ALN Sessions - for NQ2/24/25 - v1
This script does not calculate any stats.
It uses the sessions and stats from NQStats/ALNSessions
Option to draw boxes around the session times.
Options to adjust the table text/background colors/position.
The logic will determine how the Asia and London sessions interact.
Once the New York session starts (8am), it will then display the appropriate stats.
Script quirk...fyi. The script removes the stats table at 6PM.
That's just how it works. I used grok to assist with the code, and it got funky. It works, so I left it that way.
The appropriate stats table will then be displayed when the next New York session begins.
---
There is another table I used just for troubleshooting to show the values of the Asia/London session highs/lows. This can just be ignored.
3/3/25 - republished.
Statistics
US Yield Curve (2-10yr)US Yield Curve (2-10yr) by oonoon
2-10Y US Yield Curve and Investment Strategies
The 2-10 year US Treasury yield spread measures the difference between the 10-year and 2-year Treasury yields. It is a key indicator of economic conditions.
Inversion (Spread < 0%): When the 2-year yield exceeds the 10-year yield, it signals a potential recession. Investors may shift to long-term bonds (TLT, ZROZ), gold (GLD), or defensive stocks.
Steepening (Spread widening): A rising 10-year yield relative to the 2-year suggests economic expansion. Investors can benefit by shorting bonds (TBT) or investing in financial stocks (XLF). The Amundi US Curve Steepening 2-10Y ETF can be used to profit from this trend.
Monitoring the curve: Traders can track US10Y-US02Y on TradingView for real-time insights and adjust portfolios accordingly.
Autocorrelation Price Forecasting [The Quant Science]Discover how to predict future price movements using autocorrelation and linear regression models to identify potential trading opportunities.
An advanced model to predict future price movements using autocorrelation and linear regression. This script helps identify recurring market cycles and calculates potential gains, with clear visual signals for quick and informed decisions.
Main function
This script leverages an autocorrelation model to estimate the future price of an asset based on historical price relationships. It also integrates linear regression on percentage returns to provide more accurate predictions of price movements.
Insights types
1) Red label on a green candle: Bearish forecast and swing trading opportunity.
2) Red label on a red candle: Bearish forecast and trend-following opportunity.
3) Green label on a red candle: Bullish forecast and swing trading opportunity.
4) Green label on a green candle: Bullish forecast and trend-following opportunity.
IMPORTANT!
The indicator displays a future price forecast. When negative, it estimates a future price drop.
When positive, it estimates a future price increase.
Key Features
Customizable inputs
Analysis Length: number of historical bars used for autocorrelation calculation. Adjustable between 1 and 200.
Forecast Colors: customize colors for bullish and bearish signals.
Visual insights
Labels: hypothetical gains or losses are displayed as labels above or below the bars.
Dynamic coloring: bullish (green) and bearish (red) signals are highlighted directly on the chart.
Forecast line: A continuous line is plotted to represent the estimated future price values.
Practical applications
Short-term Trading: identify repetitive market cycles to anticipate future movements.
Visual Decision-making: colored signals and labels make it easier to visualize potential profit or loss for each trade.
Advanced Customization: adjust the data length and colors to tailor the indicator to your strategies.
Limitations
Prediction price models have some limitations. Trading decisions should be made with caution, considering additional market factors and risk management strategies.
Price AltimeterThis indicator should help visualize the price, inspired by a Digital Altimeter in a Pilots HUD.
It's by default calibrated to Bitcoin, with the small levels showing every $100 and the larger levels setup to display on every $1000. But you can change this to whatever you want by changing the settings for: Small and Large Level Increments.
The default colors are grey, but can be changed to whatever you want, and there are two cause if you want they work as a gradient.
There are options to fade as the values go away from the current price action.
There are options for Forward and Backward Offsets, 0 is the current price and each value represents a candle on whatever time frame your currently on.
Other Options include the Fade Ratio, the Line Width and Style, which are all self explanatory.
Hope you Enjoy!
Backtest it in fast mode to see it in action a little better...
Known Issues:
For some reason it bug's out when either or are displaying more than 19 lines, unsure why so its limited to that for now.
Extra Note on what this may be useful for: I always wanted to make this, but didn't realize how to put things in front of the price action... Offset! Duh! Anyways, I thought of this one because I often it's hard on these charts to really get an idea for absolute price amounts across different time frames, this in an intuitive, at a glance way to see it because the regular price thing on the right always adds values between values when you zoom in and you can sometimes get lost figuring out the proportions of things.
Could also be useful for Scalping?
ATH & 52-Week High Tracker### **Indicator Name: ATH & 52-Week High Tracker**
📌 **Description:**
This indicator provides a **real-time table** displaying key stock statistics to help traders analyze price levels relative to historical highs. It includes:
✔️ **All-Time High (ATH)** price
✔️ **% Change from ATH**
✔️ **52-Week High** price
✔️ **% Change from 52-Week High**
By using this indicator, traders can quickly identify how far a stock has retraced from its **historical peaks**, which can be useful for momentum trading, breakout strategies, and trend analysis.
📊 **What You Get with This Indicator:**
✅ A clear **visual table** with important stock data
✅ Quick reference to **historical price levels**
✅ Helps in identifying potential **breakout or recovery zones**
✅ Useful for both **intraday and swing traders**
⚠️ **Disclaimer:**
This indicator is for **informational purposes only** and should not be considered **financial advice, a trading strategy, or a buy/sell signal.** Always conduct your own analysis and risk management before making trading decisions. 🚀📈
Let me know if you need any refinements! 😊
EMA Alignment & Spread Monitor (Sang Youn)Overview
The EMA Alignment & Spread Monitor is a dynamic trading script designed to monitor EMA (Exponential Moving Average) alignments, track spread deviations, and provide real-time alerts when significant conditions are met. This script allows traders to customize their EMA periods, analyze market trends based on EMA positioning, and receive visual and audio alerts when key spread conditions occur.
🔹 Key Features
✅ Customizable EMA Periods – Users can input their own EMA lengths to adapt the script to various market conditions. (Default: 5, 10, 20, 60, 120)
✅ EMA Alignment Detection – Identifies bullish alignment (all EMAs in ascending order) and bearish alignment (all EMAs in descending order).
✅ Spread Calculation & Monitoring – Computes the spread difference between each EMA and tracks the average spread over a user-defined period.
✅ Deviation Alerts – Notifies traders when:
Bullish Trend: The spread exceeds its average, indicating a potential strong uptrend.
Bearish Trend: The spread falls below its average, signaling a possible downtrend.
✅ Chart Annotations – Displays 📈 (green triangle) when bullish spread exceeds average and 📉 (red triangle) when bearish spread drops below average for easy visualization.
✅ Real-time Alerts – Sends alerts when spread conditions are met, helping traders react to market shifts efficiently.
✅ Spread Histogram – Visual representation of bullish and bearish spread levels for trend analysis.
🔹 How It Works
1️⃣ Set your EMA periods in the script settings (default: 5, 10, 20, 60, 120).
2️⃣ Define the spread average calculation length (default: 50 candles).
3️⃣ The script tracks EMA alignment to determine bullish or bearish trends.
4️⃣ If the spread deviates significantly from its average, the script:
Places a 📈 green triangle above candles in a bullish trend when spread > average.
Places a 📉 red triangle below candles in a bearish trend when spread < average.
Triggers an alert for timely decision-making.
5️⃣ Use the histogram & real-time alerts to stay ahead of market movements.
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.
