SCTI-RSKSCTI-RSK 是一个多功能技术指标合集，整合了多种常用技术指标于一个图表中，方便交易者综合分析市场状况。该指标包含以下五个主要技术指标模块，每个模块都可以单独显示或隐藏：
Stoch RSI - 随机相对强弱指数
KDJ - 随机指标
RSI - 相对强弱指数
CCI - 商品通道指数
Williams %R - 威廉指标
主要特点
模块化设计：每个指标都可以单独开启或关闭显示
交叉信号可视化：Stoch RSI和KDJ的金叉/死叉信号有彩色填充标识
多时间框架分析：支持不同长度的参数设置
直观界面：清晰的参数分组和颜色区分
适用场景
趋势判断
超买超卖区域识别
交易信号确认
多指标共振分析
English Description
SCTI-RSK is a comprehensive technical indicator that combines multiple popular indicators into a single chart for traders to analyze market conditions holistically. The indicator includes the following five main technical indicator modules, each can be toggled on/off individually:
Stoch RSI - Stochastic Relative Strength Index
KDJ - Stochastic Oscillator
RSI - Relative Strength Index
CCI - Commodity Channel Index
Williams %R - Williams Percent Range
Key Features
Modular Design: Each indicator can be shown or hidden independently
Visual Crossover Signals: Golden/Death crosses are highlighted with color fills for Stoch RSI and KDJ
Multi-Timeframe Analysis: Supports different length parameters
Intuitive Interface: Clear parameter grouping and color differentiation
Use Cases
Trend identification
Overbought/Oversold zone recognition
Trade signal confirmation
Multi-indicator confluence analysis
参数说明 (Parameter Explanation)
指标参数分为6个主要组别：
基础指标设置 - 控制各指标的显示/隐藏
Stoch RSI 设置 - 包括K值、D值、RSI长度等参数
KDJ 设置 - 包括周期、信号线等参数
RSI 设置 - 包括RSI长度、中期长度等参数
CCI 设置 - 包括CCI长度、中期长度等参数
Williams %R 设置 - 包括长度参数
使用建议 (Usage Suggestions)
初次使用时，可以先开启所有指标观察它们的相互关系
根据个人交易风格调整各指标的长度参数
关注多指标同时发出信号时的交易机会
结合价格行为和其他分析工具确认信号
更新日志 (Changelog)
v1.0 初始版本，整合五大技术指标
스크립트에서 "CCI"에 대해 찾기
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
DrawIndicatorOnTheChartLibrary "DrawIndicatorOnTheChart"
this library is used to show an indicator (such RSI, CCI, MOM etc) on the main chart with indicator's horizontal lines in a window. Location of the window is calculated dynamically by last price movemements
drawIndicator(enabled, indicatorName, indicator1, indicator2, indicator3, indicatorcolors, period, indimax_, indimin_, levels, precision, xlocation, lnwidth)
draws the realted indicator on the chart
Parameters:
enabled (bool) : if it's enabled to show
indicatorName (string) : is the indicator name as string such "RSI", "CCI" etc
indicator1 (float) : is first indicator you want to show, such rsi(close, 14), mom(close, 10) etc
indicator2 (float) : is second indicator you want to show, such -DI of DMI
indicator3 (float) : is third indicator you want to show, such ADX of DMI
indicatorcolors (array)
period (int) : is the length of the window to show
indimax_ (float) : is the maximum value of the indicator, for example for RSI it's 100.0, if the indicator (such CCI, MOM etc) doesn't have maximum value then use "na"
indimin_ (float) : is the minimum value of the indicator, for example for RSI it's 0.0, if the indicator (such CCI, MOM etc)doesn't have maximum value then use "na"
levels (array) : is the levels of the array for the horizontal lines. for example if you want horizontal lines at 30.0, and 70.0 then use array.from(30.0, 70.0). if no horizontal lines then use array.from(na)
precision (int) : is the precision/nuber of decimals that is used to show indicator values, for example for RSI set it 2
xlocation (int) : is end location of the indicator window, for example if xlocation = 0 window is created on the index of the last bar/candle
lnwidth (int) : is the line width of the indicator lines
Returns: none
MyLibrary_functions_D_S_3D_D_T_PART_1Library "MyLibrary_functions_D_S_3D_D_T_PART_1"
TODO: add library description here
color_(upcolor_txt, upcolor, dncolor_txt, dncolor, theme)
Parameters:
upcolor_txt (color)
upcolor (color)
dncolor_txt (color)
dncolor (color)
theme (string)
Source_Zigzag_F(Source)
Parameters:
Source (string)
p_lw_hg(Source_low, Source_high, Depth)
Parameters:
Source_low (float)
Source_high (float)
Depth (int)
lowing_highing(Source_low, Source_high, p_lw, p_hg, Deviation)
Parameters:
Source_low (float)
Source_high (float)
p_lw (int)
p_hg (int)
Deviation (int)
ll_lh(lowing, highing)
Parameters:
lowing (bool)
highing (bool)
down_ll_down_lh(ll, lh, Backstep)
Parameters:
ll (int)
lh (int)
Backstep (int)
down(down_ll, down_lh, lw, hg)
Parameters:
down_ll (bool)
down_lh (bool)
lw (int)
hg (int)
f_x_P_S123_lw(lw_, hg_, p_lw_, down, Source_low)
Parameters:
lw_ (int)
hg_ (int)
p_lw_ (int)
down (int)
Source_low (float)
f_x_P_S123_hg(lw_, hg_, p_hg_, down, Source_high)
Parameters:
lw_ (int)
hg_ (int)
p_hg_ (int)
down (int)
Source_high (float)
Update_lw_hg_last_l_last_h(lw, hg, last_l, last_h, p_lw, p_hg, down, Source_low, Source_high)
Parameters:
lw (int)
hg (int)
last_l (int)
last_h (int)
p_lw (int)
p_hg (int)
down (int)
Source_low (float)
Source_high (float)
x1_P_y1_P_x2_P_y2_P_x3_P_y3_P_x4_P_y4_P(lw, hg, last_l, last_h, Source)
Parameters:
lw (int)
hg (int)
last_l (int)
