Moving Average Simple Tool [OmegaTools]This TradingView script is a versatile Moving Average Tool that offers users multiple moving average types and a customizable overbought and oversold (OB/OS) sensitivity feature. It is designed to assist in identifying potential price trends, reversals, and momentum by using different average calculations and providing visual indicators for deviation levels. Below is a detailed breakdown of the settings, functionality, and visual elements within the Moving Average Simple Tool.
Indicator Overview
Indicator Name: Moving Average Simple Tool
Short Title: MA Tool
Purpose: Provides a choice of six moving average types with configurable sensitivity, which helps traders identify trend direction, potential reversal zones, and overbought or oversold conditions.
Input Parameters
Source (src): This option allows the user to select the data source for the moving average calculation. By default, it is set to close, but users can choose other options like open, high, low, or any custom price data.
Length (lnt): Defines the period length for the moving average. By default, it is set to 21 periods, allowing users to adjust the moving average sensitivity to either shorter or longer periods.
Average Type (mode): This input defines the moving average calculation type. Six types of averages are available:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume-Weighted Moving Average)
RMA (Rolling Moving Average)
Middle Line: Calculates the average between the highest and lowest price over the period specified in Length. This is useful for a mid-range line rather than a traditional moving average.
Sensitivity (sens): This parameter controls the sensitivity of the overbought and oversold levels. The sensitivity value can range from 1 to 40, where a lower value represents a higher sensitivity and a higher value allows for smoother OB/OS zones.
Color Settings:
OS (Oversold Color, upc): The color applied to deviation areas that fall below the oversold threshold.
OB (Overbought Color, dnc): The color applied to deviation areas that exceed the overbought threshold.
Middle Line Color (midc): A gradient color that visually blends between overbought and oversold colors for smoother visual transitions.
Calculation Components
Moving Average Calculation (mu): Based on the chosen Average Type, this calculation derives the moving average or middle line value for the selected source and length.
Deviation (dev): The deviation of the source value from the moving average is calculated. This is useful to determine whether the current price is significantly above or below the average, signaling potential buying or selling opportunities.
Overbought (ob) and Oversold (os) Levels: These levels are calculated using a linear percentile interpolation based on the deviation, length, and sensitivity inputs. The higher the sensitivity, the narrower the overbought and oversold zones, allowing users to capture more frequent signals.
Visual Elements
Moving Average Line (mu): This line represents the moving average based on the selected calculation method and is plotted with a dynamic color based on deviation thresholds. When the deviation crosses into overbought or oversold zones, it shifts to the corresponding OB/OS colors, providing a visual indication of potential trend reversals.
Deviation Plot (dev): This plot visualizes the deviation values as a column plot, with colors matching the overbought, oversold, or neutral states. This helps users to quickly assess whether the price is trending or reverting back to its mean.
Overbought (ob) and Oversold (os) Levels: These levels are plotted as fixed lines, helping users identify when the deviation crosses into overbought or oversold zones.
Zentrische Oszillatoren
Pulse DPO: Major Cycle Tops and Bottoms█ OVERVIEW
Pulse DPO is an oscillator designed to highlight Major Cycle Tops and Bottoms .
It works on any market driven by cycles. It operates by removing the short-term noise from the price action and focuses on the market's cyclical nature.
This indicator uses a Normalized version of the Detrended Price Oscillator (DPO) on a 0-100 scale, making it easier to identify major tops and bottoms.
Credit: The DPO was first developed by William Blau in 1991.
█ HOW TO READ IT
Pulse DPO oscillates in the range between 0 and 100. A value in the upper section signals an OverBought (OB) condition, while a value in the lower section signals an OverSold (OS) condition.
Generally, the triggering of OB and OS conditions don't necessarily translate into swing tops and bottoms, but rather suggest caution on approaching a market that might be overextended.
Nevertheless, this indicator has been customized to trigger the signal only during remarkable top and bottom events.
I suggest using it on the Daily Time Frame , but you're free to experiment with this indicator on other time frames.
The indicator has Built-in Alerts to signal the crossing of the Thresholds. Please don't act on an isolated signal, but rather integrate it to work in conjunction with the indicators present in your Trading Plan.
█ OB SIGNAL ON: ENTERING OVERBOUGHT CONDITION
When Pulse DPO crosses Above the Top Threshold it Triggers ON the OB signal. At this point the oscillator line shifts to OB color.
When Pulse DPO enters the OB Zone, please beware! In this Area the Major Players usually become Active Sellers to the Public. While the OB signal is On, it might be wise to Consider Selling a portion or the whole Long Position.
Please note that even though this indicator aims to focus on major tops and bottoms, a strong trending market might trigger the OB signal and stay with it for a long time. That's especially true on young markets and on bubble-mode markets.
█ OB SIGNAL OFF: EXITING OVERBOUGHT CONDITION
When Pulse DPO crosses Below the Top Threshold it Triggers OFF the OB signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OB Zone, please beware because a Major Top might just have occurred. In this Area the Major Players usually become Aggressive Sellers. They might wind up any remaining Long Positions and Open new Short Positions.
This might be a good area to Open Shorts or to Close/Reverse any remaining Long Position. Whatever you choose to do, it's usually best to act quickly because the market is prone to enter into panic mode.
█ OS SIGNAL ON: ENTERING OVERSOLD CONDITION
When Pulse DPO crosses Below the Bottom Threshold it Triggers ON the OS signal. At this point the oscillator line shifts to OS color.
When Pulse DPO enters the OS Zone, please beware because in this Area the Major Players usually become Active Buyers accumulating Long Positions from the desperate Public.
While the OS signal is On, it might be wise to Consider becoming a Buyer or to implement a Dollar-Cost Averaging (DCA) Strategy to build a Long Position towards the next Cycle. In contrast to the tops, the OS state usually takes longer to resolve a major bottom.
█ OS SIGNAL OFF: EXITING OVERSOLD CONDITION
When Pulse DPO crosses Above the Bottom Threshold it Triggers OFF the OS signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OS Zone, please beware because a Major Bottom might already be in place. In this Area the Major Players become Aggresive Buyers. They might wind up any remaining Short Positions and Open new Long Positions.
This might be a good area to Open Longs or to Close/Reverse any remaining Short Positions.
█ WHY WOULD YOU BE INTERESTED IN THIS INDICATOR?
This indicator is built over a solid foundation capable of signaling Major Cycle Tops and Bottoms across many markets. Let's see some examples:
Early Bitcoin Years: From 0 to 1242
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling the major early highs from 9-Jun-2011 at 31.50, to the next one on 9-Apr-2013 at 240 and the epic top from 29-Nov-2013 at 1242.
Due to the massive price movements, the OB condition stays pinned during most of the exponential price action. But as you can see, the OB condition quickly vanishes once the Cycle Top has been reached. As the market matures, the OB condition becomes more exceptional and triggers much closer from the Cycle Top.
With regards to Cycle Bottoms, the early bottom of 2 after having peaked at 31.50 doesn’t get captured by the indicator. That is the only cycle bottom that escapes the Pulse DPO when the bottom threshold is set at a value of 5. In that event, the oscillator low reached 6.95.
Bitcoin Adoption Spreading: From 257 to 73k
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling all the major highs from 17-Dec-2017 at 19k, to the next one on 14-Apr-2021 at 64k and the most recent top from 9-Nov-2021 at 68k.
During the massive run of 2017, the OB condition still stayed triggered for a few weeks on each swing top. But on the next cycles it started to signal only for a few days before each swing top actually happened. The OB condition during the last cycle top triggered only for 3 days. Therefore the signal grows in focus as the market matures.
At the time of publishing this indicator, Bitcoin printed a new All Time High (ATH) on 13-Mar-2024 at 73k. That run didn’t trigger the OB condition. Therefore, if the indicator is correct the Bitcoin market still has some way to grow during the next months.
With regards to Cycle Bottoms, the bottom of 3k after having peaked at19k got captured within the wide OS zone. The bottom of 15k after having peaked at 68k got captured too within the OS accumulation area.
Gold
Pulse DPO behaves surprisingly well on a long standing market such as Gold. Moving back to the 197x years it’s been signaling most Cycle Tops and Bottoms with precision. During the last cycle, it shows topping at 2k and bottoming at 1.6k.
The current price action is signaling OB condition in the range of 2.5k to 2.7k. Looking at past cycles, it tends to trigger on and off at multiple swing tops until reaching the final cycle top. Therefore this might indicate the first wave within a potential gold run.
Oil
On the Oil market, we can see that most of the cycle tops and bottoms since the 80s got signaled. The only exception being the low from 2020 which didn’t trigger.
EURUSD
On Forex markets the Pulse DPO also behaves as expected. Looking back at EURUSD we can see the marketing triggering OB and OS conditions during major cycle tops and bottoms from recent times until the 80s.
S&P 500
On the S&P 500 the Pulse DPO catched the lows from 2016 and 2020. Looking at present price action, the recent ATH didn’t trigger the OB condition. Therefore, the indicator is allowing room for another leg up during the next months.
