ATR, Chop, Profit Target and Stop Loss TableThe ATR Table indicator is a versatile tool that helps traders visually and quantitatively manage risk, identify market conditions, and set profit targets and stop-loss levels. It is designed to enhance decision-making by incorporating key volatility and chop (market consolidation) signals into a comprehensive table format.
Key Features:
Average True Range (ATR) Calculation : The indicator computes the ATR over a user-defined period (default 14). ATR helps to measure market volatility, providing insights into how much an asset's price typically moves within a given period.
Stop Loss and Profit Target Calculation : You can configure stop-loss and profit target levels using multipliers based on the ATR. This allows dynamic risk management that adjusts to market volatility:
Stop Loss : Defined as a multiple of the ATR to help control losses.
Profit Target : Also based on a multiple of the ATR to lock in gains. The user can specify whether they are trading long or short, and the indicator adjusts the levels accordingly.
Customizable Plot Lines : The indicator can display the Stop Loss and Profit Target levels directly on the chart. Users can toggle these lines on or off and customize their colors.
Chop Signa l: The indicator highlights potential consolidation periods (chop) using a wick-based analysis. It calculates the highest upper or lower wick values and compares them to the ATR to detect periods of indecision or consolidation.
Table Display : When these wick values exceed the ATR by a user-defined multiplier, the corresponding table rows are highlighted.
Background Alerts : Optionally, users can activate background color changes on the chart to visually alert them when chop conditions are detected.
Customizable Table Layout : A table displaying the key values (ATR, Stop Loss, Profit Target, Upper/Lower Wickiness) is placed on the chart. You can choose the table's position, adjust its color scheme, and decide which rows to display.
Chop Background Customization : For users who prefer more visual cues, the indicator allows you to enable or disable background shading when chop conditions are met. You can also choose the color of this background for better customization.
Volatility
Adaptive Smooth EMA [MacroGlide]Adaptive Smooth EMA is a powerful indicator designed to track and smooth market prices using Adaptive Exponential Moving Averages (EMAs) with dynamic phase adjustment. This tool helps traders analyze price trends and identify shifts in market momentum, making it easier to recognize potential reversals and trend continuations.
Key Features:
• Adaptive EMA Calculation: The indicator calculates multiple EMAs with adaptive smoothing based on volatility, allowing traders to capture the market's movement more accurately. These smoothed values adjust dynamically with the market, making trend detection more precise.
• Dynamic Phase Adjustment: The phase of the EMA is adjusted in real-time according to the market's volatility, ensuring that the smoothing remains responsive to changes in market conditions, reducing lag and enhancing signal clarity.
• Customizable Color Gradients: The indicator uses color gradients to visually distinguish between uptrends and downtrends, making it easier to spot shifts in market direction. Users can customize the color scheme for better visual representation and interpretation.
How to Use:
• Add the indicator to your chart and adjust the EMA length and phase adjustment settings according to your trading strategy.
• Monitor the color shifts to quickly identify potential changes in trend direction. The transition between the uptrend and downtrend colors can signal momentum shifts.
• Utilize the different EMA lengths to analyze short-term and long-term trends. The smaller EMAs will react quicker to price changes, while the longer ones provide a smoother view of the overall trend.
Methodology:
The Adaptive Smooth EMA indicator computes multiple EMAs with lengths ranging from 3 to 90 periods, dynamically adjusting the phase based on market volatility. This adaptive approach allows the indicator to respond effectively to both calm and volatile market conditions, providing a more accurate reflection of current trends. By smoothing the price data while maintaining responsiveness to market changes, the indicator helps traders avoid false signals and make more informed decisions.
Originality and Usefulness:
Adaptive Smooth EMA stands out due to its ability to dynamically adjust to market conditions, offering an adaptive smoothing approach that reduces noise while capturing essential price movements. This makes it particularly useful for identifying trends, reversals, and optimizing entry and exit points in a trading strategy.
Charts:
The indicator plots a series of smoothed EMA lines, each with a unique color gradient reflecting market sentiment. These lines help visualize price trends across different timeframes, providing a comprehensive view of the market's directional strength and momentum. The gradient color transitions further enhance the clarity of trend shifts, offering an easy-to-interpret chart for traders.
Enjoy the game!
DTT Volatility Grid [Pro+] (NINE/ANARR)Introduction:
This tool is designed to automate the Digital Time Theory (DTT) framework created by Ivan and Anarr, and leverage the DTT Volatility Grid to navigate the advanced realm of Time-based statistical trading.
Description:
Built upon the proprietary Digital Time Theory (DTT), this script equips traders with an edge in analyzing Time and price-based market behaviour. It is designed for intraday traders of all asset classes, and breaks down the entire Daily range into Time Models and Inner Time Intervals. This tool is powered by data-driven insights, helping traders anticipate expansions, understand Time distortions, and assess market volatility at specific Times of the trading day.
Key Features:
Time-Based Models and Volatility Awareness: The indicator automatically populates the chart with DTT's Time Models. These Time Models, represented by specific Time Intervals, are engineered to highlight volatility injections within key sessions, offering traders clear insights into market dynamics and potential shifts.
Average Model Range Probability (AMRP): Know the average volatility expected for specific Time Models and use AMRP Levels (and Standard Deviation) to gauge the probability of a range break or failure, based on historical price action and Time data.
Root Candles and Liquidity Draws: Visualize Root Candles as draws on liquidity, showcasing premium and discount areas, and the starting point of a Time based price movement. Understand how the opening price and equilibrium of each Root Candle can serve as a framework for your trade executions. Distribution or accumulation above or below Root Candles can also be observed and utilized.
Extended Visualization: Observe prior Model Ranges into the current Time Model, including the High, Low, and Equilibrium from the previous Time Models, helping traders visualize potential support or resistance areas.
Lookback Periods and Model Count: Use customizable lookback periods to adjust the number of past models, providing further insight into market behaviour over a chosen historical range. This can help to keep charts clean and organized with one model displayed or multiple for backtesting purposes.
Detailed Data Table: The real-Time data table allows traders to view the AMRP and range data for selected models, providing an easy reference for model behaviour and volatility dynamics. The table can depict all Time Model average ranges for reference and study, providing insights to whether the previous models have exceeded their historical range volatility, or not.
Customization Options: Customize Time Intervals with various styles (solid, dashed, dotted) and choose different colors for each model or interval. You can also select which historical models to display, alongside customizable labels.
How Traders Can Use DTT Volatility Grid Effectively:
Understand Premium and Discount Areas: By tracking Time-based ranges and using DTT's Root Candles and Previous Model Equilibrium, traders can quickly assess whether price is trading in premium or discount territory during intraday sessions.
Expecting Volatility and Time-Sensitive Trades: Knowing when a move is nearing exhaustion or when Time-based distortions are likely to cause an expansion allows traders to stay ahead of sudden market shifts. The Inner Intervals and Root Candles in combination, highlight the volatility ranges across various Timeframes, giving traders insights into which Times of the day are likely to experience heightened market activity as per DTT.
Avoiding Low Volatility Periods: The AMRP system helps traders identify times of the day where price action is likely to slow down or become choppy, encouraging traders to step aside or reduce risk during these times. If the AMRP was extended above the average of the previous Time model and the current model depicts an average range probability of low volatility, then traders can sit out in anticipation for a model with higher volatility.
Usage Guidance:
Add DTT Volatility Grid (NINE/ANARR) to your TradingView chart.
Customize your preferred time intervals, model history, and visual settings for your session.
Use the data table to track average model ranges and probabilities, ensuring you align your trades with key levels.
Incorporate DTT Volatility Grid (NINE/ANARR) into your existing strategies to fine-tune your entries and exits based on data-driven insights into volatility and price behaviour.
These tools are available ONLY on the TradingView platform.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
KAMA CloudDescription:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
Benefits:
Dynamic Sensitivity: By adapting to the market's volatility, KAMA provides more reliable signals than traditional moving averages.
Trend Clarity: The color-coded cloud visually enhances the perception of the trend’s direction and strength, making it easier for traders to decide on their trading strategy.
Versatility: Suitable for various asset classes, including stocks, forex, commodities, and cryptocurrencies, across different timeframes.
Decision Support: Helps traders understand not just the direction but the strength of trends, aiding in more informed decision-making regarding entries, exits, and risk management.
Usage:
The KAMA Cloud is ideal for traders who need a robust trend-following tool that adjusts according to market dynamics. It can be used as a standalone indicator or in conjunction with other technical analysis tools to enhance trading strategies. Look for the cloud’s color shifts as potential signals for trend reversals or continuations, and consider the cloud’s thickness as an indication of trend strength.
