MA SLope Potential Divergence - FontiramisuIndicator showing potential momentum divergences on Moving Average's Slope.
The problem with the classic divergence is that when the signal appears, it is sometimes too late to enter a trade .
The potential divergence corrects this problem by signaling the beginning of a potential divergence .
Moving average slope is a momentum indicator that offers relevant insights with divergences
Potential divergences are indicated with the letter B and a red color for Bearish Div or Green color for Bullish Div .
Potential divergence is confirmed when the line and the label "Bear"' or "Bull" appear.
You can either show fast slope's divergences or slow slope's divergences or slow/fast diff's divergences.
Komut dosyalarını "momentum" için ara
Momentum-based ZigZag (incl. QQE) NON-REPAINTINGI spent a lot of time searching for the best ZigZag indicator. Difficulty with all of them is that they are always betting on some pre-defined rules which identify or confirm pivot points. Usually it is time factor - pivot point gets confirmed after a particular number of candles. This methodology is probably the best when market is moving relatively slow, but when price starts chopping up and down, there is no way the ZigZag follows accurately. On the other hand if you set it too tight (for example pivot confirmation after only 2 or even 1 candle), you will get hundreds of zigzag lines and they will tell you nothing.
My point of view is to follow the market. If it has reversed, then it has reversed, and there is no need to wait pre-defined number of candles for the confirmation. Such reversals will always be visible on momentum indicators, such as the most popular MACD. But a single-line moving average can be also good enough to notice reversals. Or my favourite one - QQE, which I borrowed (and improved) from JustUncleL, who borrowed it from Glaz, who borrowed it from... I don't even know where Quantitative Qualitative Estimation originates from. Thanks to all these guys for their input and code.
So whichever momentum indicator you choose - yes, there is a pick-your-poison-type selector as in in-famous Moving Average indicators - once it reverses, a highest (or lowest) point from the impulse is caught and ZigZag gets printed.
One thing I need to emphasize. This indicator DOES NOT REPAINT. It might look like the lines are a bit delayed, especially when compared to all the other ZigZag indicators on TradingView, but they are actually TRUE. There is a value in this - my indicator prints pivot points and Zigzag exactly on the moment they have been noticed, not earlier faking to be faster than they could be.
As a bonus, the indicator marks which impulse had strength in it. It is very nice to see a progressing impulse, but without force - a very likely that reversal on a bigger move is happening.
I'm about to publish some more scripts based on this ZigZag algo, so follow me on TradingView to get notified.
Enjoy!
Spread for VSAЭтот индикатор сравнивает спрэд (расстояние от закрытия предыдущего бара до закрытия текущего бара или индикатор Momentum = 1) на периоде для сравнения.
На графике за 100 % принимается среднее значение спрэда за период для сравнения - красная линия. (по умолчанию период сравнения равен 3 - то есть три последних бара)
Размер бара на графике равен текущему спрэду по отношению к 100 %.
Если бар меньше 100 % то он ниже среднего, и наоборот если больше 100% то он больше среднего.
Если бар красный - спрэд отрицательный (текущее закрытие меньше предыдущего закрытия)
Если бар зелёный - спрэд положительный (текущее закрытие больше предыдущего закрытия)
Если бар меньше 75% то он будет окрашен в тусклый цвет (этот процент можно менять в настройках)
Если в настройках период спрэда указать больше 1, например 2, то спрэд будет равен закрытие мину закрытие через 1 бар назад. (это для экспериментов).
Примечание:
по умолчанию период для сравнения равен 3, но также интересен график и при значениях 15 и больше. Экспериментируйте.
По вопросам и предложениям пишите в комментариях.
Automatic translation google translate.
This indicator compares the spread (the distance from the closing of the previous bar to the closing of the current bar or the Momentum indicator = 1) on the period for comparison.
On the chart, the average spread value for the period for comparison is the red line, taken as 100%. (by default, the comparison period is 3 - that is, the last three bars)
The size of the bar on the chart is equal to the current spread with respect to 100%.
If the bar is less than 100%, then it is below average, and vice versa, if more than 100%, then it is more than average.