Amazon
On the Amazon chart the Pulse DPO is mirroring pretty accurately the major swings. Scrolling back to the early 2000s, this chart resembles early exponential swings in the crypto space.
Tesla
Moving onto a younger tech stock, Pulse DPO captures pretty accurately the major tops and bottoms. The chart is shown in logarithmic scale to better display the magnitude of the moves.
█ SETTINGS
This indicator is ideal for identifying major market turning points while filtering out short-term noise. You are free to adjust the parameters to align with your preferred trading style.
Parameters : This section allows you to customize any of the Parameters that shape the Oscillator.
Oscillator Length: Defines the period for calculating the Oscillator.
Offset: Shifts the oscillator calculation by a certain number of periods, which is typically half the Oscillator Length.
Lookback Period: Specifies how many bars to look back to find tops and bottoms for normalization.
Smoothing Length: Determines the length of the moving average used to smooth the oscillator.
Thresholds : This section allows you to customize the Thresholds that trigger the OB and OS conditions.
Top: Defines the value of the Top Threshold.
Bottom: Defines the value of the Bottom Threshold.
DeNoised Momentum [OmegaTools]The DeNoised Momentum by OmegaTools is a versatile tool designed to help traders evaluate momentum, acceleration, and noise-reduction levels in price movements. Using advanced mathematical smoothing techniques, this script provides a "de-noised" view of momentum by applying filters to reduce market noise. This helps traders gain insights into the strength and direction of price trends without the distractions of market volatility. Key components include a DeNoised Moving Average (MA), a Momentum line, and Acceleration bars to identify trend shifts more clearly.
Features:
- Momentum Line: Measures the percentage change of the de-noised source price over a specified look-back period, providing insights into trend direction.
- Acceleration (Ret) Bars: Visualizes the rate of change of the source price, helping traders identify momentum shifts.
- Normal and DeNoised Moving Averages: Two moving averages, one based on close price (Normal MA) and the other on de-noised data (DeNoised MA), enable a comparison of smoothed trends versus typical price movements.
- DeNoised Price Data Plot: Displays the current de-noised price, color-coded to indicate the relationship between the Normal and DeNoised MAs, which highlights bullish or bearish conditions.
Script Inputs:
- Length (lnt): Sets the period for calculations (default: 21). It influences the sensitivity of the momentum and moving averages. Higher values will smooth the indicator further, while lower values increase sensitivity to price changes.
The Length does not change the formula of the DeNoised Price Data, it only affects the indicators calculated on it.
Indicator Components:
1. Momentum (Blue/Red Line):
- Calculated using the log of the percentage change over the specified period.
- Blue color indicates positive momentum; red indicates negative momentum.
2. Acceleration (Gray Columns):
- Measures the short-term rate of change in momentum, shown as semi-transparent gray columns.
3. Moving Averages:
- Normal MA (Purple): A standard simple moving average (SMA) based on the close price over the selected period.
- DeNoised MA (Gray): An SMA of the de-noised source, reducing the effect of market noise.
4. DeNoised Price Data:
- Represented as colored circles, with blue indicating that the Normal MA is above the DeNoised MA (bullish) and red indicating the opposite (bearish).
Usage Guide:
1. Trend Identification:
- Use the Momentum line to assess overall trend direction. Positive values indicate upward momentum, while negative values signal downward momentum.
- Compare the Normal and DeNoised MAs: when the Normal MA is above the DeNoised MA, it indicates a bullish trend, and vice versa for bearish trends.
2. Entry and Exit Signals:
- A change in the Momentum line's color from blue to red (or vice versa) may indicate potential entry or exit points.
- Observe the DeNoised Price Data circles for early signs of a trend reversal based on the interaction between the Normal and DeNoised MAs.
3. Volatility and Noise Reduction:
- By utilizing the DeNoised MA and de-noised price data, this indicator helps filter out minor fluctuations and focus on larger price movements, improving decision-making in volatile markets.
Long Short MomentumThis indicator is designed to visualize short-term and long-term momentum trends.The indicator calculates two momentum lines based on customizable lengths: a short momentum (Short Momentum) over a smaller period and a long momentum (Long Momentum) over a longer period. These lines are plotted relative to the chosen price source, typically the closing price.
The histogram, colored dynamically based on momentum direction, gives visual cues:
Green: Both short and long momentum are positive, indicating an upward trend.
Red: Both are negative, indicating a downward trend.
Gray: Mixed momentum, suggesting potential trend indecision.
The Most Powerful TQQQ EMA Crossover Trend Trading StrategyTQQQ EMA Crossover Strategy Indicator
Meta Title: TQQQ EMA Crossover Strategy - Enhance Your Trading with Effective Signals
Meta Description: Discover the TQQQ EMA Crossover Strategy, designed to optimize trading decisions with fast and slow EMA crossovers. Learn how to effectively use this powerful indicator for better trading results.
Key Features
The TQQQ EMA Crossover Strategy is a powerful trading tool that utilizes Exponential Moving Averages (EMAs) to identify potential entry and exit points in the market. Key features of this indicator include:
**Fast and Slow EMAs:** The strategy incorporates two EMAs, allowing traders to capture short-term trends while filtering out market noise.
**Entry and Exit Signals:** Automated signals for entering and exiting trades based on EMA crossovers, enhancing decision-making efficiency.
**Customizable Parameters:** Users can adjust the lengths of the EMAs, as well as take profit and stop loss multipliers, tailoring the strategy to their trading style.
**Visual Indicators:** Clear visual plots of the EMAs and exit points on the chart for easy interpretation.
How It Works
The TQQQ EMA Crossover Strategy operates by calculating two EMAs: a fast EMA (default length of 20) and a slow EMA (default length of 50). The core concept is based on the crossover of these two moving averages:
- When the fast EMA crosses above the slow EMA, it generates a *buy signal*, indicating a potential upward trend.
- Conversely, when the fast EMA crosses below the slow EMA, it produces a *sell signal*, suggesting a potential downward trend.
This method allows traders to capitalize on momentum shifts in the market, providing timely signals for trade execution.
Trading Ideas and Insights
Traders can leverage the TQQQ EMA Crossover Strategy in various market conditions. Here are some insights:
**Scalping Opportunities:** The strategy is particularly effective for scalping in volatile markets, allowing traders to make quick profits on small price movements.
**Swing Trading:** Longer-term traders can use this strategy to identify significant trend reversals and capitalize on larger price swings.
**Risk Management:** By incorporating customizable stop loss and take profit levels, traders can manage their risk effectively while maximizing potential returns.
How Multiple Indicators Work Together
While this strategy primarily relies on EMAs, it can be enhanced by integrating additional indicators such as:
- **Relative Strength Index (RSI):** To confirm overbought or oversold conditions before entering trades.
- **Volume Indicators:** To validate breakout signals, ensuring that price movements are supported by sufficient trading volume.
Combining these indicators provides a more comprehensive view of market dynamics, increasing the reliability of trade signals generated by the EMA crossover.
Unique Aspects
What sets this indicator apart is its simplicity combined with effectiveness. The reliance on EMAs allows for smoother signals compared to traditional moving averages, reducing false signals often associated with choppy price action. Additionally, the ability to customize parameters ensures that traders can adapt the strategy to fit their unique trading styles and risk tolerance.
How to Use
To effectively utilize the TQQQ EMA Crossover Strategy:
1. **Add the Indicator:** Load the script onto your TradingView chart.
2. **Set Parameters:** Adjust the fast and slow EMA lengths according to your trading preferences.
3. **Monitor Signals:** Watch for crossover points; enter trades based on buy/sell signals generated by the indicator.
4. **Implement Risk Management:** Set your stop loss and take profit levels using the provided multipliers.
Regularly review your trading performance and adjust parameters as necessary to optimize results.
Customization
The TQQQ EMA Crossover Strategy allows for extensive customization:
- **EMA Lengths:** Change the default lengths of both fast and slow EMAs to suit different time frames or market conditions.
- **Take Profit/Stop Loss Multipliers:** Adjust these values to align with your risk management strategy. For instance, increasing the take profit multiplier may yield larger gains but could also increase exposure to market fluctuations.
This flexibility makes it suitable for various trading styles, from aggressive scalpers to conservative swing traders.
Conclusion
The TQQQ EMA Crossover Strategy is an effective tool for traders seeking an edge in their trading endeavors. By utilizing fast and slow EMAs, this indicator provides clear entry and exit signals while allowing for customization to fit individual trading strategies. Whether you are a scalper looking for quick profits or a swing trader aiming for larger moves, this indicator offers valuable insights into market trends.
Incorporate it into your TradingView toolkit today and elevate your trading performance!
Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
XAUUSD 10-Minute StrategyThis XAUUSD 10-Minute Strategy is designed for trading Gold vs. USD on a 10-minute timeframe. By combining multiple technical indicators (MACD, RSI, Bollinger Bands, and ATR), the strategy effectively captures both trend-following and reversal opportunities, with adaptive risk management for varying market volatility. This approach balances high-probability entries with robust volatility management, making it suitable for traders seeking to optimise entries during significant price movements and reversals.