Whether you are a day trader, swing trader, or long-term investor, the KAMA Cloud offers a unique approach to understanding market trends, helping you navigate the complexities of various market conditions with confidence.
Iceberg Trade Revealer [CHE]Unveiling Iceberg Trades: A Deep Dive into Low Volatility Market Phases
Introduction
In the dynamic world of trading, hidden forces often influence market movements in ways that aren't immediately apparent. One such force is the phenomenon of iceberg trades—large orders that are concealed to prevent significant market impact. This presentation explores the concept of iceberg trades, explains why they are typically hidden during periods of low volatility, and introduces an indicator designed to reveal these elusive trades.
Agenda
1. Understanding Iceberg Trades
- Definition and Purpose
- Impact on Market Dynamics
2. The Low Volatility Concealment
- Why Low Volatility Phases?
- Strategies Behind Hiding Large Orders
3. Introducing the Iceberg Trade Revealer Indicator
- How the Indicator Works
- Key Components and Calculations
4. Demonstration and Use Cases
- Interpreting the Indicator Signals
- Practical Trading Applications
5. Conclusion
- Summarizing the Insights
- Q&A Session
1. Understanding Iceberg Trades
Definition and Purpose
- Iceberg Trades are large single orders divided into smaller lots to disguise the total order quantity.
- Traders use iceberg orders to minimize market impact and avoid unfavorable price movements.
Impact on Market Dynamics
- Concealed Volume: Iceberg orders hide true supply and demand levels.
- Price Stability: They prevent sudden spikes or drops by releasing orders gradually.
- Market Sentiment: Their presence can influence perceptions of market strength or weakness.
2. The Low Volatility Concealment
Why Low Volatility Phases?
- Less Market Attention: Low volatility periods attract fewer traders, making it easier to conceal large orders.
- Reduced Slippage: Prices are more stable, reducing the risk of executing orders at unfavorable prices.
- Strategic Advantage: Large players can accumulate or distribute positions without tipping off the market.
Strategies Behind Hiding Large Orders
- Order Splitting: Breaking down large orders into smaller pieces.
- Time Slicing: Executing orders over an extended period.
- Algorithmic Trading: Using sophisticated algorithms to optimize order execution.
3. Introducing the Iceberg Trade Revealer Indicator
How the Indicator Works
- Core Thesis: Iceberg trades can be detected by analyzing periods of unusually low volatility.
- Volatility Analysis: Uses the Average True Range (ATR) and Bollinger Bands to identify low volatility phases.
- Signal Generation: Marks periods where iceberg trades are likely occurring.
Key Components and Calculations
1. Average True Range (ATR)
- Measures market volatility over a specified period.
- Lower ATR values indicate less price movement.
2. Bollinger Bands
- Creates a volatility envelope around the ATR.
- Bands tighten during low volatility and widen during high volatility.
3. Timeframe Adjustments
- Utilizes multiple timeframes to enhance signal accuracy.
- Options for auto, multiplier, or manual timeframe selection.
4. Signal Conditions
- Iceberg Trade Detection: ATR falls below the lower Bollinger Band.
- Revealed Volatility: ATR rises above the upper Bollinger Band, indicating potential market moves after iceberg trades.
4. Demonstration and Use Cases
Interpreting the Indicator Signals
- Iceberg Trade Zones: Highlighted areas where large hidden orders are likely.
- Revealed Volatility Zones: Areas indicating the market's response to the execution of iceberg trades.
Practical Trading Applications
- Entry and Exit Points: Use signals to time trades alongside institutional activity.
- Risk Management: Adjust strategies during detected low volatility phases.
- Market Analysis: Gain insights into underlying market mechanics.
5. Conclusion
Summarizing the Insights
- Iceberg Trades play a significant role in market movements, especially when concealed during low volatility phases.
- The Iceberg Trade Revealer Indicator provides a tool to uncover these hidden activities, offering traders a strategic edge.
- Understanding and utilizing this indicator can enhance trading decisions by aligning them with the actions of major market players.
Best regards Chervolino ( Volker )
Q&A Session
- Questions and Discussions: Open the floor for any queries or further explanations.
Thank You!
By delving into the hidden aspects of market activity, traders can better navigate the complexities of financial markets. The Iceberg Trade Revealer Indicator serves as a bridge between observable market data and the concealed strategies of large institutions.
References
- Average True Range (ATR): A technical analysis indicator that measures market volatility.
- Bollinger Bands: A volatility indicator that creates a band of three lines which are plotted in relation to a security's price.
- Iceberg Orders: Large orders divided into smaller lots to hide the actual order quantity.
Note: Always consider multiple factors when making trading decisions. Indicators provide tools, but they do not guarantee results.
Educational Content Disclaimer:
Disclaimer:
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Universal All Assets Strategy | viResearchUniversal All Assets Strategy | viResearch
The Universal All Assets Strategy by viResearch is a sophisticated trend-following algorithm designed to operate seamlessly across various asset classes. It leverages seven unique trend-following indicators to provide robust and adaptive trading signals. The strategy dynamically adjusts to market conditions, making it suitable for equities, commodities, forex, and cryptocurrencies.
Core Methodologies and Features:
Seven Integrated Trend Indicators:
The strategy integrates seven powerful trend-following indicators. These include directional moving averages, smoothed moving averages, RSI loops, Supertrend filters, and more. When the majority of these indicators align, the strategy generates a long or short signal, ensuring that traders are capturing significant trend opportunities while minimizing noise from market fluctuations.
Universal Asset Adaptability:
Designed to work across all assets, the strategy adjusts its parameters dynamically based on the asset being traded. Whether applied to stocks, forex, or crypto, it adapts to the specific volatility and price behavior of the instrument, ensuring reliable signal generation in any market condition.
Customizable Directional Bias and Volatility Filters:
The strategy allows for an optional directional bias and incorporates volatility-based adjustments through ATR filters and standard deviation metrics. These features provide greater flexibility, allowing users to fine-tune the strategy for both trending and ranging markets.
Operational Parameters:
User-Friendly Customization:
Universal All Assets Strategy offers comprehensive customization options, including adjustable backtesting dates, starting capital settings, plotting options, and an experimental directional bias feature. These parameters can be easily tailored to meet the trader's unique needs, allowing for optimal performance across various markets and trading styles.
Seven-Trend Confirmation System:
The algorithm relies on its seven trend-following indicators to confirm market direction. If the majority of indicators generate a long signal, the strategy will initiate a long position. Conversely, a majority short signal will trigger a short position, providing strong validation for trade entries and exits.
Thoroughly Tested for Realistic Conditions:
This strategy has been rigorously backtested and forward-tested under real-world trading conditions, accounting for slippage, commissions, and various account sizes. Its robust risk management features ensure a balanced approach to trading, reducing unnecessary drawdowns and prioritizing capital preservation over time.
Concluding Remarks:
The Universal All Assets Strategy | viResearch is designed to offer traders a powerful tool for identifying and acting on market trends across multiple asset classes. With its seven-indicator confirmation system, adaptive logic, and customizable settings, this strategy is an excellent choice for traders looking for consistency and reliability in their trading approach. Whether used for long or short opportunities, this strategy provides the flexibility and precision needed to succeed in today's markets.
Triangular Arbitrage [Starbots]Triangular arbitrage in crypto refers to a trading strategy that exploits price discrepancies between three different cryptocurrencies or currency pairs on the same exchange.
The idea is to make a series of trades that ultimately result in a profit without the risk typically involved in trading. It works by taking advantage of the inefficiencies in the pricing of cryptocurrency pairs.
Here’s how it works:
Identify the Discrepancy: A trader finds a pricing mismatch between three cryptocurrencies. For example, they identify that the exchange rates between BTC/ETH, ETH/USDT, and BTC/USDT pairs are not aligned in a way that satisfies arbitrage-free conditions.
Three Trades:
Trade 1: Start with one cryptocurrency, say USDT (Tether).
Trade 2: Use USDT to buy ETH.
Trade 3: Use ETH to buy BTC.
Final Trade: Finally, convert the BTC back into USDT.
Profit: If the exchange rates between these pairs are out of sync, the trader can end up with more USDT (or the initial cryptocurrency) than they started with. This is because the temporary price inefficiency allowed them to buy low and sell high across different pairs.
Example:
Initial position: You have 10,000 USDT.
