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Correlation prix [SP500, TESLA, BTCBefore you see this post I want to thank all the TradingView team. Every day that passes I learn better and better to use Pine script and I owe this to all those who publish and to the philosophy of TradingView. Thanks from Amos This trading indicator compares the prices of the S&P 500 Index (SP500), Tesla (TSLA), and Bitcoin (BTC) to find correlations between them. To make the prices of SP500 and Tesla comparable to the price of Bitcoin, the indicator multiplies the closing price of Tesla by 114 and the closing price of the S&P 500 Index by 5.6. In this way we can superimpose the prices on the BTC chart and see what happens. Average BTC price/ tesla price = 114, so if we multiply the tesla price by 114 times we can superimpose it on the BTC price At average BTC/SPX price = 5.6, also in this case we multiply the price of SPX by 5.6 to overlay the graph and see any correlations. The indicator then calculates the average price between SP500 and Tesla, using the formula (SP500 + Tesla) / 2. This calculation creates a new line on the chart that represents the average price between these two assets. The BTC_SP_TE variable is then calculated as the average of the closing price of Bitcoin and the previously calculated average price of SP500 and Tesla, using the formula (Btc + SP_TE) / 2. This calculation creates another line on the chart that represents the average price between Bitcoin and the previously calculated average between SP500 and Tesla. The idea behind calculating these averages is to find correlations and patterns between the prices of these assets, which can help identify potential trading opportunities. By comparing the average prices of different assets, the trader can look for trends and patterns that might not be apparent when looking at each asset individually. The indicator plots these prices on a chart and fills the area between them with either green or fuchsia, depending on which one is higher. The strategy suggests buying Bitcoin when the average price of SP500 and Tesla is higher than the current price of Bitcoin, and selling when it is lower. To add visual cues to the trading strategy, the indicator uses the plotchar function to display a small triangle below the chart when it detects a potential buying opportunity. This is done with the following parameters: Value: BTC_SP_TE < Btc and Btc > Btc1 and Btc1 > Btc , which is a logical expression that checks whether the average price of SP500 and Tesla is less than the current price of Bitcoin (BTC_SP_TE < Btc), and whether the current price of Bitcoin is higher than the price 10 bars ago (Btc > Btc1 ) and higher than the price on the previous bar (Btc1 > Btc ). Text: "Moyen BTC_SP_Te", which is the text to display inside the marker. Symbol: "▲", which is the symbol to use for the marker. In this case, it is a small triangle pointing upwards. Location: location.belowbar, which specifies that the marker should be placed below the bar. I hope this is an example of how to create an indicator on TradingView, remember that correlations do not always last, it is possible that when you see the graph this correspondence no longer exists, do your studies and get inspired.
Penunjuk Pine Script®
oleh AmosTradingSystem
TESLA: EMAS and Bollinger BandsThis script is supposed to be used as part of the "TESLA" strategy in which the default values for the EMAs will serve as a trend indicator and dynamic support and resistance. Moreover, the bollinger bands will signal an overbought or oversold condition stating statistically the price will go up or down. This script features are: - 3 EMAs which will response quicker than SMA to the new prices and will serve as dynamic support and resistance as well as trend indicators. - 1 Bollinger band which will signal overbought and oversold conditions.
Penunjuk Pine Script®
oleh adriantoro
Tesla Coil MLThis is a re-implementation of @veryfid's wonderful Tesla Coil indicator to leverage basic Machine Learning Algorithms to help classify coil crossovers. The original Tesla Coil indicator requires extensive training and practice for the user to develop adequate intuition to interpret coil crossovers. The goal for this version is to help the user understand the underlying logic of the Tesla Coil indicator and provide a more intuitive way to interpret the indicator. The signals should be interpreted as suggestions rather than as a hard-coded set of rules. NOTE: Please do NOT trade off the signals blindly. Always try to use your own intuition for understanding the coils and check for confluence with other indicators before initiating a trade.
