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Advanced Petroleum Market Model (APMM)Advanced Petroleum Market Model (APMM): A Multi-Factor Fundamental Analysis Framework for Oil Market Assessment ## 1. Introduction The petroleum market represents one of the most complex and globally significant commodity markets, characterized by intricate supply-demand dynamics, geopolitical influences, and substantial price volatility (Hamilton, 2009). Traditional fundamental analysis approaches often struggle to synthesize the multitude of relevant indicators into actionable insights due to data heterogeneity, temporal misalignment, and subjective weighting schemes (Baumeister & Kilian, 2016). The Advanced Petroleum Market Model addresses these limitations through a systematic, quantitative approach that integrates 16 verified fundamental indicators across five critical market dimensions. The model builds upon established financial engineering principles while incorporating petroleum-specific market dynamics and adaptive learning mechanisms. ## 2. Theoretical Framework ### 2.1 Market Efficiency and Information Integration The model operates under the assumption of semi-strong market efficiency, where fundamental information is gradually incorporated into prices with varying degrees of lag (Fama, 1970). The petroleum market's unique characteristics, including storage costs, transportation constraints, and geopolitical risk premiums, create opportunities for fundamental analysis to provide predictive value (Kilian, 2009). ### 2.2 Multi-Factor Asset Pricing Theory Drawing from Ross's (1976) Arbitrage Pricing Theory, the model treats petroleum prices as driven by multiple systematic risk factors. The five-factor decomposition (Supply, Inventory, Demand, Trade, Sentiment) represents economically meaningful sources of systematic risk in petroleum markets (Chen et al., 1986). ## 3. Methodology ### 3.1 Data Sources and Quality Framework The model integrates 16 fundamental indicators sourced from verified TradingView economic data feeds: Supply Indicators: - US Oil Production (ECONOMICS:USCOP) - US Oil Rigs Count (ECONOMICS:USCOR) - API Crude Runs (ECONOMICS:USACR) Inventory Indicators: - US Crude Stock Changes (ECONOMICS:USCOSC) - Cushing Stocks (ECONOMICS:USCCOS) - API Crude Stocks (ECONOMICS:USCSC) - API Gasoline Stocks (ECONOMICS:USGS) - API Distillate Stocks (ECONOMICS:USDS) Demand Indicators: - Refinery Crude Runs (ECONOMICS:USRCR) - Gasoline Production (ECONOMICS:USGPRO) - Distillate Production (ECONOMICS:USDFP) - Industrial Production Index (FRED:INDPRO) Trade Indicators: - US Crude Imports (ECONOMICS:USCOI) - US Oil Exports (ECONOMICS:USOE) - API Crude Imports (ECONOMICS:USCI) - Dollar Index (TVC:DXY) Sentiment Indicators: - Oil Volatility Index (CBOE:OVX) ### 3.2 Data Quality Monitoring System Following best practices in quantitative finance (Lopez de Prado, 2018), the model implements comprehensive data quality monitoring: Data Quality Score = Σ(Individual Indicator Validity) / Total Indicators Where validity is determined by: - Non-null data availability - Positive value validation - Temporal consistency checks ### 3.3 Statistical Normalization Framework #### 3.3.1 Z-Score Normalization The model employs robust Z-score normalization as established by Sharpe (1994) for cross-indicator comparability: Z_i,t = (X_i,t - μ_i) / σ_i Where: - X_i,t = Raw value of indicator i at time t - μ_i = Sample mean of indicator i - σ_i = Sample standard deviation of indicator i Z-scores are capped at ±3 to mitigate outlier influence (Tukey, 1977). #### 3.3.2 Percentile Rank Transformation For intuitive interpretation, Z-scores are converted to percentile ranks following the methodology of Conover (1999): Percentile_Rank = (Number of values < current_value) / Total_observations × 100 ### 3.4 Exponential Smoothing Framework Signal smoothing employs exponential weighted moving averages (Brown, 1963) with adaptive alpha parameter: S_t = α × X_t + (1-α) × S_{t-1} Where α = 2/(N+1) and N represents the smoothing period. ### 3.5 Dynamic Threshold Optimization The model implements adaptive thresholds using Bollinger Band methodology (Bollinger, 1992): Dynamic_Threshold = μ ± (k × σ) Where k is the threshold multiplier adjusted for market volatility regime. ### 3.6 Composite Score Calculation The fundamental score integrates component scores through weighted averaging: Fundamental_Score = Σ(w_i × Score_i × Quality_i) Where: - w_i = Normalized component weight - Score_i = Component fundamental score - Quality_i = Data quality adjustment factor ## 4. Implementation Architecture ### 4.1 Adaptive Parameter Framework The model incorporates regime-specific adjustments based on market volatility: Volatility_Regime = σ_price / μ_price × 100 High volatility regimes (>25%) trigger enhanced weighting for inventory and sentiment components, reflecting increased market sensitivity to supply disruptions and psychological factors. ### 4.2 Data Synchronization Protocol Given varying publication frequencies (daily, weekly, monthly), the model employs forward-fill synchronization to maintain temporal alignment across all indicators. ### 4.3 Quality-Adjusted Scoring Component scores are adjusted for data quality to prevent degraded inputs from contaminating the composite signal: Adjusted_Score = Raw_Score × Quality_Factor + 50 × (1 - Quality_Factor) This formulation ensures that poor-quality data reverts toward neutral (50) rather than contributing noise. ## 5. Usage Guidelines and Best Practices ### 5.1 Configuration Recommendations For Short-term Analysis (1-4 weeks): - Lookback Period: 26 weeks - Smoothing Length: 3-5 periods - Confidence Period: 13 weeks - Increase inventory and sentiment weights For Medium-term Analysis (1-3 months): - Lookback Period: 52 weeks - Smoothing Length: 5-8 periods - Confidence Period: 26 weeks - Balanced component weights For Long-term Analysis (3+ months): - Lookback Period: 104 weeks - Smoothing Length: 8-12 periods - Confidence Period: 52 weeks - Increase supply and demand weights ### 5.2 Signal Interpretation Framework Bullish Signals (Score > 70): - Fundamental conditions favor price appreciation - Consider long positions or reduced short exposure - Monitor for trend confirmation across multiple timeframes Bearish Signals (Score < 30): - Fundamental conditions suggest price weakness - Consider short positions or reduced long exposure - Evaluate downside protection strategies Neutral Range (30-70): - Mixed fundamental environment - Favor range-bound or volatility strategies - Wait for clearer directional signals ### 5.3 Risk Management Considerations 1. Data Quality Monitoring: Continuously monitor the data quality dashboard. Scores below 75% warrant increased caution. 2. Regime Awareness: Adjust position sizing based on volatility regime indicators. High volatility periods require reduced exposure. 3. Correlation Analysis: Monitor correlation with crude oil prices to validate model effectiveness. 4. Fundamental-Technical Divergence: Pay attention when fundamental signals diverge from technical indicators, as this may signal regime changes. ### 5.4 Alert System Optimization Configure alerts conservatively to avoid false signals: - Set alert threshold at 75+ for high-confidence signals - Enable data quality warnings to maintain system integrity - Use trend reversal alerts for early regime change detection ## 6. Model Validation and Performance Metrics ### 6.1 Statistical Validation The model's statistical robustness is ensured through: - Out-of-sample testing protocols - Rolling window validation - Bootstrap confidence intervals - Regime-specific performance analysis ### 6.2 Economic Validation Fundamental accuracy is validated against: - Energy Information Administration (EIA) official reports - International Energy Agency (IEA) market assessments - Commercial inventory data verification ## 7. Limitations and Considerations ### 7.1 Model Limitations 1. Data Dependency: Model performance is contingent on data availability and quality from external sources. 