True RSITrue RSI | MisinkoMaster
The True RSI is a sophisticated reimagining of classical momentum. While the standard Relative Strength Index has served as a cornerstone of technical analysis for decades, it possesses a fundamental limitation: it treats all price movements equally, regardless of the time elapsed since the market made its last major peak or trough. The True RSI solves this structural blind spot by merging price magnitude with temporal trend strength, weighting gains and losses according to their cyclical maturity.
By dynamically scaling price changes against the time-distance of local extremes, this indicator filters out lateral market noise, reduces false overbought and oversold readings during strong trends, and delivers highly responsive execution signals.
How It Works (The Core Architecture)
Instead of relying solely on arithmetic averages of upward and downward price closes, True RSI filters raw market data through a multi-dimensional momentum matrix:
Temporal Trend Weighting: The algorithm continuously tracks how recently the market has formed local highs and lows. Gains are mathematically weighted against the strength of the upward cycle, while losses are weighted against the strength of the downward cycle.
Cycle-Weighted Ratio: The accumulated, time-weighted gains and losses are calculated over your lookback period to establish a true relative strength ratio. If gains occur during an actively surging upward cycle, they are heavily amplified; if they occur during a dying trend, they are heavily discounted.
Smoothing and Normalization: This ratio is translated into a normalized scale bounded between 0 and 100, providing an incredibly smooth yet responsive oscillator curve alongside a secondary momentum velocity histogram.
Key Features
Time-Weighted Velocity: True RSI prevents premature exhaustion signals during strong, healthy trends because it understands the cyclical age of the current market move.
On-Chart Candle Morphing: The system automatically tracks the oscillator state and alters the colors of your main price bars to keep you visually aligned with the macro trend.
Overlay Execution Labels: Prints pristine Long and Short labels directly on your price pane the moment the underlying structural momentum shifts past your designated thresholds.
Internal Divergence Histogram: Built directly behind the main oscillator is a custom acceleration histogram that monitors the rate of change of the index, pinpointing hidden momentum shifts before they reflect in the price.
Input Parameters & Optimization Guide
Lookback Period: Controls the baseline window for both the cycle-strength calculations and the price change evaluations. A default of 21 bars balances macro trend stability with immediate short-term utility.
Long / Short Thresholds: The structural boundaries that dictate trend shifts. By default, crossing above 50 signals a bullish regime, while dropping below 50 initiates a bearish regime.
Overbought / Oversold Thresholds: Tailored extremes designed to isolate true premium and discount zones. The default 80 and 20 boundaries act as high-probability mean-reversion targets.
Trading Strategies & Execution
Trend Regime Shift
When momentum builds structural backing, the indicator updates its trend state:
A crossing of the True RSI above the Long Threshold triggers a green Long label on the chart, changing candle colors to vibrant green.
A crossing of the True RSI below the Short Threshold triggers a pink Short label, shifting candle colors to pink.
Exhaustion Reversals
Because price movement is weighted against cycle time, entering the overbought (80) or oversold (20) zones represents a market that is genuinely overstretched both in terms of price velocity and time. Reversals from these zones carry high statistical significance for counter-trend scalps or trailing-stop targets.
Acceleration Divergences
Watch the central histogram centered around the 50 line. When the price is grinding flat but the histogram starts to rise or fall aggressively, it shows that the internal speed of the True RSI is accelerating. This hidden momentum often foreshadows explosive breakout expansions.
Disclaimer: Trading financial markets involves high risk. This technical script is designed as an informational analytical tool to support your rule-based mechanical execution system and does not constitute financial advice. Indicator
Stocks: Dashboard [invincible3]Stocks Dashboard is a professional all-in-one stock analysis dashboard built directly for PulseWire charts. It is designed to help investors, traders, and analysts quickly evaluate a stock using a combination of fundamental strength, valuation, financial health, income quality, shareholder return, and price momentum.
The indicator displays a clean table-based dashboard on the chart and converts raw financial and technical data into easy-to-read category scores. Instead of checking many separate financial ratios manually, this dashboard organizes the most important stock metrics into structured sections and gives a clear visual overview of the company’s current condition.
The dashboard includes composite scoring for Quality, Value, Growth, Financial Health, Income / Shareholder Return, and Momentum. Each category is scored from 0 to 100 and classified using simple rating labels such as Elite, Strong, Fair, Weak, or Risk. This allows users to quickly compare the strength and weakness of a stock across multiple dimensions.
The Valuation section includes important valuation metrics such as P/E TTM, Forward P/E, PEG Ratio, P/S, P/B, EV/EBITDA, EV/Sales, Earnings Yield, Operating Earnings Yield, Graham Price, Graham Number Upside, EPS TTM, and BVPS. These metrics help identify whether a stock may be expensive, fairly valued, or potentially undervalued.
The Quality section focuses on profitability and business efficiency. It includes ROE, Asset Return / ROA, ROIC, Piotroski F-Score, Gross Margin, Operating Margin, Net Margin, EBITDA Margin, and Free Cash Flow Margin. These values help evaluate how efficiently the company generates profits from its assets, equity, capital, revenue, and operations.
The Growth section tracks the company’s expansion profile using Revenue Growth, EPS Growth, and Sustainable Growth Rate. These metrics help users understand whether the business is improving, stagnating, or losing earnings momentum.
The Financial Health section evaluates balance sheet strength and risk. It includes Debt / Equity, Debt / Assets, Debt / EBITDA, Net Debt / EBITDA, Cash / Debt, Current Ratio, Quick Ratio, Interest Coverage, Altman Z-Score, Operating Cash Flow, and Free Cash Flow. This section is useful for identifying companies with strong liquidity, manageable debt, and lower financial risk.
The Income / Return section is designed for dividend and shareholder-return analysis. It includes Dividend Yield, Payout Ratio, Free Cash Flow Yield, DPS, Buyback Yield, and Buyback Ratio. These metrics help investors evaluate whether a company is returning value to shareholders through dividends, buybacks, and cash generation.
The Momentum section adds a technical view of the stock. It includes RSI 14, 1-month return, 3-month return, 6-month return, 12-month return, 6-month relative strength versus a selected benchmark, 12-month relative strength versus a selected benchmark, volatility, and moving-average trend using the 20, 50, and 200 daily moving averages.
The Snapshot section provides a quick summary of the selected stock, including current price, market capitalization, enterprise value, 52-week position, and distance from the 52-week high. This gives users a fast overview of where the stock is trading relative to its recent range.
