Porcentaje sobre/debajo SMAsIdentify strong bullish reversals when:
1) Price spends <30% of time below SMA200 (extreme oversold),
2) Confirmed by RSI<20.
Regressions
Trailing Stop Loss Alert (pips)Stop Loss Indicator by Drawback
Description:
The "Stop Loss Indicator by Drawback" is a technical analysis tool designed to help traders identify potential stop loss levels based on price movement. This indicator works by calculating local highs and lows over a defined range, known as the "drawback," which is measured in pips. The primary use of this indicator is to visualize key stop loss levels and alert traders when these levels are breached by price action.
How It Works:
The indicator calculates local highs and lows based on a customizable drawback (distance) in pips.
It then plots stop loss levels on the chart when price breaks these levels, either reaching a local high or low.
Alerts are triggered when the price touches or crosses these stop loss levels, notifying you when the market potentially hits key stop loss areas.
User Inputs:
Drawback in Pips: This setting defines how many pips the price must move beyond the local high or low to consider it as a potential stop loss zone. For example, a 10 pip drawback will look for price movements of at least 10 pips above the high or below the low.
Pip Size: The pip size is automatically calculated based on the instrument being traded, but it can be adjusted if needed. This ensures accurate calculation of the drawback in price terms.
How to Use:
Set the Drawback: Adjust the "Drawback in Pips" to match your preferred threshold for stop loss identification. For instance, if you're trading an instrument like EUR/USD and want to define stop loss levels based on a 10-pip move, set the value to 10.
Watch for Stop Loss Levels: The indicator will plot red lines on the chart to indicate the potential stop loss levels (local highs and lows). These levels serve as reference points for potential stop loss zones.
Receive Alerts: Enable the alert condition to receive notifications when price reaches or crosses the calculated stop loss levels. This feature is useful for traders who want to stay updated on key price movements without constantly watching the chart.
Key Benefits:
Visualize Stop Loss Zones: Clearly marked stop loss levels help you identify potential exit points and risk management strategies.
Customizable Alerts: Set up alerts for real-time notifications when stop loss levels are breached, helping you make quick decisions.
Adaptable to Any Market: Works on any instrument or time frame, providing flexibility for different trading styles.
Ideal For:
Swing traders and day traders who use stop loss levels as part of their risk management strategy.
Traders who want an automated way to monitor key price levels and get notified when their stop loss zones are triggered.
Futuristic Trend PredictorAI-based trend predictor. converting it to a strategy for backtesting. thanks.
ML Deep Regression Pro (TechnoBlooms)ML Deep Regression Pro is a machine-learning-inspired trading indicator that integrates Polynomial Regression, Linear Regression and Statistical Deviation models to provide a powerful, data-driven approach to market trend analysis.
Designed for traders, quantitative analysts and developers, this tool transforms raw market data into predictive trend insights, allowing for better decision-making and trend validation.
By leveraging statistical regression techniques, ML Deep Regression Pro eliminates market noise and identifies key trend shifts, making it a valuable addition to both manual and algorithmic trading strategies.
REGRESSION ANALYSIS
Regression is a statistical modeling technique used in machine learning and data science to identify patterns and relationships between variables. In trading, it helps detect price trends, reversals and volatility changes by fitting price data into a predictive model.
1. Linear Regression -
The most widely used regression model in trading, providing a best-fit plotted line to track price trends.
2. Polynomial Regression -
A more advanced form of regression that fits curved price structures, capturing complex market cycles and improving trend forecasting accuracy.
3. Standard Deviation Bands -
Based on regression calculations, these bands measure price dispersion and identify overbought/ oversold conditions, similar to Bollinger Bands. By default, these lines are hidden and user can make it visible through Settings.
KEY FEATURES :-
✅ Hybrid Regression Engine – Combines Linear and Polynomial Regression to detect market trends with greater accuracy.
✅ Dynamic Trend Bias Analysis – Identifies bullish & bearish market conditions using real-time regression models.
✅ Standard Deviation Bands – Measures price volatility and potential reversals with an advanced deviation model.
✅ Adaptive EMA Crossover Signals – Generates buy/sell signals when price momentum shifts relative to the regression trend.
Higher Highs and Lower Lows Strategy with RSI Filtergives buys sell signals on basis of charts hh ll price action with the help of rsi
🌰Chestnut Trend Zones🌰Chestnut Trend Zones
Confirm new trend impulses using the indicator
Mark the trend line on the indicator from the beginning of the trend and connect the following impulses. This will allow you to identify a potential new momentum zone as you approach the marked line.
