ALMA 20, 50, 200The ALMA (Arnaud Legoux Moving Average) crossover strategy uses two ALMA lines (fast and slow) to generate buy/sell signals, aiming to reduce lag and noise compared to traditional moving averages, and is often combined with volume filters for improved accuracy.
Here's a more detailed explanation:
What it is:
The ALMA indicator is a moving average (MA) variant designed to reduce lag and improve responsiveness while maintaining a smooth curve, using a Gaussian filter.
How it works:
ALMA calculates two moving averages, one from left to right and one from right to left, and then processes the output through a customizable formula for increased smoothness or responsiveness.
Crossover Strategy:
A common ALMA strategy involves using two ALMA lines with different lengths (fast and slow). A buy signal is generated when the fast ALMA crosses above the slow ALMA, and a sell signal when the fast ALMA crosses below the slow ALMA.
Benefits:
ALMA offers advantages like reduced lag, smoothness, and filtering capabilities, making it useful for identifying trends and potential reversals.
Potential Risks:
Like any indicator, ALMA can produce false signals, so it's crucial to combine it with other indicators and analyze price action.
Parameters:
ALMA has parameters like "Length" (number of periods), "Sigma" (filter's range, affecting responsiveness), and "Offset" (for accessing data of different candles).
Other uses:
ALMA can also be used for trend identification, dynamic support and resistance, and combined with other indicators to enhance trading strategies.
在腳本中搜尋"moving average crossover"
Triple Differential Moving Average BraidThe Triple Differential Moving Average Braid weaves together three distinct layers of moving averages—short-term, medium-term, and long-term—providing a structured view of market trends across multiple time horizons. It is an integrated construct optimized exclusively for the 1D timeframe. For multi-timeframe analysis and/or trading the lower 1h and 15m charts, it pairs well the Granular Daily Moving Average Ribbon ... adjust the visibility settings accordingly.
Unlike traditional moving average indicators that use a single moving average crossover, this braid-style system incorporates both SMAs and EMAs. The dual-layer approach offers stability and responsiveness, allowing traders to detect trend shifts with greater confidence.
Users can, of course, specify their own color scheme. The indicator consists of three layered moving average pairs. These are named per their default colors:
1. Silver Thread – Tracks immediate price momentum.
2. Royal Guard – Captures market structure and developing trends.
3. Golden Section – Defines major market cycles and overall trend direction.
Each layer is color-coded and dynamically shaded based on whether the faster-moving average is above or below its slower counterpart, providing a visual representation of market strength and trend alignment.
🧵 Silver Thread
The Silver Thread is the fastest-moving layer, comprising the 21D SMA and a 21D EMA. The choice of 21 is intentional, as it corresponds to approximately one full month of trading days in a 5-day-per-week market and is also a Fibonacci number, reinforcing its use in technical analysis.
· The 21D SMA smooths out recent price action, offering a baseline for short-term structure.
· The 21D EMA reacts more quickly to price changes, highlighting shifts in momentum.
· When the SMA is above the EMA, price action remains stable.
· When the SMA falls below the EMA, short-term momentum weakens.
The Silver Thread is a leading indicator within the system, often flipping direction before the medium- and long-term layers follow suit. If the Silver Thread shifts bearish while the Royal Guard remains bullish, this can signal a temporary pullback rather than a full trend reversal.
👑 Royal Guard
The Royal Guard provides a broader perspective on market momentum by using a 50D EMA and a 200D EMA. EMAs prioritize recent price data, making this layer faster-reacting than the Golden Section while still offering a level of stability.
· When the 50D EMA is above the 200D EMA, the market is in a confirmed uptrend.
· When the 50D EMA crosses below the 200D EMA, momentum has shifted bearish.
This layer confirms medium-term trend structure and reacts more quickly to price changes than traditional SMAs, making it especially useful for trend-following traders who need faster confirmation than the Golden Section provides.
If the Silver Thread flips bearish while the Royal Guard remains bullish, traders may be seeing a momentary dip in an otherwise intact uptrend. Conversely, if both the Silver Thread and Royal Guard shift bearish, this suggests a deeper pullback or possible trend reversal.
📜 Golden Section
The Golden Section is the slowest and most stable layer of the system, utilizing a 50D SMA and a 200D SMA—a classic combination used by long-term traders and institutions.
· When the 50D SMA is above the 200D SMA the market is in a strong, sustained uptrend.
· When the 50D SMA falls below the 200D SMA the market is structurally bearish.
Because SMAs give equal weight to past price data, this layer moves slowly and deliberately, ensuring that false breakouts or temporary swings do not distort the bigger picture.
Traders can use the Golden Section to confirm major market trends—when all three layers are bullish, the market is strongly trending upward. If the Golden Section remains bullish while the Royal Guard turns bearish, this may indicate a medium-term correction within a larger uptrend rather than a full reversal.
🎯 Swing Trade Setups
Swing traders can benefit from the multi-layered approach of this indicator by aligning their trades with the overall market structure while capturing short-term momentum shifts.
· Bullish: Look for Silver Thread and Royal Guard alignment before entering. If the Silver Thread flips bullish first, anticipate a momentum shift. If the Royal Guard follows, this confirms a strong medium-term move.
· Bearish: If the Silver Thread turns bearish first, it may signal an upcoming reversal. Waiting for the Royal Guard to follow adds confirmation.
· Confirmation: If the Golden Section remains bullish, a pullback may be an opportunity to enter a trend continuation trade rather than exit prematurely.
🚨 Momentum Shifts
· If the Silver Thread flips bearish but the Royal Guard remains bullish, traders may opt to buy the dip rather than exit their positions.
· If both the Silver Thread and Royal Guard turn bearish, traders should exercise caution, as this suggests a more significant correction.
· When all three layers align in the same direction the market is in a strong trending phase, making swing trades higher probability.
⚠️ Risk Management
· A narrowing of the shaded areas suggests trend exhaustion—consider tightening stop losses.
· When the Golden Section remains bullish, but the other two layers weaken, potential support zones to enter or re-enter positions.
· If all three layers flip bearish, this may indicate a larger trend reversal, prompting an exit from long positions and/or consideration of short setups.
The Triple Differential Moving Average Braid is layered, structured tool for trend analysis, offering insights across multiple timeframes without requiring traders to manually compare different moving averages. It provides a powerful and intuitive way to read the market. Swing traders, trend-followers, and position traders alike can use it to align their trades with dominant market trends, time pullbacks, and anticipate momentum shifts.
By understanding how these three moving average layers interact, traders gain a deeper, more holistic perspective of market structure—one that adapts to both momentum-driven opportunities and longer-term trend positioning.
Waldo Momentum Cloud Bollinger Bands (WMCBB)
Title: Waldo Momentum Cloud Bollinger Bands (WMCBB)
Description:
Introducing the "Waldo Momentum Cloud Bollinger Bands (WMCBB)," an innovative trading tool crafted for those who aim to deepen their market analysis by merging two dynamic technical indicators: Dynamic RSI Bollinger Bands and the Waldo Cloud.
What is this Indicator?
WMCBB integrates the volatility-based traditional Bollinger Bands with a momentum-sensitive approach through the Relative Strength Index (RSI). Here’s how it works:
Dynamic RSI Bollinger Bands: These bands dynamically adjust according to the RSI, which tracks the momentum of price movements. By scaling the RSI to align with price levels, we generate bands that not only reflect market volatility but also the underlying momentum, offering a refined view of overbought and oversold conditions.
Waldo Cloud: This feature adds a layer of traditional Bollinger Bands, visualized as a 'cloud' on your chart. It employs standard Bollinger Band methodology but enhances it with additional moving average layers to better define market trends.
The cloud's color changes dynamically based on various market conditions, providing visual signals for trend direction and potential trend reversals.
Why Combine These Indicators?
Combining Dynamic RSI Bollinger Bands with the Waldo Cloud in WMCBB aims to:
Enhance Trend Identification: The Waldo Cloud's color-coded system aids in recognizing the overarching market trend, while the Dynamic RSI Bands give insights into momentum changes within that trend, offering a comprehensive view.
