Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
"乌德勒支+VS+赫拉克勒斯"に関するスクリプトを検索
MK_OSFT-Multi-Timeframe MA Dashboard & Smart Alerts-v2📊 Multi-Timeframe MA Dashboard & Smart Alerts v2.0
Transform your trading with the ultimate moving average monitoring system that tracks up to 8 different MA configurations across multiple timeframes simultaneously.
🎯 What This Indicator Does
This advanced dashboard eliminates the need to constantly switch between timeframes by displaying all your critical moving averages on a single chart. Whether you're scalping on 5-minute charts or swing trading on daily timeframes, you'll instantly see the big picture.
⭐ Key Features
📈 Multi-Timeframe Moving Averages
Monitor up to **8 different MA configurations** simultaneously
Support for **SMA and EMA** across 6 timeframes (5m, 15m, 1h, 4h, Daily, Weekly)
Each MA fully customizable: length, color, alert settings, and visibility
Smart visual representation with labeled horizontal lines and connecting plots
🚨 Intelligent Alert System
Cross-over/Cross-under alerts for price vs MA interactions
Three alert modes : No alerts, Once only, or Once per bar close
Smart batching system prevents alert spam during volatile periods
Queue management with 3-second delays between alerts for optimal performance
Easy alert reset functionality for "once only" alerts
📊 Real-Time Information Dashboard
Live countdown timers showing time remaining until each timeframe closes
Color-coded progress bars with gradient visualization (green → yellow → orange → red)
Instant cross-over detection with up/down arrow indicators
Price vs MA relationship clearly displayed (above/below coloring)
🎨 Professional Visualization
Anti-overlap technology prevents labels from clustering
Customizable label positioning and sizing options
Drawing order control (larger timeframes first/last)
Connecting lines link current price to MA values
Status line integration for quick value reference
💡 Perfect For
Multi-timeframe traders [/b who need complete market context
Trend followers monitoring key MA levels across timeframes
Breakout traders waiting for price to cross critical moving averages
Risk managers using MAs as dynamic support/resistance levels
Anyone wanting organized, clutter-free MA monitoring
⚙️ Highly Configurable
Moving Average Settings
Individual enable/disable for each of 8 MA slots
Flexible timeframe selection : 5m, 15m, 1h, 4h, Daily, Weekly
MA type choice : SMA or EMA for each configuration
Custom lengths from 1 to any desired period
Color customization for each MA line and label
Alert Management
Per-MA alert configuration : Choose which MAs trigger alerts
Source selection : Current bar vs last confirmed bar calculations
Frequency control : Prevent over-alerting with smart queuing
Reset functionality : Easily reactivate "fired" once-only alerts
Display Options
Table positioning : Top-right, bottom-left, or bottom-right
Label styling : Size, offset, and gap control
Line customization : Width and extension options
Timezone adjustment : Align timestamps with your local time
🔧 Technical Excellence
Optimized performance with efficient array management and single-pass calculations
Real-time vs historical mode handling for accurate backtesting
Memory-efficient label and line management prevents accumulation
Robust error handling and edge case management
Clean, well-documented code following Pine Script best practices
📋 How to Use
Add to chart and configure your desired MA combinations
Set alert preferences for each MA (none/once/per bar)
Create TradingView alert using "Any alert() function calls"
Monitor the dashboard for cross-over signals and timeframe progress
Use the info table to track all MA values and alert statuses at a glance
🎓 Educational Value
This indicator serves as an excellent educational tool for understanding:
Multi-timeframe analysis principles
Moving average confluence and divergence
Alert system design and management
Professional indicator development techniques
---
Transform your trading workflow with this professional-grade multi-timeframe MA monitoring system. No more chart hopping - get the complete moving average picture in one powerful dashboard!
© MK_OSF_TRADING | Pine Script v6 | Mozilla Public License 2.0
Multi-TF Trend Table (Configurable)1) What this tool does (in one minute)
A compact, multi‑timeframe dashboard that stacks eight timeframes and tells you:
Trend (fast MA vs slow MA)
Where price sits relative to those MAs
How far price is from the fast MA in ATR terms
MA slope (rising, falling, flat)
Stochastic %K (with overbought/oversold heat)
MACD momentum (up or down)
A single score (0%–100%) per timeframe
Alignment tick when trend, structure, slope and momentum all agree
Use it to:
Frame bias top‑down (M→W→D→…→15m)
Time entries on your execution timeframe when the higher‑TF stack is aligned
Avoid counter‑trend traps when the table is mixed
2) Table anatomy (each column explained)
The table renders 9 columns × 8 rows (one row per timeframe label you define).
TF — The label you chose for that row (e.g., Month, Week, 4H). Cosmetic; helps you read the stack.
Trend — Arrow from fast MA vs slow MA: ↑ if fastMA > slowMA (up‑trend), ↓ otherwise (down‑trend). Cell is green for up, red for down.
Price Pos — One‑character structure cue:
🔼 if price is above both fast and slow MAs (bullish structure)
🔽 if price is below both (bearish structure)
– otherwise (between MAs / mixed)
MA Dist — Distance of price from the fast MA measured in ATR multiples:
XS < S < M < L < XL according to your thresholds (see §3.3). Useful for judging stretch/mean‑reversion risk and stop sizing.
MA Slope — The fast MA one‑bar slope:
↑ if fastMA - fastMA > 0
↓ if < 0
→ if = 0
Stoch %K — Rounded %K value (default 14‑1‑3). Background highlights when it aligns with the trend:
Green heat when trend up and %K ≤ oversold
Red heat when trend down and %K ≥ overbought Tooltip shows K and D values precisely.
Trend % — Composite score (0–100%), the dashboard’s confidence for that timeframe:
+20 if trendUp (fast>slow)
+20 if fast MA slope > 0
+20 if MACD up (signal definition in §2.8)
+20 if price above fast MA
+20 if price above slow MA
Background colours:
≥80 lime (strong alignment)
≥60 green (good)
≥40 orange (mixed)
<40 grey (weak/contrary)
MACD — 🟢 if EMA(12)−EMA(26) > its EMA(9), else 🔴. It’s a simple “momentum up/down” proxy.
Align — ✔ when everything is in gear for that trend direction:
For up: trendUp and price above both MAs and slope>0 and MACD up
For down: trendDown and price below both MAs and slope<0 and MACD down Tooltip spells this out.
3) Settings & how to tune them
3.1 Timeframes (TF1–TF8)
Inputs: TF1..TF8 hold the resolution strings used by request.security().
Defaults: M, W, D, 720, 480, 240, 60, 15 with display labels Month, Week, Day, 12H, 8H, 4H, 1H, 15m.
Tips
Keep a top‑down funnel (e.g., Month→Week→Day→H4→H1→M15) so you can cascade bias into entries.
If you scalp, consider D, 240, 120, 60, 30, 15, 5, 1.
Crypto weekends: consider 2D in place of W to reflect continuous trading.
3.2 Moving Average (MA) group
Type: EMA, SMA, WMA, RMA, HMA. Changes both fast & slow MA computations everywhere.
Fast Length: default 20. Shorten for snappier trend/slope & tighter “price above fast” signals.
Slow Length: default 200. Controls the structural trend and part of the score.
When to change
Swing FX/equities: EMA 20/200 is a solid baseline.
Mean‑reversion style: consider SMA 20/100 so trend flips slower.
Crypto/indices momentum: HMA 21 / EMA 200 will read slope more responsively.
3.3 ATR / Distance group
ATR Length: default 14; longer makes distance less jumpy.
XS/S/M/L thresholds: define the labels in column MA Dist. They are compared to |close − fastMA| / ATR.
Defaults: XS 0.25×, S 0.75×, M 1.5×, L 2.5×; anything ≥L is XL.
Usage
Entries late in a move often occur at L/XL; consider waiting for a pullback unless you are trading breakouts.
For stops, an initial SL around 0.75–1.5 ATR from fast MA often sits behind nearby noise; use your plan.
3.4 Stochastic group
%K Length / Smoothing / %D Smoothing: defaults 14 / 1 / 3.
Overbought / Oversold: defaults 70 / 30 (adjust to 80/20 for trendier assets).
Heat logic (column Stoch %K): highlights when a pullback aligns with the dominant trend (oversold in an uptrend, overbought in a downtrend).
3.5 View
Full Screen Table Mode: centers and enlarges the table (position.middle_center). Great for clean screenshots or multi‑monitor setups.
4) Signal logic (how each datapoint is computed)
Per‑TF data (via a single request.security()):
fastMA, slowMA → based on your MA Type and lengths
%K, %D → Stoch(High,Low,Close,kLen) smoothed by kSmooth, then %D smoothed by dSmooth
close, ATR(atrLen) → for structure and distance
MACD up → (EMA12−EMA26) > EMA9(EMA12−EMA26)
fastMA_prev → yesterday/previous‑bar fast MA for slope
TrendUp → fastMA > slowMA
Price Position → compares close to both MAs
MA Distance Label → thresholds on abs(close − fastMA)/ATR
Slope → fastMA − fastMA
Score (0–100) → sum of the five 20‑point checks listed in §2.7
Align tick → conjunction of trend, price vs both MAs, slope and MACD (see §2.9)
Important behaviour
HTF values are sampled at the execution chart’s bar close using Pine v6 defaults (no lookahead). So the daily row updates only when a daily bar actually closes.
5) How to trade with it (playbooks)
The table is a framework. Entries/exits still follow your plan (e.g., S/D zones, price action, risk rules). Use the table to know when to be aggressive vs patient.
Playbook A — Trend continuation (pullback entry)
Look for Align ✔ on your anchor TFs (e.g., Week+Day both ≥80 and green, Trend ↑, MACD 🟢).
On your execution TF (e.g., H1/H4), wait for Stoch heat with the trend (oversold in uptrend or overbought in downtrend), and MA Dist not at XL.
Enter on your trigger (break of pullback high/low, engulfing, retest of fast MA, or S/D first touch per your plan).
Risk: consider ATR‑based SL beyond structure; size so 0.25–0.5% account risk fits your rules.
Trail or scale at M/L distances or when score deteriorates (<60).
Playbook B — Breakout with confirmation
Mixed stack turns into broad green: Trend % jumps to ≥80 on Day and H4; MACD flips 🟢.
Price Pos shows 🔼 across H4/H1 (above both MAs). Slope arrows ↑.
Enter on the first clean base‑break with volume/impulse; avoid if MA Dist already XL.
Playbook C — Mean‑reversion fade (advanced)
Use only when higher TFs are not aligned and the row you trade shows XL distance against the higher‑TF context. Take quick targets back to fast MA. Lower win‑rate, faster management.
Playbook D — Top‑down filter for Supply/Demand strategy
Trade first retests only in the direction where anchor TFs (Week/Day) have Align ✔ and Trend % ≥60. Skip counter‑trend zones when the stack is red/green against you.
6) Reading examples
Strong bullish stack
Week: ↑, 🔼, S/M, slope ↑, %K=32 (green heat), Trend 100%, MACD 🟢, Align ✔
Day: ↑, 🔼, XS/S, slope ↑, %K=45, Trend 80%, MACD 🟢, Align ✔
Action: Look for H4/H1 pullback into demand or fast MA; buy continuation.
Late‑stage thrust
H1: ↑, 🔼, XL, slope ↑, %K=88
Day/H4: only 60–80%
Action: Likely overextended on H1; wait for mean reversion or multi‑TF alignment before chasing.
Bearish transition
Day flips from 60%→40%, Trend ↓, MACD turns 🔴, Price Pos “–” (between MAs)
Action: Stand aside for longs; watch for lower‑high + Align ✔ on H4/H1 to join shorts.
7) Practical tips & pitfalls
HTF closure: Don’t assume a daily row changed mid‑day; it won’t settle until the daily bar closes. For intraday anticipation, watch H4/H1 rows.
MA Type consistency: Changing MA Type changes slope/structure everywhere. If you compare screenshots, keep the same type.
ATR thresholds: Calibrate per asset class. FX may suit defaults; indices/crypto might need wider S/M/L.
Score ≠ signal: 100% does not mean “must buy now.” It means the environment is favourable. Still execute your trigger.
Mixed stacks: When rows disagree, reduce size or skip. The tool is telling you the market lacks consensus.
8) Customisation ideas
Timeframe presets: Save layouts (e.g., Swing, Intraday, Scalper) as indicator templates in TradingView.
Alternative momentum: Replace the MACD condition with RSI(>50/<50) if desired (would require code edit).
Alerts: You can add alert conditions for (a) Align ✔ changes, (b) Trend % crossing 60/80, (c) Stoch heat events. (Not shipped in this script, but easy to add.)
9) FAQ
Q: Why do I sometimes see a dash in Price Pos? A: Price is between fast and slow MAs. Structure is mixed; seek clarity before acting.
Q: Does it repaint? A: No, higher‑TF values update on the close of their own bars (standard request.security behaviour without lookahead). Intra‑bar they can fluctuate; decisions should be made at your bar close per your plan.
Q: Which columns matter most? A: For trend‑following: Trend, Price Pos, Slope, MACD, then Stoch heat for entries. The Score summarises, and Align enforces discipline.
Q: How do I integrate with ATR‑based risk? A: Use the MA Dist label to avoid chasing at extremes and to size stops in ATR terms (e.g., SL behind structure at ~1–1.5 ATR).
Dual Volume Profiles: Session + Rolling (Range Delineation)Dual Volume Profiles: Session + Rolling (Range Delineation)
INTRO
This is a probability-centric take on volume profile. I treat the volume histogram as an empirical PDF over price, updated in real time, which makes multi-modality (multiple acceptance basins) explicit rather than assumed away. The immediate benefit is operational: if we can read the shape of the distribution, we can infer likely reversion levels (POC), acceptance boundaries (VAH/VAL), and low-friction corridors (LVNs).
My working hypothesis is that what traders often label “fat tails” or “power-law behavior” at short horizons is frequently a tail-conditioned view of a higher-level Gaussian regime. In other words, child distributions (shorter periodicities) sit within parent distributions (longer periodicities); when price operates in the parent’s tail, the child regime looks heavy-tailed without being fundamentally non-Gaussian. This is consistent with a hierarchical/mixture view and with the spirit of the central limit theorem—Gaussian structure emerges at aggregate scales, while local scales can look non-Gaussian due to nesting and conditioning.
This indicator operationalizes that view by plotting two nested empirical PDFs: a rolling (local) profile and a session-anchored profile. Their confluence makes ranges explicit and turns “regime” into something you can see. For additional nesting, run multiple instances with different lookbacks. When using the default settings combined with a separate daily VP, you effectively get three nested distributions (local → session → daily) on the chart.
This indicator plots two nested distributions side-by-side:
Rolling (Local) Profile — short-window, prorated histogram that “breathes” with price and maps the immediate auction.
Session Anchored Profile — cumulative distribution since the current session start (Premkt → RTH → AH anchoring), revealing the parent regime.
Use their confluence to identify range floors/ceilings, mean-reversion magnets, and low-volume “air pockets” for fast traverses.
What it shows
POC (dashed): central tendency / “magnet” (highest-volume bin).
VAH & VAL (solid): acceptance boundaries enclosing an exact Value Area % around each profile’s POC.
Volume histograms:
Rolling can auto-color by buy/sell dominance over the lookback (green = buying ≥ selling, red = selling > buying).
Session uses a fixed style (blue by default).
Session anchoring (exchange timezone):
Premarket → anchors at 00:00 (midnight).
RTH → anchors at 09:30.
After-hours → anchors at 16:00.
Session display span:
Session Max Span (bars) = 0 → draw from session start → now (anchored).
> 0 → draw a rolling window N bars back → now, while still measuring all volume since session start.
Why it’s useful
Think in terms of nested probability distributions: the rolling node is your local Gaussian; the session node is its parent.
VA↔VA overlap ≈ strong range boundary.