The indicator consists of an indexed modifiable SMA 300
Adaptive Trend FinderAdaptive Trend Finder - The Ultimate Trend Detection Tool
Introducing Adaptive Trend Finder, the next evolution of trend analysis on TradingView. This powerful indicator is an enhanced and refined version of Adaptive Trend Finder (Log), designed to offer even greater flexibility, accuracy, and ease of use.
What’s New?
Unlike the previous version, Adaptive Trend Finder allows users to fully configure and adjust settings directly within the indicator menu, eliminating the need to modify chart settings manually. A major improvement is that users no longer need to adjust the chart's logarithmic scale manually in the chart settings; this can now be done directly within the indicator options, ensuring a smoother and more efficient experience. This makes it easier to switch between linear and logarithmic scaling without disrupting the analysis. This provides a seamless user experience where traders can instantly adapt the indicator to their needs without extra steps.
One of the most significant improvements is the complete code overhaul, which now enables simultaneous visualization of both long-term and short-term trend channels without needing to add the indicator twice. This not only improves workflow efficiency but also enhances chart readability by allowing traders to monitor multiple trend perspectives at once.
The interface has been entirely redesigned for a more intuitive user experience. Menus are now clearer, better structured, and offer more customization options, making it easier than ever to fine-tune the indicator to fit any trading strategy.
Key Features & Benefits
Automatic Trend Period Selection: The indicator dynamically identifies and applies the strongest trend period, ensuring optimal trend detection with no manual adjustments required. By analyzing historical price correlations, it selects the most statistically relevant trend duration automatically.
Dual Channel Display: Traders can view both long-term and short-term trend channels simultaneously, offering a broader perspective of market movements. This feature eliminates the need to apply the indicator twice, reducing screen clutter and improving efficiency.
Fully Adjustable Settings: Users can customize trend detection parameters directly within the indicator settings. No more switching chart settings – everything is accessible in one place.
Trend Strength & Confidence Metrics: The indicator calculates and displays a confidence score for each detected trend using Pearson correlation values. This helps traders gauge the reliability of a given trend before making decisions.
Midline & Channel Transparency Options: Users can fine-tune the visibility of trend channels, adjusting transparency levels to fit their personal charting style without overwhelming the price chart.
Annualized Return Calculation: For daily and weekly timeframes, the indicator provides an estimate of the trend’s performance over a year, helping traders evaluate potential long-term profitability.
Logarithmic Adjustment Support: Adaptive Trend Finder is compatible with both logarithmic and linear charts. Traders who analyze assets like cryptocurrencies, where log scaling is common, can enable this feature to refine trend calculations.
Intuitive & User-Friendly Interface: The updated menu structure is designed for ease of use, allowing quick and efficient modifications to settings, reducing the learning curve for new users.
Why is this the Best Trend Indicator?
Adaptive Trend Finder stands out as one of the most advanced trend analysis tools available on TradingView. Unlike conventional trend indicators, which rely on fixed parameters or lagging signals, Adaptive Trend Finder dynamically adjusts its settings based on real-time market conditions. By combining automatic trend detection, dual-channel visualization, real-time performance metrics, and an intuitive user interface, this indicator offers an unparalleled edge in trend identification and trading decision-making.
Traders no longer have to rely on guesswork or manually tweak settings to identify trends. Adaptive Trend Finder does the heavy lifting, ensuring that users are always working with the strongest and most reliable trends. The ability to simultaneously display both short-term and long-term trends allows for a more comprehensive market overview, making it ideal for scalpers, swing traders, and long-term investors alike.
With its state-of-the-art algorithms, fully customizable interface, and professional-grade accuracy, Adaptive Trend Finder is undoubtedly one of the most powerful trend indicators available.
Try it today and experience the future of trend analysis.
This indicator is a technical analysis tool designed to assist traders in identifying trends. It does not guarantee future performance or profitability. Users should conduct their own research and apply proper risk management before making trading decisions.
// Created by Julien Eche - @Julien_Eche
CAPM Alpha & BetaThe CAPM Alpha & Beta indicator is a crucial tool in finance and investment analysis derived from the Capital Asset Pricing Model (CAPM) . It provides insights into an asset's risk-adjusted performance (Alpha) and its relationship to broader market movements (Beta). Here’s a breakdown:
1. How Does It Work?
Alpha:
Definition: Alpha measures the portion of an investment's return that is not explained by market movements, i.e., the excess return over and above what the market is expected to deliver.