Step 1: You buy ETH with USDT (at a rate of 1 ETH = 2000 USDT), getting 5 ETH.
Step 2: You buy BTC with ETH (at a rate of 1 BTC = 2.5 ETH), getting 2 BTC.
Step 3: You sell BTC back for USDT (at a rate of 1 BTC = 5200 USDT), getting 10,400 USDT.
This results in a profit of 400 USDT after completing the cycle, assuming no fees or slippage.
Key Points:
Risk-Free (In Theory): In theory, triangular arbitrage is risk-free because you’re taking advantage of price discrepancies and not market trends.
High Speed Required: Since the inefficiencies in the crypto market are usually very short-lived, this strategy often requires bots or automated systems to execute trades quickly.
Fees and Slippage: In reality, exchange fees, trading volume, and slippage (the difference between the expected price and the actual execution price) can eat into profits and should be carefully considered.
Triangular arbitrage opportunities arise in crypto markets due to the high volatility and fragmentation across different trading pairs and exchanges.
________________________________________________________________
Recommended Binance pairs: DOGE/BTC, TRX/BTC, LINK/BTC, RUNE/BTC, FET/BTC, WIF/BTC,.. Make sure they have big daily volume when you swap them.
You typically have 30 seconds to 2 minutes to complete all three orders, but the main challenge is slippage, especially if the trading volume is low.
<>How to use indicator?
For example, open the DOGE/BTC chart on Binance and set the timeframe to 30 seconds or 1 minute.
In the first input, enter DOGE/USDT, the symbol that's on the left of your slash (DOGE/BTC), and in the second, enter BTC/USDT, the symbol that's on the right of your slash (DOGE/BTC).
Next, select the investment and commissions option.
Indicator will automatically calculate the discrepancies between these three different cryptocurrency pairs and show you when it's profitable to trade it on the chart.
Follow the indicator's suggested orders and capitalize on the price discrepancies between the three cryptocurrencies on the same exchange. This is how Triangular Arbitrage work.
WPR Volume Candle [Atareum]AWPRVC (Atareum WPR Volume Candles) is clearly an awesome indicator produced by AtareumFX that is based on William’s Percent Range concepts by combination with volume. This is a new approach of volume candles that is combined with R% concepts and creates such a powerful tool to trace the market and assists traders to make better decisions surly and so much accurate. You can find this new indicator more useful because it has all benefits and advantages of William’s R% and cover its disadvantages. Also it is more powerful because of using volume in its calculations and generate a new candles which is more reliable and trustworthy.
Concept:
Using William’s Percent leading periods and calculations on redesigning new candles in combination with volume, that makes unique reform candles, but these new candles with their new cloud system clearly response to any reasonable price movement with so much information.
As you know if use R% there are some misleading fake signals generate by oscillator, also it could not show any sign of price moving trend which is almost confusing for beginners or even a pro trader! And finally this oscillator is so sensitive to price change that is so creepy to use for most of traders.
This new AWPRVC solve the problem and make all of them handy and useful for you.
The cloud system which is designed in AWPRVC shows the price trend moving from Bearish Zone (-100 to -50 percent) to Bullish Zone (-50 to 0 percent). You can trust the lead moving forward of the clouds in two separate Top and Bottom (Bull and Bear) lines which solely determine the trend and power of price moving. When clouds are close to each other means we continue the trend and when they get far away from each other means we will face powerful trend in near future. If they are in Bearish Zone we continue the selling pressure and vice versa. Following picture shows good sample of Long and Short positions in compare with so many fake signals generated on original R%.
Besides the cloud system of AWPRVC which is clearly show the price trend and it is completely enough for being sure about price moving trend, you can use moving average which is designated in it to confirm the price trend, also.
Also you can see this new AWPRVC candle by using volume within its conformation, make reasonable price candles which is no so sensitive and so creepy and make your decisions come true in peace and clear sense of market moves. You can see following picture which is showing although the real price candles are so unclear and nonsense of making decision but the AWPRVC candles lead you to make true and trustable position.
As you see this new combination of Williams R% oscillator with volume and also generating a perfect new cloud system will clearly help traders even pro to trust the signals and understand whole market movement better and all of original problems of R% solved and even make a most powerful, trustworthy and useful new indicator.
Parameters:
Section 1 : Candle colour setting for flourishing just as you desire !
Section 2 : Defining Periods of R% and source of candle data in combination with determining the smoothing type of moving averages and signal period.
Section 3 : Select using Standard candles alongside with redesigned cloud calculation type and three additional moving averages which can plot on each newly generated candles and standard candles on a chart with the type mode defined in the previous section.
Note: if you want to omit any or all of these moving averages, you can use 0 in period, instead of selecting "None" in the plot moving option!
Usage :
Overall:
Regardless of the additional moving averages which will lead to so many situations of market according to their types and designs, that is four different period for new redesign AWPRVC and three period for standard chart. You can easily select periods and type for these moving averages. Also, do not forget that signal moving averages is shown only on AWPRVC chart and have two different colour for upward and downward trends. Other moving averages are plot by just one single colour.
Cloud levels are so important because AWPRVC candles show respect to them and when they break the clouds upward or downward it is surly beginning of a trend. Do not forget we have 5 levels for tracing new AWPRVC candles move as follows : Ready for Short \ Long, Surly Short \ Long and Turn Trend which is in middle range of movement percent. Each level clearly shows what it means by its name.
Support and Resistance:
Any consolidation of AWPRVC candles in Ready for Short or Long Zones means the support or resistance level due to its nature, but important thing is how long the candles lasts in there or how many times repeated in the same level in AWPRVC chart zone in future.
For plotting the support or resistance you should trace range of AWPRVC candles consolidated and plot zone in standard chart candles just like following picture.
Divergence:
When standard price candles move downward but we see upward trend in clouds of AWPRVC candles that means we should face Bullish Trend because of the divergence and vice versa. You can see perfect example in following picture.
Signal:
Alert of Long :
Bullish candle cross both cloud down and up level simultaneously.
Confirmed Long :
AWPRVC candles cross up turn trend level and pullback to cloud up level.
Take profit of Long:
Any cross down of the AWPRVC candles from surly short level of chart.
Alert of Short :
Bearish candle cross both cloud up and down level simultaneously.
Confirmed Short :
AWPRVC candles cross down turn trend level and pullback to cloud down level.
Take profit of Short:
Any cross up of the AWPRVC candles from surly long level of chart.
Notes:
Use moving averages cross of standard chart candles as lead to be in positions more as they are good representative of trend.
As long as AWPRVC candles or Cloud levels are in Bullish Zone, you can stay in Long positions.
Cloud level thickness means the power of trend and can be use as confirmation of powerful trend, so when cloud levels tight or going to cross each other it means the trend is going to be reversed.
It is the result of many years of experience in markets and there are so many details about this AWPRVC chart which I am in the experiment phase to publish in the future, so please help me with your ideas and do not hesitate to comment and inform me any suggestions or criticism.
ATR - FSThis script calculates and visualizes the Average True Range (ATR) along with its moving average, highest, and lowest values over a defined period. The ATR is a widely used volatility indicator in trading that measures the degree of price movement within a market. By incorporating both the average ATR and the high/low ranges, this script provides a comprehensive view of market volatility dynamics.
Use Cases:
Volatility-Based Trading:
Traders can use this indicator to gauge market volatility and adjust their trading strategies accordingly. For example:
High ATR values often indicate periods of high volatility, suggesting larger price swings and more aggressive trading opportunities.
Low ATR values signal quieter market conditions, where range-bound trading or less aggressive positioning might be favorable.
Stop-Loss & Take-Profit Placement:
The ATR is commonly used to determine optimal stop-loss and take-profit levels:
During high volatility periods (high ATR values), traders might widen their stop-loss levels to accommodate larger price swings.
Conversely, during low volatility periods, traders may tighten their stop-loss levels to capture profits before the market moves against them.
Trend Identification:
The moving average of ATR helps traders identify long-term volatility trends, which can indicate the strength of a market trend:
If the average ATR is increasing, it could suggest the continuation of a strong trend.
A decreasing average ATR may indicate the start of a consolidation period or weakening trend.
Volatility Breakouts:
By analyzing the highest and lowest ATR values, traders can spot potential breakout opportunities:
A sudden spike in ATR (breaking above the green line) can indicate a breakout from a consolidation phase.
Dropping below the orange line may signal a period of market stagnation or consolidation.
