Gold Killzone Bias Suite🟡 Gold Killzone Bias Suite
The Gold Killzone Bias Suite is an advanced institutional-grade tool designed to generate high-confidence directional bias for XAU/USD (Gold) during the London and New York killzones.
Built for traders using a structured, confluence-driven approach, this tool blends price action, smart money principles, momentum, and volume into a real-time bias engine with a clean, easy-to-read dashboard.
🔧 Key Features
🕰️ Session-Based Bias (London / New York)
Independent bias calculation per session
Killzone times customizable with timezone support
Background highlighting (blue/red) for each session
📊 VWAP Engine
Reclaim & rejection detection
VWAP deviation alerts
Daily HTF VWAP integration
Score impact based on VWAP behaviour
📉 Market Structure (CHoCH / BOS)
Detects swing highs/lows
Labels bullish/bearish CHoCHs
Structure score contributes to session bias
💧 Liquidity Grabs
Detects stop hunts above highs / below lows
Confirms with candle rejection (body % filter)
Plots labels and adds to bias scoring
⚡ Momentum Filters
RSI: Bullish >55, Bearish <45
MACD: Histogram + Signal Line crossovers
Combined momentum score used in bias
🧠 Smart Money Proximity
Optional FVG/OB score toggle (placeholder for custom logic)
Adds static confluence for proximity-based setups
⏫ Higher Time Frame Context
Daily VWAP comparison
4H high/low structure breaks
Adds trend score to current session bias
🧠 How Bias Works
The suite uses a scoring model. Each confluence adds or subtracts points:
VWAP reclaim/reject: ±30
CHoCH/BOS: ±30
Liquidity grab: ±20
RSI/MACD: ±10
FVG/OB Proximity: +10
Daily VWAP trend: ±10
H4 Trend Break: ±10
Final Bias:
Bullish if score ≥ +20
Bearish if score ≤ -20
Neutral if between -19 and +19
A confidence % (capped at 100) is also shown, along with the contributing confluences (VWAP, Structure, Liquidity, etc.).
📋 Dashboard
A real-time dashboard shows for each session:
Session name and time
Bias (Bullish / Bearish / Neutral)
Confidence (%)
Confluences used
Position can be moved (Top Left, Top Right, etc.). Designed to be unobtrusive yet informative.
🧪 Best Practices
Use on 15m / 5m charts for intraday setups
Confirm with D1 or H4 structure for directional context
Combine with OB/FVG zones or SMT for entries
Use Trading View alerts for bias flips or liquidity grabs (custom logic can be added)
Bar Replay compatible for back testing and journaling bias shifts
🔐 Notes
Does not generate trade signals or alerts by default
Focused on bias generation and confluence stacking
Compatible with funded account trading models
📈 Built for traders who want a systematic, score-based approach to identifying directional edge in high-volume gold sessions.
스크립트에서 "主板非次新股，市场贝塔系数最高的10只股票"에 대해 찾기
Stochastic MACDStochastic MACD Indicator: Quick Guide
This Pine Script indicator, "Stochastic MACD" (SMACD), blends MACD and Stochastic Oscillator principles to help you spot momentum shifts and potential reversals.
What it Shows:
SMACD Line: Tracks momentum.
Signal Line: Averages the SMACD line, creating crossovers.
Histogram: Visualizes momentum strength, changing color with direction.
Overbought/Oversold Levels: (Default 10 and -10) Help identify stretched market conditions. Adjustable in settings.
Visual Signals (Triangles):
Red Down Arrow (Overbought Signal): Appears when both SMACD and Signal lines are above the Overbought level (default 10) AND SMACD crosses the Signal line upwards. This suggests strong overbought conditions and a potential reversal down.
Green Up Arrow (Oversold Signal): Appears when both SMACD and Signal lines are below the Oversold level (default -10) AND SMACD crosses the Signal line upwards. This suggests potential buying opportunities from oversold conditions and a possible reversal up.
How to Use It:
Confirm Trends: Use the histogram and line directions.
Spot Reversals: Look for the red and green triangles for quick alerts.
Combine: Always use with other analysis like price action or support/resistance.
Important: This is an analytical tool, not financial advice. Trading involves risk.
SMC Structures and FVGสวัสดีครับ! ผมจะอธิบายอินดิเคเตอร์ "SMC Structures and FVG + MACD" ที่คุณให้มาอย่างละเอียดในแต่ละส่วน เพื่อให้คุณเข้าใจการทำงานของมันอย่างถ่องแท้ครับ
อินดิเคเตอร์นี้เป็นการผสมผสานแนวคิดของ Smart Money Concept (SMC) ซึ่งเน้นการวิเคราะห์โครงสร้างตลาด (Market Structure) และ Fair Value Gap (FVG) เข้ากับอินดิเคเตอร์ MACD เพื่อใช้เป็นตัวกรองหรือตัวยืนยันสัญญาณ Choch/BoS (Change of Character / Break of Structure)
1. ภาพรวมอินดิเคเตอร์ (Overall Purpose)
อินดิเคเตอร์นี้มีจุดประสงค์หลักคือ:
ระบุโครงสร้างตลาด: ตีเส้นและป้ายกำกับ Choch (Change of Character) และ BoS (Break of Structure) บนกราฟโดยอัตโนมัติ
ผสานการยืนยันด้วย MACD: สัญญาณ Choch/BoS จะถูกพิจารณาก็ต่อเมื่อ MACD Histogram เกิดการตัดขึ้นหรือลง (Zero Cross) ในทิศทางที่สอดคล้องกัน
แสดง Fair Value Gap (FVG): หากเปิดใช้งาน จะมีการตีกล่อง FVG บนกราฟ
แสดงระดับ Fibonacci: คำนวณและแสดงระดับ Fibonacci ที่สำคัญตามโครงสร้างตลาดปัจจุบัน
ปรับตาม Timeframe: การคำนวณและการแสดงผลทั้งหมดจะปรับตาม Timeframe ที่คุณกำลังใช้งานอยู่โดยอัตโนมัติ
2. ส่วนประกอบหลักของโค้ด (Code Breakdown)
โค้ดนี้สามารถแบ่งออกเป็นส่วนหลัก ๆ ได้ดังนี้:
2.1 Inputs (การตั้งค่า)
ส่วนนี้คือตัวแปรที่คุณสามารถปรับแต่งได้ในหน้าต่างการตั้งค่าของอินดิเคเตอร์ (คลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟ)
MACD Settings (ตั้งค่า MACD):
fast_len: ความยาวของ Fast EMA สำหรับ MACD (ค่าเริ่มต้น 12)
slow_len: ความยาวของ Slow EMA สำหรับ MACD (ค่าเริ่มต้น 26)
signal_len: ความยาวของ Signal Line สำหรับ MACD (ค่าเริ่มต้น 9)
= ta.macd(close, fast_len, slow_len, signal_len): คำนวณค่า MACD Line, Signal Line และ Histogram โดยใช้ราคาปิด (close) และค่าความยาวที่กำหนด
is_bullish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดขึ้นเหนือเส้น 0 (จากค่าลบเป็นบวก)
is_bearish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดลงใต้เส้น 0 (จากค่าบวกเป็นลบ)
Fear Value Gap (FVG) Settings:
isFvgToShow: (Boolean) เปิด/ปิดการแสดง FVG บนกราฟ
bullishFvgColor: สีสำหรับ Bullish FVG
bearishFvgColor: สีสำหรับ Bearish FVG
mitigatedFvgColor: สีสำหรับ FVG ที่ถูก Mitigate (ลดทอน) แล้ว
fvgHistoryNbr: จำนวน FVG ย้อนหลังที่จะแสดง
isMitigatedFvgToReduce: (Boolean) เปิด/ปิดการลดขนาด FVG เมื่อถูก Mitigate
Structures (โครงสร้างตลาด) Settings:
isStructBodyCandleBreak: (Boolean) หากเป็น true การ Break จะต้องเกิดขึ้นด้วย เนื้อเทียน ที่ปิดเหนือ/ใต้ Swing High/Low หากเป็น false แค่ไส้เทียนทะลุก็ถือว่า Break
isCurrentStructToShow: (Boolean) เปิด/ปิดการแสดงเส้นโครงสร้างตลาดปัจจุบัน (เส้นสีน้ำเงินในภาพตัวอย่าง)
pivot_len: ความยาวของแท่งเทียนที่ใช้ในการมองหาจุด Pivot (Swing High/Low) ยิ่งค่าน้อยยิ่งจับ Swing เล็กๆ ได้, ยิ่งค่ามากยิ่งจับ Swing ใหญ่ๆ ได้
bullishBosColor, bearishBosColor: สีสำหรับเส้นและป้าย BOS ขาขึ้น/ขาลง
bosLineStyleOption, bosLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น BOS
bullishChochColor, bearishChochColor: สีสำหรับเส้นและป้าย CHoCH ขาขึ้น/ขาลง
chochLineStyleOption, chochLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น CHoCH
currentStructColor, currentStructLineStyleOption, currentStructLineWidth: สี, สไตล์ และความหนาของเส้นโครงสร้างตลาดปัจจุบัน
structHistoryNbr: จำนวนการ Break (Choch/BoS) ย้อนหลังที่จะแสดง
Structure Fibonacci (จากโค้ดต้นฉบับ):
เป็นชุด Input สำหรับเปิด/ปิด, กำหนดค่า, สี, สไตล์ และความหนาของเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) ที่จะถูกคำนวณจากโครงสร้างตลาดปัจจุบัน
2.2 Helper Functions (ฟังก์ชันช่วยทำงาน)
getLineStyle(lineOption): ฟังก์ชันนี้ใช้แปลงค่า String ที่เลือกจาก Input (เช่น "─", "┈", "╌") ให้เป็นรูปแบบ line.style_ ที่ Pine Script เข้าใจ
get_structure_highest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing High ภายในช่วง lookback ที่กำหนด
get_structure_lowest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing Low ภายในช่วง lookback ที่กำหนด
is_structure_high_broken(...): ฟังก์ชันนี้ตรวจสอบว่าราคาปัจจุบันได้ Break เหนือ _structureHigh (Swing High) หรือไม่ โดยพิจารณาจาก _highStructBreakPrice (ราคาปิดหรือราคา High ขึ้นอยู่กับการตั้งค่า isStructBodyCandleBreak)
FVGDraw(...): ฟังก์ชันนี้รับ Arrays ของ FVG Boxes, Types, Mitigation Status และ Labels มาประมวลผล เพื่ออัปเดตสถานะของ FVG (เช่น ถูก Mitigate หรือไม่) และปรับขนาด/ตำแหน่งของ FVG Box และ Label บนกราฟ
2.3 Global Variables (ตัวแปรทั่วทั้งอินดิเคเตอร์)
เป็นตัวแปรที่ประกาศด้วย var ซึ่งหมายความว่าค่าของมันจะถูกเก็บไว้และอัปเดตในแต่ละแท่งเทียน (persists across bars)
structureLines, structureLabels: Arrays สำหรับเก็บอ็อบเจกต์ line และ label ของเส้น Choch/BoS ที่วาดบนกราฟ
fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays สำหรับเก็บข้อมูลของ FVG Boxes และสถานะต่างๆ
structureHigh, structureLow: เก็บราคาของ Swing High/Low ที่สำคัญของโครงสร้างตลาดปัจจุบัน
structureHighStartIndex, structureLowStartIndex: เก็บ Bar Index ของจุดเริ่มต้นของ Swing High/Low ที่สำคัญ
structureDirection: เก็บสถานะของทิศทางโครงสร้างตลาด (1 = Bullish, 2 = Bearish, 0 = Undefined)
fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: ตัวแปรสำหรับเก็บข้อมูลและอ็อบเจกต์ของเส้น Fibonacci Levels
isBOSAlert, isCHOCHAlert: (Boolean) ใช้สำหรับส่งสัญญาณ Alert (หากมีการตั้งค่า Alert ไว้)
2.4 FVG Processing (การประมวลผล FVG)
ส่วนนี้จะตรวจสอบเงื่อนไขการเกิด FVG (Bullish FVG: high < low , Bearish FVG: low > high )
หากเกิด FVG และ isFvgToShow เป็น true จะมีการสร้าง box และ label ใหม่เพื่อแสดง FVG บนกราฟ
มีการจัดการ fvgBoxes และ fvgLabels เพื่อจำกัดจำนวน FVG ที่แสดงตาม fvgHistoryNbr และลบ FVG เก่าออก
ฟังก์ชัน FVGDraw จะถูกเรียกเพื่ออัปเดตสถานะของ FVG (เช่น การถูก Mitigate) และปรับการแสดงผล
2.5 Structures Processing (การประมวลผลโครงสร้างตลาด)
Initialization: ที่ bar_index == 0 (แท่งเทียนแรกของกราฟ) จะมีการกำหนดค่าเริ่มต้นให้กับ structureHigh, structureLow, structureHighStartIndex, structureLowStartIndex
Finding Current High/Low: highest, highestBar, lowest, lowestBar ถูกใช้เพื่อหา High/Low ที่สุดและ Bar Index ของมันใน 10 แท่งล่าสุด (หรือทั้งหมดหากกราฟสั้นกว่า 10 แท่ง)
Calculating Structure Max/Min Bar: structureMaxBar และ structureMinBar ใช้ฟังก์ชัน get_structure_highest_bar และ get_structure_lowest_bar เพื่อหา Bar Index ของ Swing High/Low ที่แท้จริง (ไม่ใช่แค่ High/Low ที่สุดใน lookback แต่เป็นจุด Pivot ที่สมบูรณ์)
Break Price: lowStructBreakPrice และ highStructBreakPrice จะเป็นราคาปิด (close) หรือราคา Low/High ขึ้นอยู่กับ isStructBodyCandleBreak
isStuctureLowBroken / isStructureHighBroken: เงื่อนไขเหล่านี้ตรวจสอบว่าราคาได้ทำลาย structureLow หรือ structureHigh หรือไม่ โดยพิจารณาจากราคา Break, ราคาแท่งก่อนหน้า และ Bar Index ของจุดเริ่มต้นโครงสร้าง
Choch/BoS Logic (ส่วนสำคัญที่ถูกผสานกับ MACD):
if(isStuctureLowBroken and is_bearish_macd_cross): นี่คือจุดที่ MACD เข้ามามีบทบาท หากราคาทำลาย structureLow (สัญญาณขาลง) และ MACD Histogram เกิด Bearish Zero Cross (is_bearish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS
หาก structureDirection == 1 (เดิมเป็นขาขึ้น) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาลง)
หาก structureDirection == 2 (เดิมเป็นขาลง) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาลง)
มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ
structureDirection จะถูกอัปเดตเป็น 1 (Bullish)
structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่
else if(isStructureHighBroken and is_bullish_macd_cross): เช่นกันสำหรับขาขึ้น หากราคาทำลาย structureHigh (สัญญาณขาขึ้น) และ MACD Histogram เกิด Bullish Zero Cross (is_bullish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS
หาก structureDirection == 2 (เดิมเป็นขาลง) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาขึ้น)
หาก structureDirection == 1 (เดิมเป็นขาขึ้น) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาขึ้น)
มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ
structureDirection จะถูกอัปเดตเป็น 2 (Bearish)
structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่
การลบเส้นเก่า: d.delete_line (หากไลบรารีทำงาน) จะถูกเรียกเพื่อลบเส้นและป้ายกำกับเก่าออกเมื่อจำนวนเกิน structHistoryNbr
Updating Structure High/Low (else block): หากไม่มีการ Break เกิดขึ้น แต่ราคาปัจจุบันสูงกว่า structureHigh หรือต่ำกว่า structureLow ในทิศทางที่สอดคล้องกัน (เช่น ยังคงเป็นขาขึ้นและทำ High ใหม่) structureHigh หรือ structureLow จะถูกอัปเดตเพื่อติดตาม High/Low ที่สุดของโครงสร้างปัจจุบัน
Current Structure Display:
หาก isCurrentStructToShow เป็น true อินดิเคเตอร์จะวาดเส้น structureHighLine และ structureLowLine เพื่อแสดงขอบเขตของโครงสร้างตลาดปัจจุบัน
Fibonacci Display:
หาก isFiboXToShow เป็น true อินดิเคเตอร์จะคำนวณและวาดเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) โดยอิงจาก structureHigh และ structureLow ของโครงสร้างตลาดปัจจุบัน
Alerts:
alertcondition: ใช้สำหรับตั้งค่า Alert ใน TradingView เมื่อเกิดสัญญาณ BOS หรือ CHOCH
plot(na):
plot(na) เป็นคำสั่งที่สำคัญในอินดิเคเตอร์ที่ไม่ได้ต้องการพล็อต Series ของข้อมูลบนกราฟ (เช่น ไม่ได้พล็อตเส้น EMA หรือ RSI) แต่ใช้วาดอ็อบเจกต์ (Line, Label, Box) โดยตรง
การมี plot(na) ช่วยให้ Pine Script รู้ว่าอินดิเคเตอร์นี้มีเอาต์พุตที่แสดงผลบนกราฟ แม้ว่าจะไม่ได้เป็น Series ที่พล็อตตามปกติก็ตาม
