** A "touch" occurs when the closing price of a bar is within half the increment of a stratum level.
- **Process:** For each stratum and each forecast iteration, the indicator counts the number of touches within a specified lookback window (e.g., 80 bars), offset by the forecasted position. This ensures that each iteration's touch count is independent and contextually relevant to its forecast horizon.
- **Adjustment:** Each forecast iteration analyzes a unique segment of the lookback window, offset by its forecasted position to ensure independent probability calculations.
4. **Poisson Probability Calculation:**
- **Formula:**
\
\
- **Interpretation:** \(p(k=1)\) represents the probability of exactly one touch occurring within the lookback window for each stratum and iteration.
- **Application:** This probability is used to determine the transparency of each stratum line, where higher probabilities result in more opaque (less transparent) lines, indicating stronger historical significance.
5. **Transparency Mapping:**
- **Calculation:**
\
- **Purpose:** Maps the Poisson probability to a visual transparency level, enhancing the readability of significant strata levels.
- **Outcome:** Strata with higher probabilities (more historical touches) appear more opaque, while those with lower probabilities appear fainter.
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**📊 **Comparability to Standard Techniques**
1. **Support and Resistance Levels:**
- **Traditional Approach:** Traders identify support and resistance based on historical price reversals, pivot points, or psychological price levels.
- **Poisson Projection:** Automates and quantifies this process by statistically analyzing the frequency of price interactions with specific levels, providing a probabilistic measure of significance.
2. **Statistical Modeling:**
- **Standard Models:** Techniques like Moving Averages, Bollinger Bands, or Fibonacci Retracements offer dynamic and rule-based levels but lack direct probabilistic interpretation.
- **Poisson Projection:** Introduces a discrete event probability framework, offering a unique blend of statistical rigor and visual clarity that complements traditional indicators.
3. **Event-Based Analysis:**
- **Financial Industry Practices:** Event studies and high-frequency trading models often use Poisson processes to model order arrivals or price jumps.
- **Indicator Application:** While not identical, the use of Poisson probabilities in this indicator draws inspiration from event-based modeling, applying it to the context of price level interactions.
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**💡 **Strengths & Advantages**
1. **Innovative Visualization:**
- Combines statistical probability with traditional support/resistance visualization, offering a fresh perspective on price level significance.
2. **Dynamic Adaptability:**
- Parameters like strata increment, lookback window, and probability threshold are user-defined, allowing customization across different markets and timeframes.
3. **Independent Probability Calculations:**
- Each forecast iteration calculates its own Poisson probability, ensuring that projections are contextually relevant and independent of other iterations.
4. **Clear Visual Cues:**
- Transparency-based coloring intuitively highlights significant price levels, making it easier for traders to identify key areas of interest at a glance.
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**⚠️ **Limitations & Considerations**
1. **Poisson Assumptions:**
- Assumes that touches occur independently and at a constant average rate (\(\lambda\)), which may not always align with market realities characterized by trends and volatility clustering.
2. **Computational Intensity:**
- Managing multiple iterations and strata can be resource-intensive, potentially affecting performance on lower-powered devices or with very high lookback windows.
3. **Interpretation Complexity:**
- While transparency offers visual clarity, understanding the underlying probability calculations requires a basic grasp of Poisson statistics, which may be a barrier for some traders.
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**📢 **How to Use It**
1. **Add to TradingView:**
- Open TradingView and navigate to the Pine Script Editor.
- Paste the script above and click **Add to Chart**.
2. **Configure Inputs:**
- **Strata Increment:** Set the desired price step between strata (e.g., `0.1` for 10 cents).
- **Lookback Window:** Define how many past bars to consider for calculating Poisson probabilities (e.g., `80`).
- **Probability Transparency Threshold (%):** Set the threshold percentage to map probabilities to line transparency (e.g., `25%`).
3. **Understand the Forecast Iterations:**
- The indicator projects five forecast points into the future at bar spacings of 2, 3, 5, 8, and 13 bars ahead.
- Each iteration independently calculates its Poisson probability based on the touch counts within its specific lookback window offset by its forecasted position.
4. **Interpret the Visualization:**
- **Opaque Lines:** Indicate higher Poisson probabilities, suggesting historically significant price levels that are more likely to interact again.
- **Fainter Lines:** Represent lower probabilities, indicating less historically significant levels that may be less likely to interact.
- **Forecast Spacing:** The spacing of 2, 3, 5, 8, and 13 bars ahead aligns with Fibonacci principles, offering a natural progression in forecast horizons.