Improve Volatility and Momentum Analysis: While traditional Bollinger Bands measure market volatility, integrating RSI adds a layer of momentum analysis, potentially leading to more accurate trading signals.
Visual Clarity: The unified color scheme for both sets of bands, which changes according to RSI levels, moving average crossovers, and price positioning, simplifies the process of gauging market sentiment at a glance.
Customization: Users have the option to toggle the visibility of moving averages (MA) through the settings, allowing for tailored analysis based on individual trading strategies.
Usage:
Utilize WMCBB to identify potential trend shifts by observing price interactions with the dynamic bands or changes in the Waldo Cloud's color.
Watch for divergences between price movements and RSI to forecast potential market reversals or continuations.
This combination shines in sideways markets where traditional indicators might fall short, as it provides additional context through RSI momentum analysis.
Settings:
Customize parameters for both the Dynamic RSI and Waldo Cloud Bollinger Bands, including the calculation source, standard deviation factors, and moving average lengths.
WMCBB is perfect for traders seeking to enhance their market analysis through the synergy of momentum and volatility, all while maintaining visual simplicity. Trade with greater insight using the Waldo Momentum Cloud Bollinger Bands!
Azlan MA Silang PLUS++Overview
Azlan MA Silang PLUS++ is an advanced moving average crossover trading indicator designed for traders who want to jump back into the market when they missed their first opportunity to take a trade. It implements a sophisticated dual moving average system with customizable settings and re-entry signals, making it suitable for both trend following and swing trading strategies.
Key Features
• Dual Moving Average System with multiple MA types (EMA, SMA, WMA, LWMA)
• Customizable price sources for each moving average
• Smart re-entry system with configurable maximum re-entries
• Visual signals with background coloring and shape markers
• Comprehensive alert system for both initial and re-entry signals
• Flexible parameter customization through input options
Input Parameters
Moving Average Configuration
• MA1 Type: Choice between SMA, EMA, WMA, LWMA (default: EMA)
• MA2 Type: Choice between SMA, EMA, WMA, LWMA (default: EMA)
• MA1 Length: Minimum value 1 (default: 8)
• MA2 Length: Minimum value 1 (default: 15)
• MA1 & MA2 Shift: Offset values for moving averages
• Price Sources: Configurable for each MA (Open, High, Low, Close, HL/2, HLC/3, HLCC/4)
Re-entry System
• Enable/Disable re-entry signals
• Maximum re-entries allowed (default: 3)
Technical Implementation
Price Source Calculation
The script implements a flexible price source system through the price_source() function:
• Supports standard OHLC values
• Includes compound calculations (HL/2, HLC/3, HLCC/4)
• Defaults to close price if invalid source specified
Moving Average Types
Implements four MA calculations:
1. SMA (Simple Moving Average)
2. EMA (Exponential Moving Average)
3. WMA (Weighted Moving Average)
4. LWMA (Linear Weighted Moving Average)
Signal Generation Logic
Initial Signals
• Buy Signal: MA1 crosses above MA2 with price above both MAs
• Sell Signal: MA1 crosses below MA2 with price below both MAs
Re-entry Signals
Re-entry system activates when:
1. Price crosses under MA1 in buy mode (or over in sell mode)
2. Price returns to cross back over MA1 (or under for sells)
3. Position relative to MA2 confirms trend direction
4. Number of re-entries hasn't exceeded maximum allowed
Visual Components
• MA1: Blue line (width: 2)
• MA2: Red line (width: 2)
• Background Colors:
o Green (60% opacity): Bullish conditions
o Red (60% opacity): Bearish conditions
• Signal Markers:
o Initial Buy/Sell: Up/Down arrows with "BUY"/"SELL" labels
o Re-entry Buy/Sell: Up/Down arrows with "RE-BUY"/"RE-SELL" labels
Alert System
Generates alerts for:
• Initial buy/sell signals
• Re-entry opportunities
• Alerts include ticker and timeframe information
• Configured for once-per-bar-close frequency
Usage Tips
1. Moving Average Selection
o Shorter periods (MA1) capture faster moves
o Longer periods (MA2) identify overall trend
o EMA responds faster to price changes than SMA
2. Re-entry System
o Best used in strong trending markets
o Limit maximum re-entries based on market volatility
o Monitor price action around MA1 for potential re-entry points
3. Risk Management
o Use additional confirmation indicators
o Set appropriate stop-loss levels
o Consider market conditions when using re-entry signals
Code Structure
The script follows a modular design with distinct sections:
1. Input parameter definitions
2. Helper functions for price and MA calculations
3. Main signal generation logic
4. Visual elements and plotting
5. Alert system implementation
This organization makes the code maintainable and easy to modify for custom needs.
Relative Strength Index Custom [BRTLab]RSI Custom — Strategy-Oriented RSI with Multi-Timeframe Precision
The Relative Strength Index Custom is designed with a focus on developing robust trading strategies. This powerful indicator leverages the logic of calculating RSI on higher timeframes (HTFs) while allowing traders to execute trades on lower timeframes (LTFs). Its unique ability to extract accurate RSI data from higher timeframes without waiting for those candles to close provides a real-time advantage, eliminating the "look-ahead" bias that often
distorts backtest results.
Key Features
Multi-Timeframe RSI for Strategy Development
This indicator stands out by allowing you to calculate RSI on higher timeframes, even while operating on lower timeframe charts. This means you can, for example, calculate RSI on the 1-hour or daily chart and execute trades on a 1-minute chart without needing to wait for the higher timeframe candle to close. This feature is crucial for strategy-building as it eliminates backtesting issues where data from the future is inadvertently used, providing more reliable backtest results.
Example: On a 15-minute chart, you can use the 1-hour RSI to open positions based on higher timeframe momentum, but you get this signal in real-time, improving timing and accuracy.
Accurate Data Extraction from Higher Timeframes
The indicator's custom logic ensures that accurate RSI data is retrieved from higher timeframes, providing an edge by delivering timely information for lower timeframe decisions. This prevents delayed signals often encountered when waiting for higher timeframe candles to close, which is crucial for high-frequency and intraday traders looking for precise entries based on multi-timeframe data.
Customizable RSI Settings for Strategy Tuning
The script offers full customization of the RSI, including length and source price (close, open, high, or low), allowing traders to tailor the RSI to fit specific trading strategies. These settings are housed in the "RSI Settings" section, enabling precise adjustments that align with your overall strategy.
No Future-Looking in Backtests
Traditional backtests often suffer from "future-looking" bias, where calculations unintentionally use data from candles that haven’t yet closed. This indicator is specifically designed to prevent such issues by calculating RSI values in real-time. This is particularly important when creating and testing strategies, as it ensures that the conditions under which trades would have been made are accurately represented in historical tests.
RSI-Based Moving Average for Additional Filtering
The built-in moving average (MA) based on RSI values helps filter out noise, making it easier to identify genuine trend shifts. This is particularly useful in strategies where moving average crossovers act as additional confirmation for trade entries and exits.
Overbought and Oversold Zone Detection
Visual gradient fills on the RSI chart help traders identify overbought and oversold zones (above 70 and below 30, respectively). These zones are crucial for timing reversal trades or confirming momentum-based strategies.
How This Indicator Enhances Your Strategy
Increased Accuracy for Intraday Strategies
For traders who operate on lower timeframes, using higher timeframe RSI data gives a broader perspective of market momentum while still maintaining precision for short-term trade entries. The real-time data extraction means you don't need to wait for HTF candles to close, which can dramatically improve your entry timing.
Strategic Edge in Backtesting
One of the greatest challenges in backtesting strategies is avoiding future-looking bias. This indicator is built to overcome this by using real-time multi-timeframe data, ensuring the accuracy and reliability of historical strategy testing, which provides confidence in your strategies when applied to live markets.
Advanced Filtering for Trend Strategies
By combining the RSI values with a customizable moving average (MA) and visualizing key momentum zones with overbought/oversold fills, the indicator allows for more refined trade filters. This ensures that signals generated by your strategy are based on solid momentum data and not short-term price fluctuations.
