ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Search in scripts for "Fractal"
RunRox - Entry Model🎯 RunRox Entry Model is an all-in-one reversal-pattern indicator engineered to help traders accurately identify key price-reversal points on their charts. It will be part of our premium indicator package and improve the effectiveness of your trading strategies.
The primary concept of this indicator is liquidity analysis, making it ideal for Smart Money traders and for trading within market structure. At the same time, the indicator is universal and can be integrated into any strategy. Below, I will outline the full concept of the indicator and its settings so you can better understand how it works.
🧬 CONCEPT
In the screenshot below, I’ll schematically illustrate the core idea of this indicator. It’s one of the patterns that the indicator automatically detects on the chart using a two-timeframe approach. We use the higher timeframe to identify liquidity zones, and the lower timeframe to capture liquidity removal and structure breaks. The schematic is shown in the screenshot below.
Our indicator includes three entry models in total , and I will discuss its functionality and features in more detail later in this post.
💡 FEATURES
Three entry models
PO3 HTF Bar
Entry Area
Optimization for each Entry Area
Filters
HTF FVG
Alert customization
Next, we will examine each entry model in detail.
🟠 ENTRY MODEL 1
The first model is the core one we’ll work with; all other models rely on its structure and construction. In the screenshot below, I’ll schematically show the complete model.
As shown in the screenshot above, we display higher-timeframe candles on the current chart to better visualize the entry model and keep the trader informed of what’s happening on the larger timeframe. The screenshot also highlights both the Long and Short models, as well as the Entry Area, which I will explain in more detail below.
The schematic model on the lower timeframe is shown in the screenshot above. It illustrates that after the Entry Model forms, we draw the Entry Area on the next candle and wait for a price pullback into this zone for the optimal trade entry. Statistically, before moving higher, the price typically revisits the Entry Area, covering the imbalances created by MSS; thus, the Entry Area represents the ideal entry point.
🟩 Entry Area
Once the Entry Model has formed, we focus on identifying the optimal pullback zone for taking a position. To determine which retracement area performs best, we conducted extensive historical backtesting on potential zones and selected those that consistently delivered the strongest results. This process yields Entry Areas with the highest probability of a successful reversal.
On the screenshot above, you can see an example of the Entry Area and which zones carry a higher versus lower probability of reversal. Zones rendered with greater transparency have historically delivered weaker results than the more opaque zones. The deeper-colored areas represent the optimal entry zones and can improve your risk-reward ratio by allowing you to enter at more favorable prices.
It’s important to remember that the entire Entry Area functions as a potential zone for scaling into a position. However, if your risk-to-reward ratio isn’t favorable, you can wait for the price to retrace to lower levels within the Entry Area and enter with a more attractive risk-to-reward.
🟢 Pattern Rating
Each entry model receives a rating in the form of green circles next to its name 🟢. The rating ranges from one to four circles, based on the historical performance of similar patterns. To calculate this rating, we backtest past data by analyzing candle behavior during the model’s formation and assign circles according to how similar patterns performed historically.
Example Ratings:
🟢 – One circle
🟢🟢 – Two circles
🟢🟢🟢 – Three circles
🟢🟢🟢🟢 – Four circles
The more green circles a model has, the more reliable it is—but it’s crucial to rely on your own analysis when identifying strong reversal points on the chart. This rating reflects the model’s historical performance and does not guarantee future results, so keep that in mind!
Below is a screenshot showing four model variations with different ratings on the chart.
⚠️ Unconfirmed Pattern
Entry Model 1 is designed so that, until the higher-timeframe candle closes, the pattern remains unconfirmed and is hidden on the chart. For traders who prefer to see setups as they form, there’s a dedicated feature that displays the unconfirmed pattern at the moment of its appearance - triggered by the Market Structure Shift - before the HTF candle closes. The screenshot below shows what the pattern looks like prior to confirmation.
‼️IMPORTANT: Until the pattern is confirmed and the higher-timeframe candle has closed, the model may disappear from the chart if price reverses and the HTF candle closes below the previous bar. Therefore, this mode is suitable only for experienced traders who want to see market moves in advance. Remember that the pattern can be removed from the chart, so we recommend waiting for the HTF candle to close before deciding to enter a trade.‼️
✂️ Filters
For the primary model, there are four filters designed to enhance entry points or exclude less-confirmed patterns. The filters available in the indicator are:
Bounce Filter
Market Shift Mode
Same Wave Filter
Only with Divergence
I will explain how each of these filters works below.
- Bounce Filter
The Bounce Filter identifies significant deviations of price from its mean and only displays the Entry Model once the asset’s price moves beyond the average level. The screenshot below illustrates how this appears on the chart.
The actual average-price calculation is more sophisticated than what’s shown in the screenshot, that image is just an illustrative example. When the price deviates significantly from the N-bar average, we start looking for the Entry Model. This approach works particularly well in range-bound markets without a clear trend, as it lets you trade strong deviations from the mean.
- Market Shift Mode
This filter works by detecting the initial impulse that triggered the liquidity sweep on the previous higher-timeframe candle, and then holding the Market Structure Shift level at that point after the sweep. If the filter is turned off, price may move higher following the liquidity removal, creating a new MSS level and potentially producing a false structure shift and entry signal on the formed model.
This filter helps you more accurately identify genuine shifts - but keep in mind that the model can still perform well without it, so choose the setting that best suits your trading style.
- Same Wave Filter
The Same Wave Filter removes entry models that form without a clear lower-timeframe structure when liquidity is swept from the previous higher-timeframe candle. In other words, if the prior HTF candle and the current one belong to the same impulse wave - without any retracements on the LTF - the model is filtered out.
Keep in mind that this filter may also exclude patterns that could have produced positive results, so whether to enable it depends on your trading system.
- Only with Divergence
The Only with Divergence filter detects divergence between the lows of successive candles and indicators like RSI. When the low that swept liquidity diverges from the previous candle’s low, the indicator displays a “DIV” label. Although RSI is cited as an example, our divergence calculation is more advanced. This filter highlights patterns where low divergence signals genuine liquidity manipulation and a likely aggressive price reversal.
🌀 Model Settings
Trade Direction: Choose whether to display models for Long or Short trades.
Fractal: Select between automatic fractal detection—which adapts the lower-timeframe (LTF) and higher-timeframe (HTF) candles—or Custom.
Custom Fractal: When Custom is selected, manually specify the LTF and HTF timeframes used to detect the patterns.
History Pattern Limit: Set the maximum number of patterns to display on the chart to keep it clean and uncluttered.
🎨 Model Style
You can flexibly customize the model’s appearance by choosing your preferred line thickness, color, and the other settings we discussed above.
🔵 ENTRY MODEL 2
This model appears under specific conditions when Model 1 cannot form. It’s a price-reversal model constructed according to different rules than the first model. The screenshot below shows how it looks on the chart.
This model forms less frequently than Model 1 but delivers equally strong performance and is displayed as a position-entry zone.
Like the Entry Area in Entry Model 1, this zone is calculated automatically and highlights the best entry levels: areas that showed the strongest historical results are rendered in a brighter shade.
🎨 Model Style
You can flexibly customize the style of Entry Model 2 - its color, opacity, visibility, and the average price of the previous candle.
🟢 ENTRY MODEL 3
Entry Model 3 is a continuation pattern that only forms after Entry Model 1 has completed and delivered the necessary price move to trigger Model 3.
Below is a schematic illustration of how Model 3 is intended to work.
🎨 Model Style
As with the previous models, you can flexibly customize the style of this zone.
⬆️ HTF CANDLES
One of the standout features of this indicator is the ability to plot higher-timeframe (HTF) candles directly on your lower-timeframe (LTF) chart, giving you clear visualization of the entry models and insight into what’s unfolding on the larger timeframe.
You can fully customize the HTF candles - select their style, the number of bars displayed, and tweak various settings to match your personal trading style.
HTF FVG
Fair Value Gaps (FVGs) can also be drawn on the HTF candles themselves, enabling you to spot key liquidity or interest zones at a glance, without switching between timeframes.
Additionally, you can view all significant historical HTF highs and lows, with demarcation lines showing where each HTF candle begins and ends.
All these options let you tailor the HTF candle display on your chart and monitor multiple timeframes’ trends in a single view.
📶 INFO PANEL
Instrument: the market symbol on which the model is detected
Fractal Timeframes: the LTF and HTF fractal periods used to locate the pattern
HTF Candle Countdown: the time remaining until the higher-timeframe candle closes
Trade Direction: the direction (Long or Short) in which the model is searched for entry
🔔 ALERT CUSTOMIZATION
And, of course, you can configure any alerts you need. There are seven alert types available:
Confirmed Entry Model 1
Unconfirmed Entry Model 1
Confirmed Entry Model 2
Confirmed Entry Model 3
Entry Area 1 Trigger
Entry Area 2 Trigger
Entry Area 3 Trigger
You also get a custom macro field where you can enter any placeholders to fully personalize your alerts. Below are example macros you can use in that field.
{{event}} - Event name ('New M1')
{{direction}} - Trade direction ('Long', 'Short')
{{area_beg}} - Entry Area Price
{{area_end}} - Entry Area Price
{{exchange}} - Exchange ('Binance')
{{ticker}} - Ticker ('BTCUSD')
{{interval}} - Timeframe ('1s', '1', 'D')
{{htf}} - High timeframe ('15', '60', 'D')
{{open}}-{{close}}-{{high}}-{{low}} - Candle price values
{{htf_open}}-{{htf_close}}-{{htf_high}}-{{htf_low}} - Last confirmed HTF candle's price
{{volume}} - Candle volume
{{time}} - Candle open time in UTC timezone
{{timenow}} - Signal time in UTC timezone
{{syminfo.currency}} - 'USD' for BTCUSD pair
{{syminfo.basecurrency}} - 'BTC' for BTCUSD pair
✅ USAGE EXAMPLES
Now I’ll demonstrate several ways to apply this indicator across different trading strategies.
