Adaptive For LoopThe Adaptive For Loop is a new advanced trend following tool that can avoid false signals while keeping a high speed.
Benefits
- Good speed
- Low noise
- High Performance on INDEX:BTCUSD
- Plotting for clear visualization of trend and values.
The Idea
Before I tried using a For Loop on a singular piece of source - but every source was noisy in different parts and was not really that good.
So I got an idea: How about I make a for loop on all of them (open, high, low, close) and filter them to get the best out of all worlds?
How it works
Calculate the For Loop for open, high, low, close -> a For Loop compares the current value to past values and scores it accordingly.
After calculating them, it picks the one with the highest absolute value. This means only the for loop with the highest strength gets applied. This filters noise and provides users with high speed even in the environments that do not support it.
Enjoy Gs!
1-BTCUSD
Cumulative Volume Delta (CVD) Suite [QuantAlgo]🟢 Overview
The Cumulative Volume Delta (CVD) Suite is a comprehensive toolkit that tracks the net difference between buying and selling pressure over time, helping traders identify significant accumulation/distribution patterns, spot divergences with price action, and confirm trend strength. By visualizing the running balance of volume flow, this indicator reveals underlying market sentiment that often precedes significant price movements.
🟢 How It Works
The indicator begins by determining the optimal timeframe for delta calculation. When auto-select is enabled, it automatically chooses a lower timeframe based on your chart period, e.g., using 1-second bars for minute charts, 5-second bars for 5-minute charts, and progressively larger intervals for higher timeframes. This granular approach captures volume flow dynamics that might be missed at the chart level.
Once the timeframe is established, the indicator calculates volume delta for each bar using directional classification:
getDelta() =>
close > open ? volume : close < open ? -volume : 0
When a bar closes higher than it opens (bullish candle), the entire volume is counted as positive delta representing buying pressure. Conversely, when a bar closes lower than its open (bearish candle), volume becomes negative delta representing selling pressure. This classification is applied to every bar in the selected lower timeframe, then aggregated upward to construct the delta for each chart bar:
array deltaValues = request.security_lower_tf(syminfo.tickerid, lowerTimeframe, getDelta())
float barDelta = 0.0
if array.size(deltaValues) > 0
for i = 0 to array.size(deltaValues) - 1
barDelta := barDelta + array.get(deltaValues, i)
This aggregation process sums all the individual delta values from the lower timeframe bars that comprise each chart bar, capturing the complete volume flow activity within that period. The resulting bar delta then feeds into the various display calculations:
rawCVD = ta.cum(barDelta) // Cumulative sum from chart start
smoothCVD = ta.sma(rawCVD, smoothingLength) // Smoothed for noise reduction
rollingCVD = math.sum(barDelta, rollingLength) // Rolling window calculation
Note: This directional bar approach differs from exchange-level orderflow CVD, which uses tick data to separate aggressive buy orders (executed at the ask price) from aggressive sell orders (executed at the bid price). While this method provides a volume flow approximation rather than pure tape-reading precision, it offers a practical and accessible way to analyze buying and selling dynamics across all timeframes and instruments without requiring specialized data feeds on TradingView.
🟢 Key Features
The indicator offers five distinct visualization modes, each designed to reveal different aspects of volume flow dynamics and cater to various trading strategies and market conditions.
1. Oscillator (Raw): Displays the true cumulative volume delta from the beginning of chart history, accompanied by an EMA signal line that helps identify trend direction and momentum shifts. When CVD crosses above the signal line, it indicates strengthening buying pressure; crosses below suggest increasing selling pressure. This mode is particularly valuable for spotting long-term accumulation/distribution phases and identifying divergences where CVD makes new highs/lows while price fails to confirm, often signaling potential reversals.
2. Oscillator (Smooth): Applies a simple moving average to the raw CVD to filter out noise while preserving the underlying trend structure, creating smoother signal line crossovers. Use this when trading trending instruments where you need confirmation of genuine volume-backed moves versus temporary volatility spikes.
3. Oscillator (Rolling): Calculates cumulative delta over only the most recent N bars (configurable window length), effectively resetting the baseline and removing the influence of distant historical data. This approach focuses exclusively on current market dynamics, making it highly responsive to recent shifts in volume pressure and particularly useful in markets that have undergone regime changes or structural shifts. This mode can be beneficial for traders when they want to analyze "what's happening now" without legacy bias from months or years of prior data affecting the readings.
4. Histogram: Renders the per-bar volume delta as individual histogram bars rather than cumulative values, showing the immediate buying or selling pressure that occurred during each specific candle. Positive (green) bars indicate that bar closed higher than it opened with buying volume, while negative (red) bars show selling volume dominance. This mode excels at identifying sudden volume surges, exhaustion points where large delta bars fail to move price, and bar-by-bar absorption patterns where one side is aggressively consuming the other's volume.
5. Candles: Transforms CVD data into OHLC candlestick format, where each candle's open represents the CVD at the start of the bar and subsequent intra-bar delta changes create the high, low, and close values. This visualization reveals the internal volume flow dynamics within each time period, showing whether buying or selling pressure dominated throughout the bar's formation and exposing intra-bar reversals or sustained directional pressure. Use candle wicks and bodies to identify volume acceptance/rejection at specific CVD levels, similar to how price candles show acceptance/rejection at price levels.
▶ Built-in Alert System: Comprehensive alerts for all display modes including bullish/bearish momentum shifts (CVD crossing signal line), buying/selling pressure detection (histogram mode), and bullish/bearish CVD candle formations. Fully customizable with exchange and timeframe placeholders.
▶ Visual Customization: Choose from 5 color presets (Classic, Aqua, Cosmic, Ember, Neon) or create your own custom color schemes. Optional price bar coloring feature overlays CVD trend colors directly onto your main chart candles, providing instant visual confirmation of volume flow and making divergences immediately apparent. Optional info label with configurable position and size displays current CVD values, data source timeframe, and mode at a glance.
Triple KDJ - CKThe Triple KDJ is a market-reading architecture based on multiscale confirmation, not a new indicator. It consists of the simultaneous use of three KDJ settings with different parameters to represent three levels of price behavior: short-, medium-, and long-term. The systemic logic is simple and robust: a move is considered tradable only when there is directional coherence across all three layers, which reduces noise, prevents entries against the dominant regime, and stabilizes decision-making.
At the slowest level, the KDJ acts as a structural regime filter. It defines whether the market is, at that moment, permissive for buying, selling, or remaining neutral. When the slow KDJ shows the hierarchy J > K > D, the environment is bullish; when J < K < D occurs, the environment is bearish. If this condition is not clear, any signal on the faster levels should be ignored, as it represents only local fluctuation without directional support.
The intermediate KDJ fulfills the role of continuity confirmation. It checks whether the impulse observed on the short-term level is supported by the developing move. In practical terms, it prevents entries based solely on micro-impulses that fail to evolve into real price displacement. When the intermediate KDJ replicates the same directional hierarchy as the slow KDJ, structure and movement are aligned.
The fast KDJ is used exclusively as a timing tool, never as a standalone signal generator. This is where the J line reacts first, often emerging from extreme zones and offering the lowest-risk entry point. In the Triple KDJ, the fast layer does not “command” the trade; it simply executes what has already been authorized by the higher levels.
The J line plays a central role in this architecture. In the fast KDJ, it anticipates the change in impulse; in the intermediate KDJ, it confirms the transformation of that impulse into movement; and in the slow KDJ, it determines whether the market accepts or rejects that direction. For this reason, in the Triple KDJ the correct reading is not about line crossovers, but about a consistent hierarchy among J, K, and D across multiple scales.
Lakshmi - Low Volatility Range Breakout (LVRB)⚡️ Overview
The Low Volatility Range Breakout (LVRB) indicator is designed to identify consolidation phases characterized by suppressed volatility and generate actionable signals when price breaks out of these ranges. The underlying premise is rooted in the market principle that periods of low volatility often precede significant directional moves—volatility contraction leads to expansion.
Important Note on Optimization: The default parameter settings of this indicator have been specifically optimized for BTCUSDT on the 2-hour (2H) timeframe. While the indicator can be applied to other instruments and timeframes, users are encouraged to adjust the parameters accordingly to suit different trading conditions and asset characteristics.
This indicator automates the detection of "quiet" accumulation/distribution zones and provides clear visual cues and alerts when a breakout occurs.
⚡️ How to Use
1. Add the indicator to your chart. Default settings are optimized for BTCUSDT 2H.
2. Wait for a gray box to appear—this indicates a qualified low-volatility range is forming.
3. Monitor for breakout signals:
• LONG (green triangle below bar): Price broke above the range. Consider entering a long position.
• SHORT (red triangle above bar): Price broke below the range. Consider entering a short position.
4. Set alerts using "LVRB LONG" or "LVRB SHORT" to receive notifications on confirmed breakouts.
5. Adjust parameters as needed for different instruments or timeframes.
Tip: Combine with volume analysis or trend filters for higher-probability setups.
⚡️ How It Works
1. Low Volatility Bar Detection
A bar is classified as "low volatility" when it meets the following criteria:
• True Range (TR) is at or below the average TR (Simple Moving Average) multiplied by a user-defined threshold.
• (Optional) Candle Body is at or below the average body size multiplied by a separate threshold.
This dual-filter approach helps isolate bars that exhibit genuine compression in both range and directional commitment.
