TVB - Thomas Volatility Bands v2.0TVB – Thomas Volatility Bands v2.0
Author: Thomas Aaroon
Concept: CIV-Driven Volatility Bands with Adaptive Vomma Scaling
Overview
TVB – Thomas Volatility Bands v2.0 is an advanced volatility-adaptive band system built on two core elements:
CIV (Composite Implied Volatility) – manually provided or proxied using an external IV index
Dynamic Vomma Scaling – a higher-order volatility response factor that adjusts band width based on the convexity of implied volatility changes
Together, these components create a continuously adapting volatility envelope that reacts smoothly to market regime shifts.
Key Features
1. Flexible CIV Input
Manual CIV mode: Enter your own CIV value (decimal or %)
Proxy CIV mode: Pulls IV data from INDIA_VIX or any custom IV symbol
Weighted blending: Adjustable α-weight for proxy influence
Automatic normalization ensures stable and bounded CIV values.
2. Adaptive Volatility Engine
CIV is smoothed using EMA for intraday and SMA for higher-timeframes
Vomma coefficient dynamically adjusts based on CIV percentile and short-term CIV volatility
Produces a volatility surface that expands during stress and contracts during calm periods.
3. Time-Scaled Band Construction
Bands automatically scale their width according to:
Timeframe multiplier
Estimated bars-per-day
Annualized volatility normalization (√252 rule)
This ensures consistent volatility geometry across all chart timeframes.
4. Dual-Layer Volatility Bands
Inner Bands (±3σ): Tactical mean-reversion boundaries
Outer Bands (±4σ): Structural deviation zones for extreme price dislocations
Smooth color-coded volatility regimes (low/moderate/high CIV).
5. Re-Entry Logic (34% Rule)
A clean, rule-based mechanism inspired by distributional penetration depth:
Tracks bars that break the ±4σ outer band
Looks for 34% penetration back toward the ±3σ region
Generates optional visual markers (buy/sell re-entry)
Designed to highlight volatility compression opportunities after extreme expansions.
6. Optional CIV Diagnostic Label
Shows:
CIV and smooth CIV
Vomma coefficient
Effective band width
Useful for strategy development and volatility research.
Intended Use
TVB v2.0 is designed for:
Volatility-based trading models
Mean-reversion and re-entry systems
Volatility regime identification
Institutional-grade market structure research
This indicator does not repaint and does not generate trade signals by default (signals can be enabled via optional shapes).
Disclaimer
This tool is for educational and analytical purposes only.
It is not financial advice, and the author is not responsible for any trading outcomes.
Statistics
OSOK - One Shot One Kill( Macros w/ Body Swings, SD Prj)What you get:
Time windows: contiguous 50→10 (HH:50–(HH+1):10) and 20→40 (HH:20–HH:40), or both.
Kill Zones & Day filter: Asian, London, NY, London Close; weekdays toggles.
Static projection TF: compute swings on 5-minute (or custom) and display on any chart TF.
Fibonacci/SD ladder: internal retracements & multi-SD extensions with optional price labels.
Stats table: per-hour counts, average/ min/ max range, plus hit-rates for +1/+2/+3/+4 and −1/−2.
Sequence logic (optional): track conditional paths (e.g., 0→+2, +1→−2, etc.) to separate continuation vs. reversal behavior.
CSV export: push current table (filtered/sorted) to a chart label for copy-out.
Multi-Asset % Performance Table | v2.1 | TCP Multi-Asset % Performance Table | v2.1 | TCP
ESSENTIAL SUMMARY:
Multi-Asset % Performance Table eliminates the need to manually draw and manage individual "Price Range" tools for every asset. It automatically tracks up to 15 tickers independently in a single dashboard, calculating a TOTAL SCORE (Portfolio Average) for you. Unlike manual drawings, it supports a Global Range while allowing Custom Dates for specific assets, ensuring each ticker is calculated based on its own precise entry/exit. The Smart Visuals dynamically draw the correct date lines only for the ticker you are currently viewing, keeping your chart automatic, accurate, and clutter-free.
FUL DESCRIPTION:
📊 What is this tool?
The Multi-Asset % Performance Table is a powerful portfolio dashboard designed to track the percentage performance of up to 15 different assets simultaneously.
Instead of checking tickers one by one or manually drawing price ranges, this indicator aggregates everything into a single, clean table. It allows you to compare the ROI (Return on Investment) of a basket of coins or stocks over a specific time period and calculates an aggregate TOTAL SCORE (Average %) for your selection.
🚀 Key Features
15 Asset Slots: Monitor up to 15 different tickers (Crypto, Stocks, Forex, etc.) in one view.
Global vs. Custom Dates: Set a "Global" start/end date for the whole portfolio, but override specific assets with Custom Dates if they entered the portfolio at a different time.
Smart Visuals: Automatically draws vertical dashed lines on your chart representing the start and end dates of the ticker you are currently viewing.
Total Score Calculation: Calculates the average percentage change of your portfolio. You can dynamically include or exclude specific assets from this average using the settings.
Status Column: A quick visual reference (✔ or ✘) in the table showing which assets are currently included in the Total Score calculation.
⚙️ How it Works
Data Fetching: The script pulls "Close" prices from the Daily timeframe to ensure accuracy across long periods.
Smart Matching: The visual lines automatically detect which asset you are viewing. For example, if you are looking at BTCUSDT and have custom dates set for it, the vertical lines will jump to those specific dates. If you view a ticker not in your list, it defaults to the Global dates.
Visual Protection: The script uses advanced logic to ensure only one set of range lines appears on the chart at a time, keeping your workspace clean.
🛠️ Instructions & Settings
1. Setting up your Assets
Open the Settings (Cogwheel icon).
Under ASSET 1 through ASSET 15, enter the tickers you want to track (e.g., BINANCE:BTCUSDT).
Include in Avg?: Uncheck this if you want to see the asset in the table but exclude it from the "TOTAL SCORE" average.
2. Defining Time Ranges
Global Settings: Set the Global Start and Global End dates at the top. This applies to all assets by default.
Custom Dates: If a specific asset (e.g., Asset 4) was bought on a different day, check the "Custom Dates?" box for that asset and enter its specific Start/End time.
3. Reading the Table
The table appears on the chart (default: Bottom Right) with three columns:
Asset: The name of the ticker.
% Change: The percentage move from Start Date to End Date. (Green = Positive, Red = Negative).
Inc: Shows a ✔ if the asset is included in the Total Score average, or a ✘ if excluded.
4. The Visual Lines
Two vertical dashed lines will appear on your chart.
Note: These lines are visual references only. You cannot drag them to change the dates. To change the dates, you must use the Settings menu.
💡 Tips
Hover for Details: Hover your mouse over the % Change value in the table to see a tooltip showing the exact Start Price and End Price used for the calculation.
Resolution: The script defaults to 1 Day resolution for optimal accuracy on historical data.
v2.1 | TCP - Custom Built for Precision Performance Tracking
Last CLOSED Bar OHLCThis TradingView Pine Script (@version=6) creates a label that displays the previous fully closed candlestick’s OHLC data on the chart.
Quant Master Flow [Cumulative Volume Delta]Quant Master Flow
The Quant Master Flow indicator is a tool that analyzes market aggression by tracking the Cumulative Volume Delta (CVD), providing critical insight into institutional participation and short-term liquidity absorption. It acts as the "Conviction Filter" to confirm the statistical signals provided by the Z-Oscillator.
Core Philosophy: Aggression vs. Absorption
The CVD measures the running total of the difference between aggressive buyer-initiated volume and aggressive seller-initiated volume. By plotting this cumulative total, the indicator reveals whether the net effect of market orders is one of accumulation (aggressive buying, driving the price up) or distribution (aggressive selling, driving the price down).
Key Components
Cumulative Tally: The indicator plots the running sum of the volume delta. A rising CVD suggests buyers are more aggressive than sellers; a falling CVD suggests the reverse.
Color Coding: The CVD is colored to visualize flow:
Green: Periods of net aggressive buying (accumulation).
Red: Periods of net aggressive selling (distribution).
Volume Thresholds (Optional/Implied): Allows for filtering of low-impact noise, ensuring the cumulative line only reflects significant shifts in order flow.
Strategic Use Cases
The power of the Quant Master Flow is realized by comparing its trajectory to the price action, validating Z-Score extremes, and spotting liquidity grabs.
1. High-Conviction Confirmation
Use the CVD to confirm a directional signal from the Z-Oscillator:
Bullish Confirmation: When the Z-Oscillator hits Oversold ($\pm 2\sigma$) and the price begins to move up, a strong rising (Green) CVD confirms that the reversal is being fueled by institutional accumulation.
Bearish Confirmation: When the Z-Oscillator hits Overbought ($\pm 2\sigma$) and the price begins to fall, a strong falling (Red) CVD confirms that the drop is being driven by institutional distribution.
2. Divergence (The Early Warning System)
Divergence between the CVD and price is the strongest signal of impending failure or reversal, indicating that the current price movement is unsupported by institutional commitment.
Bearish Divergence: Price makes a Higher High while the CVD makes a Lower High. This is a warning that institutional players are distributing into the rally, signaling a failure to continue the trend.
Bullish Divergence: Price makes a Lower Low while the CVD makes a Higher Low. This shows institutional accumulation is occurring despite falling prices, often preceding a strong reversal.
3. Flow Exhaustion
When the CVD line flattens out during a strong price rally or drop, it signals that the market aggression is exhausted. This often happens right before the Z-Oscillator hits its $\pm 3\sigma$ Extreme zone, providing the earliest warning of a statistical reversal.
Quant Master Z-Oscillator [Risk + Bias]his indicator is a statistically-driven oscillator designed to measure the extreme deviation of price from its recent mean, identifying both reversal risk and directional bias within the current trend. It reframes classic Z-Score analysis to provide a quantified framework for trade timing and risk assessment.
