Commencez

Analyse fondamentale

Devrat Smart Confluence 55% long Strategy v3ema 30/50 and 100 55% win rate trades logic : | Confluence Factor | Purpose | | ------------------------------------------ | ------------------------------ | | ✅ EMA 30/50/100 alignment | Trend confirmation | | ✅ ADX > 20 | Strong momentum filter | | ✅ Pullback to dynamic support (EMA30 zone) | Sniper entries, not chasing | | ✅ Bullish engulfing *AND* volume spike | Confirm real buyer interest | | ✅ Trade only during NY/London hours | Avoids fakeouts from Asia chop |
Stratégie Pine Script®
par devratpsspl
Greer Value📈 Greer Value This indicator evaluates the year-over-year (YoY) growth consistency of five key fundamental metrics for any stock: Book Value Per Share Free Cash Flow Operating Margin Total Revenue Net Income The script tracks whether each metric increases annually based on financial statement data (FY), then calculates both individual and aggregate increase percentages over time. A color-coded table is displayed on the most recent bar showing: Raw counts of increases vs. checks per metric Percentage of years with growth Overall "Greer Value" score indicating total consistency across all five metrics ✅ Green = Strong YoY growth ❌ Red = Weak or inconsistent growth Use this tool to help identify fundamentally improving companies with long-term value creation potential.
Indicateur Pine Script®
par controllinghand
Global Liquidity Indicator in USDglobal liquidity2 Lorem ipsum (/ˌlɔː.rəm ˈɪp.səm/ LOR-əm IP-səm) is a dummy or placeholder text commonly used in graphic design, publishing, and web development. Its purpose is to permit a page layout to be designed, independently of the copy that will subsequently populate it, or to demonstrate various fonts of a typeface without meaningful text that could be distracting. Lorem ipsum is typically a corrupted version of De finibus bonorum et malorum, a 1st-century BC text by the Roman statesman and philosopher Cicero, with words altered, added, and removed to make it nonsensical and improper Latin. The first two words are the truncation of dolorem ipsum ("pain itself"). Versions of the Lorem ipsum text have been used in typesetting since the 1960s, when advertisements for Letraset transfer sheets popularized it. Lorem ipsum was introduced to the digital world in the mid-1980s, when Aldus employed it in graphic and word-processing templates for its desktop publishing program PageMaker. Other popular word processors, including Pages and Microsoft Word, have since adopted Lorem ipsum, as have many LaTeX packages, web content managers such as Joomla! and WordPress, and CSS libraries such as Semantic UI.
Indicateur Pine Script®
par DeFiDegen
Fibonacci Retracement Buy/Sell Signal by KriptomistFibonacci Retracement Buy/Sell Signal by Kriptomist This indicator identifies potential buy and sell opportunities based on Fibonacci retracement levels. It calculates significant Fibonacci retracement levels (38.2% and 61.8%) over a customizable lookback period. How it works: Buy Signal: Triggered when the price crosses above the 61.8% Fibonacci retracement level, indicating potential bullish momentum. Sell Signal: Triggered when the price crosses below the 38.2% Fibonacci retracement level, suggesting possible bearish momentum. The signals are clearly marked on your chart with visual shapes for ease of use. Additionally, alerts can be set to notify you immediately when signals occur. Features: Customizable lookback period. Clear visual representation of key Fibonacci levels. Easy-to-follow Buy/Sell visual signals. Alert conditions for timely notifications. Developed by Kriptomist for traders seeking clarity and precision in their Fibonacci-based trading strategies.
Indicateur Pine Script®
par kriptomist
PO3 Multi-Timeframe Strategy Framework🔴 4H Wick High & 🟢 4H Wick Low (Approximated) Displays the high and low of the 4-hour wick range, calculated using the last 48 1-minute candles (approx. 4 hours). Helps identify key liquidity zones for sweep setups. 📉 Fair Value Gaps (FVGs) Marks standard FVGs where price moves with a visible gap between candles (momentum imbalances). Gray label "FVG" appears below bars when triggered. 📈 Inverse Fair Value Gaps (IFVGs) Highlights price candles that completely skip over the prior candle’s high or low — often leading to reversals. Blue triangle appears above bars when detected. 🚀 Change in State of Delivery (CISD) Indicates breakout moves where price closes above recent highs (used as entry confirmation). Green triangle appears below bars to signal breakout intent. ⏰ Time Filter (Starts after 10AM EST) All logic activates only after a 10AM EST session open to align with liquidity injections.
