BTC Futures BasisShows various basis percentages in a table and plots historical basis. Also has an alert function for backwardation events. Useful for tracking bullish/bearish sentiment in BTC futures markets.
*Currently displays March and June futures for the following exchanges: Bitmex, Binance, Deribit, Okex, and FTX
Also displays CME Continuous Next Contract. All of the symbols are customizable.
-----------
Market-wide backwardation usually occurs during a heavy sell-off (such as a liquidation cascade).
**For getting alerts of backwardation events, I recommend creating an alert on the 1 minute chart with the condition "Any alert() function call". Alert level is customizable as well.
-----------
*NOTE!! : Futures contracts expire (obviously), so the contract symbols will need to be updated periodically. I will try to keep them updated going into the future.
**NOTE2!! : The alert() function does not track the CME contract. This is to avoid false triggers.
Tìm kiếm tập lệnh với " TABLE "
SPY Sub-Sector Daily Money Flow TableThis calculates the dollar volume per candlestick (2nd row) and cumulative (3rd row) of the entire trading day for each subsector of the SPY.
The 'Total' column is the total of all the subsectors combined. It is calculated separately from SPY volume.
The money flow is calculated with (open+close)/2 which means different timeframes yield different results and won't be especially accurate day-by-day. This is useful to quickly see rotation and possible divergences.
Enjoy!
PreMarketStatsThe idea is to catch pre market information (or other relevant data), that basically consists of a single number, in a table instead of using a plot that takes up space in the chart. In this example, I added pre market volume and pre market change in %. Where the second one is as well available in the details tab of the stock, it is not available if this tab is closed or during replays.
[CLX][#01] Animation - Price Ticker (Marquee)This indicator displays a classic animated price ticker overlaid on the user’s current chart. It is possible to fully customize it or to select one of the predefined styles.
A detailed description will follow in the next few days.
Used Pinescript technics:
- varip (view/animation)
- tulip instance (config/codestructur)
- table (view/position)
By the way, for me, one of the coolest animated effects is by Duyck
We hope you enjoy it! 🎉
CRYPTOLINX - jango_blockchained 😊👍
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely.
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script.
Probability TableThe script is inspired by user NickbarComb, I suggested checking out his Price Convergence script.
Basically, this script plots a table containing the probability of the current candle closing either higher or lower based on user-define past period.
Hope that it will be helpful.
MTF Price/Volume % [Anan]Hello friends,
This is a multi-timeframe table with these features:
Display price change percentage compared with the last timeframe candle close.
Display price change percentage compared with the last timeframe candle close MA.
Displays change percentage compared with the last timeframe candle volume.
Displays change percentage compared with the last timeframe candle volume MA.
Change type/length of MA for Price/Volume.
Full control of Panel position and size.
Full control of displaying any row or column.
Average Daily Range TableThis is the last script to complete Vladimir Poltoratskiy's setup found in his books.
Poltoratskiy argues that you should not take any fractal corridors higher than 50% of the Average Daily Range. To be honest, even 40% is a lot, because then, your target will be 160% ADR away from your entry and one "fracture" just can't be enough to predict moves this big.
I chose a table to visually represent the indicator because it doesn't change its value during the day. It takes far less room on the chart.
There are also two simple moving averages. You may use the as an indicator if the relative volatility as of late is extremely low and in that case, perhaps, expect an increase in the coming days. They are applied to the Average Daily Range, not one day range!
PAC newThis indicator will alert you when a candle goes above or below the price action channel (PAC) but only on the first or second candle after a colour change in candle.
When price is above the price action channel that is a bullish sign, when price is below the PAC that is a bearish sign.
The idea is that a sudden change in price is a cause to investigate further price action moving in that direction so the indicator aims to identify reversal
Scalping strategy that works on 5 min chart and aims to gain 10 pips. Do not act on every signal. Further investigation is required, for example by looking at RSI oversolf and overbought levels. For example, at an oversold area, a buy signal is more valid
Table: Forex Central Bank Interest RatesThis tool shows CB Interest Rates for USD, JPY, CAD, CHF, EUR, GBP, NZD, AUD - basically all the majors.
Use override and input your own value if it is changed and I haven't updated the script yet.
Month/Month Percentage % Change, Historical; Seasonal TendencyTable of monthly % changes in Average Price over the last 10 years (or the 10 yrs prior to input year).
Useful for gauging seasonal tendencies of an asset; backtesting monthly volatility and bullish/bearish tendency.
~~User Inputs~~
Choose measure of average: sma(close), sma(ohlc4), vwap(close), vwma(close).
Show last 10yrs, with 10yr average % change, or to just show single year.
Chose input year; with the indicator auto calculating the prior 10 years.
Choose color for labels and size for labels; choose +Ve value color and -Ve value color.
Set 'Daily bars in month': 21 for Forex/Commodities/Indices; 30 for Crypto.
Set precision: decimal places
~~notes~~
-designed for use on Daily timeframe (tradingview is buggy on monthly timeframe calculations, and less precise on weekly timeframe calculations).
-where Current month of year has not occurred yet, will print 9yr average.
-calculates the average change of displayed month compared to the previous month: i.e. Jan22 value represents whole of Jan22 compared to whole of Dec21.
-table displays on the chart over the input year; so for ES, with 2010 selected; shows values from 2001-2010, displaying across 2010-2011 on the chart.
-plots on seperate right hand side scale, so can be shrunk and dragged vertically.
-thanks to @gabx11 for the suggestion which inspired me to write this
Koalafied Risk ManagementTables and labels/lines showing trade levels and risk/reward. Use to manage trade risk compared to portfolio size.
Initial design optimised for tickers denominated against USD.
Multi-Session High/Low Trackertable that shows rth eth and full weekly range high and low with range difference from high and low
Table ATH and DayQuotes in the middle of a chartJust important things at a glance ..
AlltimeHigh and Daily High/Low
Trend Gazer v666: Unified ICT Trading System# Trend Gazer v666: Unified ICT Trading System
※日本語説明もあります。 Japanese Description follows;
## 📊 Overview
**Trend Gazer v666** is a revolutionary **all-in-one institutional trading system** that eliminates the need for multiple separate indicators. This unified framework synthesizes **ICT Smart Money Structure**, **Multi-Timeframe Order Blocks**, **Fair Value Gaps**, **Smoothed Heiken Ashi**, **Volumetric Weighted Cloud**, and **Non-Repaint STDEV bands** into a single coherent overlay.
Unlike traditional approaches that require traders to juggle 5-10 different scripts, Trend Gazer v666 delivers **complete market context** through intelligent script synthesis, eliminating conflicting signals and analysis paralysis.
---
## 🎯 Why Script Synthesis is Essential
### The Problem with Multiple Independent Scripts
Traditional trading setups suffer from critical inefficiencies:
1. **Information Overload** - Running 5-10 separate scripts clutters your chart, making pattern recognition nearly impossible
2. **Conflicting Signals** - Order Block script says BUY, Structure script shows Bearish CHoCH, Momentum indicator points down
3. **Missed Context** - You spot an Order Block but miss the CHoCH that invalidates it because they're on different indicators
4. **Analysis Paralysis** - Too many data points without unified logic leads to hesitation and missed entries
5. **Performance Degradation** - Multiple `request.security()` calls from different scripts slow down TradingView significantly
### The Institutional Reality
Professional trading desks don't use fragmented tools. They use **integrated platforms** where:
- Market structure automatically filters signals
- Order Blocks are validated against momentum
- Fair Value Gaps are displayed only when relevant to current structure
- All components communicate to provide unified trade recommendations
**Trend Gazer v666 brings institutional-grade integration to retail traders.**
---
## 🔧 How Script Synthesis Works in v666
### Unified Data Flow Architecture
Instead of independent scripts calculating the same data redundantly, v666 uses a **single-pass analysis system**:
```
┌─────────────────────────────────────────────────────┐
│ Multi-Timeframe Data Ingestion (1m/3m/15m/60m) │
│ ─ Single request.security() call per timeframe │
│ ─ Shared across all components │
└──────────────────┬──────────────────────────────────┘
│
┌─────────┴─────────┐
│ │
┌────▼────┐ ┌────▼────┐
│ OB │ │ CHoCH │
│ Detection│ │Detection │
└────┬────┘ └────┬────┘
│ │
└─────────┬─────────┘
│
┌───────▼────────┐
│ Unified Logic │ ◄── Smoothed HA Filter
│ - OB blocks │ ◄── VWC Confirmation
│ signals │ ◄── NPR Band Validation
│ - CHoCH gates│ ◄── EMA Trend Context
│ all signals│
└───────┬────────┘
│
┌──────▼─────┐
│ Signals │
│ #0 - #5 │
└────────────┘
```
### Key Synthesis Techniques
#### 1. **Cross-Component Validation**
**Signal 5 (OB Strong 70%+)**:
- Detects Order Block creation
- Checks volume distribution (70%+ threshold)
- Validates against Smoothed Heiken Ashi trend
- Confirms with VWC momentum
- Gates with CHoCH structure filter
- **Result**: Only displays when ALL conditions align
**Traditional Multi-Script Approach**:
- OB script shows OB (doesn't know about HA trend)
- HA script shows bearish (doesn't know about OB)
- Structure script shows no CHoCH yet
- **Result**: Conflicting information, no clear action
#### 2. **Intelligent Signal Gating**
**ICT Structure Filter** (optional, default OFF):
```pinescript
if not is_signal_after_ms
// Hide ALL signals (including Signal 0) until CHoCH occurs
buySig0 := false
buySig := false
buySig4 := false
buySig10 := false
```
This prevents the classic mistake of trading against market structure because your OB indicator doesn't communicate with your structure indicator. **All signals (S0-S5) are subject to this filter when enabled.**
#### 3. **OB Direction Filter**
When 2+ consecutive Bullish OBs are detected:
- **Automatically blocks ALL SELL signals** across Signals #0-5
- Fair Value Gaps below price are visually de-emphasized
- CHoCH labels still appear (structure always visible)
**Why This Matters**: Your Order Block script and signal generation script now "talk" to each other. No more taking SELL signals when institutional buying zones are stacked below.
#### 4. **Smoothed Heiken Ashi Integration**
The Smoothed HA doesn't just display candles—it **filters every signal** (including Signal #0):
```pinescript
if enableSmoothedHAFilter
if smoothedHA_isBullish // BLACK candles
sellSig0 := false // Block Signal 0 SELL
sellSig := false // Block counter-trend SELLs
else // WHITE candles
buySig0 := false // Block Signal 0 BUY
buySig := false // Block counter-trend BUYs
```
**Traditional Approach**: Run separate Smoothed HA script, manually compare candle color to signals. Easy to miss.
#### 5. **Fair Value Gap Context Awareness**
FVGs in v666 know about:
- Current market structure (CHoCH direction)
- Active Order Blocks (don't clutter OB zones)
- Time relevance (auto-fade after break)
They're not just boxes on a chart—they're **contextualized inefficiencies** that update as market conditions change.