Core Philosophy
The primary goal is to determine the statistical probability of a mean-reversion event. By measuring how many standard deviations the current price is away from its simple moving average (the basis), the indicator identifies moments of maximum risk (Extremes) and optimal entry (Oversold/Overbought zones).
Key Components
Z-Score Calculation:
Measures the distance of the closing price from the Lookback Length Simple Moving Average (SMA), normalized by the Standard Deviation (Volatility).
The raw score is then smoothed using an Exponential Moving Average (EMA) to filter noise, providing a clearer reading of the underlying statistical position.
Statistical Thresholds:
$\pm 2\sigma$ (High/Low): Defines the standard Overbought/Oversold zones (Trigger Zones). Movement into these areas suggests a pullback or reversal is increasingly likely.
$\pm 3\sigma$ (Extreme): Defines the "Kill Zone" of maximum statistical risk. Price reaching this level is highly unlikely to sustain itself, triggering an Extreme Overbought/Oversold warning.
Risk & Bias Dashboard (Table):
A real-time dashboard displayed on the chart (bottom right) provides a quantified summary of the current market state:
Current Z: The exact Z-Score value and its gradient color (green for positive pressure, red for negative).
Market Risk: Flags the statistical risk (e.g., OVERBOUGHT or EXTREME OVERSOLD ⚠️) based on the $\sigma$ thresholds.
Next Bias: Suggests the immediate directional bias (e.g., LONG SETUP NEXT or SHORT REVERSAL), helping the user prepare for the next high-probability setup based on the Z-Score's position relative to the mean.
Divergence Engine:
Detects standard Bullish and Bearish divergences between the Z-Score and the price action, signaling potential trend exhaustion or hidden momentum shifts.
Interpretation & Use
Risk Management: Treat the $\pm 3\sigma$ (Extreme) levels as mandatory profit-taking or high-alert reversal zones. Trading against these extremes carries the highest statistical risk.
Entry Timing: High-probability entries are found when the Z-Score is at $\pm 2\sigma$ (Oversold/Overbought) and a momentum shift (e.g., a green bar after an Oversold red sequence) is observed.
Trend Confirmation: When the Z-Score operates between $0$ and $\pm 2\sigma$, it confirms the direction of the current trend (Positive Z-Score = Bullish bias).
Bitcoin vs M2 Global Liquidity (Lead 3M) - Table Ticker═══════════════════════════════════════════════════════════════
Bitcoin vs M2 Global Liquidity - Regression Indicator
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TECHNICAL SPECS
• Pine Script v6
• Overlay: false (separate pane)
• Data sources: 5 M2 series + 4 FX pairs (request.security)
• Calculation: Rolling OLS linear regression with configurable lead
• Output: Regression line + ±1σ/±2σ confidence bands + R² ticker
CORE FUNCTIONALITY
Aggregates M2 money supply from 5 central banks (CN, US, EU, JP, GB),
converts to USD, applies time-lead, runs rolling linear regression
vs Bitcoin price, plots predicted value with confidence intervals.
CONFIGURABLE PARAMETERS
Input Controls:
• Lead Period: 0-365 days (default: 90)
• Lookback Window: 50-2000 bars (default: 750)
• Bands: Toggle ±1σ and ±2σ visibility
• Colors: BTC, M2, regression line, confidence zones
• Ticker: Position, size, colors, transparency
Advanced Settings:
• Table display: R², lead, M2 total, country breakdown (%)
• Ticker customization: 9 position options, 6 text sizes
• Border: Width 0-10px, color, outline-only mode
DATA AGGREGATION
Sources (via request.security):
• ECONOMICS:CNM2, USM2, EUM2, JPM2, GBM2
• FX_IDC:CNYUSD, JPYUSD (others: FX:EURUSD, GBPUSD)
• Conversion: All M2 → USD → Sum / 1e12 (trillions)
REGRESSION ENGINE
• Arrays: m2Array, btcArray (dynamic sizing, auto-trim)
• Window: Rolling (lookbackPeriod bars)
• Lead: Time-shift via array indexing (i + leadPeriodDays)
• Calc: Manual OLS (covariance/variance), no built-in ta functions
• Outputs: slope, intercept, r2, stdResiduals
CONFIDENCE BANDS
±1σ and ±2σ calculated from standard deviation of residuals.
Fill zones between upper/lower bounds with configurable transparency.
ALERTS
5 pre-configured alertcondition():
• Divergence > 15%
• Price crosses ±1σ bands (up/down)
• Price crosses ±2σ bands (up/down)
TICKER TABLE
Dynamic table.new() with 9 rows:
• R² value (4 decimals)
• Lead period (days + months)
• M2 Global total (trillions USD)
• Country breakdown: CN, US, EU, JP, GB (absolute + %)
• Optional: Hide/show M2 details
VISUAL CUSTOMIZATION
All plot() elements support:
• Color picker inputs (group="Couleurs")
• Line width: 1-3px
• Transparency: 0-100% for zones
• Offset: M2 plot has +leadPeriodDays offset option
PERFORMANCE
• Max arrays size: lookbackPeriod + leadPeriodDays + 200
• Calculations: Only when array.size >= lookbackPeriod + leadPeriodDays
• Table update: barstate.islast (once per bar)
• Request.security: gaps_off mode
CODE STRUCTURE
1. Inputs (lines 7-54)
2. Data fetch (lines 56-76)
3. M2 aggregation (line 78)
4. Array management (lines 84-95)
5. Regression calc (lines 97-172)
6. Prediction + bands (lines 174-183)
7. Plots (lines 185-199)
8. Ticker table (lines 201-236)
9. Alerts (lines 238-246)
DEPENDENCIES
None. Pure Pine Script v6. No external libraries.
LIMITATIONS
• Daily timeframe recommended (1D)
• Requires 750+ bars history for optimal calculation
• M2 data availability: TradingView ECONOMICS feed
• Max lines: 500 (declared in indicator())
CUSTOMIZATION EXAMPLES
• Shorter lookback (200d): More reactive, lower R²
• Longer lookback (1500d): More stable, regime mixing
• No bands: Set showBands=false for clean view
• Different lead: Test 60d, 120d for sensitivity analysis
TECHNICAL NOTES
• Manual OLS implementation (no ta.linreg)
• Array-based lead application (not plot offset)
• M2 values stored in trillions (/ 1e12) for readability
• Residuals array cleared/rebuilt each calculation
OPEN SOURCE
Code fully visible. Modify, fork, analyze freely.
No hidden calculations. No proprietary data.
VERSION
1.0 | November 2025 | Pine Script v6
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High Volume Bars (Advanced)High Volume Bars (Advanced)
High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”:
Classic: volume > moving average + N × standard deviation
Change-based: large change in volume vs previous bar
Z-score: statistically extreme volume values
Robust mode (optional): median + MAD, less sensitive to outliers
It can:
Recolor candles when volume is high
Optionally highlight the background
Optionally plot volume bands (center ± spread × multiplier)
⸻
1. How it works
At each bar the script:
Picks the volume source:
If Use Volume Change vs Previous Bar? is off → uses raw volume
If on → uses abs(volume - volume )
Computes baseline statistics over the chosen source:
Lookback bars
Moving average (SMA or EMA)
Standard deviation
Optionally replaces mean/std with robust stats:
Center = median (50th percentile)
Spread = MAD (median absolute deviation, scaled to approx σ)
Builds bands:
upper = center + spread * multiplier
lower = max(center - spread * multiplier, 0)
Flags a bar as “high volume” if:
It passes the mode logic:
Classic abs: volume > upper
Change mode: abs(volume - volume ) > upper
Z-score mode: z-score ≥ multiplier
AND the relative filter (optional): volume > average_volume * Min Volume vs Avg
AND it is past the first Skip First N Bars from the start of the chart
Colors the bar and (optionally) the background accordingly.
⸻
2. Inputs
2.1. Statistics
Lookback (len)
Number of bars used to compute the baseline stats (mean / median, std / MAD).
Typical values: 50–200.
StdDev / Z-Score Multiplier (mult)
How far from the baseline a bar must be to count as “high volume”.
In classic mode: volume > mean + mult × std
In z-score mode: z ≥ mult
Typical values: 1.0–2.5.
Use EMA Instead of SMA? (smooth_with_ema)
Off → uses SMA (slower but smoother).
On → uses EMA (reacts faster to recent changes).
Use Robust Stats (Median & MAD)? (use_robust)
Off → mean + standard deviation
On → median + MAD (less sensitive to a few insane spikes)
Useful for assets with occasional volume blow-ups.
⸻
2.2. Detection Mode
These inputs control how “unusual” is defined.
• Use Volume Change vs Previous Bar? (mode_change)
• Off (default) → uses absolute volume.
• On → uses abs(volume - volume ).
You then detect jumps in volume rather than absolute size.
Note: This is ignored if Z-Score mode is switched on (see below).
• Use Z-Score on Volume? (Overrides change) (mode_zscore)
• Off → high volume when raw value exceeds the upper band.
• On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier.
Z-score mode can be combined with robust stats for more stable thresholds.
• Min Volume vs Avg (Filter) (min_rel_mult)
An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful.
• 0.0 → no filter (all stats-based candidates allowed).
• 1.0 → high-volume bar must also be at least equal to average volume.
• 1.5 → bar must be ≥ 1.5 × average volume.
• Skip First N Bars (from start of chart) (skip_open_bars)
Skips the first N bars of the chart when evaluating high-volume conditions.
This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill.
⸻
2.3. Visuals
• Show Volume Bands? (show_bands)
• If on, plots:
• Upper band (upper)
• Lower band (lower)
• Center line (vol_center)
These are plotted on the same pane as the script (usually the price chart).
