Multi Oscillator OB/OS Signals v3 - Scope TestIndicator Description: Multi Oscillator OB/OS Signals
Purpose:
The "Multi Oscillator OB/OS Signals" indicator is a TradingView tool designed to help traders identify potential market extremes and momentum shifts by monitoring four popular oscillators simultaneously: RSI, Stochastic RSI, CCI, and MACD. Instead of displaying these oscillators in separate panes, this indicator plots distinct visual symbols directly onto the main price chart whenever specific predefined conditions (typically related to overbought/oversold levels or line crossovers) are met for each oscillator. This provides a consolidated view of potential signals from these different technical tools.
How It Works:
The indicator calculates the values for each of the four oscillators based on user-defined settings (like length periods and price sources) and then checks for specific signal conditions on every bar:
Relative Strength Index (RSI):
It monitors the standard RSI value.
When the RSI crosses above the user-defined Overbought (OB) level (e.g., 70), it plots an "Overbought" symbol (like a downward triangle) above that price bar.
When the RSI crosses below the user-defined Oversold (OS) level (e.g., 30), it plots an "Oversold" symbol (like an upward triangle) below that price bar.
Stochastic RSI:
This works similarly to RSI but is based on the Stochastic calculation applied to the RSI value itself (specifically, the %K line of the Stoch RSI).
When the Stoch RSI's %K line crosses above its Overbought level (e.g., 80), it plots its designated OB symbol (like a downward arrow) above the bar.
When the %K line crosses below its Oversold level (e.g., 20), it plots its OS symbol (like an upward arrow) below the bar.
Commodity Channel Index (CCI):
It tracks the CCI value.
When the CCI crosses above its Overbought level (e.g., +100), it plots its OB symbol (like a square) above the bar.
When the CCI crosses below its Oversold level (e.g., -100), it plots its OS symbol (like a square) below the bar.
Moving Average Convergence Divergence (MACD):
Unlike the others, MACD signals here are not based on fixed OB/OS levels.
It identifies when the main MACD line crosses above its Signal line. This is considered a bullish crossover and is indicated by a specific symbol (like an upward label) plotted below the price bar.
It also identifies when the MACD line crosses below its Signal line. This is a bearish crossover, indicated by a different symbol (like a downward label) plotted above the price bar.
Visualization:
All these signals appear as small, distinct shapes directly on the price chart at the bar where the condition occurred. The shapes, their colors, and their position (above or below the bar) are predefined for each signal type to allow for quick visual identification. Note: In the current version of the underlying code, the size of these shapes is fixed (e.g., tiny) and not user-adjustable via the settings.
Configuration:
Users can access the indicator's settings to customize:
The calculation parameters (Length periods, smoothing, price source) for each individual oscillator (RSI, Stoch RSI, CCI, MACD).
The specific Overbought and Oversold threshold levels for RSI, Stoch RSI, and CCI.
The colors associated with each type of signal (OB, OS, Bullish Cross, Bearish Cross).
(Limitation Note: While settings exist to toggle the visibility of signals for each oscillator individually, due to a technical workaround in the current code, these toggles may not actively prevent the shapes from plotting if the underlying condition is met.)
Alerts:
The indicator itself does not automatically generate pop-up alerts. However, it creates the necessary "Alert Conditions" within TradingView's alert system. This means users can manually set up alerts for any of the specific signals generated by the indicator (e.g., "RSI Overbought Enter," "MACD Bullish Crossover"). When creating an alert, the user selects this indicator, chooses the desired condition from the list provided by the script, and configures the alert actions.
Intended Use:
This indicator aims to provide traders with convenient visual cues for potential over-extension in price (via OB/OS signals) or shifts in momentum (via MACD crossovers) based on multiple standard oscillators. These signals are often used as potential indicators for:
Identifying areas where a trend might be exhausted and prone to a pullback or reversal.
Confirming signals generated by other analysis methods or trading strategies.
Noting shifts in short-term momentum.
Disclaimer: As with any technical indicator, the signals generated should not be taken as direct buy or sell recommendations. They are best used in conjunction with other forms of analysis (price action, trend analysis, volume, fundamental analysis, etc.) and within the framework of a well-defined trading plan that includes risk management. Market conditions can change, and indicator signals can sometimes be false or misleading.
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TriTrend Nexus[BullByte]TriTrend Nexus is a comprehensive market analysis tool that consolidates three well-established signals into a single, easy-to-read interface. It is designed to help traders quickly assess the market’s current condition and make more informed decisions about potential trend shifts.
Key Features and Functionality
Composite Signal System
Multi-Faceted Approach :
The indicator combines insights from three distinct market signals into one composite score. This approach provides a more holistic view of market conditions compared to relying on a single indicator.
Clear Classification :
Based on the composite score, TriTrend Nexus categorizes the market into:
Strong Signals : When all three underlying conditions are met, indicating a robust and established trend.
Early Signals : When two out of the three conditions are met, offering an early hint of a potential trend.
Neutral/Choppy : When conditions are ambiguous or conflicting, suggesting a lack of clear market direction.
Trend Qualifiers :
In addition to the composite score, the indicator subtly refines its signal by noting whether a trend is “Rising” or “Fading.” This further aids traders in understanding the momentum behind the signal.
Dynamic Signal Identification
Timely Alerts :
By analyzing the composite data in real time, the indicator quickly identifies when market conditions shift, offering early warning signals that help traders stay ahead of the market.
Adaptive Analysis :
The built-in signal assessment continuously monitors market changes. Whether the market is in the early stages of a move or firmly committed to a trend, TriTrend Nexus adapts its messaging to reflect the evolving conditions.
User-Friendly Dashboard
Integrated Display :
A customizable dashboard provides an at-a-glance summary of key metrics. Users can choose between a detailed view for comprehensive insights or a compact version for a streamlined experience.
Key Metrics Displayed :
Primary Signal : The overall market status, such as “Bullish Strong” or “Bearish Early.”
Composite Nexus Score : A numerical value representing the strength of the current market conditions.
Supporting Data : Essential values that help explain the current signal without overwhelming the trader.
Easy Interpretation :
The dashboard is designed with clarity in mind. Clear labeling and a consistent layout ensure that even traders new to composite indicators can quickly interpret the displayed information.
Visual Clarity and Aesthetic
Color-Coded Signals :
The indicator uses a vibrant color scheme to highlight market conditions:
Bright Green : Signifies a strong bullish trend.
Light Green : Indicates an emerging bullish trend.
Red : Represents a strong bearish trend.
Light Red/Pink : Denotes an early bearish signal.
Gray : Used when market conditions are neutral or choppy.
Graphical Enhancements :
The plotted oscillator visually reinforces the signal classifications with dynamic color transitions. Horizontal markers provide reference points to help traders easily compare the current readings against standard levels.
Customization Options
Adjustable Settings :
Traders can personalize the indicator by modifying input settings such as sensitivity thresholds and period lengths. This flexibility allows the tool to adapt to different market environments and trading styles.
Dashboard Flexibility :
The option to toggle between a full dashboard and a shorter version means that both novice and experienced traders can configure the display to best suit their needs. A more detailed dashboard offers extensive insights, while the compact mode provides a minimalist view for those who prefer simplicity.
Tailored User Experience :
With multiple adjustable parameters, users can fine-tune the indicator to respond precisely to their preferred timeframes and market conditions. This adaptability makes TriTrend Nexus a versatile tool for various trading strategies.
Benefits for Traders
Quick and Informed Decision-Making :
With a single glance at the dashboard and visual cues from the oscillator, traders can quickly gauge whether the market is poised for a strong move, is in the early stages of a trend, or is too volatile for clear signals. This helps in planning timely entries and exits.
Enhanced Market Insight :
By integrating multiple perspectives into one coherent score, the indicator filters out market noise and highlights the prevailing trend more reliably. This can be particularly useful during periods of market uncertainty.
Reduced Analysis Time:
The combination of clear, color-coded signals and an intuitive dashboard reduces the time spent analyzing various individual indicators, allowing traders to focus more on strategy execution.
Customization for Diverse Strategies :
The ability to adjust various input parameters and the dashboard layout ensures that traders can tailor the tool to fit their unique analysis style and market conditions, making it a versatile addition to any trading toolkit.
User-Friendly Interface :
Even for those who are not technically inclined, the clear visual design and straightforward signal descriptions make it easy to understand the current market situation without needing to interpret complex data.
NasyI## NasyI - Multi-Timeframe Technical Analysis Toolkit
### English Description
**NasyI** is a comprehensive technical analysis indicator designed to provide traders with a complete view of market dynamics across multiple timeframes. This indicator combines the power of Exponential Moving Averages (EMAs), Simple Moving Averages (MAs), Volume Weighted Average Price (VWAP), and key support/resistance levels to help traders identify trend direction, potential reversal points, and optimal entry/exit opportunities.
#### Key Features
1. **Multi-Timeframe Analysis System**
- 2-minute EMAs (13, 48) for ultra-short-term trend identification
- 5-minute EMAs (9, 13, 21, 48, 200) for short-term trend confirmation
- Daily EMAs (5, 13, 21, 48, 100, 200) and MAs (20, 50, 100, 200) for longer-term perspective
- Color-coded bands between key EMAs to visually identify trend strength and direction
2. **Advanced VWAP Integration**
- Daily VWAP for intraday support/resistance
- Weekly VWAP for medium-term price reference
- Monthly VWAP for long-term institutional price levels
- All VWAPs properly reset at their respective time period boundaries
3. **Critical Price Level Identification**
- Previous day high/low lines for identifying key breakout and breakdown levels
- Pre-market high/low tracking to identify potential intraday support/resistance zones
- All levels displayed with distinct line styles for easy identification
4. **Dynamic Trend Analysis**
- Color-coded bands between EMAs display trend strength and direction:
- Green bands indicate uptrend conditions (9 EMA > 21 EMA > 48 EMA)
- Red bands indicate downtrend conditions (9 EMA < 21 EMA < 48 EMA)
- Yellow bands indicate neutral/confused market conditions
- Visual representation makes trend changes immediately apparent
5. **Comprehensive Customization Options**
- Fully customizable colors for all indicators and bands
- Adjustable transparency settings for visual clarity
- Optional price labels with customizable placement and appearance
- Ability to show/hide specific components based on trading preferences
#### Trading Applications
This indicator is particularly valuable for:
1. **Day Trading & Scalping**: The 2-minute and 5-minute EMAs with color bands provide clear short-term trend direction and potential reversal signals.
2. **Swing Trading**: Daily EMAs and MAs offer perspective on the larger trend, helping to align short-term trades with the broader market direction.
3. **Gap Trading**: Previous day and pre-market levels help identify potential gap fill scenarios and breakout/breakdown opportunities.
4. **VWAP Trading Strategies**: Multiple timeframe VWAPs allow for identifying institutional participation levels and potential reversal zones.
5. **EMA Cross Systems**: The various EMAs can be used to identify golden crosses and death crosses across multiple timeframes.
#### How the Components Work Together
The power of NasyI comes from the integration of these different technical elements:
1. The short-timeframe EMAs (2m, 5m) provide immediate trend information, while the daily EMAs/MAs provide context about the larger market structure.
2. The color bands between EMAs offer instant visual confirmation of trend alignment or divergence across timeframes.
3. Previous day and pre-market levels add horizontal support/resistance zones to complement the dynamic moving averages.
4. Multiple timeframe VWAPs provide additional confirmation of institutional activity levels and potential reversal points.
By combining these elements, traders can develop a comprehensive market view that integrates price action, trend direction, and key support/resistance levels all in one indicator.
#### Usage Instructions
1. Apply the NasyI indicator to your chart (works best on intraday timeframes from 1-minute to 30-minute).
2. Observe the relationship between price and the various EMAs:
- Price above the 2m/5m EMAs with green bands indicates bullish short-term conditions
- Price below the 2m/5m EMAs with red bands indicates bearish short-term conditions
3. Use the daily EMAs/MAs and VWAPs as targets for potential price movements and reversal zones.
4. Previous day and pre-market high/low lines provide key levels to watch for breakouts or breakdowns.
5. Customize the appearance according to your preferences using the extensive settings options.
This indicator represents a unique approach to technical analysis by combining multiple timeframe perspectives into a single, visually intuitive display that helps traders make more informed decisions based on a comprehensive view of market conditions.
### 中文描述
**NasyI** 是一个全面的技术分析指标，旨在为交易者提供跨多个时间周期的完整市场动态视图。该指标结合了指数移动平均线（EMA）、简单移动平均线（MA）、成交量加权平均价格（VWAP）和关键支撑/阻力水平的力量，帮助交易者识别趋势方向、潜在反转点和最佳进出场机会。
#### 主要特点
1. **多时间周期分析系统**
- 2分钟EMAs（13，48）用于超短期趋势识别
- 5分钟EMAs（9，13，21，48，200）用于短期趋势确认
- 日线EMAs（5，13，21，48，100，200）和MAs（20，50，100，200）用于更长期的视角
- 关键EMAs之间的彩色带状区域直观显示趋势强度和方向
2. **高级VWAP整合**
- 日内VWAP作为日内支撑/阻力
- 周内VWAP作为中期价格参考
- 月内VWAP作为长期机构价格水平
- 所有VWAP在各自的时间周期边界正确重置
3. **关键价格水平识别**
- 前一交易日高点/低点线用于识别关键突破和跌破水平
- 盘前高点/低点跟踪用于识别潜在的日内支撑/阻力区域
- 所有水平以不同的线条样式显示，便于识别
4. **动态趋势分析**
- EMAs之间的彩色带状区域显示趋势强度和方向：
- 绿色带状区域表示上升趋势（9 EMA > 21 EMA > 48 EMA）
- 红色带状区域表示下降趋势（9 EMA < 21 EMA < 48 EMA）
- 黄色带状区域表示中性/混乱市场条件
- 视觉表示使趋势变化立即显现
5. **全面的自定义选项**
- 所有指标和带状区域的颜色完全可定制
- 可调节的透明度设置，提高视觉清晰度
- 可选的价格标签，带有可定制的位置和外观
- 能够根据交易偏好显示/隐藏特定组件
#### 交易应用
此指标对以下方面特别有价值：
1. **日内交易和短线交易**：2分钟和5分钟EMAs与色带提供清晰的短期趋势方向和潜在反转信号。
2. **摇摆交易**：日线EMAs和MAs提供对更大趋势的视角，帮助将短期交易与更广泛的市场方向对齐。
3. **缺口交易**：前一日和盘前水平帮助识别潜在的缺口填充情况和突破/跌破机会。
4. **VWAP交易策略**：多时间周期VWAP允许识别机构参与水平和潜在反转区域。
5. **EMA交叉系统**：各种EMAs可用于识别跨多个时间周期的黄金交叉和死亡交叉。
#### 组件如何协同工作
NasyI的强大之处在于这些不同技术元素的集成：
1. 短时间周期EMAs（2m，5m）提供即时趋势信息，而日线EMAs/MAs提供关于更大市场结构的背景。
2. EMAs之间的色带提供趋势对齐或跨时间周期分歧的即时视觉确认。
3. 前一日和盘前水平添加水平支撑/阻力区域，补充动态移动平均线。
4. 多时间周期VWAP提供机构活动水平和潜在反转点的额外确认。
通过结合这些元素，交易者可以发展出全面的市场视图，整合价格行动、趋势方向和关键支撑/阻力水平于一个指标中。
#### 使用说明
1. 将NasyI指标应用到您的图表上（最适合1分钟至30分钟的日内时间周期）。
2. 观察价格与各种EMAs之间的关系：
- 价格位于2m/5m EMAs之上，带有绿色带状区域，表示看涨的短期条件
- 价格位于2m/5m EMAs之下，带有红色带状区域，表示看跌的短期条件
3. 使用日线EMAs/MAs和VWAPs作为潜在价格移动和反转区域的目标。
4. 前一日和盘前高点/低点线提供需要关注的突破或跌破的关键水平。
5. 使用广泛的设置选项根据您的偏好自定义外观。
这个指标代表了一种独特的技术分析方法，将多个时间周期的视角结合到一个单一的、视觉直观的显示中，帮助交易者基于对市场条件的全面视图做出更明智的决策。
MACD Volume Strategy (BBO + MACD State, Reversal Type)Overview
MACD Volume Strategy (BBO + MACD State, Reversal Type) is a momentum-based reversal system that combines MACD crossover logic with volume filtering to enhance signal accuracy and minimize noise. It aims to identify structural trend shifts and manage risk using predefined parameters.
