Bear & Bull Builder // visual strategy builderAre you a trend follower?
Trend following systems have been a cornerstone of trading since the first candlestick charts were invented in 18th-century Japan by Munehisa Homma (or Honma), a legendary rice merchant who used them to analyze market sentiment and predict price movements. Since then, legendary traders like Richard Dennis and Dr. David Paul have used technical analysis—the study of turning points and trends of candlestick charts—to develop an edge and strategy for trading equity, commodity, and forex markets.
How to Utilize the Bear & Bull Builder
This script is a way to pick and choose technical methods like SMAs and EMAs to define trend exits and entries. Additionally, you can specify an ATR (Average True Range) calculated stop loss based on your individual strategy and trading plan. Within the settings panel, you can set up this script to display only Long Position values, zones, and levels—or configure it for shorts, or both.
What Makes This Original
Unlike most trend-following indicators that lock you into a single approach, this script lets you combine different indicator types (RSI, WaveTrend, CCI, EMA, SMA) across three separate trend timeframes. The originality comes from the flexibility: you can test whether momentum-based trends (like RSI) work better than moving averages for your timeframe, or experiment with mixing them together. The script also bridges the gap between manual trading and automation by providing visual position values and fill zones that show exactly where signals generate versus where orders execute—critical information most scripts ignore.
Getting Started
For this quick and easy setup example, I built a strategy that is long-only, displays only long positional data and values, and uses a 21 & 55 period exponential moving average for the short and medium-term trend in addition to an 89 period simple moving average for my longer-term outlook. I have set my ATR-based multiplier to 0.75, and have left the fill zone display turned on to help visualize when to set up the built-in alerts for automating my strategy. I have made this the default settings of the script.
Positional Values
GREEN NUMBERS → Entry signal price
YELLOW NUMBERS → Stop loss price
BLUE NUMBERS → Exit signal price
IMPORTANT
I cannot describe how useful it is to use TradingView's built-in Long and Short position tools! The whole reason for this script is that it is as manually friendly as it is automated—especially for backtesting. You can use the long position tool to measure exact profits and losses on individual trades for the strategies you build. This can really help you see clearly if you have built a system with positive expectancy.
Tables
1. Settings Display Table
Displays the trend types that are configurable in the settings panel. Shows if positional values for longs and shorts are currently displayed.
2. Back testing Table
Displays the total amount of long and short entry signals since the first bar of the chart. Additionally, it displays the average amount of bars per trade (time in trade).
Alerts & Automation
There are 4 built-in alerts for automating your strategy to an external server:
1.Long Entries
2.Long Exits
3.Short Entries
4.Short Exits
Since this script uses confirmed bar states for alert generation (to avoid repainting), all alerts and displayed position values (the green, yellow, and blue numbers) will be sent on the closing price. Each alert has a placeholder preset for further customization.
Technical Details
How the trend detection works:
Bullish state triggers when close > all three selected trends
Bearish state triggers when close < all three selected trends
Uses barstate.isconfirmed to prevent repainting
Stop loss calculation:
Long stops: highest_trend - (ATR × multiplier)
Short stops: lowest_trend + (ATR × multiplier)
ATR period is fixed at 20 bars, multiplier is user-adjustable
Entry placement logic:
Long entries execute at the highest value among the three selected trends
Short entries execute at the lowest value among the three selected trends
This ensures entries occur near the support/resistance created by the trend lines
Why calculate all indicators upfront:
The script calculates all five indicator types (EMA, SMA, RSI, CCI, WaveTrend) for all three trend lengths on every bar, then selectively uses the ones you choose in settings. This prevents Pine Script consistency warnings while maintaining flexibility.
Cari skrip untuk "Table"
ES Multi-Timeframe SMC Entry SystemOverviewThis is a comprehensive Smart Money Concepts (SMC) trading strategy for ES1! (E-mini S&P 500) futures that provides simultaneous buy and sell signals across three timeframes: Daily, Weekly, and Monthly. It incorporates your complete entry checklists, confluence scoring system, and automated risk management.Core Features1. Multi-Timeframe Signal Generation
Daily Signals (D) - For intraday/swing trades (1-3 day holds)
Weekly Signals (W) - For swing trades (3-10 day holds)
Monthly Signals (M) - For position trades (weeks to months)
All three timeframes can trigger simultaneously (pyramiding enabled)
2. Smart Money Concepts ImplementationOrder Blocks (OB)
Automatically detects bullish and bearish order blocks
Bullish OB = Down candle before strong impulse up
Bearish OB = Up candle before strong impulse down
Validates freshness (< 10 bars = higher quality)
Visual boxes displayed on chart
Fair Value Gaps (FVG)
Identifies 3-candle imbalance patterns
Bullish FVG = Gap between high and current low
Bearish FVG = Gap between low and current high
Tracks unfilled gaps as targets/entry zones
Auto-removes when filled
Premium/Discount Zones
Calculates 50-period swing range
Premium = Upper 50% (short from here)
Discount = Lower 50% (long from here)
Deep zones (<30% or >70%) for higher quality setups
Visual shading: Red = Premium, Green = Discount
Liquidity Sweeps
Sell-Side Sweep (SSL) = False break below lows → reversal up
Buy-Side Sweep (BSL) = False break above highs → reversal down
Marked with yellow labels on chart
Valid for 10 bars after occurrence
Break of Structure (BOS)
Identifies when price breaks recent swing high/low
Confirms trend continuation
Marked with small circles on chart
3. Confluence Scoring SystemEach timeframe has a 10-point scoring system based on your checklist requirements:Daily Score (10 points max)
HTF Trend Alignment (2 pts) - 4H and Daily EMAs aligned
SMC Structure (2 pts) - OB in correct zone with HTF bias
Liquidity Sweep (1 pt) - Recent SSL/BSL occurred
Volume Confirmation (1 pt) - Volume > 1.2x 20-period average
Optimal Time (1 pt) - 9:30-12 PM or 2-4 PM ET (avoids lunch)
Risk-Reward >2:1 (1 pt) - Built into exit strategy
Clean Price Action (1 pt) - BOS occurred
FVG Present (1 pt) - Near unfilled fair value gap
Minimum Required: 6/10 (adjustable)Weekly Score (10 points max)
Weekly/Monthly Alignment (2 pts) - W and M EMAs aligned
Daily/Weekly Alignment (2 pts) - D and W trends match
Premium/Discount Correct (2 pts) - Deep zone + trend alignment
Major Liquidity Event (1 pt) - SSL/BSL sweep
Order Block Present (1 pt) - Valid OB detected
Risk-Reward >3:1 (1 pt) - Built into exit
Fresh Order Block (1 pt) - OB < 10 bars old
Minimum Required: 7/10 (adjustable)Monthly Score (10 points max)
Monthly/Weekly Alignment (2 pts) - M and W trends match
Weekly OB in Monthly Zone (2 pts) - OB in deep discount/premium
Major Liquidity Sweep (2 pts) - Significant SSL/BSL
Strong Trend Alignment (2 pts) - D, W, M all aligned
Risk-Reward >4:1 (1 pt) - Built into exit
Extreme Zone (1 pt) - Price <20% or >80% of range
Minimum Required: 8/10 (adjustable)4. Entry ConditionsDaily Long Entry
✅ Daily score ≥ 6/10
✅ 4H trend bullish (price > EMAs)
✅ Price in discount zone
✅ Bullish OB OR SSL sweep OR near bullish FVG
✅ NOT during avoid times (lunch/first 5 min)Daily Short Entry
✅ Daily score ≥ 6/10
✅ 4H trend bearish
✅ Price in premium zone
✅ Bearish OB OR BSL sweep OR near bearish FVG
✅ NOT during avoid timesWeekly Long Entry
✅ Weekly score ≥ 7/10
✅ Weekly trend bullish
✅ Daily trend bullish
✅ Price in discount
✅ Bullish OB OR SSL sweepWeekly Short Entry
✅ Weekly score ≥ 7/10
✅ Weekly trend bearish
✅ Daily trend bearish
✅ Price in premium
✅ Bearish OB OR BSL sweepMonthly Long Entry
✅ Monthly score ≥ 8/10
✅ Monthly trend bullish
✅ Weekly trend bullish
✅ Price in DEEP discount (<30%)
✅ Bullish order block presentMonthly Short Entry
✅ Monthly score ≥ 8/10
✅ Monthly trend bearish
✅ Weekly trend bearish
✅ Price in DEEP premium (>70%)
✅ Bearish order block present5. Automated Risk ManagementPosition Sizing (Per Entry)
Daily: 1.0% account risk per trade
Weekly: 0.75% account risk per trade
Monthly: 0.5% account risk per trade
Formula:
Contracts = (Account Equity × Risk%) ÷ (Stop Points × $50)
Minimum = 1 contractStop Losses
Daily: 12 points ($600 per contract)
Weekly: 40 points ($2,000 per contract)
Monthly: 100 points ($5,000 per contract)
Profit Targets (Risk:Reward)
Daily: 2:1 = 24 points ($1,200 profit)
Weekly: 3:1 = 120 points ($6,000 profit)
Monthly: 4:1 = 400 points ($20,000 profit)
Example with $50,000 AccountDaily Trade:
Risk = $500 (1% of $50k)
Stop = 12 points × $50 = $600
Contracts = $500 ÷ $600 = 0.83 → 1 contract
Target = 24 points = $1,200 profit
Weekly Trade:
Risk = $375 (0.75% of $50k)
Stop = 40 points × $50 = $2,000
Contracts = $375 ÷ $2,000 = 0.18 → 1 contract
Target = 120 points = $6,000 profit
Monthly Trade:
Risk = $250 (0.5% of $50k)
Stop = 100 points × $50 = $5,000
Contracts = $250 ÷ $5,000 = 0.05 → 1 contract
Target = 400 points = $20,000 profit
6. Visual Elements on ChartKey Levels
Previous Daily High/Low - Red/Green solid lines
Previous Weekly High/Low - Red/Green circles
Previous Monthly High/Low - Red/Green crosses
Equilibrium Line - White dotted line (50% of range)
Zones
Premium Zone - Light red shading (upper 50%)
Discount Zone - Light green shading (lower 50%)
SMC Markings
Bullish Order Blocks - Green boxes with "Bull OB" label
Bearish Order Blocks - Red boxes with "Bear OB" label
Bullish FVGs - Green boxes with "FVG↑"
Bearish FVGs - Red boxes with "FVG↓"
Liquidity Sweeps - Yellow "SSL" (down) or "BSL" (up) labels
Break of Structure - Small lime/red circles
Entry Signals
Daily Long - Small lime triangle ▲ with "D" below price
Daily Short - Small red triangle ▼ with "D" above price
Weekly Long - Medium green triangle ▲ with "W" below price
Weekly Short - Medium maroon triangle ▼ with "W" above price
Monthly Long - Large aqua triangle ▲ with "M" below price
Monthly Short - Large fuchsia triangle ▼ with "M" above price
7. Information TablesConfluence Score Table (Top Right)
┌──────────┬────────┬────────┬────────┐
│ TF │ SCORE │ STATUS │ SIGNAL │
├──────────┼────────┼────────┼────────┤
│ 📊 DAILY │ 7/10 │ ✓ PASS │ 🔼 │
│ 📈 WEEKLY│ 6/10 │ ✗ WAIT │ ━ │
│ 🌙 MONTH │ 9/10 │ ✓ PASS │ 🔽 │
├──────────┴────────┴────────┴────────┤
│ P&L: $2,450 │
└─────────────────────────────────────┘
Green scores = Pass (meets minimum threshold)
Orange/Red scores = Fail (wait for better setup)
🔼 = Long signal active
🔽 = Short signal active
━ = No signal
Entry Checklist Table (Bottom Right)
┌──────────────┬───┐
│ CHECKLIST │ ✓ │
├──────────────┼───┤
│ ━ DAILY ━ │ │
│ HTF Trend │ ✓ │
│ Zone │ ✓ │
│ OB │ ✗ │
│ Liq Sweep │ ✓ │
│ Volume │ ✓ │
│ ━ WEEKLY ━ │ │
│ W/M Align │ ✓ │
│ Deep Zone │ ✗ │
│ ━ MONTHLY ━ │ │
│ M/W/D Align │ ✓ │
│ Zone: Discount│ │
└──────────────┴───┘
Green ✓ = Condition met
Red ✗ = Condition not met
Real-time updates as market conditions change
8. Alert SystemIndividual Alerts:
"Daily Long" - Triggers when daily long setup appears
"Daily Short" - Triggers when daily short setup appears
"Weekly Long" - Triggers when weekly long setup appears
"Weekly Short" - Triggers when weekly short setup appears
"Monthly Long" - Triggers when monthly long setup appears
"Monthly Short" - Triggers when monthly short setup appears
Combined Alerts:
"Any Long Signal" - Catches any bullish opportunity (D/W/M)
"Any Short Signal" - Catches any bearish opportunity (D/W/M)
Alert Messages Include:
🔼/🔽 Direction indicator
Timeframe (DAILY/WEEKLY/MONTHLY)
Current confluence score
NSE Swing Breadth NSE Swing Breadth – Market Health Dashboard (0–200, % from Neutral)
Overview
NSE Swing Breadth – Market Health Dashboard is a market-wide health and regime indicator designed to track internal strength and participation across Large-, Mid-, and Small-cap indices in the Indian equity market.