3. วิธีใช้งาน
คัดลอกโค้ดทั้งหมด ที่อยู่ในบล็อก immersive ด้านบน
ไปที่ TradingView และเปิดกราฟที่คุณต้องการ
คลิกที่เมนู "Pine Editor" ที่อยู่ด้านล่างของหน้าจอ
ลบโค้ดเดิมที่มีอยู่ และ วางโค้ดที่คัดลอกมา ลงไปแทน
คลิกที่ปุ่ม "Add to Chart"
อินดิเคเตอร์จะถูกเพิ่มลงในกราฟของคุณโดยอัตโนมัติ คุณสามารถคลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟเพื่อเข้าถึงหน้าต่างการตั้งค่าและปรับแต่งตามความต้องการของคุณได้
Hello! I will explain the "SMC Structures and FVG + MACD" indicator you provided in detail, section by section, so you can fully understand how it works.This indicator combines the concepts of Smart Money Concept (SMC), which focuses on analyzing Market Structure and Fair Value Gaps (FVG), with the MACD indicator to serve as a filter or confirmation for Choch (Change of Character) and BoS (Break of Structure) signals.1. Overall PurposeThe main purposes of this indicator are:Identify Market Structure: Automatically draw lines and label Choch (Change of Character) and BoS (Break of Structure) on the chart.Integrate MACD Confirmation: Choch/BoS signals will only be considered when the MACD Histogram performs a cross (Zero Cross) in the corresponding direction.Display Fair Value Gap (FVG): If enabled, FVG boxes will be drawn on the chart.Display Fibonacci Levels: Calculate and display important Fibonacci levels based on the current market structure.Adapt to Timeframe: All calculations and displays will automatically adjust to the timeframe you are currently using.2. Code BreakdownThis code can be divided into the following main sections:2.1 Inputs (Settings)This section contains variables that you can adjust in the indicator's settings window (click the gear icon next to the indicator's name on the chart).MACD Settings:fast_len: Length of the Fast EMA for MACD (default 12)slow_len: Length of the Slow EMA for MACD (default 26)signal_len: Length of the Signal Line for MACD (default 9) = ta.macd(close, fast_len, slow_len, signal_len): Calculates the MACD Line, Signal Line, and Histogram using the closing price (close) and the specified lengths.is_bullish_macd_cross: Checks if the MACD Histogram crosses above the 0 line (from negative to positive).is_bearish_macd_cross: Checks if the MACD Histogram crosses below the 0 line (from positive to negative).Fear Value Gap (FVG) Settings:isFvgToShow: (Boolean) Enables/disables the display of FVG on the chart.bullishFvgColor: Color for Bullish FVG.bearishFvgColor: Color for Bearish FVG.mitigatedFvgColor: Color for FVG that has been mitigated.fvgHistoryNbr: Number of historical FVG to display.isMitigatedFvgToReduce: (Boolean) Enables/disables reducing the size of FVG when mitigated.Structures (โครงสร้างตลาด) Settings:isStructBodyCandleBreak: (Boolean) If true, the break must occur with the candle body closing above/below the Swing High/Low. If false, a wick break is sufficient.isCurrentStructToShow: (Boolean) Enables/disables the display of the current market structure lines (blue lines in the example image).pivot_len: Lookback length for identifying Pivot points (Swing High/Low). A smaller value captures smaller, more frequent swings; a larger value captures larger, more significant swings.bullishBosColor, bearishBosColor: Colors for bullish/bearish BOS lines and labels.bosLineStyleOption, bosLineWidth: Style (Solid, Dotted, Dashed) and width of BOS lines.bullishChochColor, bearishChochColor: Colors for bullish/bearish CHoCH lines and labels.chochLineStyleOption, chochLineWidth: Style (Solid, Dotted, Dashed) and width of CHoCH lines.currentStructColor, currentStructLineStyleOption, currentStructLineWidth: Color, style, and width of the current market structure lines.structHistoryNbr: Number of historical breaks (Choch/BoS) to display.Structure Fibonacci (from original code):A set of inputs to enable/disable, define values, colors, styles, and widths for various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) that will be calculated from the current market structure.2.2 Helper FunctionsgetLineStyle(lineOption): This function converts the selected string input (e.g., "─", "┈", "╌") into a line.style_ format understood by Pine Script.get_structure_highest_bar(lookback): This function attempts to find the Bar Index of the Swing High within the specified lookback period.get_structure_lowest_bar(lookback): This function attempts to find the Bar Index of the Swing Low within the specified lookback period.is_structure_high_broken(...): This function checks if the current price has broken above _structureHigh (Swing High), considering _highStructBreakPrice (closing price or high price depending on isStructBodyCandleBreak setting).FVGDraw(...): This function takes arrays of FVG Boxes, Types, Mitigation Status, and Labels to process and update the status of FVG (e.g., whether it's mitigated) and adjust the size/position of FVG Boxes and Labels on the chart.2.3 Global VariablesThese are variables declared with var, meaning their values are stored and updated on each bar (persists across bars).structureLines, structureLabels: Arrays to store line and label objects for Choch/BoS lines drawn on the chart.fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays to store FVG box data and their respective statuses.structureHigh, structureLow: Stores the price of the significant Swing High/Low of the current market structure.structureHighStartIndex, structureLowStartIndex: Stores the Bar Index of the start point of the significant Swing High/Low.structureDirection: Stores the status of the market structure direction (1 = Bullish, 2 = Bearish, 0 = Undefined).fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: Variables to store data and objects for Fibonacci Levels.isBOSAlert, isCHOCHAlert: (Boolean) Used to trigger alerts in TradingView (if alerts are configured).2.4 FVG ProcessingThis section checks the conditions for FVG formation (Bullish FVG: high < low , Bearish FVG: low > high ).If FVG occurs and isFvgToShow is true, a new box and label are created to display the FVG on the chart.fvgBoxes and fvgLabels are managed to limit the number of FVG displayed according to fvgHistoryNbr and remove older FVG.The FVGDraw function is called to update the FVG status (e.g., whether it's mitigated) and adjust its display.2.5 Structures ProcessingInitialization: At bar_index == 0 (the first bar of the chart), structureHigh, structureLow, structureHighStartIndex, and structureLowStartIndex are initialized.Finding Current High/Low: highest, highestBar, lowest, lowestBar are used to find the highest/lowest price and its Bar Index of it in the last 10 bars (or all bars if the chart is shorter than 10 bars).Calculating Structure Max/Min Bar: structureMaxBar and structureMinBar use get_structure_highest_bar and get_structure_lowest_bar functions to find the Bar Index of the true Swing High/Low (not just the highest/lowest in the lookback but a complete Pivot point).Break Price: lowStructBreakPrice and highStructBreakPrice will be the closing price (close) or the Low/High price, depending on the isStructBodyCandleBreak setting.isStuctureLowBroken / isStructureHighBroken: These conditions check if the price has broken structureLow or structureHigh, considering the break price, previous bar prices, and the Bar Index of the structure's starting point.Choch/BoS Logic (Key Integration with MACD):if(isStuctureLowBroken and is_bearish_macd_cross): This is where MACD plays a role. If the price breaks structureLow (bearish signal) AND the MACD Histogram performs a Bearish Zero Cross (is_bearish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 1 (previously bullish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bearish).If structureDirection == 2 (already bearish), it will be labeled "BOS" (confirming bearish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 1 (Bullish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.else if(isStructureHighBroken and is_bullish_macd_cross): Similarly for bullish breaks. If the price breaks structureHigh (bullish signal) AND the MACD Histogram performs a Bullish Zero Cross (is_bullish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 2 (previously bearish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bullish).If structureDirection == 1 (already bullish), it will be labeled "BOS" (confirming bullish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 2 (Bearish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.Deleting Old Lines: d.delete_line (if the library works) will be called to delete old lines and labels when their number exceeds structHistoryNbr.Updating Structure High/Low (else block): If no break occurs, but the current price is higher than structureHigh or lower than structureLow in the corresponding direction (e.g., still bullish and making a new high), structureHigh or structureLow will be updated to track the highest/lowest point of the current structure.Current Structure Display:If isCurrentStructToShow is true, the indicator draws structureHighLine and structureLowLine to show the boundaries of the current market structure.Fibonacci Display:If isFiboXToShow is true, the indicator calculates and draws various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) based on the structureHigh and structureLow of the current market structure.Alerts:alertcondition: Used to set up alerts in TradingView when BOS or CHOCH signals occur.plot(na):plot(na) is an important statement in indicators that do not plot data series directly on the chart (e.g., not plotting EMA or RSI lines) but instead draw objects (Line, Label, Box).Having plot(na) helps Pine Script recognize that this indicator has an output displayed on the chart, even if it's not a regularly plotted series.3. How to UseCopy all the code in the immersive block above.Go to TradingView and open your desired chart.Click on the "Pine Editor" menu at the bottom of the screen.Delete any existing code and paste the copied code in its place.Click the "Add to Chart" button.The indicator will be added to your chart automatically. You can click the gear icon next to the indicator's name on the chart to access the settings window and customize it to your needs.I hope this explanation helps you understand this indicator in detail. If anything is unclear, or you need further adjustments, please let me know.
Bollinger Levels Table - Horizontal Support ZonesBollinger Levels Table - Horizontal Support Zones Indicator (with Customizable Options)
The "Bollinger Levels Table - Horizontal Support Zones" indicator is a comprehensive tool designed to help you identify potential support areas on your chart using moving averages and Bollinger Bands. The indicator displays an organized table of key price levels and draws horizontal lines on the chart, providing clear visibility of potential support zones.
What Does This Indicator Do?
This indicator aims to simplify support analysis by consolidating and displaying significant price levels derived from three different Bollinger Band settings: BB10, BB20, and BB50. It calculates both the Mid-line (Basis) and the Lower Band for each of these settings.
Furthermore, the indicator automatically arranges these levels from highest to lowest in an easy-to-read table, assigning a "Payment" label to each level. These "Payments" are simply labels to help you track the levels in descending order.
How Does This Indicator Work?
Bollinger Band Calculations: The indicator uses the standard Bollinger Band formula:
Mid-line (Basis): A Simple Moving Average (SMA) of the closing price over a specified period.
Standard Deviation (Dev): The standard deviation of the closing price over the same period, multiplied by a Multiplier.
Lower Band: The Mid-line minus the Standard Deviation.
These calculations are applied to three different periods: 10, 20, and 50, providing a variety of potential support levels based on different timeframes. You can adjust the values for these lengths (10, 20, 50) and the Multiplier through the indicator's settings.
Table Construction: A dynamic table is created on the chart (which can be positioned in the top or bottom right corner based on the current price's position). This table displays:
Indicator: The name of the Bollinger Band level (e.g., BB10 Mid, BB20 Lower).
Price: The exact price value of that level.
Payments: A label indicating the level's order in the table.
Level Ordering: All calculated levels are dynamically sorted from highest to lowest to present them in a logical order within the table.
Horizontal Line Plotting: Horizontal lines are drawn on the chart for each selected level, providing a visual representation of the potential support areas. These lines are colored black and have a consistent width for easy identification.