Key Components and Logic:
MACD (12, 26, 9):
Generates buy signals on MACD Line crossovers above the Signal Line and sell signals on crossovers below the Signal Line, helping to capture momentum shifts.
RSI (14):
Utilizes oversold (below 35) and overbought (above 65) levels as a secondary filter to validate entries and avoid overextended price zones.
Bollinger Bands (20, 2):
Uses upper and lower Bollinger Bands to identify potential overbought and oversold conditions, aiming to enter long trades near the lower band and short trades near the upper band.
ATR-Based Stop Loss and Take Profit:
Stop Loss and Take Profit levels are dynamically set as multiples of ATR (3x for stop loss, 5x for take profit), ensuring flexibility with market volatility to optimise exit points.
Entry & Exit Conditions:
Buy Entry: T riggered when any of the following conditions are met:
MACD Line crosses above the Signal Line
RSI is oversold
Price drops below the lower Bollinger Band
Sell Entry: Triggered when any of the following conditions are met:
MACD Line crosses below the Signal Line
RSI is overbought
Price moves above the upper Bollinger Band
Exit Strategy: Trades are closed based on opposing entry signals, with adaptive spread adjustments for realistic exit points.
Backtesting Configuration & Results:
Backtesting Period: July 21, 2024, to October 30, 2024
Symbol Info: XAUUSD, 10-minute timeframe, OANDA data source
Backtesting Capital: Initial capital of $700, with each trade set to 10 contracts (equivalent to approximately 0.1 lots based on the broker’s contract size for gold).
Users should confirm their broker's contract size for gold, as this may differ. This script uses 10 contracts for backtesting purposes, aligned with 0.1 lots on brokers offering a 100-contract specification.
Key Backtesting Performance Metrics:
Net Profit: $4,733.90 USD (676.27% increase)
Total Closed Trades: 526
Win Rate: 53.99%
Profit Factor: 1.44 (1.96 for Long trades, 1.14 for Short trades)
Max Drawdown: $819.75 USD (56.33% of equity)
Sharpe Ratio: 1.726
Average Trade: $9.00 USD (0.04% of equity per trade)
This backtest reflects realistic conditions, with a spread adjustment of 38 points and no slippage or commission applied. The settings aim to simulate typical retail trading conditions. However, please adjust the initial capital, contract size, and other settings based on your account specifics for best results.
Usage:
This strategy is tuned specifically for XAUUSD on a 10-minute timeframe, ideal for both trend-following and reversal trades. The ATR-based stop loss and take profit levels adapt dynamically to market volatility, optimising entries and exits in varied conditions. To backtest this script accurately, ensure your broker’s contract specifications for gold align with the parameters used in this strategy.
SuperATR 7-Step Profit - Strategy [presentTrading] Long time no see!
█ Introduction and How It Is Different
The SuperATR 7-Step Profit Strategy is a multi-layered trading approach that integrates adaptive Average True Range (ATR) calculations with momentum-based trend detection. What sets this strategy apart is its sophisticated 7-step take-profit mechanism, which combines four ATR-based exit levels and three fixed percentage levels. This hybrid approach allows traders to dynamically adjust to market volatility while systematically capturing profits in both long and short market positions.
Traditional trading strategies often rely on static indicators or single-layered exit strategies, which may not adapt well to changing market conditions. The SuperATR 7-Step Profit Strategy addresses this limitation by:
- Using Adaptive ATR: Enhances the standard ATR by making it responsive to current market momentum.
- Incorporating Momentum-Based Trend Detection: Identifies stronger trends with higher probability of continuation.
- Employing a Multi-Step Take-Profit System: Allows for gradual profit-taking at predetermined levels, optimizing returns while minimizing risk.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy revolves around detecting strong market trends and capitalizing on them using an adaptive ATR and momentum indicators. Below is a detailed breakdown of each component of the strategy.
🔶 1. True Range Calculation with Enhanced Volatility Detection
The True Range (TR) measures market volatility by considering the most significant price movements. The enhanced TR is calculated as:
TR = Max
Where:
High and Low are the current bar's high and low prices.
Previous Close is the closing price of the previous bar.
Abs denotes the absolute value.
Max selects the maximum value among the three calculations.
🔶 2. Momentum Factor Calculation
To make the ATR adaptive, the strategy incorporates a Momentum Factor (MF), which adjusts the ATR based on recent price movements.
Momentum = Close - Close
Stdev_Close = Standard Deviation of Close over n periods
Normalized_Momentum = Momentum / Stdev_Close (if Stdev_Close ≠ 0)
Momentum_Factor = Abs(Normalized_Momentum)
Where:
Close is the current closing price.
n is the momentum_period, a user-defined input (default is 7).
Standard Deviation measures the dispersion of closing prices over n periods.
Abs ensures the momentum factor is always positive.
🔶 3. Adaptive ATR Calculation
The Adaptive ATR (AATR) adjusts the traditional ATR based on the Momentum Factor, making it more responsive during volatile periods and smoother during consolidation.
Short_ATR = SMA(True Range, short_period)
Long_ATR = SMA(True Range, long_period)
Adaptive_ATR = /
Where:
SMA is the Simple Moving Average.
short_period and long_period are user-defined inputs (defaults are 3 and 7, respectively).
🔶 4. Trend Strength Calculation
The strategy quantifies the strength of the trend to filter out weak signals.
Price_Change = Close - Close
ATR_Multiple = Price_Change / Adaptive_ATR (if Adaptive_ATR ≠ 0)
Trend_Strength = SMA(ATR_Multiple, n)
🔶 5. Trend Signal Determination
If (Short_MA > Long_MA) AND (Trend_Strength > Trend_Strength_Threshold):
Trend_Signal = 1 (Strong Uptrend)
Elif (Short_MA < Long_MA) AND (Trend_Strength < -Trend_Strength_Threshold):
Trend_Signal = -1 (Strong Downtrend)
Else:
Trend_Signal = 0 (No Clear Trend)
🔶 6. Trend Confirmation with Price Action
Adaptive_ATR_SMA = SMA(Adaptive_ATR, atr_sma_period)
If (Trend_Signal == 1) AND (Close > Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Elif (Trend_Signal == -1) AND (Close < Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Else:
Trend_Confirmed = False
Local Performance
🔶 7. Multi-Step Take-Profit Mechanism
The strategy employs a 7-step take-profit system
█ Trade Direction
The SuperATR 7-Step Profit Strategy is designed to work in both long and short market conditions. By identifying strong uptrends and downtrends, it allows traders to capitalize on price movements in either direction.
Long Trades: Initiated when the market shows strong upward momentum and the trend is confirmed.
Short Trades: Initiated when the market exhibits strong downward momentum and the trend is confirmed.
█ Usage
To implement the SuperATR 7-Step Profit Strategy:
1. Configure the Strategy Parameters:
- Adjust the short_period, long_period, and momentum_period to match the desired sensitivity.
- Set the trend_strength_threshold to control how strong a trend must be before acting.
2. Set Up the Multi-Step Take-Profit Levels:
- Define ATR multipliers and fixed percentage levels according to risk tolerance and profit goals.
- Specify the percentage of the position to close at each level.
3. Apply the Strategy to a Chart:
- Use the strategy on instruments and timeframes where it has been tested and optimized.
- Monitor the positions and adjust parameters as needed based on performance.
4. Backtest and Optimize:
- Utilize TradingView's backtesting features to evaluate historical performance.
- Adjust the default settings to optimize for different market conditions.
█ Default Settings
Understanding default settings is crucial for optimal performance.
Short Period (3): Affects the responsiveness of the short-term MA.
Effect: Lower values increase sensitivity but may produce more false signals.
Long Period (7): Determines the trend baseline.
Effect: Higher values reduce noise but may delay signals.
Momentum Period (7): Influences adaptive ATR and trend strength.
Effect: Shorter periods react quicker to price changes.
Trend Strength Threshold (0.5): Filters out weaker trends.
Effect: Higher thresholds yield fewer but stronger signals.
ATR Multipliers: Set distances for ATR-based exits.
Effect: Larger multipliers aim for bigger moves but may reduce hit rate.
Fixed TP Levels (%): Control profit-taking on smaller moves.
Effect: Adjusting these levels affects how quickly profits are realized.
Exit Percentages: Determine how much of the position is closed at each TP level.
Effect: Higher percentages reduce exposure faster, affecting risk and reward.
Adjusting these variables allows you to tailor the strategy to different market conditions and personal risk preferences.
By integrating adaptive indicators and a multi-tiered exit strategy, the SuperATR 7-Step Profit Strategy offers a versatile tool for traders seeking to navigate varying market conditions effectively. Understanding and adjusting the key parameters enables traders to harness the full potential of this strategy.