If the bar is red, the spread is negative (the current close is less than the previous close)
If the bar is green, the spread is positive (the current close is greater than the previous close)
If the bar is less than 75%, then it will be painted in a dull color (this percentage can be changed in the settings)
If in the settings the period of the spread is specified more than 1, for example 2, then the spread will be equal to closing mine closing after 1 bar back. (this is for experimentation).
Note:
the default period for comparison is 3, but the chart is also interesting for values of 15 or more. Experiment.
For questions and suggestions, write in the comments.
Momentum on EMA oscillatorThis script calculates the momentum on a 2 ema oscillator.
Ema crossing often (always ?) come too late. The momentum is used to anticipate the moment where the 2 ema will cross, offering a signal to take into account.
Does not work well in a range market. Signal comes too late and is thus in opposition with the market timing.
Seeing such a trap is trivial as you won't buy if the market is slightly bear or flat.
Signals is quite good to detect the begining of a bull/bear market.
Can display some interesting divergence.
Function : Know Sure Thing ! (KST)Firstly : Know Sure Thing, or KST , is a momentum oscillator developed by Martin Pring to make rate-of-change readings easier for traders to interpret. In a 1992 Stocks and Commodities article, Mr. Pring referred to the indicator as "Summed Rate of Change ( KST )," but the KST term stuck with technical analysts. The indicator is relatively common among technical analysts preferring momentum oscillators to make decisions.
References : Investopedia (www.investopedia.com )
Let's start :
Simply :
KST : Above point 0 means long position (positive zone), below point 0 (negative zone) means short position.
I liked this indicator more than RSI because we can evaluate the breaking points of the channels we draw on the indicator according to the regions.
Plus area (positive area), breaking the channel upwards may indicate a very strong rise, and minus area (negative area) the channel downwards may indicate a very strong fall.
As a person who is very keen to identify major trends in advance, I like the KST indicator to approach the target quickly and simply. I also find it very successful in terms of divergences.
CAUTION : This indicator has been written before many times on TV. I have no effort on it. I saved loads only for variable periods. But I have enough experience to say that you are successful in trends with KST . Nevertheless, do not use it alone, as other promoters may benefit.
For example , I divided the standard periods into 4 as in the script. With a correct adaptive period, it has the potential to contribute greatly to accurate moves! You can use with mutable variable periods. Abundant trend lines can be drawn on the indicator and divergences between price and indicator can be sought. Best regards!
Momentum Exhaustion + Swing Points ComboCombination of @Zeenobit's Swing Points and Momentum Exhaustion indicators; RSX instead of RSI from @jaggedsoft/@everget's RSX Divergences script(s).
Momentum VisualizerA colorful indicator that visually shows momentum, fast-moving average on the outside and the slower moving averages in the core. The outer moving average is to see where the outer momentum breaks below again.
Momentum Strategy, rev.2This is a revised version of the Momentum strategy listed in the built-ins.
For more information check out this resource:
www.forexstrategiesresources.com
Momentum Signals – Real-time (Repainting)This indicator generates real-time BUY/SELL signals using a confluence of VWMA trend, 3-bar momentum, and volume, then filters them by a strength score.
⚠️ **WARNING:** This version **repaints**; signals can appear and disappear before the bar closes.
Momentum Signals – Real-time (Repainting)This indicator generates real-time BUY/SELL signals using a confluence of VWMA trend, 3-bar momentum, and volume, then filters them by a strength score.
⚠️ WARNING: This version repaints; signals can appear and disappear before the bar closes.
Momentum Candle DetectorThe momentum candle indicator highlights a candle with a body having a defined % of the range, and a close within a defined % of the high/low.
Momentum Reversal StrategyBEST USE IN 15MIN TIME FRAME EURUSD / XAUSUD
1. Strategy Overview
This strategy hunts short-term momentum reversals at key levels during high-liquidity sessions.
Timeframes: 5-minute for entries; 15-minute for trend context
Sessions: London for EUR/USD & GBP/USD; New York for XAU/USD
Pairs: EUR/USD, GBP/USD, XAU/USD
Indicators (3 max):
EMA(20) and EMA(50) (close)
MACD (12, 26, 9) histogram
Optional: RSI(14) (for divergence filter)
2. Entry Rules
Trend Filter (15 min):
Long only if EMA20 > EMA50; short only if EMA20 < EMA50.