2. Range Box Formation
When consecutive low-volatility bars are detected, the indicator begins constructing a consolidation box:
• The box expands to encompass the high and low of qualifying bars.
• A minimum number of bars and a minimum fraction of low-volatility bars are required for the box to become "qualified" (active).
• A configurable tolerance allows for a limited number of consecutive non-low-vol bars within the sequence, accommodating minor noise without invalidating the range.
• If the box height exceeds a maximum threshold (defined as a multiple of the base ATR at sequence start), the range is invalidated.
3. Breakout Detection
Once a qualified range is established, the indicator monitors for breakouts:
• Wick Mode: Requires both a wick pierce beyond the range boundary AND a close outside the range.
• Close Mode: Requires only a close beyond the range boundary.
• (Optional) Breakout Body Filter: The breakout candle's body must exceed a multiple of the average body size at range formation.
• (Optional) Candle Direction Filter: Bullish breakouts require a green candle; bearish breakouts require a red candle.
Signals are displayed in real-time and confirmed upon bar close.
⚡️ Inputs & Parameters
• Volatility Window: Lookback period for calculating average TR and average body size.
• TR Multiplier: A bar's TR must be ≤ avgTR × this value to qualify as low-vol.
• Body Multiplier: A bar's body must be ≤ avgBody × this value (if body filter is enabled).
• Use Body Filter: Toggle the body size filter on/off.
• Min Bars in Box: Minimum number of bars required for a range to become qualified.
• Min Low-Vol Fraction: Minimum proportion of bars in the sequence that must be low-vol.
• Allowed Consecutive Non-Low-Vol Bars: Tolerance for consecutive bars that do not meet low-vol criteria.
• Max Box Height: Maximum allowed range height as a multiple of the base ATR.
• Breakout Mode: Choose between "Wick" (pierce + close) or "Close" (close only).
• Breakout Body Multiplier: Require breakout candle body ≥ avgBody × this value (1.0 = OFF).
• Require Candle Direction: Enforce green candle for LONG, red candle for SHORT.
⚡️ Visual Features
• Consolidation Boxes: Displayed in neutral (gray) color during formation. Upon a confirmed breakout, the box is colored green for bullish breakouts or red for bearish breakouts.
• Breakout Signals:
• LONG: Green upward triangle displayed below the price bar with "LONG" label.
• SHORT: Red downward triangle displayed above the price bar with "SHORT" label.
• Range Levels: Optional horizontal plots for the active range's high and low.
• Invalidated Boxes: Optionally retained in neutral (gray) color or deleted from the chart.
• Full Customization: Colors, transparency, and border width are all adjustable.
⚡️ Alerts
Two alert conditions are available:
• LVRB LONG: Triggered on a confirmed bullish breakout (bar close).
• LVRB SHORT: Triggered on a confirmed bearish breakout (bar close).
⚡️ Use Cases
• Breakout Trading: Enter positions when price escapes a well-defined low-volatility range.
• Volatility Expansion Plays: Anticipate increased volatility following periods of compression.
• Filtering Choppy Markets: Avoid trading during extended consolidation; wait for confirmed breakouts.
• Multi-Timeframe Analysis: Use on higher timeframes to identify major consolidation zones.
⚡️ Notes
• Best used in conjunction with volume analysis, trend context, or support/resistance levels for confirmation.
• Performance varies across instruments and timeframes; backtesting and parameter optimization are recommended.
⚡️ Credits
Developed by Lakshmi. Inspired by volatility contraction principles and range breakout methodologies.
⚡️ Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of profits. Trading financial instruments involves substantial risk, and you may lose more than your initial investment. Past performance, whether indicated by backtesting or historical analysis, does not guarantee future results. The use of this indicator does not ensure or promise any profits or protection against losses. Users are solely responsible for their own trading decisions and should conduct their own research and/or consult with a qualified financial advisor before making any investment decisions. By using this indicator, you acknowledge and accept that you bear full responsibility for any trading outcomes.
Adaptive MA SuperTrend 2.0The Adaptive MA SuperTrend 2.0 is a new cutting edge SuperTrend that adapts to the environment and provides users with fast, smooth signals that can enhance the strategies of any user.
How does it work?
This indicator combines the classic ATR with Moving Average of users choice, and filters the data. It uses a condition, that flips the Moving Average between the past and current value, adapting and trying to enhance the accuracy of the indicator
Chan Theory - Chanlun|CCZT# Chan Theory - Chanlun|CCZT
## Overview
This indicator implements Chan Theory (缠论) structural analysis framework using Pine Script v5. It automatically identifies fractals, pens, segments, and pivot zones from price movements, providing objective structure-based trading signals.
**Key Features:**
- Real-time fractal and pen recognition with 4 pen type options
- Multi-level segment analysis (sub-level and main-level)
- Dynamic pivot zone identification and visualization
- Type I/II/III trading signal detection
- Customizable display settings for all structural components
## How It Works
### 1. Candlestick Containment Processing
Eliminates noise by processing candlestick containment relationships:
- **Uptrend**: Takes higher highs and higher lows
- **Downtrend**: Takes lower highs and lower lows
### 2. Fractal Recognition
Identifies top/bottom fractals on processed candlesticks:
- **Top Fractal**: Middle candlestick high > both adjacent highs
- **Bottom Fractal**: Middle candlestick low < both adjacent lows
### 3. Pen Construction (4 Types Available)
Connects valid fractals to form pens:
- **Classic Pen (老笔)**: Requires 5+ processed K-lines per pen
- **Optimized Pen (新笔)**: 4+ processed K-lines with 5+ raw K-lines
- **4K Pen**: 4 raw K-lines with specific conditions
- **Strict Pen (严笔)**: 5+ K-lines with directional validation
### 4. Segment Partitioning (3 Modes)
Groups pens into higher-level segments:
- **Dynamic Correction**: Real-time adjustment with new data
- **Strict Mode**: Full compliance with classical definitions
- **Extension Mode**: Flexible trend continuation handling
### 5. Pivot Zone Recognition
Identifies consolidation zones at multiple levels:
- Sub-level pivot zones (pen-based)
- Main-level pivot zones (segment-based)
- Real-time pivot extension visualization
## Trading Signals
### Type I Signals (1buy/1sell)
Trend reversal signals based on momentum divergence within segments. Requires pivot zone formation or sufficient pen count.
### Type II Signals (2buy/2sell)
Pullback entry signals occurring after Type I, identified by sub-level fractal confirmations.
### Type III Signals (3buy/3sell)
Breakout confirmation signals when price breaks and holds beyond prior pivot zones.
### Quasi-Type II Signals (L2buy/L2sell)
Similar to Type II but with less strict conditions.
## Settings Guide
| Setting | Description |
|---------|-------------|
| **Pen Type** | Choose Classic/Optimized/4K/Strict based on volatility |
| **Segment Mode** | Select calculation method matching your strategy |
| **Show Pivot Zones** | Toggle sub-level/main-level pivots |
| **Show Running Pen** | Display currently forming unconfirmed pen |
| **Fast Pen Mode** | Allow pens without complete fractals |
## Display Options
- Pen lines with customizable colors and width
- Segment lines for different levels
- Pivot zone boxes with gradient colors
- Trading signals at fractal positions
## Upcoming Features (Coming Soon)
The following features are planned for future releases:
### Pen & Fractal Enhancements
- Right containment check (启用右包含检查)
- Pen endpoint mode: Strict highest/lowest vs Secondary high/low (笔端点模式)
- Pen extension correction in secondary mode (次高次低模式启用笔延伸修正)
- Single pen to segment (单笔成段)
- Segment formation conditions (成段条件)
- K-line count for segment (K线数量)
- Pen/Segment count for trend (笔/段数量)
- Trend line start filter (趋势线起点过滤)
- Local extremum filter (局部极值过滤)
- Lookback period (回溯周期)
- 3K interval filter (3K间隔过滤)
- Raw K-line fractal display (显示原始K线分型)
- Raw fractal for pen (原始分型用于笔)
- Single pen to segment ratio (单笔成段比例)
- Top/Bottom to pen ratio (顶底成笔)
### Segment Enhancements
- Super trend line display (显示大趋势线)
- Trend line extension (趋势线延伸)
- Super trend line extension (大趋势线延伸)
- Super trend segment colors (线段颜色-大趋势线)
- Single segment to trend (单段成趋势)
- Trend breakthrough (趋势突破)
- Feature sequence gap detection (启用特征序列缺口检测)
### Pivot Zone Enhancements
- Big-level pivot zone display (大级别中枢)
- Early draw big-level pivot (提前绘制大级别中枢)
- Big-level pivot colors (大级别中枢颜色)
### Trading Signal Enhancements
- Big-level trading signals (大级别买卖点)
