S&P 500 Scalper Pro [Trend + MACD] 5 minfor scalping 5 min S&P on 5 min chart put SL on 20 min ma and take 2:1 risk
ابحث في النصوص البرمجية عن "美国标普500指数成分股"
GainzAlgo V2 [Alpha]// © GainzAlgo
//@version=5
indicator('GainzAlgo V2 ', overlay=true, max_labels_count=500)
show_tp_sl = input.bool(true, 'Display TP & SL', group='Techical', tooltip='Display the exact TP & SL price levels for BUY & SELL signals.')
rrr = input.string('1:2', 'Risk to Reward Ratio', group='Techical', options= , tooltip='Set a risk to reward ratio (RRR).')
tp_sl_multi = input.float(1, 'TP & SL Multiplier', 1, group='Techical', tooltip='Multiplies both TP and SL by a chosen index. Higher - higher risk.')
tp_sl_prec = input.int(2, 'TP & SL Precision', 0, group='Techical')
candle_stability_index_param = 0.7
rsi_index_param = 80
candle_delta_length_param = 10
disable_repeating_signals_param = input.bool(true, 'Disable Repeating Signals', group='Techical', tooltip='Removes repeating signals. Useful for removing clusters of signals and general clarity.')
GREEN = color.rgb(29, 255, 40)
RED = color.rgb(255, 0, 0)
TRANSPARENT = color.rgb(0, 0, 0, 100)
label_size = input.string('huge', 'Label Size', options= , group='Cosmetic')
label_style = input.string('text bubble', 'Label Style', , group='Cosmetic')
buy_label_color = input(GREEN, 'BUY Label Color', inline='Highlight', group='Cosmetic')
sell_label_color = input(RED, 'SELL Label Color', inline='Highlight', group='Cosmetic')
label_text_color = input(color.white, 'Label Text Color', inline='Highlight', group='Cosmetic')
stable_candle = math.abs(close - open) / ta.tr > candle_stability_index_param
rsi = ta.rsi(close, 14)
atr = ta.atr(14)
bullish_engulfing = close < open and close > open and close > open
rsi_below = rsi < rsi_index_param
decrease_over = close < close
var last_signal = ''
var tp = 0.
var sl = 0.
bull_state = bullish_engulfing and stable_candle and rsi_below and decrease_over and barstate.isconfirmed
bull = bull_state and (disable_repeating_signals_param ? (last_signal != 'buy' ? true : na) : true)
bearish_engulfing = close > open and close < open and close < open
rsi_above = rsi > 100 - rsi_index_param
increase_over = close > close
bear_state = bearish_engulfing and stable_candle and rsi_above and increase_over and barstate.isconfirmed
bear = bear_state and (disable_repeating_signals_param ? (last_signal != 'sell' ? true : na) : true)
round_up(number, decimals) =>
factor = math.pow(10, decimals)
math.ceil(number * factor) / factor
if bull
last_signal := 'buy'
dist = atr * tp_sl_multi
tp_dist = rrr == '2:3' ? dist / 2 * 3 : rrr == '1:2' ? dist * 2 : rrr == '1:4' ? dist * 4 : dist
tp := round_up(close + tp_dist, tp_sl_prec)
sl := round_up(close - dist, tp_sl_prec)
if label_style == 'text bubble'
label.new(bar_index, low, 'BUY', color=buy_label_color, style=label.style_label_up, textcolor=label_text_color, size=label_size)
else if label_style == 'triangle'
label.new(bar_index, low, 'BUY', yloc=yloc.belowbar, color=buy_label_color, style=label.style_triangleup, textcolor=TRANSPARENT, size=label_size)
else if label_style == 'arrow'
label.new(bar_index, low, 'BUY', yloc=yloc.belowbar, color=buy_label_color, style=label.style_arrowup, textcolor=TRANSPARENT, size=label_size)
label.new(show_tp_sl ? bar_index : na, low, 'TP: ' + str.tostring(tp) + '\nSL: ' + str.tostring(sl), yloc=yloc.price, color=color.gray, style=label.style_label_down, textcolor=label_text_color)
if bear
last_signal := 'sell'
dist = atr * tp_sl_multi
tp_dist = rrr == '2:3' ? dist / 2 * 3 : rrr == '1:2' ? dist * 2 : rrr == '1:4' ? dist * 4 : dist
tp := round_up(close - tp_dist, tp_sl_prec)
sl := round_up(close + dist, tp_sl_prec)
if label_style == 'text bubble'
label.new(bear ? bar_index : na, high, 'SELL', color=sell_label_color, style=label.style_label_down, textcolor=label_text_color, size=label_size)
else if label_style == 'triangle'
label.new(bear ? bar_index : na, high, 'SELL', yloc=yloc.abovebar, color=sell_label_color, style=label.style_triangledown, textcolor=TRANSPARENT, size=label_size)
else if label_style == 'arrow'
label.new(bear ? bar_index : na, high, 'SELL', yloc=yloc.abovebar, color=sell_label_color, style=label.style_arrowdown, textcolor=TRANSPARENT, size=label_size)
label.new(show_tp_sl ? bar_index : na, low, 'TP: ' + str.tostring(tp) + '\nSL: ' + str.tostring(sl), yloc=yloc.price, color=color.gray, style=label.style_label_up, textcolor=label_text_color)
alertcondition(bull or bear, 'BUY & SELL Signals', 'New signal!')
alertcondition(bull, 'BUY Signals (Only)', 'New signal: BUY')
alertcondition(bear, 'SELL Signals (Only)', 'New signal: SELL')
Average True Range (ATR)Strategy Name: ATR Trend-Following System with Volatility Filter & Dynamic Risk Management
Short Name: ATR Pro Trend System
Current Version: 2025 Edition (fully tested and optimized)Core ConceptA clean, robust, and highly profitable trend-following strategy that only trades when three strict conditions are met simultaneously:Clear trend direction (price above/below EMA 50)
Confirmed trend strength and trailing stop (SuperTrend)
Sufficient market volatility (current ATR(14) > its 50-period average)
This combination ensures the strategy stays out of choppy, low-volatility ranges and only enters during high-probability, trending moves with real momentum.Key Features & ComponentsComponent
Function
Default Settings
EMA 50
Primary trend filter
50-period exponential
SuperTrend
Dynamic trailing stop + secondary trend confirmation
Period 10, Multiplier 3.0
ATR(14) with RMA
True volatility measurement (Wilder’s original method)
Length 14
50-period SMA of ATR
Volatility filter – only trade when current ATR > average ATR
Length 50
Background coloring
Visual position status: light green = long, light red = short, white = flat
–
Entry markers
Green/red triangles at the exact entry bar
–
Dynamic position sizing
Fixed-fractional risk: exactly 1% of equity per trade
1.00% risk
Stop distance
2.5 × ATR(14) – fully adaptive to current volatility
Multiplier 2.5
Entry RulesLong: Close > EMA 50 AND SuperTrend bullish AND ATR(14) > SMA(ATR,50)
Short: Close < EMA 50 AND SuperTrend bearish AND ATR(14) > SMA(ATR,50)
Exit RulesPosition is closed automatically when SuperTrend flips direction (acts as volatility-adjusted trailing stop).
Money ManagementRisk per trade: exactly 1% of current account equity
Position size is recalculated on every new entry based on current ATR
Automatically scales up in strong trends, scales down in low-volatility regimes
Performance Highlights (2015–Nov 2025, real backtests)CAGR: 22–50% depending on market
Max Drawdown: 18–28%
Profit Factor: 1.89–2.44
Win Rate: 57–62%
Average holding time: 10–25 days (daily timeframe)
Best Markets & TimeframesExcellent on: Bitcoin, S&P 500, Nasdaq-100, DAX, Gold, major Forex pairs
Recommended timeframes: 4H, Daily, Weekly (Daily is the sweet spot)
NQUSB Sector Industry Stocks Strength
A Comprehensive Multi-Industry Performance Comparison Tool
The complete Pine Script code and supporting Python automation scripts are available on GitHub:
GitHub Repository: github.com
Original idea from by www.tradingview.com
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═══ WHAT'S NEW ═══
4-Level Hierarchical Navigation:
Primary: All 11 NQUSB sectors (NQUSB10, NQUSB15, NQUSB20, etc.)
Secondary (Default): Broad sectors like Technology, Energy
Tertiary: Industry groups within sectors
Quaternary: Individual stocks within industries (37 semiconductors)
Enhanced Stock Coverage:
1,176 total stocks across 129 industries
37 semiconductor stocks
Market-cap weighted selection: 60% tech / 35% others
Range: 1-37 stocks per industry
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
═══ CORE FEATURES ═══
1. Drill-Down/Drill-Up Navigation
View NVDA at different granularity levels:
Quaternary: ● NVDA ranks #3 of 37 semiconductors
Tertiary: ✓ Semiconductors at 85% (strongest in tech hardware)
Secondary: ✓ Tech Hardware at 82% (stronger than software)
Primary: ✓ Technology at 78% (#1 sector overall)
Insight: One indicator, one stock, four perspectives - instantly see if strength is stock-specific, industry-specific, or sector-wide.
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2. Visual Current Stock Identification
Violet Markers - Instant Recognition:
● (dot) marker when current stock is in top N performers
✕ (cross) marker when current stock is below top N
Violet color (#9C27B0) on both symbol and value labels
Example: "NVDA ● ranks #3 of 37"
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3. Rank Display in Title
Dynamic title shows performance context:
"Semiconductors (RS Rating - 3 Months) | NVDA ranks #3 of 37"
#1 = Best performer, higher number = lower rank
Total adjusts if current stock auto-added
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4. Auto-Add Current Stock
Always Included:
Current stock automatically added if not in predefined list
Example: Viewing PRSO → "PRSO ranks #37 of 39 ✕"
Works for any stock - from NVDA to obscure small-caps
Violet markers ensure visibility even when ranked low
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═══ DUAL PERFORMANCE METRICS ═══
RS Rating (Relative Strength):
Normalized strength score 1-99
Compare stocks across different price ranges
Default benchmark: SPX
% Return:
Simple percentage price change
Direct performance comparison
11 Time Periods:
1 Week, 2 Weeks, 1 Month, 2 Months, 3 Months (Default) , 6 Months, 1 Year, YTD, MTD, QTD, Custom (1-500 days)
Result: 22 analytical combinations (2 metrics × 11 periods)
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═══ USE CASES ═══
Sector Rotation Analysis:
Is NVDA's strength semiconductors-specific or tech-wide?
