Tìm kiếm tập lệnh với "创业板+etf"
Fetti Fields Header (Presets)This is for individuals that like to customize their charts and add some style and motivation
30m stratDefine a time range, and the indicator will highlight it with a shaded area
This indicator lets you visualize higher timeframe levels while viewing a lower timeframe chart.
Whole number highlightsThis very simple indicator provides what should be a built-in TradingView feature: it highlights the whole number currency amounts (dollar, pound, euro, etc.) on your chart with a simple and unobtrusive dotted line. By default, a slightly thicker dotted line is used on whole number multiples of ten.
These are important to highlight because they often act as "psychological levels" in the marketplace, especially when they coincide with more significant levels of support or resistance. They can also help provide a sense of scale to the chart, which is useful when switching between various zoom levels.
It's open-source, so it can be easily combined into other indicators, which should especially be useful for those with limited plans who are allowed a limited number of indicators.
Disclaimer : Use at your own risk. This indicator and the strategy described herein are not in any way financial advice, nor does the author of this script make any claims about the effectiveness of this indicator or of any related strategy, which may depend highly on the discretion and skill of the trader executing it, among many other factors outside of the author's control. The author of this script accepts no liability, and is not responsible for any trading decisions that you may or may not make as a result of this indicator. You should expect to lose money if using this indicator.
TA█ TA Library
📊 OVERVIEW
TA is a Pine Script technical analysis library. This library provides 25+ moving averages and smoothing filters , from classic SMA/EMA to Kalman Filters and adaptive algorithms, implemented based on academic research.
🎯 Core Features
Academic Based - Algorithms follow original papers and formulas
Performance Optimized - Pre-calculated constants for faster response
Unified Interface - Consistent function design
Research Based - Integrates technical analysis research
🎯 CONCEPTS
Library Design Philosophy
This technical analysis library focuses on providing:
Academic Foundation
Algorithms based on published research papers and academic standards
Implementations that follow original mathematical formulations
Clear documentation with research references
Developer Experience
Unified interface design for consistent usage patterns
Pre-calculated constants for optimal performance
Comprehensive function collection to reduce development time
Single import statement for immediate access to all functions
Each indicator encapsulated as a simple function call - one line of code simplifies complexity
Technical Excellence
25+ carefully implemented moving averages and filters
Support for advanced algorithms like Kalman Filter and MAMA/FAMA
Optimized code structure for maintainability and reliability
Regular updates incorporating latest research developments
🚀 USING THIS LIBRARY
Import Library
//@version=6
import DCAUT/TA/1 as dta
indicator("Advanced Technical Analysis", overlay=true)
Basic Usage Example
// Classic moving average combination
ema20 = ta.ema(close, 20)
kama20 = dta.kama(close, 20)
plot(ema20, "EMA20", color.red, 2)
plot(kama20, "KAMA20", color.green, 2)
Advanced Trading System
// Adaptive moving average system
kama = dta.kama(close, 20, 2, 30)
= dta.mamaFama(close, 0.5, 0.05)
// Trend confirmation and entry signals
bullTrend = kama > kama and mamaValue > famaValue
bearTrend = kama < kama and mamaValue < famaValue
longSignal = ta.crossover(close, kama) and bullTrend
shortSignal = ta.crossunder(close, kama) and bearTrend
plot(kama, "KAMA", color.blue, 3)
plot(mamaValue, "MAMA", color.orange, 2)
plot(famaValue, "FAMA", color.purple, 2)
plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green)
plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red)
📋 FUNCTIONS REFERENCE
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float) : Series of values to process.
alpha (series float) : The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag. Developed by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio. Developed by Perry J. Kaufman.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the efficiency calculation.
fastLength (simple int) : Fast EMA length for smoothing calculation. Optional. Default is 2.
slowLength (simple int) : Slow EMA length for smoothing calculation. Optional. Default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics. Developed by Tim Tillson.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
volumeFactor (simple float) : Volume factor affecting responsiveness. Optional. Default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness. Based on digital signal processing principles by John Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations. Based on the Kalman Filter algorithm developed by Rudolf Kalman (1960).
