ProScalper📊 ProScalper - Professional 1-Minute Scalping System
🎯 Overview
ProScalper is a sophisticated, multi-confluence scalping indicator designed specifically for 1-minute chart trading. Combining advanced technical analysis with intelligent signal filtering, it provides high-probability trade setups with clear entry, stop loss, and take profit levels.
✨ Key Features
🔺 Smart Signal Detection
Range Filter Technology: Fast-responding trend detection (25-period) optimized for 1-minute timeframe
Medium-sized triangles appear above/below candles for clear buy/sell signals
Only most recent signal shown - no chart clutter
Automatically deletes old signals when new ones appear
📋 Real-Time Signal Table
Top-center display shows complete trade breakdown
Grade system: A+, A, B+, B, C+ ratings for every setup
All confluence reasons listed with checkmarks
Score and R:R displayed for instant trade quality assessment
Color-coded: Green for LONG, Red for SHORT
📐 Multi-Confluence Analysis
ProScalper combines 10+ technical factors:
✅ EMA Trend: 4 EMAs (200, 48, 13, 8) for multi-timeframe alignment
✅ VWAP: Dynamic support/resistance
✅ Fibonacci Retracement: Golden ratio (61.8%), 50%, 38.2%, 78.6%
✅ Range Filter: Adaptive trend confirmation
✅ Pivot Points: Smart reversal detection
✅ Volume Analysis: Spike detection and volume profile
✅ Higher Timeframe: 5-minute trend confirmation
✅ HTF Support/Resistance: Key levels from higher timeframes
✅ Liquidity Sweeps: Smart money detection
✅ Opening Range Breakout: First 15-minute range
💰 Complete Trade Management
Entry Lines: Dashed green (LONG) or red (SHORT) showing exact entry
Stop Loss: Red dashed line with price label
Take Profit: Blue dashed line with price label and R:R
Partial Exits: 1R level marked with orange dashed line
All lines extend 10 bars for clean alignment with Fibonacci levels
📊 Dynamic Risk/Reward
Adaptive R:R calculation based on market volatility
Targets adjusted for pivot distances
Minimum 1.2:1 to maximum 3.5:1 for scalping
Position sizing based on account risk percentage
🎨 Professional Visualization
Clean chart layout - no clutter, only essential information
Custom EMA colors: Red (200), Aqua (48), Green (13), White (8)
Gold VWAP line for key support/resistance
Color-coded Fibonacci: Bright yellow (61.8%), white (50%), orange (38.2%), fuchsia (78.6%)
No shaded zones - pure price action focus
📈 Performance Tracking
Real-time statistics table (optional)
Win rate, total trades, P&L tracking
Average R:R and win/loss ratios
Setup-specific performance metrics
⚙️ Settings & Customization
Risk Management
Adjustable account risk per trade (default: 0.5%)
ATR-based stop loss multiplier (default: 0.8 for tight scalping)
Dynamic position sizing
Signal Sensitivity
Confluence Score Threshold: 40-100 (default: 55 for balanced signals)
Range Filter Period: 25 bars (fast signals for 1-min)
Range Filter Multiplier: 2.2 (tighter bands for more signals)
Visual Controls
Toggle signal table on/off
Show/hide Fibonacci levels
Control EMA visibility
Adjust table text size
Partial Exits
1R: 50% (default)
2R: 30% (default)
3R: 20% (default)
Fully customizable percentages
Trailing Stops
ATR-Based (best for scalping)
Pivot-Based
EMA-Based
Breakeven trigger at 0.8R
🎯 Best Use Cases
Ideal For:
✅ 1-minute scalping on liquid instruments
✅ Day traders looking for quick 2-8 minute trades
✅ High-frequency trading with 8-15 signals per session
✅ Trending markets where Range Filter excels
✅ Crypto, Forex, Futures - works on all liquid assets
Trading Style:
Timeframe: 1-minute (can work on 3-5 min with adjusted settings)
Hold Time: 3-8 minutes average
Target: 1.2-3R per trade
Frequency: 8-15 signals per day
Win Rate: 45-55% (with proper risk management)
📋 How to Use
Step 1: Wait for Signal
Watch for green triangle (BUY) or red triangle (SELL)
Signal table appears at top center automatically
Step 2: Review Confluence
Check grade (prefer A+, A, B+ for best quality)
Review all reasons listed in table
Confirm score is above your threshold (55+ recommended)
Note the R:R ratio
Step 3: Enter Trade
Enter at current market price
Set stop loss at red dashed line
Set take profit at blue dashed line
Mark 1R level (orange line) for partial exit
Step 4: Manage Trade
Exit 50% at 1R (orange line)
Move to breakeven after 0.8R
Trail remaining position using your chosen method
Exit fully at TP or opposite signal
🎨 Chart Setup Recommendations
Optimal Display:
Timeframe: 1-minute
Chart Type: Candles or Heikin Ashi
Background: Dark theme for best color visibility
Volume: Enable volume bars below chart
Complementary Indicators (optional):
Order flow/Delta for institutional confirmation
Market profile for key levels
Economic calendar for news avoidance
⚠️ Important Notes
Risk Disclaimer:
Not financial advice - for educational purposes only
Always use proper risk management (0.5-1% per trade max)
Past performance doesn't guarantee future results
Test on demo account before live trading
Best Practices:
✅ Trade during high liquidity hours (9:30-11 AM, 2-4 PM EST)
✅ Avoid news events and market open/close (first/last 2 minutes)
✅ Use tight stops (0.8-1.0 ATR) for 1-minute scalping
✅ Take partial profits quickly (1R = 50% off)
✅ Respect max daily loss limits (3% recommended)
✅ Focus on A and B grade setups for consistency
What Makes This Different:
🎯 Complete system - not just signals, but full trade management
📊 Multi-confluence - 10+ factors analyzed per trade
🎨 Professional visualization - clean, focused chart design
⚡ Optimized for 1-min - settings specifically tuned for fast scalping
📋 Transparent reasoning - see exactly why each trade was taken
🏆 Grade system - instantly know trade quality
🔧 Technical Details
Pine Script Version: 5
Overlay: Yes (plots on price chart)
Max Lines: 500
Max Labels: 100
Non-repainting: All signals confirmed on bar close
Alerts: Compatible with TradingView alerts
📞 Support & Updates
This indicator is actively maintained and optimized for 1-minute scalping. Settings can be adjusted for different timeframes and trading styles, but default configuration is specifically tuned for high-frequency 1-minute scalping.
🚀 Get Started
Add ProScalper to your 1-minute chart
Adjust settings to your risk tolerance
Wait for signals (green/red triangles)
Follow the signal table guidance
Manage trades using provided levels
Track performance with stats table
Happy Scalping! 📊⚡💰
Hareketli Ortalamalar
Trend Pullback System```{"variant":"standard","id":"36492","title":"Trend Pullback System Description"}
Trend Pullback System is a price-action trend continuation model that looks to enter on pullbacks, not breakouts. It’s designed to find high-quality long/short entries inside an already established trend, place the stop at meaningful structure, trail that stop as structure evolves, and warn you when the trade thesis is no longer valid.
Developed by: Mohammed Bedaiwi
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HOW IT WORKS
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1. Trend Detection
• The strategy defines overall bias using moving averages.
• Bullish environment (“uptrend”): price above the slower MA, fast MA above slow MA, and the slow MA is sloping up.
• Bearish environment (“downtrend”): price below the slower MA, fast MA below slow MA, and the slow MA is sloping down.
This prevents trading against chop and focuses on continuation moves in the dominant direction.
2. Pullback + Re-entry Logic
• The script waits for price to pull back into structure (support in an uptrend, resistance in a downtrend), and then push back in the direction of the main trend.
• That “push back” is the setup trigger. We don’t chase the first breakout candle — we buy/sell the retest + resume.
3. Structural Levels (“Diamonds”)
• Green diamond (below bar): bullish pivot low formed while the trend is bullish. This marks defended support.
- Use it as a re-entry zone for longs.
- Use it to trail a stop higher when you’re already long.
- Shorts can take profit here because buyers stepped in.
• Red diamond (above bar): bearish pivot high formed while the trend is bearish. This marks defended resistance.
- Use it as a re-entry zone for shorts.
- Use it to trail a stop lower when you’re already short.
- Longs can take profit here because sellers stepped in.
4. Entry Signals
• BUY arrow (green triangle up under the candle, text like “BUY” / “BUY Zone”):
- LongSetup is true.
- Trend is bullish or turning bullish.
- Price just bounced off recent defended support (green diamond) and reclaimed short-term momentum.
Meaning: enter long here or cover/exit shorts.
• SELL arrow (red triangle down above the candle):
- ShortSetup is true.
- Trend is bearish or turning bearish.
- Price just rolled down from defended resistance (red diamond) and lost short-term momentum.
Meaning: enter short here or take profit on longs.
These are the primary trade entries. They are meant to be actionable.
5. Weak Setups (“W” in yellow)
• Yellow triangle with “W”:
- A possible long/short idea is trying to form, BUT the higher-timeframe confirmation is not fully there yet.
- Think of it as early pressure / early caution, not a full signal.
• You usually watch these areas rather than jumping in immediately.
6. Exit Warning (orange “EXIT” label above a bar)
• The strategy will raise an EXIT marker when you’re in a trade and the *opposite* side just produced a confirmed setup.
- You’re short and a valid longSetup appears → EXIT.