Drill through all 4 levels to find answer
Identify which industry groups are leading/lagging
Finding Hidden Gems:
JPM ranks #3 of 13 in Major Banks
But Financials sector weak overall (68%)
= Relative strength play in weak sector
Cross-Industry Comparison:
129 industries covered
Market-wide scan capability
Find strongest performers across all sectors
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═══ TECHNICAL SPECIFICATIONS ═══
V32 Stats:
Total Industries: 129
Total Stocks: 1,176
File Size: 82,032 bytes (80.1 KB)
Request Limit: 39 max (Semiconductors), 10-16 typical
Granularity Levels: 4 (Primary → Quaternary)
Smart Stock Allocation:
Technology industries: 60% coverage
Other industries: 35% coverage
Market-cap weighted selection
Formula: MIN(39, MAX(5, CEILING(total × percentage)))
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═══ KEY ADVANTAGES ═══
vs. Single Industry Tools:
✓ 129 industries vs 1
✓ Market-wide perspective
✓ Hierarchical navigation
✓ Sector rotation detection
vs. Manual Comparison:
✓ No ETF research needed
✓ Instant visual markers
✓ Automatic ranking
✓ One-click drill-down
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For complete documentation, Python automation scripts, and CSV data files:
github.com
Version: V32
Last Updated: 2025-11-30
Pine Script Version: v5
Viprasol Elite Flow Pro - Premium Order Flow & Trend System═══════════════════════════════════════════════════════════════
🔥 VIPRASOL ELITE FLOW PRO
Professional Order Flow & Trend Detection System
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📊 WHAT IS THIS INDICATOR?
Viprasol Elite Flow Pro is a comprehensive trading system that combines institutional order flow analysis with adaptive trend detection. Unlike basic indicators, this tool identifies high-probability setups by analyzing where smart money is likely positioning, while filtering signals through multiple confirmation layers.
This indicator is designed for traders who want to:
✓ Identify premium (supply) and discount (demand) zones automatically
✓ Detect trend direction with adaptive cloud technology
✓ Spot high-volume rejection points before major moves
✓ Filter low-quality signals with intelligent confirmation logic
✓ Track market strength in real-time via elite dashboard
═══════════════════════════════════════════════════════════════
🎯 CORE FEATURES
═══════════════════════════════════════════════════════════════
1️⃣ ELITE TREND ENGINE
• Adaptive Moving Average system (Fast/Adaptive/Smooth modes)
• Dynamic trend cloud that expands/contracts with volatility
• Real-time trend state tracking (Bullish/Bearish/Ranging)
• Trend strength meter (0-10 scale)
• ATR-based volatility adjustments
2️⃣ ORDER FLOW DETECTION
• Automatic Premium Zone (Supply) identification
• Automatic Discount Zone (Demand) identification
• Smart zone extension - zones remain valid until broken
• Zone rejection detection with price action confirmation
• Customizable zone strength (5-30 bars lookback)
3️⃣ VOLUME INTELLIGENCE
• Volume spike detection (configurable threshold)
• Climax bar identification (exhaustion signals)
• Volume filter for signal validation
• Institutional activity detection
4️⃣ SMART SIGNAL SYSTEM
• 3 Signal Modes: Aggressive, Balanced, Conservative
• Multi-layer confirmation logic
• Automatic profit targets (2:1 risk-reward)
• Stop loss suggestions based on ATR
• Prevents overtrading with bars-since-signal filter
5️⃣ ELITE DASHBOARD (HUD)
• Real-time trend direction and strength
• Volume status monitoring
• Active zones counter
• Market volatility gauge
• Current signal status
• 4 positioning options, compact mode available
6️⃣ PREMIUM STYLING
• 4 Professional color themes (Cyber/Gold/Ocean/Fire)
• Adjustable transparency and label sizes
• Clean, institutional-grade visuals
• Optimized for all chart types
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📖 HOW TO USE THIS INDICATOR
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STEP 1: TREND IDENTIFICATION
→ Green Cloud = Bullish trend - look for LONG opportunities
→ Red Cloud = Bearish trend - look for SHORT opportunities
→ Purple Cloud = Ranging - wait for breakout or fade extremes
STEP 2: ZONE ANALYSIS
→ PREMIUM (Red) zones = Potential resistance/supply areas
→ DISCOUNT (Green) zones = Potential support/demand areas
→ Price rejecting from zones = high-probability setups
STEP 3: SIGNAL CONFIRMATION
→ Wait for "LONG" or "SHORT" labels to appear
→ Check dashboard for trend strength (Moderate/Strong preferred)
→ Confirm volume status is "HIGH" or "CLIMAX"
→ Entry: Enter when label appears
→ Stop Loss: Use dotted line (1 ATR away)
→ Take Profit: Use dashed line (2 ATR away)
STEP 4: RISK MANAGEMENT
→ Never risk more than 1-2% per trade
→ Use the provided stop loss levels
→ Trail stops as price moves in your favor
→ Avoid trading during low volatility periods
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⚙️ RECOMMENDED SETTINGS
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FOR SCALPING (1M - 5M):
- Trend Type: Fast
- Sensitivity: 15
- Signal Mode: Aggressive
- Zone Strength: 8
FOR DAY TRADING (15M - 1H):
- Trend Type: Adaptive
- Sensitivity: 21 (default)
- Signal Mode: Balanced
- Zone Strength: 12 (default)
FOR SWING TRADING (4H - Daily):
- Trend Type: Smooth
- Sensitivity: 34
- Signal Mode: Conservative
- Zone Strength: 20
BEST MARKETS:
✓ Crypto (BTC, ETH, major altcoins)
✓ Forex (Major pairs: EUR/USD, GBP/USD)
✓ Indices (S&P 500, NASDAQ, DAX)
✓ High-liquidity stocks
═══════════════════════════════════════════════════════════════
🎓 UNDERSTANDING THE METHODOLOGY
═══════════════════════════════════════════════════════════════
This indicator is built on three core concepts:
1. ORDER FLOW THEORY
Markets move between premium (expensive) and discount (cheap) zones. Smart money accumulates in discount zones and distributes in premium zones. This indicator identifies these zones automatically.
2. ADAPTIVE TREND FOLLOWING
Unlike fixed-period moving averages, the Elite Trend Engine adjusts to current market volatility, providing more accurate trend signals in both trending and ranging conditions.
3. CONFLUENCE-BASED ENTRIES
Signals only trigger when multiple conditions align:
- Price in correct zone (premium for shorts, discount for longs)
- Trend confirmation (cloud color matches direction)
- Volume validation (spike or climax present)
- Price action strength (strong rejection candles)
This multi-layer approach dramatically reduces false signals.
═══════════════════════════════════════════════════════════════
🔔 ALERT SETUP
═══════════════════════════════════════════════════════════════
This indicator includes 5 alert types:
1. Long Signal → Triggers when buy conditions met
2. Short Signal → Triggers when sell conditions met
3. Volume Climax → Warns of pot
Trendshift [CHE] StrategyTrendshift Strategy — First-Shift Structural Regime Trading
Profitfactor 2,603
Summary
Trendshift Strategy implements a structural regime-shift trading model built around the earliest confirmed change in directional structure. It identifies major swing highs and lows, validates breakouts through optional ATR-based conviction, and reacts only to the first confirmed shift in each direction. After a regime reversal, the strategy constructs a premium and discount band between the breakout candle and the previous opposite swing. This band is used as contextual bias and may optionally inform stop placement and position sizing.
The strategy focuses on clear, interpretable structural events rather than continuous signal generation. By limiting entries to the first valid shift, it reduces false recycles and allows the structural state to stabilize before a new trade occurs. All signals operate on closed-bar logic, and the strategy avoids higher-timeframe calls to stabilize execution behavior.
Motivation: Why this design?
Many structure-based systems repeatedly trigger as price fluctuates around prior highs and lows. This often leads to multiple flips during volatile or choppy conditions. Trendshift Strategy addresses this problem by restricting execution to the first confirmed structural event in each direction. ATR-based filters help differentiate genuine structural breaks from noise, while the contextual band ensures that the breakout is meaningful in relation to recent volatility.
The design aims to represent a minimalistic structural trading framework focused on regime turns rather than continuous trend signaling. This reduces chart noise and clarifies where the market transitions from one regime to another.
What’s different vs. standard approaches?
Baseline reference
Typical swing-based structure indicators report every break above or below recent swing points.
Architecture differences
First-shift-only regime logic that blocks repeated signals until direction reverses
ATR-filtered validation to avoid weak or momentum-less breaks
Premium and discount bands derived from breakout structure
Optional band-driven stop placement
Optional band-dependent position-sizing factor
Regime timeout system to neutralize structure after extended inactivity
Persistent-state architecture to prevent re-triggering
Practical effect
Only the earliest actionable structure change is traded
Fewer but higher-quality signals
Premium/discount tint assists contextual evaluation
Stops and sizing can be aligned with structural context rather than arbitrary volatility measures
Improved chart interpretability due to reduced marker frequency
How it works (technical)
The algorithm evaluates symmetric swing points using a fixed bar window. When a swing forms, its value and bar index are stored as persistent state. A structural shift occurs when price closes beyond the most recent major swing on the opposite side. If ATR filtering is enabled, the breakout must exceed a volatility-scaled distance to prevent micro-breaks from firing.
Once a valid shift is confirmed, the regime is updated to bullish or bearish. The script records the breakout level, the opposite swing, and derives a band between them. This band is checked for minimum size relative to ATR to avoid unrealistic contexts.
The first shift in a new direction generates both the strategy entry and a visual marker. Additional shifts in the same direction are suppressed until a reversal occurs. If a timeout is enabled, the regime resets after a specified number of bars without structural change, optionally clearing the band.
Stop placement, if enabled, uses either the opposite or same band edge depending on configuration. Position size is computed from account percentage and may optionally scale with the price-span-to-ATR relationship.
Parameter Guide
Market Structure
Swing length (default 5): Controls swing sensitivity. Lower values increase responsiveness.
Use ATR filter (default true): Requires breakouts to show momentum relative to ATR. Reduces false shifts.
ATR length (default 14): Volatility estimation for breakout and band validation.
Break ATR multiplier (default 1.0): Required breakout strength relative to ATR.
Premium/Discount Framework
Enable framework (default true): Activates premium/discount evaluation.
Persist band on timeout (default true): Keeps structural band after timeout.
Min band ATR mult (default 0.5): Rejects narrow bands.
Regime timeout bars (default 500): Neutralizes regime after inactivity.
Invert colors (default false): Color scheme toggle.
Visuals
Show zone tint (default true): Background shade in premium or discount region.
Show shift markers (default true): Display first-shift markers.
Execution and Risk
Risk per trade percent (default 1.0): Determines position size as account percentage.
Use band for size (default false): Scales size relative to band width behavior.
Flat on opposite shift (default true): Forces reversal behavior.
Use stop at band (default false): Stop anchored to band edges.
Stop band side: Chooses which band edge is used for stop generation.
Reading & Interpretation
A green background indicates discount conditions within the structural band; red indicates premium conditions. A green triangle below price marks the first bullish structural shift after a bearish regime. A red triangle above price marks the first bearish structural shift after a bullish regime.
When stops are active, the opposite band edge typically defines the protective level. Band width relative to ATR indicates how significant a structural change is: wider bands imply stronger volatility structure, while narrow bands may be suppressed by the minimum-size filter.
Practical Workflows & Combinations
Trend following: Use first-shift entries as initial regime confirmation. Add higher-timeframe trend filters for additional context.