last_h (int)
Source (string)
x1_P_os(lw, hg, x2_D, Diverjence_MACD_Line_, Diverjence_MACD_Histagram_, Diverjence_RSI_, Diverjence_Stochastic_, Diverjence_volume_, Diverjence_CCI_, Diverjence_MFI_, Diverjence_Momentum_, Diverjence_OBV_, Diverjence_ADX_, MACD, hist_MACD, RSI, volume_ok, Stochastic_K, CCI, MFI, momentum, OBV, adx)
Parameters:
lw (int)
hg (int)
x2_D (int)
Diverjence_MACD_Line_ (bool)
Diverjence_MACD_Histagram_ (bool)
Diverjence_RSI_ (bool)
Diverjence_Stochastic_ (bool)
Diverjence_volume_ (bool)
Diverjence_CCI_ (bool)
Diverjence_MFI_ (bool)
Diverjence_Momentum_ (bool)
Diverjence_OBV_ (bool)
Diverjence_ADX_ (bool)
MACD (float)
hist_MACD (float)
RSI (float)
volume_ok (float)
Stochastic_K (float)
CCI (float)
MFI (float)
momentum (float)
OBV (float)
adx (float)
x3_P_os(lw, hg, x2_D, x4_D, Diverjence_MACD_Line_, Diverjence_MACD_Histagram_, Diverjence_RSI_, Diverjence_Stochastic_, Diverjence_volume_, Diverjence_CCI_, Diverjence_MFI_, Diverjence_Momentum_, Diverjence_OBV_, Diverjence_ADX_, MACD, hist_MACD, RSI, volume_ok, Stochastic_K, CCI, MFI, momentum, OBV, adx)
Parameters:
lw (int)
hg (int)
x2_D (int)
x4_D (int)
Diverjence_MACD_Line_ (bool)
Diverjence_MACD_Histagram_ (bool)
Diverjence_RSI_ (bool)
Diverjence_Stochastic_ (bool)
Diverjence_volume_ (bool)
Diverjence_CCI_ (bool)
Diverjence_MFI_ (bool)
Diverjence_Momentum_ (bool)
Diverjence_OBV_ (bool)
Diverjence_ADX_ (bool)
MACD (float)
hist_MACD (float)
RSI (float)
volume_ok (float)
Stochastic_K (float)
CCI (float)
MFI (float)
momentum (float)
OBV (float)
adx (float)
Err_test(lw, hg, x1, y1, x2, y2, y_d, start, finish, Err_Rate)
Parameters:
lw (int)
hg (int)
x1 (int)
y1 (float)
x2 (int)
y2 (float)
y_d (float)
start (int)
finish (int)
Err_Rate (float)
divergence_calculation(Feasibility_RD, Feasibility_HD, Feasibility_ED, lw, hg, Source_low, Source_high, x1_P_pr, x3_P_pr, x1_P_os, x3_P_os, x2_P_pr, x4_P_pr, oscillator, Fix_Err_Mid_Point_Pr, Fix_Err_Mid_Point_Os, Err_Rate_permissible_Mid_Line_Pr, Err_Rate_permissible_Mid_Line_Os, Number_of_price_periods_R_H, Permissible_deviation_factor_in_Pr_R_H, Number_of_oscillator_periods_R_H, Permissible_deviation_factor_in_OS_R_H, Number_of_price_periods_E, Permissible_deviation_factor_in_Pr_E, Number_of_oscillator_periods_E, Permissible_deviation_factor_in_OS_E)
Parameters:
Feasibility_RD (bool)
Feasibility_HD (bool)
Feasibility_ED (bool)
lw (int)
hg (int)
Source_low (float)
Source_high (float)
x1_P_pr (int)
x3_P_pr (int)
x1_P_os (int)
x3_P_os (int)
x2_P_pr (int)
x4_P_pr (int)
oscillator (float)
Fix_Err_Mid_Point_Pr (bool)
Fix_Err_Mid_Point_Os (bool)
Err_Rate_permissible_Mid_Line_Pr (float)
Err_Rate_permissible_Mid_Line_Os (float)
Number_of_price_periods_R_H (int)
Permissible_deviation_factor_in_Pr_R_H (float)
Number_of_oscillator_periods_R_H (int)
Permissible_deviation_factor_in_OS_R_H (float)
Number_of_price_periods_E (int)
Permissible_deviation_factor_in_Pr_E (float)
Number_of_oscillator_periods_E (int)
Permissible_deviation_factor_in_OS_E (float)
label_txt(label_ID, zigzag_Indicator_1_, zigzag_Indicator_2_, zigzag_Indicator_3_)
Parameters:
label_ID (string)
zigzag_Indicator_1_ (bool)
zigzag_Indicator_2_ (bool)
zigzag_Indicator_3_ (bool)
delet_scan_item_1(string_, NO_1, GAP)
Parameters:
string_ (string)
NO_1 (int)
GAP (int)
delet_scan_item_2(string_, NO_1, GAP)
Parameters:
string_ (string)
NO_1 (int)
GAP (int)
calculation_Final_total(MS_MN, Scan_zigzag_NO, zigzag_Indicator, zigzag_Indicator_1, zigzag_Indicator_2, zigzag_Indicator_3, LW_hg_P2, LW_hg_P1, lw_1, lw_2, lw_3, hg_1, hg_2, hg_3, lw_hg_D_POINT_ad_Array, lw_hg_D_POINT_id_Array, Array_Regular_MS, Array_Hidden_MS, Array_Exaggerated_MS, Array_Regular_MN, Array_Hidden_MN, Array_Exaggerated_MN)
Parameters:
MS_MN (string)
Scan_zigzag_NO (string)
zigzag_Indicator (bool)
zigzag_Indicator_1 (bool)
zigzag_Indicator_2 (bool)
zigzag_Indicator_3 (bool)
LW_hg_P2 (int)
LW_hg_P1 (int)
lw_1 (int)
lw_2 (int)
lw_3 (int)
hg_1 (int)
hg_2 (int)
hg_3 (int)
lw_hg_D_POINT_ad_Array (array)
lw_hg_D_POINT_id_Array (array)
Array_Regular_MS (array)
Array_Hidden_MS (array)
Array_Exaggerated_MS (array)
Array_Regular_MN (array)
Array_Hidden_MN (array)
Array_Exaggerated_MN (array)
Search_piote_1(array_id_7, scan_no)
Parameters:
array_id_7 (array)
scan_no (int)
Composite Momentum IndicatorComposite Momentum Indicator" combines the signals from several oscillators, including Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) by averaging the standardized values (Z-Scores). Since it is a Z-Score based indicators the values will be typically be bound between +3 and -3 oscillating around 0. Here's a summary of the code:
Input Parameters: Users can customize the look-back period and set threshold values for overbought and oversold conditions. They can also choose which oscillators to include in the composite calculation.