- Type 2 chain detection (类2链式判断)
- Type 3 search range (类3搜索范围)
- Type 3 rapid reversal alert (3类买卖点急速反转警报)
### MACD Divergence (Complete Module)
- Sub-level divergence display (显示次级别背驰)
- Main-level divergence display (显示本级别背驰)
- Type 1 divergence detection method (1买卖点背驰判断方式)
- Type 1 pivot requirement (1买卖点中枢要求)
- Type 1 divergence detection toggle (1类买卖点启用背驰判断)
### Signal Filtering (Complete Module)
- Fractal validity filter (买卖点分型过滤)
- Basic fractal filter (买卖点分型基础过滤)
- Type 1 MACD divergence filter (1买卖macd背驰过滤)
- Type 2 signal filter (2买卖点过滤)
- False signal trap avoidance (防狼术)
- Expected signal display (显示预期买卖点)
- Alert differentiation (警报区分)
### Feature Sequence (Complete Module)
- Feature sequence display (显示特征序列)
- Up/Down segment colors for feature sequence
## Notes
- This script is for technical analysis reference only
- Does not constitute investment advice
- Users should make independent trading decisions
- Best used in conjunction with Chan Theory MACD Divergence indicator
---
# 概述
本指标基于缠论(Chan Theory)技术分析框架，使用Pine Script v5实现价格结构的自动识别。自动解析分型、笔、线段和中枢等核心组件，提供客观的结构化交易信号。
**核心功能：**
- 实时分型和笔识别，提供4种笔类型选择
- 多级别线段分析（次级别和本级别）
- 动态中枢识别与可视化
- 一、二、三类买卖点检测
- 所有结构组件可自定义显示设置
## 工作原理
### 1. K线包含处理
消除K线包含关系带来的噪音：
- **上涨趋势**：取高点高值、低点高值
- **下跌趋势**：取高点低值、低点低值
### 2. 分型识别
在处理后的K线上识别顶底分型：
- **顶分型**：中间K线高点 > 两侧高点
- **底分型**：中间K线低点 < 两侧低点
### 3. 笔的构建（4种类型）
连接有效分型形成笔结构：
- **老笔**：每笔至少5根处理后K线
- **新笔**：4根处理后K线 + 5根原始K线
- **4K笔**：4根原始K线满足特定条件
- **严笔**：5根K线 + 方向验证
### 4. 线段划分（3种模式）
将笔组合成更高级别的线段：
- **当下延伸后修正**：随新数据实时调整
- **严格模式**：完全符合经典定义
- **延伸模式**：灵活处理趋势延续
### 5. 中枢识别
识别多级别的盘整区域：
- 次级别中枢（基于笔）
- 本级别中枢（基于线段）
- 实时中枢延伸可视化
## 买卖点信号
### 一类买卖点 (1buy/1sell)
基于线段内动量背驰的趋势反转信号，需要中枢形成或足够笔数。
### 二类买卖点 (2buy/2sell)
一类之后的回调入场信号，通过次级别分型确认识别。
### 三类买卖点 (3buy/3sell)
价格突破并站稳中枢边界的突破确认信号。
### 类二买卖点 (L2buy/L2sell)
条件较宽松的类似二类信号。
## 设置说明
| 设置项 | 说明 |
|--------|------|
| **笔的类型** | 根据波动性选择老笔/新笔/4K/严笔 |
| **线段模式** | 选择符合策略的计算方式 |
| **显示中枢** | 切换次级别/本级别中枢显示 |
| **运行中的笔** | 显示当前形成中的未确认笔 |
| **急速成笔** | 允许无完整分型成笔 |
## 显示选项
- 笔线条，可自定义颜色和宽度
- 不同级别的线段线条
- 中枢区域带渐变色
- 买卖点信号显示在分型位置
## 即将推出的功能（敬请期待）
以下功能计划在后续版本中发布：
### 分型、笔增强功能
- 启用右包含检查
- 笔端点模式：严格最高最低点 / 允许次高次低点
- 次高次低模式启用笔延伸修正
- 单笔成段
- 成段条件（突破极值/数量条件/任一满足）
- K线数量要求
- 笔/段数量要求
- 趋势线起点过滤
- 局部极值过滤
- 回溯周期
- 3K间隔过滤
- 显示原始K线分型
- 原始分型用于笔
- 单笔成段比例
- 顶底成笔
### 线段增强功能
- 显示大趋势线
- 趋势线延伸
- 大趋势线延伸
- 线段颜色（大趋势线）
- 单段成趋势
- 趋势突破
- 启用特征序列缺口检测
### 中枢增强功能
- 是否显示大级别中枢
- 提前绘制大级别中枢
- 大级别中枢颜色设置
### 买卖点增强功能
- 大级别买卖点
- 启用类2链式判断
- 类3搜索范围
- 启用3类买卖点急速反转警报
### MACD背驰模块（完整模块）
- 显示次级别背驰
- 显示本级别背驰
- 1买卖点背驰判断方式
- 1买卖点中枢要求
- 1类买卖点启用背驰判断
### 买卖点过滤模块（完整模块）
- 买卖点分型过滤
- 买卖点分型基础过滤
- 1买卖macd背驰过滤
- 2买卖点过滤
- 防狼术
- 显示预期买卖点
- 警报区分
### 特征序列模块（完整模块）
- 显示特征序列
- 上涨/下跌线段特征序列颜色
## 声明
- 本脚本仅供技术分析参考
- 不构成投资建议
- 用户应自行做出交易决策
- 建议结合缠论macd背驰指标使用
Fourier Smoothed Volume Zone Oscillator Forecast [QuantAlgo]🟢 Overview
Volume tells the story that price alone cannot. When thousands of contracts change hands on an upward move versus a handful on a downward drift, the market communicates something meaningful about conviction and participation. The Fourier Smoothed Volume Zone Oscillator (FSVZO) captures this relationship by measuring directional volume flow, producing readings that reveal whether buyers or sellers control the tape with genuine commitment. Building on this foundation, this FSVZO Forecast indicator adds a forward-looking dimension through three distinct projection engines: a market structure model that interprets swing dynamics, a volume-weighted approach that examines accumulation and distribution flows, and a linear regression method that extrapolates recent directional behavior. What distinguishes this implementation is its dual forecasting architecture. Since FSVZO fundamentally depends on the interplay between price movement and volume activity, the indicator projects both elements independently before calculating future oscillator values, creating coherent framework for mean reversion trading across multiple asset classes and timeframes, from intraday scalping on liquid futures to swing trading equities and cryptocurrencies.
🟢 How It Works
The indicator begins by calculating a Volume Zone Oscillator using a directional volume approach: it multiplies volume by the sign of price change (positive when price rises, negative when price falls), applies a weighted moving average to this directional volume, then divides by a simple moving average of total volume. The result scales to a percentage, typically oscillating between -100 and +100, with readings beyond these levels indicating exceptional momentum conditions. Multiple smoothing passes, including a triple-smoothed SMA sequence and optional additional smoothing, reduce noise while preserving meaningful signals.
The forecasting mechanism operates through a two-stage process that distinguishes this indicator from simpler projection tools. First, the system estimates future price levels using the selected forecasting method. Second, it independently projects future volume using one of three volume models: average (baseline historical volume), momentum (volume adjusted for recent acceleration or deceleration), or mean reversion (volume gravitating toward longer-term norms). These dual projections then feed into a simulated FSVZO engine that replicates the actual oscillator's mathematics, calculating directional volume relationships and applying identical smoothing operations to produce projected values.
Since momentum oscillators rarely travel in straight lines, the projection system incorporates dynamic price oscillation. This mechanism draws from stored patterns of recent price changes, applies mathematical wave functions tied to current volatility conditions, and factors in momentum characteristics to create natural-looking forecast trajectories. The Price Volatility input allows traders to adjust the degree of fluctuation in projections. Higher settings produce more waviness, while lower settings generate smoother trend-like forecasts. The complete system generates up to 20 bars of projected FSVZO and MA values, rendered as dashed lines extending beyond current price action.
🟢 Key Features
1. Market Structure Model
This projection method analyzes price action through the lens of swing point dynamics and structural shifts. The algorithm identifies pivot highs and pivot lows within a configurable lookback range, then evaluates whether the market exhibits bullish characteristics (successive higher highs and higher lows) or bearish patterns (successive lower highs and lower lows). When price breaks previous swing levels, the model recognizes these as potential changes of character that inform projection direction.
Price forecasts under this model incorporate proximity analysis to key structural levels and aggregate trend strength, measured by counting trend-confirming swings across recent history. Bullish structure combined with price near support zones biases projections upward, generating forecasted FSVZO readings that reflect potential buying momentum. Bearish structure near resistance creates downward-biased projections. ATR scaling keeps projections proportional to current market volatility.
▶ Practical Implications:
Designed for traders who build strategies around support, resistance, and swing-based entries
Structure-based projections provide context around pivot zones where FSVZO direction changes may coincide with price reactions
Can help visualize potential divergence setups as structural shifts in price may precede FSVZO direction changes
Shows how FSVZO projections shift based on proximity to detected swing highs and lows
Works best when markets display clear directional swings rather than choppy consolidation
May produce less useful output during extended consolidation phases with overlapping swing points
Day traders can combine structural projections with session pivots for intraday momentum context
2. Volume-Weighted Model
This method synthesizes multiple volume indicators to construct informed price projections that subsequently drive FSVZO forecasts. The algorithm tracks On-Balance Volume to measure cumulative buying and selling pressure over time, monitors the Accumulation/Distribution Line to assess where price settles within each bar's range relative to volume, and computes volume-weighted returns that emphasize high-activity price movements. Directional slopes of these metrics reveal whether volume patterns confirm or contradict prevailing price direction.
Significant volume spikes receive heightened attention, with their directional bias incorporated into forecast calculations. When OBV slope, A/D line slope, and volume momentum align in the same direction, the model generates more assertive price projections, translating to stronger FSVZO movements. Conflicting volume signals produce dampened projections, suggesting FSVZO may consolidate rather than extend. The Volume Influence parameter allows traders to weight how heavily volume analysis affects the final projection versus pure price trend extrapolation.