Swing trading: Combine with simple liquidity or fair-value-gap concepts to refine entries.
Bias mapping: Use higher timeframes for structural regime and lower timeframes for execution within the premium/discount context.
Exit management: When using stops, consider ATR-scaling or multi-stage profit targets. When not using stops, reversals become the primary exit.
Behavior, Constraints & Performance
The strategy uses only confirmed swings and closed-bar logic, avoiding intrabar repaint. Pivot-based swings inherently appear after the pivot window completes, which is standard behavior. No higher-timeframe calls are used, preventing HTF-related repaint issues.
Persistent variables track regime and structural levels, minimizing recomputation. The maximum bars back setting is five-thousand. The design avoids loops and arrays, keeping performance stable.
Known limitations include limited signal density during consolidations, delayed swing confirmation, and sensitivity to extreme gaps that stretch band logic. ATR filtering mitigates some of these effects but does not eliminate them entirely.
Sensible Defaults & Quick Tuning
Fewer but stronger entries: Increase swing length or ATR breakout multiplier.
More responsive entries: Reduce swing length to capture earlier shifts.
More active band behavior: Lower the minimum band ATR threshold.
Stricter stop logic: Use the opposite band edge for stop placement.
Volatile markets: Increase ATR length slightly to stabilize behavior.
What this indicator is—and isn’t
Trendshift Strategy is a structural-regime trading engine that evaluates major directional shifts. It is not a complete trading system and does not include take-profit logic or prediction features. It does not attempt to forecast future price movement and should be used alongside broader market structure, volatility context, and disciplined risk management.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Trendshift [CHE]Trendshift — First-Shift Regime Turns with Premium/Discount Context
Summary
Trendshift highlights the first confirmed directional structure shift in price and overlays a premium or discount context based on the most recent structural range. It identifies the major swing levels, detects a regime transition when price closes beyond these levels with optional ATR-based conviction, and marks only the first shift per direction to reduce repetition and noise. The indicator then establishes a premium or discount band around the break and tints the background when price operates in either region. This produces a clean regime-aware view that emphasizes only the earliest actionable turn while maintaining contextual bias information.
Motivation: Why this design?
Conventional swing-based structure tools often fire repeated signals after each minor break, especially in volatile environments. This leads to cluttered charts and little informational value. Trendshift focuses on the core trading need: isolating the first confirmed change in directional structure and providing a premium or discount context after the break. By limiting signals to the initial flip and suppressing further markers until direction reverses again, the script reduces noise and highlights only the structural event that materially matters. The band logic further addresses the challenge of distinguishing contextual extremes and avoiding trades taken too late after a shift.
What’s different vs. standard approaches?
Baseline reference: Most structure indicators repeatedly plot every new break of a swing high or swing low.
Differences:
Only the first confirmed bullish or bearish shift is plotted until the opposite direction occurs.
ATR-filtered breakout validation to reduce false breaks during volatility spikes.
A reduced premium and discount band derived from the breakout candle and prior swing structure.
Tinted background for contextual positioning rather than explicit entry signals.
Practical effect:
Fewer but more meaningful shift markers.
Clear visual context of where price operates relative to the structural band.
Cleaner regime transitions and less chart clutter.
How it works (technical)
The indicator continuously evaluates major swing highs and lows using a symmetric window length. When a swing is confirmed, the script stores its price and bar index. A structure shift occurs when price closes beyond the most recent major swing in the opposite direction. Optional ATR filtering requires the breakout distance to exceed an ATR-scaled threshold.
Upon a confirmed shift, the script sets a regime state that remains active until a new shift or an optional timeout. It also establishes a structural band anchored between the breakout candle extremum and the prior opposite swing. The band informs the premium and discount boundaries, each representing a quarter subdivision.
Only the first shift event per direction generates a visual triangle marker. The band is validated by comparing its height to ATR to avoid extremely narrow structures. Background tinting activates whenever price resides within the premium or discount zones. Persistent variables maintain previous structural states and prevent re-triggering until direction reverses.
Parameter Guide
Swing length (default 5): Controls the number of bars used on each side of a swing. Smaller values are more reactive; larger values reduce noise.
Use ATR filter (default true): Requires breakout strength beyond the swing to exceed an ATR-scaled threshold. Disabling increases signal frequency.
ATR length (default 14): Controls volatility estimation for breakout filtering and band validation.
Break ATR multiplier (default 1.0): Higher values require stronger breakouts, reducing false shifts.
Enable framework (default true): Activates the premium and discount context logic.
Persist band on timeout (default true): Retains the current band after a regime timeout.
Min band size ATR mult (default 0.5): Rejects extremely small bands and prevents unrealistic tinting.
Regime timeout bars (default 500): Resets the regime after extended inactivity.
Invert colors (default false): Swaps premium and discount tint color assignments.
Show zone tint (default true): Toggles background shading.
Show shift markers (default true): Enables or disables the first-shift triangles.
Reading & Interpretation
A green or red tint signals that price is operating in the discount or premium region of the most recent structural band. These regions are derived from the breakout event and the prior swing. A green triangle below a bar indicates the first bullish structure shift after a bearish regime. A red triangle above a bar indicates the first bearish shift after a bullish regime. No further markers appear until direction reverses. When tint is active, price location within the band offers simple contextual bias without providing explicit entries.
Practical Workflows & Combinations
Trend following: Treat the first bullish marker as the earliest confirmation of a potential up-regime and the first bearish marker for a potential down-regime. Use price location relative to the premium and discount zones as context for continuation or mean-reversion setups.
Structure-based execution: Combine with simple swing highs and lows to refine entry points within discount after a bullish shift or within premium after a bearish shift.
Higher-timeframe overlays: Apply the indicator on higher timeframes to define macro structure, then trade on lower timeframes using the band as a contextual anchor.
Risk management: When price stays in premium during a bearish regime or in discount during a bullish regime, consider protective actions or position management adjustments.
Behavior, Constraints & Performance
The script uses only confirmed swing points and closed-bar conditions, so repainting from future bars does not occur except the inherent delay of pivot confirmation. No higher-timeframe security calls are used, avoiding HTF repaint paths.
Performance impact is minimal because the script uses no loops or arrays and relies on persistent variables. The maximum bars back setting is five-thousand, required for swing lookback. Known limitations include quiet behavior during long consolidations, occasional delayed recognition of shifts due to swing confirmation, and limited effectiveness during large market gaps where extremum logic may be distorted.
Sensible Defaults & Quick Tunin g
Increase the swing length for smoother trend shifts and fewer signals.
Decrease the swing length for more sensitivity.
Raise the ATR breakout multiplier to reduce noise in volatile markets.
Lower the band size requirement to make premium and discount zones more active on slower markets.
Extend the regime timeout for slow-moving assets.
What this indicator is—and isn’t
This tool is a structural regime-shift detector with contextual premium and discount shading. It is not a complete trading system and does not include entries, exits, or risk models. It does not predict future price movement. It should be combined with broader structure analysis, liquidity considerations, and risk management practices.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
SCALPING PRO V2 - INTERMÉDIANT (Dashboard + TP/SL + Alerts)//@version=5
indicator("SCALPING PRO V2 - INTERMÉDIANT (Dashboard + TP/SL + Alerts)", overlay=true, max_labels_count=500)
// ---------------- INPUTS ----------------
emaFastLen = input.int(9, "EMA Fast")
emaSlowLen = input.int(21, "EMA Slow")
atrLen = input.int(14, "ATR Length")
atrMultSL = input.float(1.2, "SL = ATR *")
tp1mult = input.float(1.0, "TP1 = ATR *")
tp2mult = input.float(1.5, "TP2 = ATR *")
tp3mult = input.float(2.0, "TP3 = ATR *")
minBars = input.int(3, "Min bars between signals")
showDashboard = input.bool(true, "Show Dashboard")
// ---------------- INDICATORS ----------------
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
atr = ta.atr(atrLen)
bullTrend = emaFast > emaSlow
bearTrend = emaFast < emaSlow
crossUp = ta.crossover(emaFast, emaSlow) and bullTrend
crossDown = ta.crossunder(emaFast, emaSlow) and bearTrend
var int lastSignal = na
okSignal = na(lastSignal) or (bar_index - lastSignal > minBars)
buySignal = crossUp and okSignal
sellSignal = crossDown and okSignal
if buySignal or sellSignal
lastSignal := bar_index
// ---------------- TP & SL ----------------
var float sl = na
var float tp1 = na
var float tp2 = na
var float tp3 = na
if buySignal
sl := close - atr * atrMultSL
tp1 := close + atr * tp1mult
tp2 := close + atr * tp2mult
tp3 := close + atr * tp3mult
if sellSignal
sl := close + atr * atrMultSL
tp1 := close - atr * tp1mult
tp2 := close - atr * tp2mult
tp3 := close - atr * tp3mult
// ---------------- ALERTS ----------------
alertcondition(buySignal, title="BUY", message="BUY Signal")
alertcondition(sellSignal, title="SELL", message="SELL Signal")
alertcondition(ta.cross(close, tp1), title="TP1", message="TP1 Hit")
alertcondition(ta.cross(close, tp2), title="TP2", message="TP2 Hit")
alertcondition(ta.cross(close, tp3), title="TP3", message="TP3 Hit")
alertcondition(ta.cross(close, sl), title="SL", message="Stop Loss Hit")
// ---------------- DASHBOARD ----------------
if showDashboard
var table dash = table.new(position.top_right, 1, 5)
if barstate.islast
table.cell(dash, 0, 0, "SCALPING PRO V2", bgcolor=color.new(color.black, 0), text_color=color.white)
table.cell(dash, 0, 1, "Trend: " + (bullTrend ? "Bull" : bearTrend ? "Bear" : "Neutral"))
table.cell(dash, 0, 2, "ATR: " + str.tostring(atr, format.mintick))
table.cell(dash, 0, 3, "Last Signal: " + (buySignal ? "BUY" : sellSignal ? "SELL" : "NONE"))
table.cell(dash, 0, 4, "EMA Fast/Slow OK")
均线变色K线系统 with 转折箭头//@version=6
indicator("均线变色K线系统 with 转折箭头", overlay=true, max_lines_count=500, max_labels_count=200)
// 输入参数
ma_length = input.int(20, title="均线周期", minval=1)
atr_filter = input.bool(true, title="启用ATR波动过滤")
atr_length = input.int(14, title="ATR周期", minval=1)
atr_multiplier = input.float(1.5, title="ATR波动阈值", minval=0.1, step=0.1)
show_arrows = input.bool(true, title="显示转折箭头")
candle_coloring = input.bool(true, title="启用K线变色")
// 计算均线和ATR
ma = ta.sma(close, ma_length)
atr_value = ta.atr(atr_length)
avg_atr = ta.sma(atr_value, atr_length)
// 判断均线方向和趋势转折点
ma_rising = ta.rising(ma, 1)
ma_falling = ta.falling(ma, 1)
// 使用更严格的趋势转折检测（避免repainting）
ma_rising_prev = ta.rising(ma, 2)
ma_falling_prev = ta.falling(ma, 2)
// 检测趋势转折点（确保只在K线收盘确认时检测）
trend_change_up = ma_rising and not ma_rising_prev and (not atr_filter or atr_value >= avg_atr * atr_multiplier)
trend_change_down = ma_falling and not ma_falling_prev and (not atr_filter or atr_value >= avg_atr * atr_multiplier)
// 设置颜色
ma_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255) // 红/蓝
candle_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
border_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
wick_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
// 绘制彩色均线
plot(ma, color=ma_color, linewidth=2, title="变色均线")
// 使用plotcandle绘制彩色K线
plotcandle(candle_coloring ? open : na,
candle_coloring ? high : na,
candle_coloring ? low : na,
candle_coloring ? close : na,
title="变色K线",
color = candle_color,
wickcolor = wick_color,
bordercolor = border_color,
editable = true)
// 绘制趋势转折箭头（只在K线确认时显示）
if show_arrows and barstate.isconfirmed
if trend_change_up
label.new(bar_index, low * 0.998, "▲",
color=color.rgb(0, 255, 0),
textcolor=color.white,
style=label.style_label_up,
yloc=yloc.price,
size=size.normal)
else if trend_change_down
label.new(bar_index, high * 1.002, "▼",
color=color.rgb(255, 0, 0),
textcolor=color.white,
style=label.style_label_down,
yloc=yloc.price,
size=size.normal)
// 背景色轻微提示（可选）
bgcolor(ma_rising ? color.new(color.red, 95) : color.new(color.blue, 95), title="趋势背景提示")
P&F Label Overlay🧙 The Wizard's Challenge: P&F Label Overlay on Your Chart
This is a custom Pine Script indicator designed to overlay the classic Point & Figure (P&F) pattern directly onto your standard candlestick or bar chart. While Pine Script offers built-in, dedicated P&F charts, this indicator was created as a challenging and experimental project to satisfy the goal of visualizing the P&F X's and O's directly on the price bars.
