FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
Crypto
[ayana] TFPS - TradFi Pressure ScoreTFPS - TradFi Pressure Score: Your Market Pressure Barometer
Understand what moves Wall Street, before it moves Crypto.
This indicator is your real-time barometer for the influence of traditional financial markets (TradFi) on Crypto. It measures the combined pressure from four key quadrants—Risk Appetite (S&P 500), Market Stress (VIX), Liquidity (DXY), and Macro Expectations (US10Y)—to answer one question: "Do I have a tailwind or a headwind from the global markets?"
How to Read Your "Cockpit" in 60 Seconds
The Main Line (Overall Market Pressure)
GREEN / ABOVE 0: Bullish Tailwind. The macro environment is supportive for Crypto.
RED / BELOW 0: Bearish Headwind. The macro environment is creating pressure on Crypto.
BRIGHT Color: Pressure is ACCELERATING.
DARK Color: Pressure is DECELERATING (losing momentum).
The Dashboard (Your Command Center)
Lead/Lag Analysis: The game-changer. Tells you if TradFi is currently leading the price or vice-versa. This is your key to knowing whether to watch macro news or focus on crypto-specifics.
TradFi Influence (R²): Shows you HOW RELEVANT the macro pressure is right now. High R² means Wall Street's influence is dominant. Low R² means crypto is moving on its own narrative.
Dynamic Weights: Reveals the market's primary NARRATIVE. Is the pressure coming from Fear (VIX), Liquidity (DXY), or general Risk Appetite (SPX)?
Extreme Signals (Reversal Zones)
Stress Cloud (Z-Score): Large, opaque bars warn of statistically EXTREME greed or fear levels.
Extreme Dots: Pinpoint the moments when pressure has likely reached an unsustainable peak, often preceding turning points.
Key Strategies & Use Cases
As a Trend Filter: Simply avoid fighting the color. Don't force long trades when the TFPS shows a strong red headwind.
For Precision Entry/Exits: Use the Extreme Dots and a decelerating color on the Main Line to time your entries in confluence with your own strategy.
For Strategic Decisions: Use the Lead/Lag and R² metrics to decide where to focus your attention and how to manage portfolio risk based on the current macro regime.
Configuration
For best results, leave the engine settings on their default (auto-adaptive) mode. The indicator's core intelligence lies in its ability to adapt to changing market dynamics automatically. You can adjust the visual theme to match your chart.
MP Master VWAP [BackQuant]MP Master VWAP
Overview
MP Master VWAP is an, volume-weighted average price suite. It re-anchors automatically to any time partition you select—Day, Week, Month, Quarter or Year—and builds an adaptive standard-deviation envelope, optional pivot clusters and context-aware candle colouring so you can read balance, imbalance and auction edges in a single glance. We use private methods on calculating key levels, making them adaptive and more responsive. This is not just a plain VWAP.
Key Components
• Anchored VWAP core – The engine resets VWAP the instant a new session for the chosen anchor begins. Separator lines and a live high–low box make those rotations obvious.
• Dynamic sigma bands – Three upper and three lower bands, scaled by real-time standard deviation. 1-σ filters noise, 2-σ marks momentum, 3-σ flags exhaustion.
• Previous-period memory – The prior session’s VWAP and bands stay on-screen in a muted style so you can trade retests of last month’s value without clutter.
• High-precision price labels – VWAP and every active band print their prices on the hard right edge; labels vanish if you want a cleaner chart.
• Pivot package – Choose Traditional, Fibonacci or Camarilla calculations on a Daily, Weekly or Monthly look-back. Levels plot as subtle circles that complement, not compete with, the VWAP map.
• Context candles – Bars tint relative to their location: vivid red above U2, soft red between U1-U2, neutral grey inside value, soft green between L2-L1, vivid green below L2.
Customisation Highlights
Period section
• Anchor reset drop-down
• Toggles for separator lines and period high/low
Band section
• Independent visibility for L1/U1, L2/U2, L3/U3
• Individual multipliers to fit any volatility profile
• Optional real-time price labels
Pivot section
• Three formula choices
• Independent timeframe—mix a Monthly VWAP with Weekly Camarilla for confluence
Visual section
• Separate switches for current vs previous envelopes
• Candle-colour toggle for traders who prefer raw price bars
Colour section
• Full palette selectors to match dark or light themes instantly
Some Potential Ways it can be used:
Mean-reversion fade – Price spikes into U2 or U3 and stalls (especially at a pivot). Fade back toward VWAP; scale out at U1 and VWAP.
Trend continuation – Close above U1 on rising volume; trail a stop behind U1. Mirror setup for shorts under L1.
Breakout validation – Session gaps below previous VWAP but quickly reclaims it. Use the cross-above alert to automate entry and target U1 / U2.
Overnight inventory flush – Globex extremes that tag L2 / U2 often reverse at the cash open; scalp rotations back to VWAP.
Risk framing – Let the gap between VWAP and L2 / U2 dictate position size, keeping reward-to-risk consistent across assets.
Alerts Included
• Cross above / below current VWAP
• Cross first sigma bands (U1 / L1)
• Break above second sigma bands (U2) or below L2
• Touch of third sigma bands (U3 / L3)
• Cross of previous-period VWAP
• New period high or low
Best Practices
• Tighten sigma multipliers on thin-liquidity symbols; widen them on index futures or high-cap crypto.
• Pair the envelope with order-flow or footprint tools to confirm participation at band edges.
• On intraday charts, anchor a higher-timeframe VWAP (e.g., Monthly on a 15-minute) to reveal institutional accumulation.
• Treat the previous period’s VWAP as yesterday’s fair value—gaps that never revisit it often morph into trend days.
Final Notes
MP Master VWAP condenses auction-market theory into one readable overlay: automatic period resets, adaptive deviation bands, historical memory, multi-style pivots and self-explanatory colour coding. You can deploy it on equities, futures, crypto or FX—wherever volume meets time, VWAP remains the benchmark of true price discovery.
TFPS - TradFi Pressure ScoreThe Data-Driven Answer to a New Market Reality.
This indicator quantifies the pressure exerted by Wall Street on the crypto market across four critical dimensions: Risk Appetite, Fear, Liquidity Flows, and the Opportunity Cost of Capital. Our research has found that the correlation between this 4-dimensional pressure vector and crypto price action reaches peak values of 0.87. This is your decisive macro edge, delivered in real-time.
The Irreversible Transformation
A fundamental analysis of the last five years of market data proves an irreversible transformation: The crypto market has matured into a high-beta risk asset, its fate now inextricably linked to Traditional Finance (TradFi).
The empirical data is clear:
Bitcoin increasingly behaves like a leveraged version of the S&P 500.
The correlation to major stock indices is statistically significant and persistent.
The "digital gold" narrative is refuted by the data; the correlation to gold is virtually non-existent.
This means standard technical indicators are no longer sufficient. Tools like RSI or MACD are blind to the powerful, external macro context that now dominates price action. They see the effect, but not the cause.
The Solution: A 4-Dimensional Macro-Lens
The TradFi Pressure Score (TFPS) is the answer. It is an institutional-grade dashboard that aggregates the four most dominant external forces into a single, actionable score:
S&P 500 (SPY): The Pulse of Risk Appetite. A rising S&P signals a "risk-on" environment, fueling capital flows into crypto.
VIX: The Market's Fear Gauge. A rising VIX signals a "risk-off" flight to safety, draining liquidity from crypto.
DXY (US-Dollar Index): The Anchor of Global Liquidity. A strong Dollar (rising DXY) tightens financial conditions, creating powerful headwinds for risk assets like Bitcoin.
US 10Y Yield: The Opportunity Cost of Capital. Rising yields make risk-free assets more attractive, pulling capital away from non-yielding assets like crypto.
What makes the TFPS truly unique?
1. Dynamic Weighting (The Secret Weapon):
Which macro factor matters most right now? Is it a surging Dollar or a collapsing stock market? The TFPS answers this automatically. It continuously analyzes the correlation of all four components to your chosen asset (e.g., Bitcoin) and adjusts their influence in real-time. The dashboard shows you the exact live weights, ensuring you are always focused on the factor that is currently driving the market.