▶ Practical Implications:
Designed for traders who incorporate volume confirmation into their analysis
Helps identify whether current price moves are accompanied by supportive volume patterns
Volume-based projections can provide additional context when evaluating divergences between price and momentum
Best suited for instruments with meaningful volume data
Swing traders can assess whether breakout moves show volume commitment
3. Linear Regression Model
The most mathematically direct of the three approaches, linear regression fits an optimal straight line through recent price data using least-squares methodology and extends that trajectory forward. These projected prices, combined with volume forecasts, generate corresponding FSVZO projections without conditional market interpretation or structural analysis. The forecast simply addresses one question: if price continues at its current rate of change with projected volume conditions, where would FSVZO readings be in upcoming bars?
▶ Practical Implications:
Functions well during sustained, orderly trends where price progression remains relatively linear
Responds more slowly to sudden directional shifts or volatility regime changes
Works effectively on higher timeframes where trends develop more gradually
Useful benchmark for comparing against structure or volume models to gauge projection differences
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the FSVZO Forecast indicator projects future oscillator positions that may assist with:
▶ Mean Reversion Trading at Extreme Zones: FSVZO displays defined overbought and oversold territories that create potential mean reversion opportunities. When FSVZO enters these upper or lower extremes, traders can monitor for potential exhaustion and reversal setups as the oscillator moves back toward neutral. Projections add a timing dimension to this analysis by showing where FSVZO may travel in upcoming bars, allowing traders to anticipate when the oscillator might approach or exit extreme zones.
▶ Trend Following with the Colored Band: The filled band between FSVZO main line and offset line delivers immediate trend visualization across all forecast models. Green coloring indicates rising FSVZO (current value higher than previous = long/buy opportunity), while red coloring indicates falling FSVZO (current value lower than previous = short/sell opportunity). This visual system provides quick reference for current momentum direction. For trend following applications, traders can monitor band color for directional bias and watch for color transitions as potential warning signals. Projections extend this visualization into future bars, showing whether the forecast anticipates continued momentum or potential direction changes. Combining band direction with FSVZO's position relative to zero provides layered context: green band above zero suggests bullish momentum, red band below zero suggests bearish momentum, while mixed readings suggest transitional conditions.
▶ Divergence Detection: Built-in divergence scanning identifies regular (R label) and hidden (H label) divergences between price and FSVZO. Regular divergences occur when price makes a higher high while FSVZO makes a lower high (bearish) or price makes a lower low while FSVZO makes a higher low (bullish). Hidden divergences signal potential trend continuation. Projections can provide context for whether developing divergences might continue or resolve.
▶ Signal Line Crossovers: The indicator tracks crossovers between FSVZO and its moving average. Crossovers from below occur when FSVZO rises above the MA, while crossovers from above occur when FSVZO falls below the MA. Projections may help anticipate when these crossovers could occur.
▶ Zero Line Analysis: FSVZO crossing above zero indicates a shift to positive directional volume flow. Crossing below zero indicates a shift to negative directional volume flow. Projections can show whether the oscillator may approach or cross the zero line in upcoming bars.
▶ White Noise Filtering: The optional Ehlers White Noise overlay displays an additional oscillator that measures the degree of randomness in price movement. This can help identify periods when price movements lack clear directional commitment, providing context for when momentum signals may be less meaningful.
▶ Multi-Model Comparison: Running different projection methods and noting where they agree or disagree provides additional analytical context. When multiple methods project similar trajectories, this alignment may warrant special attention.
▶ Trade Management: Reference projected FSVZO levels when planning stops, position adjustments, or profit targets based on anticipated momentum conditions.
🟢 Important Considerations
▶ This indicator requires volume data to function correctly. Instruments that do not report volume or report unreliable volume data will produce meaningless or zero readings.
▶ These forecasts derive from mathematical analysis of recent price and volume behavior. Markets operate as dynamic systems influenced by countless factors that no technical indicator can fully anticipate. Projected FSVZO values represent potential momentum scenarios based on current conditions, and actual readings may follow different paths than those visualized. Historical tendencies and mathematical extrapolations provide no guarantee of future market behavior. Consider these projections as one component within a comprehensive trading methodology that includes disciplined risk management, appropriate position sizing, and multiple analytical perspectives. The primary value of this script lies not in expecting precise forecasts but in developing forward-looking awareness of possible market conditions and structuring your trades accordingly.
Valex Bot - V3Valex Bot V3 is a macro trend intelligence indicator designed to cut through market noise and highlight the most important directional shifts in price. Built for traders who prioritize clarity and confidence, it delivers clean, visually intuitive trend guidance along with precise buy and sell signals that align with major market cycles. By anchoring its analysis to higher-timeframe market structure, Valex Bot V3 helps users stay on the right side of powerful trends while avoiding emotional overtrading and false signals common on lower timeframes. Whether used as a standalone trend system or as a directional filter for entries, it excels at identifying high-probability market phases across crypto, forex, and traditional markets.
Volume-Weighted Price Z-Score [QuantAlgo]🟢 Overview
The Volume-Weighted Price Z-Score indicator quantifies price deviations from volume-weighted equilibrium using statistical standardization. It combines volume-weighted moving average analysis with logarithmic deviation measurement and volatility normalization to identify when prices have moved to statistically extreme levels relative to their volume-weighted baseline, helping traders and investors spot potential mean reversion opportunities across multiple timeframes and asset classes.
🟢 How It Works
The indicator's core methodology lies in its volume-weighted statistical approach, where price displacement is measured through normalized deviations from volume-weighted price levels:
volumeWeightedAverage = ta.vwma(priceSource, lookbackPeriod)
logDeviation = math.log(priceSource / volumeWeightedAverage)
volatilityMeasure = ta.stdev(logDeviation, lookbackPeriod)
The script uses logarithmic transformation to capture proportional price changes rather than absolute differences, ensuring equal treatment of percentage moves regardless of price level:
rawZScore = logDeviation / volatilityMeasure
zScore = ta.ema(rawZScore, smoothingPeriod)
First, it establishes the volume-weighted baseline which gives greater weight to price levels where significant trading occurred, creating a more representative equilibrium point than simple moving averages.
Then, the logarithmic deviation measurement converts the price-to-average ratio into a normalized scale:
logDeviation = math.log(priceSource / volumeWeightedAverage)
Next, statistical normalization is achieved by dividing the deviation by its own historical volatility, creating a standardized z-score that measures how many standard deviations the current price sits from the volume-weighted mean.
Finally, EMA smoothing filters noise while preserving the signal's responsiveness to genuine market extremes:
rawZScore = logDeviation / volatilityMeasure
zScore = ta.ema(rawZScore, smoothingPeriod)
This creates a volume-anchored statistical oscillator that combines price-volume relationship analysis with volatility-adjusted normalization, providing traders with probabilistic insights into market extremes and mean reversion potential based on standard deviation thresholds.
🟢 Signal Interpretation
▶ Positive Values (Above Zero): Price trading above volume-weighted average indicating potential overvaluation relative to volume-weighted equilibrium = Caution on longs, potential mean reversion downward = Short/sell opportunities
▶ Negative Values (Below Zero): Price trading below volume-weighted average indicating potential undervaluation relative to volume-weighted equilibrium = Caution on shorts, potential mean reversion upward = Long/buy opportunities
▶ Zero Line Crosses: Mean reversion transitions where price crosses back through volume-weighted equilibrium, indicating shift from overvalued to undervalued (or vice versa) territory
▶ Extreme Positive Zone (Above +2.5σ default): Statistically rare overvaluation representing 98.8%+ confidence level deviation, indicating extremely stretched bullish conditions with high mean reversion probability = Strong correction warning/short signal
▶ Extreme Negative Zone (Below -2.5σ default): Statistically rare undervaluation representing 98.8%+ confidence level deviation, indicating extremely stretched bearish conditions with high mean reversion probability = Strong buying opportunity signal
▶ ±1σ Reference Levels: Moderate deviation zones (±1 standard deviation) marking common price fluctuation boundaries where approximately 68% of price action occurs under normal distribution
▶ ±2σ Reference Levels: Significant deviation zones (±2 standard deviations) marking unusual price extremes where approximately 95% of price action should be contained under normal conditions
🟢 Features
▶ Preconfigured Presets: Three optimized parameter sets accommodate different analytical approaches, instruments and timeframes. "Default" provides balanced statistical measurement suitable for swing trading and daily/4-hour analysis, offering deviation detection with moderate responsiveness to price dislocations. "Fast Response" delivers heightened sensitivity optimized for intraday trading and scalping on 15-minute to 1-hour charts, using shorter statistical windows and minimal smoothing to capture rapid mean reversion opportunities as they develop. "Smooth Trend" offers conservative extreme identification ideal for position trading on daily to weekly charts, employing extended statistical periods and heavy noise filtering to isolate only the most significant market extremes.
▶ Built-in Alerts: Seven alert conditions enable comprehensive automated monitoring of statistical extremes and mean reversion events. Extreme Overbought triggers when z-score crosses above the extreme threshold (default +2.5σ) signaling rare overvaluation, Extreme Oversold activates when z-score crosses below the negative extreme threshold (default -2.5σ) signaling rare undervaluation. Exit Extreme Overbought and Exit Extreme Oversold alert when prices begin reverting from these statistical extremes back toward the mean. Bullish Mean Reversion notifies when z-score crosses above zero indicating shift to overvalued territory, while Bearish Mean Reversion triggers on crosses below zero indicating shift to undervalued territory. Any Extreme Level provides a combined alert for any extreme threshold breach regardless of direction. These notifications allow you to capitalize on statistically significant price dislocations without continuous chart monitoring.
▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and visual preferences, ensuring optimal contrast for identifying positive versus negative deviations across trading environments. The adjustable fill transparency control (0-100%) allows fine-tuning of the gradient area prominence between the z-score line and zero baseline, with higher opacity values creating subtle background context while lower values produce bold deviation emphasis. Optional bar coloring extends the z-score gradient directly to the indicator pane bars, providing immediate visual reinforcement of current deviation magnitude and direction without requiring reference to the plotted line itself.
*Note: This indicator requires volume data to function correctly, as it calculates deviations from a volume-weighted price average. Tickers with no volume data or extremely limited volume will not produce meaningful results, i.e., the indicator may display flat lines, erratic values, or fail to calculate properly. Using this indicator on assets without volume data (certain forex pairs, synthetic indices, or instruments with unreported/unavailable volume) will produce unreliable or no results at all. Additionally, ensure your chart has sufficient historical data to cover the selected lookback period, e.g., using a 100-bar lookback on a chart with only 50 bars of history will yield incomplete or inaccurate calculations. Always verify your chosen ticker has consistent, accurate volume information and adequate price history before applying this indicator.
Stress & Recovery Daily Stock/BTC This indicator is a stress → recovery regime tool designed for Daily charts (Bitcoin and equities). It combines Williams Vix Fix (WVF) to detect panic/capitulation conditions (potential bottoms) with RSI vs EMA(RSI) to confirm the start of a recovery phase — but only when that recovery occurs within a configurable number of bars after a WVF panic event.
It is not a generic trend indicator. It focuses on one specific sequence:
Panic spike (WVF) → Recovery confirmation (RSI crossing above EMA(RSI)).
What it Shows
1) Red Bottom Shadow (Panic Zone)
A red shaded area below the baseline appears when WVF triggers a panic condition. This highlights periods where downside pressure and “panic-like” behavior are elevated.
To avoid clutter, the red triangle marker (▼) is plotted only once per red cluster, specifically on the last bar of the panic cluster (end of the WVF signal streak).
2) Green State Ribbon (Recovery Regime)
A green ribbon above the baseline indicates a recovery regime. You can choose how the green signal behaves:
Crossover only: green is active only on the single bar where RSI crosses above EMA(RSI).
State (RSI > EMA): green stays active as long as RSI remains above EMA(RSI).
3) Amber Ribbon (Conflict State)
If panic (WVF) and recovery (green state) overlap, the ribbon turns amber.
This indicates a mixed condition: panic is still present, but momentum is attempting to reverse.
4) Green Triangle Marker (▲) — Validated Recovery Start
A green triangle (▲) appears only when RSI crosses above EMA(RSI) AND that crossover happens within N bars from the most recent WVF panic zone. This time-window filter helps avoid unrelated RSI crossovers that occur far from capitulation events.
How to Use
- Treat red shadow as a “panic/stress zone”.
- Look for the green triangle (▲) as the first validated recovery trigger after panic.
- Use green ribbon as a recovery regime filter (especially in “State” mode).
- Use amber ribbon as a caution zone (overlap = mixed signals).
This indicator is best used as a context and timing filter, not as a complete trading system by itself.
Notes:
- Designed and tuned for Daily timeframe usage.
- Signals may behave differently on intraday timeframes or illiquid assets.
RSI Forecast [QuantAlgo]🟢 Overview
While standard RSI excels at measuring current momentum and identifying overbought or oversold conditions, it only reflects what has already happened in the market. The RSI Forecast indicator builds upon this foundation by projecting potential RSI trajectories into future bars, giving traders a framework to consider where momentum might head next. Three analytical models power these projections: a market structure approach that reads swing highs and lows, a volume analysis method that weighs accumulation and distribution patterns, and a linear regression model that extrapolates recent trend behavior. Each model processes market data differently, allowing traders to choose the approach that best fits their analytical style and the asset they're trading.
🟢 How It Works
At its foundation, the indicator calculates RSI using the standard methodology: comparing average upward price movements against average downward movements over a specified period, producing an oscillator that ranges from 0 to 100. Traders can apply an optional signal line using various moving average types (e.g., SMA, EMA, SMMA/RMA, WMA, or VWMA), and when SMA smoothing is selected, Bollinger Bands can be added to visualize RSI volatility ranges.
The forecasting mechanism operates by first estimating future price levels using the chosen projection method. These estimated prices then pass through a simulated RSI engine that mirrors the actual indicator's mathematics. The simulation updates the internal gain and loss averages bar by bar, applying the same RMA smoothing that powers real RSI calculations, to produce authentic projected values.
Since RSI characteristically moves in waves rather than straight lines, the projection system incorporates dynamic oscillation. This draws from stored patterns of recent RSI movements, factors in the tendency for RSI to pull back from extreme readings, and applies mathematical wave functions tied to current momentum conditions. The Oscillation Intensity control lets traders adjust how much waviness appears in projections. Signal line (RSI-based MA) projections follow the same logic, advancing the chosen moving average type forward using its proper mathematical formula. The complete system generates 15 bars of projected RSI and signal line values, displayed as dashed lines extending beyond current price action.
🟢 Key Features
1. Market Structure Model
This projection method reads price action through swing point analysis. It scans for pivot highs and pivot lows within a defined lookback range, then evaluates whether the market is building bullish patterns (successive higher highs and higher lows) or bearish patterns (successive lower highs and lower lows). The algorithm recognizes structural shifts when price violates previous swing levels in either direction.
Price projections under this model factor in proximity to key swing levels and overall trend strength, measured by tallying trend-confirming swings over recent history. When bullish structure prevails and price hovers near support, upward price bias enters the projection, pushing forecasted RSI higher. Bearish structure near resistance creates the opposite effect. The model scales its projections using ATR to keep them proportional to current volatility conditions.
▶ Practical Implications for Traders:
Aligns well with traders who focus on support, resistance, and swing-based entries
Provides context for where RSI might travel as price interacts with structural levels
Tends to perform better when markets display clear directional swings
May produce less useful output during consolidation phases with overlapping swings
Offers early visualization of potential divergence setups
Swing traders can use structure-based projections to time entries around key pivot zones
Position traders could benefit from the trend strength component when holding through larger moves
On lower timeframes, it helps scalpers identify micro-structure shifts for quick momentum plays
Useful for mapping out potential RSI behavior around breakout and breakdown levels
Day traders can combine structural projections with session highs and lows for intraday context
2. Volume-Weighted Model
This method blends multiple volume indicators to inform its price projections. It tracks On-Balance Volume to gauge cumulative buying and selling pressure, monitors the Accumulation/Distribution Line to assess where price closes relative to its range on each bar, and calculates volume-weighted returns to give heavier influence to high-volume price movements. The model examines the directional slope of these metrics to assess whether volume confirms or contradicts price direction.
Unusually high volume bars receive special attention, with their directional bias factored into projections. When all volume metrics point the same direction, the model produces more aggressive price forecasts and consequently stronger RSI movements. Conflicting volume signals lead to more muted projections, suggesting RSI may move sideways rather than trending.
▶ Practical Implications for Traders:
Suited for traders who incorporate volume confirmation into their analysis
Works best with instruments that report accurate, meaningful volume data
Useful for identifying situations where momentum lacks volume support
Less applicable to instruments with sparse or unreliable volume information
Scalpers on liquid markets can spot volume-backed momentum for quick entries and exits
Helps intraday traders distinguish between genuine moves and low-volume fakeouts
Position traders can assess whether institutional participation supports longer-term trends
Effective during news events or market opens when volume spikes often drive directional moves
Swing traders can use volume divergence in projections to anticipate potential reversals
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. These projected prices then generate corresponding RSI forecasts. This creates a clean momentum projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent price trend continues at its current rate of change, where would RSI be in the coming bars?
▶ Practical Implications for Traders:
Delivers a clean, mathematically neutral projection baseline
Functions well during sustained, orderly trends
Involves fewer parameters and produces consistent, reproducible output
Responds more slowly when trend direction shifts
Works best in trending environments rather than ranging markets
Ideal for position traders who want to ride established trends
Useful for swing traders to gauge trend exhaustion when actual RSI deviates from linear projections
Scalpers can use the smooth output as a reference point to measure short-term momentum deviations
Effective baseline for comparing against structure or volume models to measure market complexity
Works particularly well on higher timeframes where trends develop more gradually
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future RSI positions that may help with:
▶ Overbought/Oversold Planning: See whether RSI trajectories point toward extreme zones, giving you time to prepare responses before conditions develop
▶ Entry and Exit Timing: Factor projected RSI levels into your timing decisions for opening or closing positions
▶ Crossover Anticipation: Watch for projected crossings between RSI and its signal line (RSI-based MA) that might indicate upcoming momentum shifts
▶ Mean Reversion Context: When RSI sits at extremes, projections can illustrate potential paths back toward the midline
▶ Momentum Evaluation: Assess whether current directional strength appears likely to continue or fade based on projection direction
▶ Divergence Awareness: Use forecast trajectories alongside price action to spot potential divergence formations earlier
▶ Comparative Analysis: Run different projection methods and note where they agree or disagree, using alignment as an additional filter, for instance
▶ Multi-Timeframe Context: Compare RSI projections across different timeframes to identify alignment or conflict in momentum outlook
▶ Trade Management: Reference projected RSI levels when adjusting stops, scaling positions, or setting profit targets
▶ Rule-Based Systems: Incorporate projected RSI conditions into systematic trading approaches for more forward-looking signal generation
Note: It is essential to recognize that these forecasts derive from mathematical analysis of recent price behavior. Markets are dynamic environments shaped by innumerable factors that no technical tool can fully capture or foresee. The projected RSI values represent potential scenarios for how momentum might develop, and actual readings can take different paths than those visualized. Historical tendencies and past patterns offer no guarantee of future behavior. Consider these projections as one element within a comprehensive trading approach that encompasses disciplined risk management, appropriate position sizing, and diverse analytical methods. The true benefit lies not in expecting precise forecasts but in developing a forward-thinking perspective on possible market conditions and planning your responses accordingly.