Penunjuk Pine Script®
oleh jdehorty
25
Script_Algo - ORB Strategy with Filters🔍 Core Concept: This strategy combines three powerful technical analysis tools: Range Breakout, the SuperTrend indicator, and a volume filter. Additionally, it features precise customization of the number of candles used to construct the breakout range, enabling optimized performance for specific assets. 🎯 How It Works: The strategy defines a trading range at the beginning of the trading session based on a selected number of candles. It waits for a breakout above the upper or below the lower boundary of this range, requiring a candle close. It filters signals using the SuperTrend indicator for trend confirmation. It utilizes trading volume to filter out false breakouts. ⚡ Strategy Features 📈 Entry Points: Long: Candle close above the upper range boundary + SuperTrend confirmation Short: Candle close below the lower range boundary + SuperTrend confirmation 🛡️ Risk Management: Stop-Loss: Set at the opposite range boundary. Take-Profit: Calculated based on a risk/reward ratio (3:1 by default). Position Size: 10 contracts (configurable). ⚠️ IMPORTANT SETTINGS 🕐 Time Parameters: Set the correct time and time zone! ❕ATTENTION: The strategy works ONLY with correct time settings! Set the time corresponding to your location and trading session. 📊 This strategy is optimized for trading TESLA stock! Parameters are tailored to TESLA's volatility, and trading volumes are adequate for signal filtering. Trading time corresponds to the American session. 📈 If you look at the backtesting results, you can see that the strategy could potentially have generated about 70 percent profit on Tesla stock over six months on 5m timeframe. However, this does not guarantee that results will be repeated in the future; remain vigilant. ⚠️ For other assets, the following is required: Testing and parameter optimization Adjustment of time intervals and the number of candles forming the range Calibration of stop-loss and take-profit levels ⚠️ Limitations and Drawbacks 🔗 Automation Constraints: ❌ Cannot be directly connected via Webhook to CFD brokers! Additional IT solutions are required for automation, thus only manual trading based on signals is possible. 📉 Risk Management: Do not risk more than 2-3% of your account per trade. Test on historical data before live use. Start with a demo account. 💪 Strategy Advantages ✅ Combined approach – multiple signal filters ✅ Clear entry and exit rules ✅ Visual signals on the chart ✅ Volume-based false breakout filtering ✅ Automatic position management 🎯 Usage Recommendations Always test the strategy on historical data. Start with small trading volumes. Ensure time settings are correct. Adapt parameters to current market volatility. Use only for stocks – futures and Forex require adaptation. 📚 Suitable Timeframes - M1-M15 Only highly liquid stocks 🍀 I wish all subscribers good luck in trading and steady profits! 📈 May your charts move in the right direction! ⚠️ Remember: Trading involves risk. Do not invest money you cannot afford to lose!
Strategi Pine Script®
oleh Script_Algo
Tesla CoilThis indicator reads the charts as frequency because the charts are just waves after all. This is an excellent tool for finding "Booms" and detecting dumps. Booms are found when all the frequencies pull under the red 20 line. Dumps are detected when all the lines drag themselves along the 20 line as seen is screenshots below. Below is another 2 examples of a "boom". Everything sucks in before exploding out. Below is an example of a dump:
Penunjuk Pine Script®
oleh veryfid
99
Hosoda’s CloudsMany investors aim to develop trading systems with a high win rate, mistakenly associating it with substantial profits. In reality, high returns are typically achieved through greater exposure to market trends, which inevitably lowers the win rate due to increased risk and more volatile conditions. The system I present, called “Hosoda’s Clouds” in honor of Goichi Hosoda , the creator of the Ichimoku Kinko Hyo indicator, is likely one of the first profitable systems many traders will encounter. Designed to capture trends, it performs best in markets with clear directional movements and is less suitable for range-bound markets like Forex, which often exhibit lateral price action. This system is not recommended for low timeframes, such as minute charts, due to the random and emotionally driven nature of price movements in those periods. For a deeper exploration of this topic, I recommend reading my article “Timeframe is Everything”, which discusses the critical importance of selecting the appropriate timeframe. I suggest testing and applying the “Hosoda’s Clouds” strategy on assets with a strong trending nature and a proven track record of performance. Ideal markets include Tesla (1-hour, 4-hour, and daily), BTC/USDT (daily), SPY (daily), and XAU/USD (daily), as these have consistently shown clear directional trends over time. Commissions and Configuration Commissions can be adjusted in the system’s settings to suit individual needs. For evaluating the effectiveness of “Hosoda’s Clouds,” I’ve used a standard commission of $1 per order as a baseline, though this can be modified in the code to accommodate different brokers or preferences. The margin per trade is set to $1,000 by default, but users are encouraged to experiment with different margin settings in the configuration to match their trading style. Rules of the “Hosoda’s Clouds” System (Bullish Strategy) This strategy is designed to capture trending movements in bullish markets using the Ichimoku Kinko Hyo indicator. The rules are as follows: Long Entry: A long position is triggered when the Tenkan-sen crosses above the Kijun-sen below the Ichimoku cloud, identifying potential reversals or bounces in a bearish context. Stop Loss (SL): Placed at the low of the candle 12 bars prior to the entry candle. This setting has proven optimal in my tests, but it can be adjusted in the code based on risk tolerance. Take Profit (TP): The position is closed when the Tenkan-sen crosses below the bottom of the Ichimoku cloud (the minimum of Senkou Span A and Senkou Span B). Notes on the Code