Purpose: It represents the value a fund manager or strategy adds (or subtracts) from an investment’s performance, adjusting for market risk.
Calculation:
Alpha is derived from comparing actual returns to expected returns predicted by CAPM:
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Alpha = Actual Return − (Risk-Free Rate + β × (Market Return − Risk-Free Rate))
Interpretation:
Positive Alpha: The investment outperformed its CAPM prediction (good performance for additional value/risk).
Negative Alpha: The investment underperformed its CAPM prediction.
Beta:
Definition: Beta measures the sensitivity of an asset's returns relative to the overall market's returns. It quantifies systematic risk.
Purpose: Indicates how volatile or correlated an investment is relative to the market benchmark (e.g., S&P 500).
Calculation:
Beta is computed as the ratio of the covariance of the asset and market returns to the variance of the market returns:
β = Covariance (Asset Return, Market Return) / Variance (Market Return)
β = Variance (Market Return) Covariance (Asset Return, Market Return)
Interpretation:
Beta = 1: The asset’s price moves in line with the market.
Beta > 1: The asset is more volatile than the market (higher risk/higher potential reward).
Beta < 1: The asset is less volatile than the market (lower risk/lower reward).
Beta < 0: The asset moves inversely to the market.
2. How to Use It?
Using Alpha:
Portfolio Evaluation: Investors use Alpha to gauge whether a portfolio manager or a strategy has successfully outperformed the market on a risk-adjusted basis.
If Alpha is consistently positive, the portfolio may deliver higher-than-expected returns for the given level of risk.
Stock/Asset Selection: Compare Alpha across multiple securities. Positive Alpha signals that the asset may be a good addition to your portfolio for excess returns.
Adjusting Investment Strategy: If Alpha is negative, reassess the asset's role in the portfolio and refine strategies.
Using Beta:
Risk Management:
A high Beta (e.g., 1.5) indicates higher sensitivity to market movements. Use such assets if you want to take on more risk during bullish market phases or expect higher returns.
A low Beta (e.g., 0.7) indicates stability and is useful in diversifying risk in volatile or bearish markets.
Portfolio Diversification: Combine assets with varying Betas to achieve the desired level of market responsiveness and smooth out portfolio volatility.
Monitoring Systematic Risk: Beta helps identify whether an investment aligns with your risk tolerance. For example, high-Beta stocks may not be suitable for conservative investors.
Practical Application:
Use both Alpha and Beta together:
Assess performance with Alpha (excess returns).
Assess risk exposure with Beta (market sensitivity).
Example: A stock with a Beta of 1.2 and a highly positive Alpha might suggest a solid performer that is slightly more volatile than the market, making it a suitable pick for risk-tolerant, return-maximizing investors.
In conclusion, the CAPM Alpha & Beta indicator gives a comprehensive view of an asset's performance and risk. Alpha enables performance evaluation on a risk-adjusted basis, while Beta reveals the level of market risk. Together, they help investors make informed decisions, build optimal portfolios, and align investments with their risk-return preferences.
Simple APF Strategy Backtesting [The Quant Science]Simple backtesting strategy for the quantitative indicator Autocorrelation Price Forecasting. This is a Buy & Sell strategy that operates exclusively with long orders. It opens long positions and generates profit based on the future price forecast provided by the indicator. It's particularly suitable for trend-following trading strategies or directional markets with an established trend.
Main functions
1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles.
2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions.
Logic
The strategy works as follow:
Entry Condition: Go long if the hypothetical gain exceeds the threshold gain (configurable by user interface).
Position Management: Sets a take-profit level based on the future price.
Position Sizing: Automatically calculates the order size as a percentage of the equity.
No Stop-Loss: this strategy doesn't includes any stop loss.
Example Use Case
A trader analyzes a dayli period using 7 historical bars for autocorrelation.
Sets a threshold gain of 20 points using a 5% of the equity for each trade.
Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance.
User Interface
Length: Set the length of the data used in the autocorrelation price forecasting model.
Thresold Gain: Minimum value to be considered for opening trades based on future price forecast.
Order Size: percentage size of the equity used for each single trade.
Strategy Limit
This strategy does not use a stop loss. If the price continues to drop and the future price forecast is incorrect, the trader may incur a loss or have their capital locked in the losing trade.
Disclaimer!