※This strategy is for educational and research purposes only. All results are based on historical simulations and do not guarantee future performance.
Strategy Objectives
Identify early trend transitions with high probability
Filter entries using volume dynamics to validate momentum
Maintain continuous exposure using a reversal-style model
Apply a consistent 1:1.5 risk-to-reward ratio per trade
Key Features
Integrated MACD and volume oscillator filtering
Zero repainting (all signals confirmed on closed candles)
Automatic position flipping for seamless direction shifts
Stop-loss and take-profit based on recent structural highs/lows
Trading Rules
Long Entry Conditions
MACD crosses above the zero line (BBO Buy arrow)
Volume oscillator is positive (short EMA > long EMA)
MACD is above the signal line
Close any existing short and enter a new long
Short Entry Conditions
MACD crosses below the zero line (BBO Sell arrow)
Volume oscillator is positive
MACD is below the signal line
Close any existing long and enter a new short
Exit Rules
Take Profit (TP) = Entry ± (risk distance × 1.5)
Stop Loss (SL) = Recent swing low (for long) or high (for short)
Early Exit = Triggered when a reversal signal appears (flip logic)
Risk Management Parameters
Pair: ETH/USD
Timeframe: 10-minute
Starting Capital: $3,000
Commission: 0.02%
Slippage: 2 pip
Risk per Trade: 5% of account equity (adjusted for sustainable practice)
Total Trades: 312 (backtest on selected dataset)
※Risk parameters are fully configurable and should be adjusted to suit each trader's personal setup and broker conditions.
Parameters & Configurations
Volume Short Length: 6
Volume Long Length: 12
MACD Fast Length: 11
MACD Slow Length: 21
Signal Smoothing: 10
Oscillator MA Type: SMA
Signal Line MA Type: SMA
Visual Support
Green arrow = Long entry
Red arrow = Short entry
MACD lines, signal line, and histogram
SL/TP markers plotted directly on the chart
Strategic Advantages & Uniqueness
Volume filtering eliminates low-participation, weak signals
Structurally aligned SL/TP based on recent market pivots
No repainting — decisions are made only on closed candles
Always in the market due to the reversal-style framework
Inspirations & Attribution
This strategy is inspired by the excellent work of:
Bitcoinblockchainonline – “BBO_Roxana_Signals MACD + vol”
Leveraging MACD zero-line cross and volume oscillator for intuitive signal generation.
HasanRifat – “MACD Fake Filter ”
Introduced a signal filter using MACD wave height averaging to reduce false positives.
This strategy builds upon those ideas to create a more automated, risk-aware, and technically adaptive system.
Summary
MACD Volume Strategy is a clean, logic-first automated trading system built for precision-seeking traders. It avoids discretionary bias and provides consistent signal logic under backtested historical conditions.
100% mechanical — no discretionary input required
Designed for high-confidence entries
Can be extended with filters, alerts, or trailing stops
※Strategy performance depends on market context. Past performance is not indicative of future results. Use with proper risk management and careful configuration.
ATR and Moving AverageUsing ATR and Moving Average: A Technical Analysis Strategy
The Average True Range (ATR) and the Moving Average are two important technical analysis tools that can be used together to identify trading opportunities in the market. In this article, we will explore how to use these two tools and how the crossover between them can indicate changes in the market.
What is ATR?
The Average True Range (ATR) is a measure of the volatility of an asset, which calculates the average true range of an asset over a period of time. The true range is the difference between the closing price and the opening price of an asset, or the difference between the closing price and the highest or lowest price of the day. ATR is an important measure of volatility, as it helps to identify the magnitude of price fluctuations of an asset.
What is Moving Average?
The Moving Average is a technical analysis tool that calculates the average price of an asset over a period of time. The Moving Average can be used to identify trends and price patterns, and is an important tool for traders. There are different types of Moving Averages, including the Simple Moving Average (SMA), the Exponential Moving Average (EMA), and the Weighted Moving Average (WMA).
Crossover between ATR and Moving Average
The crossover between ATR and Moving Average can be an important indicator of changes in the market. When ATR crosses above the Moving Average, it may indicate that the volatility of the asset is increasing and that the price may be about to rise. This occurs because ATR is increasing, which means that the true range of the asset is increasing, and the Moving Average is being surpassed, which means that the price is rising.
On the other hand, when ATR crosses below the Moving Average, it may indicate that the volatility of the asset is decreasing and that the price may be about to fall. This occurs because ATR is decreasing, which means that the true range of the asset is decreasing, and the Moving Average is being surpassed, which means that the price is falling.
Trading Strategies
There are several trading strategies that can be used with the crossover between ATR and Moving Average. Some of these strategies include:
Buying when ATR crosses above the Moving Average, with the expectation that the price will rise.
Selling when ATR crosses below the Moving Average, with the expectation that the price will fall.
Using the crossover between ATR and Moving Average as a filter for other trading strategies, such as trend analysis or pattern recognition.
In summary, the crossover between ATR and Moving Average can be an important indicator of changes in the market, and can be used as a technical analysis tool to identify trading opportunities. However, it is important to remember that no trading strategy is foolproof, and that it is always important to use a disciplined approach and manage risk adequately.
Fibonacci Circle Zones🟩 The Fibonacci Circle Zones indicator is a technical visualization tool, building upon the concept of traditional Fibonacci circles. It provides configurable options for analyzing geometric relationships between price and time, used to identify potential support and resistance zones derived from circle-based projections. The indicator constructs these Fibonacci circles based on two user-selected anchor points (Point A and Point B), which define the foundational price range and time duration for the geometric analysis.
Key features include multiple mathematical Circle Formulas for radius scaling and several options for defining the circle's center point, enabling exploration of complex, non-linear geometric relationships between price and time distinct from traditional linear Fibonacci analysis. Available formulas incorporate various mathematical constants (π, e, φ variants, Silver Ratio) alongside traditional Fibonacci ratios, facilitating investigation into different scaling hypotheses. Furthermore, selecting the Center point relative to the A-B anchors allows these circular time-price patterns to be constructed and analyzed from different geometric perspectives. Analysis can be further tailored through detailed customization of up to 12 Fibonacci levels, including their mathematical values, colors, and visibility..
📚 THEORY and CONCEPT 📚
Fibonacci circles represent an application of Fibonacci principles within technical analysis, extending beyond typical horizontal price levels by incorporating the dimension of time. These geometric constructions traditionally use numerical proportions, often derived from the Fibonacci sequence, to project potential zones of price-time interaction, such as support or resistance. A theoretical understanding of such geometric tools involves considering several core components: the significance of the chosen geometric origin or center point , the mathematical principles governing the proportional scaling of successive radii, and the fundamental calculation considerations (like chart scale adjustments and base radius definitions) that influence the resulting geometry and ensure its accurate representation.
⨀ Circle Center ⨀
The traditional construction methodology for Fibonacci circles begins with the selection of two significant anchor points on the chart, usually representing a key price swing, such as a swing low (Point A) and a subsequent swing high (Point B), or vice versa. This defined segment establishes the primary vector—representing both the price range and the time duration of that specific market move. From these two points, a base distance or radius is derived (this calculation can vary, sometimes using the vertical price distance, the time duration, or the diagonal distance). A center point for the circles is then typically established, often at the midpoint (time and price) between points A and B, or sometimes anchored directly at point B.
Concentric circles are then projected outwards from this center point. The radii of these successive circles are calculated by multiplying the base distance by key Fibonacci ratios and other standard proportions. The underlying concept posits that markets may exhibit harmonic relationships or cyclical behavior that adheres to these proportions, suggesting these expanding geometric zones could highlight areas where future price movements might decelerate, reverse, or find equilibrium, reflecting a potential proportional resonance with the initial defining swing in both price and time.
The Fibonacci Circle Zones indicator enhances traditional Fibonacci circle construction by offering greater analytical depth and flexibility: it addresses the origin point of the circles: instead of being limited to common definitions like the midpoint or endpoint B, this indicator provides a selection of distinct center point calculations relative to the initial A-B swing. The underlying idea is that the geometric source from which harmonic projections emanate might vary depending on the market structure being analyzed. This flexibility allows for experimentation with different center points (derived algorithmically from the A, B, and midpoint coordinates), facilitating exploration of how price interacts with circular zones anchored from various perspectives within the defining swing.
Potential Center Points Setup : This view shows the anchor points A and B , defined by the user, which form the basis of the calculations. The indicator dynamically calculates various potential Center points ( C through N , and X ) based on the A-B structure, representing different geometric origins available for selection in the settings.
Point X holds particular significance as it represents the calculated midpoint (in both time and price) between A and B. This 'X' point corresponds to the default 'Auto' center setting upon initial application of the indicator and aligns with the centering logic used in TradingView's standard Fibonacci Circle tool, offering a familiar starting point.
The other potential center points allow for exploring circles originating from different geometric anchors relative to the A-B structure. While detailing the precise calculation for each is beyond the scope of this overview, they can be broadly categorized: points C through H are derived from relationships primarily within the A-B time/price range, whereas points I through N represent centers projected beyond point B, extrapolating the A-B geometry. Point J, for example, is calculated as a reflection of the A-X midpoint projected beyond B. This variety provides a rich set of options for analyzing circle patterns originating from historical, midpoint, and extrapolated future anchor perspectives.
Default Settings (Center X, FibCircle) : Using the default Center X (calculated midpoint) with the default FibCircle . Although circles begin plotting only after Point B is established, their curvature shows they are geometrically centered on X. This configuration matches the standard TradingView Fib Circle tool, providing a baseline.
Centering on Endpoint B : Using Point B, the user-defined end of the swing, as the Center . This anchors the circular projections directly to the swing's termination point. Unlike centering on the midpoint (X) or start point (A), this focuses the analysis on geometric expansion originating precisely from the conclusion of the measured A-B move.
Projected Center J : Using the projected Point J as the Center . Its position is calculated based on the A-B swing (conceptually, it represents a forward projection related to the A-X midpoint relationship) and is located chronologically beyond Point B. This type of forward projection often allows complete circles to be visualized as price develops into the corresponding time zone.
Time Symmetry Projection (Center L) : Uses the projected Point L as the Center . It is located at the price level of the start point (A), projected forward in time from B by the full duration of the A-B swing . This perspective focuses analysis on temporal symmetry , exploring geometric expansions from a point representing a full time cycle completion anchored back at the swing's origin price level.
⭕ Circle Formula
Beyond the center point , the expansion of the projected circles is determined by the selected Circle Formula . This setting provides different mathematical methods, or scaling options , for scaling the circle radii. Each option applies a distinct mathematical constant or relationship to the base radius derived from the A-B swing, allowing for exploration of various geometric proportions.
eScaled
Mathematical Basis: Scales the radius by Euler's number ( e ≈ 2.718), the base of natural logarithms. This constant appears frequently in processes involving continuous growth or decay.
Enables investigation of market geometry scaled by e , exploring relationships potentially based on natural exponential growth applied to time-price circles, potentially relevant for analyzing phases of accelerating momentum or volatility expansion.
FibCircle
Mathematical Basis: Scales the radius to align with TradingView’s built-in Fibonacci Circle Tool.
Provides a baseline circle size, potentially emulating scaling used in standard drawing tools, serving as a reference point for comparison with other options.
GoldenFib
Mathematical Basis: Scales the radius by the Golden Ratio (φ ≈ 1.618).
Explores the fundamental Golden Ratio proportion, central to Fibonacci analysis, applied directly to circular time-price geometry, potentially highlighting zones reflecting harmonic expansion or retracement patterns often associated with φ.
GoldenContour
Mathematical Basis: Scales the radius by a factor derived from Golden Ratio geometry (√(1 + φ²) / 2 ≈ 0.951). It represents a specific geometric relationship derived from φ.
Allows analysis using proportions linked to the geometry of the Golden Rectangle, scaled to produce circles very close to the initial base radius. This explores structural relationships often associated with natural balance or proportionality observed in Golden Ratio constructions.
SilverRatio
Mathematical Basis: Scales the radius by the Silver Ratio (1 + √2 ≈ 2.414). The Silver Ratio governs relationships in specific regular polygons and recursive sequences.
Allows exploration using the proportions of the Silver Ratio, offering a significant expansion factor based on another fundamental metallic mean for comparison with φ-based methods.
PhiDecay
Mathematical Basis: Scales the radius by φ raised to the power of -φ (φ⁻ᵠ ≈ 0.53). This unique exponentiation explores a less common, non-linear transformation involving φ.
Explores market geometry scaled by this specific phi-derived factor which is significantly less than 1.0, offering a distinct contractile proportion for analysis, potentially relevant for identifying zones related to consolidation phases or decaying momentum.
PhiSquared
Mathematical Basis: Scales the radius by φ squared, normalized by dividing by 3 (φ² / 3 ≈ 0.873).
Enables investigation of patterns related to the φ² relationship (a key Fibonacci extension concept), visualized at a scale just below 1.0 due to normalization. This scaling explores projections commonly associated with significant trend extension targets in linear Fibonacci analysis, adapted here for circular geometry.
PiScaled
Mathematical Basis: Scales the radius by Pi (π ≈ 3.141).
Explores direct scaling by the fundamental circle constant (π), investigating proportions inherent to circular geometry within the market's time-price structure, potentially highlighting areas related to natural market cycles, rotational symmetry, or full-cycle completions.