2. Adaptive Engine:
The forces driving a 15-minute chart are different from those driving a daily chart. The TFPS engine automatically recalibrates its internal lookback periods to your chosen timeframe. This ensures the score is always optimally relevant, whether you are a day trader or a swing trader.
3. Designed for Actionable Insights
The Pressure Line: The indicator's core output. Is its value > 0 (tailwind) or < 0 (headwind)? This provides an instant, unambiguous read on the macro environment for your trade.
The Z-Score (The Contrarian Signal): The background "Stress Cloud" and the discrete dots provide early warnings of extreme macro greed or fear. Readings above +2 or below -2 have historically pinpointed moments of market exhaustion that often precede major trend reversals.
Lead/Lag Status: Gain a critical edge by knowing who is in the driver's seat. The dashboard tells you if TradFi is leading the price action or if crypto is moving independently, allowing you to validate your trade thesis against the dominant market force.
This is a public indicator with protected source code
Access is now available for traders who understand the new market reality at the intersection of crypto and traditional finance.
You are among the first to leverage what is a new standard for macro analysis in crypto trading. Your feedback is highly valued as I continue to refine this tool.
Follow for updates and trade with the full context!
TFPS - TradFi-Pressure-Score (Adaptive)The data-driven answer to an irreversible market reality.
This indicator quantifies the combined pressure from the S&P 500, VIX, DXY, and US10Y, whose correlation to crypto has reached peak values of 0.87. Your decisive macro edge, in real-time.
This indicator is built on a fundamental analysis of market data from the last five years. The analysis proves an irreversible transformation: The crypto market has evolved into a high-beta risk asset, its fate inextricably linked to Traditional Finance (TradFi).
The empirical data is clear:
Bitcoin increasingly behaves like a leveraged version of the S&P 500.
The correlation to stock indices, with peak values of up to 0.87, is statistically highly significant.
The "digital gold" safe-haven narrative is refuted by the data; the correlation to gold (0.04) is virtually non-existent and statistically insignificant.
This means: Standard indicators like RSI or MACD are insufficient for today's market conditions. They only see price, ignoring the powerful external context that now dominates price action.
The TradFi Pressure Score (TFPS) is the answer to this data-driven reality. It's your institutional-grade macro dashboard, aggregating the four most dominant external forces into a single, actionable score:
S&P 500 (SPY): The pulse of global risk appetite. A rising S&P signals a "risk-on" environment, fueling capital flows into crypto.
VIX: The market's "Fear Gauge". A rising VIX signals a "risk-off" flight to safety, draining liquidity from crypto.
DXY (US-Dollar Index): The counter-pole to risk assets. A strong Dollar (rising DXY) tightens global liquidity, creating significant headwinds for Bitcoin.
US 10Y Yield: The opportunity cost of capital. Rising yields make risk-free assets more attractive, pulling capital away from non-yielding assets like crypto.
What makes TFPS truly unique?
Dynamic Weighting (its secret weapon): Which factor matters most today? The DXY or the VIX? TFPS continuously analyzes the correlation of all four factors to your chosen asset (e.g., Bitcoin) and automatically adjusts their weight in real-time. This ensures you're always focused on what's currently driving the market.
Adaptive Engine : What drives a 15-minute chart is different from a daily chart. The TFPS engine automatically adapts its lookback periods and calculations to your chosen timeframe for optimal relevance.
Clear, Actionable Signals Designed for Traders:
Pressure Line (>0 or <0): Instantly see if the world's largest financial forces are providing a tailwind or a headwind for your trade.
Z-Score (Extreme Readings) : Get early warnings of extreme macro "Greed" or "Fear". Readings above +2 or below -2 have historically pinpointed moments of market exhaustion that often precede major trend reversals.
Regime Change : A fundamental shift in the nature of TradFi pressure is visualized with a clear signal, providing unambiguous macro insights.
Lead/Lag Status : Gain a critical edge by knowing who's in the driver's seat. The dashboard tells you if TradFi is LEADING the price action or if crypto is moving independently, allowing you to focus on the right information source.
This is a private beta. I am granting exclusive access to a limited number of traders who understand this new market reality. In exchange for your valuable feedback, you will be among the first to leverage what I believe is the new standard for macro analysis in crypto trading.
Request access to trade with the full context.
Spent Output Profit Ratio Z-Score | Vistula LabsOverview
The Spent Output Profit Ratio (SOPR) Z-Score indicator is a sophisticated tool designed by Vistula Labs to help cryptocurrency traders analyze market sentiment and identify potential trend reversals. It leverages on-chain data from Glassnode to calculate the Spent Output Profit Ratio (SOPR) for Bitcoin and Ethereum, transforming this metric into a Z-Score for easy interpretation.
What is SOPR?
Spent Output Profit Ratio (SOPR) measures the profit ratio of spent outputs (transactions) on the blockchain:
SOPR > 1: Indicates that, on average, coins are being sold at a profit.
SOPR < 1: Suggests that coins are being sold at a loss.
SOPR = 1: Break-even point, often seen as a key psychological level.
SOPR provides insights into holder behavior—whether they are locking in profits or cutting losses—making it a valuable gauge of market sentiment.
How It Works
The indicator applies a Z-Score to the SOPR data to normalize it relative to its historical behavior:
Z-Score = (Smoothed SOPR - Moving Average of Smoothed SOPR) / Standard Deviation of Smoothed SOPR
Smoothed SOPR: A moving average (e.g., WMA) of SOPR over a short period (default: 30 bars) to reduce noise.
Moving Average of Smoothed SOPR: A longer moving average (default: 180 bars) of the smoothed SOPR.
Standard Deviation: Calculated over a lookback period (default: 200 bars).
This Z-Score highlights how extreme the current SOPR is compared to its historical norm, helping traders spot significant deviations.
Key Features
Data Source:
Selectable between BTC and ETH, using daily SOPR data from Glassnode.
Customization:
Moving Average Types: Choose from SMA, EMA, DEMA, RMA, WMA, or VWMA for both smoothing and main averages.
Lengths: Adjust the smoothing period (default: 30) and main moving average length (default: 180).
Z-Score Lookback: Default is 200 bars.
Thresholds: Set levels for long/short signals and overbought/oversold conditions.
Signals:
Long Signal: Triggered when Z-Score crosses above 1.02, suggesting potential upward momentum.
Short Signal: Triggered when Z-Score crosses below -0.66, indicating potential downward momentum.
Overbought/Oversold Conditions:
Overbought: Z-Score > 2.5, signaling potential overvaluation.
Oversold: Z-Score < -2.0, indicating potential undervaluation.
Visualizations:
Z-Score Plot: Teal for long signals, magenta for short signals.
Threshold Lines: Dashed for long/short, solid for overbought/oversold.
Candlestick Coloring: Matches signal colors.
Arrows: Green up-triangles for long entries, red down-triangles for short entries.
Background Colors: Magenta for overbought, teal for oversold.
Alerts:
Conditions for Long Opportunity, Short Opportunity, Overbought, and Oversold.
Usage Guide
Select Cryptocurrency: Choose BTC or ETH.
Adjust Moving Averages: Customize types and lengths for smoothing and main averages.
Set Thresholds: Define Z-Score levels for signals and extreme conditions.
Monitor Signals: Use color changes, arrows, and background highlights to identify opportunities.
Enable Alerts: Stay informed without constant chart watching.
Interpretation
High Z-Score (>1.02): SOPR is significantly above its historical mean, potentially indicating overvaluation or strong bullish momentum.
Low Z-Score (<-0.66): SOPR is below its mean, suggesting undervaluation or bearish momentum.
Extreme Conditions: Z-Scores above 2.5 or below -2.0 highlight overbought or oversold markets, often preceding reversals.
Conclusion
The SOPR Z-Score indicator combines on-chain data with statistical analysis to provide traders with a clear, actionable view of market sentiment. Its customizable settings, visual clarity, and alert system make it an essential tool for both novice and experienced traders seeking an edge in the cryptocurrency markets.