#### 6. **Unified Alert System**
**💎 STRONG BUY/SELL**:
- Triggers when: 70%+ OB creation OR Signal #5 fires
- **Why synthesis matters**: Alert knows about both OB creation AND signal generation because they share the same codebase
**Traditional Approach**: Set separate alerts on OB script and Signal script, get duplicate/conflicting notifications.
---
## 🔥 Core Components & Their Integration
### 1️⃣ ICT Smart Money Structure (Donchian Method)
**Purpose**: Identify institutional trend shifts that precede major moves.
**Components**:
- **1.CHoCH** (Bullish) - Lower low broken, bullish structure shift
- **A.CHoCH** (Bearish) - Higher high broken, bearish structure shift
- **SiMS/BoMS** - Momentum continuation confirmations
**Integration**:
- **Gates ALL signals** - No signal displays before first CHoCH
- **Directional bias** - After 1.CHoCH, only BUY signals pass filters
- **Pattern tracking** - Triple CHoCH sequences tracked for STRONG signals
**Credit**: Based on *ICT Donchian Smart Money Structure* by Zeiierman (CC BY-NC-SA 4.0)
---
### 2️⃣ Multi-Timeframe Order Blocks
**Purpose**: Map institutional supply/demand zones across timeframes.
**Timeframes**: 1m, 3m, 15m, 60m, Current TF
**Key Features**:
- **70%+ Volume Detection** - Identifies high-conviction institutional zones
- **Volumetric Analysis** - Each OB shows volume distribution (e.g., "12.5M 85%")
- **Time/Date Display** - "14:30 today" or "14:30 yday" for temporal context
- **Breaker Tracking** - Failed OBs that flip polarity
**Integration**:
- **OB Direction Filter** - 2+ consecutive Bullish OBs block ALL SELL signals
- **Signal Enhancement** - Signals inside OB zones get priority markers
- **CHoCH Validation** - OBs without CHoCH confirmation are visually subdued
**Display Format**:
```
12.5M 85% OB 15m 14:30 today
└─┬─┘ └┬┘ └┬┘ └──┬─┘ └─┬─┘
│ │ │ │ └─ Temporal marker
│ │ │ └──────── Time (JST)
│ │ └────────────── Timeframe
│ └───────────────────── Volume percentage
└────────────────────────── Total volume
```
---
### 3️⃣ Fair Value Gaps (FVG)
**Purpose**: Identify price inefficiencies institutions must correct.
**Detection Logic**:
```
Bullish FVG: high < low → Gap up (expect downward fill)
Bearish FVG: low > high → Gap down (expect upward fill)
```
**Integration**:
- **Structure-Aware** - Only highlights FVGs aligned with CHoCH direction
- **OB Interaction** - FVGs inside active OBs are de-emphasized
- **Volume Attribution** - Shows dominant volume side (Bull vs Bear)
**Display Format**:
```
8.3M 85% FVG 5m 09:15 today
```
**Why Integration Matters**: Standalone FVG indicators show ALL gaps. v666 shows only **actionable** gaps based on current market structure.
---
### 4️⃣ Smoothed Heiken Ashi
**Purpose**: Filter noise and provide clear trend context.
**Calculation**:
- EMA smoothing of Heiken Ashi components
- Eliminates false reversals common in raw HA
**Color Coding**:
- **BLACK (Bullish)** - Clean uptrend, BUY signals prioritized
- **WHITE (Bearish)** - Clean downtrend, SELL signals prioritized
**Integration**:
- **Signal Gating** - Blocks counter-trend signals by default
- **First Signal Only** - Optional: Show only first signal after HA color change
- **Structure Alignment** - HA trend must match CHoCH direction
---
### 5️⃣ Volumetric Weighted Cloud (VWC)
**Purpose**: Track institutional momentum across 6 timeframes.
**Timeframes**: 1m, 3m, 5m, 15m, 60m, 240m
**Visual**:
- Real-time status table (bottom-left by default)
- Shows RSI, Structure, and EMA status per timeframe
**Integration**:
- **Signal 2 Generator** - VWC directional changes trigger entries
- **Momentum Confirmation** - Validates OB bounces
- **Multi-TF Alignment** - Displays timeframe confluence
---
### 6️⃣ Non-Repaint STDEV (NPR) + Bollinger Bands
**Purpose**: Identify extreme mean-reversion points without repainting.
**Timeframes**: 15m, 60m
**Integration**:
- **Signal 4** - 60m NPR/BB bounce with EMA slope validation
- **Volatility Context** - Informs OB size expectations
- **Extreme Detection** - "Close INSIDE bands" logic prevents knife-catching
---
## 🚀 Six-Signal Trading System
### Signal Hierarchy
**💎 HIGHEST PRIORITY**:
- **Signal #5 (OB Strong 70%+)** - Institutional conviction zones
**⭐ HIGH PRIORITY**:
- **Signal #4** - 60m NPR/BB bounce with EMA filter
**🎯 STANDARD SIGNALS**:
- **Signal #0** - Smoothed HA Touch & Breakout (ALL filters apply)
- **Signal #1** - RSI Shift + Structure (Strictest)
- **Signal #2** - VWC Switch (Most frequent)
- **Signal #3** - Structure Change
### Signal #5: OB Strong (Star Signal) ⭐
**Trigger Conditions**:
1. 70%+ volume Order Block created (Bullish or Bearish)
2. Smoothed HA aligns with OB direction
3. Market structure supports direction (optional: CHoCH occurred)
**Label Format**:
```
🌟BUY #5
@ HL and/or
EMA converg.
85% (12.5K)
```
**Why It's Reliable**:
- 70%+ volume threshold eliminates weak OBs
- Combines OB detection + signal generation + trend filter
- Historically shows 65-75% win rate in trending markets
---
## 🎯 Advanced Features
### OB Direction Filter (Default ON)
**Bullish OB Scenario**:
```
Chart shows: consecutive Bullish OBs
Result:
✅ All BUY signals (#0-5) allowed
❌ All SELL signals blocked (red zone is institutional support)
✅ 1.CHoCH can still occur (structure always visible)
```
**Why This Matters**: Prevents the costly mistake of shorting into institutional buying zones.
### Smoothed HA First Signal Only
**Without Filter**:
```
HA: BLACK─┐ ┌─BLACK
└─WHITE──┘
Signals: ↓BUY BUY BUY SELL SELL SELL BUY BUY BUY BUY
```
**With Filter (Enabled)**:
```
HA: BLACK─┐ ┌─BLACK
└─WHITE──┘
Signals: ↓BUY SELL BUY
FIRST FIRST FIRST
```
**Result**: 70% fewer signals, 40% higher win rate (reduced noise). **Applies to all signals including Signal #0 (HA Touch & Breakout).**
### Bullish OB Bypass Filter (Default ON)
**Special Rule**: When last OB is Bullish → **Force enable ALL BUY signals**
This overrides:
- ICT Structure Filter
- EMA Trend Filter
- Range Market Filter
- Smoothed HA Filter
**Rationale**: Fresh Bullish OB = institutional buying. Trust the big players.
---
## 📡 Alert System (Simplified)
### Essential Alerts Only
1. **💎 STRONG BUY** - 70%+ OB OR Signal #5
2. **💎 STRONG SELL** - 70%+ OB OR Signal #5
3. **🎯 ALL BUY SIGNALS** - Any BUY (#0-5 / OB↑ / 1.CHoCH)
4. **🎯 ALL SELL SIGNALS** - Any SELL (#0-5 / OB↓ / A.CHoCH)
5. **🔔 ANY ALERT** - BUY or SELL detected
**Alert Format**:
```
BTCUSDT 5 💎 STRONG BUY
ETHUSDT 15 BUY SIGNAL (Check chart for #0-5/OB↑/1.CHoCH)
```
**Why Unified Alerts Matter**: Single script = single alert system. No duplicate notifications from overlapping scripts.
---
## ⚙️ Configuration
### Essential Settings
**ICT Structure Filter** (Default: OFF):
- When ON: Only show signals after CHoCH/SiMS/BoMS
- Recommended for beginners to avoid counter-trend trades
**OB Direction Filter** (Default: ON):
- Blocks SELL signals when Bullish OBs dominate
- Core synthesis feature—keeps signals aligned with institutional zones
**Smoothed HA Filter** (Default: ON):
- Blocks counter-trend signals based on HA candle color
- Pair with "First Signal Only" for cleanest chart
**Show Lower Timeframes** (Default: OFF):
- Display 1m/3m OBs on higher timeframe charts
- Disabled by default for performance on 60m+ charts
### Style Settings
**Multi-Timeframe Order Blocks**:
- Enable/disable specific timeframes (1m/3m/15m/60m)
- Combine Overlapping OBs: Merges confluence zones
- Extend Zones: 40 bars (dynamic until broken)
**Fair Value Gaps**:
- Current timeframe only (prevents clutter)
- Mitigation source: Close or High/Low
**Status Table**:
- Position: Bottom Left (default)
- Displays: 4H, 1H, 15m, 5m status
- Columns: RSI, Structure, EMA state
---
## 📚 How to Use
### For Scalpers (1m-5m Charts)
1. Enable **1m and 3m Order Blocks**
2. Wait for **BLACK Smoothed HA** (bullish) or **WHITE** (bearish)
3. Take **Signal #5** (OB Strong) or **Signal #0** (HA Breakout)
4. Use FVGs as micro-targets
5. Set stop below nearest OB
**Alert Setup**: `💎 STRONG BUY` + `💎 STRONG SELL`
### For Day Traders (15m-60m Charts)
1. Enable **15m and 60m Order Blocks**
2. Wait for **1.CHoCH** or **A.CHoCH** (structure shift)
3. Look for **Signal #5** (OB 70%+) or **Signal #4** (NPR bounce)
4. Confirm with VWC table (15m/60m should align)
5. Target previous swing high/low or next OB zone
**Alert Setup**: `🎯 ALL BUY SIGNALS` + `🎯 ALL SELL SIGNALS`
### For Swing Traders (4H-Daily Charts)
1. Enable **60m Order Blocks** (renders as larger zones on HTF)
2. Wait for **Market Structure confirmation** (CHoCH)
3. Focus on **Signal #1** (RSI + Structure) for highest conviction
4. Use **EMA 200/400/800** for macro trend alignment
5. Target major FVG fills or structure levels
**Alert Setup**: `🔔 ANY ALERT` (covers all scenarios)
### Universal Strategy (Recommended)
**Phase 1: Build Confidence** (Weeks 1-4)
- Trade ONLY **💎 STRONG BUY/SELL** signals
- Ignore all other signals (they're for context)
- Paper trade to observe accuracy
**Phase 2: Add Confirmation** (Weeks 5-8)
- Add **Signal #4** (NPR bounce) to your arsenal
- Require Smoothed HA alignment
- Still avoid Signals #0-3
**Phase 3: Full System** (Weeks 9+)
- Gradually incorporate Signals #0-3 for **additional entries**
- Use them to add to existing positions from #4/#5
- Never trade #0-3 alone without higher signal confirmation
---
## 🏆 What Makes v666 Unique
### 1. **True Script Synthesis**
**Other "all-in-one" indicators**: Copy-paste multiple scripts into one file. Components don't communicate.
**Trend Gazer v666**: Purpose-built unified logic where:
- OB detection informs signal generation
- CHoCH gates all signals automatically
- Smoothed HA filters entries in real-time
- VWC provides momentum confirmation
- All components share data structures (single-pass efficiency)
### 2. **Intelligent Signal Prioritization**
Not all signals are equal:
- **30% transparency** = 💎 STRONG / ⭐ Star (trade these)
- **70% transparency** = Standard signals (use as confirmation)
**Visual hierarchy** eliminates analysis paralysis.
### 3. **Institutional Zone Mapping**
**Multi-Timeframe Order Blocks** with:
- Volumetric analysis (12.5M 85%)
- Temporal context (today/yday)
- Confluence detection (combined OBs)
- Break tracking (stops extending when invalidated)
No other free indicator provides this level of OB detail.
### 4. **Non-Repaint Architecture**
Every component uses `barstate.isconfirmed` checks. What you see in backtests = what you'd see in real-time. No false confidence from repainting.
### 5. **Performance Optimized**
- Single `request.security()` call per timeframe (most scripts call it separately per component)
- Memory-efficient OB storage (max 100 OBs vs unlimited in some scripts)
- Dynamic rendering (only visible OBs drawn)
- Smart garbage collection (old FVGs auto-removed)
**Result**: Faster than running 3 separate OB/Structure/Signal scripts.
### 6. **Educational Transparency**
- All logic documented in code comments
- Signal conditions clearly explained
- Credits given to original algorithm authors
- Open-source (MPL 2.0) - learn and modify
---
## 💡 Educational Value
### Learning ICT Concepts
Use v666 as a **visual teaching tool**:
- **Market Structure**: See CHoCH/SiMS/BoMS in real-time
- **Order Blocks**: Understand institutional positioning
- **Fair Value Gaps**: Learn inefficiency correction
- **Smart Money Behavior**: Watch footprints unfold
### Backtesting Insights
Test these hypotheses:
1. Do 70%+ OBs have higher win rates than standard OBs?
2. Does trading after CHoCH improve risk/reward?
3. Which timeframe OBs (1m/3m/15m/60m) work best for your style?
4. Does Smoothed HA "First Signal Only" reduce false entries?
**v666 makes ICT concepts measurable.**
---
## ⚠️ Important Disclaimers
### Risk Warning
This indicator is for **educational and informational purposes only**. It is **NOT** financial advice.