This is a simple template. Use the code as a starting point rather than a finished solution. The script does not include important parameters, so use it solely for educational purposes or as a boilerplate.
Ethereum Logarithmic Regression Bands (Fine-Tuned)This indicator, "Ethereum Logarithmic Regression Bands (Fine-Tuned)," is my attempt to create a tool for estimating long-term trends in Ethereum (ETH/USD) price action using logarithmic regression bands. Please note that I am not an expert in financial modeling or coding—I developed this as a personal project to serve as a rough estimation rather than a precise or professional trading tool. The data was fitted to non-bubble periods of Ethereum's history to provide a general trendline, but it’s far from perfect.
I’m sharing this because I couldn’t find a similar indicator available, and I thought it might be useful for others who are also exploring ETH’s long-term behavior. The bands start from Ethereum’s launch price and are adjustable via input parameters, but they are based on my best effort to align with historical data. With some decent coding experience, I’m sure someone could refine this further—perhaps by optimizing the coefficients or incorporating more advanced fitting techniques. Feel free to tweak the code, suggest improvements, or use it as a starting point for your own projects!
How to Use:
** THIS CHART IS SPECIFICALLY CODED FOR ETH/USD (KRAKEN) ON THE WEEKLY TIMEFRAME IN LOG VIEW**
The main band (blue) represents the logarithmic regression line.
The upper (red) and lower (green) bands provide a range around the main trend, adjustable with multipliers.
Adjust the "Launch Price," "Base Coefficient," "Growth Coefficient," and other inputs to experiment with different fits.
Disclaimer:
This is not financial advice. Use at your own risk, and always conduct your own research before making trading decisions.
ICT Session by LasinsName: ICT Session by Lasins
Purpose: To visually identify and differentiate between the Asian, London, and New York trading sessions on the chart.
Features:
Highlights the background of the chart during each session.
Includes a mini dashboard in the top-right corner to show the active session.
Allows customization of time zones (exchange timezone or UTC).
Displays copyright and author information.
Key Components
Inputs:
useExchangeTimezone: A boolean input to toggle between using the exchange timezone or UTC for session times.
showDashboard: A boolean input to toggle the visibility of the mini dashboard.
Session Times:
The script defines three trading sessions:
Asian Session: 2000-0000 UTC (or adjusted for exchange timezone).
London Session: 0200-0500 UTC (or adjusted for exchange timezone).
New York Session: 0700-1000 UTC (or adjusted for exchange timezone).
Session Detection:
The is_session function checks if the current time falls within a specified session using the time function.
Background Coloring:
The bgcolor function is used to highlight the chart background during each session:
Asian Session: Red background.
London Session: Green background.
New York Session: Blue background.
Mini Dashboard:
A table is created in the top-right corner of the chart to display the active session and its corresponding color.
The dashboard includes:
A header row with "Session" and "Color".
Rows for each session (Asian, London, New York) with their respective colors.
Copyright and Author Information:
A label is added to the chart to display the copyright and author information ("© ICT Session by Lasins Raj").
How It Works
The script checks the current time and compares it to the predefined session times.
If the current time falls within a session, the chart background is highlighted with the corresponding color.
The mini dashboard updates to reflect the active session.
The copyright and author information is displayed at the bottom of the chart.
Customization
You can adjust the session times in the script to match your preferred timezone or trading hours.
The useExchangeTimezone input allows you to switch between UTC and the exchange timezone.
The showDashboard input lets you toggle the visibility of the mini dashboard.
Example Use Case
Traders who follow the ICT (Inner Circle Trader) methodology can use this indicator to identify key trading sessions and plan their trades accordingly.
The visual representation of sessions helps traders quickly recognize when major markets are open and active.
TASC 2025.03 A New Solution, Removing Moving Average Lag█ OVERVIEW
This script implements a novel technique for removing lag from a moving average, as introduced by John Ehlers in the "A New Solution, Removing Moving Average Lag" article featured in the March 2025 edition of TASC's Traders' Tips .
█ CONCEPTS
In his article, Ehlers explains that the average price in a time series represents a statistical estimate for a block of price values, where the estimate is positioned at the block's center on the time axis. In the case of a simple moving average (SMA), the calculation moves the analyzed block along the time axis and computes an average after each new sample. Because the average's position is at the center of each block, the SMA inherently lags behind price changes by half the data length.
As a solution to removing moving average lag, Ehlers proposes a new projected moving average (PMA) . The PMA smooths price data while maintaining responsiveness by calculating a projection of the average using the data's linear regression slope.