Primarily, it’s most effective within the Smart Money framework - where liquidity and manipulation are the core focus - so it integrates seamlessly into your SMC-based approach.
However, it can also be employed in other strategies, such as classic technical analysis or Elliott Wave, to capitalize on reversal points on the chart.
Example 1
The first example illustrates forming a downtrend using a Smart Money strategy. After the market structure shifts and the first BOS is broken, we begin looking for a short entry.
Once Entry Model 1 is established, a Fair Value Gap appears, which we use as our position-entry zone. The nearest target becomes the newly formed BOS level.
In this trade, it was crucial to wait for a strong downtrend to develop before hunting for entries. Therefore, we waited for the first BOS to break and entered the trade to ride the continuation of the downtrend down to the next BOS level.
Example 2
The next example illustrates a downtrend developing with a Fair Value Gap on the 1-hour timeframe. The FVG is also displayed directly on the HTF candles in the chart.
The pattern forms within the HTF Fair Value Gap, indicating that we can balance this inefficiency and ride the continuation of the downtrend.
The target can simply be a 1:2 or 1:3 risk–reward ratio, as in our case.
📌 CONCLUSION
These two examples illustrate how this indicator can be used to identify reversals or trend continuations. In truth, there are countless ways to incorporate this tool, and each trader can adapt the model to fit their own strategy.
Always remember to rely on your own analysis and only enter trades when you feel confident in them.
DT_KEY_LEVELSDT_Key_Levels: Powerful Market Structure Analysis Indicator
DT_Key_Levels is an advanced indicator for fundamental market structure analysis, optimized for higher timeframes (D1, W, M). The indicator combines three powerful technical analysis tools — fractals, Fair Value Gaps (FVG), and psychological levels — in one comprehensive solution.
Three Components of the Indicator
1. Enhanced Fractal System
The indicator uses an improved version of Bill Williams' classic fractals, allowing for deeper market structure analysis:
Dual Identification System:
Standard 5-bar fractals (displayed with thick lines) for analyzing reliable support/resistance levels
Light 3-bar fractals (displayed with thin lines) for early identification of potential reversal points
Intelligent Tracking System:
Automatic detection and filtering of completed fractals
Marking fractals with corresponding timeframe designation (HTF-1D, HTF-1W, HTF-1M)
Tracking and marking the All-Time High (ATH)
2. Fair Value Gaps (FVG) System
The indicator identifies and visualizes price gaps in market structure — zones that often act as magnets for future price movements:
Precise Identification of Inefficient Zones:
Bullish FVG: when the current candle's low is above the -2 candle's high
Bearish FVG: when the current candle's high is below the -2 candle's low
Detailed Visualization:
Clear display of upper and lower boundaries of each FVG
Midline (0.5 FVG) for determining key reaction levels within the gap
Marking each FVG with "FF" (Fair value Fill) label for quick identification
Dynamic Management:
Automatic removal of FVGs when they are filled by price movement
Customizable line extension for improved tracking of target zones
3. Intelligent Psychological Levels
The indicator automatically determines key psychological levels with adaptation to the type of instrument being traded:
Specialized Calibration for Various Assets:
Forex (EUR/USD, GBP/USD, USD/JPY): optimization for standard figures and round values
Precious metals (XAUUSD): adaptation to typical gold reaction zones with a $50 step
Cryptocurrencies (BTC, ETH): dynamic step adjustment depending on current price zone
Stock indices (NASDAQ, S&P500, DAX): accounting for the movement characteristics of each index
Smart Adaptation System:
Automatic determination of the optimal step for any instrument
Generation of up to 24 key levels, evenly distributed around the current price
Intelligent filtering to display only significant levels
Practical Application
Strategic Analysis
Identifying Key Structural Levels:
Use monthly and weekly fractals to determine strategic support/resistance zones
Look for coincidences of fractals with psychological levels to identify particularly strong zones of interest
Determine long-term barriers using type 5 fractals on higher timeframes
Analysis of Market Inefficiencies:
Track the formation of FVGs as potential targets for future movements
Use FVG midlines (0.5) as important internal reaction levels
Analyze the speed of FVG filling to understand trend strength
Tactical Trading Decisions
Entry Points and Risk Management:
Use bounces from fractals in the direction of the larger trend as a signal for entry
Place stop-losses behind fractal levels or key psychological levels
Monitor the formation of new fractals as a signal of potential reversal
Determining Target Levels:
Use unfilled FVGs as natural price targets
Apply nearby psychological levels for partial position closing
Project higher timeframe fractals to determine long-term goals
Indicator Advantages
Comprehensive Approach: combining three methodologies for a complete understanding of market structure
Intelligent Adaptation: automatic adjustment to the characteristics of different types of assets
Clean Visual Presentation: despite the abundance of information, the indicator maintains clarity of display
Effective Signal Filtering: automatic removal of completed levels to reduce visual noise
Higher Timeframe Optimization: specifically designed for daily, weekly and monthly charts
Usage Recommendations
Use the indicator only on D1, W, and M timeframes for the most reliable signals
Pay special attention to areas where different types of signals coincide (e.g., fractal + psychological level)
Use higher timeframe fractals as key zones for medium and long-term trading
Track FVGs as potential target zones and focus on their filling
ZenAlgo - DetectorThis script combines multiple volume data sources, calculates several forms of volume-based metrics, displays a table for Spot vs. Perpetual volumes, and visualizes several technical elements (such as cumulative delta, divergences, fractals, and specialized moving averages). The primary objective is to help analyze volume activity across different exchanges, compare Spot vs. Perpetual markets, and observe how shifting volumes may coincide with price action characteristics. This description aims to clarify each component, explain how the calculations are performed, and show you how to interpret the various chart markings.
Why Combine These Metrics in One Script?
Many publicly available volume-related tools focus only on a single exchange or a single type of volume (like spot or futures). This script merges multiple exchange sources for spot and perpetual data into a unified view. By doing so, users can detect discrepancies or confirm alignment between different markets without juggling multiple indicators. It also processes volume-derived signals (delta, divergences, fractals, etc.) in one place, sparing you from manually combining various standalone scripts. Through this integration, it becomes easier to observe how price and volume interact across different market segments.
Core Concept: Aggregated Volume
The script begins by collecting volumes from multiple exchanges in two categories:
Spot volumes – Typically aggregated under symbols ending with "USDT" or a user-selected currency, and
Perpetual volumes – From perpetual futures contracts (e.g., symbols ending in "USD.P" or "USDT.P").
All these exchange volumes are requested via the built-in request.security() function in a single line for each exchange. The user can enable or disable each exchange in the inputs. The script then calculates an "aggregated volume" for Spot, an aggregated volume for Perpetual, and an overall combined total.
This aggregated volume is used later to break down how much of each bar's volume can be considered "buy" or "sell" based on the bar's candle structure (body vs. wicks).
Volume-Based Calculations: Buy vs. Sell Volume and Delta
For each bar, the script estimates how much of the aggregated volume can be associated with a "buy side" and a "sell side."
Volume Buy is computed if the bar's close is above the open , giving more weight to the candle's body and allocating some portion of volume to the wicks as well.
Volume Sell is similarly computed if the bar's close is below the open .
This results in a Delta value: Delta = (Buy Volume) – (Sell Volume).
Additionally, the script accumulates these values over a user-defined "lookback length" to provide Cumulative Delta . This can help show longer-term directional volume bias.
Table: Spot vs. Perpetual Comparison
There is a toggle ("Show Spot vs Perpetual Table") that displays an on-chart table comparing volumes:
Buy Volume and Sell Volume for each aggregated category (Spot, Perp, and their sum).
Delta (the difference between Buy and Sell).
Percentage breakdowns of buy vs. sell portions.
This table only appears on the most recent bar and helps users quickly assess how Spot and Perpetual volumes compare, plus the overall total.
PVSRA Color Coding
A "PVSRA-style" color approach classifies each bar based on volume and candle range:
Climax Up (lime) or Climax Down (red) occurs if volume is extremely high relative to a simple moving average of volume and range.
Above-Average Up (blue) or Down (fuchsia) occurs if volume is moderately higher than average.
Otherwise, colors fall back to neutral up/down colors.
This allows you to spot potentially high-volume "climax" bars vs. bars with only moderate or typical volume levels.
Fractals and Divergences
The script detects certain fractal points on the aggregated volumes (sum of buy or sell volumes). It looks for a 5-bar pattern (with the current bar in the middle for top or bottom fractals).
When a fractal is confirmed on buy volume, the script checks if new higher price highs coincide with lower buy-volume peaks (or vice versa) to highlight regular or hidden divergences.
Similar logic is applied on the sell-volume side if new lower price lows occur alongside higher sell-volume troughs (or the opposite).
If enabled in the settings, lines and labels may appear on the chart to mark these divergence points.
"Delta Dot" Events
This script draws small circles above or below bars when the total delta changes magnitude relative to the previous bar by certain user-defined multipliers. It segregates "tiny," "small," "large," and "extra" expansions in bullish or bearish delta.
Bullish Dots : Appear above the bar when the new positive delta is multiple times bigger than the previous positive delta.
Bearish Dots : Appear below the bar in a similar fashion for negative delta.
These dots emphasize large or sudden shifts in buy/sell pressure from one bar to the next.
Delta MA and its Direction
A moving average is calculated on the total delta and optionally multiplied by a factor (in the code, by 4) to make it visually prominent. The user can pick from SMA, EMA, WMA, RMA, or HMA as the smoothing technique.