margin_long=0: Ideal for strategies requiring a fixed position size, particularly useful for manual entries or testing with a constant capital allocation. margin_long=100: Recommended for high-frequency systems where positions are closed quickly, simulating gradual growth based on realized profits and reflecting real-world broker constraints. System Performance The following performance metrics account for $1 per order commissions and were tested on the specified assets and timeframes: Tesla (H1) Trades: 148 Win Rate: 29.05% Period: Jan 2, 2014 – Jan 6, 2020 (+172%) Simple Annual Growth Rate: +34.3% Trades: 130 Win Rate: 30.77% Period: Jan 2, 2020 – Sep 24, 2025 (+858.90%) Simple Annual Growth Rate: +150.7% Tesla (H4) Trades: 102 Win Rate: 32.35% Period: Jun 29, 2010 – Sep 24, 2025 (+11,356.36%) Simple Annual Growth Rate: +758.5% Tesla (Daily) Trades: 56 Win Rate: 35.71% Period: Jun 29, 2010 – Sep 24, 2025 (+3,166.64%) Simple Annual Growth Rate: +211.5% BTC/USDT (Daily) Trades: 44 Win Rate: 31.82% Period: Sep 30, 2017 – Sep 24, 2025 (+2,592.23%) Simple Annual Growth Rate: +324.8% SPY (Daily) Trades: 81 Win Rate: 37.04% Period: Jan 23, 1993 – Sep 24, 2025 (+476.90%) Simple Annual Growth Rate: +14.3% XAU/USD (Daily) Trades: 216 Win Rate: 32.87% Period: Jan 6, 1833 – Sep 24, 2025 (+5,241.73%) Simple Annual Growth Rate: +27.1% SPX (Daily) Trades: 217 Win Rate: 38.25% Period: Feb 1, 1871 – Sep 24, 2025 (+16,791.02%) Simple Annual Growth Rate: +108.1% Conclusion With the “ Hosoda’s Clouds ” strategy, I aim to showcase the potential of technical analysis to generate consistent profits in trending markets, challenging recent doubts about its effectiveness. My goal is for this system to serve as both a practical tool for traders and a source of inspiration for the trading community I deeply respect. I hope it encourages the creation of new strategies, fosters creativity in technical analysis, and empowers traders to approach the markets with confidence and discipline.
Strategi Pine Script®
oleh Especulador96
2
Income Ratio■ Income Statement Ratio This script will provide how distribution of income statement of a comany is. it also allows us to see a clear picture how the business of a company develop. For example TESLA. in term of value, its revenue is 13,757K in the last quarter and it seam to be stable. while the cost of goods sold (COGS) also increase. In term of percent, it shows that the gross profit margin is growing up as well as net profit margin. moreover, depreciation and amortization has declined as well as COGS. This information like this will help us make a better trading plan. ■ Idea. 1. Each items such as Cost of Goods Sold, Gross Profit will be divided by total revenue. 2. 2 types of data after calculation, Value in Million and Percent by comparing with "Total Revenue". ■ How to use it. In the menu, you can select the type of data to show 1. Select data type, it is available in Value in Million and Percent. 2. Select the financial period : FY for Financial Year and FQ for Financial Quarter. Enjoy.
Penunjuk Pine Script®
oleh morzor61
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations. --- 💡 Why I Created This Indicator As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term . What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later. Traditional DCA tracking tools exist, but they share similar limitations: Require manual data entry and complex spreadsheets Use fixed assumptions that don't reflect real market behavior Can't show future projections overlaid on actual price charts Lose the visual context of what's happening in the market Make compound growth feel abstract rather than tangible I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time. This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore. --- 🎯 What This Indicator Does This educational indicator provides DCA analysis tools. Users can input investment scenarios to study: Theoretical Performance: Educational calculations based on historical return data Comparative Analysis: Study differences between actual and theoretical scenarios Historical Projections: Theoretical projections for educational analysis (not predictions) Performance Metrics: CAGR, ROI, and other analytical metrics for study Historical Analysis: Calculates historical return data for reference purposes --- 🚀 Key Features Volatility-Adjusted Historical Return Calculation Analyzes 3-20 years of actual price data for any symbol Automatically detects high-volatility stocks (meme stocks, growth stocks) Uses median returns for volatile stocks, standard CAGR for stable stocks Provides conservative estimates when extreme outlier years are detected Smart fallback to manual percentages when data insufficient Customizable Performance Dashboard Educational DCA performance analysis with compound growth calculations Customizable table sizing (Tiny to Huge text options) 9 positioning options (Top/Middle/Bottom + Left/Center/Right) Theme-adaptive colors (automatically adjusts to dark/light mode) Multiple display layout options Future Projection System Visual future growth projections Timeframe-aware calculations (Daily/Weekly/Monthly charts) 1-30 year projection options Shows projected portfolio value and total investment amounts Investment Insights Performance vs benchmark comparison ROI from initial investment tracking Monthly average return analysis Investment milestone alerts (25%, 50%, 100% gains) Contribution tracking and next milestone indicators --- 📊 Step-by-Step Setup Guide 1. Investment Settings 💰 Initial Investment: Enter your starting lump sum (e.g., $60,000) Monthly Contribution: Set your regular DCA amount (e.g., $500/month) Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed % Historical Period: Select 3-20 years for auto calculations (default: 10 years) Start Year: When you began investing (e.g., 2020) Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000) 2. Display Settings 📊 Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right Visibility Toggles: Show/hide Main Table and Stats Table independently 3. Future Projection 🔮 Enable Projections: Toggle on to see future growth visualization Projection Years: Set 1-30 