How to Use This Indicator:
This indicator is intended to provide potential entry points or accumulation zones for trades, especially for traders employing Dollar-Cost Averaging (DCA) strategies or building positions in stages. The levels displayed in the table and on the chart can represent potential support levels where one might consider initiating or adding to a position.
In the indicator's settings, you'll find important options:
Multiplier: Controls the width of the Bollinger Bands (default 2.0).
BB Lengths: Allows you to adjust the periods for the moving averages (default 20, 50, 10).
Visible Levels: This is the new feature! Here, you can select which levels you wish to see in the table and on the chart. Simply check or uncheck the boxes next to each level (BB10 Mid, BB10 Lower, and so on) to customize the indicator's display according to your strategy and needs.
Underlying Concepts:
This indicator is based on the principle that Bollinger Bands can act as dynamic support and resistance zones.
Mid-line (SMA): Often functions as a medium-term support or resistance.
Lower Band: Typically indicates that the price is relatively low and may find support, making it a potential area for buying or starting to build a position.
By combining different Bollinger Band timeframes (10, 20, 50), the indicator gives you a multi-timeframe perspective on support areas, helping you identify the most relevant levels for your strategy.
Note: While the indicator provides "Payments" for the levels, this is purely a sequential labeling within the table to assist your position-building strategy. There is no actual payment functionality associated with this indicator.
Candle Channel█ OVERVIEW
The "Candle Channel" indicator is a versatile technical analysis tool that plots a price channel based on the Simple Moving Average (SMA) of candlestick midpoints. The channel bands, calculated based on candlestick volatility, form dynamic support and resistance levels that adapt to price movements. The script generates signals for reversals from the bands and SMA breakouts, making it useful for both short-term and long-term traders. By adjusting the SMA length, the channel can vary in nature—from a wide channel encapsulating price movement to narrower support/resistance or trend-following bands. The channel width can be further customized using a scaling parameter, allowing adaptation to different trading styles and markets.
█ MECHANISM
Band Calculation
The indicator is based on the following calculations:
Candlestick Midpoint: Calculated as the arithmetic average of the candle’s high and low prices: (high + low) / 2.
Simple Moving Average (SMA): The average of candlestick midpoints over a specified length (default: 20 candles), forming the channel’s centerline.
Average Candle Height: Calculated as the average difference between the high and low prices (high - low) over the same SMA length, serving as a measure of market volatility.
Band Scaling: The user specifies a percentage of the average candle height (default: 200%), which is multiplied by the average height to create an offset. The upper band is SMA + offset, and the lower band is SMA - offset.Example: For an average candle height of 10 points and 200% scaling, the offset is 20 points, meaning the bands are ±20 points from the SMA.
Channel Characteristics: The SMA length determines the channel’s dynamics. Shorter SMA values (10–30) create a wide channel that contains price movement, ideal for scalping or short-term trading. Longer SMA values (above 30, e.g., 50–100) transform the channel into narrower support/resistance or trend-following bands, suitable for longer-term analysis. Band scaling further adjusts the channel width to match market volatility.
Signals
Reversal from Bands: Signals are generated when the price closes outside the band (above the upper or below the lower) and then returns to the channel, indicating a potential trend reversal.
SMA Breakout: Signals are generated when the price crosses the SMA upward (bullish signal) or downward (bearish signal), suggesting potential trend changes.
Visualization
Centerline: The SMA of candlestick midpoints, displayed as a thin line.
Channel Bands: Upper and lower channel boundaries, with customizable colors.
Fill: Options include a gradient (smooth color transition between bands) or solid color. The fill can also be disabled for greater clarity.
█ FEATURES AND SETTINGS
SMA Length: Determines the moving average period (default: 20). Values of 10–30 are suitable for a wide channel containing price movement, ideal for short-term timeframes. Longer values (e.g., 50–100) create narrower support/resistance or trend-following bands, better suited for higher timeframes.
Band Scaling: Percentage of the average candle height (default: 200%). Adjusts the channel width to match market volatility—smaller values (e.g., 50–100%) for narrower bands, larger values (e.g., 200–300%) for wider channels.
Fill Type: Gradient, solid, or no fill, allowing customization to user preferences.
Colors: Options to change the colors of bands, fill, and signals for better readability.
Signals: Options to enable/disable reversal signals from bands and SMA breakout signals.
█ HOW TO USE
Add the script to your chart in TradingView by clicking "Add to Chart" in the Pine Editor.
Adjust input parameters in the script settings:
SMA Length: Set to 10–30 for a wide channel containing price movement, suitable for scalping or short-term trading. Set above 30 (e.g., 50–100) for narrower support/resistance or trend-following bands.
Band Scaling: Adjust the channel width to market volatility. Smaller values (50–100%) for tighter support/resistance bands, larger values (200–300%) for wider channels containing price movement.
Fill Type and Colors: Choose a gradient for aesthetics or a solid fill for clarity.
Analyze signals:
Reversal Signals: Triangles above (bearish) or below (bullish) candles indicate potential reversal points.
SMA Breakout Signals: Circles above (bearish) or below (bullish) candles indicate trend changes.
Test the indicator on different instruments and timeframes to find optimal settings for your trading style.
█ LIMITATIONS
The indicator may generate false signals in highly volatile or consolidating markets.
On low-liquidity charts (e.g., exotic currency pairs), the bands may be less reliable.
Effectiveness depends on properly matching parameters to the market and timeframe.
Volume Surge Detector[SpeculationLab]Volume Surge Detector
This tool is especially useful for spotting early signs of breakouts, news-driven spikes, or institutional activity that are often preceded by abnormal surges in volume.
For better chart readability, the volume bar colors are based on TradingView’s original Volume indicator, while all other code is fully original by Speculation Lab.
You can customize both the SMA line and the volume bar colors to fit your style.
The logic compares the current volume against its SMA (default length: 14, fully adjustable).
The script comes with two surge levels:
Surge Level 1 (default = 5) → When volume is more than 5× the SMA, the bar turns aqua.
Surge Level 2 (default = 10) → When volume is more than 10× the SMA, the bar turns yellow.
It also includes built-in alerts, so you’ll be notified instantly whenever a surge is detected.
This makes it easier to spot potential breakout moves or large market participation in real time.
Disclaimer: This script is for educational purposes only. It does not provide financial advice.
这是一个用于探测 成交量爆发 的指标。
为了图表美观，成交量柱的颜色借鉴了 TradingView 原始 Volume 指标，其余代码均为 Speculation Lab 原创。
用户可以自由调整成交量柱和其对应的 SMA 均线的颜色。
指标通过对比成交量和其 SMA（默认长度为14，可自定义长度和颜色）的比例来检测放量。
默认设置了两个放量级别：
Surge Level 1（默认=5）：当成交量超过均量的 5 倍时，量柱变为水蓝色。
Surge Level 2（默认=10）：当成交量超过均量的 10 倍时，量柱变为黄色。
脚本还设置了 内置警报功能，方便交易者在出现放量时实时收到通知。
Active PMI Support/Resistance Levels [EdgeTerminal]The PMI Support & Resistance indicator revolutionizes traditional technical analysis by using Pointwise Mutual Information (PMI) - a statistical measure from information theory - to objectively identify support and resistance levels. Unlike conventional methods that rely on visual pattern recognition, this indicator provides mathematically rigorous, quantifiable evidence of price levels where significant market activity occurs.
- The Mathematical Foundation: Pointwise Mutual Information
Pointwise Mutual Information measures how much more likely two events are to occur together compared to if they were statistically independent. In our context:
Event A: Volume spikes occurring (high trading activity)
Event B: Price being at specific levels
The PMI formula calculates: PMI = log(P(A,B) / (P(A) × P(B)))
Where:
P(A,B) = Probability of volume spikes occurring at specific price levels
P(A) = Probability of volume spikes occurring anywhere
P(B) = Probability of price being at specific levels
High PMI scores indicate that volume spikes and certain price levels co-occur much more frequently than random chance would predict, revealing genuine support and resistance zones.
- Why PMI Outperforms Traditional Methods
Subjective interpretation: What one trader sees as significant, another might ignore
Confirmation bias: Tendency to see patterns that confirm existing beliefs
Inconsistent criteria: No standardized definition of "significant" volume or price action
Static analysis: Doesn't adapt to changing market conditions
No strength measurement: Can't quantify how "strong" a level truly is
PMI Advantages:
✅ Objective & Quantifiable: Mathematical proof of significance, not visual guesswork
✅ Statistical Rigor: Levels backed by information theory and probability
✅ Strength Scoring: PMI scores rank levels by statistical significance
✅ Adaptive: Automatically adjusts to different market volatility regimes
✅ Eliminates Bias: Computer-calculated, removing human interpretation errors
✅ Market Structure Aware: Reveals the underlying order flow concentrations
- How It Works
Data Processing Pipeline:
Volume Analysis: Identifies volume spikes using configurable thresholds
Price Binning: Divides price range into discrete levels for analysis
Co-occurrence Calculation: Measures how often volume spikes happen at each price level
PMI Computation: Calculates statistical significance for each price level
Level Filtering: Shows only levels exceeding minimum PMI thresholds
Dynamic Updates: Refreshes levels periodically while maintaining historical traces
Visual System:
Current Levels: Bright, thick lines with PMI scores - your actionable levels
Historical Traces: Faded previous levels showing market structure evolution
Strength Tiers: Line styles indicate PMI strength (solid/dashed/dotted)
Color Coding: Green for support, red for resistance
Info Table: Real-time display of strongest levels with scores
- Indicator Settings:
Core Parameters
Lookback Period (Default: 200)
Lower (50-100): More responsive to recent price action, catches short-term levels
Higher (300-500): Focuses on major historical levels, more stable but less responsive
Best for: Day trading (100-150), Swing trading (200-300), Position trading (400-500)
Volume Spike Threshold (Default: 1.5)
Lower (1.2-1.4): More sensitive, catches smaller volume increases, more levels detected
Higher (2.0-3.0): Only major volume surges count, fewer but stronger signals
Market dependent: High-volume stocks may need higher thresholds (2.0+), low-volume stocks lower (1.2-1.3)
Price Bins (Default: 50)
Lower (20-30): Broader price zones, less precise but captures wider areas
Higher (70-100): More granular levels, precise but may be overly specific
Volatility dependent: High volatility assets benefit from more bins (70+)
Minimum PMI Score (Default: 0.5)
Lower (0.2-0.4): Shows more levels including weaker ones, comprehensive view
Higher (1.0-2.0): Only statistically strong levels, cleaner chart
Progressive filtering: Start with 0.5, increase if too cluttered
Max Levels to Show (Default: 8)
Fewer (3-5): Clean chart focusing on strongest levels only
More (10-15): Comprehensive view but may clutter chart
Strategy dependent: Scalpers prefer fewer (3-5), swing traders more (8-12)
Historical Tracking Settings
Update Frequency (Default: 20 bars)
Lower (5-10): More frequent updates, captures rapid market changes
Higher (50-100): Less frequent updates, focuses on major structural shifts
Timeframe scaling: 1-minute charts need lower frequency (5-10), daily charts higher (50+)
Show Historical Levels (Default: True)
Enables the "breadcrumb trail" effect showing evolution of support/resistance
Disable for cleaner charts focusing only on current levels
Max Historical Marks (Default: 50)
Lower (20-30): Less memory usage, shorter history
Higher (100-200): Longer historical context but more resource intensive
Fade Strength (Default: 0.8)
Lower (0.5-0.6): Historical levels more visible
Higher (0.9-0.95): Historical levels very subtle
Visual Settings
Support/Resistance Colors: Choose colors that contrast well with your chart theme Line Width: Thicker lines (3-4) for better visibility on busy charts Show PMI Scores: Toggle labels showing statistical strength Label Size: Adjust based on screen resolution and chart zoom level
- Most Effective Usage Strategies
For Day Trading:
Setup: Lookback 100-150, Volume Threshold 1.8-2.2, Update Frequency 10-15
Use PMI levels as bounce/rejection points for scalp entries
Higher PMI scores (>1.5) offer better probability setups
Watch for volume spike confirmations at levels
For Swing Trading:
Setup: Lookback 200-300, Volume Threshold 1.5-2.0, Update Frequency 20-30
Enter on pullbacks to high PMI support levels
Target next resistance level with PMI score >1.0
Hold through minor levels, exit at major PMI levels
For Position Trading:
Setup: Lookback 400-500, Volume Threshold 2.0+, Update Frequency 50+
Focus on PMI scores >2.0 for major structural levels
Use for portfolio entry/exit decisions
Combine with fundamental analysis for timing
- Trading Applications:
Entry Strategies:
PMI Bounce Trades
Price approaches high PMI support level (>1.0)
Wait for volume spike confirmation (orange triangles)
Enter long on bullish price action at the level
Stop loss just below the PMI level
Target: Next PMI resistance level
PMI Breakout Trades
Price consolidates near high PMI level
Volume increases (watch for orange triangles)
Enter on decisive break with volume
Previous resistance becomes new support
Target: Next major PMI level
PMI Rejection Trades
Price approaches PMI resistance with momentum
Watch for rejection signals and volume spikes
Enter short on failure to break through
Stop above the PMI level
Target: Next PMI support level
Risk Management:
Stop Loss Placement
Place stops 0.1-0.5% beyond PMI levels (adjust for volatility)
Higher PMI scores warrant tighter stops
Use ATR-based stops for volatile assets
Position Sizing
Larger positions at PMI levels >2.0 (highest conviction)
Smaller positions at PMI levels 0.5-1.0 (lower conviction)
Scale out at multiple PMI targets
- Key Warning Signs & What to Watch For
Red Flags:
🚨 Very Low PMI Scores (<0.3): Weak statistical significance, avoid trading
🚨 No Volume Confirmation: PMI level without recent volume spikes may be stale
🚨 Overcrowded Levels: Too many levels close together suggests poor parameter tuning
🚨 Outdated Levels: Historical traces are reference only, not tradeable
Optimization Tips:
✅ Regular Recalibration: Adjust parameters monthly based on market regime changes
✅ Volume Context: Always check for recent volume activity at PMI levels
✅ Multiple Timeframes: Confirm PMI levels across different timeframes
✅ Market Conditions: Higher thresholds during high volatility periods
Interpreting PMI Scores
PMI Score Ranges:
0.5-1.0: Moderate statistical significance, proceed with caution
1.0-1.5: Good significance, reliable for most trading strategies
1.5-2.0: Strong significance, high-confidence trade setups
2.0+: Very strong significance, institutional-grade levels
Historical Context: The historical trace system shows how support and resistance evolve over time. When current levels align with multiple historical traces, it indicates persistent market memory at those prices, significantly increasing the level's reliability.
