VWAP-Anchored MACD [BOSWaves]VWAP-Anchored MACD - Volume-Weighted Momentum Mapping With Zero-Line Filtering
Overview
The VWAP-Anchored MACD delivers a refined momentum model built on volume-weighted price rather than raw closes, giving you a more grounded view of trend strength during sessions, weeks, or months.
Instead of tracking two EMAs of price like a standard MACD, this tool reconstructs the MACD engine using anchored VWAP as the core input. The result is a momentum structure that reacts to real liquidity flow, filters out weak crossovers near the zero line, and visualizes acceleration shifts with clear, high-contrast gradients.
This indicator acts as a precise momentum map that adapts in real time. You see how weighted price is accelerating, where valid crossovers form, and when trend conviction is strong enough to justify execution.
It uses gradient line coloring to show bullish or bearish momentum, histogram shading to highlight energy shifts, cross dots to mark valid crossovers, optional buy/sell diamonds for execution cues, and candle coloring to display trend strength at a glance.
Theoretical Foundation
Traditional MACD compares the difference between two exponential moving averages of price.
This variant replaces price with anchored VWAP, making the calculation sensitive to actual traded volume across your chosen period (Session, Week, or Month).
Three principles drive the logic:
Anchored VWAP Momentum : Price is weighted by volume and aggregated across the selected anchor. The fast and slow VWAP-EMAs then expose how liquidity-corrected momentum is expanding or contracting.
Zero-Line Distance Filtering : Crossover signals that occur too close to the zero line are removed. This eliminates the common MACD problem of generating weak, directionless signals in choppy phases.
Directional Visualization : MACD line, signal line, histogram, candle colors, and optional diamond markers all react to shifts in VWAP-momentum, giving you a clean structural read on market pressure.
Anchoring VWAP to session, weekly, or monthly resets creates a systematic framework for tracking how capital flow is driving momentum throughout each trading cycle.
How It Works
The core engine processes momentum through several mapped layers:
VWAP Aggregation : Price × volume is accumulated until the anchor resets. This creates a continuous, liquidity-corrected VWAP curve.
MACD Construction : Fast and slow VWAP-EMAs define the MACD line, while a smoothed signal line identifies edges where momentum shifts.
Zero-Line Distance Filter : MACD and signal must both exceed a threshold distance from zero for a crossover to count as valid. This prevents fake crossovers during compression.
Visual Momentum Layers : It uses gradient line coloring to show bullish or bearish momentum, histogram shading to highlight energy shifts, cross dots to mark valid crossovers, optional buy/sell diamonds for execution cues, and candle coloring to display trend strength at a glance.
This layered structure ensures you always know whether momentum is strengthening, fading, or transitioning.
Interpretation
You get a clean, structural understanding of VWAP-based momentum:
Bullish Phases : MACD > Signal, histogram expands, candles turn bullish, and crossovers occur above the threshold.
Bearish Phases : MACD < Signal, histogram drives lower, candles shift bearish, and downward crossovers trigger below the threshold.
Neutral/Compression : Both lines remain near the zero boundary, histogram flattens, and signals are suppressed to avoid noise.
This creates a more disciplined version of MACD momentum reading - less noise, more conviction, and better alignment with liquidity.
Strategy Integration
Trend Continuation : Use VWAP-MACD crossovers that occur far from the zero line as higher-conviction entries.
Zero-Line Rejection : Watch for histogram contractions near zero to anticipate flattening momentum and potential reversal setups.
Session/Week/Month Anchors : Session anchor works best for intraday flows. Weekly or monthly anchor structures create cleaner macro momentum reads for swing trading.
Signal-Only Execution : Optional buy/sell diamonds give you direct points to trigger trades without overanalyzing the chart.
This indicator slots cleanly into any momentum-following system and offers higher signal quality than classic MACD variants due to the volume-weighted core.
Technical Implementation Details
VWAP Reset Logic : Session (D), Week (W), or Month (M)
Dynamic Fast/Slow VWAP EMAs : Fully configurable lengths, smoothing and anchor settings
MACD/Signal Line Framework : Traditional structure with volume-anchored input
Zero-Line Filtering : Adjustable threshold for structural confirmation
Dual Visualization Layers : MACD body + histogram + crosses + candle coloring
Optimized Performance : Lightweight, fast rendering across all timeframes
Optimal Application Parameters
Timeframes:
1- 15 min : Short-term momentum scalping and rapid trend shifts
30- 240 min : Balanced momentum mapping with clear structural filtering
Daily : Macro VWAP regime identification
Suggested Configuration:
Fast Length : 12
Slow Length : 26
Signal Length : 9
Zero Threshold : 200 - 500 depending on asset range
These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance.
Performance Characteristics
High Effectiveness:
Assets with strong intraday or session-based volume cycles
Markets where volume-weighted momentum leads price swings
Trend environments with strong acceleration
Reduced Effectiveness:
Ultra-choppy markets hugging the VWAP axis
Sessions with abnormally low volume
Ranges where MACD naturally compresses
Disclaimer
The VWAP-Anchored MACD is a structural momentum tool designed to enhance directional clarity - not a guaranteed predictor. Performance depends on market regime, volatility, and disciplined execution. Use it alongside broader trend, volume, and structural analysis for optimal results.
ค้นหาในสคริปต์สำหรับ "美股标普500"
ShooterViz Lazy Trader EMA SystemShooterViz Lazy Trader EMA System - Complete User Guide
What This Script Does
This is a position scaling indicator that tells you exactly when to enter, add to, and exit trades using a simplified 5-EMA system. It removes the guesswork and decision fatigue from trading by giving you clear visual signals.
The Core Concept
3 entry signals that build your position from 20% → 50% → 100%
2 exit signals that scale you out at 50% → 50% (complete exit)
1 higher timeframe filter that keeps you on the right side of the trend
No Fibonacci calculations, no RSI divergence, no multi-indicator confusion. Just EMAs and price action.
What You'll See On Your Chart
1. Colored EMA Lines
Blue Lines (Entry Zone):
3 EMA (lightest blue) - Early reversal detector
5 EMA (darker blue) - Confirmation line
Green Lines (Add Zone):
21 EMA (bright green) - First add location
34 EMA (lighter green) - Final add location
Red Lines (Exit Zone):
89 EMA (lighter red) - First exit trigger
144 EMA (darker red) - Final exit trigger
Orange Lines (Hyper Frame - optional):
Hyper 21 EMA (from higher timeframe) - Trend direction
Hyper 34 EMA (from higher timeframe) - Bias confirmation
2. Triangle Signals
Green Triangles (Below Price) = BUY/ADD:
Lime triangle with "20%" = Entry 1: Price reclaimed 3→5 EMA (starter position)
Green triangle with "30%" = Entry 2: Price bounced off 21 EMA (first add)
Teal triangle with "50%" = Entry 3: Price broke out from 34 EMA compression (final add)
Red Triangles (Above Price) = SELL:
Orange triangle with "50% OFF" = Exit 1: Price broke below 89 EMA (take half off)
Red triangle with "EXIT ALL" = Exit 2: Price broke below 144 EMA (close remaining position)
3. Background Color (Trend Bias)
Light green background = Hyper frame EMAs trending up (bias LONG)
Light red background = Hyper frame EMAs trending down (bias SHORT)
Gray background = Neutral/choppy (be cautious)
4. Info Table (Top Right Corner)
A live status dashboard showing:
Which entry signals are currently active (✓ or —)
Which exit signals are currently active (⚠ or ⛔)
Current hyper frame bias (🟢 LONG / 🔴 SHORT / ⚪ NEUTRAL)
Which timeframe you're using for hyper frame filtering
How to Install and Set Up
Step 1: Add the Script to TradingView
Open TradingView
Click "Pine Editor" at the bottom of the screen
Copy the entire script code
Paste it into the Pine Editor
Click "Add to Chart"
Step 2: Configure Your Settings
Click the gear icon ⚙️ next to "LazyEMA" in your indicators list.
Critical Settings to Configure:
Hyper Frame Selection (Most Important!)
Location: "Hyper Frame (Pick ONE)" section
Setting: "Timeframe"
What to choose:
Trading 15min or 1H charts? → Use "240" (4-hour)
Trading 4H or Daily charts? → Use "D" (Daily)
Trading Daily or Weekly charts? → Use "W" (Weekly)
Why this matters: This filter keeps you aligned with the bigger trend. Only take longs when this timeframe is green, shorts when it's red.