MA Cross HeatmapThe Moving Average Cross Heatmap Created by Technicator , visualizes the crossing distances between multiple moving averages using a heat map style color coding.
The main purpose of this visualization is to help identify potential trend changes or trading opportunities by looking at where the moving averages cross over each other.
Key Features:
Can plot up to 9 different moving average with their cross lengths you set
Uses a heat map to show crossing distances between the MAs
Adjustable settings like crossing length percentage, color scheme, color ceiling etc.
Overlay style separates the heat map from the price chart
This is a unique way to combine multiple MA analysis with a visual heat map representation on one indicator. The code allows you to fine-tune the parameters to suit your trading style and preferences. Worth checking out if you trade using multiple moving average crossovers as part of your strategy.
Gabriels Trend Regularity Adaptive Moving Average Dragon This is an improved version of the trend following Williams Alligator, through the use of five Trend Regularity Adaptive Moving Averages (TRAMA) instead of three smoothed averages (SMMA). This indicator can double as a TRAMA Ribbon indicator by reducing the offset to zero. Whereas the active offset can double as a forecasting indicator for options and futures.
This indicator uses five TRAMAs, set at 8, 21, 55, 144, and 233 periods. They make up the Lips, Teeth, Jaws, Wings, and Tail of the Dragon. This indicator uses convergence-divergence relationships to build trading signals, with the Tail making the slowest turns and the Lips making the fastest turns. The Lips crossing downwards through the other lines signal a short opportunity, whereas Lips crossing upwards through other lines signal a buying opportunity. The downward cross can be referred to as the Dragon "Sleeping" , and the upward cross as the Dragon "Awakening" .
In particular, but not limited to, the Wings and Tail movements possess a Roar-like forecast effect on the market. Respectively, they can be referred to as the Dragon "Spreading its Wings" or "Swinging its Tail" .
The first three lines, stretching apart and constantly moving higher or lower, denote periods in which long or short equity positions should be managed and maintained. This can be referred to as the Dragon "Eating with a mouth wide open" . Whereas indicator lines converging into narrow bands and shifting into a horizontal position can denote a trending period coming to an end, signaling the need for profit-taking and position realignment. Conversely, a previous flat line moving can denote a new trending period starting.
This indicator can double as a Multiple TRAMAs indicator by reducing the offset to zero. As such, very interesting results can be observed when used in a moving average crossover system such as the Williams Alligator or as trailing support and resistance.
The following moving average adapts to the average of the highest high and lowest low made over a specific period, thus adapting to trend strength. The TRAMA can be used like most moving averages, with the advantage of being smoother during ranging markets because it is calculated through exponential averaging.
It is calculating, using a smoothing factor, the squared simple moving average of the number of highest highs or lowest lows previously made. Where the highest highs and lowest lows are calculated using rolling maximums and minimums. Therefore, squaring allows the moving average to penalize lower values, thus appearing stationary during ranging markets.
As with all moving averages, it is still a lagging indicator, and it can suffer whipsaws when the market moves too violently or when it consolidates in ranging conditions. Despite it working in all timeframes, it won't be as formidable in the 1–5-minute scalping timeframes due to that. I would suggest 5 to 45 minutes if you are a swing trader, or hourly, daily, and weekly if you are a long-term investor.
I hope you enjoy this indicator! It's the first indicator I made, so constructive criticism would be appreciated. Thanks!
LoTek - CT Moving Average Crossover Indicator - MTF [CT/LoTek]This is a shameless fork of Caretaker's excellent CT MAC indicator. This indicator has 2 new features. I've added the ability to select a different timeframe for each moving average. This way you can set a Daily 10, or a weekly 20 or any other of your favorite lines and it will always be there on your chart. The other new features is the ability to select VWMA as well as SMA and EMA for each moving average. VWMA is pretty nice to watch as well, and with 9 moving averages to mix and match, I'm sure you'll find something worth keeping.
To fork this, I created a new "resolution" variable for each MA. I also created a new function that uses the request.security call to get the specific timeframe resolution. I backtested this with CT's OG script and the numbers stay the same... but I have a sneaky suspicion that VWMAs are not showing proper crossover values. So keep that in mind. The drawn lines are fine, but the crossover data when using VWMA may be off. I wrote the new function to default to EMA, so if it fails at VWMA, it will just show you EMA data.
Let's see, what else... please tell me if you find any bugs or want any other features baked in.
Portfolio Backtester Engine█ OVERVIEW
Portfolio Backtester Engine (PBTE). This tool will allow you to backtest strategies across multiple securities at once. Allowing you to easier understand if your strategy is robust. If you are familiar with the PineCoders backtesting engine , then you will find this indicator pleasant to work with as it is an adaptation based on that work. Much of the functionality has been kept the same, or enhanced, with some minor adjustments I made on the account of creating a more subjectively intuitive tool.
█ HISTORY
The original purpose of the backtesting engine (`BTE`) was to bridge the gap between strategies and studies . Previously, strategies did not contain the ability to send alerts, but were necessary for backtesting. Studies on the other hand were necessary for sending alerts, but could not provide backtesting results . Often, traders would have to manage two separate Pine scripts to take advantage of each feature, this was less than ideal.
The `BTE` published by PineCoders offered a solution to this issue by generating backtesting results under the context of a study(). This allowed traders to backtest their strategy and simultaneously generate alerts for automated trading, thus eliminating the need for a separate strategy() script (though, even converting the engine to a strategy was made simple by the PineCoders!).
Fast forward a couple years and PineScript evolved beyond these issues and alerts were introduced into strategies. The BTE was not quite as necessary anymore, but is still extremely useful as it contains extra features and data not found under the strategy() context. Below is an excerpt of features contained by the BTE:
"""
More than `40` built-in strategies,
Customizable components,
Coupling with your own external indicator,
Simple conversion from Study to Strategy modes,
Post-Exit analysis to search for alternate trade outcomes,
Use of the Data Window to show detailed bar by bar trade information and global statistics, including some not provided by TV backtesting,
Plotting of reminders and generation of alerts on in-trade events.
"""
Before I go any further, I want to be clear that the BTE is STILL a good tool and it is STILL very useful. The Portfolio Backtesting Engine I am introducing is only a tangental advancement and not to be confused as a replacement, this tool would not have been possible without the `BTE`.
█ THE PROBLEM
Most strategies built in Pine are limited by one thing. Data. Backtesting should be a rigorous process and researchers should examine the performance of their strategy across all market regimes; that includes, bullish and bearish markets, ranging markets, low volatility and high volatility. Depending on your TV subscription The Pine Engine is limited to 5k-20k historical bars available for backtesting, which can often leave the strategy results wanting. As a general rule of thumb, strategies should be tested across a quantity of historical bars which will allow for at least 100 trades. In many cases, the lack of historical bars available for backtesting and frequency of the strategy signals produces less than 100 trades, rendering your strategy results inconclusive.
█ THE SOLUTION
In order to be confident that we have a robust strategy we must test it across all market regimes and we must have over 100 trades. To do this effectively, researchers can use the Portfolio Backtesting Engine (PBTE).
By testing a strategy across a carefully selected portfolio of securities, researchers can now gather 5k-20k historical bars per security! Currently, the PTBE allows up to 5 securities, which amounts to 25k-100k historical bars.
█ HOW TO USE
1 — Add the indicator to your chart.
• Confirm inputs. These will be the most important initial values which you can change later by clicking the gear icon ⚙ and opening up the settings of the indicator.
2 — Select a portfolio.
• You will want to spend some time carefully selecting a portfolio of securities.
• Each security should be uncorrelated.
• The entire portfolio should contain a mix of different market regimes.
You should understand that strategies generally take advantage of one particular type of market regime. (trending, ranging, low/high volatility)
For example, the default RSI strategy is typically advantageous during ranging markets, whereas a typical moving average crossover strategy is advantageous in trending markets.