Staccked SMA - Regime Switching & Persistance StatisticsThis indicator is designed to identify the prevailing market regime by analyzing the behavior of a "stack" of Simple Moving Averages (SMAs). It helps you understand whether the market is currently trending, mean-reverting, or moving randomly.
Core Concept: SMA Correlation
At its heart, the indicator examines the relationship between a set of nine SMAs with different lengths (3, 5, 8, 13, 21, 34, 55, 89, 144) and the lengths themselves.
In a strong trending market (either up or down), the SMAs will be neatly "stacked" in order of their length. The shortest SMA will be furthest from the longest SMA, creating a strong, almost linear visual pattern. When we measure the statistical correlation between the SMA values and their corresponding lengths, we get a value close to +1 (perfect uptrend stack) or -1 (perfect downtrend stack). The absolute value of this correlation will be very high (close to 1).
In a mean-reverting or sideways market, the SMAs will be tangled and crisscrossing each other. There is no clear order, and the relationship between an SMA's length and its price value is weak. The correlation will be close to 0.
This indicator calculates this Pearson correlation on every bar, giving a continuous measure of how ordered or "trendy" the SMAs are. An absolute correlation above 0.8 is considered strongly trending, while a value between 0.4 and 0.8 suggests a mean-reverting character. Below 0.4, the market is likely random or choppy.
Regime Classification and Statistics
The indicator doesn't just look at the current correlation; it analyzes its behavior over a user-defined lookback window (default is 252 bars) to classify the overall market "regime."
It presents its findings in a clear table:
📊 |SMA Correlation| Regime Table: This main table provides a snapshot of the current market character.
Median: Shows the median absolute correlation over the lookback period, giving a central tendency of the market's behavior.
% > 0.80: The percentage of time the market was in a strong trend during the lookback period.
% < 0.80 & > 0.40: The percentage of time the market showed mean-reverting characteristics.
🧠 Regime: The final classification. It's labeled "📈 Trend-Dominant" if the median correlation is high and it has spent a significant portion of the time trending. It's labeled "🔄 Mean-Reverting" if the median is in the middle range and it has spent significant time in that state. Otherwise, it's considered "⚖️ Random/ Choppy".
📐 Regime Significance: This tells you how statistically confident you can be in the current regime classification, using a Z-score to compare its occurrence against random chance. ⭐⭐⭐ indicates high confidence (99%), while "❌ Not Significant" means the pattern could be random.
Regime Transition Probabilities
Optionally, a second table can be displayed that shows the historical probability of the market transitioning from one regime to another over different time horizons (t+5, t+10, t+15, and t+20 bars).
📈 → 🔄 → ⚖️ Transition Table: This table answers questions like, "If the market is trending now (From: 📈), what is the probability it will be mean-reverting (→ 🔄) in 10 bars?"
This provides powerful insights into the market's cyclical nature, helping you anticipate future behavior based on past patterns. For example, you might find that after a period of strong trending, a transition to a choppy state is more likely than a direct switch to a mean-reverting
Indicator Settings
Lookback Window for Regime Classification: This sets the number of recent bars (default is 252) the script analyzes to determine the current market regime (Trending, Mean-Reverting, or Random). A larger number provides a more stable, long-term view, while a smaller number makes the classification more sensitive to recent price action.
Show Regime Transition Table: A simple toggle (on/off) to show or hide the table that displays the probabilities of the market switching from one regime to another.
Lookback Offset for Starting Regime: This determines the "starting point" in the past for calculating regime transitions. The default is 20 bars ago. The script looks at the regime at this point and then checks what it became at later points.
Step 1, 2, 3, 4 Offset (bars): These define the future time intervals (5, 10, 15, and 20 bars by default) for the transition probability table. For example, the script checks the regime at the "Lookback Offset" and then sees what it transitioned to 5, 10, 15, and 20 bars later.
Significance Filter Settings
Use Regime Significance Filter: When enabled, this filter ensures that the regime transition statistics only count transitions that were "statistically significant." This helps to filter out noise and focus on more reliable patterns.
Min Stars Required (1=90%, 2=95%, 3=99%): This sets the minimum confidence level required for a regime to be included in the transition statistics when the significance filter is on.
1 ⭐: Requires at least 90% confidence.
2 ⭐⭐: Requires at least 95% confidence (default).
3 ⭐⭐⭐: Requires at least 99% confidence.
Position Size Calculator with Fees# Position Size Calculator with Portfolio Management - Manual
## Overview
The Position Size Calculator with Portfolio Management is an advanced Pine Script indicator designed to help traders calculate optimal position sizes based on their total portfolio value and risk management strategy. This tool automatically calculates your risk amount based on portfolio allocation percentages and determines the exact position size needed while accounting for trading fees.
## Key Features
- **Portfolio-Based Risk Management**: Calculates risk based on total portfolio value
- **Tiered Risk Allocation**: Separates trading allocation from total portfolio
- **Automatic Trade Direction Detection**: Determines long/short based on entry vs stop loss
- **Fee Integration**: Accounts for trading fees in position size calculations
- **Risk Factor Adjustment**: Allows scaling of position size up or down
- **Visual Display**: Shows all calculations in a clear, color-coded table
- **Automatic Risk Calculation**: No need to manually input risk amount
## Input Parameters
### Total Portfolio ($)
- **Purpose**: The total value of your investment portfolio
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
- **Example**: If your total portfolio is worth $100,000, enter 100000
### Trading Portfolio Allocation (%)
- **Purpose**: The percentage of your total portfolio allocated to active trading
- **Default**: 20.0%
- **Range**: 0.0% to 100.0%
- **Step**: 0.01
- **Example**: If you allocate 20% of your portfolio to trading, enter 20
### Risk from Trading (%)
- **Purpose**: The percentage of your trading allocation you're willing to risk per trade
- **Default**: 0.1%
- **Range**: Any positive value
- **Step**: 0.01
- **Example**: If you risk 0.1% of your trading allocation per trade, enter 0.1
### Entry Price ($)
- **Purpose**: The price at which you plan to enter the trade
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Stop Loss ($)
- **Purpose**: The price at which you will exit if the trade goes against you
- **Default**: 0.0
- **Range**: Any positive value
- **Step**: 0.01
### Risk Factor
- **Purpose**: A multiplier to scale your position size up or down
- **Default**: 1.0 (no scaling)
- **Range**: 0.0 to 10.0
- **Step**: 0.1
- **Examples**:
- 1.0 = Normal position size
- 2.0 = Double the position size
- 0.5 = Half the position size
### Fee (%)
- **Purpose**: The percentage fee charged per transaction
- **Default**: 0.01% (0.01)
- **Range**: 0.0% to 1.0%
- **Step**: 0.001
## How Risk Amount is Calculated
The script automatically calculates your risk amount using this formula:
```
Risk Amount = Total Portfolio × Trading Allocation (%) × Risk % ÷ 10,000
```
### Example Calculation:
- Total Portfolio: $100,000
- Trading Allocation: 20%
- Risk per Trade: 0.1%
**Risk Amount = $100,000 × 20 × 0.1 ÷ 10,000 = $20**
This means you would risk $20 per trade, which is 0.1% of your $20,000 trading allocation.
## Portfolio Structure Example
Let's say you have a $100,000 portfolio:
### Allocation Structure:
- **Total Portfolio**: $100,000
- **Trading Allocation (20%)**: $20,000
- **Long-term Investments (80%)**: $80,000
### Risk Management:
- **Risk per Trade (0.1% of trading)**: $20
- **Maximum trades at risk**: Could theoretically have 1,000 trades before risking entire trading allocation
## How Position Size is Calculated
### Trade Direction Detection
- **Long Trade**: Entry price > Stop loss price
- **Short Trade**: Entry price < Stop loss price
### Position Size Formulas
#### For Long Trades:
```
Position Size = -Risk Factor × Risk Amount / (Stop Loss × (1 - Fee) - Entry Price × (1 + Fee))
```
#### For Short Trades:
```
Position Size = -Risk Factor × Risk Amount / (Entry Price × (1 - Fee) - Stop Loss × (1 + Fee))
```
## Output Display
The indicator displays a comprehensive table with color-coded sections:
### Portfolio Information (Light Blue Background)
- **Portfolio (USD)**: Your total portfolio value
- **Trading Portfolio Allocation (%)**: Percentage allocated to trading
- **Risk as % of Trading**: Risk percentage per trade
### Trade Setup (Gray Background)
- **Entry Price**: Your specified entry price
- **Stop Loss**: Your specified stop loss price
- **Fee (%)**: Trading fee percentage
- **Risk Factor**: Position size multiplier
### Risk Analysis (Red Background)
- **Risk Amount**: Automatically calculated dollar risk
- **Effective Entry**: Actual entry cost including fees
- **Effective Exit**: Actual exit value including fees
- **Expected Loss**: Calculated loss if stop loss is hit
- **Deviation from Risk %**: Accuracy of risk calculation
### Final Result (Blue Background)
- **Position Size**: Number of shares/units to trade
## Usage Examples
### Example 1: Conservative Long Trade
- **Total Portfolio**: $50,000
- **Trading Allocation**: 15%
- **Risk per Trade**: 0.05%
- **Entry Price**: $25.00
- **Stop Loss**: $24.00
- **Risk Factor**: 1.0
- **Fee**: 0.01%
**Calculated Risk Amount**: $50,000 × 15% × 0.05% ÷ 100 = $3.75
### Example 2: Aggressive Short Trade
- **Total Portfolio**: $200,000
- **Trading Allocation**: 30%
- **Risk per Trade**: 0.2%
- **Entry Price**: $150.00
- **Stop Loss**: $155.00
- **Risk Factor**: 2.0
- **Fee**: 0.01%
**Calculated Risk Amount**: $200,000 × 30% × 0.2% ÷ 100 = $120
**Actual Risk**: $120 × 2.0 = $240 (due to risk factor)
## Color Coding System
- **Green/Red Header**: Trade direction (Long/Short)
- **Light Blue**: Portfolio management parameters
- **Gray**: Trade setup parameters
- **Red**: Risk-related calculations and results
- **Blue**: Final position size result
## Best Practices
### Portfolio Management
1. **Keep trading allocation reasonable** (typically 10-30% of total portfolio)
2. **Use conservative risk percentages** (0.05-0.2% per trade)
3. **Don't risk more than you can afford to lose**
### Risk Management
1. **Start with small risk factors** (1.0 or less) until comfortable
2. **Monitor your total exposure** across all open positions
3. **Adjust risk based on market conditions**
### Trade Execution
1. **Always validate calculations** before placing trades
2. **Account for slippage** in volatile markets
3. **Consider position size relative to liquidity**
## Risk Management Guidelines
### Conservative Approach
- Trading Allocation: 10-20%
- Risk per Trade: 0.05-0.1%
- Risk Factor: 0.5-1.0
### Moderate Approach
- Trading Allocation: 20-30%
- Risk per Trade: 0.1-0.15%
- Risk Factor: 1.0-1.5
### Aggressive Approach
- Trading Allocation: 30-40%
- Risk per Trade: 0.15-0.25%
- Risk Factor: 1.5-2.0
## Troubleshooting
### Common Issues
1. **Position Size shows 0**
- Verify all portfolio inputs are greater than 0
- Check that entry price differs from stop loss
- Ensure calculated risk amount is positive
2. **Very small position sizes**
- Increase risk percentage or risk factor
- Check if your risk amount is too small for the price difference
3. **Large risk deviation**
- Normal for very small positions
- Consider adjusting entry/stop loss levels
### Validation Checklist
- Total portfolio value is realistic
- Trading allocation percentage makes sense
- Risk percentage is conservative
- Entry and stop loss prices are valid
- Trade direction matches your intention
## Advanced Features
### Risk Factor Usage
- **Scaling up**: Use risk factors > 1.0 for high-confidence trades
- **Scaling down**: Use risk factors < 1.0 for uncertain trades
- **Never exceed**: Risk factors that would risk more than your comfort level
### Multiple Timeframe Analysis
- Use different risk factors for different timeframes
- Consider correlation between positions
- Adjust trading allocation based on market conditions
## Disclaimer
This tool is for educational and planning purposes only. Always verify calculations manually and consider market conditions, liquidity, and correlation between positions. The automated risk calculation assumes you're comfortable with the mathematical relationship between portfolio allocation and individual trade risk. Past performance doesn't guarantee future results, and all trading involves risk of loss.
Institutional Momentum Scanner [IMS]Institutional Momentum Scanner - Professional Momentum Detection System
Hunt explosive price movements like the professionals. IMS identifies maximum momentum displacement within 10-bar windows, revealing where institutional money commits to directional moves.
KEY FEATURES:
▪ Scans for strongest momentum in rolling 10-bar windows (institutional accumulation period)
▪ Adaptive filtering reduces false signals using efficiency ratio technology
▪ Three clear states: LONG (green), SHORT (red), WAIT (gray)
▪ Dynamic volatility-adjusted thresholds (8% ATR-scaled)
▪ Visual momentum flow with glow effects for signal strength
BASED ON:
- Pocket Pivot concept (O'Neil/Morales) applied to price momentum
- Adaptive Moving Average principles (Kaufman KAMA)
- Market Wizards momentum philosophy
- Institutional order flow patterns (5-day verification window)
HOW IT WORKS:
The scanner finds the maximum price displacement in each 10-bar window - where the market showed its hand. An adaptive filter (5-bar regression) separates real moves from noise. When momentum exceeds the volatility-adjusted threshold, states change.
IDEAL FOR:
- Momentum traders seeking explosive moves
- Swing traders (especially 4H timeframe)
- Position traders wanting institutional footprints
- Anyone tired of false breakout signals
Default parameters (10,5) optimized for 4H charts but adaptable to any timeframe. Remember: The market rewards patience and punishes heroes. Wait for clear signals.