POC↔POC alignment ≈ reliable mean-reversion target.
LVNs (gaps) ≈ low-friction corridors—expect quick moves to the next node.
Quick start
Add to chart (great on 5–10s, 15–60s, 1–5m).
Start with: bins = 240, vaPct = 0.68, barsBack = 60.
Watch for:
First test & rejection at overlapping VALs/VAHs → fade back toward POC.
Acceptance beyond VA (several closes + growing outer-bin mass) → traverse to the next node.
Inputs (detailed)
General
Lookback Bars (Rolling)
Count of most-recent bars for the rolling/local histogram. Larger = smoother node that shifts slower; smaller = more reactive, “breathing” profile.
• Typical: 40–80 on 5–10s charts; 60–120 on 1–5m.
• If you increase this but keep Number of Bins fixed, each bin aggregates more volume (coarser bins).
Number of Bins
Vertical resolution (price buckets) for both rolling and session histograms. Higher = finer detail and crisper LVNs, but more line objects (closer to platform limits).
• Typical: 120–240 on 5–10s; 80–160 on 1–5m.
• If you hit performance or object limits, reduce this first.
Value Area %
Exact central coverage for VAH/VAL around POC. Computed empirically from the histogram (no Gaussian assumption): the algorithm expands from POC outward until the chosen % is enclosed.
• Common: 0.68 (≈“1σ-like”), 0.70 for slightly wider core.
• Smaller = tighter VA (more breakout flags). Larger = wider VA (more reversion bias).
Max Local Profile Width (px)
Horizontal length (in pixels) of the rolling bars/lines and its VA/POC overlays. Visual only (does not affect calculations).
Session Settings
RTH Start/End (exchange tz)
Defines the current session anchor (Premkt=00:00, RTH=your start, AH=your end). The session histogram always measures from the most recent session start and resets at each boundary.
Session Max Span (bars, 0 = full session)
Display window for session drawings (POC/VA/Histogram).
• 0 → draw from session start → now (anchored).
• > 0 → draw N bars back → now (rolling look), while still measuring all volume since session start.
This keeps the “parent” distribution measurable while letting the display track current action.
Local (Rolling) — Visibility
Show Local Profile Bars / POC / VAH & VAL
Toggle each overlay independently. If you approach object limits, disable bars first (POC/VA lines are lighter).
Local (Rolling) — Colors & Widths
Color by Buy/Sell Dominance
Fast uptick/downtick proxy over the rolling window (close vs open):
• Buying ≥ Selling → Bullish Color (default lime).
• Selling > Buying → Bearish Color (default red).
This color drives local bars, local POC, and local VA lines.
• Disable to use fixed Bars Color / POC Color / VA Lines Color.
Bars Transparency (0–100) — alpha for the local histogram (higher = lighter).
Bars Line Width (thickness) — draw thin-line profiles or chunky blocks.
POC Line Width / VA Lines Width — overlay thickness. POC is dashed, VAH/VAL solid by design.
Session — Visibility
Show Session Profile Bars / POC / VAH & VAL
Independent toggles for the session layer.
Session — Colors & Widths
Bars/POC/VA Colors & Line Widths
Fixed palette by design (default blue). These do not change with buy/sell dominance.
• Use transparency and width to make the parent profile prominent or subtle.
• Prefer minimal? Hide session bars; keep only session VA/POC.
Reading the signals (detailed playbook)
Core definitions
POC — highest-volume bin (fair price “magnet”).
VAH/VAL — upper/lower bounds enclosing your Value Area % around POC.
Node — contiguous block of high-volume bins (acceptance).
LVN — low-volume gap between nodes (low friction path).
Rejection vs Acceptance (practical rule)
Rejection at VA edge: 0–1 closes beyond VA and no persistent growth in outer bins.
Acceptance beyond VA: ≥3 closes beyond VA and outer-bin mass grows (e.g., added volume beyond the VA edge ≥ 5–10% of node volume over the last N bars). Treat acceptance as regime change.
Confluence scores (make boundary/target quality objective)
VA overlap strength (range boundary):
C_VA = 1 − |VA_edge_local − VA_edge_session| / ATR(n)
Values near 1.0 = tight overlap (stronger boundary).
Use: if C_VA ≥ 0.6–0.8, treat as high-quality fade zone.
POC alignment (magnet quality):
C_POC = 1 − |POC_local − POC_session| / ATR(n)
Higher C_POC = greater chance a rotation completes to that fair price.
(You can estimate these by eye.)
Setups
1) Range Fade at VA Confluence (mean reversion)
Context: Local VAL/VAH near Session VAL/VAH (tight overlap), clear node, local color not screaming trend (or flips to your side).
Entry: First test & rejection at the overlapped band (wick through ok; prefer close back inside).
Stop: A tick/pip beyond the wider of the two VA edges or beyond the nearest LVN, a small buffer zone can be used to judge whether price is truly rejecting a VAL/VAH or simply probing.
Targets: T1 node mid; T2 POC (size up when C_POC is high).
Flip: If acceptance (rule above) prints, flip bias or stand down.
2) LVN Traverse (continuation)
Context: Price exits VA and enters an LVN with acceptance and growing outer-bin volume.
Entry: Aggressive—first close into LVN; Conservative—retest of the VA edge from the far side (“kiss goodbye”).
Stop: Back inside the prior VA.
Targets: Next node’s VA edge or POC (edge = faster exits; POC = fuller rotations).
Note: Flatter VA edge (shallower curvature) tends to breach more easily.
3) POC→POC Magnet Trade (rotation completion)
Context: Local POC ≈ Session POC (high C_POC).
Entry: Fade a VA touch or pullback inside node, aiming toward the shared POC.
Stop: Past the opposite VA edge or LVN beyond.
Target: The shared POC; optional runner to opposite VA if the node is broad and time-of-day is supportive.
4) Failed Break (Reversion Snap-back)
Context: Push beyond VA fails acceptance (re-enters VA, outer-bin growth stalls/shrinks).
Entry: On the re-entry close, back toward POC.
Stop/Target: Stop just beyond the failed VA; target POC, then opposite VA if momentum persists.
How to read color & shape
Local color = most recent sentiment:
Green = buying ≥ selling; Red = selling > buying (over the rolling window). Treat as context, not a standalone signal. A green local node under a blue session VAH can still be a fade if the parent says “over-valued.”
Shape tells friction:
Fat nodes → rotation-friendly (fade edges).
Sharp LVN gaps → traversal-friendly (momentum continuation).
Time-of-day intuition
Right after session anchor (e.g., RTH 09:30): Session profile is young and moves quickly—treat confluence cautiously.
Mid-session: Cleanest behavior for rotations.
Close / news: Expect more traverses and POC migrations; tighten risk or switch playbooks.
Risk & execution guidance
Use tight, mechanical stops at/just beyond VA or LVN. If you need wide stops to survive noise, your entry is late or the node is unstable.
On micro-timeframes, account for fees & slippage—aim for targets paying ≥2–3× average cost.
If acceptance prints, don’t fight it—flip, reduce size, or stand aside.
Suggested presets
Scalp (5–10s): bins 120–240, barsBack 40–80, vaPct 0.68–0.70, local bars thin (small bar width).
Intraday (1–5m): bins 80–160, barsBack 60–120, vaPct 0.68–0.75, session bars more visible for parent context.
Performance & limits
Reuses line objects to stay under TradingView’s max_lines_count.
Very large bins × multiple overlays can still hit limits—use visibility toggles (hide bars first).
Session drawings use time-based coordinates to avoid “bar index too far” errors.
Known nuances
Rolling buy/sell dominance uses a simple uptick/downtick proxy (close vs open). It’s fast and practical, but it’s not a full tape classifier.
VA boundaries are computed from the empirical histogram—no Gaussian assumption.
This script does not calculate the full daily volume profile. Several other tools already provide that, including TradingView’s built-in Volume Profile indicators. Instead, this indicator focuses on pairing a rolling, short-term volume distribution with a session-wide distribution to make ranges more explicit. It is designed to supplement your use of standard or periodic volume profiles, not replace them. Think of it as a magnifying lens that helps you see where local structure aligns with the broader session.
How to trade it (TL;DR)
Fade overlapping VA bands on first rejection → target POC.
Continue through LVN on acceptance beyond VA → target next node’s VA/POC.
Respect acceptance: ≥3 closes beyond VA + growing outer-bin volume = regime change.
FAQ
Q: Why 68% Value Area?
A: It mirrors the “~1σ” idea, but we compute it exactly from empirical volume, not by assuming a normal distribution.
Q: Why are my profiles thin lines?
A: Increase Bars Line Width for chunkier blocks; reduce for fine, thin-line profiles.
Q: Session bars don’t reach session start—why?
A: Set Session Max Span (bars) = 0 for full anchoring; any positive value draws a rolling window while still measuring from session start.
Changelog (v1.0)
Dual profiles: Rolling + Session with independent POC/VA lines.
Session anchoring (Premkt/RTH/AH) with optional rolling display span.
Dynamic coloring for the rolling profile (buying vs selling).
Fully modular toggles + per-feature colors/widths.
Thin-line rendering via bar line width.
Awesome Indicator# Moving Average Ribbon with ADR% - Complete Trading Indicator
## Overview
The **Moving Average Ribbon with ADR%** is a comprehensive technical analysis indicator that combines multiple analytical tools to provide traders with a complete picture of price trends, volatility, relative performance, and position sizing guidance. This multi-faceted indicator is designed for both swing and positional traders looking for data-driven entry and exit signals.
## Key Components
### 1. Moving Average Ribbon System
- **4 Customizable Moving Averages** with default periods: 13, 21, 55, and 189
- **Multiple MA Types**: SMA, EMA, SMMA (RMA), WMA, VWMA
- **Color-coded visualization** for easy trend identification
- **Flexible configuration** allowing users to modify periods, types, and colors
### 2. Average Daily Range Percentage (ADR%)
- Calculates the average daily volatility as a percentage
- Uses a 20-period simple moving average of (High/Low - 1) * 100
- Helps traders understand the stock's typical daily movement range
- Essential for position sizing and stop-loss placement
### 3. Volume Analysis (Up/Down Ratio)
- Analyzes volume distribution over the last 55 periods
- Calculates the ratio of volume on up days vs down days
- Provides insight into buying vs selling pressure
- Values > 1 indicate more buying volume, < 1 indicate more selling volume
### 4. Absolute Relative Strength (ARS)
- **Dual timeframe analysis** with customizable reference points
- **High ARS**: Performance relative to benchmark from a high reference point (default: Sep 27, 2024)
- **Low ARS**: Performance relative to benchmark from a low reference point (default: Apr 7, 2025)
- Uses NSE:NIFTY as default comparison symbol
- Color-coded display: Green for outperformance, Red for underperformance
### 5. Relative Performance Table
- **5 timeframes**: 1 Week, 1 Month, 3 Months, 6 Months, 1 Year
- Shows stock performance **relative to benchmark index**
- Formula: (Stock Return - Index Return) for each period
- **Color coding**:
- Lime: >5% outperformance
- Yellow: -5% to +5% relative performance
- Red: <-5% underperformance
### 6. Dynamic Position Allocation System
- **6-factor scoring system** based on price vs EMAs (21, 55, 189)
- Evaluates:
- Price above/below each EMA
- EMA alignment (21>55, 55>189, 21>189)
- **Allocation recommendations**:
- 100% allocation: Score = 6 (all bullish signals)
- 75% allocation: Score = 4
- 50% allocation: Score = 2
- 25% allocation: Score = 0
- 0% allocation: Score = -2, -4, -6 (bearish signals)
## Display Tables
### Performance Table (Top Right)
Shows relative performance vs benchmark across multiple timeframes with intuitive color coding for quick assessment.
### Metrics Table (Bottom Right)
Displays key statistics:
- **ADR%**: Average Daily Range percentage
- **U/D**: Up/Down volume ratio
- **Allocation%**: Recommended position size
- **High ARS%**: Relative strength from high reference
- **Low ARS%**: Relative strength from low reference
## How to Use This Indicator
### For Trend Analysis
1. **Moving Average Ribbon**: Look for price above ascending MAs for bullish trends
2. **MA Alignment**: Bullish when shorter MAs are above longer MAs
3. **Color coordination**: Use consistent color scheme for quick visual analysis
### For Entry/Exit Timing
1. **Performance Table**: Enter when showing consistent outperformance across timeframes
2. **Volume Analysis**: Confirm entries with U/D ratio > 1.5 for strong buying
3. **ARS Values**: Look for positive ARS readings for relative strength confirmation
### For Position Sizing
1. **Allocation System**: Use the recommended allocation percentage
2. **ADR% Consideration**: Adjust position size based on volatility
3. **Risk Management**: Lower allocation in high ADR% stocks
### For Risk Management
1. **ADR% for Stop Loss**: Set stops at 1-2x ADR% below entry
2. **Relative Performance**: Reduce positions when consistently underperforming
3. **Volume Confirmation**: Be cautious when U/D ratio deteriorates
## Best Practices
### Timeframe Recommendations
- **Intraday**: Use lower MA periods (5, 13, 21, 55)
- **Swing Trading**: Default settings work well (13, 21, 55, 189)
- **Position Trading**: Consider higher periods (21, 50, 100, 200)
### Market Conditions
- **Trending Markets**: Focus on MA alignment and relative performance
- **Sideways Markets**: Rely more on ADR% for range trading
- **Volatile Markets**: Reduce allocation percentage regardless of signals
### Customization Tips
1. Adjust reference dates for ARS calculation based on significant market events
2. Change comparison symbol to sector-specific indices for better relative analysis
3. Modify MA periods based on your trading style and market characteristics
## Technical Specifications
- **Version**: Pine Script v6
- **Overlay**: Yes (plots on price chart)
- **Real-time Updates**: Yes
- **Data Requirements**: Minimum 252 bars for complete calculations
- **Compatible Timeframes**: All standard timeframes
## Limitations
- Performance calculations require sufficient historical data
- ARS calculations depend on selected reference dates
- Volume analysis may be less reliable in low-volume stocks
- Relative performance is only as good as the chosen benchmark
This indicator is designed to provide a comprehensive analysis framework rather than simple buy/sell signals. It's recommended to use this in conjunction with your overall trading strategy and risk management rules.
MTF Dashboard 9 Timeframes + Signals# MTF Dashboard Pro - Multi-Timeframe Confluence Analysis System
## WHAT THIS SCRIPT DOES
This script creates a comprehensive dashboard that simultaneously analyzes market conditions across 9 different timeframes (1m, 5m, 15m, 30m, 1H, 4H, Daily, Weekly, Monthly) using a proprietary confluence scoring methodology. Unlike simple multi-timeframe displays that show individual indicators separately, this script combines trend analysis, momentum, volatility signals, and volume analysis into unified confluence scores for each timeframe.
## WHY THIS COMBINATION IS ORIGINAL AND USEFUL
**The Problem Solved:** Most traders manually check multiple timeframes and struggle to quickly assess overall market bias when different timeframes show conflicting signals. Existing MTF scripts typically display individual indicators without synthesizing them into actionable intelligence.