Risk Management:
The ATR is a critical tool in risk management, helping traders set stop-losses and position sizes based on market conditions:
Higher ATR values might prompt a trader to reduce their position size to account for larger potential losses.
Lower ATR values may encourage a trader to take on larger positions, as the market risk is lower.
Theoretical price by volumeThis code is used to calculate a theoretical price range based on volume and price change and display it on the chart. Specifically, it calculates the “theoretical price volatility” based on price changes and volume, from which the upper and lower price limits are derived.
The price volatility is calculated by dividing the price change by the volume as the change unit volume.
Based on this volatility, we calculate the theoretical variation relative to the current price (“Theoretical Variance Difference”).
Based on the results, **Theoretical High Price (p_price) and Theoretical Low Price (m_price)** are calculated.
The chart displays the upper and lower bounds of these theoretical prices in color, and also calculates their mean and standard deviation (in the form of a Bollinger band) and plots them.
The background color on the chart indicates whether the price is within the theoretical price range, and at the same time, the mean and standard deviation of the theoretical prices are used to visualize price movements in more detail.
This indicator helps traders understand the impact of volume on price movements and helps them determine if prices are staying within the theoretical range or if there are unusual movements.
Burst PowerThe Burst Power indicator is to be used for Indian markets where most stocks have a maximum price band limit of 20%.
This indicator is intended to identify stocks with high potential for significant price movements. By analysing historical price action over a user-defined lookback period, it calculates a Burst Power score that reflects the stock's propensity for rapid and substantial moves. This can be helpful for stock selection in strategies involving momentum bursts, swing trading, or identifying stocks with explosive potential.
Key Components
____________________
Significant Move Counts:
5% Moves: Counts the number of days within the lookback period where the stock had a positive close-to-close move between 5% and 10%.
10% Moves: Counts the number of days with a positive close-to-close move between 10% and 19%.
19% Moves: Counts the number of days with a positive close-to-close move of 19% or more.
Maximum Price Move (%):
Identifies the largest positive close-to-close percentage move within the lookback period, along with the date it occurred.
Burst Power Score:
A composite score calculated using the counts of significant moves: Burst Power =(Count5%/5) +(Count10%/2) + (Count19%/0.5)
The score is then rounded to the nearest whole number.
A higher Burst Power score indicates a higher frequency of significant price bursts.
Visual Indicators:
Table Display: Presents all the calculated data in a customisable table on the chart.
Markers on Chart: Plots markers on the chart where significant moves occurred, aiding visual analysis.
Using the Lookback Period
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The lookback period determines how much historical data the indicator analyses. Users can select from predefined options:
3 Months
6 Months
1 Year
3 Years
5 Years
A shorter lookback period focuses on recent price action, which may be more relevant for short-term trading strategies. A longer lookback period provides a broader historical context, useful for identifying long-term patterns and behaviors.
Interpreting the Burst Power Score
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High Burst Power Score (≥15):
Indicates the stock frequently experiences significant price moves.
Suitable for traders seeking quick momentum bursts and swing trading opportunities.
Stocks with high scores may be more volatile but offer potential for rapid gains.
Moderate Burst Power Score (10 to 14):
Suggests occasional significant price movements.
May suit traders looking for a balance between volatility and stability.
Low Burst Power Score (<10):
Reflects fewer significant price bursts.
Stocks are more likely to exhibit longer, sustainable, but slower price trends.
May be preferred by traders focusing on steady growth or longer-term investments.
Note: Trading involves uncertainties, and the Burst Power score should be considered as one of many factors in a comprehensive trading strategy. It is essential to incorporate broader market analysis and risk management practices.
Customisation Options
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The indicator offers several customisation settings to tailor the display and functionality to individual preferences:
Display Mode:
Full Mode: Shows the detailed table with all components, including significant move counts, maximum price move, and the Burst Power score.
Mini Mode: Displays only the Burst Power score and its corresponding indicator (green, orange, or red circle).
Show Latest Date Column:
Toggle the display of the "Latest Date" column in the table, which shows the most recent occurrence of each significant move category.
Theme (Dark Mode):
Switch between Dark Mode and Light Mode for better visual integration with your chart's color scheme.
Table Position and Size:
Position: Place the table at various locations on the chart (top, middle, bottom; left, center, right).
Size: Adjust the table's text size (tiny, small, normal, large, huge, auto) for optimal readability.
Header Size: Customise the font size of the table headers (Small, Medium, Large).
Color Settings:
Disable Colors in Table: Option to display the table without background colors, which can be useful for printing or if colors are distracting.
Bullish Closing Filter:
Another customisation here is to count a move only when the closing for the day is strong. For this, we have an additional filter to see if close is within the chosen % of the range of the day. Closing within the top 1/3, for instance, indicates a way more bullish day tha, say, closing within the bottom 25%.
Move Markers on chart:
The indicator also marks out days with significant moves. You can choose to hide or show the markers on the candles/bars.
Practical Applications
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Momentum Trading: High Burst Power scores can help identify stocks that are likely to experience rapid price movements, suitable for momentum traders.
Swing Trading: Traders looking for short- to medium-term opportunities may focus on stocks with moderate to high Burst Power scores.
Positional Trading: Lower Burst Power scores may indicate steadier stocks that are less prone to volatility, aligning with long-term investment strategies.
Risk Management: Understanding a stock's propensity for significant moves can aid in setting appropriate stop-loss and take-profit levels.
Disclaimer: Trading involves significant risk, and past performance is not indicative of future results. The Burst Power indicator is intended for educational purposes and should not be construed as financial advice. Always conduct thorough research and consult with a qualified financial professional before making investment decisions.
Qualitative and Quantitative Candlestick Score [CHE] Qualitative and Quantitative Candlestick Score
Overview
The Qualitative and Quantitative Candlestick Score is a powerful indicator for TradingView that combines both qualitative and quantitative analyses of candlestick patterns. This indicator provides traders with a comprehensive assessment of market conditions to make informed trading decisions.
Key Features
- Quantitative Analysis: Calculates a quantitative score based on the price movement of each candle.
- Qualitative Analysis: Evaluates candles based on body size, wick size, trend, and trading volume.
- Cumulative Scores: Displays cumulative green (bullish) and red (bearish) scores over a defined period.
- Trend Analysis: Identifies trend direction, strength, and provides trading recommendations (Long/Short).
- Customizable Settings: Adjust parameters for time periods, thresholds, and volume analysis.
Settings and Customizations
1. Time Period Settings:
- Period: Number of periods to calculate moving averages and cumulative scores (Default: 14).
2. Qualitative Evaluation:
- Body Size Threshold (%): Minimum size of the candle body to be considered significant (Default: 0.5%).
- Wick Size Threshold (%): Maximum size of the wicks to be considered minimal (Default: 0.3%).
3. Volume Settings:
- Include Volume in Evaluation: Whether to include trading volume in the qualitative score (Default: Enabled).
- Volume MA Period: Number of periods to calculate the moving average of volume (Default: 14).
4. Trend Settings:
- Moving Average Length: Number of periods for the Simple Moving Average used to determine the trend (Default: 50).
Calculations and Visualizations
- Quantitative Score: Difference between the closing and opening price, normalized to the opening price.
- Qualitative Score: Evaluation based on body size, wick size, trend, and volume.
- Cumulative Scores: Average of green and red scores over the defined period.
- Score Difference: Difference between cumulative green and red scores to determine trend direction.
- Trend Analysis Table: Displays trend direction, trend strength, and trading recommendation in an easy-to-read table.
Plotting and Display
- Cumulative Scores: Displays cumulative green and red scores in green and red colors.
- Score Difference: Blue line chart to visualize the difference between green and red scores.
- Zero Line: Horizontal gray line as a reference point.
- Trend Analysis Table: Table in the top right of the chart showing current trend direction, strength, and trading recommendation.
Use Cases
- Trend Identification: Use the score difference and trend analysis table to quickly assess the current market sentiment.
- Trading Recommendations: Based on the table, decide whether a long or short entry is appropriate.
- Volume Analysis: Including volume helps to better understand the strength of a trend.
Benefits
- Comprehensive Analysis: Combines quantitative and qualitative methods for a deeper market analysis.
- User-Friendly: Easy parameter adjustments allow for personalized use.
- Visually Appealing: Clear charts and tables facilitate data interpretation.
- Flexible: Adaptable to various trading strategies and timeframes.
Installation and Usage
1. Installation:
- Copy the provided Pine Script code.
- Go to TradingView and open the Pine Script Editor.
- Paste the code and save the script.