• Also Highlight Background? (use_bg)
• If on, fills the background on high-volume bars with High-Vol Background.
• High-Vol Bar Transparency (0–100) (bar_transp)
Controls the opacity of the high-volume bar colors (up / down).
• 0 → fully opaque
• 100 → fully transparent (no visible effect)
• Up Color (upColor) / Down Color (dnColor)
• Regular bar colors (non high-volume) for up and down bars.
• Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase)
Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp.
• High-Vol Background (bgHighVolColor)
Background color used when Also Highlight Background? is enabled.
⸻
3. What gets colored and how
• Bar color (barcolor)
• Up bar:
• High volume → Up High-Vol Color
• Normal volume → Up Color
• Down bar:
• High volume → Down High-Vol Color
• Normal volume → Down Color
• Flat bar → neutral gray
• Background color (bgcolor)
• If Also Highlight Background? is on, high-volume bars get High-Vol Background.
• Otherwise, background is unchanged.
⸻
4. Alerts
The indicator exposes three alert conditions:
• High Volume Bar
Triggers whenever is_high is true (up or down).
• High Volume Up Bar
Triggers only when is_high is true and the bar closed up (close > open).
• High Volume Down Bar
Triggers only when is_high is true and the bar closed down (close < open).
You can use these in TradingView’s “Create Alert” dialog to:
• Get notified of potential breakout / exhaustion bars.
• Trigger webhook events for bots / custom infra.
⸻
5. Recommended presets
5.1. “Classic” high-volume detector (closest to original)
• Lookback: 150–200
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: off
• Use Robust Stats?: off
• Use Volume Change vs Previous Bar?: off
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.0–1.0
Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation.
⸻
5.2. Volatility-aware (Z-score) mode
• Lookback: 100–200
• StdDev / Z-Score Multiplier: 1.5–2.0
• Use EMA Instead of SMA?: on
• Use Robust Stats?: on (if asset has huge spikes)
• Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode)
• Use Z-Score on Volume?: on
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time.
⸻
5.3. “Wake-up bar” (volume acceleration)
• Lookback: 50–100
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: on
• Use Robust Stats?: optional
• Use Volume Change vs Previous Bar?: on
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period.
⸻
6. Limitations / notes
• Time-of-day effects
The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to:
• Use a shorter Lookback, or
• Add a session-aware filter in a future iteration.
• Illiquid symbols
On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems.
• Overlay behavior
overlay = true means:
• Bars are recolored on the price pane.
• Volume bands are also drawn on the price pane if enabled.
If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
Reversal Correlation Pressure [OmegaTools]Reversal Correlation Pressure is a quantitative regime-detection and signal-filtering framework designed to enhance both reversal timing and breakout validation across intraday and multi-session markets.
It is built for discretionary and systematic traders who require a statistically grounded filter capable of adapting to changing market conditions in real time.
1. Purpose and Overview
Market conditions constantly rotate through phases of expansion, contraction, trend persistence, and noise-driven mean reversion. Many strategies break down not because the signal is wrong, but because the regime is unsuitable.
This indicator solves that structural problem.
The tool measures the evolving correlation relationship between highs and lows — a robust proxy for how “organized” or “fragmented” price discovery currently is — and transforms it into a regime pressure reading. This reading is then used as the core variable to validate or filter reversal and breakout opportunities.
Combined with an internal performance-based filter that learns from its past signals, the indicator becomes a dynamic decision engine: it highlights only the signals that statistically perform best under the current market regime.
2. Core Components
2.1 Correlation-Based Regime Mapping
The relationship between highs and lows contains valuable information about market structure:
High correlation generally corresponds to coherent, directional markets where momentum and breakouts tend to prevail.
Low or unstable correlation often appears in overlapping, rotational phases where price oscillates and mean-reversion behavior dominates.
The indicator continuously evaluates this correlation, normalizes it statistically, and displays it as a pressure histogram:
Higher values indicate regimes favorable to trend continuation or momentum breakouts.
Lower values indicate regimes where reversals, pullbacks, and fade setups historically perform better.
This regime mapping is the foundation upon which the adaptive filter operates.
2.2 Reversal Stress & Breakout Stress Signaling
Raw directional opportunities are identified using statistically significant deviations from short-term equilibrium (overbought/oversold dynamics).
However, unlike traditional mean-reversion or breakout tools, signals here are not automatically taken. They must first be validated by the regime framework and then compared against the performance of similar past setups.
This dual evaluation sharply reduces the noise associated with reversal attempts during strong trends, while also preventing breakout attempts during choppy, anti-directional conditions.
2.3 Adaptive Regime-Selection Backtester
A key innovation of this indicator is its embedded micro-backtester, which continuously tracks how reversal or breakout signals have performed under each correlation regime.
The system evaluates two competing hypotheses:
Signals perform better during high-correlation regimes.
Signals perform better during low-correlation or neutral regimes.
For each new trigger, the indicator looks back at a rolling sample of past setups and measures short-term performance under both regimes. It then automatically selects the regime that currently demonstrates the superior historical edge.
In other words, the indicator:
Learns from recent market behavior
Determines which regime supports reversals
Determines which regime supports breakouts
Applies the optimal filter in real time
Highlights only the signals that historically outperformed under similar conditions
This creates a dynamic, statistically supervised approach to signal filtering — a substantial improvement over static or fixed-threshold systems.
2.4 Visual Components
To support rapid decision-making:
Correlation Pressure Histogram:
Encodes regime strength through a gradient-based color system, transitioning from neutral contexts into strong structural phases.
Directional Markers:
Visual arrows appear when a signal passes all filters and conditions.
Bar Coloring:
Bars can optionally be recolored to reflect active bullish or bearish bias after the adaptive filter approves a signal.
These components integrate seamlessly to give the trader a concise but complete view of the underlying conditions.
3. How to Use This Indicator
3.1 Identifying Regimes
The histogram is the anchor:
High, brightly colored columns suggest trend-friendly behavior where breakout alignment and directional follow-through have historically been stronger.
Low or muted columns suggest mean-reversion contexts where counter-trend opportunities and reversal setups gain reliability.
3.2 Filtering Signals
The indicator automatically decides whether a reversal or breakout trigger should be respected based on:
the current correlation regime,
the learned performance of recent signals under similar conditions, and
the directional stress detected in price.
The user does not need to adjust anything manually.
3.3 Integration with Other Tools
This indicator works best when combined with:
VWAP or session levels
Market internals and breadth metrics
Volume, order flow, or delta-based tools
Local structural frameworks (support/resistance, liquidity highs and lows)
Its strength is in telling you when your other signals matter and when they should be ignored.
4. Strengths of the Framework
Automatically adapts to changing micro-regimes
Reduces false reversals during strong trends
Avoids false breakouts in overlapping, rotational markets
Learns from recent historical performance
Provides a statistically driven confirmation layer
Works on all liquid assets and timeframes
Suitable for both discretionary and automated environments
5. Disclaimer
This indicator is provided strictly for educational and analytical purposes.
It does not constitute trading advice, investment guidance, or a recommendation to buy or sell any financial instrument.
Past performance of any statistical filter or adaptive method does not guarantee future results.
All trading involves significant risk, and users are responsible for their own decisions and risk management.
By using this indicator, you acknowledge that you are fully responsible for your trading activity.
Spot-Futures SpreadSpot-Futures Spread Indicator
A comprehensive indicator that automatically calculates and visualizes the percentage spread between spot and perpetual futures prices across multiple exchanges.
Key Features:
Automatic Exchange Detection - Automatically detects your current exchange and finds the corresponding spot/futures pair
Smart Fallback System - If the counterpart isn't available on your exchange, it automatically searches across 7+ major exchanges (Binance, Bybit, OKX, Gate.io, MEXC, KuCoin, HTX) and uses the first valid match
Multi-Exchange Support - Works with 14 exchanges including Binance, Bybit, OKX, MEXC, BitGet, Gate.io, KuCoin, and more
Clear Exchange Attribution - Shows exactly which exchanges are providing spot and futures data in the statistics table
Configurable Moving Average - Track the average spread with customizable period
Standard Deviation Bands - Identify unusual spread conditions with Bollinger-style bands
Built-in Alerts - Get notified when spread crosses bands or zero (parity)
Statistics Table - Real-time stats showing current spread, MA, std dev, and bands
Manual Override Options - Advanced users can manually specify exchanges and symbols
How It Works:
The indicator calculates the spread as: (Futures Price - Spot Price) / Spot Price × 100
Positive spread = Futures trading at a premium (contango)
Negative spread = Futures trading at a discount (backwardation)
Zero = Parity between spot and futures
Use Cases:
Funding Rate Analysis - Correlates with perpetual funding rates
Arbitrage Opportunities - Identify significant spot-futures divergences
Market Sentiment - Premium/discount indicates bullish/bearish positioning
Cross-Exchange Analysis - Compare spreads when spot and futures are on different exchanges
Smart Features:
Works whether you're viewing a spot or futures chart
Automatically handles exchange-specific perpetual contract naming (.P, PERP, SWAP, etc.)
Color-coded visualization (green for premium, red for discount)
Customizable colors and display options
Background shading based on spread direction
Perfect For:
Crypto traders monitoring funding rates, arbitrage traders, market makers, and anyone interested in spot-futures dynamics across multiple exchanges.
Getting Started:
Simply add the indicator to any spot or perpetual futures chart. It will automatically detect the exchange and find the corresponding pair. The statistics table shows which exchanges are being used for maximum transparency.
Note: The indicator automatically ignores invalid symbols, so you'll never see errors even if a specific pair doesn't exist on a particular exchange.
Kudos to @AlekMel that made the "Spot - Fut Spread v2" indicator that I enhance the Automatic detection feature which was not working in some case.