**Trading involves substantial risk of loss**. Past performance does not predict future results. No indicator guarantees profitable trades.
**Before trading**:
- ✅ Practice on paper/demo accounts (minimum 30 days)
- ✅ Consult qualified financial advisors
- ✅ Understand you are solely responsible for your decisions
- ✅ Losses are part of trading—accept this reality
### Performance Expectations
**Realistic Win Rates** (when used correctly):
- 💎 STRONG Signals (#5 + 70% OB): 60-75%
- ⭐ Signal #4 (NPR bounce): 55-70%
- ✅ Use proper risk management (never risk >1-2% per trade)
- 🎯 Signals #0-3 (confirmation): 50-65%
**Key Factors**:
- Higher win rates in trending markets
- Lower win rates in choppy/ranging conditions
- Win rate alone doesn't predict profitability (R:R matters)
### Not a "Holy Grail"
v666 doesn't:
- ❌ Predict the future
- ❌ Work in all market conditions (ranging markets = lower accuracy)
- ❌ Replace proper trade management
- ❌ Eliminate the need for education
It's a **tool**, not a trading bot. Your discretion, risk management, and psychology determine success.
---
## 🔗 Credits & Licenses
### Component Sources
1. **ICT Donchian Smart Money Structure**
Author: Zeiierman
License: CC BY-NC-SA 4.0
Modifications: Integrated with signal system, added CHoCH pattern tracking
2. **Reverse RSI Signals**
Author: AlgoAlpha
License: MPL 2.0
Modifications: Adapted for internal signal logic
3. **Multi-Timeframe Order Blocks & FVG**
Custom implementation based on ICT concepts
Enhanced with volumetric analysis and confluence detection
4. **Smoothed Heiken Ashi**
Custom EMA-smoothed implementation
Integrated as real-time signal filter
### This Indicator's License
**Mozilla Public License 2.0 (MPL 2.0)**
You are free to:
- ✅ Use commercially
- ✅ Modify and distribute
- ✅ Use privately
Conditions:
- 📄 Disclose source
- 📄 Include license and copyright notice
- 📄 Use same license for modifications
---
## 📞 Support & Best Practices
### Reporting Issues
If you encounter bugs, provide:
1. Chart timeframe and symbol
2. Settings configuration (screenshot)
3. Description of unexpected behavior
4. Expected vs actual result
### Recommended Workflow
**Week 1-2**: Chart observation only
- Don't take trades yet
- Observe Signal #5 appearances
- Note when OB Direction Filter blocks signals
- Watch CHoCH/structure shifts
**Week 3-4**: Paper trading
- Trade only 💎 STRONG signals
- Document every trade (screenshot + notes)
- Track: Win rate, R:R, setup quality
**Week 5+**: Small live size
- Start with minimum position sizing
- Gradually increase as confidence builds
- Review trades weekly
---
## 🎓 Recommended Learning Path
**Phase 1: Foundation** (2-4 weeks)
1. Study ICT Concepts (YouTube: Inner Circle Trader)
- Market Structure (CHoCH, BOS)
- Order Blocks
- Fair Value Gaps
2. Watch v666 on charts daily (don't trade)
3. Learn to identify 1.CHoCH and A.CHoCH manually
**Phase 2: OB Mastery** (2-4 weeks)
1. Focus only on Signal #5 (OB Strong 70%+)
2. Paper trade these exclusively
3. Understand why 70%+ volume matters
4. Learn OB Direction Filter behavior
**Phase 3: Structure Integration** (2-4 weeks)
1. Add ICT Structure Filter (ON)
2. Only trade signals after CHoCH
3. Understand structure-signal relationship
4. Learn to wait for structure confirmation
**Phase 4: Multi-TF Analysis** (4-8 weeks)
1. Study MTF Order Block confluence
2. Learn when 15m + 60m OBs align
3. Understand timeframe hierarchy
4. Use VWC table for momentum confirmation
**Phase 5: Full System** (Ongoing)
1. Gradually add Signals #4, #0-3
2. Develop personal filter preferences
3. Refine entry/exit timing
4. Build consistent edge
---
## ✅ Quick Start Checklist
- Add indicator to chart
- Set timeframe (recommend 15m for learning)
- Enable **OB Direction Filter** (ON)
- Enable **Smoothed HA Filter** (ON)
- Keep **ICT Structure Filter** (OFF initially to see all signals)
- Enable **1m, 3m, 15m, 60m Order Blocks**
- Set **Status Table** to Bottom Left
- Set up **💎 STRONG BUY** and **💎 STRONG SELL** alerts
- Paper trade for 30 days minimum
- Document every Signal #5 setup
- Review weekly performance
- Adjust filters based on results
---
## 🚀 Version History
### v666 - Unified ICT System (Current)
- ✅ Synthesized 5+ independent scripts into unified framework
- ✅ Added OB Direction Filter (institutional zone awareness)
- ✅ Integrated Smoothed Heiken Ashi as real-time signal filter
- ✅ Implemented 70%+ volumetric OB detection
- ✅ Added temporal markers (today/yday) to OB/FVG
- ✅ Simplified alert system (5 essential alerts only)
- ✅ Performance optimized (single-pass MTF analysis)
- ✅ Status table redesigned (4H/1H/15m/5m only)
### v5.0 - Simplified ICT Mode (Previous)
- ICT-focused feature set
- Basic OB/FVG detection
- 8-signal system
- Separate script components
---
## 💬 Final Thoughts
### Why "Script Synthesis" Matters
Imagine trading with:
- **TradingView Chart** (price action)
- **OB Indicator #1** (doesn't know about structure)
- **Structure Indicator #2** (doesn't filter OB signals)
- **Momentum Indicator #3** (doesn't gate signals)
- **Smoothed HA Indicator #4** (you manually compare candle color)
- **FVG Indicator #5** (shows all gaps, no prioritization)
**Result**: 5 scripts, conflicting info, missed signals, slow charts.
**Trend Gazer v666**: All 5 components + signal generation **unified**. They communicate, validate each other, and present a single coherent view.
### What Success Looks Like
**Month 1**: You understand the system
**Month 2**: You're profitable on paper
**Month 3**: You start small live trades
**Month 4+**: Confidence grows, size increases
**The goal**: Use v666 to learn institutional order flow thinking. Eventually, you'll rely on the indicator less and your pattern recognition more.
### Trade Smart. Trade Safe. Trade with Structure.
---
**© rasukaru666 | 2025 | Mozilla Public License 2.0**
*This indicator is published as open source to contribute to the trading education community. If it helps you, please share your experience and help others learn.*
---
# Trend Gazer v666: 統合型ICTトレーディングシステム
## 📊 概要
**Trend Gazer v666**は、複数の独立したインジケータを不要にする革新的な**オールインワン機関投資家向けトレーディングシステム**です。この統合フレームワークは、**ICTスマートマネーストラクチャー**、**マルチタイムフレームオーダーブロック**、**フェアバリューギャップ**、**スムーズ平均足**、**出来高加重クラウド**、**ノンリペイントSTDEVバンド**を単一の統合オーバーレイに集約しています。
従来の5〜10個の異なるスクリプトを使い分ける必要があるアプローチとは異なり、Trend Gazer v666はインテリジェントなスクリプト合成によって**完全な市場コンテキスト**を提供し、相反するシグナルや分析麻痺を解消します。
---
## 🎯 なぜスクリプトの合成が不可欠なのか
### 複数の独立したスクリプトの問題点
従来のトレーディングセットアップには深刻な非効率性があります：
1. **情報過多** - 5〜10個の独立したスクリプトを実行すると、チャートが煩雑になり、パターン認識がほぼ不可能になります
2. **相反するシグナル** - オーダーブロックスクリプトは買いシグナル、ストラクチャースクリプトは弱気CHoCH、モメンタム指標は下向き
3. **文脈の欠落** - オーダーブロックを発見したが、それを無効化するCHoCHを見逃す（異なるインジケータに表示されているため）
4. **分析麻痺** - 統一されたロジックなしに多数のデータポイントがあると、躊躇してエントリーを逃します
5. **パフォーマンス低下** - 異なるスクリプトからの複数の`request.security()`呼び出しがTradingViewを大幅に遅くします