Price-Action Zone (5 min):
Identify recent swing high/low within past 20 bars.
Draw horizontal support (for longs) or resistance (for shorts).
Indicator Alignment (5 min):
MACD histogram crossing from negative to positive for longs, positive to negative for shorts.
Candle close beyond EMA20 in direction of trade.
Candle Confirmation:
Bullish engulfing or hammer at support for longs; bearish engulfing or shooting star at resistance for shorts.
Entry Execution:
Place market order on candle close that meets all above.
3. Exit Rules
Stop-Loss (SL):
Long: 1.5× ATR(14) below entry candle low.
Short: 1.5× ATR(14) above entry candle high.
Take-Profit (TP):
Set at 2× SL distance (RR 1:2).
Trailing SL:
After price moves 1× SL in profit, trail SL to breakeven.
Partial Booking:
Close 50% at 1× SL (50% of TP), move SL to entry.
Close remaining at full TP.
4. Trade Management
False Signal Filter: Skip trades when RSI(14) > 70 for longs or < 30 for shorts (avoids overbought/oversold extremes).
One Trade at a Time: No multiple positions on same pair.
Session Cutoff: Close any open trade 15 minutes before session end.
5. Risk Parameters
Risk per Trade: 1% of account equity.
Reward Target: ≥2% (1:2 RR) per trade.
Win-Rate Expectancy: ≥75% based on indicator confluence and price-action confirmation.
RSI Mansfield +RSI Mansfield+ – Adaptive Relative Strength Indicator with Divergences
Overview
RSI Mansfield+ is an advanced relative strength indicator that compares your instrument’s performance against a configurable benchmark index or asset (e.g., Bitcoin Dominance, S&P 500). It combines Mansfield normalization, adaptive smoothing techniques, and automatic detection of bullish and bearish divergences (regular and hidden), delivering a comprehensive tool for assessing relative strength across any market and timeframe.
Originality and Motivation
Unlike traditional relative strength scripts, this indicator introduces several distinctive improvements:
Mansfield Normalization: Scales the ratio between the asset and the benchmark relative to its moving average, transforming it into a normalized oscillator that fluctuates around zero, making it easier to spot outperformance or underperformance.
Adaptive Smoothing: Automatically selects whether to use EMA or SMA based on the market type (crypto or stocks) and timeframe (intraday, daily, weekly, monthly), avoiding manual configuration and providing more robust results under varying volatility conditions.
Divergence Detection: Identifies four types of divergences in the Mansfield oscillator to help anticipate potential reversal points or trend confirmations.
Multi-Market Support: Offers benchmark selection among major crypto and global stock indices from a single input.
These enhancements make RSI Mansfield+ more practical and powerful than conventional relative strength scripts with static benchmarks or without divergence capabilities.
Core Concepts
Relative Strength (RS): Compares price evolution between your asset and the selected benchmark.
Mansfield Normalization: Measures how much the RS deviates from its historical moving average, expressed as a scaled oscillator.
Divergences: Detects regular and hidden bullish or bearish divergences within the Mansfield oscillator.
Timeframe Adaptation: Dynamically adjusts moving average lengths based on timeframe and market type.
How It Works
Benchmark Selection
Choose among over 10 indices or market domains (BTC Dominance, ETH Dominance, S&P 500, European indices, etc.).
Ratio Calculation
Computes the price-to-benchmark ratio and smooths it with the adaptive moving average.
Normalization and Scaling
Transforms deviations into a Mansfield oscillator centered around zero.
Dynamic Coloring
Green indicates relative outperformance, red signals underperformance.
Divergence Detection
Automatically identifies bullish and bearish (regular and hidden) divergences by comparing oscillator pivots against price pivots.
Baseline Reference
A clear zero line helps interpret relative strength trends.
Usage Guidelines
Benchmark Comparison
Ideal for traders analyzing whether an asset is outperforming or lagging its sector or market.
Divergence Analysis
Helps detect potential reversal or continuation signals in relative strength.
Multi-Timeframe Compatibility
Can be applied to intraday, daily, weekly, or monthly charts.
Interpretation
Oscillator >0 and green: outperforming the benchmark.
Oscillator <0 and red: underperforming.
Bullish divergences: potential relative strength reversal to the upside.