Users can customize the stock symbol, benchmark/index symbol, financial period, table position, text size, color theme, and visible sections. The dashboard supports multiple professional themes, including Dark Terminal, Light Terminal, Emerald Pro, Royal Blue, and Amber Desk. Users can also enable or disable colored score backgrounds, score bars, alternating rows, colorful section headers, and directional symbols.
This indicator is useful for:
* Long-term stock analysis
* Fundamental screening
* Valuation comparison
* Dividend and shareholder-return review
* Financial health analysis
* Momentum confirmation
* Relative strength comparison against an index or benchmark
* Building a structured watchlist review process
The goal of this dashboard is to provide a fast, organized, and visually professional stock overview without requiring users to switch between multiple financial websites or separate indicators. It combines fundamental data and technical momentum into one compact chart-based table, making it easier to identify strong, weak, undervalued, overvalued, or financially risky stocks.
Note: The dashboard uses PulseWire’s available financial data. Some metrics may appear unavailable depending on the selected symbol, exchange, market, or financial data coverage. This tool is intended for analysis and research purposes only and should not be considered financial advice.
Indicator
Stock: Snowflake Analysis [invincible3]Stock: Snowflake Analysis
Stock: Snowflake Analysis is a visual fundamental-analysis radar indicator designed for stock traders and investors who want a quick, structured view of a company’s financial quality. The indicator converts multiple PulseWire financial metrics into normalized 0–100 scores and displays them as a six-axis snowflake/radar chart directly on the price chart.
The purpose of the indicator is to help users quickly evaluate a stock from several key perspectives: dividend quality, growth, inventory efficiency, financial stability, valuation, and future outlook. Each metric is transformed into a score, then plotted visually so the user can immediately see where a company is strong or weak. The current script uses six core categories: Dividend, Growth, Inventory, Stability, Valuation, and Future, and it draws the final score as a radar/snowflake polygon.
Main Concept
The indicator works by pulling available stock financial data from PulseWire using request.financial(). These values are then normalized into scores from 0 to 100.
A score near 100 means the company is strong in that category.
A score near 50 means the company is neutral or average.
A score near 0 means the company is weak in that category.
The final snowflake shape gives a quick visual overview:
A large, balanced snowflake suggests broad financial strength.
A small or uneven snowflake suggests weakness or imbalance.
A stretched shape shows that the company is strong in some areas but weak in others.
Six Core Financial Categories
1. Dividend Score
The Dividend axis evaluates whether the stock provides attractive and sustainable shareholder distributions.
It uses:
Dividend yield
Dividend payout ratio
The dividend yield is scored positively when it is higher, while the payout ratio is scored negatively if it becomes too high. A very high payout ratio may suggest that the dividend is less sustainable.
A strong Dividend score usually means the company has a decent yield without excessive payout pressure.
2. Growth Score
The Growth axis measures how well the company is expanding.
It uses:
Revenue growth
EPS growth
Gross margin
The score rewards companies with improving sales, stronger earnings, and healthier gross margins. A company with strong revenue growth but weak margins may receive a mixed score, while a company with both growth and profitability receives a stronger score.
This category is especially useful for identifying companies with improving business momentum.
3. Inventory Score
The Inventory axis evaluates operating efficiency, especially for businesses where inventory management matters.
It uses:
Inventory turnover
Inventory-to-revenue ratio
Higher inventory turnover is considered positive because it suggests that the company sells its inventory efficiently. A lower inventory-to-revenue ratio is also considered positive because it indicates that inventory is not becoming too heavy relative to sales.
This score is more useful for retail, manufacturing, consumer goods, and industrial companies. It is less meaningful for banks, software companies, or service businesses.
4. Stability Score
The Stability axis measures balance-sheet strength.
It uses:
Current ratio
Debt-to-equity ratio
A higher current ratio generally suggests better short-term liquidity, while a lower debt-to-equity ratio suggests lower financial leverage.
A strong Stability score means the company appears financially safer and less dependent on debt. A weak score may suggest liquidity pressure or excessive leverage.
5. Valuation Score
The Valuation axis evaluates whether the stock appears reasonably priced relative to earnings.
It uses:
Earnings yield
Price-to-earnings ratio
Earnings yield is calculated as:
Earnings Yield = EPS / Price × 100
A higher earnings yield is better. A lower P/E ratio is also better, up to a reasonable threshold. This category rewards companies that generate meaningful earnings relative to their current share price.
A high Valuation score may suggest the stock is cheaper relative to earnings, while a low score may suggest the stock is expensive or earnings are weak.
6. Future Score
The Future axis attempts to measure forward-looking quality.
It uses:
Sustainable growth rate
Forward P/E ratio
A higher sustainable growth rate improves the score, while a very high forward P/E lowers the score. This category tries to balance future growth potential with future valuation risk.
A strong Future score suggests the stock may have a reasonable combination of expected growth and forward valuation.
Final Score
The center score is calculated as the average of the six category scores:
Final Score = Average of Dividend, Growth, Inventory, Stability, Valuation, and Future
This final score is displayed in the center of the radar chart.
The score color changes dynamically using the indicator’s heatmap color theme:
Low scores appear in purple tones.
Mid scores appear in teal tones.
High scores appear in green/yellow tones.
This makes it easier to visually identify weak, neutral, and strong readings.
Visual Design
The indicator displays a clean snowflake/radar chart directly on the price chart. Each axis represents one financial category.
Visual components include:
Hexagonal grid rings
Axis lines
Category labels
Score markers
Filled snowflake polygon
Dynamic color based on total score
Optional historical comparison snowflake
The snowflake is placed to the right side of the latest bar using the Right Offset setting, so it does not cover the current candles.
Historical Comparison
The indicator includes an optional Historical Snowflake feature.
When enabled, it compares the current snowflake with a previous score profile based on the selected historical lookback.
For example:
Historical Lookback = 252 bars
This can be used as an approximate one-year comparison on daily charts.
The historical snowflake helps users see whether the company’s fundamental profile has improved, weakened, or remained stable over time.
Stock-Only Protection
This indicator is designed for stock symbols only because financial metrics such as dividend yield, payout ratio, EPS, revenue growth, and debt-to-equity are stock/company-specific data.
For non-stock symbols such as:
Gold
Forex pairs
Crypto
Indices
Commodities
the script does not draw the snowflake. Instead, it displays a warning message explaining that financial metrics are not available for those instruments.
This prevents misleading neutral scores from appearing on symbols where company financial data does not exist.
Input Settings
Radar Layout
Right Offset Bars
Moves the snowflake to the right side of the latest candle.