MACD Cloud with Moving Average and ATR BandsThe algorithm implements a technical analysis indicator that combines the MACD Cloud, Moving Averages (MA), and volatility bands (ATR) to provide signals on market trends and potential reversal points. It is divided into several sections:
🎨 Color Bars:
Activated based on user input.
Controls bar color display according to price relative to ATR levels and moving average (MA).
Logic:
⚫ Black: Potential bearish reversal (price above the upper ATR band).
🔵 Blue: Potential bullish reversal (price below the lower ATR band).
o
🟢 Green: Bullish trend (price between the MA and upper ATR band).
o
🔴 Red: Bearish trend (price between the lower ATR band and MA).
o
📊 MACD Bars:
Description:
The MACD Bars section is activated by default and can be modified based on user input.
🔴 Red: Indicates a bearish trend, shown when the MACD line is below the Signal line (Signal line is a moving average of MACD).
🔵 Blue: Indicates a bullish trend, shown when the MACD line is above the Signal line.
Matching colors between MACD Bars and MACD Cloud visually confirms trend direction.
MACD Cloud Logic: The MACD Cloud is based on Moving Average Convergence Divergence (MACD), a momentum indicator showing the relationship between two moving averages of price.
MACD and Signal Lines: The cloud visualizes the MACD line relative to the Signal line. If the MACD line is above the Signal line, it indicates a potential bullish trend, while below it suggests a potential bearish trend.
☁️ MA Cloud:
The MA Cloud uses three moving averages to analyze price direction:
Moving Average Relationship: Three MAs of different periods are plotted. The cloud turns green when the shorter MA is above the longer MA, indicating an uptrend, and red when below, suggesting a downtrend.
Trend Visualization: This graphical representation shows the trend direction.
📉 ATR Bands:
The ATR bands calculate overbought and oversold limits using a weighted moving average (WMA) and ATR.
Center (matr): Shows general trend; prices above suggest an uptrend, while below indicate a downtrend.
Up ATR 1: Marks the first overbought level, suggesting a potential bearish reversal if the price moves above this band.
Down ATR 1: Marks the first oversold level, suggesting a possible bullish reversal if the price moves below this band.
Up ATR 2: Extends the overbought range to an extreme, reinforcing the possibility of a bearish reversal at this level.
Down ATR 2: Extends the oversold range to an extreme, indicating a stronger bullish reversal possibility if price reaches here.
Español:
El algoritmo implementa un indicador de análisis técnico que combina la nube MACD, promedios móviles (MA) y bandas de volatilidad (ATR) para proporcionar señales sobre tendencias del mercado y posibles puntos de reversión. Se divide en varias secciones:
🎨 Barras de Color:
- Activado según la entrada del usuario.
- Controla la visualización del color de las barras según el precio en relación con los niveles de ATR y el promedio móvil (MA).
- **Lógica:**
- ⚫ **Negro**: Reversión bajista potencial (precio por encima de la banda superior ATR).
- 🔵 **Azul**: Reversión alcista potencial (precio por debajo de la banda inferior ATR).
- 🟢 **Verde**: Tendencia alcista (precio entre el MA y la banda superior ATR).
- 🔴 **Rojo**: Tendencia bajista (precio entre la banda inferior ATR y el MA).
### 📊 Barras MACD:
- **Descripción**:
- La sección de barras MACD se activa por defecto y puede modificarse según la entrada del usuario.
- 🔴 **Rojo**: Indica una tendencia bajista, cuando la línea MACD está por debajo de la línea de señal (la línea de señal es una media móvil de la MACD).
- 🔵 **Azul**: Indica una tendencia alcista, cuando la línea MACD está por encima de la línea de señal.
- La coincidencia de colores entre las barras MACD y la nube MACD confirma visualmente la dirección de la tendencia.
### 🌥️ Nube MACD:
- **Lógica de la Nube MACD**: Basada en el indicador de convergencia-divergencia de medias móviles (MACD), que muestra la relación entre dos medias móviles del precio.
- **Líneas MACD y de Señal**: La nube visualiza la relación entre la línea MACD y la línea de señal. Si la línea MACD está por encima de la de señal, indica una tendencia alcista potencial; si está por debajo, sugiere una tendencia bajista.
### ☁️ Nube MA:
- **Relación entre Medias Móviles**: Se trazan tres medias móviles de diferentes períodos. La nube se vuelve verde cuando la media más corta está por encima de la más larga, indicando una tendencia alcista, y roja cuando está por debajo, sugiriendo una tendencia bajista.
- **Visualización de Tendencias**: Proporciona una representación gráfica de la dirección de la tendencia.
### 📉 Bandas ATR:
- Las bandas ATR calculan límites de sobrecompra y sobreventa usando una media ponderada y el ATR.
- **Centro (matr)**: Muestra la tendencia general; precios por encima indican tendencia alcista y debajo, bajista.
- **Up ATR 1**: Marca el primer nivel de sobrecompra, sugiriendo una reversión bajista potencial si el precio sube por encima de esta banda.
- **Down ATR 1**: Marca el primer nivel de sobreventa, sugiriendo una reversión alcista potencial si el precio baja por debajo de esta banda.
- **Up ATR 2**: Amplía el rango de sobrecompra a un nivel extremo, reforzando la posibilidad de reversión bajista.
- **Down ATR 2**: Extiende el rango de sobreventa a un nivel extremo, sugiriendo una reversión alcista más fuerte si el precio alcanza esta banda.
EMA Ribbon + ADX MomentumHere's a description for your TradingView indicator publication:
The EMA Ribbon + ADX Momentum indicator combines exponential moving averages (EMA) with the Average Directional Index (ADX) to identify strong trends and potential trading opportunities. This powerful tool offers:
🎯 Key Features:
EMA Ribbon (10, 21, 34, 55) for trend direction
ADX integration for trend strength confirmation
Clear visual signals with color-coded backgrounds
Real-time trend status display
Strength metrics with exact percentage values
📊 How It Works:
EMA Ribbon: Four EMAs form a ribbon pattern that shows trend direction through their stacking order
ADX Integration: Confirms trend strength when above the threshold (default 25)
Visual Signals:
Green background: Strong bullish trend
Red background: Strong bearish trend
Gray background: Neutral or weak trend
📈 Trading Signals:
STRONG BULL: EMAs properly stacked bullish + high ADX + DI+ > DI-
STRONG BEAR: EMAs properly stacked bearish + high ADX + DI- > DI+
BULL/BEAR TREND: Shows regular trend conditions without strength confirmation
NEUTRAL: No clear trend structure
🔧 Customizable Parameters:
ADX Length: Adjust trend calculation period
ADX Threshold: Modify strength confirmation level
ADX Panel Toggle: Show/hide the ADX indicator panel
💡 Best Uses:
Trend following strategies
Entry/exit timing
Trade confirmation
Market structure analysis
Risk management tool
This indicator helps traders identify not just trend direction, but also trend strength, making it particularly useful for both position entry timing and risk management. The clear visual signals and real-time metrics make it suitable for traders of all experience levels.
Note: As with all technical indicators, best results are achieved when used in conjunction with other forms of analysis and proper risk management.
EMD Oscillator (Zeiierman)█ Overview
The Empirical Mode Decomposition (EMD) Oscillator is an advanced indicator designed to analyze market trends and cycles with high precision. It breaks down complex price data into simpler parts called Intrinsic Mode Functions (IMFs), allowing traders to see underlying patterns and trends that aren’t visible with traditional indicators. The result is a dynamic oscillator that provides insights into overbought and oversold conditions, as well as trend direction and strength. This indicator is suitable for all types of traders, from beginners to advanced, looking to gain deeper insights into market behavior.
█ How It Works
The core of this indicator is the Empirical Mode Decomposition (EMD) process, a method typically used in signal processing and advanced scientific fields. It works by breaking down price data into various “layers,” each representing different frequencies in the market’s movement. Imagine peeling layers off an onion: each layer (or IMF) reveals a different aspect of the price action.
⚪ Data Decomposition (Sifting): The indicator “sifts” through historical price data to detect natural oscillations within it. Each oscillation (or IMF) highlights a unique rhythm in price behavior, from rapid fluctuations to broader, slower trends.
⚪ Adaptive Signal Reconstruction: The EMD Oscillator allows traders to select specific IMFs for a custom signal reconstruction. This reconstructed signal provides a composite view of market behavior, showing both short-term cycles and long-term trends based on which IMFs are included.
⚪ Normalization: To make the oscillator easy to interpret, the reconstructed signal is scaled between -1 and 1. This normalization lets traders quickly spot overbought and oversold conditions, as well as trend direction, without worrying about the raw magnitude of price changes.
The indicator adapts to changing market conditions, making it effective for identifying real-time market cycles and potential turning points.
█ Key Calculations: The Math Behind the EMD Oscillator
The EMD Oscillator’s advanced nature lies in its high-level mathematical operations:
⚪ Intrinsic Mode Functions (IMFs)
IMFs are extracted from the data and act as the building blocks of this indicator. Each IMF is a unique oscillation within the price data, similar to how a band might be divided into treble, mid, and bass frequencies. In the EMD Oscillator:
Higher-Frequency IMFs: Represent short-term market “noise” and quick fluctuations.