Stochastic Ensembling of OutputsStochastic Ensembling of Outputs
🙏🏻 This is a simple tool/method that would solve naturally many well known problems:
“Price reversed 1 tick before the actual level, not executing my limit order”
“I consider intraday trend change by checking whether price is above/below VWAP, but is 1 tick enough? What to do, price is now whipsawing around vwap...”.
“I want to gradually accumulate a position around a chosen anchor. But where exactly should I put my orders? And I want to automate it ofc.“
“All these DSP adepts are telling you about some kind of noise in the markets… But how can I actually see it?”
The easy fix is to make things more analog less digital, by synthesizing numerous noise instances & adding it to any price-applied metric of yours. The ones who fw techno & psytrance, and other music, probably don’t need any more explanations. Then by checking not just 2 lines or 1 process against another one, you will be checking cloud vs cloud of lines, even allowing you to introduce proxies of probabilities. More crosses -> more confirmation to act.
How-to use:
The tool has 2 inputs: source and target:
Sources should always be the underlying process. If you apply the tool to price based metric, leave it hlcc4 unless you have a better one point estimate for each bar;
Target is your target, e.g if you want to apply it to VWAP, pick VWAP as target. You can thee on the chart above how trading activity recently never exactly touched VWAP, however noised instances of VWAP 'were' touched
The code is clean and written in modular form, you can simply copy paste it to any script of yours if you don't want to have multiple study-on-study script pairs.
^^ applied to prev days highs and lows
^^ applied to MBAD extensions and basis
^^ applied to input series itself
Here’s how it works, no ML, no “AI”, no 1k lines of code, just stats:
The problem with metrics, even if they are time aware like WMA, is that they still do not directly gain information about “changes” between datapoints. If we pick noise characteristics to match these changes, we’d effectively introduce this info into our ops.
^^ this screenshot represents 2 very different processes: a sine wave and white noise, see how the noise instances learned from each process differ significantly.
Changes can be represented as AR1 process . It’s dead simple, no PHD needed, it’s just how the current datapoint is related (or not) to the previous datapoint, no more than 1, and how this relationship holds/evolves over time. Unlike the mainstream approach like MLE, I estimate this relationship (phi parameter) via MoM but giving more weights to more recent datapoints via exponential smoothing over all the data available on your charts (so I encode temporal information), algocomplexity is O(1), lighting fast, just one pass. <- that gives phi , we’d use it as color for our noise generator
Then we just need to estimate noise amplitude ( gamma ) via checking what AR1 model actually thought vs the reality, variance of these innovations. Same via exponential smoothing, time aware, O(1), one pass, it’s all it does.
Then we generate white gaussian noise, and apply 2 estimated parameters (phi and gamma), and that’s all.
Omg, I think I just made my first real DSP script xd
Just like Monte Carlo for risk management, this is so simple and natural I can’t believe so many “pros” hide it and never talk about it in open access. Sharing it here on TradingView would’ve not done anything critical for em, but many would’ve benefited.
∞
Global M2 Money Supply Growth (GDP-Weighted)📊 Global M2 Money Supply Growth (GDP-Weighted)
This indicator tracks the weighted aggregate M2 money supply growth across the world's four largest economies: United States, China, Eurozone, and Japan. These economies represent approximately 69.3 trillion USD in combined GDP and account for the majority of global liquidity, making this a comprehensive macro indicator for analyzing worldwide monetary conditions.
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🔧 KEY FEATURES:
📈 GDP-Weighted Aggregation
Each economy is weighted proportionally by its nominal GDP using 2025 IMF World Economic Outlook data:
• United States: 44.2% (30.62 trillion USD)
• China: 28.0% (19.40 trillion USD)
• Eurozone: 21.6% (15.0 trillion USD)
• Japan: 6.2% (4.28 trillion USD)
The weights are fully adjustable through the indicator settings, allowing you to update them annually as new IMF forecasts are released (typically April and October).
⏱️ Multiple Time Period Options
Choose between three calculation methods to analyze different timeframes:
• YoY (Year-over-Year): 12-month growth rate for identifying long-term liquidity trends and cycles
• MoM (Month-over-Month): 1-month growth rate for detecting short-term monetary policy shifts
• QoQ (Quarter-over-Quarter): 3-month growth rate for medium-term trend analysis
🔄 Advanced Offset Function
Shift the entire indicator forward by 0-365 days to test lead/lag relationships between global liquidity and asset prices. Research suggests a 56-70 day lag between M2 changes and Bitcoin price movements, but you can experiment with different offsets for various assets (equities, gold, commodities, etc.).
🌍 Individual Country Breakdown
Real-time display of each economy's M2 growth rate with:
• Current percentage change (YoY/MoM/QoQ)
• GDP weight contribution
• Color-coded values (green = monetary expansion, red = contraction)
📊 Smart Overlay Capability
Displays directly on your main price chart with an independent left-side scale, allowing you to visually correlate global liquidity trends with any asset's price action without cluttering the chart.
🔧 Customizable GDP Weights
All GDP values can be adjusted through the indicator settings without editing code, making annual updates simple and accessible for all users.
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📡 DATA SOURCES:
All M2 money supply data is sourced from ECONOMICS (Trading Economics) for consistency and reliability:
• ECONOMICS:USM2 (United States)
• ECONOMICS:CNM2 (China)
• ECONOMICS:EUM2 (Eurozone)
• ECONOMICS:JPM2 (Japan)
All values are normalized to USD using current daily exchange rates (USDCNY, EURUSD, USDJPY) before GDP-weighted aggregation, ensuring accurate cross-country comparisons.
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💡 USE CASES & APPLICATIONS:
🔹 Liquidity Cycle Analysis
Track global monetary expansion/contraction cycles to identify when central banks are coordinating loose or tight monetary policies.
🔹 Market Timing & Risk Assessment
High M2 growth (>10%) historically correlates with risk-on environments and rising asset prices across crypto, equities, and commodities. Negative M2 growth signals monetary tightening and potential market corrections.
🔹 Bitcoin & Crypto Correlation
Compare with Bitcoin price using the offset feature to identify the optimal lag period. Many traders use 60-70 day offsets to predict crypto market movements based on liquidity changes.
🔹 Macro Portfolio Allocation
Use as a regime filter to adjust portfolio exposure: increase risk assets during liquidity expansion, reduce during contraction.
🔹 Central Bank Policy Divergence
Monitor individual country metrics to identify when major central banks are pursuing divergent policies (e.g., Fed tightening while China eases).
🔹 Inflation & Economic Forecasting
Rapid M2 growth often leads inflation by 12-18 months, making this a leading indicator for future inflation trends.
🔹 Recession Early Warning
Negative M2 growth is extremely rare and has preceded major recessions, making this a valuable risk management tool.
════════════════════════════════════════════
📊 INTERPRETATION GUIDE:
🟢 +10% or Higher
Aggressive monetary expansion, typically during crises (2001, 2008, 2020). The COVID-19 period saw M2 growth reach 20-27%, which preceded significant inflation and asset price surges. Strong bullish signal for risk assets.
🟢 +6% to +10%
Above-average liquidity growth. Central banks are providing stimulus beyond normal levels. Generally favorable for equities, crypto, and commodities.
🟡 +3% to +6%
Normal/healthy growth rate, roughly in line with GDP growth plus 2% inflation targets. Neutral environment with moderate support for risk assets.
🟠 0% to +3%
Slowing liquidity, potential tightening phase beginning. Central banks may be raising rates or reducing balance sheets. Caution warranted for high-beta assets.
🔴 Negative Growth
Monetary contraction - extremely rare. Only occurred during aggressive Fed tightening in 2022-2023. Strong warning signal for risk assets, often precedes recessions or major market corrections.
════════════════════════════════════════════
🎯 OPTIMAL USAGE:
📅 Recommended Timeframes:
• Daily or Weekly charts for macro analysis
• Monthly charts for very long-term trends
💹 Compatible Asset Classes:
• Cryptocurrencies (especially Bitcoin, Ethereum)
• Equity indices (S&P 500, NASDAQ, global markets)
• Commodities (Gold, Silver, Oil)
• Forex majors (DXY correlation analysis)
⚙️ Suggested Settings:
• Default: YoY calculation with 0 offset for current liquidity conditions
• Bitcoin traders: YoY with 60-70 day offset for predictive analysis
• Short-term traders: MoM with 0 offset for recent policy changes
• Quarterly rebalancers: QoQ with 0 offset for medium-term trends
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📋 VISUAL DISPLAY:
The indicator plots a blue line showing the selected growth metric (YoY/MoM/QoQ), with a dashed reference line at 0% to clearly identify expansion vs. contraction regimes.
A comprehensive table in the top-right corner displays:
• Current global M2 growth rate (large, prominent display)
• Individual country breakdowns with their GDP weights
• Color-coded growth rates (green for positive, red for negative)
════════════════════════════════════════════
🔄 MAINTENANCE & UPDATES:
GDP weights should be updated annually (ideally in April or October) when the IMF releases new World Economic Outlook forecasts. Simply adjust the four GDP input parameters in the indicator settings - no code editing required.
The relative GDP proportions between the Big 4 economies change very gradually (typically <1-2% per year), so even if you update weights once every 1-2 years, the impact on the indicator's accuracy is minimal.
════════════════════════════════════════════
💭 TRADING PHILOSOPHY:
This indicator embodies the principle that "liquidity drives markets." By tracking the combined M2 money supply of the world's largest economies, weighted by their economic size, you gain insight into the fundamental liquidity conditions that underpin all asset prices.
Unlike single-country M2 indicators, this GDP-weighted approach captures the true global picture, accounting for the fact that US monetary policy has 2x the impact of Japanese policy due to economic size differences.