"The market is honest. Are you?"
F&O Time Zones – Final Fixed📌 This indicator highlights high-probability intraday time zones used in Indian F&O (Futures & Options) strategies. Ideal for scalping, breakout setups, and trap avoidance.
🕒 Covered Time Zones:
• 9:15 – 9:21 AM → Flash Trades (first 1-minute volatility)
• 9:21 – 9:30 AM → Smart Money Trap (VWAP fakeouts)
• 9:30 – 9:50 AM → Fake Breakout Zone
• 9:50 – 10:15 AM → Institutional Entry Timing
• 10:15 – 10:45 AM → VWAP Range Scalps
• 10:45 – 11:15 AM → Second Trap Zone
• 11:15 – 1:00 PM → Trend Continuation Window
• 1:00 – 1:45 PM → Volatility Compression
• 1:45 – 2:15 PM → Institutional Exit Phase 1
• 2:15 – 2:45 PM → Trend Acceleration / Reversals
• 2:45 – 3:15 PM → Expiry Scalping Zone
• 3:15 – 3:30 PM → Dead Zone (square-off time)
🔧 Features:
✓ Clean vertical lines per zone
✓ Optional label positions (top or bottom)
✓ Adjustable line style, width, and color
🧠 Best used on: NIFTY, BANKNIFTY, FINNIFTY (5-min or lower)
---
🔒 **Disclaimer**:
This script is for **educational purposes only**. It is not financial advice. Trading involves risk. Please consult a professional or do your own research before taking any positions.
—
👤 Script by: **JoanJagan**
🛠️ Built in Pine Script v5
Multi-Confluence Swing Hunter V1# Multi-Confluence Swing Hunter V1 - Complete Description
Overview
The Multi-Confluence Swing Hunter V1 is a sophisticated low timeframe scalping strategy specifically optimized for MSTR (MicroStrategy) trading. This strategy employs a comprehensive point-based scoring system that combines optimized technical indicators, price action analysis, and reversal pattern recognition to generate precise trading signals on lower timeframes.
Performance Highlight:
In backtesting on MSTR 5-minute charts, this strategy has demonstrated over 200% profit performance, showcasing its effectiveness in capturing rapid price movements and volatility patterns unique to MicroStrategy's trading behavior.
The strategy's parameters have been fine-tuned for MSTR's unique volatility characteristics, though they can be optimized for other high-volatility instruments as well.
## Key Innovation & Originality
This strategy introduces a unique **dual scoring system** approach:
- **Entry Scoring**: Identifies swing bottoms using 13+ different technical criteria
- **Exit Scoring**: Identifies swing tops using inverse criteria for optimal exit timing
Unlike traditional strategies that rely on simple indicator crossovers, this system quantifies market conditions through a weighted scoring mechanism, providing objective, data-driven entry and exit decisions.
## Technical Foundation
### Optimized Indicator Parameters
The strategy utilizes extensively backtested parameters specifically optimized for MSTR's volatility patterns:
**MACD Configuration (3,10,3)**:
- Fast EMA: 3 periods (vs standard 12)
- Slow EMA: 10 periods (vs standard 26)
- Signal Line: 3 periods (vs standard 9)
- **Rationale**: These faster parameters provide earlier signal detection while maintaining reliability, particularly effective for MSTR's rapid price movements and high-frequency volatility
**RSI Configuration (21-period)**:
- Length: 21 periods (vs standard 14)
- Oversold: 30 level
- Extreme Oversold: 25 level
- **Rationale**: The 21-period RSI reduces false signals while still capturing oversold conditions effectively in MSTR's volatile environment
**Parameter Adaptability**: While optimized for MSTR, these parameters can be adjusted for other high-volatility instruments. Faster-moving stocks may benefit from even shorter MACD periods, while less volatile assets might require longer periods for optimal performance.
### Scoring System Methodology
**Entry Score Components (Minimum 13 points required)**:
1. **RSI Signals** (max 5 points):
- RSI < 30: +2 points
- RSI < 25: +2 points
- RSI turning up: +1 point
2. **MACD Signals** (max 8 points):
- MACD below zero: +1 point
- MACD turning up: +2 points
- MACD histogram improving: +2 points
- MACD bullish divergence: +3 points
3. **Price Action** (max 4 points):
- Long lower wick (>50%): +2 points
- Small body (<30%): +1 point
- Bullish close: +1 point
4. **Pattern Recognition** (max 8 points):
- RSI bullish divergence: +4 points
- Quick recovery pattern: +2 points
- Reversal confirmation: +4 points
**Exit Score Components (Minimum 13 points required)**:
Uses inverse criteria to identify swing tops with similar weighting system.
## Risk Management Features
### Position Sizing & Risk Control
- **Single Position Strategy**: 100% equity allocation per trade
- **No Overlapping Positions**: Ensures focused risk management
- **Configurable Risk/Reward**: Default 5:1 ratio optimized for volatile assets
### Stop Loss & Take Profit Logic
- **Dynamic Stop Loss**: Based on recent swing lows with configurable buffer
- **Risk-Based Take Profit**: Calculated using risk/reward ratio
- **Clean Exit Logic**: Prevents conflicting signals
## Default Settings Optimization
### Key Parameters (Optimized for MSTR/Bitcoin-style volatility):
- **Minimum Entry Score**: 13 (ensures high-conviction entries)
- **Minimum Exit Score**: 13 (prevents premature exits)
- **Risk/Reward Ratio**: 5.0 (accounts for volatility)
- **Lower Wick Threshold**: 50% (identifies true hammer patterns)
- **Divergence Lookback**: 8 bars (optimal for swing timeframes)
### Why These Defaults Work for MSTR:
1. **Higher Score Thresholds**: MSTR's volatility requires more confirmation
2. **5:1 Risk/Reward**: Compensates for wider stops needed in volatile markets
3. **Faster MACD**: Captures momentum shifts quickly in fast-moving stocks
4. **21-period RSI**: Reduces noise while maintaining sensitivity
## Visual Features
### Score Display System
- **Green Labels**: Entry scores ≥10 points (below bars)
- **Red Labels**: Exit scores ≥10 points (above bars)
- **Large Triangles**: Actual trade entries/exits
- **Small Triangles**: Reversal pattern confirmations
### Chart Cleanliness
- Indicators plotted in separate panes (MACD, RSI)
- TP/SL levels shown only during active positions
- Clear trade markers distinguish signals from actual trades
## Backtesting Specifications
### Realistic Trading Conditions
- **Commission**: 0.1% per trade
- **Slippage**: 3 points
- **Initial Capital**: $1,000
- **Account Type**: Cash (no margin)
### Sample Size Considerations
- Strategy designed for 100+ trade sample sizes
- Recommended timeframes: 4H, 1D for swing trading
- Optimal for trending/volatile markets
## Strategy Limitations & Considerations
### Market Conditions
- **Best Performance**: Trending markets with clear swings
- **Reduced Effectiveness**: Highly choppy, sideways markets
- **Volatility Dependency**: Optimized for moderate to high volatility assets
### Risk Warnings
- **High Allocation**: 100% position sizing increases risk
- **No Diversification**: Single position strategy
- **Backtesting Limitation**: Past performance doesn't guarantee future results
## Usage Guidelines
### Recommended Assets & Timeframes
- **Primary Target**: MSTR (MicroStrategy) - 5min to 15min timeframes
- **Secondary Targets**: High-volatility stocks (TSLA, NVDA, COIN, etc.)
- **Crypto Markets**: Bitcoin, Ethereum (with parameter adjustments)
- **Timeframe Optimization**: 1min-15min for scalping, 30min-1H for swing scalping
### Timeframe Recommendations
- **Primary Scalping**: 5-minute and 15-minute charts
- **Active Monitoring**: 1-minute for precise entries
- **Swing Scalping**: 30-minute to 1-hour timeframes
- **Avoid**: Sub-1-minute (excessive noise) and above 4-hour (reduces scalping opportunities)
## Technical Requirements
- **Pine Script Version**: v6
- **Overlay**: Yes (plots on price chart)
- **Additional Panes**: MACD and RSI indicators
- **Real-time Compatibility**: Confirmed bar signals only
## Customization Options
All parameters are fully customizable through inputs:
- Indicator lengths and levels
- Scoring thresholds
- Risk management settings
- Visual display preferences
- Date range filtering
## Conclusion
This scalping strategy represents a comprehensive approach to low timeframe trading that combines multiple technical analysis methods into a cohesive, quantified system specifically optimized for MSTR's unique volatility characteristics. The optimized parameters and scoring methodology provide a systematic way to identify high-probability scalping setups while managing risk effectively in fast-moving markets.
The strategy's strength lies in its objective, multi-criteria approach that removes emotional decision-making from scalping while maintaining the flexibility to adapt to different instruments through parameter optimization. While designed for MSTR, the underlying methodology can be fine-tuned for other high-volatility assets across various markets.
**Important Disclaimer**: This strategy is designed for experienced scalpers and is optimized for MSTR trading. The high-frequency nature of scalping involves significant risk. Past performance does not guarantee future results. Always conduct your own analysis, consider your risk tolerance, and be aware of commission/slippage costs that can significantly impact scalping profitability.
CandelaCharts - 1st Presented FVG 📝 Overview
The ICT 1st Presented Fair Value Gap refers to the first FVG that forms after the market opens at 9:30 AM New York local time. In a sideways market, it often acts as a catalyst for price movement in either direction, while in trending conditions, it tends to support and reinforce the prevailing trend.
This indicator automatically identifies the first Fair Value Gap (FVG) that forms after the New York session opens at 9:30 AM local time. Based on concepts taught by Inner Circle Trader (ICT), the 1st Presented FVG is a key institutional price imbalance that often sets the tone for the trading day.
📦 Features
Customize FVG session time (e.g. 09:30 – 10:00)
Show/hide session dividers
FVG visibility filter (e.g. Bullish / Bearish)
Advanced styling
Hide overlapping FVGs
Extend FVGs
Opening prices
⚙️ Settings
Show: Controls whether all, bullish only, or bearish only FVGs are displayed on the chart.
Session: Sets a specific time window (e.g. 09:30–10:00) to filter which FVGs are displayed.
Dividers: Toggles vertical session divider on the chart for visual separation.
Midline: Displays a midpoint (CE) line through the FVG; customizable color and thickness.
Border: Adds a border around each FVG zone.
Labels: Toggles label display for FVGs.
Hide Overlap: Hides overlapping FVGs to reduce visual clutter.
Extend: Extends each FVG forward in time.
Alerts: Enables alerts when price interacts with an FVG zone.
Opening Prices: Allows defining custom time-based levels (e.g. 00:00–00:01 and 18:00–18:01) with color and style options.
⚡️ Showcase
Simple
Labels
Bordered
Consequent Encroachment
Extended
Dividers
📒 Usage
How to Use the ICT 1st Presented Fair Value Gap in Trading
To apply the ICT 1st Presented Fair Value Gap (FVG), identify the first fair value gap of the day and extend it across the chart until 3:45 PM New York time.
You’ll often notice that some of the best trade setups form around this level. It tends to act as a key reference point for price action during the day—especially on trending days, where price frequently returns to this gap before continuing in its direction.
This level can also serve as an inverse fair value gap, offering opportunities in the opposite direction under the right conditions.
How to Disqualify the 1st Presented Fair Value Gap?
When the first fair value gap forms after 9:30 AM New York time, check the candles that came just before it.
If the candlestick that creates the FVG doesn’t break above or below the range of those previous candles, then it’s not a true inefficiency. In that case, it’s considered a disqualified 1st Presented Fair Value Gap—meaning it shouldn’t be used as a key reference level.
Refer to the example below to see what this looks like on the chart.
🚨 Alerts
This script provides alert options for all signals.
Bearish Signal
A bearish signal is triggered when the bearish 1st P.FVG is formed in interval 09:30 - 10:00.
Bullish Signal
A bullish signal is triggered when the bullish 1st P.FVG is formed in interval 09:30 - 10:00.
⚠️ Disclaimer
Trading involves significant risk, and many participants may incur losses. The content on this site is not intended as financial advice and should not be interpreted as such. Decisions to buy, sell, hold, or trade securities, commodities, or other financial instruments carry inherent risks and are best made with guidance from qualified financial professionals. Past performance is not indicative of future results.
MC Geopolitical Tension Events📌 Script Title: Geopolitical Tension Events
📖 Description:
This script highlights key geopolitical and military tension events from 1914 to 2024 that have historically impacted global markets.
It automatically plots vertical dashed lines and labels on the chart at the time of each major event. This allows traders and analysts to visually assess how markets have responded to global crises, wars, and significant political instability over time.
🧠 Use Cases:
Historical backtesting: Understand how market responded to past geopolitical shocks.
Contextual analysis: Add macro context to technical setups.
🗓️ List of Geopolitical Tension Events in the Script
Date Event Title Description
1914-07-28 WWI Begins Outbreak of World War I following the assassination of Archduke Franz Ferdinand.
1929-10-24 Wall Street Crash Black Thursday, the start of the 1929 stock market crash.
1939-09-01 WWII Begins Germany invades Poland, starting World War II.
1941-12-07 Pearl Harbor Japanese attack on Pearl Harbor; U.S. enters WWII.
1945-08-06 Hiroshima Bombing First atomic bomb dropped on Hiroshima by the U.S.
1950-06-25 Korean War Begins North Korea invades South Korea.
1962-10-16 Cuban Missile Crisis 13-day standoff between the U.S. and USSR over missiles in Cuba.
1973-10-06 Yom Kippur War Egypt and Syria launch surprise attack on Israel.
1979-11-04 Iran Hostage Crisis U.S. Embassy in Tehran seized; 52 hostages taken.
1990-08-02 Gulf War Begins Iraq invades Kuwait, triggering U.S. intervention.
2001-09-11 9/11 Attacks Coordinated terrorist attacks on the U.S.
2003-03-20 Iraq War Begins U.S.-led invasion of Iraq to remove Saddam Hussein.
2008-09-15 Lehman Collapse Bankruptcy of Lehman Brothers; peak of global financial crisis.