Lower-Frequency IMFs: Capture broader market trends, showing more stable and long-term patterns.
⚪ Sifting Process: The Heart of EMD
The sifting process isolates each IMF by repeatedly separating and refining the data. Think of this as filtering water through finer and finer mesh sieves until only the clearest parts remain. Mathematically, it involves:
Extrema Detection: Finding all peaks and troughs (local maxima and minima) in the data.
Envelope Calculation: Smoothing these peaks and troughs into upper and lower envelopes using cubic spline interpolation (a method for creating smooth curves between data points).
Mean Removal: Calculating the average between these envelopes and subtracting it from the data to isolate one IMF. This process repeats until the IMF criteria are met, resulting in a clean oscillation without trend influences.
⚪ Spline Interpolation
The cubic spline interpolation is an advanced mathematical technique that allows smooth curves between points, which is essential for creating the upper and lower envelopes around each IMF. This interpolation solves a tridiagonal matrix (a specialized mathematical problem) to ensure that the envelopes align smoothly with the data’s natural oscillations.
To give a relatable example: imagine drawing a smooth line that passes through each peak and trough of a mountain range on a map. Spline interpolation ensures that line is as smooth and close to reality as possible. Achieving this in Pine Script is technically demanding and demonstrates a high level of mathematical coding.
⚪ Amplitude Normalization
To make the oscillator more readable, the final signal is scaled by its maximum amplitude. This amplitude normalization brings the oscillator into a range of -1 to 1, creating consistent signals regardless of price level or volatility.
█ Comparison with Other Signal Processing Methods
Unlike standard technical indicators that often rely on fixed parameters or pre-defined mathematical functions, the EMD adapts to the data itself, capturing natural cycles and irregularities in real-time. For example, if the market becomes more volatile, EMD adjusts automatically to reflect this without requiring parameter changes from the trader. In this way, it behaves more like a “smart” indicator, intuitively adapting to the market, unlike most traditional methods. EMD’s adaptive approach is akin to AI’s ability to learn from data, making it both resilient and robust in non-linear markets. This makes it a great alternative to methods that struggle in volatile environments, such as fixed-parameter oscillators or moving averages.
█ How to Use
Identify Market Cycles and Trends: Use the EMD Oscillator to spot market cycles that represent phases of buying or selling pressure. The smoothed version of the oscillator can help highlight broader trends, while the main oscillator reveals immediate cycles.
Spot Overbought and Oversold Levels: When the oscillator approaches +1 or -1, it may indicate that the market is overbought or oversold, signaling potential entry or exit points.
Confirm Divergences: If the price movement diverges from the oscillator's direction, it may indicate a potential reversal. For example, if prices make higher highs while the oscillator makes lower highs, it could be a sign of weakening trend strength.
█ Settings
Window Length (N): Defines the number of historical bars used for EMD analysis. A larger window captures more data but may slow down performance.
Number of IMFs (M): Sets how many IMFs to extract. Higher values allow for a more detailed decomposition, isolating smaller cycles within the data.
Amplitude Window (L): Controls the length of the window used for amplitude calculation, affecting the smoothness of the normalized oscillator.
Extraction Range (IMF Start and End): Allows you to select which IMFs to include in the reconstructed signal. Starting with lower IMFs captures faster cycles, while ending with higher IMFs includes slower, trend-based components.
Sifting Stopping Criterion (S-number): Sets how precisely each IMF should be refined. Higher values yield more accurate IMFs but take longer to compute.
Max Sifting Iterations (num_siftings): Limits the number of sifting iterations for each IMF extraction, balancing between performance and accuracy.
Source: The price data used for the analysis, such as close or open prices. This determines which price movements are decomposed by the indicator.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Volume-Adjusted Schaff Trend Cycle (VASTC)Volume-Adjusted Schaff Trend Cycle (VASTC)
The VASTC is a fairly fast-moving oscillator designed to identify trends early and signal when trends may be nearing their end. While it can be used for both trend-following and mean-reversion strategies , it shines in trend-following setups. It’s particularly useful for catching the start of a trend and giving early warnings that a trend might end soon, making it a valuable addition to a multi-indicator system.
How It Works:
The VASTC adapts the traditional Schaff Trend Cycle by adjusting the MACD component with volume data. This volume-adjusted MACD is run through two stochastic processes , applying exponential smoothing to enhance responsiveness. Volume sensitivity allows the VASTC to adapt dynamically to periods of high or low trading activity, providing more reliable trend signals.
Recommended Use:
Use VASTC in confluence with other indicators to confirm trend entries and exits. It’s best for identifying early trend setups rather than sustaining prolonged trend trades. When used alongside other indicators, especially those with a longer-term outlook or momentum based trend indicators, you’ll gain a clearer signal for potential exits or entries. Always backtest the VASTC on your chosen assets to determine the most effective input parameters, as the defaults may not suit all markets or assets. Different assets behave differently, and adjustments in parameters can improve its ability to analyze the assets you're looking at.
Parameters:
Length : Sets the primary smoothing length.
Fast/Slow Length : Adjust the speed of the volume-adjusted MACD component.
Factor : Controls the final smoothing applied to the STC.
Overbought/Oversold Levels : Defines overbought/oversold levels.
Experiment with these settings to customize the VASTC to your trading strategy and asset.
Disclaimer : This indicator is a tool to complement your trading analysis and should not be used in isolation. Always backtest and use other confluence signals for best results. The assets I looked at when making this indicator are almost certainly different than what you're looking at.
Dynamic Volume-Based Buy/Sell IndicatorThis script provides a powerful volume-based indicator that visualizes buy and sell volumes, issues alerts for volume spikes, and adjusts color intensity dynamically based on volume size. It includes customizable settings for volume averaging and thresholds, making it adaptable to various trading strategies.
Divergence for Many Indicators v4 Screener▋ INTRODUCTION:
The “Divergence for Many Indicators v4 Screener” is developed to provide an advanced monitoring solution for up to 24 symbols simultaneously. It efficiently collects signals from multiple symbols based on the “ Divergence for Many Indicators v4 ” and presents the output in an organized table. The table includes essential details starting with the symbol name, signal price, corresponding divergence indicator, and signal time.
_______________________
▋ CREDIT:
The divergence formula adapted from the “ Divergence for Many Indicators v4 ” script, originally created by @LonesomeTheBlue . Full credit to his work.
_______________________
▋ OVERVIEW:
The chart image can be considered an example of a recorded divergence signal that occurred in $BTCUSDT.
_______________________
▋ APPEARANCE:
The table can be displayed in three formats:
1. Full indicator name.
2. First letter of the indicator name.
3. Total number of divergences.
_______________________
▋ SIGNAL CONFIRMATION:
The table distinguishes signal confirmation by using three different colors:
1. Not-Confirmed (Orange): The signal is not confirmed yet, as the bar is still open.
2. Freshly Confirmed (Green): The signal was confirmed 1 or 2 bars ago.
3. Confirmed (Gray): The signal was confirmed 3 or more bars ago.
_______________________
▋ INDICATOR SETTINGS:
Section(1): Table Settings
(1) Table location on the chart.
(2) Table’s cells size.
(3) Chart’s timezone.
(4) Sorting table.
- Signal: Sorts the table by the latest signals.
- None: Sorts the table based on the input order.
(5) Table’s colors.
(6) Signal Confirmation type color. Explained above in the SIGNAL CONFIRMATION section
Section(2): Divergence for Many Indicators v4 Settings
As seen on the Divergence for Many Indicators v4
* Explained above in the APPEARANCE section
Section(3): Symbols
(1) Enable/disable symbol in the screener.
(2) Entering a symbol.
_______________________
▋ FINAL COMMENTS:
For best performance, add the Screener indicator to an active symbol chart, such as QQQ, SPY, AAPL, BTCUSDT, ES, EURUSD, etc., and avoid mixing symbols from different market allocations.
The Divergence for Many Indicators v4 Screener indicator is not a primary tool for making trading decisions.
Momentum Nexus Oscillator [UAlgo]The "Momentum Nexus Oscillator " indicator is a comprehensive momentum-based tool designed to provide traders with visual cues on market conditions using multiple oscillators. By combining four popular technical indicators—RSI (Relative Strength Index), VZO (Volume Zone Oscillator), MFI (Money Flow Index), and CCI (Commodity Channel Index)—this heatmap offers a holistic view of the market's momentum.
The indicator plots two lines: one representing the current chart’s combined momentum score and the other representing a higher timeframe’s (HTF) score, if enabled. Through smooth gradient color transitions and easy-to-read signals, the Momentum Nexus Heatmap allows traders to easily identify potential trend reversals or continuation patterns.
Traders can use this tool to detect overbought or oversold conditions, helping them anticipate possible long or short trade opportunities. The option to use a higher timeframe enhances the flexibility of the indicator for longer-term trend analysis.