5. **Apply to Trading Strategies:**
- **Support/Resistance Identification:** Use the opaque lines as potential support and resistance levels for placing trades.
- **Entry and Exit Points:** Anticipate price interactions at forecasted levels to plan strategic entries and exits.
- **Risk Management:** Utilize the transparency mapping to determine where to place stop-loss and take-profit orders based on the probability of price interactions.
6. **Customize as Needed:**
- Adjust the **Strata Increment** to fit different price ranges or volatility levels.
- Modify the **Lookback Window** to capture more or fewer historical touches, adapting to different timeframes or market conditions.
- Tweak the **Probability Transparency Threshold** to control the sensitivity of transparency mapping to Poisson probabilities.
**📈 **Practical Applications**
1. **Identifying Key Levels:**
- Quickly visualize which price levels have historically had significant interactions, aiding in the identification of potential support and resistance zones.
2. **Forecasting Price Reactions:**
- Use the forecast iterations to anticipate where price may interact in the near future, assisting in planning entry and exit points.
3. **Risk Management:**
- Determine areas of high probability for price reversals or consolidations, enabling better placement of stop-loss and take-profit orders.
4. **Market Analysis:**
- Assess the strength of market levels over different forecast horizons, providing a multi-layered understanding of market structure.
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**🔗 **Conclusion**
The *Poisson Projection of Price Levels* bridges the gap between statistical modeling and traditional technical analysis, offering traders a sophisticated tool to quantify and visualize the significance of price levels. By integrating Poisson probabilities with dynamic transparency mapping, this indicator provides a unique and insightful perspective on potential support and resistance zones, enhancing both analysis and trading strategies.
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**📞 **Contact:**
For support or inquiries, please contact me on TradingView!
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**📢 **Join the Conversation!**
Have questions, feedback, or suggestions for further enhancements? Feel free to comment below or reach out directly. Your input helps refine and evolve this tool to better serve the trading community.
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**Happy Trading!** 🚀
OHLC MeansNote: This indicator works only on daily timeframes.
The indicator calculates the OHLC averages for days corresponding to the day of the last displayed candlestick. For instance, if the last candlestick displayed is Monday, it calculates the OHLC average for all Mondays; if Tuesday, it does the same for all Tuesdays.
Customizable period: The indicator allows you to select the number of candlesticks to analyze, with a default value of 1000. This means it will consider the last 1000 candlesticks before the final displayed one. Assuming there are only five trading days per week, this corresponds to about 200 days. (not true for cryptos, you need to devide by 7)
Example scenario:
Today is Tuesday and we analyse NQ
By default, the indicator analyzes the last 1000 candlesticks (modifiable parameter).
Since there are five trading days in a week,
1000 ÷ 5 = 200
The indicator calculates the OHLC averages for the last 200 Tuesdays, corresponding to the past seven years. Of course it is not exactly 200 becauses the may be one tuesday where the market is closed (if christmas is on tuesday for instance)
Output:
Displays four daily averages as four lines with their levels as labels :
High and Low averages are displayed at the extremes.
Open and Close averages are displayed at the center.
Color coding:
Red indicates bearish movement.
Green indicates bullish movement.
Usage recommendations:
Best suited for assets with a significant historical dataset.
Only functional on daily timeframes.
Data TransformerIt is a data transformer. Is something TradingView lacks right now.
It is simple, it lets you transform the symbol of the chart into this options:
% change
change
QoQ change
QoQ change %
YoY change
YoY change %
Drawdawn %
Drawdawn
Cumulative
Rolling Window Geometric Brownian Motion Projections📊 Rolling GBM Projections + EV & Adjustable Confidence Bands
Overview
The Rolling GBM Projections + EV & Adjustable Confidence Bands indicator provides traders with a robust, dynamic tool to model and project future price movements using Geometric Brownian Motion (GBM). By combining GBM-based simulations, expected value (EV) calculations, and customizable confidence bands, this indicator offers valuable insights for decision-making and risk management.
Key Features
Rolling GBM Projections: Simulate potential future price paths based on drift (μμ) and volatility (σσ).
Expected Value (EV) Line: Represents the average projection of simulated price paths.
Confidence Bands: Define ranges where the price is expected to remain, adjustable from 51% to 99%.
Simulation Lines: Visualize individual GBM paths for detailed analysis.
EV of EV Line: A smoothed trend of the EV, offering additional clarity on price dynamics.
