Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0โ100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, itโs a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. Itโs a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency componentsยน.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
---
Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
Cari dalam skrip untuk "Exponential Moving Average"
Trend_Trader_WMA (Momentum)<---> Caution! This is first test version of indicator. I am ready to get more ideas+feedback to develop it more. <--->
The "Momentum_Trader_WMA" indicator is a versatile technical analysis tool designed to help traders identify potential trend changes and momentum shifts in the market. It combines multiple indicators and moving averages to provide a comprehensive view of price action and momentum.
Key Features:
Weighted Moving Averages (WMAs): The indicator calculates two different WMAs with user-defined lengths, providing a smoothed representation of price data.
Average True Range (ATR) Bands: ATR is used to calculate dynamic bands around the WMA Average. These bands can help traders gauge market volatility and potential breakout points. The color of the ATR bands can be seen as an early signal of trends or the continuation of current trends.
Commodity Channel Index (CCI): CCI is a momentum oscillator that measures the relative strength of price changes. The indicator calculates CCI values based on a user-defined period.
Exponential Moving Average (EMA) of CCI: An EMA of CCI is plotted to help identify trends and momentum shifts.
Color-Coded Bands: The ATR bands change colors based on CCI conditions, providing visual cues for potential trading opportunities. When ATR bands transition from narrow (indicating low volatility) to wide (indicating increased volatility), it can be seen as an early signal of a potential trend change or the continuation of the current trend.
Buy and Sell Signals: The indicator generates buy and sell signals based on crossovers of WMAs and CCI thresholds, making it easier for traders to identify entry and exit points.
Customizable Moving Averages: Traders can enable or disable different moving averages (e.g., SMA, EMA, WMA, RMA, VWMA, HMA) with various periods and colors to adapt the indicator to their trading preferences.
CCI Dot Alerts: Dots are displayed at the bottom of the chart based on CCI values, helping traders spot extreme CCI conditions.
How to Use:
Trend Identification: The WMAs and ATR bands can help identify the current trend direction and its strength. When the WMAs are in an uptrend (green) and the ATR bands widen, it may indicate a strong bullish trend. Conversely, when the WMAs are in a downtrend (red) and the ATR bands narrow, it may suggest a weakening bearish trend.
Momentum Confirmation: The CCI and its EMA provide insights into market momentum. Look for CCI crossovers above 100 for potential bullish momentum and below -100 for potential bearish momentum.
Buy and Sell Signals: Pay attention to the buy and sell signals generated by the indicator. Buy when the WMAs cross over and CCI crosses above 100. Sell when the WMAs cross under and CCI crosses below -100.
ATR Bands as Early Signals: The color changes in the ATR bands can be seen as early signals of trends or the continuation of current trends. Wide ATR bands may indicate increased volatility and potential trend changes, while narrow ATR bands suggest reduced volatility and potential trend continuation.
Moving Averages: Customize the indicator by enabling or disabling specific moving averages according to your preferred trading strategy.
CCI Dots: Use the CCI dots to identify extreme CCI conditions, which may indicate overbought or oversold market conditions.
PS:
Recommended to use Indicator with price action conecpts(eg. support and resistance) as they play important role in any market.
Buy and sell signals are not really accurate. I would personally look for trend shift in WMA middle line and confirmation from CCI dots at bottom. For example. If middle line turns green and within recent 3-4 candles (or next 3-4 candles) dots tunrns green also, that means momentum has been rised in the direction of bulls.
pls, take s/r concepts first when working. I am thinking to add more precise buy sell signal method to make it easier to trade.
Good luck with your trades :)
WaveTrend 3Dโโ OVERVIEW
WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm.
โโ BACKGROUND
The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first ported to PineScript in 2014 by the user @LazyBear, and since then, it has ascended to become one of the Top 5 most popular scripts on TradingView.
The WT algorithm appears to have origins in a lesser-known proprietary algorithm called Trading Channel Index (TCI), created by AIQ Systems in 1986 as an integral part of their commercial software suite, TradingExpert Pro. The softwareโs reference manual states that โTCI identifies changes in price directionโ and is โan adaptation of Donald R. Lambertโs Commodity Channel Index (CCI)โ, which was introduced to the world six years earlier in 1980. Interestingly, a vestige of this early beginning can still be seen in the source code of LazyBearโs script, where the final EMA calculation is stored in an intermediate variable called โtciโ in the code.
โโ IMPLEMENTATION DETAILS
WaveTrend 3D is an alternative implementation of WaveTrend that directly addresses some of the known shortcomings of the indicator, including its unbounded extremes, susceptibility to whipsaw, and lack of insight into other timeframes.
In the canonical WT approach, an exponential moving average (EMA) for a given lookback window is used to assess the variability between price and two other EMAs relative to a second lookback window. Since the difference between the average price and its associated EMA is essentially unbounded, an arbitrary scaling factor of 0.015 is typically applied as a crude form of rescaling but still fails to capture 20-30% of values between the range of -100 to 100. Additionally, the trigger signal for the final EMA (i.e., TCI) crossover-based oscillator is a four-bar simple moving average (SMA), which further contributes to the net lag accumulated by the consecutive EMA calculations in the previous steps.
The core idea behind WT3D is to replace the EMA-based crossover system with modern Digital Signal Processing techniques. By assuming that price action adheres approximately to a Gaussian distribution, it is possible to sidestep the scaling nightmare associated with unbounded price differentials of the original WaveTrend method by focusing instead on the alteration of the underlying Probability Distribution Function (PDF) of the input series. Furthermore, using a signal processing filter such as a Butterworth Filter, we can eliminate the need for consecutive exponential moving averages along with the associated lag they bring.
Ideally, it is convenient to have the resulting probability distribution oscillate between the values of -1 and 1, with the zero line serving as a median. With this objective in mind, it is possible to borrow a common technique from the field of Machine Learning that uses a sigmoid-like activation function to transform our data set of interest. One such function is the hyperbolic tangent function (tanh), which is often used as an activation function in the hidden layers of neural networks due to its unique property of ensuring the values stay between -1 and 1. By taking the first-order derivative of our input series and normalizing it using the quadratic mean, the tanh function performs a high-quality redistribution of the input signal into the desired range of -1 to 1. Finally, using a dual-pole filter such as the Butterworth Filter popularized by John Ehlers, excessive market noise can be filtered out, leaving behind a crisp moving average with minimal lag.
Furthermore, WT3D expands upon the original functionality of WT by providing:
First-class support for multi-timeframe (MTF) analysis
Kernel-based regression for trend reversal confirmation
Various options for signal smoothing and transformation
A unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
โโ SETTINGS
This is a summary of the settings used in the script listed in roughly the order in which they appear. By default, all default colors are from Google's TensorFlow framework and are considered to be colorblind safe.
Source: The input series. Usually, it is the close or average price, but it can be any series.
Use Mirror: Whether to display a mirror image of the source series; for visualizing the series as a 3D waveform similar to a soundwave.
Use EMA: Whether to use an exponential moving average of the input series.
EMA Length: The length of the exponential moving average.
Use COG: Whether to use the center of gravity of the input series.
COG Length: The length of the center of gravity.
Speed to Emphasize: The target speed to emphasize.
Width: The width of the emphasized line.
Display Kernel Moving Average: Whether to display the kernel moving average of the signal. Like PCA, an unsupervised Machine Learning technique whereby neighboring vectors are projected onto the Principal Component.
Display Kernel Signal: Whether to display the kernel estimator for the emphasized line. Like the Kernel MA, it can show underlying shifts in bias within a more significant trend by the colors reflected on the ribbon itself.
Show Oscillator Lines: Whether to show the oscillator lines.
Offset: The offset of the emphasized oscillator plots.
Fast Length: The length scale factor for the fast oscillator.
Fast Smoothing: The smoothing scale factor for the fast oscillator.
Normal Length: The length scale factor for the normal oscillator.
Normal Smoothing: The smoothing scale factor for the normal frequency.
Slow Length: The length scale factor for the slow oscillator.
Slow Smoothing: The smoothing scale factor for the slow frequency.
Divergence Threshold: The number of bars for the divergence to be considered significant.
Trigger Wave Percent Size: How big the current wave should be relative to the previous wave.
Background Area Transparency Factor: Transparency factor for the background area.
Foreground Area Transparency Factor: Transparency factor for the foreground area.
Background Line Transparency Factor: Transparency factor for the background line.
Foreground Line Transparency Factor: Transparency factor for the foreground line.
Custom Transparency: Transparency of the custom colors.
Total Gradient Steps: The maximum amount of steps supported for a gradient calculation is 256.
Fast Bullish Color: The color of the fast bullish line.
Normal Bullish Color: The color of the normal bullish line.
Slow Bullish Color: The color of the slow bullish line.
Fast Bearish Color: The color of the fast bearish line.
Normal Bearish Color: The color of the normal bearish line.
Slow Bearish Color: The color of the slow bearish line.
Bullish Divergence Signals: The color of the bullish divergence signals.
Bearish Divergence Signals: The color of the bearish divergence signals.
โโ ACKNOWLEDGEMENTS
@LazyBear - For authoring the original WaveTrend port on TradingView
@PineCoders - For the beautiful color gradient framework used in this indicator
@veryfid - For the inspiration of using mirrored signals for cycle analysis and using multiple lookback windows as proxies for other timeframes
Trend Thrust Indicator - RafkaThis indicator defines the impact of volume on the volume-weighted moving average, emphasizing trends with greater volume.
What determines a securityโs value? Price is the agreement to exchange despite the possible disagreement in value. Price is the conviction, emotion, and volition of investors. It is not a constant but is influenced by information, opinions, and emotions over time. Volume represents this degree of conviction and is the embodiment of information and opinions flowing through investor channels. It is the asymmetry between the volume being forced through supply (offers) and demand (bids) that facilitates price change. Quantifying the extent of asymmetry between price trends and the corresponding volume flows is a primary objective of volume analysis. Volume analysis research reveals that volume often leads price but may also be used to confirm the present price trend.
Trend thrust indicator
The trend thrust indicator (TTI), an enhanced version of the volume-weighted moving average convergence/divergence (VW-Macd) indicator, was introduced in Buff Pelz Dormeier's book 'Investing With Volume Analysis'. The TTI uses a volume multiplier in unique ways to exaggerate the impact of volume on volume-weighted moving averages. Like the VW-Macd, the TTI uses volume-weighted moving averages as opposed to exponential moving averages. Volume-weighted averages weigh closing prices proportionally to the volume traded during each time period, so the TTI gives greater emphasis to those price trends with greater volume and less emphasis to time periods with lighter volume. In the February 2001 issue of Stocks & Commodities, I showed that volume-weighted moving averages (Buff averages, or Vwmas) improve responsiveness while increasing reliability of simple moving averages.
Like the Macd and VW-Macd, the TTI calculates a spread by subtracting the short (fast) average from the long (slow) average. This spread combined with a volume multiplier creates the Buff spread
eha MA CrossIn the study of time series, and specifically technical analysis of the stock market, a moving-average cross occurs when, the traces of plotting of two moving averages each based on different degrees of smoothing cross each other. Although it does not predict future direction but at least shows trends.
This indicator uses two moving averages, a slower moving average and a faster-moving average. The faster moving average is a short term moving average. A short term moving average is faster because it only considers prices over a short period of time and is thus more reactive to daily price changes.
On the other hand, a long term moving average is deemed slower as it encapsulates prices over a longer period and is more passive. However, it tends to smooth out price noises which are often reflected in short term moving averages.
There are a bunch of parameters that you can set on this indicator based on your needs.
Moving Averages Algorithm
You can choose between three types provided of Algorithms
Simple Moving Average
Exponential Moving Average
Weighted Moving Average
I will update this study with more educational materials in the near future so be informed by following the study and let me know what you think about it.
Please hit the like button if this study is useful for you.
Trend Volume Accumulation R1 by JustUncleLThis simple indicator shows the Accumulated Volume within the current uptrend or downtrend. The uptrend/downtrend is detected by a change in direction of the candles which works very well with Heikin Ashi and Renko charts. Alternatively you can use a Moving average direction to indicate trend direction, which should work on any candle type.
You can select between 11 different types of moving average:
SMA = Simple Moving Average.
EMA = Exponential Moving Average.
WMA = Weighted Moving Average
VWMA = Volume Weighted Moving Average
SMMA = Smoothed Simple Moving Average.
DEMA = Double Exponential Moving Average
TEMA = Triple Exponential Moving Average.
HullMA = Hull Moving Average
SSMA = Ehlers Super Smoother Moving average
ZEMA = Near Zero Lag Exponential Moving Average.
TMA = Triangular (smoothed) Simple Moving Average.
Here is a sample chart using EMA length 6 for trend Direction:
GC Magic(EMA/RMA) V1This is the second script I am posting on TV . This is a Trend based indicator with the option of using it as Exponential Moving Averages or Rsi Moving Averages.The RMA's are giving better signal than the Exponential Moving Averages. The script has the option to select either of them. Works on all time frames. The default options are working good on all time frames.