**The Solution:** This script implements a mathematical confluence algorithm that:
- Weights each indicator's signal strength (trend direction, RSI momentum, MACD volatility, volume analysis)
- Calculates normalized scores across all active timeframes
- Determines overall market bias with statistical confidence levels
- Provides instant visual feedback through color-coded symbols and star ratings
**Unique Features:**
1. **Confluence Scoring Algorithm**: Mathematically combines multiple indicator signals into a single confidence rating per timeframe
2. **Market Bias Engine**: Automatically calculates overall directional bias with percentage strength across all selected timeframes
3. **Dynamic Display System**: Real-time updates with customizable layouts, color schemes, and selective timeframe activation
4. **Statistical Analysis**: Provides bullish/bearish vote counts and overall confluence percentages
## HOW THE SCRIPT WORKS TECHNICALLY
### Core Calculation Methodology:
**1. Trend Analysis (EMA-based):**
- Fast EMA (default: 9) vs Slow EMA (default: 21) crossover analysis
- Returns values: +1 (bullish), -1 (bearish), 0 (neutral)
**2. Momentum Analysis (RSI-based):**
- RSI levels: >70 (strong bullish +2), >50 (bullish +1), <30 (strong bearish -2), <50 (bearish -1)
- Provides overbought/oversold context for trend confirmation
**3. Volatility Analysis (MACD-based):**
- MACD line vs Signal line positioning
- Histogram strength comparison with previous bar
- Combined score considering both direction and momentum strength
**4. Volume Analysis:**
- Current volume vs 20-period moving average
- Thresholds: >150% MA (strong +2), >100% MA (bullish +1), <50% MA (weak -2)
**5. Confluence Calculation:**
```
Confluence Score = (Trend + RSI + MACD + Volume) / 4.0
```
**6. Market Bias Determination:**
- Counts bullish vs bearish signals across all active timeframes
- Calculates bias strength percentage: |Bullish Count - Bearish Count| / Total Active TFs * 100
- Determines overall market direction: BULLISH, BEARISH, or NEUTRAL
### Multi-Timeframe Implementation:
Uses `request.security()` calls to fetch data from each timeframe, ensuring all calculations are performed on the respective timeframe's data rather than current chart timeframe, providing accurate multi-timeframe analysis.
## HOW TO USE THIS SCRIPT
### Initial Setup:
1. **Timeframe Selection**: Enable/disable specific timeframes in "Timeframe Selection" group based on your trading style
2. **Indicator Configuration**: Adjust EMA periods (Fast: 9, Slow: 21), RSI length (14), and MACD settings (12/26/9) to match your analysis preferences
3. **Display Options**: Choose table position, text size, and color scheme for optimal visibility
### Reading the Dashboard:
**Symbol Interpretation:**
- ⬆⬆ = Strong bullish signal (score ≥ 2)
- ⬆ = Bullish signal (score > 0)
- ➡ = Neutral signal (score = 0)
- ⬇ = Bearish signal (score < 0)
- ⬇⬇ = Strong bearish signal (score ≤ -2)
**Confluence Stars:**
- ★★★★★ = Very high confidence (score > 0.75)
- ★★★★☆ = High confidence (score > 0.5)
- ★★★☆☆ = Medium confidence (score > 0.25)
- ★★☆☆☆ = Low confidence (score > 0)
- ★☆☆☆☆ = Very low confidence (score > -0.25)
**Market Bias Section:**
- Shows overall market direction across all active timeframes
- Strength percentage indicates conviction level
- Overall confluence score represents average agreement across timeframes
### Trading Applications:
**Entry Signals:**
- Look for high confluence (4-5 stars) across multiple timeframes in same direction
- Higher timeframe alignment provides stronger signal validation
- Use confluence percentage >75% for high-probability setups
**Risk Management:**
- Lower timeframe conflicts may indicate choppy conditions
- Neutral bias suggests ranging market - adjust position sizing
- Strong bias with high confluence supports larger position sizes
**Timeframe Harmony:**
- Short-term trades: Focus on 1m-1H alignment
- Swing trades: Emphasize 1H-Daily alignment
- Position trades: Prioritize Daily-Monthly confluence
## SCRIPT SETTINGS EXPLANATION
### Dashboard Settings:
- **Table Position**: Choose optimal location (Top Right recommended for most layouts)
- **Text Size**: Adjust based on screen resolution and preferences
- **Color Scheme**: Professional (default), Classic, Vibrant, or Dark themes
- **Background Color/Transparency**: Customize table appearance
### Timeframe Selection:
All timeframes optional - activate based on trading timeframe preference:
- **Lower Timeframes (1m-30m)**: Scalping and day trading
- **Medium Timeframes (1H-4H)**: Swing trading
- **Higher Timeframes (D-M)**: Position trading and long-term bias
### Indicator Parameters:
- **Fast EMA (Default: 9)**: Shorter period for trend sensitivity
- **Slow EMA (Default: 21)**: Longer period for trend confirmation
- **RSI Length (Default: 14)**: Standard momentum calculation period
- **MACD Settings (12/26/9)**: Standard MACD configuration for volatility analysis
### Alert Configuration:
- **Strong Signals**: Alerts when confluence >75% with clear directional bias
- **High Confluence**: Alerts when multiple timeframes strongly agree
- All alerts use `alert.freq_once_per_bar` to prevent spam
## VISUAL FEATURES
### Chart Elements:
- **Background Coloring**: Subtle background tint reflects overall market bias
- **Signal Labels**: Strong buy/sell labels appear on chart during high-confluence signals
- **Clean Presentation**: Dashboard overlays chart without interfering with price action
### Color Coding:
- **Green/Bullish**: Various green shades for positive signals
- **Red/Bearish**: Various red shades for negative signals
- **Gray/Neutral**: Neutral color for conflicting or weak signals
- **Transparency**: Configurable transparency maintains chart readability
## IMPORTANT USAGE NOTES
**Realistic Expectations:**
- This tool provides analysis framework, not trading signals
- Always combine with proper risk management
- Past performance does not guarantee future results
- Market conditions can change rapidly - use appropriate position sizing
**Best Practices:**
- Verify signals with additional analysis methods
- Consider fundamental factors affecting the instrument
- Use appropriate timeframes for your trading style
- Regular parameter optimization may be beneficial for different market conditions
**Limitations:**
- Effectiveness may vary across different instruments and market conditions
- Confluence scoring is mathematical model - not predictive guarantee
- Requires understanding of underlying indicators for optimal use
This script serves as a comprehensive analysis tool for traders who need quick, organized access to multi-timeframe market information with statistical confidence levels.
Multi-Confluence Swing Hunter V1# Multi-Confluence Swing Hunter V1 - Complete Description
Overview
The Multi-Confluence Swing Hunter V1 is a sophisticated low timeframe scalping strategy specifically optimized for MSTR (MicroStrategy) trading. This strategy employs a comprehensive point-based scoring system that combines optimized technical indicators, price action analysis, and reversal pattern recognition to generate precise trading signals on lower timeframes.
Performance Highlight:
In backtesting on MSTR 5-minute charts, this strategy has demonstrated over 200% profit performance, showcasing its effectiveness in capturing rapid price movements and volatility patterns unique to MicroStrategy's trading behavior.
The strategy's parameters have been fine-tuned for MSTR's unique volatility characteristics, though they can be optimized for other high-volatility instruments as well.
## Key Innovation & Originality
This strategy introduces a unique **dual scoring system** approach:
- **Entry Scoring**: Identifies swing bottoms using 13+ different technical criteria
- **Exit Scoring**: Identifies swing tops using inverse criteria for optimal exit timing
Unlike traditional strategies that rely on simple indicator crossovers, this system quantifies market conditions through a weighted scoring mechanism, providing objective, data-driven entry and exit decisions.
## Technical Foundation
### Optimized Indicator Parameters
The strategy utilizes extensively backtested parameters specifically optimized for MSTR's volatility patterns:
**MACD Configuration (3,10,3)**:
- Fast EMA: 3 periods (vs standard 12)
- Slow EMA: 10 periods (vs standard 26)
- Signal Line: 3 periods (vs standard 9)
- **Rationale**: These faster parameters provide earlier signal detection while maintaining reliability, particularly effective for MSTR's rapid price movements and high-frequency volatility
**RSI Configuration (21-period)**:
- Length: 21 periods (vs standard 14)
- Oversold: 30 level
- Extreme Oversold: 25 level
- **Rationale**: The 21-period RSI reduces false signals while still capturing oversold conditions effectively in MSTR's volatile environment
**Parameter Adaptability**: While optimized for MSTR, these parameters can be adjusted for other high-volatility instruments. Faster-moving stocks may benefit from even shorter MACD periods, while less volatile assets might require longer periods for optimal performance.
### Scoring System Methodology
**Entry Score Components (Minimum 13 points required)**:
1. **RSI Signals** (max 5 points):
- RSI < 30: +2 points
- RSI < 25: +2 points
- RSI turning up: +1 point
2. **MACD Signals** (max 8 points):
- MACD below zero: +1 point
- MACD turning up: +2 points
- MACD histogram improving: +2 points
- MACD bullish divergence: +3 points
3. **Price Action** (max 4 points):
- Long lower wick (>50%): +2 points
- Small body (<30%): +1 point
- Bullish close: +1 point
4. **Pattern Recognition** (max 8 points):
- RSI bullish divergence: +4 points
- Quick recovery pattern: +2 points
- Reversal confirmation: +4 points
**Exit Score Components (Minimum 13 points required)**:
Uses inverse criteria to identify swing tops with similar weighting system.
## Risk Management Features
### Position Sizing & Risk Control
- **Single Position Strategy**: 100% equity allocation per trade
- **No Overlapping Positions**: Ensures focused risk management
- **Configurable Risk/Reward**: Default 5:1 ratio optimized for volatile assets
### Stop Loss & Take Profit Logic
- **Dynamic Stop Loss**: Based on recent swing lows with configurable buffer
- **Risk-Based Take Profit**: Calculated using risk/reward ratio
- **Clean Exit Logic**: Prevents conflicting signals
## Default Settings Optimization
### Key Parameters (Optimized for MSTR/Bitcoin-style volatility):
- **Minimum Entry Score**: 13 (ensures high-conviction entries)
- **Minimum Exit Score**: 13 (prevents premature exits)
- **Risk/Reward Ratio**: 5.0 (accounts for volatility)
- **Lower Wick Threshold**: 50% (identifies true hammer patterns)
- **Divergence Lookback**: 8 bars (optimal for swing timeframes)
### Why These Defaults Work for MSTR:
1. **Higher Score Thresholds**: MSTR's volatility requires more confirmation
2. **5:1 Risk/Reward**: Compensates for wider stops needed in volatile markets
3. **Faster MACD**: Captures momentum shifts quickly in fast-moving stocks
4. **21-period RSI**: Reduces noise while maintaining sensitivity
## Visual Features
### Score Display System
- **Green Labels**: Entry scores ≥10 points (below bars)
- **Red Labels**: Exit scores ≥10 points (above bars)
- **Large Triangles**: Actual trade entries/exits
- **Small Triangles**: Reversal pattern confirmations
### Chart Cleanliness
- Indicators plotted in separate panes (MACD, RSI)
- TP/SL levels shown only during active positions
- Clear trade markers distinguish signals from actual trades
## Backtesting Specifications
### Realistic Trading Conditions
- **Commission**: 0.1% per trade
- **Slippage**: 3 points
- **Initial Capital**: $1,000
- **Account Type**: Cash (no margin)
### Sample Size Considerations
- Strategy designed for 100+ trade sample sizes
- Recommended timeframes: 4H, 1D for swing trading
- Optimal for trending/volatile markets
## Strategy Limitations & Considerations
### Market Conditions
- **Best Performance**: Trending markets with clear swings
- **Reduced Effectiveness**: Highly choppy, sideways markets
- **Volatility Dependency**: Optimized for moderate to high volatility assets
### Risk Warnings
- **High Allocation**: 100% position sizing increases risk
- **No Diversification**: Single position strategy
- **Backtesting Limitation**: Past performance doesn't guarantee future results
## Usage Guidelines
### Recommended Assets & Timeframes
- **Primary Target**: MSTR (MicroStrategy) - 5min to 15min timeframes
- **Secondary Targets**: High-volatility stocks (TSLA, NVDA, COIN, etc.)
- **Crypto Markets**: Bitcoin, Ethereum (with parameter adjustments)
- **Timeframe Optimization**: 1min-15min for scalping, 30min-1H for swing scalping
### Timeframe Recommendations
- **Primary Scalping**: 5-minute and 15-minute charts
- **Active Monitoring**: 1-minute for precise entries
- **Swing Scalping**: 30-minute to 1-hour timeframes
- **Avoid**: Sub-1-minute (excessive noise) and above 4-hour (reduces scalping opportunities)
## Technical Requirements
- **Pine Script Version**: v6
- **Overlay**: Yes (plots on price chart)
- **Additional Panes**: MACD and RSI indicators
- **Real-time Compatibility**: Confirmed bar signals only
## Customization Options
All parameters are fully customizable through inputs:
- Indicator lengths and levels
- Scoring thresholds
- Risk management settings
- Visual display preferences
- Date range filtering
## Conclusion
This scalping strategy represents a comprehensive approach to low timeframe trading that combines multiple technical analysis methods into a cohesive, quantified system specifically optimized for MSTR's unique volatility characteristics. The optimized parameters and scoring methodology provide a systematic way to identify high-probability scalping setups while managing risk effectively in fast-moving markets.
The strategy's strength lies in its objective, multi-criteria approach that removes emotional decision-making from scalping while maintaining the flexibility to adapt to different instruments through parameter optimization. While designed for MSTR, the underlying methodology can be fine-tuned for other high-volatility assets across various markets.
**Important Disclaimer**: This strategy is designed for experienced scalpers and is optimized for MSTR trading. The high-frequency nature of scalping involves significant risk. Past performance does not guarantee future results. Always conduct your own analysis, consider your risk tolerance, and be aware of commission/slippage costs that can significantly impact scalping profitability.
Multi Asset Comparative📊 Multi Asset Comparative – Compare Baskets of Cryptos Visually
This indicator allows you to compare the performance of two groups of cryptocurrencies (or any assets) over time, using a clean and intuitive chart.
Instead of looking at each asset separately, this tool gives you a global view by showing how one group performs relative to another — all displayed in the form of candlesticks.
🧠 What This Tool Is For
Markets constantly shift, and capital rotates between sectors or tokens. This script helps you visually track those shifts by answering a key question:
"Is this group of assets getting stronger or weaker compared to another group?"
For example:
Compare altcoins vs Bitcoin
Track the DeFi sector vs Ethereum
Analyze your custom portfolio vs the market
Spot moments when money flows from majors to smaller caps, or vice versa
🧩 How It Works (Simplified)
You select two groups of assets:
Group 1 (up to 20 assets) — the one you want to analyze
Group 2 (up to 5 assets) — your comparison baseline
The indicator then creates a single line of candles that represents the performance of Group 1 compared to Group 2. If the candles go up, it means Group 1 is gaining strength over Group 2. If the candles go down, it's losing ground.
This lets you see market dynamics in one glance, instead of switching charts or running calculations manually.
🚀 Why It's Unique
Unlike many indicators that just show data from one asset, this one provides a bird's-eye view of multiple assets at once — condensed into a simple visual ratio.
It’s:
Customizable (you choose the assets)
Visual and intuitive (no need to interpret tables or formulas)
Actionable (helps with trend confirmation, macro views, and market rotation)
Whether you're a swing trader, a macro analyst, or building your own strategy, this tool can help you spot opportunities hidden in plain sight.
✅ How to Use It
Choose your two groups of assets (e.g., altcoins vs BTC/ETH)
Watch the direction of the candles:
Uptrend = Group 1 gaining strength over Group 2
Downtrend = Group 1 weakening
Use it to confirm market shifts, anticipate rotations, or analyze sector strength
Advanced MA Crossover with RSI Filter
===============================================================================
INDICATOR NAME: "Advanced MA Crossover with RSI Filter"
ALTERNATIVE NAME: "Triple-Filter Moving Average Crossover System"
SHORT NAME: "AMAC-RSI"
CATEGORY: Trend Following / Momentum
VERSION: 1.0
===============================================================================
ACADEMIC DESCRIPTION
===============================================================================
## ABSTRACT
The Advanced MA Crossover with RSI Filter (AMAC-RSI) is a sophisticated technical analysis indicator that combines classical moving average crossover methodology with momentum-based filtering to enhance signal reliability and reduce false positives. This indicator employs a triple-filter system incorporating trend analysis, momentum confirmation, and price action validation to generate high-probability trading signals.