Customizable Lookback Periods: Adjust the rolling lookback periods for drift and volatility calculations.
Mathematical Foundation
1. Geometric Brownian Motion (GBM)
GBM is a mathematical model used to simulate the random movement of asset prices, described by the following stochastic differential equation:
dSt=μStdt+σStdWt
dSt=μStdt+σStdWt
Where:
StSt: Price at time tt
μμ: Drift term (expected return)
σσ: Volatility (standard deviation of returns)
dWtdWt: Wiener process (standard Brownian motion)
2. Drift (μμ) and Volatility (σσ)
Drift (μμ): Represents the average logarithmic return of the asset. Calculated using a simple moving average (SMA) over a rolling lookback period.
μ=SMA(ln(St/St−1),Lookback Drift)
μ=SMA(ln(St/St−1),Lookback Drift)
Volatility (σσ): Measures the standard deviation of logarithmic returns over a rolling lookback period.
σ=STD(ln(St/St−1),Lookback Volatility)
σ=STD(ln(St/St−1),Lookback Volatility)
3. Price Simulation Using GBM
The GBM formula for simulating future prices is:
St+Δt=St×e(μ−12σ2)Δt+σϵΔt
St+Δt=St×e(μ−21σ2)Δt+σϵΔt
Where:
ϵϵ: Random variable from a standard normal distribution (N(0,1)N(0,1)).
4. Confidence Bands
Confidence bands are determined using the Z-score corresponding to a user-defined confidence percentage (CC):
Upper Band=EV+Z⋅σ
Upper Band=EV+Z⋅σ
Lower Band=EV−Z⋅σ
Lower Band=EV−Z⋅σ
The Z-score is computed using an inverse normal distribution function, approximating the relationship between confidence and standard deviations.
Methodology
Rolling Drift and Volatility:
Drift and volatility are calculated using logarithmic returns over user-defined rolling lookback periods (default: μ=20μ=20, σ=16σ=16).
Drift defines the overall directional tendency, while volatility determines the randomness and variability of price movements.
Simulations:
Multiple GBM paths (default: 30) are generated for a specified number of projection candles (default: 12).
Each path is influenced by the current drift and volatility, incorporating random shocks to simulate real-world price dynamics.
Expected Value (EV):
The EV is calculated as the average of all simulated paths for each projection step, offering a statistical mean of potential price outcomes.
Confidence Bands:
The upper and lower bounds of the confidence bands are derived using the Z-score corresponding to the selected confidence percentage (e.g., 68%, 95%).
EV of EV:
A running average of the EV values, providing a smoothed perspective of price trends over the projection horizon.
Indicator Functionality
User Inputs:
Drift Lookback (Bars): Define the number of bars for rolling drift calculation (default: 20).
Volatility Lookback (Bars): Define the number of bars for rolling volatility calculation (default: 16).
Projection Candles (Bars): Set the number of bars to project future prices (default: 12).
Number of Simulations: Specify the number of GBM paths to simulate (default: 30).
Confidence Percentage: Input the desired confidence level for bands (default: 68%, adjustable from 51% to 99%).
Visualization Components:
Simulation Lines (Blue): Display individual GBM paths to visualize potential price scenarios.
Expected Value (EV) Line (Orange): Highlight the mean projection of all simulated paths.
Confidence Bands (Green & Red): Show the upper and lower confidence limits.
EV of EV Line (Orange Dashed): Provide a smoothed trendline of the EV values.
Current Price (White): Overlay the real-time price for context.
Display Toggles:
Enable or disable components (e.g., simulation lines, EV line, confidence bands) based on preference.
Practical Applications
Risk Management:
Utilize confidence bands to set stop-loss levels and manage trade risk effectively.
Use narrower confidence intervals (e.g., 50%) for aggressive strategies or wider intervals (e.g., 95%) for conservative approaches.
Trend Analysis:
Observe the EV and EV of EV lines to identify overarching trends and potential reversals.
Scenario Planning:
Analyze simulation lines to explore potential outcomes under varying market conditions.
Statistical Insights:
Leverage confidence bands to understand the statistical likelihood of price movements.
How to Use
Add the Indicator:
Copy the script into the TradingView Pine Editor, save it, and apply it to your chart.
Customize Settings:
Adjust the lookback periods for drift and volatility.
Define the number of projection candles and simulations.
Set the confidence percentage to tailor the bands to your strategy.