Price Exhaustion Envelope [BackQuant]Price Exhaustion Envelope
Visual preview of the bands:
What it is
The Price Exhaustion Envelope (PEE) is a multi‑factor overextension detector wrapped inside a dynamic envelope framework. It measures how “tired” a move is by blending price stretch, volume surges, momentum and acceleration, plus optional RSI divergence. The result is a composite exhaustion score that drives both on‑chart signals and the adaptive width of three optional envelope bands around a smoothed baseline. When the score spikes above or below your chosen threshold, the script can flag exhaustion, paint candles, tint the background and fire alerts.
How it works under the hood
Exhaustion score
Price component: distance of close from its mean in standard deviation units.
Volume component: normalized volume pressure that highlights unusual participation.
Momentum component: rate of change and acceleration of price, scaled by their own volatility.
RSI divergence (optional): bullish and bearish divergences gently push the score lower or higher.
Mode control: choose Price, Volume, Momentum or Composite. Composite averages the main pieces for a balanced view.
Energy scale (0 to 100)
The composite score is pushed through a logistic transform to create an “energy” value. High energy (above 70 to 80) signals a move that may be running hot, while very low energy (below 20 to 30) points to exhaustion on the downside.
Envelope engine
Baseline: EMA of price over the main lookback length.
Width: base width is standard deviation times a multiplier.
Type selector:
• Static keeps the width fixed.
• Dynamic expands width in proportion to the absolute exhaustion score.
• Adaptive links width to the energy reading so bands breathe with market “heat.”
Smoothing: a short EMA on the width reduces jitter and keeps bands pleasant to trade around.
Band architecture
You can toggle up to three symmetric bands on each side of the baseline. They default to 1.0, 1.6 and 2.2 multiples of the smoothed width. Soft transparent fills create a layered thermograph of extension. The outermost band often maps to true blow‑off extremes.
On‑chart elements
Baseline line that flips color in real time depending on where price sits.
Up to three upper and lower bands with progressive opacity.
Triangle markers at fresh exhaustion triggers.
Tiny warning glyphs at extreme upper or lower breaches.
Optional bar coloring to visually tag exhausted candles.
Background halo when energy > 80 or < 20 for instant context.
A compact info table showing State, Score, Energy, Momentum score and where price sits inside the envelope (percent).
How to use it in trading
Mean reversion plays
When price pierces the outer band and an exhaustion marker prints, look for reversal candles or lower‑timeframe confirmation to fade the move back toward the baseline.
For conservative entries, wait for the composite score to roll back under the threshold or for energy to drop from extreme to neutral.
Set stops just beyond the extreme levels (use extreme_upper and extreme_lower as natural invalidation points). Targets can be the baseline or the opposite inner band.
Trend continuation with smart pullbacks
In strong trends, the first tag of Band 1 or Band 2 against the dominant direction often offers low‑risk continuation entries. Use energy readings: if energy is low on a pullback during an uptrend, a bounce is more likely.
Combine with RSI divergence: hidden bullish divergence near a lower band in an uptrend can be a powerful confirmation.
Breakout filtering
A breakout that occurs while the composite score is still moderate (not exhausted) has a higher chance of follow‑through. Skip signals when energy is already above 80 and price is punching the outer band, as the move may be late.
Watch env_position (Envelope %) in the table. Breakouts near 40 to 60 percent of the envelope are “healthy,” while those at 95 percent are stretched.
Scaling out and risk control
Use exhaustion alerts to trim positions into strength or weakness.
Trail stops just outside Band 2 or Band 3 to stay in trends while letting the envelope expand in volatile phases.
Multi‑timeframe confluence
Run the script on a higher timeframe to locate exhaustion context, then drill down to a lower timeframe for entries.
Opposite signals across timeframes (daily exhaustion vs. 5‑minute breakout) warn you to reduce size or tighten management.
Key inputs to experiment with
Lookback Period: larger values smooth the score and envelope, ideal for swing trading. Shorter values make it reactive for scalps.
Exhaustion Threshold: raise above 2.0 in choppy assets to cut noise, drop to 1.5 for smooth FX pairs.
Envelope Type: Dynamic is great for crypto spikes, Adaptive shines in stocks where volume and volatility wave together.
RSI Divergence: turn off if you prefer a pure price/volume model or if divergence floods the score in your asset.
Alert set included
Fresh upper exhaustion
Fresh lower exhaustion
Extreme upper breach
Extreme lower breach
RSI bearish divergence
RSI bullish divergence
Hook these to TradingView notifications so you get pinged the moment a move hits exhaustion.
Best practices
Always pair exhaustion signals with structure. Support and resistance, liquidity pools and session opens matter.
Avoid blindly shorting every upper signal in a roaring bull market. Let the envelope type help you filter.
Use the table to sanity‑check: a very high score but mid‑range env_position means the band may still be wide enough to absorb more movement.
Backtest threshold combinations on your instrument. Different tickers carry different volatility fingerprints.
Final note
Price Exhaustion Envelope is a flexible framework, not a turnkey system. It excels as a context layer that tells you when the crowd is pressing too hard or when a move still has fuel. Combine it with sound execution tactics, risk limits and market awareness. Trade safe and let the envelope breathe with the market.
NAIFCHART_Fresh Algo v24# NAIFCHART Fresh Algo v24: Advanced Multi-Mode Trading System Analysis
I recently discovered this sophisticated trading system through the active community at t.me and wanted to share a detailed analysis of the NAIFCHART Fresh Algo v24 indicator. This represents an advanced evolution of multi-component trading systems that adapts to various market conditions through sophisticated operational configurations and enhanced analytical capabilities.
## Primary Signal Generation Framework
The Fresh Algo v24 operates through two fundamental signal generation approaches that accommodate different market perspectives and trading philosophies. The Trending Signals Mode serves as the primary trend-following mechanism, combining Wave Trend Oscillator analysis with Supertrend directional signals and Squeeze Momentum breakout detection. This mode incorporates ADX filtering that requires values exceeding 20 to ensure sufficient trend strength exists before signal activation, making it particularly effective during sustained directional market movements where momentum persistence creates profitable trading opportunities.
The Contrarian Signals Mode provides an alternative approach targeting reversal opportunities through extreme market condition identification. This mode activates when the Wave Trend Oscillator reaches critical threshold levels, specifically when readings surpass 65 indicating potential bearish reversal conditions or drop below 35 suggesting bullish reversal opportunities. This methodology proves valuable during overextended market phases where mean reversion becomes statistically probable.
## Advanced Filtering Mechanisms
The system incorporates multiple sophisticated filtering mechanisms designed to enhance signal quality and reduce false positive occurrences. The High Volume Filter requires volume expansion confirmation before signal activation, utilizing exponential moving average calculations to ensure institutional participation accompanies price movements. This filter substantially improves signal reliability by eliminating low-conviction breakouts that lack adequate volume support from professional market participants.
The Strong Filter provides additional trend confirmation through 200-period exponential moving average analysis. Long position signals require price action above this benchmark level, while short position signals necessitate price action below it. This ensures strategic alignment with longer-term trend direction and reduces the probability of trading against major market movements that could invalidate shorter-term signals.
## Cloud Filter Configuration System
The Fresh Algo v24 offers four distinct cloud filter configurations, each optimized for specific trading timeframes and market approaches. The Smooth Cloud Filter utilizes the mathematical relationship between 150-period and 250-period exponential moving averages, providing stable trend identification suitable for position trading strategies. This configuration generates signals exclusively when price action aligns with cloud direction, creating a more deliberate but highly reliable signal generation process.
The Swing Cloud Filter employs modified Supertrend calculations with parameters specifically optimized for swing trading timeframes. This filter achieves optimal balance between responsiveness and stability, adapting effectively to medium-term price movements while filtering excessive market noise that typically affects shorter-term analytical systems.
For active intraday traders, the Scalping Cloud Filter utilizes accelerated Supertrend calculations designed to capture rapid trend changes effectively. This configuration provides enhanced signal generation frequency suitable for compressed timeframe strategies. The advanced Scalping+ Cloud Filter incorporates Hull Moving Average confirmation, delivering maximum responsiveness for ultra-short-term trading while maintaining signal quality through additional momentum validation processes.