The slope of linear regression on a block of financial time series data can be expressed as the covariance between prices and sample points divided by the variance of the sample points. Ehlers derives the PMA by adding this slope across half the data length to the SMA, creating a first-order prediction that substantially reduces lag:
PMA = SMA + Slope * Length / 2
In addition, the article includes methods for calculating predictions of the PMA and the slope based on second-order and fourth-order differences. The formulas for these predictions are as follows:
PredictPMA = PMA + 0.5 * (Slope - Slope ) * Length
PredictSlope = 1.5 * Slope - 0.5 * Slope
Ehlers suggests that crossings between the predictions and the original values can help traders identify timely buy and sell signals.
█ USAGE
This indicator displays the SMA, PMA, and PMA prediction for a specified series in the main chart pane, and it shows the linear regression slope and prediction in a separate pane. Analyzing the difference between the PMA and SMA can help to identify trends. The differences between PMA or slope and its corresponding prediction can indicate turning points and potential trade opportunities.
The SMA plot uses the chart's foreground color, and the PMA and slope plots are blue by default. The plots of the predictions have a green or red hue to signify direction. Additionally, the indicator fills the space between the SMA and PMA with a green or red color gradient based on their differences:
Users can customize the source series, data length, and plot colors via the inputs in the "Settings/Inputs" tab.
█ NOTES FOR Pine Script® CODERS
The article's code implementation uses a loop to calculate all necessary sums for the slope and SMA calculations. Ported into Pine, the implementation is as follows:
pma(float src, int length) =>
float PMA = 0., float SMA = 0., float Slope = 0.
float Sx = 0.0 , float Sy = 0.0
float Sxx = 0.0 , float Syy = 0.0 , float Sxy = 0.0
for count = 1 to length
float src1 = src
Sx += count
Sy += src
Sxx += count * count
Syy += src1 * src1
Sxy += count * src1
Slope := -(length * Sxy - Sx * Sy) / (length * Sxx - Sx * Sx)
SMA := Sy / length
PMA := SMA + Slope * length / 2
However, loops in Pine can be computationally expensive, and the above loop's runtime scales directly with the specified length. Fortunately, Pine's built-in functions often eliminate the need for loops. This indicator implements the following function, which simplifies the process by using the ta.linreg() and ta.sma() functions to calculate equivalent slope and SMA values efficiently:
pma(float src, int length) =>
float Slope = ta.linreg(src, length, 0) - ta.linreg(src, length, 1)
float SMA = ta.sma(src, length)
float PMA = SMA + Slope * length * 0.5
To learn more about loop elimination in Pine, refer to this section of the User Manual's Profiling and optimization page.
UM-Optimized Linear Regression ChannelDESCRIPTION
This indicator was inspired by Dr. Stoxx at drstoxx.com. Shout out to him and his services for introducing me to this idea. This indicator is a slightly different take on the standard linear regression indicator.
It uses two standard deviations to draw bands and dynamically attempts to best-fit the data lookback period using an R-squared statistical measure. The R-squared value ranges between zero and one with zero being no fit to the data at all and 1 being a 100% match of the data to linear regression line. The R-squared calculation is weighted exponentially to give more weight to the most recent data.
The label provides the number of periods identified as the optimal best-fit period, the type of loopback period determination (Manual or Auto) and the R-squared value (0-100, 100% being a perfect fit). >=90% is a great fit of the data to the regression line. <50% is a difficult fit and more or less considered random data.
The lookback mode can also be set manually and defaults to a value of 100 periods.
DEFAULTS
The defaults are 1.5 and 2.0 for standard deviation. This creates 2 bands above and below the regression line. The default mode for best-fit determination with "Auto" selected in the dropdown. When manual mode is selected, the default is 100. The modes, manual lookback periods, colors, and standard deviations are user-configurable.
HOW TO USE
Overlay this indicator on any chart of any timeframe. Look for turning points at extremes in the upper and lower bands. Look for crossovers of the centerline. Look at the Auto-determination for best fit. Compare this to your favorite Manual mode setting (Manual Mode is set to 100 by default lookback periods.)
When price is at an extreme, look for turnarounds or reversals. Use your favorite indicators, in addition to this indicator, to determine reversals. Try this indicator against your favorite securities and timeframes.
CHART EXAMPLE
The chart I used for an example is the daily chart of IWM. I illustrated the extremes with white text. This is where I consider proactively exiting an existing position and/or begin looking for a reversal.