MA Type (Optional, default is fine)
Location: "MA Config" section
Default: EMA (recommended)
Options: EMA, SMA, WMA, HMA, RMA, VWMA
Most traders should stick with EMA
Visual Toggles (Customize your view)
Entry Zone: Turn individual EMAs on/off (3, 5, 21, 34)
Exit Zone: Turn individual EMAs on/off (89, 144)
Hyper Frame: Toggle the higher timeframe EMAs on/off
Step 3: Clean Up Your Chart
Turn OFF these if visible:
Volume bars (they clutter the view)
Any other indicators you have loaded
Grid lines (optional, but cleaner)
Keep ONLY:
Price candles
Your ShooterViz Lazy Trader EMA System
Maybe support/resistance levels if you manually draw them
How to Trade With This Script
The Basic Workflow
Before the Market Opens:
Check the background color and info table bias
Green background? Look for LONG setups only
Red background? Look for SHORT setups only
Gray background? Stay flat or trade small
During the Trading Session:
LONGS (When hyper frame is bullish):
Wait for Entry 1 signal:
Lime triangle appears with "20%"
Price has reclaimed the 5 EMA after dipping to 3 EMA
Action: Enter 20% of your intended position
Stop loss: Place below the 5 EMA or recent swing low
Wait for Entry 2 signal:
Green triangle appears with "30%"
Price pulled back to 21 EMA and bounced
Action: Add 30% more (you're now at 50% total)
Move stop: Trail it up to below 21 EMA
Wait for Entry 3 signal:
Teal triangle appears with "50%"
Price compressed at 34 EMA and broke out
Action: Add final 50% (you're now 100% loaded)
Move stop: Trail it up to below 34 EMA
Wait for Exit 1 signal:
Orange triangle appears with "50% OFF"
Price broke below 89 EMA
Action: Exit 50% of your position immediately
Move stop on rest: Trail to 89 EMA or lock in profits
Wait for Exit 2 signal:
Red triangle appears with "EXIT ALL"
Price broke below 144 EMA
Action: Exit remaining 50% (you're now flat)
Or: Stop gets hit at 89 EMA (same result)
SHORTS (When hyper frame is bearish):
Same process, but inverted
Triangles appear above price instead of below
Look for breakdowns below EMAs instead of bounces off them
Exit when price reclaims 89 and 144 EMAs
Real-World Example Walkthrough
Setup: Trading ES (S&P 500 Futures) on 1H Chart
Chart Configuration:
Timeframe: 1 Hour
Hyper Frame: 240 (4-hour)
Ticker: ES
Pre-Market Check:
Background is light green
Info table shows "🟢 LONG" for Hyper Bias
Decision: Only look for long entries today
9:30 AM - Market Opens
Price dips and touches 3 EMA
Watch for: Reclaim of 5 EMA
9:45 AM - Entry 1 Triggers
Lime triangle appears below bar
Price closed above 5 EMA at $4,550
Action taken:
Enter long 20% position (2 contracts if targeting 10 total)
Stop loss at $4,545 (below 5 EMA)
Risk: $10 per contract × 2 = $20 risk
10:30 AM - Entry 2 Triggers
Price rallied to $4,565, pulls back
Green triangle appears at 21 EMA ($4,555)
Action taken:
Add 30% (3 more contracts, now have 5 total)
Move stop to $4,550 (below 21 EMA)
Current P/L: +$25 ($5 gain on original 2 contracts, break-even on new 3)
11:15 AM - Entry 3 Triggers
Price consolidates at 34 EMA around $4,560
Teal triangle appears as price breaks to $4,568
Action taken:
Add final 50% (5 more contracts, now have 10 total)
Move stop to $4,555 (below 34 EMA)
Current P/L: +$70
1:00 PM - Price Extends
Price rallies to $4,595 (on track)
89 EMA is at $4,575
No action yet, let it run
2:15 PM - Exit 1 Triggers
Price pulls back from $4,600
Orange triangle appears as price breaks below 89 EMA at $4,580
Action taken:
Exit 50% (5 contracts closed at $4,580)
Keep 5 contracts with stop at 89 EMA ($4,575)
Banked: +$150 average gain on closed 5 contracts
2:45 PM - Exit 2 Triggers
Price continues down
Red triangle appears as price breaks 144 EMA at $4,570
Action taken:
Exit remaining 5 contracts at $4,570
Banked: +$100 on remaining 5 contracts
Final Results:
Total gain: $250 on the trade
Initial risk: $50 (if stopped out at Entry 1)
Risk/Reward: 5:1
Time in trade: ~5 hours
Common Questions
"What if I miss Entry 1? Can I still take Entry 2?"
Yes! Each entry is independent. If you miss the 3→5 reclaim, wait for the 21 EMA bounce. You'll start with a 30% position instead of 20%, but that's fine.
Rule: Never chase. Wait for the next EMA setup.
"What if multiple entry signals trigger at the same bar?"
Rare, but possible. If you see both Entry 1 and Entry 2 trigger together:
Take Entry 1 first (20%)
If the next bar confirms Entry 2 is still valid, add 30%
When in doubt, scale in gradually
"The hyper frame is green but I'm seeing short signals?"
Don't take them. The hyper frame is your bias filter. If it says "go long," ignore short setups. They're usually lower probability and will get stopped out.
"Can I use this for swing trading overnight?"
Absolutely. Just switch your hyper frame:
If you're on Daily charts, use Weekly hyper frame
If you're on 4H charts, use Daily hyper frame
Adjust position sizes for overnight risk
"What if the signal appears right at market close?"
Don't chase it. Wait for the next bar (next day) to confirm. Signals that appear in the last 5 minutes are often noise.
"How do I set up alerts?"
Right-click on the chart
Select "Add Alert"
Choose "LazyEMA" from the condition dropdown
Select which signal you want alerts for:
Entry 1: 3→5 Reclaim
Entry 2: 21 EMA Add
Entry 3: 34 EMA Breakout
Exit 1: 89 EMA Break
Exit 2: 144 EMA Break
Click "Create"
Pro tip: Set up all 5 alerts so you never miss a signal.
Position Sizing Guide see
swingtradenotes.substack.com
Critical Rule: Know your total risk BEFORE you take Entry 1. Don't wing it.
Customization Tips
For Day Traders (Scalpers)
Use 5min or 15min charts
Hyper frame: 1H or 4H
Expect 2-4 setups per day
Tighter stops (0.5% risk per entry)
For Swing Traders
Use 4H or Daily charts
Hyper frame: Daily or Weekly
Expect 1-2 setups per week
Wider stops (1-2% risk per entry)
For Position Traders
Use Daily or Weekly charts
Hyper frame: Weekly or Monthly
Expect 1-2 setups per month
Widest stops (2-3% risk per entry)
The "Don't Be Stupid" Checklist
Before taking ANY signal from this script, ask:
✅ Is the hyper frame bias pointing in my direction?
✅ Is the signal clean (not at a weird time or during news)?
✅ Do I know my stop loss level?
✅ Do I know my position size?
✅ Can I afford to lose if this trade fails?
If you answered "no" to ANY of these, skip the trade.
Troubleshooting
"I'm not seeing any signals"
Possible causes:
The "Show Lazy Trader System" toggle is off (turn it on)
Your chart timeframe is too high (try 1H or 4H)
Market is in a tight range (EMAs are compressed)
You need to refresh the chart
"Too many signals, getting whipsawed"
Fixes:
Increase your chart timeframe (go from 15m to 1H)
Switch to a less volatile ticker
Only trade when hyper frame bias is STRONG (not neutral)
Add a minimum bar count between signals
"The info table is covering my price action"
Fix:
Edit the script
Find the line: table.new(position.top_right, ...
Change position.top_right to position.bottom_right or position.top_left
"Signals appear then disappear"
This is normal (repainting). Some signals (especially compression breakouts) can disappear if the next bar reverses. This is why you:
Wait for bar close before acting
Use alerts that only fire on confirmed bars
Don't chase signals mid-bar
Final Thoughts
This script is a decision-making tool, not a crystal ball. It shows you high-probability setups based on EMA dynamics and trend structure. You still need to:
Manage your risk
Choose your position size
Stick to the rules
Accept losses when they happen
The system works when YOU work the system.
Print this guide, tape it next to your monitor, and follow it religiously for 20 trades before making ANY changes.
Good luck, and stay lazy (the smart way).
RV − IV Spread Alert (SPY vs VIX)Realized vs Implied Volatility Spread (RV − IV) for the S&P 500 / SPY.
Plots the daily difference between 30-day realized volatility (SPY) and implied volatility (VIX) in basis points.
Key insight from the research: when the spread turns and stays above ≈ +50 bps, forward returns historically degrade and volatility of returns rises sharply — a useful early-warning regime flag.