Delta MA Direction : The script compares the current delta MA to a short SMA of itself to define whether it is rising or falling.
A color is assigned—blue if rising, orange if falling, gray if they're roughly equal.
This helps quickly visualize longer-term momentum in the net delta metric.
Divergences on the Delta MA
After computing the "Delta MA" line, the script detects pivot highs or lows on that line. If the price makes a new high but the Delta MA pivot is lower (and vice versa), it draws lines and small labels indicating potential divergence.
Bearish Divergence : Price makes a higher high, while the Delta MA pivot forms a lower high.
Bullish Divergence : Price makes a lower low, while the Delta MA pivot forms a higher low.
RSI + MFI Computation
The script also calculates a simplified form of RSI+MFI by comparing (close – open) / (high – low) * a multiplier , then smoothing it with a simple average. This is purely for an optional observational measure to see if the price action is leaning bullish or bearish in terms of these combined indicators.
EMA Overlay and Diamond Shapes
There are two standard EMAs (13 and 21). The script checks whether price is above or below these EMAs, in addition to other conditions (like changes in delta, volume, or RSI+MFI direction) to draw diamond shapes at the top or bottom of the chart:
Green Diamonds near the bottom if the conditions line up to suggest that the environment is more favorable for bullish pressure.
Red Diamonds near the top if the environment suggests more bearish pressure.
These diamonds come in two sizes:
Normal – More pronounced, typically plotted if RSI+MFI result is above/below zero.
Small – Plotted if RSI+MFI is on the other side of that threshold.
An optional "Hardcore Mode" adds special tiny diamonds under specific delta color/condition mismatches.
How to Interpret the Chart Elements
Line Plots of Buy and Sell Volumes : A positive line for buy volume, a negative line for sell volume, and a zero-line for reference. This provides at-a-glance perspective on how buy or sell volumes add up per bar.
Histogram "Total Delta" : A color-coded bar that quickly shows whether overall buy vs. sell volume is dominant. The color is governed by the PVSRA logic (e.g., potential climax or above-average conditions).
Volume Table (when enabled): Summarizes volumes in numeric and percentage form for Spot, Perp, and total categories on the last bar.
Delta Dots : Small circles highlighting abrupt changes in delta magnitude. Larger multiples indicate bigger jumps compared to the previous bar.
Fractals & Divergence Lines : Connect pivot points in buy/sell volume or in the Delta MA line with price highs/lows to indicate potential divergences.
Delta MA Plot : Smooth curve (scaled up x4) to reflect longer-term accumulation or distribution in the delta. Colored by whether the MA is above or below a short average of itself.
Diamonds : Appear when certain volume, price, RSI+MFI, and delta conditions converge. Green diamonds near the bottom typically coincide with bullish conditions, red diamonds near the top with bearish conditions.
Practical Usage Notes
Use the Spot vs. Perp breakdown to see if these two market segments differ significantly in their contributions to total volume. This can be informative when a certain type of market (futures vs. spot) might be "driving" price action.
The PVSRA color scheme highlights "climax" or "above-average" volume bars, which can sometimes appear around major reversals or breakouts.
Observing divergences in aggregated buy/sell volume (or in the Delta MA line) can provide additional context on whether certain price moves are backed by strong volume involvement.
The script's fractal divergences rely on short pivot detection. Signals will appear only after enough bars have passed for confirmation, so these are effectively "after-the-fact" notations to illustrate possible volume/price divergences.
The diamonds do not necessarily instruct any buy/sell action; rather, they mark conditions where multiple volume and momentum criteria line up in one direction.
Important Considerations
This script displays aggregated volumes from potentially multiple exchanges. Each exchange or pair might have different time zones, liquidity, or data availability, which can occasionally result in incomplete or zero values.
All references to "buy" or "sell" volume are approximate breakdowns based on candle structure. They are not absolute measures of real-time order flow.
Divergences and fractal points are provided strictly for analytical insight. They can repaint or shift if the fractal conditions were not fully confirmed in real time.
The color-coded lines, histograms, diamonds, and tables are strictly to guide analysis of volume fluctuations and do not claim to predict future price performance.
If you enable "Hardcore Mode," you will see additional diamond markers. This mode is mainly intended as an extra highlight of certain "contradictory" delta conditions.
Summary
The "ZenAlgo - Detector" script brings together a variety of volume-based analyses:
Aggregated volumes from multiple exchanges
A breakdown into Spot vs. Perpetual activity
Delta calculations, fractal divergences, and a specialized Delta Moving Average
Color-coded bars reflecting possible PVSRA concepts
A table to highlight numeric differences and percentages
Additional overlays (e.g., diamonds, RSI+MFI synergy, etc.)
In contrast to many free, single-exchange indicators, this script centralizes multiple exchange volumes in one place, making it easier to observe and compare volume flows across different market types (spot vs. perpetual). Users no longer need to rely on scattered tools or separate overlays to check volume divergences, fractals, or specialized MA calculations—everything is unified here. By carefully monitoring the table, Delta histogram, color-coded bars, divergence lines, and diamond markers, traders can more comprehensively evaluate how volume and price interact. Each plot is designed to showcase different aspects of volume flow—such as whether spot or derivatives markets dominate, if volume is skewed toward buying or selling, and if there are divergences between volume momentum and price movement.
All computations are displayed to help you carry out a more informed market analysis. It is strongly advised to combine these observations with other risk management or analytical methods, rather than relying on any single indicator alone.
Market Overview TableThis script creates a market overview table that aggregates the signals from seven technical indicators into a single overall market trend. The goal of the table is to provide a quick summary of the market condition based on the combined behavior of multiple popular indicators. Instead of displaying each individual indicator's trend separately, it summarizes them into one overall market signal, displayed as a triangle (either up or down). This simplifies the decision-making process by focusing on an easy-to-read visual cue.
how it works
The table pulls in signals from seven indicators:
rsi (relative strength index): Measures if the asset is overbought (above 70) or oversold (below 30). In this case, the condition checks if the rsi is above 50, indicating a bullish trend.
ema (exponential moving average): A trend-following indicator that gives more weight to recent prices. It checks if the current price is above the ema value, which suggests an upward market trend.
sma (simple moving average): Similar to ema, it calculates the average price over a set period. When the price is above the sma, it indicates a bullish trend.
vwma (volume-weighted moving average): This average takes volume into account. It checks if the price is above the vwma, indicating higher trading activity in the direction of the trend.
bb (bollinger bands): The script compares the price to the upper bollinger band. If the price is above the upper band, it suggests that the price is in an overbought condition, signaling a bullish market.
williams fractals: A pattern recognition indicator that detects market turning points. In this case, it checks if the price is above the fractal high, indicating a bullish breakout.
momentum: Measures the rate of change in price over a set period. If the momentum is positive (price is rising), it indicates a bullish trend.
overall market calculation
The overall market condition is determined by the sum of bullish conditions across all seven indicators. For each indicator, if it shows a bullish signal (e.g., price above the moving average, rsi above 50), it is counted as a bullish indicator. The total number of bullish indicators is then tallied up:
If 4 or more indicators are bullish, the market is considered bullish overall.
If less than 4 indicators are bullish, the market is considered bearish overall.
This method aggregates the data from all seven indicators into a single market trend signal, represented by a triangle.
the triangle
The triangle (▲ or ▼) is used as the visual signal for the overall market trend. If the market is determined to be bullish (4 or more bullish indicators), the triangle will point up (▲), indicating a positive or upward trend. If the market is bearish (fewer than 4 bullish indicators), the triangle will point down (▼), signaling a negative or downward trend.
difference from individual indicators
The main difference between this approach and traditional indicator-based methods is the aggregation of multiple indicators into one simple signal. Instead of displaying seven separate signals for each indicator, which can be overwhelming and difficult to interpret quickly, this table combines them into one clear visual cue for the overall market condition. This makes it easier for traders to make quick decisions without having to analyze each individual indicator in detail.
Here’s what makes this approach unique:
Simplicity: Rather than plotting individual indicator signals on the chart, which can clutter the screen, the table condenses the market’s trend into a single up or down triangle, which is easier to interpret at a glance.
Comprehensive view: By aggregating seven indicators, the table considers multiple aspects of the market (e.g., momentum, trend, volume) to give a more comprehensive view of the market’s behavior, rather than relying on just one or two indicators.
Dynamic nature: As market conditions change and indicators fluctuate, the overall market trend dynamically updates, providing real-time feedback on the market’s direction.
table structure
The table is structured with two columns:
The first column contains the "OVERALL MARKET" label.
The second column displays the triangle (▲ or ▼) indicating the market trend based on the combined signal from all seven indicators.
By keeping it simple and focusing only on the overall market trend, this table allows traders to quickly grasp the market’s condition without having to sift through individual indicator data.
conclusion
This table simplifies the complexity of analyzing multiple indicators by summarizing their signals into a single, easy-to-read visual indicator. It is ideal for traders who want a quick, comprehensive view of market conditions without diving deep into the details of each individual indicator. The approach of aggregating multiple indicators into one overall market trend provides a clearer picture and saves time while maintaining the reliability of a multi-indicator analysis.
Kapua Whenua LANDZZ1Kapua Whenua means Earth's Clouds in Maori language, this indicator was created to show impulses and trends of the asset's price movement both up and down.
The Indicator was made based on key numbers of the golden ratio:
Conversion Line: Kw 17 (Purple Color)
Kw 34 - Short Period Fractal (Light Blue Color)
Kw 72 - Short/Medium Period Fractal (Orange)
Kw 144 - Medium Period Fractal (Yellow Color)
Kw 305 - Medium/Long Period Fractal (Dark Blue Color)
Kw 610 - Long Period Fractal (Grey)
Kw 1292 - Long Period Fractal+ (Black Color)
The baseline or also called the conversion line is identified by the Color Purple of value 17.