CandelaCharts - Fib Retracement (OTE) 📝 Overview
The CandelaCharts Fib Retracement (OTE) indicator is a precision tool designed to help traders identify Optimal Trade Entry (OTE) levels based on Fibonacci retracement principles, as taught in ICT (Inner Circle Trader) methodology.
This indicator automatically plots Fibonacci retracement levels between a selected swing high and swing low, highlighting the key OTE zone between the 61.8% and 78.6% retracement levels—a prime area for potential reversals in trending markets.
📦 Features
Automatic & Custom lookback modes
Customizable fib levels
Dynamic coloring
Reverse & extend
⚙️ Settings
Lookback: Controls the number of bars to look back. You can choose between **Automatic** or **Custom** mode.
Line Style: Sets the line style for the Fibonacci levels.
Levels: 0, 0.236, 0.0.382, 0.500, 0.620, 0.705, 0.790, 0.886, 1.000. Allows you to toggle the visibility of Fibonacci levels.
Dynamic Coloring: Colors Fibonacci levels according to trend direction.
Show Labels: Shows the price value at each Fibonacci level.
Reverse: Flips the Fibonacci levels in the opposite direction.
Extend Left: Extends the Fibonacci levels to the left.
⚡️ Showcase
Dynamic Coloring
Manual Coloring
Fib Retracement
Extended
Custom Length
📒 Usage
Using the CandelaCharts Fib Retracement (OTE) is pretty straightforward—just follow these steps to spot high-probability trade setups and refine your entries.
Identify the Trend – Determine whether the market is in an uptrend or downtrend.
Select Swing Points – The indicator automatically plots from the most recent swing high to swing low (or vice versa).
Wait for Price to Enter OTE Zone – Look for price action confirmation within the optimal entry zone (61.8%-78.6%).
Enter the Trade – Consider longs in an uptrend at the OTE zone, and shorts in a downtrend.
Set Stop & Target – Place stops below/above the swing low/high and target extension levels (127.2%, 161.8%).
🎯 Key takeways
The CandelaCharts Fib Retracement (OTE) is a must-have tool for traders looking to refine their entries and maximize risk-reward potential with precision-based ICT trading strategies. 🚀
🚨 Alerts
The indicator does not provide any alerts!
⚠️ Disclaimer
Trading involves significant risk, and many participants may incur losses. The content on this site is not intended as financial advice and should not be interpreted as such. Decisions to buy, sell, hold, or trade securities, commodities, or other financial instruments carry inherent risks and are best made with guidance from qualified financial professionals. Past performance is not indicative of future results.
MOKI V1The "MOKI V1" script is a trading strategy on the TradingView platform that uses a combination of two key indicators to identify buy and sell signals:
EMA200 (Exponential Moving Average 200): Used to determine the overall market trend. This line helps ensure that trades are made in the direction of the primary market trend.
RSI (Relative Strength Index): Used to measure the strength or weakness of a trend. In this strategy, a reading above 50 for the RSI indicates stronger buy signals.
Engulfing Pattern: This candlestick pattern occurs when a green (bullish) candle completely engulfs the previous red (bearish) candle. It is used as a buy signal when combined with the other indicators.
Pearson Correlation CoefficientDescription: The Pearson Correlation Coefficient measures the strength and direction of the linear relationship between two data series. Its value ranges from -1 to +1, where:
+1 indicates a perfect positive linear correlation: as one asset increases, the other asset increases proportionally.
0 indicates no linear correlation: variations in one asset have no relation to variations in the other asset.
-1 indicates a perfect negative linear correlation: as one asset increases, the other asset decreases proportionally.
This measure is widely used in technical analysis to assess the degree of correlation between two financial assets. The "Pearson Correlation (Manual Compare)" indicator allows users to manually select two assets and visually display their correlation relationship on a chart.
Features:
Correlation Period: The time period used for calculating the correlation can be adjusted (default: 50).
Comparison Asset: Users can select a secondary asset for comparison.
Visual Plots: The chart includes reference lines for perfect correlations (+1 and -1) and strong correlations (+0.7 and -0.7).
Alerts: Set alerts for when the correlation exceeds certain threshold values (e.g., +0.7 for strong positive correlation).
How to Select the Second Asset:
Primary Asset Selection: The primary asset is the one you select for viewing on the chart. This can be done by simply opening the chart for the desired asset.