years ahead for analysis Live Example - NASDAQ:META Analysis: Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value --- 🔬 Pine Script Code Examples Core DCA Calculations: // Calculate total invested over time months_elapsed = (year - start_year) * 12 + month - 1 total_invested = initial_investment + (monthly_contribution * months_elapsed) // Compound growth formula for initial investment theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed) // Future Value of Annuity for monthly contributions monthly_rate = annual_return / 12 fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate) // Total expected value theoretical_total = theoretical_initial_growth + fv_contributions Volatility Detection Logic: // Detect extreme years for volatility adjustment extreme_years = 0 for i = 1 to historical_years yearly_return = ((price_current / price_i_years_ago) - 1) * 100 if yearly_return > 100 or yearly_return < -50 extreme_years += 1 // Use median approach for high volatility stocks high_volatility = (extreme_years / historical_years) > 0.2 calculated_return = high_volatility ? median_of_returns : standard_cagr Performance Metrics: // Calculate key performance indicators absolute_gain = actual_value - total_invested total_return_pct = (absolute_gain / total_invested) * 100 roi_initial = ((actual_value - initial_investment) / initial_investment) * 100 cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100 --- 📊 Real-World Examples See the indicator in action across different investment types: Stable Index Investments: AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking. MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections. MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies. NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used. High-Volatility Growth Stocks: NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods. NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections. NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments. Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods. --- 📈 Performance Metrics Explained Current Portfolio Value: Your actual investment worth today Expected Value: What you should have based on historical returns (Auto) or your target return (Manual) Total Invested: Your actual money invested (initial + all monthly contributions) Total Gains/Loss: Absolute dollar difference between current value and total invested Total Return %: Percentage gain/loss on your total invested amount ROI from Initial Investment: How your starting lump sum has performed CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy) vs Benchmark: How you're performing compared to the expected returns --- ⚠️ Important Notes & Limitations Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended) CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking --- 📚 Educational Use & Disclaimer This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns. Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes. EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions. --- © 2025 TradeVizion. All rights reserved.
Penunjuk Pine Script®
oleh TradeVizion
4
[Kpt-Ahab] PnL-calculatorThe PnL-Cal shows how much you’re up or down in your own currency, based on the current exchange rate. Let’s say your home currency is EUR. On October 10, 2022, you bought 10 Tesla stocks at $219 apiece. Back then, with an exchange rate of 0.9701, you spent €2,257.40. If you sold the 10 Tesla shares on April 17, 2025 for $241.37 each, that’s around a 10% gain in USD. But if you converted the USD back to EUR on the same day at an exchange rate of 1.1398, you’d actually end up with an overall loss of about 6.2%. Right now, only a single entry point is supported. If you bought shares on different days with different exchange rates, you’ll unfortunately have to enter an average for now. For viewing on a phone, the table can be simplified.
Penunjuk Pine Script®
oleh Kpt-Ahab
MTF Fibonacci Pivots with Mandelbrot FractalsMTF Fibonacci Pivots with Mandelbrot Fractals: Advanced Market Structure Analysis Overview The MTF Fibonacci Pivots with Mandelbrot Fractals indicator represents a significant advancement in technical analysis by combining multi-timeframe Fibonacci pivot levels with sophisticated fractal pattern recognition. This powerful tool identifies key support and resistance zones while predicting potential price reversals with remarkable accuracy. Key Capabilities This indicator provides traders with three distinct layers of market structure analysis: Automatic Timeframe Adaptation: The primary pivot set automatically adjusts to your chart's timeframe, ensuring relevant support and resistance levels for your specific trading horizon. 1-Year Fibonacci Pivots: The second layer displays yearly pivots that reveal long-term market cycles and institutional price levels that often act as significant reversal points. 3-Year Fibonacci Pivots: The third layer unveils major market structure zones that typically remain relevant for extended periods, offering strategic context for position trading and long-term investment decisions. Predictive Technology What truly distinguishes this indicator is its advanced predictive capability powered by: Mandelbrot Fractal Pattern Recognition: The indicator implements a sophisticated fractal detection algorithm that identifies recurring price patterns across multiple timeframes. Unlike conventional fractal indicators, it incorporates noise filtering and adaptive sensitivity to market volatility. Tesla's 3-6-9 Principle Integration: The system incorporates Nikola Tesla's mathematical principle through a cubic Mandelbrot equation (Z_{n+1} = Z_n^3 + C where Z_0 = 0), creating a unique approach to pattern recognition that aligns with natural market rhythms. Historical Pattern Matching: When a current price pattern exhibits strong similarity to historical formations, the indicator generates predictive targets with confidence ratings. Each prediction undergoes rigorous validation against multiple parameters including trend alignment, volatility context, and mathematical coherence. Visual Intelligence System The indicator's visual presentation enhances trading