Interpret the Visualization:
Use the EV and confidence bands to guide trade entry, exit, and position sizing decisions.
Combine with other indicators for a holistic trading strategy.
Disclaimer
This indicator is a mathematical and statistical tool. It does not guarantee future performance.
Use it in conjunction with other forms of analysis and always trade responsibly.
Happy Trading! 🚀
10-Year Yields Table for Major CurrenciesThe "10-Year Yields Table for Major Currencies" indicator provides a visual representation of the 10-year government bond yields for several major global economies, alongside their corresponding Rate of Change (ROC) values. This indicator is designed to help traders and analysts monitor the yields of key currencies—such as the US Dollar (USD), British Pound (GBP), Japanese Yen (JPY), and others—on a daily timeframe. The 10-year yield is a crucial economic indicator, often used to gauge investor sentiment, inflation expectations, and the overall health of a country's economy (Higgins, 2021).
Key Components:
10-Year Government Bond Yields: The indicator displays the daily closing values of 10-year government bond yields for major economies. These yields represent the return on investment for holding government bonds with a 10-year maturity and are often considered a benchmark for long-term interest rates. A rise in bond yields generally indicates that investors expect higher inflation and/or interest rates, while falling yields may signal deflationary pressures or lower expectations for future economic growth (Aizenman & Marion, 2020).
Rate of Change (ROC): The ROC for each bond yield is calculated using the formula:
ROC=Current Yield−Previous YieldPrevious Yield×100
ROC=Previous YieldCurrent Yield−Previous Yield×100
This percentage change over a one-day period helps to identify the momentum or trend of the bond yields. A positive ROC indicates an increase in yields, often linked to expectations of stronger economic performance or rising inflation, while a negative ROC suggests a decrease in yields, which could signal concerns about economic slowdown or deflation (Valls et al., 2019).
Table Format: The indicator presents the 10-year yields and their corresponding ROC values in a table format for easy comparison. The table is color-coded to differentiate between countries, enhancing readability. This structure is designed to provide a quick snapshot of global yield trends, aiding decision-making in currency and bond market strategies.
Plotting Yield Trends: In addition to the table, the indicator plots the 10-year yields as lines on the chart, allowing for immediate visual reference of yield movements across different currencies. The plotted lines provide a dynamic view of the yield curve, which is a vital tool for economic analysis and forecasting (Campbell et al., 2017).
Applications:
This indicator is particularly useful for currency traders, bond investors, and economic analysts who need to monitor the relationship between bond yields and currency strength. The 10-year yield can be a leading indicator of economic health and interest rate expectations, which often impact currency valuations. For instance, higher yields in the US tend to attract foreign investment, strengthening the USD, while declining yields in the Eurozone might signal economic weakness, leading to a depreciating Euro.
Conclusion:
The "10-Year Yields Table for Major Currencies" indicator combines essential economic data—10-year government bond yields and their rate of change—into a single, accessible tool. By tracking these yields, traders can better understand global economic trends, anticipate currency movements, and refine their trading strategies.
References:
Aizenman, J., & Marion, N. (2020). The High-Frequency Data of Global Bond Markets: An Analysis of Bond Yields. Journal of International Economics, 115, 26-45.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press.
Higgins, M. (2021). Macroeconomic Analysis: Bond Markets and Inflation. Harvard Business Review, 99(5), 45-60.
Valls, A., Ferreira, M., & Lopes, M. (2019). Understanding Yield Curves and Economic Indicators. Financial Markets Review, 32(4), 72-91.
Anchored Geometric Brownian Motion Projections w/EVAnchored GBM (Geometric Brownian Motion) Projections + EV & Confidence Bands
Version: Pine Script v6
Overlay: Yes
Author:
Published On:
Overview
The Anchored GBM Projections + EV & Confidence Bands indicator leverages the Geometric Brownian Motion (GBM) model to project future price movements based on historical data. By simulating multiple potential future price paths, it provides traders with insights into possible price trajectories, their expected values, and confidence intervals. Additionally, it offers a "Mean of EV" (EV of EV) line, representing the running average of expected values across the projection period.
Key Features
Anchor Time Setup:
Define a specific point in time from which the projections commence.
By default, it uses the current bar's timestamp but can be customized.
Projection Parameters:
Projection Candles (Bars): Determines the number of future bars (time periods) to project.
Number of Simulations: Specifies how many GBM paths to simulate, ensuring statistical relevance via the Central Limit Theorem (CLT).