Market Snap Shot with Pine ScreenerMarket Snap Shot
The Market Snap Shot is a comprehensive technical analysis tool designed to track and display key price metrics across multiple timeframes, including daily, weekly, monthly, quarterly, and yearly data. This script provides a range of essential calculations for traders and investors, enabling them to assess price action, volatility, and market trends at a glance.
Features:
Daily Metrics: Includes the daily percentage change, closing range relative to the daily high/low.
Weekly Metrics: Tracks weekly open, close, range, and calculates week-to-date performance for a broader market view.
Monthly Metrics: Provides similar calculations for monthly price action, offering insights into monthly performance.
Quarter-to-Date (QTD): Displays performance for the current quarter, offering insights into quarterly price movements.
Year-to-Date (YTD): Calculates year-to-date price change, helping users track performance relative to the start of the year.
52-Week High/Low: Displays the current price's distance from the 52-week high and low, giving context to long-term price levels.
Usage:
Traders can use this screener to quickly assess the current market position and make informed decisions based on short-term and long-term trends.
Investors can leverage the 52-week and YTD metrics to gauge the overall strength of an asset in the market.
The tool is versatile for both active traders looking for real-time performance data and for those focusing on longer-term market trends.
Instructions:
This script displays multiple metrics such as percentage changes and range data for daily, weekly, monthly, quarterly, and yearly timeframes. It is designed to be used as a screener tool to assess price action and monitor performance across these key time periods.
Warning:
The "Market Snap Shot" does not provide buy or sell signals but rather serves as a performance tracking tool. Users are encouraged to use this data in conjunction with other technical and fundamental analysis tools.
Trend Detector [victhoreb]Trend Detector is a streamlined indicator that uses the Pearson correlation coefficient between the average price and time to determine market trends. It measures how closely price movement follows the progression of time over a user-defined period, providing a clear gauge of trend direction on a scale from -1 to 1.
How It Works:
The indicator calculates the correlation between price and time. A positive correlation means that as time advances, the price generally rises—signaling an uptrend. Conversely, a negative correlation indicates that the price tends to fall over time, highlighting a downtrend.
With its simple yet effective approach, Trend Detector offers traders an immediate visual and quantitative insight into prevailing market trends.
Ceres Trader NYSE Tick Indicator With Threshold AlertsThe Ceres Trader NYSE Tick Indicator provides real-time alerts for significant shifts in the NYSE Tick, empowering traders to identify potential overbought and oversold market conditions. It displays labels directly on the chart when the Tick reaches predefined thresholds, offering a clear and immediate visual representation of market sentiment.
Key Features:
Threshold-Based Alerts:
Highlights "High Tick" and "Low Tick" conditions when the Tick exceeds user-defined thresholds (default: 400 and -400).
Identifies "Extreme High Tick" and "Extreme Low Tick" conditions for more significant shifts (adjusted default: 800 and -800).
Visual Labels:
Displays colored labels directly on the price chart, indicating the type of Tick event and its value.
Green labels signal potential overbought conditions, while red labels indicate potential oversold conditions.
Low tick labels are placed below the price bar, and high tick labels are placed above the price bar for improved visibility.
Real-Time Data:
Utilizes the NYSE Tick symbol ("TICK") to provide up-to-the-minute market data.
User-Friendly Design:
Simple and intuitive design, suitable for traders of all experience levels.
How to Use:
Add the "Ceres Trader NYSE Tick Indicator with Threshold Alerts" to your TradingView chart.
Observe the colored labels that appear when the Tick reaches the specified thresholds.
Use these alerts to identify potential trading opportunities based on overbought or oversold market conditions.
Consider adjusting the threshold values within the indicator settings to align with your specific trading strategy.
Global Liquidity Indicator in USDThis indicator aggregates the total central bank balance sheets and M2 money supply for the USA, Canada, China, European Union, Japan, and the UK, converting all values to USD and normalizing them to trillions for easy visualization. It plots three lines: Total Balance Sheet, Total M2, and Combined Total, providing a comprehensive view of global liquidity trends.
Key Features:
Dynamic Coloring: Customize line colors based on direction—green for upward trends, red for downward (or any colors you choose), with independent on/off toggles for each line.
Real-Time Currency Conversion: Uses live forex rates (e.g., USD/CNY, USD/EUR) for accurate USD conversions.
Nirmal Fair Value GapsICT Fair Value Gaps
Trade Wisely
How a Fair Value Gap Works
Formation:
A Fair Value Gap occurs when a strong price movement (usually from institutional orders) creates an imbalance between buyers and sellers.
This is typically seen in a three-candle pattern, where the middle candle has a large body, and the two surrounding candles have wicks but little overlap with the middle candle’s range.
Identification:
The FVG is marked between the high of the first candle and the low of the third candle (for bullish gaps).
For bearish gaps, it’s the low of the first candle and the high of the third candle.
Market Behavior Around FVG:
Price often retraces into the gap before resuming its original direction.
This happens because the market seeks to "fill" the imbalance where few trades occurred.
Traders use FVGs as potential entry zones for trend continuation trades.
Trading Fair Value Gaps
In an Uptrend:
Look for bullish fair value gaps as potential support zones for buy entries.
Price may dip into the gap and then continue upward.
In a Downtrend:
Look for bearish fair value gaps as potential resistance zones for sell entries.
Price may retrace into the gap and then drop further.
Confluence Factors:
FVGs work best when combined with other strategies like order blocks, liquidity zones, or key Fibonacci levels.
Futures Open/High/Low TablesAdds (up to) 3 tables to a chart, displaying Open/High/Low data for today (RTH and extended hours), yesterday, and the current week / month -- to help with intraday analysis of a futures ticker.
The tables only appear on intraday charts (5min, 30min, etc). On a Daily/Weekly/etc chart they are not calculated or shown.
In addition to Open/High/Low, the "Current" table in the top-right shows a live measurement of # of points from the open, the RTH open, and the highs/lows.
Lastly, the 9:30am ET open and the 4pm RTH close are by default marked with a shaded background (on intraday charts) for easy visual reference, and also to help with adjusting the session time to accommodate time zone issues if they occur.
Tested on ES in Eastern Time Zone, but should work on any futures instrument and any time zone by adjusting the Session Time setting.
Celestial Pair Spread Hello friends, after a very long time!
Today, I tried to put into code an idea that came to my mind spontaneously and suddenly.
Note :
This script is experimental and improvable.
I haven't had a chance to try it yet.
TIMEFRAME : 1D (Daily Bars)
CELESTIAL SPREAD
The spread moves in a very limited area and is consistent within itself, especially on days far from the end of the contract.
That's why there is a reassuring sky atmosphere. That's why this name was given completely improvised.
Basic logic of the script
We enter the name of the CME Futures contract we want to enter:
Ex : CL1! , ES1! , ZC1! , NQ1!
The script creates us a pair trade parity divided into secondary contracts.
Example : ES1!/ES2!
What is pair trading?
I will explain briefly here.
For users who are wondering:
www.investopedia.com
Let's get back to our topic.
Now we have created a parity that does not actually exist.
This parity is the manifestation of the relative movements of two contracts.
When the parity rises, ES1! increased,ES2! has fallen.
In the opposite case, We can say: ES1! Contract has been dropped ES2! has increased.
Pair trading is generally a trade that needs to be kept in mind from time to time.
It is a method preferred by professionals who can process very quickly.
Market risk is minimal, but since 2 contracts are purchased, more money is paid and very low percentage profits are made.
It is very expensive to do pair trading, especially with oil and its derivatives and interest security derivatives.
The contract we are considering has micros. (small-item contracts tied to the same value)
So when we switch to our broker MES1!/MES2! We will trade.