How to read the indicator:
Every time the conversion line (Purple Color 17 periods) crosses a Kw value (Kapua Whenua) it will always look for the next KW line above or below the value as support or resistance.
For example:
If we are in a bull market, and the price crosses below the KW17 conversion line it will go towards KW 34 as support, if the price breaks KW 34 it will go towards the next line below KW 34 as support.
Every time the conversion line or the price crosses a higher value of Kapua Whenua (KW) this trend will be stronger, it means, if the conversion line (KW 17) crosses above KW 305 it will indicate more strength than if it had crossed above the KW 72 or 144 for example. So to get better results trading with the trend, always observe if the conversion line and the price are below or above some Medium/Long Period KW.
Note also that, in an uptrend, it could be that all KW are below each other. In a downtrend, it could be that all KW are on top of each other. This indicates that the farther the price is from the fractals the stronger the trend is, also, when there is a narrowing of the fractals means that the price will start to go sideways. If the price is between 2 or more Fractals, it will indicate consolidation.
A really good trend is considered when the price or the Short/Medium Period Fractals are all above or below at KW 610, which is a long period fractal, meaning a strong uptrend or downtrend.
A larger KW can be at the same point as a smaller KW, however, the stronger color will be shown above the weaker one.
***Larger chart timeframes are better to see longer KW fractals that are above or below the price, if your chart timeframe doesn't show a bigger support or resistance fractal, change the chart time to another longer period**
Tip - Get used to looking at line colors as your indicators, just like moving averages. You can also take or place any fractal at any time in the configuration menu.
Kawabunga Swing Failure Points Candles (SFP) by RRBKawabunga Swing Failure Points Candles (SFP) by RagingRocketBull 2019
Version 1.0
This indicator shows Swing Failure Points (SFP) and Swing Confirmation Points (SCP) as candles on a chart.
SFP/SCP candles are used by traders as signals for trend confirmation/possible reversal.
The signal is stronger on a higher volume/larger candle size.
A Swing Failure Point (SFP) candle is used to spot a reversal:
- up trend SFP is a failure to close above prev high after making a new higher high => implies reversal down
- down trend SFP is a failure to close below prev low after making a new lower low => implies reversal up
A Swing Confirmation Point (SCP) candle is just the opposite and is used to confirm the current trend:
- up trend SCP is a successful close above prev high after making a new higher high => confirms the trend and implies continuation up
- down trend SCP is a successful close below prev low after making a new lower low => confirms the trend and implies continuation down
Features:
- uses fractal pivots with optional filter
- show/hide SFP/SCP candles, pivots, zigzag, last min/max pivot bands
- dim lag zones/hide false signals introduced by lagging fractals or
- use unconfirmed pivots to eliminate fractal lag/false signals. 2 modes: fractals 1,1 and highest/lowest
- filter only SFP/SCP candles confirmed with volume/candle size
- SFP/SCP candles color highlighting, dim non-important bars
Usage:
- adjust fractal settings to get pivots that best match your data (lower values => more frequent pivots. 0,0 - each candle is a pivot)
- use one of the unconfirmed pivot modes to eliminate false signals or just ignore all signals in the gray lag zones
- optionally filter only SFP/SCP candles with large volume/candle size (volume % change relative to prev bar, abs candle body size value)
- up/down trend SCP (lime/fuchsia) => continuation up/down; up/down trend SFP (orange/aqua) => possible reversal down/up. lime/aqua => up; fuchsia/orange => down.
- when in doubt use show/hide pivots/unconfirmed pivots, min/max pivot bands to see which prev pivot and min/max value were used in comparisons to generate a signal on the following candle.
- disable offset to check on which bar the signal was generated
Notes:
Fractal Pivots:
- SFP/SCP candles depend on fractal pivots, you will get different signals with different pivot settings. Usually 4,4 or 2,2 settings are used to produce fractal pivots, but you can try custom values that fit your data best.
- fractal pivots are a mixed series of highs and lows in no particular order. Pivots must be filtered to produce a proper zigzag where ideally a high is followed by a low and another high in orderly fashion.
Fractal Lag/False Signals:
- only past fractal pivots can be processed on the current bar introducing a lag, therefore, pivots and min/max pivot bands are shown with offset=-rightBars to match their target bars. For unconfirmed pivots an offset=-1 is used with a lag of just 1 bar.
- new pivot is not a confirmed fractal and "does not exist yet" while the distance between it and the current bar is < rightBars => prev old fractal pivot in the same dir is used for comparisons => gives a false signal for that dir
- to show false signals enable lag zones. SFP/SCP candles in lag zones are false. New pivots will be eventually confirmed, but meanwhile you get a false signal because prev pivot in the same dir was used instead.
- to solve this problem you can either temporary hide false signals or completely eliminate them by using unconfirmed pivots of a smaller degree/lag.
- hiding false signals only works for history and should be used only temporary (left disabled). In realtime/replay mode it disables all signals altogether due to TradingView's bug (barcolor doesn't support negative offsets)
Unconfirmed Pivots:
- you have 2 methods to check for unconfirmed pivots: highest/lowest(rightBars) or fractals(1,1) with a min possible step. The first is essentially fractals(0,0) where each candle is a pivot. Both produce more frequent pivots (weaker signals).
- an unconfirmed pivot is used in comparisons to generate a valid signal only when it is a higher high (> max high) or a lower low (< min low) in the dir of a trend. Confirmed pivots of a higher degree are not affected. Zigzag is not affected.
- you can also manually disable the offset to check on which bar the pivot was confirmed. If the pivot just before an SCP/SFP suddenly jumps ahead of it - prev pivot was used, generating a false signal.
- last max high/min low bands can be used to check which value was used in candle comparison to generate a signal: min(pivot min_low, upivot min_low) and max(pivot max_high, upivot max_high) are used
- in the unconfirmed pivots mode the max high/min low pivot bands partially break because you can't have a variable offset to match the random pos of an unconfirmed pivot (anywhere in 0..rightBars from the current bar) to its target bar.
- in the unconfirmed pivots mode h (green) and l (red) pivots become H and L, and h (lime) and l (fuchsia) are used to show unconfirmed pivots of a smaller degree. Some of them will be confirmed later as H and L pivots of a higher degree.
Pivot Filter:
- pivot filter is used to produce a better looking zigzag. Essentially it keeps only higher highs/lower lows in the trend direction until it changes, skipping:
- after a new high: all subsequent lower highs until a new low
- after a new low: all subsequent higher lows until a new high
- you can't filter out all prev highs/lows to keep just the last min/max pivots of the current swing because they were already confirmed as pivots and you can't delete/change history
- alternatively you could just pick the first high following a low and the first low following a high in a sequence and ignore the rest of the pivots in the same dir, producing a crude looking zigzag where obvious max high/min lows are ignored.
- pivot filter affects SCP/SFP signals because it skips some pivots
- pivot filter is not applied to/not affected by the unconfirmed pivots
- zigzag is affected by pivot filter, but not by the unconfirmed pivots. You can't have both high/low on the same bar in a zigzag. High has priority over Low.
- keep same bar pivots option lets you choose which pivots to keep when there are both high/low pivots on the same bar (both kept by default)
SCP/SFP Filters:
- you can confirm/filter only SCP/SFP signals with volume % change/candle size larger than delta. Higher volume/larger candle means stronger signal.
- technically SCP/SFP is always the first matching candle, but it can be invalidated by the following signal in the opposite dir which in turn can be negated by the next signal.
- show first matching SCP/SFP = true - shows only the first signal candle (and any invalidations that follow) and hides further duplicate signals in the same dir, does not highlight the trend.
- show first matching SCP/SFP = false - produces a sequence of candles with duplicate signals, highlights the whole trend until its dir changes (new pivot).
Good Luck! Feel free to learn from/reuse the code to build your own indicators!
AuriumFlowAURIUM (GOLD-Weighted Average with Fractal Dynamics)
Aurium is a cutting-edge indicator that blends volume-weighted moving averages (VWMA), fractal geometry, and Fibonacci-inspired calculations to deliver a precise and holistic view of market trends. By dynamically adjusting to price and volume, Aurium uncovers key levels of confluence for trend reversals and continuations, making it a powerful tool for traders.
Key Features:
Dynamic Trendline (GOLD):
The central trendline is a weighted moving average based on price and volume, tuned using Fibonacci-based fast (34) and slow (144) exponential moving average lengths. This ensures the trendline adapts seamlessly to the flow of market dynamics.
Formula:
GOLD = VWMA(34) * Volume Factor + VWMA(144) * (1 - Volume Factor)
Fractal Highs and Lows:
Detects pivotal market points using a fractal lookback period (default 5, odd-numbered). Fractals identify local highs and lows over a defined window, capturing the structure of market cycles.
Trend Background Highlighting:
Bullish Zone: Price above the GOLD line with a green background.
Bearish Zone: Price below the GOLD line with a red background.
Buy and Sell Alerts:
Generates actionable signals when fractals align with GOLD. Bullish fractals confirm continuation or reversal in an uptrend, while bearish fractals validate a downtrend.
The Math Behind Aurium:
Volume-Weighted Adjustments:
By integrating volume into the calculation, Aurium dynamically emphasizes price levels with greater participation, giving traders insight into zones of institutional interest.
Formula:
VWMA = EMA(Close * Volume) / EMA(Volume)
Fractal Calculations:
Fractals are identified as local maxima (highs) or minima (lows) based on the surrounding bars, leveraging the natural symmetry in price behavior.
Fibonacci Relationships:
The 34 and 144 EMA lengths are Fibonacci numbers, offering a natural alignment with price cycles and market rhythms.