Bearish divergences: possible loss of momentum or reversal to the downside.
Credits
The concept of Mansfield Relative Strength is based on Stan Weinstein’s original work on relative performance analysis. This script was built entirely from scratch in TradingView Pine Script v6, incorporating original logic for adaptive smoothing, normalized scaling, and divergence detection, without reusing any external open-source code.
OA - SMESSmart Money Entry Signals (SMES)
The SMES indicator is developed to identify potential turning points in market behavior by analyzing internal price dynamics, rather than relying on external volume or sentiment data. It leverages normalized price movement, directional volatility, and smoothing algorithms to detect potential areas of accumulation or distribution by market participants.
Core Concepts
Smart Money Flow calculation based on normalized price positioning
Directional VHF (Vertical Horizontal Filter) used to enhance signal directionality
Overbought and Oversold regions defined with optional glow visualization
Entry and Exit signals based on dynamic crossovers
Highly customizable input parameters for precision control
Key Inputs
Smart Money Flow Period
Smoothing Period
Price Analysis Length
Fibonacci Lookback Length
Visual toggle options (zones, glow effects, signal display)
Usage
This tool plots the smoothed smart money flow as a standalone oscillator, designed to help traders identify potential momentum shifts or extremes in market sentiment. Entry signals are generated through crossover logic, while optional filters based on price behavior can refine those signals. Exit signals are shown when the smart money line exits extreme regions.
Important Notes
This indicator does not repaint
Works on all timeframes and instruments
Best used as a confirmation tool with other technical frameworks
All calculations are based strictly on price data
Disclaimer
This script is intended for educational purposes only. It does not provide financial advice or guarantee performance. Please do your own research and apply appropriate risk management before making any trading decisions.
Institutional Composite Moving Average (ICMA) [Volume Vigilante]Institutional Composite Moving Average (ICMA)
The Next Evolution of Moving Averages — Built for Real Traders.
ICMA blends the strength of four powerful averages (SMA, EMA, WMA, HMA) into a single ultra-responsive, ultra-smooth signal.
It reacts faster than traditional MAs while filtering out noise, giving you clean trend direction with minimal lag.
🔹 Key Features:
• Faster reaction than SMA, EMA, or WMA individually
• Smoother and more stable than raw HMA
• Naturally adapts across trend, momentum, and consolidation conditions
• Zero gimmicks. Zero repainting. Full institutional quality.
🔹 Designed For:
• Scalping
• Swing trading
• Signal engines
• Algorithmic systems
📎 How to Use:
• Overlay it on any chart
• Fine-tune the length per timeframe
• Combine with your entries/exits for maximum edge
Created by Volume Vigilante 🧬 — Delivering Real-World Trading Tools.
Catalyst TrendCatalyst Trend – A Comprehensive Trend and Regime Analyzer
The Catalyst Trend indicator was designed to dynamically and intuitively merge various classic analytical techniques. The goal is to filter out short-term market noise and reveal reliable trend phases or potential turning points. Below is a detailed explanation of its core elements and practical usage.
1. Concept and Idea
Multidimensional Trend Detection
This indicator goes beyond a simple momentum or volatility focus. It factors in multiple measurements to provide a more well-rounded market perspective.
Versatile Indicator Fusion
Linear Regression (LinReg): Multiple LinReg calculations are combined to smooth out price fluctuations and produce a robust trendline—known here as the “Cycle Reduced Line.”
ADX (Average Directional Index): Measures trend strength.
RSI (Relative Strength Index): Flags potential overbought or oversold conditions, in both the current timeframe and a higher timeframe.
ATR (Average True Range): Assesses volatility; used to dynamically adjust calculation lengths.
By weaving these elements together, the indicator adds value beyond simply stacking multiple indicators. It adapts to real-time market conditions, aiming to highlight genuine trends and reduce false signals.
2. Key Functions and Calculations
Dynamic Length & Smoothing
A blend of volatility (ATR), ADX values, and RSI inputs determines how many candles are used in the LinReg calculations and how heavily the data is smoothed.
This allows the indicator to respond promptly during periods of high volatility, while automatically adjusting to filter out unnecessary noise in quieter phases.c
Cycle Reduced Line
The script averages several offset LinReg calculations to produce a cleaner overall signal. Random outliers are thus minimized, making the trend path more visually consistent.