For all CME futures :
www.cmegroup.com
Anyway, let's continue:
The script created the parity showing its relationship with the next contract and plotted it as bars.
Celestial bands are just like Bollinger bands, but they consist of 3 bands based on percentage changes rather than standard deviation.
The middle band is obtained from moving averages.
The upper and lower bands are the middle band subjected to a threshold value.
The threshold value can be changed.
0.15 percent was charged for this script.
CAUTION :
As can be seen in the example below;
The most important thing is not to make any transactions when the contract switch dates are approaching.
Therefore, it is recommended to use it just below the main chart.
The blue bars in the parity are
Values that outside the upper and lower threshold values are colored blue.
For this condition
Alerts has been added.
Don't forget to add alert and edit.
MAIN PURPOSE
It is aimed to start a pair trade when such conditions come and to quickly close the trades when the parity basis reaches the value.
OTHER IMPORTANT POINTS
Other issues are broker related issues.
Difference between initial margins and maintanence margins of contracts (between 1! and 2!)
It shouldn't be too high.
The commission should not be too high.
Leverage must be high because the profit percentage is very low.
To calculate leverage you must divide your contract size by the relevant margin requirement.
Sample margin requirement table:
www.interactivebrokers.com
RISKS
It is an experimental and intellectual script,
the risk of contract price differences (maybe it will not leave a profit except for very extreme values)
I remind you of the quickness risk that comes from a two-legged trade.
Alerts definitely synchronized with an audible alert sent to a smartphone as an e-mail notification and displayed on the locked screen for quick action.
Best regards!
DCSessionStatsOHLC_v1.0DCSessionStatsOHLC_v1.0
© dc_77 | Pine Script™ v6 | Licensed under Mozilla Public License 2.0
This indicator overlays customizable session-based OHLC (Open, High, Low, Close) statistics on your TradingView chart. It tracks price action within user-defined sessions, calculates average manipulation and distribution levels based on historical data, and visually projects these levels with lines and labels. Additionally, it provides a session count table to monitor bullish and bearish sessions.
Key Features:
Session Customization: Define session time (e.g., "0000-1600") and time zone (e.g., UTC, America/New_York). Analyze up to 20 historical sessions.
Anchor Line: Displays a vertical line at session start with customizable style, color, and optional label.
Session Open Line: Plots a horizontal line at the session’s opening price with adjustable appearance and label.
Manipulation Levels: Calculates and projects average price extensions (high/low relative to open) for manipulative moves, shown as horizontal lines with labels.
Distribution Levels: Displays average price ranges (high/low beyond open) for distribution phases, with customizable lines and labels.
Visual Flexibility: Adjust line styles (solid, dashed, dotted), colors, widths, label sizes, and projection offsets (bars beyond session start).
Session Stats Table: Optional table showing counts of bullish (close > open) and bearish (close < open) sessions, with configurable position and size.
How It Works:
Tracks OHLC data within each session and identifies session start/end based on the specified time range.
Computes averages for manipulation (e.g., low below open in bullish sessions) and distribution (e.g., high above open) levels from past sessions.
Projects these levels forward as horizontal lines, extending them by a user-defined offset for easy reference.
Updates a table with real-time bullish/bearish session counts.
Use Case:
Ideal for traders analyzing intraday or custom session behavior, identifying key price levels, and gauging market sentiment over time.
Toggle individual elements on/off and fine-tune visuals to suit your trading style.
SuperTrend + Relative Volume (Kernel Optimized)Introducing our new KDE Optimized Supertrend + Relative Volume Indicator!
This innovative indicator combines the power of the Supertrend indicator along with Relative Volume. It utilizes the Kernel Density Estimation (KDE) to estimate the probability of a candlestick marking a significant trend break or reversal.
❓How to Interpret the KDE %:
The KDE % is a crucial metric that reflects the likelihood that the current candlestick represents a true break in the SuperTrend line, supported by an increase in relative volume. It estimates the probability of a trend shift or continuation based on historical SuperTrend breaks and volume patterns:
Low KDE %: A lower probability that the current break is significant. Price action is less likely to reverse, and the trend may continue.
Moderate KDE - High KDE %: An increased possibility that a trend reversal or consolidation could occur. Traders should start watching for confirmation signals.
📌How Does It Work?
The SuperTrend indicator uses the Average True Range (ATR) to determine the direction of the trend and identifies when the price crosses the SuperTrend line, signaling a potential trend reversal. Here's how the KDE Optimized SuperTrend Indicator works:
SuperTrend Calculation: The SuperTrend indicator is calculated, and when the price breaks above (bullish) or below (bearish) the SuperTrend line, it is logged as a significant event.
Relative Volume: For each break in the SuperTrend line, we calculate the relative volume (current volume vs. the average volume over a defined period). High relative volume can suggest stronger confirmation of the trend break.
KDE Array Calculation: KDE is applied to the break points and relative volume data:
Define the KDE options: Bandwidth, Number of Steps, and Array Range (Array Max - Array Min).
Create a density range array using the defined number of steps, corresponding to potential break points.
Apply a Gaussian kernel function to the break points and volume data to estimate the likelihood of the trend break being significant.
KDE Value and Signal Generation: The KDE array is updated as each break occurs. The KDE % is calculated for the breakout candlestick, representing the likelihood of the trend break being significant. If the KDE value exceeds the defined activation threshold, a darker bullish or bearish arrow is plotted after bar confirmation. If the KDE value falls below the threshold, a more transparent arrow is drawn, indicating a possible but lower probability break.
⚙️Settings:
SuperTrend Settings:
ATR Length: The period over which the Average True Range (ATR) is calculated.
Multiplier: The multiplier applied to the ATR to determine the SuperTrend threshold.
KDE Settings:
Bandwidth: Determines the smoothness of the KDE function and the width of the influence of each break point.
Number of Bins (Steps): Defines the precision of the KDE algorithm, with higher values offering more detailed calculations.
KDE Threshold %: The level at which relative volume is considered significant for confirming a break.
Relative Volume Length: The number of historic candles used in calculating KDE %
DynamicMALibrary "DynamicMA"
Dynamic Moving Averages Library
Introduction
The Dynamic Moving Averages Library is a specialized collection of custom built functions designed to calculate moving averages dynamically, beginning from the first available bar. Unlike standard moving averages, which rely on fixed length lookbacks, this library ensures that indicators remain fully functional from the very first data point, making it an essential tool for analysing assets with short time series or limited historical data.
This approach allows traders and developers to build robust indicators that do not require a preset amount of historical data before generating meaningful outputs. It is particularly advantageous for:
Newly listed assets with minimal price history.
High-timeframe trading, where large lookback periods can lead to delayed or missing data.
By eliminating the constraints of fixed lookback periods, this library enables the seamless construction of trend indicators, smoothing functions, and hybrid models that adapt instantly to market conditions.
Comprehensive Set of Custom Moving Averages
The library includes a wide range of custom dynamic moving averages, each designed for specific analytical use cases:
SMA (Simple Moving Average) – The fundamental moving average, dynamically computed.
EMA (Exponential Moving Average) – Adaptive smoothing for better trend tracking.
DEMA (Double Exponential Moving Average) – Faster trend detection with reduced lag.
TEMA (Triple Exponential Moving Average) – Even more responsive than DEMA.
WMA (Weighted Moving Average) – Emphasizes recent price action while reducing noise.
VWMA (Volume Weighted Moving Average) – Accounts for volume to give more weight to high-volume periods.
HMA (Hull Moving Average) – A superior smoothing method with low lag.
SMMA (Smoothed Moving Average) – A hybrid approach between SMA and EMA.
