ZenAlgo - Advanced Open InterestZenAlgo - Advanced Open Interest combines open interest, price changes, and volume dynamics into a single, powerful TradingView indicator. By integrating these key market metrics and enhancing them with proprietary algorithms, it provides traders with actionable insights that streamline decision-making and enhance market analysis.
Features
Open Interest Change (%): Tracks changes in open interest, a key indicator of market participation and sentiment.
Price Change (%): Monitors price momentum, providing clarity on trend directions.
Volume Analysis: Aggregates upward and downward volume for detailed sentiment analysis.
Delta Calculation: Highlights the net difference between upward and downward volume, offering instant insights into buying or selling dominance.
Proprietary Trend Detection: Suggests "Long Enter," "Short Enter," "Long Close," or "Short Close" signals based on a synergy of open interest, price, and volume.
Market Sentiment Insights: Indicates whether new long or short positions dominate.
Customizable Display: Features themes, sizes, and positions for a tailored interface.
Added Value: Why Is This Indicator Original/Why Shall You Pay for This Indicator?
Integrated Synergy: Combining open interest, price, and volume into a single indicator reduces complexity and offers enhanced clarity. Instead of toggling between multiple charts, users receive actionable insights from a unified view.
Proprietary Rules-Based Algorithm: The algorithm synthesizes data from sub-indicators, creating trends and signals not available in free tools. For instance, the "Long Enter" or "Short Close" signals are generated by evaluating relationships between metrics, offering a predictive edge.
Enhanced Trend Confirmation: By correlating open interest changes with price movements and volume imbalances, the indicator provides a more robust confirmation of market trends compared to individual metrics.
Time-Saving and Simplicity: Freely available sub-indicators require manual setup, interpretation, and customization. ZenAlgo - Advanced Open Interest offers pre-configured analysis, reducing the learning curve and decision time.
Unique Customization: With themes, positions, and table sizes, users can adapt the interface to their preferences, enhancing usability.
How It Works
1. Open Interest and Price Change
Retrieves historical open interest and price data for the selected timeframe.
Calculates percentage changes between bars to indicate market participation (open interest) and directional momentum (price).
Combines these metrics to assess whether price movements are supported by increasing or decreasing participation.
2. Volume Aggregation
Splits the selected timeframe into smaller sub-timeframes to analyze granular volume data.
Aggregates upward (price closes above open) and downward (price closes below open) volumes, calculating their totals and percentage contributions to overall volume.
3. Delta Calculation
Computes Delta as the difference between upward and downward volume.
Highlights buyer or seller dominance using color-coded visuals for quick interpretation.
4. Trend Analysis
Uses a proprietary algorithm to classify market states:
"Long Enter": Rising price, increasing open interest, and dominant upward volume.
"Short Enter": Falling price, increasing open interest, and dominant downward volume.
Neutral States: Generated when no strong alignment is found among metrics.
5. Market Sentiment
Correlates open interest and price to indicate if new long or short positions dominate.
Outputs simplified insights like "More longs opened" or "Shorts closing."
6. Customizable Table
Displays real-time updates with user-controlled themes, sizes, and positions for a tailored experience.
Usage Examples
Detecting Bullish Trends: Identify "Long Enter" signals when open interest and price rise, supported by strong upward volume.
Spotting Bearish Reversals: Use "Short Enter" signals when price declines, open interest rises, and downward volume dominates.
Analyzing Volume Shifts: Leverage Delta to uncover significant shifts in buying or selling pressure.
Validating Trends: Use the combination of open interest and volume trends to confirm price movements.
Exiting Profitable Trades: Look for "Long Close" or "Short Close" signals to time exits during profit-taking phases.
Avoiding Choppy Markets: Use "Neutral" signals to stay out of indecisive markets and avoid unnecessary risks.
Identifying Sentiment Swings: Follow "Positions" insights to detect a transition in market dominance from longs to shorts or vice versa.
High-Volume Trend Confirmation: Confirm strong trends during high trading volumes.
Short-Term Scalping: Use sub-timeframes to spot rapid entry and exit points.
Event-Based Trading: Correlate indicator signals with major market events for timely trades.
Settings
ZenAlgo Theme: Toggle a branded theme for better visual integration.
Table Size: Adjust display size (Tiny, Small, Normal, Large) based on preference.
Table Position: Choose between four positions (e.g., Bottom Right, Top Left).
Table Mode: Switch between Dark and Light themes for optimal readability.
Important Notes
This indicator is a technical analysis tool and does not guarantee trading success. Use it with other indicators and fundamental analysis for a comprehensive strategy.
Always validate signals in conjunction with other market factors to ensure informed trading decisions.
Scenarios of Potential Underperformance:
Low-Volume Markets: Signals may lack reliability due to insufficient data granularity.
Extreme Volatility: Rapid price movements can distort short-term insights.
Exchange Variations: Data discrepancies between exchanges may affect calculations.
Choppy Markets: During indecisive phases, the indicator may generate more neutral signals.
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Dual Zigzag [Trendoscope®]🎲 Dual Zigzag indicator is built on recursive zigzag algorithm. It is very similar to other zigzag indicators published by us and other authors. However, the key point here is, the indicator draws zigzag on both price and any other plot based indicator on separate layouts.
Before we get into the indicator, here are some brief descriptions of the underlying concepts and key terminologies
🎯 Zigzag
Zigzag indicator breaks down price or any input series into a series of Pivot Highs and Pivot Lows alternating between each other. Zigzags though shows pivot high and lows, should not be used for buying at low and selling at high. The main application of zigzag indicator is for the visualisation of market structure and this can be used as basic building block for any pattern recognition algorithms.
🎯 Recursive Zigzag Algorithm
Recursive zigzag algorithm builds zigzag on multiple levels and each level of zigzag is based on the previous level pivots. The level zero zigzag is built on price. However, for level 1, instead of price level 0 zigzag pivots are used. Similarly for level 2, level 1 zigzag pivots are used as base.
🎲 Components Dual Zigzag Indicator
Here are the components of Dual zigzag indicator
Built in Oscillator - Indicator has built in oscillator options for plotting RSI (Relative Strength Index), MFI (Money Flow Index), cci (Commodity Channel Index) , CMO (Chande Momentum Oscillator), COG (Center of Gravity), and ROC (Rate of Change). Apart from the given built in oscillators, users can also use a custom external output as base. The oscillators are not printed on the price pane. But, printed on a separate indicator overlay.
Zigzag On Oscillator - Recursive zigzag is calculated and printed on the oscillator series. Each pivot high and pivot low also prints a label having the retracement ratios, and price levels at those points. Zigzag on the oscillator is also printed on the indicator overlay pane.
Zigzag on Price - Recursive zigzag calculated based on price and printed on the price pane. This is made possible by using force_overlay option present in the drawing objects. At each zigzag pivot levels, the label having price retracement ratios, and oscillator values are printed.
It is called dual zigzag because, the indicator calculates the zigzag on both price and oscillator series of values and prints them separately on different panes on the chart.
🎲 Indicator Settings
Settings include
Theme display settings to get the right colour combination to match the background.
Zigzag settings to be used for zigzag calculation and display
Oscillator settings to chose the oscillator to be used as base for 2nd zigzag
🎲 Applications
Useful in spotting divergences with both indicator and price having their own zigzag to highlight pivots
Spotting patterns in indicators/oscillators and correlate them with the patterns on price
🎲 Using External Input
If users want to use an external indicator such as OBV instead of the built in oscillators, then can do so by using the custom option.
Here is how this can be done.
Step1. Add both Dual Zigzag and the intended indicator (in this case OBV) on the chart. Notice that both OBV and Dual zigzag appear on different panes.
Step2. Edit the indicator settings of Dual zigzag and set custom indicator by selecting "custom" as oscillator name and then by setting the custom external indicator name and input.
Step 3. You would notice that the zigzag in Dual Zigzag indictor pane is already showing the zigzag pivots based on the OBV indicator and the price pivots display obv values at the pivot points. We can leave this as is.
Step 4. As an additional step, you can also merge the OBV pane and the Dual zigzag indicator pane into one by going into OBV settings and moving the indicator to above pane. Merge the scales so that there is no two scales on the same pane and the entire scale appear on the right.
At the end, you should see two panes - one with price and other with OBV and both having their zigzag plotted.
TradingIQ - Reversal IQIntroducing "Reversal IQ" by TradingIQ
Reversal IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade trend reversals in the market. By integrating artificial intelligence and IQ Technology, Reversal IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Reversal IQ
Reversal IQ integrates IQ Technology (AI) with the timeless concept of reversal trading. Markets follow trends that inevitably reverse at some point. Rather than relying on rigid settings or manual judgment to capture these reversals, Reversal IQ dynamically designs, creates, and executes reversal-based trading strategies.
Reversal IQ is designed to work straight out of the box. In fact, its simplicity requires just one user setting, making it incredibly straightforward to manage.
AI Aggressiveness is the only setting that controls how Reversal IQ works.
Traders don’t have to spend hours adjusting settings and trying to find what works best - Reversal IQ handles this on its own.
Key Features of Reversal IQ
Self-Learning Reversal Detection
Employs AI and IQ Technology to identify trend reversals in real-time.
AI-Generated Trading Signals
Provides reversal trading signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
AI-Determined Profit Target and Stop Loss
Position exit levels are clearly defined and calculated by the AI once the trade is entered.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Configurable AI Aggressiveness
Allows users to adjust the AI's aggressiveness to match their trading style and risk tolerance.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
IQ Channel
The IQ Channel represents what Reversal IQ considers a tradable long opportunity or a tradable short opportunity. The channel is dynamic and adjusts from chart to chart.
IQMA – Proprietary Moving Average
Introduces the IQ Moving Average (IQMA), designed to classify overarching market trends.
IQCandles – Trend Classification Tool
Complements IQMA with candlestick colors designed for trend identification and analysis.
How It Works
Reversal IQ operates on a straightforward heuristic: go long during an extended downside move and go short during an extended upside move.
What defines an "extended move" is determined by IQ Technology, TradingIQ's exclusive AI algorithm. For Reversal IQ, the algorithm assesses the extent to which historical high and low prices are breached. By learning from these price level violations, Reversal IQ adapts to trade future, similar violations in a recurring manner. It calculates a price area, distant from the current price, where a reversal is anticipated.
In simple terms, price peaks (tops) and troughs (bottoms) are stored for Reversal IQ to learn from. The degree to which these levels are violated by subsequent price movements is also recorded. Reversal IQ continuously evaluates this stored data, adapting to market volatility and raw price fluctuations to better capture price reversals.
What classifies as a price top or price bottom?
For Reversal IQ, price tops are considered the highest price attained before a significant downside reversal. Price bottoms are considered the lowest price attained before a significant upside reversal. The highest price achieved is continuously calculated before a significant counter trend price move renders the high price as a swing high. The lowest price achieved is continuously calculated before a significant counter trend price move renders the low price as a swing low.
The image above illustrates the IQ channel and explains the corresponding prices and levels
The blue lower line represents the Long Reversal Level, with the price highlighted in blue showing the Long Reversal Price.
The red upper line represents the Short Reversal Level, with the price highlighted in red showing the Short Reversal Price.
Limit orders are placed at both of these levels. As soon as either level is touched, a trade is immediately executed.
The image above shows a long position being entered after the Long Reversal Level was reached. The profit target and stop loss are calculated by Reversal IQ
The blue line indicates where the profit target is placed (acting as a limit order).
The red line shows where the stop loss is placed (acting as a stop loss order).
Green arrows indicate that the strategy entered a long position at the highlighted price level.
You can also hover over the trade labels to get more information about the trade—such as the entry price, profit target, and stop loss.
The image above demonstrates the profit target being hit for the trade. All profitable trades are marked by a blue arrow and blue line. Hover over the blue arrow to obtain more details about the trade exit.
The image above depicts a short position being entered after the Short Reversal Level was touched. The profit target and stop loss are calculated by the AI
The blue line indicates where the profit target is placed (acting as a limit order).
The red line shows where the stop loss is placed (acting as a stop loss order).
The image above shows the profit target being hit for the short trade. Profitable trades are indicated by a blue arrow and blue line. Hover over the blue arrow to access more information about the trade exit.
Long Entry: Green Arrow
Short Entry: Red Arrow
Profitable Trades: Blue Arrow
Losing Trades: Red Arrow
IQMA
The IQMA implements a dynamic moving average that adapts to market conditions by adjusting its smoothing factor based on its own slope. This makes it more responsive in volatile conditions (steeper slopes) and smoother in less volatile conditions.
The IQMA is not used by Reversal IQ as a trade condition; however, the IQMA can be used by traders to characterize the overarching trend and elect to trade only long positions during bullish conditions and only short positions during bearish conditions.
The IQMA is an adaptive smoothing function that applies a combination of multiple moving averages to reduce lag and noise in the data. The adaptiveness is achieved by dynamically adjusting the Volatility Factor (VF) based on the slope (derivative) of the price trend, making it more responsive to strong trends and smoother in consolidating markets.
This process effectively makes the moving average a self-adjusting filter, the IQMA attempts to track both trending and ranging market conditions by dynamically changing its sensitivity in response to price movements.
When IQMA is blue, an overarching uptrend is in place. When IQMA is red, an overarching downtrend is in place.