With the help of Indicator Properties following Options can be changed
a. Type of moving averages for using Guppy method
b. Option to use higher time frame Signal moving average of your choice along with higher time frame
c. Enable or disable to show signal EMA/RMA on chart .
d. Enable or disable to show Guppy EMA/RMA on chart
Indicator Properties:
1. Select to use EMA , Uncheck to use RMA: --> Check to Select EMA based Guppy or Uncheck to use RMA based Guppy
2. Fast EMA/RMA For Cross --> Fast EMA/RMA cross Length
3. Slow EMA/RMA For Cross --> Slow EMA/RMA Length
4. Signal EMA/RMA --> Moving average to use for Signal filters. This moving average will be based on the timeframe u will be selecting below
5. Time interval for Signal EMA/RMA (W, D, ) --> Which time frame moving average you want for the above Signal EMA
6. Do you want to use Signal EMA/RMA for Signals? --> Do you want to use Signal EMA as filter or just the cross of Guppy . Check to use and uncheck for just cross
7. Show Signal EMA on Chart? --> Do you want to display higher timeframe Signal EMA on chart
8. Show Guppy-Slow-Red On Chart? --> Shows/Hides Slow EMA/RMAs
9. Show Guppy-Fast-Green On Chart? --> Shows/Hides Fast EMA/RMAs
Examples:
GbpAud 15m
GbpNzd 1hr
Oil 4hr
AudUSD 1hr
MACD Enhanced [DCAUT]โ MACD Enhanced
๐ ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
๐ MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
๐ COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
๐ฏ STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45ยฐ) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
๐ DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
๐ PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
Combined EMA/Smiley & DEM System## ๐ท General Overview
This script creates an advanced technical analysis system for TradingView, combining multiple Exponential Moving Averages (EMAs), Simple Moving Averages (SMAs), dynamic Fibonacci levels, and ATR (Average True Range) analysis. It presents the results clearly through interactive, real-time tables directly on the chart.
---
## ๐น Indicator Structure
The script consists of two main parts:
### **1. EMA & SMA Combined System with Fibonacci**
- **Purpose:**
Provides visual insights by comparing multiple EMA/SMA periods and identifying significant dynamic price levels using Fibonacci ratios around a calculated "Golden" line.
- **Components:**
- **Moving Averages (MAs)**:
- 20 EMAs (periods from 20 to 400)
- 20 SMAs (also from 20 to 400)
- **Golden Line:**
Calculated as the average of all EMAs and SMAs.
- **Dynamic Fibonacci Levels:**
Key ratios around the Golden line (0.5, 0.618, 0.786, 1.0, 1.272, 1.414, 1.618, 2.0) dynamically adjust based on market conditions.
- **Fibonacci Labels:**
Labels are shown next to Fibonacci lines, indicating their numeric value clearly on the chart.
- **Table (Top Right Corner):**
- Displays:
- **Input:** EMA/SMA periods sorted by their current average price levels.
- **AVG:** The average of corresponding EMA & SMA pairs.
- **EMA & SMA Values:** Individual EMA/SMA values clearly marked.
- **Dynamic Highlighting:** Highlights the row whose average (EMA+SMA)/2 is closest to the current price, helping identify immediate price action significance.
- **Sorting Logic:**
Each EMA/SMA pair is dynamically sorted based on their average values. Color coding (red/green) is used:
- **Green:** EMA/SMA pairs with shorter periods when their average is lower.
- **Red:** EMA/SMA pairs with longer periods when their average is lower.
- **Star (โญ):** Represents the "Golden" average clearly.
---
### **2. DEM System (Dynamic EMA/ATR Metrics)**
- **Purpose:**
Provides detailed ATR statistics to assess market volatility clearly and quickly.
- **Components:**
- **Moving Averages:**
- SMA lines: 25, 50, 100, 200.
- **Bollinger Bands:**
- Based on 20-period SMA of highs and standard deviation of lows.
- **ATR Analysis:**
- ATR calculations for multiple periods (1-day, 10, 20, 30, 40, 50).
- **ATR Premium:** Average ATR of all calculated periods, providing an overarching volatility indicator.
- **ATR Table (Bottom Right Corner):**
- Displays clearly structured ATR values and percentages relative to the current close price:
- Columns: **ATR Period**, **Value**, and **% of Close**.
- Rows: Each specific ATR (1D, 10, 20, 30, 40, 50), plus ATR premium.
- The ATR premium is highlighted in yellow to signify its importance clearly.
---
## ๐น Key Features and Logic Explained
- **Dynamic EMA/SMA Sorting:**
The script computes the average of each EMA/SMA pair and sorts them dynamically on each bar, highlighting their relative importance visually. This allows traders to easily interpret the strength of current support/resistance levels based on moving averages.
- **Closest EMA/SMA Pair to Current Price:**
Calculates the absolute difference between the current price and all EMA/SMA averages, highlighting the closest one for quick reference.
- **Fibonacci Ratios:**
- Dynamically calculated Fibonacci levels based on the "Golden" EMA/SMA average give clear visual guidance for potential targets, supports, and resistances.
- Labels are continuously updated and placed next to levels for clarity.
- **ATR Volatility Analysis:**
- Provides immediate insight into market volatility with absolute and relative (percentage-based) ATR values.
- ATR premium summarizes volatility across multiple timeframes clearly.
---
## ๐น Practical Use Case:
- Traders can quickly identify support/resistance and critical price zones through EMA/SMA and Fibonacci combinations.
- Useful in assessing immediate volatility, guiding stop-loss and take-profit levels through detailed ATR metrics.
- The dynamic highlighting in tables provides intuitive, real-time decision support for active traders.
---
## ๐น How to Use this Script:
1. **Adjust EMA & SMA Lengths** from indicator settings if different periods are preferred.
2. **Monitor dynamic Fibonacci levels** around the "Golden" average to identify possible reversal or continuation points.
3. **Check EMA/SMA table:** Rows highlighted indicate immediate significance concerning current market price.
4. **ATR table:** Use volatility metrics for better risk management.
---
## ๐ท Conclusion
This advanced Pine Script indicator efficiently combines multiple EMAs, SMAs, dynamic Fibonacci retracement levels, and volatility analysis using ATR into a comprehensive real-time analytical tool, enhancing traders' decision-making capabilities by providing clear and actionable insights directly on the TradingView chart.
Green/Red Candle Probability (EMA 7, SMA 20, SMA 200)### Strategy Explanation for Candle Probability Indicator
This script is designed to calculate the **probability of bullish (green) and bearish (red) candles** over a given analysis period. It leverages three key moving averages to identify market trends and display these probabilities directly on the chart, making it easier for traders to make informed decisions.
#### **How the Script Works:**
1. **Trend Detection Using Moving Averages:**
- The script calculates three moving averages:
- **EMA (Exponential Moving Average) over 7 periods**
- **SMA (Simple Moving Average) over 20 periods**
- **SMA over 200 periods**
The trend is classified as:
- **Bullish:** When EMA 7 > SMA 20 > SMA 200
- **Bearish:** When EMA 7 < SMA 20 < SMA 200
2. **Candle Analysis:**
The script analyzes the last "n" candles (based on the user-defined lookback period) to count the number of bullish and bearish candles:
- **Bullish (green) candle:** The closing price is higher than the opening price.
- **Bearish (red) candle:** The closing price is lower than the opening price.
3. **Probability Calculation:**
The probabilities are calculated as a percentage of bullish and bearish candles in the lookback period:
- **Green Probability (%) = (Number of Green Candles / Lookback Period) ร 100**
- **Red Probability (%) = (Number of Red Candles / Lookback Period) ร 100**
4. **Displaying Results in a Table:**
The results are displayed in a table on the chart, including:
- **Green Probability (%)**
- **Red Probability (%)**
- **Current Trend (Bullish, Bearish, or Neutral)**
#### **Strategy Overview:**
This indicator provides traders with a quick overview of the candle probabilities and the current market trend based on moving averages. It helps traders:
- Gauge the likelihood of bullish or bearish candles appearing in the near future.
- Identify the prevailing trend (bullish, bearish, or neutral).
- Adjust their trading strategies based on statistical probabilities rather than assumptions.
### **Important Notes:**
- The lookback period can be customized between **10 and 200 periods**.
- The indicator does not provide buy/sell signals but gives insights into market behavior.
By understanding the candle probabilities and the trend, traders can better assess market conditions and improve their decision-making process.
Super CCI By Baljit AujlaThe indicator you've shared is a custom CCI (Commodity Channel Index) with multiple types of Moving Averages (MA) and Divergence Detection. It is designed to help traders identify trends and reversals by combining the CCI with various MAs and detecting different types of divergences between the price and the CCI.
Key Components of the Indicator:
CCI (Commodity Channel Index):
The CCI is an oscillator that measures the deviation of the price from its average price over a specific period. It helps identify overbought and oversold conditions and the strength of a trend.
The CCI is calculated by subtracting a moving average (SMA) from the price and dividing by the average deviation from the SMA. The CCI values fluctuate above and below a zero centerline.
Multiple Moving Averages (MA):
The indicator allows you to choose from a variety of moving averages to smooth the CCI line and identify trend direction or support/resistance levels. The available types of MAs include:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
HMA (Hull Moving Average)
RMA (Running Moving Average)
SMMA (Smoothed Moving Average)
TEMA (Triple Exponential Moving Average)
DEMA (Double Exponential Moving Average)
VWMA (Volume-Weighted Moving Average)
ZLEMA (Zero-Lag Exponential Moving Average)
You can select the type of MA to use with a specified length to help identify the trend direction or smooth out the CCI.
Divergence Detection:
The indicator includes a divergence detection mechanism to identify potential trend reversals. Divergences occur when the price and an oscillator like the CCI move in opposite directions, signaling a potential change in price momentum.
Four types of divergences are detected:
Bullish Divergence: Occurs when the price makes a lower low, but the CCI makes a higher low. This indicates a potential reversal to the upside.
Bearish Divergence: Occurs when the price makes a higher high, but the CCI makes a lower high. This indicates a potential reversal to the downside.
Hidden Bullish Divergence: Occurs when the price makes a higher low, but the CCI makes a lower low. This suggests a continuation of the uptrend.
Hidden Bearish Divergence: Occurs when the price makes a lower high, but the CCI makes a higher high. This suggests a continuation of the downtrend.
Each type of divergence is marked on the chart with arrows and labels to alert traders to potential trading opportunities. The labels include the divergence type (e.g., "Bull Div" for Bullish Divergence) and have customizable text colors.
Visual Representation:
The CCI and its associated moving average are plotted on the indicator panel below the price chart. The CCI is plotted as a line, and its color changes depending on whether it is above or below the moving average:
Green when the CCI is above the MA (indicating bullish momentum).
Red when the CCI is below the MA (indicating bearish momentum).
Horizontal lines are drawn at specific levels to help identify key CCI thresholds:
200 and -200 levels indicate extreme overbought or oversold conditions.
75 and -75 levels represent less extreme levels of overbought or oversold conditions.
The 0 level acts as a neutral or baseline level.
A background color fill between the 75 and -75 levels helps highlight the neutral zone.
Customization Options:
CCI Length: You can customize the length of the CCI, which determines the period over which the CCI is calculated.
MA Length: The length of the moving average applied to the CCI can also be adjusted.
MA Type: Choose from a variety of moving averages (SMA, EMA, WMA, etc.) to smooth the CCI.
Divergence Detection: The indicator automatically detects the four types of divergences (bullish, bearish, hidden bullish, hidden bearish) and visually marks them on the chart.
How to Use the Indicator:
Trend Identification: When the CCI is above the selected moving average, it suggests bullish momentum. When the CCI is below the moving average, it suggests bearish momentum.
Overbought/Oversold Conditions: The CCI values above 100 or below -100 indicate overbought and oversold conditions, respectively.
Divergence Analysis: The detection of bullish or bearish divergences can signal potential trend reversals. Hidden divergences may suggest trend continuation.
Trading Signals: You can use the divergence markers (arrows and labels) as potential buy or sell signals, depending on whether the divergence is bullish or bearish.
Practical Application:
This indicator is useful for traders who want to:
Combine the CCI with different moving averages for trend-following strategies.
Identify overbought and oversold conditions using the CCI.
Use divergence detection to anticipate potential trend reversals or continuations.
Have a highly customizable tool for various trading strategies, including trend trading, reversal trading, and divergence-based trading.
Overall, this is a comprehensive tool that combines multiple technical analysis techniques (CCI, moving averages, and divergence) in a single indicator, providing traders with a robust way to analyze price action and spot potential trading opportunities.
Uptrick: RSI Histogram
1. **Introduction to the RSI and Moving Averages**
2. **Detailed Breakdown of the Uptrick: RSI Histogram**
3. **Calculation and Formula**
4. **Visual Representation**
5. **Customization and User Settings**
6. **Trading Strategies and Applications**
7. **Risk Management**
8. **Case Studies and Examples**
9. **Comparison with Other Indicators**
10. **Advanced Usage and Tips**
---
## 1. Introduction to the RSI and Moving Averages
### **1.1 Relative Strength Index (RSI)**
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder and introduced in his 1978 book "New Concepts in Technical Trading Systems." It is widely used in technical analysis to measure the speed and change of price movements.
**Purpose of RSI:**
- **Identify Overbought/Oversold Conditions:** RSI values range from 0 to 100. Traditionally, values above 70 are considered overbought, while values below 30 are considered oversold. These thresholds help traders identify potential reversal points in the market.
- **Trend Strength Measurement:** RSI also indicates the strength of a trend. High RSI values suggest strong bullish momentum, while low values indicate bearish momentum.
**Calculation of RSI:**
1. **Calculate the Average Gain and Loss:** Over a specified period (e.g., 14 days), calculate the average gain and loss.
2. **Compute the Relative Strength (RS):** RS is the ratio of average gain to average loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS))
### **1.2 Moving Averages (MA)**
Moving Averages are used to smooth out price data and identify trends by filtering out short-term fluctuations. Two common types are:
**Simple Moving Average (SMA):** The average of prices over a specified number of periods.
**Exponential Moving Average (EMA):** A type of moving average that gives more weight to recent prices, making it more responsive to recent price changes.
**Smoothed Moving Average (SMA):** Used to reduce the impact of volatility and provide a clearer view of the underlying trend. The RMA, or Running Moving Average, used in the USH script is similar to an EMA but based on the average of RSI values.