PlasticNumber
Mathematical Basis: Scales the radius by the Plastic Number (approx 1.3247), the third metallic mean. Like φ and the Silver Ratio, it is the solution to a specific cubic equation and relates to certain geometric forms.
Introduces another distinct fundamental mathematical constant for geometric exploration, comparing market proportions to those potentially governed by the Plastic Number.
SilverFib
Mathematical Basis: Scales the radius by the reciprocal Golden Ratio (1/φ ≈ 0.618).
Explores proportions directly related to the core 0.618 Fibonacci ratio, fundamental within Fibonacci-based geometric analysis, often significant for identifying primary retracement levels or corrective wave structures within a trend.
Unscaled
Mathematical Basis: No scaling applied.
Provides the base circle defined by points A/B and the Center setting without any additional mathematical scaling, serving as a pure geometric reference based on the A-B structure.
🧪 Advanced Calculation Settings
Two advanced settings allow further refinement of the circle calculations: matching the chart's scale and defining how the base radius is calculated from the A-B swing.
The Chart Scale setting ensures geometric accuracy by aligning circle calculations with the chart's vertical axis display. Price charts can use either a standard (linear) or logarithmic scale, where vertical distances represent price changes differently. The setting offers two options:
Standard : Select this option when the price chart's vertical axis is set to a standard linear scale.
Logarithmic : It is necessary to select this option if the price chart's vertical axis is set to a logarithmic scale. Doing so ensures the indicator adjusts its calculations to maintain correct geometric proportions relative to the visual price action on the log-scaled chart.
The Radius Calc setting determines how the fundamental base radius is derived from the A-B swing, offering two primary options:
Auto : This is the default setting and represents the traditional method for radius calculation. This method bases the radius calculation on the vertical price range of the A-B swing, focusing the geometry on the price amplitude.
Geometric : This setting provides an alternative calculation method, determining the base radius from the diagonal distance between Point A and Point B. It considers both the price change and the time duration relative to the chart's aspect ratio, defining the radius based on the overall magnitude of the A-B price-time vector.
This choice allows the resulting circle geometry to be based either purely on the swing's vertical price range ( Auto ) or on its combined price-time movement ( Geometric ).
🖼️ CHART EXAMPLES 🖼️
Default Behavior (X Center, FibCircle Formula) : This configuration uses the midpoint ( Center X) and the FibCircle scaling Formula , representing the indicator's effective default setup when 'Auto' is selected for both options initially. This is designed to match the output of the standard TradingView Fibonacci Circle drawing tool.
Center B with Unscaled Formula : This example shows the indicator applied to an uptrend with the Center set to Point B and the Circle Formula set to Unscaled . This configuration projects the defined levels (0.236, 0.382, etc.) as arcs originating directly from the swing's termination point (B) without applying any additional mathematical scaling from the formulas.
Visualization with Projected Center J : Here, circles are centered on the projected point J, calculated from the A-B structure but located forward in time from point B. Notice how using this forward-projected origin allows complete inner circles to be drawn once price action develops into that zone, providing a distinct visual representation of the expanding geometric field compared to using earlier anchor points. ( Unscaled formula used in this example).
PhiSquared Scaling from Endpoint B : The PhiSquared scaling Formula applied from the user-defined swing endpoint (Point B). Radii expand based on a normalized relationship with φ² (the square of the Golden Ratio), creating a unique geometric structure and spacing between the circle levels compared to other formulas like Unscaled or GoldenFib .
Centering on Swing Origin (Point A) : Illustrates using Point A, the user-defined start of the swing, as the circle Center . Note the significantly larger scale and wider spacing of the resulting circles. This difference occurs because centering on the swing's origin (A) typically leads to a larger base radius calculation compared to using the midpoint (X) or endpoint (B). ( Unscaled formula used).
Center Point D : Point D, dynamically calculated from the A-B swing, is used as the origin ( Center =D). It is specifically located at the price level of the swing's start point (A) occurring precisely at the time coordinate of the swing's end point (B). This offers a unique perspective, anchoring the geometric expansion to the initial price level at the exact moment the defining swing concludes. ( Unscaled formula shown).
Center Point G : Point G, also dynamically calculated from the A-B swing, is used as the origin ( Center =G). It is located at the price level of the swing's endpoint (B) occurring at the time coordinate of the start point (A). This provides the complementary perspective to Point D, anchoring the geometric expansion to the final price level achieved but originating from the moment the swing began . As observed in the example, using Point G typically results in very wide circle projections due to its position relative to the core A-B action. ( Unscaled formula shown).
Center Point I: Half-Duration Projection : Using the dynamically calculated Point I as the Center . Located at Point B's price level but projected forward in time by half the A-B swing duration , Point I's calculated time coordinate often falls outside the initially visible chart area. As the chart progresses, this origin point will appear, revealing large, sweeping arcs representing geometric expansions based on a half-cycle temporal projection from the swing's endpoint price. ( Unscaled formula shown).
Center Point M : Point M, also dynamically calculated from the A-B swing, serves as the origin ( Center =M). It combines the midpoint price level (derived from X) with a time coordinate projected forward from Point B by the full duration of the A-B swing . This perspective anchors the geometric expansion to the swing's balance price level but originates from the completion point of a full temporal cycle relative to the A-B move. Like other projected centers, using M allows for complete circles to be visualized as price progresses into its time zone. ( SilverFib formula shown).
Geometric Validation & Functionality : Comparing the indicator (red lines), using its default settings ( Center X, FibCircle Formula ), against TradingView's standard Fib Circle tool (green lines/white background). The precise alignment, particularly visible at the 1.50 and 2.00 levels shown, validates the core geometry calculation.
🛠️ CONFIGURATION AND SETTINGS 🛠️
The Fibonacci Circle Zones indicator offers a range of configurable settings to tailor its functionality and visual representation. These options allow customization of the circle origin, scaling method, level visibility, visual appearance, and input points.
Center and Formula
Settings for selecting the circle origin and scaling method.
Center : Dropdown menu to select the origin point for the circles.
Auto : Automatically uses point X (the calculated midpoint between A and B).
Selectable points including start/end (A, B), midpoint (X), plus various points derived from or projected beyond the A-B swing (C-N).
Circle Formula : Dropdown menu to select the mathematical method for scaling circle radii.
Auto : Automatically selects a default formula ('FibCircle' if Center is 'X', 'Unscaled' otherwise).
Includes standard Fibonacci scaling ( FibCircle, GoldenFib ), other mathematical constants ( PiScaled, eScaled ), metallic means ( SilverRatio ), phi transformations ( PhiDecay, PhiSquared ), and others.
Fib Levels
Configuration options for the 12 individual Fibonacci levels.
Advanced Settings
Settings related to core calculation methods.
Radius Calc : Defines how the base radius is calculated (e.g., 'Auto' for vertical price range, 'Geometric' for diagonal price-time distance).
Chart Scale : Aligns circle calculations with the chart's vertical axis setting ('Standard' or 'Logarithmic') for accurate visual proportions.
Visual Settings
Settings controlling the visual display of the indicator elements.
Plots : Dropdown controlling which parts of the calculated circles are displayed ( Upper , All , or Lower ).
Labels : Dropdown controlling the display of the numerical level value labels ( All , Left , Right , or None ).
Setup : Dropdown controlling the visibility of the initial setup graphics ( Show or Hide ).
Info : Dropdown controlling the visibility of the small information table ( Show or Hide ).
Text Size : Adjusts the font size for all text elements displayed by the indicator (Value ranges from 0 to 36).
Line Width : Adjusts the width of the circle plots (1-10).
Time/Price
Inputs for the anchor points defining the base swing.
These settings define the start (Point A) and end (Point B) of the price swing used for all calculations.
Point A (Time, Price) : Input fields for the exact time coordinate and price level of the swing's starting point (A).
Point B (Time, Price) : Input fields for the exact time coordinate and price level of the swing's ending point (B).
Interactive Adjustment : Points A and B can typically be adjusted directly by clicking and dragging their markers on the chart (if 'Setup' is set to 'Show'). Changes update settings automatically.
📝 NOTES 📝
Fibonacci circles begin plotting only once the time corresponding to Point B has passed and is confirmed on the chart. While potential center locations might be visible earlier (as shown in the setup graphic), the final circle calculations require the complete geometry of the A-B swing. This approach ensures that as new price bars form, the circles are accurately rendered based on the finalized A-B relationship and the chosen center and scaling.
The indicator's calculations are anchored to user-defined start (A) and end (B) points on the chart. When switching between charts with significantly different price scales (e.g., from an index at 5,000 to a crypto asset at $0.50), it is typically necessary to adjust these anchor points to ensure the circle elements are correctly positioned and scaled.
⚠️ DISCLAIMER ⚠️
The Fibonacci Circle Zones indicator is a visual analysis tool designed to illustrate Fibonacci relationships through geometric constructions incorporating curved lines, providing a structured framework for identifying potential areas of price interaction. Like all technical and visual indicators, these visual representations may visually align with key price zones in hindsight, reflecting observed price dynamics. It is not intended as a predictive or standalone trading signal indicator.
The indicator calculates levels and projections using user-defined anchor points and Fibonacci ratios. While it aims to align with TradingView’s standard Fibonacci circle tool by employing mathematical and geometric formulas, no guarantee is made that its calculations are identical to TradingView's proprietary methods.
🧠 BEYOND THE CODE 🧠
The Fibonacci Circle Zones indicator, like other xxattaxx indicators , is designed with education and community collaboration in mind. Its open-source nature encourages exploration, experimentation, and the development of new Fibonacci and grid calculation indicators and tools. We hope this indicator serves as a framework and a starting point for future Innovation and discussions.
Auto TrendLines [TradingFinder] Support Resistance Signal Alerts🔵 Introduction
The trendline is one of the most essential tools in technical analysis, widely used in financial markets such as Forex, cryptocurrency, and stocks. A trendline is a straight line that connects swing highs or swing lows and visually indicates the market’s trend direction.
Traders use trendlines to identify price structure, the strength of buyers and sellers, dynamic support and resistance zones, and optimal entry and exit points.
In technical analysis, trendlines are typically classified into three categories: uptrend lines (drawn by connecting higher lows), downtrend lines (formed by connecting lower highs), and sideways trends (moving horizontally). A valid trendline usually requires at least three confirmed touchpoints to be considered reliable for trading decisions.
Trendlines can serve as the foundation for a variety of trading strategies, such as the trendline bounce strategy, valid breakout setups, and confluence-based analysis with other tools like candlestick patterns, divergences, moving averages, and Fibonacci levels.
Additionally, trendlines are categorized into internal and external, and further into major and minor levels, each serving unique roles in market structure analysis.
🔵 How to Use
Trendlines are a key component in technical analysis, used to identify market direction, define dynamic support and resistance zones, highlight strategic entry and exit points, and manage risk. For a trendline to be reliable, it must be drawn based on structural principles—not by simply connecting two arbitrary points.
🟣 Selecting Pivot Types Based on Trend Direction
The first step is to determine the market trend: uptrend, downtrend, or sideways.
Then, choose pivot points that match the trend type :
In an uptrend, trendlines are drawn by connecting low pivots, especially higher lows.
In a downtrend, trendlines are formed by connecting high pivots, specifically lower highs.
It is crucial to connect pivots of the same type and structure to ensure the trendline is valid and analytically sound.
🟣 Pivot Classification
This indicator automatically classifies pivot points into two categories :
Major Pivots :
MLL : Major Lower Low
MHL : Major Higher Low
MHH : Major Higher High
MLH : Major Lower High
These define the primary structure of the market and are typically used in broader structural analysis.
Minor Pivots :
mLL: minor Lower Low
mHL: minor Higher Low
mHH: minor Higher High
mLH: minor Lower High
These are used for drawing more precise trendlines within corrective waves or internal price movements.
Example : In a downtrend, drawing a trendline from an MHH to an mHH creates structural inconsistency and introduces noise. Instead, connect points like MHL to MHL or mLH to mLH for a valid trendline.
🟣 Drawing High-Precision Trendlines
To ensure a reliable trendline :
Use pivots of the same classification (Major with Major or Minor with Minor).
Ensure at least three valid contact points (three touches = structural confirmation).
Draw through candles with the least deviation (choose wicks or bodies based on confluence).
Preferably draw from right to left for better alignment with current market behavior.
Use parallel lines to turn a single trendline into a trendline zone, if needed.
🟣 Using Trendlines for Trade Entries
Bounce Entry: When price approaches the trendline and shows signs of reversal (e.g., a reversal candle, divergence, or support/resistance), enter in the direction of the trend with a logical stop-loss.
Breakout Entry: When price breaks through the trendline with strong momentum and a confirmation (such as a retest or break of structure), consider trading in the direction of the breakout.
🟣 Trendline-Based Risk Management
For bounce entries, the stop-loss is placed below the trendline or the last pivot low (in an uptrend).
For breakout entries, the stop-loss is set behind the breakout candle or the last structural level.
A broken trendline can also act as an exit signal from a trade.
🟣 Combining Trendlines with Other Tools (Confluence)
Trendlines gain much more strength when used alongside other analytical tools :
Horizontal support and resistance levels
Moving averages (such as EMA 50 or EMA 200)
Fibonacci retracement zones
Candlestick patterns (e.g., Engulfing, Pin Bar)
RSI or MACD divergences
Market structure breaks (BoS / ChoCH)
🔵 Settings
Pivot Period : This defines how sensitive the pivot detection is. A higher number means the algorithm will identify more significant pivot points, resulting in longer-term trendlines.
Alerts
Alert :
Enable or disable the entire alert system
Set a custom alert name
Choose how often alerts trigger (every time, once per bar, or on bar close)
Select the time zone for alert timestamps (e.g., UTC)
Each trendline type supports two alert types :
Break Alert : Triggered when price breaks the trendline
React Alert : Triggered when price reacts or bounces off the trendline
These alerts can be independently enabled or disabled for all trendline categories (Major/Minor, Internal/External, Up/Down).
Display :
For each of the eight trendline types, you can control :
Whether to show or hide the line
Whether to delete the previous line when a new one is drawn
Color, line style (solid, dashed, dotted), extension direction (e.g., right only), and width
Major lines are typically thicker and more opaque, while minor lines appear thinner and more transparent.
All settings are designed to give the user full control over the appearance, behavior, and alert system of the indicator, without requiring manual drawing or adjustments.
🔵 Conclusion
A trendline is more than just a line on the chart—it is a structural, strategic, and flexible tool in technical analysis that can serve as the foundation for understanding price behavior and making trading decisions. Whether in trending markets or during corrections, trendlines help traders identify market direction, key zones, and high-potential entry and exit points with precision.
The accuracy and effectiveness of a trendline depend on using structurally valid pivot points and adhering to proper market logic, rather than relying on guesswork or personal bias.
