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.
ابحث في النصوص البرمجية عن "TESLA"
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.
NQ Pro Dashboard (Master Fix)This indicator is a "Head-Up Display" designed specifically for trading NQ (Nasdaq-100 Futures). It aggregates data from the broader market (volatility) and the specific stocks that drive the Nasdaq index (The "Magnificent 7") to give you a single Trend Power Score.
Here is a breakdown of how the logic works under the hood:
1. The Inputs (Data Feed)
The script watches 9 specific assets in real-time (daily timeframe data):
Fear Gauges:
VIX: The volatility index for the S&P 500.
VXN: The volatility index specifically for the Nasdaq-100.
The Engine (Mag 7):
NVDA, AAPL, MSFT, AMZN, GOOGL, META, TSLA.
2. The Logic: "Weighted" Market Strength
Instead of treating every stock equally, the script applies a Weighting Multiplier to the Mag 7 stocks based on their approximate impact on the Nasdaq-100 index.
Heavyweights (1.5x): NVDA, AAPL, MSFT (These move the market the most).
Middleweights (1.0x): AMZN, GOOGL, META.
Lightweight (0.7x): TSLA (Has the least pull of the group).
It calculates a single percentage number (MAG 7 (W)) representing the combined push or pull of these stocks.
3. The "Trend Power" Score (0-100)
This is the core signal. It starts at a neutral 50 and adds/subtracts points based on market conditions.
Fear Factor:
If VIX or VXN drops > 2% (Fear dying), it adds points (Bullish).
If VIX or VXN spikes > 2% (Fear rising), it subtracts points (Bearish).
Stock Strength:
If the Weighted Mag 7 Average is > 1.0% (Strong Rally), it adds a massive 30 points.
If it's negative (Sell-off), it subtracts points.
The Score Breakdown:
80 - 100 (Green): STRONG BULL. The engines are firing (stocks up) and the brakes are off (VIX down). Do not short.
0 - 20 (Red): STRONG BEAR. Panic selling is occurring. Do not buy.
40 - 60 (Orange): CHOP / RANGE. Conflicting signals (e.g., stocks are up but VIX is also up). Be careful.
4. The "Exhaustion" Meter (ATR)
The RANGE row tells you if the market has "gas left in the tank."
It compares Today's Range (High - Low) to the 14-Day Average Range (ATR).
< 50% (Yellow): Compressed. The market hasn't moved much yet. Expect a breakout soon.
> 120% (Purple): Extended. The market has moved massive amounts today. A reversal or pause is statistically likely (mean reversion).
5. The Visuals (Leaders Row)
The bottom row gives you a quick visual scan of the individual stocks:
N▲ (Green): Nvidia is up.
T▼ (Red): Tesla is down.
This helps you spot "divergences"—for example, if the Trend Score is high but NVDA is Red, the rally might be fragile.
Advanced Multi-Level S/R ZonesAdvanced Multi-Level S/R Zones: The Comprehensive Guide
1. Introduction: The Evolution of Support & Resistance:
Support and Resistance (S/R) is the backbone of technical analysis. However, traditional methods of drawing these levels are often plagued by subjectivity. Two traders looking at the same chart will often draw two different lines. Furthermore, standard indicators often treat every price point equally, ignoring the critical context of Volume and Time.
The Advanced Multi-Level S/R Zones script represents a paradigm shift. It moves away from subjective line drawing and toward Quantitative Zoning. By utilizing statistical measures of variability (Standard Deviation, MAD, IQR) combined with Volume-Weighting and Time-Decay algorithms, this tool identifies where price is mathematically most likely to react. It treats S/R not as thin lines, but as dynamic zones of probability.
2. Core Logic and Mathematical Foundation:
To understand how to use this tool optimally, one must understand the "engine" under the hood. The script operates on four distinct pillars of logic:
A. Session-Based Data Collection:
The script does not look at every single tick. Instead, it aggregates data into "Sessions" (daily bars by default logic). It extracts the High, Low, and Total Volume for every session within the user-defined lookback period. This filters out intraday noise and focuses on the macro structure of the market.
B. Adaptive Statistical Variability:
Most Bollinger Band-style indicators use Standard Deviation (StdDev) to measure width. However, StdDev is heavily influenced by outliers (extreme wicks). This script offers a sophisticated Adaptive Method-Skewness Detection: The script calculates the skewness of the price distribution. Adaptive Selection: If the data is highly skewed (lots of outliers, typical in Crypto), it switches to MAD (Median Absolute Deviation). MAD is robust and ignores outliers. If the data is moderately skewed, it uses IQR (Interquartile Range). If the data is normal (Gaussian), it uses StdDev.
Benefit: This ensures the zone widths are accurate regardless of whether you are trading a stable Forex pair or a volatile Altcoin.
C. The Weighting Engine (Volume + Time)
Not all price history is equal. This script assigns a "Weight Score" to every session based on two factors:
Volume Weighting: Sessions with massive volume (institutional activity) are given higher importance. A high formed on low volume is less significant than a high formed on peak volume.
Time Decay: Recent price action is more relevant than price action from 50 bars ago. The script applies a decay factor (default 0.85). This means a session from yesterday has 100% impact, while a session from 10 days ago has significantly less influence on the zone calculation.
D. Clustering Algorithm
Once the data is weighted, the script runs a clustering algorithm. It looks for price levels where multiple session Highs (for Resistance) or Lows (for Support) congregate.
It requires a minimum number of points to form a zone (User Input: minPoints).
It merges nearby levels based on the Cluster Separation Factor.
This results in "Primary," "Secondary," and "Tertiary" zones based on the strength and quantity of data points in that cluster.
3. Detailed Features and Inputs Breakdown:
Group 1: Main Settings
Lookback Sessions (Default: 10): Defines how far back the script looks for pivots. A higher number (e.g., 50) creates long-term structural zones. A lower number (e.g., 5) creates short-term scalping zones.
Variability Method (Adaptive): As described above, leave this on "Adaptive" for the best results across different assets.
Zone Width Multiplier (Default: 0.75): Controls the vertical thickness of the zones. Increase this to 1.0 or 1.5 for highly volatile assets to ensure you catch the wicks.
Minimum Points per Zone: The strictness filter. If set to 3, a price level must be hit 3 times within the lookback to generate a zone. Higher numbers = fewer, but stronger zones.
Group 2: Weighting
Volume-Weighted Zones: Crucial for identifying "Smart Money" levels. Keep this TRUE.
Time Decay: Ensures the zones update dynamically. If price moves away from a level for a long time, the zone will fade in significance.
ATR-Normalized Zone Width: This is a dynamic volatility filter. If TRUE, the zone width expands and contracts based on the Average True Range. This is vital for maintaining accuracy during market breakouts or crashes.
Group 3: Zone Strength & Scoring
The script calculates a "Score" (0-100%) for every zone based on:
-Point Count: More hits = higher score.
-Touches: How many times price wicked into the zone recently.
-Intact Status: Has the zone been broken?
-Weight: Volume/Time weight of the constituent points.
-Track Zone Touches: Looks back n bars to see how often price respected this level.
-Touch Threshold: The sensitivity for counting a "touch."
Group 4: Visuals & Display
Extend Bars: How far to the right the boxes are drawn.