2. US Market Focus: Primary data sources are US-centric, potentially limiting global applicability. 3. Lag Effects: Some fundamental indicators exhibit publication lags that may delay signal generation. 4. Regime Shifts: Structural market changes may require model recalibration. ### 7.2 Market Environment Considerations The model is optimized for normal market conditions. During extreme events (e.g., geopolitical crises, pandemics), additional qualitative factors should be considered alongside quantitative signals. ## References Baumeister, C., & Kilian, L. (2016). Forty years of oil price fluctuations: Why the price of oil may still surprise us. *Journal of Economic Perspectives*, 30(1), 139-160. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. McGraw-Hill. Brown, R. G. (1963). *Smoothing, Forecasting and Prediction of Discrete Time Series*. Prentice-Hall. Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. *Journal of Business*, 59(3), 383-403. Conover, W. J. (1999). *Practical Nonparametric Statistics* (3rd ed.). John Wiley & Sons. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. *Journal of Finance*, 25(2), 383-417. Hamilton, J. D. (2009). Understanding crude oil prices. *Energy Journal*, 30(2), 179-206. Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. *American Economic Review*, 99(3), 1053-1069. Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons. Ross, S. A. (1976). The arbitrage theory of capital asset pricing. *Journal of Economic Theory*, 13(3), 341-360. Sharpe, W. F. (1994). The Sharpe ratio. *Journal of Portfolio Management*, 21(1), 49-58. Tukey, J. W. (1977). *Exploratory Data Analysis*. Addison-Wesley.
Pine Script®指標
由EdgeTools提供
Gibbs - Algorithmic Macro TrackerThis script plots visual markers (lines and labels) on the price chart to highlight specific macro announcement windows (aka “macro times”) during the trading day. Specifically: It marks time windows like 08:20–08:40, 09:50–10:10, 03:20–03:40, etc., depending on session (US, London, Early US). It draws vertical lines at the start and end of each window. It optionally extends projection lines (dotted) up to the current high. It places labels with the word “MACRO” and the time range, so you know visually when you’re in or near macro-sensitive periods. The display works only on intraday timeframes (≤5min). You can turn each macro window on or off using the input panel. It adapts the timezone you set (default GMT-4, i.e., New York).
Pine Script®指標
由Mewann提供
Intra-day mean crossover indicator📈 Intra-day mean crossover + Key Daily MA Touch (Bullish & Bearish) This indicator generates buy/sell signals when the MACD line crosses the signal line and price has interacted with key daily moving averages: 10 EMA 21 EMA 50 SMA 🔍 How It Works Bullish Signal (🟢 Green Arrow Up) Triggered when: MACD line crosses above the signal line (bullish crossover) Price has touched any of the key daily MAs within the past N candles (default: 10) Bearish Signal (🔴 Red Arrow Down) Triggered when: MACD line crosses below the signal line (bearish crossover) Price has touched any of the same MAs within the same lookback window. ⚙️ Inputs MACD Fast / Slow / Signal Lengths (default: 12 / 26 / 9) Number of candles to check for MA touch (default: 10) Tolerance (%) for how close price must come to an MA to count as a "touch" (default: 0.1%) ⏰ Alerts This script includes built-in alert conditions: Bullish Signal Alert – Fires when the 🟢 green arrow appears Bearish Signal Alert – Fires when the 🔴 red arrow appears You can create alerts via the TradingView interface using these conditions. 👤 Author Created by: O-Hyphen Version: 1.0 Date: May 2025 🛑 Disclaimer This script is provided for educational and informational purposes only. It is not financial advice. Use at your own risk. Always do your research before making trading decisions. Past performance does not guarantee future results.