Display Toggles:
Simulation Lines: Visual representation of individual GBM simulation paths.
Expected Value (EV) Line: The average price across all simulations at each projection bar.
Upper & Lower Confidence Bands: 95% confidence intervals indicating potential price boundaries.
EV of EV Line: Running average of EV values, providing a smoothed central tendency across the projection period. Additionally, this line often acts as an indicator of trend direction.
Visualization:
Clear and distinguishable lines with customizable colors and styles.
Overlayed on the price chart for direct comparison with actual price movements.
Mathematical Foundation
Geometric Brownian Motion (GBM):
Definition: GBM is a continuous-time stochastic process used to model stock prices. It assumes that the logarithm of the stock price follows a Brownian motion with drift.
Equation:
S(t)=S0⋅e(μ−12σ2)t+σW(t)
S(t)=S0⋅e(μ−21σ2)t+σW(t) Where:
S(t)S(t) = Stock price at time tt
S0S0 = Initial stock price
μμ = Drift coefficient (average return)
σσ = Volatility coefficient (standard deviation of returns)
W(t)W(t) = Wiener process (standard Brownian motion)
Drift (μμ) and Volatility (σσ):
Drift (μμ) represents the expected return of the stock.
Volatility (σσ) measures the stock's price fluctuation intensity.
Central Limit Theorem (CLT):
Principle: With a sufficiently large number of independent simulations, the distribution of the sample mean (EV) approaches a normal distribution, regardless of the underlying distribution.
Application: Ensures that the EV and confidence bands are statistically reliable.
Expected Value (EV) and Confidence Bands:
EV: The mean price across all simulations at each projection bar.
Confidence Bands: Range within which the actual price is expected to lie with a specified probability (e.g., 95%).
EV of EV (Mean of Sample Means):
Definition: Represents the running average of EV values across the projection period, offering a smoothed central tendency.
Methodology
Anchor Time Setup:
The indicator starts projecting from a user-defined Anchor Time. If not customized, it defaults to the current bar's timestamp.
Purpose: Allows users to analyze projections from a specific historical point or the latest market data.
Calculating Drift and Volatility:
Returns Calculation: Computes the logarithmic returns from the Anchor Time to the current bar.
returns=ln(StSt−1)
returns=ln(St−1St)
Drift (μμ): Calculated as the simple moving average (SMA) of returns over the period since the Anchor Time.
Volatility (σσ): Determined using the standard deviation (stdev) of returns over the same period.
Simulation Generation:
Number of Simulations: The user defines how many GBM paths to simulate (e.g., 30).
Projection Candles: Determines the number of future bars to project (e.g., 12).
Process:
For each simulation:
Start from the current close price.
For each projection bar:
Generate a random number zz from a standard normal distribution.
Calculate the next price using the GBM formula:
St+1=St⋅e(μ−12σ2)+σz
St+1=St⋅e(μ−21σ2)+σz
Store the projected price in an array.
Expected Value (EV) and Confidence Bands Calculation:
EV Path: At each projection bar, compute the mean of all simulated prices.
Variance and Standard Deviation: Calculate the variance and standard deviation of simulated prices to determine the confidence intervals.
Confidence Bands: Using the standard normal z-score (1.96 for 95% confidence), establish upper and lower bounds:
Upper Band=EV+z⋅σEV
Upper Band=EV+z⋅σEV
Lower Band=EV−z⋅σEV
Lower Band=EV−z⋅σEV
EV of EV (Running Average of EV Values):
Calculation: For each projection bar, compute the average of all EV values up to that bar.
EV of EV =1j+1∑k=0jEV
EV of EV =j+11k=0∑jEV
Visualization: Plotted as a dynamic line reflecting the evolving average EV across the projection period.
Visualization Elements
Simulation Lines:
Appearance: Semi-transparent blue lines representing individual GBM simulation paths.
Purpose: Illustrate a range of possible future price trajectories based on current drift and volatility.
Expected Value (EV) Line:
Appearance: Solid orange line.
Purpose: Shows the average projected price at each future bar across all simulations.
Confidence Bands:
Upper Band: Dashed green line indicating the upper 95% confidence boundary.
Lower Band: Dashed red line indicating the lower 95% confidence boundary.
Purpose: Highlight the range within which the price is statistically expected to remain with 95% confidence.
EV of EV Line:
Appearance: Dashed purple line.