### 機関投資家の現実
プロのトレーディングデスクは断片的なツールを使用しません。彼らは**統合プラットフォーム**を使用します：
- マーケットストラクチャーが自動的にシグナルをフィルタリング
- オーダーブロックがモメンタムに対して検証される
- フェアバリューギャップは現在のストラクチャーに関連する場合にのみ表示
- すべてのコンポーネントが通信して統一されたトレード推奨を提供
**Trend Gazer v666は、機関投資家レベルの統合を個人トレーダーにもたらします。**
---
## 🔧 v666におけるスクリプト合成の仕組み
### 統合データフローアーキテクチャ
独立したスクリプトが同じデータを冗長に計算するのではなく、v666は**シングルパス分析システム**を使用します：
```
┌─────────────────────────────────────────────────────┐
│ マルチタイムフレームデータ取得 (1m/3m/15m/60m) │
│ ─ タイムフレームごとに1回のrequest.security()呼び出し │
│ ─ すべてのコンポーネントで共有 │
└──────────────────┬──────────────────────────────────┘
│
┌─────────┴─────────┐
│ │
┌────▼────┐ ┌────▼────┐
│ OB │ │ CHoCH │
│ 検出 │ │ 検出 │
└────┬────┘ └────┬────┘
│ │
└─────────┬─────────┘
│
┌───────▼────────┐
│ 統合ロジック │ ◄── スムーズ平均足フィルター
│ - OBがシグナル│ ◄── VWC確認
│ をブロック │ ◄── NPRバンド検証
│ - CHoCHが │ ◄── EMAトレンドコンテキスト
│ すべての │
│ シグナルを │
│ ゲート │
└───────┬────────┘
│
┌──────▼─────┐
│ シグナル │
│ #0 - #5 │
└────────────┘
```
### 主要な合成技術
#### 1. **コンポーネント間検証**
**シグナル5（OB Strong 70%+）**：
- オーダーブロック作成を検出
- 出来高分布を確認（70%以上の閾値）
- スムーズ平均足トレンドに対して検証
- VWCモメンタムで確認
- CHoCHストラクチャーフィルターでゲート
- **結果**：すべての条件が揃った場合のみ表示
**従来のマルチスクリプトアプローチ**：
- OBスクリプトはOBを表示（平均足トレンドを知らない）
- 平均足スクリプトは弱気を表示（OBを知らない）
- ストラクチャースクリプトはまだCHoCHを表示しない
- **結果**：相反する情報、明確なアクションなし
#### 2. **インテリジェントシグナルゲーティング**
**ICTストラクチャーフィルター**（オプション、デフォルトOFF）：
```pinescript
if not is_signal_after_ms
// CHoCHが発生するまですべてのシグナル（シグナル0を含む）を非表示
buySig0 := false
buySig := false
buySig4 := false
buySig10 := false
```
これにより、OBインジケータがストラクチャーインジケータと通信しないために、マーケットストラクチャーに逆らってトレードするという古典的なミスを防ぎます。**有効化時にはすべてのシグナル（S0-S5）がこのフィルターの対象となります。**
#### 3. **OB方向フィルター**
2つ以上の連続した強気OBが検出された場合：
- **すべてのSELLシグナルを自動的にブロック**（シグナル#0-5全体で）
- 価格下のフェアバリューギャップは視覚的に抑制される
- CHoCHラベルは依然として表示される（ストラクチャーは常に表示）
**これが重要な理由**：オーダーブロックスクリプトとシグナル生成スクリプトが「会話」するようになります。機関投資家の買いゾーンが下に積み重なっているときにSELLシグナルを取ることはもうありません。
#### 4. **スムーズ平均足統合**
スムーズ平均足は単にローソク足を表示するだけでなく、**すべてのシグナル（シグナル#0を含む）をフィルタリング**します：
```pinescript
if enableSmoothedHAFilter
if smoothedHA_isBullish // 黒いローソク足
sellSig0 := false // シグナル0 SELLをブロック
sellSig := false // 逆張りSELLをブロック
else // 白いローソク足
buySig0 := false // シグナル0 BUYをブロック
buySig := false // 逆張りBUYをブロック
```
**従来のアプローチ**：別のスムーズ平均足スクリプトを実行し、手動でローソク足の色をシグナルと比較。見逃しやすい。
#### 5. **フェアバリューギャップのコンテキスト認識**
v666のFVGは以下を認識しています：
- 現在のマーケットストラクチャー（CHoCH方向）
- アクティブなオーダーブロック（OBゾーンを煩雑にしない）
- 時間的関連性（ブレイク後自動フェード）
これらは単なるチャート上のボックスではなく、市場状況の変化に応じて更新される**コンテキスト化された非効率性**です。
#### 6. **統合アラートシステム**
**💎 STRONG BUY/SELL**：
- トリガー条件：70%以上のOB作成またはシグナル#5発火
- **合成が重要な理由**：アラートはOB作成とシグナル生成の両方を認識します（同じコードベースを共有しているため）
**従来のアプローチ**：OBスクリプトとシグナルスクリプトに別々のアラートを設定し、重複/相反する通知を受け取る。
---
## 🔥 コアコンポーネントとその統合
### 1️⃣ ICTスマートマネーストラクチャー（ドンチャン法）
**目的**：大きな動きに先行する機関投資家のトレンドシフトを特定します。
**コンポーネント**：
- **1.CHoCH**（強気） - 安値を下抜け、強気ストラクチャーシフト
- **A.CHoCH**（弱気） - 高値を上抜け、弱気ストラクチャーシフト
- **SiMS/BoMS** - モメンタム継続確認
**統合**：
- **すべてのシグナルをゲート** - 最初のCHoCHの前にシグナルを表示しない
- **方向バイアス** - 1.CHoCH後、BUYシグナルのみがフィルターを通過
- **パターン追跡** - トリプルCHoCHシーケンスを追跡してSTRONGシグナルを生成
**クレジット**：Zeiierman氏の*ICT Donchian Smart Money Structure*に基づく（CC BY-NC-SA 4.0）
---
### 2️⃣ マルチタイムフレームオーダーブロック
**目的**：タイムフレーム全体で機関投資家の需給ゾーンをマッピングします。
**タイムフレーム**：1m、3m、15m、60m、現在のTF
**主要機能**：
- **70%以上の出来高検出** - 高確信度の機関投資家ゾーンを特定
- **出来高分析** - 各OBは出来高分布を表示（例：「12.5M 85%」）
- **時刻/日付表示** - 「14:30 today」または「14:30 yday」による時間的コンテキスト
- **ブレーカー追跡** - 極性を反転させた失敗したOB
**統合**：
- **OB方向フィルター** - 2つ以上の連続した強気OBがすべてのSELLシグナルをブロック
- **シグナル強化** - OBゾーン内のシグナルは優先マーカーを取得
- **CHoCH検証** - CHoCH確認のないOBは視覚的に抑制される
**表示形式**：
```
12.5M 85% OB 15m 14:30 today
└─┬─┘ └┬┘ └┬┘ └──┬─┘ └─┬─┘
│ │ │ │ └─ 時間マーカー
│ │ │ └──────── 時刻（JST）
│ │ └────────────── タイムフレーム
│ └───────────────────── 出来高パーセンテージ
└────────────────────────── 総出来高
```
---
### 3️⃣ フェアバリューギャップ（FVG）
**目的**：機関投資家が修正しなければならない価格の非効率性を特定します。
**検出ロジック**：
```
強気FVG: high < low → ギャップアップ（下向きの埋めを予想）
弱気FVG: low > high → ギャップダウン（上向きの埋めを予想）
```
**統合**：
- **ストラクチャー認識** - CHoCH方向と一致するFVGのみをハイライト
- **OB相互作用** - アクティブなOB内のFVGは抑制される
- **出来高属性** - 支配的な出来高サイドを表示（強気vs弱気）
**表示形式**：
```
8.3M 85% FVG 5m 09:15 today
```
**統合が重要な理由**：スタンドアロンのFVGインジケータはすべてのギャップを表示します。v666は、現在のマーケットストラクチャーに基づいて**実行可能な**ギャップのみを表示します。
---
### 4️⃣ スムーズ平均足
**目的**：ノイズをフィルタリングし、明確なトレンドコンテキストを提供します。
**計算**：
- 平均足コンポーネントのEMAスムージング
- 生の平均足に共通する誤った反転を排除
**色分け**：
- **黒（強気）** - クリーンな上昇トレンド、BUYシグナル優先
- **白（弱気）** - クリーンな下降トレンド、SELLシグナル優先
**統合**：
- **シグナルゲーティング** - デフォルトで逆張りシグナルをブロック
- **最初のシグナルのみ** - オプション：平均足の色変化後の最初のシグナルのみを表示
- **ストラクチャー調整** - 平均足トレンドはCHoCH方向と一致する必要があります
---
### 5️⃣ 出来高加重クラウド（VWC）
**目的**：6つのタイムフレームにわたる機関投資家のモメンタムを追跡します。
**タイムフレーム**：1m、3m、5m、15m、60m、240m
**ビジュアル**：
- リアルタイムステータステーブル（デフォルトで左下）
- タイムフレームごとにRSI、ストラクチャー、EMAステータスを表示
**統合**：
- **シグナル2ジェネレーター** - VWC方向変化がエントリーをトリガー
- **モメンタム確認** - OBバウンスを検証
- **マルチTF整列** - タイムフレームのコンフルエンスを表示
---
### 6️⃣ ノンリペイントSTDEV（NPR）+ ボリンジャーバンド
**目的**：リペイントなしで極端な平均回帰ポイントを特定します。
**タイムフレーム**：15m、60m
**統合**：
- **シグナル4** - EMAスロープ検証を伴う60m NPR/BBバウンス
- **ボラティリティコンテキスト** - OBサイズの期待値を通知
- **極端検出** - 「バンド内のクローズ」ロジックがナイフキャッチを防止
---
## 🚀 6シグナルトレーディングシステム
### シグナル階層
**💎 最高優先度**：
- **シグナル#5（OB Strong 70%+）** - 機関投資家の確信ゾーン
**⭐ 高優先度**：
- **シグナル#4** - EMAフィルター付き60m NPR/BBバウンス
**🎯 標準シグナル**：
- **シグナル#0** - スムーズ平均足タッチ＆ブレイクアウト（全フィルター適用）
- **シグナル#1** - RSIシフト + ストラクチャー（最も厳格）
- **シグナル#2** - VWCスイッチ（最も頻繁）
- **シグナル#3** - ストラクチャー変更
### シグナル#5：OB Strong（スターシグナル）⭐
**トリガー条件**：
1. 70%以上の出来高オーダーブロック作成（強気または弱気）
2. スムーズ平均足がOB方向と一致
3. マーケットストラクチャーが方向をサポート（オプション：CHoCH発生）
**ラベル形式**：
```
🌟BUY #5
@ HL and/or
EMA converg.
85% (12.5K)
```
**信頼性が高い理由**：
- 70%以上の出来高閾値が弱いOBを排除
- OB検出 + シグナル生成 + トレンドフィルターを組み合わせ
- トレンド市場で歴史的に65-75%の勝率を示す
---
## 🎯 高度な機能
### OB方向フィルター（デフォルトON）
**強気OBシナリオ**：
```
チャート表示: 連続する強気OB
結果：
✅ すべてのBUYシグナル（#0-5）が許可される
❌ すべてのSELLシグナルがブロックされる（赤ゾーンは機関投資家のサポート）
✅ 1.CHoCHは依然として発生可能（ストラクチャーは常に表示）
```
**これが重要な理由**：機関投資家の買いゾーンにショートすることによる高コストのミスを防ぎます。
### スムーズ平均足「最初のシグナルのみ」
**フィルターなし**：
```
平均足: 黒─┐ ┌─黒
└─白──┘
シグナル: ↓BUY BUY BUY SELL SELL SELL BUY BUY BUY BUY
```
**フィルター有効時**：
```
平均足: 黒─┐ ┌─黒
└─白──┘
シグナル: ↓BUY SELL BUY
最初 最初 最初
```
**結果**：シグナルが70%減少、勝率が40%向上（ノイズ削減）。**シグナル#0（平均足タッチ＆ブレイクアウト）を含むすべてのシグナルに適用されます。**
### 強気OBバイパスフィルター（デフォルトON）
**特別ルール**：最後のOBが強気の場合 → **すべてのBUYシグナルを強制的に有効化**
これは以下をオーバーライドします：
- ICTストラクチャーフィルター
- EMAトレンドフィルター
- レンジマーケットフィルター
- スムーズ平均足フィルター
**理由**：新鮮な強気OB = 機関投資家の買い。大口投資家を信頼する。
---
## 📡 アラートシステム（簡素化）
### 必須アラートのみ
1. **💎 STRONG BUY** - 70%以上のOBまたはシグナル#5
2. **💎 STRONG SELL** - 70%以上のOBまたはシグナル#5
3. **🎯 ALL BUY SIGNALS** - 任意のBUY（#0-5 / OB↑ / 1.CHoCH）
4. **🎯 ALL SELL SIGNALS** - 任意のSELL（#0-5 / OB↓ / A.CHoCH）
5. **🔔 ANY ALERT** - BUYまたはSELLが検出された
**アラート形式**：
```
BTCUSDT 5 💎 STRONG BUY
ETHUSDT 15 BUY SIGNAL (Check chart for #0-5/OB↑/1.CHoCH)
```
**統合アラートが重要な理由**：単一のスクリプト = 単一のアラートシステム。重複するスクリプトからの重複通知はありません。
---
## ⚙️ 設定
### 必須設定
**ICTストラクチャーフィルター**（デフォルト：OFF）：
- ONの場合：CHoCH/SiMS/BoMS後にのみシグナルを表示
- 初心者には、逆張りトレードを避けるために推奨
**OB方向フィルター**（デフォルト：ON）：
- 強気OBが支配的な場合にSELLシグナルをブロック
- コア合成機能 - シグナルを機関投資家ゾーンと整合させる
**スムーズ平均足フィルター**（デフォルト：ON）：
- 平均足のローソク足色に基づいて逆張りシグナルをブロック
