CycleSync | QuantEdgeBIntroducing CycleSync by QuantEdgeB
Overview
CycleSync is a powerful valuation and cycle-tracking system designed to provide insights into asset price behavior across different phases of market cycles. It integrates on-chain data, price-based indicators, and risk-adjusted metrics to offer a comprehensive valuation model that helps traders and investors identify accumulation, distribution, and momentum shifts.
This system is ideal for those who want data-driven confirmation of market tops and bottoms, leveraging a blend of statistical measures, trend-following techniques, and historical on-chain valuations.
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Key Features
1. Multi-Factor Valuation Framework
Incorporates a blend of on-chain, momentum, and price-based indicators to assess market cycles in real-time. Helps determine if an asset is overvalued, fairly valued, or undervalued over long term horizon.
2.Market Cycle Recognition
Tracks key macro and micro cycle shifts, identifying trends such as accumulation, expansion, distribution, and contraction phases.
3.Dynamic Valuation
CycleSync employs Z-score standardization and adaptive rescaling to continuously refine overbought and oversold thresholds based on evolving market conditions. Unlike static valuation models, which rely on fixed levels, CycleSync dynamically recalibrates these boundaries by analyzing historical price distributions and deviations from the mean.
4.Comprehensive Dashboard
Presents cycle indicators and valuation scores in a structured table format.
Displays color-coded overbought and oversold signals for quick interpretation.
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How It Works
1.On-Chain & Price-Based Data Collection
Gathers key market cycle indicators like MVRV, NUPL, SOPR, CVDD, VWAP, Pi-Cycle, RSI, and Risk Ratios to assess historical valuation.
2.Standardization & Rescaling
Each metric is normalized using either Z-score calculations or high-low rescaling, ensuring fair contribution across different data sources. By applying statistical normalization techniques, the system ensures that extreme valuations are detected relative to the asset's own historical behavior rather than arbitrary thresholds.
3.Valuation Score & Interpretation
🔹 CycleSync Score Ranges
- 📉 Strongly Oversold (-2 and below) → Market is extremely undervalued; potential reversal.
- 📉 Moderately Oversold (-1.5 to -2) → Discounted market conditions, buying interest may emerge.
- 📉 Slightly Oversold (-0.5 to -1.5) → Possible accumulation phase.
- ⚖ Fair Value (-0.5 to +0.5) → Market trading at equilibrium.
- 📈 Slightly Overbought (+0.5 to +1.5) → Initial signs of market strength.
- 📈 Moderately Overbought (+1.5 to +2) → Market heating up, caution warranted, selling interest may emerge.
- 📈 Strongly Overbought (+2 and above) → Extreme valuation, increased risk of correction.
This classification helps traders gauge overall market sentiment and make better allocation decisions.
Note : Past valuations and buy/sell signals generated by CycleSync do not guarantee future performance. Market conditions can change, and proper risk management should always be applied.
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Use Cases
✅ Crypto Traders & Long-Term Investors
Identify potential major market tops and bottoms using on-chain and price-based cycle indicators.Confirm long-term accumulation or distribution phases with CycleSync’s multi-cycle tracking.
✅ Macro Trend Followers
Detect macro bull and bear cycle shifts by integrating valuation metrics with trend-following strategies.
✅ Mean Reversion & Rotational Traders
Exploit valuation mean reversion strategies when assets enter extreme overvaluation or undervaluation zones. Rotate capital efficiently between risk-on and risk-off assets based on CycleSync’s valuation models.
✅ Risk Management & Portfolio Allocation
Adjust portfolio exposure by scaling in/out of positions based on historical valuation insights.
Use CycleSync’s Risk Ratios & CVDD metrics to refine entry and exit strategies.
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📊 Optimized for Bitcoin , Yet "Universally" Adaptable 🔄
CycleSync is primarily optimized for Bitcoin , leveraging their extensive on-chain and market data to provide robust long-term valuation insights. However, the system remains flexible and can be applied to other assets 📉📈—provided they have sufficient historical price data to support reliable statistical calculations.
Since CycleSync incorporates volume-based metrics, it is essential that the selected chart's ticker provides accurate volume data to function properly. For assets with limited history, results may be less reliable, as long-term valuation models depend on deep market data for precision.
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Conclusion
CycleSync is a powerful full-cycle valuation system designed to provide deep market insights 📊 by blending on-chain metrics, statistical rescaling, and technical analysis. Whether you're tracking Bitcoin or other assets with sufficient historical data, this tool offers a structured framework for identifying overbought/oversold conditions, potential cycle tops/bottoms, and long-term market positioning.
With its dynamic adaptability, intuitive scaling mechanisms, and multi-metric integration ⚡, CycleSync empowers traders and investors to make more informed, data-driven decisions 📈. While no valuation model is infallible, combining CycleSync with broader market context and risk management strategies enhances its effectiveness.
🔹 Who Should Use Sentival?
✅ Swing Traders & Long-Term Investors looking for structured valuation metrics.
✅ Quantitative & Systematic Traders incorporating multi-factor models.
✅ Portfolio Managers optimizing exposure to different market regimes.
✅ Use CycleSync as a guiding framework—not a standalone signal— and gain a clearer perspective on the ever-evolving market cycles!