LSMA (Least Squares Moving Average) – Uses linear regression for trend detection.
RMA (Relative Moving Average) – Used in RSI-based calculations for smooth momentum readings.
ALMA (Arnaud Legoux Moving Average) – A Gaussian-weighted MA for superior signal clarity.
Hyperbolic MA (HyperMA) – A mathematically optimized averaging method with dynamic weighting.
Each function dynamically adjusts its calculation length to match the available bar count, ensuring instant functionality on all assets.
Fully Optimized for Pine Script v6
This library is built on Pine Script v6, ensuring compatibility with modern TradingView indicators and scripts. It includes exportable functions for seamless integration into custom indicators, making it easy to develop trend-following models, volatility filters, and adaptive risk-management systems.
Why Use Dynamic Moving Averages?
Traditional moving averages suffer from a common limitation: they require a fixed historical window to generate meaningful values. This poses several problems:
New Assets Have No Historical Data - If an asset has only been trading for a short period, traditional moving averages may not be able to generate valid signals.
High Timeframes Require Massive Lookbacks - On 1W or 1M charts, a 200-period SMA would require 200 weeks or months of data, making it unusable on newer assets.
Delayed Signal Initialization - Standard indicators often take dozens of bars to stabilize, reducing effectiveness when trading new trends.
The Dynamic Moving Averages Library eliminates these issues by ensuring that every function:
Starts calculation from bar one, using available data instead of waiting for a lookback period.
Adapts dynamically across timeframes, making it equally effective on low or high timeframes.
Allows smoother, more responsive trend tracking, particularly useful for volatile or low-liquidity assets.
This flexibility makes it indispensable for custom script developers, quantitative analysts, and discretionary traders looking to build more adaptive and resilient indicators.
Final Summary
The Dynamic Moving Averages Library is a versatile and powerful set of functions designed to overcome the limitations of fixed-lookback indicators. By dynamically adjusting the calculation length from the first bar, this library ensures that moving averages remain fully functional across all timeframes and asset types, making it an essential tool for traders and developers alike.
With built-in adaptability, low-lag smoothing, and support for multiple moving average types, this library unlocks new possibilities for quantitative trading and strategy development - especially for assets with short price histories or those traded on higher timeframes.
For traders looking to enhance signal reliability, minimize lag, and build adaptable trading systems, the Dynamic Moving Averages Library provides an efficient and flexible solution.
SMA(sourceData, maxLength)
Dynamic SMA
Parameters:
sourceData (float)
maxLength (int)
EMA(src, length)
Dynamic EMA
Parameters:
src (float)
length (int)
DEMA(src, length)
Dynamic DEMA
Parameters:
src (float)
length (int)
TEMA(src, length)
Dynamic TEMA
Parameters:
src (float)
length (int)
WMA(src, length)
Dynamic WMA
Parameters:
src (float)
length (int)
HMA(src, length)
Dynamic HMA
Parameters:
src (float)
length (int)
VWMA(src, volsrc, length)
Dynamic VWMA
Parameters:
src (float)
volsrc (float)
length (int)
SMMA(src, length)
Dynamic SMMA
Parameters:
src (float)
length (int)
LSMA(src, length, offset)
Dynamic LSMA
Parameters:
src (float)
length (int)
offset (int)
RMA(src, length)
Dynamic RMA
Parameters:
src (float)
length (int)
ALMA(src, length, offset_sigma, sigma)
Dynamic ALMA
Parameters:
src (float)
length (int)
offset_sigma (float)
sigma (float)
HyperMA(src, length)
Dynamic HyperbolicMA
Parameters:
src (float)
length (int)
DCStatCalcs_v0.1DCStatCalcs_v0.1 - Session-Based Statistical Projections
This Pine Script indicator overlays customizable horizontal lines on your chart to visualize a session's opening price and its statistical projections based on historical standard deviation (SD). Designed for traders who want to analyze price behavior within defined time sessions, it calculates and plots the session open price along with optional projection lines at 0.5, 1.0, 1.5, 2.0, and 2.5 standard deviations above and below the open, derived from past session data.
Key Features:
Customizable Sessions: Define your session time (e.g., 0600-1500) and timezone (e.g., America/New_York).
Historical Analysis: Uses a user-specified number of past sessions (default: 20) to compute the standard deviation of price movements relative to the session open.
Projection Lines: Displays toggleable lines at multiple SD levels with adjustable styles, colors, and widths for easy visualization.
Flexible Display: Extend lines beyond the current bar with an offset setting, and adjust label sizes for clarity.
Real-Time Updates: Lines dynamically extend as the session progresses, keeping projections relevant to the current bar.
How It Works:
At the start of each user-defined session, the indicator records the opening price and calculates the SD based on price deviations from the open across historical sessions. It then plots the open price line and, if enabled, projection lines at the specified SD intervals. These lines help traders identify potential support, resistance, or volatility zones based on statistical norms.
Use Case:
Ideal for day traders or analysts working with intraday charts to gauge price ranges and volatility within specific trading sessions, such as market opens or key economic hours.
Published under the Mozilla Public License 2.0. Created by dc_77.
TICK Indikator
English:
The TICK Indicator measures in real time the number of up ticking stocks minus the number of down ticking stocks on the New York Stock Exchange (NYSE). It can display either the current TICK value ("Normal" mode) or the cumulative TICK values over the trading day ("Cumulative" mode). Positive values indicate market strength, while negative values signal weakness. Colored bars visualize momentum: green shades for rising, red for falling values. The zero line acts as a reference between buying and selling pressure.
Interpretation:
> +1000 and/or continuos lows above 0 → strong buying pressure
< -1000 and/or continuos highs below 0 → strong selling pressure
Around 0 → balanced market
Deutsch:
Der TICK Indikator misst in Echtzeit die Anzahl der Aktien, die an der New York Stock Exchange (NYSE) steigen, minus der Anzahl der fallenden Aktien. Der Indikator kann im "Normal"-Modus den aktuellen TICK-Wert anzeigen oder im "Cumulative"-Modus die kumulierten TICK-Werte über den Tag hinweg summieren. Positive Werte deuten auf eine allgemeine Markstärke hin, während negative Werte Schwäche signalisieren. Farbige Balken visualisieren die Dynamik: grüne Töne bei steigenden, rote bei fallenden Werten. Die Nullinie dient als Referenzpunkt zwischen Kauf- und Verkaufsdruck.
Interpretation:
> +1000 und/oder mehrere aufeinander folgende Tiefs über 0 → starker Kaufdruck
< -1000 und/oder mehrere aufeinander folgende Hochs unter 0 → starker Verkaufsdruck
Nahe 0 → ausgeglichener Markt
ValueAtTime█ OVERVIEW
This library is a Pine Script® programming tool for accessing historical values in a time series using UNIX timestamps . Its data structure and functions index values by time, allowing scripts to retrieve past values based on absolute timestamps or relative time offsets instead of relying on bar index offsets.
█ CONCEPTS
UNIX timestamps
In Pine Script®, a UNIX timestamp is an integer representing the number of milliseconds elapsed since January 1, 1970, at 00:00:00 UTC (the UNIX Epoch ). The timestamp is a unique, absolute representation of a specific point in time. Unlike a calendar date and time, a UNIX timestamp's meaning does not change relative to any time zone .
This library's functions process series values and corresponding UNIX timestamps in pairs , offering a simplified way to identify values that occur at or near distinct points in time instead of on specific bars.
Storing and retrieving time-value pairs
This library's `Data` type defines the structure for collecting time and value information in pairs. Objects of the `Data` type contain the following two fields:
• `times` – An array of "int" UNIX timestamps for each recorded value.