Show Labels: Displays the Score, Tier (Primary/Secondary), and Status (Retesting).
Detect Pivot Zones (Overlap): This is a killer feature. It detects where a Support Zone overlaps with a Resistance Zone.
Significance: These are "Flip Zones" (Old Resistance becomes New Support). They are colored differently (Orange by default) and represent high-probability entry areas.
Group 5: Signals & Alerts
Entry Signals: Plots Buy/Sell labels when price rejects a zone.
Detect Break & Retest: specifically looks for the "Break -> Pullback -> Bounce" pattern, labeled as "RETEST BUY/SELL".
Proximity Alert: Triggers when price gets within x% of a zone.
4. Understanding the Visuals (Interpreting the Chart)
When you load the script, you will see several visual elements. Here is how to read them:
The Boxes (Zones)
Red Shades: Resistance Zones.
Dark Red (Solid Border): Primary Resistance. The strongest wall.
Lighter Red (Dashed Border): Secondary/Tertiary. Weaker, but still relevant.
Green Shades: Support Zones.
Dark Green (Solid Border): Primary Support. The strongest floor.
Orange Boxes: Pivot Zones. These are areas where price has historically reacted as both support and resistance. These are the "Line in the Sand" for trend direction.
The Labels & Emojis
The script assigns emojis to zone strength:
🔥 (Fire): Score > 80%. A massive level. Expect a strong reaction.
⭐ (Star): Score > 60%. A solid structural level.
✓ (Check): Score > 40%. A standard level.
"⟳ RETESTING": Appears when a zone was broken, and price is currently pulling back to test it from the other side.
The Dashboard (Top Right)
A statistics table provides a "Head-Up Display" for the asset:
High/Low σ (Sigma): The variability of the highs and lows. If High σ is much larger than Low σ, it implies the tops are erratic (wicks) while bottoms are clean (flat).
Method: Shows which statistical method the Adaptive engine selected (e.g., "MAD (auto)").
ATR: Current volatility value used for normalization.
5. Strategies for Optimum Output
To get the most out of this script, you should not just blindly follow the lines. Use these specific strategies:
Strategy A: The "Zone Fade" (Range Trading)
This works best in sideways markets.
Identify a Primary Support (Green) and Primary Resistance (Red).
Wait for price to enter the zone.
Look for the "SUPPORT BOUNCE" or "RESISTANCE REJECTION" signal label.
Entry: Enter against the zone (Buy at support, Sell at resistance).
Stop Loss: Place just outside the zone width. Because the zones are calculated using volatility stats, a break of the zone usually means the trade is invalid.
Strategy B: The "Pivot Flip" (Trend Following)
This is the highest probability setup in trending markets.
Look for an Orange Pivot Zone.
Wait for price to break through a Resistance Zone cleanly.
Wait for the price to return to that zone (which may now turn Orange or act as Support).
Look for the "RETEST BUY" label.
Logic: Old resistance becoming new support is a classic sign of trend continuation. The script automates the detection of this exact geometric phenomenon.
Strategy C: The Volatility Squeeze
Look at the Dashboard. Compare High σ and Low σ.
If the values are dropping rapidly or becoming very small, the zones will contract (become narrow).
Narrow zones indicate a "Squeeze" or compression in price.
Prepare for a violent breakout. Do not fade (trade against) narrow zones; look to trade the breakout.
6. Optimization & Customization Guide
Different markets require different settings. Here is how to tune the script:
For Crypto & Volatile Stocks (Tesla, Nvidia)
Method: Set to Adaptive (Mandatory, as these assets have "Fat Tails").
Multiplier: Increase to 1.0 - 1.25. Crypto wicks are deep; you need wider zones to avoid getting stopped out prematurely.
Lookback: 20-30 sessions. Crypto has a long memory; short lookbacks generate too much noise.
For Forex (EURUSD, GBPJPY)
Method: You can force StdDev or IQR. Forex is more mean-reverting and Gaussian.
Multiplier: Decrease to 0.5 - 0.75. Forex levels are often very precise to the pip.
Volume Weighting: You may turn this OFF for Forex if your broker's volume data is unreliable (since Forex has no centralized volume), though tick volume often works fine.
For Scalping (1m - 15m Timeframes)
Lookback: Decrease to 5-10. You only care about the immediate session history.
Decay Factor: Decrease to 0.5. You want the script to forget about yesterday's price action very quickly.
Touch Lookback: Decrease to 20 bars.
For Swing Trading (4H - Daily Timeframes)
Lookback: Increase to 50.
Decay Factor: Increase to 0.95. Structural levels from weeks ago are still highly relevant.
Min Points: Increase to 3 or 4. Only show levels that have been tested multiple times.
7. Advantages Over Standard Tools:
Feature Standard S/R Indicator, Advanced Multi-Level S/R Calculation, Uses simple Pivots or Fractals, Uses Statistical Distributions (MAD/IQR). Zone Width Arbitrary or Fixed Adaptive based on Volatility & ATR.
Context Ignores Volume Volume Weighted (Smart Money tracking).
Time Relevance Old levels = New levels Time Decay (Recency bias applied).
Overlaps Usually ignores overlaps Detects Pivot Zones (Res/Sup Flip).
Scoring None 0-100% Strength Score per zone.
8. Conclusion:
The Advanced Multi-Level S/R Zones script is not just a drawing tool; it is a statistical analysis engine. By accounting for the skewness of data, the volume behind the moves, and the decay of time, it provides a strictly objective roadmap of the market structure.
For the optimum output, combine the Pivot Zone identification with the Retest Signals. This aligns you with the underlying flow of order blocks and prevents trading against the statistical probabilities of the market.