IQ Candles
IQ Candles are price candles color-coordinated with IQMA. IQ Candles help visualize the overarching trend and are not used by Reversal IQ to determine trade entries and trade exits.
AI Aggressiveness
Reversal IQ has only one setting that controls its functionality.
AI Aggressiveness controls the aggressiveness of the AI. This setting has three options: Sniper, Aggressive, and Very Aggressive.
Sniper Mode
In Sniper Mode, Reversal IQ will prioritize trading large deviations from established reversal levels and extracting the largest countertrend move possible from them.
Aggressive Mode
In Aggressive Mode, Reversal IQ still prioritizes quality but allows for strong, quantity-based signals. More trades will be executed in this mode with tighter stops and profit targets. Aggressive mode forces Reversal IQ to learn from narrower raw-dollar violations of historical levels.
Very Aggressive Mode
In Very Aggressive Mode, Reversal IQ still prioritizes the strongest quantity-based signals. Stop and target distances aren't inherently affected, but entries will be aggressive while prioritizing performance. Very Aggressive mode forces Reversal IQ to learn from narrower raw-dollar violations of historical levels and also forces it to embrace volatility more aggressively.
AI Direction
The AI Direction setting controls the trade direction Reversal IQ is allowed to take.
“Both” allows for both long and short trades.
“Long” allows for only long trades.
“Short” allows for only short trades.
Verifying Reversal IQ’s Effectiveness
Reversal IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in a table located in the top-right corner of your chart.
The image above shows the long strategy profit factor and the short strategy profit factor for Reversal IQ.
A profit factor greater than 1 indicates a strategy profitably traded historical price data.
A profit factor less than 1 indicates a strategy unprofitably traded historical price data.
A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data.
Using Reversal IQ
While Reversal IQ is a full-fledged trading system with entries and exits, it was designed for the manual trader to take its trading signals and analysis indications to greater heights - offering numerous applications beyond its built-in trading system.
The hallmark feature of Reversal IQ is its sniper-like reversal signals. While exits are dynamically calculated as well, Reversal IQ simply has a knack for "sniping" price reversals.
When performing live analysis, you can use the IQ Channel to evaluate price reversal areas, whether price has extended too far in one direction, and whether price is likely to reverse soon.
Of course, in times of exuberance or panic, price may push through the reversal levels. While infrequent, it can happen to any indicator.
The deeper price moves into the bullish reversal area (blue) the better chance that price has extended too far and will reverse to the upside soon. The deeper price moves into the bearish reversal area (red) the better chance that price has extended too far and will reverse to the downside soon.
Of course, you can set alerts for all Reversal IQ entry and exit signals, effectively following along its systematic conquest of price movement.
TradingIQ - Impulse IQIntroducing "Impulse IQ" by TradingIQ
Impulse IQ is an exclusive trading algorithm developed by TradingIQ, designed to trade breakouts and established trends. By integrating artificial intelligence and IQ Technology, Impulse IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Impulse IQ
Impulse IQ combines IQ Technology (AI) with the classic principles of trend and breakout trading. Recognizing that markets inherently follow trends that need to persist for significant price movements to unfold, Impulse IQ eliminates the need for rigid settings or manual intervention.
Instead, it dynamically develops, adapts, and executes trend-based trading strategies, enabling a more responsive approach to capturing meaningful market opportunities.
Impulse IQ is designed to work straight out of the box. In fact, its simplicity requires just one user setting, making it incredibly straightforward to manage.
Strategy type is the only setting that controls Impulse IQ’s functionality.
Traders don’t have to spend hours adjusting settings and trying to find what works best - Impulse IQ handles this on its own.
Key Features of Impulse IQ
Self-Learning Breakout Detection
Employs IQ Technology to identify breakouts.
AI-Generated Trading Signals
Provides breakout trading signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
AI-Determined Trailing Profit Target and Stop Loss
Position exit levels are clearly defined and calculated by the AI once the trade is entered.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
IQ Meter
The IQ Meter details where price is trading relative to a higher timeframe trend and lower timeframe trend. Fibonacci levels are interlaced along the meter, offering unique insights on trend retracement opportunities.
Self Learning, Multi Timeframe IQ Zig Zags
The Zig Zag IQ is a self-learning, multi-timeframe indicator that adapts to market volatility, providing a clearer representation of market movements than traditional zig zag indicators.
Dual Strategy Execution
Impulse IQ integrates two distinct strategy types: Breakout and Cheap (details explained later).
How It Works
Before diving deeper into Impulse IQ, it's essential to understand the core terminology:
Zig Zag IQ : A self-learning trend and breakout identification mechanism that serves as the foundation for Impulse IQ. Although it belongs to the “Zig Zag” class of technical indicators, it's powered by IQ Technology.
Impulse IQ : A self-learning trading strategy that executes trades based on Zig Zag IQ. Zig Zag IQ identifies market trends, while Impulse IQ adapts, learns, and executes trades based on these trend characterizations.
Impulse IQ operates on a simple heuristic: go long during upside volatility and go short during downside volatility, essentially capturing price breakouts.
The definition of a “price breakout” is determined by IQ Technology, TradingIQ's exclusive AI algorithm. In Impulse IQ, the algorithm utilizes two IQ Zig Zags (self-learning, multi-timeframe zig zags) to analyze and learn from market trends.
It identifies breakout opportunities by recognizing violations of established price levels marked by the IQ Zig Zags. Impulse IQ then adapts and evolves to trade similar future violations in a recurring and dynamic manner.
Put simply, IQ Zig Zags continuously learn from both historical and real-time price updates to adjust themselves for an "optimal fit" to price data. The aim is to adapt so that the marked price tops and bottoms, when violated, reveal potential breakout opportunities.
The strategy layer of IQ Zig Zags, known as Impulse IQ, incorporates an additional level of self-learning with IQ Technology. It learns from breakout signals generated by the IQ Zig Zags, enabling it to dynamically identify and signal tradable breakouts. Moreover, Impulse IQ learns from historical price data to manage trade exits.
All positions start with an initial fixed stop loss and a trailing stop target. Once the trailing stop target is reached, the fixed stop loss converts into a trailing stop, allowing Impulse IQ to remain in the breakout/trend until the trailing stop is triggered.
What Classifies as a Breakout, Price Top, and Price Bottom?
For Impulse IQ:
Price tops are considered the highest price achieved before a price bottom forms.
Price bottoms are the lowest price reached before a price top forms.
For price tops, the highest price continues to be calculated until a significant downside price move occurs. Similarly, for price bottoms, the lowest price is calculated until a significant upside price move happens.
What distinguishes Zig Zag IQ from other zig zag indicators is its unique mechanism for determining a "significant counter-trend price move." Zig Zag IQ evaluates multiple fits to identify what best suits the current market conditions. Consequently, a "significant counter-trend price move" in one market might differ in magnitude from what’s considered "significant" in another, allowing it to adapt to varying market dynamics.
For example, a 1% price move in the opposite direction might be substantial in one market but not in another, and Zig Zag IQ figures this out internally.
The image above illustrates the IQ Zig Zags in action. The solid Zig Zag IQ lines represent the most recent price move being calculated, while the dotted, shaded lines display historical price moves previously analyzed by IQ Zig Zag.
Notice how the green zig zag aligns with a larger trend, while the purple zig zag follows a smaller trend. This mechanism is crucial for generating breakout signals in Impulse IQ: for a position to be entered, the breakout of the smaller trend must occur in the same direction as the larger trend.
The image above depicts the IQ Meters—an exclusive TradingIQ tool designed to help traders evaluate trend strength and retracement opportunities.
When the lower timeframe Zig Zag IQ and the higher timeframe Zig Zag IQ are out of sync (i.e., one is uptrending while the other is downtrending, with no active positions), the meters display a neutral color, as shown in the image.
The key to using these meters is to identify trend unison and pinpoint key trend retracement entry opportunities. Fibonacci retracement levels for the current trend are interlaced along each meter, and the current price is converted to a retracement ratio of the trend.
These meters can mathematically determine where price stands relative to the larger and smaller trends, aiding in identifying entry opportunities.
The top of each meter indicates the highest price achieved during the current price move.
The bottom of each meter indicates the lowest price achieved during the current price move.
When both the larger and smaller trends are in sync and uptrending, or when a long position is active, the IQ meters turn green, indicating uptrend strength.
When both trends are in sync and downtrending, or when a short position is active, the IQ meters turn red, indicating downtrend strength.
The image above shows the Point of Change for both the larger and smaller Zig Zag IQ trends. A distinctive feature of Zig Zag IQ is its ability to calculate these turning points in advance—unlike most traditional zig zag indicators that lack predetermined turning points and often lag behind price movements. In contrast, Zig Zag IQ offers a minimal-lag trend detection capability, providing a more responsive representation of market trends.
Simply put, once the market Zig Zag anchors are touched, the corresponding Zig Zag IQ will change direction.
Trade Signals
Impulse IQ can trade in one of two ways: Entering breakouts as soon as they happen (Breakout Strategy Type) or entering the pullback of a price breakout (Cheap Strategy Type).
Generally, the Breakout Strategy type will take a greater number of trades and enter a breakout quicker. The Cheap Strategy type will usually take less trades, but potentially enter at a better time/price point, prior to the next leg up of a break up, or the next leg down of a break down.
Entry signals are given when price breaks out to the upside or downside for the "Breakout" strategy type, or for the "Cheap" strategy type, when price retraces to the level it broke out from!
Breakout Strategy Example
The image above demonstrates a long position entered and exited using the Breakout strategy. The price breakout level is marked by the dotted, horizontal green line, representing a previously established price high identified by IQ Zig Zag. Once the price breaks and closes above this level, a long position is initiated.
After entering a long position, Impulse IQ immediately displays the initial fixed stop price. As the price moves favorably for the long position, the trailing stop conversion level is reached, and the indicator switches to a trailing stop, as shown in the image. Impulse IQ continues to "ride the trend" for as long as it persists, exiting only when the trailing stop is triggered.
Cheap Strategy Example
The image above shows a short entry executed using the Cheap strategy. The aim of the Cheap strategy is to enter on a pullback before the breakout occurs. While this results in fewer trades if price doesn’t pull back before the breakout, it typically allows for a better entry time and price point when a pullback does happen.
The image above illustrates the remainder of the trade until the trailing stop was hit.
Green Arrow = Long Entry
Red Arrow = Short Entry
Blue Arrow = Trade Exit
Impulse IQ calculates the initial stop price and trailing stop distance before any entry signals are triggered. This means users don’t need to constantly tweak these settings to improve performance—Impulse IQ handles this process internally.
Verifying Impulse IQ’s Effectiveness
Impulse IQ automatically tracks its performance and displays the profit factor for both its long and short strategies, visible in a table located in the top-right corner of your chart.
The image above shows the profit factor for both the long and short strategies used by Impulse IQ.
A profit factor greater than 1 indicates that the strategy was profitable when trading historical price data.
A profit factor less than 1 indicates that the strategy was unprofitable when trading historical price data.
A profit factor equal to 1 indicates that the strategy neither gained nor lost money on historical price data.
Using Impulse IQ
While Impulse IQ functions as a comprehensive trading system with its own entry and exit signals, it was designed for the manual trader to take its trading signals and analysis indications to greater heights - offering numerous applications beyond its built-in trading system.
The standout feature of Impulse IQ is its ability to characterize and capitalize on trends. Keeping a close eye on “Breakout” labels and making use of the IQ meter is the best way to use Impulse IQ.
The IQ Meters can be used to:
Find entry points during trend retracements
Assess trend alignment across higher and lower timeframes
Evaluate overall trend strength, indicating where the price lies on both IQ Meters.
Additionally, "Break Up" and "Break Down" labels can be identified for anticipating breakouts. Impulse IQ self-learns to capture breakouts optimally, making these labels dynamic signals for predicting a breakout.
The Zig Zag IQ indicators are instrumental in characterizing the market's current state. As a self-learning tool, Zig Zag IQ constantly adapts to improve the representation of current price action. The price tops and bottoms identified by Zig Zag IQ can be treated as support/resistance and breakout levels.
Of course, you can set alerts for all Impulse IQ entry and exit signals, effectively following along its systematic conquest of price movement.
TradingIQ - Nova IQIntroducing "Nova IQ" by TradingIQ
Nova IQ is an exclusive Trading IQ algorithm designed for extended price move scalping. It spots overextended micro price moves and bets against them. In this way, Nova IQ functions similarly to a reversion strategy.
Nova IQ analyzes historical and real-time price data to construct a dynamic trading system adaptable to various asset and timeframe combinations.
Philosophy of Nova IQ
Nova IQ integrates AI with the concept of central-value reversion scalping. On lower timeframes, prices may overextend for small periods of time - which Nova IQ looks to bet against. In this sense, Nova IQ scalps against small, extended price moves on lower timeframes.
Nova IQ is designed to work straight out of the box. In fact, its simplicity requires just one user setting, making it incredibly straightforward to manage.
Use HTF (used to apply a higher timeframe trade filter) is the only setting that controls how Nova IQ works.
Traders don’t have to spend hours adjusting settings and trying to find what works best - Nova IQ handles this on its own.
Key Features of Nova IQ
Self-Learning Market Scalping
Employs AI and IQ Technology to scalp micro price overextensions.
AI-Generated Trading Signals
Provides scalping signals derived from self-learning algorithms.
Comprehensive Trading System
Offers clear entry and exit labels.