decision-making through: Confidence-Based Visualization: Predictions display with intuitive star ratings, percentage confidence scores, and contextual information including price movement magnitude and estimated time to target. Adaptive Color Harmonization: The color system intelligently adjusts to provide optimal visibility while maintaining a professional appearance suitable for any chart setup. Trend Alignment Indicators: Each prediction includes references to the broader trend context, helping traders avoid counter-trend trades unless the reversal signal carries exceptional strength. Strategic Applications This indicator excels in multiple trading scenarios: Intraday Trading: Identify high-probability reversal zones with precise timing Swing Trading: Anticipate significant market turns at key structural levels Position Trading: Recognize major cycle shifts for strategic entry and exit The automatic 1-year and 3-year Fibonacci pivots provide institutional-grade reference points that typically define major market movements. These longer timeframes reveal critical zones that might be invisible on shorter-term analysis, giving you a significant edge in understanding where price is likely to encounter substantial buying or selling pressure. This innovative approach to market analysis combines classical Fibonacci mathematics with cutting-edge fractal theory to create a comprehensive market structure visualization system that illuminates both present support/resistance levels and future price targets with exceptional clarity. Setting Up MTF Fibonacci Pivots with Mandelbrot Fractals Initial Setup Adding this indicator to your TradingView charts is straightforward: Navigate to the "Indicators" button on your chart toolbar Search for "MTF Fibonacci Pivots with Mandelbrot Fractals" Select the indicator to add it to your chart A configuration panel will appear with various setting categories Recommended Settings The indicator comes pre-configured with optimal default settings, but you may want to adjust them based on your trading style: For Day Trading (Timeframes 1-minute to 1-hour) Pivots Timeframe 1: Auto (automatically adapts to your chart) Pivots Timeframe 2: Daily Pivots Timeframe 3: Weekly Fractal Sensitivity: 2-3 Fractal Lookback Period: 20 Prediction Strength: 2 Color Theme: High Contrast or Dark Mode For Swing Trading (Timeframes 4-hour to Daily) Pivots Timeframe 1: Daily Pivots Timeframe 2: Weekly Pivots Timeframe 3: Monthly Fractal Sensitivity: 1-2 Fractal Lookback Period: 30 Prediction Strength: 2-3 Color Theme: Default or Dimmed For Position Trading (Timeframes Daily to Weekly) Pivots Timeframe 1: Weekly Pivots Timeframe 2: Monthly Pivots Timeframe 3: Quarterly Fractal Sensitivity: 1 Fractal Lookback Period: 50 Prediction Strength: 1 Color Theme: Monochrome or Pastel Restoring Default Settings If you've adjusted settings and wish to return to the defaults: Right-click on the indicator name on your chart Select "Settings" from the context menu In the settings dialog, look for the "Reset All" button at the bottom Confirm the reset when prompted Alternatively, you can remove the indicator and add it again for a fresh start with default settings. Advanced Settings Guidance Visual Appearance Use Gradient Colors: Enable for better visual differentiation between pivot levels Color Transparency: 15% provides an optimal balance between visibility and chart clutter Line Width: 1-2 for cleaner charts, 3+ for enhanced visibility Fractal Analysis Enable Fractal Analysis: Keep enabled for prediction capabilities Fractal Box Spacing: Higher values (5-10) for cleaner displays, lower values (1-3) for more signals Maximum Forecast Bars: 20 is optimal for most timeframes, adjust higher for longer predictions Performance Considerations Enable Self-Optimization: Keep enabled to maintain smooth chart performance Resource Priority: Use "Balanced" for most computers, "Performance" for older systems Force Pivot Display: Enable only when checking specific historical periods Common Setup Mistakes to Avoid Setting all timeframes too close together (e.g., Daily, Daily, Weekly) reduces the multi-timeframe advantage Using high fractal sensitivity (4+) on noisy markets creates excessive signals Setting fractal box spacing too low causes cluttered prediction boxes Disabling self-optimization may cause performance issues on complex charts Using incompatible color themes for your chart background reduces visibility The indicator's power comes from its default 1-year and 3-year Fibonacci pivot settings, which highlight institutional levels while the auto-timeframe setting adapts to your trading horizon. These carefully balanced defaults provide an excellent starting point for most traders. For optimal results, I recommend making minimal adjustments at first, then gradually customizing settings as you become familiar with the indicator's behavior in your specific markets and timeframes. Screenshots:
Penunjuk Pine Script®
oleh UnknownUnicorn18653794
2
Magnificent 7 Overall Percentage Change with MA and Angle LabelsMagnificent 7 Overall Percentage Change with MA and Angle Labels Overview: The "Magnificent 7 Overall Percentage Change with MA and Angle Labels" indicator tracks the percentage change of seven key tech stocks (Apple, Microsoft, Amazon, NVIDIA, Tesla, Meta, and Alphabet) and displays their overall average percentage change on the chart. It also provides a moving average of this overall change and calculates the angle of the moving average to help traders gauge the momentum and direction of the overall trend. How it works: Real-Time Percentage Change: The indicator calculates the percentage change of each of the "Magnificent 7" stocks compared to their previous day's closing price, giving a snapshot of the market's performance. Overall Average: It then computes the average of the seven stocks' percentage changes to reflect the broader movement of these major tech companies. Moving Average: The indicator offers a choice of four types of moving averages (SMA, EMA, WMA, or VWMA) to smooth the overall percentage change, allowing traders to focus on the trend rather than short-term fluctuations. Slope and Angle