Market Regime# MARKET REGIME IDENTIFICATION & TRADING SYSTEM
## Complete User Guide
---
## 📋 TABLE OF CONTENTS
1. (#overview)
2. (#regimes)
3. (#indicator-usage)
4. (#entry-signals)
5. (#exit-signals)
6. (#regime-strategies)
7. (#confluence)
8. (#backtesting)
9. (#optimization)
10. (#examples)
---
## OVERVIEW
### What This System Does
This is a **complete market regime identification and trading system** that:
1. **Identifies 6 distinct market regimes** automatically
2. **Adapts trading tactics** to each regime
3. **Provides high-probability entry signals** with confluence scoring
4. **Shows optimal exit points** for each trade
5. **Can be backtested** to validate performance
### Two Components Provided
1. **Indicator** (`market_regime_indicator.pine`)
- Visual regime identification
- Entry/exit signals on chart
- Dynamic support/resistance
- Info tables with live data
- Use for manual trading
2. **Strategy** (`market_regime_strategy.pine`)
- Fully automated backtestable version
- Same logic as indicator
- Position sizing and risk management
- Performance metrics
- Use for backtesting and automation
---
## THE 6 MARKET REGIMES
### 1. 🟢 BULL TRENDING
**Characteristics:**
- Strong uptrend
- Price above SMA50 and SMA200
- ADX > 25 (strong trend)
- Higher highs and higher lows
- DI+ > DI- (bullish momentum)
**What It Means:**
- Market has clear upward direction
- Buyers in control
- Pullbacks are buying opportunities
- Strongest regime for long positions
**How to Trade:**
- ✅ **BUY dips to EMA20 or SMA20**
- ✅ Enter when RSI < 60 on pullback
- ✅ Hold through minor corrections
- ❌ Don't short against the trend
- ❌ Don't sell too early
**Expected Behavior:**
- Pullbacks are shallow (5-10%)
- Bounces are strong
- Support at moving averages holds
- Volume increases on rallies
---
### 2. 🔴 BEAR TRENDING
**Characteristics:**
- Strong downtrend
- Price below SMA50 and SMA200
- ADX > 25 (strong trend)
- Lower highs and lower lows
- DI- > DI+ (bearish momentum)
**What It Means:**
- Market has clear downward direction
- Sellers in control
- Rallies are selling opportunities
- Strongest regime for short positions
**How to Trade:**
- ✅ **SELL rallies to EMA20 or SMA20**
- ✅ Enter when RSI > 40 on bounce
- ✅ Hold through minor bounces
- ❌ Don't buy against the trend
- ❌ Don't cover shorts too early
**Expected Behavior:**
- Rallies are weak (5-10%)
- Selloffs are strong
- Resistance at moving averages holds
- Volume increases on declines
---
### 3. 🔵 BULL RANGING
**Characteristics:**
- Bullish bias but consolidating
- Price near or above SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Uptrend is pausing
- Accumulation phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **BUY at support zone**
- ✅ Enter when RSI < 40
- ✅ Take profits at resistance
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Support bounces repeatedly
- Resistance rejections common
- Eventually breaks higher (usually)
---
### 4. 🟠 BEAR RANGING
**Characteristics:**
- Bearish bias but consolidating
- Price near or below SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Downtrend is pausing
- Distribution phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **SELL at resistance zone**
- ✅ Enter when RSI > 60
- ✅ Take profits at support
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Resistance holds repeatedly
- Support bounces are weak
- Eventually breaks lower (usually)
---
### 5. ⚪ CONSOLIDATION
**Characteristics:**
- No clear direction
- Range compression
- Very low ADX (< 15 often)
- Price inside tight range
- Neutral sentiment
**What It Means:**
- Market is coiling
- Building energy for next move
- Indecision between buyers/sellers
- Calm before the storm
**How to Trade:**
- ✅ **WAIT for breakout direction**
- ✅ Enter on high-volume breakout
- ✅ Direction becomes clear
- ❌ Don't trade inside the range
- ❌ Avoid choppy scalping
**Expected Behavior:**
- Narrow range
- Low volume
- False breakouts possible
- Explosive move when it breaks
---
### 6. 🟣 CHAOS (High Volatility)
**Characteristics:**
- Extreme volatility
- No clear direction
- Erratic price swings
- ATR > 2x average
- Unpredictable
**What It Means:**
- Market panic or euphoria
- News-driven moves
- Emotion dominates logic
- Highest risk environment
**How to Trade:**
- ❌ **STAY OUT!**
- ❌ No positions
- ❌ Wait for stability
- ✅ Protect existing positions
- ✅ Reduce risk
**Expected Behavior:**
- Large intraday swings
- Gaps up/down
- Stop hunts
- Whipsaws
- Eventually calms down
---
## INDICATOR USAGE
### Visual Elements
#### 1. Background Colors
- **Light Green** = Bull Trending (go long)
- **Light Red** = Bear Trending (go short)
- **Light Teal** = Bull Ranging (buy dips)
- **Light Orange** = Bear Ranging (sell rallies)
- **Light Gray** = Consolidation (wait)
- **Purple** = Chaos (stay out!)
#### 2. Regime Labels
- Appear when regime changes
- Show new regime name
- Positioned at highs (bullish) or lows (bearish)
#### 3. Entry Signals
- **Green "LONG"** labels = Buy here
- **Red "SHORT"** labels = Sell here
- Number shows confluence score (X/5 signals)
- Hover for details (stop, target, RSI, etc.)
#### 4. Exit Signals
- **Orange "EXIT LONG"** = Close long position
- **Orange "EXIT SHORT"** = Close short position
- Shows exit reason in tooltip
#### 5. Support/Resistance Lines
- **Green line** = Dynamic support (buy zone)
- **Red line** = Dynamic resistance (sell zone)
- Adapts to regime automatically
#### 6. Moving Averages
- **Blue** = SMA 20 (short-term trend)
- **Orange** = SMA 50 (medium-term trend)
- **Purple** = SMA 200 (long-term trend)
### Information Tables
#### Top Right Table (Main Info)
Shows real-time market conditions:
- **Current Regime** - What regime we're in
- **Bias** - Long, Short, Breakout, or Stay Out
- **ADX** - Trend strength (>25 = strong)
- **Trend** - Strong, Moderate, or Weak
- **Volatility** - High or Normal
- **Vol Ratio** - Current vs average volatility
- **RSI** - Momentum (>70 overbought, <30 oversold)
- **vs SMA50/200** - Price position relative to MAs
- **Support/Resistance** - Exact price levels
- **Long/Short Signals** - Confluence scores (X/5)
#### Bottom Right Table (Regime Guide)
Quick reference for each regime:
- What action to take
- What strategy to use
- Color-coded for quick identification
---
## ENTRY SIGNALS EXPLAINED
### Confluence Scoring System (5 Factors)
Each entry signal is scored 0-5 based on how many factors align:
#### For LONG Entries:
1. ✅ **Regime Alignment** - In Bull Trending or Bull Ranging
2. ✅ **RSI Pullback** - RSI between 35-50 (not overbought)
3. ✅ **Near Support** - Price within 2% of dynamic support
4. ✅ **MACD Turning Up** - Momentum shifting bullish
5. ✅ **Volume Confirmation** - Above average volume
#### For SHORT Entries:
1. ✅ **Regime Alignment** - In Bear Trending or Bear Ranging
2. ✅ **RSI Rejection** - RSI between 50-65 (not oversold)
3. ✅ **Near Resistance** - Price within 2% of dynamic resistance