## Specialized Assistant Functionality
The system includes two distinct assistant modes that provide supplementary market analysis capabilities. The Trend Assistant Mode activates advanced cloud analysis overlays that display dynamic support and resistance zones calculated through adaptive volatility algorithms. These levels automatically adjust to current market conditions, providing visual guidance for identifying trend continuation patterns and potential reversal areas with mathematical precision.
The Trend Tracker Mode concentrates on long-term trend identification by displaying major exponential moving averages with color-coded fill areas that clarify directional bias. This mode maintains visual simplicity while providing comprehensive trend context evaluation, enabling traders to quickly assess broader market direction and align shorter-term strategies accordingly.
## Dynamic Risk Management System
The integrated risk management system automatically adapts across all operational modes, calculating stop loss and take profit targets using Average True Range multiples that adjust to current market volatility. This approach ensures consistent risk parameters regardless of selected operational mode while maintaining relevance to prevailing market conditions.
Stop loss placement occurs at 3x ATR distance from entry points, while three progressive take profit targets establish at 1x, 2x, and 3x ATR multiples respectively. The system automatically updates these levels upon trend direction changes, ensuring current market volatility influences all risk calculations and maintains appropriate risk-reward ratios throughout trade management.
## Comprehensive Market Analysis Dashboard
The sophisticated dashboard provides real-time market analysis including volatility measurements, institutional activity assessment, and multi-timeframe trend evaluation across five-minute through four-hour periods. This comprehensive market context assists traders in selecting appropriate operational modes based on current market characteristics rather than relying exclusively on historical performance data.
The multi-timeframe analysis ensures mode selection considers broader market context beyond the primary trading timeframe, improving overall strategic alignment and reducing conflicts between different temporal market perspectives. The dashboard displays market state classification, volatility percentages, institutional activity levels, current trading session information, and trend pressure indicators.
## Enhanced Trading Assistants
The Fresh Algo v24 includes specialized trading assistant features that complement the primary signal generation system. The Reversal Dot functionality identifies potential reversal points through Wave Trend Oscillator analysis, displaying small circles when crossover conditions occur at extreme levels. These reversal indicators provide early warning signals for potential trend changes before they appear in the primary signal system.
The Dynamic Take Profit Labels feature automatically identifies optimal profit-taking opportunities through RSI threshold analysis, marking potential exit points at 70, 75, and 80 levels for long positions and 30, 25, and 20 levels for short positions. This automated profit management system helps traders optimize exit timing without requiring constant manual monitoring.
## Advanced Alert System
The comprehensive alert system accommodates all operational modes while providing granular notification control for various signal types and risk management events. Traders can configure separate alerts for normal buy signals, strong buy signals, normal sell signals, strong sell signals, stop loss triggers, and individual take profit target achievements.
Cloud crossover alerts notify traders when trend direction changes occur, providing early indication of potential strategy adjustments. The alert system includes detailed trade setup information, timeframe data, and relevant entry and exit levels, ensuring traders receive complete context for informed decision-making.
## Technical Foundation Architecture
The Fresh Algo v24 combines multiple proven technical analysis components including Wave Trend Oscillator for momentum assessment, Supertrend for directional bias determination, Squeeze Momentum for volatility analysis, and various exponential moving averages for trend confirmation. Each component contributes specific market insights while the unified system provides comprehensive market evaluation through their mathematical integration.
The multi-component approach reduces dependency on individual indicator limitations while leveraging the analytical strengths of each technical tool. This creates a robust analytical framework capable of adapting to diverse market conditions through appropriate mode selection and parameter optimization.
## Implementation Strategy Considerations
Successful implementation requires careful matching of operational modes to prevailing market conditions and individual trading objectives. Trending modes demonstrate optimal performance during directional markets with sustained momentum characteristics, while contrarian modes excel during range-bound or overextended market conditions where reversal probability increases.
The cloud filter configurations provide varying degrees of confirmation strength, with smoother settings reducing false signal occurrence at the expense of some responsiveness to price changes. Traders must balance signal quality against signal frequency based on their risk tolerance and available trading time.
## Community Development Framework
This indicator represents ongoing community-driven development through the team at t.me where continuous discussions focus on optimization techniques, practical implementation strategies, and real-world performance feedback. The collaborative development approach ensures the system remains relevant to actual market conditions while incorporating insights from active professional traders.
Understanding these operational modes and their specific applications enables traders to optimize the NAIFCHART Fresh Algo v24 system according to their particular requirements while maintaining consistent risk management principles across all market environments. The inherent flexibility in the multi-mode design allows strategic adaptation to changing market conditions without requiring complete methodology overhaul.
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*Source: NAIFCHART Fresh Algo v24 available through t.me
TheEdgeKeeper MM
📉 Still losing in the long run despite using risk management? Stuck at breakeven, or making profits that barely move the needle on your financial freedom?
You're not alone — this is the story of over 90% of traders.
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We built a smarter way to manage capital — one that adapts to any performance phase, maximizes gains in profitable cycles, and protects you in rough patches.
Not just about surviving — but growing.
⚙️ That’s the philosophy behind **The Edgekeeper**, a capital management **indicator** specifically designed for **crypto traders** here on **TradingView**.
✨ Try it once — and watch how math reshapes your trading game.
📲 (t.me)
Previous Price Action## Previous Price Action - Market Structure Visualization Tool
**Three time-segmented boxes for enhanced market structure analysis:**
🟢 **240 Candles Box (Green)** - Historical context (candles -240 to -120)
🟡 **120 Candles Box (Yellow)** - Medium-term trend (candles -120 to -10)
🔴 **10 Candles Box (Red)** - Recent price action (last 10 candles)
**Key Features:**
- Non-overlapping time segments for clear trend analysis
- Uniform height based on 240-candle range for easy comparison
- 50% transparency to maintain chart readability
- Ideal for identifying momentum vs mean reversion conditions
**Perfect for:**
- Crypto day trading and scalping
- Market regime identification (trending vs choppy)
- Entry timing and trade management
- Duration of trend analysis
**Settings:** Fully customizable colors, transparency, and individual box toggle switches.
Weighted Multi-Mode Oscillator [BackQuant]Weighted Multi‑Mode Oscillator
1. What Is It?
The Weighted Multi‑Mode Oscillator (WMMO) is a next‑generation momentum tool that turns a dynamically‑weighted moving average into a 0‑100 bounded oscillator.
It lets you decide how each bar is weighted (by volume, volatility, momentum or a hybrid blend) and how the result is normalised (Percentile, Z‑Score or Min‑Max).
The outcome is a self‑adapting gauge that delivers crystal‑clear overbought / oversold zones, divergence clues and regime shifts on any market or timeframe.
2. How It Works
• Dynamic Weight Engine
▪ Volume – emphasises bars with exceptional participation.
▪ Volatility – inverse ATR weighting filters noisy spikes.
▪ Momentum – amplifies strong directional ROC bursts.
▪ Hybrid – equal‑weight blend of the three dimensions.
• Multi‑Mode Smoothing
Choose from 8 MA types (EMA, DEMA, HMA, LINREG, TEMA, RMA, SMA, WMA) plus a secondary smoothing factor to fine‑tune lag vs. responsiveness.
• Normalization Suite
▪ Percentile – rank vs. recent history (context aware).
▪ Z‑Score – standard deviations from mean (statistical extremes).
▪ Min‑Max – scale between rolling high/low (trend friendly).
3. Reading the Oscillator
Zone Default Level Interpretation
Bull > 80 Acceleration; momentum buyers in control
Neutral 20 – 80 Consolidation / no edge
Bear < 20 Exhaustion; sellers dominate
Gradient line/area automatically shades from bright green (strong bull) to deep red (strong bear).
Optional bar‑painting colours price bars the same way for rapid chart scanning.
4. Typical Use‑Cases
Trend Confirmation – Set Weight = Hybrid, Smoothing = EMA. Enter pullbacks only when WMMO > 50 and rising.
Mean Reversion – Weight = Volatility, reduce upper / lower bands to 70 / 30 and fade extremes.