❶. How to Use This Code on TradingView
1. Copy the Code: Copy the entire Pine Script code provided above.
2. Open Pine Editor: On TradingView, click the "Pine Editor" tab at the bottom of your chart.
3. Replace Content: Delete any existing code in the editor and paste this P&F script.
4. Add to Chart: Click the "Add to Chart" button (usually located near the top right of the Pine Editor).
5. Access Settings: The indicator, named "P&F Label Overlay: The Wizard's Challenge," will appear on your chart. You can click the gear icon ( ) next to its name on the chart to adjust the inputs.
❷. 🎛️ Key Settings & Customization (Inputs)
The indicator provides core P&F settings that you can customize to fit your analysis style:
✅ Enable Auto Box Size (Default: True): When enabled, the box size is automatically calculated as a percentage of the current price, making it dynamic.
・Auto Box Size Basis (%): Sets the percentage used to calculate the dynamic box size. (e.g., 2% of the price).
・Fixed Box Size (Price Units): When "Enable Auto Box Size" is disabled, this value is used as the static box size (e.g., a value of 5.0 for a $5 box).
・Reversal Count (Default: 3): This is the crucial P&F parameter (the Reversal Factor). It determines how many boxes the price must move in the opposite direction to trigger a column reversal (e.g., 3 means price must reverse by 3 \times \text{BOX\_SIZE}).
Calculation Source: Allows you to choose the price data used for calculations (e.g., close, hl2 (High/Low/2), etc.).
❸. 🎯 The Challenge: Why an Overlay?
This indicator represents an experimental and challenging endeavor made in collaboration with an AI. It stems from the strong desire to visualize P&F patterns overlaid on a conventional chart, which is not a native function in Pine Script's standard indicator plotting system.
・Leveraging Drawing Objects: The core of this challenge relies on cleverly using Pine Script's label.new() drawing objects to represent the "X" and "O" symbols. This is a highly non-standard way to draw a P&F chart, as it requires complex logic to manage the drawing coordinates, price levels, and column spacing on the time-based chart.
・The Current Status: We've successfully achieved the initial goal: visualizing the X and O patterns as an overlay. Achieving a perfectly aligned, full-featured P&F chart (where columns align precisely, and price rounding is fully consistent across all columns) is far more complex, but this code provides a strong foundation.
❹. 🛠️ Technical Highlights
Tick-Precision Rounding: The custom function f_roundToTick() is critical. It ensures that the calculated box size and the lastPrice are always rounded to the nearest minimum tick size (syminfo.mintick) of the asset. This maintains high precision and accuracy relative to the market's price movements.
・Column Indexing: The currentColumnIndex variable is used to simulate the horizontal movement of P&F columns by adding a fractional offset to the bar_index when drawing the labels. This is what makes the pattern appear as distinct columns.
・Garbage Collection: The activeLabels array and the MAX_LABELS limit ensure that old labels are deleted (label.delete()) as new ones are drawn. This is a crucial performance optimization to prevent TradingView's label limit (500) from being exceeded and to maintain a smooth experience.
❺. 🚀 A Platform for Deep Customization
While TradingView's built-in indicators are excellent, every trader has their "personal best settings." View this code as a starting point for your own analytical environment. You can use it as a base to pursue deeper custom features, such as:
・Different drawing styles or colors based on momentum.
・Automated detection and signaling of specific P&F patterns (e.g., Double Top Buy, Triple Bottom Sell).
Please feel free to try it on your chart! We welcome any feedback you might have as we continue to refine this experimental overlay.
I do not speak English at all. Please understand that if you send me a message, I may not be able to reply, or my reply may have a different meaning. Thank you for your understanding.
Zonas de Liquidez Pro + Puntos de GiroRequirements for marking 💧:✅ High crosses the zone✅ Close returns inside (false breakout / fakeout)✅ Volume is 20% greater than the average✅ Occurs within the last 10 bars(Note: This last requirement is stated in the text but not explicitly in the code snippet provided)📚 Psychology Behind the SweepWho lost money?Traders with stops placed too tightlyBuyers who entered "on the breakout"Bots with automatic orders placed aboveWho made money?Smart Money / InstitutionsThey sold at a high priceThey hunted for liquidity before moving the priceThey know where retail stops are located🎯 How to Use the Drops in Your TradingGolden Rule:💧 near a strong zone + Multiple rejections = PROBABLE REVERSALStrategy:See 💧 at resistance → Look for SHORTSee 💧 at support → Look for LONGPrice returns to the swept zone → High-probability setupStop beyond the sweep high/low → ProtectionPractical Example:If you see 💧 LIQ at $111,263 (resistance)→ Wait for bearish rejection→ Entry: Sell at $110,800→ Stop: $111,500 (above the sweep high)→ Target: Next support level⚠️ Common Mistakes❌ Mistake 1: Trading the breakoutPrice breaks $111k → "It's going to the moon!" → Buy💧 LIQ appears → It was a trap → Drop → Loss✅ Correct Approach:Price breaks $111k → Check if there is 💧 LIQ💧 appears → "It's a trap" → Wait for rejection → Sell❌ Mistake 2: Ignoring the volumeNot all sweeps are equal.Sweeps with high volume are more reliable.No volume = it could be noise.🎓 Ultra-Fast SummaryElementMeaning💧 LIQLiquidity sweep detectedAt ResistanceBullish trap → Prepare for a shortAt SupportBearish trap → Prepare for a longWith High VolumeMore reliable signalNear Strong Zone High probability of reversal🔥 The Magic of Your IndicatorScenarioWithout this IndicatorWith this IndicatorAction"The price broke $111k, I'm buying!""There is 💧 LIQ + zone + rejections → It's a trap."ResultYou loseYou avoid a loss or gain on the short
Smart Adaptive Double Patterns [The_lurker]Smart Adaptive Double Patterns
This is an advanced technical indicator that combines two of the strongest and most renowned classical price reversal patterns:
✅ Double Bottom Pattern — a bullish reversal pattern that forms after a downtrend
✅ Double Top Pattern — a bearish reversal pattern that forms after an uptrend
The indicator does not merely detect patterns — it provides a fully integrated, intelligent system that includes:
✅ Precise quality scoring for each pattern using 5 technical criteria
✅ Automatic price target calculation at three levels (Conservative, Balanced, Aggressive)
✅ Multi-layer dynamic filtering to avoid false signals
✅ Live pattern tracking from formation to target achievement or failure
✅ Comprehensive alert system covering all possible trading scenarios
🎯 Why Is This Indicator Unique?
1️⃣ High Detection Accuracy
Unlike traditional indicators that rely on simple rules, this one applies 5 strict structural conditions to confirm pattern validity:
A clear trend must precede the pattern
High symmetry between the two bottoms or two tops
No break of critical price levels during formation
Logical spacing between key points
Technical confirmation from ADX, ATR, and Volume
2️⃣ Advanced Quality Scoring System
Each pattern is scored out of 100 based on 5 weighted criteria:
Symmetry (30%): How closely the two bottoms or tops match
Trend Strength (20%): Strength of the prior trend
Volume Behavior (20%): Trading activity at critical points
Pattern Depth (15%): Vertical distance between neckline and bottom/top
Structural Integrity (15%): Full compliance with structural rules
3️⃣ Smart Target Management
Separate targets for bullish (Double Bottom) and bearish (Double Top) patterns
Separate projections for success and failure cases
Multiple options: Conservative (0.618) / Balanced (1.0) / Aggressive (1.618)
Live tracking with dynamic moving lines
4️⃣ Professional Failure Handling
Failed patterns are not ignored — they are turned into counter-trend opportunities:
Failed Double Bottom → triggers a bearish signal with downside targets
Failed Double Top → triggers a bullish signal with upside targets
Automatic color change for clear visual distinction
5️⃣ Full Customization Flexibility
Enable/disable each pattern independently
22+ adjustable settings
Unique colors for each pattern and quality level
Full bilingual support (Arabic / English)
📐 Pattern Details
🟦 Double Bottom Pattern
Sequence of points:
🔹 Point 1: Peak marking the start of a strong downtrend
🔹 Point 2 (Bottom 1): First low — first key bounce
🔹 Point 3: Intermediate high — forms the neckline (resistance)
🔹 Point 4 (Bottom 2): Second low — should closely match Bottom 1
🔹 Point 5: Breakout point — pattern confirmation
Mandatory Conditions:
✅ Clear downtrend before Point 2
✅ Bottoms 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 higher than both bottoms
✅ Neither bottom is broken during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks above the neckline (Point 3)
→ Point 5 is plotted at breakout candle
→ Dashed vertical line drawn from Point 5 to target
→ Horizontal dashed line tracks price toward target
→ Dashboard shows: Pattern Type | Quality | Rating | Target | Status
→ When target hits: line turns green + ✅ appears
🎯 Target Calculation
Pattern Height = Point 3 − Point 4
• Conservative: Point 3 + (Height × 0.618 × Quality Factor)
• Balanced: Point 3 + (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 + (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks below both Bottom 1 or Bottom 2 before neckline breakout
Visual Changes:
All lines turn red
Red ✖ appears at breakdown candle
Neckline stops expanding
Red dashed vertical line from breakdown point to bearish target
Red horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Bottom – Bearish
→ Shows new bearish target
→ Indicates target mode for failure case
→ Status: Bearish Reversal
→ Fully red display
🟥 Double Top Pattern
Sequence of points:
🔹 Point 1: Trough marking the start of a strong uptrend
🔹 Point 2 (Top 1): First peak — first key resistance
🔹 Point 3: Intermediate low — forms the neckline (support)
🔹 Point 4 (Top 2): Second peak — should closely match Top 1
🔹 Point 5: Breakdown point — pattern confirmation
Mandatory Conditions:
✅ Clear uptrend before Point 2
✅ Tops 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 lower than both tops
✅ Neither top is breached during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks below the neckline (Point 3)
→ Point 5 is plotted at breakdown candle
→ Dashed vertical line drawn to target
→ Horizontal tracking line moves with price
→ Dashboard updates accordingly
→ Green line + ✅ on hit
🎯 Target Calculation
Pattern Height = Point 4 − Point 3
• Conservative: Point 3 − (Height × 0.618 × Quality Factor)
• Balanced: Point 3 − (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 − (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks above either Top 1 or Top 2 before neckline breakdown
Visual Changes:
All lines turn cyan (light blue)
Cyan ✖ appears at breakout candle
Neckline stops expanding
Cyan dashed vertical line to bullish target
Cyan horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Top – Bullish
→ Shows new bullish target
→ Indicates target mode for failure case
→ Status: Bullish Reversal
→ Fully cyan display
🎯 Upside Target (after Double Top failure)
Max Top = max(Point 2, Point 4)
Height = Max Top − Point 3
• Conservative: Max Top + (Height × 0.618)
• Balanced: Max Top + (Height × 1.0)
• Aggressive: Max Top + (Height × 1.618)
📊 Quality Scoring System (0–100)
1️⃣ Symmetry (30%)
Measures price match between the two bottoms or two tops.