Instead of focusing on price alone, this tool measures how strongly each segment is behaving relative to its own swing trend, normalizes those movements, and combines them into a single Market Health score. The result is a clean, objective dashboard that helps traders identify Risk-On, Caution, and Risk-Off regimes.
This indicator is best used for position sizing, exposure control, and timing aggressiveness, rather than individual stock entries.
Data Used
The indicator internally tracks three broad NSE indices:
Large Caps → NIFTY100EQUALWEIGHT
Mid Caps → NIFTYMIDCAP150
Small Caps → NIFTYSMLCAP250
Using equal-weighted and broad indices ensures the signal reflects true market participation, not just index heavyweights.
Core Logic
1. Swing Strength Model
For each index, the script calculates normalized swing strength:
Price is compared to its EMA swing baseline
The deviation from the EMA is normalized using the EMA of absolute deviations
This creates a volatility-adjusted strength value, allowing fair comparison across market regimes
This answers the question:
Is this segment pushing meaningfully above or below its recent trend?
2. Strength Converted to % from Neutral (Baseline = 100)
Each segment’s strength is converted into percentage-style points around a neutral baseline of 100:
100 = Neutral
+15 = +15% strength above neutral
–20 = –20% weakness below neutral
These values are plotted as three smooth lines:
Blue → Large Caps
Orange → Mid Caps
Purple → Small Caps
This makes relative leadership and divergence immediately visible.
3. Market Health Score (0–100)
The indicator combines all three segments into a single Market Health score:
Large Caps → 40% weight
Mid Caps → 35% weight
Small Caps → 25% weight
Extreme values are clamped to avoid distortion, and the final score is normalized to a 0–100 scale:
70–100 → Strong, broad participation
40–69 → Mixed / unstable participation
0–39 → Weak, risk-off conditions
Visual Components
📊 Market Health Histogram
A vertical histogram displays Market Health (0–100) with enhanced visibility:
🟢 Green (≥ 70) → Strong Risk-On regime
🟠 Orange (40–69) → Caution / Transition
🔴 Red (< 40) → Risk-Off regime
The histogram is visually compact and designed to reflect true market health, not exaggerated spikes.
📈 Strength Lines (Baseline = 100)
Three strength lines show % deviation from neutral:
Above 100 → Positive internal strength
Below 100 → Internal weakness
These lines help identify:
Leadership (which segment is driving the market)
Early deterioration (small/mid caps weakening first)
Broad confirmation (all segments rising together)
Dashboard Tables
📌 Market Regime Table (Bottom-Left)
Displays the current market regime:
🟢 RISK ON
🟡 CAUTION
🔴 RISK OFF
Along with the exact Market Health score (0–100).
📌 Strength Table (Top-Right)
Shows Large / Mid / Small cap strength as % from neutral, for example:
+18% → 18% above neutral
–12% → 12% below neutral
This avoids misleading interpretations and keeps values intuitive and actionable.
How to Use This Indicator
Risk-On (Green)
Favor full position sizes, trend-following strategies, and broader participation trades.
Caution (Orange)
Reduce leverage, tighten stops, and be selective. Expect choppiness.
Risk-Off (Red)
Prioritize capital protection, reduce exposure, and avoid aggressive longs.
This indicator is not an entry signal — it is a market environment filter.
⚠️ Important Style Setting (Required)
For correct visualization:
Settings → Style → Uncheck “Labels on price scale”
This prevents the indicator’s internal 0–200 model scale from interfering with the chart’s price scale and keeps the pane clean and readable.
Summary
NSE Swing Breadth – Market Health Dashboard provides a clear, objective view of market internals, helping traders align their risk with the true underlying condition of the market — not just price movement.
It is especially effective for:
Market regime identification
Exposure management
Avoiding false breakouts in weak breadth environments
Unmitigated MTF High Low - Cave Diving Plot
IntroductionThe Unmitigated MTF High Low -
Cave Diving Plot is a multi-timeframe (MTF) indicator designed for NQ and ES futures traders who want to identify high-probability entry and exit zones based on unmitigated price levels. The "Cave Diving" visualization helps you navigate between support (floor) and resistance (ceiling) zones, while the integrated Strat analysis provides directional context.
Who Is This For?
Futures traders (NQ, ES) trading during ETH and RTH sessions
Scalpers and day traders looking for precise entry/exit levels
Traders using The Strat methodology for directional analysis
Anyone seeking confluence between price action and key levels
Core Concepts
1. Unmitigated Level:
An unmitigated level is a price high or low that has been created but not yet tested (touched) by price. These levels act as magnets - price often returns to test them.Key Properties:
Resistance (Highs): Price has created a high but hasn't revisited it
Support (Lows): Price has created a low but hasn't revisited it
Mitigation: When price touches a level, it becomes "mitigated" and loses strength
2. The Cave Diving MetaphorThink of trading as cave diving between two zones:
┌─────────────────────────────────┐
│ CEILING (Upper Band) │ ← 1st & 2nd Unmitigated Highs
│ 🟥 Resistance Zone │
├─────────────────────────────────┤
│ │
│ THE TUNNEL │ ← Price navigates here
│ (Trading Channel) │
│ │
├─────────────────────────────────┤
│ 🟢 Support Zone │
│ FLOOR (Lower Band) │ ← 1st & 2nd Unmitigated Lows
└─────────────────────────────────┘
Trading Concept:
Ceiling: Formed by the 1st and 2nd most recent unmitigated highs
Floor: Formed by the 1st and 2nd most recent unmitigated lows
Tunnel: The space between ceiling and floor where price operates
Cave Diving: Navigating between these zones for entries and exits
3. Session-Based Age TrackingLevels are tracked by session age:
Session: 6:00 PM to 5:00 PM NY time (23-hour window)
Age 0: Created in the current session (today)
Age 1: Created 1 session ago (yesterday)
Age 2+: Older levels (more significant)
Why Age Matters:
Older unmitigated levels are typically stronger magnets
Fresh levels (Age 0) may be weaker and easier to break
Age 2+ levels often provide high-probability reversal zones
Indicator Components
Visual Elements
1. Colored Bands (Cave Zones)Upper Band (Pink/Maroon - 95% transparency)
Space between 1st and 2nd unmitigated highs
Acts as resistance zone
Price often hesitates or reverses here
Lower Band (Teal - 95% transparency)
Space between 1st and 2nd unmitigated lows
Acts as support zone
Price often finds buyers here
2. Information Table Located in your chosen corner (default: Bottom Right), the table displays:
5 most recent unmitigated highs (top section)
Tunnel row (middle separator)
5 most recent unmitigated lows (bottom section)
Reading the TableTable Structure
┌────────┬──────────┬────────┬───────┐
│ Level │ $ │ Points │ Age │
├────────┼──────────┼────────┼───────┤
│ ↑↑↑↑↑ │ 21,450.25│ +45.30 │ 3 │ ← 5th High (oldest)
│ ↑↑↑↑ │ 21,425.50│ +32.75 │ 2 │ ← 4th High
│ ↑↑↑ │ 21,410.00│ +25.00 │ 1 │ ← 3rd High
│ ↑↑ │ 21,400.75│ +18.50 │ 1 │ ← 2nd High
│ ↑ │ 21,395.25│ +12.00 │ 0 │ ← 1st High (newest)
├────────┼──────────┼────────┼───────┤
│ Tunnel │ 🟢 │ Δ 85.50│ 2U │ ← Current State
├────────┼──────────┼────────┼───────┤
│ ↓ │ 21,310.00│ -15.25 │ 0 │ ← 1st Low (newest)
│ ↓↓ │ 21,295.50│ -22.75 │ 1 │ ← 2nd Low
│ ↓↓↓ │ 21,280.25│ -30.00 │ 1 │ ← 3rd Low
│ ↓↓↓↓ │ 21,265.75│ -38.50 │ 2 │ ← 4th Low
│ ↓↓↓↓↓ │ 21,250.00│ -45.00 │ 3 │ ← 5th Low (oldest)
└────────┴──────────┴────────┴───────┘Column
Breakdown
Column 1: Level (Arrows)
Green arrows (↑): Resistance levels above current price
Red arrows (↓): Support levels below current price
Arrow count: Indicates recency (1 arrow = newest, 5 arrows = oldest)
Why This Matters:
More arrows = older level = stronger magnet for price
Column 2: $ (Price)
Exact price of the unmitigated level
Use this for limit orders and stop placement
Column 3: Points (Distance)
Positive (+) for highs: Points above current price
Negative (-) for lows: Points below current price
Helps gauge proximity to key levels
Trading Application:
If you're +2.50 points from resistance, a reversal may be imminent
If you're -45.00 points from support, you're far from the floor
Column 4: Age (Sessions)
Number of full 6pm-5pm sessions the level has survived
Age 0: Created today (current session)
Age 1+: Created in previous sessions
Significance Ladder:
Age 0: Weak, may break easily
Age 1-2: Medium strength
Age 3+: Strong, high-probability reaction zone
Tunnel Row (Critical Information)│ Tunnel │ 🟢 │ Δ 85.50│ 2U │
└─┬─┘ └─┬─┘ └──┬──┘ └─┬─┘
│ │ │ │
Label Direction Range Strat
1. Tunnel Label: Identifies the separator row
2. Direction Indicator (🟢/🔴)
🟢 Green Circle: Current 15m bar closed bullish (above previous close)
🔴 Red Circle: Current 15m bar closed bearish (below previous close)
3. Δ (Delta/Range)
Distance in points between 1st High and 1st Low
Shows the tunnel width (trading range)
Example: Δ 85.50 = 85.50 points between ceiling and floor
Trading Use:
Wide tunnel (>100 points): More room to trade, consider range strategies
Narrow tunnel (<50 points): Tight range, expect breakout
4. Strat Pattern
1: Inside bar (consolidation)
2U: 2 Up (bullish directional bar)
2D: 2 Down (bearish directional bar)
3: Outside bar (expansion/volatility)
Color Coding:
Green: 2U (bullish)
Red: 2D (bearish)
Yellow: 3 (expansion)
Gray: 1 (inside/neutral)
Annual Lump Sum: Yearly & CompoundedAnnual Lump Sum Investment Analyzer (Yearly vs. Compounded)
Overview
This Pine Script indicator simulates a disciplined "Lump Sum" investing strategy. It calculates the performance of buying a fixed dollar amount (e.g., $10,000) on the very first trading day of every year and holding it indefinitely.
Unlike standard backtesters that only show a total percentage, this tool breaks down performance by "Vintage" (the year of purchase), allowing you to see which specific years contributed most to your wealth.
Key Features
Automated Execution: Automatically detects the first trading bar of every new year to simulate a buy.
Dual-Yield Analysis: The table provides two distinct ways to view returns:
Yearly %: How the market performed specifically during that calendar year (Jan 1 to Dec 31).
Compounded %: The total return of that specific year's investment from the moment it was bought until today.
Live Updates: For the current year, the "End Price" and "Yields" update in real-time with market movements.
Portfolio Summary: Displays your Total Invested Capital vs. Total Current Value at the top of the table.
Table Column Breakdown
The dashboard in the bottom-right corner displays the following:
Year: The vintage year of the investment.
Buy Price: The price of the asset on the first trading day of that year.
End Price: The price on the last trading day of that year (or the current price if the year is still active).
Yearly %: The isolated performance of that specific calendar year. (Green = The market ended the year higher than it started).
Compounded %: The "Diamond Hands" return. This shows how much that specific $10,000 tranche is up (or down) right now relative to the current price.
How to Use
Add the script to your chart.
