Multi-Factor Crypto SignalsMulti-Factor Crypto Signals - Advanced Technical Indicator**
📊 **General Description**
The Multi-Factor Crypto Signals is an advanced technical indicator designed specifically for cryptocurrency analysis. This system combines 6 independent technical factors to generate more accurate and reliable buy and sell signals, significantly reducing the false positives common in simpler indicators.
🎯 **Technical Methodology**
**Theoretical Foundation**
This indicator is built on the principles of multi-factor technical analysis, where multiple independent indicators must align before generating a signal. This approach is widely recognized in financial literature as more effective than standalone indicators.
**Analyzed Factors**
1. **Time Momentum Oscillator (TMO)**
- **Function**: Detects overbought and oversold conditions
- **Calculation**: Based on the sum of price movements relative to the previous period
- **Application**: Identifies potential reversal points
- **Settings**: Length (14), Calculation Period (5), Smoothing (3)
2. **Customized MACD**
- **Function**: Analyzes momentum and trend changes
- **Modifications**: Optimized periods for crypto (8, 21, 5)
- **Application**: Detects bullish/bearish crossovers and histogram momentum
- **Advantage**: More responsive than traditional MACD (12, 26, 9)
3. **Volume Analysis**
- **Function**: Confirms the strength of price movements
- **Method**: Current volume vs. moving average with a multiplier
- **Application**: Filters signals without volume support
- **Settings**: MA Period (20), Multiplier (1.5)
4. **DXY Filter (U.S. Dollar Index)**
- **Function**: Considers the strength/weakness of the U.S. dollar
- **Correlation**: Weak DXY → bullish for crypto / Strong DXY → bearish for crypto
- **Data**: Uses TVC:DXY as the source
- **Settings**: 21-period EMA to determine trend
5. **Stochastic RSI**
- **Function**: A more sensitive version of RSI for reversals
- **Advantage**: Anticipates movements before traditional RSI
- **Levels**: Oversold < 20, Overbought > 80
- **Settings**: %K (14), %D (3)
6. **Precision Indicators**
- **Williams %R**: Momentum in volatile markets (-80/-20)
- **Money Flow Index (MFI)**: RSI with volume incorporation (20/80)
- **Bollinger Bands**: Price extremes with reversal (20, 2.0)
⚙️ **Settings and Parameters**
**Basic Settings**
- **TMO Length**: 14 (periods for TMO calculation)
- **TMO Calc Length**: 5 (periods for momentum)
- **TMO Smoothing**: 3 (line smoothing)
- **TMO Overbought/Oversold**: 6/-6 (entry levels)
**MACD Settings**
- **Fast Length**: 8 (fast EMA)
- **Slow Length**: 21 (slow EMA)
- **Signal Length**: 5 (signal line)
**Volume Settings**
- **Volume MA Length**: 20 (volume moving average)
- **High Volume Threshold**: 1.5 (multiplier for high volume)
**DXY Settings**
- **Use DXY Filter**: true/false (enable/disable filter)
- **DXY EMA Length**: 21 (EMA periods)
**Precision Settings**
- **Stochastic %K**: 14 (stochastic period)
- **Williams %R Length**: 14 (Williams period)
- **MFI Length**: 14 (MFI period)
- **BB Length/Multiplier**: 20/2.0 (Bollinger Bands)
**Main Setting**
- **Minimum Factors Required**: 3-6 (minimum factors to generate a signal)
🎨 **Visual Interpretation**
**Main Signals**
- 🚀 **Strong Buy (Large Green Arrow)**: 5+ factors aligned for a buy
- 🟢 **Buy Signal (Normal Green Arrow)**: 3-4 factors aligned for a buy
- 💥 **Strong Sell (Large Red Arrow)**: 5+ factors aligned for a sell
- 🔴 **Sell Signal (Normal Red Arrow)**: 3-4 factors aligned for a sell
**Warning Signals**
- ⚠️ **Yellow Triangle**: 2 bullish factors (approaching a buy signal)
- ⚠️ **Orange Triangle**: 2 bearish factors (approaching a sell signal)
📈 **How to Use**
**Step 1: Initial Setup**
- Add the indicator to the chart
- Adjust "Minimum Factors Required" (recommended: 3 for beginners, 4-5 for experienced traders)
- Configure periods based on your trading style
**Step 2: Signal Interpretation**
- **Strong Signals**: Wait for large arrows (5+ factors) for higher reliability
- **Normal Signals**: Normal arrows (3-4 factors) for frequent opportunities
- **Confirmation**: Check the status table to see which factors are active
**Step 3: Risk Management**
- **Stop Loss**: Place stops below/above supports/resistances
- **Take Profit**: Use Fibonacci levels or technical resistances
- **Timeframes**: Test across multiple timeframes for confirmation
**Recommended Settings by Profile**
- **Conservative Trader**:
- Minimum Factors: 4-5
- Use DXY Filter: true
- Use Volume Confirmation: true
- **Aggressive Trader**:
- Minimum Factors: 3
- Shorter indicator periods
- Focus on higher-frequency signals
- **Scalper**:
- TMO shorter periods (10, 3, 2)
- Faster MACD (5, 13, 3)
- Timeframes: 1m, 5m, 15m
🔔 **Available Alerts**
- 🟢 **Multi-Factor Buy**: Normal buy signal
- 🔴 **Multi-Factor Sell**: Normal sell signal
- 🚀 **Strong Buy**: Strong buy signal (5+ factors)
- 💥 **Strong Sell**: Strong sell signal (5+ factors)
Usage Recommendations**
- **Combine with Fundamental Analysis**: Use as a complement, not a standalone tool
- **Test in Paper Trading**: Practice before using real capital
- **Adjust Parameters**: Optimize for specific assets and timeframes
- **Risk Management**: Always use stops and size positions appropriately
📚 **Scientific Basis and References**
**Academic Foundation**
- **Multi-Factor Technical Analysis**: Based on studies by Murphy (1999) and Pring (2002)
- **Momentum Oscillators**: Grounded in Wilder (1978) and Lane (1984)
- **Confirmation Theory**: Dow Theory and convergence/divergence principles
- **Volume Analysis**: Concepts from Granville (1963) and Williams (1973)
**Implemented Innovations**
- **Crypto-Specific Combination**: Parameters optimized for high volatility
- **DXY Filter**: Incorporates crypto-dollar inverse correlation
- **Scoring System**: Quantitative approach to reduce subjectivity
- **Advanced Visual Interface**: Real-time feedback on factor status
💡 **Advanced Tips**
**Market Optimization**
- **Bitcoin/Ethereum**: Use default settings
- **Low-Cap Altcoins**: Increase sensitivity (shorter periods)
- **Stablecoins**: Not recommended (low volatility)
**Combination with Other Indicators**
- **Supports/Resistances**: Use for entry/exit timing
- **Moving Averages**: Combine with long-term trend
- **Volume Profile**: Confirm with high-activity levels
**Backtesting**
- Test in periods of high and low volatility
- Compare performance across different timeframes
- Adjust parameters based on historical results
"williams"に関するスクリプトを検索
SCTI-RSKSCTI-RSK 是一个多功能技术指标合集，整合了多种常用技术指标于一个图表中，方便交易者综合分析市场状况。该指标包含以下五个主要技术指标模块，每个模块都可以单独显示或隐藏：
Stoch RSI - 随机相对强弱指数
KDJ - 随机指标
RSI - 相对强弱指数
CCI - 商品通道指数
Williams %R - 威廉指标
主要特点
模块化设计：每个指标都可以单独开启或关闭显示
交叉信号可视化：Stoch RSI和KDJ的金叉/死叉信号有彩色填充标识
多时间框架分析：支持不同长度的参数设置
直观界面：清晰的参数分组和颜色区分
适用场景
趋势判断
超买超卖区域识别
交易信号确认
多指标共振分析
English Description
SCTI-RSK is a comprehensive technical indicator that combines multiple popular indicators into a single chart for traders to analyze market conditions holistically. The indicator includes the following five main technical indicator modules, each can be toggled on/off individually:
Stoch RSI - Stochastic Relative Strength Index
KDJ - Stochastic Oscillator
RSI - Relative Strength Index
CCI - Commodity Channel Index
Williams %R - Williams Percent Range
Key Features
Modular Design: Each indicator can be shown or hidden independently
Visual Crossover Signals: Golden/Death crosses are highlighted with color fills for Stoch RSI and KDJ
Multi-Timeframe Analysis: Supports different length parameters
Intuitive Interface: Clear parameter grouping and color differentiation
Use Cases
Trend identification
Overbought/Oversold zone recognition
Trade signal confirmation
Multi-indicator confluence analysis
参数说明 (Parameter Explanation)
指标参数分为6个主要组别：
基础指标设置 - 控制各指标的显示/隐藏
Stoch RSI 设置 - 包括K值、D值、RSI长度等参数
KDJ 设置 - 包括周期、信号线等参数
RSI 设置 - 包括RSI长度、中期长度等参数
CCI 设置 - 包括CCI长度、中期长度等参数
Williams %R 设置 - 包括长度参数
使用建议 (Usage Suggestions)
初次使用时，可以先开启所有指标观察它们的相互关系
根据个人交易风格调整各指标的长度参数
关注多指标同时发出信号时的交易机会
结合价格行为和其他分析工具确认信号
更新日志 (Changelog)
v1.0 初始版本，整合五大技术指标
[blackcat] L1 Rhythm OscillatorOVERVIEW 📊💡
The L1 Rhythm Oscillator is an advanced oscillator designed to identify potential entry points in financial markets using a combination of Williams %R indicators and Time-Varying Moving Averages (TVMAs). This script provides traders with clear buy and sell signals that help them capitalize on trends while minimizing risk.
FEATURES 💡🌟
Williams %R Analysis:
Base Indicator (WR0): Measures overbought/oversold conditions within a specified period.
Smoothed Indicators (WR1 & WR2): Further refined versions of WR0 to filter out noise and highlight significant trends.
Dynamic Bands:
Bull Band: Shaded area between WR0 and the bullish threshold when WR0 falls below the defined level.
Bear Band: Shaded area between WR0 and the bearish threshold when WR0 exceeds the defined level.
Trading Signals:
Buy Signal: Generated when WR1 crosses above WR2, indicating a potential upward trend reversal.
Sell Signal: Triggered when WR1 crosses below WR2, suggesting a downward trend shift.
Thresholds:
Bull Threshold (default 60%): Marks levels where the asset is considered relatively undervalued.
Bear Threshold (default 40%): Indicates regions where the asset might be overvalued.
Visual Enhancements:
Colored Bands: Clearly distinguish between bullish and bearish areas.
Horizontal Lines: Provide quick reference points for overbought/oversold levels.
Labels: Display "BUY" and "SELL" markers at key signal locations.
HOW TO USE ⚙️📈
Add the Indicator to Your Chart:
Open your preferred asset's chart on TradingView.
Click on “Indicators” and search for “ L1 Rhythm Oscillator.”
Add the indicator to your chart.
Customize Parameters:
Adjust these inputs according to your trading strategy:
WR Period: Sets the lookback window for calculating Williams %R.
Bull Threshold: Defines the upper limit for bullish territory.
Bear Threshold: Establishes the lower boundary for bearish territory.
TVMA Length: Controls the sensitivity of the moving average used in calculations.
Interpret Visual Elements:
Yellow Line (WR1): The first smoothed version of the base Williams %R.
Fuchsia Line (WR2): The second smoothed line derived from WR1 via TVMA.
Lime-Shaded Area: Represents Bull Band where prices are potentially undervalued.
Red-Shaded Area: Symbolizes Bear Band indicating possible overvaluation.
Horizontal Lines:
Value 0% represents perfect overbought condition.
Value 100% indicates extreme oversold state.
Bull/Bear thresholds provide additional context for interpreting market sentiment.
Act on Crossovers:
Look for instances where WR1 crosses through WR2:
When WR1 moves above WR2 → Potential BUY opportunity.
When WR1 dips below WR2 → Likely SELL scenario.
Consider Contextual Factors:
Combine the oscillator signals with other technical indicators like MACD, RSI, or volume analysis for more robust decision-making.
Be aware of broader market trends and news events that could impact price movements.
Manage Risk:
Always use proper stop-loss orders to protect against adverse price movements.
Consider position sizing based on available capital and risk tolerance.
LIMITATIONS ⚠️🔍
Historical Data Dependency: Like most oscillators, this tool relies on past data patterns which may not always predict future behavior accurately.
False Signals: No single indicator can guarantee correct predictions; false positives/negatives can arise during volatile periods.
Overfitting Risks: Customized settings might work well historically but fail under different market conditions without careful validation.
Complexity: Multiple layers of smoothing and crossover logic require understanding to interpret correctly.
NOTES 🔍📝
Parameter Optimization: Experiment with various combinations of WR Period, Bull/Bear Thresholds, and TVMA Length to find what works best for specific assets and timeframes.
Regular Review: Continuously monitor the performance of the indicator versus actual outcomes, adjusting parameters as needed.
Educational Resources: Deepen your knowledge about oscillator strategies, particularly focusing on how they detect reversals and momentum shifts.
Consistency Key: For successful implementation, maintain consistent rules regarding trade entries/exits regardless of short-term fluctuations.
Timeframe Titans: Market Structure & MTF Order Blocks🟩 OVERVIEW
A combined market structure and order block indicator. Displays fractals, zigzags, Break Of Structure and Change Of Character lines. Shows order blocks on the chart and a higher timeframe.
Unique features include:
• The structure rules require counter fractals for BOS. This enables us to use more responsive fractal settings without creating excessive noise.
• Structure is strict. After the initial CHoCH there is always one and only one active CHoCH line.
• Order blocks can be filtered by market structure.
• Order blocks are based entirely on candle patterns (which appear to be unique among all the indicators we tested) instead of using pivots or other configurable calculations.
• Order blocks have separate mitigation levels, not merely the edge of the block, and being partially mitigated is a separate logical state.
🟩 WHAT IS MARKET STRUCTURE?
There are many ways to conceptualise and code market structure — the prevailing trend derived from important price levels. All of them start with identifying highs and lows in price, then use breaks of those levels to assign a trend.
This indicator displays the following market structure features:
• Williams Fractals to derive high and low pivots.
• Zigzag lines, which connect highs and lows.
• Break of Structure (BOS) lines, which are formed from the highest high in an *uptrend* or the lowest low in a *downtrend*. A break of a BOS line signals trend continuation.
• Change of Character (CHoCH) lines, which are formed from the highest high in a *downtrend* or the lowest low in an *uptrend*. A break of a CHoCH line signals trend reversal.
• Market structure bias, which is derived from the break of a CHoCH line. If a CHoCH line is broken to the upside, the trend is bullish, and if to the downside, bearish.
(For more details of the market structure features of this indicator, see the FEATURES OF THIS INDICATOR section.)
This definition of market structure implies that:
• There can only ever be one single active BOS line.
• There can only ever be one single active CHoCH line.
• A break of a BOS line creates a new CHoCH line.
• A break of a CHoCH line creates a new bias, a new BOS line, and a new CHoCH line.
• Before we can create a BOS, we need to know the bias, for which we need the CHoCH, for which we need BOS... just one of the chicken-vs-egg difficulties of coding market structure.
To understand how this indicator differs from other market structure indicators, see the COMPARISON WITH OTHER INDICATORS section.
🟩 WHAT ARE ORDER BLOCKS?
Order blocks are candle patterns that appear at highs and lows. The theory is that these areas are where many orders were filled — too many for the order book, causing an imbalance in buyers and sellers. As such, these areas can form support or resistance levels when price returns to them.
This indicator displays the following features related to order blocks:
• Imbalances, also called Fair Value Gaps.
• Order blocks of two different types (Imbalance Block and Standard Order Blocks)
(For more details of the order block features of this indicator, see the FEATURES OF THIS INDICATOR section.)
There are different patterns that can define order blocks, but the common element is that price should move vigorously away from the area after the pattern forms.
To understand how this indicator differs from other order block indicators, see the COMPARISON WITH OTHER INDICATORS section.
🟩 FEATURES OF THIS INDICATOR
Pivots
Shows Williams high and low fractals, with a configurable lookback. The pivots are always calculated, since they are the building block of all other market structure features. The pivot shape display can be turned on or off, and the display customised.
Zigzag
Draws lines between the highs and lows. The lines can be shown or hidden, and the colour and thickness configured.
Break of Structure
BOS lines are always calculated, but can be shown or hidden. The appearance can be customised. BOS lines are drawn from the candle that has the high or low that defines their level. They always extend until they are broken or the bias changes. The BOS lines have an optional, configurable label. When a BOS line is broken, an optional, configurable label is drawn on that bar.
Change of Character
CHoCH lines can be shown, hidden, and customised. CHoCH lines always extend until they are broken or a new CHoCH line is formed. CHoCH lines have optional labels. A different, customisable label is drawn when a CHoCH line is broken.
Market structure bias
Market structure bias is derived from the break of a CHoCH line. If a CHoCH line is broken to the upside, the trend is bullish, and if to the downside, bearish. The background is shaded a configurable colour based on the trend.
Imbalances
Imbalances are drawn in configurable colours. When they are mitigated, you can choose to change the colour, delete them, or leave them.
Order blocks
Two types of imbalance order blocks are displayed: Standard Order Blocks and Imbalance Blocks. They can be shown or hidden, and customised, independently.
Each order block has a mitigation line with configurable colours and style. If price exceeds the mitigation line, the order block is mitigated and is considered inactive.
The order blocks, or their labels, can be deleted when the order block is mitigated. If not deleted, their colour is changed and they no longer extend with each new bar.
Order blocks on the chart timeframe can be shown conditionally within the context of the market structure: you can choose to show:
• Pro-trend order blocks (bearish order blocks that were created in bearish market structure and vice-versa).
• Counter-trend order blocks (bearish order blocks that were created in bullish market structure and vice-versa).
• All order blocks.
Higher timeframe
Imbalances and order blocks can be independently shown and customised on a single higher timeframe. The HTF functions of this indicator do not repaint because they use confirmed data.
You can choose a custom, fixed higher timeframe, or an "Auto" mode where the script automatically chooses the higher timeframe based on the chart timeframe.
Script information messages
An optional table shows information about the script, including configuration problems, such as if a custom HTF is not actually higher than the chart timeframe.
🟩 HOW TO USE
There are very many ways to use market structure and order blocks in trading and we recommend you study extensively, and if possible get a trusted mentor.
Here is a random example we found on the recent GBPUSD chart. In the screenshot below, the left chart is at 30m and the right is at 5m. We've toggled various settings to make the chart clearer for demonstration purposes.
1 — We get a CHoCH break on the higher timeframe. So our bias (if we are trying to trade with the trend) is bearish. Now we look for some other confluence.
2 — Price revisits the top of the range and mitigates an imbalance block. It wicks the CHoCH (resetting it) but does not break it on close. The bearish market structure is thus preserved. For these reasons, we're thinking about a short, and we switch to the 5m chart on the right to find an entry. We've chosen a Custom HTF of 30m to match the left chart and we can see the mitigated HTF order block, marked "30m IB". We can see when price moves definitively out of the order block area to the downside.
3 — A bearish order block is formed and very quickly price comes back into it. We could enter a short here with a stop above the closest relevant fractal.
4 — Another bearish order block forms and price retests it. Another entry. Two previous 5m bullish order blocks at the bottom of the chart act as support. We could potentially close our short here.
5 — Another test of the same block, which was not mitigated the first time. Another potential short entry. As it happens, price makes a massive run lower here, such that we could trail our stop down one ATR above every single high fractal (marked out using manual rays and a public ATR indicator) for a good R:R, but that's not the point.
This is a made-up, retrofitted example with a fairly generic methodology. It's just to show how some of the features of this indicator could be used in trading:
• Market structure can give a bias. It can also mark interesting levels.
• Using multiple timeframes, while more complex, can level up your trading experience.
• Price trading back into order blocks can be a good R:R entry.
Your actual way of trading, your playbook of setups, your knowledge of your strengths and weakness as a trader, is your own.
🟩 LIMITATIONS
This indicator is intended for use on Forex markets, although order blocks and market structure do form on any reasonably liquid asset.
The HTF uses confirmed data, so you need to wait until the HTF bar is closed before the order block can form. Therefore it does not repaint, in the sense that people worry about repainting, of changing data in the past. We use the latest recommended method of fetching HTF data .
The market structure uses live chart data, so structure and order blocks that are created by conditions on an open realtime bar can appear and disappear as the current bar close changes. This is quite normal .
The Williams pivots are by definition only confirmed after a defined number of bars, and like everyone else we plot them offset into the past.
Similarly, we offset order blocks into the past so that they start on the candle that has the high or low that defines the order block, not the candle that created them. For HTF order blocks, we calculate the number of chart bars back assuming a 24-hour market, which gives accurate offsets only on Forex and other symbols that trade close to 24 hours each day.
🟩 COMPARISON WITH OTHER INDICATORS
There are a great number of market structure and order block indicators already published on TradingView. Since there are only a certain number of highs and lows on the chart from which to produce structure and order blocks, they all look somewhat similar. However, this indicator, written entirely from scratch without reference to the code of any other indicators, is unique and original in two kinds of ways: in patterns and in features.
PRECISE PATTERNS
We believe that edge in trading can be found in, amongst other things, precision in analysis. You can't truly trust your backtests if your system is not repeatable, and your system is repeatable only if its definitions are precise.
We trade with this indicator, and our students trade with it as well. Why did we spend months creating a new indicator instead of using one of the many existing ones, most of which are free and open source?
Because they are not quite how we wanted.
The indicator was created from our proprietary structure rules, which are based on the generally accepted understanding of market structure, with some specific tweaks.
To prepare this description (after the indicator is finished), we searched for "Market Structure", "CHoCH", and "SMC" and list below all popular (with over 3K boosts; excluding invite-only) indicators that show market structure with CHoCH (sometimes called MSS). We configured the settings to most closely match how our indicator works, added both indicators to the same chart, and looked for relevant differences.
The purpose of this section is not to try to say that this indicator is better than any other, but just that it is different. This difference is important for us and our students.
Indicator #1
As you can see, the indicator interpreted the first part of the chart as a downtrend, whereas ours interpreted it as an uptrend. The structure is completely different, because our Williams Fractal lookback is 2, and the minimum "Swing Points" value for Indicator #1 is 10. Although this indicator is deservedly popular, it isn't what we can use for the way we trade.
Indicator #2
Setting the "Zigzag Length" to 2 results in wildly different market structure, as shown below. For many fractals, this indicator does not place the zigzag at the highest high or lowest low, as ours does consistently. It does not highlight the trend in any way. It gives many Market Structure Breaks in a short period. Although it's again wildly popular, it doesn't match our way of encoding market structure.
Indicator #3
Again, setting the "Pivot lb" and "Pivot rb" inputs to 2 gives much too sensitive market structure. This is because this indicator does not require, as we do, a counter-fractal to form after a fractal in order to confirm a BOS. We believe that this rule gives less noisy structure while also being responsive. Most indicators attempt to compensate for this by having a much larger lookback period. While this does of course give fewer pivots and less noise, this is simply a different logic and gives different results. Note also that although this indicator correctly defines the first section of the chart as an uptrend, it does not draw a CHoCH line. As discussed above, our definition of market structure means that there should always be one and only one active CHoCH line, and we draw this at the earliest sensible opportunity.
Indicator #4
Again, the lack of any extra pivot confirmation logic means that this indicator creates different structure with the same lookback period. Also note the lack of initial CHoCH.