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Fundamental Analysis
Higher Time Frame Fair Value Gap [ZeroHeroTrading]A fair value gap (FVG) highlights an imbalance area between market participants, and has become popular for technical analysis among price action traders.
A bullish (respectively bearish) fair value gap appears in a triple-candle pattern when there is a large candle whose previous candle’s high (respectively low) and subsequent candle’s low (respectively high) do not fully overlap the large candle. The space between these wicks is known as the fair value gap.
The following script aims at identifying higher timeframe FVG's within a lower timeframe chart. As such, it offers a unique perspective on the formation of FVG's by combining the multiple timeframe data points in the same context.
You can change the indicator settings as you see fit to achieve the best results for your use case.
Features
It draws higher timeframe bullish and bearish FVG's on the chart.
For bullish (respectively bearish) higher timeframe FVG's, it adds the buying (respectively selling) pressure as a percentage ratio of the up (respectively down) volume of the second higher timeframe bar out of the total up (respectively down) volume of the first two higher timeframe bars.
It adds a right extended trendline from the most recent lowest low (respectively highest high) to the top (respectively bottom) of the higher timeframe bullish (respectively bearish) FVG.
It detects and displays higher timeframe FVG's as early as one starts forming.
It detects and displays lower timeframe (i.e. chart's timeframe) FVG's upon confirmation.
It allows for skipping inside first bars when evaluating FVG's.
It allows for dismissing higher timeframe FVG's if there is no update for any period of the chart's timeframe. For instance, this can occur at lower timeframes during low trading activity periods such as extended hours.
Settings
Higher Time Frame FVG dropdown: Selects the higher timeframe to run the FVG detection on. Default is 15 minutes. It must be higher than, and a multiple of, the chart's timeframe.
Higher Time Frame FVG color select: Selects the color of the text to display for higher timeframe FVG's. Default is black.
Show Trend Line checkbox: Turns on/off trendline display. Default is on.
Show Lower Time Frame FVG checkbox: Turns on/off lower timeframe (i.e. chart's timeframe) FVG detection. Default is on.
Show Lower Time Frame FVG color select: Selects the color of the border for lower timeframe (i.e. chart's timeframe) FVG's. Default is white.
Include Inside Bars checkbox: Turns on/off the inclusion of inside first bars when evaluating FVG's. Default is on.
With Consistent Updates checkbox: Turns on/off consistent updates requirement. Default is on.
Cross Market AdvancedEnglish Description:
Cross Market Advanced is a TradingView indicator designed for intermarket analysis between two tickers. It compares the price of the primary market (the chart’s ticker) with a secondary market (user-selectable) by calculating their ratio—optionally with a configurable time delay. This feature is especially useful for markets such as oil and gold. Statistical studies show that the gold price typically lags behind the oil price by about 140–150 days, meaning that an oil rally can potentially trigger a gold rally after approximately 145 days.
Key features include:
• Intermarket Comparison: Analyzes the relationship between two different markets.
• Customizable Ratio Calculation: Choose between EMA and SMA smoothing, set evaluation periods, and apply a time delay for the secondary ticker. This delay can be tailored to match market-specific relationships, like the oil-to-gold lag.
• Overbought & Oversold Levels: Visual cues are provided with horizontal reference lines at 70 (overbought) and 30 (oversold), with a shaded zone between, helping to identify extreme market conditions.
• Divergence Detection: Identifies potential divergences between the ticker price and the scaled index. Divergence signals are color-coded (red for bearish, green for bullish) as a rough guide—note that this does not replace detailed technical analysis.
• Additional Plots: Optionally display the raw ratio and z‑score.
• Visual Aids: The indicator displays the selected tickers on the chart along with clear reference levels.
This tool is ideal for traders looking to explore cross-market relationships, identify early signs of potential market reversals, and incorporate time delay effects—particularly in markets like oil and gold.
Deutsche Beschreibung:
Cross Market Advanced ist ein TradingView-Indikator, der eine Intermarket-Analyse zwischen zwei Tickern ermöglicht. Er vergleicht den Kurs des primären Marktes (dem im Chart dargestellten Ticker) mit einem sekundären Markt (frei wählbar), indem er deren Verhältnis berechnet – optional mit einem einstellbaren Zeitversatz. Dieser Zeitversatz ist besonders nützlich, wenn beispielsweise Öl und Gold verglichen werden. Statistischen Untersuchungen zufolge läuft der Goldpreis etwa 140–150 Tage hinter dem Ölpreis her, was bedeuten kann, dass eine Öl-Rallye zu einer Gold-Rallye mit einem Verzögerungsfaktor von etwa 145 Tagen führen kann.
Wichtige Funktionen im Überblick:
• Intermarket-Vergleich: Analyse der Beziehung zwischen zwei unterschiedlichen Märkten.
• Anpassbare Verhältnis-Berechnung: Auswahl zwischen EMA und SMA, Festlegung der Evaluationsperiode sowie Anwendung eines Zeitversatzes für den zweiten Ticker. Dieser Versatz kann an marktspezifische Zusammenhänge angepasst werden, wie z.B. das Öl-Gold-Verhältnis.
• Überkauft & Überverkauft: Mit horizontalen Referenzlinien bei 70 (Überkauft) und 30 (Überverkauft) wird die aktuelle Marktlage visualisiert – der dazwischen liegende, hervorgehobene Bereich hilft, extreme Marktbedingungen zu erkennen.