Oscillator Calculations: The code calculates four separate oscillators - Stochastic, RSI, Ultimate Oscillator, and CCI - each measuring different aspects of market momentum.
Z-Scores Calculation: For each oscillator, the code calculates a Z-Score, which normalizes the oscillator's values based on its historical standard deviation and mean. This allows for a consistent comparison of oscillator values across different timeframes.
Composite Z-Score: The code aggregates the Z-Scores from the selected oscillators, taking into account user preferences (whether to include each oscillator). It then calculates an average Z-Score to create the "Composite Momentum Oscillator."
Conditional Color Coding: The composite oscillator is color-coded based on its average Z-Score value. It turns green when it's above the overbought threshold, red when it's below the oversold threshold, and blue when it's within the specified range.
Horizontal Lines: The code plots horizontal lines at key levels, including 0, ±3, ±2, and ±1, to help users identify important momentum levels.
Gradient Fills: It adds gradient fills above the overbought threshold and below the oversold threshold to visually highlight extreme momentum conditions.
Combining the Stochastic, RSI, Ultimate Oscillator, and Commodity Channel Index (CCI) into one composite indicator offers several advantages for traders and technical analysts:
Comprehensive Insight: Each of these oscillators measures different aspects of market momentum and price action. Combining them into one indicator provides a more comprehensive view of the market's behavior, as it takes into account various dimensions of momentum simultaneously.
Reduced Noise: Standalone oscillators can generate conflicting signals and produce noisy readings, especially during choppy market conditions. A composite indicator smoothes out these discrepancies by averaging the signals from multiple indicators, potentially reducing false signals.
Confirmation and Divergence: By combining multiple oscillators, traders can seek confirmation or divergence signals. When multiple oscillators align in the same direction, it can strengthen a trading signal. Conversely, divergence between the oscillators can warn of potential reversals or weakening trends.
Customization: Traders can tailor the composite indicator to their specific trading strategies and preferences. They have the flexibility to include or exclude specific oscillators, adjust look-back periods, and set threshold levels. This adaptability allows for a more personalized approach to technical analysis.
Clarity and Efficiency: Rather than cluttering the chart with multiple individual oscillators, a composite indicator condenses the information into a single plot. This enhances the clarity of the chart and makes it easier for traders to quickly interpret market conditions.
Overbought/Oversold Identification: Combining these oscillators can improve the identification of overbought and oversold conditions. It reduces the likelihood of false signals since multiple indicators must align to trigger these extreme conditions.
Educational Tool: For novice traders and analysts, a composite indicator can serve as an educational tool by demonstrating how different oscillators interact and influence each other's signals. It allows users to learn about multiple technical indicators in one glance.
Efficient Use of Screen Space: A single composite indicator occupies less screen space compared to multiple separate indicators. This is especially beneficial when analyzing multiple markets or timeframes simultaneously.
Holistic Approach: Instead of relying on a single indicator, a composite approach encourages a more holistic assessment of market conditions. Traders can consider a broader range of factors before making trading decisions.
Increased Confidence: A composite indicator can boost traders' confidence in their decisions. When multiple reliable indicators align, it can provide a stronger basis for taking action in the market.
In summary, combining the Stochastic, RSI, Ultimate Oscillator, and CCI into one composite indicator enhances the depth and reliability of technical analysis. It simplifies the decision-making process, reduces noise, and offers a more complete picture of market momentum, ultimately helping traders make more informed and well-rounded trading decisions.
* Feel free to compare against individual oscillatiors*
Reversion Zone IndexThe Reversion Zone Index (RZI) is an indicator that combines the Commodity Channel Index (CCI), Choppiness Index (CI), and Bollinger Bands Percentage (BBPct) to identify mean reversion signals in the market. It is plotted as an Exponential Moving Average (EMA) smoothed oscillator with overbought and oversold zones, and mean reversion signals are represented by red and green arrows.
The three indicators are combined to benefit from their complementary aspects and create a more comprehensive view of mean reversion conditions. Here's a brief overview of each indicator's benefits:
1. Commodity Channel Index (CCI): CCI measures the current price level relative to its average over a specified period. It helps identify overbought and oversold conditions, as well as potential trend retracements. By incorporating CCI, the RZI gains insights into momentum and potential turning points.
2. Choppiness Index (CI): CI quantifies the market's choppiness or trendiness by analyzing the range between the highest high and lowest low over a specific period. It indicates whether the market is in a trending or ranging phase. CI provides valuable information about the market state, which can be useful in mean reversion analysis.
3. Bollinger Bands Percentage (BBPct): BBPct measures the current price's position relative to the Bollinger Bands. It calculates the percentage difference between the current price and the bands, identifying potential overbought or oversold conditions. BBPct helps gauge the market's deviation from its typical behavior and highlights potential reversal opportunities.
The RZI combines the three indicators by taking an average of their values and applying further calculations. It smooths the combined oscillator using an EMA to reduce noise and enhance the visibility of the trends. Smoothing with EMA provides a more responsive representation of the overall trend and helps filter out short-term fluctuations.
The overbought and oversold zones are marked on the chart as reference levels. When the combined oscillator is above the overbought zone or below the oversold zone, it suggests a potential mean reversion signal. Red and green arrows are displayed to visually indicate these mean retracement signals.
The RZI is a valuable tool for identifying mean reversion opportunities in the market. It incorporates multiple indicators, each providing unique insights into different aspects of mean reversion, such as momentum, volatility, and price positioning. Traders can use this indicator to spot potential turning points and time their trades accordingly.
EventsLibrary "Events"
events()
Returns the list of dates supported by this library as a string array.
Returns: array : Names of events supported by this library
fomcMeetings()
Gets the FOMC Meeting Dates. The FOMC meets eight times a year to determine the course of monetary policy. The FOMC announces its decision on the federal funds rate at the conclusion of each meeting and also issues a statement that provides information on the economic outlook and the Committee's assessment of the risks to the outlook.