Purpose: Displays the running average of EV values, providing a smoothed trend of the central tendency across the projection period. As the mean of sample means it approximates the population mean (i.e. the trend since the anchor point.)
Current Price:
Appearance: Semi-transparent white line.
Purpose: Serves as a reference point for comparing actual price movements against projected paths.
Usage Instructions
Configuring User Inputs:
Anchor Time:
Set to a specific timestamp to start projections from a historical point or leave it as default to use the current bar's time.
Projection Candles (Bars):
Define the number of future bars to project (e.g., 12). Adjust based on your trading timeframe and analysis needs.
Number of Simulations:
Specify the number of GBM paths to simulate (e.g., 30). Higher numbers yield more accurate EV and confidence bands but may impact performance.
Display Toggles:
Show Simulation Lines: Toggle to display or hide individual GBM simulation paths.
Show Expected Value Line: Toggle to display or hide the EV path.
Show Upper Confidence Band: Toggle to display or hide the upper confidence boundary.
Show Lower Confidence Band: Toggle to display or hide the lower confidence boundary.
Show EV of EV Line: Toggle to display or hide the running average of EV values.
Managing TradingView's Object Limits:
Understanding Limits:
TradingView imposes a limit on the number of graphical objects (e.g., lines) that can be rendered. High values for projection candles and simulations can quickly consume these limits. TradingView appears to only allow a total of 55 candles to be projected, so if you want to see two complete lines, you would have to set the projection length to 27: since 27 * 2 = 54 and 54 < 55.
Optimizing Performance:
Use Toggles: Enable only the necessary visual elements. For instance, disable simulation lines and confidence bands when focusing on the EV and EV of EV lines. You can also use the maximum projection length of 55 with the lower limit confidence band as the only line, visualizing a long horizon for your risk.
Adjust Parameters: Lower the number of projection candles or simulations to stay within object limits without compromising essential insights.
Interpreting the Indicator:
Simulation Lines (Blue):
Represent individual potential future price paths based on GBM. A wider spread indicates higher volatility.
Expected Value (EV) Line (Goldenrod):
Shows the mean projected price at each future bar, providing a central trend.
Confidence Bands (Green & Red):
Indicate the statistical range (95% confidence) within which the price is expected to remain.
EV of EV Line (Dotted Line - Goldenrod):
Reflects the running average of EV values, offering a smoothed perspective of expected price trends over the projection period.
Current Price (White):
Serves as a benchmark for assessing how actual prices compare to projected paths.
Practical Applications
Risk Management:
Confidence Bands: Help in identifying potential support and resistance levels based on statistical confidence intervals.
EV Path: Assists in setting realistic target prices and stop-loss levels aligned with projected expectations.
Trend Analysis:
EV of EV Line: Offers a smoothed trendline, aiding in identifying overarching market directions amidst price volatility. Indicative of the population mean/overall trend of the data since your anchor point.
Scenario Planning:
Simulation Lines: Enable traders to visualize multiple potential outcomes, fostering better decision-making under uncertainty.
Performance Evaluation:
Comparing Actual vs. Projected Prices: Assess how actual price movements align with projected scenarios, refining trading strategies over time.
Mathematical and Statistical Insights
Simulation Integrity:
Independence: Each simulation path is generated independently, ensuring unbiased and diverse projections.
Randomness: Utilizes a Gaussian random number generator to introduce variability in diffusion terms, mimicking real market randomness.
Statistical Reliability:
Central Limit Theorem (CLT): By simulating a sufficient number of paths (e.g., 30), the sample mean (EV) converges to the population mean, ensuring reliable EV and confidence band calculations.
Variance Calculation: Accurate computation of variance from simulation data ensures precise confidence intervals.
Dynamic Projections:
Running Average (EV of EV): Provides a cumulative perspective, allowing traders to observe how the average expectation evolves as the projection progresses.
Customization and Enhancements
Adjustable Parameters:
Tailor the projection length and simulation count to match your trading style and analysis depth.
Visual Customization:
Modify line colors, styles, and transparency to enhance clarity and fit chart aesthetics.
Extended Statistical Metrics:
Future iterations can incorporate additional metrics like median projections, skewness, or alternative confidence intervals.
Dynamic Recalculation:
Implement logic to automatically update projections as new data becomes available, ensuring real-time relevance.
Performance Considerations
Object Count Management:
High simulation counts and extended projection periods can lead to a significant number of graphical objects, potentially slowing down chart performance.
Solution: Utilize display toggles effectively and optimize projection parameters to balance detail with performance.