This indicator is built to solve that exact problem. It automatically detects and draws multiple types of trendlines based on actual price structure, separating them into Major/Minor and Internal/External categories, and respecting professional analytical principles such as pivot type, trend direction, and structural location.
zone trading stratThis only works for DOGEUSD , I made it for the 8cap chart so only use it for that.
If you want this for other symbols/charts you need to comment below or msg me.
# Price Zone Trading System: Technical Explanation
## Core Concept
The Price Zone Tracker is built on the concept that price tends to respect certain key levels or "zones" on the chart. These zones act as support and resistance areas where price may bounce or break through. The system combines zone analysis with multiple technical indicators to generate high-probability trading signals.
## Zone Analysis
The system tracks 9 predefined price zones. Each zone has both a high and low boundary, except for Zone 5 which is represented by a single line. When price enters a zone, the system monitors whether it stays within the zone, breaks above it (bullish), or breaks below it (bearish).
This zone behavior establishes the foundational bias of the system:
- When price closes above its previous zone: Zone State = Bullish
- When price closes below its previous zone: Zone State = Bearish
- When price remains within a zone: Zone State = Neutral
## Trend Analysis Components
The system performs multi-timeframe analysis using several technical components:
1. **Higher Timeframe Analysis** (±3 points in scoring)
- Uses 15-minute charts for sub-5-minute timeframes
- Uses 30-minute charts for 5-minute timeframes
- Uses 60-minute charts for timeframes above 5 minutes
- Evaluates candlestick patterns and EMA crossovers on the higher timeframe
2. **EMA Direction** (±1 point in scoring)
- Compares 12-period and 26-period EMAs
- Bullish when fast EMA > slow EMA
- Bearish when fast EMA < slow EMA
3. **MACD Analysis** (±1 point in scoring)
- Uses standard 12/26/9 MACD settings
- Bullish when MACD line crosses above signal line with positive histogram
- Bearish when MACD line crosses below signal line with negative histogram
4. **Price Action** (±2 points in scoring)
- Evaluates whether price is making higher highs/higher lows (uptrend)
- Or lower highs/lower lows (downtrend)
- Also considers ATR-based volatility and strength of movements
## Trend Score Calculation
All these components are weighted and combined into a trend score:
- Higher timeframe components have stronger weights (±2-3 points)
- Current timeframe components have moderate weights (±1 point)
- Price action components have varied weights (±0.5-2 points)
The final trend state is determined by thresholds:
- Score > +3: Trend Analysis State = Bullish
- Score < -3: Trend Analysis State = Bearish
- Score between -3 and +3: Trend Analysis State = Neutral
## Signal Generation Logic
The system combines the Zone State with the Trend Analysis State:
1. If Zone State and Trend Analysis State are both bullish:
- Combined State = Bullish
- Line Color = Green
2. If Zone State and Trend Analysis State are both bearish:
- Combined State = Bearish
- Line Color = Red
3. If Zone State and Trend Analysis State contradict each other:
- Combined State = Neutral
- Line Color = Black
This implements a safety mechanism requiring both zone analysis and technical indicators to agree before generating a directional signal.
## Trading Signals
Trading signals are generated based on changes in the Combined State:
- When Combined State changes from neutral/bearish to bullish:
- Trading Signal = LONG (green triangle appears on chart)
- When Combined State changes from neutral/bullish to bearish:
- Trading Signal = SHORT (red triangle appears on chart)
- When Combined State changes from bullish/bearish to neutral:
- Trading Signal = EXIT (yellow X appears on chart)
- When Combined State remains unchanged:
- Trading Signal = NONE (no new marker appears)
## Reversal Warning
The system also monitors for potential reversal conditions:
- When Combined State is bullish but both RSI and MFI are overbought (>70)
- When Combined State is bearish but both RSI and MFI are oversold (<30)
In these cases, a yellow diamond appears on the chart as a warning that a reversal might be imminent.
## Visual Elements
The indicator provides multiple visual elements:
1. Zone boundaries as translucent orange areas
2. A single colored line below price (green/red/black) showing the current signal
3. Trading signals as shapes on the chart
4. An information panel showing all relevant indicator values and signals
## Usage Limitations
The indicator is designed to work optimally on timeframes below 30 minutes. On higher timeframes, a warning appears and analysis is disabled.
Fuzzy SMA with DCTI Confirmation[FibonacciFlux]FibonacciFlux: Advanced Fuzzy Logic System with Donchian Trend Confirmation
Institutional-grade trend analysis combining adaptive Fuzzy Logic with Donchian Channel Trend Intensity for superior signal quality
Conceptual Framework & Research Foundation
FibonacciFlux represents a significant advancement in quantitative technical analysis, merging two powerful analytical methodologies: normalized fuzzy logic systems and Donchian Channel Trend Intensity (DCTI). This sophisticated indicator addresses a fundamental challenge in market analysis – the inherent imprecision of trend identification in dynamic, multi-dimensional market environments.
While traditional indicators often produce simplistic binary signals, markets exist in states of continuous, graduated transition. FibonacciFlux embraces this complexity through its implementation of fuzzy set theory, enhanced by DCTI's structural trend confirmation capabilities. The result is an indicator that provides nuanced, probabilistic trend assessment with institutional-grade signal quality.
Core Technological Components
1. Advanced Fuzzy Logic System with Percentile Normalization
At the foundation of FibonacciFlux lies a comprehensive fuzzy logic system that transforms conventional technical metrics into degrees of membership in linguistic variables:
// Fuzzy triangular membership function with robust error handling
fuzzy_triangle(val, left, center, right) =>
if na(val)
0.0
float denominator1 = math.max(1e-10, center - left)
float denominator2 = math.max(1e-10, right - center)
math.max(0.0, math.min(left == center ? val <= center ? 1.0 : 0.0 : (val - left) / denominator1,
center == right ? val >= center ? 1.0 : 0.0 : (right - val) / denominator2))
The system employs percentile-based normalization for SMA deviation – a critical innovation that enables self-calibration across different assets and market regimes:
// Percentile-based normalization for adaptive calibration
raw_diff = price_src - sma_val
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
normalized_diff_raw = raw_diff / diff_abs_percentile
normalized_diff = useClamping ? math.max(-clampValue, math.min(clampValue, normalized_diff_raw)) : normalized_diff_raw
This normalization approach represents a significant advancement over fixed-threshold systems, allowing the indicator to automatically adapt to varying volatility environments and maintain consistent signal quality across diverse market conditions.
2. Donchian Channel Trend Intensity (DCTI) Integration
FibonacciFlux significantly enhances fuzzy logic analysis through the integration of Donchian Channel Trend Intensity (DCTI) – a sophisticated measure of trend strength based on the relationship between short-term and long-term price extremes:
// DCTI calculation for structural trend confirmation
f_dcti(src, majorPer, minorPer, sigPer) =>
H = ta.highest(high, majorPer) // Major period high
L = ta.lowest(low, majorPer) // Major period low
h = ta.highest(high, minorPer) // Minor period high
l = ta.lowest(low, minorPer) // Minor period low
float pdiv = not na(L) ? l - L : 0 // Positive divergence (low vs major low)
float ndiv = not na(H) ? H - h : 0 // Negative divergence (major high vs high)
float divisor = pdiv + ndiv
dctiValue = divisor == 0 ? 0 : 100 * ((pdiv - ndiv) / divisor) // Normalized to -100 to +100 range
sigValue = ta.ema(dctiValue, sigPer)
DCTI provides a complementary structural perspective on market trends by quantifying the relationship between short-term and long-term price extremes. This creates a multi-dimensional analysis framework that combines adaptive deviation measurement (fuzzy SMA) with channel-based trend intensity confirmation (DCTI).
Multi-Dimensional Fuzzy Input Variables
FibonacciFlux processes four distinct technical dimensions through its fuzzy system:
Normalized SMA Deviation: Measures price displacement relative to historical volatility context
Rate of Change (ROC): Captures price momentum over configurable timeframes
Relative Strength Index (RSI): Evaluates cyclical overbought/oversold conditions
Donchian Channel Trend Intensity (DCTI): Provides structural trend confirmation through channel analysis
Each dimension is processed through comprehensive fuzzy sets that transform crisp numerical values into linguistic variables:
// Normalized SMA Deviation - Self-calibrating to volatility regimes
ndiff_LP := fuzzy_triangle(normalized_diff, norm_scale * 0.3, norm_scale * 0.7, norm_scale * 1.1)
ndiff_SP := fuzzy_triangle(normalized_diff, norm_scale * 0.05, norm_scale * 0.25, norm_scale * 0.5)
ndiff_NZ := fuzzy_triangle(normalized_diff, -norm_scale * 0.1, 0.0, norm_scale * 0.1)
ndiff_SN := fuzzy_triangle(normalized_diff, -norm_scale * 0.5, -norm_scale * 0.25, -norm_scale * 0.05)
ndiff_LN := fuzzy_triangle(normalized_diff, -norm_scale * 1.1, -norm_scale * 0.7, -norm_scale * 0.3)
// DCTI - Structural trend measurement
dcti_SP := fuzzy_triangle(dcti_val, 60.0, 85.0, 101.0) // Strong Positive Trend (> ~85)
dcti_WP := fuzzy_triangle(dcti_val, 20.0, 45.0, 70.0) // Weak Positive Trend (~30-60)
dcti_Z := fuzzy_triangle(dcti_val, -30.0, 0.0, 30.0) // Near Zero / Trendless (~+/- 20)
dcti_WN := fuzzy_triangle(dcti_val, -70.0, -45.0, -20.0) // Weak Negative Trend (~-30 - -60)
dcti_SN := fuzzy_triangle(dcti_val, -101.0, -85.0, -60.0) // Strong Negative Trend (< ~-85)
Advanced Fuzzy Rule System with DCTI Confirmation
The core intelligence of FibonacciFlux lies in its sophisticated fuzzy rule system – a structured knowledge representation that encodes expert understanding of market dynamics:
// Base Trend Rules with DCTI Confirmation
cond1 = math.min(ndiff_LP, roc_HP, rsi_M)
strength_SB := math.max(strength_SB, cond1 * (dcti_SP > 0.5 ? 1.2 : dcti_Z > 0.1 ? 0.5 : 1.0))
// DCTI Override Rules - Structural trend confirmation with momentum alignment
cond14 = math.min(ndiff_NZ, roc_HP, dcti_SP)
strength_SB := math.max(strength_SB, cond14 * 0.5)
The rule system implements 15 distinct fuzzy rules that evaluate various market conditions including:
Established Trends: Strong deviations with confirming momentum and DCTI alignment
Emerging Trends: Early deviation patterns with initial momentum and DCTI confirmation
Weakening Trends: Divergent signals between deviation, momentum, and DCTI
Reversal Conditions: Counter-trend signals with DCTI confirmation
Neutral Consolidations: Minimal deviation with low momentum and neutral DCTI
A key innovation is the weighted influence of DCTI on rule activation. When strong DCTI readings align with other indicators, rule strength is amplified (up to 1.2x). Conversely, when DCTI contradicts other indicators, rule impact is reduced (as low as 0.5x). This creates a dynamic, self-adjusting system that prioritizes high-conviction signals.
Defuzzification & Signal Generation
The final step transforms fuzzy outputs into a precise trend score through center-of-gravity defuzzification:
// Defuzzification with precise floating-point handling
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10
fuzzyTrendScore := (strength_SB * STRONG_BULL + strength_WB * WEAK_BULL +
strength_N * NEUTRAL + strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1.0 (Strong Bear) to +1.0 (Strong Bull), with critical threshold zones at ±0.3 (Weak trend) and ±0.7 (Strong trend). The histogram visualization employs intuitive color-coding for immediate trend assessment.
Strategic Applications for Institutional Trading
FibonacciFlux provides substantial advantages for sophisticated trading operations:
Multi-Timeframe Signal Confirmation: Institutional-grade signal validation across multiple technical dimensions
Trend Strength Quantification: Precise measurement of trend conviction with noise filtration
Early Trend Identification: Detection of emerging trends before traditional indicators through fuzzy pattern recognition
Adaptive Market Regime Analysis: Self-calibrating analysis across varying volatility environments
Algorithmic Strategy Integration: Well-defined numerical output suitable for systematic trading frameworks
Risk Management Enhancement: Superior signal fidelity for risk exposure optimization
Customization Parameters
FibonacciFlux offers extensive customization to align with specific trading mandates and market conditions:
Fuzzy SMA Settings: Configure baseline trend identification parameters including SMA, ROC, and RSI lengths
Normalization Settings: Fine-tune the self-calibration mechanism with adjustable lookback period, percentile rank, and optional clamping
DCTI Parameters: Optimize trend structure confirmation with adjustable major/minor periods and signal smoothing
Visualization Controls: Customize display transparency for optimal chart integration
These parameters enable precise calibration for different asset classes, timeframes, and market regimes while maintaining the core analytical framework.
Implementation Notes
For optimal implementation, consider the following guidance:
Higher timeframes (4H+) benefit from increased normalization lookback (800+) for stability
Volatile assets may require adjusted clamping values (2.5-4.0) for optimal signal sensitivity
DCTI parameters should be aligned with chart timeframe (higher timeframes require increased major/minor periods)
The indicator performs exceptionally well as a trend filter for systematic trading strategies
Acknowledgments
FibonacciFlux builds upon the pioneering work of Donovan Wall in Donchian Channel Trend Intensity analysis. The normalization approach draws inspiration from percentile-based statistical techniques in quantitative finance. This indicator is shared for educational and analytical purposes under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Past performance does not guarantee future results. All trading involves risk. This indicator should be used as one component of a comprehensive analysis framework.
Shout out @DonovanWall
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory
Elevate your technical analysis with institutional-grade fuzzy logic implementation
Research Genesis & Conceptual Framework
This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends.
Core Fuzzy Logic Principles
At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty:
// Improved fuzzy_triangle function with guard clauses for NA and invalid parameters.
fuzzy_triangle(val, left, center, right) =>
if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks
0.0
else if left == center and center == right // Crisp set (single point)
val == center ? 1.0 : 0.0
else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right)
val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center)
else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center)
val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left)
else // Standard triangle
math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center)))
This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions.
Dynamic Percentile Normalization
A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation:
// ----- Deviation Scale Estimation using Percentile -----
// Calculate the percentile rank of the *absolute* deviation over the lookback period.
// This gives an estimate of the 'typical maximum' deviation magnitude recently.
diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10
// ----- Normalize the Raw Deviation -----
// Divide the raw deviation by the estimated 'typical max' magnitude.
normalized_diff = raw_diff / diff_abs_percentile
// ----- Clamp the Normalized Deviation -----
normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff))
This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments.
