Holy-Cow IndicatorHoly-Cow Indicator
Advanced Multi-Pattern Candlestick Analyzer (HC, NV, RN)
Description
Overview
This indicator is designed to detect key candlestick patterns based on foundational principles of price action analysis, now presented with a modernized approach for easier recognition and enhanced utility. Drawing inspiration from the work of candlestick charting pioneer Steve Nison (Japanese Candlestick Charting Techniques) and advanced price action strategies popularized by Linda Raschke (Street Smarts), this tool identifies and highlights unique combinations such as the Holy-Cow (HC), Nova (NV), and Red-Nova (RN) patterns.
These patterns simplify the complexities of price action into actionable setups, making them suitable for all types of traders.
Features
Inside Bars (IB):
Marks consolidation zones where the current candle’s range is inside the previous candle’s range. Useful for spotting breakout opportunities.
Outside Bars (OB):
Identifies candles where the current range exceeds the previous candle’s range.
Indicates potential reversals or trap setups.
Holy-Cow Pattern (HC):
Inspired by: Trap + Consolidation Setup
An Outside Bar followed by an Inside Bar.
Highlights volatility followed by tight consolidation, often preceding a breakout.
Nova Pattern (NV):
Inspired by: Breakout Trap + Expansion
An Inside Bar followed by an Outside Bar.
Signals breakout traps and subsequent volatility, useful for reversals or momentum moves.
Red-Nova Pattern (RN):
Inspired by: Volatility Trap + Major Breakout Zone
A combination of Holy-Cow (HC) and Nova (NV) patterns.
Represents a highly compressed market state, often leading to significant, high-intensity moves.
Bollinger Bands Integration:
Provides context on volatility and potential overbought/oversold levels.
Customizable Visibility:
Toggle specific patterns (IB, OB, HC, NV, RN) to suit your trading strategy.
How to Use
Pattern Detection:
Enable the patterns you want to focus on through the settings panel (e.g., HC for breakout setups, RN for high-intensity moves).
Observe highlighted zones and labels for actionable trade setups.
Trading Strategies:
Breakout Trades:
Look for HC, NV, or RN patterns near support/resistance and trade in the breakout direction.
Reversal Trades:
Use NV or RN patterns forming near key levels to catch reversals.
Stop-Loss:
Place below/above the low/high of the identified pattern.
Take Profit:
Use support/resistance levels, Fibonacci extensions, or a predefined risk-reward ratio (e.g., 2:1).
Recommended Timeframes:
1–15 minutes: Scalping or intraday trading.
1-hour: Intraday and swing trading.
Daily/Weekly: Ideal for significant trend analysis and major market moves.
Acknowledgments
This indicator is based on well-established trading principles and enhanced with unique combinations for modern trading. The foundational ideas behind these patterns are drawn from:
Steve Nison: Who introduced candlestick charting to Western traders in his seminal work, Japanese Candlestick Charting Techniques, and popularized concepts such as Inside and Outside Bars.
Linda Raschke: Who furthered the application of price action patterns in her book Street Smarts, showcasing multi-bar setups and momentum strategies.
While the patterns Holy-Cow (HC), Nova (NV), and Red-Nova (RN) are unique to this indicator, they build upon these foundational principles to offer traders actionable insights.
Disclaimer
This indicator is an independent creation inspired by publicly available price action principles. It is not affiliated with any proprietary tool or service. Back testing and proper risk management are strongly advised before live trading.
Forecasting
Eroina Trend Reversal Indicator with ConfirmationsEroina Trend Reversal Indicator with Confirmations
Overview (English):
The Trend Reversal Indicator with Confirmations is designed to identify potential trend reversals by analyzing dynamic resistance and support levels. This script uses a robust confirmation system to reduce false signals, making it ideal for traders who seek disciplined, data-driven decisions.
Key Features:
• Dynamic Levels: Calculates resistance and support levels based on user-defined lengths.
• Breakout Confirmation: Confirms trend reversals by validating price action over a specified number of candles.
• Visual Cues: Displays “LONG” and “SHORT” signals directly on the chart, alongside resistance/support levels.
• Customizable Parameters: Adaptable to different timeframes and market conditions.
How It Works:
1. Resistance & Support Levels:
• Resistance: Calculated as the highest high over the last N bars.
• Support: Calculated as the lowest low over the last N bars.
2. Breakout Detection:
• A resistance breakout occurs when the price closes above the resistance level.
• A support breakout occurs when the price closes below the support level.
3. Confirmation Logic:
• Signals are validated only if the price remains above/below the levels for a user-defined number of candles.
4. Entry Signals:
• “LONG” signals indicate a confirmed breakout above resistance.
• “SHORT” signals indicate a confirmed breakdown below support.
Settings:
• Resistance Length: Defines the number of candles used to calculate resistance levels.
• Support Length: Defines the number of candles used to calculate support levels.
• Confirmation Candles: Specifies how many candles are required to confirm breakouts.
Usage:
This indicator is ideal for identifying trend reversals and optimizing entry points. Combine it with volume analysis or other technical indicators to enhance accuracy. For example:
• Use in conjunction with RSI to avoid overbought/oversold conditions.
• Combine with moving averages to confirm the trend direction.
Overview (Additional Language):
(Your additional language description can go here after English, e.g., Russian, Spanish, etc.)
IU 4 Bar UP StrategyIU 4 Bar UP Strategy
The IU 4 Bar UP Strategy is a trend-following strategy designed to identify and execute long trades during strong bullish momentum, combined with confirmation from the SuperTrend indicator. This strategy is suitable for traders aiming to capitalize on sustained upward market movements.
Features :
1. SuperTrend Confirmation: Incorporates the SuperTrend indicator as a dynamic support/resistance line to filter trades in the direction of the trend.
2. 4 Consecutive Bullish Bars: Detects a series of 4 bullish candles as a signal for strong upward momentum, ensuring robust trade setups.
3. Dynamic Alerts: Sends alerts for trade entries and exits to keep traders informed.
4. Visual Enhancements:
- Plots the SuperTrend indicator on the chart.
- Changes the background color while a trade is active for easy visualization.
Inputs :
- SuperTrend ATR Period: The period used to calculate the Average True Range (ATR) for the SuperTrend indicator.
- SuperTrend ATR Factor: The multiplier for the ATR in the SuperTrend calculation.
Entry Conditions :
A long entry is triggered when:
1. The last 4 consecutive candles are bullish (closing prices are higher than opening prices).
2. The current price is above the SuperTrend line.
3. The strategy is not already in a position.
4. The bar is confirmed (not a partially formed bar).
When all these conditions are met, the strategy enters a long position and provides an alert:
"Long Entry triggered"
Exit Conditions :
The strategy exits the long position when:
1. The closing price drops below the SuperTrend line.
2. An alert is generated: "Close the long Trade"
Visualization :
- The SuperTrend line is plotted, dynamically colored:
- Green when the trend is bullish.
- Red when the trend is bearish.
- The background color turns semi-transparent green while a trade is active, indicating a long position.
Do use proper risk management while using this strategy.
Line Break Chart StrategyHello All!
We should not pass this year without a gift!
My last publication in 2024 is Complete Line Break Chart Strategy with many features!
What is Line Break Chart?
" Line Break is a Japanese chart style that disregards time intervals and only focuses on price movements, similar to the Kagi and Renko chart styles. Line Break charts form a series of up and down bars (referred to as lines). Up lines represent rising prices, and down lines represent falling prices. New confirmed lines only form on the chart when closing prices break the range covered by previous lines. Users can control the number of past lines used in the calculation via the "Number of Lines" input in the chart settings. The typical "Number of Lines" setting is 3, meaning the chart forms a new up line when the closing price is above the high prices of the last three lines, and it forms a new down line when the closing price is below the past three lines' low prices. If the current price is higher, it is an up line and if it is lower, it is a down line. If the current closing price is the same or the move in the opposite direction is not large enough to warrant a reversal, l then no new line is draw n" by Tradingview. You can find it here
Now let's start examining the features of the indicator:
By using Line break reversals it shows trend on the main chart. You can create alert .