• Divergenzerkennung: Ermittelt mögliche Divergenzen zwischen dem Ticker-Kurs und dem skalierten Index. Divergenzsignale werden farblich hervorgehoben (rot für bärisch, grün für bullisch) und dienen als grobe Orientierung – sie ersetzen jedoch keine ausführliche charttechnische Analyse.
• Zusatzplots: Optionale Darstellung des Rohwerts des Verhältnisses sowie des z‑Scores.
• Visuelle Hilfen: Der Indikator zeigt die ausgewählten Ticker im Chart sowie die klaren Referenzniveaus an.
Dieser Indikator eignet sich ideal für Trader, die Cross-Market-Beziehungen untersuchen, frühzeitig Hinweise auf mögliche Trendumkehrungen erkennen und auch Verzögerungseffekte – wie etwa beim Öl-Gold-Verhältnis – in ihre Analyse einbeziehen möchten.
QoQ Economic & Financial Indicator ChangesA straightforward indicator for analyzing quarter-over-quarter (QoQ) percentage changes in economic and financial data series. Perfect for visualizing dynamic changes in:
Economic Indicators (GDP, House Price Indices, Employment Figures)
Company Financial Metrics (Revenue, EPS, Operating Margins)
Balance Sheet Items (Assets, Liabilities, Equity)
Cash Flow Statement Components
Other Quarterly Economic & Financial Data
Features:
Automatically calculates QoQ percentage changes
Color-coded visualization (green for positive, red for negative changes)
Displays exact percentage values
Includes adjustable scale factor for different data series
Zero line reference for easy trend identification
Enhanced Interval Candle with Breakout Detection and Detailed InThis indicator visualizes the last candle of a user-defined time interval (e.g., 1 hour, 4 hours, 1 day) on the current chart, providing enhanced details and breakout detection. It fetches the open, high, low, and close prices of the interval candle and draws a stylized representation of it, offset to the right of the current bar. The candle body and wicks are colored according to whether the interval candle closed bullishly (green) or bearishly (red). In addition to the candle itself, the indicator displays horizontal dotted lines representing the high, low, and midpoint of the interval candle, along with labels showing their exact values. These labels are dynamically updated as the interval candle changes. Furthermore, the script detects and visualizes breakouts of the interval candle's high or low. When the current price closes above the interval high, a green dashed line and a "Bullish Breakout" label are displayed. Conversely, when the current price closes below the interval low, a red dashed line and a "Bearish Breakout" label are shown. The breakout lines and labels are also dynamically updated. This indicator helps traders easily track the price action of a higher timeframe candle and spot potential breakouts based on that candle's range. The user can configure the time interval to suit their trading needs.
TradFi Fundamentals: Momentum Trading with Macroeconomic DataIntroduction
This indicator combines traditional price momentum with key macroeconomic data. By retrieving GDP, inflation, unemployment, and interest rates using security calls, the script automatically adapts to the latest economic data. The goal is to blend technical analysis with fundamental insights to generate a more robust momentum signal.
Original Research Paper by Mohit Apte, B. Tech Scholar, Department of Computer Science and Engineering, COEP Technological University, Pune, India
Link to paper
Explanation
Price Momentum Calculation:
The indicator computes price momentum as the percentage change in price over a configurable lookback period (default is 50 days). This raw momentum is then normalized using a rolling simple moving average and standard deviation over a defined period (default 200 days) to ensure comparability with the economic indicators.
Fetching and Normalizing Economic Data:
Instead of manually inputting economic values, the script uses TradingView’s security function to retrieve:
GDP from ticker "GDP"
Inflation (CPI) from ticker "USCCPI"
Unemployment rate from ticker "UNRATE"
Interest rates from ticker "USINTR"
Each series is normalized over a configurable normalization period (default 200 days) by subtracting its moving average and dividing by its standard deviation. This standardization converts each economic indicator into a z-score for direct integration into the momentum score.
Combined Momentum Score:
The normalized price momentum and economic indicators are each multiplied by user-defined weights (default: 50% price momentum, 20% GDP, and 10% each for inflation, unemployment, and interest rates). The weighted components are then summed to form a comprehensive momentum score. A horizontal zero line is plotted for reference.
Trading Signals:
Buy signals are generated when the combined momentum score crosses above zero, and sell signals occur when it crosses below zero. Visual markers are added to the chart to assist with trade timing, and alert conditions are provided for automated notifications.
Settings
Price Momentum Lookback: Defines the period (in days) used to compute the raw price momentum.
Normalization Period for Price Momentum: Sets the window over which the price momentum is normalized.
Normalization Period for Economic Data: Sets the window over which each macroeconomic series is normalized.
Weights: Adjust the influence of each component (price momentum, GDP, inflation, unemployment, and interest rate) on the overall momentum score.
Conclusion
This implementation leverages TradingView’s economic data feeds to integrate real-time macroeconomic data into a momentum trading strategy. By normalizing and weighting both technical and economic inputs, the indicator offers traders a more holistic view of market conditions. The enhanced momentum signal provides additional context to traditional momentum analysis, potentially leading to more informed trading decisions and improved risk management.
The next script I release will be an improved version of this that I have added my own flavor to, improving the signals.
Full Cycle Valuation | QuantumResearchQuantumResearch Full Cycle Valuation Indicator for BTC only!