Crucial: Set your chart timeframe to Daily (D). This ensures the script correctly identifies the first trading day of the year.
Open the Settings (Inputs) to adjust:
Annual Investment Amount: Default is $10,000.
Table Size: Adjust text size (Tiny, Small, Normal, Large).
Max Rows: Limit how many historical years are shown to keep the chart clean.
Use Case
This tool is perfect for investors who want to visualize the power of long-term holding. It allows you to see that even if a specific year had a bad "Yearly Yield" (e.g., buying in 2008), the "Compounded Yield" might still be massive today due to time in the market.
Altcoin Relative Macro StrengthAltcoin Relative Macro Strength
Overview
The Altcoin Relative Macro Strength indicator measures the altcoin market's price performance relative to global macroeconomic conditions. By comparing TOTAL3ES (total altcoin market capitalization excluding Bitcoin, Ethereum and stable coins) against a composite macro trend, the indicator identifies periods of relative overvaluation and undervaluation.
Methodology
Global Macro Trend Calculation:
The macro trend synthesizes three primary components:
- ISM PMI – A proxy for the business cycle phase
- Global Liquidity – An aggregate measure of major central bank balance sheets and broad money supply
- IWM (Russell 2000) – Small-cap equity exposure, reflecting risk-on/risk-off market sentiment
Global Liquidity is calculated as:
Fed Balance Sheet - Reverse Repo - Treasury General Account + U.S. M2 + China M2
The final Global Macro Trend is:
ISM PMI × Global Liquidity × IWM
Theoretical Framework:
The global macro trend integrates liquidity expansion/contraction with business cycle dynamics and small-cap equity performance. The inclusion of IWM reflects altcoins' tendency to behave as high-beta risk assets, exhibiting sensitivity similar to small-cap equities. This composite exhibits strong directional correlation with altcoin market movements, capturing the risk-on/risk-off dynamics that drive altcoin performance.
Interpretation
Primary Signal:
The histogram displays the rolling percentage change of TOTAL3ES relative to the global macro trend (default: 21-period average). Positive divergence indicates altcoins are outperforming macro conditions; negative divergence suggests underperformance relative to the underlying economic and risk environment.
Data Tables:
Alts/Macro Change – Percentage deviation of the altcoin market's average value from the Global Macro Trend's average over the specified period
Macro Trend – Directional assessment of the macro trend based on slope and trend agreement:
🔵 BULLISH ▲ – Positive slope with upward trend
⚪ NEUTRAL → – Slope and trend direction disagree
🟣 BEARISH ▼ – Negative slope with downward trend
Macro Slope – Percentage rate of change in the global macro trend
Altcoin Valuation – Relative valuation category based on TOTAL3/Macro deviation:
🟢 Extreme Discount / Deep Discount / Discount
🟡 Fair Value
🔴 Premium / Large Premium / Extreme Premium
TOTAL3ES Mcap – Current total altcoin market capitalization (in billions)
Visual Components:
📊 Histogram: Alts/Macro Change
🟢 Green = Positive deviation (altcoins outperforming)
🔴 Red = Negative deviation (altcoins underperforming)
📈 Macro Slope Line
Color-coded to match trend assessment
Scaled for visibility (adjustable in settings)
Application
This indicator is designed to identify mean reversion opportunities by highlighting periods when the altcoin market materially diverges from fundamental macro and risk conditions. Extreme positive values may indicate overvaluation; extreme negative values may signal undervaluation relative to the prevailing economic and risk appetite backdrop.
Strategy Considerations:
- Identify extremes: Look for periods when the histogram reaches elevated positive or negative levels
- Assess valuation: Use the Altcoin Valuation reading to gauge relative over/undervaluation
Confirm with risk sentiment: Check whether macro conditions and risk appetite support or contradict current price levels
- Mean reversion: Consider that significant deviations from trend historically tend to revert
Note: This indicator identifies relative valuation based on macro conditions and risk sentiment—it does not predict price direction or timing.
Settings
Lookback Period – 21 bars (default) – Number of bars for calculating rolling averages
Macro Slope Scale – 3.0 (default) – Multiplier for macro slope line visibility
D+P All-in-OneD+P=DARVAS+PIVOT
In this script i tried make small combo of multiple metrics.
Along with Darvas+Pivot we have EMA10,20&RSI d,w,m table. i fixed this table to middle right so that its easy to use while using phone.
There is floater table having Day Low& Previous Day Low-% differnce from current price
We have RS rating of O'Neil
Small table having MarketCap,Industry and sector.
Ichimoku MultiTF WillyArt v1.0.0What this indicator does
Ichimoku WillyArt turns the Ichimoku lines into angle-based momentum across multiple timeframes (W, D, 4H, 1H, 30m, 5m).
For each TF it computes the slope (angle in degrees) of:
Tenkan-sen
Kijun-sen
Senkou Span A
Senkou Span B
Angles are normalized so they’re comparable across assets and scales. You get a table with the angle per line and a quick emoji direction (↑, →, ↓), optional plots of the chosen line, and ready-to-use alerts.
Why angle?
Slope-as-degrees is an intuitive proxy for momentum/impulse:
Positive angle → line rising (bullish impulse).
Negative angle → line falling (bearish impulse).
Near zero → flat/indecisive.
Two normalization modes
ATR (default): slope / ATR. Robust across instruments; less sensitive to price level.
%Price: slope / price. More sensitive; can highlight subtle turns on low-volatility symbols.
Inputs you’ll actually care about
Timeframes: W, D, 4H, 1H, 30m, 5m (all fetched MTF, independent of chart TF).
Ichimoku lengths: Tenkan (9), Kijun (26), Span B (52) — standard defaults.
Bars for slope (ΔN): How many bars back the slope is measured. Higher = smoother, slower.
Threshold (°) for “strong”: Angle magnitude that qualifies as strong ↑/↓.
What you’ll see
Matrix/Table (top-right): For each TF, the angle (°) of Tenkan, Kijun, Span A, Span B + an emoji:
↑ above threshold, ↓ below −threshold, → in between.
Optional plots: Toggle “Plot angles” to visualize the chosen series’ angle across TFs.
Alerts included (ready to pick in “Create Alert”)
Sustained state: e.g., “Kijun 4H: strong ↑ angle” triggers while angle > threshold.
Threshold cross (one-shot): e.g., “Kijun 1H: upward threshold cross” fires on crossing.
Consensus (multi-TF): “Kijun consensus ↑ (D/4H/1H/30m/5m)” when all selected TFs align up (and the symmetric down case).
Messages are constant strings (TradingView requirement), so they compile cleanly. If you want dynamic text (current angle, threshold value, etc.), enable your own alert() calls—this script structure supports adding them.
How to use it (workflow)
Add to chart. No need to switch chart TF; the script pulls W/D/4H/1H/30m/5m internally.
Pick normalization. Start with ATR. Switch to %Price if you want more sensitivity.
Set ΔN & threshold.
Intraday momentum: try ΔN = 3–5 and threshold ≈ 4–8°.
Swing/position: ΔN = 5–9 and threshold ≈ 3–6° (with ATR).
Scan the table. Look for alignment (multiple TFs with ↑ or ↓ on Kijun/Spans).
Kijun + Span A up together → trending push.
Span B up/down → cloud baseline tilting (trend quality).
Turn on alerts that match your style: reactive cross for entries, sustained for trend follow, consensus to filter noise.
Reading tips
Kijun angle: great “trend backbone.” Strong ↑ on several TFs = higher-probability pullback buys.
Span A vs. Span B:
Span A reacts faster (momentum).
Span B is slower (structure).
When both tilt the same way, the cloud is genuinely rotating.
Mixed signals? Use higher TFs (W/D/4H) as bias, lower TFs (1H/30m/5m) for timing.
Good to know (limits & best practices)
Angles measure rate of change, not overbought/oversold. Combine with price structure and risk rules.
Extremely low volatility or illiquid symbols can produce tiny angles—%Price mode may help.
ΔN and thresholds are contextual: adapt per market (crypto vs FX vs equities).
Want me to bundle a “pro template” of alert presets (intraday / swing) and a heatmap color scale for the table? Happy to ship v2. 🚀
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
SFC Bollinger Band and Bandit概述 (Overview)
SFC 布林通道與海盜策略 (SFC Bollinger Band and Bandit Strategy) 是一個基於 Pine Script™ v6 的技術分析指標，結合布林通道 (Bollinger Bands)、移動平均線 (Moving Averages) 以及布林海盜 (Bollinger Bandit) 交易策略，旨在為交易者提供多時間框架的趨勢分析與進出場訊號。該腳本支援風險管理功能，並提供視覺化圖表與交易訊號提示，適用於多種金融市場。
This script, written in Pine Script™ v6, combines Bollinger Bands, Moving Averages, and the Bollinger Bandit strategy to provide traders with multi-timeframe trend analysis and entry/exit signals. It includes risk management features and visualizes data through charts and trading signals, suitable for various financial markets.
功能特點 (Key Features)
布林通道 (Bollinger Bands)
提供可調整的標準差參數 (σ1, σ2)，支援多層布林通道顯示。
進場訊號基於價格穿越布林通道上下軌，並結合連續K線確認機制。
Provides adjustable standard deviation parameters (σ1, σ2) for multi-layer Bollinger Bands display.
Entry signals are based on price crossing the upper/lower bands, combined with a consecutive bar confirmation mechanism.
移動平均線 (Moving Averages)
支援簡單移動平均線 (SMA) 或指數移動平均線 (EMA)，可自訂快、中、慢線週期。
Supports Simple Moving Average (SMA) or Exponential Moving Average (EMA) with customizable fast, medium, and slow line periods.
布林海盜策略 (Bollinger Bandit Strategy)
基於變動率 (ROC) 與布林通道動態止損，提供做多與做空訊號。
包含動態止損均線與平倉天數設定，增強交易靈活性。
Utilizes Rate of Change (ROC) and Bollinger Bands with dynamic stop-loss for long and short signals.
Includes dynamic stop-loss moving average and liquidation days for enhanced trading flexibility.
多時間框架分析 (Multi-Timeframe Analysis)
支援六個時間框架 (5分、15分、1小時、4小時、日線、週線) 的趨勢分析。
通過表格顯示各時間框架的連續上漲/下跌趨勢，輔助交易決策。
Supports trend analysis across six timeframes (5m, 15m, 1h, 4h, daily, weekly).
Displays consecutive up/down trends in a table to aid decision-making.
風險管理 (Risk Management)
提供基於 ATR 或布林通道的停利/停損設定。
自動計算交易手數，根據報價貨幣匯率調整風險敞口。
Offers take-profit/stop-loss settings based on ATR or Bollinger Bands.
Automatically calculates trading lots, adjusting risk exposure based on quote currency exchange rates.
視覺化與提示 (Visualization and Alerts)
繪製布林通道、移動平均線、海盜策略動態止損線及交易訊號。
提供多時間框架趨勢表格、交易手數標籤及浮水印。
支援交易訊號快訊，方便即時監控。
Plots Bollinger Bands, Moving Averages, Bandit strategy stop-loss lines, and trading signals.
Includes multi-timeframe trend tables, trading lot labels, and watermark.
Supports alert conditions for real-time trade monitoring.
使用說明 (Usage Instructions)
設置參數 (Parameter Setup)
布林通道 (Bollinger Bands): 可調整週期 (預設21)、標準差 (σ1=1, σ2=2) 及停利/停損依據 (ATR 或 BAND)。
移動平均線 (Moving Averages): 可選擇顯示快線 (10)、中線 (20)、慢線 (60)，並切換 SMA/EMA。
布林海盜 (Bollinger Bandit): 調整通道週期 (50)、平倉均線週期 (50) 及 ROC 週期 (30)。
時間框架 (Timeframes): 自訂六個時間框架，預設為 5分、15分、1小時、4小時、日線、週線。
Adjust Bollinger Band period (default 21), standard deviations (σ1=1, σ2=2), and take-profit/stop-loss basis (ATR or BAND).
Configure Moving Averages (fast=10, medium=20, slow=60) and toggle SMA/EMA.
Set Bollinger Bandit parameters: channel period (50), liquidation MA period (50), ROC period (30).
Customize six timeframes (default: 5m, 15m, 1h, 4h, daily, weekly).
交易訊號 (Trading Signals)
買入訊號 (Buy): 價格穿越下軌且滿足連續K線條件。
賣出訊號 (Sell): 價格穿越上軌且滿足連續K線條件。
海盜策略訊號: 基於 ROC 與布林通道穿越，結合動態止損。
Buy signal: Price crosses below lower band with consecutive bar confirmation.