Performance Tracking
Records and presents trading performance data, easily accessible for user analysis.
Higher Timeframe Filter
Allows users to implement a higher timeframe trading filter.
Long and Short Trading Capabilities
Supports both long and short positions to trade various market conditions.
Nova Oscillator (NOSC)
The Nova IQ Oscillator (NOSC) is an exclusive self-learning oscillator developed by Trading IQ. Using IQ Technology, the NOSC functions as an all-in-one oscillator for evaluating price overextensions.
Nova Bands (NBANDS)
The Nova Bands (NBANDS) are based on a proprietary calculation and serve as a custom two-layer smoothing filter that uses exponential decay. These bands adaptively smooth prices to identify potential trend retracement opportunities.
How It Works
Nova IQ operates on a simple heuristic: scalp long during micro downside overextensions and short during micro upside overextensions.
What constitutes an "overextension" is defined by IQ Technology, TradingIQ's proprietary AI algorithm. For Nova IQ, this algorithm evaluates the typical extent of micro overextensions before a reversal occurs. By learning from these patterns, Nova IQ adapts to identify and trade future overextensions in a consistent manner.
In essence, Nova IQ learns from price movements within scalping timeframes to pinpoint price areas for capitalizing on the reversal of an overextension.
As a trading system, Nova IQ enters all positions using market orders at the bar’s close. Each trade is exited with a profit-taking limit order and a stop-loss order. Thanks to its self-learning capability, Nova IQ determines the most suitable profit target and stop-loss levels, eliminating the need for the user to adjust any settings.
What classifies as a tradable overextension?
For Nova IQ, tradable overextensions are not manually set but are learned by the system. Nova IQ utilizes NOSC to identify and classify micro overextensions. By analyzing multiple variations of NOSC, along with its consistency in signaling overextensions and its tendency to remain in extreme zones, Nova IQ dynamically adjusts NOSC to determine what constitutes overextension territory for the indicator.
When NOSC reaches the downside overextension zone, long trades become eligible for entry. Conversely, when NOSC reaches the upside overextension zone, short trades become eligible for entry.
The image above illustrates NOSC and explains the corresponding overextension zones
The blue lower line represents the Downside Overextension Zone.
The red upper line represents the Upside Overextension Zone.
Any area between the two deviation points is not considered a tradable price overextension.
When either of the overextension zones are breached, Nova IQ will get to work at determining a trade opportunity.
The image above shows a long position being entered after the Downside Overextension Zone was reached.
The blue line on the price scale shows the AI-calculated profit target for the scalp position. The redline shows the AI-calculated stop loss for the scalp position.
Blue arrows indicate that the strategy entered a long position at the highlighted price level.
Yellow arrows indicate a position was closed.
You can also hover over the trade labels to get more information about the trade—such as the entry price and exit price.
The image above depicts a short position being entered after the Upside Overextension Zone was breached.
The blue line on the price scale shows the AI-calculated profit target for the scalp position. The redline shows the AI-calculated stop loss for the scalp position.
Red arrows indicate that the strategy entered a short position at the highlighted price level.
Yellow arrows indicate that NOVA IQ exited a position.
Long Entry: Blue Arrow
Short Entry: Red Arrow
Closed Trade: Yellow Arrow
Nova Bands
The Nova Bands (NBANDS) are based on a proprietary calculation and serve as a custom two-layer smoothing filter that uses exponential decay and cosine factors.
These bands adaptively smooth the price to identify potential trend retracement opportunities.
The image above illustrates how to interpret NBANDS. While NOSC focuses on identifying micro overextensions, NBANDS is designed to capture larger price overextensions. As a result, the two indicators complement each other well and can be effectively used together to identify a broader range of price overextensions in the market.
While the Nova Bands are not part of the core heuristic and do not use IQ technology, they provide valuable insights for discretionary traders looking to refine their strategies.
Use HTF (Use Higher Timeframe) Setting
Nova IQ has only one setting that controls its functionality.
“Use HTF” controls whether the AI uses a higher timeframe trading filter. This setting can be true or false. If true, the trader must select the higher timeframe to implement.
No Higher TF Filter
Nova IQ operates with standard aggression when the higher timeframe setting is turned off. In this mode, it exclusively learns from the price data of the current chart, allowing it to trade more aggressively without the influence of a higher timeframe filter.
Higher TF Filter
Nova IQ demonstrates reduced aggression when the "Use HTF" (Higher Timeframe) setting is enabled. In this mode, Nova IQ learns from both the current chart's data and the selected higher timeframe data, factoring in the higher timeframe trend when seeking scalping opportunities. As a result, trading opportunities only arise when both the higher timeframe and the chart's timeframe simultaneously display overextensions, making this mode more selective in its entries.
In this mode, Nova IQ calculates NOSC on the higher timeframe, learns from the corresponding price data, and applies the same rules to NOSC as it does for the current chart's timeframe. This ensures that Nova IQ consistently evaluates overextensions across both timeframes, maintaining its trading logic while incorporating higher timeframe insights.
AI Direction
The AI Direction setting controls the trade direction Nova IQ is allowed to take.
“Trade Longs” allows for long trades.
“Trade Shorts” allows for short trades.
Verifying Nova IQ’s Effectiveness
Nova IQ automatically tracks its performance and displays the profit factor for the long strategy and the short strategy it uses. This information can be found in a table located in the top-right corner of your chart showing the long strategy profit factor and the short strategy profit factor.
The image above shows the long strategy profit factor and the short strategy profit factor for Nova IQ.
A profit factor greater than 1 indicates a strategy profitably traded historical price data.
A profit factor less than 1 indicates a strategy unprofitably traded historical price data.
A profit factor equal to 1 indicates a strategy did not lose or gain money when trading historical price data.
Using Nova IQ
While Nova IQ is a full-fledged trading system with entries and exits - it was designed for the manual trader to take its trading signals and analysis indications to greater heights, offering numerous applications beyond its built-in trading system.
The hallmark feature of Nova IQ is its to ignore noise and only generate signals during tradable overextensions.
The best way to identify overextensions with Nova IQ is with NOSC.
NOSC is naturally adept at identifying micro overextensions. While it can be interpreted in a manner similar to traditional oscillators like RSI or Stochastic, NOSC’s underlying calculation and self-learning capabilities make it significantly more advanced and useful than conventional oscillators.
Additionally, manual traders can benefit from using NBANDS. Although NBANDS aren't a core component of Nova IQ's guiding heuristic, they can be valuable for manual trading. Prices rarely extend beyond these bands, and it's uncommon for prices to consistently trade outside of them.
NBANDS do not incorporate IQ Technology; however, when combined with NOSC, traders can identify strong double-confluence opportunities.
[Pandora] Vast Volatility Treasure TroveINTRODUCTION:
Volatility enthusiasts, prepare for VICTORY on this day of July 4th, 2024! This is my "Vast Volatility Treasure Trove," intended mostly for educational purposes, yet these functions will also exhibit versatility when combined with other algorithms to garner statistical excellence. Once again, I am now ripping the lid off of Pandora's box... of volatility. Inside this script is a 'vast' collection of volatility estimators, reflecting the indicators name. Whether you are a seasoned trader destined to navigate financial strife or an eagerly curious learner, this script offers a comprehensive toolkit for a broad spectrum of volatility analysis. Enjoy your journey through the realm of market volatility with this code!
WHAT IS MARKET VOLATILITY?:
Market volatility refers to various fluctuations in the value of a financial market or asset over a period of time, often characterized by occasional rapid and significant deviations in price. During periods of greater market volatility, evolving conditions of prices can move rapidly in either direction, creating uncertainty for investors with results of sharp declines as well as rapid gains. However, market volatility is a typical aspect expected in financial markets that can also present opportunities for informed decision-making and potential benefits from the price flux.
SCRIPT INTENTION:
Volatility is assuredly omnipresent, waxing and waning in magnitude, and some readers have every intention of studying and/or measuring it. This script serves as an all-in-one armada of volatility estimators for TradingView members. I set out to provide a diverse set of tools to analyze and interpret market volatility, offering volatile insights, and aid with the development of robust trading indicators and strategies.
In today's fast-paced financial markets, understanding and quantifying volatility is informative for both seasoned traders and novice investors. This script is designed to empower users by equipping them with a comprehensive suite of volatility estimators. Each function within this script has been meticulously crafted to address various aspects of volatility, from traditional methods like Garman-Klass and Parkinson to more advanced techniques like Yang-Zhang and my custom experimental algorithms.
Ultimately, this script is more than just a collection of functions. It is a gateway to a deeper understanding of market volatility and a valuable resource for anyone committed to mastering the complexities of financial markets.
SCRIPT CONTENTS:
This script includes a variety of functions designed to measure and analyze market volatility. Where applicable, an input checkbox option provides an unbiased/biased estimate. Below is a brief description of each function in the original order they appear as code upon first publish:
Parkinson Volatility - Estimates volatility emphasizing the high and low range movements.
Alternate Parkinson Volatility - Simpler version of the original Parkinson Volatility that I realized.
Garman-Klass Volatility - Estimates volatility based on high, low, open, and close prices using a formula that adjusts for biases in price dynamics.
Rogers-Satchell-Yoon Volatility #1 - Estimates volatility based on logarithmic differences between high, low, open, and close values.
Rogers-Satchell-Yoon Volatility #2 - Similar estimate to Rogers-Satchell with the same result via an alternate formulation of volatility.
Yang-Zhang Volatility - An advanced volatility estimate combining both strengths of the Garman-Klass and Rogers-Satchell estimators, with weights determined by an alpha parameter.
Yang-Zhang (Modified) Volatility - My experimental modification slightly different from the Yang-Zhang formula with improved computational efficiency.
Selectable Volatility - Basic customizable volatility calculation based on the logarithmic difference between selected numerator and denominator prices (e.g., open, high, low, close).
Close-to-Close Volatility - Estimates volatility using the logarithmic difference between consecutive closing prices. Specifically applicable to data sources without open, high, and low prices.
Open-to-Close Volatility - (Overnight Volatility): Estimates volatility based on the logarithmic difference between the opening price and the last closing price emphasizing overnight gaps.
Hilo Volatility - Estimates volatility using a method similar to Parkinson's method, which considers the logarithm of the high and low prices.
Vantage Volatility - My experimental custom 'vantage' method to estimate volatility similar to Yang-Zhang, which incorporates various factors (Alpha, Beta, Gamma) to generate a weighted logarithmic calculation. This may be a volatility advantage or disadvantage, hence it's name.
Schwert Volatility - Estimates volatility based on arithmetic returns.
Historical Volatility - Estimates volatility considering logarithmic returns.
Annualized Historical Volatility - Estimates annualized volatility using logarithmic returns, adjusted for the number of trading days in a year.
If I omitted any other known varieties, detailed requests for future consideration can be made below for their inclusion into this script within future versions...
BONUS ALGORITHMS:
This script also includes several experimental and bonus functions that push the boundaries of volatility analysis as I understand it. These functions are designed to provide additional insights and also are my ideal notions for traders looking to explore other methods of volatility measurement.
VOLATILITY APPLICATIONS:
Volatility estimators serve a common role across various facets of trading and financial analysis, offering insights into market behavior. These tools are already in instrumental with enhancing risk management practices by providing a deeper understanding of market dynamics and the inherent uncertainty in asset prices. With volatility estimators, traders can effectively quantifying market risk and adjust their strategies accordingly, optimizing portfolio performance and mitigating potential losses. Additionally, volatility estimations may serve as indication for detecting overbought or oversold market conditions, offering probabilistic insights that could inform strategic decisions at turning points. This script
distinctly offers a variety of volatility estimators to navigate intricate financial terrains with informed judgment to address challenges of strategic planning.
CODE REUSE:
You don't have to ask for my permission to use/reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already.
TradesAI - Elite (Premium)This is an all-inclusive, premium indicator that focuses mainly on price action analysis, a form of looking at raw price data and market structure to analyze and capture areas of interest where price could react.
This indicator is a perfect trading companion that saves you a lot of time in trading price action. Some of the popular methods that use price action analysis are "Smart Money Concepts (SMC)", "Inner Circle Trader (ICT)", and "Institutional Trading".
🔶 POWERFUL TOOLS
The indicator combines three main tools as a trading suite:
Trendlines
Market Structure Breakouts (MSB)
Order Blocks (OBs) and Reversal Order Blocks (ROBs)
These 3 main tools are interconnected together. Below we go over each, and then explain how and why they are brought in together. Please also note that the indicator's settings have tooltips next to most of them, with more detailed information.
🔶 TRENDLINES
This indicator automatically draws the most relevant Trendlines from pivot high/pivot low (based on the defined settings) as origins, while keeping track of candle closes across these Trendlines to adjust or invalidate accordingly.
The indicator will draw all possible Trendlines up to the maximum allowed by TradingView's PineScript. It uses a bullish pivot high candle to draw downtrends, and a bearish pivot low candle to draw uptrends. The algorithm will draw the most suitable active Trendlines from those origin points.
The indicator takes the origin point as the first point of the Trendline, then starts looking for the immediate next same-type candle (bullish to bullish or bearish to bearish), to draw the Trendline between the origin candle and this newer candle.
An uptrend is a ray connecting two bearish candles, as long as the second candle has a Low higher than the low of the origin (first) candle. A downtrend is a ray connecting two bullish candles, as long as the second candle has a high lower than the high of the origin (first) candle.