Scale Lookback
Controls the price range used to vertically scale the snowflake on the chart. This does not affect the financial scores.
Width
Controls the horizontal size of the radar.
Height %
Controls the vertical size of the radar relative to the recent price range.
Label Distance
Controls how far the category labels are placed from the radar center.
Visual Styling
Show Grid Rings
Turns the hexagonal background rings on or off.
Show Axis Lines
Turns the center-to-axis guide lines on or off.
Show Axis Labels
Shows or hides the category names and scores.
Show Score Markers
Shows or hides circular markers at each snowflake point.
Dynamic Colors
When enabled, the snowflake color changes based on the total average score.
Marker Size
Controls the size of the score markers.
Historical Comparison
Show Historical Snowflake
Displays a previous score profile for comparison.
Historical Lookback Bars
Defines how far back the comparison should be calculated.
How to Interpret the Snowflake
A strong stock usually shows:
A broad and balanced snowflake
High Growth and Stability scores
Reasonable Valuation score
Improving Future score
A risky or weak stock may show:
A compressed snowflake
Very low Stability
Weak Growth
Expensive Valuation
Poor Future score
An unbalanced company may show a stretched snowflake. For example, a high Growth score but low Stability and Valuation scores may indicate a fast-growing but financially risky or expensive company.
Best Use Cases
This indicator is useful for:
Quick stock screening
Comparing companies visually
Identifying financial strengths and weaknesses
Monitoring changes in a company’s financial profile
Combining fundamentals with technical analysis
Long-term investing and swing-trading research
It is especially helpful when comparing stocks within the same sector.
Important Notes
The indicator uses a generic scoring model. Different sectors naturally have different financial structures.
For example:
Banks do not use inventory metrics in the same way as retail companies.
Utilities often carry higher debt.
Technology companies may have low dividend scores but strong growth.
Retail and manufacturing companies rely more heavily on inventory efficiency.
Because of this, the scores should be interpreted with sector context.
Limitations
This indicator should not be used as a standalone buy or sell signal.
Financial data can be delayed, unavailable, or inconsistent depending on the symbol and exchange. When a financial metric is missing, the script uses a neutral internal fallback value to keep the radar visually stable.
The indicator is best used as a summary and comparison tool, not as a complete valuation model.
Disclaimer
Snowflake Analysis is an educational and research tool. It is not financial advice. Users should always combine this indicator with their own analysis, sector research, risk management, and broader market context before making trading or investment decisions. Indicator
Relative Valuation Oscillator [QuantAlgo]🟢 Overview
The Relative Valuation Oscillator identifies statistical price deviations from fair value using logarithmic price analysis and standard deviation bands. It calculates how far current price has deviated from its mean on a logarithmic scale, normalized by volatility, to generate a centered oscillator that highlights periods when price is statistically stretched above or below its historical average, helping traders identify potential mean reversion opportunities and extreme valuation conditions across different timeframes and markets.
🟢 How It Works
The indicator's core methodology lies in its statistical approach to price valuation, where deviations are measured using logarithmic returns and normalized by standard deviation:
log_price = math.log(close)
mean_log_price = ta.sma(log_price, lookback_period)
standard_deviation = ta.stdev(log_price, lookback_period)
valuation_score = (log_price - mean_log_price) / standard_deviation
First, the script converts price to logarithmic form to account for percentage-based price movements rather than absolute dollar changes, ensuring the indicator works consistently across different price levels and asset classes.
Then, it calculates the mean log price over the specified lookback period to establish a baseline fair value reference:
mean_log_price = ta.sma(log_price, lookback_period)
Next, standard deviation measurement quantifies the typical volatility of log price around this mean, providing a statistical framework for defining normal versus extreme price behavior:
standard_deviation = ta.stdev(log_price, lookback_period)
The valuation score is then derived by measuring how many standard deviations the current log price sits from its mean, creating a normalized oscillator that fluctuates around zero:
valuation_score = (log_price - mean_log_price) / standard_deviation
Finally, threshold-based signal detection identifies extreme conditions when the valuation score exceeds user-defined standard deviation multiples:
is_overvalued = valuation_score > threshold_mult
is_undervalued = valuation_score < -threshold_mult
This creates a statistical mean reversion system that identifies when price has deviated significantly from its historical average on a volatility-adjusted basis, providing traders with objective measurements of relative over or undervaluation.
🟢 Signal Interpretation
▶ Undervalued Zone (Below Negative Threshold): Oscillator falling below the negative threshold line indicates price has deviated significantly below its statistical mean = Potential long/buy opportunities for mean reversion strategies
▶ Overvalued Zone (Above Positive Threshold): Oscillator rising above the positive threshold line indicates price has deviated significantly above its statistical mean = Potential short/sell or profit-taking opportunities
▶ Fair Value Range (Between Thresholds): Oscillator remaining between positive and negative threshold lines indicates price is trading within normal statistical bounds. Within this range, the zero line acts as a directional filter: oscillator above zero but below the upper threshold suggests bullish trend/momentum with price trading above its statistical mean = Trend-following long positions can be maintained; oscillator below zero but above the lower threshold suggests bearish trend/momentum with price trading below its statistical mean = Trend-following short positions can be maintained. The oscillator can remain in these directional zones during sustained trends until mean reversion occurs, signaled by crosses back toward zero or transitions to the opposite extreme threshold.
▶ Zero Line Crosses: Oscillator crossing above zero indicates transition from below-average to above-average valuation, confirming shift to bullish momentum = Potential trend-following long entry; crossing below zero indicates transition from above-average to below-average valuation, confirming shift to bearish momentum = Potential trend-following short entry or long exit. These crosses can signal both the start of directional trends and early mean reversion from extreme conditions.
🟢 Features
▶ Preconfigured Presets: Three optimized parameter sets for different trading approaches and timeframes. "Default" provides balanced sensitivity for swing trading on 4-hour and daily charts, generating signals at statistically significant deviations. "Fast Response" delivers more frequent signals for intraday trading on 5-minute to 1-hour charts, reacting quickly to short-term deviations with increased signal frequency. "Smooth Trend" focuses on major extremes for position trading on daily to weekly timeframes, filtering noise to identify only the most significant statistical outliers.
▶ Built-in Alerts: Five alert conditions enable automated monitoring of valuation extremes and transitions. "Overvalued Threshold Crossed" triggers when the oscillator crosses above the positive threshold, signaling potential overvaluation. "Undervalued Threshold Crossed" activates when the oscillator crosses below the negative threshold, signaling potential undervaluation. "Crossed Above Fair Value (0)" and "Crossed Below Fair Value (0)" provide alerts for zero line transitions, indicating shifts between above-average and below-average valuation. "Any Extreme Valuation" offers a combined alert for any threshold breach regardless of direction, allowing traders to monitor both extremes with a single alert setup.