Ideal For:
Traders seeking a precise and intuitive indicator for aligning with trends and detecting reversals.
Strategies inspired by Bill Williams, with added volume and fractal-based insights.
Short-term scalpers and long-term trend-followers alike.
Unlock deeper market insights and trade with precision using Aurium!
GKD-C FDI-Adaptive Supertrend [Loxx]Giga Kaleidoscope GKD-C FDI-Adaptive Supertrend is a Volatility/Volume module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Damiani Volatmeter as shown on the chart above
Confirmation 1: FDI-Adaptive Supertrend as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
█ GKD-C FDI-Adaptive Supertrend
What is the Fractal Dimension Index?
The Fractal Dimension Index (FDI) is a measure of the complexity or irregularity of a geometric shape or pattern. It is a mathematical concept that quantifies the degree of self-similarity or self-affinity of an object at different scales. The FDI is a real number that represents the scaling behavior of an object in a particular space, and it can be used to characterize a wide range of natural and synthetic phenomena, from coastlines to fractal art.
The FDI is based on the concept of fractals, which are objects that exhibit self-similar or self-affine patterns at different scales. Fractals are characterized by their fractional dimensionality, which is a non-integer number that describes their complexity. The FDI is a measure of this fractional dimensionality, and it can be calculated using a variety of mathematical techniques, including box counting, wavelet analysis, and Fourier analysis.
In practical terms, the FDI can be used to quantify the complexity or roughness of natural surfaces, such as soil or rock, as well as the irregularity of synthetic materials, such as concrete or ceramics. It is also used in image analysis and pattern recognition to characterize the complexity of digital images and to detect patterns that are difficult to discern with traditional methods.
In forex trading, the Fractal Dimension Index (FDI) is a technical indicator used to analyze market trends and price movements. The FDI is calculated based on the fractal geometry of price charts and is used to identify support and resistance levels, as well as potential changes in trend direction.
The FDI indicator works by measuring the fractal dimensionality of price movements. Fractals are self-similar or self-affine patterns that repeat at different scales, and they can be used to identify key levels of support and resistance in the market. The FDI indicator calculates the fractal dimension of price movements over a specified time period, and it plots the result as a line on the price chart.
Traders use the FDI indicator to identify potential trend changes and to confirm trend direction. When the FDI line crosses above or below a key level, such as 1.5, it may indicate a potential trend reversal. Additionally, when the FDI line is trending in the same direction as the price, it can confirm the current trend and provide additional confidence for traders.
Overall, the Fractal Dimension Index is a technical indicator that can be used to analyze market trends and price movements in forex trading. By measuring the fractal dimensionality of price movements, traders can identify potential support and resistance levels and confirm trend direction.
What is Supertrend?
Supertrend is a popular technical indicator used in trading to identify trends in the market. It is a trend-following indicator that helps traders to identify the direction of the market trend and to enter or exit trades accordingly.
The Supertrend indicator is based on the Average True Range (ATR) and the price action of an asset. It plots a line on the price chart that follows the trend of the asset and indicates potential support and resistance levels. The Supertrend line changes its color when the trend changes, which can be used as a signal to enter or exit trades.
The Supertrend indicator is used to identify both long-term and short-term trends in the market. When the Supertrend line is above the price, it indicates a downtrend, and when it is below the price, it indicates an uptrend. Traders can use the Supertrend indicator to identify potential entry and exit points for their trades, as well as to set stop-loss orders and take-profit levels.
Supertrend is a popular indicator among traders because it is easy to use and can be applied to a variety of markets and timeframes. However, like any technical indicator, it is not perfect and can produce false signals in certain market conditions. Therefore, it is important to use the Supertrend indicator in combination with other indicators and to have a solid trading strategy in place.
What is FDI-Adaptive Supertrend?
FDI-Adaptive Supertrend uses FDI to adapt the period inputs into Supertrend to make Supertrend FDI-adaptive.
Requirements
Inputs
Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Outputs
Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator
Confirmation 1: GKD-C Confirmation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest strategy
Additional features will be added in future releases.
Remora4 indicadores en 1
Esto se logró combinando algunas de las ideas de otros tres indicadores:
- Williams Alligator
- Fractales y niveles
- Oscilador Asombroso
- Bitrader4.0
Como operar:
El Alligator nos muestra la tendencia, operar a favor de la tendencia.
Ubicar los fractales y determinar los breakouts con ellos.
El indicador Bitrader4.0 nos muestra la compra y venta de acuerdo a la configuración de los parámetros setup
Funcional en todos los timeframe.
////////////////////////////////
4 indicators in 1
This was achieved by combining some of the ideas from three other indicators:
- True Williams Alligator
- Fractals and Levels
- Awesome Oscillator
- Bitrader4.0
How to operate:
The trend will show us the alligator, operate in favor of the trend.
Locate the fractals and determine the breakouts with them.
The Bitrader4.0 indicator will show us buying and selling according to the setup parameters.
Functional in all timeframe.
MSSM - Multi-Session Structural Map (Precision Sweeps)MSSM – Multi-Session Structural Map (Precision Sweeps)
This indicator provides a structured view of the market based on four key components:
1). Previous session levels
2). Confirmed fractal swing points
3). Volume pocket highlights
4). Non-repainting precision liquidity sweep markers
It is designed to help analyze how price interacts with important reference areas and structural points. This tool does not generate signals or predictions. All information is visual and educational only.
HOW THE INDICATOR WORKS
PREVIOUS SESSION LEVELS
The script plots the previous session’s High, Low, and Mid. These levels help observe how the current session behaves around the prior day’s range. They act as reference areas only.
FRACTAL SWING MAP (NON-REPAINTING)
Confirmed fractals are used to mark historical swing highs and swing lows. Since fractals confirm after a certain number of bars, the swings do not repaint once formed. These swings provide a clearer view of market structure.
VOLUME POCKETS
The indicator highlights areas where volume expands relative to a rolling volume average. These regions show increased participation or activity. The highlights are informational and do not imply direction.
PRECISION LIQUIDITY SWEEPS (NON-REPAINTING)
A sweep is tagged only when:
• Price trades beyond a confirmed swing high or swing low
• Price closes back inside the previous swing level
• A wick rejection occurs
• Volume expands relative to a recent rolling average
These markers simply show where price interacted with liquidity around prior structural levels. They do not indicate a trading signal or bias.
HOW TO ADD THE INDICATOR
Open the Pine Editor in TradingView
Search the indicator name and add to favorites.
Click “Add to chart”
Adjust settings as needed (fractals, sweeps, volume pockets, or session levels)
HOW TO READ AND USE THE INDICATOR
SESSION LEVELS
Observe whether price respects, rejects, compresses around, or expands beyond the previous session high, low, or midpoint. These are observational reference levels only.
FRACTALS
Fractal highs and lows help visualize structural turning points. They provide a clearer picture of where liquidity may rest above or below past swing levels.
VOLUME POCKETS
When volume expands compared to the recent average, the candle is shaded. These areas may show increased participation, but no directional meaning is implied.
PRECISION SWEEPS
Sweeps highlight when price reaches beyond a prior confirmed swing level and then rejects that area with displacement. These markers identify interactions with liquidity, but they are not signals and do not forecast future outcomes.
CUSTOMIZATION OPTIONS
Users can adjust:
• Session level visibility
• Fractal sensitivity
• Volume pocket threshold
• Sweep sensitivity and visibility
• Transparency and styling
This makes the tool flexible across different symbols and timeframes.
IMPORTANT NOTES AND POLICY COMPLIANCE
• The indicator does not provide buy or sell signals
• The indicator does not predict price or direction
• All plotted elements are based on past price behavior
• All components are informational only
• Users should perform their own analysis and risk evaluation
• Past behavior does not guarantee future performance
SUMMARY
MSSM provides a structured view of price by combining previous session levels, confirmed swing structure, volume expansion zones, and non-repainting sweep identification. Its purpose is to assist traders in visually analyzing market structure while staying fully aligned with TradingView’s House Rules and content policies.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Musashi_Fractal_Dimension === Musashi-Fractal-Dimension ===
This tool is part of my research on the fractal nature of the markets and understanding the relation between fractal dimension and chaos theory.
To take full advantage of this indicator, you need to incorporate some principles and concepts:
- Traditional Technical Analysis is linear and Euclidean, which makes very difficult its modeling.
- Linear techniques cannot quantify non-linear behavior
- Is it possible to measure accurately a wave or the surface of a mountain with a simple ruler?
- Fractals quantify what Euclidean Geometry can’t, they measure chaos, as they identify order in apparent randomness.
- Remember: Chaos is order disguised as randomness.
- Chaos is the study of unstable aperiodic behavior in deterministic non-linear dynamic systems
- Order and randomness can coexist, allowing predictability.
- There is a reason why Fractal Dimension was invented, we had no way of measuring fractal-based structures.
- Benoit Mandelbrot used to explain it by asking: How do we measure the coast of Great Britain?
- An easy way of getting the need of a dimension in between is looking at the Koch snowflake.
- Market prices tend to seek natural levels of ranges of balance. These levels can be described as attractors and are determinant.
Fractal Dimension Index ('FDI')
Determines the persistence or anti-persistence of a market.
- A persistent market follows a market trend. An anti-persistent market results in substantial volatility around the trend (with a low r2), and is more vulnerable to price reversals
- An easy way to see this is to think that fractal dimension measures what is in between mainstream dimensions. These are:
- One dimension: a line
- Two dimensions: a square
- Three dimensions: a cube.
--> This will hint you that at certain moment, if the market has a Fractal Dimension of 1.25 (which is low), the market is behaving more “line-like”, while if the market has a high Fractal Dimension, it could be interpreted as “square-like”.