Indicateur Pine Script®
par drewschnek
200 EMA Trend Direction [Dr.K.C.Prakash]📘 Indicator Description: 200 EMA Trend Direction The "200 EMA Trend Direction " indicator is a visual trend-following tool designed to identify and confirm major market direction using the slope of the 200-period Exponential Moving Average (EMA). It’s ideal for traders who want to stay on the right side of the trend and avoid noise. 🔍 Key Components 1. 200 EMA (Exponential Moving Average) The 200 EMA is a widely used long-term trend indicator in technical analysis. It reacts faster than the simple moving average (SMA) due to its weighting toward recent prices. 2. Trend Detection Logic Uptrend is detected when the current EMA value is higher than the previous candle’s EMA. Downtrend is detected when the EMA starts sloping downward, i.e., current EMA < previous EMA. 🎨 Visual Representation Green EMA Line: Indicates the 200 EMA is rising → bullish trend (uptrend). Red EMA Line: Indicates the 200 EMA is falling → bearish trend (downtrend). This color-coding helps you instantly understand the market context without needing to analyze multiple indicators. ✅ Best Use Cases 📈 Trend Confirmation: Use it to confirm the direction before entering a trade. ⚠️ Avoid Counter-Trend Trades: Helps in staying aligned with the dominant trend. 🧠 Supports All Timeframes: Although best suited for 5m, 15m, or higher, it can also be used for 1-minute charts in scalping systems when aligned with higher timeframes. ⚙️ Customization Possibilities You can extend this indicator with: 🔔 Trend change alerts 🟩🟥 Background shading based on trend 💹 Buy/Sell signals when price crosses above/below the 200 EMA
Indicateur Pine Script®
par prakash1126
Discount to Net Asset ValueOverview This indicator helps investors and analysts identify when a company’s stock is trading below or above its intrinsic value. A persistent discount may highlight potential value opportunities, while a sustained premium could signal overvaluation or strong market sentiment. By visually shading the background and plotting the discount/premium percentage, users can quickly screen for undervalued stocks, confirm fundamental research, and make more informed buy or sell decisions. Usage Calculates the Net Asset Value discount (or premium) for any symbol. By default the indicator uses the current chart symbol’s TOTAL_ASSETS, TOTAL_LIABILITIES, and TOTAL_SHARES_OUTSTANDING. You can select another symbol by checking “Use Custom Symbol” and entering the ticker of the other symbol. This is useful for comparing the Discount to NAV across symbols. Shows the percentage discount (or premium) of market capitalization relative to net asset value. Recommended timeframes: daily bars or higher. Reporting Periods: • FQ = Fiscal Quarter • FY = Fiscal Year • TTM = Trailing Twelve Months Note: NAV is pulled on the chosen reporting period (FQ, FY, TTM). By default the background is shaded red to illustrate a discount to NAV and green to illustrate a premium to NAV. This can be toggled in the settings.
Indicateur Pine Script®
par quickshiftinn
Futures vs CFD Price Display 🎯 Trading the same asset in CFDs and Futures but tired of switching charts to compare prices? This is your indicator! Stop the constant chart hopping! This live price comparison shows you instantly where the better conditions are. ✨ What you get: Bidirectional: Works in both Futures AND CFD charts Live prices: Real-time comparison of both markets Spread calculation: Automatic difference in points and percentage Fully customizable: Colors, position, size to your liking Professional design: Clean display with symbol header 🎯 Perfect for: Gold traders (Futures vs CFD) Arbitrage strategies Spread monitoring Multi-broker comparisons ⚙️ Customization: 3 sizes (Small/Normal/Large) for all screens 4 positions available Individual color schemes Toggle features on/off 💡 Simply enter the symbol and keep both markets in sight! Notice: "Co-developed with Claude AI (Anthropic) - because even AI needs to pay the server bills! 😄"
Indicateur Pine Script®
par GoldenDayTrader
Session HighlightsCrypto relevant global equity market open/close indicator, high opacity background highlights follow the following color scheme & daily time ranges (times in EST): Orange: 8:00 PM to 9:30 PM (Sunday - Thursday): Japan/South Korea Yellow: 9:30 PM to +1D 4:00 AM (Sunday - Thursday): Hong Kong Aqua: 8:00 AM to 9:30 AM (Monday - Friday): US Premarket / Macro Data Release Blue: 9:30 AM to 4:00 PM (Monday - Friday): US White: 4:00 PM to +2D 6:00 PM (Friday - Sunday): Weekend *Market Holidays not accounted for
Indicateur Pine Script®
par bobbyjacowleff
NIFTY Option Buy Strategy MASTER v1This script is a complete option buying strategy framework for NIFTY, designed for both intraday and positional swing trades. 🔹 Built using multi-timeframe analysis (EMAs, MACD, RSI) 🔹 Combines key macro filters: India VIX, PCR, FII/DII net cash flows 🔹 Supports both Call (CE) and Put (PE) entries 🔹 Includes manual input dashboard for real-time market context 🔹 Trade logic includes: Bollinger Band breakouts Volume confirmation VWAP filtering EMA crossover + MACD alignment Resistance/support proximity from option chain (manual) 📈 Smart Trade Management: Multi-target system (e.g., exit 50% at RR=1, 50% at RR=2) Trailing stop-loss after target 1 hits Automatic exit on SL/TP or reverse signals Visual markers for all entries, exits, and stops 📊 Built-in Dashboard: Displays India VIX, PCR, FII/DII flows, and S/R levels Strike price selection (ATM + offset logic) 🧪 Ideal for backtesting, alerts, and real-time execution. Can be used with alerts + webhook for automated trading or signal generation. ⚠️ Note: This script is for educational purposes only. Always test on paper trading before going live.