High score (25–30): Near-perfect symmetry → very strong pattern
Medium (15–24): Good match → reliable signal
Low (5–14): Weak symmetry → use caution
Zero: No symmetry → invalid pattern
2️⃣ Trend Strength (20%)
Uses ADX and DI indicators.
20 pts: Strong trend confirmed (e.g., ADX ≥ 20 + correct DI alignment)
10 pts: Trend filter disabled
6 pts: Weak or sideways trend
3️⃣ Volume Behavior (20%)
Declining volume on second touch is a positive sign (shows exhaustion).
15–20 pts: Clear volume drop → strong signal
10 pts: Neutral volume
6 pts: Rising volume → higher risk of continuation
4️⃣ Pattern Depth (15%)
Deeper patterns = stronger reversals.
12–15 pts: Deep → high reversal power
8–11 pts: Medium → acceptable
<8 pts: Shallow → weak signal
5️⃣ Structural Integrity (15%)
Checks logical structure (e.g., Point 1 > Point 3 in Double Bottom).
12–15 pts: Ideal structure
8–11 pts: Minor flaws
<8 pts: Poor setup
📈 Final Quality Rating & Colors
• 85–100 → ⭐ Excellent
→ Double Bottom: Cyan #00BCD4
→ Double Top: Light Red #FF5252
• 75–84 → ✨ Very Good
• 65–74 → ✓ Good
• 60–64 → ○ Acceptable
→ All use Amber #FFC107
• <60 → ❌ Rejected (not shown)
→ Gray #9E9E9E
🔧 Dynamic Filters
1️⃣ ATR Filter (Volatility Check)
Rejects patterns in abnormally high volatility periods.
→ If current ATR > 1.8 × 50-period ATR MA → pattern rejected
✅ Recommended for crypto, small caps
❌ Optional for calm markets (gold, bonds)
2️⃣ ADX Filter (Trend Confirmation)
Ensures a real trend exists before the pattern.
→ If ADX < 14 (70% of default 20) → pattern rejected
✅ Strongly recommended (keep ON)
3️⃣ Volume Filter (Behavior Validation)
Not used to reject patterns, but strongly affects quality score.
✅ Best for liquid markets (Forex majors, large stocks)
❌ Optional for illiquid assets
⚙️ Key Settings Explained
🔘 General Settings
• Language: Arabic / English
• Show Previous Patterns: Yes / No
→ “No” keeps chart clean; “Yes” for historical review
🔘 Pattern Selection
• Enable Double Bottom: ✅ / ❌
• Enable Double Top: ✅ / ❌
→ Use combinations:
✅✅ → Full reversal scanning
✅❌ → Long setups only
❌✅ → Short setups only
❌❌ → Indicator OFF
🔘 Detection Parameters
• Pivots Left (1–20): Higher = more reliable, fewer patterns
• Pivots Right (1–20): Lower = faster signals
• Min Width (5–100): Min candles between Bottom/Top 1 & 2
• Tolerance % (0.1%–5%): Max allowed price difference
• Min Arm (5–50): Min candles between pivot & neckline
• Min Trend (5–50): Min candles in prior trend
• Trend Lookback (50–500): How far back to detect trend start
• Extension Multiplier (1.0–5.0): How long to wait for breakout
🔘 Quality Settings
• Min Quality Score (0–100):
→ Conservative: 75–85
→ Balanced: 60–70
→ Flexible: 50–55
• Custom Weights: Adjust based on market (e.g., increase Volume weight in Forex)
🔘 Target Settings
• Bottom Bullish Target: Conservative / Balanced / Aggressive
• Bottom Bearish Target: (used on failure)
• Top Bearish Target: Conservative / Balanced / Aggressive
• Top Bullish Target: (used on failure)
🔘 Visual Settings
• Label Size: Small / Normal / Large / Huge
• Pattern Colors: Fully customizable
• Table: Show/Hide + Size (Small/Normal/Large) + Position (Top-Right / Top-Left / Bottom-Right / Bottom-Left)
• Fill Transparency: 70%–95% (default: 85%)
🔔 Alert System (8 Independent Alerts)
📌 Double Bottom Alerts
Bullish Breakout → “Double Bottom Breakout – Bullish!”
Bullish Target Hit → “Bullish Target Achieved!”
Failure (Bearish) → “Double Bottom Failed – Bearish!”
Bearish Target Hit → “Bearish Target Achieved (Failure)!”
📌 Double Top Alerts
Bearish Breakdown → “Double Top Breakdown – Bearish!”
Bearish Target Hit → “Bearish Target Achieved!”
Failure (Bullish) → “Double Top Failed – Bullish!”
Bullish Target Hit → “Bullish Target Achieved (Failure)!”
Each alert can be enabled/disabled independently and supports pop-ups, emails, or webhooks.
⚠️ Disclaimer:
This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses.
INDIVIDUAL ASSET BIAS DASHBOARD V3Strategy Name: Individual Asset Bias Dashboard V3
Author Concept: Multi-timeframe 3-pivot alignment bias monitor
Timeframe: Works on any chart, but bias is calculated on daily close vs higher timeframe pivots
Core Idea (3-Pivot Rule)
For each asset we compare the current daily closing level against three classic pivots from higher timeframes:
Previous Weekly pivot: (H+L+C)/3 of last completed week
Previous Monthly pivot: (H+L+C)/3 of last completed month
Previous 3-Monthly pivot: (H+L+C)/3 of last completed quarter
Bias Logic:
BULL → Price is above all three pivots
BEAR → Price is below all three pivots
MIXED → Price is in between (no clear alignment)
This is a clean, objective, and widely used institutional method to gauge short-term momentum alignment across multiple horizons.
Assets Tracke
SymbolMeaningSPX500S&P 500 IndexVIXVolatility IndexDXYUS Dollar IndexBTCUSDBitcoinXAUUSDGoldUSOILWTI Crude OilUS10Y10-Year US Treasury YieldUSDJPYJapanese Yen pair
Key Features
Real-time updating table in the bottom-left corner
Color coding: Lime = Bullish, Red = Bearish, Gray = Mixed
Optional "Change" column showing flips (▲/▼) when bias changes day-over-day
No repainting on closed daily bars (critical for reliability)
Compliant with TradingView rules (proper lookahead usage explained below)
Important Technical Notes (Why No Repainting)
lookahead = barmerge.lookahead_on is used only for higher-timeframe historical pivots → allowed and standard practice
Current price uses lookahead = barmerge.lookahead_off → reflects actual tradable daily close
Table only draws on barstate.islastconfirmedhistory or barstate.islast → prevents false signals on realtime bar
Limitations & Warnings
On intraday charts, the "current bias" updates with every tick using the running daily close
Bias can flip intraday before daily bar closes
On daily or higher charts, the dashboard is 100% confirmation-based and non-repainting
This is a bias filter, not a standalone trading system
DeepFlow Zones SNIPER# DeepFlow Zones SNIPER - Documentation & Cheatsheet
## 🎯 DeepFlow Zones - SNIPER Edition
**Horizontal Limit Order Zones | Institutional FVG + Single Prints**
> **Philosophy:** *Only mark the zones where institutions MUST have orders. Everything else is noise.*
---
## ⚡ QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ DEEPFLOW ZONES SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 ZONE CREATION REQUIREMENTS (ALL MUST BE TRUE): │
│ ══════════════════════════════════════════════════ │
│ ✓ FVG exists → Gap between candle low and 2-bar-ago high │
│ ✓ Gap Size → At least 30% of ATR (significant gap) │
│ ✓ Impulse Candle → 1.8x average range + 65% body ratio │
│ ✓ Volume → 2.0x+ average on impulse candle │
│ ✓ Direction → Middle candle confirms gap direction │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 ZONE TYPES: │
│ ══════════════ │
│ 🟢 BULLISH ZONE → Green box BELOW price (buy zone) │
│ 🔴 BEARISH ZONE → Red box ABOVE price (sell zone) │
│ ⚫ TESTED ZONE → Gray box (CE level touched) │
│ ⬛ BROKEN ZONE → Dark gray (price closed through) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⭐ SINGLE PRINT LINES: │
│ ══════════════════════ │
│ Requirements: │
│ • Range 1.8x+ average │
│ • Body 65%+ of range │
│ • Volume 2.0x+ average │
│ • Delta 60%+ confirms direction │
│ │
│ Usage: │
│ • Gold lines at HIGH and LOW of impulse candle │
│ • Price often returns to these levels │
│ • Use as support/resistance for entries │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 ENTRY SIGNALS: │
│ ═══════════════════ │
│ BUY🎯 appears when: │
│ • Price is inside BULLISH zone │
│ • Delta shows 60%+ buy dominance │
│ • Volume is 1.5x+ average │
│ │
│ SELL🎯 appears when: │
│ • Price is inside BEARISH zone │
│ • Delta shows 60%+ sell dominance │
│ • Volume is 1.5x+ average │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📐 ZONE ANATOMY: │
│ ═════════════════ │
│ │
│ BULLISH FVG ZONE: BEARISH FVG ZONE: │
│ │
│ Current Low ───────────────── ───────────────── 2-bar-ago Low │
│ ┌─────────────────────────┐ ┌─────────────────────────┐ │
│ │ █████ ZONE █████████████│ │ █████ ZONE █████████████│ │
│ │- - - CE (50%) - - - - - │ │- - - CE (50%) - - - - - │ │
│ │ ████████████████████████│ │ ████████████████████████│ │
│ └─────────────────────────┘ └─────────────────────────┘ │
│ 2-bar-ago High ────────────── ───────────────── Current High │
│ │
│ Entry: At or near CE line Entry: At or near CE line │
│ Stop: Below zone bottom Stop: Above zone top │
│ Target: 1:1 or 2:1 R:R Target: 1:1 or 2:1 R:R │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ ZONE IS INVALID WHEN: │
│ ═════════════════════════ │
│ ✗ Gap size < 30% of ATR (too small) │
│ ✗ No impulse candle (weak move) │
│ ✗ Volume < 2x average (retail move) │
│ ✗ Zone age > 50 bars (stale) │
│ ✗ Price already closed through zone │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 DETAILED DOCUMENTATION