• `values` – An array of "float" values for each saved timestamp.
Each index in both arrays refers to a specific time-value pair. For instance, the `times` and `values` elements at index 0 represent the first saved timestamp and corresponding value. The library functions that maintain `Data` objects queue up to one time-value pair per bar into the object's arrays, where the saved timestamp represents the bar's opening time .
Because the `times` array contains a distinct UNIX timestamp for each item in the `values` array, it serves as a custom mapping for retrieving saved values. All the library functions that return information from a `Data` object use this simple two-step process to identify a value based on time:
1. Perform a binary search on the `times` array to find the earliest saved timestamp closest to the specified time or offset and get the element's index.
2. Access the element from the `values` array at the retrieved index, returning the stored value corresponding to the found timestamp.
Value search methods
There are several techniques programmers can use to identify historical values from corresponding timestamps. This library's functions include three different search methods to locate and retrieve values based on absolute times or relative time offsets:
Timestamp search
Find the value with the earliest saved timestamp closest to a specified timestamp.
Millisecond offset search
Find the value with the earliest saved timestamp closest to a specified number of milliseconds behind the current bar's opening time. This search method provides a time-based alternative to retrieving historical values at specific bar offsets.
Period offset search
Locate the value with the earliest saved timestamp closest to a defined period offset behind the current bar's opening time. The function calculates the span of the offset based on a period string . The "string" must contain one of the following unit tokens:
• "D" for days
• "W" for weeks
• "M" for months
• "Y" for years
• "YTD" for year-to-date, meaning the time elapsed since the beginning of the bar's opening year in the exchange time zone.
The period string can include a multiplier prefix for all supported units except "YTD" (e.g., "2W" for two weeks).
Note that the precise span covered by the "M", "Y", and "YTD" units varies across time. The "1M" period can cover 28, 29, 30, or 31 days, depending on the bar's opening month and year in the exchange time zone. The "1Y" period covers 365 or 366 days, depending on leap years. The "YTD" period's span changes with each new bar, because it always measures the time from the start of the current bar's opening year.
█ CALCULATIONS AND USE
This library's functions offer a flexible, structured approach to retrieving historical values at or near specific timestamps, millisecond offsets, or period offsets for different analytical needs.
See below for explanations of the exported functions and how to use them.
Retrieving single values
The library includes three functions that retrieve a single stored value using timestamp, millisecond offset, or period offset search methods:
• `valueAtTime()` – Locates the saved value with the earliest timestamp closest to a specified timestamp.
• `valueAtTimeOffset()` – Finds the saved value with the earliest timestamp closest to the specified number of milliseconds behind the current bar's opening time.
• `valueAtPeriodOffset()` – Finds the saved value with the earliest timestamp closest to the period-based offset behind the current bar's opening time.
Each function has two overloads for advanced and simple use cases. The first overload searches for a value in a user-specified `Data` object created by the `collectData()` function (see below). It returns a tuple containing the found value and the corresponding timestamp.
The second overload maintains a `Data` object internally to store and retrieve values for a specified `source` series. This overload returns a tuple containing the historical `source` value, the corresponding timestamp, and the current bar's `source` value, making it helpful for comparing past and present values from requested contexts.
Retrieving multiple values
The library includes the following functions to retrieve values from multiple historical points in time, facilitating calculations and comparisons with values retrieved across several intervals:
• `getDataAtTimes()` – Locates a past `source` value for each item in a `timestamps` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified timestamps.
• `getDataAtTimeOffsets()` – Finds a past `source` value for each item in a `timeOffsets` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified millisecond offsets behind the current bar's opening time.
• `getDataAtPeriodOffsets()` – Finds a past value for each item in a `periods` array. Each retrieved value's timestamp represents the earliest time closest to one of the specified period offsets behind the current bar's opening time.
Each function returns a tuple with arrays containing the found `source` values and their corresponding timestamps. In addition, the tuple includes the current `source` value and the symbol's description, which also makes these functions helpful for multi-interval comparisons using data from requested contexts.
Processing period inputs
When writing scripts that retrieve historical values based on several user-specified period offsets, the most concise approach is to create a single text input that allows users to list each period, then process the "string" list into an array for use in the `getDataAtPeriodOffsets()` function.
This library includes a `getArrayFromString()` function to provide a simple way to process strings containing comma-separated lists of periods. The function splits the specified `str` by its commas and returns an array containing every non-empty item in the list with surrounding whitespaces removed. View the example code to see how we use this function to process the value of a text area input .
Calculating period offset times
Because the exact amount of time covered by a specified period offset can vary, it is often helpful to verify the resulting times when using the `valueAtPeriodOffset()` or `getDataAtPeriodOffsets()` functions to ensure the calculations work as intended for your use case.
The library's `periodToTimestamp()` function calculates an offset timestamp from a given period and reference time. With this function, programmers can verify the time offsets in a period-based data search and use the calculated offset times in additional operations.
For periods with "D" or "W" units, the function calculates the time offset based on the absolute number of milliseconds the period covers (e.g., `86400000` for "1D"). For periods with "M", "Y", or "YTD" units, the function calculates an offset time based on the reference time's calendar date in the exchange time zone.
Collecting data
All the `getDataAt*()` functions, and the second overloads of the `valueAt*()` functions, collect and maintain data internally, meaning scripts do not require a separate `Data` object when using them. However, the first overloads of the `valueAt*()` functions do not collect data, because they retrieve values from a user-specified `Data` object.
For cases where a script requires a separate `Data` object for use with these overloads or other custom routines, this library exports the `collectData()` function. This function queues each bar's `source` value and opening timestamp into a `Data` object and returns the object's ID.
This function is particularly useful when searching for values from a specific series more than once. For instance, instead of using multiple calls to the second overloads of `valueAt*()` functions with the same `source` argument, programmers can call `collectData()` to store each bar's `source` and opening timestamp, then use the returned `Data` object's ID in calls to the first `valueAt*()` overloads to reduce memory usage.
The `collectData()` function and all the functions that collect data internally include two optional parameters for limiting the saved time-value pairs to a sliding window: `timeOffsetLimit` and `timeframeLimit`. When either has a non-na argument, the function restricts the collected data to the maximum number of recent bars covered by the specified millisecond- and timeframe-based intervals.
NOTE : All calls to the functions that collect data for a `source` series can execute up to once per bar or realtime tick, because each stored value requires a unique corresponding timestamp. Therefore, scripts cannot call these functions iteratively within a loop . If a call to these functions executes more than once inside a loop's scope, it causes a runtime error.
█ EXAMPLE CODE
The example code at the end of the script demonstrates one possible use case for this library's functions. The code retrieves historical price data at user-specified period offsets, calculates price returns for each period from the retrieved data, and then populates a table with the results.
The example code's process is as follows:
1. Input a list of periods – The user specifies a comma-separated list of period strings in the script's "Period list" input (e.g., "1W, 1M, 3M, 1Y, YTD"). Each item in the input list represents a period offset from the latest bar's opening time.
2. Process the period list – The example calls `getArrayFromString()` on the first bar to split the input list by its commas and construct an array of period strings.
3. Request historical data – The code uses a call to `getDataAtPeriodOffsets()` as the `expression` argument in a request.security() call to retrieve the closing prices of "1D" bars for each period included in the processed `periods` array.
4. Display information in a table – On the latest bar, the code uses the retrieved data to calculate price returns over each specified period, then populates a two-row table with the results. The cells for each return percentage are color-coded based on the magnitude and direction of the price change. The cells also include tooltips showing the compared daily bar's opening date in the exchange time zone.