🔶 Key Features
Multi-Oscillator Approach: Combines four popular momentum oscillators (RSI, VZO, MFI, and CCI) to generate a weighted score, providing a comprehensive picture of market momentum.
Dynamic Color Heatmap: Utilizes a smooth gradient transition between bullish and bearish colors, reflecting market momentum across different thresholds.
Higher Timeframe (HTF) Compatibility: Includes an optional higher timeframe input that displays a separate score line based on the same momentum metrics, allowing for multi-timeframe analysis.
Customizable Parameters: Adjustable RSI, VZO, MFI, and CCI lengths, as well as overbought and oversold levels, to match the trader’s strategy or preference.
Signal Alerts: Built-in alert conditions for both the current chart and higher timeframe scores, notifying traders when long or short entry signals are triggered.
Buy/Sell Signals: Displays visual signals (▲ and ▼) on the chart when combined scores reach overbought or oversold levels, providing clear entry cues.
User-Friendly Visualization: The heatmap is separated into four sections representing each indicator, providing a transparent view of how each contributes to the overall momentum score.
🔶 Interpreting Indicator:
Combined Score
The indicator generates a combined score by weighing the individual contributions of RSI, VZO, MFI, and CCI. This score ranges from 0 to 100 and is plotted as a line on the chart. Lower values suggest potential oversold conditions, while higher values indicate overbought conditions.
Color Heatmap
The indicator divides the combined score into four distinct sections, each representing one of the underlying momentum oscillators (RSI, VZO, MFI, and CCI). Bullish (greenish) colors indicate upward momentum, while bearish (grayish) colors suggest downward momentum.
Long/Short Signals
When the combined score drops below the oversold threshold (default is 26), a long signal (▲) is displayed on the chart, indicating a potential buying opportunity.
When the combined score exceeds the overbought threshold (default is 74), a short signal (▼) is shown, signaling a potential sell or short opportunity.
Higher Timeframe Analysis
If enabled, the indicator also plots a line representing the combined score for a higher timeframe. This can be used to align lower timeframe trades with the broader trend of a higher timeframe, providing added confirmation.
Signals for long and short entries are also plotted for the higher timeframe when its combined score reaches overbought or oversold levels.
🔶Purpose of Using Multiple Technical Indicators
The combination of RSI, VZO, MFI, and CCI in the Momentum Nexus Heatmap provides a comprehensive approach to analyzing market momentum by leveraging the unique strengths of each indicator. This multi-indicator method minimizes the limitations of using just one tool, resulting in more reliable signals and a clearer understanding of market conditions.
RSI (Relative Strength Index)
RSI contributes by measuring the strength and speed of recent price movements. It helps identify overbought or oversold levels, signaling potential trend reversals or corrections. Its simplicity and effectiveness make it one of the most widely used indicators in technical analysis, contributing to momentum assessment in a straightforward manner.
VZO (Volume Zone Oscillator)
VZO adds the critical element of volume to the analysis. By assessing whether price movements are supported by significant volume, VZO distinguishes between price changes that are driven by real market conviction and those that might be short-lived. It helps validate the strength of a trend or alert the trader to potential weakness when price moves are unsupported by volume.
MFI (Money Flow Index)
MFI enhances the analysis by combining price and volume to gauge money flow into and out of an asset. This indicator provides insight into the participation of large players in the market, showing if money is pouring into or exiting the asset. MFI acts as a volume-weighted version of RSI, giving more weight to volume shifts and helping traders understand the sustainability of price trends.
CCI (Commodity Channel Index)
CCI contributes by measuring how far the price deviates from its statistical average. This helps in identifying extreme conditions where the market might be overextended in either direction. CCI is especially useful for spotting trend reversals or continuations, particularly during market extremes, and for identifying divergence signals.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Divergence Indicator Multi [TradingFinder] MACD AO RSI DIV Chart🔵 Introduction
🟣 What is Divergence in Financial Markets?
Divergence in technical analysis happens when the price of a stock moves in a direction opposite to certain indicators. This is a crucial concept in financial markets as it can signal either a trend reversal or a continuation of the current correction in the trend. Understanding divergence helps traders and analysts make more informed decisions.
🟣 Positive Regular Divergence (RD+)
A positive regular divergence occurs at the end of a downtrend, where two price lows form. This divergence appears when the price chart shows a new low, but the indicator does not follow, signaling potential buying opportunities.
Positive divergence indicates increased buying pressure and reduced selling pressure, making it a useful signal for forecasting price increases.
🟣 Negative Regular Divergence (RD-)
A negative regular divergence is seen during an uptrend when two price highs form. The price chart records a new high, but the indicator does not reflect this change, suggesting that a market downturn is likely.
This type of divergence shows strong selling pressure and weaker buying activity, which can help identify selling opportunities.
Both positive and negative divergences are powerful tools for identifying potential trend reversals and key support and resistance levels. For example, when an indicator trends upward while the price moves downward, this creates divergence, warning traders to reconsider their investment strategy.
🟣 Different Types of Divergence in Trading
1. Regular Divergence :
o Positive Regular Divergence (RD+)
o Negative Regular Divergence (RD-)
2. Hidden Divergence :
o Positive Hidden Divergence (HD+)
o Negative Hidden Divergence (HD-)
3.Time Divergence.
Note : This guide focuses specifically on Regular Divergence.
🟣 What is Regular Divergence?
Regular Divergence, often referred to as convergence, occurs when price action and indicators show conflicting patterns, usually signaling the end of a trend. Detecting regular divergence helps traders anticipate potential trend reversals or the formation of reversal patterns.
🔵 How to Use
To optimize the detection of divergence, you can adjust the Fractal Period to specify the length of time for identifying divergence patterns.
Additionally, with the Divergence Detection Method, you can select oscillators like the MACD, RSI, or AO to base divergence detection on.
Divergence in MACD :
MACD divergence occurs when the price chart forms an opposite pattern compared to the MACD line, indicating a potential price reversal.
Divergence in RSI :
In a downtrend, if the price chart forms two consecutive lows with the second lower than the first, but the RSI shows two lows with the second higher, this indicates positive regular divergence, which is a buy signal.
On the other hand, during an uptrend, if the price forms two highs with the second higher than the first, but the RSI shows the second high lower, this points to negative regular divergence, indicating a sell signal.
Divergence in AO (Awesome Oscillator) :
The AO indicator calculates histograms using the difference between 5-period and 34-period simple moving averages. It compares peaks and troughs of these histograms with price movements, detecting divergence and plotting lines and arrows to signal divergence.
🔵 Table
The following table breaks down the main features of the oscillator. It covers four critical categories: Exist, Consecutive, Divergence Quality, and Change Phase Indicator.
Exist : If divergence is detected, a "+" will appear in this row.
Consecutive: Shows the number of consecutive divergences that have formed in a short period.
Divergence Quality : Evaluates the quality of the divergence based on the number of occurrences. One is labeled "Normal," two are "Good," and three or more are considered "Strong."
Change Phase Indicator : If a phase change is detected between two oscillation peaks, this is marked in the table.
Adaptive MA Scalping StrategyAdaptive MA Scalping Strategy
The Adaptive MA Scalping Strategy is an innovative trading approach that merges the strengths of the Kaufman's Adaptive Moving Average (KAMA) with the Moving Average Convergence Divergence (MACD) histogram. This combination results in a momentum-adaptive moving average that dynamically adjusts to market conditions, providing traders with timely and reliable signals.
How It Works
Kaufman's Adaptive Moving Average (KAMA): Unlike traditional moving averages, KAMA adjusts its sensitivity based on market volatility. It becomes more responsive during trending markets and less sensitive during periods of consolidation, effectively filtering out market noise.
MACD Histogram Integration: The strategy incorporates the MACD histogram, a momentum indicator that measures the difference between a fast and a slow exponential moving average (EMA). By adding the MACD histogram values to the KAMA, the strategy creates a new line—the momentum-adaptive moving average (MOMA)—which captures both trend direction and momentum.
Signal Generation:
Long Entry: The strategy enters a long position when the closing price crosses above the MOMA. This indicates a potential upward momentum shift.
Exit Position: The position is closed when the closing price crosses below the MOMA, signaling a potential decline in momentum.
Cloud Calculation Detail
The MOMA is calculated by adding the MACD histogram value to the KAMA of the price. This addition effectively adjusts the KAMA based on the momentum indicated by the MACD histogram. When momentum is strong, the MACD histogram will have higher values, causing the MOMA to adjust accordingly and provide earlier entry or exit signals.
Performance on Stocks
This strategy has demonstrated excellent performance on stocks when applied to the 1-hour timeframe. Its adaptive nature allows it to respond swiftly to market changes, capturing profitable trends while minimizing the impact of false signals caused by market noise. The combination of KAMA's adaptability and MACD's momentum detection makes it particularly effective in volatile market conditions commonly seen in stock trading.
Key Parameters
KAMA Length (malen): Determines the sensitivity of the KAMA. A length of 100 is used to balance responsiveness with noise reduction.