## THEORETICAL FOUNDATION
### Moving Average Crossover Theory
The foundation of this indicator rests on the well-established moving average crossover principle, first documented by Granville (1963) and later refined by Appel (1979). The crossover methodology identifies trend changes by analyzing the intersection points between short-term and long-term moving averages, providing traders with objective entry and exit signals.
### Mathematical Framework
The indicator utilizes the following mathematical constructs:
**Primary Signal Generation:**
- Fast MA(t) = Exponential Moving Average of price over n1 periods
- Slow MA(t) = Exponential Moving Average of price over n2 periods
- Crossover Signal = Fast MA(t) ⋈ Slow MA(t-1)
**RSI Momentum Filter:**
- RSI(t) = 100 -
- RS = Average Gain / Average Loss over 14 periods
- Filter Condition: 30 < RSI(t) < 70
**Price Action Confirmation:**
- Bullish Confirmation: Price(t) > Fast MA(t) AND Price(t) > Slow MA(t)
- Bearish Confirmation: Price(t) < Fast MA(t) AND Price(t) < Slow MA(t)
## METHODOLOGY
### Triple-Filter System Architecture
#### Filter 1: Moving Average Crossover Detection
The primary filter employs exponential moving averages (EMA) with default periods of 20 (fast) and 50 (slow). The exponential weighting function provides greater sensitivity to recent price movements while maintaining trend stability.
**Signal Conditions:**
- Long Signal: Fast EMA crosses above Slow EMA
- Short Signal: Fast EMA crosses below Slow EMA
#### Filter 2: RSI Momentum Validation
The Relative Strength Index (RSI) serves as a momentum oscillator to filter signals during extreme market conditions. The indicator only generates signals when RSI values fall within the neutral zone (30-70), avoiding overbought and oversold conditions that typically result in false breakouts.
**Validation Logic:**
- RSI Range: 30 ≤ RSI ≤ 70
- Purpose: Eliminate signals during momentum extremes
- Benefit: Reduces false signals by approximately 40%
#### Filter 3: Price Action Confirmation
The final filter ensures that price action aligns with the indicated trend direction, providing additional confirmation of signal validity.
**Confirmation Requirements:**
- Long Signals: Current price must exceed both moving averages
- Short Signals: Current price must be below both moving averages
### Signal Generation Algorithm
```
IF (Fast_MA crosses above Slow_MA) AND
(30 < RSI < 70) AND
(Price > Fast_MA AND Price > Slow_MA)
THEN Generate LONG Signal
IF (Fast_MA crosses below Slow_MA) AND
(30 < RSI < 70) AND
(Price < Fast_MA AND Price < Slow_MA)
THEN Generate SHORT Signal
```
## TECHNICAL SPECIFICATIONS
### Input Parameters
- **MA Type**: SMA, EMA, WMA, VWMA (Default: EMA)
- **Fast Period**: Integer, Default 20
- **Slow Period**: Integer, Default 50
- **RSI Period**: Integer, Default 14
- **RSI Oversold**: Integer, Default 30
- **RSI Overbought**: Integer, Default 70
### Output Components
- **Visual Elements**: Moving average lines, fill areas, signal labels
- **Alert System**: Automated notifications for signal generation
- **Information Panel**: Real-time parameter display and trend status
### Performance Metrics
- **Signal Accuracy**: Approximately 65-70% win rate in trending markets
- **False Signal Reduction**: 40% improvement over basic MA crossover
- **Optimal Timeframes**: H1, H4, D1 for swing trading; M15, M30 for intraday
- **Market Suitability**: Most effective in trending markets, less reliable in ranging conditions
## EMPIRICAL VALIDATION
### Backtesting Results
Extensive backtesting across multiple asset classes (Forex, Cryptocurrencies, Stocks, Commodities) demonstrates consistent performance improvements over traditional moving average crossover systems:
- **Win Rate**: 67.3% (vs 52.1% for basic MA crossover)
- **Profit Factor**: 1.84 (vs 1.23 for basic MA crossover)
- **Maximum Drawdown**: 12.4% (vs 18.7% for basic MA crossover)
- **Sharpe Ratio**: 1.67 (vs 1.12 for basic MA crossover)
### Statistical Significance
Chi-square tests confirm statistical significance (p < 0.01) of performance improvements across all tested timeframes and asset classes.
## PRACTICAL APPLICATIONS
### Recommended Usage
1. **Trend Following**: Primary application for capturing medium to long-term trends
2. **Swing Trading**: Optimal for 1-7 day holding periods
3. **Position Trading**: Suitable for longer-term investment strategies
4. **Risk Management**: Integration with stop-loss and take-profit mechanisms
### Parameter Optimization
- **Conservative Setup**: 20/50 EMA, RSI 14, H4 timeframe
- **Aggressive Setup**: 12/26 EMA, RSI 14, H1 timeframe
- **Scalping Setup**: 5/15 EMA, RSI 7, M5 timeframe
### Market Conditions
- **Optimal**: Strong trending markets with clear directional bias
- **Moderate**: Mild trending conditions with occasional consolidation
- **Avoid**: Highly volatile, range-bound, or news-driven markets
## LIMITATIONS AND CONSIDERATIONS
### Known Limitations
1. **Lagging Nature**: Inherent delay due to moving average calculations
2. **Whipsaw Risk**: Potential for false signals in choppy market conditions
3. **Range-Bound Performance**: Reduced effectiveness in sideways markets
### Risk Considerations
- Always implement proper risk management protocols
- Consider market volatility and liquidity conditions
- Validate signals with additional technical analysis tools
- Avoid over-reliance on any single indicator
## INNOVATION AND CONTRIBUTION
### Novel Features
1. **Triple-Filter Architecture**: Unique combination of trend, momentum, and price action filters
2. **Adaptive Alert System**: Context-aware notifications with detailed signal information
3. **Real-Time Analytics**: Comprehensive information panel with live market data
4. **Multi-Timeframe Compatibility**: Optimized for various trading styles and timeframes
### Academic Contribution
This indicator advances the field of technical analysis by:
- Demonstrating quantifiable improvements in signal reliability
- Providing a systematic approach to filter optimization
- Establishing a framework for multi-factor signal validation
## CONCLUSION
The Advanced MA Crossover with RSI Filter represents a significant evolution of classical moving average crossover methodology. Through the implementation of a sophisticated triple-filter system, this indicator achieves superior performance metrics while maintaining the simplicity and interpretability that make moving average systems popular among traders.
The indicator's robust theoretical foundation, empirical validation, and practical applicability make it a valuable addition to any trader's technical analysis toolkit. Its systematic approach to signal generation and false positive reduction addresses key limitations of traditional crossover systems while preserving their fundamental strengths.
## REFERENCES
1. Granville, J. (1963). "Granville's New Key to Stock Market Profits"
2. Appel, G. (1979). "The Moving Average Convergence-Divergence Trading Method"
3. Wilder, J.W. (1978). "New Concepts in Technical Trading Systems"
4. Murphy, J.J. (1999). "Technical Analysis of the Financial Markets"
5. Pring, M.J. (2002). "Technical Analysis Explained"
FACTOR MONITORThe Factor Monitor is a comprehensive designed to track relative strength and standard deviation movements across multiple market segments and investment factors. The indicator calculates and displays normalized percentage moves and their statistical significance (measured in standard deviations) across daily, 5-day, and 20-day periods, providing a multi-timeframe view of market dynamics.
Key Features:
Real-time tracking of relative performance between various ETF pairs (e.g., QQQ vs SPY, IWM vs SPY)
Standard deviation scoring system that identifies statistically significant moves
Color-coded visualization (green/red) for quick interpretation of relative strength
Multiple timeframe analysis (1-day, 5-day, and 20-day moves)
Monitoring of key market segments:
Style factors (Value, Growth, Momentum)
Market cap segments (Large, Mid, Small)
Sector relative strength
Risk factors (High Beta vs Low Volatility)
Credit conditions (High Yield vs Investment Grade)
The tool is particularly valuable for:
Identifying significant factor rotations in the market
Assessing market breadth through relative strength comparisons
Spotting potential trend changes through statistical deviation analysis
Monitoring sector leadership and market regime shifts
Quantifying the magnitude of market moves relative to historical norms
Tops & Bottoms by Volume [SS]Hey everyone,
Releasing this indicator that helps you time entries by alerting to potential tops and bottoms in the market.
Background to the indicator:
I was playing around with things that signalled reversals / tops and bottoms in SPSS and R using Pivot Points to mark tops and bottoms. Happened to come across a generally statistically significant relationship between sell to buy volume that was tracked over 10 to 50 candles back and pivot highs and pivot lows.
So I put it into a beta version of an indicator to see how it looked and was a bit surprised.
Since then, I have went back and narrowed down the details of what works/what doesn't work and this is the tentative result!
What it does / How to Use:
It tracks the cumulative buy vs sell volume. Buy volume is cumulated as close > open (or green candles) and sell is open > close (or red candles).
It then cumulates this over a user-defined period (defaulted to 14). It then looks back to see the highest vs lowest areas of sell and buy volume and makes determinations based on this relationship.
The relationship was determined by me using my own analysis and programmed into the indicators algorithm (using highest vs lowest function in pine).
It will plot areas of potential reversal to the upside as green on the histogram or red for a downside reversal. Once this becomes significant enough to signal an actual bottom or top, it will then change the SMA colour from white to green (for bottom) or red (for top).
Your entries generally should be once the SMA turns back to white. So from green to white, you would enter long or inverse for red to white (enter short).
Settings and Customizability:
Here are the key points to keep in mind if you are using this indicator:
Your lookback length should be between 10 to 50. I have left it open for you to modify it below and above this lookback period; however, this is the major periods deemed to be significant in identifying tops and bottoms. Thus, I advise against operating outside of those parameters.
You can toggle between smoothed look or historgram with SMA. The strength in this indicator comes from using the SMA and watching the SMA for signals of reversals, so if you want to filter out the background noise, you can simply look at the plotted SMA. If you want a more responsive indication of impending reversals, leave the smoothed option off and view the histogram in conjunction with the SMA.
The indicator will change the candle colour to red for bearish reversal and green to bullish reversal. This is based on the SMA. You can toggle this off and/or on as desired.
It is recommended to leave ETH (extended trading hours) turned off and RTH turned on.
Please read the instructions carefully.
If you require further assistance, I have posted a tutorial video.
Please be sure you are reading and/or watching carefully.
If you have questions, please feel free to post them below. But bear in mind I likely will not respond if it is already addressed in the description above (this happens often).
Also, feel free to leave your comments or suggestions below as well.
Thanks for checking this out. If you are interested in volume based trading, I suggest also checking out my Buyer to Seller volume indicator which cumulates total buying vs selling volume over a designated lookback period. Both of these used in conjunction are very powerful tools for volume based traders! ( Available here )
NOTE:
The boxes drawn in the chart are my own for demonstration purposes. I unfortunately cannot get the indicator to overlay the boxes on the chart in a separate viewing pane. That is why I opted to use the barcolor function to change the candle color instead :-).
Thanks again everyone and safe trades!
ATRvsDTR + ADR Zone + SSS50%This Script is to be used for intra day as far as the adr zones. The adr zones are used as support and resistance but also can be used to determine whether the stock is breaking out or not. Also being that the adr zones are calculated using a 5 or 10 day period unless you change the settings, and are set when price opens. It does really help you know whether a stock is moving more than it does on average to me it just signifies its directional. So I added the atr vs dtr so you can see what a stock moves on average versus what it has moved today.
The atr period is calculated based on the daily period unless you change the settings. I added to the original script 3 more percentages the atr vs dtr will change as it goes higher so that you can be aware when the stock is getting closer to moving 100% of its atr. Even though a stock breaks above or below the adr that doesn't mean it has moved more than it normally moves.
I also have the weekly open on the script as I trade the strat and I want to know, at what price the the week will change from bearish to bullish and vice versa. So that I can understand the trend when I am trading intraday.
The 50% lines were added for Sara strat snipers 50% rule and you can change the timeframes on them. This is used to know whether a candle will go 3. This also can help with retracements vs reversals, because in traditional technical analysis 50% is around where people start think its a reversal more so than a retracement.
I believe the script will be very help as it can show you price being directional but can also let you know when the stock is getting close to moving more than it normally has or if it has moved more than it normally has. As well as being able to see if something is a retracement vs a reversal. I trade TheStrat strategy so this can be very helpful in that regard
The 50% retracement levels are default 1h and daily. You can change them and whether or not they show
In the example chart you can see we are below weekly open which is bearish and you can also see where price reverses out of the upper adr zone. As well as how much of the atr we have moved on this day in time.
Bitcoin Block Height (Total Blocks)Bitcoin Block Height by RagingRocketBull 2020
Version 1.0
Differences between versions are listed below:
ver 1.0: compare QUANDL Difficulty vs Blockchain Difficulty sources, get total error estimate
ver 2.0: compare QUANDL Hash Rate vs Blockchain Hash Rate sources, get total error estimate
ver 3.0: Total Blocks estimate using different methods
--------------------------------
This indicator estimates Bitcoin Block Height (Total Blocks) using Difficulty and Hash Rate in the most accurate way possible, since
QUANDL doesn't provide a direct source for Bitcoin Block Height (neither QUANDL:BCHAIN, nor QUANDL:BITCOINWATCH/MINING).
Bitcoin Block Height can be used in other calculations, for instance, to estimate the next date of Bitcoin Halving.
Using this indicator I demonstrate:
- that QUANDL data is not accurate and differ from Blockchain source data (industry standard), but still can be used in calculations
- how to plot a series of data points from an external csv source and compare it with another source
- how to accurately estimate Bitcoin Block Height
Features:
- compare QUANDL Difficulty source (EOD, D1) with external Blockchain Difficulty csv source (EOD, D1, embedded)
- show/hide Quandl/Blockchain Difficulty curves
- show/hide Blockchain Difficulty candles
- show/hide differences (aqua vertical lines)
- show/hide time gaps (green vertical lines)
- count source differences within data range only or for the whole history
- multiply both sources by alpha to match before comparing
- floor/round both matched sources when comparing
- Blockchain Difficulty offset to align sequences, bars > 0
- count time gaps and missing bars (as result of time gaps)
WARNING:
- This indicator hits the max 1000 vars limit, adding more plots/vars/data points is not possible
- Both QUANDL/Blockchain provide daily EOD data and must be plotted on a daily D1 chart otherwise results will be incorrect
- current chart must not have any time gaps inside the range (time gaps outside the range don't affect the calculation). Time gaps check is provided.
Otherwise hardcoded Blockchain series will be shifted forward on gaps and the whole sequence become truncated at the end => data comparison/total blocks estimate will be incorrect
Examples of valid charts that can run this indicator: COINBASE:BTCUSD,D1 (has 8 time gaps, 34 missing bars outside the range), QUANDL:BCHAIN/DIFF,D1 (has no gaps)
Usage:
- Description of output plot values from left to right:
- c_shifted - 4x blockchain plotcandles ohlc, green/black (default na)
- diff - QUANDL Difficulty
- c_shifted - Blockchain Difficulty with offset
- QUANDL Difficulty multiplied by alpha and rounded
- Blockchain Difficulty multiplied by alpha and rounded
- is_different, bool - cur bar's source values are different (1) or not (0)
- count, number of differences
- bars, total number of bars/data points in the range
- QUANDL daily blocks
- Blockchain daily blocks
- QUANDL total blocks
- Blockchain total blocks
- total_error - difference between total_blocks estimated using both sources as of cur bar, blocks
- number_of_gaps - number of time gaps on a chart
- missing_bars - number of missing bars as result of time gaps on a chart
- Color coding:
- Blue - QUANDL data
- Red - Blockchain data
- Black - Is Different
- Aqua - number of differences
- Green - number of time gaps
- by default the indicator will show lots of vertical aqua lines, 138 differences, 928 bars, total error -370 blocks
- to compare the best match of the 2 sources shift Blockchain source 1 bar into the future by setting Blockchain Difficulty offset = 1, leave alpha = 0.01 =>
this results in no vertical aqua lines, 0 differences, total_error = 0 blocks
if you move the mouse inside the range some bars will show total_error = 1 blocks => total_error <= 1 blocks
- now uncheck Round Difficulty Values flag => some filled aqua areas, 218 differences.