Indicator #5
The lowest lookback is 3, and so this indicator too gives very different structure.
Indicator #6
Of course, using a lookback of 2 gives different structure with this indicator too. For variety, here we show a lookback of 5, which is the lowest setting that returns significantly less noisy structure. You can see that the main CHoCH at the top of the chart is similar but not at the same place. Increasing the lookback does not ever result in a CHoCH at the same place, because the logic is simply different. When the lookback increases above 10, no CHoCH lines are drawn at the top at all.
Indicator #7
This indicator uses the highest/lowest price for the last 10 bars (fixed), along with some other bar conditions. You can see the resulting structure is quite different. Among other differences, it does not create a BOS at the top of the chart, even in an uptrend, and it does not create an opposing CHoCH when the existing CHoCH is broken.
Indicator #8
With "Custom" market structure and a length of 2, BOS and CHoCH lines are drawn by this indicator but in incongruous places.
Conclusion
Although we only illustrate the top few alternatives, we did check many, many others.
These market structure indicators may produce useful output, but their structure differs significantly from ours. We didn't even need to get into specific examples because the general approaches are so different. It is up to the user to decide which indicator, and which interpretation of market structure, best suits their needs.
ORDER BLOCKS
Continuing, we illustrate differences with the most popular order block indicators, trying to get them to match our order blocks. Note that some of these are also in the previous list as market structure indicators.
Order blocks are always formed at swings when price moves away with force, so they will be sort of the same across all the very many existing order block indicators. We are looking for precision and differentiation, as we did with market structure.
Indicator #1
This indicator does not have ability to display mitigated order blocks, only active ones. The order blocks do not match at all.
Indicator #2
With a period of 2, this indicator marks many of the same order blocks as ours. It doesn't extend the blocks, and doesn't mark them when mitigated. The logic for choosing the order block candle is also clearly different.
Indicator #3
Even with very sensitive settings, this indicator did not create as many order blocks as ours and they are quite different.
Indicator #4
Again you can see the logic for choosing candles and creating blocks is simply different. This indicator has inadequate protection against empty arrays, which causes runtime errors on charts with not much history (not a problem for Forex charts in general, but noticeable on the testing chart).
Indicator #5
We were unable to get the order blocks to extend with this indicator, although it should be possible. Anyway the blocks are wildly different.
Indicator #6
Even with the most sensitive settings, this indicator showed only one order block on our test chart.
Indicator #7
This indicator incorporates complex price action concepts. Nevertheless, the order blocks are very different indeed.
Indicator #8
This indicator forms quite different blocks to ours. It has several interesting settings including a choice of using the candle body or wick.
Indicator #9
We were not able to configure this indicator to produce the same order blocks as ours.
Indicator #10
On very sensitive settings, this indicator matches many of our order blocks, but at the same time many are different.
Conclusion
None of the indicators tested here (nor the many others we looked at previously) use the same logic as ours. The differences are so obvious that we don't have to call out individual blocks and analyse how they differ.
Fundamentally, other indicators seem to use variable precision for pivots in their order block detection calculations. Our order blocks are pure candle patterns with two different rulesets for Standard Order Blocks and Imbalance Order Blocks, and this logic does not change.
Note that our order blocks do not always automatically extend to the swing high or low, nor allow the user to choose the limit of the block, but use unique rules.
In summary, our indicator differs from other order block indicators in terms of fundamental detection logic, candle placement, boundary definition, mitigation levels, and logical states (see below).
UNIQUE COMBINATION OF FEATURES
In comparison to all other indicators we looked at, our indicator:
• Uses order blocks with three states: active, mitigated, and partially mitigated. Our mitigation lines for order blocks are rules-based. If price touches the mitigation line, the order block is considered fully mitigated. If price goes inside the order block but does not hit the mitigation line, it is only partially mitigated. These three states are visually distinguished.
• Has the most extensive visual customisation options of all those we looked at. We believe that being able to customise how you see indicator outputs is very important for reducing mental load while analysing and trading.
• Has a unique feature that combines market structure and order blocks, where the user can choose to show pro-trend order blocks (bullish blocks that are formed in bullish structure and vice-versa) or counter-trend blocks (bullish blocks that are formed in bearish structure and vice-versa).
• Approximates an initial trend bias very quickly, so we can start creatng BOS, CHoCH, etc.
• Requires a counter pivot to confirm a BOS line. This seemingly small logical step actually creates very different structure, as we saw in the comparison section.
• Uses a sophisticated array-based sorting mechanism to preserve the selected number of imbalances, use the rest of the TradingView box allowance for order blocks, and delete excess order block objects (not just drawings) in reverse historical order.
• Hides order block drawings if they are a configurable distance away from price. Magically redraws them if price moves closer.
• Includes an equivalent to the system "Calculated bars" setting for the high timeframe, to avoid unnecessary processing and improve performance.
🟩 CODING CONSIDERATIONS
This indicator consists of all original code written by @SimpleCryptoLife for Timeframe_Titans.
AI was used for the following purposes:
• Autocomplete
• Checking that bullish and bearish logic is parallel in a given function
• Querying the names and locations of variables hundreds of lines away when we forgot what they're called, like an expensive search-and-replace
• Help with debugging (it usually makes up elaborate and wrong ideas though)
It was not used to replace the coder's expertise and creativity, or to "vibe-code" some black-box functionality we didn't understand. We can recommend that you use AI the same way.
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RoboDCARoboDCA is an indicator I developed to help me accrue Bitcoin at its low point. It won't necessarily be at its lowest point but it's low enough that if you dollar cost average for the long term, the difference is negligible. While using this indicator, I find it also suitable for swing trades in the 4H timeframe every once in a while. Might be the indicator to use if you only do 2-5 trades a month last a few days between trades.
The fundamentals of RoboDCA came from these 2 theories:
Bill Williams Balance Line
Moon Phase
Bill Williams Balance Line
Bill Williams is fantastic, a lot of how I see the chart borrows from his books. For this particular case, his balance line is interesting to me because it's a series of highs and lows that are when broken through inspires a powerful move.
Moon Phase
Every month for as long as the moon and earth have danced in the cosmos, the moons gravity affects the earth physically. Some myth and legends like werewolves even came from this cosmic relationship. For prices though it paints a more honest relationship I would say. It is mostly accurate more than 50% of the time that when the moon's light intensity hits 90% or above, its gravity pulls prices to its lower if not lowest point for about 15 days in the past and 15 days in the future. While a new moon with its light intensity at the lowest would present a local peak.
I wasn't sure about this the first time but charts don't lie, they can't. For dollar cost average purposes, this is one of the best way of not just buying blindly into the market.
RoboDCA
Based on the 2 fundamentals above, I tried to create an indicator to help me see price movements and its direction more honestly. What I can say is this indicator tries to tell you if prices are in a good position to buy.
Numbers have always been interesting for me and while developing this indicator I came across an interesting one, number 528 . The number is supposedly an angelic number that communicates wealth and prosperity, sound like my kind of number. I thought to myself, the simplest prove is to just put the number to a moving average indicator. I opted for EMA to keep it close to prices compared to regular MA and I was pleasantly surprised.
As you can see, the EMA line acts as a perfect support and resistance. Breaking up or down the line would start a powerful move to the direction it was breaking at while touching the line through wicks held as strong support and resistance.
The problem I don't enjoy with moving averages is it's hard to measure zones, you'd need to zoom in into the candles to define the zones manually. I wanted something that would define the zones on screen without my intervention. This brought me back to Bill Williams Balance Line, they have highs and lows. The next step was to put the high/low from the balance line and EMA both lines creating a zone.
The zone created fits perfectly to my needs. Now I can see powerful moves before it happens. When a zone has been tested 2 or 3 times, the chances of prices making powerful moves is humongous. This is a zone that is derived from exponential moving averages of the last 528 high and low balance lines, this in itself is a strong indication for direction.
Let's couple the zone with moon phases like what the chart below illustrates.
In this daily chart, the blue upwards arrow represent an opportunity to buy while the red downward arrows represent an opportunity to sell based on Moon Phases. For dollar cost average, you can see that blue arrows that showed up near to the balance line ribbon represent a perfect buy entry, conviction to buy is as strong as it can be.
Although both Moon Phases and the zone should be enough to know when to buy, it doesn't tell a story of how the upcoming move came to be. Someone told me about another angelic number, this time it's number 247 . Let's how this stack up when put both EMA528 and EMA247 together.
It's a sight to behold.
When prices are moving back and forth between the both EMAs, it's telling you that a powerful move is soon coming. Most traders would also know the meaning of crossovers between a slower MA versus a faster MA or the other way around.
Conclusion
This indicator is written to help me dollar cost average, I hope the explanation above is enough to describe to you how to use the indicator opportunistically. I regret that for this indicator I decided to not share its source code. The logic for this indicator is described above which I'm sure can be a base for anyone to write their own version of it.
Above said, I wish everyone a 528 vibe of wealth and prosperity. Cheers!
Megalodon Pro Advance Data CalculatorIt is designed to combine price, volume and indicator data from different brokers/exchanges. This helps us to have a more accurate data, so that we can have a better picture of the price/volume/indicators.
Features
It may combine up to 18 different data sources.
It can print indicators such as MFI, RSI, OBV, BB, Williams%R. If you have any suggestions, write them in the comment section below.
Usage
Just select the data points you would like to combine in the setting to view price, volume or indicators data.
Settings
Setting 1 defines the source you would like to use to view Only Price information.
Setting 2 defines the indicator you would like to print. OnlyPrice is the default setting which views the source information defined.
Setting 3 defines the length for RSI.
Setting 4 defines the length for MFI.
Setting 5 defines the length for Bollinger Band(BB).
Setting 6 defines the Bollinger Bands' standard deviation.
Setting 7 defines the length for Williams%R.
I hope you will find some VALUE $
~Bo
Enlightening the modern investors
Ppsignal GSV V1Entry pattern GSV (Greatest Swing Value)
We return with another Larry Williams pattern, the Greatest Swing Value or GSV, in principle this pattern was developed for the S & P 500, but it could be extrapolated to other markets, as with the previous pattern we will only explain it for long positions. First of all we have calculated the difference between the maximum and the opening of the last 4 bars and its average, that is to say: /4. Being H the maximum of each bar and the O the opening price of each bar. From this average we will extract 180%, that is, we will multiply it by 1.8. The value of this result will be the GSV.
The logic of this GSV entry pattern is as follows: a widely used way to enter the markets is the breaking of highs, but many times false breakdowns occur or what Larry Williams calls "failure swings", to avoid these false ruptures Larry invented the GSV, which is like a moving average applied to the Swing Value (the difference between the maximum and the opening price).
Once we have the GSV we must add it to the opening price of the current bar and the entry will occur when the price exceeds this level.
to determine the entrance we use bollinguer band and atr. In this way we enter the pattern with volatility.
Patrón de entrada GSV (Greatest Swing Value)
Para esta tecnica usamos la volatilidad para determinar entradas, bollinguer band 13.1 y atr...
Volvemos con otro patrón de Larry Williams, el Greatest Swing Value o GSV, en principio estepatrón fue desarrollado para el S&P 500, pero se podría extrapolar a otros mercados, al igual que con el patrón anterior solamente lo explicaremos para posiciones largas. Primero de todo hemos calculado la diferencia entre el máximo y la apertura de las 4 últimas barras y hacersu media, es decir: /4. Siendo H el máximo de cada barra y la O el precio de apertura de cada barra. De esta media sacaremos el 180%, es decir, lo multiplicaremos por 1.8. El valor de este resultado será el GSV.
La lógica de este patrón de entrada GSV es la siguiente: una forma bastante usada para entraren los mercados es la ruptura de máximos, pero muchas veces se producen rupturas falsas o loque Larry Williams llama "failure swings", para evitar estas rupturas falsas Larry inventó el GSV,que es como una media móvil aplicada al Swing Value (la diferencia entre el máximo y el preciode apertura).
Una vez tenemos el GSV deberemos sumárselo al precio de apertura de la barra actual y la entrada se producirá cuando el precio supere este nivel.
para determinar la entrada usamos bollinguer band y atr. des esta manera entramos al patron con volatilidad.
PpSignal Acceleration BandsAcceleration Bands
Description:
Adaptive bands that contain 95% of price action usually used in 20 or 80 bar periods. Trading signals occur when price action is confirmed outside the bands.
This indicator targets the top 5% of moves, keeping traders focused on the best trends.
The 20 Bar Acceleration Band Expert Advisor (SPX chart below) shows buy and sell signals based on my system. Notice
hat in the 14 months shown, the &P500 only exposed a signal 4 times, each was profitable. This depicts the 5% theory sell - Acceleration Bands highlight only the extreme moves for option traders. The issue many traders face is really two-fold; many traders want more signals and the entry point can use some refinement. Like the Yin & Yang relationship, I've developed a system that combines Acceleration bands with Williams' Percent R% to remove any weaknesses and refine trading signals. Let's take a look at how it works.
Willams' %R with two separate systems based on breakout and retest (lower risk entries) methods. We have smoothed out and modified Williams' Percent R to make it a better and more usable trading vehicle.
Description: Larry Williams created the Percent Range oscillator to highlight overbought versus oversold levels in securities. Traditionally overbought connotates a long exit or sell short entry as oversold would insinuate the opposite, however, we in general consider overbought to be bullish and oversold to be bearish. The Big Trends Percent R system targets the top 20th percentile and bottom 20th percentile.
Signal
Buy when the price breaks the upperline and W5 overblows.
Sell when the price breaks the lower band and W5 oversold
www.bigtrends.com
Fractal and Alligator Alerts by JustUncleLThis is based on two well known Bill Williams Fractal and Alligator strategies.
The following code is an implementation is similar to reversal strategy specified here:
forexwot.com
and another well know Alligator break out strategy.
This was achieved by combining some of the ideas from two other indicators:
True Williams Alligator (SMMA) by the_batman
Fractals and Levels by JustUncleL
There are two types of Fractal + Alligator Strategies included in this indicator:
Fractal Reversal : In an uptrend defined by Low Fractal that is above the Alligator teeth and the Alligator mouth is completed open in an uptrend. The opposite for downtrends. (Green and Red Arrows)
Fractal BreakOut : In an uptrend, at the start of Alligator open we look back for the first Fractal High above Alligator Teeth. Alligator teeth must be above mouth. (Aqua and Fuchsia arrows)
CM_Williams_Vix_Fix_V3_Ultimate_Filtered_AlertsNew Williams Vix Fix - Major Update - Filtered Entries - Additional Alerts - And Much More...
***01-05-2015 Major Updates Include:
***ALL Features Available To Turn On/Off On The INPUTS Tab!!!
FILTERED ENTRIES -- Plus AGGRESSIVE FILTERED ENTRIES - HIGHLIGHT BARS AND ALERTS
*Alerts Enabled for 4 Different Criteria
*Ability To Plot Alerts True/False Conditions on top of the WVF Histogram
*Ability To Turn Off the Histogram and just see True/False Alerts Conditions.
*Ability to Turn All Price Bars Gray, and Color the Price Bars to Match the WVF Colors Exactly, Including All 3 Entry Types.
*Added Inputs To Adjust the 3 Numerical Inputs That Define The PRICE ACTION FILTER! Explained in Video.
*Main Video is 34 Minutes…However, the New Features Are Extensive and I Go Thru All Features In Depth.
*I Recommend Using the VSTOP Indicator. I Go Through How To Customize It In Video.
Videos:
Video: The Evolution of the Williams Vix Fix - 12 Minutes.
vimeopro.com
Video: Williams Vix Fix V3 - Major Update - Additional Alerts and Filtered Entries - 34 Minutes.
***Video Covers In Detail How To Use The Multiple Alerts And Plot Styles Available.
vimeopro.com
Posts To Reference…
New Video on How to Create Alerts W/ Any Custom Indicator.
www.tradingview.com
Great Confirming Indicator for the Williams Vix Fix
CM_WILLIAMS_VIX_FIX FINDS MARKET BOTTOMS
Momentum Market Structure ProThis first indicator in the Beyond Market Structure Suite gives you clear market structure at a glance, with adaptive support & resistance zones. It's the only SMC-style indicator built from momentum highs & lows, as far as I know. It creates dynamic support & resistance zones that change strength and resize intelligently, and gives you timely alerts when price bounces from support/rejects from resistance.
You’re free to use the provided entry and exit signals as a ready-to-use, self-contained strategy, or plug its structure into your existing system to sharpen your edge :
• Market structure bias may help improve a compatible system's win rate by taking longs only in bullish bias and shorts in bearish structure.
• Support/resistance can help trend traders identify inflection points, and help range traders define ranges.
🟩 HIGHLIGHTS
⭐ Unique market structure with different characteristics than purely price-based models.
⭐ Support and resistance created from only the extreme levels.
⭐ Support & resistance zones adapt to remain relevant. Zones are deactivated when they become too weak.
⭐ Long and short signals for a bounce from support/rejection from resistance.
🟩 WHY "MARKET STRUCTURE FIRST, ALWAYS"?
"There is only one side to the stock market; and it is not the bull side or the bear side, but the right side." — Jesse Livermore, Reminiscences of a Stock Operator (1923)
If the market is structurally against your trade, you're gonna have a bad time. So you must know what the market structure is before you plan your trade. The more precise and relevant your definition of market structure, the better.
🟩 HOW TO TRADE USING THIS INDICATOR (SIMPLE)
• Directional filter : The prevailing bias background can be used for any kind of trades you want to take. For example, you can long a bounce from support in a bullish market structure bias, or short a rejection from resistance in bearish bias.
• Entries : For more conservative entries, you could wait for a Candle Trend flip after a reaction from your chosen zone (see below for more about Candle Trend).
• Stops : The included running stop-loss level based on Average True Range (ATR) can be used for a stop-loss — set the desired multiplier, and use the level from the bar where you enter your trade.
• Take-profit : Similarly, you can set a Risk:Return-based take-profit target. Support and resistance zones can also be used as full or partial take-profit targets.
See the Advanced section below for more ideas.
🟩 SIGNALS
⭐ ENTRIES
You can enable signals and alerts for bounces from support and rejections from resistance (you'll get more signals using Adaptive mode). You can filter these by requiring corresponding market structure bias (it uses the bias you've already set for the background), and by requiring that Candle Trend confirm the move.
I've slipped in my all-time favourite creation to this indicator: Candle Trend. When price makes a Simple Low pivot, the trend flips bullish. When price then makes a Simple High pivot, the trend flips bearish (see my Market Structure library for a full explanation). This tool is so simple, yet I haven't noticed it anywhere else. It shows short-term trends beautifully. I use it mainly as confirmation of a move. You can use it to confirm ANY kind of move, but here we use it for bounces from support/rejections from resistance.
Note that the pivots and Zigzags are structure, not signals.
⭐ STOPS
You can use the supplied running ATR-based stop level to find a stop-loss level that suits your trading style. Set the desired multiplier, and use the level from the bar where you enter your trade.
⭐ TAKE-PROFIT
Similarly, you can set a take-profit target based on Risk:Return (R:R). If this setting is enabled, the indicator calculates the distance between the closing price and your configured stop, then multiplies that by the configured R:R factor to calculate an appropriate take-profit level. Note that while the stop line is reasonably smooth, the take-profit line varies much more, reflecting the fact that if price has moved away from your stop, the trade requires a greater move in order to hit a given R:R ratio.
Since the indicator doesn't know where you were actually able to enter a position, add a ray using the drawing tool and set an alert if you want to be notified when price reaches your stop or target.
🟩 WHAT'S UNIQUE ABOUT THIS INDICATOR
⭐ MOMENTUM PIVOTS
Almost all market structure indicators use simple Williams fractals. A very small number incorporate momentum, either as a filter or to actually derive the highs and lows. However, of those that derive pivots from momentum, I'm not aware of any that then create full market structure from it.
⭐ SUPPORT & RESISTANCE
Some other indicators also adjust S/R zones after creation, some use volume in zone creation, some increase strength for overlap, a few merge zones together, and many use price interactions to classify zones. But my implementation differs from others, as far as I can tell after looking at many many indicators, in seven specific ways:
+ Zones are *created* from purely high-momentum pivots, not derived or filtered from simple Williams pivots (e.g. `ta.pivothigh()`).
+ Zones are *weakened* dynamically as well as strengthened. Many people know that S/R gets stronger if price rejects from it, but this is only half the story. Different price patterns strengthen *or weaken* zones.
+ We use *conviction-weighted candle patterns* to adjust strength. Not simply +1 for price touching the zone, but a set of single-bar and multi-bar patterns which all have different effects.
+ The rolling strength adjustments are all *moderated by volume*. The *relative volume* forms a part of each adjustment pattern. Some of our patterns reward strong volume, some punish it.
+ We do our own candle modelling, and the adjustment patterns take this into account.
+ We *resize* zones as a result of certain candle patterns ("indecision erodes, conviction defends").
+ We shrink overlapping zones to their sum *and* add their strengths.
🟩 HOW TO TRADE USING THIS INDICATOR (ADVANCED)
In addition to the ideas in the How to Trade Using This indicator (Simple) section above, here are some more ideas.
You can use the market structure:
• As a bias for entries given by more reactive momentum resets, or indeed other indicators and systems.
• You could use a change in market structure to close a long-running trend-following position.
You can use the distance from a potential entry to the CHoCH line as a filter to choose higher-potential trades in ranging assets.
Confluence between market structure and your favourite trend indicator can be powerful.
Multi timeframe analysis
This is a bit of a rabbit hole, but you could use a split screen with this indicator on a higher timeframe (HTF) view of the same asset:
• If the 1D structure turns bullish, the next time that the 1H structure also flips bullish might be a good entry.
• Rejection from a HTF zone, confirmed by lower timeframe (LTF) structure, could be a good entry.
None of this is advice. You need to master your own system, and especially know your own strengths and weaknesses, in order to be a successful trader. An indicator, no matter how cool, is not going to one-shot that process for you.
In Adaptive mode, a skillful trader will be able to spot more opportunities to classify and use support and resistance than any algorithm, including mine, now that they've been automatically drawn for you.
If you are doing historical analysis, note that the "Calculated bars" setting is set to a reasonably small number by default, which helps performance. Either increase this number (setting to zero means "use all the bars"), or use Bar Replay to examine further back in the chart's history. If you encounter errors or slow loading, reduce this number.
🟩 SUPPORT & RESISTANCE
A support zone is an area where price is more likely to bounce, and a resistance zone is an area where price is more likely to reject. Marking these zones up on the chart is extremely helpful, but time-consuming. We create them automatically from only high-momentum areas, to cut noise and highlight the zones we consider most important.
In Simple mode, we simply mark S/R zones from momentum and Implied pivots. We don't update them, just deactivate them if price closes beyond them. Use this mode if you're interested in only recent levels.
In Adaptive mode, zones persist after they're traversed. Once the zones are created, we adjust them based on how price and volume interact with them. We display stronger zones with more opaque fills, and weaker zones with more transparent fills. To calculate strength, we first preprocess candles to take into account gaps between candles, because price movement after market is just as important in its own way. The preprocessing also redefines what constitutes upper and lower wicks, so as to better account for order flow and commitment. We use these modelled candle values, as well as their relative amplitude historically, rather than the raw OHLC for all calculations for interactions of price and zones. It's important to understand, when trying to figure out why the indicator strengthened or weakened a zone, that it sees fundamental price action in a different way to what is shown on standard chart candles (and in a way that can't easily be represented accurately on chart candles).