Features:
- Clean daily plot of RV − IV in bps
- Horizontal lines at 0, −50 bps and +50 bps
- Red background when spread > +50 bps
- Built-in alert condition that fires once per bar close when spread closes above +50 bps
- Optional “all-clear” alert when it drops back below
Use on SPY or ES1! daily chart. Perfect for anyone wanting a simple notification when the market enters the “risk-on” volatility regime highlighted by Machina Quanta and the original Bali & Hovakimian (2007) paper.
Reversal WaveThis is the type of quantitative system that can get you hated on investment forums, now that the Random Walk Theory is back in fashion. The strategy has simple price action rules, zero over-optimization, and is validated by a historical record of nearly a century on both Gold and the S&P 500 index.
Recommended Markets
SPX (Weekly, Monthly)
SPY (Monthly)
Tesla (Weekly)
XAUUSD (Weekly, Monthly)
NVDA (Weekly, Monthly)
Meta (Weekly, Monthly)
GOOG (Weekly, Monthly)
MSFT (Weekly, Monthly)
AAPL (Weekly, Monthly)
System Rules and Parameters
Total capital: $10,000
We will use 10% of the total capital per trade
Commissions will be 0.1% per trade
Condition 1: Previous Bearish Candle (isPrevBearish) (the closing price was lower than the opening price).
Condition 2: Midpoint of the Body The script calculates the exact midpoint of the body of that previous bearish candle.
• Formula: (Previous Open + Previous Close) / 2.
Condition 3: 50% Recovery (longCondition) The current candle must be bullish (green) and, most importantly, its closing price must be above the midpoint calculated in the previous step.
Once these parameters are met, the system executes a long entry and calculates the exit parameters:
Stop Loss (SL): Placed at the low of the candle that generated the entry signal.
Take Profit (TP): Calculated by projecting the risk distance upward.
• Calculation: Entry Price + (Risk * 1).
Risk:Reward Ratio of 1:1.
About the Profit Factor
In my experience, TradingView calculates profits and losses based on the percentage of movement, which can cause returns to not match expectations. This doesn’t significantly affect trending systems, but it can impact systems with a high win rate and a well-defined risk-reward ratio. It only takes one large entry candle that triggers the SL to translate into a major drop in performance.
For example, you might see a system with a 60% win rate and a 1:1 risk-reward ratio generating losses, even though commissions are under control relative to the number of trades.
My recommendation is to manually calculate the performance of systems with a well-defined risk-reward ratio, assuming you will trade using a fixed amount per trade and limit losses to a fixed percentage.
Remember that, even if candles are larger or smaller in size, we can maintain a fixed loss percentage by using leverage (in cases of low volatility) or reducing the capital at risk (when volatility is high).
Implementing leverage or capital reduction based on volatility is something I haven’t been able to incorporate into the code, but it would undoubtedly improve the system’s performance dramatically, as it would fix a consistent loss percentage per trade, preventing losses from fluctuating with volatility swings.
For example, we can maintain a fixed loss percentage when volatility is low by using the following formula:
Leverage = % of SL you’re willing to risk / % volatility from entry point to exit or SL
And if volatility is high and exceeds the fixed percentage we want to expose per trade (if SL is hit), we could reduce the position size.
For example, imagine we only want to risk 15% per SL on Tesla, where volatility is high and would cause a 23.57% loss. In this case, we subtract 23.57% from 15% (the loss percentage we’re willing to accept per trade), then subtract the result from our usual position size.
23.57% - 15% = 8.57%
Suppose I use $200 per trade.
To calculate 8.57% of $200, simply multiply 200 by 8.57/100. This simple calculation shows that 8.57% equals about $17.14 of the $200. Then subtract that value from $200:
$200 - $17.14 = $182.86
In summary, if we reduced the position size to $182.86 (from the usual $200, where we’re willing to lose 15%), no matter whether Tesla moves up or down 23.57%, we would still only gain or lose 15% of the $200, thus respecting our risk management.
Final Notes
The code is extremely simple, and every step of its development is detailed within it.
If you liked this strategy, which complements very well with others I’ve already published, stay tuned. Best regards.
MeanReversion_tradeALERTOverview The Apex Reversal Predictor v2.5 is a specialized mean reversion strategy designed for scalping high-volatility assets like NQ (Nasdaq), ES (S&P 500), and Crypto. While most indicators chase breakouts, this system hunts for "Liquidity Sweeps"—moments where the market briefly breaks a key level to trap retail traders before snapping back to the true value (VWAP).
This is not just a signal indicator; it is a full Trade Manager that calculates your Entry, Stop Loss, and Take Profit levels automatically based on volatility (ATR).
The Logic: Why This Works Markets act like a rubber band. They can only stretch so far from their average price before snapping back. This script combines three layers of logic to identify these snap-back points:
The Stretch (Sigma Score): Measures how far price is from the VWAP relative to ATR. If the score > 2.0, the "rubber band" is overextended.
The Trap (Liquidity Sweep): Identifies Pivot Highs/Lows. It waits for price to break a pivot (luring in breakout traders) and then immediately reverse (trapping them).
The Exhaustion (RSI): Confirms that momentum is Overbought/Oversold to prevent trading against a strong trend.
Key Features
Dynamic Lines: Automatically draws Blue (Entry), Red (SL), and Green (TP) lines on the chart for active trades.
Smart Targets: Two modes for taking profit:
Mean Reversion: Targets the VWAP line (High Win Rate).
Fixed Ratio: Targets a specific Risk:Reward (e.g., 1:2).
Live Dashboard: Tracks Win Rate, Net Points, and the live "Stretch Score" in the bottom right corner.
Alert Ready: Formatted JSON alerts for easy integration with Discord or trading bots.
How & When to Use (User Guide)
1. Best Timeframes
5-Minute (5m): Best for NQ and volatile stocks (TSLA, NVDA). Filters out 1-minute noise but catches the intraday reversals.
15-Minute (15m): Best for Forex or slower-moving indices (ES).
2. The Setup Checklist Before taking a trade, look at the Dashboard in the bottom right:
Step 1: Check the "Stretch (Sigma)". Is it Orange or Red? This means price is extended and ripe for a reversal. If it's Green, the market is calm—be careful.
Step 2: Wait for the Signal.
"Apex BUY" (Green Label): Price swept a low and closed green.
"Apex SELL" (Red Label): Price swept a high and closed red.
Step 3: Execute. Enter at the close of the signal candle. Set your stop loss at the Red Line provided by the script.
3. Warning / When NOT to Use
Strong Trending Days: If the market is trending heavily (e.g., creating higher highs all day without looking back), do not fight the trend.
News Events: Avoid using this during CPI, FOMC, or NFP releases. The "rubber band" logic breaks during news because volatility expands indefinitely.
Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET
Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions.
Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts.
The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence.
THE FOUNDATION
The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology.
Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk.
Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods.
Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses.
HOW THE INDICATOR CALCULATES VOLATILITY
The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility.
For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options.
Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature.
The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements.
The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process.
The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful.
THE CORE VRP CALCULATION
Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress.
The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag.
INTERPRETING THE REGIMES
The indicator classifies market conditions into five distinct regimes based on VRP levels.
The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes.
The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding.
The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates.
The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress.
The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging.
TERM STRUCTURE ANALYSIS
Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows.
Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship.
When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms.
The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics.
MEAN REVERSION CHARACTERISTICS
One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies.
The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low.
Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization.
These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter.
MOMENTUM ANALYSIS
The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium.
The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels.
PRACTICAL APPLICATION
For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk.
For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes.
For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system.
LIMITATIONS AND CONSIDERATIONS
No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention.
The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period.
Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies.
The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources.
Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics.
STATISTICAL OUTPUTS
The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation.
Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions.
ALERT CONDITIONS
The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes.
REFERENCES
Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492.
Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78.
Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
Relative Strength Line by QuantxThe Relative Strength Line compares the price performance of a stock against a benchmark index (e.g., NIFTY, S&P 500, Bank Nifty, etc.).
It does not indicate momentum of the stock itself — it indicates whether the stock is outperforming or underperforming the market.
🔍 How To Read It
RSL Behavior Meaning
RSL moving up Stock is outperforming the benchmark (strong leadership)
RSL moving down Stock is underperforming the benchmark (weakness vs market)
RSL breaking above previous highs Strong institutional demand, leadership candidate
RSL trending sideways Stock is performing similar to the index (no leadership)
📈 Why It Matters
Institutional traders and top-performing strategies focus on stocks showing relative strength BEFORE price breakout.
A stock making new RSL highs even before a price breakout often becomes a top performer in the coming trend.
🧠 Core Trading Edge
You don’t need to predict the market.
Just identify which stocks are being accumulated and leading the market right now — that’s what the Relative Strength Line reveals.