4. ✅ **MACD Turning Down** - Momentum shifting bearish
5. ✅ **Volume Confirmation** - Above average volume
### Confluence Requirements
**Minimum Confluence** (default = 2):
- 2/5 = Entry signal triggered
- 3/5 = Good signal
- 4/5 = Strong signal
- 5/5 = Excellent signal (rare)
**Higher confluence = Higher probability = Better trades**
### Specific Entry Patterns
#### 1. Bull Trending Entry
```
Requirements:
- Regime = Bull Trending
- Price pulls back to EMA20
- Close above EMA20 (bounce)
- Up candle (close > open)
- RSI < 60
- Confluence ≥ 2
```
#### 2. Bear Trending Entry
```
Requirements:
- Regime = Bear Trending
- Price rallies to EMA20
- Close below EMA20 (rejection)
- Down candle (close < open)
- RSI > 40
- Confluence ≥ 2
```
#### 3. Bull Ranging Entry
```
Requirements:
- Regime = Bull Ranging
- RSI < 40 (oversold)
- Price at or below support
- Up candle (reversal)
- Confluence ≥ 1 (more lenient)
```
#### 4. Bear Ranging Entry
```
Requirements:
- Regime = Bear Ranging
- RSI > 60 (overbought)
- Price at or above resistance
- Down candle (rejection)
- Confluence ≥ 1 (more lenient)
```
#### 5. Consolidation Breakout
```
Requirements:
- Regime = Consolidation
- Price breaks above/below range
- Volume > 1.5x average (explosive)
- Strong directional candle
```
---
## EXIT SIGNALS EXPLAINED
### Three Types of Exits
#### 1. Regime Change Exits (Automatic)
- **Long Exit**: Regime changes to Bear Trending or Chaos
- **Short Exit**: Regime changes to Bull Trending or Chaos
- **Reason**: Market character changed, strategy no longer valid
#### 2. Support/Resistance Break Exits
- **Long Exit**: Price breaks below support by 2%
- **Short Exit**: Price breaks above resistance by 2%
- **Reason**: Key level violated, trend may be reversing
#### 3. Momentum Exits
- **Long Exit**: RSI > 70 (overbought) AND down candle
- **Short Exit**: RSI < 30 (oversold) AND up candle
- **Reason**: Overextension, take profits
### Stop Loss & Take Profit
**Stop Loss** (Automatic in strategy):
- Placed at Entry - (ATR × 2)
- Adapts to volatility
- Protected from whipsaws
- Typically 2-4% for stocks, 5-10% for crypto
**Take Profit** (Automatic in strategy):
- Placed at Entry + (Stop Distance × R:R Ratio)
- Default 2.5:1 reward:risk
- Example: $2 risk = $5 reward target
- Allows winners to run
---
## TRADING EACH REGIME
### BULL TRENDING - Most Profitable Long Environment
**Strategy: Buy Every Dip**
**Entry Rules:**
1. Wait for pullback to EMA20 or SMA20
2. Look for RSI < 60
3. Enter when candle closes above MA
4. Confluence should be 2+
**Stop Loss:**
- Below the recent swing low
- Or 2 × ATR below entry
**Take Profit:**
- At previous high
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, pulls back to $98 (EMA20)
Entry: $98.50 (close above EMA)
Stop: $96.50 (2 ATR)
Target: $103.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BEAR TRENDING - Most Profitable Short Environment
**Strategy: Sell Every Rally**
**Entry Rules:**
1. Wait for bounce to EMA20 or SMA20
2. Look for RSI > 40
3. Enter when candle closes below MA
4. Confluence should be 2+
**Stop Loss:**
- Above the recent swing high
- Or 2 × ATR above entry
**Take Profit:**
- At previous low
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, rallies to $102 (EMA20)
Entry: $101.50 (close below EMA)
Stop: $103.50 (2 ATR)
Target: $96.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BULL RANGING - Buy Low, Sell High
**Strategy: Range Trading (Long Bias)**
**Entry Rules:**
1. Wait for price at support zone
2. Look for RSI < 40
3. Enter on reversal candle
4. Confluence should be 1-2+
**Stop Loss:**
- Below support zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At resistance zone
- Don't hold through resistance
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $96 (at support, RSI 35)
Stop: $94 (below support)
Target: $104 (at resistance)
Risk: $2, Reward: $8 (4:1)
```
---
### BEAR RANGING - Sell High, Buy Low
**Strategy: Range Trading (Short Bias)**
**Entry Rules:**
1. Wait for price at resistance zone
2. Look for RSI > 60
3. Enter on rejection candle
4. Confluence should be 1-2+
**Stop Loss:**
- Above resistance zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At support zone
- Don't hold through support
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $104 (at resistance, RSI 65)
Stop: $106 (above resistance)
Target: $96 (at support)
Risk: $2, Reward: $8 (4:1)
```
---
### CONSOLIDATION - Wait for Breakout
**Strategy: Breakout Trading**
**Entry Rules:**
1. Identify consolidation range
2. Wait for VOLUME SURGE (1.5x+ avg)
3. Enter on close outside range
4. Direction must be clear
**Stop Loss:**
- Opposite side of range
- Or 2 ATR
**Take Profit:**
- Measure range height, project it
- Example: $10 range = $10 move expected
**Position Size:**
- Reduce to 1% risk
- 50% false breakout rate
**Example Trade:**
```
Consolidation: $98-$102 (4-point range)
Breakout: $102.50 (high volume)
Entry: $103
Stop: $100 (back in range)
Target: $107 (4-point range projected)
Risk: $3, Reward: $4
```
---
### CHAOS - STAY OUT!
**Strategy: Preservation**
**What to Do:**
- ❌ NO new positions
- ✅ Close existing positions if near entry
- ✅ Tighten stops on profitable trades
- ✅ Reduce position sizes dramatically
- ✅ Wait for regime to stabilize
**Why It's Dangerous:**
- Stop hunts are common
- Whipsaws everywhere
- News-driven volatility
- No technical reliability
- Even "perfect" setups fail
**When Does It End:**
- Volatility ratio drops < 1.5
- ADX starts rising (direction appears)
- Price respects support/resistance again
- Usually 1-5 days
---
## CONFLUENCE SYSTEM
### How It Works
The system scores each potential entry on 5 factors. More factors aligning = higher probability.
### Confluence Requirements by Regime
**Trending Regimes** (strictest):
- Minimum 2/5 required
- 3/5 = Good
- 4-5/5 = Excellent
**Ranging Regimes** (moderate):
- Minimum 1-2/5 required
- 2/5 = Good
- 3+/5 = Excellent
**Consolidation** (breakout only):
- Volume is most critical
- Direction confirmation
- Less confluence needed
### Adjusting Minimum Confluence
**If too few signals:**
- Lower from 2 to 1
- More trades, lower quality
**If too many false signals:**
- Raise from 2 to 3
- Fewer trades, higher quality
**Recommendation:**
- Start at 2
- Adjust based on win rate
- Aim for 55-65% win rate
---
## STRATEGY BACKTESTING
### Loading the Strategy
1. Copy `market_regime_strategy.pine`
2. Open Pine Editor in TradingView
3. Paste and "Add to Chart"
4. Strategy Tester tab opens at bottom
### Initial Settings
```
Risk Per Trade: 2%
ATR Stop Multiplier: 2.0
Reward:Risk Ratio: 2.5
Trade Longs: ✓
Trade Shorts: ✓
Trade Trending Only: ✗ (test both)
Avoid Chaos: ✓
Minimum Confluence: 2
```
### What to Look For
**Good Results:**
- Win Rate: 50-60%
- Profit Factor: 1.8-2.5
- Net Profit: Positive
- Max Drawdown: <20%
- Consistent equity curve
**Warning Signs:**
- Win Rate: <45% (too many losses)
- Profit Factor: <1.5 (barely profitable)
- Max Drawdown: >30% (too risky)