- 'FDI' is trend agnostic, which means that doesn't consider trend. This makes it super useful as gives you clean information about the market without trying to include trend stuff.
Question: If we have a game where you must choose between two options.
1. a horizontal line
2. a vertical line.
Each iteration a Horizontal Line or a Square will appear as continuation of a figure. If it that iteration shows a square and you bet vertical you win, same as if it is horizontal and it is a line.
- Wouldn’t be useful to know that Fractal dimension is 1.8? This will hint square. In the markets you can use 'FD' to filter mean-reversal signals like Bollinger bands, stochastics, Regular RSI divergences, etc.
- Wouldn’t be useful to know that Fractal dimension is 1.2? This will hint Line. In the markets you can use 'FD' to confirm trend following strategies like Moving averages, MACD, Hidden RSI divergences.
Calculation method:
Fractal dimension is obtained from the ‘hurst exponent’.
'FDI' = 2 - 'Hurst Exponent'
Musashi version of the Classic 'OG' Fractal Dimension Index ('FDI')
- By default, you get 3 fast 'FDI's (11,12,13) + 1 Slow 'FDI' (21), their interaction gives useful information.
- Fast 'FDI' cross will give you gray or red dots while Slow 'FDI' cross with the slowest of the fast 'FDI's will give white and orange dots. This are great to early spot trend beginnings or trend ends.
- A baseline (purple) is also provided, this is calculated using a 21 period Bollinger bands with 1.618 'SD', once calculated, you just take midpoint, this is the 'TDI's (Traders Dynamic Index) way. The indicator will print purple dots when Slow 'FDI' and baseline crosses, I see them as Short-Term cycle changes.
- Negative slope 'FDI' means trending asset.
- Positive most of the times hints correction, but if it got overextended it might hint a rocket-shot.
TDI Ranges:
- 'FDI' between 1.0≤ 'FDI' ≤1.4 will confirm trend following continuation signals.
- 'FDI' between 1.6≥ 'FDI' ≥2.0 will confirm reversal signals.
- 'FDI' == 1.5 hints a random unpredictable market.
Fractal Attractors
- As you must know, fractals tend orbit certain spots, this are named Attractors, this happens with any fractal behavior. The market of course also shows them, in form of Support & Resistance, Supply Demand, etc. It’s obvious they are there, but now we understand that they’re not linear, as the market is fractal, so simple trendline might not be the best tool to model this.
- I’ve noticed that when the Musashi version of the 'FDI' indicator start making a cluster of multicolor dots, this end up being an attractor, I tend to draw a rectangle as that area as price tend to come back (I still researching here).
Extra useful stuff
- Momentum / speed: Included by checking RSI Study in the indicator properties. This will add two RSI’s (9 and a 7 periods) plus a baseline calculated same way as explained for 'FDI'. This gives accurate short-term trends. It also includes RSI divergences (regular and hidden), deactivate with a simple check in the RSI section of the properties.
- BBWP (Bollinger Bands with Percentile): Efficient way of visualizing volatility as the percentile of Bollinger bands expansion. This line varies color from Iced blue when low volatility and magma red when high. By default, comes with the High vols deactivated for better view of 'FDI' and RSI while all studies are included. DDWP is trend agnostic, just like 'FDI', which make it very clean at providing information.
- Ultra Slow 'FDI': I noticed that while using BBWP and RSI, the indicator gets overcrowded, so there is the possibility of adding only one 'FDI' + its baseline.
Final Note: I’ve shown you few ways of using this indicator, please backtest before using in real trading. As you know trading is more about risk and trade management than the strategy used. This still a work in progress, I really hope you find value out of it. I use it combination with a tool named “Musashi_Katana” (also found in TradingView).
Best!
Musashi
Obbar
in case you don’t know what fractal is fractal is a way of a trading by finding critical price levels those critical price levels formed every five candle The middle candle is the critical one, The high or low of this candle is considered support or resistant at the current moment.
The current implementation of fractals indicators across many platforms, tradingview one of them not that Great although it undefined the fractals however at least the hard bar for your brain to process you need to link these factors together to form a trend line , has no directions for the trend and for sure it doesn’t show you the trigger candle.
I have been working for the best three years in a new way to trade fractals A new ecosystem that has it all, support and resistance level showing the trend and also show you the trigger candle where are you can but your buy or sell order. and I call it Obbar.
Identifying the trade:
now let’s see how you can trade this indicator, first let’s identify the trend because you know trend is your friend, and since fractal does not tell you the trend, Will use OBV for this task by applying fractals concepts to OBV we can easily see in which level buyer or seller appeal, since that level is important we should also see a huge volume on those levels, and by applying this concepts we have three area bluish The green one where you should only look for buy signal, a bearish area the red one where you should only look for sell signal, indecision area the blue where are you should just ignore the market.
The trigger candle:
it’s yellow when it’s buying signal don’t forget the trend of course so if the trend is bluish and you have yellow candle The trigger candle will be buying candle.
it’s red when it’s a sell signal added to the trend you should only sell when the trend is bearish of course.
hope this indicator improve your fractal trading, have fun
🔥 SMC Reversal Engine v3.5 – Clean FVG + DashboardSMC Reversal Engine v3.5 – Clean FVG + Dashboard
The SMC Reversal Engine is a precision-built Smart Money Concepts tool designed to help traders understand market structure the single most important foundation in reading price action. It reveals how institutions move liquidity, where structure shifts occur, and how Fair Value Gaps (FVGs) align with these changes to signal potential reversals or continuations.
Understanding How It Works
At its core, the script detects CHoCH (Change of Character) and BOS (Break of Structure)—the two key turning points in institutional order flow. A CHoCH shows that the market has reversed intent (for example, from bearish to bullish), while a BOS confirms a continuation of the current trend. Together, they form the backbone of structure-based trading.
To refine this logic, the engine uses fractal pivots clusters of candles that confirm swing highs and lows. Fractals filter out noise, identifying points where price truly changes direction. The script lets you set this sensitivity manually or automatically adapts it depending on the timeframe. Lower fractal sensitivity captures smaller intraday swings for scalpers, while higher sensitivity locks onto major swing structures for swing and position traders.
The dashboard gives you a real-time reading of the trend, the last high and low, and what the market is likely to do next—for example, “Expect HL” or “Wait for LH.” It even tracks the accuracy of these structure predictions over time, giving an educational feedback loop to help you learn price behavior.
Fair Value Gaps and Tap Entries
Fair Value Gaps (FVGs) mark moments when price moves too quickly, leaving inefficiencies that institutions often revisit. When price taps into an FVG, it often acts as a high-probability entry zone for reversals or continuations. The script automatically detects, extends, and deletes old FVGs, keeping only relevant zones visible for a clean chart.
Traders can enable markTapEntry to visually confirm when an FVG gets filled. This is a simple but powerful trigger that often aligns with CHoCH or BOS moments.
Recommended Settings for Different Traders
For Scalpers, use a lower HTF structure such as 1 minute or 5 minutes. Keep Auto Fractals on for faster reaction, and limit FVG zones to 2–3. This gives you a clean, real-time reflection of order flow.
For Intraday Traders, 15-minute to 1-hour structure gives the perfect balance between reactivity and stability. Fractal sensitivity around 3–5 captures the most actionable levels without excessive noise.
For Swing Traders, use 4-hour, 1-day, or even 3-day structure. The chart becomes smoother, showing higher-order CHoCH and BOS that define true institutional transitions. Combine this with EMA confirmation for higher conviction.
For Position or Macro Traders, select Weekly or Monthly structure. The dynamic label system expands automatically to keep more historical BOS/CHoCH points visible, allowing you to see long-term shifts clearly.
Educational Value
This indicator is built to teach traders how to see structure the way professionals and smart money do. You’ll learn to recognize how markets transition from one phase to another from accumulation to manipulation to expansion. Each CHoCH or BOS helps you decode where liquidity is being taken and where new intent begins.
The included SMC Quick Guide explains each structural cue right on your chart. Within days of using it, you’ll start noticing patterns that reveal how price really moves, instead of guessing based on indicators.
Settings and How to Use Them
Everything in the SMC Reversal Engine is designed to adapt to your trading style and help you read structure like a professional.
When you open the Inputs Panel, you’ll see sections like Fractal Settings, FVG Settings, Buy/Sell Confirmation, and Educational Tools.
Under Fractal Settings, you can choose the higher timeframe (HTF) that defines structure—from minutes to weeks. The Auto Fractal Sensitivity option automatically adjusts how tight or wide swing points are detected. Lower sensitivity captures short-term fluctuations (great for scalpers), while higher values filter noise and isolate major swing highs and lows (perfect for swing traders).
The Fair Value Gap (FVG) options manage imbalance zones—the footprints of institutional orders. You can show or hide these zones, extend them into the future, and control how long they remain before auto-deletion. The Mark Entry When FVG is Tapped option places a small label when price revisits the gap—a potential entry signal that aligns with smart money logic.
EMA Confirmation adds a layer of confluence. The script can automatically scale EMA lengths based on timeframe, or you can input your preferred values (for example, 9/21 for intraday, 50/200 for swing). Require EMA Crossover Confirmation helps filter false moves, keeping you trading only with aligned momentum.
The Educational section gives traders visual reinforcement. When enabled, you’ll see tags like HH (Higher High), HL (Higher Low), LH (Lower High), and LL (Lower Low). These show structure shifts in real time, helping you learn visually what market structure really means. The Cheat Sheet panel summarizes each term, always visible in the corner for quick reference.
Early Top Warnings use wick size and RSI divergence to signal when price may be overextended—a useful heads-up before potential CHoCH formations.
Finally, the Narrative and Accuracy System translates structure into simple English—messages like Trend Bullish → Wait for HL or BOS Bearish → Expect LL. Over time, you can monitor how accurate these expectations have been, training your pattern recognition and confidence.