## 2. Detailed Breakdown of the Uptrick: RSI Histogram
### **2.1 Indicator Overview**
The Uptrick: RSI Histogram (USH) is a technical analysis tool that combines the RSI with a moving average to create a histogram that reflects momentum and trend strength.
**Key Components:**
- **RSI Calculation:** Determines the relative strength of price movements.
- **Moving Average Application:** Smooths the RSI values to provide a clearer trend indication.
- **Histogram Plotting:** Visualizes the deviation of the smoothed RSI from a neutral level.
### **2.2 Indicator Purpose**
The primary purpose of the USH is to provide a clear visual representation of the market's momentum and trend strength. It helps traders identify:
- **Bullish and Bearish Trends:** By showing how far the smoothed RSI is from the neutral 50 level.
- **Potential Reversal Points:** By highlighting changes in momentum.
### **2.3 Indicator Design**
**RSI Moving Average (RSI MA):** The RSI MA is a smoothed version of the RSI, calculated using a running moving average. This smooths out short-term fluctuations and provides a clearer indication of the underlying trend.
**Histogram Calculation:**
- **Neutral Level:** The histogram is plotted relative to the neutral level of 50. This level represents a balanced market where neither bulls nor bears have dominance.
- **Histogram Values:** The histogram bars show the difference between the RSI MA and the neutral level. Positive values indicate bullish momentum, while negative values indicate bearish momentum.
## 3. Calculation and Formula
### **3.1 RSI Calculation**
The RSI calculation involves:
1. **Average Gain and Loss:** Calculated over the specified length (e.g., 14 periods).
2. **Relative Strength (RS):** RS = Average Gain / Average Loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS)).
### **3.2 Moving Average Calculation**
For the USH indicator, the RSI is smoothed using a running moving average (RMA). The RMA formula is similar to that of the EMA but is based on averaging RSI values over the specified length.
### **3.3 Histogram Calculation**
The histogram value is calculated as:
- **Histogram Value = RSI MA - 50**
**Plotting the Histogram:**
- **Positive Histogram Values:** Indicate that the RSI MA is above the neutral level, suggesting bullish momentum.
- **Negative Histogram Values:** Indicate that the RSI MA is below the neutral level, suggesting bearish momentum.
## 4. Visual Representation
### **4.1 Histogram Bars**
The histogram is plotted as bars on the chart:
- **Bullish Bars:** Colored green when the RSI MA is above 50.
- **Bearish Bars:** Colored red when the RSI MA is below 50.
### **4.2 Customization Options**
Traders can customize:
- **RSI Length:** Adjust the length of the RSI calculation to match their trading style.
- **Bull and Bear Colors:** Choose colors for histogram bars to enhance visual clarity.
### **4.3 Interpretation**
**Bullish Signal:** A histogram bar that moves from red to green indicates a potential shift to a bullish trend.
**Bearish Signal:** A histogram bar that moves from green to red indicates a potential shift to a bearish trend.
## 5. Customization and User Settings
### **5.1 Adjusting RSI Length**
The length parameter determines the number of periods over which the RSI is calculated and smoothed. Shorter lengths make the RSI more sensitive to price changes, while longer lengths provide a smoother view of trends.
### **5.2 Color Settings**
Traders can adjust:
- **Bull Color:** Color of histogram bars indicating bullish momentum.
- **Bear Color:** Color of histogram bars indicating bearish momentum.
**Customization Benefits:**
- **Visual Clarity:** Traders can choose colors that stand out against their chartโs background.
- **Personal Preference:** Adjust settings to match individual trading styles and preferences.
## 6. Trading Strategies and Applications
### **6.1 Trend Following**
**Identifying Entry Points:**
- **Bullish Entry:** When the histogram changes from red to green, it signals a potential entry point for long positions.
- **Bearish Entry:** When the histogram changes from green to red, it signals a potential entry point for short positions.
**Trend Confirmation:** The histogram helps confirm the strength of a trend. Strong, consistent green bars indicate robust bullish momentum, while strong, consistent red bars indicate robust bearish momentum.
### **6.2 Swing Trading**
**Momentum Analysis:**
- **Entry Signals:** Look for significant shifts in the histogram to time entries. A shift from bearish to bullish (red to green) indicates potential for upward movement.
- **Exit Signals:** A shift from bullish to bearish (green to red) suggests a potential weakening of the trend, signaling an exit or reversal point.
### **6.3 Range Trading**
**Market Conditions:**
- **Consolidation:** The histogram close to zero suggests a range-bound market. Traders can use this information to identify support and resistance levels.
- **Breakout Potential:** A significant move away from the neutral level may indicate a potential breakout from the range.
### **6.4 Risk Management**
**Stop-Loss Placement:**
- **Bullish Positions:** Place stop-loss orders below recent support levels when the histogram is green.
- **Bearish Positions:** Place stop-loss orders above recent resistance levels when the histogram is red.
**Position Sizing:** Adjust position sizes based on the strength of the histogram signals. Strong trends (indicated by larger histogram bars) may warrant larger positions, while weaker signals suggest smaller positions.
## 7. Risk Management
### **7.1 Importance of Risk Management**
Effective risk management is crucial for long-term trading success. It involves protecting capital, managing losses, and optimizing trade setups.
### **7.2 Using USH for Risk Management**
**Stop-Loss and Take-Profit Levels:**
- **Stop-Loss Orders:** Use the histogram to set stop-loss levels based on trend strength. For instance, place stops below support levels in bullish trends and above resistance levels in bearish trends.
- **Take-Profit Targets:** Adjust take-profit levels based on histogram changes. For example, lock in profits as the histogram starts to shift from green to red.
**Position Sizing:**
- **Trend Strength:** Scale position sizes based on the strength of histogram signals. Larger histogram bars indicate stronger trends, which may justify larger positions.
- **Volatility:** Consider market volatility and adjust position sizes to mitigate risk.
## 8. Case Studies and Examples
### **8.1 Example 1: Bullish Trend**
**Scenario:** A trader notices a transition from red to green histogram bars.
**Analysis:**
- **Entry Point:** The transition indicates a potential bullish trend. The trader decides to enter a long position.
- **Stop-Loss:** Set stop-loss below recent support levels.
- **Take-Profit:** Consider taking profits as the histogram moves back towards zero or turns red.
**Outcome:** The bullish trend continues, and the histogram remains green, providing a profitable trade setup.
### **8.2 Example 2: Bearish Trend**
**Scenario:** A trader observes a transition from green to red histogram bars.
**Analysis:**
- **Entry Point:** The transition suggests a potential
bearish trend. The trader decides to enter a short position.
- **Stop-Loss:** Set stop-loss above recent resistance levels.
- **Take-Profit:** Consider taking profits as the histogram approaches zero or shifts to green.
**Outcome:** The bearish trend continues, and the histogram remains red, resulting in a successful trade.
## 9. Comparison with Other Indicators
### **9.1 RSI vs. USH**
**RSI:** Measures momentum and identifies overbought/oversold conditions.
**USH:** Builds on RSI by incorporating a moving average and histogram to provide a clearer view of trend strength and momentum.
### **9.2 RSI vs. MACD**
**MACD (Moving Average Convergence Divergence):** A trend-following momentum indicator that uses moving averages to identify changes in trend direction.
**Comparison:**
- **USH:** Provides a smoothed RSI perspective and visual histogram for trend strength.
- **MACD:** Offers signals based on the convergence and divergence of moving averages.
### **9.3 RSI vs. Stochastic Oscillator**
**Stochastic Oscillator:** Measures the level of the closing price relative to the high-low range over a specified period.
**Comparison:**
- **USH:** Focuses on smoothed RSI values and histogram representation.
- **Stochastic Oscillator:** Provides overbought/oversold signals and potential reversals based on price levels.
## 10. Advanced Usage and Tips
### **10.1 Combining Indicators**
**Multi-Indicator Strategies:** Combine the USH with other technical indicators (e.g., Moving Averages, Bollinger Bands) for a comprehensive trading strategy.
**Confirmation Signals:** Use the USH to confirm signals from other indicators. For instance, a bullish histogram combined with a moving average crossover may provide a stronger buy signal.
### **10.2 Customization Tips**
**Adjust RSI Length:** Experiment with different RSI lengths to match various market conditions and trading styles.
**Color Preferences:** Choose histogram colors that enhance visibility and align with personal preferences.
### **10.3 Continuous Learning**
**Backtesting:** Regularly backtest the USH with historical data to refine strategies and improve accuracy.
**Education:** Stay updated with trading education and adapt strategies based on market changes and personal experiences.
Advanced Keltner Channel/Oscillator [MyTradingCoder]This indicator combines a traditional Keltner Channel overlay with an oscillator, providing a comprehensive view of price action, trend, and momentum. The core of this indicator is its advanced ATR calculation, which uses statistical methods to provide a more robust measure of volatility.
Starting with the overlay component, the center line is created using a biquad low-pass filter applied to the chosen price source. This provides a smoother representation of price than a simple moving average. The upper and lower channel lines are then calculated using the statistically derived ATR, with an additional set of mid-lines between the center and outer lines. This creates a more nuanced view of price action within the channel.
The color coding of the center line provides an immediate visual cue of the current price momentum. As the price moves up relative to the ATR, the line shifts towards the bullish color, and vice versa for downward moves. This color gradient allows for quick assessment of the current market sentiment.
The oscillator component transforms the channel into a different perspective. It takes the price's position within the channel and maps it to either a normalized -100 to +100 scale or displays it in price units, depending on your settings. This oscillator essentially shows where the current price is in relation to the channel boundaries.
The oscillator includes two key lines: the main oscillator line and a signal line. The main line represents the current position within the channel, smoothed by an exponential moving average (EMA). The signal line is a further smoothed version of the oscillator line. The interaction between these two lines can provide trading signals, similar to how MACD is often used.
When the oscillator line crosses above the signal line, it might indicate bullish momentum, especially if this occurs in the lower half of the oscillator range. Conversely, the oscillator line crossing below the signal line could signal bearish momentum, particularly if it happens in the upper half of the range.
The oscillator's position relative to its own range is also informative. Values near the top of the range (close to 100 if normalized) suggest that price is near the upper Keltner Channel band, indicating potential overbought conditions. Values near the bottom of the range (close to -100 if normalized) suggest proximity to the lower band, potentially indicating oversold conditions.
One of the strengths of this indicator is how the overlay and oscillator work together. For example, if the price is touching the upper band on the overlay, you'd see the oscillator at or near its maximum value. This confluence of signals can provide stronger evidence of overbought conditions. Similarly, the oscillator hitting extremes can draw your attention to price action at the channel boundaries on the overlay.
The mid-lines on both the overlay and oscillator provide additional nuance. On the overlay, price action between the mid-line and outer line might suggest strong but not extreme momentum. On the oscillator, this would correspond to readings in the outer quartiles of the range.
The customizable visual settings allow you to adjust the indicator to your preferences. The glow effects and color coding can make it easier to quickly interpret the current market conditions at a glance.
Overlay Component:
The overlay displays Keltner Channel bands dynamically adapting to market conditions, providing clear visual cues for potential trend reversals, breakouts, and overbought/oversold zones.
The center line is a biquad low-pass filter applied to the chosen price source.
Upper and lower channel lines are calculated using a statistically derived ATR.
Includes mid-lines between the center and outer channel lines.
Color-coded based on price movement relative to the ATR.
Oscillator Component:
The oscillator component complements the overlay, highlighting momentum and potential turning points.
Normalized values make it easy to compare across different assets and timeframes.
Signal line crossovers generate potential buy/sell signals.
Advanced ATR Calculation:
Uses a unique method to compute ATR, incorporating concepts like root mean square (RMS) and z-score clamping.
Provides both an average and mode-based ATR value.
Customizable Visual Settings:
Adjustable colors for bullish and bearish moves, oscillator lines, and channel components.
Options for line width, transparency, and glow effects.
Ability to display overlay, oscillator, or both simultaneously.
Flexible Parameters:
Customizable inputs for channel width multiplier, ATR period, smoothing factors, and oscillator settings.
Adjustable Q factor for the biquad filter.
Key Advantages:
Advanced ATR Calculation: Utilizes a statistical method to generate ATR, ensuring greater responsiveness and accuracy in volatile markets.
Overlay and Oscillator: Provides a comprehensive view of price action, combining trend and momentum analysis.
Customizable: Adjust settings to fine-tune the indicator to your specific needs and trading style.
Visually Appealing: Clear and concise design for easy interpretation.
The ATR (Average True Range) in this indicator is derived using a sophisticated statistical method that differs from the traditional ATR calculation. It begins by calculating the True Range (TR) as the difference between the high and low of each bar. Instead of a simple moving average, it computes the Root Mean Square (RMS) of the TR over the specified period, giving more weight to larger price movements. The indicator then calculates a Z-score by dividing the TR by the RMS, which standardizes the TR relative to recent volatility. This Z-score is clamped to a maximum value (10 in this case) to prevent extreme outliers from skewing the results, and then rounded to a specified number of decimal places (2 in this script).
These rounded Z-scores are collected in an array, keeping track of how many times each value occurs. From this array, two key values are derived: the mode, which is the most frequently occurring Z-score, and the average, which is the weighted average of all Z-scores. These values are then scaled back to price units by multiplying by the RMS.
Now, let's examine how these values are used in the indicator. For the Keltner Channel lines, the mid lines (top and bottom) use the mode of the ATR, representing the most common volatility state. The max lines (top and bottom) use the average of the ATR, incorporating all volatility states, including less common but larger moves. By using the mode for the mid lines and the average for the max lines, the indicator provides a nuanced view of volatility. The mid lines represent the "typical" market state, while the max lines account for less frequent but significant price movements.