Returns: array : FOMC Meeting Dates as timestamps
fomcMinutes()
Gets the FOMC Meeting Minutes Dates. The FOMC Minutes are released three weeks after each FOMC meeting. The Minutes provide information on the Committee's deliberations and decisions at the meeting.
Returns: array : FOMC Meeting Minutes Dates as timestamps
ppiReleases()
Gets the Producer Price Index (PPI) Dates. The Producer Price Index (PPI) measures the average change over time in the selling prices received by domestic producers for their output. The PPI is a leading indicator of CPI, and CPI is a leading indicator of inflation.
Returns: array : PPI Dates as timestamps
cpiReleases()
Gets the Consumer Price Index (CPI) Rekease Dates. The Consumer Price Index (CPI) measures changes in the price level of a market basket of consumer goods and services purchased by households. The CPI is a leading indicator of inflation.
Returns: array : CPI Dates as timestamps
csiReleases()
Gets the CSI release dates. The Consumer Sentiment Index (CSI) is a survey of consumer attitudes about the economy and their personal finances. The CSI is a leading indicator of consumer spending.
Returns: array : CSI Dates as timestamps
cciReleases()
Gets the CCI release dates. The Conference Board's Consumer Confidence Index (CCI) is a survey of consumer attitudes about the economy and their personal finances. The CCI is a leading indicator of consumer spending.
Returns: array : CCI Dates as timestamps
nfpReleases()
Gets the NFP release dates. Nonfarm payrolls is an employment report released monthly by the Bureau of Labor Statistics (BLS) that measures the change in the number of employed people in the United States.
Returns: array : NFP Dates as timestamps
eciReleases()
Gets the ECI The Employment Cost Index (ECI) is a measure of the change in the cost of labor,
SH_LibraryLibrary "SH_Library"
events()
Returns the list of dates supported by this library as a string array.
Returns: array : Names of events supported by this library
fomcMeetings()
Gets the FOMC Meeting Dates. The FOMC meets eight times a year to determine the course of monetary policy. The FOMC announces its decision on the federal funds rate at the conclusion of each meeting and also issues a statement that provides information on the economic outlook and the Committee's assessment of the risks to the outlook.
Returns: array : FOMC Meeting Dates as timestamps
fomcMinutes()
Gets the FOMC Meeting Minutes Dates. The FOMC Minutes are released three weeks after each FOMC meeting. The Minutes provide information on the Committee's deliberations and decisions at the meeting.
Returns: array : FOMC Meeting Minutes Dates as timestamps
ppiReleases()
Gets the Producer Price Index (PPI) Dates. The Producer Price Index (PPI) measures the average change over time in the selling prices received by domestic producers for their output. The PPI is a leading indicator of CPI, and CPI is a leading indicator of inflation.
Returns: array : PPI Dates as timestamps
cpiReleases()
Gets the Consumer Price Index (CPI) Rekease Dates. The Consumer Price Index (CPI) measures changes in the price level of a market basket of consumer goods and services purchased by households. The CPI is a leading indicator of inflation.
Returns: array : CPI Dates as timestamps
csiReleases()
Gets the CSI release dates. The Consumer Sentiment Index (CSI) is a survey of consumer attitudes about the economy and their personal finances. The CSI is a leading indicator of consumer spending.
Returns: array : CSI Dates as timestamps
cciReleases()
Gets the CCI release dates. The Conference Board's Consumer Confidence Index (CCI) is a survey of consumer attitudes about the economy and their personal finances. The CCI is a leading indicator of consumer spending.
Returns: array : CCI Dates as timestamps
nfpReleases()
Gets the NFP release dates. Nonfarm payrolls is an employment report released monthly by the Bureau of Labor Statistics (BLS) that measures the change in the number of employed people in the United States.
Returns: array : NFP Dates as timestamps
eciReleases()
Gets the ECI The Employment Cost Index (ECI) is a measure of the change in the cost of labor,
TASC 2022.12 Short-Term Continuation And Reversal Signals█ OVERVIEW
TASC's December 2022 edition Traders' Tips includes an article by Barbara Star titled "Short-Term Continuation And Reversal Signals". This is the code that implements the concepts presented in this publication.
█ CONCEPTS
The article takes two classic indicators, the Commodity Channel Index (CCI) and the Directional Movement Indicator (DMI), makes changes to the traditional ways of visualizing their readings, and uses them together to generate potential signals. The author first discusses the benefits of converting the DMI indicator to an oscillator format by subtracting the −DI from the +DI, which is then displayed as a histogram. Next, the author shows how the use of an on-chart visual framework (i.e., choosing the line style and color, coloring price bars, etc.) can help traders interpret the signals produced the considered pair of indicators.
█ CALCULATIONS
The article offers the following signals based on the readings of the DMI and CCI pair, suitable for several types of trades:
• Short-term trend change signals:
A DMI oscillator above zero indicates that prices are in an uptrend. A DMI oscillator below the zero line and falling means that selling pressure is dominating and price is trending down. The sign of the DMI oscillator is indicated by the color of the price bars (which correlates with the color of the DMI histogram). Namely, green, red and grey price bars correspond to the DMI oscillator above, below and equal to zero . Colored price bars and the DMI oscillator make it easy for trend traders to recognize changes in short-term trends.
• Trend continuation signals:
Blue circles appear near the bottom of the oscillator chart border when the DMI is above the zero line and the price is above its simple moving average in an uptrend . Dark red circles appear near the top of the chart in a downtrend when the DMI oscillator is below its zero line and below the 18-period moving average. Trend continuation signals are useful for those looking to add to existing positions, as well as for traders waiting for a pullback after a trend has started.
• Reversal signals:
The CCI signals a reversal to the downside when it breaks out of its +100 and then returns at some point, crossing below the +100 level. This is indicated by a magenta-colored diamond shape near the top the chart. The CCI signals a reversal to the upside when it moves below its −100 level and then at some point comes back to cross above the −100 level. This is indicated by a yellow diamond near the bottom of the chart. Reversal signals offer short-term rallies for countertrend traders as well as for swing traders looking for longer-term moves using the interplay between continuation and reversal signals.
EconomicCalendarLibrary "EconomicCalendar"
This library is a data provider for important dates and times from the Economic Calendar.
events()
Returns the list of dates supported by this library as a string array.