Pine Script®指標
由osakpamwan提供
Monthly Session Divider (Alt Background) | Chart_BullyEasily visualize monthly transitions with alternating background shading. Designed for traders who like to spot macro trends, monthly opens, and institutional order flow. ✅ Alternates background color each month ✅ Auto-detects new months using live date logic ✅ Great for RTH or ETH intraday and swing strategies ✅ Clean gray overlay with low opacity ✅ Works on intraday, daily, and weekly charts ✅ Built for clarity, not clutter Use this tool to: Identify monthly pivots or volume rotations Anchor monthly VWAPs or FVGs with visual context Frame long-term setups with clean visual breaks
Pine Script®指標
由Chart_Bully提供
Weekly Session Divider (Alt Background) | Chart_BullyThis tool adds subtle alternating background shading for each new week, helping you visually distinguish trading sessions at a glance. ✅ Alternates background by weekly session ✅ Works great on intraday and daily timeframes ✅ Ideal for traders who rely on weekly pivots, volume profiles, or macro structure ✅ Compatible with both RTH and ETH charts ✅ Clean design for easy chart integration Use it to improve your session awareness, spot emerging weekly trends, and avoid mental fatigue when reading extended charts.
Pine Script®指標
由Chart_Bully提供
Alternate Day Divider Background | Chart_BullyThis free utility shades every other trading day on your chart, helping you visually separate sessions and spot daily rhythm or pattern shifts more easily. ✅ Automatically alternates background shading by day ✅ Works on both Regular Trading Hours (RTH) and Extended Trading Hours (ETH) ✅ Especially useful on intraday and daily timeframes ✅ Helps identify breakout setups, trend shifts, or volume cycles by session Great for scalpers, day traders, and anyone who wants a subtle visual edge without chart clutter.
Pine Script®指標
由Chart_Bully提供
alfifi 📈 مؤشر "alfifi" – إشارات صعود وهبوط ذكية مؤشر احترافي لتوليد إشارات صعود وهبوط دقيقة تساعدك على اتخاذ قرارات الدخول والخروج بسهولة. 💡 المميزات: - إشارات صعود تظهر عند بداية الزخم الإيجابي - إشارات هبوط عند بداية الهبوط الحقيقي - تصميم بسيط ونظيف بدون تشويش بصري - مناسب لجميع الأطر الزمنية (من الدقيقة حتى اليومي) يجب اختبار الفريمات المتطابقة - لا يحتوي على أوامر بيع/شراء، فقط إشارات تحليلية بصرية 🔒 هذا المؤشر خاص (Invite-only) للحصول على صلاحية الوصول، يرجى التواصل عبر: 📩 تيليجرام: 📈 "alfifi" Indicator – Smart Buy & Sell Signals A professional indicator that provides precise buy and sell signals based on calculated moving average crossovers. Perfect for traders seeking clarity and confidence in entry and exit points. 💡 Features: - "Buy" signal appears at the beginning of strong bullish momentum - "Sell" signal at the early stage of bearish reversals - Clean and minimal design with no visual clutter - Suitable for all timeframes (from 1-minute to daily) – we recommend testing to find your optimal timeframe - No buy/sell orders – purely visual signals for analysis 🔒 This is an invite-only script. To get access, please contact us via: 📩 Telegram:
Pine Script®指標
由l6_5提供
1
IALGO Composite Ema BIST ScreenerIALGO Composite EMA BIST Screener™ is a precision-engineered trend analysis and screener tool built for traders who seek clarity, structure, and actionable signals in the dynamic Turkish equity markets. Combining normalized multi-timeframe EMAs with a proprietary algorithm, it creates a comprehensive decision matrix by filtering these trends through momentum and MACD conditions—offering refined insight into market opportunities. What sets IALGO Composite EMA apart is its focus on identifying potential Major Opportunities, uncovering early-stage trends, spotting oversold bottoming stocks, and signaling potential exit zones, all while highlighting different types of trends and helping confirm their validity. This screener covers the entire BIST stock universe with a built-in comprehensive group list and also allows users to create custom watchlists, set alarms, and monitor them effortlessly. Rather than manually checking each chart, IALGO provides traders with a strategic overview of where a stock stands technically—helping to assess opportunities without getting lost in market noise. It also takes into account trend-aligned Fibonacci levels, presented visually within the same matrix for deeper context in trend-based decisions. 