An additional EMA smoothing (“Final Smoothing”) further stabilizes this trendline, reducing the impact of minor price fluctuations.
Channel Bands (Optional)
These bands are derived from the standard deviation of the price residual (the difference between the smoothed price and the trendline).
They highlight potential over-extension zones: the upper band can mark short-term overbought areas, while the lower band might indicate oversold conditions.
Trend and Sideways Determination
Slope Calculation: The slope of the trendline (comparing the current bar to the previous one) helps identify short-term directional shifts.
DX Threshold: Once the ADX surpasses a user-defined threshold and the slope is positive, it may indicate a developing uptrend. Similarly, if the slope is negative and ADX > threshold, it could signal a potential downtrend.
Multi-Level Color Coding
Original Mode: Interpolated colors reflect uptrends, downtrends, and sideways phases, factoring in metrics like ADX and RSI.
Single Color: For a neutral look, the indicator can be displayed in one uniform color.
HTF RSI: This mode uses the higher-timeframe RSI to color the trendline (Long/Short/Neutral), offering a quick gauge of overarching market pressure.
3. Use Cases and Interpretation
Timeframes & Markets
The indicator is versatile and adapts well to different intervals, from 5-minute charts to weekly views.
It can be applied to various markets—crypto, forex, stocks—since volatility and trend strength are universal concepts.
Signal Recognition
Color Swings into a more pronounced upward hue (e.g., green) may signal mounting strength.
Neutral or mixed tones often point to sideways phases, which breakout traders might watch for potential price surges.
A shift to downward colors (e.g., red) may indicate a growing bearish trend.
Channel Bands & Volatility
When the bands spread widely, it’s wise to proceed with caution: abrupt spikes above the upper band or below the lower band can flag rapid short-term extremes.
These bands are more of a reference for potential overextension than a strict buy or sell trigger.
Additional Confirmations
Not a standalone panacea: The Catalyst Trend indicator is an analytical tool, best used alongside other methods such as volume analysis or price action (candlestick patterns, support/resistance levels) to bolster confidence in trading decisions.
4. Practical Tips
Parameter Adjustments
Depending on the market—crypto vs. traditional currency pairs—different ADX, RSI, or smoothing periods may be more effective. Experiment with the settings to tailor the indicator to your preferred timeframe.
Strategic Integration
Trailing Stops: For those riding a trend, the trendline or the channel bands may serve as a reference to trail stop-loss orders.
Trend Confirmation: Using RSI and ADX filters can help traders avoid sideways markets or stay the course when the trend is strong.
5. Important Final Notes
No Guarantee of Profits
No indicator can predict the future. Markets are inherently volatile and often unpredictable.
Responsible Risk Management
Test the indicator in a demo environment or with smaller positions before committing to large trades.
RSI+EMA+MZONES with DivergencesFeatures:
1. RSI Calculation:
Uses user-defined periods to calculate the RSI and visualize momentum shifts.
Plots key RSI zones, including upper (overbought), lower (oversold), and middle levels.
2. EMA of RSI:
Includes an Exponential Moving Average (EMA) of the RSI for trend smoothing and confirmation.
3. Bullish and Bearish Divergences:
Detects Regular divergences (labeled as “Bull” and “Bear”) for classic signals.
Identifies Hidden divergences (labeled as “H Bull” and “H Bear”) for potential trend continuation opportunities.
4. Customizable Labels:
Displays divergence labels directly on the chart.
Labels can be toggled on or off for better chart visibility.
5. Alerts:
Predefined alerts for both regular and hidden divergences to notify users in real time.
6. Fully Customizable:
Adjust RSI period, lookback settings, divergence ranges, and visibility preferences.
Colors and styles are easily configurable to match your trading style.
How to Use:
RSI Zones: Use RSI and its zones to identify overbought/oversold conditions.
EMA: Look for crossovers or confluence with divergences for confirmation.
Divergences: Monitor for “Bull,” “Bear,” “H Bull,” or “H Bear” labels to spot key reversal or continuation signals.
Alerts: Set alerts to be notified of divergence opportunities without constant chart monitoring.