- Erratic equity curve (unstable)
### Testing Different Regimes
**Test 1: Trending Only**
```
Trade Trending Only: ✓
Result: Higher win rate, fewer trades
```
**Test 2: All Regimes**
```
Trade Trending Only: ✗
Result: More trades, potentially lower win rate
```
**Test 3: Long Only**
```
Trade Longs: ✓
Trade Shorts: ✗
Result: Works in bull markets
```
**Test 4: Short Only**
```
Trade Longs: ✗
Trade Shorts: ✓
Result: Works in bear markets
```
---
## SETTINGS OPTIMIZATION
### Key Parameters to Adjust
#### 1. Risk Per Trade (Most Important)
- **0.5%** = Very conservative
- **1.0%** = Conservative (recommended for beginners)
- **2.0%** = Moderate (recommended)
- **3.0%** = Aggressive
- **5.0%** = Very aggressive (not recommended)
**Impact:** Higher risk = higher returns BUT bigger drawdowns
#### 2. Reward:Risk Ratio
- **2:1** = More wins needed, hit target faster
- **2.5:1** = Balanced (recommended)
- **3:1** = Fewer wins needed, hold longer
- **4:1** = Very patient, best in trending
**Impact:** Higher R:R = can have lower win rate
#### 3. Minimum Confluence
- **1** = More signals, lower quality
- **2** = Balanced (recommended)
- **3** = Fewer signals, higher quality
- **4** = Very selective
- **5** = Almost never triggers
**Impact:** Higher = fewer but better trades
#### 4. ADX Thresholds
- **Trending: 20-30** (default 25)
- Lower = detect trends earlier
- Higher = only strong trends
- **Ranging: 15-25** (default 20)
- Lower = identify ranging earlier
- Higher = only weak trends
#### 5. Trend Period (SMA)
- **20-50** = Short-term trends
- **50** = Medium-term (default, recommended)
- **100-200** = Long-term trends
**Impact:** Longer period = slower regime changes, more stable
### Optimization Workflow
**Step 1: Baseline**
- Use all default settings
- Test on 3+ years
- Record: Win Rate, PF, Drawdown
**Step 2: Risk Optimization**
- Test 1%, 1.5%, 2%, 2.5%
- Find best risk-adjusted return
- Balance profit vs drawdown
**Step 3: R:R Optimization**
- Test 2:1, 2.5:1, 3:1
- Check which maximizes profit factor
- Consider holding time
**Step 4: Confluence Optimization**
- Test 1, 2, 3
- Find sweet spot for win rate
- Aim for 55-65% win rate
**Step 5: Regime Filter**
- Test with/without trend filter
- Test with/without chaos filter
- Find what works for your asset
---
## REAL TRADING EXAMPLES
### Example 1: Bull Trending - SPY
**Setup:**
- Regime: BULL TRENDING
- Price pulls back from $450 to $445
- EMA20 at $444
- RSI drops to 45
- Confluence: 4/5
**Entry:**
- Price closes at $445.50 (above EMA20)
- LONG signal appears
- Enter at $445.50
**Risk Management:**
- Stop: $443 (2 ATR = $2.50)
- Target: $451.75 (2.5:1 = $6.25)
- Risk: $2.50 per share
- Position: 80 shares (2% of $10k = $200 risk)
**Outcome:**
- Price rallies to $452 in 3 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk (excellent)
---
### Example 2: Bear Ranging - AAPL
**Setup:**
- Regime: BEAR RANGING
- Range: $165-$175
- Price rallies to $174
- Resistance at $175
- RSI at 68
- Confluence: 3/5
**Entry:**
- Rejection candle at $174
- SHORT signal appears
- Enter at $173.50
**Risk Management:**
- Stop: $176 (above resistance)
- Target: $166 (support)
- Risk: $2.50
- Position: 80 shares
**Outcome:**
- Price drops to $167 in 2 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk
---
### Example 3: Consolidation Breakout - BTC
**Setup:**
- Regime: CONSOLIDATION
- Range: $28,000 - $30,000
- Compressed for 2 weeks
- Volume declining
**Breakout:**
- Price breaks $30,000
- Volume surges 200%
- Close at $30,500
- LONG signal
**Entry:**
- Enter at $30,500
**Risk Management:**
- Stop: $29,500 (back in range)
- Target: $32,000 (range height = $2k)
- Risk: $1,000
- Position: 0.2 BTC ($200 risk on $10k)
**Outcome:**
- Price runs to $33,000
- Target exceeded
- Profit: $2,500 × 0.2 = $500
- Return: 2.5 × risk
---
### Example 4: Avoiding Chaos - Tesla
**Setup:**
- Regime: BULL TRENDING
- LONG position from $240
- Elon tweets something crazy
- Regime changes to CHAOS
**Action:**
- EXIT signal appears
- Close position immediately
- Current price: $242 (small profit)
**Outcome:**
- Next 3 days: wild swings
- High $255, Low $230
- By staying out, avoided:
- Potential stop out
- Whipsaw losses
- Stress
**Result:**
- Small profit preserved
- Capital protected
- Re-enter when regime stabilizes
---
## ALERTS SETUP
### Available Alerts
1. **Bull Trending Regime** - Market goes bullish
2. **Bear Trending Regime** - Market goes bearish
3. **Chaos Regime** - High volatility, stay out
4. **Long Entry Signal** - Buy opportunity
5. **Short Entry Signal** - Sell opportunity
6. **Long Exit Signal** - Close long
7. **Short Exit Signal** - Close short
### How to Set Up
1. Click **⏰ (Alert)** icon in TradingView
2. Select **Condition**: Choose indicator + alert type
3. **Options**: Popup, Email, Webhook, etc.
4. **Message**: Customize notification
5. Click **Create**
### Recommended Alert Strategy
**For Active Traders:**
- Long Entry Signal
- Short Entry Signal
- Long Exit Signal
- Short Exit Signal
**For Position Traders:**
- Bull Trending Regime (enter longs)
- Bear Trending Regime (enter shorts)
- Chaos Regime (exit all)
**For Conservative:**
- Only regime change alerts
- Manually review entries
- More selective
---
## TIPS FOR SUCCESS
### 1. Start Small
- Paper trade first
- Then 0.5% risk
- Build to 1-2% over time
### 2. Follow the Regime
- Don't fight it
- Adapt your style
- Different tactics for each
### 3. Trust the Confluence
- 4-5/5 = Best trades
- 2-3/5 = Good trades
- 1/5 = Skip unless desperate
### 4. Respect Exits
- Don't hope and hold
- Cut losses quickly
- Take profits at targets
### 5. Avoid Chaos
- Seriously, just stay out
- Protect your capital
- Wait for clarity
### 6. Keep a Journal
- Record every trade
- Note regime and confluence
- Review weekly
- Learn patterns
### 7. Backtest Thoroughly
- 3+ years minimum
- Multiple market conditions
- Different assets
- Walk-forward test
### 8. Be Patient
- Best setups are rare
- 1-3 trades per week is normal
- Quality over quantity
- Compound over time
---
## COMMON QUESTIONS
**Q: How many trades per month should I expect?**
A: Depends on timeframe and settings. Daily chart: 5-15 trades/month. 4H chart: 15-30 trades/month.
**Q: What's a good win rate?**
A: 55-65% is excellent. 50-55% is good. Below 50% needs adjustment.
**Q: Should I trade all regimes?**
A: Beginners: Only trending. Intermediate: Trending + ranging. Advanced: All except chaos.
**Q: Can I use this on any timeframe?**
A: Best on Daily and 4H. Works on 1H with more noise. Not recommended <1H.
**Q: What if I'm in a trade and regime changes?**
A: Exit immediately (if using indicator) or let strategy handle it automatically.
**Q: How do I know if I'm over-optimizing?**
A: If results are perfect on one period but fail on another. Use walk-forward testing.
**Q: Should I always take 5/5 confluence trades?**
A: Yes, but they're rare (1-2/month). Don't wait only for these.