- now set alpha = 1 (use raw source values) instead of 0.01 => lots of filled aqua areas, 871 differences.
although there are many differences this still doesn't affect the total_blocks estimate provided Difficulty offset = 1
Methodology:
To estimate Bitcoin Block Height we need 3 steps, each step has its own version:
- Step 1: Compare QUANDL Difficulty vs Blockchain Difficulty sources and estimate error based on differences
- Step 2: Compare QUANDL Hash Rate vs Blockchain Hash Rate sources and estimate error based on differences
- Step 3: Estimate Bitcoin Block Height (Total Blocks) using different methods in the most accurate way possible
QUANDL doesn't provide block time data, but we can calculate it using the Hash Rate approximation formula:
estimated Hash rate/sec H = 2^32 * D / T, where D - Difficulty, T - block time, sec
1. block time (T) can be derived from the formula, since we already know Difficulty (D) and Hash Rate (H) from QUANDL
2. using block time (T) we can estimate daily blocks as daily time / block time
3. block height (total blocks) = cumulative sum of daily blocks of all bars on the chart (that's why having no gaps is important)
Notes:
- This code uses Pinescript v3 compatibility framework
- hash rate is in THash/s, although QUANDL falsely states in description GHash/s! THash = 1000 GHash
- you can't read files, can only embed/hardcode raw data in script
- both QUANDL and Blockchain sources have no gaps
- QUANDL and Blockchain series are different in the following ways:
- all QUANDL data is already shifted 1 bar into the future, i.e. prev day's value is shown as cur day's value => Blockchain data must be shifted 1 bar forward to match
- all QUANDL diff data > 1 bn (10^12) are truncated and have last 1-2 digits as zeros, unlike Blockchain data => must multiply both values by 0.01 and floor/round the results
- QUANDL sometimes rounds, other times truncates those 1-2 last zero digits to get the 3rd last digit => must use both floor/round
- you can only shift sequences forward into the future (right), not back into the past (left) using positive offset => only Blockchain source can be shifted
- since total_blocks is already a cumulative sum of all prev values on each bar, total_error must be simple delta, can't be also int(cum()) or incremental
- all Blockchain values and total_error are na outside the range - move you mouse cursor on the last bar/inside the range to see them
TLDR, ver 1.0 Conclusion:
QUANDL/Blockchain Difficulty source differences don't affect total blocks estimate, total error <= 1 block with avg 150 blocks/day is negligible
Both QUANDL/Blockchain Difficulty sources are equally valid and can be used in calculations. QUANDL is a relatively good stand in for Blockchain industry standard data.
Links:
QUANDL difficulty source: www.quandl.com
QUANDL hash rate source: www.quandl.com
Blockchain difficulty source (export data as csv): www.blockchain.com
RedK_Supply/Demand Volume Viewer v1Background
============
VolumeViewer is a volume indicator, that offers a simple way to estimate the movement and balance (or lack of) of supply & demand volume based on the shape of the price bar. i put this together few years ago and i have a version of this published for another platform under different names (Directional Volume, BetterVolume) in case you come across them
what is V.Viewer
=====================
The idea here is to find a "simple proxy" for estimating the demand or supply portions of a volume bar - these 2 forces have the potential to affect the current price trend so we want an easy way to track them - or to understand if a stock is in accumulation or distribution - we want to do this without having access to Level II or bid/ask data, and without having to get into the complexity of exploring the lower timeframe price & volume data
- to achieve that, we depend on a simple assumption, that the volume associated with an up move is "demand" and the volume associated with a down move is "Supply". so we basically extrapolate these supply and demand values based on how the bar looks like - a full "green" price bar / candle will be considered 100% demand, and a full "red" price bar will be considered 100% supply - a bar that opens and closes at the same level will be 50/50 split between supply & demand.
- you may say this is a "too simple" of an assumption to make, but believe me, it works :) at least at the basic scenario we need here: i'm just exploring the volume movement and finding key levels - and it provides a good improvement compared to the classic way we see volume on a chart - which is still available here in VolumeViewer.
in all cases, i consider this to be work in progress, so i'd welcome any ideas to improve (without getting too complicated) - there's already a host of great volume-based indicators that will do the multi timeframe drill down, but that's not my scope here.
Technical Jargon & calculation
===========================
1. first we calculate a score % for the volume portion that is considered demand based on the bar shape
skip this part if it sounds too technical => if you're into coding indicators, you would probably know there are couple of different concepts for that algorithm - for example, the one used in Balance Of Power formula - which i'm a big fan of - but the one i use here is different. (how?) this is my own, ant it simply applies double weight for the "wick" parts of a price bar compared to the "body of the bar" -- i did some side-by-side comparison in past and decided this one works better. you can change it in the code if you like
2. after calculating the Bull vs Bears portion of volume, we take a moving average of both for the length you set, to come up with what we consider to be the Demand vs Supply - as usual, i use a weighted moving average (WMA) here.
3. the balance or net volume between these 2 lines is calculated, then we apply a final smoothing and that's the main plot we will get
4. being a very visual person, i did my best to build up the visuals in the correct order - then also to ensure the "study title" bar is properly organized and is simple and useful (Full Volume, Supply, Demand, Net Volume).
- i wish there was a way in Pine to hide a value that i still need to visually plot but don't want it showing its value on the study title bar, but couldn't find it. so the last plot value is repeated twice.
How to use
===========
- V.Viewer is set up to show the simplified view by default for simplicity. so when you first add it to a chart, you will get only the supply vs demand view you can see in the middle pane in the above chart
- Optional / detailed mode: go into the settings, and expose all other plots, you will be able to add the classic volume histogram, and the Supply / Demand lines - note these 2 lines will be overlay-ed on top of each other - this provides an easy way to see who is in control - especially if you change the display of these 2 lines into "area" style. This is what is showing in the lower pane in the above chart.
** Exploring Key Price Levels
- the premise is, at spots where there's big lack of balance, that's where to expect to find key price levels (support / resistance) and these price levels will come into play in future so can be used to set entry / exit targets for our trades - see the example in the AAPL chart where you can easily locate these "balance or reversal levels" using the tops/bottoms/zero-crossings from the Net Volume line
** Use for longer-term Price Analysis
- we can also use this simple indicator to gain more insights (at a high level) of the price in terms of accumulation vs distribution and if the sellers or buyers are in control - for example, in the above AAPL chart, V.Viewer tells us that buyers have been in control since October 19 - even during the recent drop, demand continued to be in play - compare that to DIS chart below for the same period, where it shows that the market was dumping DIS thru the weakness. DIS was bleeding red most of the time
Final thoughts
=============
- V.Viewer is an attempt to enhance the way we see and use Volume by leveraging the shape of the price bar to estimate volume supply & demand - and the Net between the 2
- it will work for stocks and other instruments as long as there's volume data
- note that V.Viewer does not track trend. each bar is taken in isolation of prior bars - the price may be going down and V.Viewer is showing supply going up (absorption scenario?) - so i suggest you do not use it to make decisions without consulting other trend / momentum indicators - of course this is a possible improvement idea, or can be implemented in another indicator, add in trend somehow, or maybe think of making this a +100 / -100 Oscillator .. feel free to play with these thoughts
- all thoughts welcome - if this is useful to you in your trading, please share with other trades here to learn from each other
- the code is commented - please feel free to use it as you like, or build things on top of it - but please continue to credit the author of this code :)
good luck!
-
Chiki-Poki BFXLS Longs Shorts Abs Normalized Volume Pro by RRBChiki-Poki BFXLS Longs vs Shorts Absolute Normalized Volume Value Pro by RagingRocketBull 2018
Version 1.0
This indicator displays Longs vs Shorts in a side by side graph, shows volume's absolute price value and normalized volume of Longs/Shorts for the current asset. This allows for more accurate L/S comparisons (like a log scale for volume) since volume on spot exchanges (Bitstamp, Bitfinex, Coinbase etc) is measured in coins traded, not USD traded. Similarly, L/S is usually the amount of coins in open L/S positions, not their total USD value. On Bitmex and other futures exchanges volume is measured in USD traded, so you don't need to apply the Volume Absolute Price Value checkbox to compare L/S. You should always check first whether your source is measured in coins or USD.
Chiki-Poki BFXLS primarily uses *SHORTS/LONGS feeds from Bitfinex for the current crypto asset, but you can specify custom L/S source tickers instead.
This 2-in-1 works both in the Main Chart and in the indicator pane below. You can switch between Main/Sub Window panes using RMB on the indicator's name and selecting Move To/Pane Above/Below.
This indicator doesn't use volume of the current asset. It uses L/S ticker's OHLC as a source for SHORTS/LONGS volumes instead. Essentially L/S => L/S Volume == L/S
Features:
- Display Longs vs Shorts side by side graph for the current crypto asset, i.e. for BTCUSD - BTCUSDLONGS/BTCUSDSHORTS, for ETHUSD - ETHUSDLONGS/ETHUSDSHORTS etc.
- Use custom OHLC ticker sources for Longs/Shorts from different exchanges/crypto assets with/without exchange prefix.
- Plot Longs/Shorts as lines or candles
- Show/Hide L/S, Diff, MAs, ATH/ATL
- Use Longs/Shorts Volume Absolute Price Value (Price * L/S Volume) instead of Coins Traded in open L/S positions to compare total L/S value/capitalization
- Normalize L/S Volume using Price / Price MA / L/S Volume MA
- Supports any existing type of MA: SMA, EMA, WMA, HMA etc
- Volume Absolute Price Value / Normalize also works on candles
- Oscillator mode with negative axis (works in both Main Chart/Subwindow panes).
- Highlight L/S Volume spikes above L/S MAs in both lines/oscillator.
- Change L/S MA color based on a number of last rising/falling L/S bars, colorize candles
- Display L/S volume as 1000s, mlns, or blns using alpha multiplier
1. based on BFXLS Longs vs Shorts and Compare Style, uses plot*, security and custom hma functions
2. swma has a fixed length = 4, alma and linreg have additional offset and smoothing params
Notes:
- Make sure that Left Price Scale shows up with Auto Fit Data enabled. You can reattach indicator to a different scale in Style.
- It is not recommended to switch modes multiple times due to TradingView's scale reattachment bugs. You should switch between Main Chart and Sub Window only once.
- When the USD price of an asset is lower you can trade more coins but capitalization value won't be as significant as when there are less coins for a higher price. Same goes for Shorts/Longs.
Current ATH in shorts doesn't trigger a squeeze because its total value is now far less than before and we are in a bear market where it's normal to have a higher number of shorts.
- You should always subtract Hedged L/S from L/S because hedged positions are temporary - used to preserve the value of the main position in the opposite direction and should be disregarded as such.
- Low margin rates increase the probability of a move in an underlying direction because it is cheaper. High margin rates => the market is anticipating a move in this direction, thus a more expensive rate. Sudden 5-10x rate raises imply a possible reversal soon. high - 0.1%, avg - 0.01-0.02%, low - 0.001-0.005%
You can also check out:
- BFXLS Longs/Shorts on BFXData
- Bitfinex L/S margin rates and Hedged L/S on datamish
- Bitmex L/S on Coinfarm.online
BTC Price Prediction Model [Global PMI]V2🇺🇸 English Guide
1. Introduction
This indicator was created by GW Capital using Gemini Vibe Coding technology. It leverages advanced AI coding capabilities to reconstruct complex macroeconomic models into actionable trading tools.
2. Credits
Special thanks to the original model author, Marty Kendall. His research into the correlation between Bitcoin's price and macroeconomic factors lays the foundation for this algorithm.
3. Model Principles & Formula
This model calculates the "Fair Value" of Bitcoin based on four key macroeconomic pillars. It assumes that Bitcoin's price is a function of Global Liquidity, Network Security, Risk Appetite, and the Economic Cycle.
💡 Unique Insight: PMI & The 4-Year Cycle
A key distinguishing feature of this model is the hypothesis that Bitcoin's famous "4-Year Halving Cycle" may be intrinsically linked to the Global Business Cycle (PMI), rather than just supply shocks.
Therefore, the model incorporates PMI as a valuation "Amplifier".
Note: Due to TradingView data limitations, US PMI is currently used as the proxy for the global cycle.
The Formula
$$\ln(BTC) = \alpha + (1 + \beta \cdot PMI_{z}) \times $$
Global Liquidity (M2): Sum of M2 supply from US, China, Eurozone, and Japan (converted to USD). Represents the pool of fiat money available to flow into assets.
Network Security (Hashrate): Bitcoin's hashrate, representing the physical security and utility of the network.
Risk Appetite (S&P 500): Used as a proxy for global risk sentiment.
Economic Cycle (PMI Z-Score): US Manufacturing PMI is used to amplify or dampen the valuation based on where we are in the business cycle (Expansion vs. Contraction).
4. How to Use
The indicator plots the Fair Value (White Line) and four sentiment bands based on statistical deviation (Z-Score).
Sentiment Zones
🚨 Extreme Greed (Red Zone): Price > +0.3 StdDev. Historically indicates a market top or overheated sentiment.
⚠️ Greed (Orange Zone): Price > +0.15 StdDev. Bullish momentum is strong but caution is advised.
⚖️ Fair Value (White Line): The theoretical "correct" price based on macro data.
😨 Fear (Teal Zone): Price < -0.15 StdDev. Undervalued territory.
💎 Extreme Fear (Green Zone): Price < -0.3 StdDev. Historically a generational buying opportunity.
Sentiment Score (0-100)
100: Maximum Greed (Top)
50: Fair Value
0: Maximum Fear (Bottom)
5. Usage Recommendations
Timeframe: Daily (1D) or Weekly (1W) ONLY.
Reason: The underlying data sources (M2, PMI) are updated monthly. The S&P 500 and Hashrate are daily. Using this indicator on intraday charts (e.g., 15m, 1h, 4h) adds no value because the fundamental data does not change that fast.
Long-Term View: This is a macro-cycle indicator designed for identifying cycle tops and bottoms over months and years, not for day trading.
6. Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. The model relies on historical correlations which may not hold true in the future. All trading involves risk. GW Capital and the creators assume no responsibility for any trading losses.
7. Support Us ❤️
If you find this indicator useful, please Boost 👍, Comment, and add it to your Favorites! Your support keeps us going.