If you were to use the standard RSI strategy during a trending market, you might be selling when you should be buying.
Similarily, if you use an SMA crossover during a ranging market, you will find that the MA's may produce many false signals.
Even if you build a strategy that is designed to be used only in a trending market, it is still best to select a portfolio of all market regimes
as you will be able to test how your strategy will perform when the market does something unexpected.
3 — Test a built-in strategy or add your own.
• Navigate to gear icon ⚙ (settings) of strategy.
• Choose your options.
• Select a Main Entry Strat and Alternate Entry Strat .
• If you want to add your own strategy, you will need to modify the source code and follow the built-in example.
• You will only need to generate (buy 1 / sell -1/ neutral 0) signals.
• Select a Filter , by default these are all off.
• Select an Entry Stop - This will be your stop loss placed at the trade entry.
• Select Pyamiding - This will allow you to stack positions. By default this is off.
• Select Hard Exits - You can also think of these as Take Profits.
• Let the strategy run and take note of the display tables results.
• Portfolio - Shows each security.
• The strategy runs on each asset in your portfolio.
• The initial capital is equally distributed across each security.
So if you have 5 securities and a starting capital of 100,000$ then each security will run the strategy starting with 20,000$
The total row will aggregate the results on a bar by bar basis showing the total results of your initial capital.
• Net Profit (NP) - Shows profitability.
• Number of Trades (#T) - Shows # of trades taken during backtesting period.
• Typically will want to see this number greater than 100 on the "Total" row.
• Average Trade Length (ATL) - Shows average # of days in a trade.
• Maximum Drawdown (MD ) - Max peak-to-valley equity drawdown during backtesting period.
• This number defines the minimum amount of capital required to trade the system.
• Typically, this shouldn’t be lower than 34% and we will want to allow for at least 50% beyond this number.
• Maximum Loss (ML) - Shows largest loss experienced on a per-trade basis.
• Normally, don’t want to exceed more than 1-2 % of equity.
• Maximum Drawdown Duration (MDD) - The longest duration of a drawdown in equity prior to a new equity peak.
• This number is important to help us psychologically understand how long we can expect to wait for a new peak in account equity.
• Maximum Consecutive Losses (MCL) - The max consecutive losses endured throughout the backtesting period.
• Another important metric for trader psychology, this will help you understand how many losses you should be prepared to handle.
• Profit to Maximum Drawdown (P:MD) - A ratio for the average profit to the maximum drawdown.
• The higher the ratio is, the better. Large profits and small losses contribute to a good PMD.
• This metric allows us to examine the profit with respect to risk.
• Profit Loss Ratio (P:L) - Average profit over the average loss.
• Typically this number should be higher in trend following systems.
• Mean reversion systems show lower values, but compensate with a better win %.
• Percent Winners (% W) - The percentage of winning trades.
• Trend systems will usually have lower win percentages, since statistically the market is only trending roughly 30% of the time.
• Mean reversion systems typically should have a high % W.
• Time Percentage (Time %) - The amount of time that the system has an open position.
• The more time you are in the market, the more you are exposed to market risk, not to mention you could be using that money for something else right?
• Return on Investment (ROI) - Your Net Profit over your initial investment, represented as a percentage.
• You want this number to be positive and high.
• Open Profit (OP) - If the strategy has any open positions, the floating value will be represented here.
• Trading Days (TD) - An important metric showing how many days the strategy was active.
• This is good to know and will be valuable in understanding how long you will need to run this strategy in order to achieve results.
█ FEATURES
These are additional features that extend the original `BTE` features.
- Portfolio backtesting.
- Color coded performance results.
- Circuit Breakers that will stop trading.
- Position reversals on exit. (Simulating the function of always in the market. Similar to strategy.entry functionality)
- Whipsaw Filter
- Moving Average Filter
- Minimum Change Filter
- % Gain Equity Exit
- Popular strategies, (MACD, MA cross, supertrend)
Below are features that were excluded from the original `BTE`
- 2 stage in-trade stops with kick-in rules (This was a subjective decision to remove. I found it to be complex and thwarted my use of the `BTE` for some time.)
- Simple conversion from Study to Strategy modes. (Not possible with multiple securities)
- Coupling with your own external indicator (Not really practical to use with multiple securities, but could be used if signals were generated based on some indicator which was not based on the current chart)
- Use of the Data Window to show detailed bar by bar trade information and global statistics.
- Post Exit Analysis.
- Plotting of reminders and generation of alerts on in-trade events.
- Alerts (These may be added in the future by request when I find the time.)
█ THANKS
The whole PineCoders team for all their shared knowledge and original publication of the BTE and Richard Weismann for his ideas on building robust strategies.
═════════════════════════════════════════════════════════════════════════
Professor Snipe: A superadaptive moving average. Prof. Snipe is a superadaptive, multi-purpose indicator I developed in order to judge market trend strength and show high probability entry points.
The indicator is focused around a zero lag moving average algorithm (SUPER-MA, ), that changes its parameters depending on the volatility (ATR) and trend strength (ADX).
If the price (black 3 period MA) is above the Super-MA, this indicates market momentum and strength. If price is below the Super-MA, price and momentum are showing weakness.
Micro-Signals are given based on smaller lag-free moving average crossovers (blue and red arrows), but entries will depend on the location of price, with respect to the super-MA.
Furthermore, to judge the current price position with respect to high timeframe averages, the algo will automatically show the location of the nearest moving averages for support and resistance.
/////////////////////////
Entry Conditions example.:
For Longs:
Wait until the 4 hour trend flips bullish, price above Super-MA. Once it does, it will often retest the Super-MA as support. When that happens, use the next entry signal to go long.
For further safety, check the safety net (dotted hull moving average) to see if price has broken above that too, for an optimal long.
-- use caution when entering longs if: price is floating around the super-ma (very weak trend) and if price is below super-ma.
For Shorts:
Wait until the 4 hour trend flips bearish, price below Super-MA. Once it does, use lower timeframes to find short entry points using the MA signals.
-- use caution when entering shorts if: price is floating around the super-ma (very weak trend) and if price is above super-ma.
DYOR and test it yourself to find what works for you.
BE AWARE!
Just following the entry and exit signals (arrows) will not give you perfect results.
Summary:
Overall, this is probably the best indicator I have ever created, and has a very high success rate when used properly.
Best,
MM
Easy Loot Golden CrossGolden/Death Cross Moving Average Indicator
30, 100 & 200 period Simple Moving Average (SMA).
30 = Yellow
100 = Green
200 = Black
Black crosses mark the 'golden crosses' as well as the 'death crosses'. These black crosses appear when the 30 crosses the 100 & when the 100 crosses the 200. These black crosses don't tell you when to buy/sell, but simply indicate interest in the market.
This code is open-source so feel free to add this indicator to your chart and play around with the different moving average timeframes & color schemes.
Golden Cross
The golden cross occurs when a short-term moving average crosses over a major long-term moving average to the upside and is interpreted by analysts and traders as signaling a definitive upward turn in a market. Basically, the short-term average trends up faster than the long-term average, until they cross.
There are three stages to a golden cross:
A downtrend that eventually ends as selling is depleted
A second stage where the shorter moving average crosses up through the longer moving average
Finally, the continuing uptrend, hopefully leading to higher prices
Death Cross
Conversely, a similar downside moving average crossover constitutes the death cross and is understood to signal a decisive downturn in a market. The death cross occurs when the short term average trends down and crosses the long-term average, basically going in the opposite direction of the golden cross.
The death cross preceded the economic downturns in 1929, 1938, 1974, and 2008.
Combo Strategy 123 Reversal & EMA & MA Crossover This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The Moving Average Crossover trading strategy is possibly the most popular
trading strategy in the world of trading. First of them were written in the
middle of XX century, when commodities trading strategies became popular.
This strategy is a good example of so-called traditional strategies.
Traditional strategies are always long or short. That means they are never
out of the market. The concept of having a strategy that is always long or
short may be scary, particularly in today’s market where you don’t know what
is going to happen as far as risk on any one market. But a lot of traders
believe that the concept is still valid, especially for those of traders who
do their own research or their own discretionary trading.