**Q: Can I combine this with other indicators?**
A: Yes, but keep it simple. RSI, MACD already included. Maybe add volume profile.
**Q: What assets work best?**
A: Liquid stocks, major crypto, futures. Avoid forex spot (use futures), penny stocks.
**Q: How long to hold positions?**
A: Trending: Days to weeks. Ranging: Hours to days. Breakout: Days. Let the regime guide you.
---
## FINAL THOUGHTS
This system gives you:
- ✅ Clear market context (regime)
- ✅ High-probability entries (confluence)
- ✅ Defined exits (automatic signals)
- ✅ Adaptable tactics (regime-specific)
- ✅ Backtestable results (strategy version)
**Success requires:**
- 📚 Understanding each regime
- 🎯 Following the signals
- 💪 Discipline to wait
- 🧠 Emotional control
- 📊 Proper risk management
**Start your journey:**
1. Load the indicator
2. Watch for 1 week (no trading)
3. Identify regime patterns
4. Paper trade for 1 month
5. Go live with small size
6. Scale up as you gain confidence
**Remember:** The market will always be here. There's no rush. Master one regime at a time, and you'll be profitable in all conditions!
Good luck! 🚀
Average True Range % infoATR% is a modified version of the classic Average True Range indicator that displays price volatility as a percentage of the instrument's value, rather than in absolute values. This allows you to easily compare the volatility of different assets (e.g., Bitcoin vs Tesla stock) regardless of their price.
Main Features
1. ATR% Chart
The red line shows the average volatility from the last N candles (default 14), expressed as a percentage. For example:
ATR% = 2.5% means that the average daily move is approximately 2.5% of the asset's value
Higher values = greater volatility (higher profit potential, but also greater risk)
Lower values = lower volatility (calmer market)
2. Volatility Trend Analysis
The indicator automatically detects whether volatility is rising, falling, or stable:
Up arrow (↑) - volatility is rising (price becomes more "nervous")
Down arrow (↓) - volatility is falling (market is calming down)
Horizontal arrow (⮆) - volatility is stable (within ±3% of the moving average)
3. Information Table
In the upper right corner of the chart you will see Current ATR% value and Trend arrow with color coding:
- Green = rising volatility
- Red = falling volatility
- Gray = stable volatility
Parameters to Configure
Indicator Length (default: 14) - How many candles back to include in calculations:
Lower values (5-10): more sensitive to sudden changes, reacts faster
Higher values (20-30): more smoothed, shows long-term volatility picture
Trend Length (default: 10) - Period to analyze whether volatility is rising/falling:
Lower values: faster trend change signals
Higher values: more reliable, but slower signals
Sample Interpretations
ATR% Volatility Asset Type/Situation
< 1% Very low Stable blue-chip stocks, calm market
1-3% Low-medium Typical stocks, normal conditions
3-5% Medium-high Volatile stocks, cryptocurrencies at rest
5-10% High Cryptocurrencies, penny stocks
> 10% Extremely high Market panic, crash, pump & dump
Current State: Overbought/Oversold + Trend KAPIL GOYALThis Pine Script calculates the RSI (Relative Strength Index) and compares it against preset thresholds to classify the market as Deep/Moderate/Mild Oversold or Deep/Moderate/Mild Overbought. It also checks whether the current price is above or below the 50-day moving average to define the trend as Uptrend or Downtrend. The script then combines both signals into one clean, real-time text output—like “Moderate Oversold + Uptrend”—displayed in a small table at the chart corner. It’s designed to give a quick, clutter-free snapshot of the current market state without plotting multiple indicators.
How to use:
Apply this indicator to any chart (e.g., Tesla on TradingView). It will show one line of text describing the current condition based on RSI and trend. Use it for quick decision cues:
“Oversold + Uptrend” suggests potential accumulation or rebound zones.
“Overbought + Downtrend” warns of exhaustion or profit-taking zones.
Combine it with your entry/exit strategy—like your 30DMA/50DMA rule or momentum filters—to confirm timing rather than act alone.
Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide
Introduction to Correlation Analysis
What is Correlation?
Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor.
Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed
Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions
Zero Correlation (0): The dancers move completely independently of each other
In financial markets, correlation helps us understand relationships between different assets, which is crucial for:
Portfolio diversification
Risk management
Pairs trading strategies
Hedging positions
Market analysis
Why This Script is Special
This script goes beyond simple correlation calculations by providing:
Two different correlation methods (Pearson and Spearman)
Statistical significance testing to ensure results are meaningful
Rolling correlation analysis to track how relationships change over time
Visual representation for easy interpretation
Comprehensive statistics table with detailed metrics
Deep Dive into the Script's Components
1. Input Parameters Explained-
Symbol Selection:
This allows you to select the second asset to compare with the chart's primary asset
Default is Apple (NASDAQ:AAPL), but you can change this to any symbol
Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated
Correlation Window (60): This is the number of periods used to calculate the main correlation
Larger values (e.g., 100-500) provide more stable, long-term correlation measures
Smaller values (e.g., 10-50) are more responsive to recent price movements
60 is a good balance for most daily charts (about 3 months of trading days)
Rolling Correlation Window (20): A shorter window to detect recent changes in correlation
This helps identify when the relationship between assets is strengthening or weakening
Default of 20 is roughly one month of trading days
Return Type: This determines how price changes are calculated
Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price
Easy to understand: "The asset went up 2% today"
Log Returns: Natural logarithm of (Today's Price / Yesterday's Price)
More mathematically elegant for statistical analysis
Better for time-additive properties (returns over multiple periods)
Less sensitive to extreme values.
Confidence Level (95%): This determines how certain we want to be about our results
95% confidence means we accept a 5% chance of being wrong (false positive)
Higher confidence (e.g., 99%) makes the test more strict
Lower confidence (e.g., 90%) makes the test more lenient
95% is the standard in most scientific research
Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance.
Display options control what you see on the chart:
Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off
Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues)
Show Statistical Tests: Enables the detailed statistics table
Table Text Size: Adjusts the size of text in the statistics table
2.Functions explained-
calcReturns():
This function calculates price returns based on your selected method:
Log Returns:
Formula: ln(Price_t / Price_t-1)
Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995%
Benefits: More symmetric, time-additive, and better for statistical modeling
Simple Returns:
Formula: (Price_t - Price_t-1) / Price_t-1
Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1%
Benefits: More intuitive and easier to understand
rankArray():
This function calculates the rank of each value in an array, which is used for Spearman correlation:
How ranking works:
The smallest value gets rank 1
The second smallest gets rank 2, and so on
For ties (equal values), they get the average of their ranks
Example: For values
Sorted:
Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5)
Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships.