▶ Color Customization: Six visual themes (Classic, Aqua, Cosmic, Cyber, Neon, plus Custom) accommodate different chart backgrounds and visual preferences, with distinct colors for overvalued, undervalued, and fair value conditions. Optional background highlighting with adjustable transparency (0-100%) tints the main chart background during extreme valuation periods, providing immediate visual context without requiring continuous oscillator monitoring. Optional overlay signals display small circle markers directly on the price chart above bars during overvaluation and below bars during undervaluation, allowing correlation of statistical extremes with specific price levels and candlestick patterns.
Indicator
Adaptive RSIAdaptive RSI
Adaptive RSI is an enhanced version of the classic Relative Strength Index designed to automatically adjust its behavior to changing market conditions. The indicator can operate both as a mean-reversion oscillator and as a trend-following momentum tool, allowing traders to detect high/low value zones while also capturing directional moves.
Unlike the traditional RSI, which uses a fixed smoothing method, Adaptive RSI dynamically changes its calculation speed depending on market activity. This helps reduce false signals in slow or choppy markets while allowing faster responses during strong moves.
🔍 Concept & Idea
The goal behind Adaptive RSI is to make RSI responsive when opportunities appear and more conservative during uncertain or low-activity environments.
By automatically adjusting its internal smoothing and reaction speed, the indicator attempts to balance:
• Early entries during strong market moves
• Reduced noise during consolidation
• Mean-reversion opportunities in ranging markets
• Momentum confirmation in trending markets
This adaptive behavior makes the oscillator more versatile across multiple market conditions.
⚙️ How It Works
The indicator evaluates market activity using three drivers:
• True Range (volatility)
• Volume activity
• Rate of price change
Users can define which of these factors has priority. The script then checks up to three conditions; the more conditions that are satisfied, the faster and more responsive the RSI calculation becomes.
This creates multiple internal speed tiers ranging from smooth and conservative to highly responsive.
After the adaptive RSI is calculated, an additional adaptive smoothing layer is applied using the same logic, improving signal clarity while preserving responsiveness.
An optional feature allows the RSI to use a special Rate-of-Change weighted price source. This feature is more advanced and mainly intended for users who understand how weighted price construction affects oscillators.
A divergence measure between the base RSI and the smoothed Adaptive RSI is also plotted to help visualize shifts in momentum strength.
⚙️ Key Features
• Adaptive RSI calculation speed
• Works for both trend-following and mean-reversion approaches
• Adjustable long and short signal thresholds
• Overbought and oversold zone highlighting
• Divergence histogram between RSI and adaptive smoothing
• Trend-based coloring and visual signal markers
• Optional ROC-weighted source for advanced users
🧩 Inputs Overview
• RSI calculation length and smoothing length
• Price source selection or optional special weighted source
• Speed tier selection (slow, medium, fast behavior)
• Activity priority order (volatility, volume, momentum)
• Long/short and overbought/oversold thresholds
📌 Usage Notes
• Can be used both for trend continuation and mean-reversion strategies.
• Adaptive logic helps reduce noise during sideways markets.
• Strong moves may cause faster RSI transitions due to adaptive speed selection.
• Signals may update intrabar on lower timeframes.
• Works best when combined with risk management and confirmation tools.
• No indicator is perfect; always test before live use.
This script is intended for analytical purposes only and does not provide financial advice. Indicator
Indicator
MADZ - Moving Average Deviation Z-ScoreMADZ - Moving Average Deviation Z-Score
MADZ is a powerful valuation oscillator that measures how far the current price has deviated from a user-selected moving average, expressed in statistical terms as a Z-Score. This normalization makes it easier to identify overvalued and undervalued conditions across different assets, timeframes, and market environments.
Overview
The indicator works by:
Calculating the percentage deviation of price from a customizable moving average (SMA, EMA, WMA, VWMA, HMA, or RMA).
Applying a Z-Score transformation to this deviation over a chosen lookback period — showing how many standard deviations the current deviation is from its historical average. Smoothing the result for a clean, responsive oscillator centered around zero.
Positive values indicate price is trading above the moving average (potentially overvalued), while negative values suggest price is below (potentially undervalued). The further from zero, the greater the relative valuation extreme.
Key Features
Customizable base moving average (type and length)
Z-Score normalization for statistically meaningful readings
Final smoothing for reduced noise
Static overbought/oversold levels (default ±1.5) — line changes color when crossed (red above, green below)
Dynamic extreme bands (±3σ) — optional display of bands calculated from the oscillator’s own volatility over a user-defined period
Extreme zone highlighting — background shading activates only during truly rare valuation events
Extreme Zone Highlighting Explained
The highlighted extreme zones (background shading) are not based on the fixed static levels. Instead, they signal statistically significant outliers using dynamic bands:
Overbought extreme zone (red background): Triggered when MADZ rises above the upper dynamic band (+3 standard deviations of the MADZ line itself over the dynamic length period).
Oversold extreme zone (green background): Triggered when MADZ falls below the lower dynamic band (-3 standard deviations).
These ±3σ bands adapt to the recent behavior of the oscillator. Because they represent three standard deviations from the mean of MADZ, crossings are rare and often precede major reversals or trend accelerations — making them valuable for spotting potential turning points in valuation extremes.
How to Use
Use zero-line crosses for trend changes or mean-reversion setups.
Watch static level crossings (±1.5 default) for early overbought/oversold warnings.
Pay special attention to extreme zone shading — these highlight high-conviction valuation dislocations that may offer superior risk/reward opportunities.
Designed on the BTC chart, but can be used on other assets.
Settings
Moving Average Settings: Type, length, source
Z-Score & Smoothing: Lookback period and smoothing length
Threshold Levels: Static overbought/oversold thresholds
Display Options: Toggle dynamic bands and extreme background highlighting
This is an educational tool designed to aid in valuation analysis. The information provided is not financial advice. Always conduct your own research and consider multiple factors before making trading decisions. Trade at your own risk.
