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.
Komut dosyalarını "TESLA" için ara
TESLA: RSI and StochasticThis script is part of the "TESLA" strategy and will help traders identifying overbought and oversold condition as well as other applications such as divergence. The features for this script are the following:
- 1 RSI index in order to identify market buying/selling strength
- 2x Stochastic in order to have fast and slow overbought and oversold zones.
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.
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.
BTC Longs & Shorts HeatmapBitfinex Bitcoin Long and Short positions visualization with colored background.
Original author: @autemox
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:
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.
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.
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💡 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.
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🎯 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
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🚀 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
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📊 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
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🔬 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
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📊 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.
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📈 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
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⚠️ 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
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📚 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.
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© 2025 TradeVizion. All rights reserved.
[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.
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:
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.
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.
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.
Canuck Trading Trader StrategyCanuck Trading Trader Strategy
Overview
The Canuck Trading Trader Strategy is a high-performance, trend-following trading system designed for NASDAQ:TSLA on a 15-minute timeframe. Optimized for precision and profitability, this strategy leverages short-term price trends to capture consistent gains while maintaining robust risk management. Ideal for traders seeking an automated, data-driven approach to trading Tesla’s volatile market, it delivers strong returns with controlled drawdowns.
Key Features
Trend-Based Entries: Identifies short-term trends using a 2-candle lookback period and a minimum trend strength of 0.2%, ensuring responsive trade signals.
Risk Management: Includes a configurable 3.0% stop-loss to cap losses and a 2.0% take-profit to lock in gains, balancing risk and reward.
High Precision: Utilizes bar magnification for accurate backtesting, reflecting realistic trade execution with 1-tick slippage and 0.1 commission.
Clean Interface: No on-chart indicators, providing a distraction-free trading experience focused on performance.
Flexible Sizing: Allocates 10% of equity per trade with support for up to 2 simultaneous positions (pyramiding).
Performance Highlights
Backtested from March 1, 2024, to June 20, 2025, on NASDAQ:TSLA (15-minute timeframe) with $1,000,000 initial capital:
Net Profit: $2,279,888.08 (227.99%)
Win Rate: 52.94% (3,039 winning trades out of 5,741)
Profit Factor: 3.495
Max Drawdown: 2.20%
Average Winning Trade: $1,050.91 (0.55%)
Average Losing Trade: $338.20 (0.18%)
Sharpe Ratio: 2.468
Note: Past performance is not indicative of future results. Always validate with your own backtesting and forward testing.
Usage Instructions
Setup:
Apply the strategy to a NASDAQ:TSLA 15-minute chart.
Ensure your TradingView account supports bar magnification for accurate results.
Configuration:
Lookback Candles: Default is 2 (recommended).
Min Trend Strength: Set to 0.2% for optimal trade frequency.
Stop Loss: Default 3.0% to cap losses.
Take Profit: Default 2.0% to secure gains.
Order Size: 10% of equity per trade.
Pyramiding: Allows up to 2 orders.
Commission: Set to 0.1.
Slippage: Set to 1 tick.
Enable "Recalculate After Order is Filled" and "Recalculate on Every Tick" in backtest settings.
Backtesting:
Run backtests over March 1, 2024, to June 20, 2025, to verify performance.
Adjust stop-loss (e.g., 2.5%) or take-profit (e.g., 1–3%) to suit your risk tolerance.
Live Trading:
Use with a compatible broker or TradingView alerts for automated execution.
Monitor execution for slippage or latency, especially given the high trade frequency (5,741 trades).
Validate in a demo account before deploying with real capital.
Risk Disclosure
Trading involves significant risk and may result in losses exceeding your initial capital. The Canuck Trading Trader Strategy is provided for educational and informational purposes only. Users are responsible for their own trading decisions and should conduct thorough testing before using in live markets. The strategy’s high trade frequency requires reliable execution infrastructure to minimize slippage and latency.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Ichimoku Cloud Breakout Only LongThis is a very simple trading strategy based exclusively on the Ichimoku Cloud. There are no additional indicators or complex rules involved. The key condition is that we only open long positions when the price is clearly above the cloud — indicating a bullish trend.