Returns: array : Names of events supported by this library
fomcMeetings()
Gets the FOMC Meeting Dates. The FOMC meets eight times a year to determine the course of monetary policy. The FOMC announces its decision on the federal funds rate at the conclusion of each meeting and also issues a statement that provides information on the economic outlook and the Committee's assessment of the risks to the outlook.
Returns: array : FOMC Meeting Dates as timestamps
fomcMinutes()
Gets the FOMC Meeting Minutes Dates. The FOMC Minutes are released three weeks after each FOMC meeting. The Minutes provide information on the Committee's deliberations and decisions at the meeting.
Returns: array : FOMC Meeting Minutes Dates as timestamps
ppiReleases()
Gets the Producer Price Index (PPI) Dates. The Producer Price Index (PPI) measures the average change over time in the selling prices received by domestic producers for their output. The PPI is a leading indicator of CPI, and CPI is a leading indicator of inflation.
Returns: array : PPI Dates as timestamps
cpiReleases()
Gets the Consumer Price Index (CPI) Rekease Dates. The Consumer Price Index (CPI) measures changes in the price level of a market basket of consumer goods and services purchased by households. The CPI is a leading indicator of inflation.
Returns: array : CPI Dates as timestamps
csiReleases()
Gets the CSI release dates. The Consumer Sentiment Index (CSI) is a survey of consumer attitudes about the economy and their personal finances. The CSI is a leading indicator of consumer spending.
Returns: array : CSI Dates as timestamps
cciReleases()
Gets the CCI release dates. The Conference Board's Consumer Confidence Index (CCI) is a survey of consumer attitudes about the economy and their personal finances. The CCI is a leading indicator of consumer spending.
Returns: array : CCI Dates as timestamps
nfpReleases()
Gets the NFP release dates. Nonfarm payrolls is an employment report released monthly by the Bureau of Labor Statistics (BLS) that measures the change in the number of employed people in the United States.
Returns: array : NFP Dates as timestamps
Buy / Sell alert indicator [Crypto_BCT]Hello everyone!
I bring to your attention a indicator to determine the point of buy and sell purchase.
It is based on oscillators and a moving average.
It can be used to work with bots, for example 3COMMAS DCA bot.
Signal Condition Settings:
ATR
The current candle is larger than the ATR for this period
EMA
The signal is necessarily below (for buy) and above (for sell) the EMA of the specified period
(Buy) RSI low
The RSI index is below this value
(Sell) RSI High
The RSI index is higher than this value
(Buy) MFI low
The MFI index is below this value
(Sell) MFI High
The MFI index is higher than this value
(Buy) CCI low
CCI index is below this value
(Sell) CCI High
The CCI index is higher than this value
(Buy) Lowest bar from
The closing of the current bar is lower than the closing of the bars back in this range
(Sell) Highest bar from
The closing of the current bar is higher than the closing of bars in this range
(Buy) Lowest EMA bar ago
During a given distance back, the EMA value only decreased
(Sell) Highest EMA bar ago
At a given distance back, the EMA value only increased
I hope it will be useful!
DrawIndicatorOnTheChartLibrary "DrawIndicatorOnTheChart"
this library is used to show an indicator (such RSI, CCI, MOM etc) on the main chart with indicator's horizontal lines in a window. Location of the window is calculated dynamically by last price movemements
drawIndicator(indicatorName, indicator, indicatorcolor, period, indimax_, indimin_, levels, precision, xlocation) draws the related indicator on the chart
Parameters:
indicatorName : is the indicator name as string such "RSI", "CCI" etc
indicator : is the indicator you want to show, such rsi(close, 14), mom(close, 10) etc
indicatorcolor : is the color of indicator line
period : is the length of the window to show
indimax_ : is the maximum value of the indicator, for example for RSI it's 100.0, if the indicator (such CCI, MOM etc) doesn't have maximum value then use "na"
indimin_ : is the minimum value of the indicator, for example for RSI it's 0.0, if the indicator (such CCI, MOM etc)doesn't have maximum value then use "na"
levels : is the levels of the array for the horizontal lines. for example if you want horizontal lines at 30.0, and 70.0 then use array.from(30.0, 70.0). if no horizontal lines then use array.from(na)
precision : is the precision/number of decimals that is used to show indicator values, for example for RSI set it 2
xlocation : is end location of the indicator window, for example if xlocation = 0 window is created on the index of the last bar/candle
Returns: none
MJ ECT== One Line Introduction ==
ECT is a multi-level, trend focused technical indicator based on a three-step hierarchical approach - comprising the tide, wave, and ripple - to trend identification.
== Indicator Philosophy ==
The author believes that market trends can be understood in a three-step hierarchy, with tide at the top, wave in the middle, and ripple at the bottom, corresponding to long-, middle-, and short-term momentum in the stock price. This indicator therefore comprises three technical indicators which aims to reflect the abovementioned features of a trend. These three components are True Strength Index (TSI), Exponential Moving Averages ( EMA ), and Commodity Channel Index ( CCI ).
== Indicator Components and Breakdown ==
True Strength Index (TSI) -> Tide
A 20-period TSI is used to visualize the bullish or bearish sentiment surrounding the stock. Crossovers above the zero line are interpreted as bullish while crossovers below the zero line are interpreted as bearish . This is painted into the background where green represents bullish and red represents bearish . While the background is red ( bearish ), no bullish positions should be taken. Hence, the TSI painted background acts as a directional bias filter and going against the bias is not recommended. After understanding the directional bias, the user can delve further into the areas of value for the stock in the Wave.
Exponential Moving Averages ( EMA ) -> Wave
Four EMA are used (20, 50, 100, 200) to identify the dynamic support and resistance waves in a trending market. Stock price pullbacks into any of these EMA represent areas of value where the user can consider taking positions. The correct EMA to use depends on individual stock's behavior, with multiple bounces on a specified EMA being the priority. After understanding which wave best reflects the area of value of a stock, the user can move on to the Ripple to time their entries.
Commodity Channel Index ( CCI ) -> Ripple
A 5-period CCI is used to identify short-term oversold conditions where prices are on discount. Discount is defined by the 5-period CCI crossing below -100 as it reflects a weekly oversold condition. The indicator will display a small triangle below the candle when this condition is met.
== Ready To Deploy Field Manual ==
When background is painted red, do nothing.