- You’re long and a valid shortSetup appears → EXIT.
• This is basically: “Close or reduce — the other side just took control.”
• It’s not just a trailing stop hit; it’s a regime flip warning.
7. Stop, Target, and Trailing
• On every new setup, the script records:
- Initial stop: recent swing beyond the defended level (below support for longs, above resistance for shorts).
- Initial target: recent opposing swing.
• While you’re in position, if new confirming diamonds print in your favor, the stop can trail toward the new defended level.
• This creates structure-based risk management (not just fixed % or ATR).
8. Reference Levels
• The strategy also plots prior higher-timeframe closes (last week’s close, last month’s close, last year’s close). These can behave as magnets or stall points.
• They’re helpful for take-profit timing and for reading “are we trading above or below last month’s close?”
9. Momentum Panel (hidden by default)
• Internally, the script calculates an SMI-style momentum oscillator with overbought/oversold zones.
• This is optional visual confirmation and does not drive the core entry/exit logic.
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WHAT A TRADE LOOKS LIKE IN REAL PRICE ACTION
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Early warning
• Yellow W + red diamonds + red down arrows = “This is getting weak. Short setups are here.”
• You may also see something like “My Short Entry Id.” That’s where the short side actually engages.
Bearish follow-through, then exhaustion
• Price bleeds down.
• Then the orange EXIT appears.
→ Translation: “If you’re still short, close it. Buyers are stepping in hard. Risk of reversal is now high.”
Regime flip
• Right after EXIT, multiple green BUY arrows fire together (“BUY”, “BUYZone”).
• That’s the true long trigger.
→ This is where you either enter long or flip from short to long.
Expansion leg
• After that flip, price rips up for multiple candles / days / weeks.
• While it runs:
- Green diamonds appear under pullbacks → “dip buy zones / trail stop up here.”
- More BUY arrows show on minor pullbacks → continuation long / scale adds.
Distribution / topping
• Later, you start seeing new yellow W triangles again near local highs. That’s your “careful, this might be topping” warning.
• You finally get a hard red candle, and green diamonds stop stacking.
→ That’s where you tighten risk, scale out, or assume the move is mature.
In plain terms, the model is doing the following for you:
• It puts you short during weakness.
• It tells you when to get OUT of the short.
• It flips you long right as control changes.
• It gives you a structure-based trail the whole way up.
• It warns you again when momentum at the top starts cracking.
That is exactly how the logic was designed.
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QUICK INTERPRETATION CHEAT SHEET
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🔻 Red triangle + “Short Entry” near a red diamond
→ Short entry zone (or take profit on a long).
🟥 Red diamond above bar
→ Sellers defended here. Treat it as resistance. Good place to trail short stops just above that level. Avoid chasing longs straight into it.
🟨 Yellow W
→ Attention only. Early pressure / possible turn. Not fully confirmed.
🟧 EXIT (orange label)
→ The opposite side just printed a real setup. Close the old idea (cover shorts if you’re short, exit longs if you’re long). Thesis invalid.
🟩 Burst of green BUY triangles after EXIT
→ Long entry. Also a “cover shorts now” alert. This is the core money entry in bullish reversals.
💎 Green diamond below bar
→ Bulls defended that level. Good for trailing your long stop up, and good “buy the dip in trend” locations.
📈 Blue / teal MAs stacked and rising
→ Confirmed bullish structure. You’re in trend continuation mode, so dips are opportunities, not automatic exits.
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COLOR / SHAPE KEY
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• Green triangle up (“BUY”, “BUY Zone”):
Long entry / cover shorts / continuation long trigger.
• Red triangle down:
Short entry / take profit on longs / continuation short trigger.
• Orange “EXIT” label:
Opposite side just fired a real setup. The previous trade thesis is now invalid.
• Green diamond below price:
Bullish defended support in an uptrend. Use for dip buys, trailing stops on longs, and objective cover zones for shorts.
• Red diamond above price:
Bearish defended resistance in a downtrend. Use for re-entry shorts, trailing stops on shorts, and objective scale-out zones for longs.
• Yellow “W”:
Weak / early potential setup. Watch it, don’t blindly trust it.
• Moving average bands (fast MA, slow MA, Hull MA):
When stacked and rising, bullish control. When stacked and falling, bearish control.
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INTENT
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This system is built to:
• Trade with momentum, not against it.
• Enter on pullbacks into proven structure, not chase stretched breakouts.
• Automate stop/target logic around actual defended swing levels.
• Warn you when the other side takes over so you don’t give back gains.
Typical usage:
1. In an uptrend, wait for price to pull back, print a green diamond (support proved), then take the first BUY arrow that fires.
2. In a downtrend, wait for a bounce into resistance, print a red diamond (sellers proved), then take the first SELL arrow that fires.
3. Respect EXIT when it appears — that’s the model saying “this trade is done.”
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DISCLAIMER
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This script is for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security, cryptoasset, or derivative. Markets carry risk. Past performance does not guarantee future results. You are fully responsible for your own decisions, position sizing, risk management, and compliance with all applicable laws and regulations.
HaP OtoDinamikFİBO+EMA1. Automatic Dynamic Fibonacci Levels
This is the first and primary feature of the indicator. Instead of manually drawing the Fibonacci tool, this section automates the entire process.
Zigzag Algorithm: It automatically detects significant high and low points (swings) on the chart. While making these detections, it adjusts its own period based on the chart's timeframe (e.g., weekly, daily, 4-hour).
Automatic Fibo Drawing: It automatically draws Fibonacci retracement and extension levels between the last two significant high and low points it has detected.
Dynamic Updates: When a new and more significant low or high is formed in the market, it automatically updates the Fibonacci levels to reflect this new structure. As Fibonacci levels are breached, the next relevant levels are automatically displayed.
Customization: Users can toggle the visibility of the Fibonacci lines, adjust their thickness and colors (e.g., different colors for levels between 0-1 and for levels above 1), and choose whether the price labels appear on the right or left side of the chart.
2. Dynamic EMA (Exponential Moving Average) Support/Resistance Zones
Instead of cluttering the chart with dozens of moving averages, this feature is an intelligent system that displays only the most relevant ones at any given time.
Multi-Timeframe Analysis: It calculates numerous popular EMA values (e.g., 5, 8, 13, 21, ..., 1000) in the background on both the Daily (D) and Weekly (W) timeframes. This allows you to see where the daily and weekly EMAs are located, even when you are viewing a 1-hour chart.
Displaying the Closest Levels: Based on the current price location, it finds the nearest EMA above it (resistance) and the nearest EMA below it (support), and displays only these two on the chart.
Informative Labels: It adds labels to these EMA lines, indicating their timeframe (D/W) and period (e.g., D EMA 55), their price value, and their percentage distance from the current price.
Special Condition Handling: If the price moves above all calculated EMAs or drops below all of them (indicating a strong trend), it will only show the 5-period EMA to keep the chart clean.
Comprehensive Settings: Users can toggle the visibility of all EMAs, the price line, and labels. They can fully customize the colors, thickness, and style of the lines, as well as the size, color, and position of the labels according to their preferences.
Summary
In summary, this indicator is a powerful and flexible tool designed to help traders with the following:
Identifying potential support, resistance, and target levels through automatic and effortless Fibonacci analysis.
Instantly viewing the most critical dynamic support and resistance zones for the current price by utilizing EMAs from multiple timeframes.
Simplifying the analysis process by keeping the chart clean and understandable.
Phase Coherence Index (PCI) — Hilbert Prototype v0.1Phase Coherence Index (PCI) — Hilbert Prototype v0.1
✨ Concept:
The Phase Coherence Index (PCI) measures the degree of phase alignment among multiple trend components derived from band-filtered price signals.
In simpler terms, PCI quantifies how “in-sync” short, mid, and long-term market oscillations are.
A high PCI (values near 100) means the market’s cyclical components are moving together — a state of phase alignment that typically accompanies sustained, directional trends.
A low PCI (values near 0) indicates phase disorder or divergence between timeframes — often seen during transitions, chop, or volatility compression phases.
✨ How it works:
1. Multi-Band Extraction
The price series is decomposed into three frequency bands using a simple EMA-difference
bandpass filter:
Short-term band
Mid-term band
Long-term band
2. Hilbert Quadrature Approximation
For each band, a Hilbert-style quadrature signal (Q) is estimated using an Ehlers-inspired FIR kernel with coefficients h0=0.0962 and h1=0.5769.
The original bandpass (I) and its quadrature (Q) form a complex phasor that carries instantaneous phase information.
3. Phase Extraction
The arctangent of Q/I gives the phase angle (−π to π).
Each band’s phase represents the “timing” of its oscillation relative to price movement.
4. Coherence Calculation
All three band phases are converted to unit phasors (cos φ, sin φ).
The average of these phasors is taken, and the magnitude of the resulting vector defines the PCI value:
PCI = |mean_phasor| × 100
PCI = 100 → perfect phase alignment
PCI = 0 → complete phase randomness
5. Smoothing and Regime Coloring
The raw PCI is lightly smoothed by EMA to stabilize readings.
A simple SuperTrend regime filter (ATR length 5, factor 6) colors the PCI line:
Green: uptrend regime
Red: downtrend regime
Gray: low-coherence or neutral zone
✨ How to interpret PCI
High PCI (≥70):
Indicates strong phase coherence — multi-timeframe alignment, stable directional energy.
Often coincides with trend persistence or mature momentum phases.