Liquidation Heatmap [Alpha Extract]A sophisticated liquidity zone visualization system that identifies and maps potential liquidation levels based on swing point analysis with volume-weighted intensity measurement and gradient heatmap coloring. Utilizing pivot-based pocket detection and ATR-scaled zone heights, this indicator delivers institutional-grade liquidity mapping with dynamic color intensity reflecting relative liquidity concentration. The system's dual-swing detection architecture combined with configurable weight metrics creates comprehensive liquidation level identification suitable for strategic position planning and market structure analysis.
🔶 Advanced Pivot-Based Pocket Detection
Implements dual swing width analysis to identify potential liquidation zones at pivot highs and lows with configurable lookback periods for comprehensive level coverage. The system detects primary swing points using main pivot width and optional secondary swing detection for increased pocket density, creating layered liquidity maps that capture both major and minor liquidation levels across extended price history.
🔶 Multi-Metric Weight Calculation Engine
Features flexible weight source selection including Volume, Range (high-low spread), and Volume × Range composite metrics for liquidity intensity measurement. The system calculates pocket weights based on market activity at pivot formation, enabling traders to identify which liquidation levels represent higher concentration of potential stops and liquidations with configurable minimum weight thresholds for noise filtering.
🔶 ATR-Based Zone Height Framework
Utilizes Average True Range calculations with percentage-based multipliers to determine pocket vertical dimensions that adapt to market volatility conditions. The system creates ATR-scaled bands above swing highs for short liquidation zones and below swing lows for long liquidation zones, ensuring zone heights remain proportional to current market volatility for accurate level representation.
🔶 Dynamic Gradient Heatmap Visualization
Implements sophisticated color gradient system that maps pocket weights to intensity scales, creating intuitive visual representation of relative liquidity concentration. The system applies power-law transformation with configurable contrast adjustment to enhance differentiation between weak and strong liquidity pockets, using cyan-to-blue gradients for long liquidations and yellow-to-orange for short liquidations.
🔶 Intelligent Pocket State Management
Features advanced pocket tracking system that monitors price interaction with liquidation zones and updates pocket states dynamically. The system detects when price trades through pocket midpoints, marking them as "hit" with optional preservation or removal, and manages pocket extension for untouched levels with configurable forward projection to maintain visibility of approaching liquidity zones.
🔶 Real-Time Liquidity Scale Display
Provides gradient legend showing min-max range of pocket weights with 24-segment color bar for instant liquidity intensity reference. The system positions the scale at chart edge with volume-formatted labels, enabling traders to quickly assess relative strength of visible liquidation pockets without numerical clutter on the main chart area.
🔶 Touched Pocket Border System
Implements visual confirmation of executed liquidations through border highlighting when price trades through pocket zones. The system applies configurable transparency to touched pocket borders with inverted slider logic (lower values fade borders, higher values emphasize them), providing clear historical record of liquidated levels while maintaining focus on active untouched pockets.
🔶 Dual-Swing Density Enhancement
Features optional secondary swing width parameter that creates additional pocket layer with tighter pivot detection for increased liquidation level density. The system runs parallel pivot detection at both primary and secondary swing widths, populating chart with comprehensive liquidity mapping that captures both major swing liquidations and intermediate level clusters.
🔶 Adaptive Pocket Extension Framework
Utilizes intelligent time-based extension that projects untouched pockets forward by configurable bar count, maintaining visibility as price approaches potential liquidation zones. The system freezes touched pocket right edges at hit timestamps while extending active pockets dynamically, creating clear distinction between historical liquidations and forward-projected active levels.
🔶 Weight-Based Label Integration
Provides floating labels on untouched pockets displaying volume-formatted weight values with dynamic positioning that follows pocket extension. The system automatically manages label lifecycle, creating labels for new pockets, updating positions as pockets extend, and removing labels when pockets are touched, ensuring clean chart presentation with relevant liquidity information.
🔶 Performance Optimization Framework
Implements efficient array management with automatic clean-up of old pockets beyond lookback period and optimized box/label deletion to maintain smooth performance. The system includes configurable maximum object counts (500 boxes, 50 labels, 100 lines) with intelligent removal of oldest elements when limits are approached, ensuring consistent operation across extended timeframes.
This indicator delivers sophisticated liquidity zone analysis through pivot-based detection and volume-weighted intensity measurement with intuitive heatmap visualization. Unlike simple support/resistance indicators, the Liquidation Heatmap combines swing point identification with market activity metrics to identify where concentrated liquidations are likely to occur, while the gradient color system instantly communicates relative liquidity strength. The system's dual-swing architecture, configurable weight metrics, ATR-adaptive zone heights, and intelligent state management make it essential for traders seeking strategic position planning around institutional liquidity levels across cryptocurrency, forex, and futures markets. The visual heatmap approach enables instant identification of high-probability reversal zones where cascading liquidations may trigger significant price reactions.
P_NQ Futures Daily Bias & Structure ProOverview The Master Sniper is a professional-grade execution system designed for high-volatility assets like NQ (Nasdaq 100) and ES (S&P 500). Unlike standard indicators that generate blind signals, this script uses a Multi-Timeframe Logic Engine to first establish a daily bias and then hunt for specific intraday triggers.
It features a Hybrid Strategy that can automatically switch between Trend Following (Smart Money Concepts) and Mean Reversion (Gap Fades), giving you a complete toolkit for any market condition.
Key Features
1. Macro Bias Engine (The Filter) Before generating any signal, the script analyzes the Daily Chart in the background:
Structure: Checks for Higher Highs/Lows vs. Lower Highs/Lows.
Momentum: Uses RSI and the 200 EMA to ensure you aren't buying the top or selling the bottom.
Result: It generates a directional bias (Bullish/Bearish) that filters out low-probability trades.
2. Hybrid Entry Logic
Trend Mode (SMC): Identifies Fair Value Gaps (FVG) within "Discount" or "Premium" zones. It only triggers if the price pulls back into a value area aligned with the Daily Bias.
Reversal Mode (Elasticity): Detects when price is over-extended (2.0 Standard Deviations from VWAP) or when a "Liquidity Sweep" occurs, signaling a snap-back trade.
Gap Rejection (Morning Fade): A dedicated engine that monitors the Opening Gap. If the market gaps significantly but fails to hold, it triggers a "Fade" trade to target the gap fill.
3. Professional Trade Management Visualizes your trade plan instantly on the chart:
Split Targets: Draws targets for Contract 1 (Scalp) and Contract 2 (Runner).
Auto-Break Even: The moment TP1 is hit, the Stop Loss line visually moves to your Entry Price, signaling a "Risk-Free" trade.
Infinite Target Lines: Extends target lines to the right until the trade concludes, keeping your chart clean.
4. Risk Filters
Range Filter: Prevents buying in the Top 1/3 or selling in the Bottom 1/3 of the daily range.
Proximity Filter: Blocks trades that are squeezing too tight against the 100-candle High/Low.
How to Use
Timeframe: Optimized for the 5-Minute (5m) chart on Futures (NQ/ES) or Tech Stocks.
Dashboard: Check the bottom-right panel. Ensure "Status" says "SCANNING" and Filters show "Active."
Execution: Wait for the alert (e.g., "🟢 ENTER LONG"). Place your orders at the Blue Line with SL at the Red Line.
One Point Global Net Liquidity The "Fuel" Behind the MarketMost traders look at price action, but price is often just a reflection of the money supply available in the system. This indicator tracks Global Net Liquidity—the actual amount of fiat currency available to flow into risk assets like Crypto and Equities.
Unlike standard "Money Supply" (M2) charts, this indicator focuses on Central Bank Balance Sheets, which is a more direct proxy for "Quantitative Easing" (QE) and "Quantitative Tightening" (QT).
How It Works (The Formula)
This script aggregates the balance sheets of the "Big 4" Central Banks, which represent ~90% of global liquidity. It automatically converts all values to USD Trillions for a standardized view.
{Global Liquidity} = {US Net Liquidity} + {ECB} + {PBoC} + {BoJ}
1. US Net Liquidity (The "Trader's" Formula) We do not just use the Fed's Total Assets. We subtract the money that is "stuck" outside the private economy:
(+) Fed Balance Sheet: Total Assets.
(-) TGA (Treasury General Account): The government's checking account. When this goes up, liquidity is drained from markets.
(-) RRP (Reverse Repo): Money parked by banks at the Fed overnight. When this goes up, liquidity is removed from the system.
2. Global Additions
ECB (Eurozone): Converted to USD.
PBoC (China): Converted to USD.
BoJ (Japan): Converted to USD.
How to Use This Indicator This indicator is designed as an Overlay on the main chart (using the Left Scale).
Correlation: Generally, when the Orange Line (Liquidity) trends up, Bitcoin and the S&P 500 trend up. When Central Banks tighten (line down), risk assets struggle.