2014-03-01 Crimea Crisis Russia annexes Crimea from Ukraine.
2020-01-03 Soleimani Strike U.S. drone strike kills Iranian General Qasem Soleimani.
2022-02-24 Ukraine Invasion Russia launches full-scale invasion of Ukraine.
2023-10-07 Hamas-Israel War Hamas launches attack on Israel, sparking war in Gaza.
2024-01-12 Red Sea Crisis Houthis attack ships in Red Sea, prompting Western naval response.
Market Matrix ViewThis technical indicator is designed to provide traders with a quick and integrated view of market dynamics by combining several popular indicators into a single tool. It's not a magic bullet, but a practical aid for analyzing buying/selling pressure, trends, volume, and divergences, saving you time in the decision-making process. Built for flexibility, the indicator adapts to various trading styles (scalping, swing, or long-term) and offers customizable settings to suit your needs.
🟡 Multi-Timeframe Trends
➤ This section displays the trend direction (bullish, bearish, or neutral) across 15-minute, 1-hour, 4-hour, and Daily timeframes, providing multi-timeframe market context. Timeframes lower than the one currently selected will show "N/A."
➤It utilizes fast and slow Exponential Moving Averages (EMAs) for each timeframe:
15m: Fast EMA 42, Slow EMA 170
1h: Fast EMA 40, Slow EMA 100
4h: Fast EMA 36, Slow EMA 107
Daily: Fast EMA 20, Slow EMA 60
🟡 Smart Flow & RVOL
➤ This section displays "Buying Pressure" or "Selling Pressure" signals based on indicator confluence, alongside volume activity ("High Activity," "Normal Activity," or "Low Activity").
➤ Smart Flow combines Chaikin Money Flow (CMF) and Money Flow Index (MFI) to detect buying/selling pressure. CMF measures money flow based on price position within the high-low range, while MFI analyzes money flow considering typical price and volume. A signal is generated only when both indicators simultaneously increase/decrease beyond an adjustable threshold ("Buy/Sell Sensitivity") and volume exceeds a Simple Moving Average (SMA) scaled by the "Volume Multiplier."
➤ RVOL (Relative Volume) calculates relative volume separately for bullish and bearish candles, comparing recent volume (fast SMA) with a reference volume (slow SMA). Thresholds are adjusted based on the selected mode.
🟡 ADX & RSI
This section displays trend strength ("Strong," "Moderate," or "Weak"), its direction ("Bullish" or "Bearish"), and the RSI momentum status ("Overbought," "Oversold," "Buy/Sell Momentum," or "Neutral").
➤ ADX (Average Directional Index) measures trend strength (above 40 = "Strong," 20–40 = "Moderate," below 20 = "Weak"). Direction is determined by comparing +DI (upward movement) with -DI (downward movement). Additionally, an arrow indicates whether the trend's strength is decreasing or increasing.
➤RSI (Relative Strength Index) evaluates price momentum. Extreme levels (above 80/85 = "Overbought," below 15/20 = "Oversold") and intermediate zones (47–53 = "Neutral," above 53 = "Buy Momentum," below 47 = "Sell Momentum") are adjusted based on the selected mode.
🟡 When these signals are active for a potential trade setup, the table's background lights up green or red, respectively.
🟡 Volume Spikes
➤This feature highlights bars with significantly higher volume than the recent average, coloring them yellow on the chart to draw attention to intense market activity.
➤It uses the Z-Score method to detect volume anomalies. Current volume is compared to a 10-bar Simple Moving Average (SMA) and the standard deviation of volume over the same period. If the Z-Score exceeds a certain threshold, the bar is marked as a volume spike.
🟡 Divergences (Volume Divergence Detection)
➤ This feature marks divergences between price and technical indicators on the chart, using diamond-shaped labels (green for bullish divergences, red for bearish divergences) to signal potential trend reversals.
➤ It compares price deviations from a Simple Moving Average (SMA) with deviations of three indicators: Chaikin Money Flow (CMF), Money Flow Index (MFI), and On-Balance Volume (OBV). A bullish divergence occurs when price falls below its average, but CMF, MFI, and OBV rise above their averages, indicating hidden accumulation. A bearish divergence occurs when price rises above its average, but CMF, MFI, and OBV fall, suggesting distribution. The length of the moving averages is adjustable (default 13/10/5 bars for Scalping/Balanced/Swing), and detection thresholds are scaled by "Divergence Sensitivity" (default 1.0).
🟡 Adaptive Stop-Loss (ATR)
➤Draws dynamic stop-loss lines (red, dashed) on the chart for buy or sell signals, helping traders manage risk.Uses the Average True Range (ATR) to calculate stop-loss levels, set at low/high ± ATR × multiplier
🟡 Alerts for trend direction changes in the Info Panel:
➤ Triggers notifications when the trend shifts to Bullish (when +DI crosses above -DI) or Bearish (when +DI crosses below -DI), helping you stay informed about key market shifts.
How to use: Set alerts in Trading View for “Trend Changed to Bullish” or “Trend Changed to Bearish” with “Once Per Bar Close” for reliable signals.
🟡 Settings (Inputs)
➤ The indicator offers customizable settings to fit your trading style, but it's already optimized for Scalping (1m–15m), Balanced (16m–3h59m), and Swing (4h–Daily) modes, which automatically adjust based on the selected timeframe. The visible inputs allow you to adjust the following parameters:
Show Info Panel: Enables/disables the information panel (default: enabled).
Show Volume Spikes: Turns on/off coloring for volume spike bars (default: enabled).
Spike Sensitivity: Controls the Z-Score threshold for detecting volume spikes (default: 2.0; lower values increase signal frequency).
Show Divergence: Enables/disables the display of divergence labels (default: enabled).
Divergence Sensitivity: Adjusts the thresholds for divergence detection (default: 1.0; higher values reduce sensitivity).
Divergence Lookback Length: Sets the length of the moving averages used for divergences (default: 5, automatically adjusted to 13/10/5 for Scalping/Balanced/Swing).
RVOL Reference Period: Defines the reference period for relative volume (default: 20, automatically adjusted to 7/15/20).
RSI Length: Sets the RSI length (default: 14, automatically adjusted to 5/10/14).
Buy Sensitivity: Controls the increase threshold for Buying Pressure signals (default: 0.007; higher values reduce frequency).
Sell Sensitivity: Controls the decrease threshold for Selling Pressure signals (default: 0.007; higher values reduce frequency).
Volume Multiplier (B/S Pressure): Adjusts the volume threshold for Smart Flow signals (default: 0.6; higher values require greater volume).
🟡 This indicator is created to simplify market analysis, but I am not a professional in Pine Script or technical indicators. This indicator is not a standalone solution. For optimal results, it must be integrated into a well-defined trading strategy that includes risk management and other confirmations.
Multifractal Forecast [ScorsoneEnterprises]Multifractal Forecast Indicator
The Multifractal Forecast is an indicator designed to model and forecast asset price movements using a multifractal framework. It uses concepts from fractal geometry and stochastic processes, specifically the Multifractal Model of Asset Returns (MMAR) and fractional Brownian motion (fBm), to generate price forecasts based on historical price data. The indicator visualizes potential future price paths as colored lines, providing traders with a probabilistic view of price trends over a specified trading time scale. Below is a detailed breakdown of the indicator’s functionality, inputs, calculations, and visualization.
Overview
Purpose: The indicator forecasts future price movements by simulating multiple price paths based on a multifractal model, which accounts for the complex, non-linear behavior of financial markets.
Key Concepts:
Multifractal Model of Asset Returns (MMAR): Models price movements as a multifractal process, capturing varying degrees of volatility and self-similarity across different time scales.
Fractional Brownian Motion (fBm): A generalization of Brownian motion that incorporates long-range dependence and self-similarity, controlled by the Hurst exponent.
Binomial Cascade: Used to model trading time, introducing heterogeneity in time scales to reflect market activity bursts.
Hurst Exponent: Measures the degree of long-term memory in the price series (persistence, randomness, or mean-reversion).
Rescaled Range (R/S) Analysis: Estimates the Hurst exponent to quantify the fractal nature of the price series.
Inputs
The indicator allows users to customize its behavior through several input parameters, each influencing the multifractal model and forecast generation:
Maximum Lag (max_lag):
Type: Integer
Default: 50
Minimum: 5
Purpose: Determines the maximum lag used in the rescaled range (R/S) analysis to calculate the Hurst exponent. A higher lag increases the sample size for Hurst estimation but may smooth out short-term dynamics.
2 to the n values in the Multifractal Model (n):
Type: Integer
Default: 4
Purpose: Defines the resolution of the multifractal model by setting the size of arrays used in calculations (N = 2^n). For example, n=4 results in N=16 data points. Larger n increases computational complexity and detail but may exceed Pine Script’s array size limits (capped at 100,000).
Multiplier for Binomial Cascade (m):
Type: Float
Default: 0.8
Purpose: Controls the asymmetry in the binomial cascade, which models trading time. The multiplier m (and its complement 2.0 - m) determines how mass is distributed across time scales. Values closer to 1 create more balanced cascades, while values further from 1 introduce more variability.
Length Scale for fBm (L):
Type: Float
Default: 100,000.0
Purpose: Scales the fractional Brownian motion output, affecting the amplitude of simulated price paths. Larger values increase the magnitude of forecasted price movements.
Cumulative Sum (cum):
Type: Integer (0 or 1)
Default: 1
Purpose: Toggles whether the fBm output is cumulatively summed (1=On, 0=Off). When enabled, the fBm series is accumulated to simulate a price path with memory, resembling a random walk with long-range dependence.
Trading Time Scale (T):
Type: Integer
Default: 5
Purpose: Defines the forecast horizon in bars (20 bars into the future). It also scales the binomial cascade’s output to align with the desired trading time frame.
Number of Simulations (num_simulations):
Type: Integer
Default: 5
Minimum: 1
Purpose: Specifies how many forecast paths are simulated and plotted. More simulations provide a broader range of possible price outcomes but increase computational load.
Core Calculations
The indicator combines several mathematical and statistical techniques to generate price forecasts. Below is a step-by-step explanation of its calculations:
Log Returns (lgr):
The indicator calculates log returns as math.log(close / close ) when both the current and previous close prices are positive. This measures the relative price change in a logarithmic scale, which is standard for financial time series analysis to stabilize variance.
Hurst Exponent Estimation (get_hurst_exponent):
Purpose: Estimates the Hurst exponent (H) to quantify the degree of long-term memory in the price series.
Method: Uses rescaled range (R/S) analysis:
For each lag from 2 to max_lag, the function calc_rescaled_range computes the rescaled range:
Calculate the mean of the log returns over the lag period.
Compute the cumulative deviation from the mean.
Find the range (max - min) of the cumulative deviation.
Divide the range by the standard deviation of the log returns to get the rescaled range.
The log of the rescaled range (log(R/S)) is regressed against the log of the lag (log(lag)) using the polyfit_slope function.
The slope of this regression is the Hurst exponent (H).
Interpretation:
H = 0.5: Random walk (no memory, like standard Brownian motion).
H > 0.5: Persistent behavior (trends tend to continue).
H < 0.5: Mean-reverting behavior (price tends to revert to the mean).
Fractional Brownian Motion (get_fbm):
Purpose: Generates a fractional Brownian motion series to model price movements with long-range dependence.
Inputs: n (array size 2^n), H (Hurst exponent), L (length scale), cum (cumulative sum toggle).
Method:
Computes covariance for fBm using the formula: 0.5 * (|i+1|^(2H) - 2 * |i|^(2H) + |i-1|^(2H)).
Uses Hosking’s method (referenced from Columbia University’s implementation) to generate fBm:
Initializes arrays for covariance (cov), intermediate calculations (phi, psi), and output.
Iteratively computes the fBm series by incorporating a random term scaled by the variance (v) and covariance structure.
Applies scaling based on L / N^H to adjust the amplitude.
Optionally applies cumulative summation if cum = 1 to produce a path with memory.
Output: An array of 2^n values representing the fBm series.
Binomial Cascade (get_binomial_cascade):
Purpose: Models trading time (theta) to account for non-uniform market activity (e.g., bursts of volatility).
Inputs: n (array size 2^n), m (multiplier), T (trading time scale).
Method:
Initializes an array of size 2^n with values of 1.0.
Iteratively applies a binomial cascade:
For each block (from 0 to n-1), splits the array into segments.
Randomly assigns a multiplier (m or 2.0 - m) to each segment, redistributing mass.
Normalizes the array by dividing by its sum and scales by T.
Checks for array size limits to prevent Pine Script errors.
Output: An array (theta) representing the trading time, which warps the fBm to reflect market activity.
Interpolation (interpolate_fbm):
Purpose: Maps the fBm series to the trading time scale to produce a forecast.
Method:
Computes the cumulative sum of theta and normalizes it to .
Interpolates the fBm series linearly based on the normalized trading time.
Ensures the output aligns with the trading time scale (T).
Output: An array of interpolated fBm values representing log returns over the forecast horizon.
Price Path Generation:
For each simulation (up to num_simulations):
Generates an fBm series using get_fbm.
Interpolates it with the trading time (theta) using interpolate_fbm.
Converts log returns to price levels:
Starts with the current close price.
For each step i in the forecast horizon (T), computes the price as prev_price * exp(log_return).
Output: An array of price levels for each simulation.
Visualization:
Trigger: Updates every T bars when the bar state is confirmed (barstate.isconfirmed).
Process:
Clears previous lines from line_array.
For each simulation, plots a line from the current bar’s close price to the forecasted price at bar_index + T.
Colors the line using a gradient (color.from_gradient) based on the final forecasted price relative to the minimum and maximum forecasted prices across all simulations (red for lower prices, teal for higher prices).
Output: Multiple colored lines on the chart, each representing a possible price path over the next T bars.
How It Works on the Chart
Initialization: On each bar, the indicator calculates the Hurst exponent (H) using historical log returns and prepares the trading time (theta) using the binomial cascade.
Forecast Generation: Every T bars, it generates num_simulations price paths:
Each path starts at the current close price.
Uses fBm to model log returns, warped by the trading time.
Converts log returns to price levels.