- 最もクリーンなチャートのために「最初のシグナルのみ」と組み合わせる
**低タイムフレーム表示**（デフォルト：OFF）：
- 高タイムフレームチャートに1m/3m OBを表示
- 60m以上のチャートでのパフォーマンスのためにデフォルトで無効
### スタイル設定
**マルチタイムフレームオーダーブロック**：
- 特定のタイムフレーム（1m/3m/15m/60m）の有効/無効
- 重複するOBを結合：コンフルエンスゾーンをマージ
- ゾーン延長：40バー（ブレイクされるまで動的）
**フェアバリューギャップ**：
- 現在のタイムフレームのみ（煩雑さを防ぐ）
- 緩和ソース：クローズまたは高値/安値
**ステータステーブル**：
- 位置：左下（デフォルト）
- 表示：4H、1H、15m、5mステータス
- 列：RSI、ストラクチャー、EMAステート
---
## 📚 使用方法
### スキャルパー向け（1m-5mチャート）
1. **1mと3mオーダーブロック**を有効化
2. **黒のスムーズ平均足**（強気）または**白**（弱気）を待つ
3. **シグナル#5**（OB Strong）または**シグナル#0**（平均足ブレイクアウト）を取る
4. FVGをマイクロターゲットとして使用
5. 最寄りのOBの下にストップを設定
**アラート設定**：`💎 STRONG BUY` + `💎 STRONG SELL`
### デイトレーダー向け（15m-60mチャート）
1. **15mと60mオーダーブロック**を有効化
2. **1.CHoCH**または**A.CHoCH**（ストラクチャーシフト）を待つ
3. **シグナル#5**（OB 70%+）または**シグナル#4**（NPRバウンス）を探す
4. VWCテーブルで確認（15m/60mが整列する必要がある）
5. 前のスイング高値/安値または次のOBゾーンをターゲットにする
**アラート設定**：`🎯 ALL BUY SIGNALS` + `🎯 ALL SELL SIGNALS`
### スイングトレーダー向け（4H-日足チャート）
1. **60mオーダーブロック**を有効化（HTFでより大きなゾーンとしてレンダリング）
2. **マーケットストラクチャー確認**（CHoCH）を待つ
3. 最高確信度のために**シグナル#1**（RSI + ストラクチャー）に焦点を当てる
4. マクロトレンド整列のために**EMA 200/400/800**を使用
5. 主要なFVGフィルまたはストラクチャーレベルをターゲットにする
**アラート設定**：`🔔 ANY ALERT`（すべてのシナリオをカバー）
### ユニバーサル戦略（推奨）
**フェーズ1：信頼構築**（1-4週間）
- **💎 STRONG BUY/SELL**シグナルのみでトレード
- 他のすべてのシグナルを無視（それらはコンテキスト用）
- ペーパートレードで精度を観察
**フェーズ2：確認追加**（5-8週間）
- 武器庫に**シグナル#4**（NPRバウンス）を追加
- スムーズ平均足の整列を要求
- シグナル#0-3は依然として避ける
**フェーズ3：フルシステム**（9週間以降）
- シグナル#0-3を徐々に**追加エントリー**として組み込む
- #4/#5からの既存のポジションに追加するために使用
- #0-3を高シグナル確認なしで単独でトレードしない
---
## 🏆 v666のユニークな点
### 1. **真のスクリプト合成**
**他の「オールインワン」インジケータ**：複数のスクリプトを1つのファイルにコピー＆ペースト。コンポーネントは通信しない。
**Trend Gazer v666**：目的別に構築された統合ロジックで：
- OB検出がシグナル生成に通知
- CHoCHがすべてのシグナルを自動的にゲート
- スムーズ平均足がリアルタイムでエントリーをフィルタリング
- VWCがモメンタム確認を提供
- すべてのコンポーネントがデータ構造を共有（シングルパス効率）
### 2. **インテリジェントシグナル優先順位付け**
すべてのシグナルが等しいわけではありません：
- **30%透明度** = 💎 STRONG / ⭐ スター（これらをトレード）
- **70%透明度** = 標準シグナル（確認として使用）
**視覚的階層**が分析麻痺を排除します。
### 3. **機関投資家ゾーンマッピング**
以下を含む**マルチタイムフレームオーダーブロック**：
- 出来高分析（12.5M 85%）
- 時間的コンテキスト（today/yday）
- コンフルエンス検出（結合OB）
- ブレイク追跡（無効化されたときに延長を停止）
他の無料インジケータは、このレベルのOB詳細を提供しません。
### 4. **ノンリペイントアーキテクチャ**
すべてのコンポーネントは`barstate.isconfirmed`チェックを使用します。バックテストで見るもの = リアルタイムで見るもの。リペイントによる誤った信頼はありません。
### 5. **パフォーマンス最適化**
- タイムフレームごとに単一の`request.security()`呼び出し（ほとんどのスクリプトはコンポーネントごとに別々に呼び出します）
- メモリ効率的なOBストレージ（最大100 OB vs 一部のスクリプトでは無制限）
- 動的レンダリング（表示可能なOBのみ描画）
- スマートガベージコレクション（古いFVGは自動削除）
**結果**：3つの独立したOB/ストラクチャー/シグナルスクリプトを実行するよりも高速。
### 6. **教育的透明性**
- すべてのロジックがコードコメントで文書化
- シグナル条件が明確に説明されている
- 元のアルゴリズム作成者にクレジットを付与
- オープンソース（MPL 2.0）- 学習と修正が可能
---
## 💡 教育的価値
### ICTコンセプトの学習
v666を**視覚的な教育ツール**として使用します：
- **マーケットストラクチャー**：リアルタイムでCHoCH/SiMS/BoMSを確認
- **オーダーブロック**：機関投資家のポジショニングを理解
- **フェアバリューギャップ**：非効率性の修正を学ぶ
- **スマートマネーの行動**：足跡が展開するのを観察
### バックテストインサイト
これらの仮説をテストします：
1. 70%以上のOBは標準OBよりも高い勝率を持つか？
2. CHoCH後のトレードはリスク/リワードを改善するか？
3. どのタイムフレームOB（1m/3m/15m/60m）が自分のスタイルに最適か？
4. スムーズ平均足「最初のシグナルのみ」は誤ったエントリーを減らすか？
**v666はICTコンセプトを測定可能にします。**
---
## ⚠️ 重要な免責事項
### リスク警告
このインジケータは**教育および情報提供のみを目的として**います。これは金融アドバイスでは**ありません**。
**トレーディングには大きな損失のリスクが伴います**。過去のパフォーマンスは将来の結果を予測しません。インジケータは利益のあるトレードを保証しません。
**トレーディング前に**：
- ✅ ペーパー/デモアカウントで練習（最低30日）
- ✅ 適切なリスク管理を使用（トレードあたり1-2%以上をリスクにしない）
- ✅ 資格のある金融アドバイザーに相談
- ✅ あなたが決定に対して単独で責任を負うことを理解
- ✅ 損失はトレーディングの一部である - この現実を受け入れる
### パフォーマンス期待値
**現実的な勝率**（正しく使用した場合）：
- 💎 STRONGシグナル（#5 + 70% OB）：60-75%
- ⭐ シグナル#4（NPRバウンス）：55-70%
- 🎯 シグナル#0-3（確認）：50-65%
**主要な要因**：
- トレンド市場でより高い勝率
- 変動的/レンジ状態でより低い勝率
- 勝率だけでは収益性を予測しない（R:Rが重要）
### 「聖杯」ではない
v666は以下を行いません：
- ❌ 未来を予測
- ❌ すべての市場状況で機能（レンジ市場 = より低い精度）
- ❌ 適切なトレード管理を置き換える
- ❌ 教育の必要性を排除
これは**ツール**であり、トレーディングボットではありません。あなたの裁量、リスク管理、心理学が成功を決定します。
---
## 🔗 クレジットとライセンス
### コンポーネントソース
1. **ICT Donchian Smart Money Structure**
作者：Zeiierman
ライセンス：CC BY-NC-SA 4.0
修正：シグナルシステムと統合、CHoCHパターン追跡を追加
2. **Reverse RSI Signals**
作者：AlgoAlpha
ライセンス：MPL 2.0
修正：内部シグナルロジック用に適応
3. **マルチタイムフレームオーダーブロック & FVG**
ICTコンセプトに基づくカスタム実装
出来高分析とコンフルエンス検出で強化
4. **スムーズ平均足**
カスタムEMAスムーズ実装
リアルタイムシグナルフィルターとして統合
### このインジケータのライセンス
**Mozilla Public License 2.0（MPL 2.0）**
自由に以下が可能です：
- ✅ 商業利用
- ✅ 修正と配布
- ✅ プライベート使用
条件：
- 📄 ソース開示
- 📄 ライセンスと著作権表示を含める
- 📄 修正に同じライセンスを使用
---
## 📞 サポートとベストプラクティス
### 問題報告
バグが発生した場合、以下を提供してください：
1. チャートのタイムフレームとシンボル
2. 設定構成（スクリーンショット）
3. 予期しない動作の説明
4. 期待される結果 vs 実際の結果
### 推奨ワークフロー
**第1-2週**：チャート観察のみ
- まだトレードしない
- シグナル#5の出現を観察
- OB方向フィルターがシグナルをブロックするタイミングに注意
- CHoCH/ストラクチャーシフトを観察
**第3-4週**：ペーパートレーディング
- 💎 STRONGシグナルのみをトレード
- すべてのトレードを文書化（スクリーンショット + メモ）
- 追跡：勝率、R:R、セットアップの質
**第5週以降**：小額実トレード
- 最小ポジションサイズから始める
- 信頼が高まるにつれて徐々に増やす
- 毎週トレードをレビュー
---
## 🎓 推奨学習パス
**フェーズ1：基礎**（2-4週間）
1. ICTコンセプトを学習（YouTube：Inner Circle Trader）
- マーケットストラクチャー（CHoCH、BOS）
- オーダーブロック
- フェアバリューギャップ
2. 毎日チャートでv666を観察（トレードしない）
3. 1.CHoCHとA.CHoCHを手動で識別することを学ぶ
**フェーズ2：OBマスタリー**（2-4週間）
1. シグナル#5（OB Strong 70%+）のみに焦点を当てる
2. これらを排他的にペーパートレード
3. 70%以上の出来高が重要な理由を理解
4. OB方向フィルターの動作を学ぶ
**フェーズ3：ストラクチャー統合**（2-4週間）
1. ICTストラクチャーフィルターを追加（ON）
2. CHoCH後のシグナルのみをトレード
3. ストラクチャー-シグナル関係を理解
4. ストラクチャー確認を待つことを学ぶ
**フェーズ4：マルチTF分析**（4-8週間）
1. MTFオーダーブロックコンフルエンスを学習
2. 15mと60m OBが整列するタイミングを学ぶ
3. タイムフレーム階層を理解
4. モメンタム確認にVWCテーブルを使用
**フェーズ5：フルシステム**（継続中）
1. 徐々にシグナル#4、#0-3を追加
2. 個人的なフィルター設定を開発
3. エントリー/イグジットタイミングを洗練
4. 一貫したエッジを構築
---
## ✅ クイックスタートチェックリスト
- インジケータをチャートに追加
- タイムフレームを設定（学習には15mを推奨）
- **OB方向フィルター**を有効化（ON）
- **スムーズ平均足フィルター**を有効化（ON）
- **ICTストラクチャーフィルター**を保持（すべてのシグナルを確認するため最初はOFF）
- **1m、3m、15m、60mオーダーブロック**を有効化
- **ステータステーブル**を左下に設定
- **💎 STRONG BUY**と**💎 STRONG SELL**アラートを設定
- 最低30日間ペーパートレード
- すべてのシグナル#5セットアップを文書化
- 毎週パフォーマンスをレビュー
- 結果に基づいてフィルターを調整
---
## 🚀 バージョン履歴
### v666 - 統合ICTシステム（現行）
- ✅ 5つ以上の独立したスクリプトを統合フレームワークに合成
- ✅ OB方向フィルターを追加（機関投資家ゾーン認識）
- ✅ リアルタイムシグナルフィルターとしてスムーズ平均足を統合
- ✅ 70%以上の出来高OB検出を実装
- ✅ OB/FVGに時間マーカー（today/yday）を追加
- ✅ アラートシステムを簡素化（5つの必須アラートのみ）
- ✅ パフォーマンス最適化（シングルパスMTF分析）
- ✅ ステータステーブル再設計（4H/1H/15m/5mのみ）
### v5.0 - 簡素化ICTモード（以前）
- ICT重視の機能セット
- 基本的なOB/FVG検出
- 8シグナルシステム
- 独立したスクリプトコンポーネント
---
## 💬 最後の言葉
### なぜ「スクリプト合成」が重要なのか
以下でトレーディングを想像してください：
- **TradingViewチャート**（価格アクション）
- **OBインジケータ#1**（ストラクチャーを知らない）
- **ストラクチャーインジケータ#2**（OBシグナルをフィルタリングしない）
- **モメンタムインジケータ#3**（シグナルをゲートしない）
- **スムーズ平均足インジケータ#4**（手動でローソク足色を比較）
- **FVGインジケータ#5**（すべてのギャップを表示、優先順位付けなし）
**結果**：5つのスクリプト、相反する情報、見逃したシグナル、遅いチャート。
**Trend Gazer v666**：5つのコンポーネント + シグナル生成がすべて**統合**。それらは通信し、相互に検証し、単一の統合ビューを提示します。
### 成功とはどのようなものか
**1ヶ月目**：システムを理解
**2ヶ月目**：ペーパーで収益性がある
**3ヶ月目**：小額の実トレードを開始
**4ヶ月目以降**：信頼が高まり、サイズが増加
**目標**：v666を使用して機関投資家のオーダーフロー思考を学ぶ。最終的には、インジケータへの依存が減り、パターン認識が増えます。
### スマートにトレード。安全にトレード。ストラクチャーでトレード。
---
**© rasukaru666 | 2025 | Mozilla Public License 2.0**
*このインジケータは、トレーディング教育コミュニティに貢献するためにオープンソースとして公開されています。役立った場合は、経験を共有し、他の人の学習を支援してください。*
H1 Regression Channel + Levels + RSI Divergence (NEON UI)This indicator combines multiple tools for H1 trading analysis:
Features:
Regression Channel: Automatically plots the upper, middle, and lower regression lines based on H1 data.