The "Divergence" Signal (Alpha):
Bullish: If Price makes a Lower Low but Liquidity makes a Higher Low, it often signals seller exhaustion and a potential bottom.
Bearish: If Price makes a New High but Liquidity fails to follow (or drops), the rally may be unsupported and prone to a reversal.
Settings
Scale: This indicator is pinned to the Scale Left to allow it to overlay price action without distortion.
Data: Uses daily data from ECONOMICS and FRED feeds.
Self-Organized Criticality - Avalanche DistributionHere's all you need to know: This indicator applies Self-Organized Criticality (SOC) theory to financial markets, measuring the power-law exponent (alpha) of price drawdown distributions. It identifies whether markets are in stable Gaussian regimes or critical states where large cascading moves become more probable.
Self-Organized Criticality
SOC theory, introduced by Per Bak, Tang, and Wiesenfeld (1987), describes how complex systems naturally evolve toward critical (fragile) states. An example is a sand pile: adding grains creates avalanches whose sizes follow a power-law distribution rather than a normal distribution.
Financial markets exhibit similar behavior. Price movements aren't purely random walks—they display:
Fat-tailed distributions (more extreme events than Gaussian models predict)
Scale invariance (no characteristic avalanche size)
Intermittent dynamics (periods of calm punctuated by large cascades)
Power-Law Distributions
When a system is in a critical state, the probability of an avalanche of size s follows:
P(s) ∝ s^(-α)
Where:
α (alpha) is the power-law exponent
Higher α → distribution resembles Gaussian (large events rare)
Lower α → heavy tails dominate (large events common)
This indicator estimates α from the empirical distribution of price drawdowns.
Mathematical Method
1. Avalanche Detection
The indicator identifies local price peaks (highest point in a lookback window), then measures the percentage drawdown to the next trough. A dynamic ATR-based threshold filters out noise—small drops in calm markets count, but the bar rises in volatile periods.
2. Logarithmic Binning
Avalanche sizes are sorted into logarithmically-spaced bins (e.g., 1-2%, 2-4%, 4-8%) rather than linear bins. This captures power-law behavior across multiple scales - a 2% drop and 20% crash both matter. The indicator creates 12 adaptive bins spanning from your smallest to largest observed avalanche.
3. Bin-to-Bin Ratio Estimation
For each pair of adjacent bins, we calculate:
α ≈ log(N₁/N₂) / log(s₂/s₁)
Where N₁ and N₂ are avalanche counts, s₁ and s₂ are bin sizes.
Example: If 2% drops happen 4× more often than 4% drops, then α ≈ log(4)/log(2) ≈ 2.0.
We get 8-11 independent estimates and average them. This is more robust than fitting one line through all points—outliers can't dominate.
4. Rolling Window Analysis
Alpha recalculates using only recent avalanches (default: last 500 bars). Old data drops out as new avalanches occur, so the indicator tracks regime shifts in real-time.
Regime Classification
🟢 Gaussian α ≥ 2.8 Normal distribution behavior; large moves are rare outliers
🟡 Transitional 1.8 ≤ α < 2.8 Moderate fat tails; system approaching criticality
🟠 Critical 1.0 ≤ α < 1.8 Heavy tails; large avalanches increasingly common
🔴 Super-Critical α < 1.0 Extreme tail risk; system prone to cascading failures
What Alpha Tells You
Declining alpha → Market moving toward criticality; tail risk increasing
Rising alpha → Market stabilizing; returns to normal distribution
Persistent low alpha → Sustained fragility; heightened crash probability
Supporting Metrics
Heavy Tail %: Concentration of total drawdown in largest 10% of events
Populated Bins: Data coverage quality (11-12 out of 12 is ideal)
Avalanche Count: Sample size for statistical reliability
Limitations
This is a distributional measure, not a timing indicator. Low alpha indicates increased systemic risk but doesn't predict when a cascade will occur. Only that the probability distribution has shifted toward larger events.
How This Differs from the Per Bak Fragility Index
The SOC Avalanche Distribution calculates the power-law exponent (alpha) directly from price drawdown distributions - a pure mathematical analysis requiring only price data. The Per Bak Fragility Index aggregates external stress indicators (VIX, SKEW, credit spreads, put/call ratios) into a weighted composite score.
Technical Notes
Default settings optimized for daily and weekly timeframes on major indices
Requires minimum 200 bars of history for stable estimates
ATR-based dynamic sizing prevents scale-dependent bias
Alerts available for regime transitions and super-critical entry
References
Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters.
Sornette, D. (2003). Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press.
Z-score RegimeThis indicator compares equity behaviour and credit behaviour by converting both into z-scores. It calculates the z-score of SPX and the z-score of a credit proxy based on the HYG divided by LQD ratio.
SPX z-score shows how far the S&P 500 is from its rolling average.
Credit z-score shows how risk-seeking or risk-averse credit markets are by comparing high-yield bonds to investment-grade bonds.
When both z-scores move together, the market is aligned in either risk-on or risk-off conditions.
When SPX z-score is strong but credit z-score is weak, this may signal equity strength that is not supported by credit markets.
When credit z-score is stronger than SPX z-score, credit markets may be leading risk appetite.
The indicator plots the two z-scores as simple lines for clear regime comparison.
50 EMA Rejection Strategy V4 (Correct Signal Logic)//@version=6
indicator("50 EMA Rejection Strategy V4 (Correct Signal Logic)", overlay=true, max_labels_count=500)
//================ INPUTS ================//
group50 = "EMA 50 Trio"
ema50HighLen = input.int(50,"EMA50 High",group=group50)
ema50CloseLen = input.int(50,"EMA50 Close",group=group50)
ema50LowLen = input.int(50,"EMA50 Low",group=group50)
groupBase = "Additional EMAs"
ema10Len = input.int(10,"EMA10")
ema200Len = input.int(200,"EMA200")
ema600Len = input.int(600,"EMA600")
ema2400Len = input.int(2400,"EMA2400")
useTrendFilter = input.bool(false,"Use Higher Time EMA Filter")
groupRR = "Risk Reward Settings"
RR1 = input.float(1.0,"TP1 RR",step=0.5)
RR2 = input.float(2.0,"TP2 RR",step=0.5)
//================ CALCULATIONS ================//
Correlation Scanner📊 CORRELATION SCANNER - Financial Instruments Correlation Analyzer
🎯 ORIGINALITY AND PURPOSE
Correlation Scanner is a professional tool for analyzing correlation relationships between different financial instruments. Unlike standard correlation indicators that show the relationship between only two instruments, this script allows you to simultaneously track the correlation of up to 10 customizable instruments with a selected base asset.
The indicator is designed for traders working with cross-market analysis, portfolio diversification, and searching for related assets for arbitrage strategies.
🔧 HOW IT WORKS
The indicator uses the built-in ta.correlation() function to calculate the Pearson correlation coefficient between instrument closing prices over a specified period. Mathematical foundation:
1. Correlation Calculation: for each instrument, the correlation coefficient with the base asset is calculated over N bars (default 60)
2. Results Sorting: instruments are automatically ranked by absolute correlation value (from strongest to weakest)
3. Visualization: results are displayed in a table with color coding:
- Green: positive correlation (instruments move in the same direction)
- Red: negative correlation (instruments move in opposite directions)
- Color intensity depends on correlation strength
4. Correlation Strength Classification:
- Very Strong (💪💪💪): |r| > 0.8 — very strong relationship
- Strong (💪💪): |r| > 0.6 — strong relationship
- Medium (💪): |r| > 0.4 — medium relationship
- Weak: |r| > 0.2 — weak relationship
- Very Weak: |r| ≤ 0.2 — very weak relationship
📋 SETTINGS AND USAGE
MAIN PARAMETERS:
• Main Instrument — base instrument for comparison (default TVC:DXY - US Dollar Index)
• Correlation Period — calculation period in bars (10-500, default 60)
• Number of Instruments to Display — number of instruments to show (1-10)
• Table Position — table location on the chart
INSTRUMENT CONFIGURATION:
The indicator allows configuring up to 10 instruments for analysis. For each, you can specify:
• Instrument — instrument ticker (e.g., FX_IDC:EURUSD)
• Name — display name (emojis supported)
VISUAL SETTINGS:
• Show Chart Label with Correlation — display current chart's correlation with base instrument
• Table Header Color — table header color
• Table Row Background — table row background color
💡 USAGE EXAMPLES
1. DOLLAR IMPACT ANALYSIS: set DXY as the base instrument and track how dollar index changes affect currency pairs, gold, and cryptocurrencies
2. HEDGING ASSETS SEARCH: find instruments with strong negative correlation for risk diversification
3. PAIRS TRADING: identify assets with high positive correlation to find divergences and arbitrage opportunities
4. CROSS-MARKET ANALYSIS: track relationships between stocks, bonds, commodities, and currencies
5. SYSTEMIC RISK ASSESSMENT: identify periods of increased correlation between assets, which may indicate systemic risks
⚠️ IMPORTANT NOTES
• Correlation does NOT imply causation
• Correlation can change over time — regularly review the analysis period
• High past correlation doesn't guarantee the relationship will persist in the future
• Recommended to use the indicator in combination with fundamental analysis
🔔 ALERTS
The indicator includes a built-in alert condition: triggers when strong correlation (|r| > 0.8) is detected between the current chart and the base instrument.