Low PCI (≤30):
Suggests phase dispersion or regime transition — signals are desynchronized across bands.
Common near reversals, volatility compressions, or sideways congestion.
Cross-phase Transitions:
Rapid drops in PCI often precede periods of uncertainty or trend fatigue.
Rising PCI after a contraction suggests emerging order or trend formation.
✨ Visualization
PCI Line:
Displays phase coherence magnitude (0–100).
Color changes dynamically with SuperTrend regime.
Dotted Reference Levels:
70 (high coherence), 30 (low coherence), 50 (midline).
Optional Label:
When PCI is high, a text label appears indicating whether the average phase bias leans bullish or bearish.
✨ Practical Use
Combine PCI with momentum or volatility tools (e.g., RSI, ATR, DFI) to detect when multiple timeframes become synchronized.
Use PCI drops (<30) as early warnings for possible trend exhaustion or market disorder.
Use PCI rises (>70) as confirmation of trend continuation or emerging coherence.
✨ Limitations
PCI measures phase alignment, not directional bias. It should be combined with a directional filter (e.g., SuperTrend, slope, or moving average).
The Hilbert approximation is not a true analytic signal, but a real-time proxy optimized for stability.
PCI is best interpreted as a structural context indicator rather than a direct entry signal.
✨ Educational intent
This script is designed for quantitative study and visualization of phase alignment, not for direct trading advice.
It demonstrates how Hilbert-style phasors and vector averaging can reveal coherence across timescales — a foundational concept for spectral, cyclical, and structural analysis.
✨ Disclaimer
This script is provided for educational purposes only.
It does not constitute financial advice.
Trading involves risk; use at your own discretion.
© 2025 Trading Playground Labs — “Phase Coherence Index (Hilbert Prototype v0.1)
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📘 한국어 설명 (Korean translation below)
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Phase Coherence Index (PCI) — Hilbert Prototype v0.1 (한국어 설명)
✨ 개념
Phase Coherence Index (PCI) 는 여러 주기의 추세 구성 요소들 간에 위상의 정합 정도를 측정하는 지표입니다.
쉽게 말하면, 시장의 단기·중기·장기 진동(사이클)들이 서로 얼마나 “같은 박자”로 움직이는지를 수치화한 것입니다.
PCI 값이 100에 가까울수록 시장의 주기적 구성 요소들이 함께 움직이고 있다는 뜻이며,
이는 일반적으로 지속적인 방향성 추세(trend persistence) 상태를 의미합니다.
반대로 PCI 값이 0에 가까울수록 각 주기의 움직임이 서로 엇박자 상태로 흐르며,
이는 전환기(chop), 혼돈(chaos), 혹은 압축(squeeze) 상태를 반영합니다.
✨ 작동 원리
1. 다중 밴드 추출
가격 데이터를 3개의 주파수 대역으로 분리합니다.
단기 (short-term band)
중기 (mid-term band)
장기 (long-term band)
각 대역은 빠른 EMA와 느린 EMA의 차이(EMA difference)로 얻은 단순한 밴드패스로 계산됩니다.
2. Hilbert 직교 성분 근사
각 밴드에 대해 Ehlers 계열 FIR 필터를 이용한 Hilbert 근사 신호(Q)를 계산합니다.
원본 밴드패스 신호(I)와 Q를 결합하면 복소 위상(phasor) 를 구성할 수 있고,
이를 통해 각 대역의 순간 위상(instantaneous phase) 을 추정합니다.
3. 위상 계산
Q/I의 아크탄젠트 값을 구하면 위상각 φ(−π ~ π)가 도출됩니다.
이 위상은 해당 주기의 진동 타이밍을 나타냅니다.
4. 위상 정합(Phase Coherence) 계산
각 밴드의 위상을 단위 페이저 벡터 (cos φ, sin φ)로 변환합니다.
세 벡터의 평균을 구하고, 그 평균 벡터의 크기를 0~100 스케일로 변환한 값이 PCI입니다.
PCI = |평균 페이저| × 100
PCI = 100 → 완전한 위상 일치
PCI = 0 → 완전한 위상 무질서
5. 스무딩 및 추세 컬러링
계산된 PCI는 EMA로 약간 평활화되어 안정적으로 표시됩니다.
또한, SuperTrend 레짐 필터(ATR=5, Factor=6) 로 추세 방향에 따라 색이 바뀝니다:
초록색: 상승 추세
빨간색: 하락 추세
회색: 저정합 / 중립 구간
✨ 해석 방법
PCI ≥ 70 (높은 정합)
여러 타임프레임이 같은 방향으로 동기화되어 있는 상태입니다.
이는 추세 지속성, 모멘텀 유지, 구조적 안정 상태를 의미합니다.
PCI ≤ 30 (낮은 정합)
각 주기 간 위상 불일치가 심해지며, 시장이 방향을 잃거나 변곡점에 근접한 상황입니다.
보통 조정기, 변동성 축소, 횡보 국면에서 자주 나타납니다.
급격한 변동 (Phase Transition)
PCI가 급격히 하락하는 구간은 추세 피로(trend fatigue) 또는 혼돈 국면 진입의 신호일 수 있으며,
이후 PCI가 상승하기 시작하면 새로운 질서(추세 형성) 가 시작될 가능성을 의미합니다.
✨ 시각화 구성
PCI 라인:
위상 정합도의 크기(0~100)를 표시합니다.
SuperTrend 방향에 따라 색이 동적으로 변합니다.
점선 기준선:
70(높은 정합), 50(중간), 30(낮은 정합) 기준으로 시각화됩니다.
평균 위상 라벨:
PCI가 높을 때, 평균 위상의 방향이 상승(bullish) 인지 하락(bearish) 인지를 라벨로 표시합니다.
✨ 활용 방법
RSI, ATR, DFI 등 다른 모멘텀/변동성 지표와 함께 사용하면, 복수 타임프레임 동기화 구간을 탐지할 수 있습니다.
PCI 하락(30 이하)은 추세 피로 또는 혼돈 진입의 신호로, PCI 상승(70 이상)은 질서 회복 또는 추세 지속의 신호로 해석할 수 있습니다.
추세 전환, 조정, 또는 모멘텀 재편성 구간을 감지하는 데 유용합니다.
✨ 한계점
PCI는 방향성(Direction)을 직접 측정하지 않고, 구조적 정합(Alignment) 만 측정합니다. 따라서 반드시 방향 필터(SuperTrend, MA 기울기 등)와 함께 해석해야 합니다.
Hilbert 근사는 완전한 복소 해석 신호가 아니라, 실시간 안정성을 중시한 근사 모델입니다.
PCI는 진입 시그널용보다는 시장 구조 인식용 보조 지표로 적합합니다.
✨ 교육적 목적
이 스크립트는 위상 정합(phase coherence) 개념의 시각화와 정량적 연구를 위한 목적으로 설계되었습니다.
힐버트 변환 기반 위상 벡터(phasor) 와 벡터 평균화(vector averaging) 의 개념을 통해
시장의 다중 시간 구조 간 일관성(coherence)을 탐색할 수 있습니다.
✨면책
이 지표는 교육 목적으로만 제공됩니다.
투자 조언이 아니며, 모든 트레이딩 결정의 책임은 사용자에게 있습니다.
© 2025 Trading Playground Labs — Phase Coherence Index (Hilbert Prototype v0.1)
Developed for structural & spectral analysis of market phase alignment.
MTF 20 SMA Table - DXY**MTF 20 SMA Table - Multi-Timeframe Trend Analysis Dashboard**
**Overview:**
This indicator provides a comprehensive multi-timeframe analysis dashboard that displays the relationship between price and the 20-period Simple Moving Average (SMA) across four key timeframes: 15-minute, 1-hour, 4-hour, and Daily. It's designed to help traders quickly identify trend alignment and potential trading opportunities across multiple timeframes at a glance. It's definitely not perfect but has helped me speed up my backtesting efforts as it's worked well for me eliminating flipping back and forth between timeframes excpet when I have confluence on the table, then I check the HTF.
**How It Works:**
The indicator creates a table overlay on your chart showing three critical metrics for each timeframe:
1. **Price vs SMA (Row 1):** Shows whether price is currently above (bullish) or below (bearish) the 20 SMA
- Green = Price Above SMA
- Red = Price Below SMA
2. **SMA Direction (Row 2):** Indicates the trend direction of the SMA itself over a lookback period
- Green (↗ Rising) = Uptrend
- Red (↘ Falling) = Downtrend
- Gray (→ Flat) = Ranging/Consolidation
3. **Strength (Row 3):** Displays the distance between current price and the SMA in pips
- Purple background = Strong move (>50 pips away)
- Orange background = Moderate move (20-50 pips)
- Gray background = Weak/consolidating (<20 pips)
- Text color: Green for positive distance, Red for negative
**Key Features:**
- **Customizable Table Position:** Place the table anywhere on your chart (9 position options)
- **Adjustable SMA Lengths:** Modify the SMA period for each timeframe independently (default: 20)
- **Direction Lookback Settings:** Fine-tune how far back the indicator looks to determine SMA direction for each timeframe
- **Flat Threshold:** Set the pip threshold for determining when an SMA is "flat" vs trending (default: 5 pips)
- **DXY Optimized:** Calculations are calibrated for the US Dollar Index (1 pip = 0.01)
**Best Use Cases:**
1. **Trend Alignment:** Identify when multiple timeframes align in the same direction for higher probability trades
2. **Divergence Spotting:** Detect when lower timeframes diverge from higher timeframes (potential reversals)
3. **Entry Timing:** Use lower timeframe signals while higher timeframes confirm overall trend
4. **Strength Assessment:** Gauge how extended price is from the mean (SMA) to avoid overextended entries
**Settings Guide:**
- **SMA Settings Group:** Adjust the SMA period for each timeframe (15M, 1H, 4H, Daily)
- **SMA Direction Group:** Control lookback periods to determine trend direction
- 15M: Default 5 candles
- 1H: Default 10 candles
- 4H: Default 15 candles
- Daily: Default 20 candles
- **Flat Threshold:** Set sensitivity for "flat" detection (lower = more sensitive to ranging markets)
**Trading Strategy Examples:**
1. **Trend Following:** Look for all timeframes showing the same direction (all green or all red)
2. **Pullback Trading:** When Daily/4H are green but 15M/1H show red, wait for lower timeframes to flip green for entry
3. **Ranging Markets:** When multiple SMAs show "flat", consider range-bound strategies
**Important Notes:**
- This is a reference tool only, not a standalone trading system
- Always use proper risk management and combine with other analysis methods
- Best suited for trending instruments like indices and major forex pairs
- Calculations are optimized for DXY but can be used on other instruments (pip calculations may need adjustment)
**Credits:**
Feel free to modify and improve this code! Suggestions for enhancements are welcome in the comments.