Parameters:
source (series float) : Series of values to process.
processNoise (simple float) : Process noise variance (Q). Controls adaptation speed. Optional. Default is 0.05.
measurementNoise (simple float) : Measurement noise variance (R). Controls smoothing. Optional. Default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes. Developed by John R. McGinley Jr.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float) : Series of values to process.
fastLimit (simple float) : Maximum alpha (responsiveness). Optional. Default is 0.5.
slowLimit (simple float) : Minimum alpha (smoothing). Optional. Default is 0.05.
Returns: ( ) Tuple containing values.
laguerreFilter(source, length, gamma, order)
Calculates the standard N-order Laguerre Filter of a given data series. Standard Laguerre Filter uses uniform weighting across all polynomial terms. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Higher order increases lag. Optional. Default is 8.
Returns: (float) The calculated standard Laguerre Filter value.
laguerreBinomialFilter(source, length, gamma)
Calculates the Laguerre Binomial Filter of a given data series. Uses 6-pole feedback with binomial weighting coefficients. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Length for UltimateSmoother preprocessing.
gamma (simple float) : Feedback coefficient (0-1). Lower values reduce lag. Optional. Default is 0.5.
Returns: (float) The calculated Laguerre Binomial Filter value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology. Developed by John F. Ehlers.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Period for the filter calculation.
Returns: (float) The calculated Super Smoother value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the average range calculation.
multiplier (simple float) : Multiplier for the smooth range. Higher values increase filtering. Optional. Default is 2.618.
Returns: ( ) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals. Developed by Igor Livshin.
Parameters:
source (series float) : Series of values to process.
rsiLength (simple int) : Number of bars for the RSI calculation. Optional. Default is 14.
rsiSmooth (simple int) : Number of bars for smoothing the RSI. Optional. Default is 5.
qqeFactor (simple float) : QQE factor for volatility band width. Optional. Default is 4.236.
Returns: ( ) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
Returns: ( ) Tuple containing SSL Up and SSL Down lines.
ma(source, length, maType)
Calculates a Moving Average based on the specified type. Universal interface supporting all moving average algorithms.
Parameters:
source (series float) : Series of values to process.
length (simple int) : Number of bars for the moving average calculation.
maType (simple MaType) : Type of moving average to calculate. Optional. Default is SMA.
Returns: (float) The calculated moving average value based on the specified type.
atr(length, maType)
Calculates the Average True Range (ATR) using the specified moving average type. Developed by J. Welles Wilder Jr.
Parameters:
length (simple int) : Number of bars for the ATR calculation.
maType (simple MaType) : Type of moving average to use for smoothing. Optional. Default is RMA.
Returns: (float) The calculated Average True Range value.
macd(source, fastLength, slowLength, signalLength, maType, signalMaType)
Calculates the Moving Average Convergence Divergence (MACD) with customizable MA types. Developed by Gerald Appel.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
signalLength (simple int) : Period for the signal line moving average.
maType (simple MaType) : Type of moving average for main MACD calculation. Optional. Default is EMA.
signalMaType (simple MaType) : Type of moving average for signal line calculation. Optional. Default is EMA.
Returns: ( ) Tuple containing MACD line, signal line, and histogram values.
dmao(source, fastLength, slowLength, maType)
Calculates the Dual Moving Average Oscillator (DMAO) of a given data series. Uses the same algorithm as the Percentage Price Oscillator (PPO), but can be applied to any data series.
Parameters:
source (series float) : Series of values to process.
fastLength (simple int) : Period for the fast moving average.
slowLength (simple int) : Period for the slow moving average.
maType (simple MaType) : Type of moving average to use for both calculations. Optional. Default is EMA.