The Full Cycle Valuation indicator is an advanced on-chain valuation model that synthesizes multiple fundamental Bitcoin metrics into a single, normalized score.
By leveraging Power Law Corridor, Pi Cycle Top, Crosby Ratio, MVRV Z-Score, SOPR Z-Score, NUPL Z-Score, BAERM, and other key valuation signals, this tool provides traders and investors with an intuitive way to assess Bitcoin’s market cycle positioning and identify potential overbought or undervalued conditions. 🚀📊
1. Overview
This indicator helps users:
Identify Market Cycles – Uses a blend of statistical and fundamental models to determine whether Bitcoin is undervalued or overvalued.🔄
Normalize On-Chain & Valuation Metrics – Standardizes multiple valuation indicators through Z-score transformation for clearer insights📉📈
Assess Risk & Reward – Generates an Average Valuation Z-score, offering a high-level overview of current market positioning. ⚖️
Customize Visual Preferences – Dynamic color-coded signals, background fills, and table-based valuation metrics enhance usability. 🎨
2. How It Works
A. Power Law Corridor
The Power Law Model provides a long-term price corridor for Bitcoin based on a logarithmic regression formula. 🔢
The indicator evaluates where the current price sits relative to the Power Law Support & Resistance levels. 📊
Normalized Z-score Calculation: A standardized metric indicating overvaluation or undervaluation. 🎯
B. Pi Cycle Top
Uses the 111-day and 350-day moving averages to identify cyclical market peaks. 🔺
Generates a Z-score that measures deviation from historical overbought conditions. ⚠️
C. Crosby Ratio
Measures market momentum by analyzing Heikin-Ashi candle trends and ATR-based volatility. 📊
Provides a weekly trend strength score that is normalized into a Z-score. 📈
D. MVRV Z-Score
Compares Bitcoin's Market Cap to Realized Cap to assess whether price is above or below fair value. 💰
The higher the MVRV Z-score, the more overvalued Bitcoin is; lower scores indicate undervaluation. 🔻
E. SOPR Z-Score
Spent Output Profit Ratio (SOPR) measures profit-taking behavior in the market. 💵
SOPR is smoothed and standardized to filter out noise and highlight macro trends. 📊
F. NUPL Z-Score
Net Unrealized Profit/Loss (NUPL) calculates the proportion of coins held in profit versus loss. 📉📈
High Z-score values indicate speculative euphoria, while low values suggest capitulation. ⚠️
G. BAERM (Bitcoin AR Model)
BAERM is a statistical model that incorporates Bitcoin's supply, halvings, and historical growth trends to estimate fair value. 📉
This model is adjusted with a damping function to remove excess noise. 🎛️
H. Composite Full Cycle Z-Score
The indicator calculates a weighted average Z-score across all valuation models to generate a final Full Cycle Valuation Score. 📊
The score is used to define five distinct market states:
Very Undervalued (-3 to -2 Z-score): Ideal accumulation zone. 🟢
Undervalued (-2 to -1.5 Z-score): Accumulate Bitcoin at a discount. 🔵
Neutral (-1.5 to +1.5 Z-score): Fair market conditions. ⚪
Overheated (+1.5 to +2 Z-score): Potential distribution phase. 🟠
Very Overheated (>2 Z-score): High risk of market tops. 🔴
3. Visual Representation
This indicator offers multiple dynamic visual elements to improve clarity and ease of use:
Color-Coded Background Fill
Green Background – Indicates undervalued market conditions (Accumulation). 🟢
Blue Background – Signals overheated conditions (Distribution). 🔵
Table Display for Z-Scores
Displays each individual valuation model’s Z-score in a compact, color-coded format. 📊
The final average Z-score is highlighted, along with a corresponding market action recommendation. 🎯
4. Customization & Parameters
Traders and analysts can fine-tune the Full Cycle Valuation indicator to match their specific strategies:
On-Chain Valuation Metrics MVRV, SOPR, and NUPL Z-score lengths can be adjusted for different market perspectives.
Market Cycle Models Power Law Corridor: Adjustable regression parameters to modify long-term projections.
Pi Cycle & Crosby Ratio: Customizable smoothing lengths.
Threshold Adjustments Modify overvaluation and undervaluation thresholds to fine-tune signal sensitivity.
Visual Settings
Valuation Mode: Allows traders to switch between default mode and valuation-focused color themes. 🎨
5. Trading Applications
This indicator is not just for trading—it serves as a powerful macro analysis tool:
Long-Term Investing – Helps Bitcoin investors identify key accumulation and distribution phases. 📈
Market Timing – Guides traders in recognizing overbought and oversold conditions based on fundamental valuation. ⏳
Risk Management – Provides a systematic way to assess whether Bitcoin is fundamentally cheap or expensive. ⚠️
Cyclical Trend Analysis – Helps long-term investors compare past market cycles and spot repeating patterns. 🔄
6. Final Thoughts
The Full Cycle Valuation Indicator is a comprehensive macro valuation tool that combines multiple on-chain, statistical, and fundamental models into one easy-to-interpret score.
Whether you are a long-term investor looking to time Bitcoin cycles or a trader searching for additional confluence, this tool offers invaluable insights.