Plotting: Draws lines from the current bar to the forecasted price T bars ahead, with colors indicating relative price levels.
Dynamic Updates: The forecast updates every T bars, replacing old lines with new ones based on the latest price data and calculations.
Key Features
Multifractal Modeling: Captures complex market dynamics by combining fBm (long-range dependence) with a binomial cascade (non-uniform time).
Customizable Parameters: Allows users to adjust the forecast horizon, model resolution, scaling, and number of simulations.
Probabilistic Forecast: Multiple simulations provide a range of possible price outcomes, helping traders assess uncertainty.
Visual Clarity: Gradient-colored lines make it easy to distinguish bullish (teal) and bearish (red) forecasts.
Potential Use Cases
Trend Analysis: Identify potential price trends or reversals based on the direction and spread of forecast lines.
Risk Assessment: Evaluate the range of possible price outcomes to gauge market uncertainty.
Volatility Analysis: The Hurst exponent and binomial cascade provide insights into market persistence and volatility clustering.
Limitations
Computational Intensity: Large values of n or num_simulations may slow down execution or hit Pine Script’s array size limits.
Randomness: The binomial cascade and fBm rely on random terms (math.random), which may lead to variability between runs.
Assumptions: The model assumes log-normal price movements and fractal behavior, which may not always hold in extreme market conditions.
Adjusting Inputs:
Set max_lag based on the desired depth of historical analysis.
Adjust n for model resolution (start with 4–6 to avoid performance issues).
Tune m to control trading time variability (0.5–1.5 is typical).
Set L to scale the forecast amplitude (experiment with values like 10,000–1,000,000).
Choose T based on your trading horizon (20 for short-term, 50 for longer-term for example).
Select num_simulations for the number of forecast paths (5–10 is reasonable for visualization).
Interpret Output:
Teal lines suggest bullish scenarios, red lines suggest bearish scenarios.
A wide spread of lines indicates high uncertainty; convergence suggests a stronger trend.
Monitor Updates: Forecasts update every T bars, so check the chart periodically for new projections.
Chart Examples
This is a daily AMEX:SPY chart with default settings. We see the simulations being done every T bars and they provide a range for us to analyze with a few simulations still in the range.
On this intraday PEPPERSTONE:COCOA chart I modified the Length Scale for fBm, L, parameter to be 1000 from 100000. Adjusting the parameter as you switch between timeframes can give you more contextual simulations.
On BITSTAMP:ETHUSD I modified the L to be 1000000 to have a more contextual set of simulations with crypto's volatile nature.
With L at 100000 we see the range for NASDAQ:TLT is correctly simulated. The recent pop stays within the bounds of the highest simulation. Note this is a cherry picked example to show the power and potential of these simulations.
Technical Notes
Error Handling: The script includes checks for array size limits and division by zero (math.abs(denominator) > 1e-10, v := math.max(v, 1e-10)).
External Reference: The fBm implementation is based on Hosking’s method (www.columbia.edu), ensuring a robust algorithm.
Conclusion
The Multifractal Forecast is a powerful tool for traders seeking to model complex market dynamics using a multifractal framework. By combining fBm, binomial cascades, and Hurst exponent analysis, it generates probabilistic price forecasts that account for long-range dependence and non-uniform market activity. Its customizable inputs and clear visualizations make it suitable for both technical analysis and strategy development, though users should be mindful of its computational demands and parameter sensitivity. For optimal use, experiment with input settings and validate forecasts against other technical indicators or market conditions.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Reflexivity Resonance Factor (RRF) - Quantum Flow Reflexivity Resonance Factor (RRF) – Quantum Flow
See the Feedback Loops. Anticipate the Regime Shift.
What is the RRF – Quantum Flow?
The Reflexivity Resonance Factor (RRF) – Quantum Flow is a next-generation market regime detector and energy oscillator, inspired by George Soros’ theory of reflexivity and modern complexity science. It is designed for traders who want to visualize the hidden feedback loops between market perception and participation, and to anticipate explosive regime shifts before they unfold.
Unlike traditional oscillators, RRF does not just measure price momentum or volatility. Instead, it models the dynamic feedback between how the market perceives itself (perception) and how it acts on that perception (participation). When these feedback loops synchronize, they create “resonance” – a state of amplified reflexivity that often precedes major market moves.
Theoretical Foundation
Reflexivity: Markets are not just driven by external information, but by participants’ perceptions and their actions, which in turn influence future perceptions. This feedback loop can create self-reinforcing trends or sudden reversals.
Resonance: When perception and participation align and reinforce each other, the market enters a high-energy, reflexive state. These “resonance” events often mark the start of new trends or the climax of existing ones.
Energy Field: The indicator quantifies the “energy” of the market’s reflexivity, allowing you to see when the crowd is about to act in unison.
How RRF – Quantum Flow Works
Perception Proxy: Measures the rate of change in price (ROC) over a configurable period, then smooths it with an EMA. This models how quickly the market’s collective perception is shifting.
Participation Proxy: Uses a fast/slow ATR ratio to gauge the intensity of market participation (volatility expansion/contraction).
Reflexivity Core: Multiplies perception and participation to model the feedback loop.
Resonance Detection: Applies Z-score normalization to the absolute value of reflexivity, highlighting when current feedback is unusually strong compared to recent history.
Energy Calculation: Scales resonance to a 0–100 “energy” value, visualized as a dynamic background.
Regime Strength: Tracks the percentage of bars in a lookback window where resonance exceeded the threshold, quantifying the persistence of reflexive regimes.
Inputs:
🧬 Core Parameters
Perception Period (pp_roc_len, default 14): Lookback for price ROC.
Lower (5–10): More sensitive, for scalping (1–5min).
Default (14): Balanced, for 15min–1hr.
Higher (20–30): Smoother, for 4hr–daily.
Perception Smooth (pp_smooth_len, default 7): EMA smoothing for perception.
Lower (3–5): Faster, more detail.
Default (7): Balanced.
Higher (10–15): Smoother, less noise.
Participation Fast (prp_fast_len, default 7): Fast ATR for immediate volatility.
5–7: Scalping.
7–10: Day trading.
10–14: Swing trading.
Participation Slow (prp_slow_len, default 21): Slow ATR for baseline volatility.
Should be 2–4x fast ATR.
Default (21): Works with fast=7.
⚡ Signal Configuration
Resonance Window (res_z_window, default 50): Z-score lookback for resonance normalization.
20–30: More reactive.
50: Medium-term.
100+: Very stable.
Primary Threshold (rrf_threshold, default 1.5): Z-score level for “Active” resonance.
1.0–1.5: More signals.
1.5: Balanced.
2.0+: Only strong signals.
Extreme Threshold (rrf_extreme, default 2.5): Z-score for “Extreme” resonance.
2.5: Major regime shifts.
3.0+: Only the most extreme.
Regime Window (regime_window, default 100): Lookback for regime strength (% of bars with resonance spikes).
Higher: More context, slower.
Lower: Adapts quickly.
🎨 Visual Settings
Show Resonance Flow (show_flow, default true): Plots the main resonance line with glow effects.
Show Signal Particles (show_particles, default true): Circular markers at active/extreme resonance points.
Show Energy Field (show_energy, default true): Background color based on resonance energy.
Show Info Dashboard (show_dashboard, default true): Status panel with resonance metrics.
Show Trading Guide (show_guide, default true): On-chart quick reference for interpreting signals.
Color Mode (color_mode, default "Spectrum"): Visual theme for all elements.
“Spectrum”: Cyan→Magenta (high contrast)
“Heat”: Yellow→Red (heat map)
“Ocean”: Blue gradients (easy on eyes)
“Plasma”: Orange→Purple (vibrant)
Color Schemes
Dynamic color gradients are used for all plots and backgrounds, adapting to both resonance intensity and direction:
Spectrum: Cyan/Magenta for bullish/bearish resonance.
Heat: Yellow/Red for bullish, Blue/Purple for bearish.
Ocean: Blue gradients for both directions.
Plasma: Orange/Purple for high-energy states.
Glow and aura effects: The resonance line is layered with multiple glows for depth and signal strength.
Background energy field: Darker = higher energy = stronger reflexivity.
Visual Logic
Main Resonance Line: Shows the smoothed resonance value, color-coded by direction and intensity.
Glow/Aura: Multiple layers for visual depth and to highlight strong signals.
Threshold Zones: Dotted lines and filled areas mark “Active” and “Extreme” resonance zones.
Signal Particles: Circular markers at each “Active” (primary threshold) and “Extreme” (extreme threshold) event.
Dashboard: Top-right panel shows current status (Dormant, Building, Active, Extreme), resonance value, energy %, and regime strength.
Trading Guide: Bottom-right panel explains all states and how to interpret them.
How to Use RRF – Quantum Flow
Dormant (💤): Market is in equilibrium. Wait for resonance to build.
Building (🌊): Resonance is rising but below threshold. Prepare for a move.
Active (🔥): Resonance exceeds primary threshold. Reflexivity is significant—consider entries or exits.
Extreme (⚡): Resonance exceeds extreme threshold. Major regime shift likely—watch for trend acceleration or reversal.
Energy >70%: High conviction, crowd is acting in unison.
Above 0: Bullish reflexivity (positive feedback).
Below 0: Bearish reflexivity (negative feedback).
Regime Strength: % of bars in “Active” state—higher = more persistent regime.
Tips:
- Use lower lookbacks for scalping, higher for swing trading.
- Combine with price action or your own system for confirmation.
- Works on all assets and timeframes—tune to your style.
Alerts
RRF Activation: Resonance crosses above primary threshold.
RRF Extreme: Resonance crosses above extreme threshold.
RRF Deactivation: Resonance falls below primary threshold.
Originality & Usefulness
RRF – Quantum Flow is not a mashup of existing indicators. It is a novel oscillator that models the feedback loop between perception and participation, then quantifies and visualizes the resulting resonance. The multi-layered color logic, energy field, and regime strength dashboard are unique to this script. It is designed for anticipation, not confirmation—helping you see regime shifts before they are obvious in price.
Chart Info
Script Name: Reflexivity Resonance Factor (RRF) – Quantum Flow
Recommended Use: Any asset, any timeframe. Tune parameters to your style.
Disclaimer
This script is for research and educational purposes only. It does not provide financial advice or direct buy/sell signals. Always use proper risk management and combine with your own strategy. Past performance is not indicative of future results.
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
DECODE Moving Average ToolkitDECODE Moving Average Toolkit: Your All-in-One MA Analysis Powerhouse!
This versatile indicator is designed to be your go-to solution for analysing trends, identifying potential entry/exit points, and staying ahead of market movements using the power of Moving Averages (MAs).
Whether you're a seasoned trader or just starting out, the Decode MAT offers a comprehensive suite of features in a user-friendly package.
Key Features:
Multiple Moving Averages: Visualize up to 10 Moving Averages simultaneously on your chart.
Includes 5 Exponential Moving Averages (EMAs) and 5 Simple Moving Averages (SMAs).
Easily toggle the visibility of each MA and customize its length to suit your trading style and the asset you're analyzing.
Dynamic MA Ribbons: Gain a clearer perspective on trend direction and strength with 5 configurable MA Ribbons.
Each ribbon is formed between a corresponding EMA and SMA (e.g., EMA 20 / SMA 20).
The ribbon color changes to indicate bullish (e.g., green) or bearish (e.g., red) sentiment, providing an intuitive visual cue.
Toggle ribbon visibility with a single click.
Powerful Crossover Alerts: Never miss a potential trading opportunity with up to 5 customizable MA Crossover Alerts.
Define your own fast and slow MAs for each alert from any of the 10 available MAs.
Receive notifications directly through TradingView when your specified MAs cross over or cross under.
Optionally display visual symbols (e.g., triangles ▲▼) directly on your chart at the exact crossover points for quick identification.
Highly Customizable:
Adjust the source price (close, open, etc.) for all MA calculations.
Fine-tune the appearance (colors, line thickness) of every MA line, ribbon, and alert symbol to match your charting preferences.
User-Friendly Interface: All settings are neatly organized in the indicator's input menu, making configuration straightforward and intuitive.
How Can You Use the Decode MAT in Your Trading?
This toolkit is incredibly versatile and can be adapted to various trading strategies:
Trend Identification:
Use longer-term MAs (e.g., 50, 100, 200 period) to identify the prevailing market trend. When prices are consistently above these MAs, it suggests an uptrend, and vice-versa.
Observe the MA ribbons: A consistently green ribbon can indicate a strong uptrend, while a red ribbon can signal a downtrend. The widening or narrowing of the ribbon can also suggest changes in trend momentum.
Dynamic Support & Resistance:
Shorter-term MAs (e.g., 10, 20 period EMAs) can act as dynamic levels of support in an uptrend or resistance in a downtrend. Look for price pullbacks to these MAs as potential entry opportunities.
Crossover Signals (Entries & Exits):
Golden Cross / Death Cross: Configure alerts for classic crossover signals. For example, a 50-period MA crossing above a 200-period MA (Golden Cross) is often seen as a long-term bullish signal. Conversely, a 50-period MA crossing below a 200-period MA (Death Cross) can be a bearish signal.
Shorter-Term Signals: Use crossovers of shorter-term MAs (e.g., EMA 10 crossing EMA 20) for more frequent, shorter-term trading signals. A fast MA crossing above a slow MA can signal a buy, while a cross below can signal a sell.
Use the on-chart symbols for quick visual confirmation of these crossover events.
Confirmation Tool:
Combine the Decode MAT with other indicators (like RSI, MACD, or volume analysis) to confirm signals and increase the probability of successful trades. For instance, a bullish MA crossover combined with an oversold RSI reading could strengthen a buy signal.
Multi-Timeframe Analysis:
Apply the toolkit across different timeframes to get a broader market perspective. A long-term uptrend on the daily chart, confirmed by a short-term bullish crossover on the 1-hour chart, can provide a higher-confidence entry.
The DECODE Moving Average Toolkit empowers you to tailor your MA analysis precisely to your needs.