Sell signal: Price crosses above upper band with consecutive bar confirmation.
Bandit strategy signals: Based on ROC and band crossings with dynamic stop-loss.
視覺化 (Visualization)
布林通道以不同顏色顯示上下軌與中軌。
移動平均線以快、中、慢線區分顏色。
趨勢表格顯示各時間框架的趨勢狀態 (🔴上漲, 🟢下跌, ⚪中性)。
海盜策略顯示動態止損線與交易狀態。
Bollinger Bands display upper, lower, and middle bands in distinct colors.
Moving Averages use different colors for fast, medium, and slow lines.
Trend table shows timeframe trends (🔴 up, 🟢 down, ⚪ neutral).
Bandit strategy displays dynamic stop-loss and trading status.
RPT Position Sizer🎯 Purpose
This indicator is a position sizing and stop-loss calculator designed to help traders instantly determine:
How many shares/contracts to buy,
How much risk (₹) they are taking per trade,
How much capital will be deployed, and
The precise stop-loss price level based on user-defined parameters.
It displays all key values in a compact on-chart table (bottom-left corner) for quick trade planning.
💡 Use Case
Perfect for discretionary swing traders, systematic position traders, and risk managers who want instant visual feedback of trade sizing metrics directly on the chart — eliminating manual calculations and improving discipline.
⚙️ Key Features
Dynamic Inputs
Trading Capital (₹) — total available capital for trading.
RPT % — risk-per-trade as a percentage of total capital.
SL % — stop-loss distance in percent below CMP (Current Market Price).
CMP Source — can be linked to close, hl2, etc.
Rounding Style — round position size to Nearest, Floor, or Ceil.
Decimals Show — control number formatting precision in the table.
Core Calculations
SL Points: CMP × SL%
SL Price: CMP − SL Points
Risk Amount (₹): Capital × RPT%
Position Size: Risk ÷ SL Points
Capital Used: Position Size × CMP
Clean On-Chart Table Display
Displays:
Trading Capital
RPT %
Risk Amount (₹)
Position Size (shares/contracts)
Capital Required (₹)
Stop-Loss % & SL Price
The table uses a minimalistic white-on-black design with clear labeling and rupee formatting for quick reference.
Data Window Integration
Plots hidden values (Position Size, Risk Amount, SL Points, Capital Used) for use in TradingView’s Data Window—ideal for strategy testing and exporting values.
ASR - Average Session Range [KasTrades]This indicator displays the Average Session Range based on the session of your choice.
You can turn the tables off if you don't want to see a table version of the ASR levels. There is also a momentum table showing the current momentum, which you can also turn off.
Trend Fib Zone Bounce (TFZB) [KedArc Quant]Description:
Trend Fib Zone Bounce (TFZB) trades with the latest confirmed Supply/Demand zone using a single, configurable Fib pullback (0.3/0.5/0.6). Trade only in the direction of the most recent zone and use a single, configurable fib level for pullback entries.
• Detects market structure via confirmed swing highs/lows using a rolling window.
• Draws Supply/Demand zones (bearish/bullish rectangles) from the latest MSS (CHOCH or BOS) event.
• Computes intra zone Fib guide rails and keeps them extended in real time.
• Triggers BUY only inside bullish zones and SELL only inside bearish zones when price touches the selected fib and closes back beyond it (bounce confirmation).
• Optional labels print BULL/BEAR + fib next to the triangle markers.
What it does
Finds structure using confirmed swing highs/lows (you choose the confirmation length).
Builds the latest zone (bullish = demand, bearish = supply) after a CHOCH/BOS event.
Draws intra-zone “guide rails” (Fib lines) and extends them live.
Signals only with the trend of that zone:
BUY inside a bullish zone when price tags the selected Fib and closes back above it.
SELL inside a bearish zone when price tags the selected Fib and closes back below it.
Optional labels print BULL/BEAR + Fib next to triangles for quick context
Why this is different
Most “zone + fib + signal” tools bolt together several indicators, or fire counter-trend signals because they don’t fully respect structure. TFZB is intentionally minimal:
Single bias source: the latest confirmed zone defines direction; nothing else overrides it.
Single entry rule: one Fib bounce (0.3/0.5/0.6 selectable) inside that zone—no counter-trend trades by design.
Clean visuals: you can show only the most recent zone, clamp overlap, and keep just the rails that matter.
Deterministic & transparent: every plot/label comes from the code you see—no external series or hidden smoothing
How it helps traders
Cuts decision noise: you always know the bias and the only entry that matters right now.
Forces discipline: if price isn’t inside the active zone, you don’t trade.
Adapts to volatility: pick 0.3 in strong trends, 0.5 as the default, 0.6 in chop.
Non-repainting zones: swings are confirmed after Structure Length bars, then used to build zones that extend forward (they don’t “teleport” later)
How it works (details)
*Structure confirmation
A swing high/low is only confirmed after Structure Length bars have elapsed; the dot is plotted back on the original bar using offset. Expect a confirmation delay of about Structure Length × timeframe.
*Zone creation
After a CHOCH/BOS (momentum shift / break of prior swing), TFZB draws the new Supply/Demand zone from the swing anchors and sets it active.
*Fib guide rails
Inside the active zone TFZB projects up to five Fib lines (defaults: 0.3 / 0.5 / 0.7) and extends them as time passes.
*Entry logic (with-trend only)
BUY: bar’s low ≤ fib and close > fib inside a bullish zone.
SELL: bar’s high ≥ fib and close < fib inside a bearish zone.
*Optionally restrict to one signal per zone to avoid over-trading.
(Optional) Aggressive confirm-bar entry
When do the swing dots print?
* The code confirms a swing only after `structureLen` bars have elapsed since that candidate high/low.
* On a 5-min chart with `structureLen = 10`, that’s about 50 minutes later.
* When the swing confirms, the script plots the dot back on the original bar (via `offset = -structureLen`). So you *see* the dot on the old bar, but it only appears on the chart once the confirming bar arrives.
> Practical takeaway: expect swing markers to appear roughly `structureLen × timeframe` later. Zones and signals are built from those confirmed swings.
Best timeframe for this Indicator
Use the timeframe that matches your holding period and the noise level of the instrument:
* Intraday :
* 5m or 15m are the sweet spots.
* Suggested `structureLen`:
* 5m: 10–14 (confirmation delay \~50–70 min)
* 15m: 8–10 (confirmation delay \~2–2.5 hours)
* Keep Entry Fib at 0.5 to start; try 0.3 in strong trends, 0.6 in chop.
* Tip: avoid the first 10–15 minutes after the open; let the initial volatility set the early structure.
* Swing/overnight:
* 1h or 4h.
* `structureLen`:
* 1h: 6–10 (6–10 hours confirmation)
* 4h: 5–8 (20–32 hours confirmation)
* 1m scalping: not recommended here—the confirmation lag relative to the noise makes zones less reliable.
Inputs (all groups)
Structure
• Show Swing Points (structureTog)
o Plots small dots on the bar where a swing point is confirmed (offset back by Structure Length).
• Structure Length (structureLen)
o Lookback used to confirm swing highs/lows and determine local structure. Higher = fewer, stronger swings; lower = more reactive.
Zones
• Show Last (zoneDispNum)
o Maximum number of zones kept on the chart when Display All Zones is off.
• Display All Zones (dispAll)
o If on, ignores Show Last and keeps all zones/levels.
• Zone Display (zoneFilter): Bullish Only / Bearish Only / Both
o Filters which zone types are drawn and eligible for signals.
• Clean Up Level Overlap (noOverlap)
o Prevents fib lines from overlapping when a new zone starts near the previous one (clamps line start/end times for readability).
Fib Levels
Each row controls whether a fib is drawn and how it looks:
• Toggle (f1Tog…f5Tog): Show/hide a given fib line.
• Level (f1Lvl…f5Lvl): Numeric ratio in . Defaults active: 0.3, 0.5, 0.7 (0 and 1 off by default).
• Line Style (f1Style…f5Style): Solid / Dashed / Dotted.
• Bull/Bear Colors (f#BullColor, f#BearColor): Per-fib color in bullish vs bearish zones.
Style
• Structure Color: Dot color for confirmed swing points.
• Bullish Zone Color / Bearish Zone Color: Rectangle fills (transparent by default).
Signals
• Entry Fib for Signals (entryFibSel): Choose 0.3, 0.5 (default), or 0.6 as the trigger line.
• Show Buy/Sell Signals (showSignals): Toggles triangle markers on/off.
• One Signal Per Zone (oneSignalPerZone): If on, suppresses additional entries within the same zone after the first trigger.
• Show Signal Text Labels (Bull/Bear + Fib) (showSignalLabels): Adds a small label next to each triangle showing zone bias and the fib used (e.g., BULL 0.5 or BEAR 0.3).
How TFZB decides signals
With trend only:
• BUY
1. Latest active zone is bullish.
2. Current bar’s close is inside the zone (between top and bottom).
3. The bar’s low ≤ selected fib and it closes > selected fib (bounce).
• SELL
1. Latest active zone is bearish.
2. Current bar’s close is inside the zone.
3. The bar’s high ≥ selected fib and it closes < selected fib.
Markers & labels
• BUY: triangle up below the bar; optional label “BULL 0.x” above it.
• SELL: triangle down above the bar; optional label “BEAR 0.x” below it.
Right-Panel Swing Log (Table)
What it is
A compact, auto-updating log of the most recent Swing High/Low events, printed in the top-right of the chart.
It helps you see when a pivot formed, when it was confirmed, and at what price—so you know the earliest bar a zone-based signal could have appeared.
Columns
Type – Swing High or Swing Low.
Date – Calendar date of the swing bar (follows the chart’s timezone).
Swing @ – Time of the original swing bar (where the dot is drawn).
Confirm @ – Time of the bar that confirmed that swing (≈ Structure Length × timeframe after the swing). This is also the earliest moment a new zone/entry can be considered.
Price – The swing price (high for SH, low for SL).
Why it’s useful
Clarity on repaint/confirmation: shows the natural delay between a swing forming and being usable—no guessing.
Planning & journaling: quick reference of today’s pivots and prices for notes/backtesting.
Scanning intraday: glance to see if you already have a confirmed zone (and therefore valid fib-bounce entries), or if you’re still waiting.
Context for signals: if a fib-bounce triangle appears before the time listed in Confirm @, it’s not a valid trade (you were too early).
Settings (Inputs → Logging)
Log swing times / Show table – turn the table on/off.
Rows to keep – how many recent entries to display.
Show labels on swing bar – optional tags on the chart (“Swing High 11:45”, “Confirm SH 14:15”) that match the table.
Recommended defaults
• Structure Length: 10–20 for intraday; 20–40 for swing.
• Entry Fib for Signals: 0.5 to start; try 0.3 in stronger trends and 0.6 in choppier markets.
• One Signal Per Zone: ON (prevents over trading).
• Zone Display: Both.
• Fib Lines: Keep 0.3/0.5/0.7 on; turn on 0 and 1 only if you need anchors.
Alerts
Two alert conditions are available:
• BUY signal – fires when a with trend bullish bounce at the selected fib occurs inside a bullish zone.
• SELL signal – fires when a with trend bearish bounce at the selected fib occurs inside a bearish zone.
Create alerts from the chart’s Alerts panel and select the desired condition. Use Once Per Bar Close to avoid intrabar flicker.
Notes & tips
• Swing dots are confirmed only after Structure Length bars, so they plot back in time; zones built from these confirmed swings do not repaint (though they extend as new bars form).
• If you don’t see a BUY where you expect one, check: (1) Is the active zone bullish? (2) Did the candle’s low actually pierce the selected fib and close above it? (3) Is One Signal Per Zone suppressing a second entry?
• You can hide visual clutter by reducing Show Last to 1–3 while keeping Display All Zones off.
Glossary
• CHOCH (Change of Character): A shift where price breaks beyond the last opposite swing while local momentum flips.
• BOS (Break of Structure): A cleaner break beyond the prior swing level in the current momentum direction.
• MSS: Either CHOCH or BOS – any event that spawns a new zone.
Extension ideas (optional)
• Add fib extensions (1.272 / 1.618) for target lines.
• Zone quality score using ATR normalization to filter weak impulses.
• HTF filter to only accept zones aligned with a higher timeframe trend.