For the color coding of the center line, the mode of the ATR is used to normalize the price movement. The script calculates the difference between the current price and the price 'degree' bars ago (default is 2), and then divides this difference by the mode of the ATR. The resulting value is passed through an arctangent function and scaled to a 0-1 range. This scaled value is used to create a color gradient between the bearish and bullish colors.
Using the mode of the ATR for this color coding ensures that the color changes are based on the most typical volatility state of the market. This means that the color will change more quickly in low volatility environments and more slowly in high volatility environments, providing a consistent visual representation of price momentum relative to current market conditions.
Using a good IIR (Infinite Impulse Response) low-pass filter, such as the biquad filter implemented in this indicator, offers significant advantages over simpler moving averages like the EMA (Exponential Moving Average) or other basic moving averages.
At its core, an EMA is indeed a simple, single-pole IIR filter, but it has limitations in terms of its frequency response and phase delay characteristics. The biquad filter, on the other hand, is a two-pole, two-zero filter that provides superior control over the frequency response curve. This allows for a much sharper cutoff between the passband and stopband, meaning it can more effectively separate the signal (in this case, the underlying price trend) from the noise (short-term price fluctuations).
The improved frequency response of a well-designed biquad filter means it can achieve a better balance between smoothness and responsiveness. While an EMA might need a longer period to sufficiently smooth out price noise, potentially leading to more lag, a biquad filter can achieve similar or better smoothing with less lag. This is crucial in financial markets where timely information is vital for making trading decisions.
Moreover, the biquad filter allows for independent control of the cutoff frequency and the Q factor. The Q factor, in particular, is a powerful parameter that affects the filter's resonance at the cutoff frequency. By adjusting the Q factor, users can fine-tune the filter's behavior to suit different market conditions or trading styles. This level of control is simply not available with basic moving averages.
Another advantage of the biquad filter is its superior phase response. In the context of financial data, this translates to more consistent lag across different frequency components of the price action. This can lead to more reliable signals, especially when it comes to identifying trend changes or price reversals.
The computational efficiency of biquad filters is also worth noting. Despite their more complex mathematical foundation, biquad filters can be implemented very efficiently, often requiring only a few operations per sample. This makes them suitable for real-time applications and high-frequency trading scenarios.
Furthermore, the use of a more sophisticated filter like the biquad can help in reducing false signals. The improved noise rejection capabilities mean that minor price fluctuations are less likely to cause unnecessary crossovers or indicator movements, potentially leading to fewer false breakouts or reversal signals.
In the specific context of a Keltner Channel, using a biquad filter for the center line can provide a more stable and reliable basis for the entire indicator. It can help in better defining the overall trend, which is crucial since the Keltner Channel is often used for trend-following strategies. The smoother, yet more responsive center line can lead to more accurate channel boundaries, potentially improving the reliability of overbought/oversold signals and breakout indications.
In conclusion, this advanced Keltner Channel indicator represents a significant evolution in technical analysis tools, combining the power of traditional Keltner Channels with modern statistical methods and signal processing techniques. By integrating a sophisticated ATR calculation, a biquad low-pass filter, and a complementary oscillator component, this indicator offers traders a comprehensive and nuanced view of market dynamics.
The indicator's strength lies in its ability to adapt to varying market conditions, providing clear visual cues for trend identification, momentum assessment, and potential reversal points. The use of statistically derived ATR values for channel construction and the implementation of a biquad filter for the center line result in a more responsive and accurate representation of price action compared to traditional methods.
Furthermore, the dual nature of this indicator โ functioning as both an overlay and an oscillator โ allows traders to simultaneously analyze price trends and momentum from different perspectives. This multifaceted approach can lead to more informed decision-making and potentially more reliable trading signals.
The high degree of customization available in the indicator's settings enables traders to fine-tune its performance to suit their specific trading styles and market preferences. From adjustable visual elements to flexible parameter inputs, users can optimize the indicator for various trading scenarios and time frames.
Ultimately, while no indicator can predict market movements with certainty, this advanced Keltner Channel provides traders with a powerful tool for market analysis. By offering a more sophisticated approach to measuring volatility, trend, and momentum, it equips traders with valuable insights to navigate the complex world of financial markets. As with any trading tool, it should be used in conjunction with other forms of analysis and within a well-defined risk management framework to maximize its potential benefits.
Price Ratio Indicator [ChartPrime]The Price Ratio Indicator is a versatile tool designed to analyze the relationship between the price of an asset and its moving average. It helps traders identify overbought and oversold conditions in the market, as well as potential trend reversals.
โ User Inputs:
MA Length: Specifies the length of the moving average used in the calculation.
MA Type Fast: Allows users to choose from various types of moving averages such as Exponential Moving Average (EMA), Simple Moving Average (SMA), Weighted Moving Average (WMA), Volume Weighted Moving Average (VWMA), Relative Moving Average (RMA), Double Exponential Moving Average (DEMA), Triple Exponential Moving Average (TEMA), Zero-Lag Exponential Moving Average (ZLEMA), and Hull Moving Average (HMA).
Upper Level and Lower Level: Define the threshold levels for identifying overbought and oversold conditions.
Signal Line Length: Determines the length of the signal line used for smoothing the indicator's values.
โ Indicator Calculation:
The indicator calculates the ratio between the price of the asset and the selected moving average, subtracts 1 from the ratio, and then smooths the result using the chosen signal line length.
// ๐๐๐ฟ๐๐พ๐ผ๐๐๐ ๐พ๐ผ๐๐พ๐๐๐ผ๐๐๐๐๐
//@ Moving Average's Function
ma(src, ma_period, ma_type) =>
ma =
ma_type == 'EMA' ? ta.ema(src, ma_period) :
ma_type == 'SMA' ? ta.sma(src, ma_period) :
ma_type == 'WMA' ? ta.wma(src, ma_period) :
ma_type == 'VWMA' ? ta.vwma(src, ma_period) :
ma_type == 'RMA' ? ta.rma(src, ma_period) :
ma_type == 'DEMA' ? ta.ema(ta.ema(src, ma_period), ma_period) :
ma_type == 'TEMA' ? ta.ema(ta.ema(ta.ema(src, ma_period), ma_period), ma_period) :
ma_type == 'ZLEMA' ? ta.ema(src + src - src , ma_period) :
ma_type == 'HMA' ? ta.hma(src, ma_period)
: na
ma
//@ Smooth of Source
src = math.sum(source, 5)/5
//@ Ratio Price / MA's
p_ratio = src / ma(src, ma_period, ma_type) - 1
โ Visualization:
The main plot displays the price ratio, with color gradients indicating the strength and direction of the ratio.
The bar color changes dynamically based on the ratio, providing a visual representation of market conditions.
Invisible Horizontal lines indicate the upper and lower threshold levels for overbought and oversold conditions.
A signal line, smoothed using the specified length, helps identify trends and potential reversal points.
High and low value regions are filled with color gradients, enhancing visualization of extreme price movements.
MA type HMA gives faster changes of the indicator (Each MA has its own specifics):
MA type TEMA:
โ Additional Features:
A symbol displayed at the bottom right corner of the chart provides a quick visual reference to the current state of the indicator, with color intensity indicating the strength of the ratio.
Overall, the Price Ratio Indicator offers traders valuable insights into price dynamics and helps them make informed trading decisions based on the relationship between price and moving averages. Adjusting the input parameters allows for customization according to individual trading preferences and market conditions.
Optimal Length BackTester [YinYangAlgorithms]This Indicator allows for a โOptimal Lengthโ to be inputted within the Settings as a Source. Unlike most Indicators and/or Strategies that rely on either Static Lengths or Internal calculations for the length, this Indicator relies on the Length being derived from an external Indicator in the form of a Source Input.
This may not sound like much, but this application may allows limitless implementations of such an idea. By allowing the input of a Length within a Source Setting you may have an โOptimal Lengthโ that adjusts automatically without the need for manual intervention. This may allow for Traditional and Non-Traditional Indicators and/or Strategies to allow modifications within their settings as well to accommodate the idea of this โOptimal Lengthโ model to create an Indicator and/or Strategy that adjusts its length based on the top performing Length within the current Market Conditions.
This specific Indicator aims to allow backtesting with an โOptimal Lengthโ inputted as a โSourceโ within the Settings.
This โOptimal Lengthโ may be used to display and potentially optimize multiple different Traditional Indicators within this BackTester. The following Traditional Indicators are included and available to be backtested with an โOptimal Lengthโ inputted as a Source in the Settings:
Moving Average; expressed as either a: Simple Moving Average, Exponential Moving Average or Volume Weighted Moving Average
Bollinger Bands; expressed based on the Moving Average Type
Donchian Channels; expressed based on the Moving Average Type
Envelopes; expressed based on the Moving Average Type
Envelopes Adjusted; expressed based on the Moving Average Type
All of these Traditional Indicators likewise may be displayed with multiple โOptimal Lengthsโ. They have the ability for multiple different โOptimal Lengthsโ to be inputted and displayed, such as:
Fast Optimal Length
Slow Optimal Length
Neutral Optimal Length
By allowing for the input of multiple different โOptimal Lengthsโ we may express the โOptimal Movementโ of such an expressed Indicator based on different Time Frames and potentially also movement based on Fast, Slow and Neutral (Inclusive) Lengths.
This in general is a simple Indicator that simply allows for the input of multiple different varieties of โOptimal Lengthsโ to be displayed in different ways using Tradition Indicators. However, the idea and model of accepting a Length as a Source is unique and may be adopted in many different forms and endless ideas.
Tutorial:
You may add an โOptimal Lengthโ within the Settings as a โSourceโ as followed in the example above. This Indicator allows for the input of a:
Neutral โOptimal Lengthโ
Fast โOptimal Lengthโ
Slow โOptimal Lengthโ
It is important to account for all three as they generally encompass different min/max length values and therefore result in varying โOptimal Lengthโsโ.
For instance, say youโre calculating the โOptimal Lengthโ and you use:
Min: 1
Max: 400
This would therefore be scanning for 400 (inclusive) lengths.
As a general way of calculating you may assume the following for which lengths are being used within an โOptimal Lengthโ calculation:
Fast: 1 - 199
Slow: 200 - 400
Neutral: 1 - 400
This allows for the calculation of a Fast and Slow length within the predetermined lengths allotted. However, it likewise allows for a Neutral length which is inclusive to all lengths alloted and may be deemed the โMost Accurateโ for these reasons. However, just because the Neutral is inclusive to all lengths, doesnโt mean the Fast and Slow lengths are irrelevant. The Fast and Slow length inputs may be useful for seeing how specifically zoned lengths may fair, and likewise when they cross over and/or under the Neutral โOptimal Lengthโ.
This Indicator features the ability to display multiple different types of Traditional Indicators within the โDisplay Typeโ.
We will go over all of the different โDisplay Typesโ with examples on how using a Fast, Slow and Neutral length would impact it:
Simple Moving Average:
In this example above have the Fast, Slow and Neutral Optimal Length formatted as a Slow Moving Average. The first example is on the 15 minute Time Frame and the second is on the 1 Day Time Frame, demonstrating how the length changes based on the Time Frame and the effects it may have.
Here we can see that by inputting โOptimal Lengthsโ as a Simple Moving Average we may see moving averages that change over time with their โOptimal Lengthsโ. These lengths may help identify Support and/or Resistance locations. By using an 'Optimal Length' rather than a static length, we may create a Moving Average which may be more accurate as it attempts to be adaptive to current Market Conditions.
Bollinger Bands:
Bollinger Bands are a way to see a Simple Moving Average (SMA) that then uses Standard Deviation to identify how much deviation has occurred. This Deviation is then Added and Subtracted from the SMA to create the Bollinger Bands which help Identify possible movement zones that are โwithin rangeโ. This may mean that the price may face Support / Resistance when it reaches the Outer / Inner bounds of the Bollinger Bands. Likewise, it may mean the Price is โOverboughtโ when outside and above or โUnderboughtโ when outside and below the Bollinger Bands.
By applying All 3 different types of Optimal Lengths towards a Traditional Bollinger Band calculation we may hope to see different ranges of Bollinger Bands and how different lookback lengths may imply possible movement ranges on both a Short Term, Long Term and Neutral perspective. By seeing these possible ranges you may have the ability to identify more levels of Support and Resistance over different lengths and Trading Styles.
Donchian Channels:
Above youโll see two examples of Machine Learning: Optimal Length applied to Donchian Channels. These are displayed with both the 15 Minute Time Frame and the 1 Day Time Frame.
Donchian Channels are a way of seeing potential Support and Resistance within a given lookback length. They are a way of withholding the Highโs and Lowโs of a specific lookback length and looking for deviation within this length. By applying a Fast, Slow and Neutral Machine Learning: Optimal Length to these Donchian Channels way may hope to achieve a viable range of Highโs and Lowโs that one may use to Identify Support and Resistance locations for different ranges of Optimal Lengths and likewise potentially different Trading Strategies.
Envelopes / Envelopes Adjusted:
Envelopes are an interesting one in the sense that they both may be perceived as useful; however we deem that with the use of an โOptimal Lengthโ that the โEnvelopes Adjustedโ may work best. We will start with examples of the Traditional Envelope then showcase the Adjusted version.
Envelopes:
As you may see, a Traditional form of Envelopes even produced with a Machine Learning: Optimal Length may not produce optimal results. Unfortunately this may occur with some Traditional Indicators and they may need some adjustments as youโll notice with the โEnvelopes Adjustedโ version. However, even without the adjustments, these Envelopes may be useful for seeing โOverboughtโ and โOversoldโ locations within a Machine Learning: Optimal Length standpoint.