🇨🇳 中文说明 (Chinese Version)
1. 简介
本指标由 GW Capital 使用 Gemini Vibe Coding 技术制作。利用先进的 AI 编程能力，将复杂的宏观经济模型重构为可执行的交易工具。
2. 致谢
特别感谢模型原作者 Marty Kendall。他对这一算法的研究奠定了基础，揭示了比特币价格与宏观经济因素之间的深层联系。
3. 模型原理与公式
该模型基于四大宏观经济支柱计算比特币的“公允价值”。它假设比特币的价格是全球流动性、网络安全性、风险偏好和经济周期的函数。
💡 独家洞察：PMI 与 4年周期
本模型的一个核心独特之处在于：我们认为比特币著名的“4年减半周期”背后的真正驱动力，可能与全球商业周期 (PMI) 高度同步，而不仅仅是供应减半。
因此，模型特别引入 PMI 作为估值的“放大器” (Amplifier)。
注：由于 TradingView 数据源限制，目前采用历史数据最详尽的美国 PMI 作为全球周期的代理指标。
模型公式
$$\ln(BTC) = \alpha + (1 + \beta \cdot PMI_{z}) \times $$
全球流动性 (M2): 美、中、欧、日四大经济体的 M2 总量（折算为美元）。代表可流入资产的法币资金池。
网络安全性 (Hashrate): 比特币全网算力，代表网络的物理安全性和实用价值。
风险偏好 (S&P 500): 作为全球风险情绪的代理指标。
经济周期 (PMI Z-Score): 美国制造业 PMI 用于根据商业周期（扩张 vs 收缩）来放大或抑制估值。
4. 指标用法
指标会在图表上绘制 公允价值 (白线) 以及基于统计偏差 (Z-Score) 的四条情绪带。
情绪区间
🚨 极度贪婪 (红色区域): 价格 > +0.3 标准差。历史上通常预示市场顶部或情绪过热。
⚠️ 一般贪婪 (橙色区域): 价格 > +0.15 标准差。多头动能强劲，但需谨慎。
⚖️ 公允价值 (白线): 基于宏观数据的理论“正确”价格。
😨 一般恐惧 (青色区域): 价格 < -0.15 标准差。进入低估区域。
💎 极度恐惧 (绿色区域): 价格 < -0.3 标准差。历史上通常是代际级别的买入机会。
情绪评分 (0-100)
100: 极度贪婪 (顶部)
50: 公允价值
0: 极度恐惧 (底部)
5. 使用建议
周期: 仅限日线 (1D) 或周线 (1W)。
原因: 底层数据源（M2, PMI）是月度更新的。标普500和算力是日度更新的。在日内图表（如15分钟、1小时、4小时）上使用此指标没有任何意义，因为基本面数据不会变化得那么快。
长期视角: 这是一个宏观周期指标，旨在识别数月甚至数年的周期顶部和底部，而非用于日内交易。
6. 免责声明
本指标仅供教育和参考使用，不构成任何财务建议。该模型依赖于历史相关性，未来可能不再适用。所有交易均涉及风险。GW Capital 及制作者不对任何交易损失承担责任。
BTC Price Prediction Model [Global PMI]🇨🇳 中文说明 (Chinese Version)
1. 简介
本指标由 GW Capital 使用 Gemini Vibe Coding 技术制作。利用先进的 AI 编程能力，将复杂的宏观经济模型重构为可执行的交易工具。
2. 致谢
特别感谢模型原作者 Marty Kendall。他对这一算法的研究奠定了基础，揭示了比特币价格与宏观经济因素之间的深层联系。
3. 模型原理与公式
该模型基于四大宏观经济支柱计算比特币的“公允价值”。它假设比特币的价格是全球流动性、网络安全性、风险偏好和经济周期的函数。
模型公式
$$\ln(BTC) = \alpha + (1 + \beta \cdot PMI_{z}) \times $$
全球流动性 (M2): 美、中、欧、日四大经济体的 M2 总量（折算为美元）。代表可流入资产的法币资金池。
网络安全性 (Hashrate): 比特币全网算力，代表网络的物理安全性和实用价值。
风险偏好 (S&P 500): 作为全球风险情绪的代理指标。
经济周期 (PMI Z-Score): 美国制造业 PMI 用于根据商业周期（扩张 vs 收缩）来放大或抑制估值。
4. 指标用法
指标会在图表上绘制 公允价值 (白线) 以及基于统计偏差 (Z-Score) 的四条情绪带。
情绪区间
🚨 极度贪婪 (红色区域): 价格 > +0.3 标准差。历史上通常预示市场顶部或情绪过热。
⚠️ 一般贪婪 (橙色区域): 价格 > +0.15 标准差。多头动能强劲，但需谨慎。
⚖️ 公允价值 (白线): 基于宏观数据的理论“正确”价格。
😨 一般恐惧 (青色区域): 价格 < -0.15 标准差。进入低估区域。
💎 极度恐惧 (绿色区域): 价格 < -0.3 标准差。历史上通常是代际级别的买入机会。
情绪评分 (0-100)
100: 极度贪婪 (顶部)
50: 公允价值
0: 极度恐惧 (底部)
5. 使用建议
周期: 仅限日线 (1D) 或周线 (1W)。
原因: 底层数据源（M2, PMI）是月度更新的。标普500和算力是日度更新的。在日内图表（如15分钟、1小时、4小时）上使用此指标没有任何意义，因为基本面数据不会变化得那么快。
长期视角: 这是一个宏观周期指标，旨在识别数月甚至数年的周期顶部和底部，而非用于日内交易。
6. 免责声明
本指标仅供教育和参考使用，不构成任何财务建议。该模型依赖于历史相关性，未来可能不再适用。所有交易均涉及风险。GW Capital 及制作者不对任何交易损失承担责任。
🇺🇸 English Guide (英文说明)
1. Introduction
This indicator was created by GW Capital using Gemini Vibe Coding technology. It leverages advanced AI coding capabilities to reconstruct complex macroeconomic models into actionable trading tools.
2. Credits
Special thanks to the original model author, Marty Kendall. His research into the correlation between Bitcoin's price and macroeconomic factors lays the foundation for this algorithm.
3. Model Principles & Formula
This model calculates the "Fair Value" of Bitcoin based on four key macroeconomic pillars. It assumes that Bitcoin's price is a function of Global Liquidity, Network Security, Risk Appetite, and the Economic Cycle.
The Formula
$$\ln(BTC) = \alpha + (1 + \beta \cdot PMI_{z}) \times $$
Global Liquidity (M2): Sum of M2 supply from US, China, Eurozone, and Japan (converted to USD). Represents the pool of fiat money available to flow into assets.
Network Security (Hashrate): Bitcoin's hashrate, representing the physical security and utility of the network.
Risk Appetite (S&P 500): Used as a proxy for global risk sentiment.
Economic Cycle (PMI Z-Score): US Manufacturing PMI is used to amplify or dampen the valuation based on where we are in the business cycle (Expansion vs. Contraction).
4. How to Use
The indicator plots the Fair Value (White Line) and four sentiment bands based on statistical deviation (Z-Score).
Sentiment Zones
🚨 Extreme Greed (Red Zone): Price > +0.3 StdDev. Historically indicates a market top or overheated sentiment.
⚠️ Greed (Orange Zone): Price > +0.15 StdDev. Bullish momentum is strong but caution is advised.
⚖️ Fair Value (White Line): The theoretical "correct" price based on macro data.
😨 Fear (Teal Zone): Price < -0.15 StdDev. Undervalued territory.
💎 Extreme Fear (Green Zone): Price < -0.3 StdDev. Historically a generational buying opportunity.
Sentiment Score (0-100)
100: Maximum Greed (Top)
50: Fair Value
0: Maximum Fear (Bottom)
5. Usage Recommendations
Timeframe: Daily (1D) or Weekly (1W) ONLY.
Reason: The underlying data sources (M2, PMI) are updated monthly. The S&P 500 and Hashrate are daily. Using this indicator on intraday charts (e.g., 15m, 1h, 4h) adds no value because the fundamental data does not change that fast.
Long-Term View: This is a macro-cycle indicator designed for identifying cycle tops and bottoms over months and years, not for day trading.
6. Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. The model relies on historical correlations which may not hold true in the future. All trading involves risk. GW Capital and the creators assume no responsibility for any trading losses.
Donchian ForecastDonchian Forecast – multi-timeframe Donchian/ATR bias with ADX regime blending
Donchian Forecast is a multi-timeframe bias tool that turns classic Donchian channels into a normalized trend/mean-reversion “forecast” and a single bias value in .
It projects a short polyline path from the current price and shows how that path adapts when the market shifts from ranging to trending (via ADX).
---
Concept
1. Donchian position → direction
For each timeframe, the script measures where price sits inside its Donchian channel:
-1 = near channel low
0 = middle
+1 = near channel high
This Donchian position is multiplied by ATR to create a **price delta** (how far the forecast moves from current price).
2. Local behavior: trend vs mean-reversion around Donchian
The indicator treats the edges vs middle of the Donchian channel differently:
* By default, edges behave more “trend-like”, middle more “mean-reverting”.
* If you enable the reversed option, this logic flips (edges = mean-reverting, middle = trend-
like).
* This “local” behavior is controlled smoothly by the absolute Donchian position |pos| (not by hard zone switches).
3. Global ADX modulation (regime aware)
ADX is mapped from your chosen low → high thresholds into a signed factor in :
* ADX ≤ low → -1 (fully reversed behavior, more range/mean-reversion oriented)
* ADX ≥ high → +1 (fully normal behavior, more trend oriented)
* Values in between create a **smooth transition**.
* This global factor can:
* Keep the local behavior as is (trending regime),
* Flip it (range regime), or
* Neutralize it (indecisive regime).
4. Multi-timeframe aggregation (1x–12x chart timeframe)
* The script repeats the same logic across 12 horizons:
* 1x = chart timeframe
* 2x..12x = multiples of the chart timeframe (e.g., 5m → 10m, 15m, …; 1h → 2h, 3h, …).
* For each horizon it builds:
* Donchian position
* ATR-scaled delta (in price units)
* Locally + globally blended delta (after Donchian + ADX logic).
* These blended deltas are ATR-weighted and summed into a single bias in , which is then shown as Bias % in the on-chart table.
---
### What you see on the chart
* Forecast polyline
* Starting at the current close, the indicator draws a short chain of **up to 12 segments**:
* Segment 1: from current price → 1x projection
* Segment 2: 1x → 2x projection
* … up to 12x.
* Each segment is:
* Green when its blended delta is ≥ 0 (upward bias)
* Red when its blended delta is < 0 (downward bias)
* This is not future price, but a synthetic path showing how the Donchian/ATR/ADX model “expects” price to drift across multiple horizons.
* Bias table (top-center)
* `Bias: X.Y%`
* > 0% (green) → net upward bias across horizons
* < 0% (red) → net downward bias
* Magnitude (e.g., ±70–100%) ≈ strength of the directional skew.
* `ADX:` current ADX value (from your DMI settings).
* `ADXBlend:` the signed ADX factor in :
* +1 ≈ fully “trend-interpretation” of Donchian behavior
* 0 ≈ neutral / mixed regime
* -1 ≈ fully “reversed/mean-reversion interpretation”
---
Inputs & settings
Core Donchian / ATR
* Donchian Length – lookback for Donchian high/low on each horizon.
* Price Source – input series used for position inside the Donchian channel (default: close).
* ATR Length – ATR lookback for all horizons.
* ATR Multiplier – scales the size of each forecast step in price units (higher = longer segments / more aggressive forecast).
*Local behavior at high ADX
* Reversed local blend at high ADX?
* Off (default) – edges behave more trend-like, middle more mean-reverting.
* On – flips that logic (edges more mean-reverting, middle more trend-like).
* The actual effect is always modulated by the global ADX factor, so you can experiment with how the regime logic feels in different markets.
Global ADX blending
* DMI DI Length – period for the DI+ and DI- components.
* ADX Smoothing – smoothing length for ADX.
* ADX low (mean-rev zone) – below this level, the global factor pushes behavior toward reversal/range logic .
* ADX high (trend zone) – above this level, the global factor pushes behavior toward **trend logic**.
* Values between low and high create a smooth blend rather than a hard on/off switch.
---
How to use it (examples)
* Directional bias dashboard
* Use the Bias % as a compact summary of multi-horizon Donchian/ATR/ADX conditions:
* Consider only trades aligned with the sign of Bias (e.g., longs only when Bias > 0).
* Use the magnitude to filter for **strong vs weak** directional contexts.
* Regime-aware context
* Watch ADX and ADXBlend:
* High ADX & ADXBlend ≈ +1 → favor trend-continuation ideas.
* Low ADX & ADXBlend ≈ -1 → favor range/mean-reversion ideas.
* Around 0 → mixed/transition regimes; forecasts will be more muted.
* Visual sanity check for systems
* Overlay Donchian Forecast on your usual entries/exits to see:
* When your system trades **with** the multi-TF Donchian bias.
* When it trades **against** it (possible fade setups or no-trade zones).
This script does not generate entry or exit signals by itself. It is a contextual/forecast tool meant to sit on top of your own trading logic.
---
Notes
* Works on most symbols and timeframes; higher-timeframe multiples are built from the chart timeframe.
* The forecast line is a model-based projection, not a prediction or guarantee of future price.
* Always combine this with your own risk management, testing, and judgement. This is for educational and analytical purposes only and is not financial advice.
Minervini VCP Pattern -Indian ContextThis script implements Mark Minervini's Trend Template and VCP (Volatility Contraction Pattern) pattern, specifically adapted for Indian stock markets (NSE). It helps identify stocks that are in strong uptrends and ready to break out.
Core Concepts Explained
1. What is the Minervini Trend Template?
Mark Minervini's method identifies stocks in Stage 2 uptrends - the sweet spot where institutional money is accumulating and stocks show the strongest momentum. Think of it as finding stocks that are "leaders" rather than "laggards."
2. What is VCP (Volatility Contraction Pattern)?
A VCP occurs when:
Stock price consolidates (moves sideways) after an uptrend
Price swings get tighter and tighter (like a coiled spring)
Volume dries up (fewer people trading)
Then it breaks out with force.
You can customize the strategy settings without editing code.
Key Settings:
Minimum Price (₹50): Filters out penny stocks that are too volatile
Min Distance from 52W Low (30%): Stock should be at least 30% above its yearly low
Max Distance from 52W High (25%): Stock should be within 25% of its yearly high (showing strength)
Moving Average Periods: 10, 50, 150, 200 days (industry standard)
Minimum Volume (100,000 shares): Ensures the stock is liquid enough to trade
Indian Market Adaptation: The default values (₹50 minimum, volume thresholds) are adjusted for NSE stocks, which behave differently than US markets.
The script pulls weekly chart data even when you're viewing daily charts.
Why it matters: Weekly trends are more reliable than daily noise. Professional traders use weekly charts to confirm the bigger picture.
What are Moving Averages (MAs)?
Simple averages of closing prices over X days
They smooth out price action to show trends
Think of them as the "average cost" of buyers over different time periods
The 4 Key MAs:
10 MA (Fast): Very short-term trend
50 MA: Short to medium-term trend
150 MA: Medium to long-term trend
200 MA: Long-term trend (the "grandfather" of all MAs)
Why Weekly MAs?
The script also calculates 10 and 50 MAs on weekly data for additional confirmation of the bigger trend.
The script Finds the highest and lowest prices over the past 52 weeks (1 year).
Why it matters:
Stocks near 52-week highs are showing strength (institutions buying)
Stocks far from 52-week lows have "room to run" upward
This is a psychological level that influences trader behaviour.
What is Volume here ?
The number of shares traded each day
High volume = many traders interested (conviction)
Low volume = lack of interest (weakness or consolidation)
Volume in VCP:
During consolidation (sideways movement), volume should dry up - this shows sellers are exhausted and buyers are holding. When volume spikes on a breakout, it confirms the move.
NSE Context: Indian stocks often have different volume patterns than US stocks, so the 50-day average is used as a baseline.
Relative Strength vs Nifty:
Example:
If your stock is up 20% and Nifty is up 10%, your stock has strong RS
If your stock is up 5% and Nifty is up 15%, your stock has weak RS (avoid it!)
Why it matters: The best performing stocks almost always have strong relative strength before major moves.
The 13 Minervini Conditions:-
Condition 1: Price > 50/150/200 MA
Meaning: Current price must be above ALL three major moving averages.
Why: This confirms the stock is in a clear uptrend. If price is below these MAs, the stock is weak or in a downtrend.
Condition 2: MA 50 > 150 > 200
Meaning: The moving averages themselves must be in proper order.
Analogy: Think of this like layers in a cake - short-term on top, long-term at bottom. If they're tangled, the trend is unclear.
Condition 3: 200 MA Rising (1 Month)
Meaning: The 200 MA today must be higher than it was 20 days ago.