Indicator
Adaptive Z-Score Oscillator [QuantAlgo]🟢 Overview
The Adaptive Z-Score Oscillator transforms price action into statistical significance measurements by calculating how many standard deviations the current price deviates from its moving average baseline, then dynamically adjusting threshold levels based on historical distribution patterns. Unlike traditional oscillators that rely on fixed overbought/oversold levels, this indicator employs percentile-based adaptive thresholds that automatically calibrate to changing market volatility regimes and statistical characteristics. By offering both adaptive and fixed threshold modes alongside multiple moving average types and customizable smoothing, the indicator provides traders and investors with a robust framework for identifying extreme price deviations, mean reversion opportunities, and underlying trend conditions through the visualization of price behavior within a statistical distribution context.
🟢 How It Works
The indicator begins by establishing a dynamic baseline using a user-selected moving average type applied to closing prices over the specified length period, then calculates the standard deviation to measure price dispersion:
basis = ma(close, length, maType)
stdev = ta.stdev(close, length)
The core Z-Score calculation quantifies how many standard deviations the current price sits above or below the moving average basis, creating a normalized oscillator that facilitates cross-asset and cross-timeframe comparisons:
zScore = stdev != 0 ? (close - basis) / stdev : 0
smoothedZ = ma(zScore, smooth, maType)
The adaptive threshold mechanism employs percentile calculations over a historical lookback period to determine statistically significant extreme zones. Rather than using fixed levels like ±2.0, the indicator identifies where a specified percentage of historical Z-Score readings have fallen, automatically adjusting to market regime changes:
upperThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, upperPercentile) : fixedUpper
lowerThreshold = adaptive ? ta.percentile_linear_interpolation(smoothedZ, percentilePeriod, lowerPercentile) : fixedLower
The visualization architecture creates a four-tier coloring system that distinguishes between extreme conditions (beyond the adaptive thresholds) and moderate conditions (between the midpoint and threshold levels), providing visual gradation of statistical significance through opacity variations and immediate recognition of distribution extremes.
🟢 How to Use This Indicator
▶ Overbought and Oversold Identification:
The indicator identifies potential overbought conditions when the smoothed Z-Score crosses above the upper threshold, indicating that price has deviated to a statistically extreme level above its mean. Conversely, oversold conditions emerge when the Z-Score crosses below the lower threshold, signaling statistically significant downward deviation. In adaptive mode (default), these thresholds automatically adjust to the asset's historical behavior, i.e., during high volatility periods, the thresholds expand to accommodate wider price swings, while during low volatility regimes, they contract to capture smaller deviations as significant. This dynamic calibration reduce false signals that plague fixed-level oscillators when market character shifts between volatile and ranging conditions.
▶ Mean Reversion Trading Applications:
The Z-Score framework excels at identifying mean reversion opportunities by highlighting when price has stretched too far from its statistical equilibrium. When the oscillator reaches extreme bearish levels (below the lower threshold with deep red coloring), it suggests price has become statistically oversold and may snap back toward the mean, presenting potential long entry opportunities for mean reversion traders. Symmetrically, extreme bullish readings (above the upper threshold with bright green coloring) indicate potential short opportunities or long exit points as price becomes statistically overbought. The moderate zones (lighter colors between midpoint and threshold) serve as early warning areas where traders can prepare for potential reversals, while exits from extreme zones (crossing back inside the thresholds) often provide confirmation that mean reversion is underway.
▶ Trend and Distribution Analysis:
Beyond discrete overbought/oversold signals, the histogram's color pattern and shape reveal the underlying trend structure and distribution characteristics. Sustained periods where the Z-Score oscillates primarily in positive territory (green bars) indicate a bullish trend where price consistently trades above its moving average baseline, even if not reaching extreme levels. Conversely, predominant negative readings (red bars) suggest bearish trend conditions. The distribution shape itself provides insight into market behavior, e.g., a narrow, centered distribution clustering near zero indicates tight ranging conditions with price respecting the mean, while a wide distribution with frequent extreme readings reveals volatile trending or choppy conditions. Asymmetric distributions skewed heavily toward one side demonstrate persistent directional bias, whereas balanced distributions suggest equilibrium between bulls and bears.
▶ Built-in Alerts:
Seven alert conditions enable automated monitoring of statistical extremes and trend transitions. Enter Overbought and Enter Oversold alerts trigger when the Z-Score crosses into extreme zones, providing early warnings of potential reversal setups. Exit Overbought and Exit Oversold alerts signal when price begins reverting from extremes, offering confirmation that mean reversion has initiated. Zero Cross Up and Zero Cross Down alerts identify transitions through the neutral line, indicating shifts between above-mean and below-mean price action that can signal trend changes. The Extreme Zone Entry alert fires on any extreme threshold penetration regardless of direction, allowing unified monitoring of both overbought and oversold opportunities.
▶ Color Customization:
Six visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and aesthetic preferences, ensuring optimal contrast and readability across trading platforms. The bar transparency control (0-90%) allows fine-tuning of visual prominence, with minimal transparency creating bold, attention-grabbing bars for primary analysis, while higher transparency values produce subtle background context when using the oscillator alongside other indicators. The extreme and moderate zone coloring system uses automatic opacity variation to create instant visual hierarchy, with darkest colors highlight the most statistically significant deviations demanding immediate attention, while lighter shades mark developing conditions that warrant monitoring but may not yet justify action. Optional candle coloring extends the Z-Score color scheme directly to the price candles on the main chart, enabling traders to instantly recognize statistical extremes and trend conditions without needing to reference the oscillator panel, creating a unified visual experience where both price action and statistical analysis share the same color language.
Indicator
Market Extreme Zones IndexThe Market Extreme Zones Index is a new mean reversion (valuation) tool focused on catching long term oversold/overbought zones. Combining an enhanced RSI with a smoothed Z-score this indicator allows traders to find oppurtunities during highly oversold/overbought zones.
I will separate the explanation into the following parts:
1. How does it work?
2. Methodologies & Concepts
3. Use cases
How does it work?
The indicator attempts to catch highly unprobable events in either direction to capture reversal points over the long term. This is done by calculating the Z-Score of an enhanced RSI.
First we need to calculate the Enhanced RSI:
For this we need to calculate 2 additional lengths:
Length1 = user defined length
Length2 = Length1/2
Length3 = √Length
Now we need to calculate 3 different RSIs:
1st RSI => uses classic user defined source and classic user defined length.
2nd RSI => uses classic user defined source and Length 2.