Strong Levels: Detects pivot highs and lows with a liquidity filter (volume and candle body size) to highlight significant support and resistance levels.
RSI + Divergence: Calculates RSI and detects bullish/bearish divergences, displayed in a bright neon table.
Neon Table UI: Shows RSI value, Bullish Divergence, and Bearish Divergence clearly and brightly.
Liquidity Filter: Only considers pivots with high volume or large candle bodies to improve signal quality.
How to Use:
Watch the regression channel for trend direction.
Use strong levels as potential support/resistance.
Check the neon table for RSI readings and divergences.
Bullish divergence (YES) indicates potential upward reversal; Bearish divergence (YES) indicates potential downward reversal.
Note:
The table updates automatically based on pivot formation and RSI divergence detection.
Works best on H1 timeframe.
RSI to 50 (decimal version) - TemujinTradingSimple indicator that shows the price levels required for the RSI to get to the value of 50.
What I observe is 50 rsi often acts as support or resistance and is a fair indication of bullish/bearish sentiment and price action and bounce/rejection levels.
It provides a table showing current time frame, 4 hr, daily, weekly describing the current rsi value and the price needed for that rsi to get to 50. This table is colored red when bearish at the time frame and green when bullish (as per <50 rsi or >50rsi).
Plots historical lines of each previous candle in the series showing how price interacts.
Updated script to allow manual input of price decimals to enable more assets price to be viewable in the table format.
RSI to 50 - TemujinTradingSimple indicator that shows the price levels required for the RSI to get to the value of 50.
What I observe is 50 rsi often acts as support or resistance and is a fair indication of bullish/bearish sentiment and price action and bounce/rejection levels.
It provides a table showing current time frame, 4 hr, daily, weekly describing the current rsi value and the price needed for that rsi to get to 50. This table is colored red when bearish at the time frame and green when bullish (as per <50 rsi or >50rsi).
Plots historical lines of each previous candle in the series showing how price interacts.
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Bar Index & TimeLibrary to convert a bar index to a timestamp and vice versa.
Utilizes runtime memory to store the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 values of every bar on the chart (and optional future bars), with the ability of storing additional custom values for every chart bar.
█ PREFACE
This library aims to tackle some problems that pine coders (from beginners to advanced) often come across, such as:
I'm trying to draw an object with a 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 that is more than 10,000 bars into the past, but this causes my script to fail. How can I convert the 𝚋𝚊𝚛_𝚒𝚗𝚍𝚎𝚡 to a UNIX time so that I can draw visuals using xloc.bar_time ?
I have a diagonal line drawing and I want to get the "y" value at a specific time, but line.get_price() only accepts a bar index value. How can I convert the timestamp into a bar index value so that I can still use this function?
I want to get a previous 𝚘𝚙𝚎𝚗 value that occurred at a specific timestamp. How can I convert the timestamp into a historical offset so that I can use 𝚘𝚙𝚎𝚗 ?
I want to reference a very old value for a variable. How can I access a previous value that is older than the maximum historical buffer size of 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎 ?
This library can solve the above problems (and many more) with the addition of a few lines of code, rather than requiring the coder to refactor their script to accommodate the limitations.
█ OVERVIEW
The core functionality provided is conversion between xloc.bar_index and xloc.bar_time values.
The main component of the library is the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object, created via the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function which basically stores the 𝚝𝚒𝚖𝚎 and 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 of every bar on the chart, and there are 3 more overloads to this function that allow collecting and storing additional data. Once a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object is created, use any of the exported methods:
Methods to convert a UNIX timestamp into a bar index or bar offset:
𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙽𝚞𝚖𝚋𝚎𝚛𝙾𝚏𝙱𝚊𝚛𝚜𝙱𝚊𝚌𝚔()
Methods to retrieve the stored data for a bar index:
𝚝𝚒𝚖𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚟𝚊𝚕𝚞𝚎𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡(), 𝚐𝚎𝚝𝙰𝚕𝚕𝚅𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜𝙰𝚝𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡()
Methods to retrieve the stored data at a number of bars back (i.e., historical offset):
𝚝𝚒𝚖𝚎(), 𝚝𝚒𝚖𝚎𝙲𝚕𝚘𝚜𝚎(), 𝚟𝚊𝚕𝚞𝚎()
Methods to retrieve all the data points from the earliest bar (or latest bar) stored in memory, which can be useful for debugging purposes:
𝚐𝚎𝚝𝙴𝚊𝚛𝚕𝚒𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊(), 𝚐𝚎𝚝𝙻𝚊𝚝𝚎𝚜𝚝𝚂𝚝𝚘𝚛𝚎𝚍𝙳𝚊𝚝𝚊()
Note: the library's strong suit is referencing data from very old bars in the past, which is especially useful for scripts that perform its necessary calculations only on the last bar.
█ USAGE
Step 1
Import the library. Replace with the latest available version number for this library.
//@version=6
indicator("Usage")
import n00btraders/ChartData/
Step 2
Create a 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object to collect data on every bar. Do not declare as `var` or `varip`.
chartData = ChartData.collectChartData() // call on every bar to accumulate the necessary data
Step 3
Call any method(s) on the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object. Do not modify its fields directly.
if barstate.islast
int firstBarTime = chartData.timeAtBarIndex(0)
int lastBarTime = chartData.time(0)
log.info("First `time`: " + str.format_time(firstBarTime) + ", Last `time`: " + str.format_time(lastBarTime))
█ EXAMPLES
• Collect Future Times
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument can additionally store time values for up to 500 bars into the future.
//@version=6
indicator("Example `collectChartData(barsForward)`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData(barsForward = 500)
var rectangle = box.new(na, na, na, na, xloc = xloc.bar_time, force_overlay = true)
if barstate.islast
int futureTime = chartData.timeAtBarIndex(bar_index + 100)
int lastBarTime = time
box.set_lefttop(rectangle, lastBarTime, open)
box.set_rightbottom(rectangle, futureTime, close)
box.set_text(rectangle, "Extending box 100 bars to the right. Time: " + str.format_time(futureTime))
• Collect Custom Data
The overloaded 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() functions that accept a 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument can additionally store custom user-specified values for every bar on the chart.
//@version=6
indicator("Example `collectChartData(variables)`")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("open-close midpoint", (open + close) / 2)
variables.put("boolean", open > close ? 1 : 0)
chartData = ChartData.collectChartData(variables = variables)
var fgColor = chart.fg_color
var table1 = table.new(position.top_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
var table2 = table.new(position.bottom_right, 2, 9, color(na), fgColor, 1, fgColor, 1, true)
if barstate.isfirst
table.cell(table1, 0, 0, "ChartData.value()", text_color = fgColor)
table.cell(table2, 0, 0, "open ", text_color = fgColor)
table.merge_cells(table1, 0, 0, 1, 0)
table.merge_cells(table2, 0, 0, 1, 0)
for i = 1 to 8
table.cell(table1, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table2, 0, i, text_color = fgColor, text_halign = text.align_left, text_font_family = font.family_monospace)
table.cell(table1, 1, i, text_color = fgColor)
table.cell(table2, 1, i, text_color = fgColor)
if barstate.islast
for i = 1 to 8
float open1 = chartData.value("open", 5000 * i)
float open2 = i < 3 ? open : -1
table.cell_set_text(table1, 0, i, "chartData.value(\"open\", " + str.tostring(5000 * i) + "): ")
table.cell_set_text(table2, 0, i, "open : ")
table.cell_set_text(table1, 1, i, str.tostring(open1))
table.cell_set_text(table2, 1, i, open2 >= 0 ? str.tostring(open2) : "Error")
• xloc.bar_index → xloc.bar_time
The 𝚝𝚒𝚖𝚎 value (or 𝚝𝚒𝚖𝚎_𝚌𝚕𝚘𝚜𝚎 value) can be retrieved for any bar index that is stored in memory by the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object.
//@version=6
indicator("Example `timeAtBarIndex()`")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
if barstate.islast
int start = bar_index - 15000
int end = bar_index - 100
// line.new(start, close, end, close) // !ERROR - `start` value is too far from current bar index
start := chartData.timeAtBarIndex(start)
end := chartData.timeAtBarIndex(end)
line.new(start, close, end, close, xloc.bar_time, width = 10)
• xloc.bar_time → xloc.bar_index
Use 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡() to find the bar that a timestamp belongs to.
If the timestamp falls in between the close of one bar and the open of the next bar,
the 𝚜𝚗𝚊𝚙 parameter can be used to determine which bar to choose:
𝚂𝚗𝚊𝚙.𝙻𝙴𝙵𝚃 - prefer to choose the leftmost bar (typically used for closing times)
𝚂𝚗𝚊𝚙.𝚁𝙸𝙶𝙷𝚃 - prefer to choose the rightmost bar (typically used for opening times)
𝚂𝚗𝚊𝚙.𝙳𝙴𝙵𝙰𝚄𝙻𝚃 (or 𝚗𝚊) - copies the same behavior as xloc.bar_time uses for drawing objects
//@version=6
indicator("Example `timestampToBarIndex()`")
import n00btraders/ChartData/1
startTimeInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Session Start Time")
endTimeInput = input.time(timestamp("01 Aug 2025 15:15 -0500"), "Session End Time")
chartData = ChartData.collectChartData()
if barstate.islastconfirmedhistory
int startBarIndex = chartData.timestampToBarIndex(startTimeInput, ChartData.Snap.RIGHT)
int endBarIndex = chartData.timestampToBarIndex(endTimeInput, ChartData.Snap.LEFT)
line1 = line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
line2 = line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.new(color.green, 60), force_overlay = true)
linefill.new(line1, line2, color.new(color.green, 90))
// using Snap.DEFAULT to show that it is equivalent to drawing lines using `xloc.bar_time` (i.e., it aligns to the same bars)
startBarIndex := chartData.timestampToBarIndex(startTimeInput)
endBarIndex := chartData.timestampToBarIndex(endTimeInput)
line.new(startBarIndex, 0, startBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(endBarIndex, 0, endBarIndex, 1, extend = extend.both, color = color.yellow, width = 3)
line.new(startTimeInput, 0, startTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
line.new(endTimeInput, 0, endTimeInput, 1, xloc.bar_time, extend.both, color.new(color.blue, 85), width = 11)
• Get Price of Line at Timestamp
The pine script built-in function line.get_price() requires working with bar index values. To get the price of a line in terms of a timestamp, convert the timestamp into a bar index or offset.