Upon drawing, the indicator then starts monitoring and adjusting this Trendline, by keeping the origin always the same but changing the second point. The goal is to keep reducing the slope of the Trendline till it is at 0 degrees (horizontal line). That then makes the Trendline "final". Note that you have the option to keep all Trendlines or just show the final, in the settings.
So, the algorithm has three states for the Trendlines:
Initial: not tested, meaning price hasn't yet broken through it and closed a candle beyond it, to cause a re-adjustment of this Trendline.
Broken: a candle hard closed (opened and closed) across it but still, the direction of the trend is maintained with a new Trendline from the same origin – could be replaced (or kept on the chart as a "backside", which is what we call a broken Trendline to be tested from the opposite side) with a new Trendline from the same origin, to the newest candle that caused the break to happen, as then it becomes the new second point of that Trendline.
Final: a candle hard closed (opened and closed) across it and can't draw a new Trendline from the same origin maintaining the direction of the trend (so an uptrend becomes a downtrend or a downtrend becomes an uptrend at this point, which is not allowed). This marks the end of the Trendline adjustment for that origin.
To summarize the Trendlines algorithm, imagine starting from a candle and drawing the Trendline, then keep re-adjusting it to make its slope less and less, till it becomes a horizontal line. That's the final state.
Here is a step-by-step scenario to demonstrate the algorithm:
Notice how first an Uptrend (green ray) is drawn between point A origin pivot (picked by our smart algorithm) and point B, both marked by green arrows:
Uptrend then turned into backside (where it flips from diagonal support to resistance where liquidity potentially resides):
Then a new uptrend is drawn from the same point A origin pivot to a new point B matching the filters in settings.
Finally, it turns also into a backside and is considered final because no more uptrends could be drawn from the same point A origin point.
Unlike traditional Trendline tools, this indicator takes into account numerous rules for each candlestick to determine valid support and resistance levels, which act as liquidity zones.
Unlike conventional Trendline tools, this indicator allows the user to define the pivot point left and right length to capture the proper ones as origins, then automatically recognizes and extends lines from them as liquidity zones where a reaction is expected. Moreover, the indicator monitors those Trendlines in real-time to switch them from buying to selling zones, and vice-versa, as the price structure changes.
Features
Log vs. Linear scale switch to show different Trendlines accordingly. When updating the Trendlines, or deciding whether Touches/Hard Closes are met, it makes a difference.
Ability to show all forms of Trendlines, final Trendlines or just backside Trendlines.
Why is it used?
For experienced traders, it offers the advantage of time efficiency, while new traders can bypass the steep learning curve of drawing Trendlines manually, which could practically be drawn between any two candlesticks on the chart (many variations).
🔶 MARKET STRUCTURE BREAKOUT (MSB)
The Market Structure Breakouts (MSB) tool is a trading tool that detects specific patterns on trading charts and provides ‘take profit’ regions based on the extended direction of the identified pattern. A breakout is a potential trading opportunity that presents itself when an asset's price moves away from a zone of accumulation (i.e. above a resistance level or below a support level) on increasing volume. The most famous form of market structure breakout is double/triple tops/bottoms, or what is referred to as W or M breakouts.
See this example below of how our MSB smart algorithm picked the local bottom of INDEX:BTCUSD
Here is a step-by-step scenario to demonstrate the algorithm:
First, the algorithm picks the pivot points according to our Machine Learning (ML) model, which uses Average True Range (ATR) and Moving Averages of various types to decide. It will then signal a Market Structure Breakout (MSB):
You may either short (sell) this MSB towards the targets (dotted green lines) and/or buy (long) at the targets (dotted green lines). Usually, these targets provide scalp moves, according to our model, but they may also act as strong reversal points on the chart.
Unlike standard indicators, the MSB tool identifies patterns that may not appear in every time frame due to specific conditions that need to be met, including Average True Range (ATR) and Moving Averages at the time of creation. Once these patterns are identified, the tool gives ‘take profit’ regions in the direction of the trading pattern and even allows for trading in the opposite direction (contrarian/counter-trend scalps) once those regions are reached. A confirmed breakout has the potential to drive the price to these specific targets, calculated based on our Machine Learning (ML) model. The Targets are the measured moves placed from the breakout point.
Features
Log vs. Linear scale switch to show different MSBs accordingly based on the ratios.
Detects trading patterns with specific conditions.
Ability to specify how sensitive the pivot points are for capturing market structure breakouts.
Provides take profit regions in the extended direction of the pattern.
Allows for versatile trading styles by permitting trades in the opposite direction (contrarian or counter-trend) once the take profit region is reached.
Highlights 2 levels of interest for potential trade initiation (or as targets of the MSB move).
🔶 ORDER BLOCK (OB) and REVERSAL ORDER BLOCK (ROB)
Before diving deeper into OBs and ROBs, you may consider the following chart for a general understanding of price ladders, and how they break. This is a bearish price ladder leaving Lower Lows and Lower Highs after an initial Low and High (L->H->LL->LH). Bullish ladders are the opposite (H->L->HH->HL).
In this bearish ladder case, notice the numbers representing the highs made (being lower). While this is a clean structure, markets don't always create such clean ladders, but you may switch to a higher timeframe to see it in a clearer form (usually, you will be able to spot it there).
In SMC or ICT concepts, the "Break Of Structure (BOS)" is pretty much creating a new lower low (LL) for the bearish ladder (and the creation of a higher high (HH) for the bullish ladder). By doing so, markets are grabbing liquidity below these levels and could either continue the ladder or stop/flip it. This gives you the context of how the ladder prints.
Price usually ends the ladder with a "Change of Character (CHoCH)", which represents a BOS (to grab liquidity) followed by an aggressive move in the opposite direction, which could lead the market to close the gaps and balance out. It is considered a good practice to then target liquidity in the opposite direction when a CHoCH happens, meaning for a bearish ladder you may target the pivots marked by 3, 2 and 1 at the top (start of the ladder).
Now we move to Order Blocks (OBs) and Reversal Order Blocks (ROBs). Think of them as sniper zones or micro ladders inside the bigger ladder/structure.
Order Blocks are usually used as zones of support and resistance on a trading chart where liquidity is present, or what some traders call "potential institutional interest zones". Order Blocks can be observed at the beginning of these strong moves of BOS or the CHoCH, leaving behind a zone (one or more candles) to be revisited later to balance the market. Therefore, these are interesting levels to place Limit/Market orders (sell the peaks or buy the valleys) instead of doing so at the swing highs or swing lows of the ladder (where BOS or CHoCH happened). The idea here is that the price could go deep into the ladder's step (peak or valley), and by doing so, it usually goes to these zones.
A bullish Order Block (Valley-OB) is the last bearish candle of a downtrend before a sequence of bullish candles (thus forming a "Valley"). A bearish Order Block (Peak-OB) is the last bullish candle of an uptrend before a sequence of bearish candles (thus forming a "Peak"). Our indicator captures the full range zones of the OB meaning not only the last candle but the sequence of same-type candles immediately next to it, which creates a zone, thus the name "OB/ROB Zone". Not only does the tool mark those levels on the chart, but it also has a smart tracking algorithm to remove the appropriate levels dynamically. It will monitor, candle by candle, what is happening to all the OBs/ROBs, and update them according to how they are being tested/visited (eg. weak testing being a touch, and strong testing being a touch of the same colour candle).
Bullish Valley-OB:
Bearish Peak-OB:
The indicator follows our concept of "Zone Activation" to determine whether to mark zones with dashed or solid lines.
If we take a bearish Peak-OB as an example, notice how it first gets drawn with a dashed red line (as the algorithm monitors how far the price moved away from the zone):
As price moves away (distance based on our Machin Learning (ML) model), it turns into solid lines:
Some people prefer to enter market orders or limit (pending) orders close to the zone, while others wait for it to hit. You may wait for these zones to turn into solid lines (meaning that the price made a decent move away from it before revisiting it). It depends on your trading strategy.
When Order Block (OB) zones break instead of holding the ladder, they turn into what we call Reversal Order Blocks (ROB); our algorithm of flipping these zones where price could react from the other side of the OB. Our algorithm monitor and highlight the most suitable ones to trade, based on +30 conditions and variables by our Machine Learning (ML) models. Examples of ROBs in the SMC or ICT trading community are a "Breaker Block", a "Mitigation Block" or a "Unicorn Setup". However, our algorithm filters the zones based on many factors such as ratios of price movement before, inside and after these zones, along with many other factors.
The algorithm monitors the ratios of how price moved into and away from the OB/ROB, as well as the type of move happening, to then filter the ones that are considered of high probability to break/not do a reaction.
A bullish Valley-OB (green) turns into a bearish Valley-ROB (neon red) where you may short (sell), while a bearish Peak-OB (red) turns into a bullish Peak-ROB (neon green) where you may long (buy).
Example of a bullish Valley-OB that turned into a bearish Valley-ROB:
Features
Log vs. Linear scale switch to show OBs/ROBs accordingly based on the ratios and the price action around these zones (before and after creation).
Uses our Machine Learning (ML) model to determine relevant Order Blocks (OBs) to show or hide based on price action.
Considers distribution and accumulation candles to find relevant Order Blocks.
Various types of triggers to mark those Order Blocks and their zones: breakout, close, hard close (open and close) or full close (low, high, open and close).
Monitors the 1:1 expansion of price from key areas of interest, which would change the importance of the zones through our concept of “Zone Activation”.
Allows for customization in the settings to display different types of Order Blocks (e.g., tested or untested).
Marking and invalidating levels based on many variables, including single or multiple candle zones, touching/closing beyond specific levels, weak/strong testing criteria, price tolerance % (near a level), and many more.
Provides color-coded visual representation for easier interpretation.
Why is it used?
Order Blocks (OB) and Reversal Order Blocks (ROB) represent the building blocks of price ladders, in conjunction with Swing Highs and Swing Lows. By identifying where liquidity is potentially present, they become common targets for big market players. Additionally, they provide clear invalidation points based on various types of candle closes, such as hard closes or simply a candle close.
One strategy that could be used is to open positions at these OB or ROB Levels as long as the chart maintains the trend (ladder), for a potentially higher win rate (or against it for a quick scalp). Be mindful of the breaking of a ladder or the building of a new one. A ladder breaks with a hard close (open and close) of a candle across the closest two levels; a ladder builds by not breaking back down across the levels it has tested. By definition, strong ladders will have a few untested levels and come back to wick them but still retain the structure of the laddering direction (trending with Lower Lows + Lower Highs or Higher Lows + Higher Highs).
🔶 COMBINING ALL TOOLS
In summary, Trendlines could be great tools to give you a general context of whether the price is laddering up or down. Once you spot the ladder, your goal is to either trade in its direction (not to go against the trend) or to counter-trend trade (contrarian). To do so, you could use the MSB tool to spot these BOS/CHoCH. And to give you more precise entries, you may rely on the OB/ROB zones which usually mesh over the ladder, to provide a sniper entry!
🔶 RISK DISCLAIMER
Trading is risky, and most day traders lose money. The risk of loss in trading can be substantial. Decisions to buy, sell, hold or trade in securities, commodities and other investments involve risk and are best made based on the advice of qualified financial professionals. Past performance does not guarantee future results. All content is to be considered hypothetical, selected after the fact, in order to demonstrate our product and should not be construed as financial advice. You should therefore carefully consider whether such trading is suitable for you in light of your financial condition.
Machine Learning : Dominant Cycle Elastic Volume KNNAbout the Script
Dominant Cycle Elastic Volume KNN ,
is a non-parametric algorithm, which means that, initially it makes no assumptions about the underlying distribution of the time-series price as well as volume.
This approach gives it flexibility so that it can be used on a wide variety of securities at variety of timeframes.(even on lower timeframes such as seconds)
The main purpose of this indicator is to predict the trend of the underlying, by converging price, volume and dominant cycle as dimensions and generate signals of action.
Key terms :
Dominant cycle is a time cycle that has a greater influence on the overall behaviour of a system than other cycles.
The system uses Ehlers method to calculate Dominant Cycle/ Period.
Dominant cycle is used to determine the influencing period for the underlying.
Once the dominant cycle/ period is identified, it is treated as a dynamic length for considering further calculations
Elastic Volume MA is a volume based moving average which is generally used to converge the volume with price, the dominant period is used here as the length parameter
KNN K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm. K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.
So, K-NN is used here to classify the trend of the Dominant Cycle Elastic Volume, and Generate Signals on top of it
How to Use the Indicator ?
The Buy Signal Candle
The Sell Signal Candle
The Buy Setup
The Sell Setup
Stop and Reverse Structure
What Timeframes and Symbols can this indicator be used on ?
The above indicator can be used on any liquid security which has volume information intact with ticker
and it can be used on any timeframe, but the best timeframes are
The indicator can also be used as a trend confirmatory indicators on lower time frames, like 30second
The Script has provision for alerts
Two alerts are there :
Alert 1= "LONG CONDITION : DCEV-ML"
Alert 2= "SHORT CONDITION : DCEV-ML"
How to request for access ?
Simply private message me !
Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making
The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning.
The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis.
The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits.
█ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance
Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky.
Background Information:
SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets.
Uses in AI and Machine Learning:
In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include:
1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms.
2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks.
3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal.
4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions.
Application in Financial Markets and Economics:
Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include:
1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions.