---
**Installation Instructions:**
1. Add the indicator to your TradingView chart
2. Adjust the table position via settings to avoid overlap with price action
3. Customize SMA lengths and lookback periods to match your trading style
4. Monitor the table for timeframe alignment and trend confirmation
---
This indicator is published as open source for the community to learn from and improve upon. Happy trading! 📈
True Average PriceTrue Average Price
Overview
The indicator plots a single line representing the cumulative average closing price of any symbol you choose. It lets you project a long-term mean onto your active chart, which is useful when your favourite symbol offers limited history but you still want context from an index or data-rich feed.
How It Works
The script retrieves all available historical bars from the selected symbol, sums their closes, counts the bars, and divides the totals to compute the lifetime average. That value is projected onto the chart you are viewing so you can compare current price action to the broader historical mean.
Inputs
Use Symbol : Toggle on to select an alternate symbol; leave off to default to the current chart.
Symbol : Pick the data source used for the average when the toggle is enabled.
Line Color : Choose the display color of the average line.
Line Width : Adjust the thickness of the plotted line.
Usage Tips
Apply the indicator to exchanges with shallow history while sourcing the average from a complete index (e.g., INDEX:BTCUSD for crypto pairs).
Experiment with different symbols to understand how alternative data feeds influence the baseline level.
Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Price Action Brooks ProPrice Action Brooks Pro (PABP) - Professional Trading Indicator
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📊 OVERVIEW
Price Action Brooks Pro (PABP) is a professional-grade TradingView indicator developed based on Al Brooks' Price Action trading methodology. It integrates decades of Al Brooks' trading experience and price action analysis techniques into a comprehensive technical analysis tool, helping traders accurately interpret market structure and identify trading opportunities.
• Applicable Markets: Stocks, Futures, Forex, Cryptocurrencies
• Timeframes: 1-minute to Daily (5-minute chart recommended)
• Theoretical Foundation: Al Brooks Price Action Trading Method
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🎯 CORE FEATURES
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1️⃣ INTELLIGENT GAP DETECTION SYSTEM
Automatically identifies and marks three critical types of gaps in the market.
TRADITIONAL GAP
• Detects complete price gaps between bars
• Upward gap: Current bar's low > Previous bar's high
• Downward gap: Current bar's high < Previous bar's low
• Hollow border design - doesn't obscure price action
• Color coding: Upward gaps (light green), Downward gaps (light pink)
• Adjustable border: 1-5 pixel width options
TAIL GAP
• Detects price gaps between bar wicks/shadows
• Analyzes across 3 bars for precision
• Identifies hidden market structure
BODY GAP
• Focuses only on gaps between bar bodies (open/close)
• Filters out wick noise
• Disabled by default, enable as needed
Trading Significance:
• Gaps signal strong momentum
• Gap fills provide trading opportunities
• Consecutive gaps indicate trend continuation
✓ Independent alert system for all gap types
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2️⃣ RTH BAR COUNT (Trading Session Counter)
Intelligent counting system designed for US stock intraday trading.
FEATURES
• RTH Only Display: Regular Trading Hours (09:30-15:00 EST)
• 5-Minute Chart Optimized: Displays every 3 bars (15-minute intervals)
• Daily Auto-Reset: Counting starts from 1 each trading day
SMART COLOR CODING
• 🔴 Red (Bars 18 & 48): Critical turning moments (1.5h & 4h)
• 🔵 Sky Blue (Multiples of 12): Hourly markers (12, 24, 36...)
• 🟢 Light Green (Bar 6): Half-hour marker (30 minutes)
• ⚫ Gray (Others): Regular 15-minute interval markers
Al Brooks Time Theory:
• Bar 18 (90 min): First 90 minutes determine daily trend
• Bar 48 (4 hours): Important afternoon turning point
• Hourly markers: Track institutional trading rhythm
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3️⃣ FOUR-LINE EMA SYSTEM
Professional-grade configurable moving average system.
DEFAULT CONFIGURATION
• EMA 20: Short-term trend (Al Brooks' most important MA)
• EMA 50: Medium-short term reference
• EMA 100: Medium-long term confirmation
• EMA 200: Long-term trend and bull/bear dividing line
FLEXIBLE CUSTOMIZATION
Each EMA can be independently configured:
• On/Off toggle
• Data source selection (close/high/low/open, etc.)
• Custom period length
• Offset adjustment
• Color and transparency
COLOR SCHEME
• EMA 20: Dark brown, opaque (most important)
• EMA 50/100/200: Blue-purple gradient, 70% transparent
TRADING APPLICATIONS
• Bullish Alignment: Price > 20 > 50 > 100 > 200
• Bearish Alignment: 200 > 100 > 50 > 20 > Price
• EMA Confluence: All within <1% = major move precursor
Al Brooks Quote:
"The EMA 20 is the most important moving average. Almost all trading decisions should reference it."
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4️⃣ PREVIOUS VALUES (Key Prior Price Levels)
Automatically marks important price levels that often act as support/resistance.
THREE INDEPENDENT CONFIGURATIONS
Each group configurable for:
• Timeframe (1D/60min/15min, etc.)
• Price source (close/high/low/open/CurrentOpen, etc.)
• Line style and color
• Display duration (Today/TimeFrame/All)
SMART OPEN PRICE LABELS ⭐
• Auto-displays "Open" label when CurrentOpen selected
• Label color matches line color
• Customizable label size
TYPICAL SETUP
• 1st Line: Previous close (Support/Resistance)
• 2nd Line: Previous high (Breakout target)
• 3rd Line: Previous low (Support level)
Al Brooks Magnet Price Theory:
• Previous open: Price frequently tests opening price
• Previous high/low: Strongest support/resistance
• Breakout confirmation: Breaking prior levels = trend continuation
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5️⃣ INSIDE & OUTSIDE BAR PATTERN RECOGNITION
Automatically detects core candlestick patterns from Al Brooks' theory.
ii PATTERN (Consecutive Inside Bars)
• Current bar contained within previous bar
• Two or more consecutive
• Labels: ii, iii, iiii (auto-accumulates)
• High-probability breakout setup
• Stop loss: Outside both bars
Trading Significance:
"Inside bars are one of the most reliable breakout setups, especially three or more consecutive inside bars." - Al Brooks
OO PATTERN (Consecutive Outside Bars)
• Current bar engulfs previous bar
• Two or more consecutive
• Labels: oo, ooo (auto-accumulates)
• Indicates indecision or volatility increase
ioi PATTERN (Inside-Outside-Inside)
• Three-bar combination: Inside → Outside → Inside
• Auto-detected and labeled
• Tug-of-war pattern
• Breakout direction often very strong
SMART LABEL SYSTEM
• Auto-accumulation counting
• Dynamic label updates
• Customizable size and color
• Positioned above bars
✓ Independent alerts for all patterns
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💡 USE CASES
INTRADAY TRADING
✓ Bar Count (timing rhythm)
✓ Traditional Gap (strong signals)
✓ EMA 20 + 50 (quick trend)
✓ ii/ioi Patterns (breakout points)
SWING TRADING
✓ Previous Values (key levels)
✓ EMA 20 + 50 + 100 (trend analysis)
✓ Gaps (trend confirmation)
✓ iii Patterns (entry timing)
TREND FOLLOWING
✓ All four EMAs (alignment analysis)
✓ Gaps (continuation signals)
✓ Previous Values (targets)
BREAKOUT TRADING
✓ iii Pattern (high-reliability setup)
✓ Previous Values (targets)
✓ EMA 20 (trend direction)
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🎨 DESIGN FEATURES
PROFESSIONAL COLOR SCHEME
• Gaps: Hollow borders + light colors
• Bar Count: Smart multi-color coding
• EMAs: Gradient colors + transparency hierarchy
• Previous Values: Customizable + smart labels
CLEAR VISUAL HIERARCHY
• Important elements: Opaque (EMA 20, bar count)
• Reference elements: Semi-transparent (other EMAs, gaps)
• Hollow design: Doesn't obscure price action
USER-FRIENDLY INTERFACE
• Clear functional grouping
• Inline layout saves space
• All colors and sizes customizable
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📚 AL BROOKS THEORY CORE
READING PRICE ACTION
"Don't try to predict the market, read what the market is telling you."