Multi-Factor Fuzzy Rule System
The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors:
SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average
Rate of Change (ROC): Captures price momentum over a specified period
Relative Strength Index (RSI): Assesses overbought/oversold conditions
These factors are processed through a sophisticated fuzzy inference system with linguistic variables:
// ----- 3.1 Fuzzy Sets for Normalized Deviation -----
diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile)
diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive
diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero
diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative
diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile)
// ----- 3.2 Fuzzy Sets for ROC -----
roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0)
roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1)
roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3)
roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0)
roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0)
// ----- 3.3 Fuzzy Sets for RSI -----
rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0)
rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0)
rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0)
Advanced Fuzzy Inference Rules
The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior:
// --- Fuzzy Rules using Normalized Deviation (diffN_*) ---
cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok
strength_SB := math.max(strength_SB, cond1)
cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid
strength_WB := math.max(strength_WB, cond2)
cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high
strength_N := math.max(strength_N, cond3 * 0.6) // More neutral
strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish
This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations.
Defuzzification Process
The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength:
// --- Step 6: Defuzzification ---
denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe
if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison
fuzzyTrendScore := (strength_SB * STRONG_BULL +
strength_WB * WEAK_BULL +
strength_N * NEUTRAL +
strength_WBe * WEAK_BEAR +
strength_SBe * STRONG_BEAR) / denominator
The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators.
Advanced Visualization with Rainbow Gradient
The indicator incorporates sophisticated visualization using a rainbow gradient coloring system:
// Normalize score to for gradient function
normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2))
// Get the color based on gradient setting and normalized score
final_color = get_gradient(normalizedScore, gradient_type)
This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference.
Practical Applications
The Fuzzy SMA Trend Analyzer excels in several key applications:
Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation
Market Regime Detection: Distinguishes between trending markets and consolidation phases
Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge
Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies
Risk Management: Offers early warning of potential trend weakening or reversal
Parameter Customization
The indicator offers extensive customization options:
SMA Length: Adjusts the baseline moving average period
ROC Length: Controls momentum sensitivity
RSI Length: Configures overbought/oversold sensitivity
Normalization Lookback: Determines the adaptive calculation window for percentile normalization
Percentile Rank: Sets the statistical threshold for deviation normalization
Gradient Type: Selects the preferred color scheme for visualization
These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes.
Acknowledgments
The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators.
This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
Low Liquidity Zones [PhenLabs]📊 Low Liquidity Zones
Version: PineScript™ v6
📌 Description
Low Liquidity Zones identifies and highlights periods of unusually low trading volume on your chart, marking areas where price movement occurred with minimal participation. These zones often represent potential support and resistance levels that may be more susceptible to price breakouts or reversals when revisited with higher volume.
Unlike traditional volume analysis tools that focus on high volume spikes, this indicator specializes in detecting low liquidity areas where price moved with minimal resistance. Each zone displays its volume delta, providing insight into buying vs. selling pressure during these thin liquidity periods. This combination of low volume detection and delta analysis helps traders identify potential price inefficiencies and weak structures in the market.
🚀 Points of Innovation
• Identifies low liquidity zones that most volume indicators overlook but which often become significant technical levels
• Displays volume delta within each zone, showing net buying/selling pressure during low liquidity periods
• Dynamically adjusts to different timeframes, allowing analysis across multiple time horizons
• Filters zones by maximum size percentage to focus only on precise price levels
• Maintains historical zones until they expire based on your lookback settings, creating a cumulative map of potential support/resistance areas
🔧 Core Components
• Low Volume Detection: Identifies candles where volume falls below a specified threshold relative to recent average volume, highlighting potential liquidity gaps.
• Volume Delta Analysis: Calculates and displays the net buying/selling pressure within each low liquidity zone, providing insight into the directional bias during low participation periods.
• Dynamic Timeframe Adjustment: Automatically scales analysis periods to match your selected timeframe preference, ensuring consistent identification of low liquidity zones regardless of chart settings.
• Zone Management System: Creates, tracks, and expires low liquidity zones based on your configured settings, maintaining visual clarity on the chart.
🔥 Key Features
• Low Volume Identification: Automatically detects and highlights candles where volume falls below your specified threshold compared to the moving average.
• Volume Delta Visualization: Shows the net volume delta within each zone, providing insight into whether buyers or sellers were dominant despite the low overall volume.
• Flexible Timeframe Analysis: Analyze low liquidity zones across multiple predefined timeframes or use a custom lookback period specific to your trading style.
• Zone Size Filtering: Filters out excessively large zones to focus only on precise price levels, improving signal quality.
• Automatic Zone Expiration: Older zones are automatically removed after your specified lookback period to maintain a clean, relevant chart display.
🎨 Visualization
• Volume Delta Labels: Each zone displays its volume delta with “+” or “-” prefix and K/M suffix for easy interpretation, showing the strength and direction of pressure during the low volume period.
• Persistent Historical Mapping: Zones remain visible for your specified lookback period, creating a cumulative map of potential support and resistance levels forming under low liquidity conditions.
📖 Usage Guidelines
Analysis Timeframe
Default: 1D
Range/Options: 15M, 1HR, 3HR, 4HR, 8HR, 16HR, 1D, 3D, 5D, 1W, Custom
Description: Determines the historical period to analyze for low liquidity zones. Shorter timeframes provide more recent data while longer timeframes offer a more comprehensive view of significant zones. Use Custom option with the setting below for precise control.
Custom Period (Bars)
Default: 1000
Range: 1+
Description: Number of bars to analyze when using Custom timeframe option. Higher values show more historical zones but may impact performance.
Volume Analysis
Volume Threshold Divisor
Default: 0.5
Range: 0.1-1.0
Description: Maximum volume relative to average to identify low volume zones. Example: 0.5 means volume must be below 50% of the average to qualify as low volume. Lower values create more selective zones while higher values identify more zones.
Volume MA Length
Default: 15
Range: 1+
Description: Period length for volume moving average calculation. Shorter periods make the indicator more responsive to recent volume changes, while longer periods provide a more stable baseline.
Zone Settings
Zone Fill Color
Default: #2196F3 (80% transparency)
Description: Color and transparency of the low liquidity zones. Choose colors that stand out against your chart background without obscuring price action.
Maximum Zone Size %
Default: 0.5
Range: 0.1+
Description: Maximum allowed height of a zone as percentage of price. Larger zones are filtered out. Lower values create more precise zones focusing on tight price ranges.
Display Options
Show Volume Delta
Default: true
Description: Toggles the display of volume delta within each zone. Enabling this provides additional insight into buying vs. selling pressure during low volume periods.
Delta Text Position
Default: Right
Options: Left, Center, Right
Description: Controls the horizontal alignment of the delta text within zones. Adjust based on your chart layout for optimal readability.
✅ Best Use Cases
• Identifying potential support and resistance levels that formed during periods of thin liquidity
• Spotting price inefficiencies where larger players may have moved price with minimal volume
• Finding low-volume consolidation areas that may serve as breakout or reversal zones when revisited
• Locating potential stop-hunting zones where price moved on minimal participation
• Complementing traditional support/resistance analysis with volume context
⚠️ Limitations
• Requires volume data to function; will not work on symbols where the data provider doesn’t supply volume information
• Low volume zones don’t guarantee future support/resistance - they simply highlight potential areas of interest
• Works best on liquid instruments where volume data has meaningful fluctuations
• Historical analysis is limited by the maximum allowed box count (500) in TradingView
• Volume delta in some markets may not perfectly reflect buying vs. selling pressure due to data limitations
💡 What Makes This Unique
• Focus on Low Volume: Unlike some indicators that highlight high volume events particularly like our very own TLZ indicator, this tool specifically identifies potentially significant price zones that formed with minimal participation.
• Delta + Low Volume Integration: Combines volume delta analysis with low volume detection to reveal directional bias during thin liquidity periods.
• Flexible Lookback System: The dynamic timeframe system allows analysis across any timeframe while maintaining consistent zone identification criteria.
• Support/Resistance Zone Generation: Automatically builds a visual map of potential technical levels based on volume behavior rather than just price patterns.
🔬 How It Works
1. Volume Baseline Calculation:
The indicator calculates a moving average of volume over your specified period to establish a baseline for normal market participation. This adaptive baseline accounts for natural volume fluctuations across different market conditions.
2. Low Volume Detection:
Each candle’s volume is compared to the moving average and flagged when it falls below your threshold divisor. The indicator also filters zones by maximum size to ensure only precise price levels are highlighted.
3. Volume Delta Integration:
For each identified low volume candle, the indicator retrieves the volume delta from a lower timeframe. This delta value is formatted with appropriate scaling (K/M) and displayed within the zone.
4. Zone Management:
New zones are created and tracked in a dynamic array, with each zone extending rightward until it expires. The system automatically removes expired zones based on your lookback period to maintain a clean chart.
💡 Note:
Low liquidity zones often represent areas where price moved with minimal participation, which can indicate potential market inefficiencies. These zones frequently become important support/resistance levels when revisited, especially if approached with higher volume. Consider using this indicator alongside traditional technical analysis tools for comprehensive market context. For best results, experiment with different volume threshold settings based on the specific instrument’s typical volume patterns.
HEMA Trend Levels [AlgoAlpha]OVERVIEW
This script plots two Hull-EMA (HEMA) curves to define a color-coded dynamic trend zone and generate context-aware breakout levels, allowing traders to easily visualize prevailing momentum and identify high-probability breakout retests. The script blends smoothed price tracking with conditional box plotting, delivering both trend-following and mean-reversion signals within one system. It is designed to be simple to read visually while offering nuanced trend shifts and test confirmations.
█ CONCEPTS
The Hull-EMA (HEMA) is a hybrid moving average combining the responsiveness of short EMAs with the smoothness of longer ones. It applies layered smoothing: first by subtracting a full EMA from a half-length EMA (doubling the short EMA's weight), and then by smoothing the result again with the square root of the original length. This process reduces lag while maintaining clarity in direction changes. In this script, two HEMAs—fast and slow—are used to define the trend structure and trigger events when they cross. These crossovers generate "trend shift boxes"—temporary support or resistance zones drawn immediately after trend transitions—to detect price retests in the new direction. When price cleanly retests these levels, the script marks them as confirmations with triangle symbols, helping traders isolate better continuation setups. Color-coded bars further enhance visual interpretation: bullish bars when price is above both HEMAs, bearish when below, and neutral (gray) when indecisive.
█ FEATURES
Bullish and bearish bar coloring based on price and HEMA alignment.
Box plotting at each crossover (bullish or bearish) to create short-term decision zones.
Real-time test detection: price must cleanly test and bounce from box levels to be considered valid.
Multiple alert conditions: crossover alerts, test alerts, and trend continuation alerts.
█ USAGE
Use this indicator on any time frame and asset. Adjust HEMA lengths to match your trading style—shorter lengths for scalping or intraday, longer for swing trading. The shaded area between HEMAs helps visually define the current trend. Watch for crossovers: a bullish crossover plots a green support box just below price, and a bearish one plots a red resistance box just above. These zones act as short-term decision points. When price returns to test a box and confirms with strong rejection (e.g., closes above for bullish or below for bearish), a triangle symbol is plotted. These tests can signal strong trend continuation. For traders looking for clean entries, combining the crossover with a successful retest improves reliability. Alerts can be enabled for all key signals: trend shift, test confirmations, and continuation conditions, making it suitable for automated setups or discretionary traders tracking multiple charts.
Dynamic Momentum Bands | AlphaAlgosDynamic Momentum Bands | AlphaAlgos
Overview
The Dynamic Momentum Bands indicator is an advanced technical analysis tool that combines multiple analytical techniques to provide a comprehensive view of market momentum and trend dynamics. By integrating RSI (Relative Strength Index), volatility analysis, and adaptive moving averages, this indicator offers traders a nuanced perspective on market conditions.
Key Features
Adaptive band calculation based on price momentum
Integrated RSI-driven volatility scaling
Multiple moving average type options (EMA, SMA, VWMA)
Smooth, gradient-based band visualization
Optional price bar coloring for trend identification
Technical Methodology
The indicator employs a sophisticated approach to market analysis:
1. Momentum Calculation
Calculates RSI using a customizable length
Uses RSI to dynamically adjust band volatility
Scales band width based on distance from the 50 RSI level
2. Band Construction
Applies a selected moving average type to the price source
Calculates deviation using ATR (Average True Range)
Smooths band edges for improved visual clarity
Configuration Options
Core Settings:
Price Source: Choose the price data used for calculations
RSI Length: Customize the RSI calculation period (1-50)
Band Length: Adjust the moving average period (5-100)
Volatility Multiplier: Fine-tune band width
Band Type: Select between EMA, SMA, and VWMA
Visual Settings:
Bar Coloring: Toggle color-coded price bars
Gradient-based band visualization
Smooth color transitions for trend representation
Trend Identification
The indicator provides trend insights through:
Color-coded bands (blue for bullish, pink for bearish)
Smooth gradient visualization
Optional price bar coloring
Trading Applications
Trend Following:
- Use band position relative to price as trend indicator
- Identify momentum shifts through color changes
- Utilize gradient zones for trend strength assessment
Volatility Analysis:
Observe band width changes
Detect potential breakout or consolidation periods
Use RSI-driven volatility scaling for market context
Best Practices
Adjust RSI length to match trading timeframe
Experiment with different moving average types
Use in conjunction with other technical indicators
Consider volatility multiplier for different market conditions
This indicator is provided for informational purposes only. Always use proper risk management when trading. Past performance is not indicative of future results. Not financial Advise
Trendline Breaks with Multi Fibonacci Supertrend StrategyTMFS Strategy: Advanced Trendline Breakouts with Multi-Fibonacci Supertrend
Elevate your algorithmic trading with institutional-grade signal confluence
Strategy Genesis & Evolution
This advanced trading system represents the culmination of a personal research journey, evolving from my custom " Multi Fibonacci Supertrend with Signals " indicator into a comprehensive trading strategy. Built upon the exceptional trendline detection methodology pioneered by LuxAlgo in their " Trendlines with Breaks " indicator, I've engineered a systematic framework that integrates multiple technical factors into a cohesive trading system.
Core Fibonacci Principles
At the heart of this strategy lies the Fibonacci sequence application to volatility measurement:
// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval = 0.01, step = 0.01)
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval = 0.01, step = 0.01)
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval = 0.01, step = 0.01)
These precise Fibonacci ratios create a dynamic volatility envelope that adapts to changing market conditions while maintaining mathematical harmony with natural price movements.
Dynamic Trendline Detection
The strategy incorporates LuxAlgo's pioneering approach to trendline detection:
// Pivotal swing detection (inspired by LuxAlgo)
pivot_high = ta.pivothigh(swing_length, swing_length)
pivot_low = ta.pivotlow(swing_length, swing_length)
// Dynamic slope calculation using ATR
slope = atr_value / swing_length * atr_multiplier
// Update trendlines based on pivot detection
if bool(pivot_high)
upper_slope := slope
upper_trendline := pivot_high
else
upper_trendline := nz(upper_trendline) - nz(upper_slope)
This adaptive trendline approach automatically identifies key structural market boundaries, adjusting in real-time to evolving chart patterns.