Moreover, you can decide which trade should be taken by using Risk Management in the indicator. You can set the " Maximum Risk " and then if the risk is more than you set then the trade is not taken. When trend changed it checks the distance between reversal level and open price and compare it with the Maximum Risk
Breakout:
It can find breakouts and shows on the chart. You can create alert for breakouts
It can show breakouts on the main chart:
Flip-Flops:
Upon looking at set of price break charts, the trader will notice that there are instances when uptrend blocks is followed by one reversal block, and then by a reversal to a series of uptrend blocks. The opposite is also possible: a series of downtrend blocks is followed by one reversal box and then by an immediate reversal to downtrend. This price action is called a " Flip-Flop ". This structure usually produces trend continuation signal. when we see this then we better use Buy/Sell stop order. lets see this on the chart:
Temporal Sequence Table:
Sequence frequency shows the frequency distribution of the number of sequential highs and the number of sequential lows that have been generated. This is quite important to the trader who is seeking to join a trend or put on a trade when the price break reverses into a new trend direction. For example, if the pattern over the past year has been that there never were more than nine consecutive high closes, it would make sense not to enter a position late into the sequence of new high closes.
also you can see market structure. I have tried to formalize it and show it under the table. so you can understand if it's choppy market.
"Number of Lines" has very important role. While using low time frames such seconds/minutes time frame you may want to choose higher number of lines such 5,6. ( this may minimize the risk of a whipsaw )
Gaps feature:
You can set Gaps on/off. if Gaps on then you can see how long it takes for each box
Reversal and Continuation Probability:
The script calculated Reversal level and Continuation probability of the trend by using Sequence frequency.
It also shows unconfirmed box and current closing price level:
Last but not least it has Overlay option for all items, and can show all items in the main chart!
P.S. I added alerts :)
Wish you all a happy new year!
Enjoy!
Longest Candles HighlighterDescription:
The Longest Candles Highlighter is a simple yet effective tool that identifies and highlights candles with significant price ranges. By visually marking candles that meet specific size criteria, this indicator helps traders quickly spot high-volatility moments or significant market moves on the chart.
Features:
1. Customizable Candle Range:
- Define the minimum and maximum candle size in pips using input fields.
- Tailor the indicator to highlight candles that are most relevant to your trading strategy.
2. Flexible for Different Markets:
- Automatically adjusts pip calculation based on the instrument type (Forex or non-Forex).
- Accounts for differences in pip values, such as the 0.01 pip for JPY pairs in Forex.
3. Visual Highlighting:
- Highlights qualifying candles with a customizable background color for easy identification.
- The default color is red, but you can choose any color to match your chart theme.
4. Precision and Efficiency:
- Quickly scans and identifies candles that meet your criteria, saving you time in analyzing charts.
- Works seamlessly across all timeframes and asset classes.
How It Works:
- The indicator calculates the range of each candle in pips by subtracting the low from the high and dividing by the appropriate pip value.
- It checks whether the candle's size falls within the user-defined minimum and maximum pip range.
- If the conditions are met, the background of the candle is highlighted with the specified color, drawing your attention to significant price movements.
Use Case:
- This indicator is ideal for identifying key market moments, such as breakouts, volatility spikes, or significant price movements.
- Traders can use it to quickly locate large candles on any chart, aiding in technical analysis and strategy development.
This tool simplifies the process of spotting important candles, empowering traders to make faster and more informed trading decisions.
JCM_MadridThis indicator provides dynamic bar coloring and buy/sell signals based on EMA relationships and price momentum. It allows traders to visually identify trend changes and potential trade opportunities directly on the chart.
Indicator Basics:
Name: The script is titled "JCM_Madrid".
Overlay: It overlays its calculations and outputs directly on the price chart.
User Inputs:
-Range: Defines the length of the EMA (Exponential Moving Average).
-Ref-1 and Ref-2: Set reference lengths for secondary EMAs used in the calculations.
-Source: The price data source for EMA calculations (e.g., close, open, high, low).
-Enable Buy/Sell: Boolean toggles to activate or deactivate buy and sell signals.
Calculations:
EMA Value: Computes the main EMA based on the source and Range.
CloseMA: The difference between the close price and the EMA.
SqzMA: The difference between a secondary EMA (Ref-1) and the main EMA.
RefMA: The difference between another secondary EMA (Ref-2) and the main EMA.
Bar Coloring:
Bars are colored based on the relationship between SqzMA and CloseMA:
Purple: When SqzMA > CloseMA.
Blue: When SqzMA < CloseMA.
Buy/Sell Signals:
A Buy Signal is generated when:
CloseMA crosses from below to above 0.
The close price is higher than the previous close.
Buy signals are enabled.
A Sell Signal is generated when:
CloseMA crosses from above to below 0.
The close price is lower than the previous close.
Sell signals are enabled.
Signals are displayed as labels on the chart:
"Buy": Green label below the candle.
"Sell": Yellow label below the candle
Divergence-Weighted clouds V 1.0Comprehensive Introduction to Divergence-Weighted Clouds V 1.0 (DW)
In financial markets, the analysis of volume and price plays a fundamental role in identifying trends, reversals, and making trading decisions. Volume indicates the level of market interest and liquidity focused on an asset, while price reflects changes in supply and demand. Alongside these two elements, market volatility, support and resistance levels, and cash flow are also critical factors that help analysts form a comprehensive view of the market. The Divergence-Weighted Clouds V 1.0 (DW) indicator is designed to simultaneously analyze these fundamental elements and other important market dynamics. To achieve this, it utilizes data generated from 13 distinct indicators, each measuring specific aspects of the market:
Trend and Momentum: Analyzing the direction and strength of price movements.
Volume and Cash Flow: Understanding the inflow and outflow of capital in the market.
Oscillators: Identifying overbought and oversold conditions.
Support and Resistance Levels: Highlighting key price levels.
The Core Challenge: Standardizing Diverse Data
The primary challenge lies in the fact that the outputs of these indicators differ significantly in scale and meaning. For example:
Volume often generates very large values (e.g., millions of shares).
Oscillators provide data within fixed ranges (e.g., 0 to 100).
Price-based metrics may vary in entirely different scales (e.g., tens or hundreds of units).
These differences make direct comparison of the data impractical. The DW indicator resolves this challenge through an advanced mathematical methodology:
Normalization and Hierarchical Evaluation:
To standardize the data, a process called hierarchical EMA evaluation is employed. Initially, the raw outputs of each indicator are computed over different timeframes using Exponential Moving Averages (EMA) based on prime-number intervals.
Hierarchical Scoring:
A pyramid-like structure is used to evaluate the performance of each indicator. This method examines the relationships and distances between EMAs for each indicator and assigns a numerical score.
Final Integration and Aggregation:
The scores of all 13 indicators are then mathematically aggregated into a single number. This final value represents the overall market performance at that moment, enabling a unified interpretation of volume, price, and volatility.
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Indicators Used in DW
To achieve this comprehensive analysis, DW leverages 13 carefully selected indicators, each offering unique insights into market dynamics:
Trend and Momentum
- ALMA (Arnaud Legoux Moving Average): Reduces lag for faster trend identification.
- Aroon Up: Analyzes the stability of uptrends.
- ADX (Average Directional Index): Measures the strength of a trend.
Volume and Cash Flow
- CMF (Chaikin Money Flow): Identifies cash flow based on price and volume.
- EFI (Elder’s Force Index): Evaluates the strength of price changes alongside volume.
- Volume Delta: Tracks the balance between buying and selling pressure.
- Raw Volume: Analyzes unprocessed volume data.
Oscillators
- Fisher Transform: Normalizes data to detect price reversals.
- MFI (Money Flow Index): Identifies overbought and oversold levels.