//@version=6
indicator("Example `line.get_price()` at timestamp")
import n00btraders/ChartData/1
lineStartInput = input.time(timestamp("01 Aug 2025 08:30 -0500"), "Line Start")
chartData = ChartData.collectChartData()
var diagonal = line.new(na, na, na, na, force_overlay = true)
if time <= lineStartInput
line.set_xy1(diagonal, bar_index, open)
if barstate.islastconfirmedhistory
line.set_xy2(diagonal, bar_index, close)
if barstate.islast
int timeOneWeekAgo = timenow - (7 * timeframe.in_seconds("1D") * 1000)
// Note: could also use `timetampToBarIndex(timeOneWeekAgo, Snap.DEFAULT)` and pass the value directly to `line.get_price()`
int barsOneWeekAgo = chartData.getNumberOfBarsBack(timeOneWeekAgo)
float price = line.get_price(diagonal, bar_index - barsOneWeekAgo)
string formatString = "Time 1 week ago: {0,number,#}\n - Equivalent to {1} bars ago\n\n𝚕𝚒𝚗𝚎.𝚐𝚎𝚝_𝚙𝚛𝚒𝚌𝚎(): {2,number,#.##}"
string labelText = str.format(formatString, timeOneWeekAgo, barsOneWeekAgo, price)
label.new(timeOneWeekAgo, price, labelText, xloc.bar_time, style = label.style_label_lower_right, size = 16, textalign = text.align_left, force_overlay = true)
█ RUNTIME ERROR MESSAGES
This library's functions will generate a custom runtime error message in the following cases:
𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() is not called consecutively, or is called more than once on a single bar
Invalid 𝚋𝚊𝚛𝚜𝙵𝚘𝚛𝚠𝚊𝚛𝚍 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜 argument in the 𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊() function
Invalid 𝚕𝚎𝚗𝚐𝚝𝚑 argument in any of the functions that accept a number of bars back
Note: there is no runtime error generated for an invalid 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙 or 𝚋𝚊𝚛𝙸𝚗𝚍𝚎𝚡 argument in any of the functions. Instead, the functions will assign 𝚗𝚊 to the returned values.
Any other runtime errors are due to incorrect usage of the library.
█ NOTES
• Function Descriptions
The library source code uses Markdown for the exported functions. Hover over a function/method call in the Pine Editor to display formatted, detailed information about the function/method.
//@version=6
indicator("Demo Function Tooltip")
import n00btraders/ChartData/1
chartData = ChartData.collectChartData()
int barIndex = chartData.timestampToBarIndex(timenow)
log.info(str.tostring(barIndex))
• Historical vs. Realtime Behavior
Under the hood, the data collector for this library is declared as `var`. Because of this, the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object will always reflect the latest available data on realtime updates. Any data that is recorded for historical bars will remain unchanged throughout the execution of a script.
//@version=6
indicator("Demo Realtime Behavior")
import n00btraders/ChartData/1
var map variables = map.new()
variables.put("open", open)
variables.put("close", close)
chartData = ChartData.collectChartData(variables)
if barstate.isrealtime
varip float initialOpen = open
varip float initialClose = close
varip int updateCount = 0
updateCount += 1
float latestOpen = open
float latestClose = close
float recordedOpen = chartData.valueAtBarIndex("open", bar_index)
float recordedClose = chartData.valueAtBarIndex("close", bar_index)
string formatString = "# of updates: {0}\n\n𝚘𝚙𝚎𝚗 at update #1: {1,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #1: {2,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 at update #{0}: {3,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 at update #{0}: {4,number,#.##}\n\n"
+ "𝚘𝚙𝚎𝚗 stored in memory: {5,number,#.##}\n𝚌𝚕𝚘𝚜𝚎 stored in memory: {6,number,#.##}"
string labelText = str.format(formatString, updateCount, initialOpen, initialClose, latestOpen, latestClose, recordedOpen, recordedClose)
label.new(bar_index, close, labelText, style = label.style_label_left, force_overlay = true)
• Collecting Chart Data for Other Contexts
If your use case requires collecting chart data from another context, avoid directly retrieving the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 object as this may exceed memory limits .
//@version=6
indicator("Demo Return Calculated Results")
import n00btraders/ChartData/1
timeInput = input.time(timestamp("01 Sep 2025 08:30 -0500"), "Time")
var int oneMinuteBarsAgo = na
// !ERROR - Memory Limits Exceeded
// chartDataArray = request.security_lower_tf(syminfo.tickerid, "1", ChartData.collectChartData())
// oneMinuteBarsAgo := chartDataArray.last().getNumberOfBarsBack(timeInput)
// function that returns calculated results (a single integer value instead of an entire `ChartData` object)
getNumberOfBarsBack() =>
chartData = ChartData.collectChartData()
chartData.getNumberOfBarsBack(timeInput)
calculatedResultsArray = request.security_lower_tf(syminfo.tickerid, "1", getNumberOfBarsBack())
oneMinuteBarsAgo := calculatedResultsArray.size() > 0 ? calculatedResultsArray.last() : na
if barstate.islast
string labelText = str.format("The selected timestamp occurs 1-minute bars ago", oneMinuteBarsAgo)
label.new(bar_index, hl2, labelText, style = label.style_label_left, size = 16, force_overlay = true)
• Memory Usage
The library's convenience and ease of use comes at the cost of increased usage of computational resources. For simple scripts, using this library will likely not cause any issues with exceeding memory limits. But for large and complex scripts, you can reduce memory issues by specifying a lower 𝚌𝚊𝚕𝚌_𝚋𝚊𝚛𝚜_𝚌𝚘𝚞𝚗𝚝 amount in the indicator() or strategy() declaration statement.
//@version=6
// !ERROR - Memory Limits Exceeded using the default number of bars available (~20,000 bars for Premium plans)
//indicator("Demo `calc_bars_count` parameter")
// Reduce number of bars using `calc_bars_count` parameter
indicator("Demo `calc_bars_count` parameter", calc_bars_count = 15000)
import n00btraders/ChartData/1
map variables = map.new()
variables.put("open", open)
variables.put("close", close)
variables.put("weekofyear", weekofyear)
variables.put("dayofmonth", dayofmonth)
variables.put("hour", hour)
variables.put("minute", minute)
variables.put("second", second)
// simulate large memory usage
chartData0 = ChartData.collectChartData(variables)
chartData1 = ChartData.collectChartData(variables)
chartData2 = ChartData.collectChartData(variables)
chartData3 = ChartData.collectChartData(variables)
chartData4 = ChartData.collectChartData(variables)
chartData5 = ChartData.collectChartData(variables)
chartData6 = ChartData.collectChartData(variables)
chartData7 = ChartData.collectChartData(variables)
chartData8 = ChartData.collectChartData(variables)
chartData9 = ChartData.collectChartData(variables)
log.info(str.tostring(chartData0.time(0)))
log.info(str.tostring(chartData1.time(0)))
log.info(str.tostring(chartData2.time(0)))
log.info(str.tostring(chartData3.time(0)))
log.info(str.tostring(chartData4.time(0)))
log.info(str.tostring(chartData5.time(0)))
log.info(str.tostring(chartData6.time(0)))
log.info(str.tostring(chartData7.time(0)))
log.info(str.tostring(chartData8.time(0)))
log.info(str.tostring(chartData9.time(0)))
if barstate.islast
result = table.new(position.middle_right, 1, 1, force_overlay = true)
table.cell(result, 0, 0, "Script Execution Successful ✅", text_size = 40)
█ EXPORTED ENUMS
Snap
Behavior for determining the bar that a timestamp belongs to.
Fields:
LEFT : Snap to the leftmost bar.
RIGHT : Snap to the rightmost bar.
DEFAULT : Default `xloc.bar_time` behavior.
Note: this enum is used for the 𝚜𝚗𝚊𝚙 parameter of 𝚝𝚒𝚖𝚎𝚜𝚝𝚊𝚖𝚙𝚃𝚘𝙱𝚊𝚛𝙸𝚗𝚍𝚎𝚡().
█ EXPORTED TYPES
Note: users of the library do not need to worry about directly accessing the fields of these types; all computations are done through method calls on an object of the 𝙲𝚑𝚊𝚛𝚝𝙳𝚊𝚝𝚊 type.
Variable
Represents a user-specified variable that can be tracked on every chart bar.
Fields:
name (series string) : Unique identifier for the variable.
values (array) : The array of stored values (one value per chart bar).
ChartData
Represents data for all bars on a chart.
Fields:
bars (series int) : Current number of bars on the chart.
timeValues (array) : The `time` values of all chart (and future) bars.
timeCloseValues (array) : The `time_close` values of all chart (and future) bars.
variables (array) : Additional custom values to track on all chart bars.
█ EXPORTED FUNCTIONS
collectChartData()
Collects and tracks the `time` and `time_close` value of every bar on the chart.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
collectChartData(barsForward, variables)
Collects and tracks the `time` and `time_close` value of every bar on the chart as well as a specified number of future bars. Additionally, tracks a custom set of variables for every chart bar.
Parameters:
barsForward (simple int) : Number of future bars to collect data for.
variables (simple map) : Custom values to collect on every chart bar.
Returns: `ChartData` object to convert between `xloc.bar_index` and `xloc.bar_time`.
█ EXPORTED METHODS
method timestampToBarIndex(chartData, timestamp, snap)
Converts a UNIX timestamp to a bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
snap (series Snap) : A `Snap` enum value.
Returns: A bar index, or `na` if unable to find the appropriate bar index.
method getNumberOfBarsBack(chartData, timestamp)
Converts a UNIX timestamp to a history-referencing length (i.e., number of bars back).
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
timestamp (series int) : A UNIX time.
Returns: A bar offset, or `na` if unable to find a valid number of bars back.
method timeAtBarIndex(chartData, barIndex)
Retrieves the `time` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (int) : The bar index.
Returns: The `time` value, or `na` if there is no `time` stored for the bar index.
method time(chartData, length)
Retrieves the `time` value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time` value `length` bars ago, or `na` if there is no `time` stored for that bar.
method timeCloseAtBarIndex(chartData, barIndex)
Retrieves the `time_close` value for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: The `time_close` value, or `na` if there is no `time_close` stored for the bar index.
method timeClose(chartData, length)
Retrieves the `time_close` value of the bar that is `length` bars back from the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
length (series int) : Number of bars back.
Returns: The `time_close` value `length` bars ago, or `na` if there is none stored.
method valueAtBarIndex(chartData, name, barIndex)
Retrieves the value of a custom variable for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
barIndex (series int) : The bar index.
Returns: The value of the variable, or `na` if that variable is not stored for the bar index.
method value(chartData, name, length)
Retrieves a variable value of the bar that is `length` bars back relative to the latest bar.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
name (series string) : The variable name.
length (series int) : Number of bars back.
Returns: The value `length` bars ago, or `na` if that variable is not stored for the bar index.
method getAllVariablesAtBarIndex(chartData, barIndex)
Retrieves all custom variables for the specified bar index.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
barIndex (series int) : The bar index.
Returns: Map of all custom variables that are stored for the specified bar index.
method getEarliestStoredData(chartData)
Gets all values from the earliest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
Returns: A tuple:
method getLatestStoredData(chartData, futureData)
Gets all values from the latest bar data that is currently stored in memory.
Namespace types: ChartData
Parameters:
chartData (series ChartData) : The `ChartData` object.
futureData (series bool) : Whether to include the future data that is stored in memory.
Returns: A tuple:
Intrabar Efficiency Ratio█ OVERVIEW
This indicator displays a directional variant of Perry Kaufman's Efficiency Ratio, designed to gauge the "efficiency" of intrabar price movement by comparing the sum of movements of the lower timeframe bars composing a chart bar with the respective bar's movement on an average basis.
█ CONCEPTS
Efficiency Ratio (ER)
Efficiency Ratio was first introduced by Perry Kaufman in his 1995 book, titled "Smarter Trading". It is the ratio of absolute price change to the sum of absolute changes on each bar over a period. This tells us how strong the period's trend is relative to the underlying noise. Simply put, it's a measure of price movement efficiency. This ratio is the modulator utilized in Kaufman's Adaptive Moving Average (KAMA), which is essentially an Exponential Moving Average (EMA) that adapts its responsiveness to movement efficiency.
ER's output is bounded between 0 and 1. A value of 0 indicates that the starting price equals the ending price for the period, which suggests that price movement was maximally inefficient. A value of 1 indicates that price had travelled no more than the distance between the starting price and the ending price for the period, which suggests that price movement was maximally efficient. A value between 0 and 1 indicates that price had travelled a distance greater than the distance between the starting price and the ending price for the period. In other words, some degree of noise was present which resulted in reduced efficiency over the period.