Perfect for macro-focused traders, long-term investors, and anyone seeking to understand the "tide that lifts all boats" in financial markets.
════════════════════════════════════════════
Created for traders and investors who incorporate global liquidity trends into their decision-making process. Best used alongside other technical and fundamental analysis tools for comprehensive market assessment.
⚠️ Disclaimer: M2 money supply is a lagging macroeconomic indicator. Past correlations do not guarantee future results. Always use proper risk management and combine with other analysis methods.
Trading Sessions ConstructorHello friends,
This tool is designed for traders who want a clean, flexible way to visualize trading sessions directly on the chart. It lets you highlight key market sessions (London, New York, Tokyo, Sydney, custom specifications, etc.), add rich visual structure around them, and optionally track basic statistics - all in a highly customizable and timezone-aware format.
🛠️ How It Works
The indicator lets you define up to 8 separate sessions , each with its own name, timezone, and active days of the week. Sessions can share one common timezone or use individual timezones, depending on how you prefer to track global markets.
For each session, the script builds a visual "frame" around price action:
it can draw a box around the full range, plot high/mid/low lines, show a title label above price, and optionally display a box stats label with session metrics (such as volume or pips range).
A progress indicator at the bottom of the chart helps you see how much of the current session has already passed, while an optional summary table aggregates statistics across all visible sessions for quick comparison.
🔥 Key Features
Up to 8 configurable sessions with their own names, timezones, and weekdays
Option to use one common timezone for all sessions or separate timezones per session
Custom session titles with flexible label positioning and size
Customizable vertical start-line
Customizable session box
Per-session box stats label with selectable metrics
Independent high, mid, and low lines with full style and width control
Optional background shading to highlight active trading hours
Bottom progress indicator (◼) showing how much of the session has elapsed
Optional statistics table summarizing all visible sessions
📸 Visual Examples
1. Background + High/Mid/Low lines + Session names above high
2. Background + Boxes + Session names above high
3. Background + Vertical start-line + Session names at the bottom
4. Background + Vertical start-line + Session names at the top + Bottom progress indicator
5. Background + Session names at the bottom + Bottom progress indicator 👋 Good luck and happy trading!
Bezahltes Script
Major Crypto Relative Strength Portfolio System Majors RSPS - Relative Strength Portfolio System for Major Cryptocurrencies
Overview
Majors RSPS (Relative Strength Portfolio System) is an advanced portfolio allocation indicator that combines relative strength analysis, trend consensus, and macro risk factors to dynamically allocate capital across major cryptocurrency assets. The system leverages the NormalizedIndicators Library to evaluate both absolute trends and relative performance, creating an adaptive portfolio that automatically adjusts exposure based on market conditions.
This indicator is designed for portfolio managers, asset allocators, and systematic traders who want a data-driven approach to cryptocurrency portfolio construction with automatic rebalancing signals.
🎯 Core Concept
What is RSPS?
RSPS (Relative Strength Portfolio System) evaluates each asset on two key dimensions:
Relative Strength: How is the asset performing compared to other major cryptocurrencies?
Absolute Trend: Is the asset itself in a bullish trend?
Assets that show both strong relative performance AND positive absolute trends receive higher allocations. Weak performers are automatically filtered out, with capital reallocated to cash or stronger assets.
Dual-Layer Architecture
Layer 1: Majors Portfolio (Orange Zone)
Evaluates 14 major cryptocurrency assets
Calculates relative strength against all other majors
Applies trend filters to ensure absolute momentum
Dynamically allocates capital based on comparative strength
Layer 2: Cash/Risk Position (Navy Zone)
Evaluates macro risk factors and market conditions
Determines optimal cash allocation
Acts as a risk-off mechanism during adverse conditions
Provides downside protection through dynamic cash holdings
📊 Tracked Assets
Major Cryptocurrencies (14 Assets)
BTC - Bitcoin (Benchmark L1)
ETH - Ethereum (Smart Contract L1)
SOL - Solana (High-Performance L1)
SUI - Sui (Move-Based L1)
TRX - Tron (Payment-Focused L1)
BNB - Binance Coin (Exchange L1)
XRP - Ripple (Payment Network)
FTM - Fantom (DeFi L1)
CELO - Celo (Mobile-First L1)
TAO - Bittensor (AI Network)
HYPE - Hyperliquid (DeFi Exchange)
HBAR - Hedera (Enterprise L1)
ADA - Cardano (Research-Driven L1)
THETA - Theta (Video Network)
🔧 How It Works
Step 1: Relative Strength Calculation
For each asset, the system calculates relative strength by:
RSPS Score = Average of:
- Asset/BTC trend consensus
- Asset/ETH trend consensus
- Asset/SOL trend consensus
- Asset/SUI trend consensus
- ... (all 14 pairs)
- Asset's absolute trend consensus
Key Logic:
Each pair is evaluated using the eth_4d_cal() calibration from NormalizedIndicators
If an asset's absolute trend is extremely weak (≤ 0.1), it receives a penalty score (-0.5)
Otherwise, it gets the average of all its relative strength comparisons
Step 2: Trend Filtering
Assets must pass a trend filter to receive allocation:
Trend Score = Average of:
- Asset/BTC trend (filtered for positivity)
- Asset/ETH trend (filtered for positivity)
- Asset's absolute trend (filtered for positivity)
Only positive values contribute to the trend score, ensuring bearish assets don't receive allocation.
Step 3: Portfolio Allocation
Capital is allocated proportionally based on filtered RSPS scores:
Asset Allocation % = (Asset's Filtered RSPS Score / Sum of All Filtered Scores) × Main Portfolio %
Example:
SOL filtered score: 0.6
BTC filtered score: 0.4
All others: 0
Total: 1.0
SOL receives: (0.6 / 1.0) × Main% = 60% of main portfolio
BTC receives: (0.4 / 1.0) × Main% = 40% of main portfolio
Step 4: Cash/Risk Allocation
The system evaluates macro conditions across 6 factors:
Inverse Major Crypto Trends (40% weight)
When BTC, ETH, SOL, SUI, DOGE, etc. trend down → Cash allocation increases
Evaluates total market cap trends (TOTAL, TOTAL2, OTHERS)
Stablecoin Dominance (10% weight)
USDC dominance vs. major crypto dominances
Higher stablecoin dominance → Higher cash allocation
MVRV Ratios (10% weight)
BTC and ETH Market Value to Realized Value
High MVRV (overvaluation) → Higher cash allocation
BTC/ETH Ratio (15% weight)
Relative performance between two market leaders
Indicates market phase (BTC dominance vs. alt season)
Active Address Ratios (5% weight)
USDC active addresses vs. BTC/ETH active addresses
Network activity comparison
Macro Indicators (15% weight)
Global currency circulation (USD, EUR, CNY, JPY)
Treasury yield curve (10Y-2Y)
High yield spreads
Central bank balance sheets and money supply
Cash Allocation Formula:
Cash % = (Sum of Risk Factors × 0.5) / (Risk Factors + Majors TPI)
When risk factors are elevated, cash allocation increases, reducing exposure to volatile assets.