When background is painted green, begin thinking of bullish opportunities.
Look for the specific EMA that has the most bounces of stock price in recent months, this is the area of value to look for buying opportunity.
For the candles that intersect the EMA you identified above, watch for the appearance of a small triangle below the candle that tells you the entry timing.
When the entry timing signal triangle appears, remember the High of that candle and buy your position when the subsequent candle breaks above this High.
If the High is not broken above in the next immediate candle, remember the newer High of the newer candle (basically follow / trail the latest High until a break above is hit).
If the background turns from green to red, stop following the High and do not enter because the market sentiment has changed to bearish .
If you are holding an existing position and the background turns red, consider exiting the position. You may consider remembering the Low of the candle and exit your position if this Low is broken below on a subsequent candle.
== Best Wishes ==
The author wishes the best success for all users of this technical indicator.
Improved Commodity Channel IndexI took the normal CCI version and I made it better and more pleasantly visual.
At the same time now the CCI changes color based on the direction is going to take
We also have more levels, to define even better the current situation.
Details are simple :
green color cci = uptrend - > buy
red color cci = downtrend - > sell
Inverse Fisher Transform COMBOThis indicator is the one scripted and published by KIVANCfr3762 (fr3762 @twitter), only difference is the IFT Stochastic Momentum line to be added and also included for average IFT line calculation. Both IFT CCI and IFT CCI V2 lines are included within this script. With the options/settings menu, the lines can be added/removed for displaying on the chart up to preferences.
İndikatör , Kıvanç ( KIVANCfr3762 (fr3762 @twitter) ) hocamızın daha önceden yayınladığı indikatördür, Buna, IFT Stochastic Momentumu ekledim, ve tabi bu hesaplamayı ortalama IFT çizgisi hesabına da dahil ettim. IFT CCI ve IFT CCI V2 iki çizgi de ayrı ayrı indikatörün içinde bulunmaktadır. İstenilenler ayarlar kısmındaki kutucuklardan işaretlenerek/kaldırılarak grafiğin üzerinde gösterimi sağlanabilir.
CK INDEX Strategy Open-source code, Free, No Cost.Aqui está a tradução fiel e técnica para o inglês, ideal para a descrição do seu script no TradingView:
### 1. Requirements (The 3 Principles)
1. **Study** the code.
2. **Modify** the code.
3. **Distribute** copies or derivative versions (respecting the original credits).
Description: Direction and Strength — CK Index
The **CK Index** is a composite indicator formed by the conceptual sum of two CCIs and the PVT (Price Volume Trend) with an arithmetic mean. Its function is to simultaneously validate direction and accumulated flow.
For a **buy operation**, both CCIs must be above zero, indicating bullish dominance across different time horizons, and the PVT must be above its average. For a **sell operation**, the CCIs must be below zero and the PVT below its average.
It is important to emphasize that it acts as an **entry trigger**: the candle will turn **blue** to indicate a buy, **yellow** for a sell, and **white** when there is neutrality (meaning the color will be white when there is no clear definition—these are my personal settings). In its default form, it uses **green, red, and gray**, respectively.
Good trades, and make the world a better and freer place!
Static K-means Clustering | InvestorUnknownStatic K-Means Clustering is a machine-learning-driven market regime classifier designed for traders who want a data-driven structure instead of subjective indicators or manually drawn zones.
This script performs offline (static) K-means training on your chosen historical window. Using four engineered features:
RSI (Momentum)
CCI (Price deviation / Mean reversion)
CMF (Money flow / Strength)
MACD Histogram (Trend acceleration)
It groups past market conditions into K distinct clusters (regimes). After training, every new bar is assigned to the nearest cluster via Euclidean distance in 4-dimensional standardized feature space.
This allows you to create models like:
Regime-based long/short filters
Volatility phase detectors
Trend vs. chop separation
Mean-reversion vs. breakout classification
Volume-enhanced money-flow regime shifts
Full machine-learning trading systems based solely on regimes
Note:
This script is not a universal ML strategy out of the box.
The user must engineer the feature set to match their trading style and target market.
K-means is a tool, not a ready made system, this script provides the framework.
Core Idea
K-means clustering takes raw, unlabeled market observations and attempts to discover structure by grouping similar bars together.
// STEP 1 — DATA POINTS ON A COORDINATE PLANE
// We start with raw, unlabeled data scattered in 2D space (x/y).
// At this point, nothing is grouped—these are just observations.
// K-means will try to discover structure by grouping nearby points.
//
// y ↑
// |
// 12 | •
// | •
// 10 | •
// | •
// 8 | • •
// |
// 6 | •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 2 — RANDOMLY PLACE INITIAL CENTROIDS
// The algorithm begins by placing K centroids at random positions.
// These centroids act as the temporary “representatives” of clusters.
// Their starting positions heavily influence the first assignment step.
//
// y ↑
// |
// 12 | •
// | •
// 10 | • C2 ×
// | •
// 8 | • •
// |
// 6 | C1 × •
// |
// 4 | •
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
//
//
// STEP 3 — ASSIGN POINTS TO NEAREST CENTROID
// Each point is compared to all centroids.
// Using simple Euclidean distance, each point joins the cluster
// of the centroid it is closest to.
// This creates a temporary grouping of the data.
//
// (Coloring concept shown using labels)
//
// - Points closer to C1 → Cluster 1
// - Points closer to C2 → Cluster 2
//
// y ↑
// |
// 12 | 2
// | 1
// 10 | 1 C2 ×
// | 2
// 8 | 1 2
// |
// 6 | C1 × 2
// |
// 4 | 1
// |
// 2 |______________________________________________→ x
// 2 4 6 8 10 12 14
//
// (1 = assigned to Cluster 1, 2 = assigned to Cluster 2)
// At this stage, clusters are formed purely by distance.
Your chosen historical window becomes the static training dataset , and after fitting, the centroids never change again.
This makes the model:
Predictable
Repeatable
Consistent across backtests
Fast for live use (no recalculation of centroids every bar)
Static Training Window
You select a period with:
Training Start
Training End
Only bars inside this range are used to fit the K-means model. This window defines:
the market regime examples
the statistical distributions (means/std) for each feature
how the centroids will be positioned post-trainin
Bars before training = fully transparent
Training bars = gray
Post-training bars = full colored regimes
Feature Engineering (4D Input Vector)
Every bar during training becomes a 4-dimensional point:
This combination balances: momentum, volatility, mean-reversion, trend acceleration giving the algorithm a richer "market fingerprint" per bar.