This version uses crossover of moving average and its exponential moving average.
WARNING:
- For purpose educate only
- This script to change bars colors.
(13) Twists Swing/Day VS-478TWISTS adds a simple, but very effective twist to utilizing a multiple moving average crossover systems, enabling the effective and profitable trading of any stock, crypto or commodity. This enables trend, swing and day traders to dramatically improve their results over a similar, short-term simple, smoothed, exponential or weighted moving average crossover system.
Four distinct Laguerre filters are applied to the price, one fast, one medium one long and one very long. The default Laguerre settings are: Short = 0; Medium = 0.33, Long = 0.55 XLong = 0.77. The correlation between the length of time and the Laguerre output is adjustable in the format > inputs pane for this indicator and are referred to as gamma. The first three lengths produce two major bands or ribbons. During up trends the top band is filled with green and during down trends this top band will be filled with red. Obviously these bands or ribbons are twisting or flipping positions when the direction of the price trends change. Trading indicator dots are produced during both phases. Green dots for uptrends and red dots during down trends. During consolidation phases it is possible that there will be no dots produced because of the rule set applied to these Entry/hold and Exit/short indicator dots.
TWISTS is a triple moving average trading system using an advanced smoothing filter developed by John Ehlers. You can read about this dramatic advancement in moving averages in the following article: Time Warp -- Without Space Travel. You can find the link to this article on our site.
Access this Genie indicator for your Tradingview account, through our web site. (Links Below) This will provide you with additional educational information and reference articles, videos, input and setting options and trading strategies this indicator excels in.
Simple Moving Averages Alert Scriptcan set alerts on 3 moving averages (crossovers) , experiment with different moving average lengths in the input settings menu, there is also a toggle switch which turns off the 3rd moving average being used as a stop.
will add a backtesting version at some point
Tillson T3 Moving Average MTFMULTIPLE TIME FRAME version of Tillson T3 Moving Average Indicator
Developed by Tim Tillson, the T3 Moving Average is considered superior -1.60% to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well. However, it bears the disadvantage of overshooting the price as it attempts to realign itself to current market conditions.
It incorporates a smoothing technique which allows it to plot curves more gradual than ordinary moving averages and with a smaller lag. Its smoothness is derived from the fact that it is a weighted sum of a single EMA , double EMA , triple EMA and so on. When a trend is formed, the price action will stay above or below the trend during most of its progression and will hardly be touched by any swings. Thus, a confirmed penetration of the T3 MA and the lack of a following reversal often indicates the end of a trend.
The T3 Moving Average generally produces entry signals similar to other moving averages and thus is traded largely in the same manner. Here are several assumptions:
If the price action is above the T3 Moving Average and the indicator is headed upward, then we have a bullish trend and should only enter long trades (advisable for novice/intermediate traders). If the price is below the T3 Moving Average and it is edging lower, then we have a bearish trend and should limit entries to short. Below you can see it visualized in a trading platform.
Although the T3 MA is considered as one of the best swing following indicators that can be used on all time frames and in any market, it is still not advisable for novice/intermediate traders to increase their risk level and enter the market during trading ranges (especially tight ones). Thus, for the purposes of this article we will limit our entry signals only to such in trending conditions.
Once the market is displaying trending behavior, we can place with-trend entry orders as soon as the price pulls back to the moving average (undershooting or overshooting it will also work). As we know, moving averages are strong resistance/support levels, thus the price is more likely to rebound from them and resume its with-trend direction instead of penetrating it and reversing the trend.
And so, in a bull trend, if the market pulls back to the moving average, we can fairly safely assume that it will bounce off the T3 MA and resume upward momentum, thus we can go long. The same logic is in force during a bearish trend .
And last but not least, the T3 Moving Average can be used to generate entry signals upon crossing with another T3 MA with a longer trackback period (just like any other moving average crossover). When the fast T3 crosses the slower one from below and edges higher, this is called a Golden Cross and produces a bullish entry signal. When the faster T3 crosses the slower one from above and declines further, the scenario is called a Death Cross and signifies bearish conditions.
I Personally added a second T3 line with a volume factor of 0.618 (Fibonacci Ratio) and length of 3 (fibonacci number) which can be added by selecting the box in the input section. traders can combine the two lines to have Buy/Sell signals from the crosses.
Developed by Tim Tillson
Multiple Moving Averages Alerts ScriptAlerts script that has triggers on multiple moving average crossovers so that profit is maximised, it also has an optional control moving average, enabled by default, that when active will stop trading when the price (first ma) is below the control moving average.
Source code is open so that others can use and modify
Click Below for Backtesting version:
Disclaimers, not an expert, not intended to be financial advise.
Biffy
Tillson T3 Moving Average by KIVANÇ fr3762Developed by Tim Tillson, the T3 Moving Average is considered superior to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well. However, it bears the disadvantage of overshooting the price as it attempts to realign itself to current market conditions.
It incorporates a smoothing technique which allows it to plot curves more gradual than ordinary moving averages and with a smaller lag. Its smoothness is derived from the fact that it is a weighted sum of a single EMA , double EMA , triple EMA and so on. When a trend is formed, the price action will stay above or below the trend during most of its progression and will hardly be touched by any swings. Thus, a confirmed penetration of the T3 MA and the lack of a following reversal often indicates the end of a trend.
The T3 Moving Average generally produces entry signals similar to other moving averages and thus is traded largely in the same manner. Here are several assumptions:
If the price action is above the T3 Moving Average and the indicator is headed upward, then we have a bullish trend and should only enter long trades (advisable for novice/intermediate traders). If the price is below the T3 Moving Average and it is edging lower, then we have a bearish trend and should limit entries to short. Below you can see it visualized in a trading platform.
Although the T3 MA is considered as one of the best swing following indicators that can be used on all time frames and in any market, it is still not advisable for novice/intermediate traders to increase their risk level and enter the market during trading ranges (especially tight ones). Thus, for the purposes of this article we will limit our entry signals only to such in trending conditions.
Once the market is displaying trending behavior, we can place with-trend entry orders as soon as the price pulls back to the moving average (undershooting or overshooting it will also work). As we know, moving averages are strong resistance/support levels, thus the price is more likely to rebound from them and resume its with-trend direction instead of penetrating it and reversing the trend.
And so, in a bull trend, if the market pulls back to the moving average, we can fairly safely assume that it will bounce off the T3 MA and resume upward momentum, thus we can go long. The same logic is in force during a bearish trend .
And last but not least, the T3 Moving Average can be used to generate entry signals upon crossing with another T3 MA with a longer trackback period (just like any other moving average crossover). When the fast T3 crosses the slower one from below and edges higher, this is called a Golden Cross and produces a bullish entry signal. When the faster T3 crosses the slower one from above and declines further, the scenario is called a Death Cross and signifies bearish conditions.
I Personally added a second T3 line with a volume factor of 0.618 (Fibonacci Ratio) and length of 3 (fibonacci number) which can be added by selecting the box in the input section. traders can combine the two lines to have Buy/Sell signals from the crosses.
Developed by Tim Tillson
Oscillator SignalsOscillator Signals – Smart Trading with RSI & Stochastic
Stop waiting for false reversals—trade the confirmed moves!
Developed by Marcelo Ulisses Sobreiro Ribeiro
Many traders struggle with the Stochastic Oscillator because it can linger in overbought (OB) or oversold (OS) zones for long periods, leading to premature entries. This script solves that problem by only triggering signals when Stochastic exits these zones, combined with RSI crossovers for higher-confidence trades.
🔹 Key Features:
✅ No More False Alerts – Avoids signals inside OB/OS zones—waits for Stochastic to exit (confirming momentum shift).
✅ RSI + Moving Average Crossovers – Adds a second layer of confirmation when RSI crosses its moving average.
✅ Combined Alerts – Strongest signals occur when:
Stochastic exits oversold (OS) and RSI crosses above its MA (▲ Bullish).