- Add the indicator to your chart.
2. Customization:
- Adjust the parameters according to your trading preferences.
- Monitor the cumulative scores and the trend analysis table for trading decisions.
Conclusion
The Qualitative and Quantitative Candlestick Score offers a comprehensive analysis of market conditions by combining quantitative and qualitative evaluation methods. With its user-friendly settings and clear visualizations, this indicator is a valuable tool for traders seeking informed and precise trading decisions.
Best regards and happy trading
Chervolino
Developed by: Chervolino
Version: 1.0
License: Free to use and customize on TradingView.
For any questions or feedback, feel free to contact me through the TradingView community.
Note: This indicator is a tool to assist with trading decisions and does not replace professional financial advice. Use it responsibly and thoroughly test it before incorporating it into your trading strategies.
Options Series - MTF_Parabolic_SAR
⭐ Purpose of the Script
This script, titled "Options Series - MTF_Parabolic_SAR," is designed for analyzing price trends using the Parabolic SAR (Stop and Reverse) indicator across multiple timeframes (MTF). It dynamically highlights bullish and bearish conditions, helping traders identify trends with improved accuracy. The script uses the Parabolic SAR across three customizable timeframes (default: 5, 15, and 60 minutes) to gauge the market sentiment.
⭐ Key Features and Insights:
Multi-Timeframe Parabolic SAR: The script calculates the Parabolic SAR for three different timeframes ( input_tf_1 , input_tf_2 , and input_tf_3 ). Traders can configure these timeframes to match their trading style (e.g., intraday, swing).
The SAR plots adapt to the selected timeframe, helping traders see different perspectives of price movement, such as short-term and long-term trends.
Bullish and Bearish Conditions: The script determines bullish and bearish conditions by comparing the close price against the Parabolic SAR in each timeframe.
If at least one timeframe indicates a bullish condition (close price above SAR), the bars are colored green . Conversely, if one timeframe signals bearish conditions (close below SAR), the bars turn red .
This provides an at-a-glance view of the price trend across multiple timeframes, offering insights into the market's strength and direction.
Visual Enhancements: Bar Coloring: Bars are visually enhanced with a color scheme: green for bullish , red for bearish , and gray for neutral conditions. This makes it easy to spot market trends and reversals directly on the chart. Candle Plotting: The current candle is plotted with the corresponding color and labeled with the SAR values for each timeframe. This aids traders in tracking real-time price action.
Labeling of SAR Values: The script displays SAR values for each timeframe as floating labels next to the chart. These labels contain the timeframe and the exact SAR value, making it easier to reference without cluttering the chart.
⭐ Trading Advantages: Customizable and Adaptive: The customizable timeframes and SAR settings allow traders to adapt the script to various market conditions and their specific trading strategies. This flexibility provides a powerful tool for identifying entry and exit points. Multi-Timeframe Insights: By considering multiple timeframes, the script offers a comprehensive market view, making it easier to confirm strong trends and avoid false signals.
⭐ How It Helps Traders: Trend Identification: By visualizing Parabolic SAR across multiple timeframes, traders can quickly assess trend strength and direction. Reversal Detection: The script's color changes (green to red or vice versa) signal potential trend reversals, offering critical information for managing trades and reducing risk.
🚀 Conclusion:
This script provides traders with a multi-timeframe analysis tool for identifying trends and potential reversals using the Parabolic SAR. By offering customizable timeframes, clear visual cues, and SAR value labeling, it simplifies decision-making and enhances market insights.
XAU/USD Strategy with Correct ADX and Bollinger Bands Fill1. *Indicators Used*:
- *Exponential Moving Averages (EMAs)*: Two EMAs (20-period and 50-period) are used to identify the trend direction and potential entry points based on crossovers.
- *Relative Strength Index (RSI)*: A momentum oscillator that measures the speed and change of price movements. It identifies overbought and oversold conditions.
- *Bollinger Bands*: These consist of a middle line (simple moving average) and two outer bands (standard deviations away from the middle). They help to identify price volatility and potential reversal points.
- *Average Directional Index (ADX)*: This indicator quantifies trend strength. It's derived from the Directional Movement Index (DMI) and helps confirm the presence of a strong trend.
- *Average True Range (ATR)*: Used to calculate position size based on volatility, ensuring that trades align with the trader's risk tolerance.
2. *Entry Conditions*:
- *Long Entry*:
- The 20 EMA crosses above the 50 EMA (indicating a potential bullish trend).
- The RSI is below the oversold level (30), suggesting the asset may be undervalued.
- The price is below the lower Bollinger Band, indicating potential price reversal.
- The ADX is above a specified threshold (25), confirming that there is sufficient trend strength.
- *Short Entry*:
- The 20 EMA crosses below the 50 EMA (indicating a potential bearish trend).
- The RSI is above the overbought level (70), suggesting the asset may be overvalued.
- The price is above the upper Bollinger Band, indicating potential price reversal.
- The ADX is above the specified threshold (25), confirming trend strength.
3. *Position Sizing*:
- The script calculates the position size dynamically based on the trader's risk per trade (expressed as a percentage of the total capital) and the ATR. This ensures that the trader does not risk more than the specified percentage on any single trade, adjusting the position size according to market volatility.
4. *Exit Conditions*:
- The strategy uses a trailing stop-loss mechanism to secure profits as the price moves in the trader's favor. The trailing stop is set at a percentage (1.5% by default) below the highest price reached since entry for long positions and above the lowest price for short positions.
- Additionally, if the RSI crosses back above the overbought level while in a long position or below the oversold level while in a short position, the position is closed to prevent losses.
5. *Alerts*:
- Alerts are set to notify the trader when a buy or sell condition is met based on the strategy's rules. This allows for timely execution of trades.
### Summary
This strategy aims to capture significant price movements in the XAU/USD market by combining trend-following (EMAs, ADX) and momentum indicators (RSI, Bollinger Bands). The dynamic position sizing based on ATR helps manage risk effectively. By implementing trailing stops and alert mechanisms, the strategy enhances the trader's ability to act quickly on opportunities while mitigating potential losses.
Neural Momentum StrategyThis strategy combines Exponential Moving Average (EMA) analysis with a multi-timeframe approach. It uses a neural scoring system to evaluate market momentum and generate precise trading signals. The strategy is implemented in Pine Script v5 and is designed for use on TradingView.
Key Components
The strategy utilizes short-term (10-period) and long-term (25-period) EMAs. It calculates the difference between these EMAs to assess trend direction and strength. A neural scoring system evaluates EMA crossovers (weight: 12 points), trend strength (weight: 10 points), and price acceleration (weight: 4 points). The system implements a score smoothing algorithm using a 10-period EMA.
Multi-timeframe Analysis
The strategy automatically selects a higher timeframe based on the current chart timeframe. It calculates scores for both the current and higher timeframes, then combines these scores using a weighted average. The higher timeframe factor ranges from 3 to 6, depending on the current timeframe.
Trading Logic
Entry occurs when the final combined score turns positive after a change. Exit happens when the final combined score turns negative after a change. The strategy recalculates scores on each bar, ensuring responsive trading decisions.
Risk Management
An optional adaptive stop-loss system based on Average True Range (ATR) is available. The default ATR period is 10, and the stop factor is 1.2. Stop levels are dynamically adjusted on the higher timeframe.
Customization Options
Users can adjust EMA periods, signal line period, scoring weights, and enable/disable multi-timeframe analysis. The strategy allows setting specific date ranges for backtesting and deployment.
Position Sizing
The strategy uses a percentage-of-equity position sizing method, with a default of 30% of account equity per trade.
Code Structure
The strategy is built using TradingView's strategy framework. It employs efficient use of the request.security() function for multi-timeframe analysis. The main calculation function, calculate_score(), computes the neural score based on EMA differences and acceleration.
Performance Considerations
The strategy adapts to various market conditions through its multi-faceted scoring system. Multi-timeframe analysis helps filter out noise and identify stronger trends. The neural scoring approach aims to capture subtle market dynamics often missed by traditional indicators.
Limitations
Performance may vary across different markets and timeframes. The strategy's effectiveness relies on proper calibration of its numerous parameters. Users should thoroughly backtest and forward test before live implementation.
To summarize, the Neural Momentum Strategy represents a sophisticated approach to market analysis. It combines traditional technical indicators with advanced scoring techniques and multi-timeframe analysis. This strategy is designed for traders seeking a data-driven and adaptive method. It aims to identify high-probability trading opportunities across various market conditions.