S&P Options Patterns Detector (6-20 Candles)Pattern detector for S&P options. Detects alerts for bullish or bearish signals for any stock in S&P 500
Global M2(USD) V2This indicator tracks the total Global M2 Money Supply in USD. It aggregates economic data from the world's four largest central banks (Fed, PBOC, ECB, BOJ). The script automatically converts non-USD money supplies (CNY, EUR, JPY) into USD using real-time exchange rates to provide a unified view of global liquidity.
Usage
Macro Analysis: Overlay this on assets like Bitcoin or the S&P 500 to see if price appreciation is driven by fiat currency debasement ("money printing").
Liquidity Trends: A rising orange line indicates expanding global liquidity (generally bullish for risk assets), while a falling line suggests monetary tightening.
Real-time Data: A label at the end of the line displays the exact raw total in USD for precise tracking.
该脚本旨在追踪以美元计价的全球 M2 货币供应总量。它聚合了四大央行（美联储、中国央行、欧洲央行、日本央行）的经济数据，并通过实时汇率将非美货币（人民币、欧元、日元）统一折算为美元，从而构建出一个标准化的全球流动性指标。
用法
宏观对冲： 将其叠加在比特币或股票图表上，用于判断资产价格的上涨是否由全球法币“大放水”推动。
趋势研判： 橙色曲线向上代表全球流动性扩张（通常利好风险资产），向下则代表流动性紧缩。
数据直观： 脚本会在图表末端生成一个标签，实时显示当前全球 M2 的具体美元总额。
STRAT - MTF Dashboard + FTFC + Reversals v2.7# STRAT Indicator - Complete Description
## Overview
A comprehensive multi-timeframe STRAT trading system indicator that combines market structure analysis, flip levels, Full Timeframe Continuity (FTFC), and reversal pattern detection across 12 timeframes.
## Core Features
### 1. **Multi-Timeframe STRAT Dashboard**
- Displays STRAT combos (1, 2u, 2d, 3) across 12 timeframes: 1m, 5m, 15m, 30m, 1H, 4H, 12H, Daily, Weekly, Monthly, Quarterly, Yearly
- Color-coded directional bias (green/red/doji)
- Inside bars (●) and Outside bars (●) highlighted
- Current timeframe marked with ★
### 2. **HTF Flip Levels with Smart Grouping**
- Displays higher timeframe (HTF) flip levels (open prices) as labels on the right side
- Automatically groups multiple timeframes at the same price level (e.g., "★ 1H/4H/D")
- Current timeframe flip level always displayed with ★ marker
- Color-coded: Green (above price) / Red (below price)
### 3. **Full Timeframe Continuity (FTFC)**
- User-selectable 4 timeframes for FTFC analysis (default: D, W, M, Q)
- Green line: FTFC Up (highest open of 4 timeframes)
- Red line: FTFC Down (lowest open of 4 timeframes)
- Identifies when price is above/below all 4 timeframe opens
### 4. **Hammer & Shooting Star Detection**
- **Hammer Pattern**: Long lower wick (≥2x body), small upper wick, signals potential bottom reversal
- **Shooting Star Pattern**: Long upper wick (≥2x body), small lower wick, signals potential top reversal
- Scans last 100 bars (adjustable) and marks ALL historical patterns
- Chart markers: 🔨 (Hammer) below bars, 🔻 (Shooting Star) above bars
- Dashboard column shows reversal patterns for each timeframe
- Adjustable wick-to-body ratio sensitivity (1.5 to 5.0)
### 5. **Debug Tables**
- **FTFC Debug**: Shows close vs. 4 timeframe opens, confirms all-green/all-red conditions
- **Reversal Debug**: Real-time analysis of current bar - body size, wick measurements, ratios, and pattern qualification
## Settings
### Display Settings
- Dashboard position (9 options: top-left to bottom-right)
- Dashboard text size (tiny to huge)
- Label offset and text size
- Toggle individual features on/off
### FTFC Settings
- Select 4 custom timeframes for continuity analysis
- Default: Daily, Weekly, Monthly, Quarterly
### Reversal Settings
- **Wick to Body Ratio**: Sensitivity for pattern detection (default 2.0)
- **Lookback Bars**: How many historical bars to scan (default 100, max 500)
- Show/hide reversal markers on chart
- Show/hide reversal debug table
## Use Cases
1. **Momentum Trading**: Identify STRAT setups (2-2, 2-1-2 reversals, 3-bar plays) across multiple timeframes
2. **Swing Trading**: Use HTF flip levels as support/resistance and FTFC for trend confirmation
3. **Reversal Trading**: Catch hammer/shooting star patterns at key levels for counter-trend entries
4. **Multi-Timeframe Analysis**: Confirm alignment across timeframes before entering trades
## How to Use
### For STRAT Traders
- Look for 2-1-2 reversal setups in the dashboard
- Watch for inside bars (●) at HTF flip levels for breakout trades
- Use outside bars (●) to identify potential volatility expansion
### For Reversal Traders
- 🔨 Hammers after downtrends = potential long entries
- 🔻 Shooting stars after uptrends = potential short entries
- Combine with HTF flip levels for high-probability setups
### For Trend Followers
- FTFC green line above = bullish structure
- FTFC red line below = bearish structure
- Enter when price breaks and holds above/below FTFC levels
## Visual Elements
- **Green Labels**: HTF flip levels above current price (resistance)
- **Red Labels**: HTF flip levels below current price (support)
- **Lime Line**: FTFC Up (highest timeframe open)
- **Red Line**: FTFC Down (lowest timeframe open)
- **🔨 Icon**: Hammer pattern (potential reversal up)
- **🔻 Icon**: Shooting Star pattern (potential reversal down)
- **★ Symbol**: Current timeframe or multiple timeframes grouped
## Performance Notes
This indicator performs 12 multi-timeframe security calls and may take 15-30 seconds to calculate on initial load. This is normal for comprehensive MTF analysis.
## Version
v2.7 - Simplified reversal detection, current TF labeling, optimized performance
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**Perfect for**: STRAT traders, multi-timeframe analysts, reversal pattern traders, swing traders looking for high-probability setups with confluence across timeframes.