PABP converts core concepts into visual tools:
• Trend Assessment: EMA system
• Time Rhythm: Bar Count
• Market Structure: Gap analysis
• Trade Setups: Inside/Outside Bars
• Support/Resistance: Previous Values
PROBABILITY THINKING
• ii pattern: Medium probability
• iii pattern: High probability
• iii + EMA 20 support: Very high probability
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⚙️ TECHNICAL SPECIFICATIONS
• Pine Script Version: v6
• Maximum Objects: 500 lines, 500 labels, 500 boxes
• Alert Functions: 8 independent alerts
• Supported Timeframes: All (5-min recommended for Bar Count)
• Compatibility: All TradingView plans, Mobile & Desktop
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🚀 RECOMMENDED INITIAL SETTINGS
GAPS
• Traditional Gap: ✓
• Tail Gap: ✓
• Border Width: 2
BAR COUNT
• Use Bar Count: ✓
• Label Size: Normal
EMA
• EMA 20: ✓
• EMA 50: ✓
• EMA 100: ✓
• EMA 200: ✓
PREVIOUS VALUES
• 1st: close (Previous close)
• 2nd: high (Previous high)
• 3rd: low (Previous low)
INSIDE & OUTSIDE BAR
• All patterns: ✓
• Label Size: Large
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🌟 WHY CHOOSE PABP?
✅ Solid Theoretical Foundation
Based on Al Brooks' decades of trading experience
✅ Complete Professional Features
Systematizes complex price action analysis
✅ Highly Customizable
Every feature adjustable to personal style
✅ Excellent Performance
Optimized code ensures smooth experience
✅ Continuous Updates
Constantly improving based on feedback
✅ Suitable for All Levels
Benefits beginners to professionals
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📖 RECOMMENDED LEARNING
Al Brooks Books:
• "Trading Price Action Trends"
• "Trading Price Action Trading Ranges"
• "Trading Price Action Reversals"
Learning Path:
1. Understand basic candlestick patterns
2. Learn EMA applications
3. Master market structure analysis
4. Develop trading system
5. Continuous practice and optimization
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⚠️ RISK DISCLOSURE
IMPORTANT NOTICE:
• For educational and informational purposes only
• Does not constitute investment advice
• Past performance doesn't guarantee future results
• Trading involves risk and may result in capital loss
• Trade according to your risk tolerance
• Test thoroughly in demo account first
RESPONSIBLE TRADING:
• Always use stop losses
• Control position sizes reasonably
• Don't overtrade
• Continuous learning and improvement
• Keep trading journal
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📜 COPYRIGHT
Price Action Brooks Pro (PABP)
Author: © JimmC98
License: Mozilla Public License 2.0
Pine Script Version: v6
Acknowledgments:
Thanks to Dr. Al Brooks for his contributions to price action trading. This indicator is developed based on his theories.
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Experience professional-grade price action analysis now!
"The best traders read price action, not indicators. But when indicators help you read price action better, use them." - Al Brooks
Multi-Anchor VWAP Deviation Dashboard Overview
Multi-Anchor VWAP Deviation Dashboard (Optimized Global) is an overlay indicator that computes up to five user-defined Anchored Volume Weighted Average Prices (AVWAPs) from custom timestamps, plotting their lines and displaying real-time percentage deviations from the current close. It enables precise analysis of price positioning relative to key events (e.g., earnings, news) or periods (e.g., weekly opens), with a compact dashboard for quick scans. Optimized for performance, it uses manual iterative calculations to handle dynamic anchor changes without repainting.
Core Mechanics
The indicator focuses on efficient AVWAP computation and deviation tracking:
Anchor Configuration: Five independent anchors, each with a name, UTC timestamp (e.g., "01 Oct 2025 00:00" for monthly open), show toggle, and color. Timestamps define the calculation start—e.g., AVWAP1 from "20 Oct 2025" onward.
AVWAP Calculation: For each enabled anchor, it identifies the first bar at/after the timestamp as the reset point, then iteratively accumulates (price * volume) / total volume from there. Uses HLC3 source (customizable); handles input changes by resetting sums on new anchors.
Deviation Metric: For each AVWAP, computes % deviation = ((close - AVWAP) / AVWAP) * 100—positive = above (potential resistance), negative = below (support).
Visuals: Plots lines (linewidth 1–2, user colors); dashboard (2 columns, 6 rows) shows names (anchor-colored if enabled) and deviations (green >0%, red <0%, gray N/A), positioned user-selectable with text sizing. Updates on last bar for efficiency.
This setup scales deviations across volatilities, aiding multi-period bias assessment.
Why This Adds Value & Originality
Standard VWAPs limit to session anchors (daily/weekly); deviation tools often lack multiples. This isn't a simple mashup: Manual iterative AVWAP (no built-in ta.vwap reliance) ensures dynamic resets on timestamp tweaks—e.g., shift "Event" to FOMC date without recalc lag. The 5-anchor flexibility (arbitrary UTC times) + centralized dashboard (colored deviations at a glance) creates a "global timeline scanner" unique to event-driven trading, unlike rigid multi-VWAP scripts. It streamlines what requires 5 separate indicators, with % normalization for cross-asset comparison (e.g., SPY vs. BTC).
How to Use
Setup: Overlay on chart. Configure anchors (e.g., Anchor1: "Weekly Open" at next Monday 00:00 UTC; enable/show 2–3 for focus). Set source (HLC3 default), position (Top Right), text size (Small).
Interpret Dashboard:
Left Column: Anchor names (e.g., "Monthly Open" in orange).
Right Column: Deviations (e.g., "+1.25%" green = above, bullish exhaustion?).
Scan for confluence (e.g., all >+2% = overbought).
Trading:
Lines: Price near AVWAP = mean reversion; breaks = momentum.
Example: -0.8% below "Event" anchor post-earnings → potential bounce buy.
Use on 1H–D; adjust timestamps via calendar.
Tips: Enable 1–3 anchors to avoid clutter; test on historical events.
Limitations & Disclaimer
AVWAPs reset on anchor bars, potentially lagging mid-period; deviations are % only (add ATR for absolute). Table updates on close (no intrabar). Timestamps must be UTC/future-proof. No alerts/exits—integrate manually. Not advice; backtest deviations on your assets. Past ≠ future. Comments for ideas.
Triangular Moving Average (TRIMA)The Triangular Moving Average (TRIMA) is a technical indicator that applies a triangular weighting scheme to price data, providing enhanced smoothing compared to simpler moving averages. Originating in the early 1970s as technical analysts sought more effective noise filtering methods, the TRIMA was first popularized through the work of market technician Arthur Merrill. Its formal mathematical properties were established in the 1980s, and the indicator gained widespread adoption in the 1990s as computerized charting became standard. TRIMA effectively filters out market noise while maintaining important trends through its unique center-weighted calculation method.
## Core Concepts
* **Double-smoothing process:** TRIMA can be viewed as applying a simple moving average twice, creating more effective noise filtering
* **Triangular weighting:** Uses a symmetrical weight distribution that emphasizes central data points and reduces emphasis toward both ends
* **Constant-time implementation:** Two $O(1)$ SMA passes with circular buffers preserve exact triangular weights while keeping update cost constant per bar
* **Market application:** Particularly effective for identifying the underlying trend in noisy market conditions where standard moving averages generate too many false signals
* **Timeframe flexibility:** Works across multiple timeframes, with longer periods providing cleaner trend signals in higher timeframes
The core innovation of TRIMA is its unique triangular weighting scheme, which can be viewed either as a specialized weight distribution or as a twice-applied simple moving average with adjusted period. This creates more effective noise filtering without the excessive lag penalty typically associated with longer-period averages. The symmetrical nature of the weight distribution ensures zero phase distortion, preserving the timing of important market turning points.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For a good balance between smoothing and responsiveness, try using a TRIMA with period N instead of an SMA with period 2N - you'll get similar smoothing characteristics but with less lag.
## Calculation and Mathematical Foundation
**Simplified explanation:**
TRIMA calculates a weighted average of prices where the weights form a triangle shape. The middle prices get the most weight, and weights gradually decrease toward both the recent and older ends. This creates a smooth filter that effectively removes random price fluctuations while preserving the underlying trend.
**Technical formula:**
TRIMA = Σ(Price × Weight ) / Σ(Weight )
Where the triangular weights form a symmetric pattern:
- Weight = min(i, n-1-i) + 1
- Example for n=5: weights =
- Example for n=4: weights =
Alternatively, TRIMA can be calculated as:
TRIMA(source, p) = SMA(SMA(source, (p+1)/2), (p+1)/2)
> 🔍 **Technical Note:** The double application of SMA explains why TRIMA provides better smoothing than a single SMA or WMA. This approach effectively applies smoothing twice with optimal period adjustment, creating a -18dB/octave roll-off in the frequency domain compared to -6dB/octave for a simple moving average, and the current implementation achieves $O(1)$ complexity through circular buffers and NA-safe warmup compensation.