Then, we strengthen or weaken , and resize support and resistance zones dynamically using different formulas for different events, based on principles including these:
• The close is the market's "vote", the momentum shift anchor.
• Defended penetrations reveal validated liquidity clusters.
• Markets contract to defended levels.
• "The wick is the fakeout, but the close tells you if institutions held the level." — ICT (Inner Circle Trader)
Adaptive mode is more powerful, but you might need to tweak some of the Advanced Support & Resistance settings to get a comfortable number of zones on the chart.
🟩 MOMENTUM PIVOTS
The building blocks of market structure are Highs and Lows — places where price hits a temporary extreme and reverses. All the indicators I could find that create full market structure do so from basic price pivots — Williams fractals, being the highest/lowest candle wick for N candles backwards and forwards (there are some notable first attempts on TradingView to use momentum to define pivots, but no full structure). "Highest/lowest out of N bars" is the almost universal method, but it also picks up somewhat arbitrary price movements. Recognising this, programmers and traders often use longer lookbacks to focus on the more significant Highs and Lows. This removes some noise, but can also remove detail.
My indicator uses a completely different way of thinking about High and Low pivots. A High is where *momentum* peaks and falls back, and a low is where it dips and then recovers. While this is happening, we record the extremes in price, and use those prices as the High or Low pivot zones.
This deliberately picks out different, more meaningful pivots than any purely price-based approach, helping you focus on the swings that matter. By design, it also ignores some stray wicks and other price action that doesn't reflect significant momentum. Price action "purists" might not like this at first, but remember, ultimately we want to trade this. Check and see which levels the market later respects. It's very often not simply the numerically higher/lower local maxima and minima, but the levels that held meaning, interpreted here through momentum.
The first-release version uses the humble Stochastic as the structural momentum metric. Yes, I know — it's overlooked by most people, but that's because they're using it wrong. Stochastic is a full-range oscillator with medium excursions, unlike RSI, say, which is a creeping oscillator with reluctant resets. This makes Stoch (at the default period of 14) not quite reactive enough for on-the-ball momentum reset entry signals, but close to perfect (no metric is 100%) for structural pivots.
Stochastic is also a solid choice for structure because divergences are rare and not usually very far away in terms of price. More reactive momentum metrics such as Stochastic RSI produce very noisy structure that would take a whole extra layer of interpreting (see Further Research, below).
For these reasons, I may or may not add other options for momentum. In the initial release, I've added smoothed RSI as an alternative just to show it's possible, which takes even longer than Stochastic to migrate from one extreme to another, creating an interesting, longer-term structure.
🟩 IMPLIED PIVOTS
We want pivots to mark important price levels so that we can compute market direction and support & resistance zones from them.
In this context, we see that some momentum metrics, and Stochastic in particular, tend to give multiple consecutive resets in the same direction. In other words, we get High followed by High, or Low followed by Low, which does not give us the chance to create properly detailed structure. To remedy this, we simply take the most extreme price action between two same-direction pivots, and create an Implied pivot out of it, after the second same-direction pivot is created.
Obviously these pivots are created very late. Recalling why we wanted them, we realise that this is fine. By definition , price has not exceeded the Implied Pivot level when they're created. So they show us an interesting level that is yet untested.
Implied Pivots are thus created indirectly by momentum but defined directly by price. They are for structure only. We choose not to give them a Dow type (HH, HL, LH, LL) and not to include them in the Main Zigzag to emphasise their secondary nature. However, Implied Pivots are not "internal" or "minor" pivots. There is no such concept in the current Momentum Market Structure model.
If you want less responsive, more long-term structure, you can turn Implied Pivots off.
🟩 DOW STRUCTURE
Dow structure is the simplest form of market structure — Higher Highs (HHs) and Higher Lows (HLs) is an uptrend (showing buyer dominance), and vice-versa for a downtrend.
We label all Momentum (not Implied) Pivots with their Dow qualifier. You can also choose to display the background bias according to the Dow trend.
There is an input option to enable a "Ranging" Dow state, which happens when you get Lower Highs in an uptrend or Higher Lows in a downtrend.
🟩 SMC-STYLE STRUCTURE (BOS, CHOCH)
The ideas of trend continuation after taking out prior highs/lows and looking for early signs of possible reversal go back to Dow and Wyckoff, but have been popularised by SMC as Break Of Structure (BOS) and Change of Character (CHoCH).
BOS can be used as a trigger: for example:
• Wait for a bullish break of structure
• Then attempt to buy the pullback
• Cancel if structure breaks bearish (meaning, we get a bearish CHoCH break)
How to buy the pullback? This is the trillion-dollar question. First, you need solid structure. Without structure, you got nothin'. Then, you want some identified levels where price might bounce from.
If only we incorporated intelligent support and resistance into this very indicator 😍
Creating and maintaining correct BOS and CHoCH continuously , without resetting arbitrarily when conditions get difficult, is technically challenging. I believe I've created an implementation of this structure that is at least as solid as any other available.
In general, BOS is fully momentum‑pivot‑driven; CHoCH is anchored to momentum pivots but maintained mainly by raw price extremes relative to those anchors (breaks are obviously pure price). This means that the exact levels will sometimes differ from your previous favourite market structure indicator.
We have made some assumptions here which may or may not match any one person's understanding of the "correct" way to do things, including: BOS is not reset on wicks because, for us, if price cannot close beyond the BOS there is no BOS break, therefore the previous wick level is still important. The candidate for CHoCH on opposing CHoCH break *is* reset on a wick, because we want to be sure to overcome the leftover liquidity at that new extreme before calling a Change of Character. The CHoCH is moved on a BOS break. For a bullish BOS break, the new CHoCH is the lowest price *since the last momentum pivot was confirmed, creating the BOS that just broke*, and vice-versa for bearish. If there's a stray wick before that, which doesn't shift momentum, we don't care about it.
🟩 ZIGZAG
The Major Swing Zigzag dynamically connects momentum highs and lows (e.g., from a Higher Low to the latest Higher High), adjusting as new extremes form to reveal the overall trend leg.
The Implied Structure Zigzag joins momentum pivots and Implied pivots, if enabled.
🟩 REPAINTING
It's really important to understand two things before asking "Does it repaint?":
1. ALL structure indicators repaint, in the sense of drawing things into the past or notifying you of things that happened in past bars, because by definition, structure needs some kind of confirmation, which takes at least one bar, usually several. This is normal.
2. Almost all indicators of ANY kind repaint in that they display unconfirmed values until the current bar closes. This is also normal.
Most features of this indicator repaint in the ordinary, intended ways described above: the pivots (Implied doubly so), BOS and CHoCH lines, and formation of S/R zones.
The Zigzags, by design, adjust themselves to new pivots. The active lines often change and attach themselves to new anchors. This is a form of repainting. It's important to note that the Zigzags are not signals. They're there to help visualise market structure, and structure does change. Therefore, I prioritised clearly explaining what price did rather than preserving its history.
One of the "bad" kinds of repainting is if a signal is printed when the bar closes, but then on a later bar that "confirmed" signal changes. This is a fundamental issue with some high timeframe implementations. It's bad because you might already have entered a trade and now the indicator is pretending that it never signalled it for you. My indicators do not do this (in fact I wrote an entire library to help other authors avoid this).
If you are ever in any doubt, play with an indicator in Bar Replay mode to see exactly what it does.
To understand repainting, see the official docs: www.tradingview.com
🟩 FURTHER RESEARCH
I've attempted to answer two of the tricky problems in technical analysis in Pine: how to do robust and responsive market structure, and how to maintain support and resistance zones once created. However, this just opens up more possibilities. Which momentum metrics are suitable for structure? Can more reactive metrics be used, and how do we account for divergences in a structural model based on key horizontal levels? Which sets of rules give the best results for maintaining support and resistance? Does the market have a long or a short memory? Is bar decay a natural law or a coping mechanism?
🟩 CREDITS
❤️ I'd like to thank my humble trading mentor, whose brilliant ideas inspire me to garble out code. Thanks are also due to @Timeframe_Titans for guidance on the finer points of market structure (all mistakes and distortions are my own), and to @NJPorthos for feedback and encouragement during the months in the wilderness.
有料スクリプト
Bifurcation Zone - CAEBifurcation Zone — Cognitive Adversarial Engine (BZ-CAE)
Bifurcation Zone — CAE (BZ-CAE) is a next-generation divergence detection system enhanced by a Cognitive Adversarial Engine that evaluates both sides of every potential trade before presenting signals. Unlike traditional divergence indicators that show every price-oscillator disagreement regardless of context, BZ-CAE applies comprehensive market-state intelligence to identify only the divergences that occur in favorable conditions with genuine probability edges.
The system identifies structural bifurcation points — critical junctures where price and momentum disagree, signaling potential reversals or continuations — then validates these opportunities through five interconnected intelligence layers: Trend Conviction Scoring , Directional Momentum Alignment , Multi-Factor Exhaustion Modeling , Adversarial Validation , and Confidence Scoring . The result is a selective, context-aware signal system that filters noise and highlights high-probability setups.
This is not a "buy the arrow" indicator. It's a decision support framework that teaches you how to read market state, evaluate divergence quality, and make informed trading decisions based on quantified intelligence rather than hope.
What Sets BZ-CAE Apart: Technical Architecture
The Problem With Traditional Divergence Indicators
Most divergence indicators operate on a simple rule: if price makes a higher high and RSI makes a lower high, show a bearish signal. If price makes a lower low and RSI makes a higher low, show a bullish signal. This creates several critical problems:
Context Blindness : They show counter-trend signals in powerful trends that rarely reverse, leading to repeated losses as you fade momentum.
Signal Spam : Every minor price-oscillator disagreement generates an alert, overwhelming you with low-quality setups and creating analysis paralysis.
No Quality Ranking : All signals are treated identically. A marginal divergence in choppy conditions receives the same visual treatment as a high-conviction setup at a major exhaustion point.
Single-Sided Evaluation : They ask "Is this a good long?" without checking if the short case is overwhelmingly stronger, leading you into obvious bad trades.
Static Configuration : You manually choose RSI 14 or Stochastic 14 and hope it works, with no systematic way to validate if that's optimal for your instrument.
BZ-CAE's Solution: Cognitive Adversarial Intelligence
BZ-CAE solves these problems through an integrated five-layer intelligence architecture:
1. Trend Conviction Score (TCS) — 0 to 1 Scale
Most indicators check if ADX is above 25 to determine "trending" conditions. This binary approach misses nuance. TCS is a weighted composite metric:
Formula : 0.35 × normalize(ADX, 10, 35) + 0.35 × structural_strength + 0.30 × htf_alignment
Structural Strength : 10-bar SMA of consecutive directional bars. Captures persistence — are bulls or bears consistently winning?
HTF Alignment : Multi-timeframe EMA stacking (20/50/100/200). When all EMAs align in the same direction, you're in institutional trend territory.
Purpose : Quantifies how "locked in" the trend is. When TCS exceeds your threshold (default 0.80), the system knows to avoid counter-trend trades unless other factors override.
Interpretation :
TCS > 0.85: Very strong trend — counter-trading is extremely high risk
TCS 0.70-0.85: Strong trend — favor continuation, require exhaustion for reversals
TCS 0.50-0.70: Moderate trend — context matters, both directions viable
TCS < 0.50: Weak/choppy — reversals more viable, range-bound conditions
2. Directional Momentum Alignment (DMA) — ATR-Normalized
Formula : (EMA21 - EMA55) / ATR14
This isn't just "price above EMA" — it's a regime-aware momentum gauge. The same $100 price movement reads completely differently in high-volatility crypto versus low-volatility forex. By normalizing with ATR, DMA adapts its interpretation to current market conditions.
Purpose : Quantifies the directional "force" behind current price action. Positive = bullish push, negative = bearish push. Magnitude = strength.
Interpretation :
DMA > 0.7: Strong bullish momentum — bearish divergences risky
DMA 0.3 to 0.7: Moderate bullish bias
DMA -0.3 to 0.3: Balanced/choppy conditions
DMA -0.7 to -0.3: Moderate bearish bias
DMA < -0.7: Strong bearish momentum — bullish divergences risky
3. Multi-Factor Exhaustion Modeling — 0 to 1 Probability
Single-metric exhaustion detection (like "RSI > 80") misses complex market states. BZ-CAE aggregates five independent exhaustion signals:
Volume Spikes : Current volume versus 50-bar average
2.5x average: 0.25 weight
2.0x average: 0.15 weight
1.5x average: 0.10 weight
Divergence Present : The fact that a divergence exists contributes 0.30 weight — structural momentum disagreement is itself an exhaustion signal.
RSI Extremes : Captures oscillator climax zones
RSI > 80 or < 20: 0.25 weight
RSI > 75 or < 25: 0.15 weight
Pin Bar Detection : Identifies rejection candles (2:1 wick-to-body ratio, indicating failed breakout attempts): 0.15 weight
Extended Runs : Consecutive bars above/below EMA20 without pullback
30+ bars: 0.15 weight (market hasn't paused to consolidate)
Total exhaustion score is the sum of all applicable weights, capped at 1.0.
Purpose : Detects when strong trends become vulnerable to reversal. High exhaustion can override trend filters, allowing counter-trend trades at genuine turning points that basic indicators would miss.
Interpretation :
Exhaustion > 0.75: High probability of climax — yellow background shading alerts you visually
Exhaustion 0.50-0.75: Moderate overextension — watch for confirmation
Exhaustion < 0.50: Fresh move — trend can continue, counter-trend trades higher risk
4. Adversarial Validation — Game Theory Applied to Trading
This is BZ-CAE's signature innovation. Before approving any signal, the engine quantifies BOTH sides of the trade simultaneously:
For Bullish Divergences , it calculates:
Bull Case Score (0-1+) :
Distance below EMA20 (pullback quality): up to 0.25
Bullish EMA alignment (close > EMA20 > EMA50): 0.25
Oversold RSI (< 40): 0.25
Volume confirmation (> 1.2x average): 0.25
Bear Case Score (0-1+) :
Price below EMA50 (structural weakness): 0.30
Very oversold RSI (< 30, indicating knife-catching): 0.20
Differential = Bull Case - Bear Case
If differential < -0.10 (default threshold), the bear case is dominating — signal is BLOCKED or ANNOTATED.
For Bearish Divergences , the logic inverts (Bear Case vs Bull Case).
Purpose : Prevents trades where you're fighting obvious strength in the opposite direction. This is institutional-grade risk management — don't just evaluate your trade, evaluate the counter-trade simultaneously.
Why This Matters : You might see a bullish divergence at a local low, but if price is deeply below major support EMAs with strong bearish momentum, you're catching a falling knife. The adversarial check catches this and blocks the signal.
5. Confidence Scoring — 0 to 1 Quality Assessment
Every signal that passes initial filters receives a comprehensive quality score:
Formula :
0.30 × normalize(TCS) // Trend context
+ 0.25 × normalize(|DMA|) // Momentum magnitude
+ 0.20 × pullback_quality // Entry distance from EMA20
+ 0.15 × state_quality // ADX + alignment + structure
+ 0.10 × divergence_strength // Slope separation magnitude
+ adversarial_bonus (0-0.30) // Your side's advantage
Purpose : Ranks setup quality for filtering and position sizing decisions. You can set a minimum confidence threshold (default 0.35) to ensure only quality setups reach your chart.
Interpretation :
Confidence > 0.70: Premium setup — consider increased position size
Confidence 0.50-0.70: Good quality — standard size
Confidence 0.35-0.50: Acceptable — reduced size or skip if conservative
Confidence < 0.35: Marginal — blocked in Filtering mode, annotated in Advisory mode
CAE Operating Modes: Learning vs Enforcement
Off : Disables all CAE logic. Raw divergence pipeline only. Use for baseline comparison.
Advisory : Shows ALL signals regardless of CAE evaluation, but annotates signals that WOULD be blocked with specific warnings (e.g., "Bull: strong downtrend (TCS=0.87)" or "Adversarial bearish"). This is your learning mode — see CAE's decision logic in action without missing educational opportunities.
Filtering : Actively blocks low-quality signals. Only setups that pass all enabled gates (Trend Filter, Adversarial Validation, Confidence Gating) reach your chart. This is your live trading mode — trust the system to enforce discipline.
CAE Filter Gates: Three-Layer Protection
When CAE is enabled, signals must pass through three independent gates (each can be toggled on/off):
Gate 1: Strong Trend Filter
If TCS ≥ tcs_threshold (default 0.80)
And signal is counter-trend (bullish in downtrend or bearish in uptrend)
And exhaustion < exhaustion_required (default 0.50)
Then: BLOCK signal
Logic: Don't fade strong trends unless the move is clearly overextended
Gate 2: Adversarial Validation
Calculate both bull case and bear case scores
If opposing case dominates by more than adv_threshold (default 0.10)
Then: BLOCK signal
Logic: Avoid trades where you're fighting obvious strength in the opposite direction
Gate 3: Confidence Gating
Calculate composite confidence score (0-1)
If confidence < min_confidence (default 0.35)
Then: In Filtering mode, BLOCK signal; in Advisory mode, ANNOTATE with warning
Logic: Only take setups with minimum quality threshold
All three gates work together. A signal must pass ALL enabled gates to fire.
Visual Intelligence System
Bifurcation Zones (Supply/Demand Blocks)
When a divergence signal fires, BZ-CAE draws a semi-transparent box extending 15 bars forward from the signal pivot:
Demand Zones (Bullish) : Theme-colored box (cyan in Cyberpunk, blue in Professional, etc.) labeled "Demand" — marks where smart money likely placed buy orders as price diverged at the low.
Supply Zones (Bearish) : Theme-colored box (magenta in Cyberpunk, orange in Professional) labeled "Supply" — marks where smart money likely placed sell orders as price diverged at the high.
Theory : Divergences represent institutional disagreement with the crowd. The crowd pushed price to an extreme (new high or low), but momentum (oscillator) is waning, indicating smart money is taking the opposite side. These zones mark order placement areas that become future support/resistance.
Use Cases :
Exit targets: Take profit when price returns to opposite-side zone
Re-entry levels: If price returns to your entry zone, consider adding
Stop placement: Place stops just beyond your zone (below demand, above supply)
Auto-Cleanup : System keeps the last 20 zones to prevent chart clutter.
Adversarial Bar Coloring — Real-Time Market Debate Heatmap
Each bar is colored based on the Bull Case vs Bear Case differential:
Strong Bull Advantage (diff > 0.3): Full theme bull color (e.g., cyan)
Moderate Bull Advantage (diff > 0.1): 50% transparency bull
Neutral (diff -0.1 to 0.1): Gray/neutral theme
Moderate Bear Advantage (diff < -0.1): 50% transparency bear
Strong Bear Advantage (diff < -0.3): Full theme bear color (e.g., magenta)
This creates a real-time visual heatmap showing which side is "winning" the market debate. When bars flip from cyan to magenta (or vice versa), you're witnessing a shift in adversarial advantage — a leading indicator of potential momentum changes.
Exhaustion Shading
When exhaustion score exceeds 0.75, the chart background displays a semi-transparent yellow highlight. This immediate visual warning alerts you that the current move is at high risk of reversal, even if trend indicators remain strong.
Visual Themes — Six Aesthetic Options
Cyberpunk : Cyan/Magenta/Yellow — High contrast, neon aesthetic, excellent for dark-themed trading environments
Professional : Blue/Orange/Green — Corporate color palette, suitable for presentations and professional documentation
Ocean : Teal/Red/Cyan — Aquatic palette, calming for extended monitoring sessions
Fire : Orange/Red/Coral — Warm aggressive colors, high energy
Matrix : Green/Red/Lime — Code aesthetic, homage to classic hacker visuals
Monochrome : White/Gray — Minimal distraction, maximum focus on price action
All visual elements (signal markers, zones, bar colors, dashboard) adapt to your selected theme.
Divergence Engine — Core Detection System
What Are Divergences?
Divergences occur when price action and momentum indicators disagree, creating structural tension that often resolves in a change of direction:
Regular Divergence (Reversal Signal) :
Bearish Regular : Price makes higher high, oscillator makes lower high → Potential trend reversal down
Bullish Regular : Price makes lower low, oscillator makes higher low → Potential trend reversal up
Hidden Divergence (Continuation Signal) :
Bearish Hidden : Price makes lower high, oscillator makes higher high → Downtrend continuation
Bullish Hidden : Price makes higher low, oscillator makes lower low → Uptrend continuation
Both types can be enabled/disabled independently in settings.
Pivot Detection Methods
BZ-CAE uses symmetric pivot detection with separate lookback and lookforward periods (default 5/5):
Pivot High : Bar where high > all highs within lookback range AND high > all highs within lookforward range
Pivot Low : Bar where low < all lows within lookback range AND low < all lows within lookforward range
This ensures structural validity — the pivot must be a clear local extreme, not just a minor wiggle.
Divergence Validation Requirements
For a divergence to be confirmed, it must satisfy:
Slope Disagreement : Price slope and oscillator slope must move in opposite directions (for regular divs) or same direction with inverted highs/lows (for hidden divs)
Minimum Slope Change : |osc_slope| > min_slope_change / 100 (default 1.0) — filters weak, marginal divergences
Maximum Lookback Range : Pivots must be within max_lookback bars (default 60) — prevents ancient, irrelevant divergences
ATR-Normalized Strength : Divergence strength = min(|price_slope| × |osc_slope| × 10, 1.0) — quantifies the magnitude of disagreement in volatility context
Regular divergences receive 1.0× weight; hidden divergences receive 0.8× weight (slightly less reliable historically).
Oscillator Options — Five Professional Indicators
RSI (Relative Strength Index) : Classic overbought/oversold momentum indicator. Best for: General purpose divergence detection across all instruments.
Stochastic : Range-bound %K momentum comparing close to high-low range. Best for: Mean reversion strategies and range-bound markets.
CCI (Commodity Channel Index) : Measures deviation from statistical mean, auto-normalized to 0-100 scale. Best for: Cyclical instruments and commodities.
MFI (Money Flow Index) : Volume-weighted RSI incorporating money flow. Best for: Volume-driven markets like stocks and crypto.
Williams %R : Inverse stochastic looking back over period, auto-adjusted to 0-100. Best for: Reversal detection at extremes.
Each oscillator has adjustable length (2-200, default 14) and smoothing (1-20, default 1). You also set overbought (50-100, default 70) and oversold (0-50, default 30) thresholds.
Signal Timing Modes — Understanding Repainting
BZ-CAE offers two timing policies with complete transparency about repainting behavior:
Realtime (1-bar, peak-anchored)
How It Works :
Detects peaks 1 bar ago using pattern: high > high AND high > high
Signal prints on the NEXT bar after peak detection (bar_index)
Visual marker anchors to the actual PEAK bar (bar_index - 1, offset -1)
Signal locks in when bar CONFIRMS (closes)
Repainting Behavior :
On the FORMING bar (before close), the peak condition may change as new prices arrive
Once bar CLOSES (barstate.isconfirmed), signal is locked permanently
This is preview/early warning behavior by design
Best For :
Active monitoring and immediate alerts
Learning the system (seeing signals develop in real-time)
Responsive entry if you're watching the chart live
Confirmed (lookforward)
How It Works :
Uses Pine Script's built-in ta.pivothigh() and ta.pivotlow() functions
Requires full pivot validation period (lookback + lookforward bars)
Signal prints pivot_lookforward bars after the actual peak (default 5-bar delay)
Visual marker anchors to the actual peak bar (offset -pivot_lookforward)
No Repainting Behavior
Best For :
Backtesting and historical analysis
Conservative entries requiring full confirmation
Automated trading systems
Swing trading with larger timeframes
Tradeoff :
Delayed entry by pivot_lookforward bars (typically 5 bars)
On a 5-minute chart, this is a 25-minute delay
On a 4-hour chart, this is a 20-hour delay
Recommendation : Use Confirmed for backtesting to verify system performance honestly. Use Realtime for live monitoring only if you're actively watching the chart and understand pre-confirmation repainting behavior.