⚠️ Disclaimer This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
WASDE DatesOverview
WASDE Dates — a small, focused event indicator that displays confirmed USDA WASDE release dates for 2025 on the chart and marks each release day. The indicator is designed to be a lightweight timing tool for traders who want clean visual reminders and optional alerts around USDA WASDE publications.
Features
• Shows official WASDE release dates for 2025 in a compact chart table.
• Draws on-chart markers and a dotted vertical line on WASDE release days.
• Two alert conditions you can enable in TradingView: "WASDE Day Alert" and "WASDE 24h Reminder".
• Simple table position control (Top/Bottom, Left/Right) in the indicator settings.
• Minimal, self-contained code — no external data feeds or permissions required.
How to use
1. Apply the indicator to any chart and timeframe.
2. Use the indicator settings to choose table position.
3. Enable Alerts (if desired) via TradingView Alerts → choose “WASDE Day Alert” or “WASDE 24h Reminder”.
4. This version contains 2025 confirmed dates only — verify dates for live trading and enable alerts as needed.
Design & rationale
This indicator is intentionally not a technical trading signal. It is an event scheduler focused on clarity and low overhead: combine it with your existing setup to avoid being surprised by WASDE publications and to quickly inspect price action around these event dates.
Limitations & disclaimer
• This script shows **confirmed 2025** WASDE dates only. It does not provide trading advice or entry/exit signals. Use at your own risk.
• Double-check official USDA publishing times before executing trades.
• No external links or contact information are included in this description to comply with TradingView publishing rules.
Feature outlook (V2)
Planned V2 (future release): enhanced countdown (days → hours/minutes), optional inclusion of estimated 2026 dates marked as (TBC), and an invite-only/protected advanced version with reaction overlays (T+1/T+3) and extended alert options. V2 will be announced on this script page when ready.
Changelog
v1 — public release: 2025 confirmed dates, release markers, alerts, table position control.
Fear & Greed [theUltimator5]This indicator attempts to replicate CNN's Fear & Greed Index methodology to measure market sentiment on a scale from 0-100. It combines seven key market components into a single sentiment score, where lower values indicate fear and higher values indicate greed.
Note: It is impossible to perfectly replicate the true Fear & Greed indicator due to data limitations, so this indicator attempts to best replicate the output for each of the (7) components using available data.
The uniqueness of this indicator comes from the calculation methods for the 7 components as well as the visual representation of the data, which includes a table and selectable plots for each of the 7 components which make up the overall sentiment. Existing variants of the Fear & Greed Index have substantial flaws in the calculations of several of the components which result in warped final sentiment numbers. This indicator attempts to better track all 7 components and provide a closer model to the actual Fear & Greed index.
Here are the seven components and a brief description of how each are calculated:
1. Market Momentum
Calculation: S&P 500 current price vs. 125-day moving average
Measures how far the market has moved from its long-term trend
Uses CNN-style Z-score normalization over 252 trading days
Higher values indicate strong upward momentum (greed)
Lower values suggest declining momentum (fear)
2. Stock Strength
Calculation: S&P 500 RSI scaled to 252-day range
Uses 14-period RSI of the S&P 500 index
Normalizes RSI values based on their 252-day minimum and maximum
Measures overbought/oversold conditions relative to recent history
Higher values indicate overbought conditions (greed)
Lower values suggest oversold conditions (fear)
3. Price Breadth
Calculation: Modified McClellan Oscillator
Primary: Uses NYSE advancing vs. declining issues with 7-day smoothing
Fallback: Compares sector performance (QQQ, IWM vs. SPY)
Measures how many stocks participate in market moves
Broader participation indicates healthier trends
Narrow breadth suggests selective or weak trends
4. Put/Call Ratio
Calculation: Inverted CBOE Put/Call ratios
Primary: CBOE Equity-only Put/Call ratio (more sensitive)
Fallback: CBOE Total Put/Call ratio
Uses 5-day average and applies CNN normalization
Higher put/call ratios indicate fear (inverted to lower scores)
Lower put/call ratios suggest complacency (higher scores)
5. Market Volatility
Calculation: VIX relative to its 50-day average
Compares current VIX level to its 50-day moving average
Measures deviation from normal volatility expectations
Higher VIX relative to average indicates fear (lower scores)
Lower relative VIX suggests complacency (higher scores)
6. Safe Haven Demand
Calculation: Stock returns vs. bond yield changes
Compares 20-day smoothed S&P 500 returns to Treasury yield changes
When stocks outperform bonds, indicates risk appetite (higher scores)
When bonds outperform stocks, suggests risk aversion (lower scores)
Uses Treasury 10-year yields as the safe haven benchmark
7. Junk Bond Demand
Calculation: High-yield bond spread analysis
Measures yield spread between junk bonds (JNK ETF) and Treasuries
Compares current spread to its 5-day average
Narrowing spreads indicate risk appetite (higher scores)
Widening spreads suggest risk aversion (lower scores)
The combined sentiment is plotted as a single line which changes color based on the current sentiment value.
0-25: Extreme Fear (Red) - Market panic, oversold conditions
26-45: Fear (Orange) - Cautious sentiment, bearish bias
46-55: Neutral (Yellow) - Balanced market sentiment
56-75: Greed (Light Green) - Optimistic sentiment, bullish bias
76-100: Extreme Greed (Green) - Market euphoria, potentially overbought
There are dashed lines to represent the threshold values for each of the sentiments to better visualize transitions.
The table displays each of the (7) components of the index and their respective values. The table can be toggled on/off and the position can be moved.
An optional secondary line can be toggled on to display (1) of the (7) components as a unique color and the component name and value will highlight on the table. The secondary line can be used to dig into the main driving forces behind the overall index value.
Market Spiralyst [Hapharmonic]Hello, traders and creators! 👋
Market Spiralyst: Let's change the way we look at analysis, shall we? I've got to admit, I scratched my head on this for weeks, Haha :). What you're seeing is an exploration of what's possible when code meets art on financial charts. I wanted to try blending art with trading, to do something new and break away from the same old boring perspectives. The goal was to create a visual experience that's not just analytical, but also relaxing and aesthetically pleasing.
This work is intended as a guide and a design example for all developers, born from the spirit of learning and a deep love for understanding the Pine Script™ language. I hope it inspires you as much as it challenged me!
🧐 Core Concept: How It Works
Spiralyst is built on two distinct but interconnected engines:
The Generative Art Engine: At its core, this indicator uses a wide range of mathematical formulas—from simple polygons to exotic curves like Torus Knots and Spirographs—to draw beautiful, intricate shapes directly onto your chart. This provides a unique and dynamic visual backdrop for your analysis.
The Market Pulse Engine: This is where analysis meets art. The engine takes real-time data from standard technical indicators (RSI and MACD in this version) and translates their states into a simple, powerful "Pulse Score." This score directly influences the appearance of the "Scatter Points" orbiting the main shape, turning the entire artwork into a living, breathing representation of market momentum.
🎨 Unleash Your Creativity! This Is Your Playground
We've included 25 preset shapes for you... but that's just the starting point !
The real magic happens when you start tweaking the settings yourself. A tiny adjustment can make a familiar shape come alive and transform in ways you never expected.
I'm genuinely excited to see what your imagination can conjure up! If you create a shape you're particularly proud of or one that looks completely unique, I would love to see it. Please feel free to share a screenshot in the comments below. I can't wait to see what you discover! :)
Here's the default shape to get you started:
The Dynamic Scatter Points: Reading the Pulse
This is where the magic happens! The small points scattered around the main shape are not just decorative; they are the visual representation of the Market Pulse Score.
The points have two forms:
A small asterisk (`*`): Represents a low or neutral market pulse.
A larger, more prominent circle (`o`): Represents a high, strong market pulse.
Here’s how to read them:
The indicator calculates the Pulse Strength as a percentage (from 0% to 100%) based on the total score from the active indicators (RSI and MACD). This percentage determines the ratio of circles to asterisks.
High Pulse Strength (e.g., 80-100%): Most of the scatter points will transform into large circles (`o`). This indicates that the underlying momentum is strong and It could be an uptrend. It's a visual cue that the market is gaining strength and might be worth paying closer attention to.
Low Pulse Strength (e.g., 0-20%): Most or all of the scatter points will remain as small asterisks (`*`). This suggests weak, neutral, or bearish momentum.
The key takeaway: The more circles you see, the stronger the bullish momentum is according to the active indicators. Watch the artwork "breathe" as the circles appear and disappear with the market's rhythm!
And don't worry about the shape you choose; the scatter points will intelligently adapt and always follow the outer boundary of whatever beautiful form you've selected.
How to Use
Getting started with Spiralyst is simple:
Choose Your Canvas: Start by going into the settings and picking a `Shape` and `Palette` from the "Shape Selection & Palette" group that you find visually appealing. This is your canvas.
Tune Your Engine: Go to the "Market Pulse Engine" settings. Here, you can enable or disable the RSI and MACD scoring engines. Want to see the pulse based only on RSI? Just uncheck the MACD box. You can also fine-tune the parameters for each indicator to match your trading style.
Read the Vibe: Observe the scatter points. Are they mostly small asterisks or are they transforming into large, vibrant circles? Use this visual feedback as a high-level gauge of market momentum.
Check the Dashboard: For a precise breakdown, look at the "Market Pulse Analysis" table on the top-right. It gives you the exact values, scores, and total strength percentage.
Explore & Experiment: Play with the different shapes and color palettes! The core analysis remains the same, but the visual experience can be completely different.
⚙️ Settings & Customization
Spiralyst is designed to be highly customizable.
Shape Selection & Palette: This is your main control panel. Choose from over 25 unique shapes, select a color palette, and adjust the line extension style ( `extend` ) or horizontal position ( `offsetXInput` ).
scatterLabelsInput: This setting controls the total number of points (both asterisks and circles) that orbit the main shape. Think of it as adjusting the density or visual granularity of the market pulse feedback.
The Market Pulse engine will always calculate its strength as a percentage (e.g., 75%). This percentage is then applied to the `scatterLabelsInput` number you've set to determine how many points transform into large circles.
Example: If the Pulse Strength is 75% and you set this to `100` , approximately 75 points will become circles. If you increase it to `200` , approximately 150 points will transform.
A higher number provides a more detailed, high-resolution view of the market pulse, while a lower number offers a cleaner, more minimalist look. Feel free to adjust this to your personal visual preference; the underlying analytical percentage remains the same.
Market Pulse Engine:
`⚙️ RSI Settings` & `⚙️ MACD Settings`: Each indicator has its own group.
Enable Scoring: Use the checkbox at the top of each group to include or exclude that indicator from the Pulse Score calculation. If you only want to use RSI, simply uncheck "Enable MACD Scoring."
Parameters: All standard parameters (Length, Source, Fast/Slow/Signal) are fully adjustable.
Individual Shape Parameters (01-25): Each of the 25+ shapes has its own dedicated group of settings, allowing you to fine-tune every aspect of its geometry, from the number of petals on a flower to the windings of a knot. Feel free to experiment!
For Developers & Pine Script™ Enthusiasts
If you are a developer and wish to add more indicators (e.g., Stochastic, CCI, ADX), you can easily do so by following the modular structure of the code. You would primarily need to:
Add a new `PulseIndicator` object for your new indicator in the `f_getMarketPulse()` function.
Add the logic for its scoring inside the `calculateScore()` method.
The `calculateTotals()` method and the dashboard table are designed to be dynamic and will automatically adapt to include your new indicator!
One of the core design philosophies behind Spiralyst is modularity and scalability . The Market Pulse engine was intentionally built using User-Defined Types (UDTs) and an array-based structure so that adding new indicators is incredibly simple and doesn't require rewriting the main logic.
If you want to add a new indicator to the scoring engine—let's use the Stochastic Oscillator as a detailed example—you only need to modify three small sections of the code. The rest of the script, including the adaptive dashboard, will update automatically.
Here’s your step-by-step guide:
#### Step 1: Add the User Inputs
First, you need to give users control over your new indicator. Find the `USER INTERFACE: INPUTS` section and add a new group for the Stochastic settings, right after the MACD group.
Create a new group name: `string GRP_STOCH = "⚙️ Stochastic Settings"`
Add the inputs: Create a boolean to enable/disable it, and then add the necessary parameters (`%K`, `%D`, `Smooth`). Use the `active` parameter to link them to the enable/disable checkbox.