🔶 FEATURE HIGHLIGHTS The script includes a range of advanced features tailored for efficient trend trading: • 🔁 Normalized Multi-Period EMAs  Unique algorithmic blending of different EMA timeframes for clearer trend mapping. • 📊 MACD & Momentum Filtering  Enhances signal quality by confirming with momentum strength and trend acceleration. • 🚀 Big Opportunity Detection  Captures high-potential setups during trend transitions or accumulation zones. • 📉 Oversold Stock Scanner  Identifies bottoms or undervalued assets using EMA-momentum cross-referencing. • 🌀 Trend Start & Exit Signal Zones  Flags key reversal and exhaustion points based on composite readings. • 🔍 Dynamic Trend Type Classifier  Detects and categorizes different market trend conditions in real time. • 📈 Integrated Fibonacci Trend Zones  Visualizes Fib levels aligned with the ongoing trend in one consolidated view. • 🧭 Full BIST Coverage & Group Management  Includes BIST Tüm list by default and supports custom group creation and tracking. • 🔔 Built-in Alerts & Custom Watchlists  Track your favorites and get notified instantly when critical conditions arise. IALGO Composite EMA BIST Screener™ is not just a visual tool; it's a disciplined method to reduce subjectivity in chart analysis. While it doesn’t promise profits, it brings structure, speed, and statistical awareness to your decision-making process—giving you a consistent edge in Turkish equity markets. 🧠 Because understanding a trend is good. But recognizing the right trend at the right time? That’s IALGO. 🔶 HOW TO GET ACCESS You can see detailed instructions on Authors description to get instant access to this indicator & and related contact information
Pine Script®指標
由ialgotech提供
IALGO BIST SMART INDEX KAHIN v4IALGO BIST SMART INDEX KÂHİN™ Your Algorithmic Oracle for Turkish Index Trends IALGO BIST SMART INDEX KÂHİN™ is a purpose-built hybrid system functioning both as an indicator and a full-fledged strategy, specifically tailored for BIST index tracking. Designed to combine EMA logic, MACD filtering, and momentum strength, this tool offers a data-driven and intuitive approach to reading the rhythm of Turkish markets. Crafted with a proprietary algorithm, it doesn’t just show signals — it thinks in sequences, providing both buy/sell entries and a performance table for backtesting and real-time strategy analysis. In this way, KÂHİN acts like a modern oracle — aiming to anticipate market direction before it becomes obvious to the crowd. Unlike generic indicators, KÂHİN empowers users to prepare for market shifts ahead of time, both in uptrends and downturns. With integrated Fibonacci-based breakout zones, users gain a trader’s perspective on critical levels, offering insights into where price action may pivot next. 🔶 FEATURE HIGHLIGHTS • 🔁 EMA-Based Core Logic  Built upon a dynamic EMA framework with optimized smoothing. • 📊 MACD + Momentum Filters  Filters out false signals with dual-layered confirmation. • 🧠 Smart Signal Sequencing  Strategically timed buy & sell signals, not random alerts. • 📉 Full Strategy Mode  Includes built-in performance table to assess historical signal quality. • 📈 Index-Focused Fibonacci Zones  Overlayed levels for visualizing probable breakout or rejection areas. • 🔮 Real-Time Directional Guidance  Aims to capture early trend shifts—like a true oracle. • 💻 Educational by Design  Helps users learn structured, logic-based trend tracking. ⚠️ USAGE RESTRICTION IALGO BIST SMART INDEX KÂHİN™ is strictly built for use on BIST indices only. It is not intended for individual stocks, crypto, forex, or any other asset class. Using it outside of its scope will lead to invalid signals and incorrect results. This tool is ideal for traders looking to understand market structure, test systematic strategies, and gain edge in timing trend shifts on BIST indices. Whether you're swing trading or monitoring macro conditions, KÂHİN™ offers an advantage with logic—not guesswork. 