Pro Tips for Getting the Most Out of the SMC Reversal Engine
1. Start on Higher Timeframes First: Begin on the 4H or Daily chart where structure is cleaner and signals have more weight. Then scale down for entries once you grasp directional intent.
2. Use FVGs for Context, Not Just Entries: Observe how price behaves around unfilled FVGs—they often act as magnets or barriers, offering insight into where liquidity lies.
3. Combine With HTF Bias: Always trade in the direction of your higher timeframe trend. A bullish weekly BOS means lower timeframes should ideally align bullishly for optimal setups.
4. Clean Charts = Clear Mind: Use Minimal Mode when focusing on price action, then toggle the educational tools back on to review structure for learning.
5. Don’t Chase Every CHoCH or BOS: Focus on significant breaks that align with broader context and liquidity sweeps, not minor fluctuations.
6. Accuracy Rate Is a Feedback Tool: Use the accuracy stat as a reflection of consistency—not a trade trigger.
7. Build Narrative Awareness: Read the on-chart narrative messages to reinforce structured thinking and stay disciplined.
8. Practice Replay Mode: Step through past structures to visually connect CHoCH, BOS, and FVG behavior. It’s one of the best ways to train pattern recognition.
Summary
* Detects CHoCH and BOS automatically with fractal precision
* Identifies and manages Fair Value Gaps (FVGs) in real time
* Displays a smart dashboard with accuracy tracking
* Adapts label visibility dynamically by timeframe
* Perfect for both learning and trading with institutional clarity
This tool isn’t about predicting the market—it’s about understanding it. Once you can read structure, everything else in trading becomes secondary.
Dynamic Pivot Tracker Multi-Period - TradingEDThe "Dynamic Pivot Tracker Multi-Period" is an advanced tool designed for traders who require a detailed, multi-layered view of pivot levels across multiple time frames from a single chart. This indicator uses dynamic calculations to identify and connect high and low pivot points, aiding traders in pinpointing potential support and resistance areas and making informed decisions based on significant market structural changes.
Key Features:
Flexibility of Periods: Users can define pivot lengths for up to four different periods, ranging from as short as minutes to as long as days, allowing complete customization based on the trader's needs.
Dynamic Pivot Length Calculation: Utilizes a specialized function to adjust pivot length based on the selected time unit, ensuring pivot detection is relevant to the current time context.
Pivot Point Detection: Calculates and plots high and low pivots for each defined period using the pivothigh and pivotlow function for optimum accuracy.
Dynamic Line Management: The indicator dynamically manages the lines connecting pivots, removing old ones before plotting new to keep the chart clean and up-to-date.
Crossover Alerts: Set up automatic alerts to notify users when the price crosses above or below a pivot level, which may be indicative of an entry or exit opportunity.
Customizable Display Options:
Show Fractals: Option to visualize or hide fractals, which can indicate price reversal points.
Show Lines: Toggle the display of lines connecting the pivots, making it easier to visualize trends and ranges.
Show Pivots: Allows users to choose whether or not to see pivot points marked directly on the chart.
Practical Applications:
Trend Analysis: By observing how the price interacts with pivots across different periods, traders can gain a deeper understanding of market direction.
Support and Resistance Identification: Pivot levels can act as zones of support or resistance, providing key points for stop placement or profit taking.
Optimization of Entries/Exits: Crossover alerts provide timely signals to enter or exit positions based on significant price movements.
This indicator is ideal for day traders, swing traders, and technical traders looking to integrate pivot analysis into their trading methodology, offering a robust and customizable tool to enhance market decision-making.
Market Structure & Price Action Toolkit (Expo)█ Overview
This comprehensive Market Structure and Price Action toolkit integrates pioneering price action concepts, including fractal-based market structure, grid-price action system, retail and institutional levels/zones, liquidity concepts, and a plethora of advanced customization options to give you a trading advantage via price action automatically. Different from traditional technical indicators, which can be lagging, complex, and cluttered, this indicator focuses solely on raw price data to deliver accurate and real-time insights. All the features in this script originate exclusively from price action, concentrating on fractals-based swing highs, swing lows, and market structure. This enables users to automate their price action analysis across any market or timeframe.
The toolkit focuses on the real-time application of price data rather than historical data to ensure its usefulness for price action and smart money (ICT) traders. With this indicator, users can automate their price action analysis across various markets and timeframes, gaining a significant edge in their trading strategies.
█ Features and How They Work
█ Trading Systems
Market Structure:
Market Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts and changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market. This feature is based on real-time fractals instead of static pivot points. Fractals are based on the idea that markets are patterned, and those patterns repeat themselves on all scales – hence, the term "fractal", which means "fraction of the whole". The function uses fractal zones that refer to areas where the price is likely to experience a change in direction. These zones are identified by observing a series of fractal points.
Grid:
The grid system works similarly to the market structure but displays the data as a grid of support and resistance zones. This is a new and unique approach to understanding market structure. It might be a more convenient way for traders to understand how to act.
█ Retail Zones
Support/Resistance:
Support and Resistance zone are often seen and displayed with a delay. This feature is 100% real-time and displays SR levels as the price reacts and forms new highs and lows.
Confirmed Support/Resistance:
As the name suggests, the confirmed zone is first displayed on the chart when the price has reacted to a high/low formation over x period of time. This feature is handy to trade retest after breakouts of the zone.
We wanted to keep the retail zones simple regarding how they work and function to help all kinds of traders understand how to use them.
█ Institutional Zones
Supply/Demand:
Calculating supply and demand in its raw form is challenging due to the complexity and dynamism of financial markets. However, the function uses several concepts to gauge supply and demand levels.
Buying and Selling pressure: The buying pressure represents the highest price point (over x period and volume), while the selling pressure price represents the lowest price point (over x period and volume). The gap between the two is known as the buying/selling pressure spread. A narrow spread often signifies high liquidity and balanced supply and demand, while a wider spread might indicate imbalances.
Price Trends: Upward price movements indicate higher demand, while downward trends may suggest increased supply.
Order blocks:
Order blocks are similar to supply/demand, and the main difference is that an order block is created at specific price action and market structure patterns.
█ How to use the Market Structure Toolkit
Market Structure
Market Structure + Confirmed S/R
Grid System
Demand Zone
Supply Zone
Order Block
Support/Resistance Zones
Confirmed Support/Resistance Zone
Retest of SR Levels
█ Why Use Price Action and Market Structure
A comprehensive trading strategy often involves using both price action and market structure. Traders can use price action to understand the immediate behavior of the price and market structure to understand the broader context within which the price is moving.
Market Structure combined with Price Action refers to the observable pattern of price movement. Traders use this structure to identify trend direction (up, down, or sideways), market phase (trend or range), and key price levels (like support and resistance).
Here are some core concepts within price action trading:
Trend Identification: This is a fundamental aspect of price action trading. By simply looking at the raw price data on a chart, traders can identify whether the instrument is in an uptrend (making higher highs and higher lows), a downtrend (making lower highs and lower lows), or ranging sideways.
Support and Resistance Levels: These are horizontal lines drawn on a chart where the price has historically had difficulty moving beyond. Support is a price level where buying pressure is strong enough to prevent the price from falling further, while resistance is a level where selling pressure is strong enough to prevent further price increases.
Candlestick Patterns: Price action traders rely heavily on candlestick patterns, which can provide a lot of information about market sentiment.
Chart Patterns: In addition to individual candlestick patterns, price action traders often look for larger chart patterns like double tops/bottoms, triangles, wedges, head and shoulders patterns, and more. These patterns can take longer to form but can also provide insight into potential price movement.
Price Zones: Rather than exact price levels, many price action traders consider zones of support and resistance, understanding that market behavior isn't always perfectly precise. A zone might cover a small range of prices at which the market has repeatedly reversed in the past.
The idea behind price action trading is that the price itself can provide clues to what the market might do next. Traders who follow this approach believe that price is the final determinant of value and contains all the information needed.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ In conclusion, This toolkit is particularly useful for price action and smart money traders, as it prioritizes real-time application of price data, which in turn allows a more responsive and informed decision-making process in trading.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Paid script
Advanced Support & Resistance Zone DetectionAdvanced Support & Resistance Zone Detection Indicator: A Comprehensive Overview
The "Advanced Support & Resistance Zone Detection" indicator, developed by @HarryCTC, is a powerful tool designed to identify support and resistance levels in trading markets. This indicator helps traders identify areas where the price is likely to encounter obstacles and potentially reverse its direction. By understanding these key levels, traders can make more informed decisions and improve their overall trading strategies.
This indicator is based on the Williams Fractal Indicator.
The indicator employs a fractal-based approach to identify support and resistance levels. Fractals are patterns that indicate potential price reversals. The indicator scans the price data and looks for specific fractal patterns that signify the presence of support or resistance.
For support levels, the indicator searches for downward fractals, where a lower low is formed surrounded by higher lows on both sides. This pattern suggests a potential support level as the price has temporarily stopped declining and may reverse its direction.
Conversely, for resistance levels, the indicator looks for upward fractals, where a higher high is formed surrounded by lower highs on both sides. This pattern indicates a potential resistance level where the price has temporarily halted its ascent and may reverse downwards.
The indicator applies certain conditions and filters to ensure the significance of the identified support and resistance levels. These conditions prevent the inclusion of minor price fluctuations and focus on capturing major turning points in the market.
Once a support or resistance level is detected, the indicator plots it on the chart either as a zone or a line, based on user preferences. The indicator also keeps track of previously identified levels and updates them as new levels are found.
By utilizing fractal analysis, the "Advanced Support & Resistance Zone Detection" indicator provides traders with valuable insights into key levels where price reversals are likely to occur. Traders can use this information to plan their entries, exits, and overall trading strategies more effectively.