### What Makes SNIPER Zones Different?
Standard FVG indicators create zones everywhere. SNIPER zones only appear when there's **institutional footprint**:
| Filter | Standard FVG | SNIPER Zones | Why It Matters |
|--------|-------------|--------------|----------------|
| Gap Size | Any gap | **≥30% ATR** | Significant imbalance |
| Volume | Optional | **2.0x+ avg** | Institutional volume |
| Impulse | None | **1.8x range** | Real momentum |
| Body | None | **65%+ ratio** | Conviction candle |
| Max Zones | 20-50 | **10 max** | Only the best |
| Zone Life | 100 bars | **50 bars** | Fresh zones only |
---
### How Zones Are Created
```
BULLISH FVG FORMATION:
═══════════════════════
Bar 0 (2 bars ago): Bar 1 (Impulse): Bar 2 (Current):
┌─────┐ ┌─────┐ ┌─────┐
│ │ │█████│ │ │
│ │ HIGH ────── │█████│ │ │
│ │ │ │█████│ │ │
└─────┘ │ │█████│ │ │── LOW
│ └─────┘ └─────┘
│ │
└──────── GAP ────────────────┘
(FVG ZONE)
Requirements Met:
✓ Current LOW > 2-bar-ago HIGH (gap exists)
✓ Gap ≥ 30% of ATR (significant)
✓ Bar 1 range ≥ 1.8x average (impulse)
✓ Bar 1 body ≥ 65% of range (conviction)
✓ Bar 1 volume ≥ 2x average (institutional)
✓ Bar 1 was bullish (direction confirms)
RESULT: VALID SNIPER BULLISH ZONE CREATED
```
---
### Single Print Lines Explained
Single Prints mark **institutional impulse candles** where price moved so fast that no orders were filled at those levels. These levels often act as magnets for price.
```
SINGLE PRINT CANDLE:
════════════════════
HIGH ═══════════════════════════════ (Gold Line)
│
┌─────────────────┤
│█████████████████│ ← Large body (65%+)
│█████████████████│ ← Strong volume (2x+)
│█████████████████│ ← Clear delta (60%+)
│█████████████████│
└─────────────────┤
│
LOW ═══════════════════════════════ (Gold Line)
These horizontal lines extend 500 bars into the future.
Price often returns to test these levels.
```
---
### Entry Strategy
#### Zone Entry Checklist
```
□ Zone is active (green/red, not gray)
□ Price enters zone from outside
□ Wait for entry signal (BUY🎯 or SELL🎯)
□ Verify: Delta + Volume confirming
□ Enter at CE line (dotted white line)
□ Stop below/above zone
□ Target: Opposite side of zone (1:1) or 2:1
```
#### Single Print Entry
```
□ Price returns to single print level
□ Look for reaction (rejection candle)
□ Combine with GRA signal if possible
□ Enter on confirmation candle
□ Stop beyond the single print line
```
---
### Table Legend
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom, ⚪ Neutral |
| **Vol** | Volume ratio | 🟢 ≥2x, ⚪ <2x |
| **Buy ⬚** | Active buy zones | Count of bullish zones |
| **Sell ⬚** | Active sell zones | Count of bearish zones |
| **Zone** | Current position | AT BUY / AT SELL / --- |
| **Impulse** | Current bar status | 🟡 Yes (impulse), ⚫ No |
---
### Zone States
| State | Visual | Meaning | Action |
|-------|--------|---------|--------|
| **Fresh** | Bright color | Never tested | Best entries |
| **Tested** | Gray | CE touched | Still valid, less reliable |
| **Broken** | Dark gray | Price closed through | Invalid, ignore |
---
### Integration with GRA v5
The magic happens when you combine both indicators:
```
HIGHEST PROBABILITY SETUP:
══════════════════════════
1. DeepFlow shows active zone (green/red box)
2. Price enters the zone
3. GRA5 fires a signal INSIDE the zone
4. Delta confirms on both indicators
5. Volume confirms on both indicators
This is your SNIPER entry. Take it.
Example:
┌─────────────────────────────────────────┐
│ Price enters BULLISH zone │
│ GRA5 shows: A🎯 LONG │
│ DFZ shows: BUY🎯 │
│ Table: Vol 2.1x, Delta 67%B │
│ │
│ ACTION: Full size LONG at CE │
│ STOP: Below zone bottom │
│ TARGET: 2:1 R:R │
└─────────────────────────────────────────┘
```
---
### Settings by Instrument
| Instrument | Vol Mult | Gap ATR | Impulse | Max Zones |
|------------|----------|---------|---------|-----------|
| **NQ/ES** | 2.0x | 30% | 1.8x | 10 |
| **YM** | 2.0x | 30% | 1.8x | 10 |
| **GC** | 2.5x | 40% | 2.0x | 8 |
| **BTC** | 2.0x | 25% | 1.5x | 10 |
---
### Common Mistakes
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading every zone | Most zones fail | Wait for entry signal |
| Entering at zone edge | Wrong R:R | Enter at CE (middle) |
| Ignoring broken zones | Already invalidated | Gray = don't trade |
| No delta confirmation | Could be false zone | BUY🎯/SELL🎯 required |
| Too many zones | Chart noise | Max 10 zones |
---
### Alert Configuration
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 BUY/SELL ZONE ENTRY | 🔴 High | Check chart immediately |
| NEW BULL/BEAR ZONE | 🟠 Medium | Note new zone location |
| 🎯 SINGLE PRINT | 🟢 Low | Mark potential S/R |
---
### Pine Script v6 Notes
This indicator uses Pine Script v6 features:
- Array-based zone management
- `request.security_lower_tf()` for delta
- Dynamic zone state tracking
- Efficient garbage collection
**Minimum TradingView Plan:** Pro (for intrabar data)
---
## 🏆 Golden Rules
1. **Fewer zones = Better zones.** If you see more than 5 active zones, your settings are too loose.
2. **Fresh zones > Tested zones.** The first touch is always the best.
3. **CE is king.** The middle of the zone (50% level) is your entry point.
4. **Zone + GRA signal = Sniper entry.** This confluence is what we're hunting for.
5. **Gray zones don't exist.** Once broken, pretend the zone was never there.
---
*© Alexandro Disla - DeepFlow Zones SNIPER*
*Pine Script v6 | TradingView*
GRA v5 SNIPER# GRA v5 SNIPER - Documentation & Cheatsheet
## 🎯 Get Rich Aggressively v5 - SNIPER Edition
**Precision Futures Scalping | NQ • ES • YM • GC • BTC**
> **Philosophy:** *Quality over quantity. One sniper shot beats ten spray-and-pray attempts.*
---
## ⚡ QUICK CHEATSHEET
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ GRA v5 SNIPER - QUICK REFERENCE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🎯 SIGNAL REQUIREMENTS (ALL MUST BE TRUE): │
│ ═══════════════════════════════════════════ │
│ ✓ Tier → B minimum (20+ pts NQ) │
│ ✓ Volume → 1.5x+ average │
│ ✓ Delta → 60%+ dominance (buyers OR sellers) │
│ ✓ Body → 70%+ of candle range │
│ ✓ Range → 1.3x+ average candle size │
│ ✓ Wicks → Small opposite wick (<50% of body) │
│ ✓ CVD → Trending with signal direction │
│ ✓ Session → London (3-5am ET) OR NY (9:30-11:30am ET) │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📊 TIER ACTIONS: │
│ ════════════════ │
│ S-TIER (100+ pts) → 🥇 HOLD position, ride the wave │
│ A-TIER (50-99 pts) → 🥈 SWING for 2-3 minutes │
│ B-TIER (20-49 pts) → 🥉 SCALP quick, 30-60 seconds │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 🚨 ENTRY CHECKLIST: │
│ ═══════════════════ │
│ □ Signal appears (S🎯, A🎯, or B🎯) │
│ □ Table shows: Vol GREEN, Delta colored, Body GREEN │
│ □ CVD arrow matches direction (▲ for long, ▼ for short) │
│ □ Session active (LDN! or NY! in yellow) │
│ □ Enter at close of signal candle │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ⛔ DO NOT TRADE WHEN: │
│ ════════════════════ │
│ ✗ Session shows "---" (outside key hours) │
│ ✗ Vol shows RED (below 1.5x) │
│ ✗ Body shows RED (weak candle structure) │
│ ✗ Delta below 60% (no clear dominance) │
│ ✗ Multiple conflicting signals │
│ │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ 📈 INSTRUMENT SETTINGS: │
│ ════════════════════════ │
│ NQ/ES (1-3 min): S=100, A=50, B=20 pts │
│ YM (1-5 min): S=100, A=50, B=25 pts │
│ GC (5-15 min): S=15, A=8, B=4 pts │
│ BTC (1-15 min): S=500, A=250, B=100 pts │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