For optimal results, the recommended timeframes are 1D (daily) or 1W (weekly) charts. These higher timeframes help filter out market noise and provide more reliable trend signals.
We do not short the market under any circumstances. The focus is purely on riding upward momentum when the price breaks out or stays above the cloud.
This strategy works best when applied to growth stocks with strong upward trends and good fundamentals — such as Google (GOOGL), Tesla (TSLA), Apple (AAPL), or NVIDIA (NVDA).
Easy MA SignalsEasy MA Signals
Overview
Easy MA Signals is a versatile Pine Script indicator designed to help traders visualize moving average (MA) trends, generate buy/sell signals based on crossovers or custom price levels, and enhance chart analysis with volume-based candlestick coloring. Built with flexibility in mind, it supports multiple MA types, crossover options, and customizable signal appearances, making it suitable for traders of all levels. Whether you're a day trader, swing trader, or long-term investor, this indicator provides actionable insights while keeping your charts clean and intuitive.
Configure the Settings
The indicator is divided into three input groups for ease of use:
General Settings:
Candlestick Color Scheme: Choose from 10 volume-based color schemes (e.g., Sapphire Pulse, Emerald Spark) to highlight high/low volume candles. Select “None” for TradingView’s default colors.
Moving Average Length: Set the MA period (default: 20). Adjust for faster (lower values) or slower (higher values) signals.
Moving Average Type: Choose between SMA, EMA, or WMA (default: EMA).
Show Buy/Sell Signals: Enable/disable signal plotting (default: enabled).
Moving Average Crossover: Select a crossover type (e.g., MA vs VWAP, MA vs SMA50) for signals or “None” to disable.
Volume Influence: Adjust how volume impacts candlestick colors (default: 1.2). Higher values make thresholds stricter.
Signal Appearance Settings:
Buy/Sell Signal Shape: Choose shapes like triangles, arrows, or labels for signals.
Buy/Sell Signal Position: Place signals above or below bars.
Buy/Sell Signal Color: Customize colors for better visibility (default: green for buy, red for sell).
Custom Price Alerts:
Custom Buy/Sell Alert Price: Set specific price levels for alerts (default: 0, disabled). Enter a non-zero value to enable.
Set Up Alerts
To receive notifications (e.g., sound, popup, email) when signals or custom price levels are hit:
Click the Alert button (alarm clock icon) in TradingView.
Select Easy MA Signals as the condition and choose one of the four alert types:
MA Crossover Buy Alert: Triggers on MA crossover buy signals.
MA Crossover Sell Alert: Triggers on MA crossover sell signals.
Custom Buy Alert: Triggers when price crosses above the custom buy price.
Custom Sell Alert: Triggers when price crosses below the custom sell price.
Enable Play Sound and select a sound (e.g., “Bell”).
Set the frequency (e.g., Once Per Bar Close for confirmed signals) and create the alert.
Analyze the Chart
Moving Average Line: Displays the selected MA with color changes (green for bullish, red for bearish, gray for neutral) based on price position relative to the MA.
Buy/Sell Signals: Appear as shapes or labels when crossovers or custom price levels are hit.
Candlestick Colors: If a color scheme is selected, candles change color based on volume strength (high, low, or neutral), aiding in trend confirmation.
Why Use Easy MA Signals?
Easy MA Signals is designed to simplify technical analysis while offering advanced customization. It’s ideal for traders who want:
A clear visualization of MA trends and crossovers.
Flexible signal generation based on MA crossovers or custom price levels.
Volume-enhanced candlestick coloring to identify market strength.
Easy-to-use settings with tooltips for beginners and pros alike.
This script is particularly valuable because it combines multiple features into one indicator, reducing chart clutter and providing actionable insights without overwhelming the user.
Benefits of Easy MA Signals
Highly Customizable: Supports SMA, EMA, and WMA with adjustable lengths.