Important Disclaimer: No indicator can predict future price action with certainty. Always perform additional research and use proper risk management when making investment decisions. ⚠️📊
Blockchain Fundamentals: Liquidity Cycle MomentumLiquidity Cycle Momentum Indicator
Overview:
This indicator analyzes global liquidity trends by calculating a unique Liquidity Index and measuring its year-over-year (YoY) percentage change. It then applies a momentum oscillator to the YoY change, providing insights into the cyclical momentum of liquidity. The indicator incorporates a limited historical data workaround to ensure accurate calculations even when the chart’s history is short.
Features Breakdown:
1. Limited Historical Data Workaround
Function: The limit(length) function adjusts the lookback period when there isn’t enough historical data (i.e., near the beginning of the chart), ensuring that calculations do not break due to insufficient data.
2. Global Liquidity Calculation
Data Sources:
TVC:CN10Y (10-year yield from China)
TVC:DXY (US Dollar Index)
ECONOMICS:USCBBS (US Central Bank Balance Sheet)
FRED:JPNASSETS (Japanese assets)
ECONOMICS:CNCBBS (Chinese Central Bank Balance Sheet)
FRED:ECBASSETSW (ECB assets)
Calculation Methodology:
A ratio is computed (cn10y / dxy) to adjust for currency influences.
The Liquidity Index is then derived by multiplying this ratio with the sum of the other liquidity components.
3. Year-over-Year (YoY) Percent Change
Computation:
The indicator determines the number of bars that approximately represent one year.
It then compares the current Liquidity Index to its value one year ago, calculating the YoY percentage change.
4. Momentum Oscillator on YoY Change
Oscillator Components:
1. Calculated using the Chande Momentum Oscillator (CMO) applied to the YoY percent change with a user-defined momentum length.
2. A weighted moving average (WMA) that smooths the momentum signal.
3. Overbought and Oversold zones
Signal Generation:
Buy Signal: Triggered when the momentum crosses upward from an oversold condition, suggesting a potential upward shift in liquidity momentum.
Sell Signal: Triggered when crosses below an overbought condition, indicating potential downward momentum.
State Management:
The indicator maintains a state variable to avoid repeated signals, ensuring that a new buy or sell signal is only generated when there’s a clear change in momentum.
5. Visual Presentation and Alerts
Plots:
The oscillator value and signalline are plotted for visual analysis.
Overbought and oversold levels are marked with dashed horizontal lines.
Signal Markers:
Buy and sell signals are marked with green and maroon circles, respectively.
Background Coloration:
Optionally, the chart’s background bars are colored (yellow for buy signals and fuchsia for sell signals) to enhance visual cues when signals are triggered.
Conclusion
In summary, the Liquidity Cycle Momentum Indicator provides a robust framework to analyze liquidity trends by combining global liquidity data, YoY changes, and momentum oscillation. This makes it an effective tool for traders and analysts looking to identify cyclical shifts in liquidity conditions and potential turning points in the market.
Dollar Cost Averaging (DCA) | FractalystWhat's the purpose of this strategy?
The purpose of dollar cost averaging (DCA) is to grow investments over time using a disciplined, methodical approach used by many top institutions like MicroStrategy and other institutions.
Here's how it functions:
Dollar Cost Averaging (DCA): This technique involves investing a set amount of money regularly, regardless of market conditions. It helps to mitigate the risk of investing a large sum at a peak price by spreading out your investment, thus potentially lowering your average cost per share over time.
Regular Contributions: By adding money to your investments on a pre-determined frequency and dollar amount defined by the user, you take advantage of compounding. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
Technical Analysis: The strategy employs a market trend ratio to gauge market sentiment. It calculates the ratio of bullish vs bearish breakouts across various timeframes, assigning this ratio a percentage-based score to determine the directional bias. Once this score exceeds a user-selected percentage, the strategy looks to take buy entries, signaling a favorable time for investment based on current market trends.
Fundamental Analysis: This aspect looks at the health of the economy and companies within it to determine bullish market conditions. Specifically, we consider:
Specifically, it considers:
Interest Rate: High interest rates can affect borrowing costs, potentially slowing down economic growth or making stocks less attractive compared to fixed income.
Inflation Rate: Inflation erodes purchasing power, but moderate inflation can be a sign of a healthy economy. We look for investments that might benefit from or withstand inflation.
GDP Rate: GDP growth indicates the overall health of the economy; we aim to invest in sectors poised to grow with the economy.
Unemployment Rate: Lower unemployment typically signals consumer confidence and spending power, which can boost certain sectors.
By integrating these elements, the strategy aims to:
Reduce Investment Volatility: By spreading out your investments, you're less impacted by short-term market swings.
Enhance Growth Potential: Using both technical and fundamental filters helps in choosing investments that are more likely to appreciate over time.
Manage Risk: The strategy aims to balance the risk of market timing by investing consistently and choosing assets wisely based on both economic data and market conditions.
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What are Regular Contributions in this strategy?
Regular Contributions involve adding money to your investments on a pre-determined frequency and dollar amount defined by the user. The script will remind you to contribute based on your chosen schedule, which can be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach ensures that your returns can earn their own returns, much like interest on savings but potentially at a higher rate.
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How do regular contributions enhance compounding and reduce timing risk?
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
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How does the strategy integrate technical and fundamental analysis for investors?