Envelopes Adjusted:
By adding an adjustment to these Envelopes, we may hope to better reflect our Optimal Length within it. This is caused by adding a ratio reflection towards the current length of the Optimal Length and the max Length used. This allows for the Fast and Neutral (and potentially Slow if Neutral is greater) to achieve a potentially more accurate result.
Envelopes, much like Bollinger Bands are a way of seeing potential movement zones along with potential Support and Resistance. However, unlike Bollinger Bands which are based on Standard Deviation, Envelopes are based on percentages +/- from the Simple Moving Average.
We will conclude our Tutorial here. Hopefully this has given you some insight into how useful adding a โOptimal Lengthโ within an external (secondary) Indicator as a Source within the Settings may be. Likewise, how useful it may be for automation sake in the sense that when the โOptimal Lengthโ changes, it doesnโt rely on an alert where you need to manually update it yourself; instead it will update Automatically and you may reap the benefits of such with little manual input needed (aside from the initial setup).
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
Multi-Timeframe Trend Detector [Alifer]Here is an easy-to-use and customizable multi-timeframe visual trend indicator.
The indicator combines Exponential Moving Averages (EMA), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI) to determine the trend direction on various timeframes: 15 minutes (15M), 30 minutes (30M), 1 hour (1H), 4 hours (4H), 1 day (1D), and 1 week (1W).
EMA Trend : The script calculates two EMAs for each timeframe: a fast EMA and a slow EMA. If the fast EMA is greater than the slow EMA, the trend is considered Bullish; if the fast EMA is less than the slow EMA, the trend is considered Bearish.
MACD Trend : The script calculates the MACD line and the signal line for each timeframe. If the MACD line is above the signal line, the trend is considered Bullish; if the MACD line is below the signal line, the trend is considered Bearish.
RSI Trend : The script calculates the RSI for each timeframe. If the RSI value is above a specified Bullish level, the trend is considered Bullish; if the RSI value is below a specified Bearish level, the trend is considered Bearish. If the RSI value is between the Bullish and Bearish levels, the trend is Neutral, and no arrow is displayed.
Dashboard Display :
The indicator prints arrows on the dashboard to represent Bullish (โฒ Green) or Bearish (โผ Red) trends for each timeframe.
You can easily adapt the Dashboard colors (Inputs > Theme) for visibility depending on whether you're using a Light or Dark theme for TradingView.
Usage :
You can adjust the indicator's settings such as theme (Dark or Light), EMA periods, MACD parameters, RSI period, and Bullish/Bearish levels to adapt it to your specific trading strategies and preferences.
Disclaimer :
This indicator is designed to quickly help you identify the trend direction on multiple timeframes and potentially make more informed trading decisions.
You should consider it as an extra tool to complement your strategy, but you should not solely rely on it for making trading decisions.
Always perform your own analysis and risk management before executing trades.
The indicator will only show a Dashboard. The EMAs, RSI and MACD you see on the chart image have been added just to demonstrate how the script works.
DETAILED SCRIPT EXPLANATION
INPUTS:
theme : Allows selecting the color theme (options: "Dark" or "Light").
emaFastPeriod : The period for the fast EMA.
emaSlowPeriod : The period for the slow EMA.
macdFastLength : The fast length for MACD calculation.
macdSlowLength : The slow length for MACD calculation.
macdSignalLength : The signal length for MACD calculation.
rsiPeriod : The period for RSI calculation.
rsiBullishLevel : The level used to determine Bullish RSI condition, when RSI is above this value. It should always be higher than rsiBearishLevel.
rsiBearishLevel : The level used to determine Bearish RSI condition, when RSI is below this value. It should always be lower than rsiBullishLevel.
CALCULATIONS:
The script calculates EMAs on multiple timeframes (15-minute, 30-minute, 1-hour, 4-hour, daily, and weekly) using the request.security() function.
Similarly, the script calculates MACD values ( macdLine , signalLine ) on the same multiple timeframes using the request.security() function along with the ta.macd() function.
RSI values are also calculated for each timeframe using the request.security() function along with the ta.rsi() function.
The script then determines the EMA trends for each timeframe by comparing the fast and slow EMAs using simple boolean expressions.
Similarly, it determines the MACD trends for each timeframe by comparing the MACD line with the signal line.
Lastly, it determines the RSI trends for each timeframe by comparing the RSI values with the Bullish and Bearish RSI levels.
PLOTTING AND DASHBOARD:
Color codes are defined based on the EMA, MACD, and RSI trends for each timeframe. Green for Bullish, Red for Bearish.
A dashboard is created using the table.new() function, displaying the trend information for each timeframe with arrows representing Bullish or Bearish conditions.
The dashboard will appear in the top-right corner of the chart, showing the Bullish and Bearish trends for each timeframe (15M, 30M, 1H, 4H, 1D, and 1W) based on EMA, MACD, and RSI analysis. Green arrows represent Bullish trends, red arrows represent Bearish trends, and no arrows indicate Neutral conditions.
INFO ON USED INDICATORS:
1 โ EXPONENTIAL MOVING AVERAGE (EMA)
The Exponential Moving Average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points.
The EMA is calculated by taking the average of the true range over a specified period. The true range is the greatest of the following:
The difference between the current high and the current low.
The difference between the previous close and the current high.
The difference between the previous close and the current low.
The EMA can be used by traders to produce buy and sell signals based on crossovers and divergences from the historical average. Traders often use several different EMA lengths, such as 10-day, 50-day, and 200-day moving averages.
The formula for calculating EMA is as follows:
Compute the Simple Moving Average (SMA).
Calculate the multiplier for weighting the EMA.
Calculate the current EMA using the following formula:
EMA = Closing price x multiplier + EMA (previous day) x (1-multiplier)
2 โ MOVING AVERAGE CONVERGENCE DIVERGENCE (MACD)
The Moving Average Convergence Divergence (MACD) is a popular trend-following momentum indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a financial instrument's price.
The MACD is calculated by subtracting a longer-term Exponential Moving Average (EMA) from a shorter-term EMA. The most commonly used time periods for the MACD are 26 periods for the longer EMA and 12 periods for the shorter EMA. The difference between the two EMAs creates the main MACD line.
Additionally, a Signal Line (usually a 9-period EMA) is computed, representing a smoothed version of the MACD line. Traders watch for crossovers between the MACD line and the Signal Line, which can generate buy and sell signals. When the MACD line crosses above the Signal Line, it generates a bullish signal, indicating a potential uptrend. Conversely, when the MACD line crosses below the Signal Line, it generates a bearish signal, indicating a potential downtrend.
In addition to the MACD line and Signal Line crossovers, traders often look for divergences between the MACD and the price chart. Divergence occurs when the MACD is moving in the opposite direction of the price, which can suggest a potential trend reversal.
3 โ RELATIVE STRENGHT INDEX (RSI):
The Relative Strength Index (RSI) is another popular momentum oscillator used by traders to assess the overbought or oversold conditions of a financial instrument. The RSI ranges from 0 to 100 and measures the speed and change of price movements.
The RSI is calculated based on the average gain and average loss over a specified period, commonly 14 periods. The formula involves several steps:
Calculate the average gain over the specified period.
Calculate the average loss over the specified period.
Calculate the relative strength (RS) by dividing the average gain by the average loss.
Calculate the RSI using the following formula: RSI = 100 - (100 / (1 + RS))
The RSI oscillates between 0 and 100, where readings above 70 are considered overbought, suggesting that the price may have risen too far and could be due for a correction. Readings below 30 are considered oversold, suggesting that the price may have dropped too much and could be due for a rebound.
Traders often use the RSI to identify potential trend reversals. For example, when the RSI crosses above 30 from below, it may indicate the start of an uptrend, and when it crosses below 70 from above, it may indicate the start of a downtrend. Additionally, traders may look for bullish or bearish divergences between the RSI and the price chart, similar to the MACD analysis, to spot potential trend changes.
Moving Averages RefurbishedIntroduction
This is a collection of multiple moving averages, where you can have a rainbow of moving averages with different types that can be defined by the user.
There are already other indicators in this rainbow style, however certain averages are absent in certain indicators and present in others,
needing the merge to have a more complete solution.
Resources
Here there is the possibility to individually define each moving average.
In addition, it is possible to adjust some details, such as themes, coloring and periods.
Regarding the calculation of averages, credit goes to the following authors.
What I've done here is to group these averages together and allow them to combine.
Credits
TradingView
PineCoders
CrackingCryptocurrency
MightyZinger
Alex Orekhov (everget)
alexgrover
paragjyoti2012
Moving averages available
1. Exponential Moving Average
2. Simple Moving Average
3. Relative Moving Average
4. Weighted Moving Average
5. Ehlers Dynamic Smoothed Moving Average
6. Double Exponential Moving Average
7. Triple Exponential Moving Average
8. Smoothed Moving Average
9. Hull Moving Average
10. Fractal Adaptive Moving Average
11. Kaufman's Adaptive Moving Average
12. Volatility Adjusted Moving Average
13. Jurik Moving Average
14. Optimized Exponential Moving Average
15. Exponential Hull Moving Average
16. Arnaud Legoux Moving Average
17. Coefficient of Variation Weighted Exponential Moving Average
18. Coefficient of Variation Weighted Moving Average
19. * Ehlrs Modified Fractal Adaptive Moving Average
20. Exponential Triangular Moving Average
21. Least Squares Moving Average
22. RSI Moving average
23. Simple Triangular Moving Average
24. Triple Hull Moving Average
25. Variable Index Dynamic Average
26. Volume-weighted Moving Average
27. Zero-Lag Exponential Moving Average
28. Zero-Lag Simple Moving Average
29. Elastic Volume Weighted Moving Average
30. Tillson T3
31. Geometric Moving Average
32. Welles Wilder Moving Average
33. Adjusted Moving Average
34. Corrective Moving average
35. Exponentially Deviating Moving Average
36. EMA Range
37. Sine-Weighted Moving Average
38. Adaptive Moving Average TABLE
39. Following Adaptive Moving Average
40. Hilbert based Kaufman's Adaptive Moving Average
41. Median
42. * VWAP
43. * Rolling VWAP
44. Triangular Simple Moving Average
45. Triangular Exponential Moving Average
46. โโMoving Average Price Correlation
47. Regularized Exponential Moving Average
48. Repulsion Moving Average
49. * Symmetrically Weighted Moving Average
* fixed period averages
MovingAveragesLibraryLibrary "MovingAveragesLibrary"
This is a library allowing one to select between many different Moving Average formulas to smooth out any float variable.
You can use this library to apply a Moving Average function to any series of data as long as your source is a float.
The default application would be for applying Moving Averages onto your chart. However, the scope of this library is beyond that. Any indicator or strategy you are building can benefit from this library.
You can apply different types of smoothing and moving average functions to your indicators, momentum oscillators, average true range calculations, support and resistance zones, envelope bands, channels, and anything you can think of to attempt to smooth out noise while finding a delicate balance against lag.
If you are developing an indicator, you can use the 'ave_func' to allow your users to select any Moving Average for any function or variable by creating an input string with the following structure:
var_name = input.string(, , )
Where the types of Moving Average you would like to be provided would be included in options.
Example:
i_ma_type = input.string(title = "Moving Average Type", defval = "Hull Moving Average", options = )
Where you would add after options the strings I have included for you at the top of the PineScript for your convenience.
Then for the output you desire, simply call 'ave_func' like so:
ma = ave_func(source, length, i_ma_type)
Now the plotted Moving Average will be the same as what you or your users select from the Input.
ema(src, len) Exponential Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: Float value.
sma(src, len) Simple Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: Float value.
rma(src, len) Relative Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: Float value.
wma(src, len) Weighted Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: Float value.
dv2(len) Donchian V2 function.
โโParameters:
โโโโ len : Lookback length to use.
โโReturns: Open + Close / 2 for the selected length.
ModFilt(src, len) Modular Filter smoothing function.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: Float value.
EDSMA(src, len) Ehlers Dynamic Smoothed Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: EDSMA smoothing.
dema(x, t) Double Exponential Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: DEMA smoothing.
tema(src, len) Triple Exponential Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: TEMA smoothing.
smma(x, t) Smoothed Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: SMMA smoothing.
vwma(x, t) Volume Weighted Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: VWMA smoothing.
hullma(x, t) Hull Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: Hull smoothing.
covwma(x, t) Coefficient of Variation Weighted Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: COVWMA smoothing.
frama(x, t) Fractal Reactive Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: FRAMA smoothing.
kama(x, t) Kaufman's Adaptive Moving Average.
โโParameters:
โโโโ x : Series to use ('close' is used if no argument is supplied).
โโโโ t : Lookback length to use.
โโReturns: KAMA smoothing.
donchian(len) Donchian Calculation.
โโParameters:
โโโโ len : Lookback length to use.
โโReturns: Average of the highest price and the lowest price for the specified look-back period.
tma(src, len) Triangular Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: TMA smoothing.
VAMA(src, len) Volatility Adjusted Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: VAMA smoothing.
Jurik(src, len) Jurik Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: JMA smoothing.
MCG(src, len) McGinley smoothing.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: McGinley smoothing.
zlema(series, length) Zero Lag Exponential Moving Average.
โโParameters:
โโโโ series : Series to use ('close' is used if no argument is supplied).
โโโโ length : Lookback length to use.
โโReturns: ZLEMA smoothing.
xema(src, len) Optimized Exponential Moving Average.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ len : Lookback length to use.
โโReturns: XEMA smoothing.
EhlersSuperSmoother(src, lower) Ehlers Super Smoother.
โโParameters:
โโโโ src : Series to use ('close' is used if no argument is supplied).
โโโโ lower : Smoothing value to use.
โโReturns: Ehlers Super smoothing.