Returns: (float) The calculated Dual Moving Average Oscillator value as a percentage.
continuationIndex(source, length, gamma, order)
Calculates the Continuation Index of a given data series. The index represents the Inverse Fisher Transform of the normalized difference between an UltimateSmoother and an N-order Laguerre filter. Developed by John F. Ehlers, published in TASC 2025.09.
Parameters:
source (series float) : Series of values to process.
length (simple int) : The calculation length.
gamma (simple float) : Controls the phase response of the Laguerre filter. Optional. Default is 0.8.
order (simple int) : The order of the Laguerre filter (1-10). Optional. Default is 8.
Returns: (float) The calculated Continuation Index value.
📚 RELEASE NOTES
v1.0 (2025.09.24)
✅ 25+ technical analysis functions
✅ Complete adaptive moving average series (KAMA, FRAMA, MAMA/FAMA)
✅ Advanced signal processing filters (Kalman, Laguerre, SuperSmoother, UltimateSmoother)
✅ Performance optimized with pre-calculated constants and efficient algorithms
✅ Unified function interface design following TradingView best practices
✅ Comprehensive moving average collection (DEMA, TEMA, ZLEMA, T3, etc.)
✅ Volatility and trend detection tools (QQE, SSL Channel, Range Filter)
✅ Continuation Index - Latest research from TASC 2025.09
✅ MACD and ATR calculations supporting multiple moving average types
✅ Dual Moving Average Oscillator (DMAO) for arbitrary data series analysis
Multi Momentum 10/21/42/63 — Histogram + 2xSMAMY MM INDICATOR INDIRED BY KARADI
It averages four rate-of-change snapshots of price, all anchored at today’s close.
If “Show as %” is on, the value is multiplied by 100.
Each term is a simple momentum/ROC over a different lookback.
Combining 10, 21, 42, 63 bars blends short, medium, and intermediate horizons into one number.
Positive MM → average upward pressure across those horizons; negative MM → average downward pressure.
Why those lengths?
They roughly stack into ~2× progression (10→21≈2×10, 21→42=2×21, 63≈1.5×42). That creates a “multi-scale” momentum that’s less noisy than a single fast ROC but more responsive than a long ROC alone.
How to read the panel
Gray histogram = raw Multi-Momentum value each bar.
SMA Fast/Slow lines (defaults 12 & 26 over the MM values) = smoothing of the histogram to show the trend of momentum itself.
Typical signals
Zero-line context:
Above 0 → bullish momentum regime on average.
Below 0 → bearish regime.
Crosses of SMA Fast & Slow: momentum trend shifts (fast above slow = improving momentum; fast below slow = deteriorating).
Histogram vs SMA lines: widening distance suggests strengthening momentum; narrowing suggests momentum is fading.
Divergences: price makes a new high/low but MM doesn’t → potential exhaustion.
Compared to a classic ROC
A single ROC(20) is very sensitive to that one window.
MM averages several windows, smoothing idiosyncrasies (e.g., a one-off spike 21 bars ago) and reducing “lookback luck.”
Settings & customization
Lookbacks (10/21/42/63): you can tweak for your asset/timeframe; the idea is to mix short→medium horizons.
Percent vs raw ratio: percent is easier to compare across symbols.
SMA lengths: shorter = more reactive but choppier; longer = smoother but slower.
Practical tips
Use regime + signal: trade longs primarily when MM>0 and fast SMA>slow SMA; consider shorts when MM<0 and fast
Alerta de toque de la 200-Week SMACuando el precio toca la MMS de 200 semanas es una posible compra.
QTrade Golden, Bronze & Death, Bubonic Cross AlertsThis indicator highlights key EMA regime shifts with simple, color-coded triangles:
- Golden / Death Cross — 50 EMA crossing above/below the 200 EMA.
- Bronze / Bubonic Cross — 50 EMA crossing above/below the 100 EMA.