3rd RSI => uses RSI 2 as source and Length 2
Now calculate the divergence:
RSI_base => 2nd RSI * 3 - 1st RSI - 3rd RSI
After this we need to calculate the median of the RSI_base over √Length and make a divergence of these 2:
RSI => RSI_base*2 - median
All that remains now is the Z-score calculations:
We need:
Average RSI value
Standard Deviation = a measure of how dispersed or spread out a set of data values are from their average
Z-score = (Current Value - Average Value) / Standard Deviation
After this we just smooth the Z-score with a Weighted Moving average with √Length
Methodology & Concepts
Mean Reversion Methodology:
The methodology behind mean reversion is the theory that asset prices will eventually return to their long-term average after deviating significantly, driven by the belief that extreme moves are temporary.
Z-Score Methodology:
A Z-score, or standard score, is a statistical measure that indicates how many standard deviations a data point is from the mean of a dataset. A positive z-score means the value is above the mean, a negative score means it's below, and a score of zero means the value is equal to the mean.
You might already be able to see where I am going with this:
Z-Score could be used for the extreme moves to capture reversal points.
By applying it to the RSI rather than the Price, we get a more accurate measurement that allow us to get a banger indicator.
Use Cases
Capturing reversal points
Trend Direction
- while the main use it for mean reversion, the values can indicate whether we are in an uptrend or a downtrend.
Advantages:
Visualization:
The indicator has many plots to ensure users can easily see what the indicator signals, such as highlighting extreme conditions with background colors.
Versatility:
This indicator works across multiple assets, including the S&P500 and more, so it is not only for crypto.
Final note:
No indicator alone is perfect.
Backtests are not indicative of future performance.
Hope you enjoy Gs!
Good luck! Indicator
Indicator
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that PulseWire’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value. Indicator
Quick Valuation V.1.0 (Ibo)This Pine Script indicator performs a Quick Discounted Cash Flow (DCF)-style Valuation to estimate the intrinsic value of a stock.
It calculates a projected Fair Value and a Margin of Safety based on user inputs or automatically pulled financial data from PulseWire (like revenue, growth, margin, and exit P/E). It also automatically computes a Discount Rate using a modified CAPM model.
Key Features
Valuation Output: Calculates a target Fair Value and the resulting Margin of Safety.
Data Flexibility: Automatically pulls essential fundamentals (Revenue, Margins, Shares Outstanding, etc.) but allows the user to override any value (revenue, growth, P/E, shares, etc.) via the settings.
Automated Discount Rate: Calculates the Discount Rate (Cost of Equity) using the current 10-Year Real Yield and a computed or user-defined Beta.
Clear Display: Presents all input metrics, calculated values, and data sources (PulseWire or User Input) in a neat table on the chart. Indicator
Greer Fair Value✅ Greer Fair Value
Greer Fair Value: Graham intrinsic value + Buffett-style DCF with auto EPS/FCF and auto growth (CAGR of FCF/share), defaulting to a simple GFV badge that color-codes opportunity at a glance.
📜 Full description
Greer Fair Value is inspired by the valuation frameworks of Benjamin Graham and Warren Buffett. It combines Graham’s rate-adjusted intrinsic value with a two-stage, per-share DCF. The script auto-populates EPS (TTM) and Free Cash Flow per share (FY/FQ/TTM) from request.financial(), and can auto-estimate the near-term growth rate (g₁) using the CAGR of FCF/share over a user-selected lookback (with sensible caps). All assumptions remain editable.
Default view: only the GFV badge is shown to keep charts clean.
Badge color logic:
Gold — both DCF and Graham fair values are above the current price
Green — exactly one of them is above the current price
Red — the current price is above both values
Show more detail (optional):
Toggle “Show Graham Lines” and/or “Show DCF Lines” to plot fair values (and optional MoS bands) over time.
Toggle “Show Dashboard” for a compact data table of assumptions and outputs.
Optional summary label can be enabled for a quick on-chart readout.
Inputs you can customize: EPS source/manual fallback, FCF/share source (FY/FQ/TTM), g₁ auto-CAGR lookback & caps, terminal growth gT, discount rate r, MoS levels, step-style plots, table position, and decimals.
Note: PulseWire’s UI controls whether “Inputs/Values in Status Line” are shown. If you prefer a clean status line, open the indicator’s settings and uncheck those options, then Save as default.
Disclaimer: For educational/informational purposes only; not financial advice. Markets involve risk—do your own research. Indicator
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system. Indicator
Economic Profit (Fixed & Labeled) — Rated + PeersFRAC (Fundamental-Rated-Asset-Calculate)
FRAC is a fundamentals-driven tool designed to measure whether a company is creating or destroying shareholder value. Unlike surface ratios, FRAC uses Economic Profit (ROIC – WACC) as its engine, showing whether a business truly outperforms its cost of capital.
🔹 What FRAC Does
Calculates ROIC (Return on Invested Capital) vs. WACC (Weighted Average Cost of Capital).
Shows whether a company is creating or destroying shareholder value.
Uses tiered color coding for clarity:
🔵 Superior (Aqua Blue) → Top tier; best of the best.
🟣 Elite (Purple) → Strong value creation.
🟢 Positive (Green) → Solid, creating shareholder value.
🟡 Marginal (Yellow) → Barely covering cost of capital.
🔴 Negative (Red) → Value destruction.
🔹 Composite Ranking System (1–4)
FRAC also assigns each company a Composite Rank so you can compare multiple names side by side. The rank works like this:
Rank 1 → Superior (🔵 Aqua Blue)
Best possible rating; wide gap between ROIC and WACC.
Rank 2 → Elite (🟣 Purple)
Strongly positive; above-average capital efficiency.
Rank 3 → Positive (🟢 Green)
Creating value but only moderately; not a top compounder.
Rank 4 → Marginal/Negative (🟡/🔴)
Weak or destructive; either barely covering WACC or losing money on capital.
✅ How to Use the Ranks
When comparing a set of peers (e.g., NVDA, AMD, INTC):
FRAC will display each company’s color rating + composite rank (1–4).
You can instantly see who is strongest vs. weakest in the group.
Best decisions = overweight Rank 1 & 2 companies, avoid Rank 4 names.
🔹 Key Inputs Explained
Risk-Free Asset → Typically the 10-Year US Treasury yield (US10Y).
Corporate Tax Rate → Effective tax rate for the company’s country (e.g., USCTR).
Expected Market Return → Historical average ~8–10%, adjustable.
Beta Lookback Period → Controls how far back Beta is calculated (longer = more stable, shorter = more reactive).
👉 These must be set correctly for FRAC to calculate WACC accurately.