Standardization
To prevent any feature from dominating due to scale differences (e.g., CMF near zero vs CCI ±200), all features are standardized:
standardize(value, mean, std) =>
(value - mean) / std
Centroid Initialization
Centroids start at diverse coordinates using various curves:
linear
sinusoidal
sign-preserving quadratic
tanh compression
init_centroids() =>
// Spread centroids across using different shapes per feature
for c = 0 to k_clusters - 1
frac = k_clusters == 1 ? 0.0 : c / (k_clusters - 1.0) // 0 → 1
v = frac * 2 - 1 // -1 → +1
array.set(cent_rsi, c, v) // linear
array.set(cent_cci, c, math.sin(v)) // sinusoidal
array.set(cent_cmf, c, v * v * (v < 0 ? -1 : 1)) // quadratic sign-preserving
array.set(cent_mac, c, tanh(v)) // compressed
This makes initial cluster spread “random” even though true randomness is hardly achieved in pinescript.
K-Means Iterative Refinement
The algorithm repeats these steps:
(A) Assignment Step, Each bar is assigned to the nearest centroid via Euclidean distance in 4D:
distance = sqrt(dx² + dy² + dz² + dw²)
(B) Update Step, Centroids update to the mean of points assigned to them. This repeats iterations times (configurable).
LIVE REGIME CLASSIFICATION
After training, each new bar is:
Standardized using the training mean/std
Compared to all centroids
Assigned to the nearest cluster
Bar color updates based on cluster
No re-training occurs. This ensures:
No lookahead bias
Clean historical testing
Stable regimes over time
CLUSTER BEHAVIOR & TRADING LOGIC
Clusters (0, 1, 2, 3…) hold no inherent meaning. The user defines what each cluster does.
Example of custom actions:
Cluster 0 → Cash
Cluster 1 → Long
Cluster 2 → Short
Cluster 3+ → Cash (noise regime)
This flexibility means:
One trader might have cluster 0 as consolidation.
Another might repurpose it as a breakout-loading zone.
A third might ignore 3 clusters entirely.
Example on ETHUSD
Important Note:
Any change of parameters or chart timeframe or ticker can cause the “order” of clusters to change
The script does NOT assume any cluster equals any actionable bias, user decides.
PERFORMANCE METRICS & ROC TABLE
The indicator computes average 1-bar ROC for each cluster in:
Training set
Test (live) set
This helps measure:
Cluster profitability consistency
Regime forward predictability
Whether a regime is noise, trend, or reversion-biased
EQUITY SIMULATION & FEES
Designed for close-to-close realistic backtesting.
Position = cluster of previous bar
Fees applied only on regime switches. Meaning:
Staying long → no fee
Switching long→short → fee applied
Switching any→cash → fee applied
Fee input is percentage, but script already converts internally.
Disclaimers
⚠️ This indicator uses machine-learning but does not predict the future. It classifies similarity to past regimes, nothing more.
⚠️ Backtest results are not indicative of future performance.
⚠️ Clusters have no inherent “bullish” or “bearish” meaning. You must interpret them based on your testing and your own feature engineering.
Delta Zones Buy/Sell Pressure UT Plus Delta Zones Buy/Sell Pressure: All-in-One Smart Trading Indicator
💡 Summary: This Indicator is designed as a powerful All-in-One analysis tool, consolidating 4 crucial trading strategies: Delta Zones (Extreme Pressure), Orderblocks & Breaker Blocks (Market Structure), Multi-Indicator Signals (RSI/CCI/Stoch), and UT Bot Alerts (Trend Signals). It provides a comprehensive trading setup on a single chart.
🔎 Key Features:
Delta Zones (Extreme Buy/Sell Pressure): Utilizes Standard Deviation to spot candles with abnormal Buy/Sell Pressure, often indicating institutional activity or stop hunts.
Orderblocks & Breaker Blocks: Automatically analyzes Market Structure Shifts (MSS) to draw Orderblocks and convert them into Breaker Blocks, serving as key support/resistance zones.
Multi-Indicator Signals (RSI/CCI/Stoch): Provides confirmed Buy/Sell signals when RSI, CCI, and Stochastic are in Oversold/Overbought conditions and show reversal action (Users can select the combination).
UT Bot Alerts: Includes a ATR-based Trailing Stop system and secondary Buy/Sell signals for trend confirmation.
🚀 How to Use:
Use the "BUY/SELL" signals from the Multi-Indicator section as the primary trigger.
Use the Delta Zones or Orderblocks/Breaker Blocks as high-confidence confirmation zones for entry/exit, and as precise Stop Loss placement areas.
⚠️ Note on Performance: This Indicator uses complex logic (especially Array and Box drawing functions) and may be resource-intensive on lower timeframes.
The Ultimate Price Action & SMC Toolkit: Delta Zones, MTF IndicaThis is an All-in-One Pine Script indicator that seamlessly combines three advanced trading concepts:
Delta Zones (Wick Pressure): Uses Standard Deviation to identify extreme buying/selling pressure within the candlestick wicks, signaling potential stop hunts or liquidity absorption. These are plotted as critical Buy/Sell Boxes.
Multi-Timeframe (MTF) Indicators: Confirms signals using popular indicators (RSI, CCI, Stochastic) calculated from a separate, user-selected Timeframe or the current chart timeframe. This adds a crucial layer of context and momentum confirmation.
Smart Money Concepts (SMC): Automatically detects and plots Orderblocks (OBs) and Breaker Blocks based on confirmed Market Structure Breaks (MSB). This helps locate high-probability Supply and Demand zones.
Key Features:
Integrated plotting for combined indicator signals.
Flexible MTF selection for all standard oscillators.
Automatic Swing High/Low detection for SMC analysis.
Comprehensive Alert system for Delta Pressure, Orderblocks, and Breaker Zones.
Option 2: Focusing on SMC and Flow (Concise)
Title: "SMC Delta Flow: Advanced Orderblock, Breaker, and Wick Reversal Zones with MTF Filter."