EhlersEmaSmoother(sig, smoothK, smoothP) Ehlers EMA Smoother.
โโParameters:
โโโโ sig : Series to use ('close' is used if no argument is supplied).
โโโโ smoothK : Lookback length to use.
โโโโ smoothP : Smothing value to use.
โโReturns: Ehlers EMA smoothing.
ave_func(in_src, in_len, in_type) Returns the source after running it through a Moving Average function.
โโParameters:
โโโโ in_src : Series to use ('close' is used if no argument is supplied).
โโโโ in_len : Lookback period to be used for the Moving Average function.
โโโโ in_type : Type of Moving Average function to use. Must have a string input to select the options from that MUST match the type-casing in the function below.
โโReturns: The source as a float after running it through the Moving Average function.
Multi-Indicator by johntradingwickThe Multi-Indicator includes the functionality of the following indicators:
1. Market Structure
2. Support and Resistance
3. VWAP
4. Simple Moving Average
5. Exponential Moving Average
Functionality of the Multi-Indicator:
Market Structure
As we already know, the market structure is one of the most important things in trading. If we are able to identify the trend correctly, it takes away a huge burden. For this, I have used the Zig Zag indicator to identify price trends. It plots points on the chart whenever the prices reverse by a larger percentage than a predetermined variable. The points are then connected by straight lines that will help you to identify the swing high and low.
This will help you to filter out any small price movements, making it easier to identify the trend, its direction, and its strength levels. You can change the period in consideration and the deviation by changing the deviation % and the depth.
Support and Resistance
The indicator provides the functionality to add support and resistance levels. If you want more levels just change the timeframe it looks at in the settings. It will pull the SR levels off the timeframe specified in the settings.
You can select the timeframe for support and resistance levels. The default time frame is โsame as the chartโ.
You can also extend lines to the right and change the width and colour of the lines. There is also an option to change the criteria to select the lines as valid support or resistance. You can extend the S/R level or use the horizontal lines to mark the level when there is a change in polarity.
VWAP
Volume Weighted Average Price (VWAP) is used to measure the average price weighted by volume. VWAP is typically used with intraday charts as a way to determine the general direction of intraday prices. It's similar to a moving average in that when the price is above VWAP, prices are rising and when the price is below VWAP, prices are falling. VWAP is primarily used by technical analysts to identify market trend.
Simple Moving Average
A simple Moving Average is an unweighted Moving Average. This means that each day in the data set has equal importance and is weighted equally. As each new day ends, the oldest data point is dropped and the newest one is added to the beginning.
The multi-indicator has the ability to provide 5 moving averages. This is particularly helpful if you want to use various time periods such as 20, 50, 100, and 200. Although this is just basic functionality, it comes in handy if you are using a free account.
Exponential Moving Average
An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average. The multi-indicator provides 5 exponential moving averages. This is particularly helpful if you want to use various time periods such as 20, 50, 100, and 200.
Kalman Filter [DCAUT]โ Kalman Filter
๐ ORIGINALITY & INNOVATION
The Kalman Filter represents an important adaptation of aerospace signal processing technology to financial market analysis. Originally developed by Rudolf E. Kalman in 1960 for navigation and guidance systems, this implementation brings the algorithm's noise reduction capabilities to price trend analysis.
This implementation addresses a common challenge in technical analysis: the trade-off between smoothness and responsiveness. Traditional moving averages must choose between being smooth (with increased lag) or responsive (with increased noise). The Kalman Filter improves upon this limitation through its recursive estimation approach, which continuously balances historical trend information with current price data based on configurable noise parameters.
The key advancement lies in the algorithm's adaptive weighting mechanism. Rather than applying fixed weights to historical data like conventional moving averages, the Kalman Filter dynamically adjusts its trust between the predicted trend and observed prices. This allows it to provide smoother signals during stable periods while maintaining responsiveness during genuine trend changes, helping to reduce whipsaws in ranging markets while not missing significant price movements.
๐ MATHEMATICAL FOUNDATION
The Kalman Filter operates through a two-phase recursive process:
Prediction Phase:
The algorithm first predicts the next state based on the previous estimate:
State Prediction: Estimates the next value based on current trend
Error Covariance Prediction: Calculates uncertainty in the prediction
Update Phase:
Then updates the prediction based on new price observations:
Kalman Gain Calculation: Determines the weight given to new measurements
State Update: Combines prediction with observation based on calculated gain
Error Covariance Update: Adjusts uncertainty estimate for next iteration
Core Parameters:
Process Noise (Q): Represents uncertainty in the trend model itself. Higher values indicate the trend can change more rapidly, making the filter more responsive to price changes.
Measurement Noise (R): Represents uncertainty in price observations. Higher values indicate less trust in individual price points, resulting in smoother output.
Kalman Gain Formula:
The Kalman Gain determines how much weight to give new observations versus predictions:
K = P(k|k-1) / (P(k|k-1) + R)
Where:
K is the Kalman Gain (0 to 1)
P(k|k-1) is the predicted error covariance
R is the measurement noise parameter
When K approaches 1, the filter trusts new measurements more (responsive).
When K approaches 0, the filter trusts its prediction more (smooth).
This dynamic adjustment mechanism allows the filter to adapt to changing market conditions automatically, providing an advantage over fixed-weight moving averages.
๐ COMPREHENSIVE SIGNAL ANALYSIS
Visual Trend Indication:
The Kalman Filter line provides color-coded trend information:
Green Line: Indicates the filter value is rising, suggesting upward price momentum
Red Line: Indicates the filter value is falling, suggesting downward price momentum
Gray Line: Indicates sideways movement with no clear directional bias
Crossover Signals:
Price-filter crossovers generate trading signals:
Golden Cross: Price crosses above the Kalman Filter line, suggests potential bullish momentum development, may indicate a favorable environment for long positions, filter will naturally turn green as it adapts to price moving higher
Death Cross: Price crosses below the Kalman Filter line, suggests potential bearish momentum development, may indicate consideration for position reduction or shorts, filter will naturally turn red as it adapts to price moving lower
Trend Confirmation:
The filter serves as a dynamic trend baseline:
Price Consistently Above Filter: Confirms established uptrend
Price Consistently Below Filter: Confirms established downtrend
Frequent Crossovers: Suggests ranging or choppy market conditions
Signal Reliability Factors:
Signal quality varies based on market conditions:
Higher reliability in trending markets with sustained directional moves
Lower reliability in choppy, range-bound conditions with frequent reversals
Parameter adjustment can help adapt to different market volatility levels
๐ฏ STRATEGIC APPLICATIONS
Trend Following Strategy:
Use the Kalman Filter as a dynamic trend baseline:
Enter long positions when price crosses above the filter
Enter short positions when price crosses below the filter
Exit when price crosses back through the filter in the opposite direction
Monitor filter slope (color) for trend strength confirmation
Dynamic Support/Resistance:
The filter can act as a moving support or resistance level:
In uptrends: Filter often provides dynamic support for pullbacks
In downtrends: Filter often provides dynamic resistance for bounces
Price rejections from the filter can offer entry opportunities in trend direction
Filter breaches may signal potential trend reversals
Multi-Timeframe Analysis:
Combine Kalman Filters across different timeframes:
Higher timeframe filter identifies primary trend direction
Lower timeframe filter provides precise entry and exit timing
Trade only in direction of higher timeframe trend for better probability
Use lower timeframe crossovers for position entry/exit within major trend
Volatility-Adjusted Configuration:
Adapt parameters to match market conditions:
Low Volatility Markets (Forex majors, stable stocks): Use lower process noise for stability, use lower measurement noise for sensitivity
Medium Volatility Markets (Most equities): Process noise default (0.05) provides balanced performance, measurement noise default (1.0) for general-purpose filtering
High Volatility Markets (Cryptocurrencies, volatile stocks): Use higher process noise for responsiveness, use higher measurement noise for noise reduction
Risk Management Integration:
Use filter as a trailing stop-loss level in trending markets
Tighten stops when price moves significantly away from filter (overextension)
Wider stops in early trend formation when filter is just establishing direction
Consider position sizing based on distance between price and filter
๐ DETAILED PARAMETER CONFIGURATION
Source Selection:
Determines which price data feeds the algorithm:
OHLC4 (default): Uses average of open, high, low, close for balanced representation
Close: Focuses purely on closing prices for end-of-period analysis
HL2: Uses midpoint of high and low for range-based analysis
HLC3: Typical price, gives more weight to closing price
HLCC4: Weighted close price, emphasizes closing values
Process Noise (Q) - Adaptation Speed Control:
This parameter controls how quickly the filter adapts to changes:
Technical Meaning:
Represents uncertainty in the underlying trend model
Higher values allow the estimated trend to change more rapidly
Lower values assume the trend is more stable and slow-changing
Practical Impact:
Lower Values: Produces very smooth output with minimal noise, slower to respond to genuine trend changes, best for long-term trend identification, reduces false signals in choppy markets
Medium Values: Balanced responsiveness and smoothness, suitable for swing trading applications, default (0.05) works well for most markets
Higher Values: More responsive to price changes, may produce more false signals in ranging markets, better for short-term trading and day trading, captures trend changes earlier, adjust freely based on market characteristics
Measurement Noise (R) - Smoothing Control:
This parameter controls how much the filter trusts individual price observations:
Technical Meaning:
Represents uncertainty in price measurements
Higher values indicate less trust in individual price points
Lower values make each price observation more influential
Practical Impact:
Lower Values: More reactive to each price change, less smoothing with more noise in output, may produce choppy signals
Medium Values: Balanced smoothing and responsiveness, default (1.0) provides general-purpose filtering
Higher Values: Heavy smoothing for very noisy markets, reduces whipsaws significantly but increases lag in trend change detection, best for cryptocurrency and highly volatile assets, can use larger values for extreme smoothing
Parameter Interaction:
The ratio between Process Noise and Measurement Noise determines overall behavior:
High Q / Low R: Very responsive, minimal smoothing
Low Q / High R: Very smooth, maximum lag reduction
Balanced Q and R: Middle ground for most applications
Optimization Guidelines:
Start with default values (Q=0.05, R=1.0)
If too many false signals: Increase R or decrease Q
If missing trend changes: Decrease R or increase Q
Test across different market conditions before live use
Consider different settings for different timeframes
๐ PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional Moving Averages:
Versus Simple Moving Average (SMA):
The Kalman Filter typically responds faster to genuine trend changes
Produces smoother output than SMA of comparable length
Better noise reduction in ranging markets
More configurable for different market conditions
Versus Exponential Moving Average (EMA):
Similar responsiveness but with better noise filtering
Less prone to whipsaws in choppy conditions
More adaptable through dual parameter control (Q and R)
Can be tuned to match or exceed EMA responsiveness while maintaining smoothness
Versus Hull Moving Average (HMA):
Different noise reduction approach (recursive estimation vs. weighted calculation)
Kalman Filter offers more intuitive parameter adjustment
Both reduce lag effectively, but through different mechanisms
Kalman Filter may handle sudden volatility changes more gracefully
Response Characteristics:
Lag Time: Moderate and configurable through parameter adjustment
Noise Reduction: Good to excellent, particularly in volatile conditions
Trend Detection: Effective across multiple timeframes
False Signal Rate: Typically lower than simple moving averages in ranging markets
Computational Efficiency: Efficient recursive calculation suitable for real-time use
Optimal Use Cases:
Markets with mixed trending and ranging periods
Assets with moderate to high volatility requiring noise filtering
Multi-timeframe analysis requiring consistent methodology
Systematic trading strategies needing reliable trend identification
Situations requiring balance between responsiveness and smoothness
Known Limitations:
Parameters require adjustment for different market volatility levels
May still produce false signals during extreme choppy conditions
No single parameter set works optimally for all market conditions
Requires complementary indicators for comprehensive analysis
Historical performance characteristics may not persist in changing market conditions
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The Kalman Filter's effectiveness varies with market conditions, tending to perform better in markets with clear trending phases interrupted by consolidation. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions, but rather as part of a comprehensive trading approach.
Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always test thoroughly with different parameter settings across various market conditions before using in live trading. No technical indicator can predict future price movements with certainty, and all trading involves risk of loss.
Johnny's Machine Learning Moving Average (MLMA) w/ Trend Alerts๐ Overview
Johnny's Machine Learning Moving Average (MLMA) w/ Trend Alerts is a powerful adaptive moving average indicator designed to capture market trends dynamically. Unlike traditional moving averages (e.g., SMA, EMA, WMA), this indicator incorporates volatility-based trend detection, Bollinger Bands, ADX, and RSI, offering a comprehensive view of market conditions.
The MLMA is "machine learning-inspired" because it adapts dynamically to market conditions using ATR-based windowing and integrates multiple trend strength indicators (ADX, RSI, and volatility bands) to provide an intelligent moving average calculation that learns from recent price action rather than being static.
๐ How It Works
1๏ธโฃ Adaptive Moving Average Selection
The MLMA automatically selects one of four different moving averages:
๐ EMA (Exponential Moving Average) โ Reacts quickly to price changes.
๐ต HMA (Hull Moving Average) โ Smooth and fast, reducing lag.
๐ก WMA (Weighted Moving Average) โ Gives recent prices more importance.
๐ด VWAP (Volume Weighted Average Price) โ Accounts for volume impact.
The user can select which moving average type to use, making the indicator customizable based on their strategy.
2๏ธโฃ Dynamic Trend Detection
ATR-Based Adaptive Window ๐
The Average True Range (ATR) determines the window size dynamically.
When volatility is high, the moving average window expands, making the MLMA more stable.
When volatility is low, the window shrinks, making the MLMA more responsive.