Simple Moving Average with Regime Detection by iGrey.TradingThis indicator helps traders identify market regimes using the powerful combination of 50 and 200 SMAs. It provides clear visual signals and detailed metrics for trend-following strategies.
Key Features:
- Dual SMA System (50/200) for regime identification
- Colour-coded candles for easy trend visualisation
- Metrics dashboard
Core Signals:
- Bullish Regime: Price < 200 SMA
- Bearish Regime: Price > 200 SMA
- Additional confirmation: 50 SMA Cross-over or Cross-under (golden cross or death cross)
Metrics Dashboard:
- Current Regime Status (Bull/Bear)
- SMA Distance (% from price to 50 SMA)
- Regime Distance (% from price to 200 SMA)
- Regime Duration (bars in current regime)
Usage Instructions:
1. Apply the indicator to your chart
2. Configure the SMA lengths if desired (default: 50/200)
3. Monitor the color-coded candles:
- Green: Bullish regime
- Red: Bearish regime
4. Use the metrics dashboard for detailed analysis
Settings Guide:
- Length: Short-term SMA period (default: 50)
- Source: Price calculation source (default: close)
- Regime Filter Length: Long-term SMA period (default: 200)
- Regime Filter Source: Price source for regime calculation (default: close)
Trading Tips:
- Use bullish regimes for long positions
- Use bearish regimes for capital preservation or short positions
- Consider regime duration for trend strength
- Monitor distance metrics for potential reversals
- Combine with other systems for confluence
#trend-following #moving average #regime #sma #momentum
Risk Management:
- Not a standalone trading system
- Should be used with proper position sizing
- Consider market conditions and volatility
- Always use stop losses
Best Practices:
- Monitor multiple timeframes
- Use with other confirmation tools
- Consider fundamental factors
Version: 1.0
Created by: iGREY.Trading
Release Notes
// v1.1 Allows table overlay customisation
// v1.2 Update to v6 pinescript
Momentum-Based Buy/Sell SignalsBuy Signal:
Triggered when ROC > threshold and the MACD line crosses above the Signal line.
Sell Signal:
Triggered when ROC < threshold and the MACD line crosses below the Signal line.
Visual Elements:
Green labels with "Buy" are displayed below the bars for buy signals.
Red labels with "Sell" are displayed above the bars for sell signals.
The background turns green during a buy signal and red during a sell signal for better visual clarity.
Momentum Probability Oscillator [SS]This is the momentum based probability indicator.
What it does?
This takes the average of MFI, Stochastics and RSI and plots it out as an independent oscillator.
It then tracks bullish vs bearish instances. Bullish is defined as a greater move from open to high than open to low and inverse for bearish.
It stores this data and these averages and plots these levels as a graph.
The graph depicts the max bullish values at the top, the min bearish values at the bottom and the averages in between:
It will plot the average "threshold" value in yellow:
The threshold value is key. A ticker trading above the threshold is generally bullish. Below is bearish.
The threshold value frequently acts as support and resistance levels (see below):
Resistance:
Support:
The indicator also shows you the amount of time a ticker has spent in each region, over a defined lookback period (defaulted to 500):
When you see that cumulatively, more time has been spent in a bullish range or a bearish range, it can help you ascertain the prevailing sentiment at that time.
The indicator will also calculate the average price range based on the underlying oscillator value. It does this through use of ATR based techniques, as its not usually possible to calculate a price from an oscillator:
This is intended as a general reference and not a precise target, as it is using ATR as opposed to the actual technical value itself.
As this is an oscillator, you can use it to look for divergences as well. The advantage to having it formulated in this way is:
a) You get the power of all 3 indicators (stochastics, MFI and RSI) in one and
b) You are adding context to the underlying technical reading. The indicator is plotting out the average, max and min ranges for the selected ticker and performing assessments based on these ranges that add context to the current PA.
You also have the ability to see the specific technical levels associated with each specific technical indicator. If you open up the settings menu and select "Show Table", this will appear:
This will show you the exact values of each of the technicals the indicator is using in its range assessment.
And that is basically the bulk of the indicator!
I use this predominately on the smaller timeframes, especially when there is a lot of chop, to ascertain the overall sentiment.