This Neural Momentum Strategy is for informational and educational purposes only. It should not be considered financial advice. The strategy may exhibit slight repainting behavior due to the nature of multi-timeframe analysis and the use of the request.security() function. Historical values might change as new data becomes available.
Trading carries a high level of risk, and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment. Therefore, you should not invest money that you cannot afford to lose.
Past performance is not indicative of future results. The author and TradingView are not responsible for any losses incurred as a result of using this strategy. Always exercise caution when using this or any trading strategy, and thoroughly test it before implementing in live trading scenarios.
Users are solely responsible for any trading decisions they make based on this strategy. It is strongly recommended that you seek advice from an independent financial advisor if you have any doubts.
Feigenbaum Inspired Bifurcation IndicatorIts a work in progess but here you go. I pair it with a 50 EMA for better direction.
1. Bullish Trend Signal:
Green Labels ("Bullish") are plotted below the price chart when a bullish trend is detected.
This is based on a crossover of two simple moving averages (short and long):
The short-term moving average (SMA) crosses above the long-term moving average, indicating a potential upward trend or buying opportunity.
2. Bearish Trend Signal:
Red Labels ("Bearish") are plotted above the price chart when a bearish trend is detected.
This occurs when the short-term moving average crosses below the long-term moving average, signaling a potential downward trend or selling opportunity.
3. Mid-Range Line (Optional):
A Blue Line is plotted on the chart, representing the mid-point between the highest high and lowest low over the given period (default is 14 bars).
This line can help visualize where the price is relative to its recent range.
Summary:
Bullish Labels (Green): Appear when a bullish crossover happens.
Bearish Labels (Red): Appear when a bearish crossover happens.
Mid-Range Line (Blue): Helps identify the midpoint of recent price ranges (can be turned off if not needed).
This is a simplified trend-following indicator based on moving average crossovers, giving you a quick visual cue of when trends are shifting. Let me know if you’d like further adjustments!
Volatility Breaker Blocks [BigBeluga]The Volatility Breaker Blocks indicator identifies key market levels based on significant volatility at pivot highs and lows. It plots blocks that act as potential support and resistance zones, marked in green (support) and blue (resistance). Even after a breakout, these blocks leave behind shadow boxes that continue to impact price action. The sensitivity of block detection can be adjusted in the settings, allowing traders to customize the identification of volatility breakouts. The blocks print triangle labels (up or down) after breakouts, indicating potential areas of interest.
🔵 IDEA
The Volatility Breaker Blocks indicator is designed to highlight key areas in the market where volatility has created significant price action. These blocks, created at pivot highs and lows with increased volatility, act as potential support and resistance levels.
The idea is that even after price breaks through these blocks, the remaining shadow boxes continue to influence price movements. By focusing on volatility-driven pivot points, traders can better anticipate how price may react when it revisits these areas. The indicator also captures the natural tendency for price to retest broken resistance or support levels.
🔵 KEY FEATURES & USAGE
◉ High Volatility Breaker Blocks:
The indicator identifies areas of high volatility at pivot highs and lows, plotting blocks that represent these zones. Green blocks represent support zones (identified at pivot lows), while blue blocks represent resistance zones (identified at pivot highs).
Support:
Resistance:
◉ Shadow Blocks after Breakouts:
When price breaks through a block, the block doesn't disappear. Instead, it leaves behind a shadow box, which can still influence future price action. These shadow blocks act as secondary support or resistance levels.
If the price crosses these shadow blocks, the block stops extending, and the right edge of the box is fixed at the point where the price crosses it. This feature helps traders monitor important price levels even after the initial breakout has occurred.
◉ Triangle Labels for Breakouts:
After the price breaks through a volatility block, the indicator prints triangle labels (up or down) at the breakout points.
◉ Support and Resistance Retests:
One of the key concepts in this indicator is the retesting of broken blocks. After breaking a resistance block, price often returns to the shadow box, which then acts as support. Similarly, after breaking a support block, price tends to return to the shadow box, which becomes a resistance level. This concept of price retesting and bouncing off these levels is essential for understanding how the indicator can be used to identify potential entries and exits.
The natural tendency of price to retest broken resistance or support levels.
Additionaly indicator can display retest signals of broken support or resistance
◉ Customizable Sensitivity:
The sensitivity of volatility detection can be adjusted in the settings. A higher sensitivity captures fewer but more significant breakouts, while a lower sensitivity captures more frequent volatility breakouts. This flexibility allows traders to adapt the indicator to different trading styles and market conditions.
🔵 CUSTOMIZATION
Calculation Window: Defines the window of bars over which the breaker blocks are calculated. A larger window will capture longer-term levels, while a smaller window focuses on more recent volatility areas.
Volatility Sensitivity: Adjusts the threshold for volatility detection. Lower sensitivity captures smaller breakouts, while higher sensitivity focuses on larger, more significant moves.
Retest Signals: Display or hide retest signals of shadow boxes
$TUBR: Stop Loss IndicatorATR-Based Stop Loss Indicator for TradingView by The Ultimate Bull Run Community: TUBR
**Overview**
The ATR-Based Stop Loss Indicator is a custom tool designed for traders using TradingView. It helps you determine optimal stop loss levels by leveraging the Average True Range (ATR), a popular measure of market volatility. By adapting to current market conditions, this indicator aims to minimize premature stop-outs and enhance your risk management strategy.
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**Key Features**
- **Dynamic Stop Loss Levels**: Calculates stop loss prices based on the ATR, providing both long and short stop loss suggestions.
- **Customizable Parameters**: Adjust the ATR period, multiplier, and smoothing method to suit your trading style and the specific instrument you're trading.
- **Visual Aids**: Plots stop loss lines directly on your chart for easy visualization.
- **Alerts and Notifications** (Optional): Set up alerts to notify you when the price approaches or hits your stop loss levels.
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**Understanding the Indicator**
1. **Average True Range (ATR)**:
- **What It Is**: ATR measures market volatility by calculating the average range between high and low prices over a specified period.
- **Why It's Useful**: A higher ATR indicates higher volatility, which can help you set stop losses that accommodate market fluctuations.
2. **ATR Multiplier**:
- **Purpose**: Determines how far your stop loss is placed from the current price based on the ATR.
- **Example**: An ATR multiplier of 1.5 means the stop loss is set at 1.5 times the ATR away from the current price.
3. **Smoothing Methods**:
- **Options**: Choose from RMA (default), SMA, EMA, WMA, or Hull MA.
- **Effect**: Different smoothing methods can make the ATR more responsive or smoother, affecting where the stop loss is placed.
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**How the Indicator Works**
- **Long Stop Loss Calculation**:
- **Formula**: `Long Stop Loss = Close Price - (ATR * ATR Multiplier)`
- **Purpose**: For long positions, the stop loss is set below the current price to protect against downside risk.
- **Short Stop Loss Calculation**:
- **Formula**: `Short Stop Loss = Close Price + (ATR * ATR Multiplier)`
- **Purpose**: For short positions, the stop loss is set above the current price to protect against upside risk.
- **Plotting on the Chart**:
- **Green Line**: Represents the suggested stop loss level for long positions.
- **Red Line**: Represents the suggested stop loss level for short positions.
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**How to Use the Indicator**
1. **Adding the Indicator to Your Chart**:
- **Step 1**: Copy the PineScript code of the indicator.
- **Step 2**: In TradingView, click on **Pine Editor** at the bottom of the platform.
- **Step 3**: Paste the code into the editor and click **Add to Chart**.
- **Step 4**: The indicator will appear on your chart with the default settings.
2. **Adjusting the Settings**:
- **ATR Period**:
- **Definition**: Number of periods over which the ATR is calculated.
- **Adjustment**: Increase for a smoother ATR; decrease for a more responsive ATR.
- **ATR Multiplier**:
- **Definition**: Factor by which the ATR is multiplied to set the stop loss distance.
- **Adjustment**: Increase to widen the stop loss (less likely to be hit); decrease to tighten the stop loss.
- **Smoothing Method**:
- **Options**: RMA, SMA, EMA, WMA, Hull MA.
- **Adjustment**: Experiment to see which method aligns best with your trading strategy.
- **Display Options**:
- **Show Long Stop Loss**: Toggle to display or hide the long stop loss line.
- **Show Short Stop Loss**: Toggle to display or hide the short stop loss line.
3. **Interpreting the Indicator**:
- **Long Positions**:
- **Action**: Set your stop loss at the value indicated by the green line when entering a long trade.