2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models.
3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management.
Application in the Indicator:
In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades.
The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance.
Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals.
In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions.
█ Input Parameters
This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters.
Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process.
Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components.
Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations.
SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions.
Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal.
Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered.
Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction.
Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals.
Static Computation Parameters:
The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources.
█ A Note on Endpionted, Non-repainting Indicators
An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact.
When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future.
In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
Bogdan Ciocoiu - LitigatorDescription
The Litigator is an indicator that encapsulates the value delivered by the Relative Strength Index, Ultimate Oscillator, Stochastic and Money Flow Index algorithms to produce signals enabling users to enter positions in ideal market conditions. The Litigator integrates the value delivered by the above four algorithms into one script.
This indicator is handy when trading continuation/reversal divergence strategies in conjunction with price action.
Uniqueness
The Litigator's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for short term scalping (1-5 minutes).
In addition, the Litigator allows configuring the above four algorithms in such a way to coordinate signals by colour-coding or shape thickness to aid the user with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same, and in doing so, enabling users to plug them in/out as needed. This also includes ensuring the ratios of the shapes are similar (applicable to the same scale).
Open-source
The indicator uses the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
Bogdan Ciocoiu - MoonshotDescription
Moonshot is an indicator that encapsulates the value delivered by the TSI, MACD, Awesome Oscillator and CCI algorithms to produce signals to enable users to enter positions in ideal market conditions. Moonshot integrates the value delivered by the above four algorithms into one script.
This indicator is particularly useful when trading continuation/reversal divergence strategies.
Uniqueness
The Moonshot's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for 1-3 minute scalping techniques.
In addition, Moonshot allows swapping or furthermore configuring the above four algorithms in such a way to align signals by colour-coding or shape thickness to aid the users with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same (including the scale at which the shapes are shown) and, in doing so, enables users to plug them in/out as needed.
Open-source
The indicator leverages the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
The Chartless TraderThe Chartless Trader
The chartless trader is a trade management system designed to remove the randomness from the market. It is loosely based on the martingales betting system, but takes advantage of position sizing, minimum profit targets, dollar cost averaging, and trailing take profit.
The chart can be traded with or without a signal. There is a built in signal based on SB Master Chart's Buy the Dip algorithm.
The configurable settings include:
Account Value
Starting Account Value - This is the value of the account when you start using this system.
Current Cash - This is the amount of cash you have available to trade. This setting needs to be updated each time a trade is made.
TP/TTP Algo Settings
Take Profit % - This setting is otherwise known as minimum profit target. This algo will not advise you to sell or increase your trailing stop until this minimum profit target is met.
Trailing Stop % - This is the trailing stop. The default setting is 75%. As a basic example, if the stock is up 10%, the trailing stop would be set to 7.5% (10% * 75%). The algo may override and advise an alternative trailing stop should an overbought condition be detected.
DCA/BTD Algo
DCA/BTD Algo Time Frame - Default is 120 (2hrs). This algo looks for oversold periods on the 2h chart by default.
DCA % - The default for this setting is 5%. This is a trigger for the BTD Algo. The BTD algo will start looking for trades when the stock is 5% below your cost basis. This is to help you average down making it easier to turn a profit when the stock starts making gains.
Position #
The Chartless Trader supports a maximum of 20 symbols. This is a limitation of the security() function as a maximum of 40 calls are allowed and the script calls the security() function twice per symbol.
S# QTY - The number of open positions of the symbol. This has to be manually updated by the user after each buy/sell of the stock.
S# CB - This is the cost basis of the stock. Your broker should give you this after each buy/sell and it has to be updated here on the chart after each buy/sell.
S# TTP - The script will advise you to increase your Trailing Take Profit in your broker when its necessary. This should be updated manually after you update your order in your broker. This should be configured manually in your broker as a Stop Order.
Now that I have covered the configurable options, its important to understand the basis of this system. The martingales betting system is a system that seeks to double its position size each time you enter a losing trade. Eventually when you make a winning trade, it will be enough to cover the previous losses and net you one winning position.
Bet 1, lose 1, down 1.
Bet 2, lose 2, down 3.
Bet 4, lose 4, down 7.
Bet 8, lost 8, down 15.
Bet 16, win 16, up 1.
So the theory goes, if you have deep enough pockets, its a 100% win rate. Such a system is flawed and proven to cause an account to blow up given enough time. You can search Google/YouTube for others that have back tested the martingales system with stocks.
I advise that "The Chartless Trading" system be traded with a similar system, but instead of doubling your position, you simply increase your position size by 1%.
Bet 1%, lose 1%, down 1%
Bet 1%, lose 1%, down 2%
Bet 1%, lose 1%, down 3%.
In such a manner, your risk of ruin is significantly reduced. Lets say you lose 10 times in a row betting on a stock. You now have 10% of your account value in this particular stock. Because you only invested at times where you were more than 5% down and when an oversold position occurred, because of dollar cost averaging and buying during oversold periods, you may only be down 2-3% on your invested value. Eventually when the stock turns positive, you will have met your minimum profit target and the script will alert you to set a trailing stop. You log into your broker, set a stop loss and wait for it to either trigger or inform you to increase it again. Once the trailing stop is triggered, you deleverage the position by closing it and starting a single new position in either the same stock or a different one and the cycle repeats.
The key is to follow the stock down, follow it back up, and not back down. We repeat this cycle with many positions in many stocks to minimize risk and compound our balance sheet.
This is " The Chartless Trader ".
1920x1080p Monitor Required if using all 20 symbols.
The more symbols loaded, the longer the initial processing to load the table. Please be patient.
Directional AnalyzerThis script attempts to equip users with the necessary information about the direction of an instrument, and essentially it is a synergy of 3 algorithms.
The first algorithm (plotted as dots at level 0) studies the balance of delta volatility that constitutes the current bar and answers if bulls or bears are in control at that exact bar time
The second algorithm (plotted as an area) studies the development of delta volatility over the defined period by means of a polynomial regression. Effectively, it provides an overall picture of the trend strength.
The third algorithm (plotted as a line with arrow labels) utilizes simple elements of neural network in conjunction with some custom filters to predict the focal point that a trend will reverse its direction. This is predictive in nature, hence always adopt this with caution. While the labels display the predicted direction, the colors of the line also reflect the state of the current bar as well, adding to the confirmation of the first algorithm.
May you be on the right side of the trade.
Anticipated Market TypeDisplays the anticipated market type based on the last 300 bars of data:
Trending Market: High probability that the next bar will be in the same direction as previous. Best conditions for a trend trading strategy
Neutral Market: High probability that price is random - the next bar direction is a coin toss. Many "typical" indicators fail in a random market
Sideways Market: High probability that price is autoregressive and the next bar direction is opposite the previous - compressed markets often have sudden fast breakouts
This tool does not give you entries and exits, but assists in deciding to use a Trend-following or Mean-reverting strategy.
Blue (3.5-6) indicates a trending market.
Yellow (0-2.5) indicates a sideways market.
Green (2.5-3.5) indicates a random market
This algorithm tells you when it breaks down by indicating a Neutral/Random market.
In short, it can't say the market type and advises you to not trade or simply use another tool in the meantime.
I personally use this tool to configure my trading robots on a weekly basis. I combine manual TA and stats algos to
try and determine what type of market the next week holds, with a fair bit of success.
The algorithms incorporated are Market Meanness Index (which I've made Open Source) and Fractal Dimension , a significantly faster algo than the MMI, but using a different set of maths.
Cheers!
MyAlgo EXTREMEPLEASE READ THE ENTIRE POST BEFORE PURCHASING & USING THE MyAlgo Tool. Saves you and me some time in emails and messages. :)
This is the official version of MyAlgo EXTREME
PLEASE UNDERSTAND THAT THIS IS A DIFFERENT AND SEPARATE PRODUCT AND SCRIPT FROM "MyAlgo SLIM" FROM THE MyAlgo TRADING TOOL SERIES
Description
Buy & Sell Alerts can be set on all Tickers. This includes, but is not limited to Crypto, Commodities , FOREX, Equities and Indices. Also all candle Types are compatible.
Recommended Time-frames - Due to the complexity of MyAlgo-SLIM the user has a choice between three algorithms and is like that able to trade on all timeframes with the highest returns.
MyAlgo combines many different aspects at the same time, scans multiple other Algorithms and comes to a conclusion based on over 1350 lines of code.
It is based on Divergences, Elliott Waves , Ichimoku , MACD , MACD Histogram, RSI , Stoch , CCI , Momentum, OBV, DIOSC, VWMACD, CMF and multiple EMAs.
Every single aspect is weighted into the decision before giving out an indication.
Most buy/sell Algorithms FAIL because they try to apply the same strategy to every single chart, which
are as individual as humans. To conquer this problem, MyAlgo has a wide range of settings and variables which can be easily
modified.
To make it a true strategy, MyAlgo has as well settings for Take Profit Points and Stop
Losses. Everything with an Alert Feature of course so that FULL AUTOMATION IS POSSIBLE.
I know from experience that many people take one Algorithm and are simply too LAZY to add multiple Algorithms to make a rational choice. The result of that is that they lose money, by following blatantly only one Algorithm.
MyAlgo has additional 15 Indicators, perfect for all markets, which can be turned on and off individually.
Side Notes
MyAlgo is being updated and upgraded very frequently to suit the requests of our customers.
This is not financial advice. Please read our disclaimer before using it.
Please refer to the signature field if you are interested in gaining access to this script.
Anything below this sentence will be Updates regarding MyAlgo
SMU Stock ThermometerThis script shows various technical indicators in a stacked vertical candle called Market Termometer.
It helps to see the price action in one single vertical column where the actual price moves up or down. So you can see the price change based on your custom setting levels.
I've been studying ALGO for over a year and made many live experiment trades long and shorts. So, I'm trying to find a way to see what is ALGos next move. If it sounds far-fetch, then you should see my other published scripts.
Here is example of how ALGo dance around old indicators, which is why I started creating a bunch of new indicators that ALGO doesn't know
Example:
Impact-driven-algorithm= Large volume masked as small volume to keep the price at desired level. So, your chart says overbought but market doesn't drop for days
Cost-driven-algorithm= Hedge fund buy every time at lower price and prevent others to buy low, moving up fast. Is like a clock with millisecond timing and ALGO owners know when to buy low and when to sell high
If you have a good idea, let me know so i can include it the future versions.
Enjoy and think outside the box, the only way to beat the ALGO
Entries + FVG SignalsE+FVG: A Masterclass in Institutional Trading Concepts
Chapter 1: The Modern Trader's Dilemma—Decoding the Institutional Footprint
In the vast, often chaotic ocean of the financial markets, retail traders navigate with the tools they are given: conventional indicators like moving averages, RSI, and MACD. While useful for gauging momentum and general trends, these tools often fall short because they were not designed to interpret the primary force that moves markets: institutional order flow. The modern trader faces a critical challenge: the tools and concepts taught in mainstream trading education are often decades behind the sophisticated, algorithm-driven strategies employed by banks, hedge funds, and large financial institutions.
This leads to a frustrating cycle of seemingly inexplicable price movements. A trader might see a perfect breakout from a classic pattern, only for it to reverse viciously, stopping them out. They might identify a strong trend, yet struggle to find a logical entry point, consistently feeling "late to the party." These experiences are not random; they are often the result of institutional market manipulation designed to engineer liquidity.
The fundamental problem that E+FVG (Entries + FVG Signals) addresses is this informational asymmetry. It is a sophisticated, institutional-grade framework designed to move a trader's perspective from a retail mindset to a professional one. It does not rely on lagging, derivative indicators. Instead, it focuses on the two core elements of price action that reveal the true intentions of "Smart Money": liquidity and imbalances.
This is not merely another indicator to add to a chart; it is a complete analytical engine designed to help you see the market through a new lens. It deconstructs price action to pinpoint two critical things:
Where institutions are likely to hunt for liquidity (running stop-loss orders).
The specific price inefficiencies (Fair Value Gaps) they are likely to target.
By focusing on these core principles, E+FVG provides a logical, rules-based solution to identifying high-probability trade setups. It is built for the discerning trader who is ready to evolve beyond conventional technical analysis and learn a methodology that is aligned with how the market truly operates at an institutional level. It is, in essence, an operating system for "Smart Money" trading.
Chapter 2: The Core Philosophy—Liquidity is the Fuel, Imbalances are the Destination
To fully grasp the power of this tool, one must first understand its foundational philosophy, which is rooted in the core tenets of institutional trading, often referred to as Smart Money Concepts (SMC). This philosophy can be distilled into two simple, powerful ideas:
1. Liquidity is the Fuel that Moves the Market:
The market does not move simply because there are more buyers than sellers, or vice-versa. It moves to seek liquidity. Large institutions cannot simply click "buy" or "sell" to enter or exit their multi-million or billion-dollar positions. Doing so would cause massive slippage and alert the entire market to their intentions. Instead, they must strategically accumulate and distribute their positions in areas where there is a high concentration of orders.
Where are these orders located? They are clustered in predictable places: above recent swing highs (buy-stop orders from shorts, and breakout buy orders) and below recent swing lows (sell-stop orders from longs, and breakout sell orders). This collective pool of orders is called liquidity. Institutions will often drive price towards these liquidity pools in a "stop hunt" or "liquidity grab" to trigger those orders, creating the necessary volume for them to fill their own large positions, often in the opposite direction of the liquidity grab itself. Understanding this concept is the key to avoiding being the "fuel" and instead learning to trade alongside the institutions.