Trend Strength Filters ๐
ADX (Average Directional Index) > 25 โ Indicates a strong trend.
RSI (Relative Strength Index) > 70 or < 30 โ Identifies overbought/oversold conditions.
Price Position Relative to Upper/Lower Bands โ Determines bullish vs. bearish momentum.
3๏ธโฃ Volatility Bands & Dynamic Support/Resistance
Bollinger Bands (BB) ๐
Uses standard deviation-based bands around the MLMA to detect overbought and oversold zones.
Upper Band = Resistance, Lower Band = Support.
Helps traders identify breakout potential.
Adaptive Trend Bands ๐ต๐ด
The MLMA has built-in trend envelopes.
When price breaks the upper band, bullish momentum is confirmed.
When price breaks the lower band, bearish momentum is confirmed.
4๏ธโฃ Visual Enhancements
Dynamic Gradient Fills ๐
The trend strength (ADX-based) determines the gradient intensity.
Stronger trends = More vivid colors.
Weaker trends = Lighter colors.
Trend Reversal Arrows ๐
๐ผ Green Up Arrow: Bullish reversal signal.
๐ฝ Red Down Arrow: Bearish reversal signal.
Trend Table Overlay ๐ฅ
Displays ADX, RSI, and Trend State dynamically on the chart.
๐ข Trading Signals & How to Use It
1๏ธโฃ Bullish Signals ๐
โ
Conditions for a Long (Buy) Trade:
The MLMA crosses above the lower band.
The ADX is above 25 (confirming trend strength).
RSI is above 55, indicating positive momentum.
Green trend reversal arrow appears (confirmation of a bullish reversal).
๐น How to Trade It:
Enter a long trade when the MLMA turns bullish.
Set stop-loss below the lower Bollinger Band.
Target previous resistance levels or use the upper band as take-profit.
2๏ธโฃ Bearish Signals ๐
โ
Conditions for a Short (Sell) Trade:
The MLMA crosses below the upper band.
The ADX is above 25 (confirming trend strength).
RSI is below 45, indicating bearish pressure.
Red trend reversal arrow appears (confirmation of a bearish reversal).
๐น How to Trade It:
Enter a short trade when the MLMA turns bearish.
Set stop-loss above the upper Bollinger Band.
Target the lower band as take-profit.
๐ก What Makes This a Machine Learning Moving Average?
๐ 1๏ธโฃ Adaptive & Self-Tuning
Unlike static moving averages that rely on fixed parameters, this MLMA automatically adjusts its sensitivity to market conditions using:
ATR-based dynamic windowing ๐ (Expands/contracts based on volatility).
Adaptive smoothing using EMA, HMA, WMA, or VWAP ๐.
Multi-indicator confirmation (ADX, RSI, Volatility Bands) ๐.
๐ 2๏ธโฃ Intelligent Trend Confirmation
The MLMA "learns" from recent price movements instead of blindly following a fixed-length average.
It incorporates ADX & RSI trend filtering to reduce noise & false signals.
๐ 3๏ธโฃ Dynamic Color-Coding for Trend Strength
Strong trends trigger more vivid colors, mimicking confidence levels in machine learning models.
Weaker trends appear faded, suggesting uncertainty.
๐ฏ Why Use the MLMA?
โ
Pros
โ Combines multiple trend indicators (MA, ADX, RSI, BB).
โ Automatically adjusts to market conditions.
โ Filters out weak trends, making it more reliable.
โ Visually intuitive (gradient colors & reversal arrows).
โ Works across all timeframes and assets.
โ ๏ธ Cons
โ Not a standalone strategy โ Best used with volume confirmation or candlestick analysis.
โ Can lag slightly in fast-moving markets (due to smoothing).
300-Candle Weighted Average Zones w/50 EMA SignalsThis indicator is designed to deliver a more nuanced view of price dynamics by combining a custom, weighted price average with a volatility-based zone and a trend filter (in this case, a 50-period exponential moving average). The core concept revolves around capturing the overall price level over a relatively large lookback window (300 candles) but with an intentional bias toward recent market activity (the most recent 20 candles), thereby offering a balance between long-term context and short-term responsiveness. By smoothing this weighted average and establishing a โzoneโ of standard deviation bands around it, the indicator provides a refined visualization of both average price and its recent volatility envelope. Traders can then look for confluence with a standard trend filter, such as the 50 EMA, to identify meaningful crossover signals that may represent trend shifts or opportunities for entry and exit.
What the Indicator Does:
Weighted Price Average:
Instead of using a simple or exponential moving average, this indicator calculates a custom weighted average price over the past 300 candles. Most historical candles receive a base weight of 1.0, but the most recent 20 candles are assigned a higher weight (for example, a weight of 2.0). This weighting scheme ensures that the calculation is not simply a static lookback average; it actively emphasizes current market conditions. The effect is to generate an average line that is more sensitive to the most recent price swings while still maintaining the historical context of the previous 280 candles.
Smoothing of the Weighted Average:
Once the raw weighted average is computed, an exponential smoothing function (EMA) is applied to reduce noise and produce a cleaner, more stable average line. This smoothing helps traders avoid reacting prematurely to minor price fluctuations. By stabilizing the average line, traders can more confidently identify actual shifts in market direction.
Volatility Zone via Standard Deviation Bands:
To contextualize how far price can deviate from this weighted average, the indicator uses standard deviation. Standard deviation is a statistical measure of volatilityโhow spread out the price values are around the mean. By adding and subtracting one standard deviation from the smoothed weighted average, the indicator plots an upper band and a lower band, creating a zone or channel. The area between these bands is filled, often with a semi-transparent color, highlighting a volatility corridor within which price and the EMA might oscillate.
This zone is invaluable in visualizing โnormalโ price behavior. When the 50 EMA line and the weighted average line are both within this volatility zone, it indicates that the marketโs short- to mid-term trend and its average pricing are aligned well within typical volatility bounds.
Incorporation of a 50-Period EMA:
The inclusion of a commonly used trend filter, the 50 EMA, adds another layer of context to the analysis. The 50 EMA, being a widely recognized moving average length, is often considered a baseline for intermediate trend bias. It reacts faster than a long-term average (like a 200 EMA) but is still stable enough to filter out the market โchopโ seen in very short-term averages.
By overlaying the 50 EMA on this custom weighted average and the surrounding volatility zone, the trader gains a dual-dimensional perspective:
Trend Direction: If the 50 EMA is generally above the weighted average, the short-term trend is gaining bullish momentum; if itโs below, the short-term trend has a bearish tilt.
Volatility Normalization: The bands, constructed from standard deviations, provide a sense of whether the price and the 50 EMA are operating within a statistically โnormalโ range. If the EMA crosses the weighted average within this zone, it signals a potential trend initiation or meaningful shift, as opposed to a random price spike outside normal volatility boundaries.
Why a Trader Would Want to Use This Indicator:
Contextualized Price Level:
Standard MAs may not fully incorporate the most recent price dynamics in a large lookback window. By weighting the most recent candles more heavily, this indicator ensures that the trader is always anchored to what the market is currently doing, not just what it did 100 or 200 candles ago.
Reduced Whipsaw with Smoothing:
The smoothed weighted average line reduces noise, helping traders filter out inconsequential price movements. This makes it easier to spot genuine changes in trend or sentiment.
Visual Volatility Gauge:
The standard deviation bands create a visual representation of โnormalโ price movement. Traders can quickly assess if a breakout or breakdown is statistically significant or just another oscillation within the expected volatility range.
Clear Trade Signals with Confirmation:
By integrating the 50 EMA and designing signals that trigger only when the 50 EMA crosses above or below the weighted average while inside the zone, the indicator provides a refined entry/exit criterion. This avoids chasing breakouts that occur in abnormal volatility conditions and focuses on those crossovers likely to have staying power.
How to Use It in an Example Strategy:
Imagine you are a swing trader looking to identify medium-term trend changes. You apply this indicator to a chart of a popular currency pair or a leading tech stock. Over the past few days, the 50 EMA has been meandering around the weighted average line, both confined within the standard deviation zone.
Bullish Example:
Suddenly, the 50 EMA crosses decisively above the weighted average line while both are still hovering within the volatility zone. This might be your cue: you interpret this crossover as the 50 EMA acknowledging the recent upward shift in price dynamics that the weighted average has highlighted. Since it occurred inside the normal volatility range, itโs less likely to be a head-fake. You place a long position, setting an initial stop just below the lower band to protect against volatility.
If the price continues to rise and the EMA stays above the average, you have confirmation to hold the trade. As the price moves higher, the weighted average may follow, reinforcing your bullish stance.
Bearish Example:
On the flip side, if the 50 EMA crosses below the weighted average line within the zone, it suggests a subtle but meaningful change in trend direction to the downside. You might short the asset, placing your protective stop just above the upper band, expecting that the statistically โnormalโ level of volatility will contain the price action. If the price does break above those bands later, itโs a sign your trade may not work out as planned.
Other Indicators for Confluence:
To strengthen the reliability of the signals generated by this weighted average zone approach, traders may want to combine it with other technical studies:
Volume Indicators (e.g., Volume Profile, OBV):
Confirm that the trend crossover inside the volatility zone is supported by volume. For instance, an uptrend crossover combined with increasing On-Balance Volume (OBV) or volume spikes on up candles signals stronger buying pressure behind the price action.
Momentum Oscillators (e.g., RSI, Stochastics):
Before taking a crossover signal, check if the RSI is above 50 and rising for bullish entries, or if the Stochastics have turned down from overbought levels for bearish entries. Momentum confirmation can help ensure that the trend change is not just an isolated random event.
Market Structure Tools (e.g., Pivot Points, Swing High/Low Analysis):
Identify if the crossover event coincides with a break of a previous pivot high or low. A bullish crossover inside the zone aligned with a break above a recent swing high adds further strength to your conviction. Conversely, a bearish crossover confirmed by a breakdown below a previous swing low can make a short trade setup more compelling.
Volume-Weighted Average Price (VWAP):
Comparing where the weighted average zone lies relative to VWAP can provide institutional insight. If the bullish crossover happens while the price is also holding above VWAP, it can mean that the average participant in the market is in profit and that the trend is likely supported by strong hands.
This indicator serves as a tool to balance long-term perspective, short-term adaptability, and volatility normalization. It can be a valuable addition to a traderโs toolkit, offering enhanced clarity and precision in detecting meaningful shifts in trend, especially when combined with other technical indicators and robust risk management principles.
Ichimoku Wave Oscillator with Custom MAIchimoku Wave Oscillator with Custom MA - Pine Script Description
This script uses various types of moving averages (MA) to implement the concept of Ichimoku wave theory for wave analysis. The user can select from SMA, EMA, WMA, TEMA, SMMA to visualize the difference between short-term, medium-term, and long-term waves, while identifying potential buy and sell signals at crossover points.
Key Features:
MA Type Selection:
The user can select from SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weighted Moving Average), TEMA (Triple Exponential Moving Average), and SMMA (Smoothed Moving Average) to calculate the waves. This script is unique in that it combines TEMA and SMMA, distinguishing it from other simple moving average-based indicators.
TEMA (Triple Exponential Moving Average): Best suited for capturing short-term trends with quick responsiveness.
SMMA (Smoothed Moving Average): Useful for identifying long-term trends with minimal noise, providing more stable signals.
Wave Calculations:
The script calculates three waves: Wave 9-17, Wave 17-26, and Wave 9-26, each of which analyzes different time horizons.
Wave 9-17 (blue): Primarily used for analyzing short-term trends, ideal for detecting quick changes.
Wave 17-26 (red): Used to analyze medium-term trends, providing a more stable market direction.
Wave 9-26 (green): Represents long-term trends, suitable for understanding broader trend shifts.
Baseline (0 Line):
Each wave is visualized around the 0 line, where waves above the line indicate an uptrend and waves below the line indicate a downtrend. This allows for easy identification of trend reversals.
Crossover Signals:
CrossUp: When Wave 9-17 (short-term wave) crosses Wave 17-26 (medium-term wave) upward, it is considered a buy signal, indicating a potential upward trend shift.
CrossDown: When Wave 9-17 (short-term wave) crosses Wave 17-26 downward, it is considered a sell signal, indicating a potential downward trend shift.
Background Color for Signal:
The script visually highlights the signals with background colors. When a buy signal occurs, the background turns green, and when a sell signal occurs, the background turns red. This makes it easier to spot reversal points.
Calculation Method:
The script calculates the difference between moving averages to display the wave oscillation. Wave 9-17, Wave 17-26, and Wave 9-26 represent the difference between the moving averages for different time periods, allowing for analysis of short-term, medium-term, and long-term trends.
Wave 9-17 = MA(9) - MA(17): Represents the difference between the short-term moving averages.
Wave 17-26 = MA(17) - MA(26): Represents the difference between medium-term moving averages.
Wave 9-26 = MA(9) - MA(26): Provides insight into the long-term trend.
This calculation method effectively visualizes the oscillation of waves and helps identify trend reversals at crossover points.
Uniqueness of the Script:
Unlike other moving average-based indicators, this script combines TEMA (Triple Exponential Moving Average) and SMMA (Smoothed Moving Average) to capture both short-term sensitivity and long-term stability in trends. This duality makes the script more versatile for different market conditions.
TEMA is ideal for short-term traders who need quick signals, while SMMA is useful for long-term investors seeking stability and noise reduction. By combining these two, this script provides a more refined analysis of trend changes across various timeframes.
How to Use:
This script is effective for trend analysis and reversal detection. By visualizing the crossover points between the waves, users can spot potential buy and sell signals to make more informed trading decisions.
Scalping strategies can rely on Wave 9-17 to detect quick trend changes, while those looking for medium-term trends can analyze signals from Wave 17-26.