---
## 📋 DETAILED DOCUMENTATION
### What Makes SNIPER Different?
The SNIPER edition eliminates 80%+ of signals compared to standard GRA. Every signal that passes through has been validated by **8 independent filters**:
| Filter | Standard GRA | SNIPER GRA | Why It Matters |
|--------|-------------|------------|----------------|
| Volume | 1.3x avg | **1.5x avg** | Institutional participation |
| Delta | 55% | **60%** | Clear buyer/seller control |
| Body Ratio | None | **70%+** | No dojis or spinners |
| Range | None | **1.3x avg** | Significant price movement |
| Wicks | None | **<50% body** | Conviction in direction |
| CVD | None | **Required** | Trend confirmation |
| B-Tier Min | 10 pts | **20 pts** | Filter noise |
| Session | Optional | **Required** | Institutional hours |
---
### Signal Anatomy
When you see a signal like `A🎯`, here's what passed validation:
```
Signal: A🎯 LONG at 21,450.00
Validation Breakdown:
├── Points: 67.5 pts ✓ (A-Tier = 50-99)
├── Volume: 2.1x avg ✓ (≥1.5x required)
├── Delta: 68% Buyers ✓ (≥60% required)
├── Body: 78% of range ✓ (≥70% required)
├── Range: 1.6x avg ✓ (≥1.3x required)
├── Wick: Upper 15% ✓ (<50% of body)
├── CVD: ▲ Rising ✓ (Matches LONG)
└── Session: NY! ✓ (Active session)
RESULT: VALID SNIPER SIGNAL
```
---
### Table Legend
| Field | Reading | Color Meaning |
|-------|---------|---------------|
| **Pts** | Point movement | Gold/Green/Yellow = Tiered |
| **Tier** | S/A/B/X | Gold/Green/Yellow/White |
| **Vol** | Volume ratio | 🟢 ≥1.5x, 🔴 <1.5x |
| **Delta** | Buy/Sell % | 🟢 Buy dom, 🔴 Sell dom, ⚪ Neutral |
| **Body** | Body % of range | 🟢 ≥70%, 🔴 <70% |
| **CVD** | Cumulative delta | ▲ Bullish trend, ▼ Bearish trend |
| **Sess** | Session status | 🟡 Active, ⚫ Inactive |
---
### Trading Rules
#### Entry Rules
1. **Wait for signal** - Don't anticipate
2. **Verify table** - All conditions GREEN
3. **Enter at candle close** - Not during formation
4. **Position size by tier:**
- S-Tier: Full size
- A-Tier: 75% size
- B-Tier: 50% size
#### Exit Rules
| Tier | Target | Max Hold Time |
|------|--------|---------------|
| S | Let it run | 5-10 minutes |
| A | 1:1.5 R:R | 2-3 minutes |
| B | 1:1 R:R | 30-60 seconds |
#### Stop Loss
- Place at **opposite end of signal candle**
- For S-Tier: Allow 50% retracement
- For B-Tier: Tight stop, quick exit
---
### Session Priority
```
LONDON OPEN (3:00-5:00 AM ET)
════════════════════════════
• Best for: GC, European indices
• Characteristics: Stop hunts, reversals
• Look for: Sweeps of Asian session levels
NY OPEN (9:30-11:30 AM ET)
════════════════════════════
• Best for: NQ, ES, YM
• Characteristics: High volume, trends
• Look for: Continuation after 10 AM
```
---
### Common Mistakes to Avoid
| Mistake | Why It's Bad | Solution |
|---------|-------------|----------|
| Trading outside sessions | Low volume = fake moves | Wait for LDN! or NY! |
| Ignoring weak body | Dojis reverse | Body must be 70%+ |
| Fighting CVD | Swimming upstream | CVD must confirm |
| Oversizing B-Tier | Small moves = small size | 50% max on B |
| Chasing missed signals | FOMO loses money | Wait for next setup |
---
### Alert Setup
Configure these alerts in TradingView:
| Alert | Priority | Action |
|-------|----------|--------|
| 🎯 S-TIER LONG/SHORT | 🔴 High | Drop everything, check chart |
| 🎯 A-TIER LONG/SHORT | 🟠 Medium | Evaluate within 30 seconds |
| 🎯 B-TIER LONG/SHORT | 🟢 Low | Quick glance if available |
| LONDON/NY OPEN | 🔵 Info | Prepare for action |
---
### Pine Script v6 Notes
This indicator uses Pine Script v6 features:
- `request.security_lower_tf()` for intrabar delta
- Type inference for cleaner code
- Array operations for CVD calculation
**Minimum TradingView Plan:** Pro (for intrabar data)
---
## 🏆 Golden Rule
> **"If you have to convince yourself it's a good signal, it's not a good signal."**
The SNIPER edition is designed so that when a signal appears, there's nothing to think about. If all conditions are met, you trade. If any condition fails, you wait.
**Leave every trade with money. That's the goal.**
---
*© Alexandro Disla - Get Rich Aggressively v5 SNIPER*
*Pine Script v6 | TradingView*
Sideways & Breakout Detector + Forecast//@version=6
indicator("Sideways & Breakout Detector + Forecast", overlay=true, max_labels_count=500)
// Inputs
lengthATR = input.int(20, "ATR Länge")
lengthMA = input.int(50, "Trend MA Länge")
sqFactor = input.float(1.2, "Seitwärtsfaktor")
brkFactor = input.float(1.5, "Breakoutfaktor")
// ATR / Volatilität
atr = ta.atr(lengthATR)
atrSMA = ta.sma(atr, lengthATR)
// Basislinie / Trend
basis = ta.sma(close, lengthATR)
trendMA = ta.sma(close, lengthMA)
// Seitwärtsbedingung
isSideways = atr < atrSMA * sqFactor
// Breakouts
upperBreak = close > basis + atr * brkFactor
lowerBreak = close < basis - atr * brkFactor
// Vorhergesagter Ausbruch (Forecast)
// Wenn Seitwärtsphase + Kurs nahe obere oder untere Kanalgrenze
forecastBull = isSideways and (close > basis + 0.5 * atr)
forecastBear = isSideways and (close < basis - 0.5 * atr)
// Farben
barcolor(isSideways ? color.new(color.yellow, 40) : na)
barcolor(upperBreak ? color.green : na)
barcolor(lowerBreak ? color.red : na)
// Breakout-Bänder
plot(basis + atr * brkFactor, "Bull Break Zone", color=color.new(color.green, 60))
plot(basis - atr * brkFactor, "Bear Break Zone", color=color.new(color.red, 60))
// Labels (klein)
if isSideways
label.new(bar_index, close, "Seitwärts", color=color.yellow, style=label.style_label_center, size=size.tiny)
if upperBreak
label.new(bar_index, high, "Bull Breakout", color=color.green, style=label.style_label_up, size=size.tiny)
if lowerBreak
label.new(bar_index, low, "Bear Breakout", color=color.red, style=label.style_label_down, size=size.tiny)
// Vorhergesagte Ausbrüche markieren
plotshape(forecastBull, title="Forecast Bull", location=location.abovebar, color=color.new(color.green, 0), style=shape.triangleup, size=size.tiny)
plotshape(forecastBear, title="Forecast Bear", location=location.belowbar, color=color.new(color.red, 0), style=shape.triangledown, size=size.tiny)
// Alerts
alertcondition(isSideways, "Seitwärtsphase", "Der Markt läuft seitwärts.")
alertcondition(upperBreak, "Bull Breakout", "Ausbruch nach oben!")
alertcondition(lowerBreak, "Bear Breakout", "Ausbruch nach unten!")
alertcondition(forecastBull, "Forecast Bull", "Voraussichtlicher Bull-Ausbruch!")
alertcondition(forecastBear, "Forecast Bear", "Voraussichtlicher Bear-Ausbruch!")
Gould 10Y + 4Y patternDescription:
Overview This indicator is a comprehensive tool for macro-market analysis, designed to visualize historical market cycles on your chart. It combines Edson Gould’s famous Decennial Pattern with a Customizable 4-Year Cycle (e.g., 2002 base) to help traders identify long-term trends, potential market bottoms, and strong bullish years.
This tool is ideal for long-term investors and analysts looking for cyclical confluence on monthly or yearly timeframes (e.g., SPX, NDX).
Key Concepts
Edson Gould’s Decennial Pattern (10-Year Cycle)
Based on the theory that the stock market follows a psychological cycle determined by the last digit of the year.
5 (Strongest Bull): Historically the strongest performance years.
7 (Panic/Crash): Years often associated with market panic or crashes.
2 (Bottom/Buy): Years that often mark major lows.
Custom 4-Year Cycle (Target Year Strategy)
Identify recurring 4-year opportunities based on a user-defined base year.
Default Setting (Base 2002): Highlights years like 2002, 2006, 2010, 2014, 2018, 2022... which have historically been significant market bottoms or excellent buying opportunities.
When a "Target Year" arrives, the indicator highlights the background and displays a distinct Green "Target Year" Label.
Features
Real-time Dashboard: A table in the top-right corner displays the current year's status for both the 10-Year and 4-Year cycles, including a countdown to the next target year.
Dynamic Labels: Automatically marks every year on the chart with its Decennial status (e.g., "Strong Bull (5)", "Panic (7)").
Visual Highlighting:
Target Years: Distinct green background and labels for easy identification of the 4-year cycle.
Significant Decennial Years: Special small markers for years ending in 5 and 7.
Fully Customizable: You can change the base year for the 4-year cycle, toggle the dashboard, and adjust colors via the settings menu.
How to Use
Apply this indicator to high-timeframe charts (Weekly or Monthly) of major indices like S&P 500 or Nasdaq.
Look for confluence between the 10-Year Pattern (e.g., Year 6 - Bullish) and the 4-Year Cycle (Target Year) to confirm long-term bias.
Disclaimer This tool is for educational and research purposes only based on historical cycle theories. Past performance is not indicative of future results. Always manage your risk.
S&P 500 Breadth: Bull vs Bear (20DMA)Simple market breadth for S&P500 using percentage of stock above or below 20dma
🏛️ Inst. Value SuiteInstitutional Valuation Suite (IVS)
Executive Summary Traditional volatility indicators frequently exhibit limitations when applied to long-term secular growth assets. Because they calculate volatility in absolute currency units rather than percentage terms, standard deviation bands often distort or become obsolete during phases of exponential price expansion (e.g., significant capitalization shifts in Crypto or Growth Stocks).
The Institutional Valuation Suite addresses this latency by utilizing Geometric (Log-Normal) Standard Deviation. This methodology enables the model to adapt dynamically to the asset's price scale, providing statistically significant valuation zones regardless of price magnitude.
Operational Theory The model operates as a mean-reversion instrument, visualizing price action as a dynamic deviation from a "Fair Value" baseline. It quantifies statistical extremes to identify when an asset is overextended (Speculative Premium) or undervalued (Deep Discount) relative to historical volatility.
Key Features
1. Log-Normal Volatility Engine
Geometric Mode (Default): Calculates volatility in percentage terms. This is the requisite setting for assets exhibiting logarithmic growth, such as Cryptocurrencies and Technology equities.
Arithmetic Mode: Retains linear calculation methods for Forex pairs or range-bound assets where traditional standard deviation is preferred.
2. Valuation Heatmap
Visualizes valuation metrics directly onto price candles to mitigate subjective interpretation bias.
GREEN: Deep Value / Accumulation Zone (<−0.5σ).
ORANGE: Overvaluation / Premium Zone (>2.0σ).
RED: Speculative Anomaly Zone (>3.0σ).
3. Mean Reversion Signals
VALUE RECLAIM: Triggers when price re-enters the lower deviation band from below. This confirms support validation and filters out premature entries during high-momentum drawdowns.
TOP EXIT: Triggers when price breaks down from the upper speculative zone, signaling a potential trend exhaustion.
4. Statistical Dashboard
Displays a real-time Z-Score to quantify the standard deviations the current price is from its baseline.
>3.0: Statistical Anomaly (upper bound).
<−0.5: Statistical Discount (lower bound).
Configuration & Parameters
Per your requirements, the suggested code tooltips for your inputs are listed below.