Support, Resistance, and Price Dynamics
- Ichimoku Lines (Tenkan-sen & Kijun-sen): Analyzes support and resistance levels.
- McGinley Dynamic: Minimizes errors caused by rapid price movements.
- Price Hierarchy: Evaluates the relative position of prices across timeframes.
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Example: Hierarchical Scoring for Price Analysis
To illustrate how the DW indicator processes data, let’s take the price as an example and analyze it using the first four prime numbers (2, 3, 5, and 7) as intervals for Exponential Moving Averages (EMAs). This example will demonstrate how the indicator evaluates price relationships and assigns a hierarchical score.
Step-by-Step Calculation:
1. Raw Data:
Let’s assume the closing prices for a specific asset over recent days are as follows:
Day 1: 100
Day 2: 102
Day 3: 101
Day 4: 104
Day 5: 103
Day 6: 105
Day 7: 106
2. Calculate EMAs for Prime Number Intervals:
Using the prime-number intervals (2, 3, 5, 7), we calculate the EMAs for these timeframes:
EMA(2): Averages the last 2 closing prices equal to 105.33
EMA(3): Averages the last 3 closing prices equal to 104.25
EMA(5): Averages the last 5 closing prices equal to 103.17
EMA(7): Averages the last 7 closing prices equal to 102.67
3. Compare EMAs Hierarchically:
To assign a score, the relationships between the EMAs are analyzed hierarchically. We evaluate whether each smaller EMA is greater or less than the larger ones:
Compare EMA(2) to EMA(3), EMA(5), and EMA(7):
EMA(2) > EMA(3):105.33>104.25 => +1
EMA(2) > EMA(5): 105.33>103.17 => +1
EMA(2) > EMA(7): 105.33 > 102.67 => +1
Compare EMA(3) to EMA(5) and EMA(7):
EMA(3) > EMA(5) : 104.25>103.17 => +1
EMA(3) > EMA(7):104.25 >102.67 => +1
Compare EMA(5) to EMA(7):
EMA(5) > EMA(7):103.17>102.67 => +1
Assign a Score:
Each positive comparison adds +1 to the score. In this example:
Total Score for Price = 1+1+1+1+1+1+1=6
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Logic Behind Scoring:
The score reflects the "steepness" or "hierarchy" of price movement across different timeframes:
A higher score indicates that shorter EMAs are consistently above longer ones, signaling a strong upward trend.
A lower score or negative values would indicate the opposite (e.g., short-term prices lagging behind long-term averages, signaling weakness or potential reversal).
This method ensures that even complex data points (like price, volume, or oscillators) can be distilled into a single, comparable numerical value. When repeated across all 13 indicators, it enables the DW indicator to create a unified, normalized score that represents the overall market condition.
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Settings and Customization in Divergence-Weighted Clouds V 1.0 (DW)
The Divergence-Weighted Clouds V 1.0 (DW) indicator provides extensive customization options to empower traders to fine-tune the analysis according to their specific needs and trading strategies. Each of the 13 indicators is fully customizable through the settings menu, allowing adjustments to parameters such as lookback periods, sensitivity, and calculation methods. This flexibility ensures that DW can adapt seamlessly to a wide range of market conditions and asset classes.
Key Features of the Settings Menu
1. Global Settings:
Lookback Periods: Define the timeframe for data aggregation and analysis across all indicators.
Normalization Settings: Adjust parameters to refine the process of scaling diverse outputs to a comparable range.
Divergence Sensitivity: Control the weight given to indicators deviating from the average, enabling a focus on outliers or broader trends.
2. Indicator-Specific Settings:
Each of the 13 indicators has its own dedicated section in the settings menu for precise customization. Examples include:
ALMA (Arnaud Legoux Moving Average):
Window Size: Set the number of bars used for calculating the average.
Offset: Control the sensitivity of trend detection.
Sigma: Adjust the smoothing factor for the calculation.
Aroon Up:
Length: Modify the lookback period for identifying highs and evaluating uptrends.
ADX (Average Directional Index):
DI Length: Specify the period for calculating directional indicators (DI).
ADX Smoothing: Adjust the smoothing period for trend strength analysis.
3. Oscillator Settings:
Fisher Transform:
Length: Customize the period for normalization and detecting reversals.
Money Flow Index (MFI):
Length: Set the timeframe for analyzing overbought and oversold conditions.
4. Volume and Cash Flow Settings:
Chaikin Money Flow (CMF):
Length: Define the period for analyzing cash flow based on price and volume.
Volume Delta:
Timeframe: Select a custom timeframe for analyzing buying and selling pressure.
5. Support and Resistance Settings:
In the Support and Resistance category of the DW indicator, we address the logic behind four components:
McGinley Dynamic
Price Hierarchy
Base Line
Conversion Line
The settings structure for this section primarily focuses on McGinley Dynamic, while the other three elements—Price Hierarchy, Base Line, and Conversion Line—operate based on predefined values derived from the mathematical structure and logic of the DW indicator. Let’s explore this in detail:
McGinley Dynamic
Length: The only customizable setting in this category. Users can adjust the length parameter to tailor the responsiveness of the McGinley Dynamic to different market conditions. McGinley Dynamic adapts dynamically to the speed of price changes, reducing lag and minimizing false signals. Its flexibility allows it to serve as both a trendline and a support/resistance guide.
Price Hierarchy
The Price Hierarchy component in DW leverages a pyramid structure and triangular scoring based on prime-number intervals (e.g., 2, 3, 5, 7). This methodology ensures a mathematically robust framework for evaluating the relative position of prices across multiple timeframes.
Why No Settings for Price Hierarchy?
The unique properties of prime numbers make them ideal for constructing this hierarchical scoring system. Changing these intervals would compromise the integrity of the calculations, as they are specifically designed to ensure precision and consistency. Therefore, no customization is allowed for this component in the settings menu.
Conversion Line and Base Line
The Conversion Line (Tenkan-sen) and Base Line (Kijun-sen) are integral components derived from DW’s scoring methodology and represent short-term and medium-term equilibrium levels, respectively. These lines are calculated using the Ichimoku framework, which provides a reliable and well-recognized mathematical basis:
Conversion Line: The average of the highest high and lowest low over a fixed period of 9 bars.
Base Line: The average of the highest high and lowest low over a fixed period of 26 bars./list]
Both lines are utilized in DW as part of the 13 generated indicator variables to assess market equilibrium.
Why Default Values for Conversion and Base Lines?
These values are fixed to the default Ichimoku parameters to:
- Ensure consistency with the broader Ichimoku logic for users familiar with its methodology.
- Prevent confusion in the settings menu, as customization of these parameters is unnecessary for DW’s scoring system.
Important Note: While these lines are derived using Ichimoku logic, they are not standalone Ichimoku components but are embedded into DW’s mathematical structure. In the next section, we will elaborate on how the Ichimoku framework is employed for the graphical visualization of DW’s calculations.
Displaying the Results of 13 Indicator Integration in DW Indicator
The Divergence-Weighted Clouds V 1.0 (DW) employs a rigorous methodology to integrate 13 distinct indicators into a single, normalized output. Here's how the process works, followed by an explanation of the visualization strategy leveraging Ichimoku logic.
Simultaneous Evaluation of 13 Indicators
1. Mathematical Integration Logic:
Normalization: The outputs of all 13 indicators (e.g., ALMA, ADX, CMF) are normalized into comparable ranges, ensuring compatibility despite their diverse scales.
Hierarchical Scoring with Prime Intervals: For each indicator, Exponential Moving Averages (EMAs) are calculated using prime-number intervals (e.g., 2, 3, 5, 7). These EMAs are evaluated through a triangular scoring system, creating individual scores for each indicator.
Divergence Weighting: Indicators showing significant divergence from group averages are given higher weights, amplifying their influence on the final score.
2. Unified Score Calculation:
The normalized and weighted outputs of all 13 indicators are aggregated into a single score.