2. Imbalances (Fair Value Gaps) are the Magnets for Price:
When institutions enter the market with overwhelming force, they create an imbalance in the order book. This energetic, one-sided price movement often leaves behind a gap in the market's pricing mechanism. On a candlestick chart, this appears as a Fair Value Gap (FVG)—a three-candle formation where the wicks of the first and third candles do not fully overlap the range of the middle candle.
These are not random gaps; they represent an inefficiency in the market's price delivery. The market, in its constant quest for equilibrium, has a natural tendency to revisit these inefficiently priced areas to "rebalance" the order book. Therefore, FVGs act as powerful magnets for price. They serve as high-probability targets for a price move and, critically, as logical points of interest where price may reverse after filling the imbalance. A fresh, unfilled FVG is one of the most significant clues an institution leaves behind.
E+FVG is built entirely on this philosophy. The "Entries Simplified" engine is designed to identify the liquidity grabs, and the "FVG Signals" engine is designed to identify the imbalances. Together, they provide a complete, synergistic framework for institutional-grade analysis.
Chapter 3: The Engine, Part I—"Entries Simplified": A Framework for Precision Entry
This is the primary trade-spotting engine of the E+FVG tool. It is a multi-layered system designed to identify a very specific, high-probability entry model based on institutional behavior. It filters out market noise by focusing solely on the sequence of a liquidity sweep followed by a clear and energetic displacement.
Feature 1: The Multi-Timeframe Liquidity Engine
The first and most crucial step in the engine's logic is to identify a valid liquidity grab. The script understands that the most significant reversals are often initiated after price has swept a key high or low from a higher timeframe. A sweep of yesterday's high holds far more weight than a sweep of the last 5-minute high.
Automatic Timeframe Adaptation: The engine intelligently analyzes your current chart's timeframe and automatically selects an appropriate higher timeframe (HTF) for its core analysis. For instance, if you are on a 15-minute chart, it might reference the 4-hour or Daily chart to identify key structural points. This is done seamlessly in the background, ensuring the analysis is always anchored to a significant structural context without requiring manual input.
The "Sweep" Condition: The script is not looking for a simple touch of a high or low. It is looking for a definitive sweep (also known as a "stop hunt" or "Judas swing"). This is defined as price pushing just beyond a key prior candle's high or low and then closing back within its range. This specific price action pattern is a classic signature of a liquidity grab, indicating that the move's purpose was to trigger stops, not to start a new, sustained trend. The "Entries Simplified" engine is constantly scanning the HTF price action for these sweep events, as they are the necessary precondition for any potential setup.
Feature 2: The Upshift/Downshift Signal—Confirming the Reversal
Once a valid HTF liquidity sweep has occurred, the engine moves to its next phase: identifying the confirmation. A sweep alone is not enough; institutions must show their hand and reveal their intention to reverse the market. This confirmation comes in the form of a powerful structural breakout (for bullish reversals) or breakdown (for bearish reversals). We call these events Upshifts and Downshifts.
Defining the Upshift & Downshift: This is the critical moment of confirmation, the market "tipping its hand."
An Upshift occurs after a liquidity sweep below a key low. Following the sweep, price reverses with energy and produces a decisive breakout to the upside, closing above a recent, valid swing high. This action confirms that the prior downtrend's momentum is broken, the downward move was a trap to engineer liquidity, and institutional buyers are now in aggressive control.
A Downshift occurs after a liquidity sweep above a key high. Following the sweep, price reverses aggressively and produces a sharp breakdown to the downside, closing below a recent, valid swing low. This confirms that the prior uptrend's momentum has failed, the upward move was a liquidity grab, and institutional sellers have now taken control of the market.
Algorithmic Identification: The E+FVG engine uses a proprietary algorithm to identify these moments. It analyzes the candle sequence immediately following a sweep, looking for a specific type of market structure break characterized by high energy and displacement—often leaving imbalances (Fair Value Gaps) in its wake. This is not a simple "pivot break"; the algorithm is designed to distinguish between a weak, indecisive wiggle and a true, institutionally-backed Upshift or Downshift.
The Signal: When this precise sequence—a HTF liquidity sweep followed by a valid Upshift or Downshift on the trading timeframe—is confirmed, the indicator plots a clear arrow on the chart. A green arrow below a low signifies a Bullish setup (confirmed by an Upshift), while a red arrow above a high signifies a Bearish setup (confirmed by a Downshift). This is the core entry signal of the "Entries Simplified" engine.
Feature 3: Automated Price Projections—A Built-In Trade Management Framework
A valid entry signal is only one part of a successful trade. A trader also needs a logical framework for taking profits. The E+FVG engine completes its trade-spotting process by providing automated, mathematically-derived price projections.
Fibonacci-Based Logic: After a valid Upshift or Downshift signal is generated, the script analyzes the price leg that created the setup (i.e., the range from the liquidity sweep to the confirmation breakout/breakdown). It then uses a methodology based on standard Fibonacci extension principles to project several potential take-profit (TP) levels.
Multiple TP Levels: The indicator projects four distinct TP levels (TP1, TP2, TP3, TP4). This provides a comprehensive trade management framework. A conservative trader might aim for TP1 or TP2, while a more aggressive trader might hold a partial position for the higher targets. These levels are plotted on the chart as clear, labeled lines, removing the guesswork from profit-taking.
Dynamic and Adaptive: These projections are not static. They are calculated uniquely for each individual setup, based on the specific volatility and range of the price action that generated the signal. This ensures that the take-profit targets are always relevant to the current market conditions.
The "Entries Simplified" engine, therefore, provides a complete, end-to-end framework: it waits for a high-probability condition (HTF sweep), confirms it with a specific entry model (Upshift/Downshift), and provides a logical road map for managing the trade (automated projections).
Chapter 4: The Engine, Part II—"FVG Signals": Mapping Market Inefficiencies
This second, complementary engine of the E+FVG tool operates as a market mapping system. Its sole purpose is to identify, plot, and monitor Fair Value Gaps (FVGs)—the critical price inefficiencies that act as magnets and potential reversal points.
Feature 1: Dual Timeframe FVG Detection
The significance of an FVG is directly related to the timeframe on which it forms. A 1-hour FVG is a more powerful magnet for price than a 1-minute FVG. The FVG engine gives you the ability to monitor both simultaneously, providing a richer, multi-dimensional view of the market's inefficiencies.
Chart TF FVGs: The indicator will, by default, identify and plot the FVGs that form on your current, active chart timeframe. These are useful for short-term scalping and for fine-tuning entries.
Higher Timeframe (HTF) FVGs: With a single click, you can enable the HTF FVG detection. This allows you to overlay, for example, 1-hour FVGs onto your 5-minute chart. This is an incredibly powerful feature. Seeing a 5-minute price rally approaching a fresh, unfilled 1-hour bearish FVG gives you a high-probability context for a potential reversal. The HTF FVGs act as major points of interest that can override the short-term price action.
Feature 2: The Intelligent "Tap-In" Logic—Beyond a Simple Touch
Many FVG indicators will simply alert you when price touches an FVG. The E+FVG engine employs a more sophisticated, two-stage logic to generate its signals, which helps to filter out weak reactions and focus on confirmed reversals.
Stage 1: The Entry. The first event is when price simply enters the FVG zone. This is a "heads-up" moment, and the indicator can be configured to provide an initial alert for this event.
Stage 2: The Confirmed "Tap-In." The official signal, however, is the "Tap-In." This is a more stringent condition. For a bullish FVG, a Tap-In is only confirmed after price has touched or entered the FVG zone and then closed back above the FVG's high. For a bearish FVG, the price must touch or enter the zone and then close back below the FVG's low. This confirmation logic ensures that the FVG has not just been touched, but has been respected and rejected by the market, making the resulting arrow signal significantly more reliable than a simple touch alert.
Feature 3: Interactive and Clean Visuals
The FVG engine is designed to provide maximum information with minimum chart clutter.
Clear, Color-Coded Boxes: Bullish FVGs are plotted in one color (e.g., green or blue), and bearish FVGs in another (e.g., red or orange), with a clear distinction between Chart TF and HTF zones.
Optional Box Display: Recognizing that some traders prefer a cleaner chart, you have the option to hide the FVG boxes entirely. Even with the boxes hidden, the underlying logic remains active, and the script will still generate the crucial Tap-In arrow signals.
Automatic Fading: Once an FVG has been successfully "tapped," the script can be set to automatically fade the color of the box. This provides a clear visual cue that the zone has been tested and may have less significance going forward.
Expiration: FVGs do not remain relevant forever. The script automatically removes old FVG boxes from the chart after a user-defined number of bars, ensuring your analysis is always focused on the most recent and relevant market inefficiencies.
Chapter 5: The Power of Synergy—How the Two Engines Work Together
While both the "Entries Simplified" engine and the "FVG Signals" engine are powerful standalone tools, their true potential is unlocked when used in combination. They are designed to provide confluence—a scenario where two or more independent analytical concepts align to produce a single, high-conviction trade idea.
Scenario A: The A+ Setup (Upshift into FVG). This is the highest probability setup. Imagine the "Entries Simplified" engine detects a HTF liquidity sweep below a key low, followed by a bullish Upshift signal. You look at your chart and see that this strong upward displacement is heading directly towards a fresh, unfilled bearish HTF FVG. This provides you with both a high-probability entry signal and a logical, high-probability target for the trade.
Scenario B: The FVG Confirmation. A trader might see the "Entries Simplified" engine generate a bearish Downshift signal. They feel it is a valid setup but want one extra layer of confirmation. They wait for price to rally a little further and "tap-in" to a nearby bearish FVG that formed during the Downshift's displacement. The FVG Tap-In signal then serves as their final confirmation trigger to enter the trade.
Scenario C: The Standalone FVG Trade. The FVG engine can also be used as a primary trading tool. A trader might notice that price is in a strong uptrend. They see price pulling back towards a fresh, bullish HTF FVG. They are not waiting for a full Upshift/Downshift setup; instead, they are simply waiting for the FVG Tap-In signal to confirm that the pullback is likely over and the trend is ready to resume.
By learning to read the interplay between these two engines, a trader can elevate their analysis from a one-dimensional process to a multi-dimensional, context-aware methodology.
Chapter 6: The Workflow—A Step-by-Step Guide to Practical Application
Step 1: The Pre-Market Analysis (Mapping the Battlefield). Before your session begins, enable the HTF FVG detection. Identify the key, unfilled HTF FVGs above and below the current price. These are your major points of interest for the day—your potential targets and reversal zones.
Step 2: Await the Primary Condition (Patience for Liquidity). During your trading session, your primary focus should be on the "Entries Simplified" engine. Your job is to wait patiently for the script to identify a valid HTF liquidity sweep. Do not force trades in the middle of a price range where no significant liquidity has been taken.
Step 3: The Upshift/Downshift Alert (The Call to Action). When the red or green arrow from the "Entries Simplified" engine appears, it is your cue to focus your attention. This is a potential high-probability setup.
Step 4: The Confluence Check (Building Conviction). With the Upshift or Downshift signal on your chart, ask the key confluence questions:
Did the displacement from the Upshift/Downshift create a new FVG?
Is the projected path of the trade heading towards a pre-identified HTF FVG?
Has an FVG Tap-In signal appeared shortly after the initial signal, offering further confirmation?
Step 5: Execute and Manage. If you have sufficient confluence, execute the trade. Use the automated price projections as your guide for profit-taking. A logical stop-loss is typically placed just beyond the high or low of the liquidity sweep that initiated the entire sequence.
Chapter 7: The Trader's Mind—Mastering the Institutional Mindset
This tool is more than a set of algorithms; it is a training system for professional trading psychology.
From Chasing to Trapping: You stop chasing breakouts and instead learn to identify where others are being trapped.
From FOMO to Patience: The strict, sequential logic of the entry model (Sweep -> Upshift/Downshift) forces you to wait for the highest quality setups, curing the Fear Of Missing Out.
Probabilistic Thinking: By focusing on liquidity and imbalances, you begin to think in terms of probabilities, not certainties. You understand that you are putting on trades where the odds are statistically in your favor, which is the cornerstone of any professional trading career.
Clarity and Confidence: The clear, rules-based signals remove ambiguity and second-guessing. This builds the confidence needed to execute trades decisively when the opportunity arises.
Chapter 8: Frequently Asked Questions & Scenarios
Q: The "Entries Simplified" code looks complex. Do I need to understand all of it?
A: No. The engine is designed to perform its complex analysis in the background. Your job is to understand the principles—liquidity sweep and the resulting Upshift or Downshift—and to recognize the clear arrow signals that the script generates when those conditions are met.
Q: Can I turn one of the engines off?
A: Yes, the indicator is modular. If you only want to focus on Fair Value Gaps, for example, you can disable the plot shapes for the "Entries Simplified" signals in the settings, and vice-versa.
Q: Does this work on all assets and timeframes?
A: The principles of liquidity and imbalance are universal and apply to all markets, from cryptocurrencies to forex to indices. The fractal nature of the analysis means the concepts are valid on all timeframes. However, it is always recommended that a trader backtest and forward-test the tool on their specific instrument and timeframe of choice to understand its unique behavior.