Crypto Flow Index (CFI) - RS vs BTC/ETH ---
Crypto Flow Index, CFI
Crypto Flow Index, CFI, measures relative strength between an asset and Bitcoin or Ethereum.
You use CFI to judge whether capital favors your asset or the benchmark.
CFI does not give entry or exit signals.
You use CFI as a bias and context tool.
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What CFI measures
Relative strength money flow on the BASE/BTC or BASE/ETH pair.
Volume weighted pressure, not price alone.
Momentum blended into flow to smooth rotations.
Optional USD trend filter using fast and slow EMAs.
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How to read CFI
Above 50 means relative strength favors the asset.
Below 50 means relative strength favors BTC or ETH.
Rising CFI shows strengthening relative demand.
Falling CFI shows weakening relative demand.
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Histogram
Green bars show positive relative flow.
Red bars show negative relative flow.
Larger bars signal stronger pressure.
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Bias ribbon
Green ribbon shows bullish relative bias.
Red ribbon shows bearish relative bias.
Gray ribbon shows transition or balance.
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How to use CFI
Favor long trades when CFI stays above 50.
Avoid longs when price rises but CFI falls.
Spot rotations before price reacts.
Combine with structure, entries, and risk rules.
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Important limits
CFI compares assets only to BTC or ETH.
CFI does not represent the entire crypto market.
USD price and relative strength often diverge.
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Core question CFI answers
Is your asset gaining or losing strength versus Bitcoin or Ethereum.
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BTC Valuation ZonesBTC Valuation – Distance From 200 MA
This indicator provides a simple but powerful Bitcoin valuation framework based on how far price is from the 200-period Moving Average, a level that has historically acted as Bitcoin’s long-term equilibrium.
Instead of predicting tops or bottoms, this tool focuses on mean-reversion behavior:
When price deviates too far above the 200 MA → risk increases
When price deviates deeply below the 200 MA → long-term opportunity increases
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview
The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context.
🟢 How It Works
The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion:
basis = ma(close, length, maType)
stdev = ta.stdev(close, length)
The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons:
zScore = stdev != 0 ? (close - basis) / stdev : 0
smoothedZ = ma(zScore, smooth, maType)
The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes:
upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper
lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower
The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes.
🟢 How to Use This Indicator
▶ Overbought and Oversold Identification:
The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions.
▶ Mean Reversion Trading Applications:
The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway.
▶ Trend and Distribution Analysis:
Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears.
▶ Built-in Alerts:
Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities.
▶ Color Customization:
Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
Volume-Gated Trend Ribbon [QuantAlgo]🟢 Overview
The Volume-Gated Trend Ribbon employs a selective price-updating mechanism that filters market noise through volume validation, creating a trend-following system that responds exclusively to significant price movements. The indicator gates price updates to moving average calculations based on volume threshold crossovers, ensuring that only bars with significant participation influence the trend direction. By interpolating between fast and slow moving averages to create a multi-layered visual ribbon, the indicator provides traders and investors with an adaptive trend identification framework that distinguishes between volume-backed directional shifts and low-conviction price fluctuations across multiple timeframes and asset classes.
🟢 How It Works
The indicator first establishes a dynamic baseline by calculating the simple moving average of volume over a configurable lookback period, then applies a user-defined multiplier to determine the significance threshold:
avgVol = ta.sma(volume, volPeriod)
highVol = volume >= avgVol * volMult
The gated price mechanism employs conditional updating where the close price is only captured and stored when volume exceeds the threshold. During low-volume periods, the indicator maintains the last qualified price level rather than tracking every minor fluctuation:
var float gatedClose = close
if highVol
gatedClose := close
Dual moving averages are calculated using the gated price input, with the indicator supporting various MA types. The fast and slow periods create the outer boundaries of the trend ribbon:
fastMA = volMA(gatedClose, close, fastPeriod)
slowMA = volMA(gatedClose, close, slowPeriod)
Ribbon interpolation creates intermediate layers by blending the fast and slow moving averages using weighted combinations, establishing a gradient effect that visually represents trend strength and momentum distribution:
midFastMA = fastMA * 0.67 + slowMA * 0.33
midSlowMA = fastMA * 0.33 + slowMA * 0.67
Trend state determination compares the fast MA against the slow MA, establishing bullish regimes when the faster average trades above the slower average and bearish regimes during the inverse relationship. Signal generation triggers on state transitions, producing alerts when the directional bias shifts:
bullish = fastMA > slowMA
longSignal = trendState == 1 and trendState != 1
shortSignal = trendState == -1 and trendState != -1
The visualization architecture constructs a three-tiered opacity gradient where the ribbon's core (between mid-slow and slow MAs) displays the highest opacity, the inner layer (between mid-fast and mid-slow) shows medium opacity, and the outer layer (between fast and mid-fast) presents the lightest fill, creating depth perception that emphasizes the trend center while acknowledging edge uncertainty.
🟢 How to Use This Indicator
▶ Long and Short Signals: The indicator generates long/buy signals when the trend state transitions to bullish (fast MA crosses above slow MA) and short/sell signals when transitioning to bearish (fast MA crosses below slow MA). Because these crossovers only reflect volume-validated price movements, they represent significant level of participation rather than random noise, providing higher-conviction entry signals that filter out false breakouts occurring on thin volume.
▶ Ribbon Width Dynamics: The spacing between the fast and slow moving averages creates the ribbon width, which serves as a visual proxy for trend strength and volatility. Expanding ribbons indicate accelerating directional movement with increasing separation between short-term and long-term momentum, suggesting robust trend development. Conversely, contracting ribbons signal momentum deceleration, potential trend exhaustion, or impending consolidation as the fast MA converges toward the slow MA.
▶ Preconfigured Presets: Three optimized parameter sets accommodate different trading styles and market conditions. Default provides balanced trend identification suitable for swing trading on daily timeframes with moderate volume filtering and responsiveness. Fast Response delivers aggressive signal generation optimized for intraday scalping on 1-15 minute charts, using lower volume thresholds and shorter moving average periods to capture rapid momentum shifts. Smooth Trend offers conservative trend confirmation ideal for position trading on 4-hour to weekly charts, employing stricter volume requirements and extended periods to filter noise and identify only the most robust directional moves.
▶ Built-in Alerts: Three alert conditions enable automated monitoring: Bullish Trend Signal triggers when the fast MA crosses above the slow MA confirming uptrend initiation, Bearish Trend Signal activates when the fast MA crosses below the slow MA confirming downtrend initiation, and Trend Change alerts on any directional transition regardless of direction. These notifications allow you to respond to volume-validated regime shifts without continuous chart monitoring.
▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and display preferences, ensuring optimal contrast and visual clarity across trading environments. The adjustable fill opacity control (0-100%) allows fine-tuning of ribbon prominence, with lower opacity values create subtle background context while higher values produce bold trend emphasis. Optional bar coloring extends the trend indication directly to the price bars, providing immediate directional reference without requiring visual cross-reference to the ribbon itself.
Bollinger Bands Forecast [QuantAlgo]🟢 Overview
Bollinger Bands are widely recognized for mapping volatility boundaries around price action, but they inherently lag behind market movement since they calculate based on completed bars. The Bollinger Bands Forecast addresses this limitation by adding a predictive layer that attempts to project where the upper band, lower band, and basis line might position in the future. The indicator provides three unique analytical models for generating these projections: one examines swing structure and breakout patterns, another integrates volume flow and accumulation metrics, while the third applies statistical trend fitting. Traders can select whichever methodology aligns with their market view or trading style to gain visibility into potential future volatility zones that could inform position planning, risk management, and timing decisions across various asset classes and timeframes.
🟢 How It Works
The core calculation begins with traditional Bollinger Bands: a moving average basis line (configurable as SMA, EMA, SMMA/RMA, WMA, or VWMA) with upper and lower bands positioned at a specified number of standard deviations away. The forecasting extension works by first generating predicted price values for upcoming bars using the selected method. These projected prices then feed into a rolling calculation that simulates how the basis line would update bar by bar, respecting the mathematical properties of the chosen moving average type. As each new forecasted price enters the calculation window, the oldest historical price drops out, mimicking the natural progression of the moving average. The system recalculates standard deviation across this evolving price window and applies the multiplier to determine where upper and lower bands would theoretically sit. This process repeats for each of the forecasted bars, creating a connected chain of potential future band positions that render as dashed lines on the chart.