MACD Fast Length (fast): Sets the period for the fast EMA in the MACD calculation. A value of 24 helps in capturing short-term momentum changes.
MACD Slow Length (slow): Sets the period for the slow EMA in the MACD calculation. A value of 52 smooths out longer-term trends.
MACD Signal Length (signal): Determines the period for the signal line in the MACD calculation. An 18-period signal line is used for timely crossovers.
Advantages of the Strategy
Adaptive to Market Conditions: By adjusting to both volatility and momentum, the strategy remains effective across different market phases.
Enhanced Signal Accuracy: The fusion of KAMA and MACD reduces false signals, improving the accuracy of trade entries and exits.
Simplicity in Execution: With straightforward entry and exit rules based on price crossovers, the strategy is user-friendly for traders at all experience levels
Kurutoga Histogram with HTF and LTF
Kurutoga Histogram:
The Kurutoga Histogram is a technical analysis indicator designed to measure price divergence from the 50% level of a recent price range. By calculating how far the current price is from the midpoint of a selected base length of candles, the histogram provides insight into the momentum, strength, and potential reversals in the market. Additionally, it can be applied across multiple timeframes to provide a comprehensive view of both short- and long-term market dynamics.
Key Components:
Base Length:
The base length is the number of candles (bars) over which the high and low prices are observed. The default base length is typically 14 periods, but it can be adjusted according to the trader's preference.
This base length defines the range from which the 50% level, or midpoint, is calculated.
50% Level (Midpoint):
The midpoint is the average of the highest high and the lowest low over the selected base length. This 50% level acts as an equilibrium point around which the price fluctuates.
Formula:
Midpoint = (Highest High + Lowest Low) / 2
The price’s distance from this midpoint is an indicator of how strong the current trend or divergence is.
Price Divergence:
The main calculation of the histogram is the difference between the current closing price and the midpoint of the price range.
Formula:
Divergence = Close Price − Midpoint
A positive divergence (price above the midpoint) indicates bullish strength, while a negative divergence (price below the midpoint) indicates bearish strength.
Multi-Timeframe Analysis:
The Kurutoga Histogram can be applied to both the current timeframe and a higher timeframe (HTF), allowing traders to gauge price movement in both short-term and long-term contexts.
By comparing the histograms of multiple timeframes, traders can determine if there is alignment (confluence) between trends, which can strengthen trade signals or provide additional confirmation.
Color-Coded Histogram:
Blue Bars (Positive Divergence): Represent that the price is above the 50% level, indicating bullish momentum. Taller blue bars suggest stronger upward momentum, while shrinking bars suggest weakening strength.
Red Bars (Negative Divergence): Represent that the price is below the 50% level, indicating bearish momentum. Taller red bars suggest stronger downward momentum, while shrinking bars suggest a potential reversal or consolidation.
The histogram’s color intensity and transparency can be adjusted to enhance the visual effect, distinguishing between current timeframe (LTF) and higher timeframe (HTF) divergence.
Interpretation:
Bullish Signals: When the histogram bars are blue and growing, the price is gaining momentum above the midpoint of its recent range. This could signal an ongoing uptrend.
Bearish Signals: When the histogram bars are red and growing, the price is gaining momentum below the midpoint, signaling an ongoing downtrend.
Momentum Shifts: When the histogram bars shrink in size (whether blue or red), it could indicate that the current trend is losing strength and may reverse or enter consolidation.
Neutral or Sideways Movement: When the histogram bars hover around zero, it means the price is trading near the midpoint of its recent range, often signaling a lack of strong momentum in either direction.
Multi-Timeframe Confluence:
When the current timeframe (LTF) histogram aligns with the higher timeframe (HTF) histogram (e.g., both are showing strong bullish or bearish divergence), it may provide stronger confirmation of the trend's strength.
Divergence between timeframes (e.g., bullish on LTF but bearish on HTF) may suggest that price movements on lower timeframes are not yet reflected in the broader trend, signaling caution.
Applications:
Trend Identification: The Kurutoga Histogram is highly useful for detecting when the price is trending away from its equilibrium point, providing insight into the strength of ongoing trends.
Momentum Analysis: By measuring the divergence from the 50% level, the histogram helps traders identify when momentum is increasing or decreasing.
Reversal Detection: Shrinking histogram bars can signal weakening momentum, which often precedes trend reversals.
Consolidation and Breakouts: When the histogram remains near zero for an extended period, it suggests consolidation, which often precedes a breakout in either direction.
Advantages:
Clear Visuals: The use of a color-coded histogram makes it easy to visually assess whether the market is gaining bullish or bearish momentum.
Multi-Timeframe Utility: The ability to compare current timeframe signals with higher timeframe signals adds an extra layer of confirmation, reducing false signals.
Dynamic Adjustment: By adjusting the base length, traders can fine-tune the sensitivity of the indicator to match different markets or trading styles.
Limitations:
Lagging Indicator: Like most divergence indicators, the Kurutoga Histogram may lag slightly behind actual price movements, especially during fast, volatile markets.
Requires Confirmation: This indicator works best when used in conjunction with other technical tools like moving averages, support/resistance levels, or volume indicators, to avoid relying on divergence alone.
Conclusion:
The Kurutoga Histogram is a versatile and visually intuitive tool for measuring price divergence from a key equilibrium point, helping traders to assess the strength of trends and identify potential reversal points. Its use across multiple timeframes provides deeper insights, making it a valuable addition to any trading strategy that emphasizes momentum and trend following.
BRT MACD CustomBRT MACD Custom — Adaptive and Flexible MACD for Multi-Timeframe Analysis
The BRT MACD Custom is an advanced version of the traditional MACD indicator, offering additional flexibility and adaptability for multi-timeframe trading. This custom script allows traders to adjust the calculation parameters for MACD to suit their specific trading strategy, timeframe, and market conditions.
Key Features
Multi-Timeframe Support
Unlike the standard MACD, this indicator lets you choose a specific timeframe (different from the chart timeframe) for calculating MACD values. This feature provides more flexibility in analyzing market trends on multiple timeframes without changing the main chart.
Example: You can analyze MACD on a 15-minute timeframe even when your chart is set to 1-minute, giving you broader market insights.
Customizable EMA and Signal Settings
Users can adjust the fast and slow EMA lengths as well as the signal smoothing to better align with their preferred trading strategies. The script allows switching between the two popular types of moving averages — SMA or EMA — for both the MACD and the signal line.
Volatility-Based Adaptive EMA
The script includes an adaptive mechanism for EMA calculation. When the selected timeframe closes, the indicator dynamically adjusts the calculation, ensuring the MACD values respond quickly to market volatility. This makes the indicator more reactive compared to static MACD implementations.
Shift Options for MACD, Signal, and Histogram
The indicator allows shifting the MACD, signal line, and histogram values by one or more bars. This can be useful for backtesting and simulating strategies where you anticipate future price movements.
Signal Alerts for Long and Short Trades
The script generates visual signals when certain conditions are met, indicating potential long or short trade opportunities. These signals are based on MACD and histogram crossovers:
Long Signal: Triggered when MACD is above the signal line and both are rising.
Short Signal: Triggered when MACD is below the signal line and both are falling.
Custom Plotting
The MACD line, signal line, and histogram are plotted on the chart for easy visualization. The histogram changes colors to reflect positive or negative momentum:
Green shades when MACD is above the signal line.
Red shades when MACD is below the signal line.
Applications in Trading
The BRT MACD Custom is ideal for traders who need flexibility in their technical analysis. Its multi-timeframe capabilities and customizable moving averages make it suitable for day trading, swing trading, and long-term investing across a variety of markets.
Scalping: Use the 1-minute or 5-minute timeframe to identify short-term trends while calculating MACD on a higher timeframe such as 15 or 30 minutes.
Swing Trading: Apply the indicator on 1-hour or 4-hour charts to detect mid-term trends.
Long-Term Investing: Analyze daily or weekly charts with longer EMA periods to confirm market direction before making large investments.
Trend Following Regression CloudTrend Following Regression Cloud Indicator
The Trend Following Regression Cloud is a versatile trading tool designed to help you effortlessly identify the market's prevailing trend. By analyzing price movements over multiple time frames, it provides a clear visual representation of whether the market is trending upwards or downwards.
How It Works:
- Adaptive Analysis: The indicator calculates linear regression lines over various periods ranging from short-term to long-term (e.g., 10, 20, 50, up to 500 periods). This means it adapts quickly to recent market changes, capturing new trends as they develop.
- Noise Reduction: By comparing and weighting the slopes of these regression lines, it filters out insignificant price fluctuations (market noise). This ensures that the signals you receive are more reliable and less prone to false alarms.
- Cloud Calculation: The cloud is generated by first calculating the slopes of multiple linear regression lines over different lengths. The differences between the slopes of shorter-term and longer-term regressions are then computed and weighted by their respective lengths. By summing up these weighted differences, the indicator produces a "total distance" value. This value is applied to a baseline (such as a 100-period simple moving average) to create the cloud line. The area between the baseline and the cloud line is filled, and its color changes based on whether the total distance is positive or negative, providing a visual cue of the market's trend direction.