Buffett Quality Filter (TTM)//@version=6
indicator("Buffett Quality Filter (TTM)", overlay = true, max_labels_count = 500)
// 1. Get financial data (TTM / FY / FQ)
// EPS (TTM) for P/E
eps = request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE_BASIC", "TTM")
// Profitability & moat (annual stats)
roe = request.financial(syminfo.tickerid, "RETURN_ON_EQUITY", "FY")
roic = request.financial(syminfo.tickerid, "RETURN_ON_INVESTED_CAPITAL", "FY")
// Margins (TTM – rolling 12 months)
grossMargin = request.financial(syminfo.tickerid, "GROSS_MARGIN", "TTM")
netMargin = request.financial(syminfo.tickerid, "NET_MARGIN", "TTM")
// Balance sheet safety (quarterly)
deRatio = request.financial(syminfo.tickerid, "DEBT_TO_EQUITY", "FQ")
currentRat = request.financial(syminfo.tickerid, "CURRENT_RATIO", "FQ")
// Growth (1-year change, TTM)
epsGrowth1Y = request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE_BASIC_ONE_YEAR_GROWTH", "TTM")
revGrowth1Y = request.financial(syminfo.tickerid, "REVENUE_ONE_YEAR_GROWTH", "TTM")
// Free cash flow (TTM) and shares to build FCF per share for P/FCF
fcf = request.financial(syminfo.tickerid, "FREE_CASH_FLOW", "TTM")
sharesOut = request.financial(syminfo.tickerid, "TOTAL_SHARES_OUTSTANDING", "FQ")
fcfPerShare = (not na(fcf) and not na(sharesOut) and sharesOut != 0) ? fcf / sharesOut : na
// 2. Valuation ratios from price
pe = (not na(eps) and eps != 0) ? close / eps : na
pFcf = (not na(fcfPerShare) and fcfPerShare > 0) ? close / fcfPerShare : na
// 3. Thresholds (Buffett-style, adjustable)
minROE = input.float(15.0, "Min ROE %")
minROIC = input.float(12.0, "Min ROIC %")
minGM = input.float(30.0, "Min Gross Margin %")
minNM = input.float(8.0, "Min Net Margin %")
maxDE = input.float(0.7, "Max Debt / Equity")
minCurr = input.float(1.3, "Min Current Ratio")
minEPSG = input.float(8.0, "Min EPS Growth 1Y %")
minREVG = input.float(5.0, "Min Revenue Growth 1Y %")
maxPE = input.float(20.0, "Max P/E")
maxPFCF = input.float(20.0, "Max P/FCF")
// 4. Individual conditions
cROE = not na(roe) and roe > minROE
cROIC = not na(roic) and roic > minROIC
cGM = not na(grossMargin) and grossMargin > minGM
cNM = not na(netMargin) and netMargin > minNM
cDE = not na(deRatio) and deRatio < maxDE
cCurr = not na(currentRat) and currentRat > minCurr
cEPSG = not na(epsGrowth1Y) and epsGrowth1Y > minEPSG
cREVG = not na(revGrowth1Y) and revGrowth1Y > minREVG
cPE = not na(pe) and pe < maxPE
cPFCF = not na(pFcf) and pFcf < maxPFCF
// 5. Composite “Buffett Score” (0–10) – keep it on ONE line to avoid line-continuation errors
score = (cROE ? 1 : 0) + (cROIC ? 1 : 0) + (cGM ? 1 : 0) + (cNM ? 1 : 0) + (cDE ? 1 : 0) + (cCurr ? 1 : 0) + (cEPSG ? 1 : 0) + (cREVG ? 1 : 0) + (cPE ? 1 : 0) + (cPFCF ? 1 : 0)
// Strictness
minScoreForPass = input.int(7, "Min score to pass (0–10)", minval = 1, maxval = 10)
passes = score >= minScoreForPass
// 6. Visuals
bgcolor(passes ? color.new(color.green, 80) : na)
plot(score, "Buffett Score (0–10)", color = color.new(color.blue, 0))
// Info label on last bar
var label infoLabel = na
if barstate.islast
if not na(infoLabel)
label.delete(infoLabel)
infoText = str.format(
"Buffett score: {0}\nROE: {1,number,#.0}% | ROIC: {2,number,#.0}%\nGM: {3,number,#.0}% | NM: {4,number,#.0}%\nP/E: {5,number,#.0} | P/FCF: {6,number,#.0}\nD/E: {7,number,#.00} | Curr: {8,number,#.00}",
score, roe, roic, grossMargin, netMargin, pe, pFcf, deRatio, currentRat)
infoLabel := label.new(bar_index, high, infoText,
style = label.style_label_right,
color = color.new(color.black, 0),
textcolor = color.white,
size = size.small)
VB-MainLiteVB-MainLite – v1.0 Initial Release
Overview
VB-MainLite is a consolidated market-structure and execution framework designed to streamline decision-making into a single chart-level view. The script combines multi-timeframe trend, volatility, volume, and liquidity signals into one cohesive visual layer, reducing indicator clutter while preserving depth of information for active traders.
Core Architecture
Trend Backbone – EMA 200
Dedicated EMA 200 acts as the primary trend filter and higher-timeframe bias reference.
Serves as the “spine” of the system for contextualizing all secondary signals (swings, reversals, volume events, etc.).
Custom MA Suite (Envelope Ready)
Four configurable moving averages with flexible source, length, and smoothing.
Default configuration (preset idea: “8/89 Envelope”):
MA #1: EMA 8 on high
MA #2: EMA 8 on low
MA #3: EMA 89 on high
MA #4: EMA 89 on low
All four are disabled by default to keep the chart minimal. Users can toggle them on from the Custom MAs group for envelope or cloud-style configurations.
Nadaraya–Watson Smoother (Swing Framework)
Gaussian-kernel Nadaraya–Watson regression applied to price (hl2) to build a smooth synthetic curve.
Two layers of functionality:
Swing labels (▲ / ▼) at inflection points in the smoothed curve.
Optional curve line that visually tracks the turning structure over the last ~500 bars.
Designed to surface early swing potential before standard MAs react.
Hull Moving Average (Trend Overlay)
Optional Hull MA (HMA) for faster trend visualization.
Color-coded by slope (buy/sell bias).
Default: off to prevent overloading the chart; can be enabled under Hull MA settings.
Momentum, Exhaustion & Pattern Engine
CCI-Based Bar Coloring
CCI applied to close with configurable thresholds.
Overbought / oversold CCI zones map directly into candle coloring to visually highlight short-term momentum extremes.
RSI Top / Bottom Exhaustion Finder
RSI logic applied separately to high-driven (tops) and low-driven (bottoms) sequences.
Plots:
Top arrows where high-side RSI stretches into high-risk territory.
Bottom arrows where low-side RSI indicates exhaustion on the downside.
Useful as confluence around the Nadaraya swing turns and EMA 200 regime.
Engulfing + MA Trend Engine (“Fat Bull / Fat Bear”)
Detects bullish and bearish engulfing patterns, then combines them with MA trend cross logic.
Only when both pattern and MA regime align does the engine flag:
Fat Bull (Engulf + MA aligned long)
Fat Bear (Engulf + MA aligned short)
Candles are marked via conditional barcolor to highlight strong, structured shifts in control.
Fat Finger Detection (Wick Spikes / Stop Runs)
Identifies abnormal wick extensions relative to the prior bar’s body range with configurable tolerance.
Supports detection of potential liquidity grabs, stop runs, or “excess” that may precede reversals or mean-reversion behavior.
Volume & Liquidity Intelligence
Bull Snort (Aggressive Buy Spikes)
Flags events where:
Volume is significantly above the 50-period average, and
Price closes in the upper portion of the bar and above prior close.
Plots a labeled marker below the bar to indicate aggressive upside initiative by buyers.
Pocket Pivots (Accumulation Flags)
Compares current volume vs prior 10 sessions with a filter on prior “up” days.
Highlights pocket pivot days where current green candle volume outclasses recent down-day volumes, suggesting stealth accumulation.
Delta Volume Core (Directional Volume by Price)
Internal volume-by-price style engine over a user-defined lookback.
Splits volume into up-close and down-close buckets across dynamic price bins.
Feeds into S&R and ICT zone logic to quantify where buying vs selling pressure built up.
Structural Context: S&R and ICT Zones
S&R Power Channel
Computes local high/low band over a configurable lookback window.
Renders:
Upper and lower S&R channel lines.
Shaded support / resistance zones using boxes.
Adds Buy Power / Sell Power metrics based on the ratio of up vs down bars inside the window, displayed directly in the zone overlays.
Drops ◈ markers where price interacts dynamically with the top or bottom band, highlighting reaction points.
ICT-Style Premium / Discount & Macro Zones
Two tiered structures:
Local Premium / Discount zones over a shorter SR window.
Macro Premium / Discount zones over a longer macro window.
Each zone:
Uses underlying directional volume to annotate accumulation vs distribution bias.
Provides Delta Volume Bias shading in the mid-band region, visually encoding whether local power flows are net-buying or net-selling.
Enables traders to quickly see whether current trade location is in a local/macro discount or premium context while still respecting volume profile.
Positioning Intelligence: PCD (Stocks)
Position Cost Distribution (PCD) – Stocks Only
Available for stock symbols on intraday up to daily timeframe (≤ 1D).
Uses:
TOTAL_SHARES_OUTSTANDING fundamentals,
Daily OHLCV snapshot, and
A bucketed distribution engine
to approximate cost basis distribution across price.
Outputs:
Horizontal “PCD bars” to the right of current price, density-scaled by estimated share concentration.
Color-coding by profitability relative to current price (profitable vs unprofitable positions).
Labels for:
Current price
Average cost
Profit ratio (share % below current price)
90% cost range
70% cost range
Range overlap as a measure of clustering / concentration.
Multi-Timeframe Trend: Two-Pole Gaussian Dashboard
Two-Pole Gaussian Filter (Line + Cloud)
Smooths a user-selected source (default: close) using a two-pole Gaussian filter with tunable alpha.
Plots:
A thin Gaussian trend line, and
A thick Gaussian “cloud” line with transparency, colored by slope vs past (offsetG).
Functions as a responsive trend backbone that is more sensitive than EMA 200 but less noisy than raw price.
Multi-Timeframe Gaussian Dashboard
Evaluates Gaussian trend direction across up to six timeframes (e.g., 1H / 2H / 4H / Daily / Weekly).
Renders a compact bottom-right table:
Header: symbol + overall bias arrow (up / down) based on average trend alignment.
Row of colored cells per timeframe (green for uptrend, magenta for downtrend) with human-readable TF labels (e.g., “60M”, “4H”, “1D”).