🟢 Key Features
1. Market Structure Model
This forecasting approach interprets price through the lens of swing analysis and structural patterns. The algorithm identifies pivot highs and lows across a definable lookback window, then tracks whether price is forming higher highs and higher lows (bullish structure) or lower highs and lower lows (bearish structure). The system looks for break of structure (BOS) when price pushes beyond a previous swing point in the trending direction, or change of character (CHoCH) when price starts creating opposing swing patterns.
When projecting future prices, the model considers current distance from recent swing levels and the strength of the established trend (measured by counting higher highs versus lower lows). If bullish structure dominates and price sits near a swing low, the forecast biases upward. Conversely, bearish structure near a swing high produces downward bias. ATR scaling ensures the projection magnitude relates to actual market volatility.
Practical Implications for Traders:
Useful when you trade based on swing points and structural breaks
The Structure Influence slider (0 to 1) lets you dial in how much weight structure analysis carries versus pure trend
Helps visualize where bands could form around key structural levels you're watching
Works better in trending conditions where structure patterns are clearer
Might be less effective in choppy, sideways markets without defined swings
2. Volume-Weighted Model
This method attempts to incorporate volume flow into the price forecast. It combines three volume-based metrics: On-Balance Volume (OBV) to track cumulative buying/selling pressure, the Accumulation/Distribution Line to measure money flow, and volume-weighted price changes to emphasize moves that occur on high volume. The algorithm calculates the slope of these indicators to determine if volume is confirming price direction or diverging from it.
Volume spikes above a configurable threshold are flagged as potentially significant, with the direction of the spike (whether it occurred on an up bar or down bar) influencing the forecast. When OBV, A/D Line, and volume momentum all align in the same direction, the model projects stronger moves. When they conflict or show weak volume support, the forecast becomes more conservative.
Practical Implications for Traders:
Relevant if you use volume analysis to confirm price moves
More meaningful in markets with reliable volume data
The Volume Influence parameter (0 to 1) controls how much volume factors into the projection
Volume Spike Threshold adjusts sensitivity to what constitutes unusual volume
Helps spot scenarios where volume doesn't support a move, suggesting possible consolidation
Might be less effective in low-liquidity instruments or markets where volume reporting is unreliable
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. This creates a clean trend projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent trend continues at its current rate of change, where would price be in 10 or 20 bars?
Practical Implications for traders:
Provides a neutral, mathematical baseline for comparison
Works well when trends are steady and consistent
Can be useful for backtesting since results are deterministic
Requires minimal configuration beyond lookback period
Might not adapt to changing market conditions as dynamically as the other methods
Best suited for trending markets rather than ranging or volatile conditions
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future Bollinger Band positions that may help with:
▶ Pre-planning entries and exits: See where potential support (lower band) or resistance (upper band) might develop before price gets there
▶ Volatility context: Observe whether forecasted bands are widening (suggesting potential volatility expansion) or narrowing (possible compression or squeeze setup)
▶ Target setting: Reference projected band levels when determining profit targets or stop placement
▶ Mean reversion scenarios: Visualize potential paths back toward the basis line when price extends to a band extreme
▶ Breakout anticipation: Consider where upper or lower bands might sit if price begins a strong directional move
▶ Strategy development: Build trading rules around forecasted band interactions, such as entering when price is projected to return to the basis or exit when forecasts show band expansion
▶ Method comparison: Switch between the three forecasting models to see if they agree or diverge, potentially using consensus as a confidence filter
It's critical to understand that these forecasts are projections based on recent market behavior. Markets are complex systems influenced by countless factors that cannot be captured in a technical calculation or predicted perfectly. The forecasted bands represent one possible scenario of how volatility might unfold, so actual price action may still diverge from these projections. Past performance and historical patterns provide no assurance of future results. Use these forecasts as one input within a broader trading framework that includes proper risk management, position sizing, and multiple forms of analysis. The value lies not in prediction accuracy but in helping you think probabilistically about potential market states and plan accordingly.
The Abramelin Protocol [MPL]"Any sufficiently advanced technology is indistinguishable from magic." — Arthur C. Clarke
🌑 SYSTEM OVERVIEW
The Abramelin Protocol is not a standard technical indicator; it is a "Technomantic" trading algorithm engineered to bridge the gap between 15th-century esoteric mathematics and modern high-frequency markets.
This script is the flagship implementation of the MPL (Magic Programming Language) project—an open-source experimental framework designed to compile metaphysical intent into executable Python and Pine Script algorithms.
Unlike traditional indicators that rely on arbitrary constants (like the 14-period RSI or 200 SMA), this protocol calculates its parameters using "Dynamic Entity Gematria." We utilize a custom Python backend to analyze the ASCII vibrational frequencies of specific metaphysical archetypes, reducing them via Tesla's 3-6-9 harmonic principles to derive market-responsive periods.
🧬 WHAT IS ?
MPL (Magic Programming Language) is a domain-specific language and research initiative created to explore Technomancy—the art of treating code as a spellbook and the market as a chaotic entity to be tamed.
By integrating the logic of ancient Grimoires (such as The Book of Abramelin) with modern Data Science, MPL aims to discover hidden correlations in price action that standard tools overlook.
🔗 CONNECT WITH THE PROJECT:
If you are a developer, a trader, or a seeker of hidden knowledge, examine the source code and join the order:
• 📂 Official Project Site: hakanovski.github.io
• 🐍 MPL Source Code (GitHub): github.com
• 👨💻 Developer Profile (LinkedIn): www.linkedin.com
🔢 THE ALGORITHM: 452 - 204 - 50
The inputs for this script are mathematically derived signatures of the intelligence governing the system:
1. THE PAIMON TREND (Gravity)
• Origin: Derived from the ASCII summation of the archetype PAIMON (King of Secret Knowledge).
• Function: This 452-period Baseline acts as the market's "Event Horizon." It represents the deep, structural direction of the asset.
• Price > Line: Bullish Domain.
• Price < Line: Bearish Void.
2. THE ASTAROTH SIGNAL (Trigger)
• Origin: Derived from the ASCII summation of ASTAROTH (Knower of Past & Future), reduced by Tesla’s 3rd Harmonic.
• Function: This is the active trigger line. It replaces standard moving averages with a precise, gematria-aligned trajectory.
3. THE VOLATILITY MATRIX (Scalp)
• Origin: Based on the 9th Harmonic reduction.
• Function: Creates a "Cloud" around the signal line to visualize market noise.
🛡️ THE MILON GATE (Matrix Filter)
Unique to this script is the "MILON Gate" toggle found in the settings.
• ☑️ Active (Default): The algorithm applies the logic of the MILON Magic Square. Signals are ONLY generated if Volume and Volatility align with the geometric structure of the move. This filters out ~80% of false signals (noise).
• ⬜ Inactive: The algorithm operates in "Raw Mode," showing every mathematical crossover without the volume filter.
⚠️ OPERATIONAL USAGE
• Timeframe: Optimized for 4H (The Builder) and Daily (The Architect) charts.
• Strategy: Use the Black/Grey Line (452) as your directional bias. Take entries only when the "EXECUTE" (Long) or "PURGE" (Short) sigils appear.
Use this tool wisely. Risk responsibly. Let the harmonics guide your entries.
— Hakan Yorganci
Technomancer & Full Stack Developer
Mutanabby_AI | ONEUSDT_MR1
ONEUSDT Mean-Reversion Strategy | 74.68% Win Rate | 417% Net Profit
This is a long-only mean-reversion strategy designed specifically for ONEUSDT on the 1-hour timeframe. The core logic identifies oversold conditions following sharp declines and enters positions when selling pressure exhausts, capturing the subsequent recovery bounce.
Backtested Period: June 2019 – December 2025 (~6 years)
Performance Summary
| Metric | Value |
|--------|-------|
| Net Profit | +417.68% |
| Win Rate | 74.68% |
| Profit Factor | 4.019 |
| Total Trades | 237 |
| Sharpe Ratio | 0.364 |
| Sortino Ratio | 1.917 |
| Max Drawdown | 51.08% |
| Avg Win | +3.14% |
| Avg Loss | -2.30% |
| Buy & Hold Return | -80.44% |
Strategy Logic :
Entry Conditions (Long Only):
The strategy seeks confluence of three conditions that identify exhausted selling:
1. Prior Move Filter:*The price change from 5 bars ago to 3 bars ago must be ≥ -7% (ensures we're not entering during freefall)
2. Current Move Filter: The price change over the last 2 bars must be ≤ 0% (confirms momentum is stalling or reversing)
3. Three-Bar Decline: The price change from 5 bars ago to 3 bars ago must be ≤ -5% (confirms a significant recent drop occurred)
When all three conditions align, the strategy identifies a potential reversal point where sellers are exhausted.
Exit Conditions:
- Primary Exit: Close above the previous bar's high while the open of the previous bar is at or below the close from 9 bars ago (profit-taking on strength)
- Trailing Stop: 11x ATR trailing stop that locks in profits as price rises
Risk Management
- Position Sizing:Fixed position based on account equity divided by entry price
- Trailing Stop:11× ATR (14-period) provides wide enough room for crypto volatility while protecting gains
- Pyramiding:Up to 4 orders allowed (can scale into winning positions)
- **Commission:** 0.1% per trade (realistic exchange fees included)
Important Disclaimers
⚠️ This is NOT financial advice.