Calculation: To provide additional insights, the indicator calculates the slope of the moving average and converts it into an angle (in degrees). This can help traders determine the strength of the trend—steeper angles often indicate stronger momentum. Key Features: Percentage Change of the "Magnificent 7": Tracks the percentage change of Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), NVIDIA (NVDA), Tesla (TSLA), Meta (META), and Alphabet (GOOGL) on the current chart's timeframe. Overall Average Change: Computes the average percentage change across all seven stocks, giving a combined view of how the most influential tech stocks are performing. Customizable Moving Averages: Offers four types of moving averages (SMA, EMA, WMA, VWMA) to provide flexibility in tracking the trend of the overall percentage change. Angle Calculation: Measures the angle of the moving average in degrees, which helps assess the strength of the market’s momentum. Alerts and visual cues can be triggered based on the angle's steepness. Visual Cues: The percentage change is plotted in green when positive and red when negative, with a background color that changes accordingly. A zero line is plotted for reference. Use Case: This indicator is ideal for traders and investors looking to track the collective performance of the most dominant tech companies in the market. It provides real-time insights into how the "Magnificent 7" stocks are moving together and offers clues about potential market momentum based on the direction and angle of their average percentage change. Customization: Moving Average Type and Length: Choose between different types of moving averages (SMA, EMA, WMA, VWMA) and adjust the length to suit your preferred timeframe. Angle Threshold: Set an angle threshold to trigger alerts when the moving average slope becomes too steep, indicating strong momentum. Alerts: Alerts can be created based on the crossing of the moving average or when the angle of the moving average exceeds a specified threshold. This ensures traders are notified when the trend is accelerating or decelerating significantly. Conclusion: The "Magnificent 7 Overall Percentage Change with MA and Angle Labels" indicator is a powerful tool for those wanting to monitor the performance of the most influential tech stocks, analyze their overall trend, and receive timely alerts when market conditions shift.
Penunjuk Pine Script®
oleh readysetfire
2
Moving Average PropertiesThis indicator calculates and visualizes the Relative Smoothness (RS) and Relative Lag (RL) or call it accuracy of a selected moving average (MA) in comparison to the SMA of length 2 (the lowest possible length for a moving average and also the one closest to the price). Median RS (Relative Smoothness): Interpretation: The median RS represents the median value of the Relative Smoothness calculated for the selected moving average across a specified look-back period (max bar lookback is set at 3000). Significance: A more negative (larger) median RS suggests that the chosen moving average has exhibited smoother price behavior compared to a simple moving average over the analyzed period. A less negative value indicates a relatively choppier price movement. Median RL (Relative Lag): Interpretation: The median RL represents the median value of the Relative Lag calculated for the selected moving average compared to a simple moving average of length 2. Significance: A higher median RL indicates that the chosen moving average tends to lag more compared to a simple moving average. Conversely, lower values suggest less lag in the selected moving average. Ratio of Median RS to Median RL: Interpretation: This ratio is calculated by dividing the median RS by the median RL. Significance: Traders might use this ratio to assess the balance between smoothness and lag in the chosen moving average. This a measure of for every % of lag what is the smoothness achieved. This can be used a benchmark to decide what length to choose for a MA to get an equivalent value between two stocks. For example a TESLA stock on a 15 minute time frame with a length of 12 has a value (ratio of RS/RL) of -150 , where as APPLE stock of length 35 on a 15 minute chart also has a value (ratio of RS/RL) of -150. I imply that a MA of length 12 working on TESLA stock is equivalent to MA of length 35 on a APPLE stock. (THIS IS A EXAMPLE). My assumption is that finding the right moving average length for a stock isn't a one-size-fits-all situation. It's not just about using a fixed length; it's about adapting to the unique characteristics of each stock. I believe that what works for one stock might not work for another because they have different levels of smoothness or lag in their price movements. So, instead of applying the same length to all stocks, I suggest adjusting the length of the moving average to match the values that we know work best for achieving the desired smoothness or lag or its ratio (RS/RL). This way, we're customizing the indicator for each stock, tailoring it to their individual behaviors rather than sticking to a one-size-fits-all approach. Users can choose from various types of moving averages (EMA, SMA, WMA, VWMA, HMA) and customize the length of the moving average. RS measures the smoothness of the MA, while RL measures its lag compared to a simple moving average. The script plots the median RS and RL values, the selected MA, and the ratio of median RS to median RL on the price chart. Traders can use this information to assess the performance of different moving averages and potentially inform their trading decisions.
Penunjuk Pine Script®
oleh federalTacos5392b
Breakout Volume Can Help Confirm Other SignalsVolume can help confirm signals we might discover using other methods of technical analysis. This indicator tracks volume intelligently. Its logic spots above-average turnover and then tests against the price change. BrkVol highlights sessions with heavy volume and directional moves. This can help take out the noise and help confirm the trend. Tesla is a classic example of this, with the stock rallying after showing heavy-volume gains on October 24- 25, December 16 and January 8.