As an example, let's say that the price of an asset had moved from $15 to $14 by the end of a period, but the sum of absolute changes for each bar of data was $4. ER would be calculated like so:
ER = abs(14 - 15)/4 = 0.25
This suggests that the trend was only 25% efficient over the period, as the total distanced travelled by price was four times what was required to achieve the change over the period.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed, but fewer chart bars can display indicator information because there is a limit to the total number of intrabars that can be analyzed.
Intrabar precision
The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
Intrabar Efficiency Ratio (IER)
Intrabar Efficiency Ratio applies the concept of ER on an intrabar level. Rather than comparing the overall change to the sum of bar changes for the current chart's timeframe over a period, IER compares single bar changes for the current chart's timeframe to the sum of absolute intrabar changes, then applies smoothing to the result. This gives an indication of how efficient changes are on the current chart's timeframe for each bar of data relative to LTF bar changes on an average basis. Unlike the standard ER calculation, we've opted to preserve directional information by not taking the absolute value of overall change, thus allowing it to be utilized as a momentum oscillator. However, by taking the absolute value of this oscillator, it could potentially serve as a replacement for ER in the design of adaptive moving averages.
Since this indicator preserves directional information, IER can be regarded as similar to the Chande Momentum Oscillator (CMO) , which was presented in 1994 by Tushar Chande in "The New Technical Trader". Both CMO and ER essentially measure the same relationship between trend and noise. CMO simply differs in scale, and considers the direction of overall changes.
█ FEATURES
Display
Three different display types are included within the script:
• Line : Displays the middle length MA of the IER as a line .
Color for this display can be customized via the "Line" portion of the "Visuals" section in the script settings.
• Candles : Displays the non-smooth IER and two moving averages of different lengths as candles .
The `open` and `close` of the candle are the longest and shortest length MAs of the IER respectively.
The `high` and `low` of the candle are the max and min of the IER, longest length MA of the IER, and shortest length MA of the IER respectively.
Colors for this display can be customized via the "Candles" portion of the "Visuals" section in the script settings.
• Circles : Displays three MAs of the IER as circles .
The color of each plot depends on the percent rank of the respective MA over the previous 100 bars.
Different colors are triggered when ranks are below 10%, between 10% and 50%, between 50% and 90%, and above 90%.
Colors for this display can be customized via the "Circles" portion of the "Visuals" section in the script settings.
With either display type, an optional information box can be displayed. This box shows the LTF that the script is using, the average number of lower timeframe bars per chart bar, and the number of chart bars that contain LTF data.
Specifying intrabar precision
Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed.
The first five options allow users to specify the approximate amount of chart bars to be covered:
• Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.
This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.
• Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped LTF in relation to the current chart's timeframe.
• Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min LTF.
• Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min LTF.
The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible.
Specifying MA type
Seven MA types are included in the script for different averaging effects:
• Simple
• Exponential
• Wilder (RMA)
• Weighted
• Volume-Weighted
• Arnaud Legoux with `offset` and `sigma` set to 0.85 and 6 respectively.
• Hull
Weighting
This script includes the option to weight IER values based on the percent rank of absolute price changes on the current chart's timeframe over a specified period, which can be enabled by checking the "Weigh using relative close changes" option in the script settings. This places reduced emphasis on IER values from smaller changes, which may help to reduce noise in the output.
█ FOR Pine Script™ CODERS
• This script imports the recently published lower_ltf library for calculating intrabar statistics and the optimal lower timeframe in relation to the current chart's timeframe.
• This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post .
It works differently from the usual request.security() in that it can only be used on LTFs, and it returns an array containing one value per intrabar.
This makes it much easier for programmers to access intrabar information.
• This script implements a new recommended best practice for tables which works faster and reduces memory consumption.
Using this new method, tables are declared only once with var , as usual. Then, on the first bar only, we use table.cell() to populate the table.
Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.
This greatly reduces the resources required to render tables.
Look first. Then leap.
lower_tf█ OVERVIEW
This library is a Pine programmer’s tool containing functions to help those who use the request.security_lower_tf() function. Its `ltf()` function helps translate user inputs into a lower timeframe string usable with request.security_lower_tf() . Another function, `ltfStats()`, accumulates statistics on processed chart bars and intrabars.
█ CONCEPTS
Chart bars
Chart bars , as referred to in our publications, are bars that occur at the current chart timeframe, as opposed to those that occur at a timeframe that is higher or lower than that of the chart view.
Intrabars
Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar.
Lower timeframes (LTFs)
A lower timeframe is a timeframe that is smaller than the chart's timeframe. This framework exemplifies how authors can determine which LTF to use by examining the chart's timeframe. The LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed.
Intrabar precision
The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible.
█ `ltf()`
This function returns a timeframe string usable with request.security_lower_tf() . It calculates the returned timeframe by taking into account a user selection between eight different calculation modes and the chart's timeframe. You send it the user's selection, along with the text corresponding to the eight choices from which the user has chosen, and the function returns a corresponding LTF string.
Because the function processes strings and doesn't require recalculation on each bar, using var to declare the variable to which its result is assigned will execute the function only once on bar zero and speed up your script:
var string ltfString = ltf(ltfModeInput, LTF1, LTF2, LTF3, LTF4, LTF5, LTF6, LTF7, LTF8)
The eight choices users can select from are of two types: the first four allow a selection from the desired amount of chart bars to be covered, the last four are choices of a fixed number of intrabars to be analyzed per chart bar. Our example code shows how to structure your input call and then make the call to `ltf()`. By changing the text associated with the `LTF1` to `LTF8` constants, you can tailor it to your preferences while preserving the functionality of `ltf()` because you will be sending those string constants as the function's arguments so it can determine the user's selection. The association between each `LTFx` constant and its calculation mode is fixed, so the order of the arguments is important when you call `ltf()`.
These are the first four modes and the `LTFx` constants corresponding to each:
Covering most chart bars (least precise) — LTF1
Covers all chart bars. This is accomplished by dividing the current timeframe in seconds by 4 and converting that number back to a string in timeframe.period format using secondsToTfString() . Due to the fact that, on premium subscriptions, the typical historical bar count is between 20-25k bars, dividing the timeframe by 4 ensures the highest level of intrabar precision possible while achieving complete coverage for the entire dataset with the maximum allowed 100K intrabars.
Covering some chart bars (less precise) — LTF2
Covering less chart bars (more precise) — LTF3
These levels offer a stepped LTF in relation to the chart timeframe with slightly more, or slightly less precision. The stepped lower timeframe tiers are calculated from the chart timeframe as follows:
Chart Timeframe Lower Timeframe
Less Precise More Precise
< 1hr 1min 1min
< 1D 15min 1min
< 1W 2hr 30min
> 1W 1D 60min
Covering the least chart bars (most precise) — LTF4
Analyzes the maximum quantity of intrabars possible by using the 1min LTF, which also allows the least amount of chart bars to be covered.
The last four modes allow the user to specify a fixed number of intrabars to analyze per chart bar. Users can choose from 12, 24, 50 or 100 intrabars, respectively corresponding to the `LTF5`, `LTF6`, `LTF7` and `LTF8` constants. The value is a target; the function will do its best to come up with a LTF producing the required number of intrabars. Because of considerations such as the length of a ticker's session, rounding of the LTF to the closest allowable timeframe, or the lowest allowable timeframe of 1min intrabars, it is often impossible for the function to find a LTF producing the exact number of intrabars. Requesting 100 intrabars on a 60min chart, for example, can only produce 60 1min intrabars. Higher chart timeframes, tickers with high liquidity or 24x7 markets will produce optimal results.
█ `ltfStats()`
`ltfStats()` returns statistics that will be useful to programmers using intrabar inspection. By analyzing the arrays returned by request.security_lower_tf() in can determine:
• intrabarsInChartBar : The number of intrabars analyzed for each chart bar.
• chartBarsCovered : The number of chart bars where intrabar information is available.
• avgIntrabars : The average number of intrabars analyzed per chart bar. Events like holidays, market activity, or reduced hours sessions can cause the number of intrabars to vary, bar to bar.
The function must be called on each bar to produce reliable results.
█ DEMONSTRATION CODE
Our example code shows how to provide users with an input from which they can select a LTF calculation mode. If you use this library's functions, feel free to reuse our input setup code, including the tooltip providing users with explanations on how it works for them.
We make a simple call to request.security_lower_tf() to fetch the close values of intrabars, but we do not use those values. We simply send the returned array to `ltfStats()` and then plot in the indicator's pane the number of intrabars examined on each bar and its average. We also display an information box showing the user's selection of the LTF calculation mode, the resulting LTF calculated by `ltf()` and some statistics.
█ NOTES
• As in several of our recent publications, this script uses secondsToTfString() to produce a timeframe string in timeframe.period format from a timeframe expressed in seconds.
• The script utilizes display.data_window and display.status_line to restrict the display of certain plots.
These new built-ins allow coders to fine-tune where a script’s plot values are displayed.
• We implement a new recommended best practice for tables which works faster and reduces memory consumption.
Using this new method, tables are declared only once with var , as usual. Then, on bar zero only, we use table.cell() calls to populate the table.
Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.
This greatly reduces the resources required to render tables. We encourage all Pine Script™ programmers to do the same.
Look first. Then leap.
█ FUNCTIONS
The library contains the following functions:
ltf(userSelection, choice1, choice2, choice3, choice4, choice5, choice6, choice7, choice8)
Selects a LTF from the chart's TF, depending on the `userSelection` input string.
Parameters:
userSelection : (simple string) User-selected input string which must be one of the `choicex` arguments.
choice1 : (simple string) Input selection corresponding to "Least precise, covering most chart bars".
choice2 : (simple string) Input selection corresponding to "Less precise, covering some chart bars".
choice3 : (simple string) Input selection corresponding to "More precise, covering less chart bars".
choice4 : (simple string) Input selection corresponding to "Most precise, 1min intrabars".
choice5 : (simple string) Input selection corresponding to "~12 intrabars per chart bar".
choice6 : (simple string) Input selection corresponding to "~24 intrabars per chart bar".
choice7 : (simple string) Input selection corresponding to "~50 intrabars per chart bar".
choice8 : (simple string) Input selection corresponding to "~100 intrabars per chart bar".
Returns: (simple string) A timeframe string to be used with `request.security_lower_tf()`.
ltfStats()
Returns statistics about analyzed intrabars and chart bars covered by calls to `request.security_lower_tf()`.
Parameters:
intrabarValues : (float [ ]) The ID of a float array containing values fetched by a call to `request.security_lower_tf()`.
Returns: A 3-element tuple: [ (series int) intrabarsInChartBar, (series int) chartBarsCovered, (series float) avgIntrabars ].
Super PerformanceThe "Super Performance" script is a custom indicator written in Pine Script (version 6) for use on the TradingView platform. Its main purpose is to visually compare the performance of a selected stock or index against a benchmark index (default: NIFTYMIDSML400) over various timeframes, and to display sector-wise performance rankings in a clear, tabular format.
Key Features:
Customizable Display:
Users can toggle between dark and light color themes, enable or disable extended data columns, and choose between a compact "Mini Mode" or a full-featured table view. Table positions and sizes are also configurable for both stock and sector tables.
Performance Calculation:
The script calculates percentage price changes for the selected stock and the benchmark index over multiple periods: 1, 5, 10, 20, 50, and 200 days. It then checks if the stock is outperforming the index for each period.
Conviction Score:
For each period where the stock outperforms the index, a "conviction score" is incremented. This score is mapped to qualitative labels such as "Super solid," "Solid," "Good," etc., and is color-coded for quick visual interpretation.
Sector Performance Table:
The script tracks 19 sector indices (e.g., REALTY, IT, PHARMA, AUTO, ENERGY) and calculates their performance over 1, 5, 10, 20, and 60-day periods. It then ranks the top 5 performing sectors for each timeframe and displays them in a sector performance table.
Visual Output:
Two tables are constructed:
Stock Performance Table: Shows the stock's returns, index returns, outperformance markers (✔/✖), and the difference for each period, along with the overall conviction score.
Sector Performance Table: Ranks and displays the top 5 sectors for each timeframe, with color-coded performance values for easy comparison.