- **Short Positions**:
- **Action**: Set your stop loss at the value indicated by the red line when entering a short trade.
- **Adjusting Stop Losses**:
- **Trailing Stops**: You may choose to adjust your stop loss over time, moving it in the direction of your trade as the ATR-based stop loss levels change.
4. **Implementing in Your Trading Strategy**:
- **Risk Management**:
- **Position Sizing**: Use the stop loss distance to calculate your position size based on your risk tolerance.
- **Consistency**: Apply the same settings consistently to maintain discipline.
- **Combining with Other Indicators**:
- **Enhance Decision-Making**: Use in conjunction with trend indicators, support and resistance levels, or other technical analysis tools.
- **Alerts Setup** (If included in the code):
- **Purpose**: Receive notifications when the price approaches or hits your stop loss level.
- **Configuration**: Set up alerts in TradingView based on the alert conditions defined in the indicator.
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**Benefits of Using This Indicator**
- **Adaptive Risk Management**: By accounting for current market volatility, the indicator helps prevent setting stop losses that are too tight or too wide.
- **Minimize Premature Stop-Outs**: Reduces the likelihood of being stopped out due to normal price fluctuations.
- **Flexibility**: Customizable settings allow you to tailor the indicator to different trading instruments and timeframes.
- **Visualization**: Clear visual representation of stop loss levels aids in quick decision-making.
---
**Things to Consider**
- **Market Conditions**:
- **High Volatility**: Be cautious as ATR values—and thus stop loss distances—can widen, increasing potential losses.
- **Low Volatility**: Tighter stop losses may increase the chance of being stopped out by minor price movements.
- **Backtesting and Optimization**:
- **Historical Analysis**: Test the indicator on past data to evaluate its effectiveness and adjust settings accordingly.
- **Continuous Improvement**: Regularly reassess and fine-tune the parameters to adapt to changing market conditions.
- **Risk Per Trade**:
- **Alignment with Risk Tolerance**: Ensure the stop loss level keeps potential losses within your acceptable risk per trade (e.g., 1-2% of your trading capital).
- **Emotional Discipline**:
- **Stick to Your Plan**: Avoid making impulsive changes to your stop loss levels based on emotions rather than analysis.
---
**Example Usage Scenario**
1. **Setting Up a Long Trade**:
- **Entry Price**: $100
- **ATR Value**: $2
- **ATR Multiplier**: 1.5
- **Calculated Stop Loss**: $100 - ($2 * 1.5) = $97
- **Action**: Place a stop loss order at $97.
2. **During the Trade**:
- **Price Increases to $105**
- **ATR Remains at $2**
- **New Stop Loss Level**: $105 - ($2 * 1.5) = $102
- **Action**: Move your stop loss up to $102 to lock in profits.
---
**Final Tips**
- **Documentation**: Keep a trading journal to record your trades, stop loss levels, and observations for future reference.
- **Education**: Continuously educate yourself on risk management and technical analysis to enhance your trading skills.
- **Support**: Engage with trading communities or seek professional advice if you're unsure about implementing the indicator effectively.
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**Conclusion**
The ATR-Based Stop Loss Indicator is a valuable tool for traders looking to enhance their risk management by setting stop losses that adapt to market volatility. By integrating this indicator into your trading routine, you can improve your ability to protect capital and potentially increase profitability. Remember to use it as part of a comprehensive trading strategy, and always adhere to sound risk management principles.
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**How to Access the Indicator**
To start using the ATR-Based Stop Loss Indicator, follow these steps:
1. **Obtain the Code**: Copy the PineScript code provided for the indicator.
2. **Create a New Indicator in TradingView**:
- Open TradingView and navigate to the **Pine Editor**.
- Paste the code into the editor.
- Click **Save** and give your indicator a name.
3. **Add to Chart**: Click **Add to Chart** to apply the indicator to your current chart.
4. **Customize Settings**: Adjust the input parameters to suit your preferences and start integrating the indicator into your trading strategy.
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**Disclaimer**
Trading involves significant risk, and it's possible to lose all your capital. The ATR-Based Stop Loss Indicator is a tool to aid in decision-making but does not guarantee profits or prevent losses. Always conduct your own analysis and consider seeking advice from a financial professional before making trading decisions.
Quantoshi Global Liquidity StrategyThis strategy leverages global liquidity data alongside technical indicators like the Rate of Change (ROC) and Double Exponential Moving Average (DEMA) to identify optimal long-entry points during major market trends. The script is designed to capture long-term, sustained momentum and includes built-in risk management by filtering out rapid price spikes. It is best suited for swing trading or long-term trend trading.
Key Features:
Global Liquidity Data:
The strategy incorporates data from major global central banks and M2 money supply to calculate a comprehensive liquidity index, which is a critical component for long-term trend detection.
ROC-DEMA Crossover:
It combines the Rate of Change (ROC) and a 100-period Double Exponential Moving Average (DEMA) to identify momentum shifts. Long entries are triggered when these indicators confirm an upward trend.
Price Thresholds:
The strategy compares the current price to the price from several candles ago to ensure positions are not entered during unsustainable price surges.
Custom Alerts:
Automated alerts for long entries and exits allow users to automate their trades or receive timely notifications when market conditions are met.
How It Works:
The strategy enters long positions when ROC and DEMA signals confirm a positive trend, and the price conditions suggest a sustainable upward momentum. Long exits occur when the momentum reverses, with a clear crossover signal of ROC below DEMA. Custom alert messages make it ideal for automated trading setups.
Why It's Unique:
This strategy combines liquidity data with technical indicators to filter noise and focus on significant market shifts. It allows traders to capture major trend reversals without needing to actively monitor the charts, making it useful for those focused on swing or long-term trading.
Backtesting & Risk Management:
Given its long-term focus, this strategy generates only a few signals per decade when used on a weekly timescale. As a result, traditional backtesting show few trades, but historical analysis reveals its effectiveness in capturing major market movements.
Account Size:
The backtest is based on a $1,000 account size to represent a realistic trading scenario.
Commissions & Tick size: Commission fees of 0.1% and a tick size of 100 are applied to reflect real-world trading conditions.
Trade Size:
Risk per trade is limited to 5% of the account balance to align with sound risk management practices.
Double BBW OverlayDouble BBW Overlay Indicator
Overview
The Double BBW (Bollinger Band Width) Overlay indicator is a custom script for TradingView that combines two BBW indicators with adjustable settings. It allows traders to compare the volatility of two different periods of Bollinger Bands on the same chart. By default, the first BBW is calculated with a 10-period center line, and the second BBW with a 20-period center line, but these values can be customized.
How It Works
Bollinger Bands consist of an upper band, a lower band, and a middle band (typically a moving average). The Bollinger Band Width (BBW) measures the distance between the upper and lower bands relative to the center line. The width of these bands indicates market volatility:
Narrow Bands: Low volatility, usually preceding a breakout.
Wide Bands: High volatility, often following a strong price movement.
This indicator plots two BBW values on a non-overlay chart, making it easy to visualize and compare different market conditions over different periods.
Indicator Components
BBW 1 (default period: 10)
Calculates the BBW using a center line based on a 10-period moving average.
The width is plotted in blue by default.
BBW 2 (default period: 20)
Calculates the BBW using a center line based on a 20-period moving average.
The width is plotted in red by default.
Zero Line
A gray horizontal line at the value of 0 for reference, helping to understand the scale of BBW values.
Input Parameters
Center Line Period for BBW 1 (length1)
Default: 10
This controls the length of the moving average for the first BBW calculation. It defines how many periods are used to calculate the middle Bollinger Band for BBW 1.
Center Line Period for BBW 2 (length2)
Default: 20
This controls the length of the moving average for the second BBW calculation. It defines how many periods are used to calculate the middle Bollinger Band for BBW 2.
Standard Deviation Multiplier (mult)
Default: 2.0
This controls how far the upper and lower Bollinger Bands are from the center line. The multiplier affects how sensitive the Bollinger Bands are to price changes, with higher values producing wider bands.
How to Use
Adding the Indicator: Once the script is added to your TradingView account, simply apply the indicator to any chart. It will be displayed as a separate pane below the price chart, showing two BBW lines corresponding to the two different periods.
Customizing Periods: Use the settings panel to adjust the center line periods for BBW 1 and BBW 2 to match your desired trading strategy. For instance, you can analyze short-term versus long-term volatility by adjusting the periods.
Volatility Analysis:
When both BBW lines are narrow, it indicates low volatility across both short-term and long-term periods, which could suggest that a breakout is imminent.