- Early-Warning Proxy — tiny triangles for the 4 EMA vs. 200 EMA (4↑200 and 4↓200). These often fire before the 50/100 and 50/200 crosses.
No text clutter on the chart—just triangles. Colors: gold (50↑200), red (50↓200), darker-yellow bronze (50↑100), burgundy (50↓100), turquoise (4↑200), purple (4↓200).
What it tells you (in order of warning → confirmation)
- First warning: 4 EMA crosses the 200 EMA (proxy for price shifting around the 200 line).
- Second warning: 50 EMA crosses the 100 EMA (Bronze/Bubonic).
- Confirmation: 50 EMA crosses the 200 EMA (Golden/Death).
Alerts included
- Golden Cross (50↑200) and Death Cross (50↓200)
- Bronze Cross (50↑100) and Bubonic Cross (50↓100)
- 4 EMA vs. 200 EMA crosses (up & down) — early-warning proxy
- Price–100 EMA events (touch/cross, if enabled in settings)
NASDAQ VWAP Distance Histogram (Multi-Symbol)📊 VWAP Distance Histogram (Multi-Symbol)
This custom indicator plots a histogram of price strength relative to the VWAP (Volume-Weighted Average Price).
The zero line is VWAP.
Histogram bars above zero = price trading above VWAP (strength).
Histogram bars below zero = price trading below VWAP (weakness).
Unlike a standard VWAP overlay, this tool lets you monitor multiple symbols at once and aggregates them into a single, easy-to-read histogram.
🔑 Features
Multi-Symbol Support → Track up to 10 different tickers plus the chart symbol.
Aggregation Options → Choose between average or median deviation across enabled symbols.
Percent or Raw Values → Display distance from VWAP as % of price or raw price points.
Smoothing → Apply EMA smoothing to calm intraday noise.
Color-Coded Histogram → Green above VWAP, red below.
Alerts → Trigger when the aggregate crosses above/below VWAP.
Heads-Up Table → Shows number of symbols tracked and current aggregate reading.
⚡ Use Cases
Market Breadth via VWAP → Monitor whether your basket of stocks is trading above or below VWAP.
Index Substitution → Create your own “mini index” by tracking a hand-picked set of tickers.
Intraday Confirmation → Use aggregate VWAP strength/weakness to confirm entries and exits.
Relative Strength Spotting → Switch on/off specific tickers to see who’s holding above VWAP vs. breaking down.
🛠️ Settings
Include Chart Symbol → Toggle to include the current chart’s ticker.
Smoothing → EMA length (set to 0 to disable).
Percent Mode → Show results as % of price vs. raw difference.
Aggregate Mode → Average or median across all active symbols.
Symbol Slots (S1–S10) → Enter tickers to track alongside the chart.
⚠️ Notes
Works best on intraday charts since VWAP is session-based.
Designed for confirmation, not as a standalone entry/exit signal.
Ensure correct symbol format (e.g., NASDAQ:AAPL if needed).
✅ Tip: Combine this with your regular price action strategy. For example, if your setup triggers long and the histogram is well above zero, that’s added confirmation. If it’s below zero, caution — the basket shows weakness.
Moving Average SlopeA simple tool that allows you to choose from multiple types of moving averages (e.g. WMA, EMA, SMA, HMA) and define the MA period, and lookback period for slope calculation.
Williams Fractals BW - Flechas + Breakoutsfractal con velas en la direccion hacia donde va para menos conficion
MTF Stochastic Range FinderThis indicator compares Stochastic RSI from 2 timeframes to signal possible reversals. Default 5 minute and 2 minute. Both Stochastic RSIs are customizable.
Allows for 6 support/resistance lines and allows a tolerance to filter proximity to levels for entry.