For a broader market overview, Wave 9-26 helps users understand the long-term market trend.
This script is built on the concept of wave theory to anticipate trend changes, making it suitable for various timeframes and strategies. The user can tailor the characteristics of the waves by selecting different MA types, allowing for flexible application across different trading strategies.
Ichimoku Wave Oscillator with Custom MA - Pine Script ์ค๋ช
์ด ์คํฌ๋ฆฝํธ๋ ๋ค์ํ ์ด๋ ํ๊ท (MA) ์ ํ์ ํ์ฉํ์ฌ ์ผ๋ชฉ ํ๋๋ก ์ ๊ฐ๋
์ ๊ธฐ๋ฐ์ผ๋ก ํ๋ ๋ถ์์ ์๋ํ๋ ์งํ์
๋๋ค. ์ฌ์ฉ์๋ SMA, EMA, WMA, TEMA, SMMA ์ค ์ํ๋ ์ด๋ ํ๊ท ์ ์ ํํ ์ ์์ผ๋ฉฐ, ์ด๋ฅผ ํตํด ๋จ๊ธฐ, ์ค๊ธฐ, ์ฅ๊ธฐ ํ๋ ๊ฐ์ ์ฐจ์ด๋ฅผ ์๊ฐํํ๊ณ , ๊ต์ฐจ์ ์์ ์์น ๋ฐ ํ๋ฝ ์ ํธ๋ฅผ ํฌ์ฐฉํ ์ ์์ต๋๋ค.
์ฃผ์ ๊ธฐ๋ฅ:
์ด๋ ํ๊ท (MA) ์ ํ ์ ํ:
์ฌ์ฉ์๋ SMA(๋จ์ ์ด๋ ํ๊ท ), EMA(์ง์ ์ด๋ ํ๊ท ), WMA(๊ฐ์ค ์ด๋ ํ๊ท ), TEMA(์ผ์ค ์ง์ ์ด๋ ํ๊ท ), SMMA(ํํ ์ด๋ ํ๊ท ) ์ค ํ๋๋ฅผ ์ ํํ์ฌ ํ๋์ ๊ณ์ฐํ ์ ์์ต๋๋ค. ์ด ์คํฌ๋ฆฝํธ๋ TEMA์ SMMA์ ๋
์ฐฝ์ ์ธ ์กฐํฉ์ ํตํด ๊ธฐ์กด์ ๋จ์ํ ์ด๋ ํ๊ท ์งํ์ ์ฐจ๋ณํ๋ฉ๋๋ค.
TEMA(์ผ์ค ์ง์ ์ด๋ ํ๊ท ): ๋น ๋ฅธ ๋ฐ์์ผ๋ก ๋จ๊ธฐ ํธ๋ ๋๋ฅผ ํฌ์ฐฉํ๋ ๋ฐ ์ ํฉํฉ๋๋ค.
SMMA(ํํ ์ด๋ ํ๊ท ): ์ฅ๊ธฐ์ ์ธ ์ถ์ธ๋ฅผ ํ์
ํ๋ ๋ฐ ์ ์ฉํ๋ฉฐ, ๋
ธ์ด์ฆ๋ฅผ ์ต์ํํ์ฌ ์์ ์ ์ธ ์ ํธ๋ฅผ ์ ๊ณตํฉ๋๋ค.
ํ๋(Wave) ๊ณ์ฐ:
์ด ์คํฌ๋ฆฝํธ๋ Wave 9-17, Wave 17-26, Wave 9-26์ ์ธ ๊ฐ์ง ํ๋์ ๊ณ์ฐํ์ฌ ๊ฐ๊ฐ ๋จ๊ธฐ, ์ค๊ธฐ, ์ฅ๊ธฐ ์ถ์ธ๋ฅผ ๋ถ์ํฉ๋๋ค.
Wave 9-17 (ํ๋์): ์ฃผ๋ก ๋จ๊ธฐ ์ถ์ธ๋ฅผ ๋ถ์ํ๋ ๋ฐ ์ฌ์ฉ๋๋ฉฐ, ๋น ๋ฅธ ์ถ์ธ ๋ณํ๋ฅผ ํฌ์ฐฉํ๋ ๋ฐ ์ ์ฉํฉ๋๋ค.
Wave 17-26 (๋นจ๊ฐ์): ์ค๊ธฐ ์ถ์ธ๋ฅผ ๋ถ์ํ๋ ๋ฐ ์ฌ์ฉ๋๋ฉฐ, ์ข ๋ ์์ ์ ์ธ ์์ฅ ํ๋ฆ์ ๋ณด์ฌ์ค๋๋ค.
Wave 9-26 (๋
น์): ์ฅ๊ธฐ ์ถ์ธ๋ฅผ ๋ํ๋ด๋ฉฐ, ํฐ ํ๋ฆ์ ๋ฐฉํฅ์ฑ์ ํ์
ํ๋ ๋ฐ ์ ํฉํฉ๋๋ค.
๊ธฐ์ค์ (0 ๋ผ์ธ):
๊ฐ ํ๋์ 0 ๋ผ์ธ์ ๊ธฐ์ค์ผ๋ก ๋ณ๋์ฑ์ ์๊ฐํํฉ๋๋ค. 0 ์์ ์๋ ํ๋์ ์์น์ธ, 0 ์๋์ ์๋ ํ๋์ ํ๋ฝ์ธ๋ฅผ ๋ํ๋ด๋ฉฐ, ์ด๋ฅผ ํตํด ์ถ์ธ์ ์ ํ์ ์ฝ๊ฒ ํ์ธํ ์ ์์ต๋๋ค.
ํ๋ ๊ต์ฐจ ์ ํธ:
CrossUp: Wave 9-17(๋จ๊ธฐ ํ๋)์ด Wave 17-26(์ค๊ธฐ ํ๋)์ ์ํฅ ๊ต์ฐจํ ๋, ์์น ์ ํธ๋ก ๊ฐ์ฃผ๋ฉ๋๋ค. ์ด๋ ๋จ๊ธฐ์ ์ธ ์ถ์ธ ๋ณํ๊ฐ ๋ฐ์ํ ์ ์์์ ์๋ฏธํฉ๋๋ค.
CrossDown: Wave 9-17(๋จ๊ธฐ ํ๋)์ด Wave 17-26(์ค๊ธฐ ํ๋)์ ํํฅ ๊ต์ฐจํ ๋, ํ๋ฝ ์ ํธ๋ก ํด์๋ฉ๋๋ค. ์ด๋ ์์ฅ์ด ์ฝ์ธ๋ก ๋์์ค ๊ฐ๋ฅ์ฑ์ ๋ํ๋
๋๋ค.
๋ฐฐ๊ฒฝ ์์ ํ์:
๊ต์ฐจ ์ ํธ๊ฐ ๋ฐ์ํ ๋, ์์น ์ ํธ๋ ๋
น์ ๋ฐฐ๊ฒฝ, ํ๋ฝ ์ ํธ๋ ๋นจ๊ฐ์ ๋ฐฐ๊ฒฝ์ผ๋ก ์๊ฐ์ ์ผ๋ก ๊ฐ์กฐ๋์ด ์ฌ์ฉ์๊ฐ ์ ํธ๋ฅผ ์ฝ๊ฒ ์ธ์ํ ์ ์์ต๋๋ค.
๊ณ์ฐ ๋ฐฉ์:
์ด ์คํฌ๋ฆฝํธ๋ ์ด๋ ํ๊ท ๊ฐ์ ์ฐจ์ด๋ฅผ ๊ณ์ฐํ์ฌ ๊ฐ ํ๋์ ๋ณ๋์ฑ์ ๋ํ๋
๋๋ค. Wave 9-17, Wave 17-26, Wave 9-26์ ๊ฐ๊ฐ ์ค์ ๋ ์ฃผ๊ธฐ์ ์ด๋ ํ๊ท (MA)์ ์ฐจ์ด๋ฅผ ํตํด, ์์ฅ์ ๋จ๊ธฐ, ์ค๊ธฐ, ์ฅ๊ธฐ ์ถ์ธ ๋ณํ๋ฅผ ์๊ฐ์ ์ผ๋ก ํํํฉ๋๋ค.
Wave 9-17 = MA(9) - MA(17): ๋จ๊ธฐ ์ถ์ธ์ ์ฐจ์ด๋ฅผ ๋ํ๋
๋๋ค.
Wave 17-26 = MA(17) - MA(26): ์ค๊ธฐ ์ถ์ธ์ ์ฐจ์ด๋ฅผ ๋ํ๋
๋๋ค.
Wave 9-26 = MA(9) - MA(26): ์ฅ๊ธฐ์ ์ธ ์ถ์ธ ๋ฐฉํฅ์ ํ์
ํ ์ ์์ต๋๋ค.
์ด๋ฌํ ๊ณ์ฐ ๋ฐฉ์์ ํ๋์ ๋ณ๋์ฑ์ ํ์
ํ๋ ๋ฐ ์ ์ฉํ๋ฉฐ, ์ถ์ธ์ ๊ต์ฐจ์ ์ ํตํด ์์น/ํ๋ฝ ์ ํธ๋ฅผ ์ก์๋
๋๋ค.
์คํฌ๋ฆฝํธ์ ๋
์ฐฝ์ฑ:
์ด ์คํฌ๋ฆฝํธ๋ ๊ธฐ์กด์ ์ด๋ ํ๊ท ๊ธฐ๋ฐ ์งํ๋ค๊ณผ ๋ฌ๋ฆฌ, TEMA(์ผ์ค ์ง์ ์ด๋ ํ๊ท )์ SMMA(ํํ ์ด๋ ํ๊ท )์ ํจ๊ป ์ฌ์ฉํ์ฌ ์งง์ ์ฃผ๊ธฐ์ ๊ธด ์ฃผ๊ธฐ์ ํธ๋ ๋๋ฅผ ๋์์ ํ์
ํ ์ ์๋๋ก ์ค๊ณ๋์์ต๋๋ค. ์ด๋ฅผ ํตํด ๋จ๊ธฐ ํธ๋ ๋์ ๋ฏผ๊ฐํ ๋ณํ์ ์ฅ๊ธฐ ํธ๋ ๋์ ์์ ์ฑ์ ๋ชจ๋ ๋ฐ์ํฉ๋๋ค.
TEMA๋ ๋จ๊ธฐ ํธ๋ ์ด๋์๊ฒ ๋น ๋ฅด๊ณ ๋ฏผ์ฒฉํ ์ ํธ๋ฅผ ์ ๊ณตํ๋ฉฐ, SMMA๋ ์ฅ๊ธฐ ํฌ์์์๊ฒ ๋ณด๋ค ์์ ์ ์ด๊ณ ๊ธด ํธํก์ ํธ๋ ๋๋ฅผ ํ์
ํ๋ ๋ฐ ์ ๋ฆฌํฉ๋๋ค. ๋ ์งํ์ ๊ฒฐํฉ์ผ๋ก, ๋ค์ํ ์์ฅ ํ๊ฒฝ์์ ์ถ์ธ์ ๋ณํ๋ฅผ ๋ ์ ๊ตํ๊ฒ ๋ถ์ํ ์ ์์ต๋๋ค.
์ฌ์ฉ ๋ฐฉ๋ฒ:
์ด ์คํฌ๋ฆฝํธ๋ ์ถ์ธ ๋ถ์๊ณผ ๋ณ๊ณก์ ํฌ์ฐฉ์ ํจ๊ณผ์ ์
๋๋ค. ๊ฐ ํ๋ ๊ฐ์ ๊ต์ฐจ์ ์ ์๊ฐ์ ์ผ๋ก ํ์ธํ๊ณ , ์์น ๋๋ ํ๋ฝ ์ ํธ๋ฅผ ํฌ์ฐฉํ์ฌ ๋งค๋งค ์์ ๊ฒฐ์ ์ ๋์ธ ์ ์์ต๋๋ค.
์ค์บํ ์ ๋ต์์๋ Wave 9-17์ ์ฃผ๋ก ์ฐธ๊ณ ํ์ฌ ๋น ๋ฅด๊ฒ ์ถ์ธ ๋ณํ๋ฅผ ์ก์๋ด๊ณ , ์ค๊ธฐ ์ถ์ธ๋ฅผ ์ฐธ๊ณ ํ๊ณ ์ถ์ ๊ฒฝ์ฐ Wave 17-26์ ์ฌ์ฉํด ์ ํธ๋ฅผ ๋ถ์ํ ์ ์์ต๋๋ค.
์ฅ๊ธฐ์ ์ธ ์์ฅ ํ๋ฆ์ ํ์
ํ๊ณ ์ ํ ๋๋ Wave 9-26์ ํตํด ํฐ ํธ๋ ๋๋ฅผ ํ์ธํ ์ ์์ต๋๋ค.
์ด ์คํฌ๋ฆฝํธ๋ ํ๋ ์ด๋ก ์ ๊ฐ๋
์ ๊ธฐ๋ฐ์ผ๋ก ์์ฅ์ ์ถ์ธ ๋ณํ๋ฅผ ์์ธกํ๋ ๋ฐ ์ ์ฉํ๋ฉฐ, ๋ค์ํ ์๊ฐ๋์ ์ ๋ต์ ๋ง์ถ์ด ์ฌ์ฉํ ์ ์์ต๋๋ค. ํนํ, ์ฌ์ฉ์๊ฐ ์ ํํ MA ์ ํ์ ๋ฐ๋ผ ํ๋์ ํน์ฑ์ ๋ณํ์ํฌ ์ ์์ด, ์ฌ๋ฌ ๋งค๋งค ์ ๋ต์ ์ ์ฐํ๊ฒ ๋์ํ ์ ์์ต๋๋ค.