This score represents the overall behavior of the market, based on the simultaneous evaluation of trend, volume, oscillators, and price metrics.
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Challenge of Visualizing Results
The next challenge lies in effectively visualizing the score to make it actionable for traders. The DW indicator resolves this challenge by leveraging the Ichimoku framework.
Why Ichimoku for Visualization?
The Ichimoku system is known for its clear and predictive visualization capabilities, making it ideal for representing DW’s complex calculations:
1. Cloud-Based Display: Ichimoku Clouds (Kumo) are intuitive for identifying equilibrium zones and future price movements.
2. Projection Ability: The forward-projected Leading Spans (Senkou A and B) provide predictive insights based on past and current data.
3. Trader Familiarity: Ichimoku is widely recognized, reducing the learning curve for users.
Implementation of Ichimoku Logic
1. Mapping Score to Price:
The score is normalized and mapped to price using a scale factor, ensuring alignment with price data while preserving DW’s analytical integrity.
2. Ichimoku Cloud Lines:
Conversion Line (Tenkan-sen): Short-term equilibrium based on the score, calculated using a 9-period high-low average.
Base Line (Kijun-sen): Medium-term equilibrium calculated using a 26-period high-low average.
Leading Spans (Senkou A & B):
- Senkou A: Average of the Conversion and Base Lines.
- Senkou B: High-low average over a 52-period window.
Lagging Span (Chikou): Unlike traditional Ichimoku, DW’s Lagging Span reflects the Nebula Score shifted backward, providing a historical perspective on combined indicator behavior
3. Cloud Dynamics:
The Kumo Cloud is filled based on the relative position of Senkou A and Senkou B, using color shading to distinguish bullish and bearish conditions.
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Customization in Computational Settings
The core computational components of DW allow some customization for sensitivity adjustments:
Divergence Sensitivity: Controls the weight assigned to indicators with higher divergence.
Volatility Normalization: Adjusts the lookback period for volatility adjustments, refining the Nebula Score scaling.
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Advantages of Using Ichimoku Logic
1. Predictive Visualization:
The forward-projected cloud provides actionable insights for identifying trends and reversals earlier than traditional Ichimoku.
2. Aligned Lagging Span:
DW’s Lagging Span represents the normalized evaluation of all 13 indicators, offering a unique perspective beyond just closing price.
3. Intuitive Interpretation:
Traders familiar with Ichimoku can easily interpret DW’s outputs, making it accessible and effective.
Conclusion
By combining rigorous mathematical evaluation with Ichimoku’s visualization strengths, DW provides traders with a clear, actionable representation of market conditions. This ensures that the complex integration of 13 indicators is not only analytically robust but also visually intuitive.
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Comparison Between Divergence-Weighted Clouds V 1.0 (DW) and Traditional Ichimoku: NVIDIA 4H Chart
The chart showcases a side-by-side comparison of the Divergence-Weighted Clouds V 1.0 (DW) indicator (on the left) and the Traditional Ichimoku indicator (on the right). This comparison highlights the differences in how the two indicators interpret market trends and project equilibrium zones using their respective methodologies.
Key Observations and Insights
1. Base and Conversion Line Movements:
On Thursday, November 21, 2024, 17:30, in the DW indicator (left chart), the Base Line crosses above the Conversion Line, signaling a shift in medium-term equilibrium relative to short-term equilibrium.
On the Traditional Ichimoku (right chart), this crossover is not reflected until Monday, November 25, 2024, 17:30, occurring 4 days later.
Significance:
The DW indicator identifies the crossover and equilibrium shift significantly earlier due to its ability to process and normalize data from 13 distinct indicators.
This predictive capability provides traders with earlier insights, enabling them to anticipate changes and adjust their strategies proactively.
2. Cloud Dynamics and Leading Spans:
In both charts, the cloud (Kumo) represents the equilibrium and potential support/resistance zones.
The DW indicator’s Leading Span A and Leading Span B react faster to market changes, creating a more responsive and forward-looking cloud compared to the traditional Ichimoku.
Example:
On the DW chart (left), the cloud begins shifting to reflect the crossover earlier, signaling potential future support/resistance levels.
In the Ichimoku chart (right), the cloud reacts more slowly, lagging behind the DW indicator.
3. Lagging Span (Chikou Line):
In the DW indicator, the Lagging Span is based on the normalized output of the 13 indicators, reflecting their aggregated behavior rather than just the closing price shifted backward as in the traditional Ichimoku.
This provides a unique perspective on past market strength, aligning the Lagging Span more closely with the overall market condition derived from DW’s computations.
4. Price Alignment:
In the DW indicator, all normalized scores and values are mapped to align with price action, ensuring that the visualization remains intuitive while incorporating complex calculations.
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Advantages of DW Over Traditional Ichimoku
1.Earlier Signal Detection:
As demonstrated by the Base and Conversion Line crossover, DW detects changes in market equilibrium 4 days earlier, giving traders a significant advantage in anticipating price movements.
2. Enhanced Predictive Power:
The Leading Spans in DW’s cloud react faster, providing clearer forward-looking support and resistance zones compared to the traditional Ichimoku.
3. Comprehensive Data Integration:
While the Ichimoku relies solely on price-based calculations, DW integrates outputs from 13 distinct indicators, offering a more robust and comprehensive analysis of market conditions.
4. Alignment with Market Behavior:
The DW Lagging Span reflects the aggregated score of multiple indicators, aligning more closely with overall market sentiment and providing a deeper context than the price-based Lagging Span in Ichimoku.
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Final Note
The chart comparison illustrates how the Divergence-Weighted Clouds V 1.0 (DW) indicator outperforms traditional Ichimoku in terms of signal responsiveness and predictive accuracy. By combining the mathematical rigor of DW’s calculations with the visual clarity of Ichimoku, traders gain a powerful tool for analyzing market trends and making informed decisions.
Look at the DW chart (left) to see how early signals and cloud adjustments provide actionable insights compared to the slower reactions of the Traditional Ichimoku chart (right).
Compare TOTAL, TOTAL2, TOTAL3, and OTHERSCompare TOTAL, TOTAL2, TOTAL3, and OTHERS
This indicator compares the performance of major cryptocurrency market cap indices: TOTAL, TOTAL2, TOTAL3, and OTHERS. It normalizes each index's performance relative to its starting value and visualizes their relative changes over time.
Features
- Normalized Performance: Tracks the percentage change of each index from its initial value.
- Customizable Timeframe: Allows users to select a base timeframe for the data (e.g., daily, weekly).
- Dynamic Labels: Displays the latest performance of each index as a label on the chart, aligned to the right of the corresponding line for easy comparison.
- Color-Coded Lines: Each index is assigned a distinct color for clear differentiation:
-- TOTAL (Blue): Represents the total cryptocurrency market cap.
-- TOTAL2 (Green): Excludes Bitcoin.
-- TOTAL3 (Orange): Excludes Bitcoin and Ethereum.
-- OTHERS (Red): Represents all cryptocurrencies excluding the top 10 by market cap.
- Baseline Reference: Includes a horizontal line at 0% for reference.
Use Cases:
- Market Trends: Identify which segments of the cryptocurrency market are outperforming or underperforming over time.
- Portfolio Insights: Assess the impact of Bitcoin and Ethereum dominance on the broader market.
- Market Analysis: Compare smaller-cap coins (OTHERS) with broader indices (TOTAL, TOTAL2, and TOTAL3).
This script is ideal for traders and analysts who want a quick, visual way to track how different segments of the cryptocurrency market perform relative to each other over time.
Note: The performance is normalized to highlight percentage changes, not absolute values.
Pi Cycle MACD Inverse OscillatorPi Cycle MACD Inverse Oscillator with Gradient and Days Since Last Top
This indicator is ideal for Bitcoin traders seeking a robust tool to visualize long-term and short-term trends with enhanced clarity and actionable insights.