Breakout State Management
The strategy implements sophisticated state tracking for breakout detection:
// Track breakouts with state variables
var int upper_breakout_state = 0
var int lower_breakout_state = 0
// Update breakout state when price crosses trendlines
upper_breakout_state := bool(pivot_high) ? 0 : close > upper_trendline ? 1 : upper_breakout_state
lower_breakout_state := bool(pivot_low) ? 0 : close < lower_trendline ? 1 : lower_breakout_state
// Detect new breakouts (state transitions)
bool new_upper_breakout = upper_breakout_state > upper_breakout_state
bool new_lower_breakout = lower_breakout_state > lower_breakout_state
This state-based approach enables precise identification of the exact moment when price breaks through a significant trendline.
Multi-Factor Signal Confluence
Entry signals require confirmation from multiple technical factors:
// Define entry conditions with multi-factor confluence
long_entry_condition = enable_long_positions and
upper_breakout_state > upper_breakout_state and // New trendline breakout
di_plus > di_minus and // Bullish DMI confirmation
close > smoothed_trend // Price above Supertrend envelope
// Execute trades only with full confirmation
if long_entry_condition
strategy.entry('L', strategy.long, comment = "LONG")
This strict requirement for confluence significantly reduces false signals and improves the quality of trade entries.
Advanced Risk Management
The strategy includes sophisticated risk controls with multiple methodologies:
// Calculate stop loss based on selected method
get_long_stop_loss_price(base_price) =>
switch stop_loss_method
'PERC' => base_price * (1 - long_stop_loss_percent)
'ATR' => base_price - long_stop_loss_atr_multiplier * entry_atr
'RR' => base_price - (get_long_take_profit_price() - base_price) / long_risk_reward_ratio
=> na
// Implement trailing functionality
strategy.exit(
id = 'Long Take Profit / Stop Loss',
from_entry = 'L',
qty_percent = take_profit_quantity_percent,
limit = trailing_take_profit_enabled ? na : long_take_profit_price,
stop = long_stop_loss_price,
trail_price = trailing_take_profit_enabled ? long_take_profit_price : na,
trail_offset = trailing_take_profit_enabled ? long_trailing_tp_step_ticks : na,
comment = "TP/SL Triggered"
)
This flexible approach adapts to varying market conditions while providing comprehensive downside protection.
Performance Characteristics
Rigorous backtesting demonstrates exceptional capital appreciation potential with impressive risk-adjusted metrics:
Remarkable total return profile (1,517%+)
Strong Sortino ratio (3.691) indicating superior downside risk control
Profit factor of 1.924 across all trades (2.153 for long positions)
Win rate exceeding 35% with balanced distribution across varied market conditions
Institutional Considerations
The strategy architecture addresses execution complexities faced by institutional participants with temporal filtering and date-range capabilities:
// Time Filter settings with flexible timezone support
import jason5480/time_filters/5 as time_filter
src_timezone = input.string(defval = 'Exchange', title = 'Source Timezone')
dst_timezone = input.string(defval = 'Exchange', title = 'Destination Timezone')
// Date range filtering for precise execution windows
use_from_date = input.bool(defval = true, title = 'Enable Start Date')
from_date = input.time(defval = timestamp('01 Jan 2022 00:00'), title = 'Start Date')
// Validate trading permission based on temporal constraints
date_filter_approved = time_filter.is_in_date_range(
use_from_date, from_date, use_to_date, to_date, src_timezone, dst_timezone
)
These capabilities enable precise execution timing and market session optimization critical for larger market participants.
Acknowledgments
Special thanks to LuxAlgo for the pioneering work on trendline detection and breakout identification that inspired elements of this strategy. Their innovative approach to technical analysis provided a valuable foundation upon which I could build my Fibonacci-based methodology.
This strategy is shared under the same Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license as LuxAlgo's original work.
Past performance is not indicative of future results. Conduct thorough analysis before implementing any algorithmic strategy.
Dynamic Volume Profile Oscillator | AlphaAlgosDynamic Volume Profile Oscillator | AlphaAlgos
Overview
The Dynamic Volume Profile Oscillator is an advanced technical analysis tool that transforms traditional volume analysis into a responsive oscillator. By creating a dynamic volume profile and measuring price deviation from volume-weighted equilibrium levels, this indicator provides traders with powerful insights into market momentum and potential reversals.
Key Features
• Volume-weighted price deviation analysis
• Adaptive midline that adjusts to changing market conditions
• Beautiful gradient visualization with 10-level intensity zones
• Fast and slow signal lines for trend confirmation
• Mean reversion mode that identifies price extremes relative to volume
• Fully customizable sensitivity and smoothing parameters
Technical Components
1. Volume Profile Analysis
The indicator builds a dynamic volume profile by:
• Collecting recent price and volume data within a specified lookback period
• Calculating a volume-weighted mean price (similar to VWAP)
• Measuring how far current price has deviated from this weighted average
• Adjusting this deviation based on historical volatility
2. Oscillator Calculation
The oscillator offers two calculation methods:
• Mean Reversion Mode (default): Measures deviation from volume-weighted mean price, normalized to reflect potential overbought/oversold conditions
• Standard Mode : Normalizes volume activity to identify unusual volume patterns
3. Adaptive Zones
The indicator features dynamic zones that:
• Center around an adaptive midline that reflects the average oscillator value
• Expand and contract based on recent volatility (standard deviation)
• Visually represent intensity through multi-level gradient coloring
• Provide clear visualization of bullish/bearish extremes
4. Signal Generation
Trading signals are generated through:
• Main oscillator line position relative to the adaptive midline
• Crossovers between fast (5-period) and slow (15-period) signal lines
• Color changes that instantly identify trend direction
• Distance from the midline indicating trend strength
Configuration Options
Volume Analysis Settings:
• Price Source - Select which price data to analyze
• Volume Source - Define volume data source
• Lookback Period - Number of bars for main calculations
• Profile Calculation Periods - Frequency of profile recalculation
Oscillator Settings:
• Smoothing Length - Controls oscillator smoothness
• Sensitivity - Adjusts responsiveness to price/volume changes
• Mean Reversion Mode - Toggles calculation methodology
Threshold Settings:
• Adaptive Midline - Uses dynamic midline based on historical values
• Midline Period - Lookback period for midline calculation
• Zone Width Multiplier - Controls width of bullish/bearish zones
Display Settings:
• Color Bars - Option to color price bars based on trend direction
Trading Strategies
Trend Following:
• Enter long positions when the oscillator crosses above the adaptive midline
• Enter short positions when the oscillator crosses below the adaptive midline
• Use signal line crossovers for entry timing
• Monitor gradient intensity to gauge trend strength
Mean Reversion Trading:
• Look for oscillator extremes shown by intense gradient colors
• Prepare for potential reversals when the oscillator reaches upper/lower zones
• Use divergences between price and oscillator for confirmation
• Consider scaling positions based on gradient intensity
Volume Analysis:
• Use Standard Mode to identify unusual volume patterns
• Confirm breakouts when accompanied by strong oscillator readings
• Watch for divergences between price and volume-based readings
• Use extended periods in extreme zones as trend confirmation
Best Practices
• Adjust sensitivity based on the asset's typical volatility
• Use longer smoothing for swing trading, shorter for day trading
• Combine with support/resistance levels for optimal entry/exit points
• Consider multiple timeframe analysis for comprehensive market view
• Test different profile calculation periods to match your trading style
This indicator is provided for informational purposes only. Always use proper risk management when trading based on any technical indicator. Not financial advise.
Multi-Oscillator Adaptive Kernel | AlphaAlgosMulti-Oscillator Adaptive Kernel | AlphaAlgos
Overview
The Multi-Oscillator Adaptive Kernel (MOAK) is an advanced technical analysis tool that combines multiple oscillators through sophisticated kernel-based smoothing algorithms. This indicator is designed to provide clearer trend signals while filtering out market noise, offering traders a comprehensive view of market momentum across multiple timeframes.
Key Features
• Fusion of multiple technical oscillators (RSI, Stochastic, MFI, CCI)
• Advanced kernel smoothing technology with three distinct mathematical models
• Customizable sensitivity and lookback periods
• Clear visual signals for trend shifts and reversals
• Overbought/oversold zones for precise entry and exit timing
• Adaptive signal that responds to varying market conditions
Technical Components
The MOAK indicator utilizes a multi-layer approach to signal generation:
1. Oscillator Fusion
The core of the indicator combines normalized readings from up to four popular oscillators:
• RSI (Relative Strength Index) - Measures the speed and change of price movements
• Stochastic - Compares the closing price to the price range over a specific period
• MFI (Money Flow Index) - Volume-weighted RSI that includes trading volume
• CCI (Commodity Channel Index) - Measures current price level relative to an average price
2. Kernel Smoothing
The combined oscillator data is processed through one of three kernel functions:
• Exponential Kernel - Provides stronger weighting to recent data with exponential decay
• Linear Kernel - Applies a linear weighting from most recent to oldest data points
• Gaussian Kernel - Uses a bell curve distribution that helps filter out extreme values
3. Dual Signal Lines
• Fast Signal Line - Responds quickly to price changes
• Slow Signal Line - Provides confirmation and shows the underlying trend direction
Configuration Options
Oscillator Selection:
• Enable/disable each oscillator (RSI, Stochastic, MFI, CCI)
• Customize individual lookback periods for each oscillator
Kernel Settings:
• Kernel Type - Choose between Exponential, Linear, or Gaussian mathematical models
• Kernel Length - Adjust the smoothing period (higher values = smoother line)
• Sensitivity - Fine-tune the indicator's responsiveness (higher values = more responsive)
Display Options:
• Color Bars - Toggle price bar coloring based on indicator direction
How to Interpret the Indicator
Signal Line Direction:
• Upward movement (teal) indicates bullish momentum
• Downward movement (magenta) indicates bearish momentum
Trend Shifts:
• Small circles mark the beginning of new uptrends
• X-marks indicate the start of new downtrends
Overbought/Oversold Conditions:
• Values above +50 suggest overbought conditions (potential reversal or pullback)
• Values below -50 suggest oversold conditions (potential reversal or bounce)
Trading Strategies
Trend Following:
• Enter long positions when the signal line turns teal and shows an uptrend
• Enter short positions when the signal line turns magenta and shows a downtrend
• Use the slow signal line (area fill) as confirmation of the underlying trend
Counter-Trend Trading:
• Look for divergences between price and the indicator
• Consider profit-taking when the indicator reaches overbought/oversold areas
• Wait for trend shift signals before entering counter-trend positions
Multiple Timeframe Analysis:
• Use the indicator across different timeframes for confirmation
• Higher timeframe signals carry more weight than lower timeframe signals
Best Practices
• Experiment with different kernel types for various market conditions
• Gaussian kernels often work well in ranging markets
• Exponential kernels can provide earlier signals in trending markets
• Combine with volume analysis for higher probability trades
• Use appropriate stop-loss levels as the indicator does not guarantee price movements
This indicator is provided as-is with no guarantees of profit. Always use proper risk management when trading with any technical indicator. Nothing is financial advise.
Hourly Volatility Explorer📊 Hourly Volatility Explorer: Master The Market's Pulse
Unlock the hidden rhythms of price action with this sophisticated volatility analysis tool. The Hourly Volatility Explorer reveals the most potent trading hours across multiple time zones, giving you a strategic edge in timing your trades.
🌟 Key Features:
⏰ Multi-Timezone Analysis
• GMT (UTC+0)
• EST (UTC-5) - New York
• BST (UTC+1) - London
• JST (UTC+9) - Tokyo
• AEST (UTC+10) - Sydney
Perfect for tracking major market sessions and their overlaps!
📈 Dynamic Visualization
• Color-gradient hourly bars for instant pattern recognition
• Real-time volatility comparison
• Interactive data table with comprehensive statistics
• Automatic highlighting of peak volatility periods
🎯 Strategic Applications:
Day Trading:
• Identify optimal trading windows
• Avoid low-liquidity periods
• Capitalize on session overlaps
• Fine-tune entry/exit timing
Risk Management:
• Set appropriate stop losses based on hourly volatility
• Adjust position sizes for different market hours
• Optimize risk-reward ratios
• Plan around high-impact hours
Global Market Analysis:
• Track volatility across all major sessions
• Spot institutional trading patterns
• Identify quiet vs. active periods
• Monitor 24/7 market dynamics
💡 Perfect For:
• Forex traders navigating global sessions
• Crypto traders in 24/7 markets
• Day traders optimizing execution times
• Algorithmic traders fine-tuning strategies
• Risk managers calibrating exposure
📊 Advanced Features:
• Rolling 3-month analysis for reliable patterns
• Precise pip movement calculations
• Sample size tracking for statistical validity
• Real-time current hour comparison
• Color-coded visual system for instant insights
⚡ Pro Trading Tips:
• Use during major session overlaps for maximum opportunity
• Compare patterns across different instruments
• Combine with volume analysis for deeper insights
• Track seasonal variations in hourly patterns
• Build trading schedules around peak hours
🎓 Educational Value:
• Understand market microstructure
• Learn global market dynamics
• Master timezone relationships
• Develop timing intuition
🛠️ Customization:
• Adjustable lookback period
• Flexible pip multiplier
• Multiple timezone options
• Visual preference settings
Whether you're scalping the 1-minute chart or managing longer-term positions, the Hourly Volatility Explorer provides the precise timing intelligence needed for today's global markets.
Transform your trading schedule from guesswork to science. Know exactly when markets move, why they move, and how to position yourself for maximum opportunity.
#TechnicalAnalysis #Trading #Volatility #MarketTiming #DayTrading #Forex #Crypto #TradingView #PineScript #MarketAnalysis #TradingStrategy #RiskManagement #GlobalMarkets #FinancialMarkets #TradingTools #MarketStructure #PriceAction #Scalping #SwingTrading #AlgoTrading
Wall Street Ai**Wall Street Ai – Advanced Technical Indicator for Market Analysis**
**Overview**
Wall Street Ai is an advanced, AI-powered technical indicator meticulously engineered to provide traders with in-depth market analysis and insight. By leveraging state-of-the-art artificial intelligence algorithms and comprehensive historical price data, Wall Street Ai is designed to identify significant market turning points and key price levels. Its sophisticated analytical framework enables traders to uncover potential shifts in market momentum, assisting in the formulation of strategic trading decisions while maintaining the highest standards of objectivity and reliability.
**Key Features**
- **Intelligent Pattern Recognition:**
Wall Street Ai employs advanced machine learning techniques to analyze historical price movements and detect recurring patterns. This capability allows it to differentiate between typical market noise and meaningful signals indicative of potential trend reversals.