// Add this code block right after the GRP_MACD and MACD inputs
string GRP_STOCH = "⚙️ Stochastic Settings"
bool stochEnabledInput = input.bool(true, "Enable Stochastic Scoring", group = GRP_STOCH)
int stochKInput = input.int(14, "%K Length", minval=1, group = GRP_STOCH, active = stochEnabledInput)
int stochDInput = input.int(3, "%D Smoothing", minval=1, group = GRP_STOCH, active = stochEnabledInput)
int stochSmoothInput = input.int(3, "Smooth", minval=1, group = GRP_STOCH, active = stochEnabledInput)
#### Step 2: Integrate into the Pulse Engine (The "Factory")
Next, go to the `f_getMarketPulse()` function. This function acts as a "factory" that builds and configures the entire market pulse object. You need to teach it how to build your new Stochastic indicator.
Update the function signature: Add the new `stochEnabledInput` boolean as a parameter.
Calculate the indicator: Add the `ta.stoch()` calculation.
Create a `PulseIndicator` object: Create a new object for the Stochastic, populating it with its name, parameters, calculated value, and whether it's enabled.
Add it to the array: Simply add your new `stochPulse` object to the `array.from()` list.
Here is the complete, updated `f_getMarketPulse()` function :
// Factory function to create and calculate the entire MarketPulse object.
f_getMarketPulse(bool rsiEnabled, bool macdEnabled, bool stochEnabled) =>
// 1. Calculate indicator values
float rsiVal = ta.rsi(rsiSourceInput, rsiLengthInput)
= ta.macd(close, macdFastInput, macdSlowInput, macdSignalInput)
float stochVal = ta.sma(ta.stoch(close, high, low, stochKInput), stochDInput) // We'll use the main line for scoring
// 2. Create individual PulseIndicator objects
PulseIndicator rsiPulse = PulseIndicator.new("RSI", str.tostring(rsiLengthInput), rsiVal, na, 0, rsiEnabled)
PulseIndicator macdPulse = PulseIndicator.new("MACD", str.format("{0},{1},{2}", macdFastInput, macdSlowInput, macdSignalInput), macdVal, signalVal, 0, macdEnabled)
PulseIndicator stochPulse = PulseIndicator.new("Stoch", str.format("{0},{1},{2}", stochKInput, stochDInput, stochSmoothInput), stochVal, na, 0, stochEnabled)
// 3. Calculate score for each
rsiPulse.calculateScore()
macdPulse.calculateScore()
stochPulse.calculateScore()
// 4. Add the new indicator to the array
array indicatorArray = array.from(rsiPulse, macdPulse, stochPulse)
MarketPulse pulse = MarketPulse.new(indicatorArray, 0, 0.0)
// 5. Calculate final totals
pulse.calculateTotals()
pulse
// Finally, update the function call in the main orchestration section:
MarketPulse marketPulse = f_getMarketPulse(rsiEnabledInput, macdEnabledInput, stochEnabledInput)
#### Step 3: Define the Scoring Logic
Now, you need to define how the Stochastic contributes to the score. Go to the `calculateScore()` method and add a new case to the `switch` statement for your indicator.
Here's a sample scoring logic for the Stochastic, which gives a strong bullish score in oversold conditions and a strong bearish score in overbought conditions.
Here is the complete, updated `calculateScore()` method :
// Method to calculate the score for this specific indicator.
method calculateScore(PulseIndicator this) =>
if not this.isEnabled
this.score := 0
else
this.score := switch this.name
"RSI" => this.value > 65 ? 2 : this.value > 50 ? 1 : this.value < 35 ? -2 : this.value < 50 ? -1 : 0
"MACD" => this.value > this.signalValue and this.value > 0 ? 2 : this.value > this.signalValue ? 1 : this.value < this.signalValue and this.value < 0 ? -2 : this.value < this.signalValue ? -1 : 0
"Stoch" => this.value > 80 ? -2 : this.value > 50 ? 1 : this.value < 20 ? 2 : this.value < 50 ? -1 : 0
=> 0
this
#### That's It!
You're done. You do not need to modify the dashboard table or the total score calculation.
Because the `MarketPulse` object holds its indicators in an array , the rest of the script is designed to be adaptive:
The `calculateTotals()` method automatically loops through every indicator in the array to sum the scores and calculate the final percentage.
The dashboard code loops through the `enabledIndicators` array to draw the table. Since your new Stochastic indicator is now part of that array, it will appear automatically when enabled!
---
Remember, this is your playground! I'm genuinely excited to see the unique shapes you discover. If you create something you're proud of, feel free to share it in the comments below.
Happy analyzing, and may your charts be both insightful and beautiful! 💛
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
Global Bond Yields Monitor [MarktQuant]Global Bond Yields Monitor
The Global Bond Yields Monitor is designed to help users track and compare government bond yields across major economies. It provides an at-a-glance view of short- and long-term interest rates for multiple countries, enabling users to observe shifts in global fixed-income markets.
Key Features:
Multi-Country Coverage: Includes major advanced and emerging economies such as the United States, China, Japan, Germany, United Kingdom, Canada, Australia, and more.
Multiple Maturities: Displays yields for the 2-year, 5-year, 10-year, and 30-year maturities (20-year for Russia).
Dynamic Yield Data: Plots real-time yields for the selected country directly from TradingView’s data sources.
Weekly Change Tracking: Calculates and displays the yield change from one week ago ( ) for each maturity.
Table Visualization: Option to display a compact data table showing current yields and weekly changes, color-coded for easier interpretation.
Visual Yield Curve Comparison: Plots yield lines for short- and long-term maturities, with shaded areas between curves for visual clarity.
Customizable Display: Choose table placement and whether to show or hide the weekly change table.
Use Cases
This script is intended for analysts, traders, and investors who want to monitor shifts in sovereign bond markets. Changes in yields can reflect adjustments in monetary policy expectations, inflation outlook, or broader macroeconomic trends.
❗Important Note❗
This indicator is for market monitoring and educational purposes only. It does not generate trading signals, and it should not be interpreted as financial advice. All data is sourced from TradingView’s available market feeds, and accuracy may depend on the source data.
Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector
Advanced Support & Resistance Detection Using Mathematical Clustering
The Problem
Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly.
The Solution: Lorentzian Distance Clustering
This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis.
Data Points Clustering:
🔬 Why Euclidean Distance Fails in Trading
Traditional K-means uses Euclidean distance:
• Formula: distance = (price_A - price_B)²
• Problem: Squaring amplifies differences exponentially
• Real impact: One 5% price spike has 25x more influence than a 1% move
• Result: Clusters get pulled toward outliers, missing real support/resistance zones
Example scenario:
Prices: ← flash spike
Euclidean: Centroid gets dragged toward 150
Actual S/R zone: Around 100 (where prices actually trade)
⚡ Lorentzian Distance: The Game Changer
Our approach uses Lorentzian distance:
• Formula: distance = log(1 + (price_difference)² / σ²)
• Breakthrough: Logarithmic compression keeps outliers in check
• Real impact: Large moves still matter, but don't dominate
• Result: Clusters focus on where prices actually spend time
Same example with Lorentzian:
Prices: ← flash spike
Lorentzian: Centroid stays near 100 (real trading zone)
Outlier (150): Acknowledged but not dominant
🧠 Adaptive Intelligence
The σ parameter isn't fixed,it's calculated from market disturbance/entropy:
• High volatility: σ increases, making algorithm more tolerant of large moves
• Low volatility: σ decreases, making algorithm more sensitive to small changes
• Self-calibrating: Adapts to any instrument or market condition automatically
Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior.
🎯 Automatic K-Selection (Elbow Method)
Instead of guessing how many S/R levels to draw, the indicator:
• Tests 2-6 clusters and calculates WCSS (tightness measure)
• Finds the "elbow" - where adding more clusters stops helping much
• Uses sharpness calculation to pick the optimal number automatically
Result: Perfect balance between detail and clarity.
How It Works
1. Collect recent closing prices
2. Calculate entropy to adapt to current market volatility
3. Cluster prices using Lorentzian K-means algorithm
4. Auto-select optimal cluster count via statistical analysis
5. Draw levels at cluster centers with deviation bands
📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis)
When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually:
What WCSS tells you:
• Lower WCSS = tighter clusters = better S/R levels
• Higher WCSS = scattered clusters = weaker levels
What Sharpness tells you:
• Higher positive values = optimal elbow point = best K choice
• Lower/negative values = poor elbow definition = avoid this K
• Measures the "sharpness" of the WCSS curve drop-off
Decision strategy using both metrics:
K=2: WCSS = 150.42 | Sharpness = - | Selected =
K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice
K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected =
K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected =
Quick decision rules:
• Pick K with highest positive Sharpness (indicates optimal elbow)
• Confirm with significant WCSS drop (30%+ reduction is good)
• Avoid K values with negative or very low Sharpness (<1.0)
• K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice
Why this works:
The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality.
Elbow Method Visualization:
Traditional clustering problems:
❌ Price spikes distort results
❌ Fixed parameters don't adapt
❌ Manual tuning is subjective
❌ No way to validate choices
Lorentzian solution:
☑️ Outlier-resistant distance metric
☑️ Entropy-based adaptation to volatility
☑️ Automatic optimal K selection
☑️ Statistical validation via WCSS & Sharpness
Features
Visual:
• Color-coded levels (red=highest resistance, green=lowest support)
• Optional deviation bands showing cluster spread
• Strength scores on labels: Each cluster shows a reliability score.
• Higher scores (0.8+) = very strong S/R levels with tight price clustering
• Lower scores (0.6-0.7) = weaker levels, use with caution
• Based on cluster tightness and data point density
• Clean line extensions and labels
Analytics:
• WCSS analysis table showing why K was chosen
• Cluster metrics and statistics
• Real-time entropy monitoring
Control:
• Auto/manual K selection toggle
• Customizable sample size (20-500 bars)
• Show/hide bands and metrics tables
The Result
You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection.
Best for: Traders who want objective, data-driven S/R levels without manual chart analysis.
Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
EPS+Sales+Net Profit+MCap+Sector & Industry📄 Full Description
This script displays a comprehensive financial data panel directly on your TradingView chart, helping long-term investors and swing traders make informed decisions based on fundamental trends. It consolidates key financial metrics and business classification data into a single, visually clear table.
🔍 Key Features:
🧾 Financial Metrics (Auto-Fetched via request.financial):
EPS (Earnings Per Share) – Displayed with trend direction (QoQ or YoY).
Sales / Revenue – In ₹ Crores (for Indian stocks), trend change also included.
Net Profit – Also in ₹ Crores, along with percentage change.
Market Cap – Automatically calculated using outstanding shares × price, shown in ₹ Cr.
Free Float Market Cap – Based on float shares × price, also in ₹ Cr.
🏷️ Sector & Industry Info:
Automatically identifies and displays the Sector and Industry of the stock using syminfo.sector and syminfo.industry.
Displayed inline with metrics, making it easy to know what business the stock belongs to.
📊 Table View:
Compact and responsive table shown on your chart.
Columns: Date | EPS | QoQ | Sales | QoQ | Net Profit | QoQ | Metrics
Metrics column dynamically shows:
Market Cap
Free Float
Sector (Row 4)
Industry (Row 5)
🌗 Appearance:
Supports Dark Mode and Mini Mode toggle.
You can also customize:
Number of data points (last 4+ quarters or years)
Table position and size
🎯 Use Case:
This script is ideal for:
Fundamental-focused traders who use EPS/Sales trends to identify momentum.
Swing traders who combine price action with fundamental tailwinds.
Portfolio builders who want to see sector/industry alignment quickly.
It works best with fundamentally sound stocks where earnings and profitability are a major factor in price movements.
✅ Important Notes:
Script uses request.financial which only works with supported symbols (mostly stocks).
Market Cap and Free Float are calculated in ₹ Crores.
All financial values are rounded and formatted for readability (e.g., 1,234 Cr).
🙏 Credits:
Developed and published by Sameer Thorappa
Built with a clean, minimalist approach for high readability and functionality.
9:45am NIFTY TRADINGTime Frame: 15 Minutes | Reference Candle Time: 9:45 AM IST | Valid Trading Window: 3 Hours
📌 Introduction
This document outlines a structured trading strategy for NIFTY & BANKNIFTY Options based on a 15-minute timeframe with a 9:45 AM IST reference candle. The strategy incorporates technical indicators, probability analysis, and strict trading rules to optimize entries and exits.
📊 Core Features
1. Reference Time Trading System
9:45 AM IST Candle acts as the reference for the day.