🔹 Example Comparison
NVDA: ROIC 25% – WACC 7% = +18% → 🔵 Superior → Rank 1
AMD: ROIC 17% – WACC 8% = +9% → 🟣 Elite → Rank 2
INTC: ROIC 11% – WACC 9% = +2% → 🟢 Positive → Rank 3
FSLY: ROIC 5% – WACC 10% = –5% → 🔴 Negative → Rank 4
🔹 Why It Matters
Buffett said: “The best businesses are those that can consistently generate returns on capital above their cost of capital.”
FRAC turns that into a visual + numeric rating system (1–4), making comparisons across peers simple and actionable.
🔹 Credit
FRAC was created by Hunter Hammond (Elite x FineFir), inspired by corporate finance models of Economic Profit and Economic Value Added (EVA).
⚠️ Disclaimer: FRAC is a research framework, not financial advice. Always pair with full due diligence. Indicator
BTC Power Law Valuation BandsBTC Power Law Rainbow
A long-term valuation framework for Bitcoin based on Power Law growth — designed to help identify macro accumulation and distribution zones, aligned with long-term investor behavior.
🔍 What Is a Power Law?
A Power Law is a mathematical relationship where one quantity varies as a power of another. In this model:
Price ≈ a × (Time)^b
It captures the non-linear, exponentially slowing growth of Bitcoin over time. Rather than using linear or cyclical models, this approach aligns with how complex systems, such as networks or monetary adoption curves, often grow — rapidly at first, and then more slowly, but persistently.
🧠 Why Power Law for BTC?
Bitcoin:
Has finite supply and increasing adoption.
Operates as a monetary network , where Metcalfe’s Law and power laws naturally emerge.
Exhibits exponential growth over logarithmic time when viewed on a log-log chart .
This makes it uniquely well-suited for power law modeling.
🌈 How to Use the Valuation Bands
The central white line represents the modeled fair value according to the power law.
Colored bands represent deviations from the model in logarithmic space, acting as macro zones:
🔵 Lower Bands: Deep value / Accumulation zones.
🟡 Mid Bands: Fair value.
🔴 Upper Bands: Euphoria / Risk of macro tops.
📐 Smart Money Concepts (SMC) Alignment
Accumulation: Occurs when price consolidates near lower bands — often aligning with institutional positioning.
Markup: As price re-enters or ascends the bands, we often see breakout behavior and trend expansion.
Distribution: When price extends above upper bands, potential for exit liquidity creation and distribution events.
Reversion: Historically, price mean-reverts toward the model — rarely staying outside the bands for long.
This makes the model useful for:
Cycle timing
Long-term DCA strategy zones
Identifying value dislocations
Filtering short-term noise
⚠️ Disclaimer
This tool is for educational and informational purposes only . It is not financial advice. The power law model is a non-predictive, mathematical framework and does not guarantee future price movements .
Always use additional tools, risk management, and your own judgment before making trading or investment decisions. Indicator
Indicator
Indicator
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. Indicator
S&P 500 & Normalized CAPE Z-Score AnalyzerThis macro-focused indicator visualizes the historical valuation of the U.S. equity market using the CAPE ratio (Shiller P/E), normalized over its long-term average and standard deviations. It helps traders and investors identify overvaluation and undervaluation zones over time, combining both statistical signals and historical context.
💡 Why It’s Useful
This indicator is ideal for macro traders and long-term investors looking to contextualize equity valuations across decades. It helps identify statistical extremes in valuation by referencing the standard deviation of the CAPE ratio relative to its long-term mean. The overlay of S&P 500 price with valuation zones provides a visual confirmation tool for macro decisions or timing insights.
It includes:
✅ Three display modes:
-S&P 500 (color-coded by CAPE valuation zone)
-Normalized CAPE (vs. long-term mean)
-CAPE Z-Score (standardized measure)
🎯 How to Interpret
Dynamic coloring of the S&P 500 price based on CAPE valuation:
🔴 Z > +2σ → Highly Overvalued
🟠 Z > +1σ → Overvalued
⚪ -1σ < Z < +1σ → Neutral
🟢 Z < -1σ → Undervalued
✅ Z < -2σ → Strong Buy Zone
-Live valuation label showing the current CAPE, Z-score, and zone.
-Macro event shading: major historical events (e.g. Great Depression, Oil Crisis, Dot-com Bubble, COVID Crash) are shaded on the chart for context.
✅ Built-in alerts:
CAPE > +2σ → Potential risk zone
CAPE < -2σ → Potential opportunity zone
📊 Use Cases
This indicator is ideal for:
🧠 Macro traders seeking long-term valuation extremes.
📈 Portfolio managers monitoring systemic valuation risk.
🏛️ Long-term investors timing strategic allocation shifts.
🧪 How It Works
CAPE ratio (Shiller PE) is retrieved from Quandl (MULTPL/SHILLER_PE_RATIO_MONTH).
The script calculates the long-term average and standard deviation of CAPE.
The Z-score is computed as:
(CAPE - Mean) / Standard Deviation
Users can switch between:
S&P 500 chart, color-coded by CAPE valuation zones.
Normalized CAPE, centered around zero (historic mean).
CAPE Z-score, showing statistical positioning directly.
Visual bands represent +1σ, +2σ, -1σ, -2σ thresholds.
You can switch between modes using the “Display” dropdown in the settings panel.
📊 Data Sources
CAPE: MULTPL/SHILLER_PE_RATIO_MONTH via Quandl
S&P 500: Monthly close prices of SPX (PulseWire data)
All data updated on monthly resolution
This is not a repackaged built-in or autogenerated script. It’s a custom-built and interactive indicator designed for educational and analytical use in macroeconomic valuation studies. Indicator
FA Dashboard: Valuation, Profitability & SolvencyFundamental Analysis Dashboard: A Multi-Dimensional View of Company Quality
This script presents a structured and customizable dashboard for evaluating a company’s fundamentals across three key dimensions: Valuation, Profitability, and Solvency & Liquidity.
Unlike basic fundamental overlays, this dashboard consolidates multiple financial indicators into visual tables that update dynamically and are grouped by category. Each ratio is compared against configurable thresholds, helping traders quickly assess whether a company meets certain value investing criteria. The tables use color-coded checkmarks and fail marks (✔️ / ❌) to visually signal pass/fail evaluations.
▶️ Key Features
Valuation Ratios:
Earnings Yield: EBIT / EV
EV / EBIT and EV / FCF: Enterprise value metrics for profitability
Price-to-Book, Free Cash Flow Yield, PEG Ratio
Profitability Ratios:
Return on Invested Capital (ROIC), ROE, Operating, Net & Gross Margins, Revenue Growth
Solvency & Liquidity Ratios:
Debt to Equity, Debt to EBITDA, Current Ratio, Quick Ratio, Altman Z-Score
Each of these metrics is calculated using request.financial() and can be viewed using either annual (FY) or quarterly (FQ) data, depending on user preference.