- **Robust Noise Reduction:**
The indicator incorporates a refined volatility filtering system that minimizes the impact of minor price fluctuations. By isolating significant price movements, it ensures that the analytical output focuses on substantial market shifts rather than ephemeral variations.
- **Customizable Analytical Parameters:**
With a wide range of adjustable settings, Wall Street Ai can be fine-tuned to align with diverse trading strategies and risk appetites. Traders can modify sensitivity, threshold levels, and other critical parameters to optimize the indicator’s performance under various market conditions.
- **Comprehensive Data Analysis:**
By harnessing the power of artificial intelligence, Wall Street Ai performs a deep analysis of historical data, identifying statistically significant highs and lows. This analysis not only reflects past market behavior but also provides valuable insights into potential future turning points, thereby enhancing the predictive aspect of your trading strategy.
- **Adaptive Market Insights:**
The indicator’s dynamic algorithm continuously adjusts to current market conditions, adapting its analysis based on real-time data inputs. This adaptive quality ensures that the indicator remains relevant and effective across different market environments, whether the market is trending strongly, consolidating, or experiencing volatility.
- **Objective and Reliable Analysis:**
Wall Street Ai is built on a foundation of robust statistical methods and rigorous data validation. Its outputs are designed to be objective and free from any exaggerated claims, ensuring that traders receive a clear, unbiased view of market conditions.
**How It Works**
Wall Street Ai integrates advanced AI and deep learning methodologies to analyze a vast array of historical price data. Its core algorithm identifies and evaluates critical market levels by detecting patterns that have historically preceded significant market movements. By filtering out non-essential fluctuations, the indicator emphasizes key price extremes and trend changes that are likely to impact market behavior. The system’s adaptive nature allows it to recalibrate its analytical parameters in response to evolving market dynamics, providing a consistently reliable framework for market analysis.
**Usage Recommendations**
- **Optimal Timeframes:**
For the most effective application, it is recommended to utilize Wall Street Ai on higher timeframe charts, such as hourly (H1) or higher. This approach enhances the clarity of the detected patterns and provides a more comprehensive view of long-term market trends.
- **Market Versatility:**
Wall Street Ai is versatile and can be applied across a broad range of financial markets, including Forex, indices, commodities, cryptocurrencies, and equities. Its adaptable design ensures consistent performance regardless of the asset class being analyzed.
- **Complementary Analytical Tools:**
While Wall Street Ai provides profound insights into market behavior, it is best utilized in combination with other analytical tools and techniques. Integrating its analysis with additional indicators—such as trend lines, support/resistance levels, or momentum oscillators—can further refine your trading strategy and enhance decision-making.
- **Strategy Testing and Optimization:**
Traders are encouraged to test Wall Street Ai extensively in a simulated trading environment before deploying it in live markets. This allows for thorough calibration of its settings according to individual trading styles and risk management strategies, ensuring optimal performance across diverse market conditions.
**Risk Management and Best Practices**
Wall Street Ai is intended to serve as an analytical tool that supports informed trading decisions. However, as with any technical indicator, its outputs should be interpreted as part of a comprehensive trading strategy that includes robust risk management practices. Traders should continuously validate the indicator’s findings with additional analysis and maintain a disciplined approach to position sizing and risk control. Regular review and adjustment of trading strategies in response to market changes are essential to mitigate potential losses.
**Conclusion**
Wall Street Ai offers a cutting-edge, AI-driven approach to technical analysis, empowering traders with detailed market insights and the ability to identify potential turning points with precision. Its intelligent pattern recognition, adaptive analytical capabilities, and extensive noise reduction make it a valuable asset for both experienced traders and those new to market analysis. By integrating Wall Street Ai into your trading toolkit, you can enhance your understanding of market dynamics and develop a more robust, data-driven trading strategy—all while adhering to the highest standards of analytical integrity and performance.
Price and Volume Breakout - Jemmy TradeThe "Price and Volume Breakout" indicator is designed to identify potential breakout opportunities by analyzing both price and volume trends. It uses a combination of historical price highs, volume peaks, and a customizable Simple Moving Average (SMA) to signal bullish breakouts. When the price exceeds the highest price of the defined breakout period and is supported by high volume, the indicator triggers visual alerts on the chart. These include dotted lines, labels, and boxes highlighting accumulation zones, along with dynamically calculated stop loss and take profit levels.
Key Features:
• Breakout Detection: Compares the current closing price to the highest price and volume over specified periods to signal a breakout.
• Customizable Stop Loss Options: Offers three methods for setting stop loss levels:
o Below SMA: Positions stop loss a user-defined percentage below the SMA.
o Lowest Low: Uses the lowest low over a specific look-back period.
o Range Average: Calculates an average based on the previous price range.
• Dynamic Take Profit Calculation: Automatically computes take profit levels based on the defined risk-to-reward ratio.
• Visual Chart Elements: Draws breakout lines, stop loss and take profit indicators, labels (e.g., "🚀 Breakout Buy", "🔴 Stop Loss", "🟢 Take Profit"), and boxes marking accumulation zones for easy visualization.
• Alert Conditions: Includes alert functionality to notify traders when breakout conditions are met, enabling timely trading decisions.
How to Use:
1. Customization: Adjust settings such as the breakout periods for price and volume, the length of the SMA, stop loss options, and the risk-to-reward ratio to fit your trading strategy.
2. Signal Identification: When the price exceeds the highest value from the previous period, accompanied by high volume and confirmation from the SMA, the indicator displays a "Breakout Buy" signal.
3. Risk Management: The indicator calculates appropriate stop loss and take profit levels automatically based on your selected parameters, ensuring a balanced risk/reward setup.
4. Alerts: Utilize the built-in alert conditions to receive notifications whenever the breakout criteria are satisfied, helping you act promptly.
PLEASE USE IT AS PER YOUR OWN RISK MANAGEMENT STRATEGIES.
Keywords:
#Breakout #Trading #VolumeAnalysis #TechnicalAnalysis #PriceAction #RiskManagement #TrendFollowing #TradingSignals #PriceBreakout #SmartTrading #JemmyTrade
MLB Momentum IndicatorMLB Momentum Indicator is a no‐lookahead technical indicator designed to signal intraday trend shifts and potential reversal points. It combines several well‐known technical components—Moving Averages, MACD, RSI, and optional ADX & Volume filters—to deliver high‐probability buy/sell signals on your chart.
Below is an overview of how it works and what each part does:
1. Moving Average Trend Filter
The script uses two moving averages (fast and slow) to determine the primary trend:
isUpTrend if Fast MA > Slow MA
isDownTrend if Fast MA < Slow MA
You can select the MA method—SMA, EMA, or WMA—and customize lengths.
Why it matters: The indicator only gives bullish signals if the trend is up, and bearish signals if the trend is down, helping avoid trades that go against the bigger flow.
2. MACD Confirmation (Momentum)
Uses MACD (with user‐defined Fast, Slow, and Signal lengths) to check momentum:
macdBuySignal if the MACD line crosses above its signal line (bullish)
macdSellSignal if the MACD line crosses below its signal line (bearish)
Why it matters: MACD crossovers confirm an emerging momentum shift, aligning signals with actual price acceleration rather than random fluctuation.
3. RSI Overbought/Oversold Filter
RSI (Relative Strength Index) is calculated with a chosen length, plus Overbought & Oversold thresholds:
For long signals: the RSI must be below the Overbought threshold (e.g. 70).
For short signals: the RSI must be above the Oversold threshold (e.g. 30).
Why it matters: Prevents buying when price is already overbought or shorting when price is too oversold, filtering out possible poor‐risk trades.
4. Optional ADX Filter (Trend Strength)
If enabled, ADX must exceed a chosen threshold (e.g., 20) for a signal to be valid:
This ensures you’re only taking trades in markets that have sufficient directional momentum.
Why it matters: It weeds out choppy, sideways conditions where signals are unreliable.
5. Optional Volume Filter (High‐Participation Moves)
If enabled, the indicator checks whether current volume is above a certain multiple of its moving average (e.g., 1.5× average volume).
Why it matters: High volume often indicates stronger institutional interest, validating potential breakouts or reversals.
6. ATR & Chandelier (Visual Reference)
For reference only, the script can display ATR‐based stop levels or a Chandelier Exit line:
ATR (Average True Range) helps gauge volatility and can inform stop‐loss distances.
Chandelier Exit is a trailing stop technique that adjusts automatically as price moves.
Why it matters: Though this version of the script doesn’t execute trades, these lines help you see how far to place stops or how to ride a trend.
7. Final Bullish / Bearish Signal
When all conditions (trend, MACD, RSI, optional ADX, optional Volume) line up for a long, a green “Long” arrow appears.
When all conditions line up for a short, a red “Short” arrow appears.
Why it matters: You get a clear, on‐chart signal for each potential entry, rather than needing to check multiple indicators manually.
8. Session & Date Filtering
The script allows choosing a start/end date and an optional session window (e.g. 09:30–16:00).
Why it matters: Helps limit signals to a specific historical backtest range or trading hours, which can be crucial for day traders (e.g., stock market hours only).
Putting It All Together
Primary Trend → ensures you trade in line with the bigger direction.
MACD & RSI → confirm momentum and avoid overbought/oversold extremes.
ADX & Volume → optional filters for strong trend strength & genuine interest.
Arrows → each potential buy (Long) or sell (Short) signal is clearly shown on your chart.
Use Cases
5‐Minute Scalping: Shorter RSI/MACD lengths to catch small, frequent intraday moves.
Swing Trading: Larger MAs, bigger RSI thresholds, and using ADX to filter only major trends.
Cautious Approach: Enable volume & ADX filters to reduce false signals in choppy markets.
Benefits & Limitations
Benefits:
Consolidates multiple indicators into one overlay.
Clear buy/sell signals with optional dynamic volatility references.
Flexible user inputs adapt to different trading styles/timeframes.
Limitations:
Like all technical indicators, it can produce false signals in sideways or news‐driven markets.
Success depends heavily on user settings and the particular market’s behavior.
Summary
The MLB Momentum Indicator combines a trend filter (MAs), momentum check (MACD), overbought/oversold gating (RSI), and optional ADX/Volume filters to create clear buy/sell arrows on your chart. This approach encourages trading in sync with both trend and momentum, and helps avoid suboptimal entries when volume or trend strength is lacking. It can be tailored to scalp micro‐moves on lower timeframes or used for higher‐timeframe swing trading by adjusting the input settings.
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis.
High and Low in a Given Date/Time RangeThis Pine Script v5 indicator plots horizontal lines at both the highest and lowest price levels reached within a user-defined date/time range.
Description:
Inputs:
The user specifies a start and an end date/time by providing the year, month, day, hour, and minute for each. These inputs are converted into timestamps based on the chart’s timezone.
How It Works:
Timestamp Conversion: The script converts the provided start and end dates/times into timestamps using the chart’s timezone.
Bar Check: It examines every bar and checks if the bar’s timestamp falls between the start and end timestamps.
Price Updates:
If a bar’s time is within the specified range, the indicator updates the highest price if the current bar's high exceeds the previously recorded high, and it updates the lowest price if the current bar's low is lower than the previously recorded low.
Drawing Lines:
A red horizontal line is drawn at the highest price, and a green horizontal line is drawn at the lowest price. Both lines start from the first bar in the range and extend dynamically to the current bar, updating as new high or low values are reached.
End of Range: Once a bar's time exceeds the end timestamp, the lines stop updating.
This tool offers a clear and straightforward way to monitor key price levels during a defined period without any extra fluff.
real_time_candlesIntroduction
The Real-Time Candles Library provides comprehensive tools for creating, manipulating, and visualizing custom timeframe candles in Pine Script. Unlike standard indicators that only update at bar close, this library enables real-time visualization of price action and indicators within the current bar, offering traders unprecedented insight into market dynamics as they unfold.
This library addresses a fundamental limitation in traditional technical analysis: the inability to see how indicators evolve between bar closes. By implementing sophisticated real-time data processing techniques, traders can now observe indicator movements, divergences, and trend changes as they develop, potentially identifying trading opportunities much earlier than with conventional approaches.
Key Features
The library supports two primary candle generation approaches:
Chart-Time Candles: Generate real-time OHLC data for any variable (like RSI, MACD, etc.) while maintaining synchronization with chart bars.
Custom Timeframe (CTF) Candles: Create candles with custom time intervals or tick counts completely independent of the chart's native timeframe.
Both approaches support traditional candlestick and Heikin-Ashi visualization styles, with options for moving average overlays to smooth the data.
Configuration Requirements
For optimal performance with this library:
Set max_bars_back = 5000 in your script settings
When using CTF drawing functions, set max_lines_count = 500, max_boxes_count = 500, and max_labels_count = 500
These settings ensure that you will be able to draw correctly and will avoid any runtime errors.
Usage Examples
Basic Chart-Time Candle Visualization
// Create real-time candles for RSI
float rsi = ta.rsi(close, 14)
Candle rsi_candle = candle_series(rsi, CandleType.candlestick)
// Plot the candles using Pine's built-in function
plotcandle(rsi_candle.Open, rsi_candle.High, rsi_candle.Low, rsi_candle.Close,
"RSI Candles", rsi_candle.candle_color, rsi_candle.candle_color)
Multiple Access Patterns
The library provides three ways to access candle data, accommodating different programming styles:
// 1. Array-based access for collection operations
Candle candles = candle_array(source)
// 2. Object-oriented access for single entity manipulation
Candle candle = candle_series(source)
float value = candle.source(Source.HLC3)
// 3. Tuple-based access for functional programming styles
= candle_tuple(source)
Custom Timeframe Examples
// Create 20-second candles with EMA overlay
plot_ctf_candles(
source = close,
candle_type = CandleType.candlestick,
sample_type = SampleType.Time,
number_of_seconds = 20,
timezone = -5,
tied_open = true,
ema_period = 9,
enable_ema = true
)
// Create tick-based candles (new candle every 15 ticks)
plot_ctf_tick_candles(
source = close,
candle_type = CandleType.heikin_ashi,
number_of_ticks = 15,
timezone = -5,
tied_open = true
)
Advanced Usage with Custom Visualization
// Get custom timeframe candles without automatic plotting
CandleCTF my_candles = ctf_candles_array(
source = close,
candle_type = CandleType.candlestick,
sample_type = SampleType.Time,
number_of_seconds = 30
)
// Apply custom logic to the candles
float ema_values = my_candles.ctf_ema(14)
// Draw candles and EMA using time-based coordinates
my_candles.draw_ctf_candles_time()
ema_values.draw_ctf_line_time(line_color = #FF6D00)
Library Components
Data Types
Candle: Structure representing chart-time candles with OHLC, polarity, and visualization properties
CandleCTF: Extended candle structure with additional time metadata for custom timeframes
TickData: Structure for individual price updates with time deltas
Enumerations
CandleType: Specifies visualization style (candlestick or Heikin-Ashi)
Source: Defines price components for calculations (Open, High, Low, Close, HL2, etc.)