Volume Pulse – Intraday futures: Weight = Volume to catch participation surges before breakout candles.
Divergence Spotting – Compare price highs/lows to WMMO peaks for early reversal clues.
5. Inputs & Styling
Calculation: Source, MA Length, MA Type, Smoothing
Weighting: Volume period & factor, Volatility length, Momentum period
Normalisation: Method, Look‑back, Upper / Lower thresholds
Display: Gradient fills, Threshold lines, Bar‑colouring toggle, Line width & colours
All thresholds, colours and fills are fully customisable inside the settings panel.
6. Built‑In Alerts
WMMO Long – oscillator crosses up through upper threshold.
WMMO Short – oscillator crosses down through lower threshold.
Attach them once and receive push / e‑mail notifications the moment momentum flips.
7. Best Practices
Percentile mode is self‑adaptive and works well across assets; Z‑Score excels in ranges; Min‑Max shines in persistent trends.
Very short MA lengths (< 10) may produce jitter; compensate with higher “Smoothing” or longer look‑backs.
Pair WMMO with structure‑based tools (S/R, trend lines) for higher‑probability trade confluence.
Disclaimer
This script is provided for educational purposes only. It is not financial advice. Always back‑test thoroughly and manage risk before trading live capital.
Custom Portfolio [BackQuant]Custom Portfolio {BackQuant]
Overview
This script turns TradingView into a lightweight portfolio optimizer with institutional-grade analytics and real-time position management capabilities.
Rank up to 15 tickers every bar using a pair-wise relative-strength "league table" that compares each asset against all others through your choice of 12 technical indicators.
Auto-allocate 100% of capital to the single strongest asset and optionally apply dynamic leverage when the aggregate market is trending, with full position tracking and rebalancing logic.
Track performance against a custom buy-and-hold benchmark while watching a fully fledged stats dashboard update in real time, including 15 professional risk metrics.
How it works
Relative-strength engine – Each asset is compared against every other asset with a user-selectable indicator (default: 9/21 EMA cross). The system generates a complete comparison matrix where Asset A vs Asset B, Asset A vs Asset C, and so on, creating strength scores. The summed scores crown a weekly/daily/hourly "winner" that receives the full allocation.
Regime filter – A second indicator applied to TOTAL crypto-market cap (or any symbol you choose) classifies the environment as trending or mean-reverting . Leverage activates only in trending regimes, protecting capital during choppy or declining markets. Choose from indicators like Universal Trend Model, Relative Strength Overlay, Momentum Velocity, or Custom RSI for regime detection.
Capital & position logic – Equity grows linearly when flat and multiplicatively while invested. The system tracks entry prices, calculates returns including leverage adjustments, and handles position transitions seamlessly. Optional intra-trade leverage rebalancing keeps exposure in sync with market conditions, recalculating position sizes as regime conditions change.
Risk & performance analytics – Every confirmed bar records return, drawdown, VaR/CVaR, Sharpe, Sortino, alpha/beta vs your benchmark, gain-to-pain, Calmar, win-rate, Omega ratio, portfolio variance, skewness, and annualized statistics. All metrics render in a professional table for instant inspection with proper annualization based on your selected trading days (252 for traditional markets, 365 for crypto).
Key inputs
Backtest window – Hard-code a start date or let the script run from series' inception with full date range validation.
Asset list (15 slots) – Works with spot, futures, indices, even synthetic spreads (e.g., BYBIT:BTCUSDT.P). The script automatically cleans ticker symbols for display.
Indicator universe – Switch the comparative metric to DEMA, BBPCT, LSMAz adaptive scores, Volatility WMA, DEMA ATR, Median Supertrend, and more proprietary indicators.
With more always being added!
Leverage settings – Max leverage from 1x to any multiple, auto-rebalancing toggle, trend/reversion thresholds with precision controls.
Visual toggles – Show/hide equity curve, rolling drawdown heat-map, daily PnL spikes, position label, advanced metrics table, buy-and-hold comparison equity.
Risk-free rate input – Customize the risk-free rate for accurate Sharpe ratio calculations, supporting both percentage and decimal inputs.
On-chart visuals
Color-coded equity curve with "shadow" offset for depth perception that changes from green (profitable) to red (losing) based on recent performance momentum.
Rolling drawdown strip that fades from light to deep red as losses widen, with customizable maximum drawdown scaling for visual clarity.
Optional daily-return histogram line and zero reference for understanding day-to-day volatility patterns.
Bottom-center table prints the current winning ticker in real time with clean formatting.
Top-right metrics grid updates every bar with 15 key performance indicators formatted to three decimal places for precision.
Benchmark overlay showing buy-and-hold performance of your selected index (default: SPX) for relative performance comparison.
Typical workflow
Add the indicator on a blank chart (overlay off).
Populate ticker slots with the assets you actually trade from your broker's symbol list.
Pick your momentum or mean-reversion metric and a regime filter that matches your market hypothesis.
Set max leverage (1 = spot only) and decide if you want dynamic rebalancing.
Press the little " L " on the price axis to view the equity curve in log scale for better long-term visualization.
Enable the metrics table to monitor Sharpe, Sortino, and drawdown in real time.
Iterate through different asset combinations and indicator settings; compare performance vs buy-and-hold; refine until you find robust parameters.
Who is it for?
Systematic crypto traders looking for a one-click, cross-sectional rotation model with professional risk management.
Portfolio quants who need rapid prototyping without leaving TradingView or exporting to Python/R.
Swing traders wanting an at-a-glance health check of their multi-coin basket with instant position signals.
Fund managers requiring detailed performance attribution and risk metrics for client reporting.
Researchers backtesting momentum and mean-reversion strategies across multiple assets simultaneously.
Important notes & tips
Set Trading Days in a Year to 252 for traditional markets; 365 for 24/7 crypto to ensure accurate annualization.
CAGR and Sharpe assume the backtest start date you choose—short windows can inflate stats, so test across multiple market cycles.
Leverage is theoretical; always confirm your broker's margin rules and account for funding costs not modeled here.
The script is computationally heavy at 15 assets due to the N×N comparison matrix—reduce the list or lengthen the timeframe if you hit execution limits.
Best results often come from mixing assets with different volatility profiles rather than highly correlated instruments.
The regime filter symbol can be changed from CRYPTOCAP:TOTAL to any broad market index that represents your asset universe.
Degen Screener – ALTs vs. BTCDegen Screener – ALTs vs. BTC
🛠️ What This Script Does:
This multi-asset screener monitors up to 10 cryptocurrencies and compares their RSI strength relative to Bitcoin (BTC) — acting like BTC is the "north star." It's perfect for catching early shifts in momentum across the crypto market.
🎨 Color Logic:
RSI Column:
RSI < 30 → Green (oversold)
RSI > 70 → Red (overbought)
In between → Gray
Relative RSI Column:
0 → Green (stronger than BTC)
< 0 → Red (weaker than BTC)
Trend Column:
🤑 → Bullish shift (green background)
🖕 → Bearish shift (red background)
🔔 Alert Conditions:
Alerts fire when all three of these are true:
RSI is below 30 (oversold)
The asset is stronger than BTC
Momentum is turning bullish (🤑)
Perfect for spotting early reversals in oversold altcoins.
✅ How to Use:
Add the script to any chart (doesn’t matter which asset)
Customize the list of up to 10 symbols
Set your timeframe
Enable the alert condition: Relative RSI Signal
💡 Notes:
Script runs on whatever chart you’re on, but it pulls data from the 10 assets you select on your indicator.
⚠️ Disclaimer:
This tool is for educational and informational purposes only. It is not financial advice. Always do your own research.
Bear Market Defender [QuantraSystems]Bear Market Defender
A system to short Altcoins when BTC is ranging or falling - benefit from Altcoin bleed or collapse .
QuantraSystems guarantees that the information created and published within this document and on the TradingView platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors.
Important Note!
The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script.
Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance.
This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest.
Even More Important Note!!
The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing.
While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes.