I also will reference it on the 1 hour to see what the prevailing sentiment is and whether the stock is at an area of technical resistance or support. For example, here is what I referenced on SPY today:
QUICK NOTE:
It works best with RTH (regular trading hours) turned on and ETH (extended trading hours) turned off!
That's it!
Hopefully you like it and leave your comments and suggestions below!
Multi SMI Ergodic OscillatorThe Multi SMI Ergodic Oscillator (Multi SMIEO) indicator can be used to identify potential buy and sell signals based on the relationship between the TSI and EMA lines.
The script is creating an indicator that plots multiple (3) sets of Time Series Indicator (TSI-Indicator) and Exponential Moving Average (EMA-Signal) lines as a single indicator.
The TSI is a momentum oscillator that helps identify overbought and oversold conditions. It is calculated using the close prices of an asset, a short-term moving average, and a long-term moving average. The script uses three different pairs of input values for the short-term and long-term periods, which can be adjusted by the user.
The EMA is a type of moving average that gives more weight to recent prices. It is calculated by applying a weighting factor to the most recent price, and then adding that weighted value to the previous EMA value. The script uses three different input values for the length of the EMA, which can also be adjusted by the user.
After calculating the TSI and EMA for each set, the script plots them on the same graph, with different colors and widths to differentiate them. The three sets of TSI and EMA lines are plotted to allow the user to compare the results of different periods. The script also plots a horizontal line at zero, which is used as a reference point for the oscillations of the indicator lines.
One way to use this indicator is to look for crossovers between the TSI and the EMA lines. A bullish crossover occurs when the TSI crosses above the EMA. This suggests that the buying pressure is increasing and a potential buy signal is generated. A bearish crossover occurs when the TSI crosses below the EMA. This suggests that the selling pressure is increasing and a potential sell signal is generated.
Some other ways that the indicator can be used include:
1. Identifying trends: The TSI and EMA lines can be used to identify the direction of the trend. An uptrend is present when the TSI and EMA lines are both trending upwards, while a downtrend is present when the TSI and EMA lines are both trending downwards.
2. Overbought and oversold conditions: The TSI can be used to identify overbought and oversold conditions. When the TSI is above the upper limit of the range, the asset is considered overbought and may be due for a price correction. Conversely, when the TSI is below the lower limit of the range, the asset is considered oversold and may be due for a price rebound.
3. Confirming price action: The Multi SMIEO indicator can be used to confirm price action. If a bullish divergence is present, it confirms a potential bullish reversal. If a bearish divergence is present, it confirms a potential bearish reversal.
4. Multiple time frame analysis: By using different periods for the TSI and EMA lines, the indicator can be used to analyze the asset on multiple time frames. It can be useful to compare the results of different periods to get a better understanding of the asset's price movements.
5. Risk management: This indicator can be used as an element of risk management strategy, it can help traders to identify overbought and oversold conditions to set stop loss or take profit levels.
The Multi SMI Ergodic Oscillator (Multi SMIEO) is a versatile indicator that can be used in a number of ways to analyze the price movements of an asset. It can be used to identify potential buy and sell signals, trends, overbought and oversold conditions, and to confirm price action. By using different periods for the TSI and EMA lines, the indicator can also be used to analyze the asset on multiple time frames. However, it is important to remember that indicators are based on historical data, and past performance does not guarantee future results.
It is important to use the indicator as part of a comprehensive trading strategy that includes risk management and other analysis techniques, such as fundamental and technical analysis. It is also important to keep in mind that indicators are not a standalone solution for trading, they should be used in conjunction with other market analysis and research techniques to generate better results.
Lastly, it is important to keep in mind that trading in financial markets comes with a certain level of risk and it is crucial to always have a proper risk management plan in place. Never invest more than you can afford to lose.
Point Of ControlStrategy and indicators are explained on the Chart.
Here's how i read the chart.
Entry:
1. Let the price close above the Ichimoku cloud
2. Price is above Volume Support zone
2. Make sure that momentum indicated with Green Triangles for Long Position
Exit:
1. Orange cross at the bottom of the candle indicates price is about to weaken
2. Best time to exit is Volume Resistance + Bearish(Hammer or Engulf )
PS: Use it along with R-Smart for better results
T3 Velocity Candles [Loxx]T3 Velocity Candles is a candle coloring overlay that calculates its gradient coloring using T3 velocity.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