🧠 Because in markets, the smartest trader isn't the one who reacts the fastest—it's the one who prepares before others even see the change coming. 🔶 HOW TO GET ACCESS You can see detailed instructions on Authors description to get instant access to this indicator & and related contact information -Türkce- IALGO BIST SMART INDEX KÂHİN™ Borsa İstanbul XU100 Endeks İçin Algoritmik Öngörü Aracidir (BIST:XU100) - Günlük periyotta kullanilmalidir. IALGO BIST SMART INDEX KÂHİN™, yalnızca BIST endeksleri için özel olarak tasarlanmış, hem bir gösterge hem de strateji olarak çalışan hibrit bir sistemdir. EMA temelli algoritma, MACD filtresi ve momentum analizi ile birleştirilen bu yapı, Türk piyasalarının ritmini daha net ve yapısal biçimde okumanızı sağlar. Kendine özgü algoritması sayesinde KÂHİN, yalnızca sinyal üretmez — sıralı ve stratejik alım-satım noktaları sunar. Dahili performans tablosu sayesinde geçmiş veriler üzerinde test yapabilir, stratejinizin başarısını anlık olarak izleyebilirsiniz. Bu özelliğiyle bir kahin gibi, piyasanın yönünü kalabalıktan önce sezmeyi hedefler. Genel göstergelerin ötesine geçerek KÂHİN, trend dönüşlerine önceden hazırlanmanız için size güç verir — ister yükseliş, ister düşüş döneminde olun. Entegre Fibonacci kırılım bölgeleri, fiyat hareketlerinin muhtemel dönüş ya da sıçrama noktalarını grafik üzerinde görsel olarak sunar. 🔶 TEMEL ÖZELLİKLER • 🔁 EMA Tabanlı Çekirdek Algoritma  Optimize edilmiş EMA yapısıyla trend takibi. • 📊 MACD ve Momentum Filtreleri  Yanıltıcı sinyalleri filtrelemek için ikili onay sistemi. • 🧠 Akıllı Sinyal Sıralaması  Rastgele değil; zamanlaması mantıkla belirlenmiş al-sat sinyalleri. • 📉 Strateji Modu ve Performans Tablosu  Gerçek zamanlı strateji testi ve geçmiş başarı analizi. • 📈 Endekse Özgü Fibonacci Seviyeleri  Kırılım ve tepki bölgelerini grafik üzerinde anlık görme. • 🔮 Gerçek Zamanlı Yön Tespiti  Trend dönüşlerini erkenden yakalama hedefi. • 💻 Öğretici Yapı  Yapılandırılmış ve mantığa dayalı işlem mantığını kullanıcıya kazandırır. ⚠️ KULLANIM UYARISI IALGO BIST SMART INDEX KÂHİN™, yalnızca BIST endekslerinde kullanılmak üzere geliştirilmiştir. Hisse senetleri, kripto paralar, forex ya da başka varlıklarda kullanılamaz. Bu sınırın dışına çıkmak, geçersiz sinyaller ve yanlış sonuçlar doğuracaktır. Bu araç, piyasa yapısını anlamak, sistematik stratejileri test etmek ve trend dönüşlerini zamanında fark etmek isteyen yatırımcılar için idealdir. İster kısa vadeli işlem yapın, ister makro düzeyde yön arayın — KÂHİN™ size sezgiyle değil, mantıkla avantaj kazandırır. 🧠 Çünkü piyasada en hızlı tepki veren değil, daha değişim gelmeden hazır olan kazanır. 🔶 NASIL ERİŞİM SAĞLANIR Bu göstergeye anında erişim sağlamak ve ilgili iletişim bilgilerine ulaşmak için, yazarın açıklama bölümündeki yönergeleri detaylı bir şekilde inceleyebilirsiniz.
Pine Script®策略
由ialgotech提供
📡 ETF RADAR HUD (SPY · QQQ · SPX) Auto-detects if you’re on SPY, SPX or QQQ Shows a sleek status dashboard with: Trend condition (EMA crossover) Volatility meter (based on ATR vs price) RSI mood Volume activity Instrument tag ("SPY 🔍", "QQQ 🚀", "SPX" or "Other 🪐") 🧠 Strategy: We build a situational awareness HUD so SPY/QQQ/SPX day traders know: Are we trending or ranging? Is volatility expanding? Are we in overbought/oversold territory? Is there a volume surge?