This script combines the concept of the Pi Cycle indicator with a unique MACD-based inverse oscillator to analyze Bitcoin market trends. It introduces several features to help traders understand market conditions better:
Inverse Oscillator:
- Oscillator ranges between 1 and -1.
- A value of 1 indicates the two moving averages (350 MA and 111 MA) are equal.
- A value of -1 indicates the maximum observed distance between the moving averages during the selected lookback period.
- The oscillator dynamically adjusts to price changes using a configurable scaling factor.
Gradient Visualization:
The oscillator line transitions smoothly from green (closer to -1) to yellow (at 0) and red (closer to 1).
The color gradient provides a quick visual cue for market momentum.
Days Since Last Pi Cycle Top:
Calculates and displays the number of days since the last "Pi Cycle Top" (defined as a crossover between the two moving averages).
The label updates dynamically and appears only on the most recent bar.
Conditional Fill:
Highlights the area between 0 and 1 with a green gradient when the price is above the long moving average.
Enhances visual understanding of the oscillator's position relative to key thresholds.
Inputs:
- Long Moving Average (350 default): Determines the primary trend.
- Short Moving Average (111 default): Measures shorter-term momentum.
- Oscillator Lookback Period (100 default): Defines the range for normalizing the oscillator.
- Price Scaling Factor (0.01 default): Adjusts the normalization to account for large price fluctuations.
How to Use:
- Use the oscillator to identify potential reversal points and trend momentum.
- Look for transitions in the gradient color and the position relative to 0.
- Monitor the "Days Since Last Top" label for insights into the market's cycle timing.
- Utilize the conditional fill to quickly assess when the market is in a favorable position above the long moving average.
ForecastPro by BinhMyco1. Overview:
This Pine Script implements a custom forecasting tool on TradingView, labeled "BinhMyco." It provides a method to predict future price movements based on historical data and a comparison with similar historical patterns. The script supports two types of forecasts: **Prediction** and **Replication**, where the forecasted price can be either based on price peaks/troughs or an average direction. The script also calculates a confidence probability, showing how closely the forecasted data aligns with historical trends.
2. Inputs:
- Source (`src`): The input data source for forecasting, which defaults to `open`.
- Length (`len`): The length of the training data used for analysis (fixed at 200).
- Reference Length (`leng`): A fixed reference length for comparing similar historical patterns (set to 70).
- Forecast Length (`length`): The length of the forecast period (fixed at 60).
- Multiplier (`mult`): A constant multiplier for the forecast confidence cone (set to 4.0).
- Forecast Type (`typ`): Type of forecast, either **Prediction** or **Replication**.
- Direction Type (`dirtyp`): Defines how the forecast is calculated — either based on price **peaks/troughs** or an **average direction**.
- Forecast Divergence Cone (`divcone`): A boolean option to enable the display of a confidence cone around the forecast.
3. Color Constants:
- Green (`#00ffbb`): Color used for upward price movements.
- Red (`#ff0000`): Color used for downward price movements.
- Reference Data Color (`refcol`): Blue color for the reference data.
- Similar Data Color (`simcol`): Orange color for the most similar data.
- Forecast Data Color (`forcol`): Yellow color for forecasted data.
4. Error Checking:
- The script checks if the reference length is greater than half the training data length, and if the forecast length exceeds the reference length, raising errors if either condition is true.
5. Arrays for Calculation:
- Correlation Array (`c`): Holds the correlation values between the data source (`src`) and historical data points.
- Index Array (`index`): Stores the indices of the historical data for comparison.
6. Forecasting Logic:
- Correlation Calculation: The script calculates the correlation between the historical data (`src`) and the reference data over the given reference length. It then identifies the point in history most similar to the current data.
- Forecast Price Calculation: Based on the type of forecast (Prediction or Replication), the script calculates future prices either by predicting based on similar bars or by replicating past data. The forecasted prices are stored in the `forecastPrices` array.
- Forecast Line Drawing: The script draws lines to represent the forecasted price movements. These lines are color-coded based on whether the forecasted price is higher or lower than the current price.
7. Divergence Cone (Optional):
- If the **divcone** option is enabled, the script calculates and draws a confidence cone around the forecasted prices. The upper and lower bounds of the cone are calculated using a standard deviation factor, providing a visual representation of forecast uncertainty.
8. Probability Table:
- A table is displayed on the chart, showing the probability of the forecast being accurate. This probability is calculated using the correlation between the current data and the most similar historical pattern. If the probability is positive, the table background turns green; if negative, it turns red. The probability is presented as a percentage.
9. Key Functions:
- `highest_range` and `lowest_range`: Functions to find the highest and lowest price within a range of bars.
- `ftype`: Determines the forecast type (Prediction or Replication) and adjusts the forecasting logic accordingly.
- `ftypediff`: Computes the difference between the forecasted and actual prices based on the selected forecast type.
- `ftypelim`, `ftypeleft`, `ftyperight`: Additional functions to adjust the calculation of the forecast based on the forecast type.
10. Conclusion:
The "ForecastPro" script is a unique tool for forecasting future price movements on TradingView. It compares historical price data with similar historical trends to generate predictions. The script also offers a customizable confidence cone and displays the probability of the forecast's accuracy. This tool provides traders with valuable insights into future price action, potentially enhancing decision-making in trading strategies.
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This script provides advanced functionality for traders who wish to explore price forecasting, and can be customized to fit various trading styles.
Market Movement After OpenDescription:
This script provides a detailed visualization of market movements during key trading hours: the German market opening (08:00–09:00 UTC+1) and the US market opening (15:30–16:30 UTC+1). It is designed to help traders analyze price behavior in these critical trading periods by capturing and presenting movement patterns and trends directly on the chart and in an interactive table.
Key Features:
Market Movement Analysis:
Tracks the price movement during the German market's first hour (08:00–09:00 UTC+1) and the US market's opening session (15:30–16:30 UTC+1).
Analyzes whether the price moved up or down during these intervals.
Visual Representation:
Dynamically colored price lines indicate upward (green) or downward (red) movement during the respective periods.
Labels ("DE" for Germany and "US" for the United States) mark key moments in the chart.
Historical Data Table:
Displays the past 10 trading days' movement trends in an interactive table, including:
Date: Trading date.
German Market Movement: Up (▲), Down (▼), or Neutral (-) for 08:00–09:00 UTC+1.
US Market Movement: Up (▲), Down (▼), or Neutral (-) for 15:30–16:30 UTC+1.
The table uses color coding for easy interpretation: green for upward movements, red for downward, and gray for neutral.
Real-Time Updates:
Automatically updates during live trading sessions to reflect the most recent movements.
Highlights incomplete periods (e.g., ongoing sessions) to indicate their status.
Customizable:
Suitable for intraday analysis or broader studies of market trends.
Designed to overlay directly on any price chart.
Use Case:
This script is particularly useful for traders who focus on market openings, which are often characterized by high volatility and significant price movements. By providing a clear visual representation of historical and live data, it aids in understanding and capitalizing on market trends during these critical periods.
Notes:
The script works best when the chart is set to the appropriate timezone (UTC+1 for the German market or your local equivalent).
For precise trading decisions, consider combining this script with other technical indicators or trading strategies.
Feel free to share feedback or suggest additional features to enhance the script!
IU open equal to high/low strategyIU open equal to high/low strategy:
The "IU Open Equal to High/Low Strategy" is designed to identify and trade specific market conditions where the day's first price action shows a strong directional bias. This strategy automatically enters trades based on the relationship between the market's open price and its first high or low of the day.
Entry Conditions:
1. Long Entry: A long position is initiated when the first open price of the session equals the day's first low. This signals a potential upward move.
2. Short Entry: A short position is initiated when the first open price of the session equals the day's first high. This signals a potential downward move.
Exit Conditions:
1. Stop Loss (SL): For both long and short trades, the stop loss is calculated based on the low or high of the candle where the position was entered.
2. Take Profit (TP): The take profit is set using a Risk-to-Reward (RTR) ratio, which is customizable by the user. The TP is calculated relative to the entry price and the distance between the entry and the stop loss.