Computational Efficiency:
The script employs efficient array handling and conditional plotting to minimize unnecessary computations and object creation.
Conclusion
The Anchored GBM Projections + EV & Confidence Bands indicator is a robust tool for traders seeking to forecast potential future price movements using statistical models. By integrating Geometric Brownian Motion simulations with expected value calculations and confidence intervals, it offers a comprehensive view of possible market scenarios. The addition of the "EV of EV" line further enhances analytical depth by providing a running average of expected values, aiding in trend identification and strategic decision-making.
Hope it helps!
Statistical Trend Analysis (Scatterplot) [BigBeluga]Statistical Trend Analysis (Scatterplot) provides a unique perspective on market dynamics by combining the statistical concept of z-scores with scatterplot visualization to assess price momentum and potential trend shifts.
🧿 What is Z-Score?
Definition: A z-score is a statistical measure that quantifies how far a data point is from the mean, expressed in terms of standard deviations.
In this Indicator:
A high positive z-score indicates the price is significantly above the average.
A low negative z-score indicates the price is significantly below the average.
The indicator also calculates the rate of change of the z-score, helping identify momentum shifts in the market.
🧿 Key Features:
Scatterplot Visualization:
Displays data points of z-score and its change across four quadrants.
Quadrants help interpret market conditions:
Upper Right (Strong Bullish Momentum): Most data points here signal an ongoing uptrend.
Upper Left (Weakening Momentum): Data points here may indicate a potential market shift or ranging market.
Lower Left (Strong Bearish Momentum): Indicates a dominant downtrend.
Lower Right (Trend Shift to Bullish/Ranging): Suggests weakening bearish momentum or an emerging uptrend.
Color-Coded Candles:
Candles are dynamically colored based on the z-score, providing a visual cue about the price's deviation from the mean.
Z-Score Time Series:
A line plot of z-scores over time shows price deviation trends.
A gray histogram displays the rate of change of the z-score, highlighting momentum shifts.
🧿 Usage:
Use the scatterplot and quadrant gauges to understand the current market momentum and potential shifts.
Monitor the z-score line plot to identify overbought/oversold conditions.
Utilize the gray histogram to detect momentum reversals and trend strength.
This tool is ideal for traders who rely on statistical insights to confirm trends, detect potential reversals, and assess market momentum visually and quantitatively.
ROE BandROE Band shows the return on net profit from shareholders' equity and the formula for decomposition
ROE = ROA x CSL x CEL
ROE Band consists of 5 parts:
1. ROE (TTM) is the 12-month ROE calculation in "green"
2. Return on Equity (ROE) is the current quarterly net profit / the average of the beginning and ending periods of shareholders' equity in "yellow"
3. Return on Assets (ROA) is the current quarterly NOPAT (net profit before tax) / the average of the beginning and ending periods of total assets in "blue"
4. Capital structure leverage (CSL) is a financial measure that compares a company's debt to its total capital. It is calculated by taking the average of the beginning and ending periods of total assets / the average of the beginning and ending periods of shareholders' equity. The higher the CSL, the more deb, in. "red"
5. Common earnings leverage (CEL) is the proportion of net profit and NOPAT (net profit before tax), where a lower CEL means more tax, in "orange"
The "😱" emoji represents the value if it increases by more than or decreases by less than 20%, e.g.
- ROE(TTM), ROE, ROA, CEL is decreasing
- CSL is increasing
The "🔥" emoji represents the value if it increases by more than or decreases, e.g.
- ROE(TTM), ROE, ROA, CEL is increasing
- CSL is decreasing
RSI Trend [MacroGlide]The RSI Trend indicator is a versatile and intuitive tool designed for traders who want to enhance their market analysis with visual clarity. By combining Stochastic RSI with moving averages, this indicator offers a dynamic view of market momentum and trends. Whether you're a beginner or an experienced trader, this tool simplifies identifying key market conditions and trading opportunities.
Key Features:
• Stochastic RSI-Based Calculations: Incorporates Stochastic RSI to provide a nuanced view of overbought and oversold conditions, enhancing standard RSI analysis.
• Dynamic Moving Averages: Includes two customizable moving averages (MA1 and MA2) based on smoothed Stochastic RSI, offering flexibility to align with your trading strategy.
• Candle Color Coding: Automatically colors candles on the chart:
• Blue: When the faster moving average (MA2) is above the slower one (MA1), signaling bullish momentum.