## Interpretation Details
TRIMA can be used in various trading strategies:
* **Trend identification:** The direction of TRIMA indicates the prevailing trend
* **Signal generation:** Crossovers between price and TRIMA generate trade signals with fewer false alarms than SMA
* **Support/resistance levels:** TRIMA can act as dynamic support during uptrends and resistance during downtrends
* **Trend strength assessment:** Distance between price and TRIMA can indicate trend strength
* **Multiple timeframe analysis:** Using TRIMAs with different periods can confirm trends across different timeframes
## Limitations and Considerations
* **Market conditions:** Like all moving averages, less effective in choppy, sideways markets
* **Lag factor:** More lag than WMA or EMA due to center-weighted emphasis
* **Limited adaptability:** Fixed weighting scheme cannot adapt to changing market volatility
* **Response time:** Takes longer to reflect sudden price changes than directionally-weighted averages
* **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation
## References
* Ehlers, John F. "Cycle Analytics for Traders." Wiley, 2013
* Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013
* Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002
Savitzky-Golay Filter (SGF)The Savitzky-Golay Filter (SGF) is a digital filter that performs local polynomial regression on a series of values to determine the smoothed value for each point. Developed by Abraham Savitzky and Marcel Golay in 1964, it is particularly effective at preserving higher moments of the data while reducing noise. This implementation provides a practical adaptation for financial time series, offering superior preservation of peaks, valleys, and other important market structures that might be distorted by simpler moving averages.
## Core Concepts
* **Local polynomial fitting:** Fits a polynomial of specified order to a sliding window of data points
* **Moment preservation:** Maintains higher statistical moments (peaks, valleys, inflection points)
* **Optimized coefficients:** Uses pre-computed coefficients for common polynomial orders
* **Adaptive weighting:** Weight distribution varies based on polynomial order and window size
* **Market application:** Particularly effective for preserving significant price movements while filtering noise
The core innovation of the Savitzky-Golay filter is its ability to smooth data while preserving important features that are often flattened by other filtering methods. This makes it especially valuable for technical analysis where maintaining the shape of price patterns is crucial.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Window Size | 11 | Number of points used in local fitting (must be odd) | Increase for smoother output, decrease for better feature preservation |
| Polynomial Order | 2 | Order of fitting polynomial (2 or 4) | Use 2 for general smoothing, 4 for better peak preservation |
| Source | close | Price data used for calculation | Consider using hlc3 for more stable fitting |
**Pro Tip:** A window size of 11 with polynomial order 2 provides a good balance between smoothing and feature preservation. For sharper peaks and valleys, use order 4 with a smaller window size.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The filter fits a polynomial of specified order to a moving window of price data. The smoothed value at each point is computed from this local fit, effectively removing noise while preserving the underlying shape of the data.
**Technical formula:**
For a window of size N and polynomial order M, the filtered value is:
y = Σ(c_i × x )
Where:
- c_i are the pre-computed filter coefficients
- x are the input values in the window
- Coefficients depend on window size N and polynomial order M
> 🔍 **Technical Note:** The implementation uses optimized coefficient calculations for orders 2 and 4, which cover most practical applications while maintaining computational efficiency.
## Interpretation Details
The Savitzky-Golay filter can be used in various trading strategies:
* **Pattern recognition:** Preserves chart patterns while removing noise
* **Peak detection:** Maintains amplitude and width of significant peaks
* **Trend analysis:** Smooths price movement without distorting important transitions
* **Divergence trading:** Better preservation of local maxima and minima
* **Volatility analysis:** Accurate representation of price movement dynamics
## Limitations and Considerations
* **Computational complexity:** More intensive than simple moving averages
* **Edge effects:** First and last few points may show end effects
* **Parameter sensitivity:** Performance depends on appropriate window size and order selection
* **Data requirements:** Needs sufficient points for polynomial fitting
* **Complementary tools:** Best used with volume analysis and momentum indicators
## References
* Savitzky, A., Golay, M.J.E. "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Analytical Chemistry, 1964
* Press, W.H. et al. "Numerical Recipes: The Art of Scientific Computing," Chapter 14
* Schafer, R.W. "What Is a Savitzky-Golay Filter?" IEEE Signal Processing Magazine, 2011
Bilateral Filter (BILATERAL)The Bilateral Filter is an edge-preserving smoothing technique that combines spatial filtering with intensity filtering to achieve noise reduction while maintaining significant price structure. Originally developed in computer vision for image processing, this adaptive filter has been adapted for financial time series analysis to provide superior smoothing that preserves important market transitions. The filter intelligently reduces noise in stable price regions while preserving sharp transitions like breakouts, reversals, and other significant market structures that would be blurred by conventional filters.
## Core Concepts
* **Dual-domain filtering:** Combines traditional time-based (spatial) filtering with value-based (range) filtering for adaptive smoothing
* **Edge preservation:** Maintains important price transitions while aggressively smoothing areas of minor fluctuation
* **Adaptive processing:** Automatically adjusts filtering strength based on local price characteristics
The core innovation of the Bilateral Filter is its ability to distinguish between random noise and significant price movements. Unlike conventional filters that smooth everything equally, Bilateral filtering preserves major price transitions by reducing the influence of price points that differ significantly from the current price, effectively preserving market structure while still eliminating noise.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback window size | Increase for more context in filtering decisions, decrease for quicker response |
| Sigma_S_Ratio | 0.3 | Controls spatial (time) weighting | Lower values emphasize recent bars, higher values distribute influence more evenly |
| Sigma_R_Mult | 2.0 | Controls range (price) sensitivity | Lower values increase edge preservation, higher values increase smoothing |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For breakout trading strategies, try reducing Sigma_R_Mult to 1.0-1.5 to make the filter more sensitive to significant price moves, allowing it to preserve breakout signals while still filtering noise.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The Bilateral Filter calculates a weighted average of nearby prices, where the weights depend on two factors: how far away in time the price point is (spatial weight) and how different the price value is (range weight). Points that are close in time AND similar in value get the highest weight. This means stable price regions get smoothed while significant changes are preserved.
**Technical formula:**
BF = (1 / Wp) × Σ_{q ∈ S} G_s(||p - q||) × G_r(|I - I |) × I
Where:
- G_s is the spatial Gaussian kernel: exp(-||p - q||² / (2 × σ_s²))
- G_r is the range Gaussian kernel: exp(-|I - I |² / (2 × σ_r²))
- Wp is the normalization factor (sum of all weights)
> 🔍 **Technical Note:** The sigma_r parameter is typically calculated dynamically based on local price volatility (standard deviation) to provide adaptive filtering - this automatically adjusts filtering strength based on market conditions.
## Interpretation Details
The Bilateral Filter can be applied in various trading contexts:
* **Trend identification:** Reveals cleaner underlying price direction by removing noise while preserving trend changes
* **Support/resistance identification:** Provides clearer price levels by preserving significant turning points
* **Pattern recognition:** Maintains critical chart patterns while eliminating distracting minor fluctuations
* **Breakout trading:** Preserves sharp price transitions for more reliable breakout signals
* **Pre-processing:** Can be used as an initial filter before applying other technical indicators to reduce false signals
## Limitations and Considerations
* **Computational complexity:** More intensive calculations than traditional linear filters
* **Parameter sensitivity:** Performance highly dependent on proper parameter selection
* **Non-linearity:** Non-linear behavior may produce unexpected results in certain market conditions
* **Interpretation adjustment:** Requires different interpretation than conventional moving averages
* **Complementary tools:** Best used alongside volume analysis and traditional indicators for confirmation
## References
* Tomasi, C. and Manduchi, R. "Bilateral Filtering for Gray and Color Images," Proceedings of IEEE ICCV, 1998
* Paris, S. et al. "A Gentle Introduction to Bilateral Filtering and its Applications," ACM SIGGRAPH, 2008
EMA6 or SMA6 Touch AlertThis script monitors the market and notifies you whenever the price touches either the 6-period EMA or the 6-period SMA.
It helps identify potential pullbacks, reaction points, or entry zones, as price interaction with these moving averages often signals short-term market shifts.
What the script does:
Calculates the EMA 6 and SMA 6
Detects if price touches either moving average within the candle
Plots both lines on the chart for visibility
Allows you to set alerts to receive automatic notifications
Best suited for:
Scalping
Day Trading
Pullback Entries
Short-term trend reactions
HEK Dynamic Price Channel StrategyHEK Dynamic Price Channel Strategy
Concept
The HEK Dynamic Price Channel provides a channel structure that expands and contracts according to price momentum and time-based equilibrium.
Unlike fixed-band systems, it evaluates the interaction between price and its balance line through an adaptive channel width that dynamically adjusts to changing market conditions.
How It Works
When the price reacts to the midline, the channel bands automatically reposition themselves.
Touching the upper band indicates a strengthening trend, while touching the lower band signals weakening momentum.
This adaptive mechanism helps filter out false signals during sudden directional changes, enhancing overall signal quality.
Advantages
✅ Maintains trend continuity while avoiding overtrading.
✅ Automatically adapts to changing volatility conditions.
✅ Detects early signals of short- and mid-term trend reversals.
Applications
Directional confirmation in spot and futures markets.
A supporting tool in channel breakout strategies.
Identifying price consolidation and equilibrium zones.
Note
This strategy is intended for educational and research purposes only.
It should not be considered financial advice. Always consult a professional financial advisor before making investment decisions.
© HEK — Adaptive Channel Approach on Dynamic Market Structures
(15M) Gold Daily Signal — Invite OnlyQuick Start
Symbol XAUUSD, timeframe 15m.
Defaults: TP 50 pips, SL 150 pips.
Wait for green (long) or red (short) background after bar close.