Additional Features:
- Plots are used to visualize the entry price, stop loss, and take profit levels directly on the chart, providing clear and actionable insights.
- Labels are displayed to indicate the occurrence of the "Open == Low" or "Open == High" conditions for easier identification of potential trade setups.
- A dynamic fill highlights the areas between the entry price and the stop loss or take profit, offering a clear visual representation of the trade's risk and reward zones.
This strategy is designed for traders looking to capitalize on directional momentum at the start of the trading session. It is customizable, allowing users to set their desired Risk-to-Reward ratio and tailor the strategy to fit their trading style.
M2 Global Liquidity Index - Time-Shift - KHM2 Global Liquidity Index - Enhanced Time-Shift Indicator
Based on original work by @Mik3Christ3ns3n
Enhanced with advanced time-shift functionality and overlay capabilities.
Description:
This indicator tracks and visualizes the global M2 money supply from five major economies, allowing precise time-shift analysis for correlation studies. All values are converted to USD in real-time and aggregated to provide a comprehensive view of global liquidity conditions.
Key Features:
- Advanced time-shift capability (-1000 to +1000 days) with shape preservation
- Real-time currency conversion to USD
- Overlay functionality with main chart
- Right-scale display for better comparison
- Full historical data preservation during time shifts
Components Tracked:
- US M2 Money Supply (USM2)
- China M2 Money Supply (CNM2)
- Eurozone M2 Money Supply (EUM2)
- Japan M2 Money Supply (JPM2)
- UK M2 Money Supply (GBM2)
Primary Use Cases:
1. Correlation Analysis:
- Compare global liquidity trends with asset prices
- Identify leading/lagging relationships through time-shift
- Study monetary policy impacts across different time periods
2. Market Analysis:
- Track global liquidity conditions
- Monitor central bank policy effects
- Identify potential macro trend changes
Settings:
- Time Offset: Shift the M2 data backwards or forwards (-1000 to +1000 days)
- Positive values: Move M2 data into the future
- Negative values: Move M2 data into the past
- Zero: Current alignment
Technical Notes:
- Data updates follow central banks' M2 publication schedules
- All currency conversions performed in real-time
- Historical shape preservation during time-shifts
- Enhanced data consistency through lookahead mechanism
Credits:
Original concept and base code by @Mik3Christ3ns3n
Enhanced version includes advanced time-shift capabilities and shape preservation
License:
Pine Script™ code is subject to the terms of the Mozilla Public License 2.0
#M2 #GlobalLiquidity #MoneySupply #Macro #CentralBanks #MonetaryPolicy #TimeShift #Correlation #TradingIndicator #MacroAnalysis #LiquidityAnalysis #MarketIndicator
PO3 ExotradesPO3 Exotrades Indicator
The PO3 Exotrades indicator is designed to provide an advanced and customizable way to visualize market trends on higher timeframes. It displays scaled and color-coded candles with precise wick and body structures for better chart analysis. This indicator is ideal for traders who want to analyze and monitor higher timeframe (HTF) market data directly on lower timeframe charts.
Key Features:
Scaled and Customizable Candles: The indicator allows for adjustable candle size and spacing, making it suitable for different trading styles and preferences. You can scale up or down the candle body width while maintaining the original height to ensure accurate visual representation.
Color-Coding for Market Direction: The indicator automatically colors the body of the candles based on the market's trend. Green represents a bullish candle, while red represents a bearish candle, giving quick visual cues for price movement direction.
Wicks Visualization: The indicator also visualizes the wicks of the candles, providing detailed insight into price action and volatility. Wicks can be color-customized for both bullish and bearish movements.
Timeframe Customization: You can set the timeframe (TF) to your preferred value, allowing for flexibility in analyzing high timeframe candles on lower timeframe charts.
Chart Trading (CRT) Friendly: Ideal for Chart Trading (CRT), the indicator's clean and clear visuals help traders spot key market signals more effectively, making it a perfect tool for those who engage in intra-day or long-term chart trading.
User-Friendly Adjustments: Customize the appearance of the candles, wicks, and their spacing to suit your preferences, enhancing your chart analysis and trading strategy.
How to Use:
Apply the PO3 Exotrades indicator to your chart.
Adjust the scale to increase or decrease the candle width for better visual clarity.
Use the indicator's color-coded candles to identify bullish and bearish market conditions quickly.
Analyze the wick structures to understand volatility and price action during key market movements.
Leverage the HTF data on lower timeframes to align your trading strategies with higher timeframe trends, optimizing your entries and exits.
Whether you're a scalper, day trader, or swing trader, the PO3 Exotrades indicator enhances your technical analysis and provides an edge in your trading decisions by visualizing HTF data in a clear and actionable way.
Cumulative volume analysisAfter user define the ragion area. While break out the region area, you can try this indicator to notice when the power is ended and maybe reverse the trend.
1. The user enters the start time, and end time
2. The indicator will record the highest, lowest price, and cumulative amount during this period.
The cumulative amount is calculated by adding up the amount of each K bar, regardless of whether it rises or falls.
3. When the cumulative amount is reduced to less than or equal to 0, make a plotshape arrow
4.There are two diferent method to record the volume. Try the better way to different product.
Pivot Highs/Lows with Bar CountsWhat does the indicator do?
This indicator adds labels to a chart at swing (a.k.a., "pivot") highs and lows. Each label may contain a date, the closing price at the swing, the number of bars since the last swing in the same direction, and the number of bars from the last swing in the opposite direction. A table is also added to the chart that shows the average, min, and max number of bars between swings.
OK, but how do I use it?
Many markets -- especially sideways-moving ones -- commonly cycle between swing highs and lows at regular time intervals. By measuring the number of bars between highs and lows -- both same-sided swings (i.e., H-H and L-L) and opposite-sided swings (i.e., H-L and L-H) -- you can then project the averages of those bar counts from the last high or low swing to make predictions about where the next swing high or low should occur. Note that this indicator does not make the projection for you. You have to determine which swing you want to project from and then use the bar counts from the indicator to draw a line, place a label, etc.
Example: Chart of BTC/USD
The indicator shows pivot highs and lows with bar counts, and it displays a table of stats on those pivots.
If you focus on the center section of the chart, you can see that prices were moving in a sideways channel with very regular highs and lows. This indicator counts the bars between these pivots, and you could have used those counts to predict when the next high or low may have occurred.
The bar counts do not work as well on the more recent section of the chart because there are no regularly time swings.
Mean Reversion IndicatorSMA with Deviation and Z-Score Indicator
Overview:
This indicator combines the Simple Moving Average (SMA) with statistical measures of price deviation to identify potential buy and sell signals based on mean reversion principles. It calculates the Z-Score, which quantifies how far the current price is from its moving average in terms of standard deviations, helping traders spot when an asset might be overbought or oversold.
Key Features:
SMA Calculation: Uses a user-defined period to compute a Simple Moving Average, providing a baseline for price movement.
Z-Score: Measures the number of standard deviations the current price is from the SMA. This is crucial for identifying extreme price movements.
Formula: Z-Score = (Current Price - SMA) / Standard Deviation
Signal Generation:
Buy Signal: Generated when the Z-Score falls below a predefined threshold, suggesting the price is significantly below its mean and potentially undervalued.
Sell Signal: Triggered when the Z-Score exceeds another threshold, indicating the price is significantly above its mean and possibly overvalued.
Visual Indicators:
SMA Line: Plotted in blue on the chart for easy reference.
Z-Score Line: Available but hidden by default, can be shown if needed for deeper analysis.
Buy/Sell Signals: Represented by green up-arrows for buy signals and red down-arrows for sell signals.
Background Color: Changes to green or red subtly to indicate buy or sell zones based on Z-Score thresholds.
Z-Score Label: Provides the numerical Z-Score for each bar, aiding in precise decision-making.