A: The strategy combines technical and fundamental analysis in the following manner:
Technical Analysis: It uses a market trend ratio to determine the directional bias by calculating the ratio of bullish vs bearish breakouts. Once this ratio exceeds a user-selected percentage threshold, the strategy signals to take buy entries, optimizing the timing within the given timeframe(s).
Fundamental Analysis: This aspect assesses the broader economic environment to identify sectors or assets that are likely to benefit from current economic conditions. By understanding these fundamentals, the strategy ensures investments are made in assets with strong growth potential.
This integration allows the strategy to select investments that are both technically favorable for entry and fundamentally sound, providing a comprehensive approach to investment decisions in the crypto, stock, and commodities markets.
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How does the strategy identify market structure? What are the underlying calculations?
Q: How does the strategy identify market structure?
A: The strategy identifies market structure by utilizing an efficient logic with for loops to pinpoint the first swing candle that features a pivot of 2. This marks the beginning of the break of structure, where the market's previous trend or pattern is considered invalidated or changed.
What are the underlying calculations for identifying market structure?
A: The underlying calculations involve:
Identifying Swing Points: The strategy looks for swing highs (marked with blue Xs) and swing lows (marked with red Xs). A swing high is identified when a candle's high is higher than the highs of the candles before and after it. Conversely, a swing low is when a candle's low is lower than the lows of the candles before and after it.
Break of Structure (BOS):
Bullish BOS: This occurs when the price breaks above the swing high level of the previous structure, indicating a potential shift to a bullish trend.
Bearish BOS: This happens when the price breaks below the swing low level of the previous structure, signaling a potential shift to a bearish trend.
Structural Liquidity and Invalidation:
Structural Liquidity: After a break of structure, liquidity levels are updated to the first swing high in a bullish BOS or the first swing low in a bearish BOS.
Structural Invalidation: If the price moves back to the level of the first swing low before the bullish BOS or the first swing high before the bearish BOS, it invalidates the break of structure, suggesting a potential reversal or continuation of the previous trend.
This method provides users with a technical approach to filter market regimes, offering an advantage by minimizing the risk of overfitting to historical data, which is often a concern with traditional indicators like moving averages.
By focusing on identifying pivotal swing points and the subsequent breaks of structure, the strategy maintains a balance between sensitivity to market changes and robustness against historical data anomalies, ensuring a more adaptable and potentially more reliable market analysis tool.
What entry criteria are used in this script?
The script uses two entry models for trading decisions: BreakOut and Fractal.
Underlying Calculations:
Breakout: The script records the most recent swing high by storing it in a variable. When the price closes above this recorded level, and all other predefined conditions are satisfied, the script triggers a breakout entry. This approach is considered conservative because it waits for the price to confirm a breakout above the previous high before entering a trade. As shown in the image, as soon as the price closes above the new candle (first tick), the long entry gets taken. The stop-loss is initially set and then moved to break-even once the price moves in favor of the trade.
Fractal: This method involves identifying a swing low with a period of 2, which means it looks for a low point where the price is lower than the two candles before and after it. Once this pattern is detected, the script executes the trade. This is an aggressive approach since it doesn't wait for further price confirmation. In the image, this is represented by the 'Fractal 2' label where the script identifies and acts on the swing low pattern.
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How does the script calculate trend score? What are the underlying calculations?
Market Trend Ratio: The script calculates the ratio of bullish to bearish breakouts. This involves:
Counting Bullish Breakouts: A bullish breakout is counted when the price breaks above a recent swing high (as identified in the strategy's market structure analysis).
Counting Bearish Breakouts: A bearish breakout is counted when the price breaks below a recent swing low.
Percentage-Based Score: This ratio is then converted into a percentage-based score:
For example, if there are 10 bullish breakouts and 5 bearish breakouts in a given timeframe, the ratio would be 10:5 or 2:1. This could be translated into a score where 66.67% (10/(10+5) * 100) represents the bullish trend strength.
The score might be calculated as (Number of Bullish Breakouts / Total Breakouts) * 100.
User-Defined Threshold: The strategy uses this score to determine when to take buy entries. If the trend score exceeds a user-defined percentage threshold, it indicates a strong enough bullish trend to justify a buy entry. For instance, if the user sets the threshold at 60%, the script would look for a buy entry when the trend score is above this level.
Timeframe Consideration: The calculations are performed across the timeframes specified by the user, ensuring the trend score reflects the market's behavior over different periods, which could be daily, weekly, or any other relevant timeframe.
This method provides a quantitative measure of market trend strength, helping to make informed decisions based on the balance between bullish and bearish market movements.
What type of stop-loss identification method are used in this strategy?
This strategy employs two types of stop-loss methods: Initial Stop-loss and Trailing Stop-Loss.
Underlying Calculations:
Initial Stop-loss:
ATR Based: The strategy uses the Average True Range (ATR) to set an initial stop-loss, which helps in accounting for market volatility without predicting price direction.
Calculation:
- First, the True Range (TR) is calculated for each period, which is the greatest of:
- Current Period High - Current Period Low
- Absolute Value of Current Period High - Previous Period Close
- Absolute Value of Current Period Low - Previous Period Close
- The ATR is then the moving average of these TR values over a specified period, typically 14 periods by default. This ATR value can be used to set the stop-loss at a distance from the entry price that reflects the current market volatility.
Swing Low Based:
For this method, the stop-loss is set based on the most recent swing low identified in the market structure analysis. This approach uses the lowest point of the recent price action as a reference for setting the stop-loss.