INTRODUCTION TO THE STAR FRAMEWORK
The STAR Framework – an abbreviation for Strategic Trading with Adaptive Risk - is a bespoke portfolio-level infrastructure for dynamic, multi-asset crypto trading systems. It combines systematic position management, adaptive sizing, and “intra-system” diversification, all built on a rigorous foundation of Risk-based position sizing .
At its core, STAR is designed to facilitate:
Adaptive position sizing based on user-defined maximum portfolio risk
Capital allocation across multiple assets with dynamic weight adjustment
Execution-aware trading with robust fee and slippage adjustment
Realistic equity curve logic based on a compounding realized PnL and additive unrealized PnL
The STAR Framework is intended for use as both a standalone portfolio system or preferred as a modular component within a broader trading “global portfolio” - delivering a balance of robustness and scalability across strategy types, timeframes, and market regimes.
RISK ALLOCATION VIA "R" CALCULATIONS
The foundational concept behind STAR is the use of the R unit - a dynamic representation of risk per trade. R is defined by the distance between a trade's entry and its stoploss, making it an intuitive and universally adaptive sizing unit across any token, timeframe, or market.
Example: Suppose the entry price is $100, and the stoploss is $95. A $5 move against the position represents a 1R loss. A 15% price increase to $115 would equal a +3R gain.
This makes R-based systems highly flexible: the user defines the percentage of capital that is put at risk per R and all positions are scaled accordingly - whether the token is volatile, illiquid, or slow-moving.
R is an advantageous method for determine position sizing - instead of being tied to complex value at risk mechanisms with having layered exit criteria, or continuous volatility-based sizing criteria that need to be adjusted while in an open trade, R allows for very straightforward sizing, invalidation and especially risk control – which is the most fundamental.
REALIZED BALANCE, FEES & SLIPPAGE ACCOUNTING
All position sizing, risk metrics, and the base equity curve within STAR are calculated based on realized balance only .
This means:
No sizing adjustments are made based on unrealized profit and loss ✅
No active positions are included in the system's realized equity until fully closed ✅
Every trade is sized precisely according to current locked-in realized portfolio balance ✅
This creates the safest risk profile - especially when multiple trades are open. Unrealized gains are not used to inflate sizing, ensuring margin safety across all assets.
All calculations also incorporate slippage and fees, based on user-defined estimates – which can and should be based upon user-collected data - and updated frequently forwards in time. These are not cosmetic, or simply applied to the final equity curve - they are fully integrated into the dynamic position sizing and equity performance , ensuring:
Stoploss hits result in exactly a −1R loss, even after slippage and fees ✅
Winners are discounted based on realistic execution costs ✅
No trade is oversized due to unaccounted execution costs ✅
Example - Slippage in R Units:
Let R be defined as the distance from entry to stoploss.
Suppose that distance is $1, and the trade is closed at a win of +$2.
If execution slippage leads to a 50 cent worse entry and a 50 cent worse exit, you’ve lost $1 extra - which is an additional 1R in execution slippage. This makes the effective return 1.0R instead of the intended 2.0R.
This is equivalent to a slippage value of 50%.
Thus, slippage in STAR is tracked and modelled on an R-adjusted basis , enabling more accurate long-term performance modelling.
MULTI-ASSET, LONG/SHORT SUPPORT
STAR supports concurrent long and short positions across multiple tokens. This can sometimes result in partially hedged exposure - for example, being long one asset and short another.
This structure has key benefits:
Diversifies idiosyncratic risk by distributing exposure across multiple tokens
Allows simultaneous exploitation of relative strength and weakness
Reduces portfolio volatility via natural hedging during reduced trending periods
Even in a highly correlated market like crypto, short-term momentum behaviour often varies between tokens - making diversified, multi-directional exposure a strategic advantage .
EQUITY CURVE
The STAR framework only updates the underlying realized equity when a position is closed, and the trade outcome is known. This approach ensures:
True representation of actual capital available for trading
No exposure distortion due to unrealized gains
Risk remains tightly linked to realized results
This trade-to-trade basis for realized equity modelling eliminates the common pitfall of overallocation based on unrealized profits.
The visual equity curve represents an accurate visualization of the Total Equity however, which is equivalent to what would be the realized equity if all trades were closed on the prior bar close.
TIMEFRAME CONSIDERATIONS
Lower timeframes typically yield better performance for STAR due to:
Greater data density per day - more observations = better statistical inference
Faster compounding - more trades per week = faster capital rotation
However, lower timeframes also suffer from increased slippage and fees. STAR's execution-aware structure helps mitigate this, but users must still choose timeframes appropriate to their liquidity, costs, and operational availability.
INPUT OPTIONS
Fees (direct trading costs - the percentage of capital removed from the initial position size)
Slippage (execution delay, as a percentage. In practice, the fill price is often worse than the signal price. This directly affects R and hence position sizing)
Risk % ( Please note : this is the risk level if every position is opened at once. 5% risk for 5 assets is 1% risk per position)
System Start date
Float Precision value of displayed numbers
Table visualization - positioning and table sizes
Adjustable color options
VISUAL SIMPLICITY
To avoid usual unnecessary complexity and empower fast at-a-glance action taking, as well as enable mobile compatibility, only the most relevant information is presented.
This includes all information required to open positions in one table.
As well as a quick and straightforward overview for the system stats
Lastly, there is an optional table that can be enabled
displaying more detailed information if desired:
USAGE GUIDELINES
To use STAR effectively:
Input your average slippage and fees %
Input your maximum portfolio risk % (this controls overall leverage and is equivalent to the maximum loss that the allocation to STAR would bring if ALL positions are allocated AND hit their stop loss at the same time)
Wait for signal alerts with entry, stop, and size details
STAR will dynamically calculate sizing, risk exposure, and portfolio allocation on your behalf. Position multipliers, stop placement, and asset-specific risk are all embedded in the system logic.
Note: Leverage must be manually set to ISOLATED on your exchange platform to prevent unwanted position linking.
ABOUT THE BEAR MARKET DEFENDER STRATEGY
The first strategy to launch on the STAR Framework is the BEAR MARKET DEFENDER (BMD) - a fast-acting, trend following system based upon the Trend Titan NEUTRONSTAR. For the details of the logic behind NEUTRONSTAR, please refer to the methodology and trend aggregation section of the following indicator:
The BMD ’s short side exit calculation methodology is slightly improved compared to NEUTRONSTAR, to capture downtrends more consistently and also cut positions faster – which is crucial when considering general jump risk in the Crypto space.
Accordingly, the only focus of the BMD is to capture trends to the short side, providing the benefit of being in a spectrum from no correlation to being negatively correlated in risk and return behavior to classical Crypto long exposure.
More precisely, Crypto behavior showcases that when Bitcoin is in a ranging/mean reverting environment, most tokens that don’t fall into the “Blue-Chip” category tend to find themselves in a trend towards 0.
Typically during this period most Crypto portfolios suffer heavily due to a “Crypto-long” biased exposure.
The Bear Market Defender thrives in these chaotic, high volatility markets where most coins trend towards zero while the traditional Crypto long exposure is either flat or in a drawdown, therefore the BMD adds a source of uncorrelated risk and returns to hedge typical long exposure and bolster portfolio volatility.
Because of the BMD's short-only exposure, it will often suffer small losses during strong uptrends. During these periods, long exposure performs the best and the goal is to outperform the temporary underperformance in the BMD .
To take advantage of the abovementioned behavior of most tokens trending to zero, assets traded in the BMD are systematically updated on a quarterly basis with available liquidity being an important consideration for the tokens to be eligible for selection.
FINAL SUMMARY
The STAR Framework represents a new generation of portfolio grade trading infrastructure, built around disciplined execution, realized equity, and adaptive position sizing. It is designed to support any number of future methodologies - beginning with BMD .
The Bear Market Defender is here to hedge out commonly long biased portfolio allocations in the Crypto market, specializing in bringing uncorrelated returns during periods of sideways price action on Bitcoin, or whole-market downturns.
Together, STAR + BMD deliver a scalable, volatility tuned system that prioritizes capital preservation, signal accuracy, and adaptive risk allocation. Whether deployed standalone or within a broader portfolio, this framework is engineered for high performance, longevity, and adaptability in the ever-evolving crypto landscape.