Customizable Parameters:
SMA Length: Adjust the period over which the SMA is calculated.
Lookback Period: Set the number of periods for calculating the standard deviation and Z-Score.
Buy/Sell Z-Scores: Thresholds for generating buy and sell signals can be tailored to your strategy or market conditions. FX:EURUSD FX:EURUSD
Usage Tips:
This indicator is best used in conjunction with other forms of analysis for confirmation. Mean reversion does not always hold in trending markets.
Adjust the Z-Score thresholds based on asset volatility for more or less frequent signals.
Backtest with historical data to optimize settings for your specific trading approach.
Note: While this indicator can help identify potential trading opportunities based on statistical anomalies, it does not guarantee success and should be part of a broader trading strategy that includes risk management and market context understanding.
AHR999X IndexAHR999X Index - A Tool to Watch BITSTAMP:BTCUSD Bitcoin Tops
The AHR999X Index is designed as an extension of the well-known AHR999 Index, specifically to help identify Bitcoin's market tops. This index combines two critical components:
200-Day Fixed Investment Cost:
The average cost if you invested a fixed amount into Bitcoin every day over the last 200 days (using a geometric mean).
Growth Estimate:
A price estimate derived from a logarithmic regression model based on Bitcoin's age.
The formula for AHR999X is:
AHR999X = (Bitcoin Price ÷ 200-Day Fixed Investment Cost) × (Bitcoin Price ÷ Growth Estimate) × 3
How to Interpret AHR999X
Above 8: Accumulation Zone – Bitcoin is historically undervalued.
Between 0.45 and 8: Neutral Zone – Bitcoin is within a reasonable price range.
Below 0.45: Exit Zone – Historically signals market tops and high-risk areas.
A Cycle Observation
One important point to note:
The bottom value of AHR999X increases with every Bitcoin market cycle.
This reflects Bitcoin's long-term price appreciation and diminishing volatility over time.
Market Open Levels v3This indicator "Market Open Levels v3" allows a chart user to automatically display up to 20 previous price levels at the open price of up to 8 different markets simultaneously on one indicator.
The user can specify custom labels for each market's price level, as well as adjust the GMT Offset to allow for market open times in a different timezone than the chart's displayed time.
Displays price level at specified market open times. For instance, if a user specifies a market opens at 08:00, then a price level (horizontal line) will be drawn at the most recent 08:00 candle's open price (if GMT Offset is set to 0).
See tooltips for more information on specific inputs.
Three Step Future-Trend [BigBeluga]Three Step Future-Trend by BigBeluga is a forward-looking trend analysis tool designed to project potential future price direction based on historical periods. This indicator aggregates data from three consecutive periods, using price averages and delta volume analysis to forecast trend movement and visualize it on the chart with a projected trend line and volume metrics.
🔵 Key Features:
Three Period Analysis: Calculates price averages and delta volumes from three specified periods, creating a consolidated view of historical price movement.
Future Trend Line Projection: Plots a forward trend line based on the calculated averag of three periods, helping traders visualize potential future price movement.
Avg Delta Volume and Future Price Label: Shows a delta average Volume a long with a Future Price label at the end of the projected trend line, indicating the possible future delta volume and future Price.
Volume Data Table: Displays a detailed table showing delta and total volume for each of the three periods, allowing quick volume comparison to support the projected trend.
This indicator provides a dynamic way to anticipate market direction by blending price and volume data, giving traders insights into both volume and trend strength in upcoming periods.
DCA Order Info PlannerDescription :
This script is a Dollar-Cost Averaging (DCA) order planner designed for SPOT, LONG, and SHORT markets. It automatically calculates the optimal price levels for your orders based on configurable parameters, while also considering leverage and liquidation price.
🔹 Key Features:
1. Automatic Order Planning:
- The script calculates price levels for your orders based on an adjustable scaling coefficient (default: 1.5).
- You can set the percentage interval between each order (default: 2%).
- Displays the number of units to buy/sell at each level.
2.Leverage Management:
- Integrates a configurable leverage and computes the liquidation price for LONG and SHORT positions.
3.Clear Visual Display:
- Markers on the chart indicating order levels with customizable labels.
- A summary table shows price levels and corresponding quantities.
- Visualizes Stop Loss and Take Profit levels if defined.
4.Automatic Alerts:
- Sends alerts when the price reaches an order level.
🔹 Customizable Parameters:
- Starting Price: Initial price for calculating orders.
- Budget: Total budget for DCA orders.
- Leverage: Multiplier for LONG/SHORT positions.
- Scaling Coefficient: Adjusts the spacing between order levels.
- Maximum DCA Levels: Limits the number of generated orders.
🔹 How to Use:
1. Configure the parameters according to your strategy.
2. The script displays order levels and quantities on the chart.
3. Use the summary table to manually input orders on your favorite trading platform.
This script is particularly useful in volatile market conditions to average your entry or exit price and manage risk effectively.
IU Opening range Breakout StrategyIU Opening Range Breakout Strategy
This Pine Script strategy is designed to capitalize on the breakout of the opening range, which is a popular trading approach. The strategy identifies the high and low prices of the opening session and takes trades based on price crossing these levels, with built-in risk management and trade limits for intraday trading.
Key Features:
1. Risk Management:
- Risk-to-Reward Ratio (RTR):
Set a customizable risk-to-reward ratio to calculate target prices based on stop-loss levels.
Default: 2:1
- Max Trades in a Day:
Specify the maximum number of trades allowed per day to avoid overtrading.
Default: 2 trades in a day.
- End-of-Day Close:
Automatically closes all open positions at a user-defined session end time to ensure no overnight exposure.
Default: 3:15 PM
2. Opening Range Identification
- Opening Range High and Low:
The script detects the high and low of the first trading session using Pine Script's session functions.
These levels are plotted as visual guides on the chart:
- High: Lime-colored circles.
- Low: Red-colored circles.
3. Trade Entry Logic
- Long Entry:
A long trade is triggered when the price closes above the opening range high.
- Entry condition: Crossover of the price above the opening range high.
-Short Entry:
A short trade is triggered when the price closes below the opening range low.
- Entry condition: Crossunder of the price below the opening range low.
Both entries are conditional on the absence of an existing position.
4. Stop Loss and Take Profit
- Long Position:
- Stop Loss: Previous candle's low.
- Take Profit: Calculated based on the RTR.
- **Short Position:**
- **Stop Loss:** Previous candle's high.
- **Take Profit:** Calculated based on the RTR.
The strategy plots these levels for visual reference:
- Stop Loss: Red dashed lines.
- Take Profit: Green dashed lines.
5. Visual Enhancements
-Trade Level Highlighting:
The script dynamically shades the areas between the entry price and SL/TP levels:
- Red shading for the stop-loss region.
- Green shading for the take-profit region.
- Entry Price Line:
A silver-colored line marks the average entry price for active trades.
How to Use:
1.Input Configuration:
Adjust the Risk-to-Reward ratio, max trades per day, and session end time to suit your trading preferences.
2.Visual Cues:
Use the opening range high/low lines and shading to identify potential breakout opportunities.
3.Execution:
The strategy will automatically enter and exit trades based on the conditions. Review the plotted SL and TP levels to monitor the risk-reward setup.
Important Notes:
- This strategy is designed for intraday trading and works best in markets with high volatility during the opening session.
- Backtest the strategy on your preferred market and timeframe to ensure compatibility.
- Proper risk management and position sizing are essential when using this strategy in live markets.
Quantify [Entry Model] | FractalystWhat’s the indicator’s purpose and functionality?
Quantify is a machine learning entry model designed to help traders identify high-probability setups to refine their strategies.
➙ Simply pick your bias, select your entry timeframes, and let Quantify handle the rest for you.
Can the indicator be applied to any market approach/trading strategy?
Absolutely, all trading strategies share one fundamental element: Directional Bias
Once you’ve determined the market bias using your own personal approach, whether it’s through technical analysis or fundamental analysis, select the trend direction in the Quantify user inputs.