🧠 How to Use
Add the script to any stock chart.
Select your preferred data period (FY or FQ).
Adjust thresholds if desired to match your personal investing strategy.
Review the visual dashboard to see which metrics the company passes or fails.
💡 Why It’s Useful
This tool is ideal for traders or long-term investors looking to filter stocks using fundamental criteria. It draws inspiration from principles used by Benjamin Graham, Warren Buffett, and Joel Greenblatt, offering a fast and informative way to screen quality businesses.
This is not a repackaged built-in or autogenerated script. It’s a custom-built, interactive tool tailored for fundamental analysis using official financial data provided via Pine Script’s request.financial().
Indicator
Sharpe Ratio Z-ScoreThis indicator calculates the Sharpe Ratio and its Z-Score , which are used to evaluate the risk-adjusted return of an asset over a given period. The Sharpe Ratio is computed using the average return and the standard deviation of returns, while the Z-Score standardizes this ratio to assess how far the current Sharpe Ratio deviates from its historical average.
The Sharpe Ratio is a measure of how much return an investment has generated relative to the risk it has taken. In the context of this script, the risk-free rate is assumed to be 0, but in real applications, it would typically be the return on a safe investment, like a Treasury bond. A higher Sharpe Ratio indicates that the investment's returns are higher compared to its risk, making it a more favorable investment. Conversely, a lower Sharpe Ratio suggests that the investment may not be worth the risk.
Calculation:
Daily Returns Calculation: The script calculates the daily return of the asset. This measures the percentage change in the asset’s closing price from one period to the next.
Sharpe Ratio Calculation: The Sharpe Ratio is calculated by taking the average daily return and dividing it by the standard deviation of the returns, then multiplying by the square root of the period length.
Usage:
Traders and Investors can use the Sharpe Ratio to evaluate how well the asset is compensating for risk. A high Sharpe Ratio indicates a high return per unit of risk, whereas a low or negative Sharpe Ratio suggests poor risk-adjusted returns. In overbought times, an asset would have high/positive returns per unit of risk. In oversold times, an asset would have low/negative returns per unit of risk.
The Z-Score provides a way to compare the current Sharpe Ratio to its historical distribution, offering a more standardized view of how extreme or typical the current ratio is.
Positive Z-score: Indicates that the asset's return is significantly lower than its risk, suggesting potential oversold conditions.
Negative Z-score: Indicates that the asset's return is significantly higher than its risk, suggesting potential overbought conditions.
Red Zone (-3 to -2): Strong overbought conditions.
Green Zone (2 to 3): Strong oversold conditions.
Sharpe Ratio Limitations:
While the Sharpe Ratio is widely used to evaluate risk-adjusted returns, it has its limitations.
Fat Tails: It assumes that returns are normally distributed and does not account for extreme events or "fat tails" in the return distribution. This can be problematic for assets like cryptocurrencies, which may experience large, sudden price swings that skew the return distribution.
Single Risk Factor: The Sharpe Ratio only considers standard deviation (total volatility) as a measure of risk, ignoring other types of risks like skewness or kurtosis, which may also impact an asset’s performance.
Time Frame Sensitivity: The accuracy of the Sharpe Ratio and its Z-Score is heavily influenced by the time frame chosen for the calculation. A longer period may smooth out short-term fluctuations, while a shorter period might be more sensitive to recent volatility.
Overbought and Oversold Zones: The script marks overbought and oversold conditions based on the Z-Score, but this is not a guarantee of market reversal. It’s important to combine this tool with other technical indicators and fundamental analysis for a more comprehensive market evaluation.
Volatility: The Sharpe Ratio and Z-Score depend on the volatility (standard deviation) of the asset’s returns. For highly volatile assets, such as cryptocurrencies, the Sharpe Ratio may not fully capture the true risk or may be misleading if the volatility is transient.
Doesn't Account for Downside Risk: The Sharpe Ratio treats upside and downside volatility equally, which may not reflect how investors perceive risk. Some investors may be more concerned with downside risk, which the Sharpe Ratio does not distinguish from upside fluctuations.
Important Considerations:
The Sharpe Ratio should not be used in isolation. While it provides valuable insights into risk-adjusted returns, it is important to combine it with other performance and risk indicators to form a more comprehensive market evaluation. Relying solely on the Sharpe Ratio may lead to misleading conclusions, particularly in volatile or non-normally distributed markets.
When integrated into a broader investment strategy, the Sharpe Ratio can help traders and investors better assess the risk-return profile of an asset, identifying periods of potential overperformance or underperformance. However, it should be used alongside other tools to ensure more informed decision-making, especially in highly fluctuating markets.
Indicator
Revenue GridDescription:
The Revenue Grid indicator helps traders and investors visualize a stock’s valuation by plotting horizontal lines based on its price-to-sales (P/S) ratio. This tool displays how the stock price compares to multiples of its total revenue per share, giving a clear perspective on valuation benchmarks.
Fundamental Concept:
The price-to-sales ratio compares a company’s stock price to its revenue per share. It’s used to evaluate whether a stock is overvalued or undervalued based on its revenue.
This indicator offers a unique way to view this ratio by applying Fibonacci multiples to the revenue per share. It plots lines at these multiples to show how the stock price measures up against different valuation levels.
How It Works:
Data Inputs:
Total Revenue (TR): The company’s revenue over the past twelve months.
Total Shares Outstanding (TSO): The total number of shares in circulation.
Calculation:
Calculates the revenue per share (TR/TSO).
Plots lines at fixed Fibonacci multiples (e.g., 1x, 2x, 3x, 5x, 8x, 13x) of the revenue per share value.
How to Use:
1. Add the "Revenue Grid" indicator to your chart by searching for it in the indicator library and applying it.
2. Observe the lines plotted on the chart. If these lines are trending upwards, it indicates that the revenue is increasing.
3. Analyze how historical prices trend relative to these lines. Look for periods where the stock price supports around specific multiples, you can easily get a sense of overvaluation or undervaluation in certain periods.
Use this information to guide further analysis and investment decisions.
Benefits:
1. Clear Valuation View: Easily see how the company’s revenue translates into stock price levels.
2. Investment Insight: Identify if the stock price is lagging behind revenue growth, which might signal a buying opportunity.
3. Historical Context: Understand how the market has historically valued the company and assess the current valuation.
Do let me know your feedbacks in comments. Happy Investing :) Indicator