Stochastic exits overbought (OB) and RSI crosses below its MA (▼ Bearish).
✅ Fully Customizable – Adjust lengths, OB/OS levels, and toggle signals.
✅ Built-in Alerts – Never miss a setup.
🔹 Why It Works Better:
Traditional Stochastic signals often fail because price can stay stuck in OB/OS for extended periods. This script ignores entries inside the zones and only acts when:
Stochastic leaves OS (crosses above 20) + RSI confirms uptrend.
Stochastic leaves OB (crosses below 80) + RSI confirms downtrend.
Filters out weak reversals, focusing on high-probability breakouts.
🔹 Ideal For:
Traders tired of "whipsaws" from premature OB/OS entries.
Swing traders seeking confirmed reversals.
Combining with support/resistance for precision.
📌 Pro Tip: Pair this with price action (e.g., breakouts from key levels) for even stronger signals!
Try it now—trade less, profit more! 🚀
Key Improvements:
Problem-Solution Framework: Directly addresses the "Stochastic lingering" issue upfront.
Stronger Emphasis on Confirmation: Highlights how the script waits for OB/OS exits to avoid fakeouts.
Clearer Value Proposition: Positions the script as a filter for higher-quality signals.
ZenAlgo - Aggregated DeltaZenAlgo - Aggregated Delta is an advanced market analysis tool designed to provide traders with a holistic view of market sentiment by leveraging multi-exchange volume aggregation, cumulative delta analysis, and divergence detection. Unlike traditional indicators that rely on a single data source, this tool aggregates order flow data from multiple exchanges, reducing the impact of exchange-specific anomalies and liquidity disparities.
This indicator is ideal for traders looking to enhance their understanding of market dynamics, trend confirmations, and order flow patterns. By intelligently combining multiple analytical components, it eliminates the need for manually interpreting separate indicators and offers traders a streamlined approach to market analysis.
This indicator was inspired by aggregated volume analysis techniques. Independently developed with a focus on cumulative delta and divergence detection.
Key Features & Their Interaction
Multi-Exchange Volume Aggregation: Aggregates buy and sell volumes from up to nine major exchanges, including Binance, Bybit, Coinbase, and Kraken. Unlike traditional single-source indicators, this ensures a robust, diversified measure of market sentiment and smooths out exchange-specific volume fluctuations.
Cumulative Delta Analysis: Tracks the net difference between buy and sell volumes across all aggregated exchanges, helping traders identify true buying/selling pressure rather than misleading short-term volume spikes.
Advanced Divergence Detection: Unlike basic divergence indicators, this tool detects divergences not only between price and cumulative delta but also across multiple analytical layers, including moving averages and temperature zones, offering deeper confirmation signals.
Dynamic Market Temperature Zones: Unlike fixed overbought/oversold indicators, this feature applies adaptive standard deviation-based filtering to classify market conditions dynamically as "Extreme Hot," "Hot," "Neutral," "Cold," and "Extreme Cold."
Intelligent Market State Classification: Determines whether the market is in a Full Bull, Bearish, or Neutral state by analyzing multi-exchange volume flow, cumulative delta positioning, and market-wide liquidity trends.
Real-Time Alerts & Adaptive Visualization: Provides fully configurable real-time alerts for trend shifts, divergences, and market conditions, allowing traders to act immediately on high-confidence signals.
What Makes ZenAlgo - Aggregated Delta Unique?
Unlike free or open-source alternatives, ZenAlgo - Aggregated Delta applies a multi-layered data processing approach to smooth inconsistencies and improve signal reliability. Instead of using raw exchange feeds, the system incorporates adaptive volume aggregation and standard deviation-based market classification to ensure accuracy and reduce noise. These enhancements lead to more precise trend signals and a clearer representation of market sentiment.
Multi-Exchange Order Flow Validation: Unlike single-source indicators that rely on individual exchange feeds, this tool ensures cross-market consistency by aggregating volume data dynamically.
Fractal-Based Divergence Detection: Detects divergences using fractal logic rather than contextual volume trends, reducing false-positive divergence signals while maintaining accuracy.
Automated Sentiment Analysis: Classifies market sentiment into structured phases (Full Bull, Bearish, etc.), reducing the manual effort needed to interpret order flow trends.
How It Works (Technical Breakdown)
Multi-Exchange Volume Aggregation: The system fetches and validates buy/sell volume data from multiple exchanges, applying volume aggregation techniques to smooth out inconsistencies. It ensures that data from low-liquidity exchanges does not disproportionately influence the analysis.
Cumulative Delta Computation: Cumulative delta is computed as the net difference between buy and sell volumes over a given period. By summing up these values across multiple exchanges, traders can identify real accumulation or distribution zones, reducing false signals from isolated exchange anomalies.
Divergence Detection Methodology: The tool uses a fractal-based logic approach to detect high-confidence divergences across price, volume, and delta trends. This allows for a more structured detection process compared to simple peak/trough analysis, reducing noise in the signals.
Temperature Zones Filtering: Market conditions are dynamically classified using a rolling standard deviation model, ensuring that hot/cold states adjust automatically based on recent volatility levels. This means that instead of using arbitrary fixed thresholds, the tool adapts based on historical data behavior.
Market Sentiment State Calculation: The tool evaluates liquidity conditions, volume trends, and cumulative delta flow, categorizing the market into predefined states (Bullish, Bearish, Neutral). This helps traders assess the broader context of price movements rather than reacting to isolated signals.
Real-Time Adaptive Alerts: The system provides fully configurable alerts that notify traders about key trend shifts, high-confidence divergences, and changes in market conditions as they occur. This ensures that traders can make timely and well-informed decisions.
Why This Approach Works
By aggregating data from multiple exchanges, it reduces the impact of exchange-specific liquidity disparities and anomalies, leading to a more holistic view of order flow.
The cumulative delta analysis ensures that price movements are validated by actual buying/selling pressure, filtering out misleading short-term spikes.
Dynamic market classification adapts to current conditions rather than using outdated fixed thresholds, making it more relevant in different market regimes.
Fractal-based divergence detection avoids common pitfalls of traditional divergence analysis, reducing false signals while maintaining accuracy.
Combining real-time adaptive alerts with well-structured classification improves traders’ ability to respond to market shifts efficiently.
Practical Use Cases
Identifying High-Probability Trend Reversals: If cumulative delta shows bullish divergence while the market is in an Extreme Cold zone, it signals a strong potential for reversal.
Confirming Trend Continuation: When bullish moving average crossovers align with a rising cumulative delta, traders can enter positions with higher confidence.
Detecting Exhaustion in Market Moves: If price enters an "Extreme Hot" zone but cumulative delta starts declining, this suggests trend exhaustion and a possible reversal.
Filtering False Breakouts: If price breaks a resistance level but aggregated buy volume fails to increase, this invalidates the breakout, helping traders avoid bad trades.
Cross-Exchange Sentiment Confirmation: If cumulative delta on aggregated exchanges contradicts price action on an individual exchange, traders can identify localized exchange-based distortions.
Customization & Settings Overview
Exchange Selection: Traders can fine-tune exchange sources for aggregation, allowing for custom market-specific insights.
Adaptive Divergence Settings: Configure detection thresholds, lookback periods, and divergence filtering options to reduce noise and focus on high-confidence signals.
Moving Average Adjustments: Select custom MA types, lengths, and visualization preferences to match different trading styles.
Market Temperature Thresholds: Adjust hot/cold sensitivity to align with preferred risk levels and volatility expectations.
Configurable Alerts & Theme Customization: Full control over notification triggers, color themes, and label formatting to enhance user experience.
Important Considerations
Market Context Dependency: This tool provides order flow analysis, which should be used in conjunction with broader market context and risk management.
Data Source Variability: While multi-exchange aggregation improves reliability, some exchanges may report inaccurate or delayed data.
Extreme Volatility Handling: Large price swings can temporarily distort delta readings, so traders should always validate with additional context.
Liquidity Limitations: In low-liquidity conditions, order flow signals may be less reliable due to fragmented market participation.