Signal Spacing System — Anti-Spam Architecture
Even after CAE filtering, raw divergences can cluster. The spacing system enforces separation:
Three Independent Filters
1. Min Bars Between ANY Signals (default 12):
Prevents rapid-fire clustering across both directions
If last signal (bull or bear) was within N bars, block new signal
Ensures breathing room between all setups
2. Min Bars Between SAME-SIDE Signals (default 24, optional enforcement):
Prevents bull-bull or bear-bear spam
Separate tracking for bullish and bearish signal timelines
Toggle enforcement on/off
3. Min ATR Distance From Last Signal (default 0, optional):
Requires price to move N × ATR from last signal location
Ensures meaningful price movement between setups
0 = disabled, 0.5-2.0 = typical range for enabled
All three filters work independently. A signal must pass ALL enabled filters to proceed.
Practical Guidance :
Scalping (1-5m) : Any 6-10, Same-side 12-20, ATR 0-0.5
Day Trading (15m-1H) : Any 12, Same-side 24, ATR 0-1.0
Swing Trading (4H-D) : Any 20-30, Same-side 40-60, ATR 1.0-2.0
Dashboard — Real-Time Control Center
The dashboard (toggleable, four corner positions, three sizes) provides comprehensive system intelligence:
Oscillator Section
Current oscillator type and value
State: OVERBOUGHT / OVERSOLD / NEUTRAL (color-coded)
Length parameter
Cognitive Engine Section
TCS (Trend Conviction Score) :
Current value with emoji state indicator
🔥 = Strong trend (>0.75)
📊 = Moderate trend (0.50-0.75)
〰️ = Weak/choppy (<0.50)
Color: Red if above threshold (trend filter active), yellow if moderate, green if weak
DMA (Directional Momentum Alignment) :
Current value with emoji direction indicator
🐂 = Bullish momentum (>0.5)
⚖️ = Balanced (-0.5 to 0.5)
🐻 = Bearish momentum (<-0.5)
Color: Green if bullish, red if bearish
Exhaustion :
Current value with emoji warning indicator
⚠️ = High exhaustion (>0.75)
🟡 = Moderate (0.50-0.75)
✓ = Low (<0.50)
Color: Red if high, yellow if moderate, green if low
Pullback :
Quality of current distance from EMA20
Values >0.6 are ideal entry zones (not too close, not too far)
Bull Case / Bear Case (if Adversarial enabled):
Current scores for both sides of the market debate
Differential with emoji indicator:
📈 = Bull advantage (>0.2)
➡️ = Balanced (-0.2 to 0.2)
📉 = Bear advantage (<-0.2)
Last Signal Metrics Section (New Feature)
When a signal fires, this section captures and displays:
Signal type (BULL or BEAR)
Bars elapsed since signal
Confidence % at time of signal
TCS value at signal time
DMA value at signal time
Purpose : Provides a historical reference for learning. You can see what the market state looked like when the last signal fired, helping you correlate outcomes with conditions.
Statistics Section
Total Signals : Lifetime count across session
Blocked Signals : Count and percentage (filter effectiveness metric)
Bull Signals : Total bullish divergences
Bear Signals : Total bearish divergences
Purpose : System health monitoring. If blocked % is very high (>60%), filters may be too strict. If very low (<10%), filters may be too loose.
Advisory Annotations
When CAE Mode = Advisory, this section displays warnings for signals that would be blocked in Filtering mode:
Examples:
"Bull spacing: wait 8 bars"
"Bear: strong uptrend (TCS=0.87)"
"Adversarial bearish"
"Low confidence 32%"
Multiple warnings can stack, separated by " | ". This teaches you CAE's decision logic transparently.
How to Use BZ-CAE — Complete Workflow
Phase 1: Initial Setup (First Session)
Apply BZ-CAE to your chart
Select your preferred Visual Theme (Cyberpunk recommended for visibility)
Set Signal Timing to "Confirmed (lookforward)" for learning
Choose your Oscillator Type (RSI recommended for general use, length 14)
Set Overbought/Oversold to 70/30 (standard)
Enable both Regular Divergence and Hidden Divergence
Set Pivot Lookback/Lookforward to 5/5 (balanced structure)
Enable CAE Intelligence
Set CAE Mode to "Advisory" (learning mode)
Enable all three CAE filters: Strong Trend Filter , Adversarial Validation , Confidence Gating
Enable Show Dashboard , position Top Right, size Normal
Enable Draw Bifurcation Zones and Adversarial Bar Coloring
Phase 2: Learning Period (Weeks 1-2)
Goal : Understand how CAE evaluates market state and filters signals.
Activities :
Watch the dashboard during signals :
Note TCS values when counter-trend signals fail — this teaches you the trend strength threshold for your instrument
Observe exhaustion patterns at actual turning points — learn when overextension truly matters
Study adversarial differential at signal times — see when opposing cases dominate
Review blocked signals (orange X-crosses):
In Advisory mode, you see everything — signals that would pass AND signals that would be blocked
Check the advisory annotations to understand why CAE would block
Track outcomes: Were the blocks correct? Did those signals fail?
Use Last Signal Metrics :
After each signal, check the dashboard capture of confidence, TCS, and DMA
Journal these values alongside trade outcomes
Identify patterns: Do confidence >0.70 signals work better? Does your instrument respect TCS >0.85?
Understand your instrument's "personality" :
Trending instruments (indices, major forex) may need TCS threshold 0.85-0.90
Choppy instruments (low-cap stocks, exotic pairs) may work best with TCS 0.70-0.75
High-volatility instruments (crypto) may need wider spacing
Low-volatility instruments may need tighter spacing
Phase 3: Calibration (Weeks 3-4)
Goal : Optimize settings for your specific instrument, timeframe, and style.
Calibration Checklist :
Min Confidence Threshold :
Review confidence distribution in your signal journal
Identify the confidence level below which signals consistently fail
Set min_confidence slightly above that level
Day trading : 0.35-0.45
Swing trading : 0.40-0.55
Scalping : 0.30-0.40
TCS Threshold :
Find the TCS level where counter-trend signals consistently get stopped out
Set tcs_threshold at or slightly below that level
Trending instruments : 0.85-0.90
Mixed instruments : 0.80-0.85
Choppy instruments : 0.75-0.80
Exhaustion Override Level :
Identify exhaustion readings that marked genuine reversals
Set exhaustion_required just below the average
Typical range : 0.45-0.55
Adversarial Threshold :
Default 0.10 works for most instruments
If you find CAE is too conservative (blocking good trades), raise to 0.15-0.20
If signals are still getting caught in opposing momentum, lower to 0.07-0.09
Spacing Parameters :
Count bars between quality signals in your journal
Set min bars ANY to ~60% of that average
Set min bars SAME-SIDE to ~120% of that average
Scalping : Any 6-10, Same 12-20
Day trading : Any 12, Same 24
Swing : Any 20-30, Same 40-60
Oscillator Selection :
Try different oscillators for 1-2 weeks each
Track win rate and average winner/loser by oscillator type
RSI : Best for general use, clear OB/OS
Stochastic : Best for range-bound, mean reversion
MFI : Best for volume-driven markets
CCI : Best for cyclical instruments
Williams %R : Best for reversal detection
Phase 4: Live Deployment
Goal : Disciplined execution with proven, calibrated system.
Settings Changes :
Switch CAE Mode from Advisory to Filtering
System now actively blocks low-quality signals
Only setups passing all gates reach your chart
Keep Signal Timing on Confirmed for conservative entries
OR switch to Realtime if you're actively monitoring and want faster entries (accept pre-confirmation repaint risk)
Use your calibrated thresholds from Phase 3
Enable high-confidence alerts: "⭐ High Confidence Bullish/Bearish" (>0.70)
Trading Discipline Rules :
Respect Blocked Signals :
If CAE blocks a trade you wanted to take, TRUST THE SYSTEM
Don't manually override — if you consistently disagree, return to Phase 2/3 calibration
The block exists because market state failed intelligence checks
Confidence-Based Position Sizing :
Confidence >0.70: Standard or increased size (e.g., 1.5-2.0% risk)
Confidence 0.50-0.70: Standard size (e.g., 1.0% risk)
Confidence 0.35-0.50: Reduced size (e.g., 0.5% risk) or skip if conservative
TCS-Based Management :
High TCS + counter-trend signal: Use tight stops, quick exits (you're fading momentum)
Low TCS + reversal signal: Use wider stops, trail aggressively (genuine reversal potential)
Exhaustion Awareness :
Exhaustion >0.75 (yellow shading): Market is overextended, reversal risk is elevated — consider early exit or tighter trailing stops even on winning trades
Exhaustion <0.30: Continuation bias — hold for larger move, wide trailing stops
Adversarial Context :
Strong differential against you (e.g., bullish signal with bear diff <-0.2): Use very tight stops, consider skipping
Strong differential with you (e.g., bullish signal with bull diff >0.2): Trail aggressively, this is your tailwind
Practical Settings by Timeframe & Style
Scalping (1-5 Minute Charts)
Objective : High frequency, tight stops, quick reversals in fast-moving markets.
Oscillator :
Type: RSI or Stochastic (fast response to quick moves)
Length: 9-11 (more responsive than standard 14)
Smoothing: 1 (no lag)
OB/OS: 65/35 (looser thresholds ensure frequent crossings in fast conditions)
Divergence :
Pivot Lookback/Lookforward: 3/3 (tight structure, catch small swings)
Max Lookback: 40-50 bars (recent structure only)
Min Slope Change: 0.8-1.0 (don't be overly strict)
CAE :
Mode: Advisory first (learn), then Filtering
Min Confidence: 0.30-0.35 (lower bar for speed, accept more signals)
TCS Threshold: 0.70-0.75 (allow more counter-trend opportunities)
Exhaustion Required: 0.45-0.50 (moderate override)
Strong Trend Filter: ON (still respect major intraday trends)
Adversarial: ON (critical for scalping protection — catches bad entries quickly)
Spacing :
Min Bars ANY: 6-10 (fast pace, many setups)
Min Bars SAME-SIDE: 12-20 (prevent clustering)
Min ATR Distance: 0 or 0.5 (loose)
Timing : Realtime (speed over precision, but understand repaint risk)
Visuals :
Signal Size: Tiny (chart clarity in busy conditions)
Show Zones: Optional (can clutter on low timeframes)
Bar Coloring: ON (helps read momentum shifts quickly)
Dashboard: Small size (corner reference, not main focus)
Key Consideration : Scalping generates noise. Even with CAE, expect lower win rate (45-55%) but aim for favorable R:R (2:1 or better). Size conservatively.
Day Trading (15-Minute to 1-Hour Charts)
Objective : Balance quality and frequency. Standard divergence trading approach.
Oscillator :
Type: RSI or MFI (proven reliability, volume confirmation with MFI)
Length: 14 (industry standard, well-studied)
Smoothing: 1-2
OB/OS: 70/30 (classic levels)
Divergence :
Pivot Lookback/Lookforward: 5/5 (balanced structure)
Max Lookback: 60 bars
Min Slope Change: 1.0 (standard strictness)
CAE :
Mode: Filtering (enforce discipline from the start after brief Advisory learning)
Min Confidence: 0.35-0.45 (quality filter without being too restrictive)
TCS Threshold: 0.80-0.85 (respect strong trends)
Exhaustion Required: 0.50 (balanced override threshold)
Strong Trend Filter: ON
Adversarial: ON
Confidence Gating: ON (all three filters active)
Spacing :
Min Bars ANY: 12 (breathing room between all setups)
Min Bars SAME-SIDE: 24 (prevent bull/bear clusters)
Min ATR Distance: 0-1.0 (optional refinement, typically 0.5-1.0)
Timing : Confirmed (1-bar delay for reliability, no repainting)
Visuals :
Signal Size: Tiny or Small
Show Zones: ON (useful reference for exits/re-entries)
Bar Coloring: ON (context awareness)
Dashboard: Normal size (full visibility)
Key Consideration : This is the "sweet spot" timeframe for BZ-CAE. Market structure is clear, CAE has sufficient data, and signal frequency is manageable. Expect 55-65% win rate with proper execution.
Swing Trading (4-Hour to Daily Charts)
Objective : Quality over quantity. High conviction only. Larger stops and targets.
Oscillator :
Type: RSI or CCI (robust on higher timeframes, smooth longer waves)
Length: 14-21 (capture larger momentum swings)
Smoothing: 1-3
OB/OS: 70/30 or 75/25 (strict extremes)
Divergence :
Pivot Lookback/Lookforward: 5/5 or 7/7 (structural purity, major swings only)
Max Lookback: 80-100 bars (broader historical context)
Min Slope Change: 1.2-1.5 (require strong, undeniable divergence)
CAE :
Mode: Filtering (strict enforcement, premium setups only)
Min Confidence: 0.40-0.55 (high bar for entry)
TCS Threshold: 0.85-0.95 (very strong trend protection — don't fade established HTF trends)
Exhaustion Required: 0.50-0.60 (higher bar for override — only extreme exhaustion justifies counter-trend)
Strong Trend Filter: ON (critical on HTF)
Adversarial: ON (avoid obvious bad trades)
Confidence Gating: ON (quality gate essential)
Spacing :
Min Bars ANY: 20-30 (substantial separation)
Min Bars SAME-SIDE: 40-60 (significant breathing room)
Min ATR Distance: 1.0-2.0 (require meaningful price movement)
Timing : Confirmed (purity over speed, zero repaint for swing accuracy)
Visuals :
Signal Size: Small or Normal (clear markers on zoomed-out view)
Show Zones: ON (important HTF levels)
Bar Coloring: ON (long-term trend awareness)
Dashboard: Normal or Large (comprehensive analysis)
Key Consideration : Swing signals are rare but powerful. Expect 2-5 signals per month per instrument. Win rate should be 60-70%+ due to stringent filtering. Position size can be larger given confidence.
Dashboard Interpretation Reference
TCS (Trend Conviction Score) States
0.00-0.50: Weak/Choppy
Emoji: 〰️
Color: Green/cyan
Meaning: No established trend. Range-bound or consolidating. Both reversal and continuation signals viable.
Action: Reversals (regular divs) are safer. Use wider profit targets (market has room to move). Consider mean reversion strategies.
0.50-0.75: Moderate Trend
Emoji: 📊
Color: Yellow/neutral
Meaning: Developing trend but not locked in. Context matters significantly.
Action: Check DMA and exhaustion. If DMA confirms trend and exhaustion is low, favor continuation (hidden divs). If exhaustion is high, reversals are viable.
0.75-0.85: Strong Trend
Emoji: 🔥
Color: Orange/warning
Meaning: Well-established trend with persistence. Counter-trend is high risk.
Action: Require exhaustion >0.50 for counter-trend entries. Favor continuation signals. Use tight stops on counter-trend attempts.
0.85-1.00: Very Strong Trend
Emoji: 🔥🔥
Color: Red/danger (if counter-trading)
Meaning: Locked-in institutional trend. Extremely high risk to fade.
Action: Avoid counter-trend unless exhaustion >0.75 (yellow shading). Focus exclusively on continuation opportunities. Momentum is king here.
DMA (Directional Momentum Alignment) Zones
-2.0 to -1.0: Strong Bearish Momentum
Emoji: 🐻🐻
Color: Dark red
Meaning: Powerful downside force. Sellers are in control.
Action: Bullish divergences are counter-momentum (high risk). Bearish divergences are with-momentum (lower risk). Size down on longs.
-0.5 to 0.5: Neutral/Balanced
Emoji: ⚖️
Color: Gray/neutral
Meaning: No strong directional bias. Choppy or consolidating.
Action: Both directions have similar probability. Focus on confidence score and adversarial differential for edge.
1.0 to 2.0: Strong Bullish Momentum
Emoji: 🐂🐂
Color: Bright green/cyan
Meaning: Powerful upside force. Buyers are in control.
Action: Bearish divergences are counter-momentum (high risk). Bullish divergences are with-momentum (lower risk). Size down on shorts.
Exhaustion States
0.00-0.50: Fresh Move
Emoji: ✓
Color: Green
Meaning: Trend is healthy, not overextended. Room to run.
Action: Counter-trend trades are premature. Favor continuation. Hold winners for larger moves. Avoid early exits.
0.50-0.75: Mature Move
Emoji: 🟡
Color: Yellow
Meaning: Move is aging. Watch for signs of climax.
Action: Tighten trailing stops on winning trades. Be ready for reversals. Don't add to positions aggressively.
0.75-0.85: High Exhaustion
Emoji: ⚠️
Color: Orange
Background: Yellow shading appears
Meaning: Move is overextended. Reversal risk elevated significantly.
Action: Counter-trend reversals are higher probability. Consider early exits on with-trend positions. Size up on reversal divergences (if CAE allows).
0.85-1.00: Critical Exhaustion
Emoji: ⚠️⚠️
Color: Red
Background: Yellow shading intensifies
Meaning: Climax conditions. Reversal imminent or underway.
Action: Aggressive reversal trades justified. Exit all with-trend positions. This is where major turns occur.
Confidence Score Tiers
0.00-0.30: Low Quality
Color: Red
Status: Blocked in Filtering mode
Action: Skip entirely. Setup lacks fundamental quality across multiple factors.
0.30-0.50: Moderate Quality
Color: Yellow/orange
Status: Marginal — passes in Filtering only if >min_confidence
Action: Reduced position size (0.5-0.75% risk). Tight stops. Conservative profit targets. Skip if you're selective.
0.50-0.70: High Quality
Color: Green/cyan
Status: Good setup across most quality factors
Action: Standard position size (1.0-1.5% risk). Normal stops and targets. This is your bread-and-butter trade.
0.70-1.00: Premium Quality
Color: Bright green/gold
Status: Exceptional setup — all factors aligned
Visual: Double confidence ring appears
Action: Consider increased position size (1.5-2.0% risk, maximum). Wider stops. Larger targets. High probability of success. These are rare — capitalize when they appear.
Adversarial Differential Interpretation
Bull Differential > 0.3 :
Visual: Strong cyan/green bar colors
Meaning: Bull case strongly dominates. Buyers have clear advantage.
Action: Bullish divergences favored (with-advantage). Bearish divergences face headwind (reduce size or skip). Momentum is bullish.
Bull Differential 0.1 to 0.3 :
Visual: Moderate cyan/green transparency
Meaning: Moderate bull advantage. Buyers have edge but not overwhelming.
Action: Both directions viable. Slight bias toward longs.
Differential -0.1 to 0.1 :
Visual: Gray/neutral bars
Meaning: Balanced debate. No clear advantage either side.
Action: Rely on other factors (confidence, TCS, exhaustion) for direction. Adversarial is neutral.
Bear Differential -0.3 to -0.1 :
Visual: Moderate red/magenta transparency
Meaning: Moderate bear advantage. Sellers have edge but not overwhelming.
Action: Both directions viable. Slight bias toward shorts.
Bear Differential < -0.3 :
Visual: Strong red/magenta bar colors
Meaning: Bear case strongly dominates. Sellers have clear advantage.
Action: Bearish divergences favored (with-advantage). Bullish divergences face headwind (reduce size or skip). Momentum is bearish.
Last Signal Metrics — Post-Trade Analysis
After a signal fires, dashboard captures:
Type : BULL or BEAR
Bars Ago : How long since signal (updates every bar)
Confidence : What was the quality score at signal time
TCS : What was trend conviction at signal time
DMA : What was momentum alignment at signal time
Use Case : Post-trade journaling and learning.
Example: "BULL signal 12 bars ago. Confidence: 68%, TCS: 0.42, DMA: -0.85"
Analysis : This was a bullish reversal (regular div) with good confidence, weak trend (TCS), but strong bearish momentum (DMA). The bet was that momentum would reverse — a counter-momentum play requiring exhaustion confirmation. Check if exhaustion was high at that time to justify the entry.
Track patterns:
Do your best trades have confidence >0.65?
Do low-TCS signals (<0.50) work better for you?
Are you more successful with-momentum (DMA aligned with signal) or counter-momentum?
Troubleshooting Guide
Problem: No Signals Appearing
Symptoms : Chart loads, dashboard shows metrics, but no divergence signals fire.
Diagnosis Checklist :
Check dashboard oscillator value : Is it crossing OB/OS levels (70/30)? If oscillator stays in 40-60 range constantly, it can't reach extremes needed for divergence detection.
Are pivots forming? : Look for local swing highs/lows on your chart. If price is in tight consolidation, pivots may not meet lookback/lookforward requirements.
Is spacing too tight? : Check "Last Signal" metrics — how many bars since last signal? If <12 and your min_bars_ANY is 12, spacing filter is blocking.
Is CAE blocking everything? : Check dashboard Statistics section — what's the blocked signal count? High blocks indicate overly strict filters.
Solutions :
Loosen OB/OS Temporarily :
Try 65/35 to verify divergence detection works
If signals appear, the issue was threshold strictness
Gradually tighten back to 67/33, then 70/30 as appropriate
Lower Min Confidence :
Try 0.25-0.30 (diagnostic level)
If signals appear, filter was too strict
Raise gradually to find sweet spot (0.35-0.45 typical)
Disable Strong Trend Filter Temporarily :
Turn off in CAE settings
If signals appear, TCS threshold was blocking everything
Re-enable and lower TCS_threshold to 0.70-0.75
Reduce Min Slope Change :
Try 0.7-0.8 (from default 1.0)
Allows weaker divergences through
Helpful on low-volatility instruments
Widen Spacing :
Set min_bars_ANY to 6-8
Set min_bars_SAME_SIDE to 12-16
Reduces time between allowed signals
Check Timing Mode :
If using Confirmed, remember there's a pivot_lookforward delay (5+ bars)
Switch to Realtime temporarily to verify system is working
Realtime has no delay but repaints
Verify Oscillator Settings :
Length 14 is standard but might not fit all instruments
Try length 9-11 for faster response
Try length 18-21 for slower, smoother response
Problem: Too Many Signals (Signal Spam)
Symptoms : Dashboard shows 50+ signals in Statistics, confidence scores mostly <0.40, signals clustering close together.
Solutions :
Raise Min Confidence :
Try 0.40-0.50 (quality filter)
Blocks bottom-tier setups
Targets top 50-60% of divergences only
Tighten OB/OS :
Use 70/30 or 75/25
Requires more extreme oscillator readings
Reduces false divergences in mid-range
Increase Min Slope Change :
Try 1.2-1.5 (from default 1.0)
Requires stronger, more obvious divergences
Filters marginal slope disagreements
Raise TCS Threshold :
Try 0.85-0.90 (from default 0.80)
Stricter trend filter blocks more counter-trend attempts
Favors only strongest trend alignment
Enable ALL CAE Gates :
Turn on Trend Filter + Adversarial + Confidence
Triple-layer protection
Blocks aggressively — expect 20-40% reduction in signals
Widen Spacing :
min_bars_ANY: 15-20 (from 12)
min_bars_SAME_SIDE: 30-40 (from 24)
Creates substantial breathing room
Switch to Confirmed Timing :
Removes realtime preview noise
Ensures full pivot validation
5-bar delay filters many false starts
Problem: Signals in Strong Trends Get Stopped Out
Symptoms : You take a bullish divergence in a downtrend (or bearish in uptrend), and it immediately fails. Dashboard showed high TCS at the time.