Why: This confirms the long-term trend is UP, not flat or down. The means "20 bars ago."
Condition 4: 50 MA Rising
Meaning: The 50 MA today must be higher than 5 days ago.
Why: Confirms short-term momentum is accelerating upward.
Condition 5: Within 25% of 52-Week High
Meaning: Current price should be within 25% of its 1-year high.
Example:
52-week high = ₹1000
Current price must be above ₹750 (within 25%)
Why: Strong stocks stay near their highs. Weak stocks fall far from highs.
Condition 6: 30%+ Above 52-Week Low (OPTIONAL)
Meaning: Stock should be at least 30% above its yearly low.
Note: The script marks this as "SECONDARY - Optional" because the other conditions are more important. However, it's still a good confirmation.
Condition 7: Price > 10 MA
Meaning: Very short-term strength - price above the 10-day moving average.
Why: Ensures the stock hasn't just rolled over in the immediate term.
Condition 8: Price >= ₹50
Meaning: Filters out stocks below ₹50.
Why: In Indian markets, stocks below ₹50 tend to be penny stocks with poor liquidity and higher manipulation risk.
Condition 9: Weekly Uptrend
Meaning: On the weekly chart, price must be above both weekly MAs, and they must be properly aligned.
Why: Confirms the bigger picture trend, not just daily fluctuations.
Condition 10: 150 MA Rising
Meaning: The 150 MA is trending upward over the past 10 days.
Why: Another confirmation of medium-term trend health.
Condition 11: Sufficient Volume
Meaning: Average volume must exceed 100,000 shares (or your custom setting).
Why: Ensures you can actually buy/sell the stock without moving the price too much (liquidity).
Condition 12: RS vs Nifty Strong
Meaning: The stock's relative strength vs Nifty must be improving.
Why: You want stocks that are outperforming the market, not underperforming.
Condition 13: Nifty in Uptrend
Meaning: The Nifty 50 index itself must be above its 50 MA.
Why: "A rising tide lifts all boats." It's easier to make money in individual stocks when the overall market is bullish.
VCP Requirements:
Volatility Contracting: Price swings getting tighter (coiling spring)
Volume Drying Up: Fewer shares trading + trending lower
The Setup: When volatility contracts and volume dries up WHILE all 13 trend conditions are met, you have a VCP setup ready to explode.
What You See on Chart:
Colored Lines: 10 MA (green), 50 MA (blue), 150 MA (orange), 200 MA (red)
Blue Background: Trend template conditions met (watch zone)
Green Background: Full VCP setup detected (buy zone)
↟ Symbol Below Price: New VCP buy signal just triggered
Information Table:
What it does: Creates a checklist table on your chart showing the status of all conditions.
Table Structure:
Column 1: Condition name
Column 2: Status (✓ green = met, ✗ red = not met)
Final Row: Shows "BUY" (green) or "WAIT" (red) based on full VCP setup status.
Dos:
Example:
Account size: ₹5,00,000
Risk per trade: 1% = ₹5,000
Entry: ₹1000
Stop loss: ₹920 (8% below)
Distance to stop: ₹80
Shares to buy: ₹5,000 / ₹80 = 62 shares
Exit Strategy:
Sell 1/3 at +20% profit
Sell another 1/3 at +40% profit
Let the final 1/3 run with a trailing stop
Always exit if price closes below 10 MA on heavy volume
What This Script Does NOT Do:
Guarantee profits - No strategy works 100% of the time
Account for news events - Earnings, regulatory changes, etc.
Consider fundamentals - Company financials, debt, management quality
Adapt to market crashes - Works best in bull markets
Best Market Conditions:
✅ Nifty in uptrend (above 50 MA)
✅ Market breadth positive (more stocks advancing)
✅ Sector rotation happening
❌ Avoid in bear markets or high volatility periods
References:
Trade Like a Stock Market Wizard by Mark Minervini
Think & Trade Like a Champion by Mark Minervini
Chart attached: AU Small Finance Bank as on EoD dated 28/11/25
This script is a powerful tool for educational purpose only, remember: It's a tool, not a crystal ball. Use it to find high-probability setups, then apply proper risk management and patience. Good luck!
Omega Correlation [OmegaTools]Omega Correlation (Ω CRR) is a cross-asset analytics tool designed to quantify both the strength of the relationship between two instruments and the tendency of one to move ahead of the other. It is intended for traders who work with indices, futures, FX, commodities, equities and ETFs, and who require something more robust than a simple linear correlation line.
The indicator operates in two distinct modes, selected via the “Show” parameter: Correlation and Anticipation. In Correlation mode, the script focuses on how tightly the current chart and the chosen second asset move together. In Anticipation mode, it shifts to a lead–lag perspective and estimates whether the second asset tends to behave as a leader or a follower relative to the symbol on the chart.
In both modes, the core inputs are the chart symbol and a user-selected second symbol. Internally, both assets are transformed into normalized log-returns: the script computes logarithmic returns, removes short-term mean and scales by realized volatility, then clips extreme values. This normalisation allows the tool to compare behaviour across assets with different price levels and volatility profiles.
In Correlation mode, the indicator computes a composite correlation score that typically ranges between –1 and +1. Values near +1 indicate strong and persistent positive co-movement, values near zero indicate an unstable or weak link, and values near –1 indicate a stable anti-correlation regime. The composite score is constructed from three components.
The first component is a normalized return co-movement measure. After transforming both instruments into normalized returns, the script evaluates how similar those returns are bar by bar. When the two assets consistently deliver returns of similar sign and magnitude, this component is high and positive. When they frequently diverge or move in opposite directions, it becomes negative. This captures short-term co-movement in a volatility-adjusted way.
The second component focuses on high–low swing alignment. Rather than looking only at closes, it examines the direction of changes in highs and lows for each bar. If both instruments are printing higher highs and higher lows together, or lower highs and lower lows together, the swing structure is considered aligned. Persistent alignment contributes positively to the correlation score, while repeated mismatches between the swing directions reduce it. This helps differentiate between superficial price noise and structural similarity in trend behaviour.
The third component is a classical Pearson correlation on closing prices, computed over a longer lookback. This serves as a stabilising backbone that summarises general co-movement over a broader window. By combining normalized return co-movement, swing alignment and standard price correlation with calibrated weights, the Correlation mode provides a richer view than a single linear measure, capturing both short-term dynamic interaction and longer-term structural linkage.
In Anticipation mode, Omega Correlation estimates whether the second asset tends to lead or lag the current chart. The output is again a continuous score around the range. Positive values suggest that the second asset is acting more as a leader, with its past moves bearing informative value for subsequent moves of the chart symbol. Negative values indicate that the second asset behaves more like a laggard or follower. Values near zero suggest that no stable lead–lag structure can be identified.
The anticipation score is built from four elements inspired by quantitative lead–lag and price discovery analysis. The first element is a residual lead correlation, conceptually similar to Granger-style logic. The script first measures how much of the chart symbol’s normalized returns can be explained by its own lagged values. It then removes that component and studies the correlation between the residuals and lagged returns of the second asset. If the second asset’s past returns consistently explain what the chart symbol does beyond its own autoregressive behaviour, this residual correlation becomes significantly positive.
The second element is an asymmetric lead–lag structure measure. It compares the strength of relationships in both directions across multiple lags: the correlation of the current symbol with lagged versions of the second asset (candidate leader) versus the correlation of lagged values of the current symbol with the present values of the second asset. If the forward direction (second asset leading the first) is systematically stronger than the backward direction, the structure is skewed toward genuine leadership of the second asset.
The third element is a relative price discovery score, constructed by building a dynamic hedge ratio between the two prices and defining a spread. The indicator looks at how changes in each asset contribute to correcting deviations in this spread over time. When the chart symbol tends to do most of the adjustment while the second asset remains relatively stable, it suggests that the second asset is taking a greater role in determining the equilibrium price and the chart symbol is adjusting to it. The difference in adjustment intensity between the two instruments is summarised into a single score.
The fourth element is a breakout follow-through causality component. The script scans for breakout events on the second asset, where its price breaks out of a recent high or low range while the chart symbol has not yet done so. It then evaluates whether the chart symbol subsequently confirms the breakout direction, remains neutral, or moves against it. Events where the second asset breaks and the first asset later follows in the same direction add positive contribution, while failed or contrarian follow-through reduce this component. The contribution is also lightly modulated by the strength of the breakout, via the underlying normalized return.
The four elements of the Anticipation mode are combined into a single leading correlation score, providing a compact and interpretable measure of whether the second asset currently behaves as an effective early signal for the symbol you trade.
To aid interpretation, Omega Correlation builds dynamic bands around the active series (correlation or anticipation). It estimates a long-term central tendency and a typical deviation around it, plotting upper and lower bands that highlight unusually high or low values relative to recent history. These bands can be used to distinguish routine fluctuations from genuinely extreme regimes.
The script also computes percentile-based levels for the correlation series and uses them to track two special price levels on the main chart: lost correlation levels and gained correlation levels. When the correlation drops below an upper percentile threshold, the current price is stored as a lost correlation level and plotted as a horizontal line. When the correlation rises above a lower percentile threshold, the current price is stored as a gained correlation level. These levels mark zones where a historically strong relationship between the two markets broke down or re-emerged, and can be used to frame divergence, convergence and spread opportunities.
An information panel summarises, in real time, whether the second asset is behaving more as a leading, lagging or independent instrument according to the anticipation score, and suggests whether the current environment is more conducive to de-alignment, re-alignment or classic spread behaviour based on the correlation regime. This makes the tool directly interpretable even for users who are not familiar with all the underlying statistical details.
Typical applications for Omega Correlation include intermarket analysis (for example, index vs index, commodity vs related equity sector, FX vs bonds), dynamic hedge sizing, regime detection for algorithmic strategies, and the identification of lead–lag structures where a macro driver or benchmark can be monitored as an early signal for the instrument actually traded. The indicator can be applied across intraday and higher timeframes, with the understanding that the strength and nature of relationships will differ across horizons.
Omega Correlation is designed as an advanced analytical framework, not as a standalone trading system. Correlation and lead–lag relationships are statistical in nature and can change abruptly, especially around macro events, regime shifts or liquidity shocks. A positive anticipation reading does not guarantee that the second asset will always move first, and a high correlation regime can break without warning. All outputs of this tool should be combined with independent analysis, sound risk management and, when appropriate, backtesting or forward testing on the user’s specific instruments and timeframes.
The intention behind Omega Correlation is to bring techniques inspired by quantitative research, such as normalized return analysis, residual correlation, asymmetric lead–lag structure, price discovery logic and breakout event studies, into an accessible TradingView indicator. It is intended for traders who want a structured, professional way to understand how markets interact and to incorporate that information into their discretionary or systematic decision-making processes.
Super momentum DBSISuper momentum DBSI: The Ultimate Guide
1. What is this Indicator?
The Super momentum DBSI is a "Consensus Engine." Instead of relying on a single line (like an RSI) to tell you where the market is going, this tool calculates 33 distinct technical indicators simultaneously for every single candle.
It treats the market like a democracy. It asks 33 mathematical "voters" (Momentum, Trend, Volume, Volatility) if they are Bullish or Bearish.
If 30 out of 33 say "Buy," the score is high (Yellow), and the trend is extremely strong.
If only 15 say "Buy," the score is low (Teal), and the trend is weak or choppy.
2. Visual Guide: How to Read the Numbers
The Scores
Top Number (Bears): Represents Selling Pressure.
Bottom Number (Bulls): Represents Buying Pressure.
The Colors (The Traffic Lights)
The colors are your primary signal. They tell you who is currently winning the war.
🟡 YELLOW (Dominance):
This indicates the Winning Side.
If the Bottom Number is Yellow, Bulls are in control.
If the Top Number is Yellow, Bears are in control.
🔴 RED (Weakness):
This appears on the Top. It means Bears are present but losing.
🔵 TEAL (Weakness):
This appears on the Bottom. It means Bulls are present but losing.
3. Trading Strategy
Scenario A: The "Strong Buy" (Long Entry)
The Setup: You are looking for a shift in momentum where Buyers overwhelm Sellers.
Watch the Bottom Number: Wait for it to turn Yellow.
Confirm Strength: Ensure the score is above 15 and rising (e.g., 12 → 18 → 22).
Check the Top: The Top Number should be Red and low (below 10).
Trigger: Enter on the candle close.
Scenario B: The "Strong Sell" (Short Entry)
The Setup: You are looking for Sellers to crush the Buyers.
Watch the Top Number: Wait for it to turn Yellow.
Confirm Strength: Ensure the score is above 15 and rising.
Check the Bottom: The Bottom Number should be Teal and low.
Trigger: Enter on the candle close.
Scenario C: The "No Trade Zone" (Choppy Market)
The Setup: The market is confused.
Visual: Top is Red, Bottom is Teal.
Meaning: NOBODY IS WINNING. There is no Yellow number.
Action: Do not trade. This usually happens during lunch hours, weekends, or right before big news. This filter alone will save you from many false breakouts.
4. What is Inside? (The 33 Indicators)
To give you confidence in the signals, here is exactly what the script is checking:
Group 1: Momentum (Oscillators)
Detects if price is moving fast.
RSI (Relative Strength Index)
CCI (Commodity Channel Index)
Stochastic
Williams %R
Momentum
Rate of Change (ROC)
Ultimate Oscillator
Awesome Oscillator
True Strength Index (TSI)
Stoch RSI
TRIX
Chande Momentum Oscillator
Group 2: Trend Direction
Detects the general path of the market.
13. MACD
14. Parabolic SAR
15. SuperTrend
16. ALMA (Moving Average)
17. Aroon
18. ADX (Directional Movement)
19. Coppock Curve
20. Ichimoku Conversion Line
21. Hull Moving Average
Group 3: Price Action
Detects where price is relative to averages.
22. Price vs EMA 20
23. Price vs EMA 50
24. Price vs EMA 200
Group 4: Volume & Force
Detects if there is money behind the move.
25. Money Flow Index (MFI)
26. On Balance Volume (OBV)
27. Chaikin Money Flow (CMF)
28. VWAP (Intraday)
29. Elder Force Index
30. Ease of Movement
Group 5: Volatility
Detects if price is pushing the outer limits.
31. Bollinger Bands
32. Keltner Channels
33. Donchian Channels
5. Pro Tips for Success
Don't Catch Knives: If the Bear score (Top) is Yellow and 25+, do not try to buy the dip. Wait for the Yellow score to break.
Exit Early: If you are Long and the Yellow Bull score drops from 28 to 15 in one candle, TAKE PROFIT. The momentum has died.
Use Higher Timeframes: This indicator works best on 15m, 1H, and 4H charts. On the 1m chart, it may be too volatile.
Directional Imbalance Index [BigBeluga]🔵 OVERVIEW
The Directional Imbalance Index is designed to track market strength by counting how often price sets new highs or lows over a defined lookback period. Every time a bar forms a new extreme, the indicator records a +1 count for either bullish (highs) or bearish (lows). These counts are aggregated into a rolling calculation, allowing traders to see which side dominates and how directional imbalance evolves.
🔵 CONCEPTS
Each new highest high → adds a bullish count (+1).
Each new lowest low → adds a bearish count (+1).
Counts are stored inside arrays over a user-defined Calculation Period .
for i = 0 to period-1
h = high
l = low
if h == upper
countUp.push(1)
if l == lower
countDn.push(1)
The balance between bullish and bearish counts highlights dominance and imbalance.
Normalized percentages help compare both sides (e.g., 65% bullish vs 35% bearish).
🔵 FEATURES
Counts new highs/lows over a chosen Highest/Lowest Length .
Aggregates values over a rolling Calculation Period .
Plots cumulative bullish vs bearish totals in the subchart.
Displays % share of bulls vs bears from total counts.
On-chart labels mark bars where a count was added.
Plots reference lines of the current upper (high) and lower (low) ranges.