Description:
An essential tool for sophisticated SMC traders. This indicator provides high-precision zones:
Smart Money Blocks: Plots Orderblocks and Breaker Blocks following Market Structure Shifts (MSS). Includes a "Chop Control" feature to invalidate re-used or weak Breakers.
Delta Reversal Zones: Pinpoints candles showing extreme high-deviation wick pressure. Use these zones to confirm reversals and identify precise entry points where liquidity was captured.
MTF Confirmation: Incorporates configurable Multi-Timeframe (MTF) RSI, CCI, and Stochastic indicators to act as a momentum filter or confirmation tool.
Add this indicator to unify your analysis of Liquidity, Market Structure, and Momentum on a single chart!
SMC, SmartMoneyConcepts, Orderblock, BreakerBlock, MTF, MultiTimeframe, Delta, Wick, Liquidity, PriceAction, RSI, Stochastic, CCI
TrendMaster V2TrendMaster V2 is a comprehensive Pine Script indicator designed for TradingView. It combines multiple technical indicators and an advanced scoring logic to provide actionable trading signals. The script is highly customizable, allowing users to adjust trading modes, color themes, and signal filters according to their preferences and risk tolerance.
Key Features
Composite Scoring System:
The script calculates a composite score based on trend, momentum, pattern recognition, volume, volatility, divergence, Pearson correlation, and the CCI index. This score helps identify the best buy or sell opportunities.
Customizable Parameters:
Users can choose between “Aggressive,” “Balanced,” or “Conservative” trading modes, adjust indicator periods, and customize the color scheme of all visual elements.
Confluence Analysis:
The script evaluates the number of matching bullish or bearish signals, providing a confluence summary for higher-confidence trades.
Visual Signals:
Clear visual cues (triangles, circles, crosses) are displayed on the chart for strong buy/sell signals, confluences, and divergences.
Information Panels:
Two panels display real-time data such as score, RSI, volume, volatility, Pearson, CCI, trend, signal, and mode, along with the confluence status for quick reference.
Alert Conditions:
The script supports alerts for strong buy/sell signals, confluences, and divergences.
How It Works
Main Configuration:
Users select a trading mode (Aggressive, Balanced, or Conservative) and a color theme (Dark or Light).
Custom colors can also be set for bullish, bearish, strong, neutral, and signal elements.
Technical Indicators
Moving Averages (SMA/EMA) for trend analysis.
RSI to assess momentum and overbought/oversold conditions.
MACD for trend confirmation.
Volume and Volatility (ATR) for market activity evaluation.
Advanced Indicators
Pearson Correlation to measure trend strength.
CCI for cyclic momentum analysis.
Pattern Recognition
The script identifies common bullish and bearish reversal patterns (e.g., engulfing, hammer, morning/evening star) and continuation patterns (e.g., three white soldiers/black crows).
Composite Score
Each indicator contributes to a composite score, weighted according to the selected trading mode.
The score determines the strength of buy/sell signals.
Confluence Analysis
The script counts the number of matching bullish or bearish signals, providing a confluence summary for higher-confidence trades.
Visual Signals and Alerts
Strong buy/sell signals: triangles
Confluence signals: circles
Divergences: crosses
Alerts are triggered for strong buy/sell signals, confluences, and divergences.
Usage Instructions
Add the script to your TradingView chart.
Adjust the settings in the configuration panel to match your trading style.
Monitor the information panels and visual signals to spot trading opportunities.
Set up alerts for your preferred signal types.
Composite Momentum System⚙️ Composite Momentum System — RSI + CCI + Momentum + MFI + (DI·ADX) × MACD² (4-Color Smoothed Signal)
This advanced indicator fuses multiple momentum, volume, and trend components into one unified oscillator, dynamically visualized around a zero line. It helps traders identify powerful directional moves, trend reversals, and momentum exhaustion far earlier than traditional MACD or RSI alone.
🧩 Core Formula
Composite = ((RSI + CCI + Momentum + MFI) + (((DI− × −1) + DI+) × ADX)) × (MACD²)
RSI – captures relative strength and short-term momentum
CCI – measures deviation from price mean (volatility & cycles)
Momentum – shows raw velocity of price change
MFI – volume-weighted momentum, adds money flow confirmation
DI / ADX – directional strength and market trend intensity
MACD² – amplifies strong momentum moves and filters weak noise
🌈 Visual Design & Features
Zero-Centered Histogram:
Green = Bullish momentum, Red = Bearish momentum
MACD Signal Line (4 Colors):
🟢 Positive & Rising → strong up momentum
🟡 Positive & Falling → weakening uptrend
🔴 Negative & Falling → strong downtrend
🟠 Negative & Rising → possible bearish fade or reversal
Adjustable Signal Smoothing:
Choose MA type (SMA, EMA, RMA, WMA, VWMA) and custom smoothing length for cleaner visualization.
ATR Normalization:
Optional setting to keep MACD and composite values consistent across instruments.
Centering Options:
RSI and MFI can be centered (−50/+50) to balance oscillation around zero.
🎯 How to Use
Above 0: Bullish composite energy → favor long setups.
Below 0: Bearish composite energy → favor short setups.
Signal line color changes highlight momentum acceleration or slowdown.
Crosses through zero often precede major shifts or breakout moments.
⚡ Best Practice
Use this indicator as a momentum strength filter in confluence with price action or volume patterns.
Combine it with VWAP, higher-timeframe trend, or support/resistance zones for high-probability entries.
Adaptive Trend CatcherAdaptive Trend Catcher is an original indicator that combines Hull Moving Average smoothing, ATR-based volatility bands, and a CCI filter within an adaptive logic framework. It’s built to react intelligently to changing market conditions rather than applying fixed parameters.
The system uses hysteresis to confirm trend flips only after several consistent signals, minimizing noise and false reversals. During strong momentum bursts, it automatically tightens its internal deadzone and step size to stay responsive while maintaining stability in quieter periods.
The result is a dynamic trend engine that plots a color-shifting adaptive line — green for bullish, red for bearish — that adjusts smoothly with volatility. Optional upper/lower ATR bands can be displayed for added context.
How to use: Watch for confirmed trend color flips with supporting momentum. Bullish flips occur when price regains the lower band and CCI turns positive; bearish flips when price falls below the upper band and CCI turns negative.
Includes alert conditions for both reversals.
For educational purposes only. Not financial advice.