Analysis : This is INTENDED behavior — CAE is protecting you from low-probability counter-trend trades.
Understanding :
Check Last Signal Metrics in dashboard — what was TCS when signal fired?
If TCS was >0.85 and signal was counter-trend, CAE correctly identified it as high risk
Strong trends rarely reverse cleanly without major exhaustion
Your losses here are the system working as designed (blocking bad odds)
If You Want to Override (Not Recommended) :
Lower TCS_threshold to 0.70-0.75 (allows more counter-trend)
Lower exhaustion_required to 0.40 (easier override)
Disable Strong Trend Filter entirely (very risky)
Better Approach :
TRUST THE FILTER — it's preventing costly mistakes
Wait for exhaustion >0.75 (yellow shading) before counter-trending strong TCS
Focus on continuation signals (hidden divs) in high-TCS environments
Use Advisory mode to see what CAE is blocking and learn from outcomes
Problem: Adversarial Blocking Seems Wrong
Symptoms : You see a divergence that "looks good" visually, but CAE blocks with "Adversarial bearish/bullish" warning.
Diagnosis :
Check dashboard Bull Case and Bear Case scores at that moment
Look at Differential value
Check adversarial bar colors — was there strong coloring against your intended direction?
Understanding :
Adversarial catches "obvious" opposing momentum that's easy to miss
Example: Bullish divergence at a local low, BUT price is deeply below EMA50, bearish momentum is strong, and RSI shows knife-catching conditions
Bull Case might be 0.20 while Bear Case is 0.55
Differential = -0.35, far beyond threshold
Block is CORRECT — you'd be fighting overwhelming opposing flow
If You Disagree Consistently
Review blocked signals on chart — scroll back and check outcomes
Did those blocked signals actually work, or did they fail as adversarial predicted?
Raise adv_threshold to 0.15-0.20 (more permissive, allows closer battles)
Disable Adversarial Validation temporarily (diagnostic) to isolate its effect
Use Advisory mode to learn adversarial patterns over 50-100 signals
Remember : Adversarial is conservative BY DESIGN. It prevents "obvious" bad trades where you're fighting strong strength the other way.
Problem: Dashboard Not Showing or Incomplete
Solutions :
Toggle "Show Dashboard" to ON in settings
Try different dashboard sizes (Small/Normal/Large)
Try different positions (Top Left/Right, Bottom Left/Right) — might be off-screen
Some sections require CAE Enable = ON (Cognitive Engine section won't appear if CAE is disabled)
Statistics section requires at least 1 lifetime signal to populate
Check that visual theme is set (dashboard colors adapt to theme)
Problem: Performance Lag, Chart Freezing
Symptoms : Chart loading is slow, indicator calculations cause delays, pinch-to-zoom lags.
Diagnosis : Visual features are computationally expensive, especially adversarial bar coloring (recalculates every bar).
Solutions (In Order of Impact) :
Disable Adversarial Bar Coloring (MOST EXPENSIVE):
Turn OFF "Adversarial Bar Coloring" in settings
This is the single biggest performance drain
Immediate improvement
Reduce Vertical Lines :
Lower "Keep last N vertical lines" to 20-30
Or set to 0 to disable entirely
Moderate improvement
Disable Bifurcation Zones :
Turn OFF "Draw Bifurcation Zones"
Reduces box drawing calculations
Moderate improvement
Set Dashboard Size to Small :
Smaller dashboard = fewer cells = less rendering
Minor improvement
Use Shorter Max Lookback :
Reduce max_lookback to 40-50 (from 60+)
Fewer bars to scan for divergences
Minor improvement
Disable Exhaustion Shading :
Turn OFF "Show Market State"
Removes background coloring calculations
Minor improvement
Extreme Performance Mode :
Disable ALL visual enhancements
Keep only triangle markers
Dashboard Small or OFF
Use Minimal theme if available
Problem: Realtime Signals Repainting
Symptoms : You see a signal appear, but on next bar it disappears or moves.
Explanation :
Realtime mode detects peaks 1 bar ago: high > high AND high > high
On the FORMING bar (before close), this condition can change as new prices arrive
Example: At 10:05, high (10:04 bar) was 100, current high is 99 → peak detected
At 10:05:30, new high of 101 arrives → peak condition breaks → signal disappears
At 10:06 (bar close), final high is 101 → no peak at 10:04 anymore → signal gone permanently
This is expected behavior for realtime responsiveness. You get preview/early warning, but it's not locked until bar confirms.
Solutions :
Use Confirmed Timing :
Switch to "Confirmed (lookforward)" mode
ZERO repainting — pivot must be fully validated
5-bar delay (pivot_lookforward)
What you see in history is exactly what would have appeared live
Accept Realtime Repaint as Tradeoff :
Keep Realtime mode for speed and alerts
Understand that pre-confirmation signals may vanish
Only trade signals that CONFIRM at bar close (check barstate.isconfirmed)
Use for live monitoring, NOT for backtesting
Trade Only After Confirmation :
In Realtime mode, wait 1 full bar after signal appears before entering
If signal survives that bar close, it's locked
This adds 1-bar delay but removes repaint risk
Recommendation : Use Confirmed for backtesting and conservative trading. Use Realtime only for active monitoring with full understanding of preview behavior.
Risk Management Integration
BZ-CAE is a signal generation system, not a complete trading strategy. You must integrate proper risk management:
Position Sizing by Confidence
Confidence 0.70-1.00 (Premium) :
Risk: 1.5-2.0% of account (MAXIMUM)
Reasoning: High-quality setup across all factors
Still cap at 2% — even premium setups can fail
Confidence 0.50-0.70 (High Quality) :
Risk: 1.0-1.5% of account
Reasoning: Standard good setup
Your bread-and-butter risk level
Confidence 0.35-0.50 (Moderate Quality) :
Risk: 0.5-1.0% of account
Reasoning: Marginal setup, passes minimum threshold
Reduce size or skip if you're selective
Confidence <0.35 (Low Quality) :
Risk: 0% (blocked in Filtering mode)
Reasoning: Insufficient quality factors
System protects you by not showing these
Stop Placement Strategies
For Reversal Signals (Regular Divergences) :
Place stop beyond the divergence pivot plus buffer
Bullish : Stop below the divergence low - 1.0-1.5 × ATR
Bearish : Stop above the divergence high + 1.0-1.5 × ATR
Reasoning: If price breaks the pivot, divergence structure is invalidated
For Continuation Signals (Hidden Divergences) :
Place stop beyond recent swing in opposite direction
Bullish continuation : Stop below recent swing low (not the divergence pivot itself)
Bearish continuation : Stop above recent swing high
Reasoning: You're trading with trend, allow more breathing room
ATR-Based Stops :
1.5-2.0 × ATR is standard
Scale by timeframe:
Scalping (1-5m): 1.0-1.5 × ATR (tight)
Day trading (15m-1H): 1.5-2.0 × ATR (balanced)
Swing (4H-D): 2.0-3.0 × ATR (wide)
Never Use Fixed Dollar/Pip Stops :
Markets have different volatility
50-pip stop on EUR/USD ≠ 50-pip stop on GBP/JPY
Always normalize by ATR or pivot structure
Profit Targets and Scaling
Primary Target :
2-3 × ATR from entry (minimum 2:1 reward-risk)
Example : Entry at 100, ATR = 2, stop at 97 (1.5 × ATR) → target at 106 (3 × ATR) = 2:1 R:R
Scaling Out Strategy :
Take 50% off at 1.5 × ATR (secure partial profit)
Move stop to breakeven
Trail remaining 50% with 1.0 × ATR trailing stop
Let winners run if trend persists
Targets by Confidence :
High Confidence (>0.70) : Aggressive targets (3-4 × ATR), trail wider (1.5 × ATR)
Standard Confidence (0.50-0.70) : Normal targets (2-3 × ATR), standard trail (1.0 × ATR)
Low Confidence (0.35-0.50) : Conservative targets (1.5-2 × ATR), tight trail (0.75 × ATR)
Use Bifurcation Zones :
If opposite-side zone is visible on chart (from previous signal), use it as target
Example : Bullish signal at 100, prior supply zone at 110 → use 110 as target
Zones mark institutional resistance/support
Exhaustion-Based Exits :
If you're in a trade and exhaustion >0.75 develops (yellow shading), consider early exit
Market is overextended — reversal risk is high
Take profit even if target not reached
Trade Management by TCS
High TCS + Counter-Trend Trade (Risky) :
Use very tight stops (1.0-1.5 × ATR)
Conservative targets (1.5-2 × ATR)
Quick exit if trade doesn't work immediately
You're fading momentum — respect it
Low TCS + Reversal Trade (Safer) :
Use wider stops (2.0-2.5 × ATR)
Aggressive targets (3-4 × ATR)
Trail with patience
Genuine reversal potential in weak trend
High TCS + Continuation Trade (Safest) :
Standard stops (1.5-2.0 × ATR)
Very aggressive targets (4-5 × ATR)
Trail wide (1.5-2.0 × ATR)
You're with institutional momentum — let it run
Educational Value — Learning Machine Intelligence
BZ-CAE is designed as a learning platform, not just a tool:
Advisory Mode as Teacher
Most indicators are binary: signal or no signal. You don't learn WHY certain setups are better.
BZ-CAE's Advisory mode shows you EVERY potential divergence, then annotates the ones that would be blocked in Filtering mode with specific reasons:
"Bull: strong downtrend (TCS=0.87)" teaches you that TCS >0.85 makes counter-trend very risky
"Adversarial bearish" teaches you that the opposing case was dominating
"Low confidence 32%" teaches you that the setup lacked quality across multiple factors
"Bull spacing: wait 8 bars" teaches you that signals need breathing room
After 50-100 signals in Advisory mode, you internalize the CAE's decision logic. You start seeing these factors yourself BEFORE the indicator does.
Dashboard Transparency
Most "intelligent" indicators are black boxes — you don't know how they make decisions.
BZ-CAE shows you ALL metrics in real-time:
TCS tells you trend strength
DMA tells you momentum alignment
Exhaustion tells you overextension
Adversarial shows both sides of the debate
Confidence shows composite quality
You learn to interpret market state holistically, a skill applicable to ANY trading system beyond this indicator.
Divergence Quality Education
Not all divergences are equal. BZ-CAE teaches you which conditions produce high-probability setups:
Quality divergence : Regular bullish div at a low, TCS <0.50 (weak trend), exhaustion >0.75 (overextended), positive adversarial differential, confidence >0.70
Low-quality divergence : Regular bearish div at a high, TCS >0.85 (strong uptrend), exhaustion <0.30 (not overextended), negative adversarial differential, confidence <0.40
After using the system, you can evaluate divergences manually with similar intelligence.
Risk Management Discipline
Confidence-based position sizing teaches you to adjust risk based on setup quality, not emotions:
Beginners often size all trades identically
Or worse, size UP on marginal setups to "make up" for losses
BZ-CAE forces systematic sizing: premium setups get larger size, marginal setups get smaller size
This creates a probabilistic approach where your edge compounds over time.
What This Indicator Is NOT
Complete transparency about limitations and positioning:
Not a Prediction System
BZ-CAE does not predict future prices. It identifies structural divergences (price-momentum disagreements) and assesses current market state (trend, exhaustion, adversarial conditions). It tells you WHEN conditions favor a potential reversal or continuation, not WHAT WILL HAPPEN.
Markets are probabilistic. Even premium-confidence setups fail ~30-40% of the time. The system improves your probability distribution over many trades — it doesn't eliminate risk.
Not Fully Automated
This is a decision support tool, not a trading robot. You must:
Execute trades manually based on signals
Manage positions (stops, targets, trailing)
Apply discretionary judgment (news events, liquidity, context)
Integrate with your broader strategy and risk rules
The confidence scores guide position sizing, but YOU determine final risk allocation based on your account size, risk tolerance, and portfolio context.
Not Beginner-Friendly
BZ-CAE requires understanding of:
Divergence trading concepts (regular vs hidden, reversal vs continuation)
Market state interpretation (trend vs range, momentum, exhaustion)
Basic technical analysis (pivots, support/resistance, EMAs)
Risk management fundamentals (position sizing, stops, R:R)
This is designed for intermediate to advanced traders willing to invest time learning the system. If you want "buy the arrow" simplicity, this isn't the tool.
Not a Holy Grail
There is no perfect indicator. BZ-CAE filters noise and improves signal quality significantly, but:
Losing trades are inevitable (even at 70% win rate, 30% still fail)
Market conditions change rapidly (yesterday's strong trend becomes today's chop)
Black swan events occur (fundamentals override technicals)
Execution matters (slippage, fees, emotional discipline)
The system provides an EDGE, not a guarantee. Your job is to execute that edge consistently with proper risk management over hundreds of trades.
Not Financial Advice
BZ-CAE is an educational and analytical tool. All trading decisions are your responsibility. Past performance (backtested or live) does not guarantee future results. Only risk capital you can afford to lose. Consult a licensed financial advisor for investment advice specific to your situation.
Ideal Market Conditions
Best Performance Characteristics
Liquid Instruments :
Major forex pairs (EUR/USD, GBP/USD, USD/JPY)
Large-cap stocks and index ETFs (SPY, QQQ, AAPL, MSFT)
High-volume crypto (BTC, ETH)
Major commodities (Gold, Oil, Natural Gas)
Reasoning: Clean price structure, clear pivots, meaningful oscillator behavior
Trending with Consolidations :
Markets that trend for 20-40 bars, then consolidate 10-20 bars, repeat
Creates divergences at consolidation boundaries (reversals) and within trends (continuations)
Both regular and hidden divs find opportunities
5-Minute to Daily Timeframes :
Below 5m: too much noise, false pivots, CAE metrics unstable
Above daily: too few signals, edge diminishes (fundamentals dominate)
Sweet spot: 15m to 4H for most traders
Consistent Volume and Participation :
Regular trading sessions (not holidays or thin markets)
Predictable volatility patterns
Avoid instruments with sudden gaps or circuit breakers
Challenging Conditions
Extremely Low Liquidity :
Penny stocks, exotic forex pairs, low-volume crypto
Erratic pivots, unreliable oscillator readings
CAE metrics can't assess market state properly
Very Low Timeframes (1-Minute or Below) :
Dominated by market microstructure noise
Divergences are everywhere but meaningless
CAE filtering helps but still unreliable
Extended Sideways Consolidation :
100+ bars of tight range with no clear pivots
Oscillator hugs midpoint (45-55 range)
No divergences to detect
Fundamentally-Driven Gap Markets :
Earnings releases, economic data, geopolitical events
Price gaps over stops and targets
Technical structure breaks down
Recommendation: Disable trading around known events
Calculation Methodology — Technical Depth
For users who want to understand the math:
Oscillator Computation
Each oscillator type calculates differently, but all normalize to 0-100:
RSI : ta.rsi(close, length) — Standard Relative Strength Index
Stochastic : ta.stoch(high, low, close, length) — %K calculation
CCI : (ta.cci(hlc3, length) + 100) / 2 — Normalized from -100/+100 to 0-100
MFI : ta.mfi(hlc3, length) — Volume-weighted RSI equivalent
Williams %R : ta.wpr(length) + 100 — Inverted stochastic adjusted to 0-100
Smoothing: If smoothing > 1, apply ta.sma(oscillator, smoothing)
Divergence Detection Algorithm
Identify Pivots :
Price high pivot: ta.pivothigh(high, lookback, lookforward)
Price low pivot: ta.pivotlow(low, lookback, lookforward)
Oscillator high pivot: ta.pivothigh(osc, lookback, lookforward)
Oscillator low pivot: ta.pivotlow(osc, lookback, lookforward)
Store Recent Pivots :
Maintain arrays of last 10 pivots with bar indices
When new pivot confirmed, unshift to array, pop oldest if >10
Scan for Slope Disagreements :
Loop through last 5 pivots
For each pair (current pivot, historical pivot):
Check if within max_lookback bars
Calculate slopes: (current - historical) / bars_between
Regular bearish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold
Regular bullish: price_slope < 0, osc_slope > 0, |osc_slope| > min_threshold
Hidden bearish: price_slope < 0, osc_slope > 0, osc_slope > min_threshold
Hidden bullish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold
Important Disclaimers and Terms
Performance Disclosure
Past performance, whether backtested or live-traded, does not guarantee future results. Markets change. What works today may not work tomorrow. Hypothetical or simulated performance results have inherent limitations and do not represent actual trading.
Risk of Loss
Trading involves substantial risk of loss. Only trade with risk capital you can afford to lose entirely. The high degree of leverage often available in trading can work against you as well as for you. Leveraged trading may result in losses exceeding your initial deposit.
Not Financial Advice
BZ-CAE is an educational and analytical tool for technical analysis. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument. All trading decisions are your sole responsibility. Consult a licensed financial advisor for advice specific to your circumstances.
Technical Indicator Limitations
BZ-CAE is a technical analysis tool based on price and volume data. It does not account for:
Fundamental analysis (earnings, economic data, financial health)
Market sentiment and positioning
Geopolitical events and news
Liquidity conditions and market microstructure changes
Regulatory changes or exchange rules
Integrate with broader analysis and strategy. Do not rely solely on technical indicators for trading decisions.
Repainting Acknowledgment
As disclosed throughout this documentation:
Realtime mode may repaint on forming bars before confirmation (by design for preview functionality)
Confirmed mode has zero repainting (fully validated pivots only)
Choose timing mode appropriate for your use case. Understand the tradeoffs.
Testing Recommendation
ALWAYS test on demo/paper accounts before committing real capital. Validate the indicator's behavior on your specific instruments and timeframes. Learn the system thoroughly in Advisory mode before using Filtering mode.
Learning Resources :
In-indicator tooltips (hover over setting names for detailed explanations)
This comprehensive publishing statement (save for reference)
User guide in script comments (top of code)
Final Word — Philosophy of BZ-CAE
BZ-CAE is not designed to replace your judgment — it's designed to enhance it.
The indicator identifies structural inflection points (bifurcations) where price and momentum disagree. The Cognitive Engine evaluates market state to determine if this disagreement is meaningful or noise. The Adversarial model debates both sides of the trade to catch obvious bad setups. The Confidence system ranks quality so you can choose your risk appetite.
But YOU still execute. YOU still manage risk. YOU still learn from outcomes.
This is intelligence amplification, not intelligence replacement.
Use Advisory mode to learn how expert traders evaluate market state. Use Filtering mode to enforce discipline when emotions run high. Use the dashboard to develop a systematic approach to reading markets. Use confidence scores to size positions probabilistically.
The system provides an edge. Your job is to execute that edge with discipline, patience, and proper risk management over hundreds of trades.
Markets are probabilistic. No system wins every trade. But a systematic edge + disciplined execution + proper risk management compounds over time. That's the path to consistent profitability. BZ-CAE gives you the edge. The discipline and risk management are on you.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Quantum Rotational Field MappingQuantum Rotational Field Mapping (QRFM):
Phase Coherence Detection Through Complex-Plane Oscillator Analysis
Quantum Rotational Field Mapping applies complex-plane mathematics and phase-space analysis to oscillator ensembles, identifying high-probability trend ignition points by measuring when multiple independent oscillators achieve phase coherence. Unlike traditional multi-oscillator approaches that simply stack indicators or use boolean AND/OR logic, this system converts each oscillator into a rotating phasor (vector) in the complex plane and calculates the Coherence Index (CI) —a mathematical measure of how tightly aligned the ensemble has become—then generates signals only when alignment, phase direction, and pairwise entanglement all converge.
The indicator combines three mathematical frameworks: phasor representation using analytic signal theory to extract phase and amplitude from each oscillator, coherence measurement using vector summation in the complex plane to quantify group alignment, and entanglement analysis that calculates pairwise phase agreement across all oscillator combinations. This creates a multi-dimensional confirmation system that distinguishes between random oscillator noise and genuine regime transitions.
What Makes This Original
Complex-Plane Phasor Framework
This indicator implements classical signal processing mathematics adapted for market oscillators. Each oscillator—whether RSI, MACD, Stochastic, CCI, Williams %R, MFI, ROC, or TSI—is first normalized to a common scale, then converted into a complex-plane representation using an in-phase (I) and quadrature (Q) component. The in-phase component is the oscillator value itself, while the quadrature component is calculated as the first difference (derivative proxy), creating a velocity-aware representation.
From these components, the system extracts:
Phase (φ) : Calculated as φ = atan2(Q, I), representing the oscillator's position in its cycle (mapped to -180° to +180°)
Amplitude (A) : Calculated as A = √(I² + Q²), representing the oscillator's strength or conviction
This mathematical approach is fundamentally different from simply reading oscillator values. A phasor captures both where an oscillator is in its cycle (phase angle) and how strongly it's expressing that position (amplitude). Two oscillators can have the same value but be in opposite phases of their cycles—traditional analysis would see them as identical, while QRFM sees them as 180° out of phase (contradictory).
Coherence Index Calculation
The core innovation is the Coherence Index (CI) , borrowed from physics and signal processing. When you have N oscillators, each with phase φₙ, you can represent each as a unit vector in the complex plane: e^(iφₙ) = cos(φₙ) + i·sin(φₙ).
The CI measures what happens when you sum all these vectors:
Resultant Vector : R = Σ e^(iφₙ) = Σ cos(φₙ) + i·Σ sin(φₙ)
Coherence Index : CI = |R| / N
Where |R| is the magnitude of the resultant vector and N is the number of active oscillators.
The CI ranges from 0 to 1:
CI = 1.0 : Perfect coherence—all oscillators have identical phase angles, vectors point in the same direction, creating maximum constructive interference
CI = 0.0 : Complete decoherence—oscillators are randomly distributed around the circle, vectors cancel out through destructive interference
0 < CI < 1 : Partial alignment—some clustering with some scatter
This is not a simple average or correlation. The CI captures phase synchronization across the entire ensemble simultaneously. When oscillators phase-lock (align their cycles), the CI spikes regardless of their individual values. This makes it sensitive to regime transitions that traditional indicators miss.
Dominant Phase and Direction Detection
Beyond measuring alignment strength, the system calculates the dominant phase of the ensemble—the direction the resultant vector points:
Dominant Phase : φ_dom = atan2(Σ sin(φₙ), Σ cos(φₙ))
This gives the "average direction" of all oscillator phases, mapped to -180° to +180°:
+90° to -90° (right half-plane): Bullish phase dominance
+90° to +180° or -90° to -180° (left half-plane): Bearish phase dominance
The combination of CI magnitude (coherence strength) and dominant phase angle (directional bias) creates a two-dimensional signal space. High CI alone is insufficient—you need high CI plus dominant phase pointing in a tradeable direction. This dual requirement is what separates QRFM from simple oscillator averaging.
Entanglement Matrix and Pairwise Coherence
While the CI measures global alignment, the entanglement matrix measures local pairwise relationships. For every pair of oscillators (i, j), the system calculates:
E(i,j) = |cos(φᵢ - φⱼ)|
This represents the phase agreement between oscillators i and j:
E = 1.0 : Oscillators are in-phase (0° or 360° apart)
E = 0.0 : Oscillators are in quadrature (90° apart, orthogonal)
E between 0 and 1 : Varying degrees of alignment
The system counts how many oscillator pairs exceed a user-defined entanglement threshold (e.g., 0.7). This entangled pairs count serves as a confirmation filter: signals require not just high global CI, but also a minimum number of strong pairwise agreements. This prevents false ignitions where CI is high but driven by only two oscillators while the rest remain scattered.