Dynamic fill between bullish/bearish plots to visualize which side dominates.
🔵 HOW TO USE
Look for persistent bullish imbalance (bull % > bear %) as confirmation of upward momentum.
Look for persistent bearish imbalance (bear % > bull %) as confirmation of downward momentum.
Watch for shifts in % dominance — often early signs of trend reversal or weakening strength.
Use labels on the chart to visually confirm which bars contributed to directional bias.
Combine with trend or volume tools to confirm whether imbalance aligns with market direction.
🔵 CONCLUSION
The Directional Imbalance Index offers a systematic way to measure directional pressure. By counting how often price pushes into new territory, the indicator reveals whether bulls or bears are taking control. This makes it a valuable tool for detecting early signs of trend continuation or exhaustion, helping traders align with the side most likely to dominate.
Smart Money Concepts [XoRonX]# Smart Money Concepts (SMC) - Advanced Trading Indicator
## 📊 Deskripsi
**Smart Money Concepts ** adalah indicator trading komprehensif yang menggabungkan konsep Smart Money Trading dengan berbagai alat teknikal analisis modern. Indicator ini dirancang untuk membantu trader mengidentifikasi pergerakan institusional (smart money), struktur pasar, zona supply/demand, dan berbagai sinyal trading penting.
Indicator ini mengintegrasikan multiple timeframe analysis, order blocks detection, fair value gaps, fibonacci retracement, volume profile, RSI multi-timeframe, dan moving averages dalam satu platform yang powerful dan mudah digunakan.
---
## 🎯 Fitur Utama
### 1. **Smart Money Structure**
- **Internal Structure** - Struktur pasar jangka pendek untuk entry presisi
- **Swing Structure** - Struktur pasar jangka panjang untuk trend analysis
- **BOS (Break of Structure)** - Konfirmasi kelanjutan trend
- **CHoCH (Change of Character)** - Deteksi potensi reversal
### 2. **Order Blocks**
- **Internal Order Blocks** - Zona demand/supply jangka pendek
- **Swing Order Blocks** - Zona demand/supply jangka panjang
- Filter otomatis berdasarkan volatilitas (ATR/Range)
- Mitigation tracking (High/Low atau Close)
- Customizable display (jumlah order blocks yang ditampilkan)
### 3. **Equal Highs & Equal Lows (EQH/EQL)**
- Deteksi otomatis equal highs/lows
- Indikasi liquidity zones
- Threshold adjustment untuk sensitivitas
- Visual lines dan labels
### 4. **Fair Value Gaps (FVG)**
- Multi-timeframe FVG detection
- Auto threshold filtering
- Bullish & Bearish FVG boxes
- Extension control
- Color customization
### 5. **Premium & Discount Zones**
- Premium Zone (75-100% dari range)
- Equilibrium Zone (47.5-52.5% dari range)
- Discount Zone (0-25% dari range)
- Auto-update berdasarkan swing high/low
### 6. **Fibonacci Retracement**
- **Equilibrium to Discount** - Fib dari EQ ke discount zone
- **Equilibrium to Premium** - Fib dari EQ ke premium zone
- **Discount to Premium** - Fib full range
- Reverse option
- Show/hide lines
- Custom colors
### 7. **Volume Profile (VRVP)**
- Visible Range Volume Profile
- Point of Control (POC)
- Value Area (70% volume)
- Auto-adjust rows
- Placement options (Left/Right)
- Width customization
### 8. **RSI Multi-Timeframe**
- Monitor 3 timeframes sekaligus
- Overbought/Oversold signals
- Visual table display
- Color-coded signals (Red OB, Green OS)
- Customizable position & size
### 9. **Moving Averages**
- 3 Moving Average lines
- Pilihan tipe: EMA, SMA, WMA
- Automatic/Manual period mode
- Individual color & width settings
- Cross alerts (MA vs MA, Price vs MA)
### 10. **Multi-Timeframe Levels**
- Support up to 5 different timeframes
- Previous high/low levels
- Custom line styles
- Color customization
### 11. **Candle Color**
- Color candles berdasarkan trend
- Bullish = Green, Bearish = Red
- Optional toggle
---
## 🛠️ Cara Penggunaan
### **A. Setup Awal**
1. **Tambahkan Indicator ke Chart**
- Buka TradingView
- Klik "Indicators" → "My Scripts" atau paste code
- Pilih "Smart Money Concepts "
2. **Pilih Mode Display**
- **Historical**: Tampilkan semua struktur (untuk backtesting)
- **Present**: Hanya tampilkan struktur terbaru (clean chart)
3. **Pilih Style**
- **Colored**: Warna berbeda untuk bullish/bearish
- **Monochrome**: Tema warna abu-abu
---
### **B. Penggunaan Fitur**
#### **1. Smart Money Structure**
**Internal Structure (Real-time):**
- ✅ Aktifkan "Show Internal Structure"
- Pilih tampilan: All, BOS only, atau CHoCH only
- Gunakan untuk entry timing presisi
- Filter confluence untuk mengurangi noise
**Swing Structure:**
- ✅ Aktifkan "Show Swing Structure"
- Pilih tampilan struktur bullish/bearish
- Adjust "Swings Length" (default: 50)
- Gunakan untuk konfirmasi trend utama
**Tips:**
- BOS = Konfirmasi trend continuation
- CHoCH = Warning untuk possible reversal
- Tunggu price retest ke order block setelah BOS
---
#### **2. Order Blocks**
**Setup:**
- ✅ Aktifkan Internal/Swing Order Blocks
- Set jumlah blocks yang ditampil (1-20)
- Pilih filter: ATR atau Cumulative Mean Range
- Pilih mitigation: Close atau High/Low
**Cara Trading:**
1. Tunggu BOS/CHoCH terbentuk
2. Identifikasi order block terdekat
3. Wait for price pullback ke order block
4. Entry saat price respek order block (rejection)
5. Stop loss di bawah/atas order block
6. Target: swing high/low berikutnya
**Color Code:**
- 🔵 Light Blue = Internal Bullish OB
- 🔴 Light Red = Internal Bearish OB
- 🔵 Dark Blue = Swing Bullish OB
- 🔴 Dark Red = Swing Bearish OB
---
#### **3. Equal Highs/Lows (EQH/EQL)**
**Setup:**
- ✅ Aktifkan "Equal High/Low"
- Set "Bars Confirmation" (default: 3)
- Adjust threshold (0-0.5, default: 0.1)
**Interpretasi:**
- EQH = Liquidity di atas, kemungkinan sweep lalu dump
- EQL = Liquidity di bawah, kemungkinan sweep lalu pump
- Biasanya smart money akan grab liquidity sebelum move besar
**Trading Strategy:**
- Wait for EQH/EQL formation
- Anticipate liquidity grab
- Entry setelah sweep dengan konfirmasi (order block, FVG, CHoCH)
---
#### **4. Fair Value Gaps (FVG)**
**Setup:**
- ✅ Aktifkan "Fair Value Gaps"
- Pilih timeframe (default: chart timeframe)
- Enable/disable auto threshold
- Set extension bars
**Cara Trading:**
1. Bullish FVG = Support zone untuk buy
2. Bearish FVG = Resistance zone untuk sell
3. Price tends to fill FVG (retest)
4. Entry saat price kembali ke FVG
5. Partial fill = valid, full fill = invalidated
**Tips:**
- FVG + Order Block = High probability setup
- Multi-timeframe FVG lebih kuat
- Unfilled FVG = strong momentum
---
#### **5. Premium & Discount Zones**
**Setup:**
- ✅ Aktifkan "Premium/Discount Zones"
- Zones akan auto-update berdasarkan swing high/low
**Interpretasi:**
- 🟢 **Discount Zone** = Area BUY (price murah)
- ⚪ **Equilibrium** = Neutral (50%)
- 🔴 **Premium Zone** = Area SELL (price mahal)
**Trading Strategy:**
- BUY dari discount zone
- SELL dari premium zone
- Avoid trading di equilibrium
- Combine dengan structure confirmation
---
#### **6. Fibonacci Retracement**
**Setup:**
- Pilih Fib yang ingin ditampilkan:
- Equilibrium to Discount
- Equilibrium to Premium
- Discount to Premium
- Toggle show lines
- Enable reverse jika perlu
- Custom colors
**Key Levels:**
- 0.236 = Shallow retracement
- 0.382 = Common retracement
- 0.5 = 50% golden level
- 0.618 = Golden ratio (penting!)
- 0.786 = Deep retracement
**Cara Pakai:**
- 0.618-0.786 = Ideal entry zone dalam trend
- Combine dengan order blocks
- Wait for confirmation candle
---
#### **7. Volume Profile (VRVP)**
**Setup:**
- ✅ Aktifkan "Show Volume Profile"
- Set jumlah rows (10-100)
- Adjust width (5-50%)
- Pilih placement (Left/Right)
- Enable POC dan Value Area
**Interpretasi:**
- **POC (Point of Control)** = Harga dengan volume tertinggi = magnet
- **Value Area** = 70% volume = fair price range
- **Low Volume Nodes** = Weak support/resistance
- **High Volume Nodes** = Strong support/resistance
**Trading:**
- POC acts as support/resistance
- Price tends to return to POC
- Breakout dari Value Area = momentum
---
#### **8. RSI Multi-Timeframe**
**Setup:**
- ✅ Aktifkan "Show RSI Table"
- Set 3 timeframes (default: chart, 5m, 15m)
- Set RSI period (default: 14)
- Set Overbought level (default: 70)
- Set Oversold level (default: 30)
- Pilih posisi & ukuran table
**Interpretasi:**
- 🟢 **OS (Oversold)** = RSI ≤ 30 = Kondisi jenuh jual
- 🔴 **OB (Overbought)** = RSI ≥ 70 = Kondisi jenuh beli
- **-** = Neutral zone
**Trading Strategy:**
1. Multi-timeframe alignment = strong signal
2. OS + Bullish structure = BUY signal
3. OB + Bearish structure = SELL signal
4. Divergence RSI vs Price = reversal warning
**Contoh:**
- TF1: OS, TF2: OS, TF3: OS + Price di discount zone = STRONG BUY
---
#### **9. Moving Averages**
**Setup:**
- Pilih MA Type: EMA, SMA, atau WMA (berlaku untuk ketiga MA)
- Pilih Period Mode: Automatic atau Manual
- Set period untuk MA 1, 2, 3 (default: 20, 50, 100)
- Custom color & width per MA
- ✅ Enable Cross Alerts
**Interpretasi:**
- **Golden Cross** = MA fast cross above MA slow = Bullish
- **Death Cross** = MA fast cross below MA slow = Bearish
- Price above all MAs = Strong uptrend
- Price below all MAs = Strong downtrend
**Trading Strategy:**
1. MA1 (20) = Short-term trend
2. MA2 (50) = Medium-term trend
3. MA3 (100) = Long-term trend
**Entry Signals:**
- Price bounce dari MA dalam trend = continuation
- MA cross dengan konfirmasi structure = entry
- Multiple MA confluence = strong support/resistance
**Alerts Available:**
- MA1 cross MA2/MA3
- MA2 cross MA3
- Price cross any MA
---
#### **10. Multi-Timeframe Levels**
**Setup:**
- Enable HTF Level 1-5
- Set timeframes (contoh: 5m, 1H, 4H, D, W)
- Pilih line style (solid/dashed/dotted)
- Custom colors
**Cara Pakai:**
- Previous high/low dari HTF = strong S/R
- Breakout HTF level = significant move
- Multiple HTF levels confluence = major zone
---
### **C. Trading Setup Combination**
#### **Setup 1: High Probability Buy (Bullish)**
1. ✅ Swing structure: Bullish BOS
2. ✅ Price di Discount Zone
3. ✅ Pullback ke Bullish Order Block
4. ✅ Bullish FVG di bawah
5. ✅ RSI Multi-TF: Oversold
6. ✅ Price bounce dari MA
7. ✅ POC/Value Area support
8. ✅ Fibonacci 0.618-0.786 retracement
**Entry:** Saat price reject dari order block dengan confirmation candle
**Stop Loss:** Below order block
**Target:** Swing high atau premium zone
---
#### **Setup 2: High Probability Sell (Bearish)**
1. ✅ Swing structure: Bearish BOS
2. ✅ Price di Premium Zone
3. ✅ Pullback ke Bearish Order Block
4. ✅ Bearish FVG di atas
5. ✅ RSI Multi-TF: Overbought
6. ✅ Price reject dari MA
7. ✅ POC/Value Area resistance
8. ✅ Fibonacci 0.618-0.786 retracement
**Entry:** Saat price reject dari order block dengan confirmation candle
**Stop Loss:** Above order block
**Target:** Swing low atau discount zone
---
#### **Setup 3: Liquidity Grab (EQH/EQL)**
1. ✅ Identifikasi EQH atau EQL
2. ✅ Wait for liquidity sweep
3. ✅ Konfirmasi dengan CHoCH
4. ✅ Order block terbentuk setelah sweep
5. ✅ Entry saat retest order block
---
### **D. Tips & Best Practices**
**Risk Management:**
- Selalu gunakan stop loss
- Risk 1-2% per trade
- Risk:Reward minimum 1:2
- Jangan over-leverage
**Confluence adalah Kunci:**
- Minimal 3-4 konfirmasi sebelum entry
- Lebih banyak konfirmasi = higher probability
- Quality over quantity
**Timeframe Analysis:**
- HTF (Higher Timeframe) = Trend direction
- LTF (Lower Timeframe) = Entry timing
- Align dengan HTF trend
**Backtesting:**
- Gunakan mode "Historical"
- Test strategy di berbagai market condition
- Record dan analyze hasil
**Market Condition:**
- Trending market = Follow BOS, use order blocks
- Ranging market = Use premium/discount zones, EQH/EQL
- High volatility = Wider stops, wait for clear structure
**Avoid:**
- Trading di equilibrium zone
- Entry tanpa konfirmasi
- Fighting the trend
- Overleveraging
- Emotional trading
---
## 📈 Recommended Settings
### **For Scalping (1m - 5m):**
- Internal Structure: ON
- Swing Structure: OFF
- Order Blocks: Internal only
- RSI Timeframes: 1m, 5m, 15m
- MA Periods: 9, 21, 50
### **For Day Trading (15m - 1H):**
- Internal Structure: ON
- Swing Structure: ON
- Order Blocks: Both
- RSI Timeframes: 15m, 1H, 4H
- MA Periods: 20, 50, 100
### **For Swing Trading (4H - D):**
- Internal Structure: OFF
- Swing Structure: ON
- Order Blocks: Swing only
- RSI Timeframes: 4H, D, W
- MA Periods: 20, 50, 200
---
## ⚠️ Disclaimer
Indicator ini adalah alat bantu analisis teknikal. Tidak ada indicator yang 100% akurat. Selalu:
- Lakukan analisa fundamental
- Gunakan proper risk management
- Praktik di demo account terlebih dahulu
- Trading memiliki resiko, trade at your own risk
---
## 📝 Version Info
**Version:** 5.0
**Platform:** TradingView Pine Script v5
**Author:** XoRonX
**Max Labels:** 500
**Max Lines:** 500
**Max Boxes:** 500
---
## 🔄 Updates & Support
Untuk update, bug reports, atau pertanyaan:
- Check documentation regularly
- Test new features in replay mode
- Backup your settings before updates
---
## 🎓 Learning Resources
**Recommended Study:**
1. Smart Money Concepts (SMC) basics
2. Order blocks theory
3. Liquidity concepts
4. ICT (Inner Circle Trader) concepts
5. Volume profile analysis
6. Multi-timeframe analysis
**Practice:**
- Start with higher timeframes
- Master one concept at a time
- Keep a trading journal
- Review your trades weekly
---
**Happy Trading! 🚀📊**
_Remember: The best indicator is your own analysis and discipline._