Trailing Stop-Loss:
The strategy uses structural liquidity and structural invalidation levels across multiple timeframes to adjust the stop-loss once the trade is profitable. This method involves:
Detecting Structural Liquidity: After a break of structure, the liquidity levels are updated to the first swing high in a bullish scenario or the first swing low in a bearish scenario. These levels serve as potential areas where the price might find support or resistance, allowing the stop-loss to trail the price movement.
Detecting Structural Invalidation: If the price returns to the level of the first swing low before a bullish break of structure or the first swing high before a bearish break of structure, it suggests the trend might be reversing or invalidating, prompting the adjustment of the stop-loss to lock in profits or minimize losses.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop. The ATR-based stop-loss adapts to the current market conditions by considering the volatility, ensuring that the stop-loss is not too tight during volatile periods, which could lead to premature exits, nor too loose during calm markets, which might result in larger losses. Similarly, the swing low based stop-loss provides a logical exit point if the market structure changes unfavorably.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance. This involves backtesting the strategy with different settings for the ATR period, the distance from the swing low, and how the trailing stop-loss reacts to structural liquidity and invalidation levels.
Through this process, you can tailor the strategy to perform optimally in different market environments, ensuring that the stop-loss mechanism supports the trade's longevity while safeguarding against significant drawdowns.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP
- You can choose to set a take profit level at which your position gets fully closed.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Strategy to Create a Profitable Edge and Systems?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
Incorporation of Fundamental Analysis:
This strategy integrates fundamental analysis by considering key economic indicators such as interest rates, inflation, GDP growth, and unemployment rates. These fundamentals help in assessing the broader economic health, which in turn influences sector performance and market trends. By understanding these economic conditions, the strategy can identify sectors or assets that are likely to thrive, ensuring investments are made in environments conducive to growth. This approach allows for a more informed investment decision, aligning technical entries with fundamentally strong market conditions, thus potentially enhancing the strategy's effectiveness over time.
Technical Analysis Without Classical Methods:
The strategy's technical analysis diverges from traditional methods like moving averages by focusing on market structure through a trend score system.
Instead of using lagging indicators, it employs a real-time analysis of market trends by calculating the ratio of bullish to bearish breakouts. This provides several benefits:
Immediate Market Sentiment: The trend score system reacts more dynamically to current market conditions, offering insights into the market's immediate sentiment rather than historical trends, which can often lag behind real-time changes.
Reduced Overfitting: By not relying on moving averages or similar classical indicators, the strategy avoids the common pitfall of overfitting to historical data, which can lead to poor performance in new market conditions. The trend score provides a fresh perspective on market direction, potentially leading to more robust trading signals.
Clear Entry Signals: With the trend score, entry decisions are based on a clear percentage threshold, making the strategy's decision-making process straightforward and less subjective than interpreting moving average crossovers or similar signals.
Regular Contributions and Reminders:
The strategy encourages regular investments through a system of predefined frequency and amount, which could be weekly, bi-weekly, monthly, quarterly, or yearly. This systematic approach:
Enhances Compounding: Regular contributions leverage the power of compounding, where returns on investments can generate their own returns, potentially leading to exponential growth over time.
Reduces Timing Risk: By investing regularly, the strategy minimizes the risk associated with trying to time the market, spreading out the investment cost over time and potentially reducing the impact of volatility.
Automated Reminders: The script reminds users to make contributions based on their chosen schedule, ensuring consistency and discipline in investment practices, which is crucial for long-term success.
Long-Term Wealth Building:
Focused on long-term wealth accumulation, this strategy:
Promotes Patience and Discipline: By emphasizing regular contributions and a disciplined approach to both entry and risk management, it aligns with the principles of long-term investing, discouraging impulsive decisions based on short-term market fluctuations.
Diversification Across Asset Classes: Operating across crypto, stocks, and commodities, the strategy provides diversification, which is a key component of long-term wealth building, reducing risk through varied exposure.
Growth Over Time: The strategy's design to work with the market's natural growth cycles, supported by fundamental analysis, aims for sustainable growth rather than quick profits, aligning with the goals of investors looking to build wealth over decades.
This comprehensive approach, combining fundamental insights, innovative technical analysis, disciplined investment habits, and a focus on long-term growth, offers a unique and potentially effective pathway for investors seeking to build wealth steadily over time.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Forward Curve Visualization ToolProvide the spot symbol and the futures product root, and the script automatically scans all relevant contracts for you—no more tedious manual searches. The result is a clean, intuitive chart showing the live forward curve in real time.
It also detects contango or backwardation conditions (based on spot < F1 < F2 < F3).
Future Features:
Plot historical snapshots of the curve (1 day, 1 week, or 1 month ago) to understand market trends over time.
Display additional metrics such as annualized basis, cost of carry (CoC), and even volume or open interest for deeper insights.
If you trade futures and watch the forward curve, this script will give you the actionable data you need and get more ideas or features you’d like to see. Let’s build them together!
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Blockchain Fundamentals: Liquidity & BTC YoYLiquidity & BTC YoY Indicator
Overview:
This indicator calculates the Year-over-Year (YoY) percentage change for two critical metrics: a custom Liquidity Index and Bitcoin's price. The Liquidity Index is derived from a blend of economic and forex data representing the M2 money supply, while the BTC price is obtained from a reliable market source. A dedicated limit(length) function is implemented to handle limited historical data, ensuring that the YoY calculations are available immediately—even when the chart's history is short.