UVR Crypto TrendINDICATOR OVERVIEW: UVR CRYPTO TREND
The UVR Crypto Trend indicator is a custom-built tool designed specifically for cryptocurrency markets, utilizing advanced volatility, momentum, and trend-following techniques. It aims to identify trend reversals and provide buy and sell signals by analyzing multiple factors, such as price volatility(UVR), RSI (Relative Strength Index), CMF (Chaikin Money Flow), and EMA (Exponential Moving Average). The indicator is optimized for CRYPTO MARKETS only.
KEY FEATURES AND HOW IT WORKS
Volatility Analysis with UVR
The UVR (Ultimate Volatility Rate) is a proprietary calculation that measures market volatility by comparing significant price extremes and smoothing the data over time.
Purpose: UVR aims to reduce noise in low-volatility environments and highlight significant movements during higher-volatility periods. While it strives to improve filtering in low-volatility conditions, it does not guarantee perfect performance, making it a balanced and adaptable tool for dynamic markets like cryptocurrency.
HOW UVR (ULTIMATE VOLATILITY RATE) IS CALCULATED
UVR is calculated using a method that ensures precise measurement of market volatility by comparing price extremes across consecutive candles:
Volatility Components:
Two values are calculated to represent potential price fluctuations:
The absolute difference between the current candle's high and the previous candle's low:
Volatility Component 1=∣High−Low ∣
The absolute difference between the previous candle's high and the current candle's low:
Volatility Component 2=∣High −Low∣
Volatility Ratio:
The larger of the two components is selected as the Volatility Ratio, ensuring UVR captures the most significant movement:
Volatility Ratio=max(Volatility Component 1,Volatility Component 2)
Smoothing with SMMA:
To stabilize the volatility calculation, the Volatility Ratio is smoothed using a Smoothed Moving Average (SMMA) over a user-defined period (e.g., 14 candles):
UVR=(UVR(Previous)×(Period−1)+Volatility Ratio)/Period
This calculation ensures UVR adapts dynamically to market conditions, focusing on significant price movements while filtering out noise.
RSI FOR MOMENTUM DETECTION
RSI (Relative Strength Index) identifies overbought and oversold conditions.
Trend Confirmation at the 50 Level
RSI values crossing above 50 signal the potential start of an upward trend.
RSI values crossing below 50 indicate the potential start of a downward trend.
Key Reversals at Extreme Levels
RSI detects trend reversals at overbought (>70) and oversold (<30) levels.
For example:
Overbought Trend Reversal: RSI >70 followed by bearish price action signals a potential downtrend.
Oversold Trend Reversal: RSI <30 with bullish confirmation signals a potential uptrend.
Rare Extreme RSI Readings
Extreme levels, such as RSI <12 (oversold) or RSI >88 (overbought), are used to identify rare yet powerful reversals.
---HOW IT DIFFERS FROM OTHER INDICATORS---
Using UVR High and Low Values
The Ultimate Volatility Rate (UVR) focuses on analyzing the high and low price ranges of the market to measure volatility.
Unlike traditional trend indicators that rely primarily on momentum or moving average crossovers, UVR leverages price extremes to better identify trend reversals.
This approach ensures fewer false signals during low-volatility phases and more accurate trend detection during high-volatility conditions.
UVR as the Core Component
The indicator is fundamentally built around UVR as the primary filter, while supporting tools like RSI (momentum detection), CMF (volume confirmation), and EMA (trend validation) complement its functionality.
By integrating these additional components, the indicator provides a multidimensional analysis rather than relying solely on a single approach.
Dynamic Adaptation to Volatility
UVR dynamically adjusts to market conditions, striving to improve filtering in low-volatility phases. While not flawless, this approach minimizes false signals and adapts more effectively to varying levels of market activity.
Trend Clouds for Visual Guidance
UVR-based dynamic clouds visually mark high and low price areas, highlighting potential consolidation or retracement zones.
These clouds serve as guides for setting stop-loss or take-profit levels, offering clear risk management strategies.
BUY AND SELL SIGNAL LOGIC
BUY CONDITIONS
Momentum-Based Buy-Entry
RSI >50, CMF >0, and the close price is above EMA50.
The price difference between open and close exceeds a threshold based on UVR.
Oversold Reversal
RSI <30 and CMF >0 with a strong bullish candle (close > open and UVR-based sensitivity filter).
Breakout Confirmation
The price breaks above a previously identified resistance, with conditions for RSI and CMF supporting the breakout.
Reversal from Oversold RSI Extreme
RSI <12 on the previous candle with a strong rebound on the current candle with UVR confirmation filter.
SELL CONDITIONS
Momentum-Based Sell-Entry
RSI <50, CMF <0, and the close price is below EMA50.
The price difference between open and close exceeds the UVR threshold.
Overbought Reversal
RSI >70 with bearish price action (open > close and UVR-based sensitivity filter).
Breakdown Confirmation
The price breaks below a previously identified support, with RSI and CMF supporting the breakdown.
Reversal from Overbought RSI Extreme
RSI >88 on the previous candle with a bearish confirmation on the current candle with UVR confirmation filter.
BUY AND SELL SIGNALS VISUALIZATION
The UVR Crypto Trend Indicator visually represents buy and sell conditions using dynamic plots, making it easier for traders to interpret and act on the signals. Below is an explanation of the visual representation:
Buy Signals and Visualization
Signal Trigger:
A buy signal is generated when one of the defined Buy Conditions is met (e.g., RSI >50, CMF >0, price above EMA50).
Visual Representation:
A blue upward arrow appears at the candle where the buy condition is triggered.
A blue cloud forms above the price candles, representing the strength of the bullish trend. The cloud dynamically adapts to market volatility, using the UVR calculation to mark support zones or consolidation levels.
Purpose of the Blue Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving up
Sell Signals and Visualization
Signal Trigger:
A sell signal is generated when one of the defined Sell Conditions is met (e.g., RSI <50, CMF <0, price below EMA50).
Visual Representation:
A red downward arrow appears at the candle where the sell condition is triggered.
A red cloud forms below the price candles, representing the strength of the bearish trend. Like the blue cloud, it uses the UVR calculation to dynamically mark resistance zones or potential retracement levels.
Purpose of the Red Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving down.
CONCLUSION
The UVR Crypto Trend indicator provides a powerful tool for trend reversal detection by combining volatility analysis, momentum confirmation, and trend-following techniques. Its unique use of the Ultimate Volatility Rate (UVR) as a core element, supported by proven indicators like RSI, CMF, and EMA, ensures reliable and actionable signals tailored for the crypto market's dynamic nature. By leveraging UVR’s high and low price range analysis, it achieves a level of precision that traditional indicators lack, making it a high-performing system for cryptocurrency traders.
Traders Trend DashboardThe Traders Trend Dashboard (TTD) is a comprehensive trend analysis tool designed to assist traders in making informed trading decisions across various markets and timeframes. Unlike conventional trend-following scripts, TTD goes beyond simple trend detection by incorporating a unique combination of moving averages and a visual dashboard, providing traders with a clear and actionable overview of market trends. Here's how TTD stands out from the crowd:
Originality and Uniqueness:
TTD doesn't rely on just one moving average crossover to detect trends. Instead, it employs a dynamic approach by comparing two moving averages of distinct periods across multiple timeframes. This innovative methodology enhances trend detection accuracy and reduces false signals commonly associated with single moving average systems.
Market Applicability:
TTD is versatile and adaptable to various financial markets, including forex, stocks, cryptocurrencies, and commodities. Its flexibility ensures that traders can utilize it across different asset classes and capitalize on market opportunities.
Optimal Timeframe Utilization:
Unlike many trend indicators that work best on specific timeframes, TTD caters to traders with diverse trading preferences. It offers support for intraday trading (1m, 3m, 5m), short-term trading (15m, 30m, 1h), and swing trading (4h, D, W, M), making it suitable for a wide range of trading styles.