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing
The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing.
Please take the time to read the following:
Key Features and Benefits of Portfolio Methods
Bracketed Rebalancing:
Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold:
Daily Rebalancing:
Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action:
No Rebalancing:
For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied:
Portfolio Weights Visualization:
One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets:
Rolling Drawdown Plot:
Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns:
Daily Portfolio Returns:
Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress:
Performance Metrics
Net Profit (%):
This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance.
Maximum Drawdown (Max DD):
Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses.
Annual Mean Returns (% p/y):
This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities.
Annual Standard Deviation of Returns (% p/y):
This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns.
Variance:
Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns.
Sortino Ratio:
The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns.
Sharpe Ratio:
The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns.
Omega Ratio:
The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns.
Gain-to-Pain Ratio:
The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk.
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Compound Annual Growth Rate (CAGR) (% p/y):
CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance.
Portfolio Alpha (% p/y):
Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance.
Portfolio Beta:
Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations.
Skewness of Returns:
Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes.
Value at Risk (VaR) 95th Percentile:
VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount.
Conditional Value at Risk (CVaR):
CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level.
These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk.
Conclusion
The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
Session Status Table📌 Session Status Table
Session Status Table is an indicator that displays the real-time status of the four major trading sessions:
* 🇯🇵 Asia (Tokyo)
* 🇬🇧 London
* 🇺🇸 New York AM
* 🇺🇸 New York PM
It shows which sessions are currently open, how much time remains until they open or close, and optionally sends alerts in advance.
🧩 Features:
* Real-time session table — shows the status of each session on the chart.
* Color-coded statuses:
* 🟢 Green – Session is open
* 🔴 Red – Session is closed
* ⚪ Gray – Weekend
* Countdown timers until session open or close.
* User alerts — receive a notification a custom number of minutes before a session starts.
⚙️ Customization:
* Table position — fully configurable.
* Session colors — customizable for open, closed, and weekend states.
* Session labels — customizable with icons.
* Notifications:
* Enabled through TradingView's Alerts panel.
* User-defined lead time before session opens.
🕒 Time Zones:
All times are calculated in UTC to ensure consistency across different markets and regions, avoiding discrepancies from time zones and daylight saving time.
🚨 How to enable alerts:
1. Open the "Alerts" panel in TradingView.
2. Click "Create Alert".
3. In the condition dropdown, choose "Session Status Table".
4. Set to any alert() trigger.
5. Save — you'll be notified a set number of minutes before each session begins.
ℹ️ Technical Notes:
* Built with Pine Script version 6.
* Logically divided into clear sections: inputs, session calculations, table rendering, and alerts.
* Optimized for performance and reliability on all timeframes.
Ideal for traders who use session activity in their strategies — especially in Forex, crypto, and futures markets.
Arnaud Legoux Trend Aggregator | Lyro RSArnaud Legoux Trend Aggregator
Introduction
Arnaud Legoux Trend Aggregator is a custom-built trend analysis tool that blends classic market oscillators with advanced normalization, advanced math functions and Arnaud Legoux smoothing. Unlike conventional indicators, 𝓐𝓛𝓣𝓐 aggregates market momentum, volatility and trend strength.
Signal Insight
The 𝓐𝓛𝓣𝓐 line visually reflects the aggregated directional bias. A rise above the middle line threshold signals bullish strength, while a drop below the middle line indicates bearish momentum.
Another way to interpret the 𝓐𝓛𝓣𝓐 is through overbought and oversold conditions. When the 𝓐𝓛𝓣𝓐 rises above the +0.7 threshold, it suggests an overbought market and signals a strong uptrend. Conversely, a drop below the -0.7 level indicates an oversold condition and a strong downtrend.
When the oscillator hovers near the zero line, especially within the neutral ±0.3 band, it suggests that no single directional force is dominating—common during consolidation phases or pre-breakout compression.
Real-World Example
Usually 𝓐𝓛𝓣𝓐 is used by following the bar color for simple signals; however, like most indicators there are unique ways to use an indicator. Let’s dive deep into such ways.
The market begins with a green bar color, raising awareness for a potential long setup—but not a direct entry. In this methodology, bar coloring serves as an alert mechanism rather than a strict entry trigger.
The first long position was initiated when the 𝓐𝓛𝓣𝓐 signal line crossed above the +0.3 threshold, suggesting a shift in directional acceleration. This entry coincided with a rising price movement, validating the trade.
As price advanced, the position was exited into cash—not reversed into a short—because the short criteria for this use case are distinct. The exit was prompted by 𝓐𝓛𝓣𝓐 crossing back below the +0.3 level, signaling the potential weakening of the long trend.
Later, as 𝓐𝓛𝓣𝓐 crossed below 0, attention shifted toward short opportunities. A short entry was confirmed when 𝓐𝓛𝓣𝓐 dipped below -0.3, indicating growing downside momentum. The position was eventually closed when 𝓐𝓛𝓣𝓐 crossed back above the -0.3 boundary—signaling a possible deceleration of the bearish move.
This logic was consistently applied in subsequent setups, emphasizing the role of 𝓐𝓛𝓣𝓐’s thresholds in guiding both entries and exits.
Framework
The Arnaud Legoux Trend Aggregator (ALTA) combines multiple technical indicators into a single smoothed signal. It uses RSI, MACD, Bollinger Bands, Stochastic Momentum Index, and ATR.
Each indicator's output is normalized to a common scale to eliminate bias and ensure consistency. These normalized values are then transformed using a hyperbolic tangent function (Tanh).
The final score is refined with a custom Arnaud Legoux Moving Average (ALMA) function, which offers responsive smoothing that adapts quickly to price changes. This results in a clear signal that reacts efficiently to shifting market conditions.
⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT.
CryptoNeo - Crypto Market Directionnal StrengthCryptoNeo – Crypto Market Directional Strength is a quantitative, multi-factor trend scoring tool designed to help traders identify the directional bias of the crypto market by aggregating key flow and sentiment signals into a single visual output.
This indicator is the result of multi-alpha signal research originally developed for a systematic trading strategy. It has now been optimized for discretionary traders who want to see real-time directional consensus across stablecoin flows, market structure shifts, and premium dynamics.
The script computes a Trend Score based on multiple real-time crypto signals. Each signal contributes to the score based on user-defined weights, and the result is visualized as color-coded columns on your chart, helping you quickly assess bullish or bearish strength.
Aggregated Signals:
Stablecoin Futures Flow (bullish/bearish) - Detects leveraged sentiment via stablecoin perpetual futures rates deviations.
Stablecoin Spot Flow (bullish/bearish) - Flags potential accumulation or distribution behavior in spot markets.
Stablecoin Market Cap Trend - Captures expansion or contraction in total stablecoin supply to indicate macro liquidity shifts.
USDC/USDT Rate Divergence - Identifies subtle pressure between the leading stablecoins, hinting at stress or capital flight.
Derivatives Overleverage Signal - Detects potentially dangerous funding-driven setups in the futures market.
Coinbase Premium Trend - Highlights differences between U.S. and global crypto prices, often a proxy for retail or institutional demand.
The score is plotted using the Directional Strength Score column chart:
🟢 Green – strong bullish alignment (score ≥ 3)
🟡 Yellow – mild bullish bias (score = 1–2)
⚪ White – neutral or indecisive (score ≈ 0)
🟠 Orange – mild bearish bias (score = –1 or –2)
🔴 Red – strong bearish alignment (score ≤ –3)
All components include weight inputs, allowing you to control how much influence each factor has on the final score, enabling you to personalize the indicator for different pairs, market conditions, or strategies.
Best to use For:
Traders focused on BTC, ETH, SOL, DOGE, and other large-cap cryptocurrencies
30-minute to 4-hour timeframes
Swing trading, trend confirmation, or sentiment filtering
Spotting potential trend shifts, fakeouts, or market exhaustion
CryptoNeo - Crypto Stablecoin MatrixThe CryptoNeo – Crypto Stablecoin Matrix is a forward-looking indicator that decodes broad crypto market sentiment by analyzing how stablecoins behave across spot and futures markets.