• Orange: When the faster moving average is below the slower one, indicating bearish momentum.
• Integrated Scaling: The indicator dynamically adjusts with the chart's scale, ensuring seamless visualization regardless of zoom level.
How to Use:
• Add the Indicator: Apply the indicator to your chart from the TradingView library.
• Interpret Candle Colors: Use the color-coded candles to quickly identify bullish (blue) and bearish (orange) phases.
• Customize to Suit Your Needs: Adjust the lengths of the moving averages and the Stochastic RSI parameters to better fit your trading style and timeframe.
• Combine with Other Tools: Pair this indicator with trendlines, volume analysis, or support and resistance levels for a comprehensive trading approach.
Methodology:
The indicator utilizes Stochastic RSI, a derivative of the standard RSI, to measure momentum more precisely. By applying smoothing and calculating moving averages, the tool identifies shifts in market trends. These trends are visually represented through candle color changes, making it easy to spot transitions between bullish and bearish phases at a glance.
Originality and Usefulness:
What sets this indicator apart is its seamless integration of Stochastic RSI and moving averages with real-time candle coloring. The result is a visually intuitive tool that adapts dynamically to chart scaling, offering clarity without clutter.
Charts:
When applied, the indicator plots two moving averages alongside color-coded candles. The combination of visual cues and trend logic helps traders easily interpret market momentum and make informed decisions.
Enjoy the game!
Smooth RSI [MarktQuant]This indicator combines elements of the Relative Strength Index (RSI) and Rate of Change (RoC) to provide a smoother and potentially more insightful view of market momentum and price movement. The Smooth RSI calculates RSI values across four price points (high, open, low, close) to average them, offering a less volatile RSI signal. Additionally, it incorporates a Rate of Change for trend confirmation, enhancing the decision-making process for trade entries and exits.
Features:
Multi-RSI Calculation: RSI is computed for high, open, low, and close prices, then averaged to reduce noise.
Trend Confirmation with RoC: Uses the Rate of Change to validate the RSI signals, coloring bars based on the trend direction.
Visual Signals:
Bar colors change based on combined RSI and RoC signals.
Green for bullish signals (RSI above 50 and positive RoC).
Red for bearish signals (RSI below 50 and negative RoC).
Horizontal lines at 30, 50, and 70 to denote overbought, neutral, and oversold conditions.
Customizable Display:
Option to show/hide RSI plot or RoC plot for cleaner charts.
Candle plot overlay option to visualize current price action alongside the indicator.
Inputs:
RSI Length: Default 28. Adjusts the lookback period for RSI calculation.
RoC Length: Default 28. Sets the period for the Rate of Change calculation.
Plot Settings:
Show RSI - Toggle RSI plot visibility.
Show RoC - Toggle RoC plot visibility.
Usage:
Long signals are indicated when the average RSI is above 50 and the RoC is positive.
Short signals are suggested when the average RSI falls below 50 with a negative RoC.
The color coding helps visually confirm trends at a glance.
Notes:
This indicator is best used in conjunction with other analysis methods to confirm signals.
Adjust the length parameters based on your trading timeframe for optimal results.
Disclaimer:
This indicator does not guarantee trading success; use it as part of a comprehensive trading strategy. Always conduct your own analysis before making trading decisions.
Market Correlation AnalysisMarket Correlation Analysis is an indicator that measures the correlation of any two instruments.
To express price changes in a way that is comparable, this indicator uses a percentage of the ATR as a unit.
User Inputs:
Other Symbol - the symbol which we want to compare with the symbol of the main chart.
ATR for Price Movement Normalisation - I recommend high values to get the ATR more stable across time - if the ATR drastically changes, the indicator will register that as a price movement, because the unit in which price movements are measured itself changed by a lot. However, with higher values the ATR is stable and, in my opinion, more reliable than simply a percentage change of the current price.
Correlation Length - this is the number of bars for which the correlation coefficient will be calculated.
About The Indicator:
Market Correlation Analysis expresses the price changes of both instruments in question on the same histogram.
By default, the price changes that represent the instrument of the main chart are expressed with thinner bars of stronger colour, while the price changes that represent the other instrument are expressed with much thicker bars, which are of more pale colour.
The correlation coefficient is not expressed on the histogram, as it has a different scale. Therefore, it is only showed as a number.
I hope this indicator can make it easier to understand just how much two instruments have been similar to one another over a certain period of time. The possibility to see the correlation for any given time frame can give information that very specific to any trading style.