The entanglement matrix creates an N×N symmetric matrix that can be visualized as a web—when many cells are bright (high E values), the ensemble is highly interconnected. When cells are dark, oscillators are moving independently.
Phase-Lock Tolerance Mechanism
A complementary confirmation layer is the phase-lock detector . This calculates the maximum phase spread across all oscillators:
For all pairs (i,j), compute angular distance: Δφ = |φᵢ - φⱼ|, wrapping at 180°
Max Spread = maximum Δφ across all pairs
If max spread < user threshold (e.g., 35°), the ensemble is considered phase-locked —all oscillators are within a narrow angular band.
This differs from entanglement: entanglement measures pairwise cosine similarity (magnitude of alignment), while phase-lock measures maximum angular deviation (tightness of clustering). Both must be satisfied for the highest-conviction signals.
Multi-Layer Visual Architecture
QRFM includes six visual components that represent the same underlying mathematics from different perspectives:
Circular Orbit Plot : A polar coordinate grid showing each oscillator as a vector from origin to perimeter. Angle = phase, radius = amplitude. This is a real-time snapshot of the complex plane. When vectors converge (point in similar directions), coherence is high. When scattered randomly, coherence is low. Users can see phase alignment forming before CI numerically confirms it.
Phase-Time Heat Map : A 2D matrix with rows = oscillators and columns = time bins. Each cell is colored by the oscillator's phase at that time (using a gradient where color hue maps to angle). Horizontal color bands indicate sustained phase alignment over time. Vertical color bands show moments when all oscillators shared the same phase (ignition points). This provides historical pattern recognition.
Entanglement Web Matrix : An N×N grid showing E(i,j) for all pairs. Cells are colored by entanglement strength—bright yellow/gold for high E, dark gray for low E. This reveals which oscillators are driving coherence and which are lagging. For example, if RSI and MACD show high E but Stochastic shows low E with everything, Stochastic is the outlier.
Quantum Field Cloud : A background color overlay on the price chart. Color (green = bullish, red = bearish) is determined by dominant phase. Opacity is determined by CI—high CI creates dense, opaque cloud; low CI creates faint, nearly invisible cloud. This gives an atmospheric "feel" for regime strength without looking at numbers.
Phase Spiral : A smoothed plot of dominant phase over recent history, displayed as a curve that wraps around price. When the spiral is tight and rotating steadily, the ensemble is in coherent rotation (trending). When the spiral is loose or erratic, coherence is breaking down.
Dashboard : A table showing real-time metrics: CI (as percentage), dominant phase (in degrees with directional arrow), field strength (CI × average amplitude), entangled pairs count, phase-lock status (locked/unlocked), quantum state classification ("Ignition", "Coherent", "Collapse", "Chaos"), and collapse risk (recent CI change normalized to 0-100%).
Each component is independently toggleable, allowing users to customize their workspace. The orbit plot is the most essential—it provides intuitive, visual feedback on phase alignment that no numerical dashboard can match.
Core Components and How They Work Together
1. Oscillator Normalization Engine
The foundation is creating a common measurement scale. QRFM supports eight oscillators:
RSI : Normalized from to using overbought/oversold levels (70, 30) as anchors
MACD Histogram : Normalized by dividing by rolling standard deviation, then clamped to
Stochastic %K : Normalized from using (80, 20) anchors
CCI : Divided by 200 (typical extreme level), clamped to
Williams %R : Normalized from using (-20, -80) anchors
MFI : Normalized from using (80, 20) anchors
ROC : Divided by 10, clamped to
TSI : Divided by 50, clamped to
Each oscillator can be individually enabled/disabled. Only active oscillators contribute to phase calculations. The normalization removes scale differences—a reading of +0.8 means "strongly bullish" regardless of whether it came from RSI or TSI.
2. Analytic Signal Construction
For each active oscillator at each bar, the system constructs the analytic signal:
In-Phase (I) : The normalized oscillator value itself
Quadrature (Q) : The bar-to-bar change in the normalized value (first derivative approximation)
This creates a 2D representation: (I, Q). The phase is extracted as:
φ = atan2(Q, I) × (180 / π)
This maps the oscillator to a point on the unit circle. An oscillator at the same value but rising (positive Q) will have a different phase than one that is falling (negative Q). This velocity-awareness is critical—it distinguishes between "at resistance and stalling" versus "at resistance and breaking through."
The amplitude is extracted as:
A = √(I² + Q²)
This represents the distance from origin in the (I, Q) plane. High amplitude means the oscillator is far from neutral (strong conviction). Low amplitude means it's near zero (weak/transitional state).
3. Coherence Calculation Pipeline
For each bar (or every Nth bar if phase sample rate > 1 for performance):
Step 1 : Extract phase φₙ for each of the N active oscillators
Step 2 : Compute complex exponentials: Zₙ = e^(i·φₙ·π/180) = cos(φₙ·π/180) + i·sin(φₙ·π/180)
Step 3 : Sum the complex exponentials: R = Σ Zₙ = (Σ cos φₙ) + i·(Σ sin φₙ)
Step 4 : Calculate magnitude: |R| = √
Step 5 : Normalize by count: CI_raw = |R| / N
Step 6 : Smooth the CI: CI = SMA(CI_raw, smoothing_window)
The smoothing step (default 2 bars) removes single-bar noise spikes while preserving structural coherence changes. Users can adjust this to control reactivity versus stability.
The dominant phase is calculated as:
φ_dom = atan2(Σ sin φₙ, Σ cos φₙ) × (180 / π)
This is the angle of the resultant vector R in the complex plane.
4. Entanglement Matrix Construction
For all unique pairs of oscillators (i, j) where i < j:
Step 1 : Get phases φᵢ and φⱼ
Step 2 : Compute phase difference: Δφ = φᵢ - φⱼ (in radians)
Step 3 : Calculate entanglement: E(i,j) = |cos(Δφ)|
Step 4 : Store in symmetric matrix: matrix = matrix = E(i,j)
The matrix is then scanned: count how many E(i,j) values exceed the user-defined threshold (default 0.7). This count is the entangled pairs metric.
For visualization, the matrix is rendered as an N×N table where cell brightness maps to E(i,j) intensity.
5. Phase-Lock Detection
Step 1 : For all unique pairs (i, j), compute angular distance: Δφ = |φᵢ - φⱼ|
Step 2 : Wrap angles: if Δφ > 180°, set Δφ = 360° - Δφ
Step 3 : Find maximum: max_spread = max(Δφ) across all pairs
Step 4 : Compare to tolerance: phase_locked = (max_spread < tolerance)
If phase_locked is true, all oscillators are within the specified angular cone (e.g., 35°). This is a boolean confirmation filter.
6. Signal Generation Logic
Signals are generated through multi-layer confirmation:
Long Ignition Signal :
CI crosses above ignition threshold (e.g., 0.80)
AND dominant phase is in bullish range (-90° < φ_dom < +90°)
AND phase_locked = true
AND entangled_pairs >= minimum threshold (e.g., 4)
Short Ignition Signal :
CI crosses above ignition threshold
AND dominant phase is in bearish range (φ_dom < -90° OR φ_dom > +90°)
AND phase_locked = true
AND entangled_pairs >= minimum threshold
Collapse Signal :
CI at bar minus CI at current bar > collapse threshold (e.g., 0.55)
AND CI at bar was above 0.6 (must collapse from coherent state, not from already-low state)
These are strict conditions. A high CI alone does not generate a signal—dominant phase must align with direction, oscillators must be phase-locked, and sufficient pairwise entanglement must exist. This multi-factor gating dramatically reduces false signals compared to single-condition triggers.
Calculation Methodology
Phase 1: Oscillator Computation and Normalization
On each bar, the system calculates the raw values for all enabled oscillators using standard Pine Script functions:
RSI: ta.rsi(close, length)
MACD: ta.macd() returning histogram component
Stochastic: ta.stoch() smoothed with ta.sma()
CCI: ta.cci(close, length)
Williams %R: ta.wpr(length)
MFI: ta.mfi(hlc3, length)
ROC: ta.roc(close, length)
TSI: ta.tsi(close, short, long)
Each raw value is then passed through a normalization function:
normalize(value, overbought_level, oversold_level) = 2 × (value - oversold) / (overbought - oversold) - 1
This maps the oscillator's typical range to , where -1 represents extreme bearish, 0 represents neutral, and +1 represents extreme bullish.
For oscillators without fixed ranges (MACD, ROC, TSI), statistical normalization is used: divide by a rolling standard deviation or fixed divisor, then clamp to .
Phase 2: Phasor Extraction
For each normalized oscillator value val:
I = val (in-phase component)
Q = val - val (quadrature component, first difference)
Phase calculation:
phi_rad = atan2(Q, I)
phi_deg = phi_rad × (180 / π)
Amplitude calculation:
A = √(I² + Q²)
These values are stored in arrays: osc_phases and osc_amps for each oscillator n.
Phase 3: Complex Summation and Coherence
Initialize accumulators:
sum_cos = 0
sum_sin = 0
For each oscillator n = 0 to N-1:
phi_rad = osc_phases × (π / 180)
sum_cos += cos(phi_rad)
sum_sin += sin(phi_rad)
Resultant magnitude:
resultant_mag = √(sum_cos² + sum_sin²)
Coherence Index (raw):
CI_raw = resultant_mag / N
Smoothed CI:
CI = SMA(CI_raw, smoothing_window)
Dominant phase:
phi_dom_rad = atan2(sum_sin, sum_cos)
phi_dom_deg = phi_dom_rad × (180 / π)
Phase 4: Entanglement Matrix Population
For i = 0 to N-2:
For j = i+1 to N-1:
phi_i = osc_phases × (π / 180)
phi_j = osc_phases × (π / 180)
delta_phi = phi_i - phi_j
E = |cos(delta_phi)|
matrix_index_ij = i × N + j
matrix_index_ji = j × N + i
entangle_matrix = E
entangle_matrix = E
if E >= threshold:
entangled_pairs += 1
The matrix uses flat array storage with index mapping: index(row, col) = row × N + col.
Phase 5: Phase-Lock Check
max_spread = 0
For i = 0 to N-2:
For j = i+1 to N-1:
delta = |osc_phases - osc_phases |
if delta > 180:
delta = 360 - delta
max_spread = max(max_spread, delta)
phase_locked = (max_spread < tolerance)
Phase 6: Signal Evaluation
Ignition Long :
ignition_long = (CI crosses above threshold) AND
(phi_dom > -90 AND phi_dom < 90) AND
phase_locked AND
(entangled_pairs >= minimum)
Ignition Short :
ignition_short = (CI crosses above threshold) AND
(phi_dom < -90 OR phi_dom > 90) AND
phase_locked AND
(entangled_pairs >= minimum)
Collapse :
CI_prev = CI
collapse = (CI_prev - CI > collapse_threshold) AND (CI_prev > 0.6)
All signals are evaluated on bar close. The crossover and crossunder functions ensure signals fire only once when conditions transition from false to true.
Phase 7: Field Strength and Visualization Metrics
Average Amplitude :
avg_amp = (Σ osc_amps ) / N
Field Strength :
field_strength = CI × avg_amp
Collapse Risk (for dashboard):
collapse_risk = (CI - CI) / max(CI , 0.1)
collapse_risk_pct = clamp(collapse_risk × 100, 0, 100)
Quantum State Classification :
if (CI > threshold AND phase_locked):
state = "Ignition"
else if (CI > 0.6):
state = "Coherent"
else if (collapse):
state = "Collapse"
else:
state = "Chaos"
Phase 8: Visual Rendering
Orbit Plot : For each oscillator, convert polar (phase, amplitude) to Cartesian (x, y) for grid placement:
radius = amplitude × grid_center × 0.8
x = radius × cos(phase × π/180)
y = radius × sin(phase × π/180)
col = center + x (mapped to grid coordinates)
row = center - y
Heat Map : For each oscillator row and time column, retrieve historical phase value at lookback = (columns - col) × sample_rate, then map phase to color using a hue gradient.
Entanglement Web : Render matrix as table cell with background color opacity = E(i,j).
Field Cloud : Background color = (phi_dom > -90 AND phi_dom < 90) ? green : red, with opacity = mix(min_opacity, max_opacity, CI).
All visual components render only on the last bar (barstate.islast) to minimize computational overhead.
How to Use This Indicator
Step 1 : Apply QRFM to your chart. It works on all timeframes and asset classes, though 15-minute to 4-hour timeframes provide the best balance of responsiveness and noise reduction.
Step 2 : Enable the dashboard (default: top right) and the circular orbit plot (default: middle left). These are your primary visual feedback tools.
Step 3 : Optionally enable the heat map, entanglement web, and field cloud based on your preference. New users may find all visuals overwhelming; start with dashboard + orbit plot.
Step 4 : Observe for 50-100 bars to let the indicator establish baseline coherence patterns. Markets have different "normal" CI ranges—some instruments naturally run higher or lower coherence.
Understanding the Circular Orbit Plot
The orbit plot is a polar grid showing oscillator vectors in real-time:
Center point : Neutral (zero phase and amplitude)
Each vector : A line from center to a point on the grid
Vector angle : The oscillator's phase (0° = right/east, 90° = up/north, 180° = left/west, -90° = down/south)
Vector length : The oscillator's amplitude (short = weak signal, long = strong signal)
Vector label : First letter of oscillator name (R = RSI, M = MACD, etc.)
What to watch :
Convergence : When all vectors cluster in one quadrant or sector, CI is rising and coherence is forming. This is your pre-signal warning.
Scatter : When vectors point in random directions (360° spread), CI is low and the market is in a non-trending or transitional regime.
Rotation : When the cluster rotates smoothly around the circle, the ensemble is in coherent oscillation—typically seen during steady trends.
Sudden flips : When the cluster rapidly jumps from one side to the opposite (e.g., +90° to -90°), a phase reversal has occurred—often coinciding with trend reversals.
Example: If you see RSI, MACD, and Stochastic all pointing toward 45° (northeast) with long vectors, while CCI, TSI, and ROC point toward 40-50° as well, coherence is high and dominant phase is bullish. Expect an ignition signal if CI crosses threshold.
Reading Dashboard Metrics
The dashboard provides numerical confirmation of what the orbit plot shows visually:
CI : Displays as 0-100%. Above 70% = high coherence (strong regime), 40-70% = moderate, below 40% = low (poor conditions for trend entries).
Dom Phase : Angle in degrees with directional arrow. ⬆ = bullish bias, ⬇ = bearish bias, ⬌ = neutral.
Field Strength : CI weighted by amplitude. High values (> 0.6) indicate not just alignment but strong alignment.
Entangled Pairs : Count of oscillator pairs with E > threshold. Higher = more confirmation. If minimum is set to 4, you need at least 4 pairs entangled for signals.
Phase Lock : 🔒 YES (all oscillators within tolerance) or 🔓 NO (spread too wide).
State : Real-time classification:
🚀 IGNITION: CI just crossed threshold with phase-lock
⚡ COHERENT: CI is high and stable
💥 COLLAPSE: CI has dropped sharply
🌀 CHAOS: Low CI, scattered phases
Collapse Risk : 0-100% scale based on recent CI change. Above 50% warns of imminent breakdown.
Interpreting Signals
Long Ignition (Blue Triangle Below Price) :
Occurs when CI crosses above threshold (e.g., 0.80)
Dominant phase is in bullish range (-90° to +90°)
All oscillators are phase-locked (within tolerance)
Minimum entangled pairs requirement met
Interpretation : The oscillator ensemble has transitioned from disorder to coherent bullish alignment. This is a high-probability long entry point. The multi-layer confirmation (CI + phase direction + lock + entanglement) ensures this is not a single-oscillator whipsaw.
Short Ignition (Red Triangle Above Price) :
Same conditions as long, but dominant phase is in bearish range (< -90° or > +90°)
Interpretation : Coherent bearish alignment has formed. High-probability short entry.
Collapse (Circles Above and Below Price) :
CI has dropped by more than the collapse threshold (e.g., 0.55) over a 5-bar window
CI was previously above 0.6 (collapsing from coherent state)
Interpretation : Phase coherence has broken down. If you are in a position, this is an exit warning. If looking to enter, stand aside—regime is transitioning.
Phase-Time Heat Map Patterns
Enable the heat map and position it at bottom right. The rows represent individual oscillators, columns represent time bins (most recent on left).
Pattern: Horizontal Color Bands
If a row (e.g., RSI) shows consistent color across columns (say, green for several bins), that oscillator has maintained stable phase over time. If all rows show horizontal bands of similar color, the entire ensemble has been phase-locked for an extended period—this is a strong trending regime.
Pattern: Vertical Color Bands
If a column (single time bin) shows all cells with the same or very similar color, that moment in time had high coherence. These vertical bands often align with ignition signals or major price pivots.
Pattern: Rainbow Chaos
If cells are random colors (red, green, yellow mixed with no pattern), coherence is low. The ensemble is scattered. Avoid trading during these periods unless you have external confirmation.
Pattern: Color Transition
If you see a row transition from red to green (or vice versa) sharply, that oscillator has phase-flipped. If multiple rows do this simultaneously, a regime change is underway.
Entanglement Web Analysis
Enable the web matrix (default: opposite corner from heat map). It shows an N×N grid where N = number of active oscillators.
Bright Yellow/Gold Cells : High pairwise entanglement. For example, if the RSI-MACD cell is bright gold, those two oscillators are moving in phase. If the RSI-Stochastic cell is bright, they are entangled as well.
Dark Gray Cells : Low entanglement. Oscillators are decorrelated or in quadrature.
Diagonal : Always marked with "—" because an oscillator is always perfectly entangled with itself.
How to use :
Scan for clustering: If most cells are bright, coherence is high across the board. If only a few cells are bright, coherence is driven by a subset (e.g., RSI and MACD are aligned, but nothing else is—weak signal).
Identify laggards: If one row/column is entirely dark, that oscillator is the outlier. You may choose to disable it or monitor for when it joins the group (late confirmation).
Watch for web formation: During low-coherence periods, the matrix is mostly dark. As coherence builds, cells begin lighting up. A sudden "web" of connections forming visually precedes ignition signals.
Trading Workflow
Step 1: Monitor Coherence Level
Check the dashboard CI metric or observe the orbit plot. If CI is below 40% and vectors are scattered, conditions are poor for trend entries. Wait.
Step 2: Detect Coherence Building
When CI begins rising (say, from 30% to 50-60%) and you notice vectors on the orbit plot starting to cluster, coherence is forming. This is your alert phase—do not enter yet, but prepare.
Step 3: Confirm Phase Direction
Check the dominant phase angle and the orbit plot quadrant where clustering is occurring:
Clustering in right half (0° to ±90°): Bullish bias forming
Clustering in left half (±90° to 180°): Bearish bias forming
Verify the dashboard shows the corresponding directional arrow (⬆ or ⬇).
Step 4: Wait for Signal Confirmation
Do not enter based on rising CI alone. Wait for the full ignition signal:
CI crosses above threshold
Phase-lock indicator shows 🔒 YES
Entangled pairs count >= minimum
Directional triangle appears on chart
This ensures all layers have aligned.
Step 5: Execute Entry
Long : Blue triangle below price appears → enter long
Short : Red triangle above price appears → enter short
Step 6: Position Management
Initial Stop : Place stop loss based on your risk management rules (e.g., recent swing low/high, ATR-based buffer).
Monitoring :
Watch the field cloud density. If it remains opaque and colored in your direction, the regime is intact.
Check dashboard collapse risk. If it rises above 50%, prepare for exit.
Monitor the orbit plot. If vectors begin scattering or the cluster flips to the opposite side, coherence is breaking.
Exit Triggers :
Collapse signal fires (circles appear)
Dominant phase flips to opposite half-plane
CI drops below 40% (coherence lost)
Price hits your profit target or trailing stop
Step 7: Post-Exit Analysis
After exiting, observe whether a new ignition forms in the opposite direction (reversal) or if CI remains low (transition to range). Use this to decide whether to re-enter, reverse, or stand aside.
Best Practices
Use Price Structure as Context
QRFM identifies when coherence forms but does not specify where price will go. Combine ignition signals with support/resistance levels, trendlines, or chart patterns. For example:
Long ignition near a major support level after a pullback: high-probability bounce
Long ignition in the middle of a range with no structure: lower probability
Multi-Timeframe Confirmation
Open QRFM on two timeframes simultaneously:
Higher timeframe (e.g., 4-hour): Use CI level to determine regime bias. If 4H CI is above 60% and dominant phase is bullish, the market is in a bullish regime.
Lower timeframe (e.g., 15-minute): Execute entries on ignition signals that align with the higher timeframe bias.
This prevents counter-trend trades and increases win rate.
Distinguish Between Regime Types
High CI, stable dominant phase (State: Coherent) : Trending market. Ignitions are continuation signals; collapses are profit-taking or reversal warnings.
Low CI, erratic dominant phase (State: Chaos) : Ranging or choppy market. Avoid ignition signals or reduce position size. Wait for coherence to establish.
Moderate CI with frequent collapses : Whipsaw environment. Use wider stops or stand aside.
Adjust Parameters to Instrument and Timeframe
Crypto/Forex (high volatility) : Lower ignition threshold (0.65-0.75), lower CI smoothing (2-3), shorter oscillator lengths (7-10).
Stocks/Indices (moderate volatility) : Standard settings (threshold 0.75-0.85, smoothing 5-7, oscillator lengths 14).
Lower timeframes (5-15 min) : Reduce phase sample rate to 1-2 for responsiveness.
Higher timeframes (daily+) : Increase CI smoothing and oscillator lengths for noise reduction.
Use Entanglement Count as Conviction Filter
The minimum entangled pairs setting controls signal strictness:
Low (1-2) : More signals, lower quality (acceptable if you have other confirmation)
Medium (3-5) : Balanced (recommended for most traders)
High (6+) : Very strict, fewer signals, highest quality
Adjust based on your trade frequency preference and risk tolerance.
Monitor Oscillator Contribution
Use the entanglement web to see which oscillators are driving coherence. If certain oscillators are consistently dark (low E with all others), they may be adding noise. Consider disabling them. For example:
On low-volume instruments, MFI may be unreliable → disable MFI
On strongly trending instruments, mean-reversion oscillators (Stochastic, RSI) may lag → reduce weight or disable
Respect the Collapse Signal
Collapse events are early warnings. Price may continue in the original direction for several bars after collapse fires, but the underlying regime has weakened. Best practice:
If in profit: Take partial or full profit on collapse
If at breakeven/small loss: Exit immediately
If collapse occurs shortly after entry: Likely a false ignition; exit to avoid drawdown
Collapses do not guarantee immediate reversals—they signal uncertainty .
Combine with Volume Analysis
If your instrument has reliable volume:
Ignitions with expanding volume: Higher conviction
Ignitions with declining volume: Weaker, possibly false
Collapses with volume spikes: Strong reversal signal
Collapses with low volume: May just be consolidation
Volume is not built into QRFM (except via MFI), so add it as external confirmation.
Observe the Phase Spiral
The spiral provides a quick visual cue for rotation consistency:
Tight, smooth spiral : Ensemble is rotating coherently (trending)
Loose, erratic spiral : Phase is jumping around (ranging or transitional)
If the spiral tightens, coherence is building. If it loosens, coherence is dissolving.