- Past performance does not guarantee future results
- Backtest results may contain look-ahead bias or curve-fitting
- Real trading involves slippage, liquidity issues, and execution delays
- This strategy is optimized for ONEUSDT specifically — results may differ on other pairs
- Always test before risking real capital
Recommended Usage
- Timeframe:*1H (as designed)
- Pair: ONEUSDT (Binance)
- Account Size: Ensure sufficient capital to survive max drawdown
Source Code
Feedback Welcome
I'm sharing this strategy freely for educational purposes. Please:
- Drop a comment with your backtesting results any you analysis
- Share any modifications that improve performance
- Let me know if you spot any issues in the logic
Happy trading
As a quant trader, do you think this strategy will survive in live trading?
Yes or No? And why?
I want to hear from you guys
Stochastic RSI Forecast [QuantAlgo]🟢 Overview
The Stochastic RSI Forecast extends the classic momentum oscillator by projecting potential future K and D line values up to 10 bars ahead. Unlike traditional indicators that only reflect historical price action, this indicator uses three proprietary forecasting models, each operating on different market data inputs (price structure, volume metrics, or linear trend), to explore potential price paths. This unique approach allows traders to form probabilistic expectations about future momentum states and incorporate these projections into both discretionary and algorithmic trading and/or analysis.
🟢 How It Works
The indicator operates through a multi-stage calculation process that extends the RSI-to-Stochastic chain forward in time. First, it generates potential future price values using one of three selectable forecasting methods, each analyzing different market dimensions (structure, volume, or trend). These projected prices are then processed through an iterative RSI calculation that maintains continuity with historical gain/loss averages, producing forecasted RSI values. Finally, the system applies the full stochastic transformation (calculating the position of each forecasted RSI within its range, smoothing with K and D periods) to project potential future oscillator values.
The forecasting models adapt to market conditions by analyzing configurable lookback periods and recalculating projections on every bar update. The implementation preserves the mathematical properties of the underlying RSI calculation while extrapolating momentum trajectories, creating visual continuity between historical and forecasted values displayed as semi-transparent dashed lines extending beyond the current bar.
🟢 Key Features
1. Market Structure Model
This algorithm applies price action analysis by tracking break of structure (BOS) and change of character (CHoCH) patterns to identify potential order flow direction. The system detects swing highs and lows using configurable pivot lengths, then analyzes sequences of higher highs or lower lows to determine bullish or bearish structure bias. When price approaches recent swing points, the forecast projects moves in alignment with the established structure, scaled by ATR (Average True Range) for volatility adjustment.
Potential Benefits for Traders:
Explores potential momentum continuation scenarios during established trends
Identifies areas where structure changes might influence momentum
Could be useful for swing traders and position traders who incorporate structure-based analysis
The Structure Influence parameter (0-1 scale) allows blending between pure trend following and structure-weighted forecasts
Helps visualize potential trend exhaustion through weakening structure patterns
2. Volume-Weighted Model
This model analyzes volume patterns by combining On-Balance Volume (OBV), Accumulation/Distribution Line, and volume-weighted price returns to assess potential capital flow. The algorithm calculates directional volume momentum and identifies volume spikes above customizable thresholds to determine accumulation or distribution phases. When volume indicators align directionally, the forecast projects stronger potential moves; when volume diverges from price trends, it suggests possible reversals or consolidation.
Potential Benefits for Traders:
Incorporates volume analysis into momentum forecasting
Attempts to filter price action by volume support or lack thereof
Could be more relevant in markets where volume data is reliable (equities, crypto, major forex pairs)
Volume Influence parameter (0-1 scale) enables adaptation to different market liquidity profiles
Highlights volume climax patterns that sometimes precede trend changes
Could be valuable for traders who incorporate volume confirmation in their analysis
3. Linear Regression Model
This mathematical approach applies least-squares regression fitting to project price trends based on recent price data. Unlike the conditional logic of the other methods, linear regression provides straightforward trend extrapolation based on the best-fit line through the lookback period.
Potential Benefits for Traders:
Delivers consistent, reproducible forecasts based on statistical principles
Works better in trending markets with clear directional bias
Useful for systematic traders building quantitative strategies requiring stable inputs
Minimal parameter sensitivity (primarily controlled by lookback period)
Computationally efficient with fast recalculation on every bar
Serves as a baseline to compare against the more complex structure and volume methods
🟢 Universal Applications Across All Models
Each forecasting method projects potential future stochastic RSI values (K and D lines), which traders can use to:
▶ Anticipate potential crossovers: Visualize possible K/D crosses several bars ahead
▶ Explore overbought/oversold scenarios: Forecast when momentum might return from extreme zones
▶ Assess divergences: Evaluate how oscillator divergences might develop
▶ Inform entry timing: Consider potential points along the forecasted momentum curve for trade entry
▶ Develop systematic strategies: Build rules based on forecasted crossovers, slope changes, or threshold levels
▶ Adapt to market conditions: Switch between methods based on current market character (trending vs range-bound, high vs low volume)
In short, the indicator's flexibility allows traders to combine forecasting projections with traditional stochastic signals, using historical K/D for immediate reference while considering forecasted values for planning and analysis. As with all technical analysis tools, the forecasts represent one possible scenario among many and should be used as part of a broader trading methodology rather than as standalone signals.
Ellipse Price Action Indicator v3Successful Trade Setup Using Ellipse Price Action Indicator (EPAI) 🔥
With the help of the Ellipse Price Action Indicator, this bearish trade setup played out perfectly.
EPAI generated a Sell Signal with a red arrow.
According to the EPAI method:
🔹 If price makes a new high on the next candle — add one more unit.
🔹 Every time a new high forms, add one more unit per candle.
🔹 Exit all units at the Moving Average (MA) — this is the core risk-controlled exit rule.
This is why EPAI consistently provides successful trade setups when followed with discipline.
⚠ Important Notice — Last Chance!
The Ellipse Price Action Indicator (EPAI) will soon become PRIVATE.
Access will be restricted only to Premium Members.
✅ First 25 subscribers can use this indicator absolutely FREE.
After that, it will not be available publicly.
Green to Red Money RailsWhat this indicator does
Green to Red Money Rails (G2R Rails) is a price-action tool that draws dynamic “rails” from recent swing lows and highs. It tracks how support and resistance are shifting so you can see where trend pressure is building or weakening.
Core logic (high level)
Detects pivot lows and stores the last three (L1, L2, L3).
Builds green support “fans”: inner dotted rails L1→L2 and L2→L3, plus a main solid base rail L1→L3.
Detects pivot highs and, when the last high is lower than the previous one, draws a red resistance rail from H2→H3.
Optional labels mark the most recent swing low (“L”) and swing high (“H”).
How to use it
Use the green rails as dynamic support zones for trend-following, pullback entries, or stop placement.
Use the red rail as a visual ceiling in downtrends: breaks above it can signal the end of a sell-off; rejections at it confirm sellers still in control.
Works best on liquid markets and swing-trading timeframes (for example, 1h–1D). Always combine with your own risk management and higher-timeframe context.
This script does not auto-generate signals or manage risk for you; it is a visual framework for reading structure and building your own trading plans.
BTC STH Proxy vs Realized Price (RP) Ratio | STH : LTH📊 REALIZED PRICE MARKET SIGNAL
Indicator that builds a Short-Term Holder (STH) price proxy using a configurable moving average of Bitcoin’s market price and compares it to Bitcoin’s Realized Price (RP) derived from on-chain data.
Realized Price (RP) is calculated from CoinMetrics Realized Market Cap divided by Glassnode circulating supply.
STH Proxy is a user-defined moving average (EMA/SMA/WMA) of BTC price, designed to mimic the behavior of the true STH Realized Price.
Users can adjust the MA type, length, and RP smoothing to closely replicate the STH curve seen on Glassnode, Bitbo, and Bitcoin Magazine Pro.
Optionally, the indicator can display the STH/RP ratio, which highlights transitions between market phases.
This tool provides a simple but effective way to visualize short-term vs long-term holder cost-basis dynamics using only publicly accessible on-chain aggregates and price data.
----------
💡TLDR: An alt take on the Short-Term Holder Realized Price / Long-Term Holder Realized Price cross model | (STH/LTH cross)
- A mix of MAs are used to mimic STH.
- RP here used as a proxy for the long-term holder (LTH) cost basis.
- Bull/Bear signals are generated when the STH proxy crosses above or below RP.
⭐ Free to use • Leave feedback • Happy trading!
Eagle Eye Pro Dashboard 🔴 IMPORTANT NOTICE
This indicator is an advanced trading support tool. It helps you spot opportunities and improve your analysis, but it DOES NOT guarantee results nor replace your personal judgment.
• 🔴 Every trade remains your sole responsibility.
• 🔴 Risk is always present: the indicator does not eliminate it, only helps manage it.
• 🔴 The indicator is restricted: it ONLY generates signals during the London and New York sessions.
• 🔴 It does not generate signals outside those sessions or during weekends, to ensure better accuracy and performance.
• 🔴 It is not recommended to trade other assets or use timeframes different from those specified.
EAGLE EYE PRO V71.2 RENTAL
This indicator is built to deliver clear signals and a professional dashboard, specially optimized for BTC.
🔑 Key highlights:
• 🔴 Exclusively optimized for BTC.
• 🔴 Recommended timeframe: 15 minutes, providing cleaner and more reliable signals.
• 🔴 Adventurous mode: 1 minute, but with higher risk due to extreme volatility.
• 🔴 Restricted hours: the indicator works only during the London and New York sessions.
• 🔴 It does not operate during weekends