pearsonCorr():
This function calculates the Pearson correlation coefficient:
Mathematical Formula:
r = (nΣxy - ΣxΣy) / √
Where x and y are the two variables, and n is the sample size
What it measures:
The strength and direction of the linear relationship between two variables
Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship)
0 indicates no linear relationship
Example:
If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship
When one stock goes up, the other tends to go up in a fairly consistent proportion
spearmanCorr():
This function calculates the Spearman rank correlation:
How it works:
Convert each value in both datasets to its rank
Calculate the Pearson correlation on the ranks instead of the original values
What it measures:
The strength and direction of the monotonic relationship between two variables
A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases
It doesn't require the relationship to be linear
When to use it instead of Pearson:
When the relationship is monotonic but not linear
When there are significant outliers in the data
When the data is ordinal (ranked) rather than interval/ratio
Example:
If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship
When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship
tStatistic():
This function calculates the t-statistic for correlation:
Mathematical Formula: t = r × √((n-2)/(1-r²))
Where r is the correlation coefficient and n is the sample size
What it measures:
How many standard errors the correlation is away from zero
Used to test the null hypothesis that the true correlation is zero
Interpretation:
Larger absolute t-values indicate stronger evidence against the null hypothesis
Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level
criticalT() and pValue():
These functions provide approximations for statistical significance testing:
criticalT():
Returns the critical t-value for a given degrees of freedom (df) and significance level
The critical value is the threshold that the t-statistic must exceed to be considered statistically significant
Uses approximations since Pine Script doesn't have built-in statistical distribution functions
pValue():
Estimates the p-value for a given t-statistic and degrees of freedom
The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero
Smaller p-values indicate stronger evidence against the null hypothesis
Standard interpretation:
p < 0.01: Very strong evidence (marked with **)
p < 0.05: Strong evidence (marked with *)
p ≥ 0.05: Weak evidence, not statistically significant
stdev():
This function calculates the standard deviation of a dataset:
Mathematical Formula: σ = √(Σ(x-μ)²/(n-1))
Where x is each value, μ is the mean, and n is the sample size
What it measures:
The amount of variation or dispersion in a set of values
A low standard deviation indicates that the values tend to be close to the mean
A high standard deviation indicates that the values are spread out over a wider range
Why it matters for correlation:
Standard deviation is used in calculating the correlation coefficient
It also provides information about the volatility of each asset's returns
Comparing standard deviations helps understand the relative riskiness of the two assets.
3.Getting Price Data-
price1: The closing price of the primary asset (the chart you're viewing)
price2: The closing price of the secondary asset (the one you selected in the input parameters)
Returns are used instead of raw prices because:
Returns are typically stationary (mean and variance stay constant over time)
Returns normalize for price levels, allowing comparison between assets of different values
Returns represent what investors actually care about: percentage changes in value
4.Information Table-
Creates a table to display statistics
Only shows on the last bar to avoid performance issues
Positioned in the top right of the chart
Has 2 columns and 15 rows
Populating the Table
The script then populates the table with various statistics:
Header Row: "Metric" and "Value"
Sample Information: Sample size and return type
Pearson Correlation: Value, t-statistic, p-value, and significance
Spearman Correlation: Value, t-statistic, p-value, and significance
Rolling Correlation: Current value
Standard Deviations: For both assets
Interpretation: Text description of the correlation strength
The table uses color coding to highlight important information:
Green for significant positive results
Red for significant negative results
Yellow for borderline significance
Color-coded headers for each section
=> Practical Applications and Interpretation
How to Interpret the Results
Correlation Strength
0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation
The assets move mostly independently of each other
Good for diversification purposes
0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation
The assets show some tendency to move together (or in opposite directions)
May be useful for certain trading strategies but not extremely reliable
0.7 to 1.0 (or -0.7 to -1.0): Strong correlation
The assets show a strong tendency to move together (or in opposite directions)
Can be useful for pairs trading, hedging, or as a market indicator
Statistical Significance
p < 0.01: Very strong evidence that the correlation is real
Marked with ** in the table
Very unlikely to be due to random chance
p < 0.05: Strong evidence that the correlation is real
Marked with * in the table
Unlikely to be due to random chance
p ≥ 0.05: Weak evidence that the correlation is real
Not marked in the table
Could easily be due to random chance
Rolling Correlation
The rolling correlation shows how the relationship between assets changes over time
If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing
This can indicate:
A shift in market regime
Changing fundamentals of one or both assets
Temporary market dislocations that might present trading opportunities
Trading Applications
1. Portfolio Diversification
Goal: Reduce overall portfolio risk by combining assets that don't move together
Strategy: Look for assets with low or negative correlations
Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech
2. Pairs Trading
Goal: Profit from the relative price movements of two correlated assets
Strategy:
Find two assets with strong historical correlation
When their prices diverge (one goes up while the other goes down)
Buy the underperforming asset and short the outperforming asset
Close the positions when they converge back to their normal relationship
Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi
3. Hedging
Goal: Reduce risk by taking an offsetting position in a negatively correlated asset
Strategy: Find assets that tend to move in opposite directions
Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall
4. Market Analysis
Goal: Understand market dynamics and interrelationships
Strategy: Analyze correlations between different sectors or asset classes
Example:
If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other
If the correlation between stocks and bonds changes, it might signal a shift in market expectations
5. Risk Management
Goal: Understand and manage portfolio risk
Strategy: Monitor correlations to identify when diversification benefits might be breaking down
Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits
Advanced Interpretation and Caveats
Correlation vs. Causation
Important Note: Correlation does not imply causation
Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other
Implication: Just because two assets move together doesn't mean one causes the other to move
Solution: Look for fundamental economic reasons why assets might be correlated
Non-Stationary Correlations
Problem: Correlations between assets can change over time
Causes:
Changing market conditions
Shifts in monetary policy
Structural changes in the economy
Changes in the underlying businesses
Solution: Use rolling correlations to monitor how relationships change over time
Outliers and Extreme Events
Problem: Extreme market events can distort correlation measurements
Example: During a market crash, many assets may move in the same direction regardless of their normal relationship
Solution:
Use Spearman correlation, which is less sensitive to outliers
Be cautious when interpreting correlations during extreme market conditions
Sample Size Considerations
Problem: Small sample sizes can produce unreliable correlation estimates
Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results
Solution:
Use the default correlation length of 60 or higher
Be skeptical of correlations calculated with small samples
Timeframe Considerations
Problem: Correlations can vary across different timeframes
Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis
Solution:
Test correlations on multiple timeframes
Use the timeframe that matches your trading horizon
Look-Ahead Bias
Problem: Using information that wouldn't have been available at the time of trading
Example: Calculating correlation using future data
Solution: This script avoids look-ahead bias by using only historical data
Best Practices for Using This Script
1. Appropriate Parameter Selection
Correlation Window:
For short-term trading: 20-50 periods
For medium-term analysis: 50-100 periods
For long-term analysis: 100-500 periods
Rolling Window:
Should be shorter than the main correlation window
Typically 1/3 to 1/2 of the main window
Return Type:
For most applications: Log Returns (better statistical properties)
For simplicity: Simple Returns (easier to interpret)
2. Validation and Testing
Out-of-Sample Testing:
Calculate correlations on one time period
Test if they hold in a different time period
Multiple Timeframes:
Check if correlations are consistent across different timeframes
Economic Rationale:
Ensure there's a logical reason why assets should be correlated
3. Monitoring and Maintenance
Regular Review:
Correlations can change, so review them regularly
Alerts:
Set up alerts for significant correlation changes
Documentation:
Keep notes on why certain assets are correlated and what might change that relationship
4. Integration with Other Analysis
Fundamental Analysis:
Combine correlation analysis with fundamental factors
Technical Analysis:
Use correlation analysis alongside technical indicators
Market Context:
Consider how market conditions might affect correlations
Conclusion
This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights.