All signals (Buy/Sell/Reversal) are generated based on price action relative to this candle.
The valid trading window is 3 hours after the reference candle.
2. Signal Generation Logic
Signal Condition
Buy (B) Price breaks above reference candle high with confirmation
Sell (S) Price breaks below reference candle low with confirmation
Reversal (R) Early trend reversal signal (requires strict confirmation)
3. Probability Analysis System
The strategy calculates Win Probability (%) using 4 components:
Component Weight Calculation
Body Win Probability 30% Based on candle body strength (body % of total range)
Volume Win Probability 30% Current volume vs. average volume strength
Trend Win Probability 40% EMA crossover + RSI momentum alignment
Composite Probability - Weighted average of all 3 components
Probability Color Coding:
🟢 Green (High Probability): ≥70%
🟠 Orange (Medium Probability): 50-69%
🔴 Red (Low Probability): <50%
4. Timeframe Enforcement
Strictly 15-minute charts only (no other timeframes allowed).
System auto-disables signals if the wrong timeframe is selected.
📈 Technical Analysis Components
1. EMA System (Trend Analysis)
Short EMA (9) – Fast trend indicator
Middle EMA (20) – Intermediate trend
Long EMA (50) – Long-term trend confirmation
Rules:
Buy Signal: Price > 9 EMA > 20 EMA > 50 EMA (Bullish trend)
Sell Signal: Price < 9 EMA < 20 EMA < 50 EMA (Bearish trend)
2. Multi-Timeframe RSI (Momentum)
5M, 15M, 1H, 4H, Daily RSI values are compared for divergence/confluence.
Overbought (≥70) / Oversold (≤30) conditions help in reversal signals.
3. Volume Analysis
Volume Strength (%) = (Current Volume / Avg. Volume) × 100
Strong Volume (>120% Avg.) confirms breakout/breakdown.
4. Body Percentage (Candle Strength)
Body % = (Close - Open) / (High - Low) × 100
Strong Bullish Candle: Body > 60%
Strong Bearish Candle: Body < 40%
📊 Visual Elements
1. Information Tables
Reference Data Table (9:45 AM Candle High/Low/Close)
RSI Values Table (5M, 15M, 1H, 4H, Daily)
Signal Legend (Buy/Sell/Reversal indicators)
2. Chart Overlays
Reference Lines (9:45 AM High & Low)
EMA Lines (9, 20, 50)
Signal Labels (B, S, R)
3. Color Coding
High Probability (Green)
Medium Probability (Orange)
Low Probability (Red)
⚠️ Important Usage Guidelines
✅ Best Practices:
Trade only within the 3-hour window (9:45 AM - 12:45 PM IST).
Wait for confirmation (closing above/below reference candle).
Use probability score to filter high-confidence trades.
❌ Avoid:
Trading outside the 15-minute timeframe.
Ignoring volume & RSI divergence.
Overtrading – Stick to 1-2 high-probability setups per day.
🎯 Conclusion
This NIFTY Trading Strategy is optimized for 15-minute charts with a 9:45 AM IST reference candle. It combines EMA trends, RSI momentum, volume analysis, and probability scoring to generate high-confidence signals.
🚀 Key Takeaways:
✔ Reference candle defines the day’s bias.
✔ Probability system filters best trades.
✔ Strict 15M timeframe ensures consistency.
Happy Trading! 📈💰
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features.
Core Functionality
1. Signal Generation Logic
Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes).
Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273).
RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active.
Buy Signal: A buy is triggered if, after setup:
Price rises back above the level,
The RSI rate of change (ROC) indicates exhaustion of the drop,
The current bar shows positive momentum.
2. Trade Management
Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically.
Exit Signals: The script signals exit based on price action relative to these risk management levels.
3. Filters & Enhancements
Parabolic Move Filter: Prevents entries during extreme price moves.
Dead Cat Bounce Filter: Avoids false signals after sharp reversals.
Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts).
Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement.
User Inputs & Customization
Trade Direction: Toggle between LONG and SHORT signal generation.
Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements.
Trade Settings: Set take profit, stop loss, and trailing stop behavior.
Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis.
Backtesting: Integrated backtester with summary and detailed statistics tables.
Technical Features
Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics.
Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations.
Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart.
Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry).
Intended Use
Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD).
Timeframe: 30-second chart.
Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support.
Notable Customization & Extension Points
Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum".
Displacement Logic: Future update to use "FVG concept" for displacement.
High-Contrast RSI: Optional visual enhancements for RSI extremes.
Time-based Stop: Consideration for adding a time-based stop mechanism.
This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
Color█ OVERVIEW
This library is a Pine Script® programming tool for advanced color processing. It provides a comprehensive set of functions for specifying and analyzing colors in various color spaces, mixing and manipulating colors, calculating custom gradients and schemes, detecting contrast, and converting colors to or from hexadecimal strings.
█ CONCEPTS
Color
Color refers to how we interpret light of different wavelengths in the visible spectrum . The colors we see from an object represent the light wavelengths that it reflects, emits, or transmits toward our eyes. Some colors, such as blue and red, correspond directly to parts of the spectrum. Others, such as magenta, arise from a combination of wavelengths to which our minds assign a single color.
The human interpretation of color lends itself to many uses in our world. In the context of financial data analysis, the effective use of color helps transform raw data into insights that users can understand at a glance. For example, colors can categorize series, signal market conditions and sessions, and emphasize patterns or relationships in data.
Color models and spaces
A color model is a general mathematical framework that describes colors using sets of numbers. A color space is an implementation of a specific color model that defines an exact range (gamut) of reproducible colors based on a set of primary colors , a reference white point , and sometimes additional parameters such as viewing conditions.
There are numerous different color spaces — each describing the characteristics of color in unique ways. Different spaces carry different advantages, depending on the application. Below, we provide a brief overview of the concepts underlying the color spaces supported by this library.
RGB
RGB is one of the most well-known color models. It represents color as an additive mixture of three primary colors — red, green, and blue lights — with various intensities. Each cone cell in the human eye responds more strongly to one of the three primaries, and the average person interprets the combination of these lights as a distinct color (e.g., pure red + pure green = yellow).
The sRGB color space is the most common RGB implementation. Developed by HP and Microsoft in the 1990s, sRGB provided a standardized baseline for representing color across CRT monitors of the era, which produced brightness levels that did not increase linearly with the input signal. To match displays and optimize brightness encoding for human sensitivity, sRGB applied a nonlinear transformation to linear RGB signals, often referred to as gamma correction . The result produced more visually pleasing outputs while maintaining a simple encoding. As such, sRGB quickly became a standard for digital color representation across devices and the web. To this day, it remains the default color space for most web-based content.
TradingView charts and Pine Script `color.*` built-ins process color data in sRGB. The red, green, and blue channels range from 0 to 255, where 0 represents no intensity, and 255 represents maximum intensity. Each combination of red, green, and blue values represents a distinct color, resulting in a total of 16,777,216 displayable colors.
CIE XYZ and xyY
The XYZ color space, developed by the International Commission on Illumination (CIE) in 1931, aims to describe all color sensations that a typical human can perceive. It is a cornerstone of color science, forming the basis for many color spaces used today. XYZ, and the derived xyY space, provide a universal representation of color that is not tethered to a particular display. Many widely used color spaces, including sRGB, are defined relative to XYZ or derived from it.
The CIE built the color space based on a series of experiments in which people matched colors they perceived from mixtures of lights. From these experiments, the CIE developed color-matching functions to calculate three components — X, Y, and Z — which together aim to describe a standard observer's response to visible light. X represents a weighted response to light across the color spectrum, with the highest contribution from long wavelengths (e.g., red). Y represents a weighted response to medium wavelengths (e.g., green), and it corresponds to a color's relative luminance (i.e., brightness). Z represents a weighted response to short wavelengths (e.g., blue).
From the XYZ space, the CIE developed the xyY chromaticity space, which separates a color's chromaticity (hue and colorfulness) from luminance. The CIE used this space to define the CIE 1931 chromaticity diagram , which represents the full range of visible colors at a given luminance. In color science and lighting design, xyY is a common means for specifying colors and visualizing the supported ranges of other color spaces.
CIELAB and Oklab
The CIELAB (L*a*b*) color space, derived from XYZ by the CIE in 1976, expresses colors based on opponent process theory. The L* component represents perceived lightness, and the a* and b* components represent the balance between opposing unique colors. The a* value specifies the balance between green and red , and the b* value specifies the balance between blue and yellow .
The primary intention of CIELAB was to provide a perceptually uniform color space, where fixed-size steps through the space correspond to uniform perceived changes in color. Although relatively uniform, the color space has been found to exhibit some non-uniformities, particularly in the blue part of the color spectrum. Regardless, modern applications often use CIELAB to estimate perceived color differences and calculate smooth color gradients.
In 2020, a new LAB-oriented color space, Oklab , was introduced by Björn Ottosson as an attempt to rectify the non-uniformities of other perceptual color spaces. Similar to CIELAB, the L value in Oklab represents perceived lightness, and the a and b values represent the balance between opposing unique colors. Oklab has gained widespread adoption as a perceptual space for color processing, with support in the latest CSS Color specifications and many software applications.
Cylindrical models
A cylindrical-coordinate model transforms an underlying color model, such as RGB or LAB, into an alternative expression of color information that is often more intuitive for the average person to use and understand.
Instead of a mixture of primary colors or opponent pairs, these models represent color as a hue angle on a color wheel , with additional parameters that describe other qualities such as lightness and colorfulness (a general term for concepts like chroma and saturation). In cylindrical-coordinate spaces, users can select a color and modify its lightness or other qualities without altering the hue.
The three most common RGB-based models are HSL (Hue, Saturation, Lightness), HSV (Hue, Saturation, Value), and HWB (Hue, Whiteness, Blackness). All three define hue angles in the same way, but they define colorfulness and lightness differently. Although they are not perceptually uniform, HSL and HSV are commonplace in color pickers and gradients.
For CIELAB and Oklab, the cylindrical-coordinate versions are CIELCh and Oklch , which express color in terms of perceived lightness, chroma, and hue. They offer perceptually uniform alternatives to RGB-based models. These spaces create unique color wheels, and they have more strict definitions of lightness and colorfulness. Oklch is particularly well-suited for generating smooth, perceptual color gradients.
Alpha and transparency
Many color encoding schemes include an alpha channel, representing opacity . Alpha does not help define a color in a color space; it determines how a color interacts with other colors in the display. Opaque colors appear with full intensity on the screen, whereas translucent (semi-opaque) colors blend into the background. Colors with zero opacity are invisible.
In Pine Script, there are two ways to specify a color's alpha:
• Using the `transp` parameter of the built-in `color.*()` functions. The specified value represents transparency (the opposite of opacity), which the functions translate into an alpha value.
• Using eight-digit hexadecimal color codes. The last two digits in the code represent alpha directly.
A process called alpha compositing simulates translucent colors in a display. It creates a single displayed color by mixing the RGB channels of two colors (foreground and background) based on alpha values, giving the illusion of a semi-opaque color placed over another color. For example, a red color with 80% transparency on a black background produces a dark shade of red.
Hexadecimal color codes
A hexadecimal color code (hex code) is a compact representation of an RGB color. It encodes a color's red, green, and blue values into a sequence of hexadecimal ( base-16 ) digits. The digits are numerals ranging from `0` to `9` or letters from `a` (for 10) to `f` (for 15). Each set of two digits represents an RGB channel ranging from `00` (for 0) to `ff` (for 255).
Pine scripts can natively define colors using hex codes in the format `#rrggbbaa`. The first set of two digits represents red, the second represents green, and the third represents blue. The fourth set represents alpha . If unspecified, the value is `ff` (fully opaque). For example, `#ff8b00` and `#ff8b00ff` represent an opaque orange color. The code `#ff8b0033` represents the same color with 80% transparency.
Gradients
A color gradient maps colors to numbers over a given range. Most color gradients represent a continuous path in a specific color space, where each number corresponds to a mix between a starting color and a stopping color. In Pine, coders often use gradients to visualize value intensities in plots and heatmaps, or to add visual depth to fills.
The behavior of a color gradient depends on the mixing method and the chosen color space. Gradients in sRGB usually mix along a straight line between the red, green, and blue coordinates of two colors. In cylindrical spaces such as HSL, a gradient often rotates the hue angle through the color wheel, resulting in more pronounced color transitions.
Color schemes
A color scheme refers to a set of colors for use in aesthetic or functional design. A color scheme usually consists of just a few distinct colors. However, depending on the purpose, a scheme can include many colors.