If both BBW lines widen simultaneously, it shows that volatility is increasing in both timeframes, possibly indicating a strong trend.
Use Cases
Breakout Strategy: When the BBW lines contract significantly, it may signal that a low-volatility period is about to end, which is often followed by a price breakout in either direction.
Trend Strength: Comparing short-term and long-term BBW values can help determine if recent price movements are supported by broader market volatility or if they are isolated to the short term.
Chart Display
BBW 1: Blue line, representing the Bollinger Band Width calculated with a center line period of 10 (or your customized value).
BBW 2: Red line, representing the Bollinger Band Width calculated with a center line period of 20 (or your customized value).
Zero Line: A gray line at 0 is provided for reference, although BBW values are always positive.
Advantages of Using Double BBW
Comprehensive View of Volatility: By overlaying two BBW indicators with different timeframes, you can gain insights into both short-term and long-term market volatility trends.
Customizable: You can easily adjust the moving average periods and the standard deviation multiplier to match your preferred trading strategy or the characteristics of the asset you are trading.
Easy Visualization: The separate plots of BBW values make it easier to see shifts in market volatility, allowing you to spot potential trading opportunities.
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
MTF Squeeze Analyzer - [tradeviZion]MTF Squeeze Analyzer
Multi-Timeframe Squeeze Pro Analyzer Tool
Overview:
The MTF Squeeze Analyzer is a comprehensive tool designed to help traders monitor the TTM Squeeze indicator across multiple timeframes in a streamlined and efficient manner. Built with Pine Script™ version 5, this indicator enhances your market analysis by providing detailed insights into squeeze conditions and momentum shifts, enabling you to make more informed trading decisions.
Key Features:
1. Multi-Timeframe Monitoring:
Comprehensive Coverage: Track squeeze conditions across multiple timeframes, including 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, and daily charts.
Squeeze Counts: Keep count of the number of consecutive bars the price has been within each squeeze level (low, mid, high), helping you assess the strength and duration of consolidation periods.
2. Dynamic Table Display:
Customizable Appearance: Adjust table position, text size, and colors to suit your preferences.
Color-Coded Indicators: Easily identify squeeze levels and momentum shifts with intuitive color schemes.
Message Integration: Features rotating messages to keep you engaged and informed.
3. Alerts for Key Market Events:
Squeeze Start and Fire Alerts: Receive notifications when a squeeze starts or fires on your selected timeframes.
Custom Squeeze Count Alerts: Set thresholds for squeeze counts and get alerted when these levels are reached, allowing you to anticipate potential breakouts.
Fully Customizable: Choose which alerts you want to receive and tailor them to your trading strategy.
4. Momentum Analysis:
Momentum Oscillator: Visualize momentum using a histogram that changes color based on momentum shifts.
Detailed Insights: Determine whether momentum is increasing or decreasing to make more strategic trading decisions.
How It Works:
The indicator is based on the TTM Squeeze concept, which identifies periods of low volatility where the market is "squeezing" before a potential breakout. It analyzes the relationship between Bollinger Bands and Keltner Channels to determine squeeze conditions and uses linear regression to calculate momentum.
1. Squeeze Levels:
No Squeeze (Green): Market is not in a squeeze.
Low Compression Squeeze (Gray): Mild consolidation, potential for a breakout.
Mid Compression Squeeze (Red): Moderate consolidation, higher breakout potential.
High Compression Squeeze (Orange): Strong consolidation, significant breakout potential.
2. Squeeze Counts:
Tracks the number of consecutive bars in each squeeze condition.
Helps identify how long the market has been consolidating, providing clues about potential breakout timing.
3. Momentum Histogram:
Upward Momentum: Shown in aqua or blue, indicating increasing or decreasing upward momentum.
Downward Momentum: Displayed in red or yellow, representing increasing or decreasing downward momentum.
Using Alerts:
Stay ahead of market movements with customizable alerts:
1. Enable Alerts in Settings:
Squeeze Start Alert: Get notified when a new squeeze begins.
Squeeze Fire Alert: Be alerted when a squeeze ends, signaling a potential breakout.
Squeeze Count Alert: Set a specific number of bars for a squeeze condition, and receive an alert when this count is reached.
2. Set Up Alerts on Your Chart:
Click on the indicator name and select " Add Alert on MTF Squeeze Analyzer ".
Choose your desired alert conditions and customize the notification settings.
Click " Create " to activate the alerts.
How to Set It Up:
1. Add the Indicator to Your Chart:
Search for " MTF Squeeze Analyzer " in the TradingView Indicators library.
Add it to your chart.
2. Customize Your Settings:
Table Display:
Choose whether to show the table and select its position on the chart.
Adjust text size and colors to enhance readability.
Timeframe Selection:
Select the timeframes you want to monitor.
Enable or disable specific timeframes based on your trading strategy.
Colors & Styles:
Customize colors for different squeeze levels and momentum shifts.
Adjust header and text colors to match your chart theme.
Alert Settings:
Enable alerts for squeeze start, squeeze fire, and squeeze counts.
Set your preferred squeeze type and count threshold for alerts.
3. Interpret the Data:
Table Information:
The table displays the squeeze status and counts for each selected timeframe.
Colors indicate the type of squeeze, making it easy to assess market conditions at a glance.
Momentum Histogram:
Use the histogram to gauge the strength and direction of market momentum.
Observe color changes to identify shifts in momentum.
Why Use MTF Squeeze Analyzer ?
Enhanced Market Insight:
Gain a deeper understanding of market dynamics by monitoring multiple timeframes simultaneously.
Identify potential breakout opportunities by analyzing squeeze durations and momentum shifts.
Customizable and User-Friendly:
Tailor the indicator to fit your trading style and preferences.
Easily adjust settings without needing to delve into the code.
Time-Efficient:
Save time by viewing all relevant squeeze information in one place.
Reduce the need to switch between different charts and timeframes.
Stay Informed with Alerts:
Never miss a critical market movement with fully customizable alerts.
Focus on other tasks while the indicator monitors the market for you.
Acknowledgment:
This tool builds upon the foundational work of John Carter , who developed the TTM Squeeze concept. It also incorporates enhancements from LazyBear and Makit0 , providing a more versatile and powerful indicator. MTF Squeeze Analyzer extends these concepts by adding multi-timeframe analysis, squeeze counting, and advanced alerting features, offering traders a comprehensive solution for market analysis.
Note: Always practice proper risk management and test the indicator thoroughly to ensure it aligns with your trading strategy. Past performance is not indicative of future results.
Trade smarter with TradeVizion—unlock your trading potential today!
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms
█ Introduction and How it is Different
The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions.
What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets.
BTCUSD 8hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean.
🔶 Spread Calculation:
The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price:
Spread = CME Price - Bitfinex Price
This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity.
🔶 Bollinger Bands Calculation:
The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period.
1. Moving Average (SMA):
The simple moving average of the spread (mu_S) over a specified period P is calculated as:
mu_S = (1/P) * sum(S_i from i=1 to P)
Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior.
2. Standard Deviation:
The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread:
sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P))
3. Upper and Lower Bollinger Bands:
The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618).
- Upper Band:
Upper Band = mu_S + (k * sigma_S)
- Lower Band:
Lower Band = mu_S - (k * sigma_S)
These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities.
Local view
🔶 Entry Conditions:
- Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward.
Spread < Lower Band
- Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward.
Spread > Upper Band
🔶 Risk Management and Profit-Taking:
The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably.
Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks.
█ Trade Direction
The trade direction is based on deviations of the spread from its historical norm:
- Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow.
- Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen.
These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time.
█ Usage
The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur.
- Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes.
- Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior.
█ Default Settings
The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences.
🔶 Bollinger Bands Period (200 bars):
This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy:
- Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives.
- Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations.
🔶 Standard Deviation Multiplier (2.618):
The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades.
- Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals.
- Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability.
🔶 Take-Profit Levels:
The strategy has four take-profit levels to gradually lock in profits:
- Level 1 (3%): 25% of the position is closed at a 3% profit.
- Level 2 (8%): 20% of the position is closed at an 8% profit.
- Level 3 (14%): 15% of the position is closed at a 14% profit.
- Level 4 (21%): 10% of the position is closed at a 21% profit.
Adjusting these take-profit levels affects how quickly profits are realized:
- Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits.
- Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in.
🔶 Hold Days (20 bars):
The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this:
- Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early.
- Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions.
By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