Indicateur Pine Script®
par aman_ghadge698
COT-Index-NocTradingCOT Index Indicator The COT Index Indicator is a powerful tool designed to visualize the Commitment of Traders (COT) data and offer insights into market sentiment. The COT Index is a measurement of the relative positioning of commercial traders versus non-commercial and retail traders in the futures market. It is widely used to identify potential market reversals by observing the extremes in trader positioning. Customizable Timeframe: The indicator allows you to choose a custom time interval (in months) to visualize the COT data, making it flexible to fit different trading styles and strategies. How to Use: Visualize Market Sentiment: A COT Index near extremes (close to 0 or 100) can indicate potential turning points in the market, as it reflects extreme positioning of different market participant groups. Adjust the Time Interval: The ability to adjust the time interval (in months) gives traders the flexibility to analyze the market over different periods, which can be useful in detecting longer-term trends or short-term shifts in sentiment. Combine with Other Indicators: To enhance your analysis, combine the COT Index with your technical analysis. This tool can serve as an invaluable addition to your trading strategy, providing a deeper understanding of the market dynamics and the positioning of major market participants.
Indicateur Pine Script®
par NocTrading
PER Bands (Auto EPS)PER Bands Indicator - Technical Specification Function This PineScript v6 overlay indicator displays horizontal price bands based on Price-to-Earnings Ratio multiples. The indicator calculates price levels by multiplying earnings per share values by user-defined PER multiples, then plots these levels as horizontal lines on the chart. Data Sources The script attempts to automatically retrieve earnings per share data using TradingView's `request.financial()` function. The system first queries trailing twelve months EPS data, then annual EPS data if TTM is unavailable. When automatic retrieval fails or returns zero values, the indicator uses manually entered EPS values as a fallback. Configuration Options Users can configure five separate PER multiples (default values: 10x, 15x, 20x, 25x, 30x). Each band supports individual color customization and adjustable line width settings from 1 to 5 pixels. The indicator includes toggles for band visibility and optional fill areas between adjacent bands with 95% transparency. Visual Components The indicator plots five horizontal lines representing different PER valuation levels. Optional fill areas create colored zones between consecutive bands. A data table in the top-right corner displays current EPS source, EPS value, current PER ratio, and calculated price levels for each configured multiple. Calculation Method The indicator performs the following calculations: - Band Price = Current EPS × PER Multiple - Current PER = Current Price ÷ Current EPS These calculations update on each bar close using the most recent available EPS data. Alert System The script includes alert conditions for price crossovers above the lowest PER band and crossunders below the highest PER band. Additional alert conditions can be configured for any band level through the alert creation interface. Debug Features Debug mode displays character markers on the chart indicating when TTM or annual EPS data is available. This feature helps users verify which data source the indicator is using for calculations. Data Requirements The indicator requires positive, non-zero EPS values to function correctly. Stocks with negative earnings or zero EPS will display "N/A" for current PER calculations, though bands will still plot using the manual EPS input value. Exchange Compatibility Automatic EPS data availability varies by exchange. United States equity markets typically provide comprehensive fundamental data coverage. International markets may have limited automatic data availability, requiring manual EPS input for accurate calculations. Technical Limitations The indicator cannot fetch real-time EPS updates and relies on TradingView's fundamental data refresh schedule. Historical EPS changes are not reflected in past band positions, as the indicator uses current EPS values for all historical calculations. Display Settings The information table shows EPS source type (TTM Auto, Annual Auto, Manual, or Manual Fallback), allowing users to verify data accuracy. The table refreshes only on the last bar to optimize performance and reduce computational overhead. Code Structure Built using PineScript v6 syntax with proper scope management for plot and fill functions. The script uses global scope for all plot declarations and conditional logic within plot parameters to handle visibility settings. Version Requirements This indicator requires TradingView Pine Script version 6 or later due to the use of `request.financial()` functions and updated syntax requirements for plot titles and fill operations.
Indicateur Pine Script®
par hungryWhale69029
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.
Indicateur Pine Script®
par EdgeTools