- Visual Representation: The indicator plots two lines—a base line and a cloud line—creating a shaded area (the "cloud") between them. The color of this cloud changes based on market conditions:
- Green Cloud: Indicates that short-term trends are stronger than long-term trends, suggesting an upward market movement. This could be a good time to consider buying.
- Red Cloud: Signifies that the market may be trending downwards, as long-term trends overpower short-term ones. This could be an opportune moment to consider selling.
MACD Enhanced Strategy MTF with Stop Loss [LTB]Test strategy for MACD
This strategy, named "MACD Enhanced Strategy MTF with Stop Loss ," is a modified Moving Average Convergence Divergence (MACD) strategy with enhancements such as multi-timeframe (MTF) analysis, custom scoring, and a dynamic stop loss mechanism. Let’s break down how to effectively use it:
Key Elements of the Strategy
MACD Indicator with Modifications:
The strategy uses MACD, a well-known momentum indicator, with customizable parameters:
fastLength, slowLength, and signalLength represent the standard MACD settings.
Instead of relying solely on MACD crossovers, it introduces scoring parameters for histogram direction (histside), indicator direction (indiside), and signal cross (crossscore). This allows for a more nuanced decision-making process when determining buy and sell signals.
Multi-Timeframe Analysis (MTF):
The strategy compares the current timeframe's MACD score with that of a higher timeframe (HTF). It dynamically selects the higher timeframe based on the current timeframe. For example, if the current chart period is 1, it will select 5 as the higher timeframe.
This MTF approach aims to align trades with broader trends, filtering out false signals that could be present when analyzing only a single timeframe.
Scoring System:
A custom scoring system (count() function) is used to evaluate buy and sell signals. This includes calculations based on the direction and momentum of MACD (indi) and the histogram. The score is used to determine the strength of signals.
Positive scores indicate bullish sentiment, while negative scores indicate bearish sentiment.
This scoring mechanism aims to reduce the influence of noise and provide more reliable entries.
Entry Conditions:
Long Condition: When the Result value (a combination of MTF and current MACD analysis) changes and becomes positive, a long entry is triggered.
Short Condition: When the Result changes and becomes negative, a short entry is initiated.
Stop Loss Mechanism:
The countstop() function calculates dynamic stop loss values for both long and short trades. It is based on the Average True Range (ATR) multiplied by a factor (Mult), providing adaptive stop loss levels depending on market volatility.
The stop loss is plotted on the chart to show potential risk levels for open trades, with the line appearing only if shotsl is enabled.
How to Use the Strategy
To properly use the strategy, follow these steps:
Parameter Optimization:
Adjust the input parameters such as fastLength, slowLength, and signalLength to tune the MACD indicator to the specific asset you’re trading. The values provided are typical defaults, but optimizing these values based on backtesting can help improve performance.
Customize the scoring parameters (crossscore, indiside, histside) to balance how much weight you want to put on the direction, histogram, and cross events of the MACD indicator.
Select Appropriate Timeframes:
This strategy employs a multi-timeframe (MTF) approach, so it's important to understand how the higher timeframe (HTF) is selected based on the current timeframe. For instance, if you are trading on a 5-minute chart, the higher timeframe will be 15 minutes, which helps filter out lower timeframe noise.
Ensure you understand the relationship between the timeframe you’re using and the HTF it automatically selects. The strategy’s effectiveness can vary depending on how these timeframes align with the asset’s overall volatility.
Run Backtests:
Always backtest the strategy over historical data to determine its reliability for the asset and timeframes you’re interested in. Note that the MTF approach may require substantial data to capture how different timeframes interact.
Use the backtest results to adjust the scoring parameters or the Stop Loss Factor (Mult) for better risk management.
Stop Loss Usage:
The stop loss is calculated dynamically using ATR, which means that it adjusts with changing volatility. This can be useful to avoid being stopped out too often during periods of increased volatility.
The shotsl parameter can be set to true to visualize the stop loss line on the chart. This helps to monitor the protection level and make better decisions regarding holding or closing a trade manually.
Entry Signals and Trade Execution:
Look for changes in the Result value to determine entry points. For a long position, the Result needs to become positive, and for a short position, it must be negative.
Note that the strategy's entries are more conservative because it waits for the Result to confirm the direction using multiple factors, which helps filter out false breakouts.
Risk Management:
The adaptive stop loss mechanism reduces the risk by basing the stop level on market volatility. However, you must still consider additional risk management practices such as position sizing and profit targets.
Given the scoring mechanism, it might not enter trades frequently, which means using this strategy may result in fewer but potentially more accurate trades. It’s important to be patient and not force trades that don’t align with the calculated results.
Real-Time Monitoring:
Make sure to monitor trades actively. Since the strategy recalculates the score on each bar, real-time changes in the Result value could provide exit opportunities even if the stop loss isn't triggered.
Summary
The "MACD Enhanced Strategy MTF with Stop Loss " is a sophisticated version of the MACD strategy, enhanced with multi-timeframe analysis and adaptive stop loss. Properly using it involves optimizing MACD and scoring parameters, selecting suitable timeframes, and actively managing entries and exits based on a combination of scoring and volatility-based stop losses. Always conduct thorough backtesting before applying it in a live environment to ensure the strategy performs well on the asset you're trading.
RoC Momentum CycleRoC Momentum Cycles (RMC) is derived from RoC (Rate of Change) indicator.
Motivation behind RMC: Addressing RoC’s Shortcomings
While the Rate of Change (RoC) indicator is a valuable tool for assessing momentum, it has notable limitations that traders must be aware of. One of the primary challenges with the traditional RoC is its sensitivity to price fluctuations, which can lead to false signals in volatile markets. This often results in premature entries or exits, impacting trading performance.
By smoothing out the RoC calculations and focusing on more consistent signal generation (using SMA on smoothed RoC), RMC offers a more consistent representation of price trends.
Momentum Cycles
RMC helps visualize momentum cycles in a much better way compared to RoC.
Long Momentum Cycle : A cross-over of smoothed RoC (blue line) above averaged signal (orange line) below zero marks start of a new potential upside cycle which ends when the blue line comes back to zero line from above.
Short Momentum Cycle : A cross-under of blue line below orange line above zero marks beginning of a potential downside cycle which ends when the blue line comes back to zero from below.
Normalized ZScoreThe Normalized ZScore Indicator is a dynamic tool designed to help traders identify potential overbought and oversold conditions in the market. It calculates the ZScore of the price movement relative to a moving average, allowing users to track the deviation of price from its average and normalize it within a fixed range for clearer signal generation. The indicator can be used for both trend-following and mean-reversion strategies, offering customizable options for various trading styles.
How It Works
This indicator works by calculating two distinct ZScores:
Standard ZScore: Based on the price deviation from a simple moving average (SMA).
Fast ZScore: Calculated using price deviation from the SMA combined with standard deviation over a shorter period.
The ZScore values are normalized between -100 and 100, allowing for consistent and comparable signal outputs across different assets and timeframes.
Key Features
Customizable MA and Deviation Lengths: Adjust the length of the moving average (MA Length) and deviation (Deviation Length) to suit your trading needs.
Overbought/Oversold Zones: The indicator highlights areas where the market may be overbought or oversold using a user-defined threshold.
Color-Coded Signals: The ZScore plot changes color based on market conditions:
Positive ZScore (overbought) = Customizable Positive Color
Neutral ZScore = Customizable Middle Color
Negative ZScore (oversold) = Customizable Negative Color
Trend Filtering Option: The built-in trend filter helps to enhance signal accuracy by factoring in the overall market trend.
Signal Shapes:
Diamonds: Indicate strong long or short entry signals when ZScore crosses predefined thresholds.
X-Crosses: Indicate weaker long or short entry signals for users preferring caution in their trades.
Inputs
MA Length: Set the length of the moving average used for calculating the ZScore.
Deviation Length: Set the length used for deviation calculations.
OBS Threshold: Set the threshold for defining overbought and oversold zones.
Trend Filter: Enable or disable the trend filter for added signal confidence.
Color Settings: Customize the colors for positive, middle, and negative ZScore values.
Visual Features
ZScore Plot: A smooth and color-coded line plot to visualize the ZScore in real-time.
Overbought/Oversold Zones: Visualized with horizontal lines and fill colors to highlight extremes.
Bar Coloring: Bars change colors when ZScore exceeds overbought/oversold zones, enhancing visual clarity.
Signal Markers: Diamond or X-shaped markers appear on the chart to indicate potential trade signals.
How to Use
Entry Points: Look for the ZScore to cross into overbought/oversold regions for potential reversal trades. Use the diamonds and X-crosses for long and short entries.
Trend Filter: Enable the trend filter to avoid taking trades against the overall market trend.
Customize Settings: Adjust the lengths and colors to match your specific trading strategy and timeframe.