Place orders at the plotted Entry / TP / SL; optional scale-ins at E1/E2.
Max signals kept on chart – housekeeping only (limits old drawings).
Alerts
Turn Green → ready-to-buy signal.
Turn Red → ready-to-sell signal.
Create alerts once per bar close and keep the default message or customize.
Multi-MA Multi-TFMulti-MA Multi-TF Indicator
Visualize up to 8 customizable Moving Averages across multiple timeframes directly on your current chart. This indicator is designed to provide a comprehensive overview of trend dynamics without cluttering your workspace with numerous separate indicators.
Key Features:
Up to 8 Moving Averages: Configure each MA individually.
Flexible Configuration: For each MA, independently set:
Timeframe (5m, 15m, 1h, 4h, Daily, Weekly)
MA Type (SMA or EMA)
Length (Period)
Line Width
Color
Whether to plot the MA line
Whether to show the MA in the summary table
Whether to display its label
Informative Labels: Optional labels appear next to the MA lines, displaying:
MA identification (e.g., Daily EMA(9))
Current MA value
Optional percentage distance of the MA from the current price (+% if MA is above price, -% if MA is below).
Label Only Mode: Choose to display only the labels with short line segments, hiding the main MA lines for a cleaner look.
Summary Table: An optional table provides a quick overview of selected MAs:
Lists the MA name (Timeframe, Type, Length).
Shows the percentage distance from the current price, color-coded for clarity:
Green ▲: Price is currently above the MA.
Red ▼: Price is currently below the MA.
Select which MAs appear in the table via individual MA settings.
Position the table in any corner of the chart.
|Magical Trend Line with RSI & ADX|# 📊 Multi-Symbol RSI + ADX Trend Confirmation System
### Adaptive Multi-Layer Trend Analyzer with EMA Structure, RSI Momentum, ADX Strength & Table Dashboard
**Version:** 1.0 | **Language:** Pine Script v6 | **Author:** ask2maniish
---
## 🔍 Summary of the Script
This indicator combines **EMA trend structure**, **RSI**, and **ADX** to form a **multi-layered trend confirmation system** with rich visual cues and a live table dashboard.
---
## 🧭 1. Trend Direction System
Uses **EMA Fast**, **EMA Slow**, and **Main EMA (default 100-period)** to detect the current trend structure.
Classifies trends into 5 categories:
| Trend Type | Color | Description |
|-------------|--------|-------------|
| 🟢 Bright Green | Strong Bullish | Strong upside with momentum |
| 🟩 Green | Moderate Bullish | Controlled upward structure |
| ⚪ Gray | Sideways / Neutral | Low momentum or indecision |
| 🟧 Orange | Moderate Bearish | Controlled decline |
| 🔴 Red | Strong Bearish | Aggressive downward pressure |
---
## ⚙️ 2. RSI + ADX Momentum Filter
**RSI** detects momentum direction and exhaustion:
- RSI > 70 → Overbought (Potential reversal)
- RSI < 30 → Oversold (Potential reversal)
- RSI rising above 50 → Bullish momentum
- RSI falling below 50 → Bearish momentum
**ADX** identifies trend strength:
- ADX > 25 → Strong trend
- ADX < 25 → Weak / ranging market
✅ **Combined Logic:**
RSI defines direction, ADX confirms strength.
When both align with EMA structure, trend continuation probability increases.
---
## 📈 3. Multiple EMA Layers (7 Total)
Optionally visualize up to **7 EMAs (5, 8, 13, 21, 144-high, 144-close, 144-low)**.
Each EMA auto-colors using localized slope and RSI/ADX confluence logic.
Gives a clear view of **momentum stacking** and **trend maturity**.
---
## 📊 4. Trend Meter Table (HTF + LTF View)
A dynamic table provides both **current timeframe (LTF)** and **higher timeframe (HTF)** trend confirmation.
| Table Section | Description |
|----------------|-------------|
| **Current TF Trend** | EMA-based directional bias |
| **HTF Trend** | Optional higher timeframe confirmation |
| **RSI Status** | Momentum context |
| **ADX Strength** | Trend power |
| **Overall Bias** | Combined directional strength |
🧩 Example:
- ✅ Both LTF & HTF show Bullish → focus on longs.
- ❌ Both Bearish → focus on shorts.
- ⚪ Mixed → stand aside or scalp.
---
## 🎨 5. Background & Label System
- **Soft background shading** → shows live market bias.
- **On-chart labels** → display EMA trend, RSI/ADX values, and crossover events.
- **Color-coded EMA bands** → provide instant visual context.
---
## 🎯 How to Trade Using This Indicator
### 1️⃣ Identify Overall Market Context
Use the **Trend Meter Table** for directional bias.
- ✅ Bullish on both TFs → Focus on long setups.
- ❌ Bearish on both TFs → Focus on short setups.
- ⚪ Mixed signals → Wait for clarity or scalp only.
---
### 2️⃣ Entry Logic
**Long Setup**
- Main EMA color = 🟢 Green or Bright Green
- RSI rising above 50 (not yet overbought)
- ADX > threshold (e.g., 25)
- Price above both Fast & Slow EMA
**Short Setup**
- Main EMA color = 🟧 Orange or 🔴 Red
- RSI below 50 (not yet oversold)
- ADX > threshold
- Price below both Fast & Slow EMA
---
### 3️⃣ Exit / Reversal
- RSI enters overbought/oversold zone → take profit or tighten stop.
- Trend label shifts from “Strong” to “Moderate” → trend weakening.
- Candle closes below/above EMA cluster → exit signal.
---
### 4️⃣ Multi-Timeframe Confirmation
Always trade in the direction of **HTF bias**.
Example:
📍 On 15m → Confirm with 4H trend.
If 4H = “Strong Bullish”, only take long entries when local (15m) = bullish.
---
## ⚡ Tips
- Use with Smart Money Concepts or liquidity tools for added confluence.
- Works well for swing, intraday, and positional setups.
- Adjust RSI/ADX thresholds to match volatility regime.
---
## 📘 Disclaimer
This indicator is for **educational purposes** only and does not constitute financial advice.
Trade responsibly and use risk management at all times.
---
**© 2025 ask2maniish | Magical Trend Line with RSI & ADX**
Double Weighted Moving Average (DWMA)# DWMA: Double Weighted Moving Average
## Overview and Purpose
The Double Weighted Moving Average (DWMA) is a technical indicator that applies weighted averaging twice in sequence to create a smoother signal with enhanced noise reduction. Developed in the late 1990s as an evolution of traditional weighted moving averages, the DWMA was created by quantitative analysts seeking enhanced smoothing without the excessive lag typically associated with longer period averages. By applying a weighted moving average calculation to the results of an initial weighted moving average, DWMA achieves more effective filtering while preserving important trend characteristics.
## Core Concepts
* **Cascaded filtering:** DWMA applies weighted averaging twice in sequence for enhanced smoothing and superior noise reduction
* **Linear weighting:** Uses progressively increasing weights for more recent data in both calculation passes
* **Market application:** Particularly effective for trend following strategies where noise reduction is prioritized over rapid signal response
* **Timeframe flexibility:** Works across multiple timeframes but particularly valuable on daily and weekly charts for identifying significant trends
The core innovation of DWMA is its two-stage approach that creates more effective noise filtering while minimizing the additional lag typically associated with longer-period or higher-order filters. This sequential processing creates a more refined output that balances noise reduction and signal preservation better than simply increasing the length of a standard weighted moving average.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback period for both WMA calculations | Increase for smoother signals in volatile markets, decrease for more responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For trend following, use a length of 10-14 with DWMA instead of a single WMA with double the period - this provides better smoothing with less lag than simply increasing the period of a standard WMA.
## Calculation and Mathematical Foundation
**Simplified explanation:**
DWMA first calculates a weighted moving average where recent prices have more importance than older prices. Then, it applies the same weighted calculation again to the results of the first calculation, creating a smoother line that reduces market noise more effectively.
**Technical formula:**
```
DWMA is calculated by applying WMA twice:
1. First WMA calculation:
WMA₁ = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ)
2. Second WMA calculation applied to WMA₁:
DWMA = (WMA₁₁ × w₁ + WMA₁₂ × w₂ + ... + WMA₁ₙ × wₙ) / (w₁ + w₂ + ... + wₙ)
```
Where:
- Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc.
- n is the period length
- Sum of weights = n(n+1)/2
**O(1) Optimization - Inline Dual WMA Architecture:**
This implementation uses an advanced O(1) algorithm with two complete inline WMA calculations. Each WMA uses the dual running sums technique:
1. **First WMA (source → wma1)**:
- Maintains buffer1, sum1, weighted_sum1
- Recurrence: `W₁_new = W₁_old - S₁_old + (n × P_new)`
- Cached denominator norm1 after warmup
2. **Second WMA (wma1 → dwma)**:
- Maintains buffer2, sum2, weighted_sum2
- Recurrence: `W₂_new = W₂_old - S₂_old + (n × WMA₁_new)`
- Cached denominator norm2 after warmup
**Implementation details:**
- Both WMAs fully integrated inline (no helper functions)
- Each maintains independent state: buffers, sums, counters, norms
- Both warm up independently from bar 1
- Performance: ~16 operations per bar regardless of period (vs ~10,000 for naive O(n²) implementation)
**Why inline architecture:**
Unlike helper functions, the inline approach makes all state variables and calculations visible in a single scope, eliminating function call overhead and making the dual-pass nature explicit. This is ideal for educational purposes and when debugging complex cascaded filters.