█ NOTES
• This library's architecture relies on a user-defined type (UDT) for its data storage format. UDTs are blueprints from which scripts create objects , i.e., composite structures with fields containing independent values or references of any supported type.
• The library functions search through a `Data` object's `times` array using the array.binary_search_leftmost() function, which is more efficient than looping through collected data to identify matching timestamps. Note that this built-in works only for arrays with elements sorted in ascending order .
• Each function that collects data from a `source` series updates the values and times stored in a local `Data` object's arrays. If a single call to these functions were to execute in a loop , it would store multiple values with an identical timestamp, which can cause erroneous search behavior. To prevent looped calls to these functions, the library uses the `checkCall()` helper function in their scopes. This function maintains a counter that increases by one each time it executes on a confirmed bar. If the count exceeds the total number of bars, indicating the call executes more than once in a loop, it raises a runtime error .
• Typically, when requesting higher-timeframe data with request.security() while using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should be offset with the history-referencing operator to prevent lookahead bias on historical bars. However, the call in this script's example code enables lookahead without offsetting the `expression` because the script displays results only on the last historical bar and all realtime bars, where there is no future data to leak into the past. This call ensures the displayed results use the latest data available from the context on realtime bars.
Look first. Then leap.
█ EXPORTED TYPES
Data
A structure for storing successive timestamps and corresponding values from a dataset.
Fields:
times (array) : An "int" array containing a UNIX timestamp for each value in the `values` array.
values (array) : A "float" array containing values corresponding to the timestamps in the `times` array.
█ EXPORTED FUNCTIONS
getArrayFromString(str)
Splits a "string" into an array of substrings using the comma (`,`) as the delimiter. The function trims surrounding whitespace characters from each substring, and it excludes empty substrings from the result.
Parameters:
str (series string) : The "string" to split into an array based on its commas.
Returns: (array) An array of trimmed substrings from the specified `str`.
periodToTimestamp(period, referenceTime)
Calculates a UNIX timestamp representing the point offset behind a reference time by the amount of time within the specified `period`.
Parameters:
period (series string) : The period string, which determines the time offset of the returned timestamp. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are:
- "Y" for years.
- "M" for months.
- "W" for weeks.
- "D" for days.
- "YTD" (Year-to-date) for the span from the start of the `referenceTime` value's year in the exchange time zone. An argument with this unit cannot contain a multiplier.
referenceTime (series int) : The millisecond UNIX timestamp from which to calculate the offset time.
Returns: (int) A millisecond UNIX timestamp representing the offset time point behind the `referenceTime`.
collectData(source, timeOffsetLimit, timeframeLimit)
Collects `source` and `time` data successively across bars. The function stores the information within a `Data` object for use in other exported functions/methods, such as `valueAtTimeOffset()` and `valueAtPeriodOffset()`. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
source (series float) : The source series to collect. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: (Data) A `Data` object containing collected `source` values and corresponding timestamps over the allowed time range.
method valueAtTime(data, timestamp)
(Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to the specified `timestamp`. Callable as a method or a function.
Parameters:
data (series Data) : The `Data` object containing the collected time and value data.
timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest saved timestamp that is closest to the value.
Returns: ( ) A tuple containing the following data from the `Data` object:
- The stored value corresponding to the identified timestamp ("float").
- The earliest saved timestamp that is closest to the specified `timestamp` ("int").
valueAtTime(source, timestamp, timeOffsetLimit, timeframeLimit)
(Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to the specified `timestamp`. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timestamp (series int) : The millisecond UNIX timestamp to search. The function returns data for the earliest bar whose timestamp is closest to the value.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : (simple string) Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple containing the following data:
- The `source` value corresponding to the identified timestamp ("float").
- The earliest bar's timestamp that is closest to the specified `timestamp` ("int").
- The current bar's `source` value ("float").
method valueAtTimeOffset(data, timeOffset)
(Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest saved timestamp closest to `timeOffset` milliseconds behind the current bar's opening time. Callable as a method or a function.
Parameters:
data (series Data) : The `Data` object containing the collected time and value data.
timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest saved timestamp that is closest to the calculated offset time.
Returns: ( ) A tuple containing the following data from the `Data` object:
- The stored value corresponding to the identified timestamp ("float").
- The earliest saved timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int").
valueAtTimeOffset(source, timeOffset, timeOffsetLimit, timeframeLimit)
(Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to `timeOffset` milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timeOffset (series int) : The millisecond offset behind the bar's opening time. The function returns data for the earliest bar's timestamp that is closest to the calculated offset time.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple containing the following data:
- The `source` value corresponding to the identified timestamp ("float").
- The earliest bar's timestamp that is closest to `timeOffset` milliseconds before the current bar's opening time ("int").
- The current bar's `source` value ("float").
method valueAtPeriodOffset(data, period)
(Overload 1 of 2) Retrieves value and time data from a `Data` object's fields at the index of the earliest timestamp closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Callable as a method or a function.
Parameters:
data (series Data) : The `Data` object containing the collected time and value data.
period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are:
- "Y" for years.
- "M" for months.
- "W" for weeks.
- "D" for days.
- "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.
Returns: ( ) A tuple containing the following data from the `Data` object:
- The stored value corresponding to the identified timestamp ("float").
- The earliest saved timestamp that is closest to the calculated offset behind the bar's opening time ("int").
valueAtPeriodOffset(source, period, timeOffsetLimit, timeframeLimit)
(Overload 2 of 2) Retrieves `source` and time information for the earliest bar whose opening timestamp is closest to a calculated offset behind the current bar's opening time. The calculated offset represents the amount of time covered by the specified `period`. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
period (series string) : The period string, which determines the calculated time offset. The specified argument must contain a unit and an optional multiplier (e.g., "1Y", "3M", "2W", "YTD"). Supported units are:
- "Y" for years.
- "M" for months.
- "W" for weeks.
- "D" for days.
- "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple containing the following data:
- The `source` value corresponding to the identified timestamp ("float").
- The earliest bar's timestamp that is closest to the calculated offset behind the current bar's opening time ("int").
- The current bar's `source` value ("float").
getDataAtTimes(timestamps, source, timeOffsetLimit, timeframeLimit)
Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the UNIX timestamps specified in the `timestamps` array. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
timestamps (array) : An array of "int" values representing UNIX timestamps. The function retrieves `source` and time data for each element in this array.
source (series float) : The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple of the following data:
- An array containing a `source` value for each identified timestamp (array).
- An array containing an identified timestamp for each item in the `timestamps` array (array).
- The current bar's `source` value ("float").
- The symbol's description from `syminfo.description` ("string").
getDataAtTimeOffsets(timeOffsets, source, timeOffsetLimit, timeframeLimit)
Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to one of the time offsets specified in the `timeOffsets` array. Each offset in the array represents the absolute number of milliseconds behind the current bar's opening time. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
timeOffsets (array) : An array of "int" values representing the millisecond time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day and one week behind the current bar's opening time.
source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple of the following data:
- An array containing a `source` value for each identified timestamp (array).
- An array containing an identified timestamp for each offset specified in the `timeOffsets` array (array).
- The current bar's `source` value ("float").
- The symbol's description from `syminfo.description` ("string").
getDataAtPeriodOffsets(periods, source, timeOffsetLimit, timeframeLimit)
Retrieves `source` and time information for each bar whose opening timestamp is the earliest one closest to a calculated offset behind the current bar's opening time. Each calculated offset represents the amount of time covered by a period specified in the `periods` array. Any call to this function cannot execute more than once per bar or realtime tick.