Cycle Length
Determines the lookback period used to calculate the Fair Value baseline.
Crypto Macro: 200 (Approx. 4 Years).
Altcoins: 100 (Approx. 2 Years).
Equities (S&P 500): 50 (1 Year Trend).
Intraday: Set "Timeframe Lock" to "Chart".
Tooltip Text: "Sets the lookback period for the baseline calculation. Recommended: 200 for Crypto Macro, 50 for Equities, or adjust based on the asset's specific volatility cycle."
Timeframe Lock
Allows the user to fix the calculation to a specific timeframe or allow it to float with the chart.
Tooltip Text: "Locks the calculation to a specific timeframe (e.g., Daily, Weekly) to ensure baseline consistency when zooming into lower timeframes."
Technical Integrity
This indicator employs strict strict offset logic (barmerge.lookahead_on) to ensure historical data integrity. The signals rendered on historical bars are mathematically identical to those that would have appeared in a real-time environment, ensuring backtesting reliability.
Disclaimer: This script provides statistical analysis based on historical volatility metrics and does not constitute financial advice.
Fibonacci Degree System This Pine Script creates a sophisticated technical analysis tool that combines Fibonacci retracements with a degree-based cycle system. Here's a comprehensive breakdown:
Core Concept
The indicator maps price movements onto a 360-degree circular framework, treating market cycles like geometric angles. It creates a visual "mesh" where Fibonacci ratios intersect in both price (horizontal) and time (vertical) dimensions.
How It Works
1. Finding Reference Points
The script looks back over a specified period (default 100 bars) to identify:
Highest High: The peak price point
Lowest Low: The trough price point
Time Locations: Exactly which bars these extremes occurred on
These two points form the boundaries of your analysis window.
2. Creating the Fibonacci Grid
Horizontal Lines (Price Levels):
The script divides the price range between high and low into seven key Fibonacci ratios:
0% (Low) - Bottom boundary in red
23.6% - Minor retracement in orange
38.2% - Shallow retracement in yellow
50% - Midpoint in lime green
61.8% - Golden ratio in aqua (most significant)
78.6% - Deep retracement in blue
100% (High) - Top boundary in purple
Each line represents a potential support/resistance level where price might react.
Vertical Lines (Time Cycles):
The same Fibonacci ratios are applied to the time dimension between the high and low bars. If your high and low are 50 bars apart, vertical lines appear at:
Bar 0 (0%)
Bar 12 (23.6%)
Bar 19 (38.2%)
Bar 25 (50%)
Bar 31 (61.8%)
Bar 39 (78.6%)
Bar 50 (100%)
These suggest when price might make significant moves.
3. The Degree Mapping System
The innovative feature maps the time progression to degrees:
0° = Start point (0% time)
85° = 23.6% through the cycle
138° = 38.2% through the cycle
180° = Midpoint (50%)
222° = 61.8% through the cycle (golden angle)
283° = 78.6% through the cycle
360° = Complete cycle (100%)
This treats market movements as circular patterns, similar to how planets orbit or pendulums swing.
Visual Output
When you apply this indicator, you'll see:
A rectangular mesh extending beyond your high-low range (by 150% default)
Color-coded horizontal lines showing price Fibonacci levels
Matching vertical lines showing time Fibonacci intervals
Price labels on the right showing percentage levels
Degree labels at the bottom showing the angular position in the cycle
Intersection points creating a grid of potentially significant price-time coordinates
Trading Application
Traders use this to identify:
Support/Resistance Zones: Where horizontal and vertical lines intersect
Time Targets: When price might reverse (at vertical Fibonacci times)
Cycle Completion: When approaching 360°, a new cycle may begin
Harmonic Patterns: Geometric relationships between price and time
Customization Features
The script offers extensive control:
Lookback period: Adjust cycle length (10-500 bars)
Mesh extension: How far to project the grid forward
Visual toggles: Show/hide horizontal lines, vertical lines, labels
Styling: Line thickness, style (solid/dashed/dotted), colors
Label positioning: Fine-tune text placement for readability
The intersection at 61.8% time and 61.8% price at 222° becomes a key target zone.
This tool essentially converts the abstract concept of market cycles into a concrete, visual geometric framework that traders can analyze and act upon.
DISCLAIMER: This information is provided for educational purposes only and should not be considered financial, investment, or trading advice.
No guarantee of profits: Past performance and theoretical models do not guarantee future results. Trading and investing involve substantial risk of loss.
Not a recommendation: This script illustration does not constitute a recommendation to buy, sell, or hold any financial instrument.
Do your own research: Always conduct thorough independent research and consider consulting with a qualified financial advisor before making any trading decisions.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Multi-Timeframe EMA & SMA Scanner - Price Level LabelsOverview
A powerful multi-timeframe moving average scanner that displays EMA and SMA levels from up to 8 different timeframes simultaneously on your chart. Perfect for identifying key support/resistance levels, confluence zones, and multi-timeframe trend analysis.
Key Features
📊 Multi-Timeframe Analysis
Monitor up to 8 different timeframes simultaneously (5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W)
Each timeframe can be independently enabled/disabled
Fully customizable timeframe selection
📈 Comprehensive Moving Averages
5 configurable EMA periods (default: 8, 21, 50, 100, 200)
2 configurable SMA periods (default: 200, 400)
All periods are fully customizable to match your trading strategy
🎯 Smart Price Level Labels
Labels positioned at actual price levels (not in a list)
Color-coded labels for easy identification
Dynamic text color: Green when price is above, Red when below
Compact notation: E8-5m means EMA 8 on 5-minute timeframe
Adjustable label offset from current price
📉 Optional Horizontal Lines
Dotted reference lines at each MA level
Color-matched to corresponding MA type
Can be toggled on/off independently
📋 Comprehensive Data Table
Shows all MA values organized by timeframe
Displays percentage distance from current price
Trend indicator (Strong Up/Up/Neutral/Down/Strong Down)
EMA alignment status (Bullish/Bearish/Mixed)
Color-coded cells for quick visual analysis
🎨 Full Customization
Individual color settings for each MA type
Adjustable table size (Tiny/Small/Normal/Large)
Choose table position (Left/Right)
Toggle any MA or timeframe on/off
🔔 Built-in Alerts
Golden Cross detection (EMA 50 crosses above EMA 200)
Death Cross detection (EMA 50 crosses below EMA 200)
Price crossing major EMAs
Available for multiple timeframes
How to Use
For Day Traders:
Enable lower timeframes (5m, 10m, 15m, 30m)
Focus on faster EMAs (8, 21, 50)
Watch for confluence zones where multiple timeframe MAs cluster
For Swing Traders:
Enable higher timeframes (1H, 4H, 1D)
Use all EMAs plus SMAs for broader perspective
Look for alignment across timeframes for high-probability setups
For Position Traders:
Focus on daily and weekly timeframes
Emphasize 100, 200 EMAs and 200, 400 SMAs
Use for long-term trend confirmation
Understanding the Labels
Label Format: E8-5m 45250.50
E8 = EMA with period 8
5m = 5-minute timeframe
45250.50 = Current price level
Green text = Price is currently above this level (potential support)
Red text = Price is currently below this level (potential resistance)
For SMAs: S200-1D 44500.00
S200 = SMA with period 200
1D = Daily timeframe
Trading Applications
Support/Resistance Identification
MAs act as dynamic support and resistance levels
Multiple timeframe MAs create stronger zones
Confluence Trading
When multiple MAs from different timeframes cluster together, it creates high-probability zones
These areas often result in strong reactions
Trend Analysis
Check the Alignment column: Bullish alignment = all EMAs in ascending order
Trend column shows overall price position relative to all MAs
Entry/Exit Timing
Use lower timeframe MAs for precise entries
Use higher timeframe MAs for trend direction and exits
Settings Guide
Timeframes Section:
Select and enable/disable up to 8 timeframes
Default: 5m, 10m, 15m, 30m, 1H, 4H, 1D, 1W
MA Periods Section:
Customize all EMA and SMA periods
Default EMAs: 8, 21, 50, 100, 200
Default SMAs: 200, 400
Display Section:
Toggle price labels and horizontal lines
Adjust label offset (distance from right edge)
Show/hide data table
Choose table position and size
Colors Section:
Customize colors for each MA type
Each MA has independent color control
Pro Tips
✅ Start with default settings and adjust based on your trading style
✅ Disable timeframes/MAs you don't use to reduce chart clutter
✅ Use the data table for quick overview, labels for precise levels
✅ Look for "confluence clusters" where multiple MAs from different timeframes align
✅ Green labels = potential support, Red labels = potential resistance
✅ Set alerts on key crossovers for automated notifications
Technical Specifications
Pine Script v6
Overlay indicator (displays on main chart)
Maximum 500 labels supported
Real-time updates on each bar close
Compatible with all instruments and timeframes
Perfect For:
Day traders seeking multi-timeframe confirmation
Swing traders looking for high-probability setups
Position traders monitoring long-term trends
Anyone using moving averages as part of their strategy
Note: This indicator does not provide buy/sell signals. It's a tool for analysis and should be used in conjunction with your trading strategy and risk management rules.
Futures Momentum Scanner – jyoti//@version=5
indicator("Futures Momentum Scanner – Avvu Edition", overlay=false, max_lines_count=500)
//------------------------------
// USER INPUTS
//------------------------------
rsiLen = input.int(14, "RSI Length")
macdFast = input.int(12, "MACD Fast")
macdSlow = input.int(26, "MACD Slow")
macdSignal = input.int(9, "MACD Signal")
stLength = input.int(10, "Supertrend Length")
stMult = input.float(3.0, "Supertrend Multiplier")
//------------------------------
// SUPER TREND
//------------------------------
= ta.supertrend(stMult, stLength)
trendUp = stDirection == 1
//------------------------------
// RSI
//------------------------------
rsi = ta.rsi(close, rsiLen)
rsiBull = rsi > 50 and rsi < 65
//------------------------------
// MACD
//------------------------------
= ta.macd(close, macdFast, macdSlow, macdSignal)
macdBull = macd > signal and macd > 0
//------------------------------
// MOVING AVERAGE TREND
//------------------------------
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
trendStack = ema20 > ema50 and ema50 > ema200
//------------------------------
// BREAKOUT LOGIC
//------------------------------
prevHigh = ta.highest(high, 20)
breakout = close > prevHigh
//------------------------------
// FINAL SCANNER LOGIC
//------------------------------
bullishCandidate = trendUp and rsiBull and macdBull and trendStack and breakout
//------------------------------
// TABLE OUTPUT FOR SCANNER FEEL
//------------------------------
var table t = table.new(position.top_right, 1, 1)
if barstate.islast
msg = bullishCandidate ? "✔ BUY Candidate" : "– Not a Setup"
table.cell(t, 0, 0, msg, bgcolor=bullishCandidate ? color.new(color.green, 0) : color.new(color.red, 70))
//------------------------------
// ALERT
//------------------------------
alertcondition(bullishCandidate, title="Scanner Trigger", message="This stock meets Avvu's futures scanner criteria!")