Stablecoins are the lifeblood of the crypto ecosystem, and how they move can offer early insight into future market direction. This tool leverages that behavior to forecast potential price action across major cryptocurrencies like BTC, ETH, SOL, DOGE, and other large-cap coins that tend to move in sync with the broader market.
Originally derived from a suite of alpha signals developed for a systematic crypto trading algorithm, this indicator compresses advanced stablecoin flow analytics into a clear and intuitive visual format — designed specifically for discretionary traders.
It is optimized for trading on the 30-minute to 4-hour timeframes, where the nuances of capital flow are most actionable. Whether you’re swing trading majors or scouting key pivot points, this tool provides a fresh edge rooted in stablecoin dynamics.
The Matrix is composed of four core components that signal changes in sentiment, capital flow, and market positioning:
1. Stablecoin Futures Flow (Bullish/Bearish)
Detects shifts in leveraged positioning in the futures market based on proprietary flow dynamics.
🟩 Green squares = bullish futures flow (long bias)
🟥 Red squares = bearish futures flow (short bias)
Helps identify directional sentiment through futures-driven stablecoin movement.
2. Stablecoin Spot Flow (Bullish/Bearish)
Analyzes momentum in spot market stablecoin activity to reveal potential accumulation or distribution.
🟢 Green circles = bullish spot flow (buying pressure)
🔴 Red circles = bearish spot flow (selling pressure)
Offers early signals of demand or profit-taking pressure.
3. Futures Oversold/Overbought Level 1
Identifies early signs of exhaustion or trend slowing based on leveraged market conditions.
🟢 Green diamonds = early oversold signal
🔴 Red diamonds = early overbought signal
Useful for catching subtle turning points or slowing momentum.
4. Futures Oversold/Overbought Level 2
Flags rare and extreme positioning events that may precede major reversals.
🟢 Large green diamonds = deep oversold condition
🔴 Large red diamonds = deep overbought condition
Highlights moments of extreme imbalance or sentiment peaks.
Customization & Flexibility
Adjustable sensitivity settings allow you to fine-tune:
Bullish and bearish Spot and Futures Flow
Thresholds for Level 1 and Level 2 Overbought/Oversold signals
This ensures traders can align signal responsiveness with their trading style and market conditions.
Best Used For:
Swing trading crypto majors (BTC, ETH, SOL, DOGE, etc.)
Timeframes between 30 minutes and 4 hours
Identifying trend reversals and accumulation zones
Tracking macro market sentiment using stablecoin behavior
[Stoxello] Linear Regression Chop Zone Indicator📊 Linear Regression Chop Zone Indicator – Description
The Stoxello Linear Regression Chop Zone Indicator is a custom-built, multi-functional visual tool for identifying market trend direction, strength, and potential entry/exit signals using a combination of linear regression, EMA slope angles, and volatility-adjusted smoothing.
🧠 Core Features:
🔶 1. Chop Zone Color Coding (Trend Strength via EMA Angle)
The script calculates the angle of a 34-period EMA, representing momentum and trend steepness.
This angle is then translated into color-coded bars on the chart to help traders visually identify chop zones and trend strength.
Turquoise / Dark Green / Pale Green = Increasing bullish trend.
Lime / Yellow = Neutral or low momentum (choppy zones).
Orange / Red / Dark Red = Increasing bearish trend.
🔶 2. Linear Regression Deviation Channels (Trend Path)
A custom linear regression line is drawn with +/- deviation bands above and below it.
These lines track the expected price path and visually define upper/lower zones, similar to regression channels.
The correlation (R) and determination (R²) values are displayed as labels on the chart, measuring the strength and reliability of the linear fit.
🔶 3. Linear Regression-Adjusted EMA (Smoothing with Volatility)
A novel volatility-adaptive EMA is computed by combining a traditional EMA with distance from a linear regression line.
The result is a dynamic EMA that becomes more reactive in volatile conditions and smoother in stable ones.
Two lines are plotted:
Primary EMA (Yellow)
Trigger Line (Lagged by 2 bars, Fuchsia)
The fill color between these two helps visualize short-term bullish or bearish pressure.
🔶 4. Buy/Sell Signal Logic with De-Duplication
Buy signals are triggered when:
The adjusted EMA crosses above its previous value (bullish inflection).
Or when the EMA angle exceeds +5° (strong trend detected).
Sell signals occur when:
The adjusted EMA crosses below its previous value.
Each signal is deduplicated by tracking the last signal using var string lastSignal:
No repeat buys after a buy, or sells after a sell.
Signals are marked on the chart using clean text labels:
Buy: "•Entry• = Price"
Sell: "•Exit• = Price"
🔶 5. Alerts
Two alertconditions are included for:
BUY signals (long_signal)
SELL signals (short_signal)
Can be used with webhooks, email, or app notifications to automate or monitor trades.
🔍 Ideal Use Cases:
Traders who want a clear visual aid for market chop vs. trend.
Swing or intraday traders looking for adaptive entry/exit points.
Anyone combining regression analysis and momentum tracking into one indicator.
SectorRotationRadarThe Sector Rotation Radar is a powerful visual analysis tool designed to track the relative strength and momentum of a stock compared to a benchmark index and its associated sector ETF. It helps traders and investors identify where an asset stands within the broader market cycle and spot rotation patterns across sectors and timeframes.
🔧 Key Features:
Benchmark Comparison: Measures the relative performance (strength and momentum) of the current symbol against a chosen benchmark (default: SPX), highlighting over- or underperformance.
Automatic Sector Detection: Automatically links stocks to their relevant sector ETFs (e.g., XLK, XLF, XLU), based on an extensive internal symbol map.
Multi-Timeframe Analysis: Supports simultaneous comparison across the current, next, and even third-higher timeframes (e.g., Daily → Weekly → Monthly), providing a bigger-picture perspective of trend shifts.
Tail Visualization: Displays a "trail" of price behavior over time, visualizing how the asset has moved in terms of relative strength and momentum across a user-defined period.
Quadrant-Based Layout: The chart is divided into four dynamic main zones, each representing a phase in the strength/momentum cycle:
🔄 Improving: Gaining strength and momentum
🚀 Leading: High strength and high momentum — top performers
💤 Weakening: Losing momentum while still strong
🐢 Lagging: Low strength and low momentum — underperformers
Clean Chart Visualization:
Background grid with axis labels
Dynamic tails and data points for each symbol
Option to include the associated sector ETF for context
Descriptive labels showing exact strength/momentum values per point
⚙️ Customization Options:
Benchmark Selector: Choose any symbol to compare against (e.g., SPX, Nasdaq, custom index)
Start Date Control: Option to fix a historical start point or use the current data range
Trail Length: Set the number of previous data points to display
Additional Timeframes: Enable analysis of one or two higher timeframes beyond the current
Sector ETF Display: Toggle to show or hide the related sector ETF alongside the asset
📚 Technical Architecture:
The indicator relies on external modules for:
Statistical modeling
Relative strength and momentum calculations
Chart rendering and label drawing
These components work together to compute and display a dynamic, real-time map of asset performance over time.
🧠 Use Case:
Sector Rotation Radar is ideal for traders looking to:
Spot stocks or sectors rotating into strength or weakness
Confirm alignment across multiple timeframes
Identify sector leaders and laggards
Understand how a symbol is positioned relative to the broader market and its peers
This tool is especially valuable for swing traders, sector rotation strategies, and macro-aware investors who want a visual edge in decision-making.
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Weekend FadeThis indicator is a tool for setting a bias based on weekend price movements, with the assumption that the crypto market often experiences stronger moves over the weekend due to thinner order books. It helps identify potential fade opportunities, suggesting that price movements from Saturday and Sunday may reverse during the weekdays.
How to use:
Sets a bias based on weekend price action.
Sets a bias based on weekend price action.
Use weekday price action for confirmation before acting on the bias.
Best suited for range-bound markets, where the price tends to revert to the mean.
Avoid fading high-timeframe breakouts, as they often indicate strong trends.