Parameters:
periods (array) : An array of period strings, which determines the time offsets used in the search. The function retrieves `source` and time data for each element in this array. For example, the array ` ` specifies that the function returns data for the timestamps closest to one day, week, and month behind the current bar's opening time. Each "string" in the array must contain a unit and an optional multiplier. Supported units are:
- "Y" for years.
- "M" for months.
- "W" for weeks.
- "D" for days.
- "YTD" (Year-to-date) for the span from the start of the current bar's year in the exchange time zone. An argument with this unit cannot contain a multiplier.
source (float) : (series float) The source series to analyze. The function stores each value in the series with an associated timestamp representing its corresponding bar's opening time.
timeOffsetLimit (simple int) : Optional. A time offset (range) in milliseconds. If specified, the function limits the collected data to the maximum number of bars covered by the range, with a minimum of one bar. If the call includes a non-empty `timeframeLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
timeframeLimit (simple string) : Optional. A valid timeframe string. If specified and not empty, the function limits the collected data to the maximum number of bars covered by the timeframe, with a minimum of one bar. If the call includes a non-na `timeOffsetLimit` value, the function limits the data using the largest number of bars covered by the two ranges. The default is `na`.
Returns: ( ) A tuple of the following data:
- An array containing a `source` value for each identified timestamp (array).
- An array containing an identified timestamp for each period specified in the `periods` array (array).
- The current bar's `source` value ("float").
- The symbol's description from `syminfo.description` ("string").
Price Change IndicatorPrice Change Indicator (PCI)
Version: 1.0
Author: LazyTrader 🚀
🔍 Overview
The Price Change Indicator (PCI) helps traders visualize and compare price changes between the current bar and the previous bar. It provides a customizable display of price changes in two formats:
Percentage (%) Change – Relative price movement.
Natural Change – Absolute difference in price units.
⚙️ Key Features
✅ Customizable Calculation Method: Choose how the price change is calculated:
Opening Price
Closing Price
High
Low
✅ Flexible Display Format:
Show Percentage (%) Change.
Show Natural (Absolute) Change in price.
✅ Adjustable Sensitivity with Multiplier:
100 (Standard Change)
1000 (Small Change)
10000 (Tiny Change)
✅ Intuitive Labeling:
Green label (above bar) for increase.
Red label (below bar) for decrease.
No label if no change.
Large, easy-to-read labels for better visibility.
✅ Perfect for Any Market:
Stocks 📈
Forex 💱
Crypto 🚀
Commodities 🛢️
📊 How It Works
The indicator calculates the difference between the current and previous bar’s price based on your chosen method.
The result is displayed as either a percentage (%) or a natural price change.
If the price has increased, a green label is displayed above the bar.
If the price has decreased, a red label is displayed below the bar.
⚡ How to Use
Add the indicator to your chart.
Go to settings and customize:
Select calculation method (Open, Close, High, Low).
Choose display format (% or Natural Change).
Adjust multiplier for more sensitivity.
Analyze the labels to see price movements easily!
🔧 Settings Explained
Setting Description
Price Calculation Method: Choose Open, Close, High, or Low price for comparison.
Display Format: Show either % Change or Natural Change.
Multiplier: Apply 100, 1000, or 10000 to scale small price changes.
Show Labels: Toggle labels on/off.
🎯 Best Use Cases
🔹 Identifying strong price movements
🔹 Spotting trends and momentum shifts
🔹 Comparing price movement intensity
🔹 Works for scalping, swing trading, and long-term analysis
Uptrick: Alpha TrendIntroduction
Uptrick: Alpha Trend is a comprehensive technical analysis indicator designed to provide traders with detailed insights into market trends, momentum, and risk metrics. It adapts to various trading styles—from quick scalps to longer-term positions—by dynamically adjusting its calculations and visual elements. By combining multiple smoothing techniques, advanced color schemes, and customizable data tables, the indicator offers a holistic view of market behavior.
Originality
The Alpha Trend indicator distinguishes itself by blending established technical concepts with innovative adaptations. It employs three different smoothing techniques tailored to specific trading modes (Scalp, Swing, and Position), and it dynamically adjusts its parameters to match the chosen mode. The indicator also offers a wide range of color palettes and multiple on-screen tables that display key metrics. This unique combination of features, along with its ability to adapt in real time, sets it apart as a versatile tool for both novice and experienced traders.
Features
1. Multi-Mode Trend Line
The indicator automatically selects a smoothing method based on the trading mode:
- Scalp Mode uses the Hull Moving Average (HMA) for rapid responsiveness.
- Swing Mode employs the Exponential Moving Average (EMA) for balanced reactivity.
- Position Mode applies the Weighted Moving Average (WMA) for smoother, long-term trends.
Each method is chosen to best capture the price action dynamics appropriate to the trader’s timeframe.
2. Adaptive Momentum Thresholds
It tracks bullish and bearish momentum with counters that increment as the trend confirms directional movement. When these counters exceed a user-defined threshold, the indicator generates optional buy or sell signals. This approach helps filter out minor fluctuations and highlights significant market moves.
3. Gradient Fills
Two types of fills enhance visual clarity:
- Standard Gradient Fill displays ATR-based zones above and below the trend line, indicating potential bullish and bearish areas.
- Fading Gradient Fill creates a smooth transition between the trend line and the price, visually emphasizing the distance between them.
4. Bar Coloring and Signal Markers
The indicator can color-code bars based on market conditions—bullish, bearish, or neutral—allowing for immediate visual assessment. Additionally, signal markers such as buy and sell arrows are plotted when momentum thresholds are breached.
5. Comprehensive Data Tables
Uptrick: Alpha Trend offers several optional tables for detailed analysis:
- Insider Info: Displays key metrics like the current trend value, bullish/bearish momentum counts, and ATR.
- Indicator Metrics: Lists input settings such as trend length, damping, signal threshold, and net momentum.
- Market Analysis: Summarizes overall trend direction, trend strength, Sortino ratio, return, and volatility.
- Price & Trend Dynamics: Details price deviation from the trend, trend slope, and ATR ratio.
- Momentum & Volatility Insights: Presents RSI, standard deviation (volatility), and net momentum.
- Performance & Acceleration Metrics: Focuses on the Sortino ratio, trend acceleration, return, and trend strength.
Each table can be positioned flexibly on the chart, allowing traders to customize the layout according to their needs.
Why It Combines Specific Smoothing Techniques
Smoothing techniques are essential for filtering out market noise and revealing underlying trends. The indicator combines three smoothing methods for the following reasons:
- The Hull Moving Average (HMA) in Scalp Mode minimizes lag and responds quickly to price changes, which is critical for short-term trading.
- The Exponential Moving Average (EMA) in Swing Mode gives more weight to recent data, striking a balance between speed and smoothness. This makes it suitable for mid-term trend analysis.
- The Weighted Moving Average (WMA) in Position Mode smooths out short-term fluctuations, offering a clear view of longer-term trends and reducing the impact of transient market volatility.
By using these specific methods in their respective trading modes, the indicator ensures that the trend line is appropriately responsive for the intended time frame, enhancing decision-making while maintaining clarity.
Inputs
1. Trend Length (Default: 30)
Defines the lookback period for the smoothing calculation. A shorter trend length results in a more responsive line, while a longer length produces a smoother, less volatile trend.
2. Trend Damping (Default: 0.75)
Controls the degree of smoothing applied to the trend line. Lower values lead to a smoother curve, whereas higher values increase sensitivity to price fluctuations.