A user might choose palettes for a color scheme arbitrarily, or generate them algorithmically. There are many techniques for calculating color schemes. A few simple, practical methods are:
• Sampling a set of distinct colors from a color gradient.
• Generating monochromatic variants of a color (i.e., tints, tones, or shades with matching hues).
• Computing color harmonies — such as complements, analogous colors, triads, and tetrads — from a base color.
This library includes functions for all three of these techniques. See below for details.
█ CALCULATIONS AND USE
Hex string conversion
The `getHexString()` function returns a string containing the eight-digit hexadecimal code corresponding to a "color" value or set of sRGB and transparency values. For example, `getHexString(255, 0, 0)` returns the string `"#ff0000ff"`, and `getHexString(color.new(color.red, 80))` returns `"#f2364533"`.
The `hexStringToColor()` function returns the "color" value represented by a string containing a six- or eight-digit hex code. The `hexStringToRGB()` function returns a tuple containing the sRGB and transparency values. For example, `hexStringToColor("#f23645")` returns the same value as color.red .
Programmers can use these functions to parse colors from "string" inputs, perform string-based color calculations, and inspect color data in text outputs such as Pine Logs and tables.
Color space conversion
All other `get*()` functions convert a "color" value or set of sRGB channels into coordinates in a specific color space, with transparency information included. For example, the tuple returned by `getHSL()` includes the color's hue, saturation, lightness, and transparency values.
To convert data from a color space back to colors or sRGB and transparency values, use the corresponding `*toColor()` or `*toRGB()` functions for that space (e.g., `hslToColor()` and `hslToRGB()`).
Programmers can use these conversion functions to process inputs that define colors in different ways, perform advanced color manipulation, design custom gradients, and more.
The color spaces this library supports are:
• sRGB
• Linear RGB (RGB without gamma correction)
• HSL, HSV, and HWB
• CIE XYZ and xyY
• CIELAB and CIELCh
• Oklab and Oklch
Contrast-based calculations
Contrast refers to the difference in luminance or color that makes one color visible against another. This library features two functions for calculating luminance-based contrast and detecting themes.
The `contrastRatio()` function calculates the contrast between two "color" values based on their relative luminance (the Y value from CIE XYZ) using the formula from version 2 of the Web Content Accessibility Guidelines (WCAG) . This function is useful for identifying colors that provide a sufficient brightness difference for legibility.
The `isLightTheme()` function determines whether a specified background color represents a light theme based on its contrast with black and white. Programmers can use this function to define conditional logic that responds differently to light and dark themes.
Color manipulation and harmonies
The `negative()` function calculates the negative (i.e., inverse) of a color by reversing the color's coordinates in either the sRGB or linear RGB color space. This function is useful for calculating high-contrast colors.
The `grayscale()` function calculates a grayscale form of a specified color with the same relative luminance.
The functions `complement()`, `splitComplements()`, `analogousColors()`, `triadicColors()`, `tetradicColors()`, `pentadicColors()`, and `hexadicColors()` calculate color harmonies from a specified source color within a given color space (HSL, CIELCh, or Oklch). The returned harmonious colors represent specific hue rotations around a color wheel formed by the chosen space, with the same defined lightness, saturation or chroma, and transparency.
Color mixing and gradient creation
The `add()` function simulates combining lights of two different colors by additively mixing their linear red, green, and blue components, ignoring transparency by default. Users can calculate a transparency-weighted mixture by setting the `transpWeight` argument to `true`.
The `overlay()` function estimates the color displayed on a TradingView chart when a specific foreground color is over a background color. This function aids in simulating stacked colors and analyzing the effects of transparency.
The `fromGradient()` and `fromMultiStepGradient()` functions calculate colors from gradients in any of the supported color spaces, providing flexible alternatives to the RGB-based color.from_gradient() function. The `fromGradient()` function calculates a color from a single gradient. The `fromMultiStepGradient()` function calculates a color from a piecewise gradient with multiple defined steps. Gradients are useful for heatmaps and for coloring plots or drawings based on value intensities.
Scheme creation
Three functions in this library calculate palettes for custom color schemes. Scripts can use these functions to create responsive color schemes that adjust to calculated values and user inputs.
The `gradientPalette()` function creates an array of colors by sampling a specified number of colors along a gradient from a base color to a target color, in fixed-size steps.
The `monoPalette()` function creates an array containing monochromatic variants (tints, tones, or shades) of a specified base color. Whether the function mixes the color toward white (for tints), a form of gray (for tones), or black (for shades) depends on the `grayLuminance` value. If unspecified, the function automatically chooses the mix behavior with the highest contrast.
The `harmonyPalette()` function creates a matrix of colors. The first column contains the base color and specified harmonies, e.g., triadic colors. The columns that follow contain tints, tones, or shades of the harmonic colors for additional color choices, similar to `monoPalette()`.
█ EXAMPLE CODE
The example code at the end of the script generates and visualizes color schemes by processing user inputs. The code builds the scheme's palette based on the "Base color" input and the additional inputs in the "Settings/Inputs" tab:
• "Palette type" specifies whether the palette uses a custom gradient, monochromatic base color variants, or color harmonies with monochromatic variants.
• "Target color" sets the top color for the "Gradient" palette type.
• The "Gray luminance" inputs determine variation behavior for "Monochromatic" and "Harmony" palette types. If "Auto" is selected, the palette mixes the base color toward white or black based on its brightness. Otherwise, it mixes the color toward the grayscale color with the specified relative luminance (from 0 to 1).
• "Harmony type" specifies the color harmony used in the palette. Each row in the palette corresponds to one of the harmonious colors, starting with the base color.
The code creates a table on the first bar to display the collection of calculated colors. Each cell in the table shows the color's `getHexString()` value in a tooltip for simple inspection.
Look first. Then leap.
█ EXPORTED FUNCTIONS
Below is a complete list of the functions and overloads exported by this library.
getRGB(source)
Retrieves the sRGB red, green, blue, and transparency components of a "color" value.
getHexString(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channel values to a string representing the corresponding color's hexadecimal form.
getHexString(source)
(Overload 2 of 2) Converts a "color" value to a string representing the sRGB color's hexadecimal form.
hexStringToRGB(source)
Converts a string representing an sRGB color's hexadecimal form to a set of decimal channel values.
hexStringToColor(source)
Converts a string representing an sRGB color's hexadecimal form to a "color" value.
getLRGB(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channel values to a set of linear RGB values with specified transparency information.
getLRGB(source)
(Overload 2 of 2) Retrieves linear RGB channel values and transparency information from a "color" value.
lrgbToRGB(lr, lg, lb, t)
Converts a set of linear RGB channel values to a set of sRGB values with specified transparency information.
lrgbToColor(lr, lg, lb, t)
Converts a set of linear RGB channel values and transparency information to a "color" value.
getHSL(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of HSL values with specified transparency information.
getHSL(source)
(Overload 2 of 2) Retrieves HSL channel values and transparency information from a "color" value.
hslToRGB(h, s, l, t)
Converts a set of HSL channel values to a set of sRGB values with specified transparency information.
hslToColor(h, s, l, t)
Converts a set of HSL channel values and transparency information to a "color" value.
getHSV(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of HSV values with specified transparency information.
getHSV(source)
(Overload 2 of 2) Retrieves HSV channel values and transparency information from a "color" value.
hsvToRGB(h, s, v, t)
Converts a set of HSV channel values to a set of sRGB values with specified transparency information.
hsvToColor(h, s, v, t)
Converts a set of HSV channel values and transparency information to a "color" value.
getHWB(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of HWB values with specified transparency information.
getHWB(source)
(Overload 2 of 2) Retrieves HWB channel values and transparency information from a "color" value.
hwbToRGB(h, w, b, t)
Converts a set of HWB channel values to a set of sRGB values with specified transparency information.
hwbToColor(h, w, b, t)
Converts a set of HWB channel values and transparency information to a "color" value.
getXYZ(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of XYZ values with specified transparency information.
getXYZ(source)
(Overload 2 of 2) Retrieves XYZ channel values and transparency information from a "color" value.
xyzToRGB(x, y, z, t)
Converts a set of XYZ channel values to a set of sRGB values with specified transparency information
xyzToColor(x, y, z, t)
Converts a set of XYZ channel values and transparency information to a "color" value.
getXYY(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of xyY values with specified transparency information.
getXYY(source)
(Overload 2 of 2) Retrieves xyY channel values and transparency information from a "color" value.
xyyToRGB(xc, yc, y, t)
Converts a set of xyY channel values to a set of sRGB values with specified transparency information.
xyyToColor(xc, yc, y, t)
Converts a set of xyY channel values and transparency information to a "color" value.
getLAB(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of CIELAB values with specified transparency information.
getLAB(source)
(Overload 2 of 2) Retrieves CIELAB channel values and transparency information from a "color" value.
labToRGB(l, a, b, t)
Converts a set of CIELAB channel values to a set of sRGB values with specified transparency information.
labToColor(l, a, b, t)
Converts a set of CIELAB channel values and transparency information to a "color" value.
getOKLAB(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of Oklab values with specified transparency information.
getOKLAB(source)
(Overload 2 of 2) Retrieves Oklab channel values and transparency information from a "color" value.
oklabToRGB(l, a, b, t)
Converts a set of Oklab channel values to a set of sRGB values with specified transparency information.
oklabToColor(l, a, b, t)
Converts a set of Oklab channel values and transparency information to a "color" value.
getLCH(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of CIELCh values with specified transparency information.
getLCH(source)
(Overload 2 of 2) Retrieves CIELCh channel values and transparency information from a "color" value.
lchToRGB(l, c, h, t)
Converts a set of CIELCh channel values to a set of sRGB values with specified transparency information.
lchToColor(l, c, h, t)
Converts a set of CIELCh channel values and transparency information to a "color" value.
getOKLCH(r, g, b, t)
(Overload 1 of 2) Converts a set of sRGB channels to a set of Oklch values with specified transparency information.
getOKLCH(source)
(Overload 2 of 2) Retrieves Oklch channel values and transparency information from a "color" value.
oklchToRGB(l, c, h, t)
Converts a set of Oklch channel values to a set of sRGB values with specified transparency information.
oklchToColor(l, c, h, t)
Converts a set of Oklch channel values and transparency information to a "color" value.
contrastRatio(value1, value2)
Calculates the contrast ratio between two colors values based on the formula from version 2 of the Web Content Accessibility Guidelines (WCAG).
isLightTheme(source)
Detects whether a background color represents a light theme or dark theme, based on the amount of contrast between the color and the white and black points.
grayscale(source)
Calculates the grayscale version of a color with the same relative luminance (i.e., brightness).
negative(source, colorSpace)
Calculates the negative (i.e., inverted) form of a specified color.
complement(source, colorSpace)
Calculates the complementary color for a `source` color using a cylindrical color space.
analogousColors(source, colorSpace)
Calculates the analogous colors for a `source` color using a cylindrical color space.
splitComplements(source, colorSpace)
Calculates the split-complementary colors for a `source` color using a cylindrical color space.
triadicColors(source, colorSpace)
Calculates the two triadic colors for a `source` color using a cylindrical color space.
tetradicColors(source, colorSpace, square)
Calculates the three square or rectangular tetradic colors for a `source` color using a cylindrical color space.
pentadicColors(source, colorSpace)
Calculates the four pentadic colors for a `source` color using a cylindrical color space.
hexadicColors(source, colorSpace)
Calculates the five hexadic colors for a `source` color using a cylindrical color space.
add(value1, value2, transpWeight)
Additively mixes two "color" values, with optional transparency weighting.
overlay(fg, bg)
Estimates the resulting color that appears on the chart when placing one color over another.
fromGradient(value, bottomValue, topValue, bottomColor, topColor, colorSpace)
Calculates the gradient color that corresponds to a specific value based on a defined value range and color space.
fromMultiStepGradient(value, steps, colors, colorSpace)
Calculates a multi-step gradient color that corresponds to a specific value based on an array of step points, an array of corresponding colors, and a color space.
gradientPalette(baseColor, stopColor, steps, strength, model)
Generates a palette from a gradient between two base colors.
monoPalette(baseColor, grayLuminance, variations, strength, colorSpace)
Generates a monochromatic palette from a specified base color.
harmonyPalette(baseColor, harmonyType, grayLuminance, variations, strength, colorSpace)
Generates a palette consisting of harmonious base colors and their monochromatic variants.