The indicator utilizes several input parameters that allow users to customize its behavior according to their preferences and trading styles. Let's explore each of these parameters in detail:
1. Periods: This parameter determines the number of periods considered when identifying support and resistance levels. It indicates the length of the lookback period used to analyze price action and detect potential zones.
2. S&R Distance: The S&R distance parameter specifies the minimum distance, in pips, between the identified support and resistance levels. It helps filter out insignificant price fluctuations and focuses on significant price zones.
3. Number of S&R Levels to Show: This parameter controls the maximum number of support and resistance levels displayed on the chart. Users can set the desired number to avoid cluttering the chart with excessive information.
4. Draw S&R Zones: If enabled, this parameter allows the indicator to draw support and resistance zones on the chart. These zones represent areas where price reversals are likely to occur. Traders can visually analyze the chart and observe the significance of these zones.
5. Draw S&R Lines: This parameter determines whether the indicator should draw lines representing support and resistance levels on the chart. These lines provide a clear visual representation of the detected levels.
6. Resistance Zone Color: Users can customize the color of the resistance zones drawn on the chart. By choosing distinct colors, traders can easily differentiate between support and resistance zones.
7. Support Zone Color: Similarly, this parameter allows users to specify the color of the support zones displayed on the chart.
8. Resistance Line Color: Traders can choose the color of the lines representing resistance levels. This color customization helps in visually distinguishing resistance levels from other elements on the chart.
9. Support Line Color: This parameter determines the color of the lines representing support levels.
10. S&R Zone & Line Extension: The S&R zone and line extension parameter defines the extension of support and resistance zones and lines to the right side of the chart. It provides traders with a visual projection of the potential future behavior of these levels.
11. S&R Line Width: Users can adjust the width of the lines representing support and resistance levels. This customization option helps traders emphasize or de-emphasize these lines based on their preferences.
[JL] How Many Signals last N barsGot this idea after I found Multiple Indicators Screener from QuantNomad.
This script learnt some codes from QuantNomad's great script. Thanks to him.
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This table show how many signals happened during the last N bars.
I only take care Forex, so this table only has 28 symbols. Feel free to change it.
Calculate the following signals:
RSI cross over/under 50
Short Moving average cross over/under long moving average
Stochastic k cross over/under d
MACD hist cross over/under 0
Williams Fractals: Up and Down fractals happened.
The concept is simple: Range period will always happen more cross signals than the trend period.
When the counter is less than median of all symbols, will be set green color. So more green mean more chance to be trend.
[blackcat] L2 Ehlers FRAMALevel: 2
Background
John F. Ehlers introuced Fractal Adaptive Moving Average (FRAMA) in 2004.
Function
The objective of using filters is to separate the desired signals from the undesired signals (or noise). The practical application of moving averages often involves a tradeoff between the amount of smoothness required and the amount of lag that can be tolerated. Moving averages have this problem because the price data is not stationary, and may have different bandwidths over different time intervals. Various momentum-adaptive filtering techniques have been developed to take advantage of the nonstationary structure of prices. Adaptive filters have also been developed based on price statistics and the cyclic content of the price data . Dr. Ehlers described a different class of filters that monitor a different measure of temporal nonstationarity and alters their bandwidth in response to this measure.
There is no argument that market prices are fractal. Fractal shapes are self-similar because they tend to have the same roughness and sparseness regardless of the magnification used to view them. If you remove the labels from a 5 minute chart, a daily chart, and a weekly chart you would have difficulty telling them apart. This is the characteristic that makes them fractal. The self-similarity can be defined by the fractal dimension that describes the sparseness at all magnification levels.
To determine the fractal dimension of a generalized pattern, Dr. Ehlers cover the pattern with a number “N” of small objects of several various sizes “s”. As with any moving average, we are forced to compromise between responsiveness and smoothness. FRAMA can be a valuable weapon in your arsenal of technical indicators. It rapidly follows significant changes in price but becomes very flat in congestion zones so that bad whipsaw trades can be eliminated.
Key Signal
Filt --> FRAMA fast line
Trigger --> FRAMA slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 63th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Pinbar signal 顶/底分型 指标Top/Bottom Fractal Indicator is a technical analysis tool used to identify potential reversal points in a market trend. It is based on the concept of fractal patterns, which consist of specific candlestick formations that signal local highs (top fractals) or lows (bottom fractals).
A top fractal typically occurs when the high of a middle candlestick is higher than the highs of the two candles on either side. This formation suggests that upward momentum may be weakening and a downward reversal could follow. Conversely, a bottom fractal forms when the low of a middle candlestick is lower than the lows of the two adjacent candles, indicating a potential shift from a downtrend to an uptrend.
This indicator is commonly used to spot market turning points, determine entry or exit signals, and enhance the reliability of other indicators such as Bollinger Bands or MACD. Because of its clear structure and reliable reversal signals, the Top/Bottom Fractal Indicator is also widely applied in wave theory and price action strategies.
A Pin Bar is a powerful candlestick pattern used in technical analysis to identify potential price reversals or continuations in the market. It is characterized by a small body located at one end of the candle and a long tail or wick on the opposite side, which indicates a strong rejection of price in that direction. A bullish Pin Bar has a long lower wick, showing that sellers pushed the price down but buyers regained control, suggesting a potential upward move. Conversely, a bearish Pin Bar has a long upper wick, indicating that buyers attempted to drive prices higher but were overpowered by sellers, hinting at a possible downward move. For high-probability setups, traders typically look for Pin Bars that form at key support or resistance levels, trendlines, or Fibonacci zones, and they often use them in combination with overall trend direction. Entry strategies include entering at the close of the Pin Bar, on a retracement, or above/below the wick with stop-losses placed beyond the wick’s extreme. When used correctly in the right context, the Pin Bar can be a highly effective signal in a trader’s toolkit.
顶/底分型 指标Top/Bottom Fractal Indicator is a technical analysis tool used to identify potential reversal points in a market trend. It is based on the concept of fractal patterns, which consist of specific candlestick formations that signal local highs (top fractals) or lows (bottom fractals).
A top fractal typically occurs when the high of a middle candlestick is higher than the highs of the two candles on either side. This formation suggests that upward momentum may be weakening and a downward reversal could follow. Conversely, a bottom fractal forms when the low of a middle candlestick is lower than the lows of the two adjacent candles, indicating a potential shift from a downtrend to an uptrend.
This indicator is commonly used to spot market turning points, determine entry or exit signals, and enhance the reliability of other indicators such as Bollinger Bands or MACD. Because of its clear structure and reliable reversal signals, the Top/Bottom Fractal Indicator is also widely applied in wave theory and price action strategies.
Order Blocks v2Order Blocks v2 – Smart OB Detection with Time & FVG Filters
Order Blocks v2 is an advanced tool designed to identify potential institutional footprints in the market by dynamically plotting bullish and bearish order blocks.
This indicator refines classic OB logic by combining:
Fractal-based break conditions
Time-level filtering (Power of 3)
Optional Fair Value Gap (FVG) confirmation
Real-time plotting and auto-invalidation
Perfect for traders using ICT, Smart Money, or algorithmic timing models like Hopplipka.
🧠 What the indicator does
Detects order blocks after break of bullish/bearish fractals
Supports 3-bar or 5-bar fractal structures
Allows OB detection based on close breaks or high/low breaks
Optionally confirms OBs only if followed by a Fair Value Gap within N candles
Filters OBs based on specific time levels (3, 7, 11, 14) — core anchors in many algorithmic models
Automatically deletes invalidated OBs once price closes through the zone
⚙️ How it works
The indicator:
Tracks local fractal highs/lows
Once a fractal is broken by price, it backtracks to identify the best OB candle (highest bullish or lowest bearish)
Validates the level by checking:
OB type logic (close or HL break)
Time stamp match with algorithmic time anchors (e.g. 3, 7, 11, 14 – known from the Power of 3 concept)
Optional FVG confirmation after OB
Plots OB zones as lines (body or wick-based) and removes them if invalidated by a candle close
This ensures traders see only valid, active levels — removing noise from broken or out-of-context zones.
🔧 Customization
Choose 3-bar or 5-bar fractals
OB detection type: close break or HL break
Enable/disable OBs only on times 3, 7, 11, 14 (Hopplipka style)
Optional: require nearby FVG for validation
Line style: solid, dashed, or dotted
Adjust OB length, width, color, and use body or wick for OB height
🚀 How to use it
Add the script to your chart
Choose your preferred OB detection mode and filters
Use plotted OB zones to:
Anticipate price rejections and reversals
Validate Smart Money or ICT-based entry zones
Align setups with algorithmic time sequences (3, 7, 11, 14)
Filter out invalid OBs automatically, keeping your chart clean
The tool is useful on any timeframe but performs best when combined with a liquidity-based or time-anchored trading model.
💡 What makes it original
Combines fractal logic with OB confirmation and time anchors
Implements time-based filtering inspired by Hopplipka’s interpretation of the "Power of 3"
Allows OB validation via optional FVG follow-up — rarely available in public indicators
Auto-cleans invalidated OBs to reduce clutter
Designed to reflect market structure logic used by institutions and algorithms
💬 Why it’s worth using
Order Blocks v2 simplifies one of the most nuanced parts of SMC: identifying clean and high-probability OBs.
It removes subjectivity, adds clear timing logic, and integrates optional confluence tools — like FVG.
For traders serious about algorithmic-level structure and clean setups, this tool delivers both logic and clarity.
⚠️ Important
This indicator:
Is not a signal generator or financial advice tool
Is intended for experienced traders using OB/SMC/time-based logic
Does not predict market direction — it provides visual structural levels only