3. Signal Strength Threshold (Default: 5)
Specifies the number of consecutive bullish or bearish bars required to trigger a signal. Higher thresholds reduce the frequency of signals, focusing on stronger moves.
4. Enable Bar Coloring (Default: True)
Toggles whether each price bar is colored to indicate bullish, bearish, or neutral conditions.
5. Enable Signals (Default: True)
When enabled, this option plots buy or sell arrows on the chart once the momentum thresholds are met.
6. Enable Standard Gradient Fill (Default: False)
Activates ATR-based gradient fills around the trend line to visualize potential support and resistance zones.
7. Enable Fading Gradient Fill (Default: True)
Draws a gradual color transition between the trend line and the current price, emphasizing their divergence.
8. Trading Mode (Options: Scalp, Swing, Position)
Determines which smoothing method and ATR period to use, adapting the indicator’s behavior to short-term, medium-term, or long-term trading.
9. Table Position Inputs
Allows users to select from nine possible chart positions (top, middle, bottom; left, center, right) for each data table.
10. Show Table Booleans
Separate toggles control the display of each table (Insider Info, Indicator Metrics, Market Analysis, and the three Deep Tables), enabling a customized view of the data.
Color Schemes
(Default) - The colors in the preview image of the indicator.
(Emerald)
(Sapphire)
(Golden Blaze)
(Mystic)
(Monochrome)
(Pastel)
(Vibrant)
(Earth)
(Neon)
Calculations
1. Trend Line Methods
- Scalp Mode: Utilizes the Hull Moving Average (HMA), which computes two weighted moving averages (one at half the length and one at full length), subtracts them, and then applies a final weighted average based on the square root of the length. This method minimizes lag and increases responsiveness.
- Swing Mode: Uses the Exponential Moving Average (EMA), which assigns greater weight to recent prices, thus balancing quick reaction with smoothness.
- Position Mode: Applies the Weighted Moving Average (WMA) to focus on longer-term trends by emphasizing the entire lookback period and reducing the impact of short-term volatility.
2. Momentum Tracking
The indicator maintains separate counters for bullish and bearish momentum. These counters increase as the trend confirms directional movement and reset when the trend reverses. When a counter exceeds the defined signal strength threshold, a corresponding signal (buy or sell) is triggered.
3. Volatility and ATR Zones
The Average True Range (ATR) is calculated using a period that adapts to the selected trading mode (shorter for Scalp, longer for Position). The ATR value is then used to define upper and lower zones around the trend line, highlighting the current level of market volatility.
4. Return and Trend Acceleration
- Return is calculated as the difference between the current and previous closing prices, providing a simple measure of price change.
- Trend Acceleration is derived from the change in the trend line’s movement (its first derivative) compared to the previous bar. This metric indicates whether the trend is gaining or losing momentum.
5. Sortino Ratio and Standard Deviation
- The Sortino Ratio measures risk-adjusted performance by comparing returns to downside volatility (only considering negative price changes).
- Standard Deviation is computed over the lookback period to assess the extent of price fluctuations, offering insights into market stability.
Usage
This indicator is suitable for various time frames and market instruments. Traders can enable or disable specific visual elements such as gradient fills, bar coloring, and signal markers based on their preference. For a minimalist approach, one might choose to display only the primary trend line. For a deeper analysis, enabling multiple tables can provide extensive data on momentum, volatility, trend dynamics, and risk metrics.
Important Note on Risk
Trading involves inherent risk, and no indicator can eliminate the uncertainty of the markets. Past performance is not indicative of future results. It is essential to use proper risk management, test any new tool thoroughly, and consult multiple sources or professional advice before making trading decisions.
Conclusion
Uptrick: Alpha Trend unifies a diverse set of calculations, adaptive smoothing techniques, and customizable visual elements into one powerful tool. By combining the Hull, Exponential, and Weighted Moving Averages, the indicator is able to provide a trend line that is both responsive and smooth, depending on the trading mode. Its advanced color schemes, gradient fills, and detailed data tables deliver a comprehensive analysis of market trends, momentum, and risk. Whether you are a short-term trader or a long-term investor, this indicator aims to clarify price action and assist you in making more informed trading decisions.
DataDoodles SD + ProbabilityDataDoodles SD + Probability
Overview:
The “DataDoodles SD + Probability” indicator is designed to provide traders with a statistical edge by leveraging standard deviation and probability metrics. This advanced tool calculates the annualized standard deviation, Z-score, and probability of price movements, offering insights into potential market direction with customizable alert thresholds.
Key Features:
1. Annualized Standard Deviation (Volatility) Calculation:
• Uses a user-defined period to compute the rolling standard deviation of daily returns.
• Annualizes the volatility, giving a clear picture of expected price fluctuations.
2. Probability of Price Movement:
• Calculates the probability of price moving up or down using a corrected Z-Score.
• Displays the probability percentage for both upward and downward movements.
3. Dynamic Alerts:
• Configurable alerts for upward and downward price movement probabilities.
• Receive alerts when the probability exceeds user-defined thresholds.
4. Projections and Visuals:
• Plots projected high and low price levels based on annualized volatility.
• Displays Z-Score and probability metrics on the chart for quick reference.
5. Comprehensive Data Table:
• Bottom-center table displays key metrics:
• Daily Return
• Standard Deviation (SD)
• Annualized Standard Deviation (Yearly SD)
User Inputs:
• Annualization Period: Set the time frame for volatility annualization (Default: 252 days).
• SD Period: Define the rolling window for calculating standard deviation (Default: 252 days).
• Alert Probability Up/Down: Customize the probability thresholds for alerts (Default: 90%).
How It Works:
• Data Request and Calculation:
• Uses daily close prices to ensure consistent timeframe calculations.
• Calculates daily returns and annualizes the volatility using the square root of the time frame.
• Probability Computation:
• Employs a normal distribution CDF approximation to compute the probability of upward and downward price movements.
• Adjusts probabilities based on Z-Score to ensure accuracy.
• High and Low Projections:
• Utilizes the annualized volatility to estimate high and low price projections for the year.
• Visual Indicators and Alerts:
• Plots projected high (green) and low (red) levels on the chart.
• Displays Z-Score, probability percentages, and dynamically updates a statistics table.
Use Cases:
• Trend Analysis: Identify high-probability market movements using the probability metrics.
• Volatility Insights: Understand annualized volatility to gauge market risk and potential price ranges.
• Strategic Trading Decisions: Set alerts for high-probability scenarios to optimize entry and exit points.
Why Use “DataDoodles SD + Probability”?
This indicator provides a powerful combination of statistical analysis and visual representation. It empowers traders with:
• Quantitative Edge: By leveraging probability metrics and standard deviation, users can make informed trading decisions.
• Risk Management: Annualized volatility projections help in setting realistic stop-loss and take-profit levels.
• Actionable Alerts: Customizable probability alerts ensure users are notified of potential market moves, allowing proactive trading strategies.
Recommended Settings:
• Annualization Period: 252 (Ideal for daily data representing a trading year)
• SD Period: 252 (One trading year for consistent volatility calculations)
• Alert Probability: Set to 90% for conservative signals or lower for more frequent alerts.
Final Thoughts:
The “DataDoodles SD + Probability” indicator is a robust tool for traders looking to integrate statistical analysis into their trading strategies. It combines volatility measurement, probability calculations, and dynamic alerts to provide a comprehensive market overview.
Whether you’re a day trader or a long-term investor, this indicator can enhance your market insight and improve decision-making accuracy.
Disclaimer:
This indicator is a technical analysis tool designed for educational purposes. Past performance is not indicative of future results. Traders are encouraged to perform their own analysis and manage risk accordingly.