SampleType: Sets sampling method (Time-based or Tick-based)
Core Functions
get_tick(): Captures current price as a tick data point
candle_array(): Creates an array of candles from price updates
candle_series(): Provides a single candle based on latest data
candle_tuple(): Returns OHLC values as a tuple
ctf_candles_array(): Creates custom timeframe candles without rendering
Visualization Functions
source(): Extracts specific price components from candles
candle_ctf_to_float(): Converts candle data to float arrays
ctf_ema(): Calculates exponential moving averages for candle arrays
draw_ctf_candles_time(): Renders candles using time coordinates
draw_ctf_candles_index(): Renders candles using bar index coordinates
draw_ctf_line_time(): Renders lines using time coordinates
draw_ctf_line_index(): Renders lines using bar index coordinates
Technical Implementation Notes
This library leverages Pine Script's varip variables for state management, creating a sophisticated real-time data processing system. The implementation includes:
Efficient tick capturing: Samples price at every execution, maintaining temporal tracking with time deltas
Smart state management: Uses a hybrid approach with mutable updates at index 0 and historical preservation at index 1+
Temporal synchronization: Manages two time domains (chart time and custom timeframe)
The tooltip implementation provides crucial temporal context for custom timeframe visualizations, allowing users to understand exactly when each candle formed regardless of chart timeframe.
Limitations
Custom timeframe candles cannot be backtested due to Pine Script's limitations with historical tick data
Real-time visualization is only available during live chart updates
Maximum history is constrained by Pine Script's array size limits
Applications
Indicator visualization: See how RSI, MACD, or other indicators evolve in real-time
Volume analysis: Create custom volume profiles independent of chart timeframe
Scalping strategies: Identify short-term patterns with precisely defined time windows
Volatility measurement: Track price movement characteristics within bars
Custom signal generation: Create entry/exit signals based on custom timeframe patterns
Conclusion
The Real-Time Candles Library bridges the gap between traditional technical analysis (based on discrete OHLC bars) and the continuous nature of market movement. By making indicators more responsive to real-time price action, it gives traders a significant edge in timing and decision-making, particularly in fast-moving markets where waiting for bar close could mean missing important opportunities.
Whether you're building custom indicators, researching price patterns, or developing trading strategies, this library provides the foundation for sophisticated real-time analysis in Pine Script.
Implementation Details & Advanced Guide
Core Implementation Concepts
The Real-Time Candles Library implements a sophisticated event-driven architecture within Pine Script's constraints. At its heart, the library creates what's essentially a reactive programming framework handling continuous data streams.
Tick Processing System
The foundation of the library is the get_tick() function, which captures price updates as they occur:
export get_tick(series float source = close, series float na_replace = na)=>
varip float price = na
varip int series_index = -1
varip int old_time = 0
varip int new_time = na
varip float time_delta = 0
// ...
This function:
Samples the current price
Calculates time elapsed since last update
Maintains a sequential index to track updates
The resulting TickData structure serves as the fundamental building block for all candle generation.
State Management Architecture
The library employs a sophisticated state management system using varip variables, which persist across executions within the same bar. This creates a hybrid programming paradigm that's different from standard Pine Script's bar-by-bar model.
For chart-time candles, the core state transition logic is:
// Real-time update of current candle
candle_data := Candle.new(Open, High, Low, Close, polarity, series_index, candle_color)
candles.set(0, candle_data)
// When a new bar starts, preserve the previous candle
if clear_state
candles.insert(1, candle_data)
price.clear()
// Reset state for new candle
Open := Close
price.push(Open)
series_index += 1
This pattern of updating index 0 in real-time while inserting completed candles at index 1 creates an elegant solution for maintaining both current state and historical data.
Custom Timeframe Implementation
The custom timeframe system manages its own time boundaries independent of chart bars:
bool clear_state = switch settings.sample_type
SampleType.Ticks => cumulative_series_idx >= settings.number_of_ticks
SampleType.Time => cumulative_time_delta >= settings.number_of_seconds
This dual-clock system synchronizes two time domains:
Pine's execution clock (bar-by-bar processing)
The custom timeframe clock (tick or time-based)
The library carefully handles temporal discontinuities, ensuring candle formation remains accurate despite irregular tick arrival or market gaps.
Advanced Usage Techniques
1. Creating Custom Indicators with Real-Time Candles
To develop indicators that process real-time data within the current bar:
// Get real-time candles for your data
Candle rsi_candles = candle_array(ta.rsi(close, 14))
// Calculate indicator values based on candle properties
float signal = ta.ema(rsi_candles.first().source(Source.Close), 9)
// Detect patterns that occur within the bar
bool divergence = close > close and rsi_candles.first().Close < rsi_candles.get(1).Close
2. Working with Custom Timeframes and Plotting
For maximum flexibility when visualizing custom timeframe data:
// Create custom timeframe candles
CandleCTF volume_candles = ctf_candles_array(
source = volume,
candle_type = CandleType.candlestick,
sample_type = SampleType.Time,
number_of_seconds = 60
)
// Convert specific candle properties to float arrays
float volume_closes = volume_candles.candle_ctf_to_float(Source.Close)
// Calculate derived values
float volume_ema = volume_candles.ctf_ema(14)
// Create custom visualization
volume_candles.draw_ctf_candles_time()
volume_ema.draw_ctf_line_time(line_color = color.orange)
3. Creating Hybrid Timeframe Analysis
One powerful application is comparing indicators across multiple timeframes:
// Standard chart timeframe RSI
float chart_rsi = ta.rsi(close, 14)
// Custom 5-second timeframe RSI
CandleCTF ctf_candles = ctf_candles_array(
source = close,
candle_type = CandleType.candlestick,
sample_type = SampleType.Time,
number_of_seconds = 5
)
float fast_rsi_array = ctf_candles.candle_ctf_to_float(Source.Close)
float fast_rsi = fast_rsi_array.first()
// Generate signals based on divergence between timeframes
bool entry_signal = chart_rsi < 30 and fast_rsi > fast_rsi_array.get(1)
Final Notes
This library represents an advanced implementation of real-time data processing within Pine Script's constraints. By creating a reactive programming framework for handling continuous data streams, it enables sophisticated analysis typically only available in dedicated trading platforms.
The design principles employed—including state management, temporal processing, and object-oriented architecture—can serve as patterns for other advanced Pine Script development beyond this specific application.
------------------------
Library "real_time_candles"
A comprehensive library for creating real-time candles with customizable timeframes and sampling methods.
Supports both chart-time and custom-time candles with options for candlestick and Heikin-Ashi visualization.
Allows for tick-based or time-based sampling with moving average overlay capabilities.
get_tick(source, na_replace)
Captures the current price as a tick data point
Parameters:
source (float) : Optional - Price source to sample (defaults to close)
na_replace (float) : Optional - Value to use when source is na
Returns: TickData structure containing price, time since last update, and sequential index
candle_array(source, candle_type, sync_start, bullish_color, bearish_color)
Creates an array of candles based on price updates
Parameters:
source (float) : Optional - Price source to sample (defaults to close)
candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)
sync_start (simple bool) : Optional - Whether to synchronize with the start of a new bar
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
Returns: Array of Candle objects ordered with most recent at index 0
candle_series(source, candle_type, wait_for_sync, bullish_color, bearish_color)
Provides a single candle based on the latest price data
Parameters:
source (float) : Optional - Price source to sample (defaults to close)
candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)
wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
Returns: A single Candle object representing the current state
candle_tuple(source, candle_type, wait_for_sync, bullish_color, bearish_color)
Provides candle data as a tuple of OHLC values
Parameters:
source (float) : Optional - Price source to sample (defaults to close)
candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)
wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
Returns: Tuple representing current candle values
method source(self, source, na_replace)
Extracts a specific price component from a Candle
Namespace types: Candle
Parameters:
self (Candle)
source (series Source) : Type of price data to extract (Open, High, Low, Close, or composite values)
na_replace (float) : Optional - Value to use when source value is na
Returns: The requested price value from the candle
method source(self, source)
Extracts a specific price component from a CandleCTF
Namespace types: CandleCTF
Parameters:
self (CandleCTF)
source (simple Source) : Type of price data to extract (Open, High, Low, Close, or composite values)
Returns: The requested price value from the candle as a varip
method candle_ctf_to_float(self, source)
Converts a specific price component from each CandleCTF to a float array
Namespace types: array
Parameters:
self (array)
source (simple Source) : Optional - Type of price data to extract (defaults to Close)
Returns: Array of float values extracted from the candles, ordered with most recent at index 0
method ctf_ema(self, ema_period)
Calculates an Exponential Moving Average for a CandleCTF array
Namespace types: array
Parameters:
self (array)
ema_period (simple float) : Period for the EMA calculation
Returns: Array of float values representing the EMA of the candle data, ordered with most recent at index 0
method draw_ctf_candles_time(self, sample_type, number_of_ticks, number_of_seconds, timezone)
Renders custom timeframe candles using bar time coordinates
Namespace types: array
Parameters:
self (array)
sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips
number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips
number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips
timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips
Returns: void - Renders candles on the chart using time-based x-coordinates
method draw_ctf_candles_index(self, sample_type, number_of_ticks, number_of_seconds, timezone)
Renders custom timeframe candles using bar index coordinates
Namespace types: array
Parameters:
self (array)
sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips
number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips
number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips
timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips
Returns: void - Renders candles on the chart using index-based x-coordinates
method draw_ctf_line_time(self, source, line_size, line_color)
Renders a line representing a price component from the candles using time coordinates
Namespace types: array
Parameters:
self (array)
source (simple Source) : Optional - Type of price data to extract (defaults to Close)
line_size (simple int) : Optional - Width of the line
line_color (simple color) : Optional - Color of the line
Returns: void - Renders a connected line on the chart using time-based x-coordinates
method draw_ctf_line_time(self, line_size, line_color)
Renders a line from a varip float array using time coordinates
Namespace types: array
Parameters:
self (array)
line_size (simple int) : Optional - Width of the line, defaults to 2
line_color (simple color) : Optional - Color of the line
Returns: void - Renders a connected line on the chart using time-based x-coordinates
method draw_ctf_line_index(self, source, line_size, line_color)
Renders a line representing a price component from the candles using index coordinates
Namespace types: array
Parameters:
self (array)
source (simple Source) : Optional - Type of price data to extract (defaults to Close)
line_size (simple int) : Optional - Width of the line
line_color (simple color) : Optional - Color of the line
Returns: void - Renders a connected line on the chart using index-based x-coordinates
method draw_ctf_line_index(self, line_size, line_color)
Renders a line from a varip float array using index coordinates
Namespace types: array
Parameters:
self (array)
line_size (simple int) : Optional - Width of the line, defaults to 2
line_color (simple color) : Optional - Color of the line
Returns: void - Renders a connected line on the chart using index-based x-coordinates
plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)
Plots tick-based candles with moving average
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Type of candle chart to display
number_of_ticks (simple int) : Number of ticks per candle
timezone (simple int) : Timezone offset from UTC (-12 to +12)
tied_open (simple bool) : Whether to tie open price to close of previous candle
ema_period (simple float) : Period for the exponential moving average
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
line_width (simple int) : Optional - Width of the moving average line, defaults to 2
ema_color (color) : Optional - Color of the moving average line
use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index
Returns: void - Creates visual candle chart with EMA overlay
plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)
Plots tick-based candles without moving average
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Type of candle chart to display
number_of_ticks (simple int) : Number of ticks per candle
timezone (simple int) : Timezone offset from UTC (-12 to +12)
tied_open (simple bool) : Whether to tie open price to close of previous candle
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index
Returns: void - Creates visual candle chart without moving average
plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)
Plots time-based candles with moving average
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Type of candle chart to display
number_of_seconds (simple float) : Time duration per candle in seconds
timezone (simple int) : Timezone offset from UTC (-12 to +12)
tied_open (simple bool) : Whether to tie open price to close of previous candle
ema_period (simple float) : Period for the exponential moving average
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
line_width (simple int) : Optional - Width of the moving average line, defaults to 2
ema_color (color) : Optional - Color of the moving average line
use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index
Returns: void - Creates visual candle chart with EMA overlay
plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)
Plots time-based candles without moving average
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Type of candle chart to display
number_of_seconds (simple float) : Time duration per candle in seconds
timezone (simple int) : Timezone offset from UTC (-12 to +12)
tied_open (simple bool) : Whether to tie open price to close of previous candle
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index
Returns: void - Creates visual candle chart without moving average
plot_ctf_candles(source, candle_type, sample_type, number_of_ticks, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, enable_ema, line_width, ema_color, use_time_indexing)
Unified function for plotting candles with comprehensive options
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Optional - Type of candle chart to display
sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks)
number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)
number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)
timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12)
tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle
ema_period (simple float) : Optional - Period for the exponential moving average
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
enable_ema (bool) : Optional - Whether to display the EMA overlay
line_width (simple int) : Optional - Width of the moving average line, defaults to 2
ema_color (color) : Optional - Color of the moving average line
use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index
Returns: void - Creates visual candle chart with optional EMA overlay
ctf_candles_array(source, candle_type, sample_type, number_of_ticks, number_of_seconds, tied_open, bullish_color, bearish_color)
Creates an array of custom timeframe candles without rendering them
Parameters:
source (float) : Input price source to sample
candle_type (simple CandleType) : Type of candle chart to create (candlestick or Heikin-Ashi)
sample_type (simple SampleType) : Method for sampling data (Time or Ticks)
number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)
number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)
tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle
bullish_color (color) : Optional - Color for bullish candles
bearish_color (color) : Optional - Color for bearish candles
Returns: Array of CandleCTF objects ordered with most recent at index 0
Candle
Structure representing a complete candle with price data and display properties
Fields:
Open (series float) : Opening price of the candle
High (series float) : Highest price of the candle
Low (series float) : Lowest price of the candle
Close (series float) : Closing price of the candle
polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)
series_index (series int) : Sequential index identifying the candle in the series
candle_color (series color) : Color to use when rendering the candle
ready (series bool) : Boolean indicating if candle data is valid and ready for use
TickData
Structure for storing individual price updates
Fields:
price (series float) : The price value at this tick
time_delta (series float) : Time elapsed since the previous tick in milliseconds
series_index (series int) : Sequential index identifying this tick
CandleCTF
Structure representing a custom timeframe candle with additional time metadata
Fields:
Open (series float) : Opening price of the candle
High (series float) : Highest price of the candle
Low (series float) : Lowest price of the candle
Close (series float) : Closing price of the candle
polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)
series_index (series int) : Sequential index identifying the candle in the series
open_time (series int) : Timestamp marking when the candle was opened (in Unix time)
time_delta (series float) : Duration of the candle in milliseconds
candle_color (series color) : Color to use when rendering the candle