📈 Visual Components
Orange Zone (Majors Portfolio)
Fill: Light orange area showing aggregate portfolio strength
Line: Average trend power index (TPI) of allocated assets
Baseline: 0 level (neutral)
Interpretation:
Above 0: Bullish allocation environment
Rising: Strengthening portfolio momentum
Falling: Weakening portfolio momentum
Below 0: No allocation (100% cash)
Navy Zone (Cash Position)
Fill: Navy blue area showing cash allocation strength
Line: Risk-adjusted cash allocation signal
Baseline: 0 level
Interpretation:
Higher navy zone: Elevated risk-off signal → More cash
Lower navy zone: Risk-on environment → Less cash
Zero: No cash allocation (100% invested)
Performance Line (Orange/Blue)
Orange: Main portfolio allocation dominant (risk-on mode)
Blue: Cash allocation dominant (risk-off mode)
Tracks: Cumulative portfolio returns with dynamic rebalancing
Allocation Table (Bottom Left)
Shows real-time portfolio composition:
ColumnDescriptionAssetCryptocurrency nameRSPS ValuePercentage allocation (of main portfolio)CashDollar amount (if enabled)
Color Coding:
Orange: Active allocation
Gray: Weak signal (borderline)
Blue: Cash position
Missing: No allocation (filtered out)
⚙️ Settings & Configuration
Required Setup
Chart Symbol
MUST USE: INDEX:BTCUSD or similar major crypto index
Recommended Timeframe: 1D (Daily) or 4D (4-Day)
Why: System needs price data for all 14 majors, BTC provides stable reference
Hide Chart Candles
For clean visualization:
Right-click on chart
Select "Hide Symbol" or set candle opacity to 0
This allows the indicator fills and table to be clearly visible
User Inputs
plot_table (Default: true)
Enable/disable the allocation table
Set to false if you only want the visual zones
use_cash (Default: false)
Enable portfolio dollar value calculations
Shows actual dollar allocations per asset
cash (Default: 100)
Total portfolio size in dollars/currency units
Used when use_cash is enabled
Example: Set to 10000 for a $10,000 portfolio
💡 Interpretation Guide
Entry Signals
Strong Allocation Signal:
✓ Orange zone elevated (> 0.3)
✓ Navy zone low (< 0.2)
✓ Performance line orange
✓ Multiple assets in allocation table
→ Action: Deploy capital to allocated assets per table percentages
Risk-Off Signal:
✓ Orange zone near zero
✓ Navy zone elevated (> 0.4)
✓ Performance line blue
✓ Few or no assets in table (high cash %)
→ Action: Reduce exposure, increase cash holdings
Rebalancing Triggers
Monitor the allocation table for changes:
New assets appearing: Add to portfolio
Assets disappearing: Remove from portfolio
Percentage changes: Rebalance existing positions
Cash % changes: Adjust overall exposure
Market Regime Detection
Risk-On (Bull Market):
Orange zone high and rising
Navy zone minimal
Many assets allocated (8-12)
High individual allocations (15-30% each)
Risk-Off (Bear Market):
Orange zone near zero or negative
Navy zone elevated
Few assets allocated (0-3)
Cash allocation dominant (70-100%)
Transition Phase:
Both zones moderate
Medium number of assets (4-7)
Balanced cash/asset allocation (40-60%)
🎯 Trading Strategies
Strategy 1: Pure RSPS Following
1. Check allocation table daily
2. Rebalance portfolio to match percentages
3. Follow cash allocation strictly
4. Review weekly, act on significant changes (>5%)
Best For: Systematic portfolio managers, passive allocators
Strategy 2: Threshold-Based
Entry Rules:
- Orange zone > 0.4 AND Navy zone < 0.3
- At least 5 assets in allocation table
- Total non-cash allocation > 60%
Exit Rules:
- Orange zone < 0.1 OR Navy zone > 0.5
- Fewer than 3 assets allocated
- Cash allocation > 70%
Best For: Active traders wanting clear rules
Strategy 3: Relative Strength Overlay
1. Use RSPS for broad allocation framework
2. Within allocated assets, overweight top 3 performers
3. Scale position sizes by RSPS score
4. Use individual asset charts for entry/exit timing
Best For: Discretionary traders with portfolio focus
Strategy 4: Risk-Adjusted Position Sizing
For each allocated asset:
Position Size = Base Position × (Asset's RSPS Score / Max RSPS Score) × (1 - Cash Allocation)
Example:
- $10,000 portfolio
- SOL RSPS: 0.6 (highest)
- BTC RSPS: 0.4
- Cash allocation: 30%
SOL Size = $10,000 × (0.6/0.6) × (1-0.30) = $7,000
BTC Size = $10,000 × (0.4/0.6) × (1-0.30) = $4,667
Cash = $10,000 × 0.30 = $3,000
Best For: Risk-conscious allocators
📊 Advanced Usage
Multi-Timeframe Confirmation
Use on multiple timeframes for robust signals:
1D Chart: Tactical allocation (daily rebalancing)
4D Chart: Strategic allocation (weekly review)
Strong Confirmation:
- Both timeframes show same top 3 assets
- Both show similar cash allocation levels
- Orange zones aligned on both
Weak/Conflicting:
- Different top performers
- Diverging cash allocations
→ Wait for alignment or use shorter timeframe
Sector Rotation Analysis
Group assets by type and watch rotation:
L1 Dominance: BTC, ETH, SOL, SUI, ADA high → Layer 1 season
Alt L1s: TRX, FTM, CELO rising → Alternative platform season
Specialized: TAO, THETA, HYPE strong → Niche narrative season
Payment/Stable: XRP, BNB allocation → Risk reduction phase
Divergence Trading
Bullish Divergence:
Navy zone declining (less risk-off)
Orange zone flat or slightly rising
Few assets still allocated but strengthening
→ Early accumulation signal
Bearish Divergence:
Orange zone declining
Navy zone rising
Asset count decreasing in table
→ Distribution/exit signal
Performance Tracking
The performance line (overlay) shows cumulative strategy returns:
Compare to BTC/ETH: Is RSPS outperforming?
Drawdown analysis: How deep are pullbacks?
Correlation: Does it track market or provide diversification?
🔬 Technical Details
Data Sources
Price Data:
COINEX: Primary exchange for alt data
CRYPTO: Alternative price feeds
INDEX: Aggregated index prices (recommended for BTC)
Macro Data:
Dominance metrics (SUI.D, BTC.D, etc.)
MVRV ratios (on-chain valuation)
Active addresses (network activity)
Global money supply and macro indicators
Calculation Methodology
RSPS Scoring:
For each asset, calculate 14 relative trends (vs. all others)
Calculate asset's absolute trend
Average all 15 values
Apply penalty filter for extremely weak trends (≤ 0.1)
Trend Consensus:
Uses eth_4d_cal() from NormalizedIndicators library
Combines 8 normalized indicators per measurement
Returns value from -1 (bearish) to +1 (bullish)
Performance Calculation:
Daily Return = Σ(Asset ROC × Asset Allocation)
Cumulative Performance = Previous Perf × (1 + Daily Return / 100)
Assumes perfect rebalancing and no slippage (theoretical performance).
Filtering Logic
filter() function:
pinescriptfilter(input) => input >= 0 ? input : 0
This zero-floor filter ensures:
Only positive trend values contribute to allocation
Bearish assets receive 0 weight
No short positions or inverse allocations
Anti-Manipulation Safeguards
Null Handling:
All values wrapped in nz() to handle missing data
Prevents calculation errors from data gaps
Normalization:
Allocations always sum to 100%
Prevents over/under-allocation
Conditional Logic:
Assets need positive values on multiple metrics
Single metric cannot drive allocation alone
⚠️ Important Considerations
Required Timeframes
1D (Daily): Recommended for most users
4D (4-Day): More stable, fewer rebalances
Other timeframes: Use at your own discretion, may require recalibration
Data Requirements
Needs INDEX:BTCUSD or equivalent major crypto symbol
All 14 tracked assets must have available data
Macro indicators require specific TradingView data feeds
Rebalancing Frequency
System provides daily allocation updates
Practical rebalancing: Weekly or on significant changes (>10%)
Consider transaction costs and tax implications
Performance Notes
Theoretical returns: No slippage, fees, or execution delays
Backtest carefully: Validate on your specific market conditions
Past performance: Does not guarantee future results
Risk Warnings
⚠️ High Concentration Risk: May allocate heavily to 1-3 assets
⚠️ Volatility: Crypto markets are inherently volatile
⚠️ Liquidity: Some allocated assets may have lower liquidity
⚠️ Correlation: All assets correlated to BTC/ETH to some degree
⚠️ System Risk: Relies on continued availability of data feeds
Not Financial Advice
This indicator is a tool for analysis and research. It does not constitute:
Investment advice
Portfolio management services
Trading recommendations
Guaranteed returns
Always perform your own due diligence and risk assessment.
🎓 Use Cases
For Portfolio Managers
Systematic allocation framework
Objective rebalancing signals
Risk-adjusted exposure management
Performance tracking vs. benchmarks
For Active Traders
Identify strongest assets to focus trading on
Gauge overall market regime (risk-on/off)
Time entry/exit for portfolio shifts
Complement technical analysis with allocation data
For Institutional Allocators
Quantitative portfolio construction
Multi-asset exposure optimization
Drawdown management through cash allocation
Compliance-friendly systematic approach
For Researchers
Study relative strength dynamics in crypto markets
Analyze correlation between majors
Test macro factor impact on crypto allocations
Develop derived strategies and signals
🔧 Setup Checklist
✅ Chart Configuration
Set chart to INDEX:BTCUSD
Set timeframe to 1D or 4D
Hide chart candles for clean visualization
Add indicator from library
✅ Indicator Settings
Enable plot_table (see allocation table)
Set use_cash if tracking dollar amounts
Input your portfolio size in cash parameter
✅ Monitoring Setup
Bookmark chart for daily review
Set alerts for major allocation changes (optional)
Create spreadsheet to track allocations (optional)
Establish rebalancing schedule (weekly recommended)
✅ Validation
Verify all 14 assets appear in table (when allocated)
Check that percentages sum to ~100%
Confirm performance line is tracking
Test cash allocation calculation if enabled
📋 Quick Reference
Signal Interpretation
ConditionOrange ZoneNavy ZoneActionStrong BullHigh (>0.4)Low (<0.2)Full allocationModerate BullMid (0.2-0.4)Low-MidStandard allocationNeutralLow (0.1-0.2)Mid (0.3-0.4)Balanced allocationModerate BearVery Low (<0.1)Mid-HighReduce exposureStrong BearZero/NegativeHigh (>0.5)High cash/exit
Rebalancing Thresholds
Change TypeThresholdActionIndividual asset±5%Consider rebalanceIndividual asset±10%Strongly rebalanceCash allocation±10%Adjust exposureAsset entry/exitAnyAdd/remove position
Color Legend
Orange: Main portfolio strength/allocation
Navy: Cash/risk-off allocation
Blue text: Cash position in table
Orange text: Active asset allocation
Gray text: Weak/borderline allocation
White: Headers and labels
🚀 Getting Started
Beginner Path
Add indicator to INDEX:BTCUSD daily chart
Hide candles for clarity
Enable plot_table to see allocations
Check table daily, note top 3-5 assets
Start with small allocation, observe behavior
Gradually increase allocation as you gain confidence
Intermediate Path
Set up on both 1D and 4D charts
Enable use_cash with your portfolio size
Create tracking spreadsheet
Implement weekly rebalancing schedule
Monitor divergences between timeframes
Compare performance to buy-and-hold BTC
Advanced Path
Modify code to add/remove tracked assets
Adjust relative strength calculation methodology
Customize cash allocation factors and weights
Integrate with portfolio management platform
Develop algorithmic rebalancing system
Create alerts for specific allocation conditions
📖 Additional Resources
Related Indicators
NormalizedIndicators Library: Core calculation engine
Individual asset trend indicators for deeper analysis
Macro indicator dashboards for cash allocation factors
Complementary Analysis
On-chain metrics (MVRV, active addresses, etc.)