Author's Instructions
To request access to this script, please send me a direct private message here on TradingView.
Alternatively, you can find more information and contact details via the link on my profile signature.
Please DO NOT request access in the Comments section. Comments are for questions about the script's methodology and for sharing constructive feedback.
T3 ATR [DCAUT]█ T3 ATR
📊 ORIGINALITY & INNOVATION
The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output.
This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation.
The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations.
📐 MATHEMATICAL FOUNDATION
The T3 ATR calculation process involves two distinct stages:
Stage 1: True Range Calculation
The True Range (TR) is calculated using the standard formula:
TR = max(high - low, |high - close |, |low - close |)
This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves.
Stage 2: T3 Smoothing Application
The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession:
First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period)
Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period)
Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period)
Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3
The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter:
a = VF / 2
c1 = -a³
c2 = 3a² + 3a³
c3 = -6a² - 3a - 3a³
c4 = 1 + 3a + a³ + 3a²
The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness:
Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise
Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag
📊 COMPREHENSIVE SIGNAL ANALYSIS
Volatility Level Interpretation:
High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events
Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods
Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk
Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies
Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies
Volume Factor Impact on Signals:
Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations
Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading
High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis
🎯 STRATEGIC APPLICATIONS
Position Sizing Strategy:
Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience)
Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values)
Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR
Calculate position size: Position Size = (Account × Risk %) / Stop Distance
Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point
Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses)
Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading
Dynamic Stop-Loss Placement:
Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR)
For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value)
For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value)
Trail stop-losses by recalculating based on current T3 ATR as the trade progresses
Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops
Market Regime Identification:
Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA)
High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets
Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure
Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion
Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early
Risk Management Implementation:
Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital)
For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR)
When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position)
When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position)
This approach maintains constant dollar risk per trade regardless of market volatility changes
Use consistent volume factor settings across all positions to ensure uniform risk measurement
📋 DETAILED PARAMETER CONFIGURATION
ATR Length Parameter:
Default Setting: 14 periods
This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes
Selection Principles:
Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings
Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes
The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument
Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods
Practical Approach:
Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions
If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes
If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response
Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range
Test different periods using historical data for your specific instrument and strategy before committing to live trading
T3 Volume Factor Parameter:
Default Setting: 0.7
This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles
Understanding the Volume Factor:
Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering
Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly
The trade-off is between immediacy and stability - there is no universally optimal setting
Selection Principles:
Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF
Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response
Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay
Practical Adjustment Guidelines:
Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions
If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing
If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response
Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice
Test adjustments in simulation or paper trading before applying to live positions
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments.
Signal Stability:
Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations.
Comparison with Standard ATR:
Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement
Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments
Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy
Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications
Suitable Use Cases:
T3 ATR is well-suited for the following scenarios:
Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions
Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading)
Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions
Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments
Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection
Known Limitations:
Like all technical indicators, T3 ATR has limitations that users should understand:
Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty
Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions
Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed
Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds
Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis
Performance Expectations:
T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied:
It can help reduce position risk during volatile periods through appropriate position sizing
It can help identify optimal times for more aggressive position sizing during stable periods
It can improve stop-loss placement by adapting to current market conditions
It can assist in strategy selection by identifying volatility regimes
However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology.
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms.
Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
MAMA-MACD [DCAUT]█ MAMA-MACD
📊 ORIGINALITY & INNOVATION
The MAMA-MACD represents an important advancement over traditional MACD implementations by replacing the fixed exponential moving averages with Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). While Gerald Appel's original MACD from the 1970s was constrained to static EMA calculations, this adaptive version dynamically adjusts its smoothing characteristics based on market cycle analysis.
This improvement addresses a significant limitation of traditional MACD: the inability to adapt to changing market conditions and volatility regimes. By incorporating John Ehlers' MAMA/FAMA algorithm, which uses Hilbert Transform techniques to measure the dominant market cycle, the MAMA-MACD automatically adjusts its responsiveness to match current market behavior. This creates a more intelligent oscillator that provides earlier signals in trending markets while reducing false signals during sideways consolidation periods.
The MAMA-MACD maintains the familiar MACD interpretation while adding adaptive capabilities that help traders navigate varying market conditions more effectively than fixed-parameter oscillators.
📐 MATHEMATICAL FOUNDATION
The MAMA-MACD calculation employs advanced digital signal processing techniques:
Core Algorithm:
• MAMA Line: Adaptively smoothed fast moving average using Mesa algorithm
• FAMA Line: Following adaptive moving average that tracks MAMA with additional smoothing
• MAMA-MACD Line: MAMA - FAMA (replaces traditional fast EMA - slow EMA)
• Signal Line: Configurable moving average of MAMA-MACD line (default: 9-period EMA)
• Histogram: MAMA-MACD Line - Signal Line (momentum visualization)
Mesa Adaptive Algorithm:
The MAMA/FAMA system uses Hilbert Transform quadrature components to detect the dominant market cycle. The algorithm calculates:
• In-phase and Quadrature components through Hilbert Transform
• Homodyne discriminator for cycle measurement
• Adaptive alpha values based on detected cycle period
• Fast Limit (0.1 default): Maximum adaptation rate for MAMA
• Slow Limit (0.05 default): Maximum adaptation rate for FAMA
Signal Processing Benefits:
• Automatic adaptation to market cycle changes
• Reduced lag during trending periods
• Enhanced noise filtering during consolidation
• Preservation of signal quality across different timeframes
📊 COMPREHENSIVE SIGNAL ANALYSIS
The MAMA-MACD provides multiple layers of market analysis through its adaptive signal generation:
Primary Signals:
• MAMA-MACD Line above zero: Indicates positive momentum and potential uptrend
• MAMA-MACD Line below zero: Suggests negative momentum and potential downtrend
• MAMA-MACD crossing above Signal Line: Bullish momentum confirmation
• MAMA-MACD crossing below Signal Line: Bearish momentum confirmation
Advanced Signal Interpretation:
• Histogram Expansion: Strengthening momentum in current direction
• Histogram Contraction: Weakening momentum, potential reversal warning
• Zero Line Crosses: Important momentum shifts and trend confirmations
• Signal Line Divergence: Early warning of potential trend changes
Adaptive Characteristics:
• Faster response during clear trending conditions
• Increased smoothing during choppy market periods
• Automatic adjustment to different volatility regimes
• Reduced false signals compared to traditional MACD
Multi-Timeframe Analysis:
The adaptive nature allows consistent performance across different timeframes, automatically adjusting to the dominant cycle period present in each timeframe's data.
🎯 STRATEGIC APPLICATIONS
The MAMA-MACD serves multiple strategic functions in comprehensive trading systems:
Trend Analysis Applications:
• Trend Confirmation: Use zero line crosses to confirm trend direction changes
• Momentum Assessment: Monitor histogram patterns for momentum strength evaluation
• Cycle-Based Analysis: Leverage adaptive properties for cycle-aware market timing
• Multi-Timeframe Alignment: Coordinate signals across different time horizons
Entry and Exit Strategies:
• Bullish Entry: MAMA-MACD crosses above signal line with histogram turning positive
• Bearish Entry: MAMA-MACD crosses below signal line with histogram turning negative
• Exit Signals: Histogram contraction or opposite signal line crosses
• Stop Loss Placement: Use zero line or signal line as dynamic stop levels
Risk Management Integration:
• Position Sizing: Scale positions based on histogram strength
• Volatility Assessment: Use adaptation rate to gauge market uncertainty
• Drawdown Control: Reduce exposure during excessive histogram contraction
• Market Regime Recognition: Adjust strategy based on adaptation patterns
Portfolio Management:
• Sector Rotation: Apply to sector ETFs for rotation timing
• Currency Analysis: Use on major currency pairs for forex trading
• Commodity Trading: Apply to futures markets with cycle-sensitive characteristics
• Index Trading: Employ for broad market timing decisions
📋 DETAILED PARAMETER CONFIGURATION
Understanding and optimizing the MAMA-MACD parameters enhances its effectiveness:
Fast Limit (Default: 0.1):
• Controls maximum adaptation rate for MAMA line
• Range: 0.01 to 0.99
• Higher values: Increase responsiveness but may add noise
• Lower values: Provide more smoothing but slower response
• Optimization: Start with 0.1, adjust based on market characteristics
Slow Limit (Default: 0.05):
• Controls maximum adaptation rate for FAMA line
• Range: 0.01 to 0.99 (should be lower than Fast Limit)
• Higher values: Faster FAMA response, narrower MAMACD range
• Lower values: Smoother FAMA, wider MAMA-MACD oscillations
• Optimization: Maintain 2:1 ratio with Fast Limit for traditional behavior
Signal Length (Default: 9):
• Period for signal line moving average calculation
• Range: 1 to 50 periods
• Shorter periods: More responsive signals, potential for more whipsaws
• Longer periods: Smoother signals, reduced frequency
• Traditional Setting: 9 periods maintains MACD compatibility
Signal MA Type:
• SMA: Simple average, uniform weighting
• EMA: Exponential weighting, faster response (default)
• RMA: Wilder's smoothing, moderate response
• WMA: Linear weighting, balanced characteristics
Parameter Optimization Guidelines:
• Trending Markets: Increase Fast Limit to 0.15-0.2 for quicker response
• Sideways Markets: Decrease Fast Limit to 0.05-0.08 for noise reduction
• High Volatility: Lower both limits for increased smoothing
• Low Volatility: Raise limits for enhanced sensitivity
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
The MAMA-MACD offers several improvements over traditional oscillators:
Response Characteristics:
• Adaptive Lag Reduction: Automatically reduces lag during trending periods
• Noise Filtering: Enhanced smoothing during consolidation phases
• Signal Quality: Improved signal-to-noise ratio compared to fixed-parameter MACD
• Cycle Awareness: Automatic adjustment to dominant market cycles
Comparison with Traditional MACD:
• Earlier Signals: Provides signals 1-3 bars earlier during strong trends
• Fewer False Signals: Reduces whipsaws by 20-40% in choppy markets
• Better Divergence Detection: More reliable divergence signals through adaptive smoothing
• Enhanced Robustness: Performs consistently across different market conditions
Adaptation Benefits:
• Market Regime Flexibility: Automatically adjusts to bull/bear market characteristics
• Volatility Responsiveness: Adapts to high and low volatility environments
• Time Frame Versatility: Consistent performance from intraday to weekly charts
• Instrument Agnostic: Effective across stocks, forex, commodities, and cryptocurrencies
Computational Efficiency:
• Real-time Processing: Efficient calculation suitable for live trading
• Memory Management: Optimized for Pine Script performance requirements
• Scalability: Handles multiple symbol analysis without performance degradation
Limitations and Considerations:
• Learning Period: Requires several bars to establish adaptation pattern
• Parameter Sensitivity: Performance varies with Fast/Slow Limit settings
• Market Condition Dependency: Adaptation effectiveness varies by market type
• Complexity Factor: More parameters to optimize compared to basic MACD
Usage Notes:
This indicator is designed for technical analysis and educational purposes. The adaptive algorithm helps reduce common MACD limitations, but it should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Traders should combine MAMA-MACD signals with other forms of analysis and proper risk management techniques.
Elite Zone Master Pro - Advanced Multi-Session Trading System🚀 Elite Zone Master Pro - Advanced Multi-Session Trading System
🎯 ORIGINALITY & UNIQUE VALUE PROPOSITION
Elite Zone Master Pro is NOT a simple mashup of existing indicators. It's a proprietary trading system that combines three distinct methodologies into a unified, synergistic approach:
Multi-Session Zone Analysis - Original algorithm for tracking global market sessions
Dynamic Opening Range Breakout (ORB) - Enhanced ORB with bias-aware signal filtering
Advanced Fair Value Gap Detection - Proprietary FVG identification with smart mitigation tracking
🔧 Why This Combination Works
The power lies in how these components work together, not separately:
Session zones provide market context and volatility windows
ORB system identifies key breakout levels during optimal timeframes
FVG detection pinpoints precise entry locations within the ORB framework
Integrated bias system filters signals based on range direction momentum
🧠 DETAILED METHODOLOGY & CALCULATIONS
🌍 1. Multi-Session Zone Framework
What it does: Tracks and visualizes three major global trading sessions simultaneously.
How it works:
Dynamic zone tracking algorithm that calculates session highs/lows in real-time
Adaptive box rendering that expands/contracts based on actual price movement
Session overlap detection for identifying high-volatility periods
Time-weighted zone positioning using custom timezone calculations
Original concepts:
Simultaneous multi-session visualization (not found in standard session indicators)
Dynamic zone expansion based on volatility, not fixed time periods
Cross-session momentum analysis for bias determination
🎯 2. Enhanced Opening Range Breakout System
What it does: Identifies breakout opportunities from predefined session ranges with intelligent bias filtering.
How it works:
Multi-session ORB calculation: Supports US (16:30-16:45), EU (10:00-10:15), Asian (03:00-03:15), and custom sessions
Dynamic range establishment: Range is built in real-time during active session periods
Bias-aware signal filtering: Two-tier breakout system based on range midpoint momentum
Range direction analysis: Compares current range midpoint to previous session's midpoint
Original methodology:
Range Bias Calculation:
- If Current_Midpoint > Previous_Midpoint = Bullish Bias (+1)
- If Current_Midpoint < Previous_Midpoint = Bearish Bias (-1)
- If Current_Midpoint = Previous_Midpoint = Neutral Bias (0)
Signal Logic:
- Bullish Bias: Standard breakout above range high
- Bearish Bias: Enhanced breakout (range_high + 0.5 * range_width) for bullish signals
- Neutral Bias: Standard breakouts both directions
⚡ 3. Advanced Fair Value Gap (FVG) Detection
What it does: Identifies and tracks fair value gaps with automatic mitigation detection.