Penunjuk Pine Script®
oleh TradeStation
6
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading. "Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 THE MACHINE LEARNING Gaussian Mixture Models (GMM): Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering : Models data as coming from multiple Gaussian distributions Each market regime is a different Gaussian component Provides probability of belonging to each regime More sophisticated than simple clustering Expectation-Maximization Algorithm: The indicator continuously learns and adapts using the E-M algorithm: E-step: Calculate probability of current market belonging to each regime M-step: Update regime parameters based on new data Continuous learning without repainting Adapts to changing market conditions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THREE MARKET REGIMES The GMM identifies three distinct market states: Regime 1 - Low Volatility: Quiet, ranging markets Uses RSI-based momentum calculation Reduces false signals in choppy conditions Background: Pink tint Regime 2 - Normal Market: Standard trending conditions Uses Rate of Change momentum Balanced sensitivity Background: Gray tint Regime 3 - High Volatility: Strong trends or volatility events Uses Z-score based momentum Captures extreme moves Background: Cyan tint ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 KEY INNOVATIONS 1. Probabilistic Regime Detection: Instead of binary regime assignment, provides probabilities: 30% Regime 1, 60% Regime 2, 10% Regime 3 Smooth transitions between regimes No sudden indicator jumps 2. Weighted Momentum Calculation: Combines three different momentum formulas Weights based on regime probabilities Automatically adapts to market conditions 3. Confidence Indicator: Shows how certain the model is (white line) High confidence = strong regime identification Low confidence = transitional market state Line transparency changes with confidence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION Training Period (50-500): 50-100: Quick adaptation to recent conditions 100: Balanced (default) 200-500: Stable regime identification Number of Components (2-5): 2: Simple bull/bear regimes 3: Low/Normal/High volatility (default) 4-5: More granular regime detection Learning Rate (0.1-1.0): 0.1-0.3: Slow, stable learning 0.3: Balanced (default) 0.5-1.0: Fast adaptation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Visual Signals: Cyan gradient: Bullish momentum Magenta gradient: Bearish momentum Background color: Current regime Confidence line: Model certainty 1. Regime-Based Trading: Regime 1 (pink): Expect mean reversion Regime 2 (gray): Standard trend following Regime 3 (cyan): Strong momentum trades 2. Confidence-Filtered Signals: Only trade when confidence > 70% High confidence = clearer market state Avoid transitions (low confidence) 3. Adaptive Position Sizing: Regime 1: Smaller positions (choppy) Regime 2: Normal positions Regime 3: Larger positions (trending) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 ADVANTAGES OVER OTHER ML INDICATORS vs K-Means Clustering: Soft clustering (probabilities) vs hard boundaries Captures uncertainty and transitions More mathematically robust vs KNN (K-Nearest Neighbors): Unsupervised learning (no historical labels needed) Continuous adaptation Lower computational complexity vs Neural Networks: Interpretable (know what each regime means) No overfitting issues Works with limited data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Best Market Conditions: Markets with clear regime shifts Volatile to trending transitions Multi-timeframe analysis Cryptocurrency markets (high regime variation) Key Strengths: Automatically adapts to market changes No manual parameter adjustment needed Smooth transitions between regimes Probabilistic confidence measure ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 TECHNICAL BACKGROUND Gaussian Mixture Models are used extensively in: Speech recognition (Google Assistant) Computer vision (facial recognition) Astronomy (galaxy classification) Genomics (gene expression analysis) Finance (risk modeling at investment banks) The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 PRO TIPS Watch regime transitions: Best opportunities often occur when regimes change Combine with volume: High volume + regime change = strong signal Use confidence filter: Avoid low confidence periods Multi-timeframe: Compare regimes across timeframes Adjust position size: Scale based on identified regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTES Machine learning adapts but doesn't predict the future Best used with other confirmation indicators Allow time for model to learn (100+ bars) Not financial advice - educational purposes Backtest thoroughly on your instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides: Automatic adaptation to market conditions Clear regime identification with confidence levels Smooth, professional signal generation True unsupervised machine learning This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in: Google's speech recognition Tesla's computer vision NASA's data analysis Wall Street risk models "Let the machine learn the market regimes. Trade with statistical confidence." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Machine Learning Trading Systems Version: 1.0 Algorithm: Gaussian Mixture Model with E-M Classification: Unsupervised Learning Oscillator Not financial advice. Always DYOR.