Underlying Conditions and Interpretation:
TTD is particularly effective during trending markets, where its multi-timeframe approach helps identify consistent trends across various time horizons. In ranging markets, TTD can indicate potential reversals or areas of uncertainty when moving averages converge or cross frequently.
How to Use TTD:
1. Timeframe Selection: Choose the relevant timeframes based on your trading style and preferences. Enable or disable timeframes in the settings to focus on the most relevant ones for your strategy.
2. Dashboard Interpretation: The TTD dashboard displays green (🟢) and red (🔴) symbols to indicate the relationship between two moving averages. A green symbol suggests that the shorter moving average is above the longer one, indicating a potential bullish trend. A red symbol suggests the opposite, indicating a potential bearish trend.
3. Confirmation and Strategy: Consider TTD signals as confirmation for your trading strategy. For instance, in an uptrend, look for long opportunities when the dashboard displays consistent green symbols. Conversely, in a downtrend, focus on short opportunities when red symbols dominate.
4. Risk Management: As with any indicator, use TTD in conjunction with proper risk management techniques. Avoid trading solely based on indicator signals; instead, integrate them into a comprehensive trading plan.
Conclusion:
The Traders Trend Dashboard (TTD) offers traders a powerful edge in trend analysis, combining innovation, versatility, and clarity. By understanding its unique methodology and integrating its signals with your trading strategy, you can make more informed trading decisions across various markets and timeframes. Elevate your trading with TTD and unlock a new level of trend analysis precision.
Average sector correlations to SPYHello Traders!
This is our latest addition to MFR TradingView account: Average sector correlations to SPY.
The Average Sector Correlation indicator is a powerful tool designed to give insights into the interconnectedness of different SPY sectors in relation to the SPY itself. As an introduction, know that this indicator presents the average correlation of all SPY sectors, serving as a barometer for overall market cohesion and relative performance.
At Myfractalrange, we monitor correlations extensively as we know they serve as warning for reversals, bullish rallies, bear market allies, etc.
Before going into how subscribers can use this script, let't have a look at the different data points:
In this script, we are calculating the average sector correlations to the SPY (S&P 500 ETF).
The following data points are used for the calculation:
- XLK: Technology Select Sector SPDR Fund
- XLE: Energy Select Sector SPDR Fund
- XLF: Financial Select Sector SPDR Fund
- XLU: Utilities Select Sector SPDR Fund
- XLV: Health Care Select Sector SPDR Fund
- XLP: Consumer Staples Select Sector SPDR Fund
- XLI: Industrial Select Sector SPDR Fund
- XLY: Consumer Discretionary Select Sector SPDR Fund
- XLC: Communication Services Select Sector SPDR Fund
- XLRE: Real Estate Select Sector SPDR Fund
- XLB: Materials Select Sector SPDR Fund
These data points represent different sectors of the stock market.
The user can modify the "period" variable to specify the lookback period for calculating the correlation.
By changing the value of "Period," the user can adjust the number of historical data points used in the correlation calculation. Default value is 10 days.
How does the script work?
The script uses the ta.correlation function from TradingView's Pine Script to calculate the correlation between the daily returns of each sector ETF and the SPY. The daily return is calculated as the percentage change in price from the previous day.
The correlation calculation is performed for each sector ETF and the SPY, using the specified lookback period. The correlations are then averaged to obtain the average sector correlation to the SPY.
The resulting average sector correlation is plotted on the chart using a blue line.
How to use correlations when trading?
This script can be used to assess the overall market sentiment by measuring the average sector correlation to the SPY. When the average sector correlation is positive, it indicates that the sectors are generally moving in the same direction as the broader market (SPY). This suggests a strong market trend.
Traders can use this information to make informed trading decisions. For example, if the average sector correlation is strongly positive, it may be a signal to consider bullish positions in individual stocks or ETFs from sectors with high positive correlations. Conversely, if the average sector correlation is negative or weak, it may indicate a lack of market direction or potential sector rotation, requiring caution in trading decisions.
Furthermore, when correlation values are high and growing, it may signify a build-up of risk, suggesting that the sectors are moving in tandem due to widespread market forces. This can often be a signal of broader market participants chasing trends or reacting to panic. Therefore, this indicator can serve as a valuable tool for traders and investors who want to understand market sentiment and systemic risk at a glance.
The Average Sector Correlation indicator also provides the capability to monitor average correlations across multiple timeframes concurrently. This feature allows users to track the fluctuations of sector correlations over short, medium, and long-term periods, all simultaneously.
This function offers a more comprehensive view of the market dynamics and can alert users to changes in correlation patterns over various time horizons. Thus, users can gain insights into the immediate temperament of the market while also maintaining awareness of larger trends that may be forming or diminishing over extended periods. It presents a holistic image of market behaviour, enhancing the user's decision-making process.
Why use Correlations in combination with other indicators?
To enhance trading strategies, this script can be used in combination with other technical indicators or signals. By incorporating additional indicators such as moving averages, trend lines, or oscillators, traders can build a comprehensive trading system.
For example, traders can use the average sector correlation as a confirmation signal for other technical analysis tools. If a bullish signal is generated by another indicator, such as a moving average crossover or a breakout, the positive average sector correlation can provide additional confidence to enter or hold a long position.
Conversely, if a bearish signal is generated by another indicator, a negative average sector correlation can act as a confirmation signal to consider short positions or reduce exposure to sectors with low or negative correlations.
By combining multiple signals and indicators, traders can develop a well-rounded trading strategy that incorporates market breadth (sector correlations) along with other technical factors to increase the probability of successful trades.
It's important to note that while Correlations are a useful tool, it should not be relied upon solely for making trading decisions. It's recommended to use it in conjunction with other technical analysis tools and consider other factors such as Trend, market conditions, risk management, and fundamental analysis.
We hope that you will find these explanations useful.
Enjoy!
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorised. This script is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. Myfractalrange is not responsible for any losses you may incur. Please invest wisely.
Oliver Velez IndicatorOliver Velez is a well-known trader and educator who has developed multiple trading strategies. One of them is the 20-200sma strategy, which is a basic moving average crossover strategy. The strategy involves using two simple moving averages (SMAs) - a short-term SMA with a period of 20 and a long-term SMA with a period of 200 - on a 2-minute timeframe chart.
When the short-term SMA crosses above the long-term SMA, it signals a potential bullish trend and traders may look for opportunities to enter a long position. Conversely, when the short-term SMA crosses below the long-term SMA, it signals a potential bearish trend and traders may look for opportunities to enter a short position.
Traders using this strategy may also look for additional confirmations, such as price action signals or other technical indicators, before entering or exiting a trade. It is important to note that no trading strategy can guarantee profits, and traders should always use risk management techniques to limit potential losses.
This script is an implementation of the 2 SMA's (can also choose other types of MA's), with Elephant Bar Indicator (EBI) and the Tail Bars Indicator in TradingView.
The Elephant Bar Indicator is a technical indicator used in trading to identify potential trend reversals in the market. It is named after the large size of the bullish or bearish candlestick that it represents. The Tail Bars Indicator is a pattern recognition technique that identifies candlestick patterns with long tails or wicks.
The script starts by defining the input parameters for both indicators. For the Elephant Bar Indicator, the user inputs the lookback period and the size multiplier. For the Tail Bars Indicator, the user inputs the tail ratio and opposite wick ratio.
Next, the script calculates the moving averages of the closing price over the defined short and long periods using the Moving Average function. The script then calculates the average candle size and volume over the lookback period.
The script then identifies the Elephant Bars and Tail Bars using the input parameters and additional conditions. For Elephant Bars, the script identifies bullish and bearish bars that meet certain criteria, such as a size greater than the average candle size and volume greater than the average volume.
For Tail Bars, the script identifies bullish and bearish bars that have long tails or wicks and meet certain criteria such as opposite wick size less than or equal to the tail size multiplied by the input opposite wick ratio.
Finally, the script plots the Elephant Bar and Tail Bar signals on the chart using different colors and shapes. The script also plots the moving averages and Keltner Channels to help traders identify potential trend reversals.
It is still under development, so please, if someone has ideas to add, more than welcome