The algorithm will then adjust its calculations to provide optimal entry levels aligned with your chosen bias. This involves analyzing historical patterns to identify setups with the highest potential expected values, ensuring your setups are aligned with the selected direction.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How can this indicator help me to refine my trading strategy?
1. Focus on Positive Expected Value
• The indicator evaluates every setup to ensure it has a positive expected value, helping you focus only on trades that statistically favor long-term profitability.
2. Adapt to Market Conditions
• By analyzing real-time market behavior and historical patterns, the algorithm adjusts its calculations to match current conditions, keeping your strategy relevant and adaptable.
3. Eliminate Emotional Bias
• With clear probabilities, expected values, and data-driven insights, the indicator removes guesswork and helps you avoid emotional decisions that can damage your edge.
4. Optimize Entry Levels
• The indicator identifies optimal entry levels based on your selected bias and timeframes, improving robustness in your trades.
5. Enhance Risk Management
• Using tools like the Kelly Criterion, the indicator suggests optimal position sizes and risk levels, ensuring that your strategy maintains consistency and discipline.
6. Avoid Overtrading
• By highlighting only high-potential setups, the indicator keeps you focused on quality over quantity, helping you refine your strategy and avoid unnecessary losses.
How can I get started to use the indicator for my entries?
1. Set Your Market Bias
• Determine whether the market trend is Bullish or Bearish using your own approach.
• Select the corresponding bias in the indicator’s user inputs to align it with your analysis.
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Let the Algorithm Analyze
• Quantify evaluates historical data and real-time price action to calculate probabilities and expected values.
• It highlights setups with the highest potential based on your selected bias and timeframes.
4. Refine Your Entries
• Use the insights provided—entry levels, probabilities, and risk calculations—to align your trades with a math-driven edge.
• Avoid overtrading by focusing only on setups with positive expected value.
5. Adapt to Market Conditions
• The indicator continuously adapts to real-time market behavior, ensuring its recommendations stay relevant and precise as conditions change.
How does the indicator calculate the current range?
The indicator calculates the current range by analyzing swing points from the very first bar on your charts to the latest available bar it identifies external liquidity levels, also known as BSLQ (buy-side liquidity levels) and SSLQ (sell-side liquidity levels).
What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "■" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
What does the multi-timeframe functionality offer?
You can incorporate up to 4 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
How does the Indicator Identifies Positive Expected Values?
Quantify instantly calculates whether a trade setup has the potential to generate positive expected value (EV).
To determine a positive EV setup, the indicator uses the formula:
EV = ( P(Win) × R(Win) ) − ( P(Loss) × R(Loss))
where:
- P(Win) is the probability of a winning trade.
- R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
- P(Loss) is the probability of a losing trade.
- R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value.
How can I know that the setup I'm going to trade with has a positive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable. In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request: The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
What machine learning techniques are used in Quantify?
Quantify offers two main machine learning approaches:
1. Adaptive Learning (Fixed Sample Size): The algorithm learns from the entire dataset without resampling, maintaining a stable model that adapts to the latest market conditions.
2. Bootstrap Resampling: This method creates multiple subsets of the historical data, allowing the model to train on varying sample sizes. This technique enhances the robustness of predictions by ensuring that the model is not overfitting to a single dataset.
How does machine learning affect the expected value calculations in Quantify?
Machine learning plays a key role in improving the accuracy of expected value (EV) calculations. By analyzing historical price action, liquidity hits, and market bias patterns, the model continuously adjusts its understanding of risk and reward, allowing the expected value to reflect the most likely market movements. This results in more precise EV predictions, helping traders focus on setups that maximize profitability.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
What is a trailing stoploss, and how does it work in Quantify?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
Why would a trader prefer a trailing stoploss based on liquidity levels instead of a fixed take-profit level?
Traders who use trailing stoplosses based on liquidity levels prefer this method because:
1. Market-Driven Flexibility: The stoploss follows the market structure rather than being static at a pre-defined level. This means the stoploss is less likely to be hit by small market fluctuations or false reversals. The stoploss remains adaptive, moving as the market moves.
2. Riding the Trend: Traders can capture more profit during a sustained trend because the trailing stop will adjust only when the trend starts to reverse significantly, based on key liquidity levels. This allows them to hold positions longer without prematurely locking in profits.
3. Avoiding Premature Exits: Fixed stoploss levels may exit a trade too early in volatile markets, while liquidity-based trailing stoploss levels respect the natural flow of price action, preventing the trader from exiting too soon during pullbacks or minor retracements.
🎲 Becoming the House: Gaining an Edge Over the Market
In American roulette, the casino has a 5.26% edge due to the presence of the 0 and 00 pockets. On even-money bets, players face a 47.37% chance of winning, while true 50/50 odds would require a 50% chance. This edge—the gap between the payout odds and the true probabilities—ensures that, statistically, the casino will always win over time, even if individual players win occasionally.
From a Trader’s Perspective
In trading, your edge comes from identifying and executing setups with a positive expected value (EV). For example:
• If you identify a setup with a 55.48% chance of winning and a 1:1 risk-to-reward (RR) ratio, your trade has a statistical advantage over a neutral (50/50) probability.
This edge works in your favor when applied consistently across a series of trades, just as the casino’s edge ensures profitability across thousands of spins.
🎰 Applying the Concept to Trading
Like casinos leverage their mathematical edge in games of chance, you can achieve long-term success in trading by focusing on setups with positive EV and managing your trades systematically. Here’s how:
1. Probability Advantage: Prioritize trades where the probability of success (win rate) exceeds the breakeven rate for your chosen risk-to-reward ratio.
• Example: With a 1:1 RR, you need a win rate above 50% to achieve positive EV.
2. Risk-to-Reward Ratio (RR): Even with a win rate below 50%, you can gain an edge by increasing your RR (e.g., a 40% win rate with a 2:1 RR still has positive EV).
3. Consistency and Discipline: Just as casinos profit by sticking to their mathematical advantage over thousands of spins, traders must rely on their edge across many trades, avoiding emotional decisions or overleveraging.
By targeting favorable probabilities and managing trades effectively, you “become the house” in your trading. This approach allows you to leverage statistical advantages to enhance your overall performance and achieve sustainable profitability.
What Makes the Quantify Indicator Original?
1. Data-Driven Edge
Unlike traditional indicators that rely on static formulas, Quantify leverages probability-based analysis and machine learning. It calculates expected value (EV) and confidence levels to help traders identify setups with a true statistical edge.
2. Integration of Market Structure
Quantify uses market structure liquidity levels to dynamically adapt. It identifies key zones like swing highs/lows and liquidity traps, enabling users to align entries and exits with where the market is most likely to react. This bridges the gap between price action analysis and quantitative trading.
3. Sophisticated Risk Management
The Kelly Criterion implementation is unique. Quantify allows traders to input their maximum allowed drawdown, dynamically adjusting risk exposure to maintain optimal position sizing. This ensures risk is scientifically controlled while maximizing potential growth.
4. Multi-Timeframe and Liquidity-Based Trailing Stops
The indicator doesn’t just suggest fixed profit-taking levels. It offers market structure-based trailing stop-loss functionality, letting traders ride trends as long as liquidity and probabilities favor the position, which is rare in most tools.
5. Customizable Bias and Adaptive Learning
• Directional Bias: Traders can set a bullish or bearish bias, and the indicator recalculates probabilities to align with the trader’s market outlook.
• Adaptive Learning: The machine learning model adapts to changes in data (via resampling or bootstrap methods), ensuring that predictions stay relevant in evolving markets.
6. Positive EV Focus
The focus on positive EV setups differentiates it from reactive indicators. It shifts trading from chasing signals to acting on setups that statistically favor profitability, akin to how professional quant funds operate.
7. User Empowerment
Through features like customizable timeframes, real-time probability updates, and visualization tools, Quantify empowers users to make data-informed decisions.
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Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
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