Secondary Asset Selection: To select the secondary asset for comparison, use the input field labeled "Comparison Asset" in the script settings. You can manually enter the ticker symbol of the secondary asset you want to compare with the primary asset.
This indicator is ideal for traders looking to identify relationships and correlations between different financial assets to make informed trading decisions.
Statistical Arbitrage Pairs Trading - Long-Side OnlyThis strategy implements a simplified statistical arbitrage (" stat arb ") approach focused on mean reversion between two correlated instruments. It identifies opportunities where the spread between their normalized price series (Z-scores) deviates significantly from historical norms, then executes long-only trades anticipating reversion to the mean.
Key Mechanics:
1. Spread Calculation: The strategy computes Z-scores for both instruments to normalize price movements, then tracks the spread between these Z-scores.
2. Modified Z-Score: Uses a robust measure combining the median and Median Absolute Deviation (MAD) to reduce outlier sensitivity.
3. Entry Signal: A long position is triggered when the spread’s modified Z-score falls below a user-defined threshold (e.g., -1.0), indicating extreme undervaluation of the main instrument relative to its pair.
4. Exit Signal: The position closes automatically when the spread reverts to its historical mean (Z-score ≥ 0).
Risk management:
Trades are sized as a percentage of equity (default: 10%).
Includes commissions and slippage for realistic backtesting.
VFV Correction Levels
This Pine Script, "VFV Correction Levels," identifies significant daily price corrections and calculates corresponding investments based on fixed thresholds (paliers). Key features include:
Six predefined correction levels trigger investments between $150 and $600 based on the percentage drop.
Larger corrections correspond to higher investment amounts.
Graphical Indicators:
Visual labels mark correction levels and display investment amounts directly on the chart.
Investment Tracking:
Calculates total invested and tracks performance (yield percentage) relative to the initial correction price.
Smoothed Gaussian Trend Filter [AlgoAlpha]Experience seamless trend detection and market analysis with the Smoothed Gaussian Trend Filter by AlgoAlpha! This cutting-edge indicator combines advanced Gaussian filtering with linear regression smoothing to identify and enhance market trends, making it an essential tool for traders seeking precise and actionable signals.
Key Features :
🔍 Gaussian Trend Filtering: Utilizes a customizable Gaussian filter with adjustable length and pole settings for tailored smoothing and trend identification.
📊 Linear Regression Smoothing: Reduces noise and further refines the Gaussian output with user-defined smoothing length and offset, ensuring clarity in trend representation.
✨ Dynamic Visual Highlights: Highlights trends and signals based on volume intensity, allowing for real-time insights into market behavior.
📉 Choppy Market Detection: Identifies ranging or choppy markets, helping traders avoid false signals.
🔔 Custom Alerts: Set alerts for bullish and bearish signals, trend reversals, or choppy market conditions to stay on top of trading opportunities.
🎨 Color-Coded Visuals: Fully customizable colors for bullish and bearish signals, ensuring clear and intuitive chart analysis.
How to Use :
Add the Indicator: Add it to your favorites and apply it to your TradingView chart.
Interpret the Chart: Observe the trend line for directional changes and use the accompanying buy/sell signals for entry and exit opportunities. Choppy market conditions are flagged for additional caution.
Set Alerts: Enable alerts for trend signals or choppy market detections to act promptly without constant chart monitoring.
How It Works :
The Smoothed Gaussian Trend Filter uses a combination of advanced smoothing techniques to identify trends and enhance market clarity. First, a Gaussian filter is applied to price data, using a user-defined length (Gaussian length) and poles (smoothness level) to calculate an alpha value that determines the degree of smoothing. This creates a refined trend line that minimizes noise while preserving key market movements. The output is then further processed using linear regression smoothing, allowing traders to adjust the length and offset to flatten minor oscillations and emphasize the dominant trend. To incorporate market activity, volume intensity is analyzed through a normalized Hull Moving Average (HMA), dynamically adjusting the trend line's color transparency based on trading activity. The indicator also identifies trend direction by comparing the smoothed trend line with a calculated SuperTrend-style level, generating clear trend regimes and highlighting ranging or choppy conditions where trends are less reliable and avoiding false signals. This seamless integration of Gaussian smoothing, regression analysis, and volume dynamics provides traders with a powerful and intuitive tool for market analysis.
Price Projection by Linear RegressionPurpose:
This is a TradingView Pine Script indicator that performs a linear regression on historical price data to project potential future price levels. It's designed to help traders visualize long-term price trends and potential future price targets.