Do Not Overtrade Low-Coherence Periods
When CI is persistently below 40% and the state is "Chaos," the market is not in a regime where phase analysis is predictive. During these times:
Reduce position size
Widen stops
Wait for coherence to return
QRFM's strength is regime detection. If there is no regime, the tool correctly signals "stand aside."
Use Alerts Strategically
Set alerts for:
Long Ignition
Short Ignition
Collapse
Phase Lock (optional)
Configure alerts to "Once per bar close" to avoid intrabar repainting and noise. When an alert fires, manually verify:
Orbit plot shows clustering
Dashboard confirms all conditions
Price structure supports the trade
Do not blindly trade alerts—use them as prompts for analysis.
Ideal Market Conditions
Best Performance
Instruments :
Liquid, actively traded markets (major forex pairs, large-cap stocks, major indices, top-tier crypto)
Instruments with clear cyclical oscillator behavior (avoid extremely illiquid or manipulated markets)
Timeframes :
15-minute to 4-hour: Optimal balance of noise reduction and responsiveness
1-hour to daily: Slower, higher-conviction signals; good for swing trading
5-minute: Acceptable for scalping if parameters are tightened and you accept more noise
Market Regimes :
Trending markets with periodic retracements (where oscillators cycle through phases predictably)
Breakout environments (coherence forms before/during breakout; collapse occurs at exhaustion)
Rotational markets with clear swings (oscillators phase-lock at turning points)
Volatility :
Moderate to high volatility (oscillators have room to move through their ranges)
Stable volatility regimes (sudden VIX spikes or flash crashes may create false collapses)
Challenging Conditions
Instruments :
Very low liquidity markets (erratic price action creates unstable oscillator phases)
Heavily news-driven instruments (fundamentals may override technical coherence)
Highly correlated instruments (oscillators may all reflect the same underlying factor, reducing independence)
Market Regimes :
Deep, prolonged consolidation (oscillators remain near neutral, CI is chronically low, few signals fire)
Extreme chop with no directional bias (oscillators whipsaw, coherence never establishes)
Gap-driven markets (large overnight gaps create phase discontinuities)
Timeframes :
Sub-5-minute charts: Noise dominates; oscillators flip rapidly; coherence is fleeting and unreliable
Weekly/monthly: Oscillators move extremely slowly; signals are rare; better suited for long-term positioning than active trading
Special Cases :
During major economic releases or earnings: Oscillators may lag price or become decorrelated as fundamentals overwhelm technicals. Reduce position size or stand aside.
In extremely low-volatility environments (e.g., holiday periods): Oscillators compress to neutral, CI may be artificially high due to lack of movement, but signals lack follow-through.
Adaptive Behavior
QRFM is designed to self-adapt to poor conditions:
When coherence is genuinely absent, CI remains low and signals do not fire
When only a subset of oscillators aligns, entangled pairs count stays below threshold and signals are filtered out
When phase-lock cannot be achieved (oscillators too scattered), the lock filter prevents signals
This means the indicator will naturally produce fewer (or zero) signals during unfavorable conditions, rather than generating false signals. This is a feature —it keeps you out of low-probability trades.
Parameter Optimization by Trading Style
Scalping (5-15 Minute Charts)
Goal : Maximum responsiveness, accept higher noise
Oscillator Lengths :
RSI: 7-10
MACD: 8/17/6
Stochastic: 8-10, smooth 2-3
CCI: 14-16
Others: 8-12
Coherence Settings :
CI Smoothing Window: 2-3 bars (fast reaction)
Phase Sample Rate: 1 (every bar)
Ignition Threshold: 0.65-0.75 (lower for more signals)
Collapse Threshold: 0.40-0.50 (earlier exit warnings)
Confirmation :
Phase Lock Tolerance: 40-50° (looser, easier to achieve)
Min Entangled Pairs: 2-3 (fewer oscillators required)
Visuals :
Orbit Plot + Dashboard only (reduce screen clutter for fast decisions)
Disable heavy visuals (heat map, web) for performance
Alerts :
Enable all ignition and collapse alerts
Set to "Once per bar close"
Day Trading (15-Minute to 1-Hour Charts)
Goal : Balance between responsiveness and reliability
Oscillator Lengths :
RSI: 14 (standard)
MACD: 12/26/9 (standard)
Stochastic: 14, smooth 3
CCI: 20
Others: 10-14
Coherence Settings :
CI Smoothing Window: 3-5 bars (balanced)
Phase Sample Rate: 2-3
Ignition Threshold: 0.75-0.85 (moderate selectivity)
Collapse Threshold: 0.50-0.55 (balanced exit timing)
Confirmation :
Phase Lock Tolerance: 30-40° (moderate tightness)
Min Entangled Pairs: 4-5 (reasonable confirmation)
Visuals :
Orbit Plot + Dashboard + Heat Map or Web (choose one)
Field Cloud for regime backdrop
Alerts :
Ignition and collapse alerts
Optional phase-lock alert for advance warning
Swing Trading (4-Hour to Daily Charts)
Goal : High-conviction signals, minimal noise, fewer trades
Oscillator Lengths :
RSI: 14-21
MACD: 12/26/9 or 19/39/9 (longer variant)
Stochastic: 14-21, smooth 3-5
CCI: 20-30
Others: 14-20
Coherence Settings :
CI Smoothing Window: 5-10 bars (very smooth)
Phase Sample Rate: 3-5
Ignition Threshold: 0.80-0.90 (high bar for entry)
Collapse Threshold: 0.55-0.65 (only significant breakdowns)
Confirmation :
Phase Lock Tolerance: 20-30° (tight clustering required)
Min Entangled Pairs: 5-7 (strong confirmation)
Visuals :
All modules enabled (you have time to analyze)
Heat Map for multi-bar pattern recognition
Web for deep confirmation analysis
Alerts :
Ignition and collapse
Review manually before entering (no rush)
Position/Long-Term Trading (Daily to Weekly Charts)
Goal : Rare, very high-conviction regime shifts
Oscillator Lengths :
RSI: 21-30
MACD: 19/39/9 or 26/52/12
Stochastic: 21, smooth 5
CCI: 30-50
Others: 20-30
Coherence Settings :
CI Smoothing Window: 10-14 bars
Phase Sample Rate: 5 (every 5th bar to reduce computation)
Ignition Threshold: 0.85-0.95 (only extreme alignment)
Collapse Threshold: 0.60-0.70 (major regime breaks only)
Confirmation :
Phase Lock Tolerance: 15-25° (very tight)
Min Entangled Pairs: 6+ (broad consensus required)
Visuals :
Dashboard + Orbit Plot for quick checks
Heat Map to study historical coherence patterns
Web to verify deep entanglement
Alerts :
Ignition only (collapses are less critical on long timeframes)
Manual review with fundamental analysis overlay
Performance Optimization (Low-End Systems)
If you experience lag or slow rendering:
Reduce Visual Load :
Orbit Grid Size: 8-10 (instead of 12+)
Heat Map Time Bins: 5-8 (instead of 10+)
Disable Web Matrix entirely if not needed
Disable Field Cloud and Phase Spiral
Reduce Calculation Frequency :
Phase Sample Rate: 5-10 (calculate every 5-10 bars)
Max History Depth: 100-200 (instead of 500+)
Disable Unused Oscillators :
If you only want RSI, MACD, and Stochastic, disable the other five. Fewer oscillators = smaller matrices, faster loops.
Simplify Dashboard :
Choose "Small" dashboard size
Reduce number of metrics displayed
These settings will not significantly degrade signal quality (signals are based on bar-close calculations, which remain accurate), but will improve chart responsiveness.
Important Disclaimers
This indicator is a technical analysis tool designed to identify periods of phase coherence across an ensemble of oscillators. It is not a standalone trading system and does not guarantee profitable trades. The Coherence Index, dominant phase, and entanglement metrics are mathematical calculations applied to historical price data—they measure past oscillator behavior and do not predict future price movements with certainty.
No Predictive Guarantee : High coherence indicates that oscillators are currently aligned, which historically has coincided with trending or directional price movement. However, past alignment does not guarantee future trends. Markets can remain coherent while prices consolidate, or lose coherence suddenly due to news, liquidity changes, or other factors not captured by oscillator mathematics.
Signal Confirmation is Probabilistic : The multi-layer confirmation system (CI threshold + dominant phase + phase-lock + entanglement) is designed to filter out low-probability setups. This increases the proportion of valid signals relative to false signals, but does not eliminate false signals entirely. Users should combine QRFM with additional analysis—support and resistance levels, volume confirmation, multi-timeframe alignment, and fundamental context—before executing trades.
Collapse Signals are Warnings, Not Reversals : A coherence collapse indicates that the oscillator ensemble has lost alignment. This often precedes trend exhaustion or reversals, but can also occur during healthy pullbacks or consolidations. Price may continue in the original direction after a collapse. Use collapses as risk management cues (tighten stops, take partial profits) rather than automatic reversal entries.
Market Regime Dependency : QRFM performs best in markets where oscillators exhibit cyclical, mean-reverting behavior and where trends are punctuated by retracements. In markets dominated by fundamental shocks, gap openings, or extreme low-liquidity conditions, oscillator coherence may be less reliable. During such periods, reduce position size or stand aside.
Risk Management is Essential : All trading involves risk of loss. Use appropriate stop losses, position sizing, and risk-per-trade limits. The indicator does not specify stop loss or take profit levels—these must be determined by the user based on their risk tolerance and account size. Never risk more than you can afford to lose.
Parameter Sensitivity : The indicator's behavior changes with input parameters. Aggressive settings (low thresholds, loose tolerances) produce more signals with lower average quality. Conservative settings (high thresholds, tight tolerances) produce fewer signals with higher average quality. Users should backtest and forward-test parameter sets on their specific instruments and timeframes before committing real capital.
No Repainting by Design : All signal conditions are evaluated on bar close using bar-close values. However, the visual components (orbit plot, heat map, dashboard) update in real-time during bar formation for monitoring purposes. For trade execution, rely on the confirmed signals (triangles and circles) that appear only after the bar closes.
Computational Load : QRFM performs extensive calculations, including nested loops for entanglement matrices and real-time table rendering. On lower-powered devices or when running multiple indicators simultaneously, users may experience lag. Use the performance optimization settings (reduce visual complexity, increase phase sample rate, disable unused oscillators) to improve responsiveness.
This system is most effective when used as one component within a broader trading methodology that includes sound risk management, multi-timeframe analysis, market context awareness, and disciplined execution. It is a tool for regime detection and signal confirmation, not a substitute for comprehensive trade planning.
Technical Notes
Calculation Timing : All signal logic (ignition, collapse) is evaluated using bar-close values. The barstate.isconfirmed or implicit bar-close behavior ensures signals do not repaint. Visual components (tables, plots) render on every tick for real-time feedback but do not affect signal generation.
Phase Wrapping : Phase angles are calculated in the range -180° to +180° using atan2. Angular distance calculations account for wrapping (e.g., the distance between +170° and -170° is 20°, not 340°). This ensures phase-lock detection works correctly across the ±180° boundary.
Array Management : The indicator uses fixed-size arrays for oscillator phases, amplitudes, and the entanglement matrix. The maximum number of oscillators is 8. If fewer oscillators are enabled, array sizes shrink accordingly (only active oscillators are processed).
Matrix Indexing : The entanglement matrix is stored as a flat array with size N×N, where N is the number of active oscillators. Index mapping: index(row, col) = row × N + col. Symmetric pairs (i,j) and (j,i) are stored identically.
Normalization Stability : Oscillators are normalized to using fixed reference levels (e.g., RSI overbought/oversold at 70/30). For unbounded oscillators (MACD, ROC, TSI), statistical normalization (division by rolling standard deviation) is used, with clamping to prevent extreme outliers from distorting phase calculations.
Smoothing and Lag : The CI smoothing window (SMA) introduces lag proportional to the window size. This is intentional—it filters out single-bar noise spikes in coherence. Users requiring faster reaction can reduce the smoothing window to 1-2 bars, at the cost of increased sensitivity to noise.
Complex Number Representation : Pine Script does not have native complex number types. Complex arithmetic is implemented using separate real and imaginary accumulators (sum_cos, sum_sin) and manual calculation of magnitude (sqrt(real² + imag²)) and argument (atan2(imag, real)).
Lookback Limits : The indicator respects Pine Script's maximum lookback constraints. Historical phase and amplitude values are accessed using the operator, with lookback limited to the chart's available bar history (max_bars_back=5000 declared).
Visual Rendering Performance : Tables (orbit plot, heat map, web, dashboard) are conditionally deleted and recreated on each update using table.delete() and table.new(). This prevents memory leaks but incurs redraw overhead. Rendering is restricted to barstate.islast (last bar) to minimize computational load—historical bars do not render visuals.
Alert Condition Triggers : alertcondition() functions evaluate on bar close when their boolean conditions transition from false to true. Alerts do not fire repeatedly while a condition remains true (e.g., CI stays above threshold for 10 bars fires only once on the initial cross).
Color Gradient Functions : The phaseColor() function maps phase angles to RGB hues using sine waves offset by 120° (red, green, blue channels). This creates a continuous spectrum where -180° to +180° spans the full color wheel. The amplitudeColor() function maps amplitude to grayscale intensity. The coherenceColor() function uses cos(phase) to map contribution to CI (positive = green, negative = red).
No External Data Requests : QRFM operates entirely on the chart's symbol and timeframe. It does not use request.security() or access external data sources. All calculations are self-contained, avoiding lookahead bias from higher-timeframe requests.
Deterministic Behavior : Given identical input parameters and price data, QRFM produces identical outputs. There are no random elements, probabilistic sampling, or time-of-day dependencies.
— Dskyz, Engineering precision. Trading coherence.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Fractals & SweepThe Fractals & Sweep indicator is designed to identify key market structure points (fractals) and detect potential liquidity sweeps around those areas. It visually highlights both Bill Williams fractals and regular fractals, and alerts the user when the market sweeps liquidity above or below the most recent fractal levels.
Fractal Recognition:
Detects both bullish (low) and bearish (high) fractals on the price chart.
Users can choose between:
Bill Williams fractal logic (default), or
Regular fractal logic (when the “Filter Bill Williams Fractals” option is enabled).
Fractals are plotted directly on the chart as red downward triangles for highs and green upward triangles for lows.
Fractal Tracking:
The indicator stores the most recent high and low fractal levels to serve as reference points for potential sweep detection.
Sweep Detection:
A bearish sweep is triggered when the price wicks above the last fractal high but closes below it — suggesting a liquidity grab above resistance.
A bullish sweep is triggered when the price wicks below the last fractal low but closes above it — suggesting a liquidity grab below support.
When a sweep occurs, the indicator draws a horizontal line from the previous fractal point to the current bar.
Alert System:
Custom alerts notify the trader when a bearish sweep or bullish sweep occurs, allowing for timely reactions to potential reversals or liquidity traps.
[blackcat] L1 Value Trend IndicatorOVERVIEW
The L1 Value Trend Indicator is a sophisticated technical analysis tool designed for TradingView users seeking advanced market trend identification and trading signals. This comprehensive indicator combines multiple analytical techniques to provide traders with a holistic view of market dynamics, helping identify potential entry and exit points through various signal mechanisms. 📈 It features a main Value Trend line along with a lagged version, golden cross and dead cross signals, and multiple technical indicators including RSI, Williams %R, Stochastic %K/D, and Relative Strength calculations. The indicator also includes reference levels for support and resistance analysis, making it a versatile tool for both short-term and long-term trading strategies. ✅
FEATURES
📈 Primary Value Trend Line: Calculates a smoothed value trend using a combination of SMA and custom smoothing techniques
🔍 Value Trend Lag: Implements a lagged version of the main trend line for cross-over analysis
🚀 Golden Cross & Dead Cross Signals: Identifies buy/sell opportunities when the main trend line crosses its lagged version
💸 Multi-Indicator Integration: Combines multiple technical analysis tools for comprehensive market view
📊 RSI Calculations: Includes 6-period, 7-period, and 13-period RSI calculations for momentum analysis
📈 Williams %R: Provides overbought/oversold conditions using the Williams %R formula
📉 Stochastic Oscillator: Implements both Stochastic %K and %D calculations for momentum confirmation
📋 Relative Strength: Calculates relative strength based on highest highs and current price
✅ Visual Labels: Displays BUY and SELL labels on chart when crossover conditions are met
📣 Alert Conditions: Provides automated alert conditions for golden cross and dead cross events
📌 Reference Levels: Plots entry (25) and exit (75) reference lines for support/resistance analysis
HOW TO USE
Copy the Script: Copy the complete Pine Script code from the original file
Open TradingView: Navigate to TradingView website or application
Access Pine Editor: Go to the Pine Script editor (usually found in the chart toolbar)
Paste Code: Paste the copied script into the editor
Save Script: Save the script with a descriptive name like " L1 Value Trend Indicator"
Select Chart: Choose the chart where you want to apply the indicator
Add Indicator: Apply the indicator to your chart
Configure Parameters: Adjust input parameters to customize behavior
Monitor Signals: Watch for golden cross (BUY) and dead cross (SELL) signals
Use Reference Levels: Monitor entry (25) and exit (75) lines for support/resistance levels
LIMITATIONS
⚠️ Potential Repainting: The script may repaint due to lookahead bias in some calculations
📉 Lookahead Bias: Some calculations may reference future values, potentially causing repainting issues
🔄 Parameter Sensitivity: Results may vary significantly with different parameter settings
📉 Computational Complexity: May impact chart performance with heavy calculations on large datasets
📊 Resource Usage: Requires significant processing power for multiple indicator calculations
🔄 Data Sensitivity: Results may be affected by data quality and market conditions
NOTES
📈 Signal Timing: Cross-over signals may lag behind actual price movements
📉 Parameter Optimization: Optimal parameters may vary by market conditions and asset type
📋 Market Conditions: Performance may vary significantly across different market environments
📈 Multi-Indicator: Combine signals with other technical indicators for confirmation
📉 Timeframe Analysis: Use multiple timeframes for enhanced signal accuracy
📋 Volume Analysis: Incorporate volume data for additional confirmation
📈 Strategy Integration: Consider using this indicator as part of a broader trading strategy
📉 Risk Management: Use signals as part of a comprehensive risk management approach
📋 Backtesting: Test parameter combinations with historical data before live trading
THANKS
🙏 Original Creator: blackcat1402 creates the L1 Value Trend Indicator
📚 Community Contributions: Recognition to TradingView community for continuous improvements and contributions
📈 Collaborative Development: Appreciation for collaborative efforts in enhancing technical analysis tools
📉 TradingView Community: Special thanks to TradingView community members for their ongoing support and feedback
📋 Educational Resources: Recognition of educational resources that helped in understanding technical analysis principles
Fractals + FVG [Combined]Звісно, ось варіант опису англійською, який можна використати для публікації індикатора в TradingView.
Description
This script combines two powerful and widely-used trading concepts into a single, comprehensive indicator: Bill Williams Fractals with dynamic support/resistance lines and Fair Value Gaps (FVG) based on the popular logic from LuxAlgo.
The goal is to provide a cleaner chart by merging two essential tools, allowing traders to analyze market structure and imbalances simultaneously.
Features
1. Williams Fractals with Invalidation Lines
This part of the indicator identifies classic Bill Williams fractals and enhances them with a unique visualization feature.
Fractal Detection: Automatically identifies both bullish (bottom) and bearish (top) fractals. You can choose between a 3-bar or 5-bar pattern in the settings.
Dynamic S/R Lines: A horizontal line is automatically drawn from every confirmed fractal, acting as a potential support or resistance level.
Automatic Invalidation: A line is considered "invalidated" or breached when the body of a candle closes past it. When this happens, the line stops extending, changes its color to the "invalidated" color, and remains on the chart as a historical reference. This provides a clear, objective signal that a level has been broken.
Customization: You can fully customize the colors for the support, resistance, and invalidated lines to match your chart theme.
2. Fair Value Gaps (FVG) / Imbalance
This module incorporates the robust FVG detection logic from LuxAlgo to automatically identify and display market imbalances.
FVG Detection: Highlights bullish and bearish Fair Value Gaps on the chart with colored boxes, representing inefficiencies in price delivery.
Automatic Mitigation: The FVG boxes are automatically removed from the chart once the price has "mitigated" or filled the gap, keeping your workspace clean and focused on active imbalances.
Multi-Timeframe (MTF): You can set the indicator to find and display FVGs from a higher timeframe directly on your current chart.
Dashboard: An optional on-screen dashboard provides a quick summary of the total count of bullish/bearish FVGs and the percentage that have been mitigated.
Full Customization: Control the colors of FVG boxes, extend their length, and configure other visual style settings.
How to Use
Fractal Lines: Use the active support and resistance lines as key levels for potential bounces or breaks. A line's invalidation can serve as confirmation of a shift in market structure.
FVG Zones: Fair Value Gaps often act as "magnets" for price. Use these zones as potential targets for your trades or as areas of interest for entries when price retraces to fill the imbalance.
Combined Strategy: The true power of this indicator comes from combining both concepts. For example, a bullish FVG forming near a key fractal support level can create a high-probability confluence zone for a long entry. Similarly, a break and invalidation of a fractal resistance line might signal that price is heading towards the next bearish FVG above.
This indicator is a tool for analysis and should be used in conjunction with your own trading strategy and risk management rules.
Six Meridian Divine Swords [theUltimator5]The Six Meridian Divine Sword is a legendary martial arts technique in the classic wuxia novel “Demi-Gods and Semi-Devils” (天龙八部) by Jin Yong (金庸). The technique uses powerful internal energy (qi) to shoot invisible sword-like energy beams from the six meridians of the hand. Each of the six fingers/meridians corresponds to a “sword,” giving six different sword energies.
The Six Meridian Divine Swords indicator is a compact “signal dashboard” that fuses six classic indicators (fingers)—MACD, KDJ, RSI, LWR (Williams %R), BBI, and MTM—into one pane. Each row is a traffic-light dot (green/bullish, red/bearish, gray/neutral). When all six align, the script draws a confirmation line (“All Bullish” or “All Bearish”). It’s designed for quick consensus reads across trend, momentum, and overbought/oversold conditions.
How to Read the Dashboard
The pane has 6 horizontal rows (explained in depth later):
MACD
KDJ
RSI
LWR (Larry Williams %R)
BBI (Bull & Bear Index)
MTM (Momentum)
Each tick in the row is a dot, with sentiment identified by a color.
Green = bullish condition met
Red = bearish condition met
Gray = inside a neutral band (filtering chop), shown when Use Neutral (Gray) Colors is ON
There are two lines that track the dots on the top or bottom of the pane.
All Bullish Signal Line: appears only if all 6 are strongly bullish (default color = white)
All Bearish Signal Line: appears only if all 6 are strongly bearish (default color = fuchsia)
The Six Meridians (Indicators) — What They Mean:
1) MACD — Trend & Momentum
What it is: A trend-following momentum indicator based on the relationship between two moving averages (typically 12-EMA and 26-EMA)
Logic used: Classic MACD line (EMA12−EMA26) vs its 9-EMA signal.
Bullish: MACD > Signal and |MACD−Signal| > Neutral Threshold
Bearish: MACD < Signal and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Small crosses can whipsaw. The neutral band ignores tiny separations to reduce noise.
Inputs: Fast/Slow/Signal lengths, Neutral Threshold.
2) KDJ — Stochastic with J-line boost
What it is: A variation of the stochastic oscillator popular in Chinese trading systems
Logic used: K = SMA(Stochastic, smooth), D = SMA(K, smooth), J = 3K − 2D.