Penunjuk Pine Script®
oleh AlphaNatt
Universal Stochastic Fusion (Simplified) — v6What this indicator is This indicator is called Universal Stochastic Fusion (USF). It’s a tool that helps traders see when the market might be too high (overbought) or too low (oversold), and when it might be a good time to buy or sell. ________________________________________ How it works Think of the market like a rubber band. • If the band stretches too far up, it usually snaps back down. • If it stretches too far down, it usually bounces back up. The USF indicator measures this stretch using something called the Stochastic Oscillator (just a fancy way of saying it looks at where the current price sits compared to recent highs and lows). It shows this on a scale from 0 to 100: • Near 100 → market is stretched upward (too hot). • Near 0 → market is stretched downward (too cold). • Around 50 → normal, middle ground. ________________________________________ What’s special about USF 1. Two views at once o It lets you see the market’s stretch on your current chart and on another timeframe (like a daily view). o This way, you can see the short-term and the bigger picture together. 2. Smart levels o Instead of always using the same “too high/too low” levels (like 80 and 20), it can adjust these lines automatically depending on how wild or calm the market is. 3. Buy and Sell signals o When the market looks too low and starts turning up, it can mark a BUY. o When the market looks too high and starts turning down, it can mark a SELL. 4. Extra filter (optional) o It can also use another tool (RSI) to double-check signals, so you don’t get as many false alerts. ________________________________________ How this helps traders • It helps traders avoid buying when prices are already too high. • It helps them spot possible bottoms where prices may bounce back. • It combines short-term and long-term signals so traders don’t get tricked by quick moves. ________________________________________ Where it works This indicator is universal — meaning it works on almost any market: • Stocks (like Apple, Tesla, etc.) • Forex (currencies like EUR/USD) • Crypto (Bitcoin, Ethereum, etc.) • Commodities (Gold, Oil, etc.) • Futures and Indices (S&P 500, Nasdaq, etc.) Because all these markets share the same pattern of prices going up and down too much and then pulling back, the USF can be applied everywhere. ________________________________________ 👉 In short: The Universal Stochastic Fusion is like a heat meter for the market. It tells you when prices might be too hot (good chance to sell) or too cold (good chance to buy), and it works in all markets and timeframes. ________________________________________
Penunjuk Pine Script®
oleh SupertradeOfficial
RSI-Adaptive T3 [ChartPrime] — Strategy (Long Only, 1D)This trade has been successfully converted from an individual setup to a full strategy, and the results are truly outstanding. I’m currently testing this for Tesla options trading on the 1-day chart, and it appears to be working extremely well. A special thanks to ChartPrime for creating such a beautifully designed indicator — it’s performing impressively in these tests. If anyone would like to try it out, feel free to download and see the results for yourself. Thank you!
Strategi Pine Script®
oleh sheazadjiwani
1
Asset Premium/Discount Monitor📊 Overview The Asset Premium/Discount Monitor is a tool for analyzing the relative value between two correlated assets. It measures when one asset is trading at a premium or discount compared to its historical relationship with another asset, helping traders identify potential mean reversion opportunities, or pairs trading opportunities. 🎯 Use Cases Perfect for analyzing: NASDAQ:MSTR vs CRYPTO:BTCUSD - MicroStrategy's premium/discount to Bitcoin NASDAQ:COIN vs BITSTAMP:BTCUSD - Coinbase's relative value to Bitcoin NASDAQ:TSLA vs NASDAQ:QQQ - Tesla's premium to tech sector Regional banks AMEX:KRE vs AMEX:XLF - Individual bank stocks vs financial sector Any two correlated assets where relative value matters Example of a trade: MSTR vs BTC - When indicator shows MSTR at 95% percentile (extreme premium): Short MSTR, Buy BTC. Then exit when the spread reverts to the mean, say 40-60% percentile. 🔧 How It Works Core Calculation Ratio Analysis: Calculates the price ratio between your asset and the correlated asset Historical Baseline: Establishes the "normal" relationship using a 252-day moving average. You can change this. Premium Measurement: Measures current deviation from historical average as a percentage Statistical Context: Provides percentile rankings and standard deviation bands The Math Premium % = (Current Ratio / Historical Average Ratio - 1) × 100 🎨 Customization Options Correlated Asset: Choose any symbol for comparison Lookback Period: Adjust historical baseline (50-1000 days) Smoothing: Reduce noise with moving average (1-50 days) Visual Toggles: Show/hide bands and percentile lines Color Themes: Customize premium/discount colors 📊 Interpretation Guide Premium/Discount Reading Positive %: Asset trading above historical relationship (premium) Negative %: Asset trading below historical relationship (discount) Near 0%: Asset at fair value relative to correlation Percentile Ranking 90%+: Near recent highs - potential selling opportunity 10% and below: Near recent lows - potential buying opportunity 25-75%: Normal trading range Signal Classifications 🔴 SELL PREMIUM: Asset expensive relative to recent range 🟡 Premium Rich: Moderately expensive, monitor for reversal ⚪ NEUTRAL: Fair value territory 🟡 Discount Opportunity: Moderately cheap, potential accumulation zone 🟢 BUY DISCOUNT: Asset cheap relative to recent range 🚨 Built-in Alerts Extreme Premium Alert: Triggers when percentile > 95% Extreme Discount Alert: Triggers when percentile < 5% ⚠️ Important Notes Works best with highly correlated assets Historical relationships can change - monitor correlation strength Not investment advice - use as one factor in your analysis Backtest thoroughly before implementing any strategy 🔄 Updates & Future Features This indicator will be continuously improved based on user feedback. So... please give me your feedback!
Penunjuk Pine Script®
oleh HenriqueCentieiro