Key Components:
User Inputs:
Historical Data Points (default 1000 bars) - The amount of historical data used to calculate the trend
Years to Project (default 10 years) - How far into the future to project the price
Technical Implementation:
Uses linear regression (ta.linreg) to calculate the trend slope
Converts years to trading days using 252 trading days per year
Limits visible projection to 500 bars due to TradingView's drawing limitations
Projects prices using the formula: current_price + (slope × number_of_bars)
Visual Elements:
Blue line showing actual historical prices
Red projection line showing the expected price path
Label showing the projected price at the visible end of the line
Information table in the top-right corner showing:
Current price
Final projected price after the full time period
Limitations:
Can only display projections up to 500 bars into the future (about 2 years) due to TradingView limitations
The full projection value is still calculated and shown in the table
Past performance doesn't guarantee future results - this is a mathematical projection based on historical trends
Usage:
Traders can use this to:
Visualize potential long-term price trends
Set long-term price targets
Understand the historical trend's trajectory
Compare current prices with projected future values
Previous Candle Sweep IndicatorThis script identifies candlesticks where the current candle's high is higher than the previous candle's high, and the current candle's low is lower than the previous candle's low. If both conditions are met, the candle's body is highlighted in blue on the chart, allowing traders to quickly spot these patterns.
Features:
Highlights candles with both higher highs and lower lows.
Uses clear visual cues (blue body) for easy identification.
Ideal for traders looking to identify specific volatility patterns or reversals.
Adjust Asset for Future Interest (Brazil)Este script foi criado para ajustar o preço de um ativo com base na taxa de juros DI11!, que reflete a expectativa do mercado para os juros futuros. O objetivo é mostrar como o valor do ativo seria influenciado se fosse diretamente ajustado pela variação dessa taxa de juros.
Como funciona?
Preço do Ativo
O script começa capturando o preço de fechamento do ativo que está sendo visualizado no gráfico. Esse é o ponto de partida para o cálculo.
Taxa de Juros DI11!
Em seguida, ele busca os valores diários da taxa DI11! no mercado. Esta taxa é uma referência de juros de curto prazo, usada para ajustes financeiros e projeções econômicas.
Fator de Ajuste
Com a taxa de juros DI11!, o script calcula um fator de ajuste simples:
Fator de Ajuste
=
1
+
DI11
100
Fator de Ajuste=1+
100
DI11
Esse fator traduz a taxa percentual em um multiplicador aplicado ao preço do ativo.
Cálculo do Ativo Ajustado
Multiplica o preço do ativo pelo fator de ajuste para obter o valor ajustado do ativo. Este cálculo mostra como o preço seria se fosse diretamente influenciado pela variação da taxa DI11!.
Exibição no Gráfico
O script plota o preço ajustado do ativo como uma linha azul no gráfico, com maior espessura para facilitar a visualização. O resultado é uma curva que reflete o impacto teórico da taxa de juros DI11! sobre o ativo.
Utilidade
Este indicador é útil para entender como as taxas de juros podem influenciar ativos financeiros de forma hipotética. Ele é especialmente interessante para analistas que desejam avaliar a relação entre o mercado de renda variável e as condições de juros no curto prazo.
This script was created to adjust the price of an asset based on the DI11! interest rate, which reflects the market's expectation for future interest rates. The goal is to show how the asset's value would be influenced if it were directly adjusted by the variation of this interest rate.
How does it work?
Asset Price
The script starts by capturing the closing price of the asset that is being viewed on the chart. This is the starting point for the calculation.
DI11! Interest Rate
The script then searches for the daily values of the DI11! rate in the market. This rate is a short-term interest reference, used for financial adjustments and economic projections.
Adjustment Factor
With the DI11! interest rate, the script calculates a simple adjustment factor:
Adjustment Factor
=
1
+
DI11
100
Adjustment Factor=1+
100
DI11
This factor translates the percentage rate into a multiplier applied to the asset's price.
Adjusted Asset Calculation
Multiplies the asset price by the adjustment factor to obtain the adjusted asset value. This calculation shows how the price would be if it were directly influenced by the variation of the DI11! rate.
Display on the Chart
The script plots the adjusted asset price as a blue line on the chart, with greater thickness for easier visualization. The result is a curve that reflects the theoretical impact of the DI11! interest rate on the asset.
Usefulness
This indicator is useful for understanding how interest rates can hypothetically influence financial assets. It is especially interesting for analysts who want to assess the relationship between the equity market and short-term interest rate conditions.