Order book liquidity for execution planning
Correlation matrices for diversification analysis
Volatility indicators for position sizing
Learning Materials
Study relative strength portfolio theory
Research tactical asset allocation strategies
Understand crypto market cycles and phases
Learn about risk management in volatile assets
🎯 Key Takeaways
✅ Systematic allocation across 14 major cryptocurrencies
✅ Dual-layer approach: Asset selection + Cash management
✅ Relative strength focused: Invests in comparatively strong assets
✅ Trend filtering: Only allocates to assets in positive trends
✅ Dynamic rebalancing: Automatically adjusts to market conditions
✅ Risk-managed: Increases cash during adverse conditions
✅ Transparent methodology: Clear calculation logic
✅ Practical visualization: Easy-to-read table and zones
✅ Performance tracking: See cumulative strategy returns
✅ Highly customizable: Adjust assets, weights, and factors
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
Majors RSPS transforms complex multi-asset portfolio management into a systematic, data-driven process. By combining relative strength analysis with trend consensus and macro risk factors, it provides traders and portfolio managers with a robust framework for navigating cryptocurrency markets with discipline and objectivity.WiederholenClaude kann Fehler machen. Bitte überprüfen Sie die Antworten. Sonnet 4.5
Mean Reversion Signals (v6.4) – VWAP ±SD use with "support and resistence levels with breaks {lux algo} " at 5m tf for better results
Price Drop CounterThe Price Drop Counter is a very basic statistical indicator.
See it as an analytical tool that tracks how many times an asset's price has dropped by a specified percentage from its recent peak within a defined date range.
The indicator monitors the highest price reached and counts each occurrence when the price falls by your chosen threshold, then resets its peak tracking point after each drop is registered.
Uses
Volatility Assessment: Measure how frequently significant price corrections occur during specific periods
Market Behavior Analysis: Compare drop frequency across different timeframes or market conditions
Risk Evaluation: Identify assets or periods with higher downside volatility
Historical Pattern Recognition: Study how often major pullbacks happened during bull or bear markets
Backtesting Support: Analyze how your strategy would perform based on the frequency of drawdowns
How to use it
Add the indicator to your TradingView chart
Configure the Percent Drop (%) to define your threshold (default: 10%). The indicator will count each time price falls by this percentage from the most recent high
IMPORTANT Set your Start Date and End Date to analyze a specific period of interest
The blue step-line plot shows the cumulative count of drops within your date range
Adjust the percentage threshold based on your analysis needs - use smaller values (2-5%) for more frequent signals or larger values (15-20%) for major corrections only
The counter resets its high-water mark after each qualifying drop, allowing it to track multiple sequential drops within the same period.
indicator CalibrationIndicator Calibration - Multi-Indicator Consensus System
Overview
Indicator Calibration is a powerful consensus-based trading indicator that leverages the MyIndicatorLibrary (NormalizedIndicators) to combine multiple trend-following indicators into a single, actionable signal. By averaging the normalized outputs of up to 8 different trend indicators, this tool provides traders with a clear consensus view of market direction, reducing noise and false signals inherent in single-indicator approaches.
The indicator outputs a value between -1 (strong bearish) and +1 (strong bullish), with 0 representing a neutral market state. This creates an intuitive, easy-to-read oscillator that synthesizes multiple analytical perspectives into one coherent signal.
🎯 Core Concept
Consensus Trading Philosophy
Rather than relying on a single indicator that may give conflicting or premature signals, Indicator Calibration employs a democratic voting system where multiple indicators contribute their normalized opinion:
Each enabled indicator votes: +1 (bullish), -1 (bearish), or 0 (neutral)
The votes are averaged to create a consensus signal
Strong consensus (closer to ±1) indicates high agreement among indicators
Weak consensus (closer to 0) indicates market indecision or transition
Key Benefits
Reduced False Signals: Multiple indicators must agree before strong signals appear
Noise Filtering: Individual indicator quirks are smoothed out by averaging
Customizable: Enable/disable indicators and adjust parameters to suit your trading style
Universal Application: Works across all timeframes and asset classes
Clear Visualization: Simple line oscillator with clear bull/bear zones
📊 Included Indicators
The system can utilize up to 8 normalized trend-following indicators from the library:
1. BBPct - Bollinger Bands Percent
Parameters: Length (default: 20), Factor (default: 2)
Type: Stationary oscillator
Strength: Mean reversion and volatility detection
2. NorosTrendRibbonEMA
Parameters: Length (default: 20)
Type: Non-stationary trend follower
Strength: Breakout detection with momentum confirmation
3. RSI - Relative Strength Index
Parameters: Length (default: 9), SMA Length (default: 4)
Type: Stationary momentum oscillator
Strength: Overbought/oversold with smoothing
4. Vidya - Variable Index Dynamic Average
Parameters: Length (default: 30), History Length (default: 9)
Type: Adaptive moving average
Strength: Volatility-adjusted trend following
5. HullSuite
Parameters: Length (default: 55), Multiplier (default: 1)
Type: Fast-response moving average
Strength: Low-lag trend identification
6. TrendContinuation
Parameters: MA Length 1 (default: 50), MA Length 2 (default: 25)
Type: Dual HMA system
Strength: Trend quality assessment with neutral states
7. LeonidasTrendFollowingSystem
Parameters: Short Length (default: 21), Key Length (default: 10)
Type: Dual EMA crossover
Strength: Simple, reliable trend tracking
8. TRAMA - Trend Regularity Adaptive Moving Average
Parameters: Length (default: 50)
Type: Adaptive trend follower
Strength: Adjusts to trend stability
⚙️ Input Parameters
Source Settings
Source: Choose your price input (default: close)
Can be modified to: open, high, low, close, hl2, hlc3, ohlc4, hlcc4
Indicator Selection
Each indicator can be enabled or disabled via checkboxes:
use_bbpct: Enable/disable Bollinger Bands Percent
use_noros: Enable/disable Noro's Trend Ribbon
use_rsi: Enable/disable RSI
use_vidya: Enable/disable VIDYA
use_hull: Enable/disable Hull Suite
use_trendcon: Enable/disable Trend Continuation
use_leonidas: Enable/disable Leonidas System
use_trama: Enable/disable TRAMA
Parameter Customization
Each indicator has its own parameter group where you can fine-tune:
val 1: Primary period/length parameter
val 2: Secondary parameter (multiplier, smoothing, etc.)
📈 Signal Interpretation
Output Line (Orange)
The main output oscillates between -1 and +1:
+1.0 to +0.5: Strong bullish consensus (all or most indicators agree on uptrend)
+0.5 to +0.2: Moderate bullish bias (bullish indicators outnumber bearish)
+0.2 to -0.2: Neutral zone (mixed signals or transition phase)
-0.2 to -0.5: Moderate bearish bias (bearish indicators outnumber bullish)
-0.5 to -1.0: Strong bearish consensus (all or most indicators agree on downtrend)
Reference Lines
Green line (+1): Maximum bullish consensus
Red line (-1): Maximum bearish consensus
Gray line (0): Neutral midpoint
💡 Trading Strategies
Strategy 1: Consensus Threshold Trading
Entry Rules:
- Long: Output crosses above +0.5 (strong bullish consensus)
- Short: Output crosses below -0.5 (strong bearish consensus)
Exit Rules:
- Exit Long: Output crosses below 0 (consensus lost)
- Exit Short: Output crosses above 0 (consensus lost)
Strategy 2: Zero-Line Crossover
Entry Rules:
- Long: Output crosses above 0 (bullish shift in consensus)
- Short: Output crosses below 0 (bearish shift in consensus)
Exit Rules:
- Exit on opposite crossover
Strategy 3: Divergence Trading
Look for divergences between:
- Price making higher highs while indicator makes lower highs (bearish divergence)
- Price making lower lows while indicator makes higher lows (bullish divergence)
Strategy 4: Extreme Reading Reversal
Entry Rules:
- Long: Output reaches -0.8 or below (extreme bearish consensus = potential reversal)
- Short: Output reaches +0.8 or above (extreme bullish consensus = potential reversal)
Use with caution - best combined with other reversal signals
🔧 Optimization Tips
For Trending Markets
Enable trend-following indicators: Noro's, VIDYA, Hull Suite, Leonidas
Use higher threshold levels (±0.6) to filter out minor retracements
Increase indicator periods for smoother signals
For Range-Bound Markets
Enable oscillators: BBPct, RSI
Use zero-line crossovers for entries
Decrease indicator periods for faster response
For Volatile Markets
Enable adaptive indicators: VIDYA, TRAMA
Use wider threshold levels to avoid whipsaws
Consider disabling fast indicators that may overreact
Custom Calibration Process
Start with all indicators enabled using default parameters
Backtest on your chosen timeframe and asset
Identify which indicators produce the most false signals
Disable or adjust parameters for problematic indicators
Test different threshold levels for entry/exit
Validate on out-of-sample data
📊 Visual Guide
Color Scheme
Orange Line: Main consensus output
Green Horizontal: Bullish extreme (+1)
Red Horizontal: Bearish extreme (-1)
Gray Horizontal: Neutral zone (0)
Reading the Chart
Line above 0: Net bullish sentiment
Line below 0: Net bearish sentiment
Line near extremes: Strong consensus
Line fluctuating near 0: Indecision or transition
Smooth line movement: Stable consensus
Erratic line movement: Conflicting signals
⚠️ Important Considerations
Lag Characteristics
This is a lagging indicator by design (consensus takes time to form)
Best used for trend confirmation rather than early entry
May miss the first portion of strong moves
Reduces false entries at the cost of delayed entries
Number of Active Indicators
More indicators = smoother but slower signals
Fewer indicators = faster but potentially noisier signals
Minimum recommended: 4 indicators for reliable consensus
Optimal: 6-8 indicators for balanced performance
Market Conditions
Best: Strong trending markets (up or down)
Good: Volatile markets with clear directional moves
Poor: Choppy, sideways markets with no clear trend
Worst: Low-volume, range-bound conditions
Complementary Tools
Consider combining with:
Volume analysis for confirmation
Support/resistance levels for entry/exit points
Market structure analysis (higher timeframe trends)
Risk management tools (ATR-based stops)
🎓 Example Use Cases
Swing Trading
Timeframe: Daily or 4H
Enable: All 8 indicators with default parameters
Entry: Consensus > +0.5 or < -0.5
Hold: Until consensus reverses to opposite extreme
Day Trading
Timeframe: 15m or 1H
Enable: Faster indicators (RSI, BBPct, Noro's, Hull Suite)
Entry: Zero-line crossover with volume confirmation
Exit: Opposite crossover or profit target
Position Trading
Timeframe: Weekly or Daily
Enable: Slower indicators (TRAMA, VIDYA, Trend Continuation)
Entry: Strong consensus (±0.7) with higher timeframe confirmation
Hold: Months until consensus weakens significantly
🔬 Technical Details
Calculation Method
1. Each enabled indicator calculates its normalized signal (-1, 0, or +1)
2. All active signals are stored in an array
3. Array.avg() computes the arithmetic mean
4. Result is plotted as a continuous line
Output Range
Theoretical: -1.0 to +1.0
Practical: Typically ranges between -0.8 to +0.8
Rare: All indicators perfectly aligned at ±1.0
Performance
Lightweight calculation (simple averaging)
No repainting (all indicators are non-repainting)
Compatible with all Pine Script features
Works on all TradingView plans
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
🚀 Quick Start Guide
Add to Chart: Apply indicator to your chart
Choose Timeframe: Select appropriate timeframe for your trading style
Enable Indicators: Start with all 8 enabled
Observe Behavior: Watch how consensus forms during different market conditions
Calibrate: Adjust parameters and indicator selection based on observations
Backtest: Validate your settings on historical data
Trade: Apply with proper risk management
🎯 Key Takeaways
✅ Consensus beats individual indicators - Multiple perspectives reduce errors
✅ Customizable to your style - Enable/disable and tune to preference
✅ Simple interpretation - One line tells the story
✅ Works across markets - Stocks, crypto, forex, commodities
✅ Reduces emotional trading - Clear, objective signal generation
✅ Professional-grade - Built on proven technical analysis principles
Indicator Calibration transforms complex multi-indicator analysis into a single, actionable signal. By harnessing the collective wisdom of multiple proven trend-following systems, traders gain a powerful edge in identifying high-probability trade setups while filtering out market noise.