Pine Script®指標
由StanTheTradingMan提供
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: 1. 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 2. 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 3. Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance 4. 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 RetrySEverything that you bold i need to have the bold declarations around them for some reason you bold market states instead of what you actually bold. the first one was correct, you just more items needed to be bolded. Objects = Market states Should be Objects = Market statesEdit 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: 1. 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 2. 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 3. Product Objects (Market Equilibrium) Mathematical Definition = Universal property combining multiple objects into balanced state Market Translation = Price, volume, and volatility achieve multi-dimensional balance 4. 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) 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 Progressive Learning Path Week 1 = Master basic categorical concepts Week 2 = Understand universal properties in trading Week 3 = Learn homotopy path analysis Week 4 = Advanced consciousness detection Week 5 = Professional parameter optimization Conclusion: The Future of Market Analysis 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. Categories Primary : Trend Analysis Secondary : Mathematical Indicators Tertiary : Educational Tools "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. DAFETradingSystems.com
Pine Script®指標
由DskyzInvestments提供
6
[Top] Simple Position + SL CalculatorThis indicator is a user-friendly tool designed to help traders easily calculate optimal position sizing, determine suitable stop-loss levels, and quantify maximum potential losses in dollar terms based on their personalized trading parameters. Key Features: Position Size Calculation: Automatically computes the number of shares to purchase based on the trader’s total account size and specified percentage of the account allocated per trade. Stop-Loss Level: Suggests an appropriate stop-loss price point calculated based on the trader’s defined risk percentage per trade. Max Loss Visualization: Clearly displays the maximum potential loss (in dollars) should the stop-loss be triggered. Customizable Interface: Provides the flexibility to place the calculation table in different chart positions (Top Left, Top Right, Bottom Left, Bottom Right) according to user preference. How to Use: Enter your total Account Size. Set the desired Position Size as a percentage of your account. (Typically, 1%–5% per trade is recommended for cash accounts.) Define the Risk per Trade percentage (commonly between 0.05%–0.5%). Choose your preferred Table Position to comfortably integrate with your trading chart. Note: If you identify a technical support level below the suggested stop-loss point, consider reducing your position size to manage the increased risk effectively. Keep in mind that the calculations provided by this indicator are based solely on standard industry best practices and the specific inputs entered by you. They do not account for market volatility, news events, or any other factors outside the provided parameters. Always complement this indicator with sound technical and fundamental analysis.
Pine Script®指標
由TopTradesUA提供
My Trading mantra/playbook🧠 My Trading Mantra — Motivational Trading Reminder Overlay This indicator displays a customizable trading mantra as an overlay on your TradingView charts to keep your mindset sharp and disciplined during trading sessions. You can customize the mantra text, font size, text color, background color, transparency, and screen position (top, middle, bottom, left, right, center). It’s designed to serve as a constant motivational reminder emphasizing core trading principles like planning, risk management, patience, and learning from losses to grow profits. Features: Customizable multi-line mantra text input Adjustable font size (small to huge) Color customization for text and background Adjustable background transparency Multiple screen position options for display Lightweight and simple overlay, no performance impact Purpose: To help traders stay mentally focused and disciplined by having their personalized mantra visible at all times while analyzing charts. Additionally, it can be used as a trading plan or playbook, allowing traders to display their key rules, strategies, or reminders directly on their charts for quick reference during live trading. code is open with love. you are a good trader don't let the markets tell you diffrent.
Pine Script®指標
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EVERUP.EDU.VN - ProEverup.edu.vn is a specialized platform for in-depth Crypto technical analysis, combining a minimalist and intuitive interface with a team of highly experienced analysts. Every feature and report is designed to help you access accurate, timely, and easy-to-understand market information, empowering you to make confident and effective trading decisions. EverUp is not just a tool—it’s your trusted companion on the journey to conquer the Crypto market. Sign up today to experience advanced analysis, receive timely advice, and grow together with the EverUp community!
Pine Script®指標
由EverUp-edu-vn提供