Gives an immediate read on whether intraday, swing, and higher-timeframe flows are aligned or fragmented.
Default Configuration & Usage Guidance
Default state after adding the script:
Enabled by default:
EMA 200 trend backbone
Nadaraya–Watson swing labels and curve
CCI bar coloring
RSI top/bottom arrows
Fat Bull / Fat Bear engine
Bull Snort & Pocket Pivots
S&R Power Channel
ICT Local + Macro zones
Two-pole Gaussian line + cloud + dashboard
PCD engine for stocks (auto-active where data is available)
Disabled by default (opt-in):
Custom MA suite (4x MAs, preset as EMA 8/8/89/89)
Hull MA overlay
How traders can use VB-MainLite in practice:
Use EMA 200 + Gaussian dashboard to define top-down directional bias and avoid trading directly against multi-TF trend.
Use Nadaraya swing labels, RSI exhaustion arrows, and CCI bar colors to time entries within that higher-timeframe bias.
Use Fat Bull / Fat Bear events as structured confirmation that both pattern and MA regime have flipped in the same direction.
Use Bull Snort, Pocket Pivots, and S&R / ICT zones to align execution with liquidity, volume, and location (premium vs discount).
On stocks, use PCD as a positioning map to understand trapped supply, support zones near crowded cost basis, and where profit-taking is likely.
MTF RSI Stacked + AI + Gradient MTF RSI Stacked + AI + Gradient
Quick-start guide & best-practice rules
What the indicator does
Multi-Time-Frame RSI in one pane
• 10 time-frames (1 m → 1 M) are stacked 100 points apart (0, 100, 200 … 900).
• Each RSI is plotted with a smooth red-yellow-green gradient:
– Red = RSI below 30 (oversold)
– Yellow = RSI near 50
– Green = RSI above 70 (overbought)
• Grey 30-70 bands are drawn for every TF so you can see extremities at a glance.
Built-in AI (KNN) signal
• On every close of the chosen AI-time-frame the script:
– Takes the last 14-period RSI + normalised ATR as “features”
– Compares them to the last N bars (default 1 000)
– Votes of the k = 5 closest neighbours → BUY / SELL / NEUTRAL
• Confidence % is shown in the badge (top-right).
• A thick vertical line (green/red) is printed once when the signal flips.
How to read it
• Gradient colour tells you instantly which TFs are overbought/obove sold.
• When all or most gradients are green → broad momentum up; look for shorts only on lower-TF pullbacks.
• When most are red → broad momentum down; favour longs only on lower-TF bounces.
• Use the AI signal as a confluence filter, not a stand-alone entry:
– If AI = BUY and 3+ higher-TF RSIs just crossed > 50 → consider long.
– If AI = SELL and 3+ higher-TF RSIs just crossed < 50 → consider short.
• Divergences: price makes a higher high but 1 h/4 h RSI (gradient) makes a lower high → possible reversal.
Settings you can tweak
AI timeframe – leave empty = same as chart, or pick a higher TF (e.g. “15” or “60”) to slow the signal down.
Training bars – 500-2 000 is the sweet spot; bigger = slower but more stable.
K neighbours – 3-7; lower = more signals, higher = smoother.
RSI length – 14 is standard; 9 gives earlier turns, 21 gives fewer false swings.
Practical trading workflow
Open the symbol on your execution TF (e.g. 5 m).
Set AI timeframe to 3-5× execution TF (e.g. 15 m or 30 m) so the signal survives market noise.
Wait for AI signal to align with gradient extremes on at least one higher TF.
Enter on the first gradient reversal inside the 30-70 band on the execution TF.
Place stop beyond the swing that caused the gradient flip; target next opposing 70/30 level on the same TF or trail with structure.
Colour cheat-sheet
Bright green → RSI ≥ 70 (overbought)
Bright red → RSI ≤ 30 (oversold)
Muted colours → RSI near 50 (neutral, momentum pause)
That’s it—one pane, ten time-frames, colour-coded extremes and an AI confluence layer.
Keep the chart clean, use price action for precise entries, and let the gradient tell you when the wind is at your back.
Séparateur H4 & DailyH4 & Daily Separator - TradingView Indicator
This Pine Script v6 indicator draws infinite vertical lines to mark H4 and Daily candle separations on your chart.
Features:
H4 Separations: Marks candles starting at 3am, 7am, 11am, 3pm, 7pm, and 11pm
Daily Separations: Marks candles starting at midnight (00:00)
Fully Customizable:
Toggle H4 and/or Daily lines independently
Choose line color, thickness (1-4), and style (Solid, Dotted, Dashed)
Control the number of visible vertical lines (1-500)
Use Case:
Perfect for traders who want to visualize higher timeframe separations while trading on lower timeframes. Helps identify H4 and Daily candle opens without switching charts.
Installation:
Simply copy the code into TradingView's Pine Editor and add it to your chart. All settings are adjustable in the indicator's settings panel.
Goal Setting Strategies Viprasol# 🎯 Goal Setting Strategies Viprasol
A powerful goal tracking tool designed for disciplined traders who want to monitor their trading objectives, milestones, and progress directly on their charts.
## ✨ KEY FEATURES
### 📊 Flexible Goal Management
- Track anywhere from 1 to 20 trading goals simultaneously
- Adjustable goal count via simple input slider
- Each goal has its own unique emoji identifier
- Real-time progress counter
### ✅ Visual Tracking System
- Interactive checkbox system for goal completion
- Clear visual indicators (✅ completed, ⬜️ pending)
- Customizable goal names and descriptions
- Dynamic progress display
### 🎨 Full Customization
- **4 Position Options**: Top Left, Top Right, Bottom Left, Bottom Right
- **5 Font Sizes**: Tiny, Small, Normal, Large, Huge (optimized for all screen sizes)
- **Custom Colors**: Header, labels, background, achievement text
- **Premium Styling**: Modern cyber-themed design with professional appearance
### 💡 Perfect For:
- Daily/Weekly trading goal tracking
- Risk management milestones
- Profit target monitoring
- Trading plan compliance
- Personal development objectives
- Learning milestones
## 🔧 HOW TO USE
1. **Set Your Primary Goal**: Enter your main objective in "Primary Goal" field
2. **Choose Goal Count**: Select how many goals you want (1-20)
3. **Name Your Goals**: Customize each goal name in the "Goal Definitions" section
4. **Track Progress**: Check off goals as you complete them
5. **Customize Display**: Adjust colors, sizes, and position to match your chart setup
## 📐 INPUT GROUPS
### 🎯 Viprasol Goal Configuration
- Primary Goal Name
- Number of Goals (1-20)
### 📋 Goal Definitions
- All 20 goals with individual names and checkboxes
- Only enabled goals (based on count) will display
### 🌈 Premium Styling
- Goal Header Color
- Label Color
- Panel Background Color
- Achievement Color
- Header Font Size
- Milestone Font Size (Tiny/Small optimized for space)
### 📍 Elite Display
- Dashboard Position selector
## 💎 UNIQUE FEATURES
- **Space Efficient**: Tiny and Small font options for compact displays
- **Scalable**: Grow from 1 goal to 20 as your needs evolve
- **Non-Intrusive**: Overlay indicator that doesn't interfere with price action
- **Professional Design**: Clean, modern interface with cyber aesthetic
## 🎓 USE CASES
**Day Traders**: Track daily profit targets, trade count limits, max loss thresholds
**Swing Traders**: Monitor weekly/monthly goals, position management rules
**New Traders**: Learning milestones, strategy development checkpoints
**Experienced Traders**: Advanced risk management, portfolio objectives
## ⚙️ TECHNICAL DETAILS
- Version: Pine Script v5
- Type: Overlay Indicator
- Max Labels: 500
- Table-based display system
- No repainting
- Lightweight performance
## 🚀 GETTING STARTED
1. Add indicator to your chart
2. Set "Number of Goals" to your desired count (start small, scale up)
3. Customize goal names
4. Check boxes as you achieve goals
5. Watch your progress build!
## 📊 DISPLAY OPTIMIZATION
- Use "Tiny" or "Small" for maximum goals on small screens
- Use "Normal" or "Large" for standard monitors
- Use "Huge" for presentation or large displays
- Adjust position to avoid chart overlap
## 🎯 TRADING DISCIPLINE
This tool helps reinforce:
- Goal-oriented trading mindset
- Progress tracking accountability
- Milestone celebration
- Structured approach to trading development
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**© viprasol**
*Designed for traders who take their goals seriously.*
S&P 500 Scalper Pro [Trend + MACD] 5 minfor scalping 5 min S&P on 5 min chart put SL on 20 min ma and take 2:1 risk