Bullish: K > D and |K−D| > 2
Bearish: K < D and |K−D| > 2
Neutral: |K−D| ≤ 2
Why: K–D separation filters tiny wiggles; J offers an “extreme” early-warning context in the value label.
Inputs: Length, Smoothing.
3) RSI — Momentum balance (0–100)
What it is: A momentum oscillator measuring speed and magnitude of price changes (0–100)
Logic used: RSI(N).
Bullish: RSI > 50 + Neutral Zone
Bearish: RSI < 50 − Neutral Zone
Neutral: Between those bands
Why: Centerline/adaptive bands (around 50) give a directional bias without relying on fixed 70/30.
Inputs: Length, Neutral Zone (± around 50).
4) LWR (Williams %R) — Overbought/Oversold
What it is: An oscillator similar to stochastic, measuring how close the close is to the high-low range over N periods
Logic used: %R over N bars (0 to −100).
Bullish: %R > −50 + Neutral Zone
Bearish: %R < −50 − Neutral Zone
Neutral: Between those bands
Why: Uses a centered band around −50 instead of only −20/−80, making it act like a directional filter.
Inputs: Length, Neutral Zone (± around −50).
5) BBI (Bull & Bear Index) — Smoothed trend bias
What it is: A composite moving average, essentially the average of several different moving averages (often 3, 6, 12, 24 periods)
Logic used: Average of 4 SMAs (3/6/12/24 by default):
BBI = (MA3 + MA6 + MA12 + MA24) / 4
Bullish: Close > BBI and |Close−BBI| > 0.2% of BBI
Bearish: Close < BBI and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Multiple MAs blended together reduce single-MA whipsaw. A dynamic 0.2% band ignores tiny drift.
Inputs: 4 lengths (default 3/6/12/24). Threshold is auto-scaled at 0.2% of BBI.
6) MTM (Momentum) — Rate of change in price
What it is: A simple measure of rate of change
Logic used: MTM = Close − Close
Bullish: MTM > 0.5% of Close
Bearish: MTM < −0.5% of Close
Neutral: |MTM| ≤ threshold
Why: A percent-based gate adapts across prices (e.g., $5 vs $500) and mutes insignificant moves.
Inputs: Length. Threshold auto-scaled to 0.5% of current Close.
Display & Inputs You Can Tweak
🎨 Use Neutral (Gray) Colors
ON (default): 3-color mode with clear “no-trade”/“weak” states.
OFF: classic binary (green/red) without neutral filtering.
Pivot Candle PatternsPivot Candle Patterns Indicator
Overview
The PivotCandlePatterns indicator is a sophisticated trading tool that identifies high-probability candlestick patterns at market pivot points. By combining Williams fractals pivot detection with advanced candlestick pattern recognition, this indicator targets the specific patterns that statistically show the highest likelihood of signaling reversals at market tops and bottoms.
Scientific Foundation
The indicator is built on extensive statistical analysis of historical price data using a 42-period Williams fractal lookback period. Our research analyzed which candlestick patterns most frequently appear at genuine market reversal points, quantifying their occurrence rates and subsequent success in predicting reversals.
Key Research Findings:
At Market Tops (Pivot Highs):
- Three White Soldiers: 28.3% occurrence rate
- Spinning Tops: 13.9% occurrence rate
- Inverted Hammers: 11.7% occurrence rate
At Market Bottoms (Pivot Lows):
- Three Black Crows: 28.4% occurrence rate
- Hammers: 13.3% occurrence rate
- Spinning Tops: 13.1% occurrence rate
How It Works
1. Pivot Point Detection
The indicator uses a non-repainting implementation of Williams fractals to identify potential market turning points:
- A pivot high is confirmed when the middle candle's high is higher than surrounding candles within the lookback period
- A pivot low is confirmed when the middle candle's low is lower than surrounding candles within the lookback period
- The default lookback period is 2 candles (user adjustable from 1-10)
2. Candlestick Pattern Recognition
At identified pivot points, the indicator analyzes candle properties using these parameters:
- Body percentage threshold for Spinning Tops: 40% (adjustable from 10-60%)
- Shadow percentage threshold for Hammer patterns: 60% (adjustable from 40-80%)
- Maximum upper shadow for Hammer: 10% (adjustable from 5-20%)
- Maximum lower shadow for Inverted Hammer: 10% (adjustable from 5-20%)
3. Pattern Definitions
The indicator recognizes these specific patterns:
Single-Candle Patterns:
- Spinning Top : Small body (< 40% of total range) with significant upper and lower shadows (> 25% each)
- Hammer : Small body (< 40%), very long lower shadow (> 60%), minimal upper shadow (< 10%), closing price above opening price
- Inverted Hammer : Small body (< 40%), very long upper shadow (> 60%), minimal lower shadow (< 10%)
Multi-Candle Patterns:
- Three White Soldiers : Three consecutive bullish candles, each closing higher than the previous, with each open within the previous candle's body
- Three Black Crows : Three consecutive bearish candles, each closing lower than the previous, with each open within the previous candle's body
4. Visual Representation
The indicator provides multiple visualization options:
- Highlighted candle backgrounds for pattern identification
- Text or dot labels showing pattern names and success rates
- Customizable colors for different pattern types
- Real-time alert functionality on pattern detection
- Information dashboard displaying pattern statistics
Why It Works
1. Statistical Edge
Unlike traditional candlestick pattern indicators that simply identify patterns regardless of context, PivotCandlePatterns focuses exclusively on patterns occurring at statistical pivot points, dramatically increasing signal quality.
2. Non-Repainting Design
The pivot detection algorithm only uses confirmed data, ensuring the indicator doesn't repaint or provide false signals that disappear on subsequent candles.
3. Complementary Pattern Selection
The selected patterns have both:
- Statistical significance (high frequency at pivots)
- Logical market psychology (reflecting institutional supply/demand changes)
For example, Three White Soldiers at a pivot high suggests excessive bullish sentiment reaching exhaustion, while Hammers at pivot lows indicate rejection of lower prices and potential buying pressure.
Practical Applications
1. Reversal Trading
The primary use is identifying potential market reversals with statistical probability metrics. Higher percentage patterns (like Three White Soldiers at 28.3%) warrant more attention than lower probability patterns.
2. Confirmation Tool
The indicator works well when combined with other technical analysis methods:
- Support/resistance levels
- Trend line breaks
- Divergences on oscillators
- Volume analysis
3. Risk Management
The built-in success rate metrics help traders properly size positions based on historical pattern reliability. The displayed percentages reflect the probability of the pattern successfully predicting a reversal.
Optimized Settings
Based on extensive testing, the default parameters (Body: 40%, Shadow: 60%, Shadow Maximums: 10%, Lookback: 2) provide the optimal balance between:
- Signal frequency
- False positive reduction
- Early entry opportunities
- Pattern clarity
Users can adjust these parameters based on their timeframe and trading style, but the defaults represent the statistically optimal configuration.
Complementary Research: Reclaim Analysis
Additional research on "reclaim" scenarios (where price briefly breaks a level before returning) showed:
- Fast reclaims (1-2 candles) have 70-90% success rates
- Reclaims with increasing volume have 53.1% success rate vs. decreasing volume at 22.6%
This complementary research reinforces the importance of candle patterns and timing at critical market levels.
Multiple (12) Strong Buy/Sell Signals + Momentum
Indicator Manual: "Multiple (12) Strong Buy/Sell Signals + Momentum"
This indicator is designed to identify strong buy and sell signals based on 12 configurable conditions, which include a variety of technical analysis methods such as trend-following indicators, pattern recognition, volume analysis, and momentum oscillators. It allows for customizable alerts and visual cues on the chart. The indicator helps traders spot potential entry and exit points by displaying buy and sell signals based on the selected conditions.
Key Observations:
• The script integrates multiple indicators and pattern recognition methods to provide comprehensive buy/sell signals.
• Trend-based indicators like EMAs and MACD are combined with pattern recognition (flags, triangles) and momentum-based signals (RSI, ADX, and volume analysis).
• User customization is a core feature, allowing adjustments to the conditions and thresholds for more tailored signals.
• The script is designed to be responsive to market conditions, with multiple conditions filtering out noise to generate reliable signals.
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Key Features:
1. 12 Combined Buy/Sell Signal Conditions: This indicator incorporates a diverse set of conditions based on trend analysis, momentum, and price patterns.
2. Minimum Conditions Input: You can adjust the threshold of conditions that need to be met for the buy/sell signals to appear.
3. Alert Customization: Set alert thresholds for both buy and sell signals.
4. Dynamic Visualization: Buy and sell signals are shown as triangles on the chart, with momentum signals highlighted as circles.
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Detailed Description of the 12 Conditions:
1. Exponential Moving Averages (EMA):
o Conditions: The indicator uses EMAs with periods 3, 8, and 13 for quick trend-following signals.
o Bullish Signal: EMA3 > EMA8 > EMA13 (Bullish stack).
o Bearish Signal: EMA3 < EMA8 < EMA13 (Bearish stack).
o Reversal Signal: The crossing over or under of these EMAs can signify trend reversals.
2. MACD (Moving Average Convergence Divergence):
o Fast MACD (2, 7, 3) is used to confirm trends quickly.
o Bullish Signal: When the MACD line crosses above the signal line.
o Bearish Signal: When the MACD line crosses below the signal line.
3. Donchian Channel:
o Tracks the highest high and lowest low over a given period (default 20).
o Breakout Signal: Price breaking above the upper band is bullish; breaking below the lower band is bearish.
4. VWAP (Volume-Weighted Average Price):
o Above VWAP: Bullish condition (price above VWAP).
o Below VWAP: Bearish condition (price below VWAP).
5. EMA Stacking & Reversal:
o Tracks the order of EMAs (3, 8, 13) to confirm strong trends and reversals.
o Bullish Reversal: EMA3 < EMA8 < EMA13 followed by a crossing to bullish.
o Bearish Reversal: EMA3 > EMA8 > EMA13 followed by a crossing to bearish.
6. Bull/Bear Flags:
o Bull Flag: Characterized by a strong price movement (flagpole) followed by a pullback and breakout.
o Bear Flag: Similar to Bull Flag but in the opposite direction.
7. Triangle Patterns (Ascending and Descending):
o Detects ascending and descending triangles using pivot highs and lows.
o Ascending Triangle: Higher lows and flat resistance.
o Descending Triangle: Lower highs and flat support.
8. Volume Sensitivity:
o Identifies price moves with significant volume increases.
o High Volume: When current volume is significantly above the moving average volume (set to 1.2x of the average).
9. Momentum Indicators:
o RSI (Relative Strength Index): Confirms overbought and oversold levels with thresholds set at 65 (overbought) and 35 (oversold).
o ADX (Average Directional Index): Confirms strong trends when ADX > 28.
o Momentum Up: Momentum is upward with strong volume and bullish RSI/ADX conditions.
o Momentum Down: Momentum is downward with strong volume and bearish RSI/ADX conditions.
10. Bollinger & Keltner Squeeze:
o Squeeze Condition: A contraction in both Bollinger Bands and Keltner Channels indicates low volatility, signaling a potential breakout.
o Squeeze Breakout: Price breaking above or below the squeeze bands.
11. 3 Consecutive Candles Condition:
o Bullish: Price rises for three consecutive candles with higher highs and lows.
o Bearish: Price falls for three consecutive candles with lower highs and lows.
12. Williams %R and Stochastic RSI:
o Williams %R: A momentum oscillator with signals when the line crosses certain levels.
o Stochastic RSI: Provides overbought/oversold levels with smoother signals.
o Combined Signals: You can choose whether to require both WPR and StochRSI to signal a buy/sell.
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User Inputs (Inputs Tab):
1. Minimum Conditions for Buy/Sell:
o min_conditions: Number of conditions required to trigger a buy/sell signal on the chart (1 to 12).
o Alert_min_conditions: User-defined alert threshold (how many conditions must be met before an alert is triggered).
2. Donchian Channel Settings:
o Show Donchian: Toggle visibility of the Donchian channel.
o Donchian Length: The length of the Donchian Channel (default 20).
3. Bull/Bear Flag Settings:
o Bull Flag Flagpole Strength: ATR multiplier to define the strength of the flagpole.
o Bull Flag Pullback Length: Length of pullback for the bull flag pattern.
o Bull Flag EMA Length: EMA length used to confirm trend during bull flag pattern.
Similar settings exist for Bear Flag patterns.
4. Momentum Indicators:
o RSI Length: Period for calculating the RSI (default 9).
o RSI Overbought: Overbought threshold for the RSI (default 65).
o RSI Oversold: Oversold threshold for the RSI (default 35).
5. Bollinger/Keltner Squeeze Settings:
o Squeeze Width Threshold: The maximum width of the Bollinger and Keltner Bands for squeeze conditions.
6. Stochastic RSI Settings:
o Stochastic RSI Length: The period for calculating the Stochastic RSI.
7. WPR Settings:
o WPR Length: Period for calculating Williams %R (default 14).
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User Inputs (Style Tab):
1. Signal Plotting:
o Control the display and colors of the buy/sell signals, momentum indicators, and pattern signals on the chart.
o Buy/Sell Signals: Can be customized with different colors and shapes (triangle up for buys, triangle down for sells).
o Momentum Signals: Custom circle placement for momentum-up or momentum-down signals.
2. Donchian Channel:
o Show Donchian: Toggle visibility of the Donchian upper, lower, and middle bands.
o Band Colors: Choose the color for each band (upper, lower, middle).
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How to Use the Indicator:
1. Adjust Minimum Conditions: Set the minimum number of conditions that must be met for a signal to appear. For example, set it to 5 if you want only stronger signals.
2. Set Alert Threshold: Define the number of conditions needed to trigger an alert. This can be different from the minimum conditions for visual signals.
3. Customize Appearance: Modify the colors and styles of the signals to match your preferences.
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Conclusion:
This comprehensive trading indicator uses a combination of trend-following, pattern recognition, and momentum-based conditions to help you spot potential buy and sell opportunities. By adjusting the input settings, you can fine-tune it to match your specific trading strategy, making it a versatile tool for different market conditions.
Signal Reliability Based on Condition Count
The reliability of the buy/sell signals increases as more conditions are met. Here's a breakdown of the probabilities:
1. 1-3 Conditions Met: Lower Probability
o Signals that meet only 1-3 conditions tend to have lower reliability and are considered less probable. These signals may represent false positives or weaker market movements, and traders should approach them with caution.
2. 4 Conditions Met: More Reliable Signal
o When 4 conditions are met, the signal becomes more reliable. This indicates that multiple indicators or market patterns are aligning, increasing the likelihood of a valid buy/sell opportunity. While not foolproof, it's a stronger indication that the market may be moving in a particular direction.
3. 5-6 Conditions Met: Strong Signal
o A signal meeting 5-6 conditions is considered a strong signal. This indicates a well-confirmed move, with several technical indicators and market factors aligning to suggest a higher probability of success. These are the signals that traders often prioritize.
4. 7+ Conditions Met: Rare and High-Confidence Signal
o Signals that meet 7 or more conditions are rare and should be considered high-confidence signals. These represent a significant alignment of multiple factors, and while they are less frequent, they are highly reliable when they do occur. Traders can be more confident in acting on these signals, but they should still monitor market conditions for confirmation.
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You can adjust the number of conditions as needed, but this breakdown should give a clear structure on how the signal strength correlates with the number of conditions met!
Double Top/Bottom Fractals DetectorDouble Top/Bottom Detector with Williams Fractals (Extended + Early Signal)
This indicator combines the classic Williams Fractals methodology with an enhanced mechanism to detect potential reversal patterns—namely, double tops and double bottoms. It does so by using two separate detection schemes:
Confirmed Fractals for Pattern Formation:
The indicator calculates confirmed fractals using the traditional Williams Fractals rules. A fractal is confirmed if a bar’s high (for an up fractal) or low (for a down fractal) is the highest or lowest compared to a specified number of bars on both sides (default: 2 bars on the left and 2 on the right).
Once a confirmed fractal is identified, its price (high for tops, low for bottoms) and bar index are stored in an internal array (up to the 10 most recent confirmed fractals).
When a new confirmed fractal appears, the indicator compares it with previous confirmed fractals. If the new fractal is within a user-defined maximum bar distance (e.g., 20 bars) and the price difference is within a specified tolerance (default: 0.8%), the indicator assumes that a double top (if comparing highs) or a double bottom (if comparing lows) pattern is forming.
A signal is then generated by placing a label on the chart—SELL for a double top and BUY for a double bottom.
Early Signal Generation:
To capture potential reversals sooner, the indicator also includes an “early signal” mechanism. This uses asymmetric offsets different from the confirmed fractal calculation:
Signal Right Offset: Defines the candidate bar used for early signal detection (default is 1 bar).
Signal Left Offset: Defines the number of bars to the left of the candidate that must confirm the candidate’s price is the extreme (default is 2 bars).
For an early top candidate, the candidate bar’s high must be greater than the highs of the bars specified by the left offset and also higher than the bar immediately to its right. For an early bottom candidate, the corresponding condition applies for lows.
If the early candidate’s price level is within the acceptable tolerance when compared to any of the previously stored confirmed fractals (again, within the allowed bar distance), an early signal is generated—displayed as SELL_EARLY or BUY_EARLY.
The early signal block can be enabled or disabled via a checkbox input, allowing traders to choose whether to use these proactive signals.
Key Parameters:
n:
The number of bars used to confirm a fractal. The fractal is considered valid if the bar’s high (or low) is higher (or lower) than the highs (or lows) of the preceding and following n bars.
maxBarsApart:
The maximum number of bars allowed between two fractals for them to be considered part of the same double top or bottom pattern.
tolerancePercent:
The maximum allowed percentage difference (default: 0.8%) between the high (or low) values of two fractals to qualify them as matching for the pattern.
signalLeftOffset & signalRightOffset:
These parameters define the asymmetric offsets for early signal detection. The left offset (default: 2) specifies how many bars to look back, while the right offset (default: 1) specifies the candidate bar’s position.
earlySignalsEnabled:
A checkbox option that allows users to enable or disable early signal generation. When disabled, the indicator only uses confirmed fractal signals.
How It Works:
Fractal Calculation and Plotting:
The confirmed fractals are calculated using the traditional method, ensuring robust identification by verifying the pattern with a symmetrical offset. These confirmed fractals are plotted on the chart using triangle shapes (upwards for potential double bottoms and downwards for potential double tops).
Pattern Detection:
Upon detection of a new confirmed fractal, the indicator checks up to 10 previous fractals stored in internal arrays. If the new fractal’s high or low is within the tolerance range and close enough in terms of bars to one of the stored fractals, it signifies the formation of a double top or double bottom. A corresponding SELL or BUY label is then placed on the chart.
Early Signal Feature:
If enabled, the early signal block checks for candidate bars based on the defined asymmetric offsets. These candidates are evaluated to see if their high/low levels meet the early confirmation criteria relative to nearby bars. If they also match one of the confirmed fractal levels (within tolerance and bar distance), an early signal is issued with a label (SELL_EARLY or BUY_EARLY) on the chart.
Benefits for Traders:
Timely Alerts:
By combining both confirmed and early signals, the indicator offers a proactive approach to detect reversals sooner, potentially improving entry and exit timing.
Flexibility:
With adjustable parameters (including the option to disable early signals), traders can fine-tune the indicator to better suit different markets, timeframes, and trading styles.
Enhanced Pattern Recognition:
The dual-layered approach (confirmed fractals plus early detection) helps filter out false signals and captures the essential formation of double tops and bottoms more reliably.
Matrix Series and Vix Fix with VWAP CCI and QQE SignalsMatrix Series and Vix Fix with VWAP CCI and QQE Signals
Short Title: Advanced Matrix
Purpose
This Pine Script combines multiple technical analysis tools to create a comprehensive trading indicator. It incorporates elements like support/resistance zones, overbought/oversold conditions, Williams Vix Fix, QQE (Quantitative Qualitative Estimation) signals, VWAP CCI signals, and a 200-period SMA for trend filtering. The goal is to provide actionable buy and sell signals with enhanced visualization.
Key Features and Components
1. Matrix Series
Smoothing Input: Allows customization of EMA smoothing for the indicator (default: 5).
Support/Resistance Zones: Based on CCI (Commodity Channel Index) values.
Dynamic zones calculated with customizable parameters (SupResPeriod, SupResPercentage, PricePeriod).
Candlestick Visualization: Custom candlestick plots with colors indicating trends.
Dynamic levels for overbought/oversold conditions.
2. Overbought/Oversold Signals
Overbought and oversold levels are adjustable (ob and os).
Plots circles on the chart to highlight extreme conditions.
3. Williams Vix Fix
Identifies potential reversal points by analyzing volatility.
Uses Bollinger Bands and percentile thresholds to detect high-probability entries.
Includes two alert levels (alert1 and alert2) with customizable criteria for signal filtering.
4. QQE Signals
Based on the smoothed RSI and QQE methodology.
Detects trend changes using adaptive ATR bands (FastAtrRsiTL).
Plots long and short signals when specific conditions are met.
5. VWAP CCI Signals
Combines VWAP and CCI for additional trade signals.
Detects crossovers and crossunders of CCI levels (-200 and 200) to generate long and short signals.
6. 200 SMA
A 200-period simple moving average is plotted to act as a trend filter.
The script rules recommend buying only when the price is above the SMA200.
Customizable Inputs
General:
Smoothing, support/resistance periods, overbought/oversold levels.
Williams Vix Fix:
Lookback periods, Bollinger Band settings, percentile thresholds.
QQE:
RSI length, smoothing factor, QQE factor, and threshold values.
VWAP CCI:
Length for calculating deviations.
Visual Elements
Dynamic candlestick colors to indicate trend direction.
Overbought/oversold circles for extreme price levels.
Resistance and support lines.
Labels and shapes for buy/sell signals from Vix Fix, QQE, and VWAP CCI.
Alerts
Alerts are configured for the Matrix Series (e.g., "BUY MATRIX") and other components, ensuring traders are notified when significant conditions are met.
Intended Use
This indicator is designed for traders seeking a multi-faceted tool to analyze market trends, identify potential reversal points, and generate actionable trading signals. It combines traditional indicators with advanced techniques for comprehensive market analysis.
VIX CANDLESThe VIX CANDLES indicator is designed to visualize the Williams VIX Fix, a volatility measure that mimics the VIX index but applies to different financial instruments.
This indicator calculates a custom "VIX-like" value based on the highest close over a specified look-back period and plots the result as candlesticks. It also includes an Exponential Moving Average (EMA) to help identify trends and potential buy/sell signals.
Williams VIX Fix Calculation: Measures volatility by comparing the current low to the highest close over the given period, then expressing the result as a percentage.
Custom Candles: The VIX Fix is displayed as a set of candlesticks where the body is colored based on whether the close is higher (green) or lower (red) than the open.
Exponential Moving Average (EMA): An EMA is applied to the VIX Fix value to smooth the data and identify trend changes. The EMA's length is customizable.
Trade Direction Labels: Labels are plotted when the VIX Fix crosses above or below the EMA, signaling potential buy (▽) or sell (△) opportunities.
Customization: Colors for the candlesticks and EMA, as well as the look-back period and EMA length, can be adjusted to suit your preferences.
This indicator is useful for traders looking to track volatility and identify potential trend shifts in the market. By combining the Williams VIX Fix with an EMA, it provides a more dynamic view of market conditions, helping traders spot extreme volatility and adjust their strategies accordingly.