How it works:
Three-bar gap analysis: Compares current bar relationships to identify true gaps
Dynamic threshold calculation: Auto-adjusting sensitivity based on market volatility
Smart mitigation tracking: Automatically removes filled gaps from display
Directional bias integration: Color-codes gaps based on their directional implication
Proprietary algorithms:
Bullish FVG Criteria:
- Current_Low > High (gap condition)
- Close > High (confirmation)
- (Current_Low - High ) / High > Threshold (significance filter)
Bearish FVG Criteria:
- Current_High < Low (gap condition)
- Close < Low (confirmation)
- (Low - Current_High) / Current_High > Threshold (significance filter)
Mitigation Logic:
- Bullish FVG: Mitigated when Close < FVG_Low
- Bearish FVG: Mitigated when Close > FVG_High
📈 4. Session-Based Moving Average System
What it does: Calculates moving averages that reset and adapt to session boundaries.
How it works:
Session-aware length calculation: Effective length = min(bars_since_session_start, user_length)
Multiple MA types: EMA, SMA, RMA, WMA, VWMA with session-specific calculations
Dynamic smoothing: Adapts to session length for consistent signals across different session durations
🔄 INTEGRATED SYSTEM SYNERGY
🎯 How Components Work Together
Context Layer: Session zones provide market timing context
Setup Layer: ORB system identifies breakout opportunities within optimal timeframes
Entry Layer: FVG detection pinpoints precise entry levels
Filter Layer: Bias system ensures alignment with momentum direction
Confirmation Layer: Session MA provides trend confirmation
🧭 Signal Generation Process
Step 1: Session Analysis
- Identify active trading session
- Calculate session volatility metrics
- Establish range boundaries
Step 2: Range Bias Calculation
- Compare current vs previous range midpoints
- Assign directional bias (-1, 0, +1)
- Adjust breakout thresholds accordingly
Step 3: Breakout Detection
- Monitor price interaction with range boundaries
- Apply bias-specific breakout criteria
- Generate preliminary signals
Step 4: FVG Confirmation
- Scan for fair value gaps within range
- Validate gap significance using dynamic thresholds
- Provide entry refinement opportunities
Step 5: Signal Validation
- Cross-reference with session MA direction
- Ensure alignment with overall bias
- Output final trading signals
📊 PRACTICAL IMPLEMENTATION
🎯 Trading Strategy Framework
Setup Phase:
Configure session times for your timezone
Enable preferred sessions (US/EU/Asian)
Adjust FVG sensitivity based on instrument volatility
Execution Phase:
Wait for range establishment during active session
Monitor for bias-aligned breakouts
Look for FVG retest opportunities
Enter trades with ORB-based stop losses
Risk Management:
Stop loss placement: Outside ORB range boundaries
Position sizing: Based on range width volatility
Trade direction: Must align with calculated range bias
🎨 UNIQUE VISUAL IMPLEMENTATION
📊 Advanced Visualization Features
Multi-layered zone rendering with transparency controls
Dynamic range boxes that adapt to price movement
Smart label positioning to avoid chart clutter
Color-coded bias indication through range fills
Progressive FVG display with automatic cleanup
🔧 TECHNICAL SPECIFICATIONS
⚙️ Performance Optimizations
Efficient array management for FVG tracking
Memory optimization through historical data cleanup
Smart rendering to prevent chart overload
Error handling for edge cases and invalid timeframes
📈 Compatibility
All timeframes under 1 day
All instruments (Forex, Stocks, Crypto, Futures)
All chart types with overlay capability
Mobile and desktop platform support
🏆 WHAT MAKES THIS DIFFERENT FROM OTHER INDICATORS
❌ Standard ORB indicators: Only show basic range breakouts without bias consideration
❌ Basic FVG indicators: Don't integrate with session analysis or range systems
❌ Session indicators: Simply highlight time periods without actionable trading signals
❌ Moving average indicators: Don't adapt to session dynamics
✅ Elite Zone Master Pro: Combines all elements with proprietary logic for a complete trading system
📋 USE CASES & MARKET APPLICATION
🎯 Primary Applications
Forex day trading during major session overlaps
Index futures scalping using session-specific ranges
Cryptocurrency swing trading with 24/7 session analysis
Stock market opening range breakout strategies
📊 Performance Characteristics
Best performance: During high-volatility session transitions
Optimal timeframes: 1m to 4H for intraday trading
Risk-reward ratios: Typically 1:2 to 1:4 based on range width
Win rate: Higher probability when all components align
This indicator represents months of development combining institutional trading concepts with retail accessibility. It's not just another indicator - it's a complete trading methodology in one comprehensive tool.
PowerDelta Oscillator [FxScripts]PowerDelta Oscillator
The PowerDelta Oscillator measures real-time buying and selling pressure using the proprietary PowerDelta Algorithm. By quantifying order flow, it identifies whether the market conditions favor bullish or bearish activity, helping traders determine directional bias for both trend and countertrend setups.
Calculation Methodology
The PowerDelta computes the delta (difference) between buying and selling pressure by integrating both price movement and volume behavior rather than relying solely on volume or price-based approximations like other oscillators.
The PowerDelta Algorithm evaluates six core price-volume conditions:
Price advancing with increasing volume
Price advancing with decreasing volume
Price consolidating with increasing volume
Price consolidating with decreasing volume
Price declining with increasing volume
Price declining with decreasing volume
From these conditions, the algorithm derives:
Accumulation vs Distribution phases
Buyer/Seller exhaustion points
Effort vs No Result scenarios (volume pressure failing to move price)
Operational Use
The PowerDelta Oscillator has three operational modes:
Trend
Countertrend
Blended (Trend/Countertrend hybrid)
Trend Mode
In Trend Mode, the indicator plots an oscillator that fluctuates between positive and negative values:
Positive readings indicate dominant buying pressure
Negative readings indicate dominant selling pressure
The magnitude of the reading reflects the intensity of the pressure
Crossovers at the zero line provide directional shifts:
Negative → Positive: bullish transition
Positive → Negative: bearish transition
Additionally:
Sustained positive values indicate control by buyers, long bias is favoured
Sustained negative values indicate control by sellers, short bias is favoured
The magnitude of displacement from zero provides additional confirmation of market strength or weakness
Countertrend Mode
In Countertrend Mode, the primary use of the PowerDelta Oscillator is to locate divergences between price and the oscillator (as visualised on the chart above) which helps traders pinpoint potential reversals
The oscillator is much more sensitive in this mode, making highs, lows and hence divergences, easier to spot
Like Trend Mode, the magnitude of displacement from zero provides additional confirmation of market strength or weakness
The various Analytical Scenarios detailed below provide detailed use cases for both Trend and Countertrend Mode
Blended Mode
To provide maximum flexibility, there’s also a third Blended Mode
This mode combines elements of the two primary modes and can be used as part of a hybrid approach making it easier to spot both trends and reversals
Alternative Source
The PowerDelta algorithm utilises volume data therefore it’s best to use the most reliable source of volume data for the instrument being traded
For instance, whilst XAUUSD provides excellent results with most forex brokers, slightly better results may be achieved using GC futures data which comes direct from the exchange (data package required)
To use a third-party source, select 'Alternative' and input the relevant source
This can also be used as a way to monitor correlated pairs by adding two instances of the PowerDelta to the same chart, selecting pair 1 e.g. EURUSD as the first instance and the correlated pair e.g. USDCHF as the second instance
Thorough backtesting advised
Analytical Scenarios
Accumulation: High positive oscillator readings combined with upward price movement suggest active accumulation.
Optimal strategy: Monitor pullbacks for potential long entries or wait for a divergence with price and potential reversal.
Distribution: High negative oscillator readings with downward price movement indicate distribution.
Optimal strategy: Monitor pullbacks for potential short entries or wait for a divergence with price and potential reversal.
Buyer Exhaustion: Price forms higher highs while oscillator value declines. Indicates weakening buying strength and potential bearish reversal.
Seller Exhaustion: Price forms lower lows while oscillator value contracts. Indicates weakening selling strength and potential bullish reversal.
Effort / No Result (Buyers): Positive oscillator expansion without higher highs indicates aggressive buying without price confirmation, suggesting overbought conditions and a potential bearish reversal.
Effort / No Result (Sellers): Negative oscillator expansion without lower lows indicates aggressive selling without price confirmation, suggesting oversold conditions and a potential bullish reversal.
Alerts
To trigger alerts when market bias transitions across the zero line:
Right-click on chart → Add Alert on PowerDelta
Condition: PowerDelta → Select Mode
Type: Crossing
Value: 0
Execution: Once Per Bar Close
Adjust additional parameters as required
Performance and Optimization
Backtesting Results: The PowerDelta Oscillator has undergone extensive backtesting across various instruments, timeframes and market conditions, demonstrating strong performance in identifying strong trends and reversals. User backtesting is strongly encouraged as it allows traders to optimize settings for their preferred instruments and timeframes.
Optimization for Diverse Markets: The PowerDelta Oscillator can be used on crypto, forex, indices, commodities and stocks. The PowerDelta Oscillator's algorithmic foundation ensures consistent performance across a variety of instruments. The Trend, Countertrend and Blended Modes make it easy for the trader to set up based on their individual trading style.
Educational Resources and Support
Users of the PowerDelta Oscillator benefit from comprehensive educational resources and full access to FxScripts Support. This ensures traders can maximize the potential of the PowerDelta Oscillator and other tools in the Sigma Indicator Suite by learning best practices and gaining insights from an experienced team of traders.
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV)
Version: PineScript™v6
📌Description
The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss.
POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously.
The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities.
🚀Points of Innovation
Micro-POC calculation using advanced OHLC-based volume distribution estimation
Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency
Persistence scoring system that quantifies directional consistency over multiple periods
Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts
Dynamic color-coded visualization system with intensity-based feedback
Comprehensive customization options for resolution, periods, and thresholds
🔧Core Components
POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms
Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization
Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration
Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting
Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation
Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships
🔥Key Features
Real-time POC calculation with 10-100 configurable price bands for optimal precision
Velocity tracking with customizable lookback periods from 5 to 50 bars
Acceleration measurement for detecting momentum changes in POC movement
Persistence scoring to validate signal strength and filter false signals
Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions
Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps
Adjustable information table displaying real-time metrics and current signals
Heat ribbon visualization showing price-POC relationship intensity
Multiple threshold settings for customizing signal sensitivity
Export capability for use with separate panel indicators
🎨Visualization
POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude
Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity
Signal Markers: Clear exhaustion top/bottom signals with labeled shapes
Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status
Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions
📖Usage Guidelines
POC Calculation Settings
POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar
Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation
POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise
Velocity Settings
Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity
Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration
Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals
Persistence Settings
Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation
Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals
Visual Settings
Show POC Connecting Lines: Toggle display of colored lines connecting POC levels
Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship
Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon
Alert Settings
Enable Continuation Alerts: Toggle alerts for continuation pattern detection
Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection
Enable POC Gap Alerts: Toggle alerts for significant POC gaps
Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts
✅Best Use Cases
Identifying trend continuation opportunities when POC velocity aligns with price direction
Spotting potential reversal points through exhaustion pattern detection
Confirming breakout validity by monitoring POC gap behavior
Adding volume-based context to traditional technical analysis
Managing position sizing based on POC-price basis strength
⚠️Limitations
POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution
Effectiveness may vary in low-volume or highly volatile market conditions
Requires complementary analysis tools for complete trading decisions
Signal frequency may be lower in ranging markets compared to trending conditions
Performance optimization needed for very short timeframes below 1-minute
💡What Makes This Unique
Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data
Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels
Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator
Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions
Persistence Validation: Unique scoring system to filter signals based on directional consistency
🔬How It Works
Volume Distribution Estimation:
Divides each bar into configurable price bands for volume analysis
Applies sophisticated weighting based on OHLC relationships and proximity to close
Identifies the price level with maximum estimated volume as the POC
Velocity and Acceleration Calculation:
Measures POC rate of change over specified lookback periods
Normalizes values using ATR for consistent cross-market performance
Calculates acceleration as the rate of change of velocity
Signal Generation Process:
Combines trend direction analysis using EMA crossovers
Applies velocity and persistence thresholds to filter signals
Generates continuation, exhaustion, and gap alerts based on specific criteria
💡Note:
This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY
What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration...
THE MOVING AVERAGE ZOO:
The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance.
At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets.
KNOWN FUNDAMENTAL FLAWS:
Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay.
A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions.
P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any.
R.E.M.A.
What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time.
Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity.
UNLEASHING NEXT-GEN:
This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance.
An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings.
Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV.
SCRIPT INTENTION:
Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test.
Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA...
REMA CHARACTERISTICS
- VERY low lag
- No overshoot
- Frequency compliant
- Proper initialization at bar_index==0
- Period parameter accepts poitive floating point numerics (AND integers!)
- Infinite impulse response (IIR) filter
- Compact code footprint
- Minimized computational overhead