NEW PRICE ACTION ALGO (v2)Updated price action indicator for day trading QQQ,SPY & IWM on the 5-6min chart
D+P All-in-OneD+P=DARVAS+PIVOT
In this script i tried make small combo of multiple metrics.
Along with Darvas+Pivot we have EMA10,20&RSI d,w,m table. i fixed this table to middle right so that its easy to use while using phone.
There is floater table having Day Low& Previous Day Low-% differnce from current price
We have RS rating of O'Neil
Small table having MarketCap,Industry and sector.
Baseline Deviation Oscillator [Alpha Extract]A sophisticated normalized oscillator system that measures price deviation from a customizable moving average baseline using ATR-based scaling and dynamic threshold adaptation. Utilizing advanced HL median filtering and multi-timeframe threshold calculations, this indicator delivers institutional-grade overbought/oversold detection with automatic zone adjustment based on recent oscillator extremes. The system's flexible baseline architecture supports six different moving average types while maintaining consistent ATR normalization for reliable signal generation across varying market volatility conditions.
🔶 Advanced Baseline Construction Framework
Implements flexible moving average architecture supporting EMA, RMA, SMA, WMA, HMA, and TEMA calculations with configurable source selection for optimal baseline customization. The system applies HL median filtering to the raw baseline for exceptional smoothing and outlier resistance, creating ultra-stable trend reference levels suitable for precise deviation measurement.
// Flexible Baseline MA System
ma(src, length, type) =>
if type == "EMA"
ta.ema(src, length)
else if type == "TEMA"
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
3 * ema1 - 3 * ema2 + ema3
// Baseline with HL Median Smoothing
Baseline_Raw = ma(src, MA_Length, MA_Type)
Baseline = hlMedian(Baseline_Raw, HL_Filter_Length)
🔶 ATR Normalization Engine
Features sophisticated ATR-based scaling methodology that normalizes price deviations relative to current volatility conditions, ensuring consistent oscillator readings across different market regimes. The system calculates ATR bands around the baseline and uses half the band width as the normalization factor for volatility-adjusted deviation measurement.
🔶 Dynamic Threshold Adaptation System
Implements intelligent threshold calculation using rolling window analysis of oscillator extremes with configurable smoothing and expansion parameters. The system identifies peak and trough levels over dynamic windows, applies EMA smoothing, and adds expansion factors to create adaptive overbought/oversold zones that adjust to changing market conditions.
1D
3D
1W
🔶 Multi-Source Configuration Architecture
Provides comprehensive source selection including Close, Open, HL2, HLC3, and OHLC4 options for baseline calculation, enabling traders to optimize oscillator behavior for specific trading styles. The flexible source system allows adaptation to different market characteristics while maintaining consistent ATR normalization methodology.
🔶 Signal Generation Framework
Generates bounce signals when oscillator crosses back through dynamic thresholds and zero-line crossover signals for trend confirmation. The system identifies both standard threshold bounces and extreme zone bounces with distinct alert conditions for comprehensive reversal and continuation pattern detection.
Bull_Bounce = ta.crossover(OSC, -Active_Lower) or
ta.crossover(OSC, -Active_Lower_Extreme)
Bear_Bounce = ta.crossunder(OSC, Active_Upper) or
ta.crossunder(OSC, Active_Upper_Extreme)
// Zero Line Signals
Zero_Cross_Up = ta.crossover(OSC, 0)
Zero_Cross_Down = ta.crossunder(OSC, 0)
🔶 Enhanced Visual Architecture
Provides color-coded oscillator line with bullish/bearish dynamic coloring, signal line overlay for trend confirmation, and optional cloud fills between oscillator and signal. The system includes gradient zone fills for overbought/oversold regions with configurable transparency and threshold level visualization with automatic label generation.
snapshot
🔶 HL Median Filter Integration
Features advanced high-low median filtering identical to DEMA Flow for exceptional baseline smoothing without lag introduction. The system constructs rolling windows of baseline values, performs median extraction for both odd and even window lengths, and eliminates outliers for ultra-clean deviation measurement baseline.
🔶 Comprehensive Alert System
Implements multi-tier alert framework covering bullish bounces from oversold zones, bearish bounces from overbought zones, and zero-line crossovers in both directions. The system provides real-time notifications for critical oscillator events with customizable message templates for automated trading integration.
🔶 Performance Optimization Framework
Utilizes efficient calculation methods with optimized array management for median filtering and minimal computational overhead for real-time oscillator updates. The system includes intelligent null value handling and automatic scale factor protection to prevent division errors during extreme market conditions.
🔶 Why Choose Baseline Deviation Oscillator ?
This indicator delivers sophisticated normalized oscillator analysis through flexible baseline architecture and dynamic threshold adaptation. Unlike traditional oscillators with fixed levels, the BDO automatically adjusts overbought/oversold zones based on recent oscillator behavior while maintaining consistent ATR normalization for reliable cross-market and cross-timeframe comparison. The system's combination of multiple MA type support, HL median filtering, and intelligent zone expansion makes it essential for traders seeking adaptive momentum analysis with reduced false signals and comprehensive reversal detection across cryptocurrency, forex, and equity markets.
Earnings Move Radar (E+1 & E+2)Stop guessing earnings reactions. See them. Count them. Use them.
Earnings Move Radar turns every earnings release into a clear visual story on your chart.
What it does for you
Automatically marks the first and second trading day after each earnings (E+1 & E+2).
Shows at a glance whether the move came from a gap, an intraday trend, or a follow-through / reversal on the next day.
Packs all past earnings into a compact stats panel so you instantly know:
how big earnings moves usually are,
how often they close up vs. down,
how common your “big move” threshold really is.
Highlights simple, practical patterns like “two-day runs” and “three-day runs” around earnings that many traders care about but rarely measure.
Why traders like it
Saves time: no more clicking through old earnings dates one by one.
Puts realistic numbers behind your option ideas and post-earnings plays.
Works on any stock or ETF with earnings data, with a customizable look-back window.
All labels and the stats table are shown in Chinese, making it very friendly for Chinese-speaking traders.
How to use
Apply it to a daily chart of the stock or ETF you trade.
Scan the labels to understand how the market usually reacts to earnings for this symbol.
Use the stats panel to size your risk and define what “normal”, “large” and “extreme” earnings moves mean for you.
This indicator is an analytical tool, not a signal generator. It does not provide financial advice.
Session Range Boxes (Budapest time) GR V2.0Session Range Boxes (Budapest time)
This indicator draws intraday range boxes for the main Forex sessions based on Europe/Budapest time (CET/CEST).
Tracked sessions (Budapest time):
Asia: 01:00 – 08:00
Frankfurt (pre-London): 08:00 – 09:00
London: 09:00 – 18:00
New York: 14:30 – 23:00
For each session, the script:
Detects the session start and session end using the current chart timeframe and the Europe/Budapest time zone.
Tracks the high and low of price during the entire session.
Draws a box (rectangle) from session open to session close, covering the full price range between session high and low.
Optionally prints a small label above the first bar of each session (Asia, Fra, London, NY).
Color scheme:
Asia: soft orange box
Frankfurt: light aqua box
London: darker blue box
New York: light lime box
Use this tool to:
Quickly see which session created the high/low of the day,
Identify liquidity zones and session ranges that price may revisit,
Visually separate Asia, Frankfurt, London and New York volatility on intraday charts.
Optimized for intraday trading (Forex / indices), but it works on any symbol where session behavior matters.