Offers multiple crossover options (VWAP, SMA10, SMA20, etc.) for tailored strategies.
Custom price alerts allow precise targeting of key levels.
Volume-Based Candlestick Coloring: 10 unique color schemes highlight volume strength, helping traders confirm trends.
Adjustable volume influence ensures adaptability to different markets.
Flexible Signal Visualization: Choose from various signal shapes (triangles, arrows, labels) and positions (above/below bars).
Customizable colors improve visibility on any chart background.
Alert Integration: Built-in alert conditions for crossovers and custom prices support sound, email, and app notifications.
Easy setup for real-time trading decisions.
User-Friendly Design: Organized input groups with clear tooltips make configuration intuitive.
Suitable for beginners and advanced traders alike.
Example Use Cases
Swing Trading with MA Crossovers:
Scenario: A trader wants to trade Bitcoin (BTC/USD) on a 4-hour chart using an EMA crossover strategy.
Setup:
Set Moving Average Type to EMA, Length to 20.
Set Moving Average Crossover to “MA vs SMA50”.
Enable Show Buy/Sell Signals and choose “arrowup” for buy, “arrowdown” for sell.
Select “Emerald Spark” for candlestick colors to highlight volume surges.
Usage: Buy when the EMA20 crosses above the SMA50 (green arrow appears) and volume is high (dark green candles). Sell when the EMA20 crosses below the SMA50 (red arrow). Set alerts for real-time notifications.
Scalping with Custom Price Alerts:
Scenario: A day trader monitors Tesla (TSLA) on a 5-minute chart and wants alerts at specific support/resistance levels.
Setup:
Set Custom Buy Alert Price to 150.00 (support) and Custom Sell Alert Price to 160.00 (resistance).
Use “labelup” for buy signals and “labeldown” for sell signals.
Keep Moving Average Crossover as “None” to focus on price alerts.
Usage: Receive a sound alert and label when TSLA crosses 150.00 (buy) or 160.00 (sell). Use volume-colored candles to confirm momentum before entering trades.
When NOT to Use Easy MA Signals
High-Frequency Trading: Reason: The indicator relies on moving averages and volume, which may lag in ultra-fast markets (e.g., sub-second trades). High-frequency traders may need specialized tools with real-time tick data.
Alternative: Use order book or market depth indicators for faster execution.
Low-Volatility or Sideways Markets:
Reason: MA crossovers and custom price alerts can generate false signals in choppy, range-bound markets, leading to whipsaws.
Alternative: Use oscillators like RSI or Bollinger Bands to trade within ranges.
This indicator is tailored more towards less experienced traders. And as always, paper trade until you are comfortable with how this works if you're unfamiliar with trading! We hope you enjoy this and have great success. Thanks for your interested in Easy MA Signals!
Big 7 NASDAQ📊 Big 7 NASDAQ % Change Heatmap with Trend Arrows
This indicator displays a real-time performance table for the "Big 7" NASDAQ stocks:
Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), Meta (META), Tesla (TSLA), and Nvidia (NVDA).
🔎 Features:
Live Daily % Change: Calculates the percentage change between today’s open and the current price.
Color Gradient: Background color intensity reflects the strength of the move (from mild to strong bullish/bearish).
Trend Arrows: Visual arrows 🔺 (up) and 🔻 (down) represent the direction of movement.
Position Mode Selector:
"Buy" – highlights with green tones
"Sell" – highlights with red tones
"Neutral" – uses dynamic coloring based on individual stock moves
📍 Placement:
The table is positioned in the top-right corner of the chart for easy reference without cluttering your main view.
Lukiano BTC 369 Close MarkerThis indicator highlights candles where the closing price reduces to 3, 6, or 9 based on digital root calculation (sum of digits).
🔵 Blue dot = 3
🟢 Green dot = 6
🔴 Red dot = 9
Inspired by Tesla’s 369 theory and adapted for BTC traders who want to explore alternative energy-based signals.
Created by @Lukiano