Can filter price level by manual support/resistance levels and/or VWAP
Dual EMA (EMA Average)Simple EMA visual with 2 EMAs + Average
Includes:
2 EMAs with timeframe settings
EMA Midline - an average of EMAs
shaded space between EMA lines
Ripster EMA Cloud A+ Setup + Exit Plan + Buy/Sell TriggersUsing the Ripster Cloud Method, a conditional system that helps with entries and exits
VIM (Volume in Money)Volume in Money + MA (Short Numbers & Coloring)
This indicator visualizes the monetary volume traded in each candle, calculated as:
Money Volume
=
Volume
×
Close Price
Money Volume=Volume×Close Price
🔹 Features:
Plots bars representing the money volume (total traded value).
Coloring options:
• Prev Close → Green if the current close is higher than the previous close, Red if lower.
• Candle Direction → Green if the candle is bullish (close > open), Red if bearish (close < open).
Moving Average (default length: 14) applied on the money volume for trend analysis.
Axis values and labels displayed in a shortened format (K, M, B, T) for readability.
📊 This helps traders quickly identify whether large amounts of money flowed into or out of the asset, making it easier to detect unusual activity compared to regular volume indicators.
Opening 15-Minute Range This triggers after the third 5-minute bar from the session open
Works on any intraday timeframe (1m, 2m, 5m, etc.).
Pre‑Market Cumulative VolumeDescription:
This indicator plots the cumulative trading volume for the pre‑market session on intraday charts. It automatically detects when a bar is part of the extended pre‑market period using TradingView’s built‑in session variables, starts a new running total at the first pre‑market bar of each day, and resets at the beginning of regular trading hours. During regular market hours or the post‑market session, the indicator does not display values.
To use this script effectively, ensure extended‑hour data is enabled on the chart, and select an intraday timeframe where pre‑market data is available. The result is a simple yet powerful tool for monitoring cumulative pre‑market activity.
How to use
Add the script to a chart and make sure you are on an intraday timeframe (e.g., 1‑min, 5‑min). Extended‑hour data must be enabled; otherwise session.ispremarket will always be false.
During each pre‑market session, the indicator will reset at the first pre‑market bar and then accumulate the volume of subsequent pre‑market bars.
Outside the pre‑market (regular trading hours and post‑market), the plot outputs na, so it does not draw on those bars.
Customization (optional)
If you want to define your own pre‑market times instead of relying on TradingView’s built‑in session, you can replace the isPreMarket line with a time‑range check. For example, isPreMarket = not na(time(timeframe.period, "0400-0930")) detects bars between 04:00 and 09:30 (U.S. Eastern time). You can parameterize the session string with input.session("0400-0930", "Pre‑Market Session") to let users adjust it.
Pre‑Market Cumulative VolumeDescription:
This indicator plots the cumulative trading volume for the pre‑market session on intraday charts. It automatically detects when a bar is part of the extended pre‑market period using TradingView’s built‑in session variables, starts a new running total at the first pre‑market bar of each day, and resets at the beginning of regular trading hours. During regular market hours or the post‑market session, the indicator does not display values.
To use this script effectively, ensure extended‑hour data is enabled on the chart, and select an intraday timeframe where pre‑market data is available. The result is a simple yet powerful tool for monitoring cumulative pre‑market activity.
How to use
Add the script to a chart and make sure you are on an intraday timeframe (e.g., 1‑min, 5‑min). Extended‑hour data must be enabled; otherwise session.ispremarket will always be false.
During each pre‑market session, the indicator will reset at the first pre‑market bar and then accumulate the volume of subsequent pre‑market bars.
Outside the pre‑market (regular trading hours and post‑market), the plot outputs na, so it does not draw on those bars.
Customization (optional)
If you want to define your own pre‑market times instead of relying on TradingView’s built‑in session, you can replace the isPreMarket line with a time‑range check. For example, isPreMarket = not na(time(timeframe.period, "0400-0930")) detects bars between 04:00 and 09:30 (U.S. Eastern time). You can parameterize the session string with input.session("0400-0930", "Pre‑Market Session") to let users adjust it.