[blackcat] L2 Angle Trend TrackerOVERVIEW
The " L2 Angle Trend Tracker" is a sophisticated technical analysis tool designed to monitor trend direction and momentum using multiple Exponential Moving Averages (EMAs) with different periods. 📈 This script calculates the angles of 5 EMAs (5, 8, 10, 12, and 15 periods) and displays them with gradient colors, providing a comprehensive view of market momentum. When all EMAs cross above or below specified threshold levels, it generates Buy or Sell signals with visual alerts. The indicator helps traders identify trend reversals, potential entry/exit points, and market sentiment shifts with precision. 🚀 This powerful tool is particularly useful for traders who want to combine multiple timeframe analysis with angle-based momentum confirmation.
FEATURES
Calculates angles for 5 EMAs with customizable periods (5, 8, 10, 12, and 15)
Displays angle values with distinct colors for each EMA (Green, Blue, Purple, Orange, and Red)
Generates Buy signals when all EMAs cross above the lower threshold
Generates Sell signals when all EMAs cross below the upper threshold
Shows a zero line and threshold lines for easy reference
Customizable threshold levels for Buy/Sell signals
Visual alerts with "Buy" and "Sell" labels at the point of signal generation
The script uses a mathematical formula to calculate the angle of each EMA relative to its position 11 bars ago
Angle values are converted from radians to degrees for easier interpretation
The zero line represents no change in the EMA angle
The indicator is not overlayed on the price chart by default, but can be adjusted in the script settings 📊
HOW TO USE
Adjust the EMA periods to match your trading strategy 🛠️
Shorter periods (5, 8) are more sensitive to price changes
Longer periods (10, 12, 15) provide smoother trend confirmation
Set appropriate threshold values for Buy/Sell signals based on your risk tolerance
Default thresholds are 70 for upper threshold and -70 for lower threshold
Consider adjusting thresholds based on market volatility
Watch for Buy signals when all EMAs cross above the lower threshold (default: -70)
The signal appears as a green "Buy" label on the chart
This indicates a potential trend reversal to the upside
Watch for Sell signals when all EMAs cross below the upper threshold (default: 70)
The signal appears as a red "Sell" label on the chart
This indicates a potential trend reversal to the downside
Combine with other indicators for confirmation before making trading decisions 🧠
Consider using volume confirmation, support/resistance levels, or other oscillators
The angle tracker works well with trend-following strategies
Use the angle values to gauge momentum strength
Steeper angles indicate stronger momentum
Flatter angles suggest weakening momentum or consolidation
CONFIGURATION
EMA Periods: The script uses five different EMA periods that can be customized:
EMA Period 5: Short-term trend indicator
EMA Period 8: Medium-short term trend indicator
EMA Period 10: Medium-term trend indicator
EMA Period 12: Medium-long term trend indicator
EMA Period 15: Long-term trend indicator
Threshold Settings:
Threshold Top: Sets the upper boundary for Sell signals (default: 70)
Threshold Bot: Sets the lower boundary for Buy signals (default: -70)
These thresholds can be adjusted based on market conditions and trading style
LIMITATIONS
The script may generate false signals in ranging markets or during periods of high volatility
All EMAs must cross the threshold for a signal to appear, which may filter some valid signals
The angle calculation uses a 11-bar lookback period, which may not be suitable for all timeframes
Works best in trending markets and may produce whipsaws in choppy conditions ⚠️
The indicator is more effective on higher timeframes (4H, 1D) than on very short timeframes (1M, 5M)
Signal generation requires confirmation from multiple EMAs, which may delay entry/exit points
The angle calculation method may not be suitable for all financial instruments
ADVANCED TIPS
Use multiple instances of this indicator with different EMA settings for multi-timeframe analysis
Combine with volume analysis to confirm the strength of signals
Look for confluence with support and resistance levels for more reliable signals
Consider using the angle values as a filter for other trading strategies
The indicator can be used to identify momentum exhaustion points when angles flatten
For swing trading, consider using the Buy and Sell signals as potential entry/exit points
For day trading, you may want to use shorter EMA periods and adjust threshold values accordingly
NOTES
The script uses a mathematical formula to calculate the angle of each EMA relative to its position 11 bars ago
The angle values are converted from radians to degrees for easier interpretation
The zero line represents no change in the EMA angle
The indicator is not overlayed on the price chart by default, but can be adjusted in the script settings 📊
The angle calculation provides a dynamic view of momentum that traditional moving averages don't offer
The threshold values are based on empirical testing and can be fine-tuned for specific instruments
THANKS
Special thanks to the TradingView community for their support and feedback on this indicator. If you find this script helpful, please consider leaving a comment or sharing your experiences with it. Your feedback helps improve the tool for everyone. 🙏
Also, a nod to the original concept developers who pioneered angle-based trend analysis. This script builds upon those foundational ideas to provide a more comprehensive view of market momentum. 🌟
Market Breadth Toolkit [LuxAlgo]The Market Breadth Toolkit allows traders to use up to 6 different market breadth measures on two different exchanges, for a total of 12 different views of the market.
This toolkit includes divergence detection and allows setting custom fixed levels for traders who want to experiment with them.
🔶 USAGE
The main idea behind Breadth is to measure the number of advancing and declining issues and/or volume by exchange to have an idea of the underlying strength of the whole exchange.
On the other hand, thrusts represent big impulses in the breadth, as it is described by technicians to be the start of a new bullish trend.
By default, the Toolkit is set to "Breadth Thrust Zweig", with divergences enabled.
We will now explain all the different breadth measures available in the toolkit.
🔹 Deemer Breakaway Momentum
The "Breakaway Momentum" is a concept related to market breadth introduced by legendary technical analyst Walter Deemer.
As stated on his website:
We coined the term "breakaway momentum" in the 1970's to describe this REALLY powerful upward momentum
and:
We now know that the stock market generates breakaway momentum when the 10-day total advances on the NYSE are greater than 1.97 times the 10-day total NYSE declines OR the 20-day total advances on the NYSE are greater than 1.72 times the 20-day total NYSE declines.
As we can see in the chart above, which shows both methods, momentum is identified when the ratio of advancing issues to declining issues is greater than 1.97 for the 10-day average or 1.72 for the 20-day average.
🔹 Zweig Breadth Tools
Legendary trader and author Marting Zweig, best known as the author of "Winning on Wall Street" and the creator of the Put/Call Ratio.
In this toolkit, we feature two of his other tools:
Breadth Thrust: Number of Advancing / (Number of Advancing + Number of Declining Stocks)
Market Thrust: (Number of Advancing × Advancing Volume) — (Number of Declining Stocks × Declining Volume)
As we can see on the above chart, the Breadth Thrust printed a new signal on April 24, 2025, which is a bullish signal on the daily chart that can last several months, considering the previous signals.
On the right side, we have the Market Thrust as the delta between advancing minus declining volume weighted.
🔹 Whaley Measures
Wayne Whaley received the 2010 Charles Dow Award from the CMT Association, as stated on their website: "In 1994, the CMT Association established the Charles H. Dow Award to recognize outstanding research in technical analysis."
We include two of the tools from this paper:
Advance Decline Thrust: Number of Advancing / (Number of Advancing + Number of Declining Stocks)
Up/Down Volume Thrust Advancing Volume / (Advancing Volume + Declining Volume)
The chart above shows Thrust signals at extreme readings as described in the paper.
🔹 Divergences
The divergence detector is enabled by default, traders can disable it and fine-tune the detection length in the settings panel.
🔹 Fixed Levels
Traders can adjust the Thrust detection thresholds in the settings panel.
In the image above, we can see the Deemer Breakaway Momentum 10 with the original threshold (below) and with the 3.0 threshold (above).
🔶 SETTINGS
Breadth: Choose between 6 different breadth thrust measurement methods.
Data: Choose between NYSE or NASDAQ exchanges.
Divergences: Enable/Disable divergences and select the length detection.
🔹 Levels
Use Fixed Levels: Enable/Disable Fixed Levels.
Top Level: Select the top-level threshold.
Bottom Level: Select bottom level threshold.
Levels Style: Choose between dashed, dotted, or solid style.
🔹 Style
Breadth: Select breadth colors
Divergence: Select divergence colors
Market Sentiment Index US Top 40 [Pt]▮Overview
Market Sentiment Index US Top 40 [Pt} shows how the largest US stocks behave together. You pick one simple measure—High Low breakouts, Above Below moving average, or RSI overbought/oversold—and see how many of your chosen top 10/20/30/40 NYSE or NASDAQ names are bullish, neutral, or bearish.
This tool gives you a quick view of broad-market strength or weakness so you can time trades, confirm trends, and spot hidden shifts in market sentiment.
▮Key Features
► Three Simple Modes
High Low Index: counts stocks making new highs or lows over your lookback period
Above Below MA: flags stocks trading above or below their moving average
RSI Sentiment: marks overbought or oversold stocks and plots a small histogram
► Universe Selection
Top 10, 20, 30, or 40 symbols from NYSE or NASDAQ
Option to weight by market cap or treat all symbols equally
► Timeframe Choice
Use your chart’s timeframe or any intraday, daily, weekly, or monthly resolution
► Histogram Smoothing
Two optional moving averages on the sentiment bars
Markers show when the faster average crosses above or below the slower one
► Ticker Table
Optional on-chart table showing each ticker’s state in color
Grid or single-row layout with adjustable text size and color settings
▮Inputs
► Mode and Lookback
Pick High Low, Above Below MA, or RSI Sentiment
Set lookback length (for example 10 bars)
If using Above Below MA, choose the moving average type (EMA, SMA, etc.)
► Universe Setup
Market: NYSE or NASDAQ
Number of symbols: 10, 20, 30, or 40
Weights: on or off
Timeframe: blank to match chart or pick any other
► Moving Averages on Histogram
Enable fast and slow averages
Set their lengths and types
Choose colors for averages and markers
► Table Options
Show or hide the symbol table
Select text size: tiny, small, or normal
Choose layout: grid or one-row
Pick colors for bullish, neutral, and bearish cells
Show or hide exchange prefixes
▮How to Read It
► Sentiment Bars
Green means bullish
Red means bearish
Near zero means neutral
► Zero Line
Separates bullish from bearish readings
► High Low Line (High Low mode only)
Smooth ratio of highs versus lows over your lookback
► MA Crosses
Fast MA above slow MA hints rising breadth
Fast MA below slow MA hints falling breadth
► Ticker Table
Each cell colored green, gray, or red for bull, neutral, or bear
▮Use Cases
► Confirm Market Trends
Early warning when price makes highs but breadth is weak
Catch rallies when breadth turns strong while price is flat
► Spot Sector Rotation
Switch between NYSE and NASDAQ to see which group leads
Watch tech versus industrial breadth to track money flow
► Filter Trade Signals
Enter longs only when breadth is bullish
Consider shorts when breadth turns negative
► Combine with Other Indicators
Use RSI Sentiment with trend tools to spot overextended moves
Add volume indicators in High Low mode for breakout confirmation
► Timeframe Analysis
Daily for big-picture bias
Intraday (15-min) for precise entries and exits
Test OHLCV LibraryThis indicator, "Test OHLCV Library," serves as a practical example of how to use the OHLCVData library to fetch historical candle data from a specific timeframe (like 4H) in a way that is largely impervious to the chart's currently selected time frame.
Here's a breakdown of its purpose and how it addresses request.security limitations:
Indicator Purpose:
The main goal of this indicator is to demonstrate and verify that the OHLCVData library can reliably provide confirmed historical OHLCV data for a user-specified timeframe (e.g., 4H), and that a collection of these data points (the last 10 completed candles) remains consistent even when the user switches the chart's time frame (e.g., from 5-second to Daily).
It does this by:
Importing the OHLCVData library.
Using the library's getTimeframeData function on every bar of the chart.
Checking the isTargetBarClosed flag returned by the library to identify the exact moment a candle in the target timeframe (e.g., 4H) has closed.
When isTargetBarClosed is true, it captures the confirmed OHLCV data provided by the library for that moment and stores it in a persistent var array.
It maintains a list of the last 10 captured historical 4H candle opens in this array.
It displays these last 10 confirmed opens in a table.
It uses the isAdjustedToChartTF flag from the library to show a warning if the chart's time frame is higher than the target timeframe, indicating that the data fetched by request.security is being aligned to that higher resolution.
Circumventing request.security Limitations:
The primary limitation of request.security that this setup addresses is the challenge of getting a consistent, non-repainting collection of historical data points from a different timeframe when the chart's time frame is changed.
The Problem: Standard request.security calls, while capable of fetching data from other timeframes, align that data to the bars of the current chart. When you switch the chart's time frame, the set of chart bars changes, and the way the requested data aligns to these new bars changes. If you simply collected data on every chart bar where request.security returned a non-na value, the resulting collection would differ depending on the chart's resolution. Furthermore, using request.security without lookahead=barmerge.lookahead_off or an offset ( ) can lead to repainting on historical bars, where values change as the script recalculates.
How the Library/Indicator Setup Helps:
Confirmed Data: The OHLCVData library uses lookahead=barmerge.lookahead_off and, more importantly, provides the isTargetBarClosed flag. This flag is calculated using a reliable method (checking for a change in the target timeframe's time series) that accurately identifies the precise chart bar corresponding to the completion of a candle in the target timeframe (e.g., a 4H candle), regardless of the chart's time frame.
Precise Capture: The indicator only captures and stores the OHLCV data into its var array when this isTargetBarClosed flag is true. This means it's capturing the confirmed, finalized data for the target timeframe candle at the exact moment it closes.
Persistent Storage: The var array in the indicator persists its contents across the bars of the chart's history. As the script runs through the historical bars, it selectively adds confirmed 4H candle data points to this array only when the trigger is met.
Impervious Collection: Because the array is populated based on the completion of the target timeframe candles (detected reliably by the library) rather than simply collecting data on every chart bar, the final contents of the array (the list of the last 10 confirmed 4H opens) will be the same regardless of the chart's time frame. The table then displays this static collection.
In essence, this setup doesn't change how request.security fundamentally works or aligns data to the chart's bars. Instead, it uses the capabilities of request.security (fetching data from another timeframe) and Pine Script's execution model (bar-by-bar processing, var persistence) in a specific way, guided by the library's logic, to build a historical collection of data points that represent the target timeframe's candles and are independent of the chart's display resolution.