> 🔍 **Technical Note:** The dual-pass O(1) approach creates a filter that effectively increases smoothing without the quadratic increase in computational cost. Original O(n²) implementations required ~10,000 operations for period=100; this optimized version requires only ~16 operations, achieving a 625x speedup while maintaining exact mathematical equivalence.
## Interpretation Details
DWMA can be used in various trading strategies:
* **Trend identification:** The direction of DWMA indicates the prevailing trend
* **Signal generation:** Crossovers between price and DWMA generate trade signals, though they occur later than with single WMA
* **Support/resistance levels:** DWMA can act as dynamic support during uptrends and resistance during downtrends
* **Trend strength assessment:** Distance between price and DWMA can indicate trend strength
* **Noise filtering:** Using DWMA to filter noisy price data before applying other indicators
## Limitations and Considerations
* **Market conditions:** Less effective in choppy, sideways markets where its lag becomes a disadvantage
* **Lag factor:** More lag than single WMA due to double calculation process
* **Initialization requirement:** Requires more data points for full calculation, showing more NA values at chart start
* **Short-term trading:** May miss short-term trading opportunities due to increased smoothing
* **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation
## References
* Jurik, M. "Double Weighted Moving Averages: Theory and Applications in Algorithmic Trading Systems", Jurik Research Papers, 2004
* Ehlers, J.F. "Cycle Analytics for Traders," Wiley, 2013
Weighted Moving Average (WMA)This implementation uses O(1) algorithm that eliminates the need to loop through all period values on each bar. It also generates valid WMA values from the first bar and is not returning NA when number of bars is less than period.
## Overview and Purpose
The Weighted Moving Average (WMA) is a technical indicator that applies progressively increasing weights to more recent price data. Emerging in the early 1950s during the formative years of technical analysis, WMA gained significant adoption among professional traders through the 1970s as computational methods became more accessible. The approach was formalized in Robert Colby's 1988 "Encyclopedia of Technical Market Indicators," establishing it as a staple in technical analysis software. Unlike the Simple Moving Average (SMA) which gives equal weight to all prices, WMA assigns greater importance to recent prices, creating a more responsive indicator that reacts faster to price changes while still providing effective noise filtering.
## Core Concepts
* **Linear weighting:** WMA applies progressively increasing weights to more recent price data, creating a recency bias that improves responsiveness
* **Market application:** Particularly effective for identifying trend changes earlier than SMA while maintaining better noise filtering than faster-responding averages like EMA
* **Timeframe flexibility:** Works effectively across all timeframes, with appropriate period adjustments for different trading horizons
The core innovation of WMA is its linear weighting scheme, which strikes a balance between the equal-weight approach of SMA and the exponential decay of EMA. This creates an intuitive and effective compromise that prioritizes recent data while maintaining a finite lookback period, making it particularly valuable for traders seeking to reduce lag without excessive sensitivity to price fluctuations.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For most trading applications, using a WMA with period N provides better responsiveness than an SMA with the same period, while generating fewer whipsaws than an EMA with comparable responsiveness.
## Calculation and Mathematical Foundation
**Simplified explanation:**
WMA calculates a weighted average of prices where the most recent price receives the highest weight, and each progressively older price receives one unit less weight. For example, in a 5-period WMA, the most recent price gets a weight of 5, the next most recent a weight of 4, and so on, with the oldest price getting a weight of 1.
**Technical formula:**
```
WMA = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ)
```
Where:
- Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc.
- The sum of weights for a period n is calculated as: n(n+1)/2
- For example, for a 5-period WMA, the sum of weights is 5(5+1)/2 = 15
**O(1) Optimization - Dual Running Sums:**
The key insight is maintaining two running sums:
1. **Unweighted sum (S)**: Simple sum of all values in the window
2. **Weighted sum (W)**: Sum of all weighted values
The recurrence relation for a full window is:
```
W_new = W_old - S_old + (n × P_new)
```
This works because when all weights decrement by 1 (as the window slides), it's mathematically equivalent to subtracting the entire unweighted sum. The implementation:
- **During warmup**: Accumulates both sums as the window fills, computing denominator each bar
- **After warmup**: Uses cached denominator (constant at n(n+1)/2), updates both sums in constant time
- **Performance**: ~8 operations per bar regardless of period, vs ~100+ for naive O(n) implementation
> 🔍 **Technical Note:** Unlike EMA which theoretically considers all historical data (with diminishing influence), WMA has a finite memory, completely dropping prices that fall outside its lookback window. This creates a cleaner break from outdated market conditions. The O(1) optimization achieves 12-25x speedup over naive implementations while maintaining exact mathematical equivalence.
## Interpretation Details
WMA can be used in various trading strategies:
* **Trend identification:** The direction of WMA indicates the prevailing trend with greater responsiveness than SMA
* **Signal generation:** Crossovers between price and WMA generate trade signals earlier than with SMA
* **Support/resistance levels:** WMA can act as dynamic support during uptrends and resistance during downtrends
* **Moving average crossovers:** When a shorter-period WMA crosses above a longer-period WMA, it signals a potential uptrend (and vice versa)
* **Trend strength assessment:** Distance between price and WMA can indicate trend strength
## Limitations and Considerations
* **Market conditions:** Still suboptimal in highly volatile or sideways markets where enhanced responsiveness may generate false signals
* **Lag factor:** While less than SMA, still introduces some lag in signal generation
* **Abrupt window exit:** The oldest price suddenly drops out of calculation when leaving the window, potentially causing small jumps
* **Step changes:** Linear weighting creates discrete steps in influence rather than a smooth decay
* **Complementary tools:** Best used with volume indicators and momentum oscillators for confirmation
## References
* Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002
* Murphy, John J. "Technical Analysis of the Financial Markets." New York Institute of Finance, 1999
* Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013
Iriza4 -DAX EMA+HULL+ADX TP40 SL205 MIN SKALP. Additional filters improve accuracy: the strategy blocks trades after too many consecutive bullish or bearish candles (streak filter) and ignores signals when price is too far from the EMA (measured by ATR distance).
Each position uses a fixed risk-to-reward ratio of 1 : 2 with clear stop-loss and take-profit targets, without partial exits or breakevens. The goal is to identify clean pullbacks inside strong trends and filter out late or exhausted entries
Empire OS Trading Fully Automated Prop Firm Ready💎 Prop-Firm-Ready Momentum System v3 — The Gold-Mine Algorithm 💎
Engineered for the same standards that top prop firms demand — minimal drawdown, consistent equity growth, and precision-based execution. This isn’t a basic indicator; it’s a refined momentum engine built for traders who scale capital and manage risk like professionals.
Performance Snapshot
• Profit Factor 2.26 • Win Rate 33 % • Max Drawdown 0.9 % • Total P/L + $447 • W/L Ratio 4.6 : 1
Stress-tested on Gold (XAUUSD) across live-market conditions, it stays composed under volatility and delivers structured, data-driven consistency.
⚡ See it. Test it. Scale it.
Built for prop-firm precision — from $10 K to $300 K and beyond.
Simple Moving Average (SMA)## Overview and Purpose
The Simple Moving Average (SMA) is one of the most fundamental and widely used technical indicators in financial analysis. It calculates the arithmetic mean of a selected range of prices over a specified number of periods. Developed in the early days of technical analysis, the SMA provides traders with a straightforward method to identify trends by smoothing price data and filtering out short-term fluctuations. Due to its simplicity and effectiveness, it remains a cornerstone indicator that forms the basis for numerous other technical analysis tools.
## What’s Different in this Implementation
- **Constant streaming update:**
On each bar we:
1) subtract the value leaving the window,
2) add the new value,
3) divide by the number of valid samples (early) or by `period` (once full).
- **Deterministic lag, same as textbook SMA:**
Once full, lag is `(period - 1)/2` bars—identical to the classic SMA. You just **don’t lose the first `period-1` bars** to `na`.
- **Large windows without penalty:**
Complexity is constant per tick; memory is bounded by `period`. Very long SMAs stay cheap.
## Behavior on Early Bars
- **Bars < period:** returns the arithmetic mean of **available** samples.
Example (period = 10): bar #3 is the average of the first 3 inputs—not `na`.
- **Bars ≥ period:** behaves exactly like standard SMA over a fixed-length window.
> Implication: Crosses and signals can appear earlier than with `ta.sma()` because you’re not suppressing the first `period-1` bars.
## When to Prefer This
- Backtests needing early bars: You want signals and state from the very first bars.
- High-frequency or very long SMAs: O(1) updates avoid per-bar CPU spikes.
- Memory-tight scripts: Single circular buffer; no large temp arrays per tick.
## Caveats & Tips
Backtest comparability: If you previously relied on na gating from ta.sma(), add your own warm-up guard (e.g., only trade after bar_index >= period-1) for apples-to-apples.
Missing data: The function treats the current bar via nz(source); adjust if you need strict NA propagation.
Window semantics: After warm-up, results match the textbook SMA window; early bars are a partial-window mean by design.
## Math Notes
Running-sum update:
sum_t = sum_{t-1} - oldest + newest
SMA_t = sum_t / k where k = min(#valid_samples, period)
Lag (full window): (period - 1) / 2 bars.
## References
- Edwards & Magee, Technical Analysis of Stock Trends
- Murphy, Technical Analysis of the Financial Markets