For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications.
Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
Whales buy & sell🐋 Whales on Wall Street — Buy & Sell Signal Indicator
The Whales on Wall Street Signal Indicator is a precision-built trading tool designed to simplify your decision-making and give you real-time clarity in the market.
It automatically identifies high-probability reversal zones, momentum shifts, and trend confirmations — marking exact Buy (green) and Sell (red) signals based on price action, volume confirmation, and momentum strength.
Built for day traders and scalpers, this indicator eliminates the guesswork by combining multiple technical confluences such as:
EMA & RSI alignment for trend direction
Smart volume spikes for institutional activity
Volatility filters to reduce false signals
Dynamic alerts for entries and exits in real time
Whether you’re trading SPY, QQQ, NVDA, or Tesla, this indicator adapts to any ticker and timeframe — giving you crystal-clear entries, cleaner exits, and the confidence to trade like a whale.
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."
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🤖 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
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🎯 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
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💡 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
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⚙️ 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
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📊 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)
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🚀 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
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📈 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
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🔬 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.
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💡 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
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⚠️ 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
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🏆 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."
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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.
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.
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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.
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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.
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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.
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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.
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👉 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.
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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!
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!
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
VMA [Extreme Advanced Custom Table for BTCUSD]This indicator implements a Variable Moving Average (VMA) with a 33-period length—selected in homage to the Tesla 369 concept—to dynamically adjust to market conditions. It not only calculates the adaptive VMA but also displays a custom table of key metrics directly on the chart. Here’s how to use it:
Apply to Your Chart:
Add the indicator to your chart (optimized for BTCUSD, though it can be used on other symbols) and choose your desired source (e.g., close).
Customize Your Visuals:
Trend & Price Lines: Toggle the trend colors, price line, and bar coloring based on the VMA’s direction.
Channels & Slope: Enable the volatility channel and slope line to visualize market volatility and the VMA’s momentum.
Pivot Points & Super VMA: Activate pivot high/low markers for potential reversal points and a Super VMA (SMA of VMA) for an extra smoothing layer.
Table Customization: Adjust the table’s position, colors, and font sizes as needed for your viewing preference.
Monitor Key Metrics:
The dynamic table displays essential information:
VMA Value & Trend: See the current VMA and whether the trend is Bullish, Bearish, or Neutral.
Volatility Index (vI) & Slope: Quickly assess market volatility and the VMA’s slope (both absolute and percentage).
Price-VMA Difference & Correlation: Evaluate how far the price is from the VMA and its correlation.
Higher Timeframe VMA: Compare the current VMA with its higher timeframe counterpart (set via the “Higher Timeframe” input).
Alerts for Key Conditions:
Built-in alert conditions notify you when:
The trend changes (bullish/bearish).
The VMA slope becomes extreme.
The price and VMA correlation falls below a defined threshold.
The VMA crosses its higher timeframe average.
How to Use the Script:
Add to Your Chart:
Open TradingView and apply the indicator to your BTCUSD (or any other) chart.
The indicator will overlay on your chart, plotting the VMA along with optional elements such as the price line, volatility channels, and higher timeframe VMA.
Customize Your Settings:
Inputs:
Choose your data source (e.g., close price).
Adjust the VMA length (default is 33) if desired.
Visual Options:
Toggle trend colors, bar coloring, and additional visuals (price line, volatility channels, slope line, pivot points, and Super VMA) to suit your trading style.
Table Customization:
Set the table position, colors, border width, and font size to ensure key metrics are easily visible.
Higher Timeframe:
You can change the higher timeframe input (default is Daily) to better fit your analysis routine.
Interpret the Indicator:
Trend Analysis:
Watch the color-coded VMA line. A rising (orange) VMA suggests bullish momentum, while a falling (red) one indicates bearish conditions.
What Sets This Script Apart:
Dynamic Adaptation:
Unlike a fixed-period moving average, the VMA adjusts its sensitivity in real time by integrating a volatility measure, making it more adaptive to market swings.
Multi-Layered Analysis:
With integrated volatility channels, pivot points, slope analysis, and a higher timeframe VMA, this tool gives you a fuller picture of market dynamics.
Immediate Data at a Glance:
The real-time table consolidates multiple key metrics into one view, saving time and reducing the need for additional indicators.
Custom Alerts:
Pre-built alert conditions allow for timely notifications, ensuring you don’t miss critical market changes.
BTC vs Mag7 Combined IndexThis Mag7 Combined Index script is a custom TradingView indicator that calculates and visualizes the collective performance of the Magnificent 7 (Mag7) stocks—Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta (red line) compared to Bitcoin (blue line). It normalizes the daily closing prices of each stock to their initial value on the chart, scales them into percentages, and then computes their simple average to form a combined index. The result is plotted as a single red line, offering a clear view of the aggregated performance of these influential stocks over time compared to Bitcoin.
This indicator is ideal for analyzing the overall market impact of Bitcoin compared to the Mag7 stocks.
MAG 7 - Weighted Multi-Symbol Momentum + ExtrasOverview
This indicator aggregates the percentage change of multiple symbols into a single “weighted momentum” value. You can set individual weights to emphasize or de-emphasize particular stocks. The script plots two key items:
The default tickers in the script are:
AAPL (Apple)
AMZN (Amazon)
NVDA (NVIDIA)
MSFT (Microsoft)
GOOGL (Alphabet/Google)
TSLA (Tesla)
META (Meta Platforms/Facebook)
Raw Weighted Momentum (Histogram):
Each bar represents the combined (weighted) percentage change across your chosen symbols for that bar.
Bars are colored green if the momentum is above zero, or red if below zero.
Smoothed Momentum (Yellow Line):
An Exponential Moving Average (EMA) of the raw momentum for a smoother trend view.
Helps visualize when short-term momentum is accelerating or decelerating relative to its average.
Features
Symbol Inputs: Up to seven user-defined tickers, with weights for each symbol.
Smoothing Period: Set a custom lookback length to calculate the EMA (or switch to SMA in the code if you prefer).
Table Display: A built-in table in the top-right corner lists each symbol’s real-time percentage change, plus the total weighted momentum.
Alerts:
Configure alerts for when the weighted momentum crosses above or below user-defined thresholds.
Helps you catch major shifts in sentiment across multiple symbols.
How To Use
Select Symbols & Weights: In the indicator’s settings, specify the tickers you want to monitor and their corresponding weights. Weights default to 1 (equal weighting).
Watch the Bars vs. Zero:
Bars above zero mean a positive weighted momentum (the basket is collectively moving up).
Bars below zero mean negative weighted momentum (the basket is collectively under pressure).
Check the Yellow Line: The EMA of momentum.
If the bars consistently stay above the line, short-term momentum is stronger than its recent average.
If the bars dip below the line, momentum is weakening relative to its average.
Review the Table: Quick snapshot of each symbol’s daily percentage change plus the total basket momentum, all color-coded red or green.
Caution & Tips
This indicator measures rate of change, not absolute price levels. A rising momentum can still be part of a larger downtrend.
Always combine momentum readings with other technical and/or fundamental signals for confirmation.
For better reliability, experiment with different smoothing lengths to suit your trading style (shorter for scalping, longer for swing or positional approaches).