Features Breakdown:
1. Limited Historical Data Workaround
- Functionality: limit(length) The function dynamically adjusts the lookback period when there isn’t enough historical data. This prevents delays in displaying YoY metrics at the beginning of the chart.
2. Liquidity Calculation
- Data Sources: Combines multiple data streams:
USM2, ECONOMICS:CNM2, USDCNY, ECONOMICS:JPM2, USDJPY, ECONOMICS:EUM2, USDEUR
- Formula:
Liquidity Index = USM2 + (CNM2 / USDCNY) + (JPM2 / USDJPY) + (EUM2 / USDEUR)
[b3. Bitcoin Price Calculation
- Data Source: Retrieves Bitcoin's price from BITSTAMP:BTCUSD on the user-selected timeframe for its historical length.
4. Year-over-Year (YoY) Percent Change Calculation
- Methodology:
- The indicator uses a custom function, to autodetect the proper number of bars, based on the selected timeframe.
- It then compares the current value to that from one year ago for both the Liquidity Index and BTC price, calculating the YoY percentage change.
5. Visual Presentation
- Plotting:
- The YoY percentage changes for Liquidity (plotted in blue) and BTC price (plotted in orange) are clearly displayed.
- A horizontal zero line is added for visual alignment, making it easier to compare the two copies of the metric. You add one copy and only display the BTC YoY. Then you add another copy and only display the M2 YoY.
-The zero lines are then used to align the scripts to each other by interposing them. You scale each chart the way you like, then move each copy individually to align both zero lines on top of each other.
This indicator is ideal for analysts and investors looking to monitor macroeconomic liquidity trends alongside Bitcoin's performance, providing immediate insights.
On-chain Zscore | QuantumResearchQuantumResearch On-chain Zscore Indicator
The On-chain Zscore Indicator by QuantumResearch is a cutting-edge tool designed for traders and analysts who leverage on-chain metrics to assess Bitcoin’s market conditions. This indicator calculates a composite Z-score using three key on-chain metrics: NUPL (Net Unrealized Profit/Loss), SOPR (Spent Output Profit Ratio), and MVRV (Market Value to Realized Value). By normalizing these values through standard deviations, the indicator provides a dynamic, data-driven approach to identifying overbought and oversold conditions, improving market timing and decision-making.
1. Overview
This indicator integrates multiple on-chain metrics to:
Assess Market Cycles – Utilize Z-score normalization to detect potential tops and bottoms.
Smooth Volatility – Apply EMA and standard deviation filtering to refine signals.
Identify Buy & Sell Signals – Use adaptive thresholds to highlight market extremes.
Provide Visual Clarity – Color-coded bar signals and background fills for intuitive analysis.
2. How It Works
A. Z-score Calculation
What is a Z-score? – The Z-score measures how far a data point deviates from its historical mean in terms of standard deviations. This helps in identifying statistical extremes.
Zscore(source,mean,std)=>
zscore = (source-mean)/std
zscore
Standard Deviation Normalization – Each on-chain metric (NUPL, SOPR, MVRV) is individually standardized before being combined into a final score.
B. On-Chain Components
NUPL Z-score – Measures unrealized profits and losses relative to market cycles.
SOPR Z-score – Evaluates profit-taking behavior on spent outputs.
MVRV Z-score – Assesses whether Bitcoin is overvalued or undervalued based on market cap vs. realized cap.
C. Composite On-chain Score
The indicator computes an average Z-score of the three on-chain metrics to create a composite market assessment.
Adaptive thresholds (default: 0.73 for bullish signals, -0.44 for bearish signals) dynamically adjust based on market conditions.
3. Visual Representation
This indicator features color-coded elements and dynamic threshold visualization:
Bar Colors
Green Bars – Bullish conditions when Z-score exceeds the upper threshold.
Red Bars – Bearish conditions when Z-score drops below the lower threshold.
Gray Bars – Neutral market conditions.
Threshold Bands & Background Fill
Upper Band (Overbought) – Default threshold set at 0.73.
Middle Band – Neutral zone at 0.
Lower Band (Oversold) – Default threshold set at -0.44.
4. Customization & Parameters
This indicator is highly configurable, allowing traders to fine-tune settings based on their strategy:
On-Chain Z-score Settings
NUPL Z-score Length – Default: 126 periods
SOPR Z-score Length – Default: 111 periods
MVRV Z-score Length – Default: 111 periods
Signal Thresholds
Upper Threshold (Bullish Zone) – Default: 0.73
Lower Threshold (Bearish Zone) – Default: -0.44
Color & Visual Settings
Choose from eight customizable color modes to suit personal preferences.
5. Trading Applications
The On-chain Zscore Indicator is versatile and can be applied in various market scenarios:
Macro Trend Analysis – Identify long-term market tops and bottoms using normalized on-chain metrics.
Momentum Confirmation – Validate price action trends with SOPR & MVRV behavior.
Market Timing – Use deviation thresholds to enter at historically significant price zones.
Risk Management – Avoid overextended markets by watching for extreme Z-score readings.
6. Final Thoughts
The QuantumResearch On-chain Zscore Indicator provides a unique approach to market evaluation by combining three critical on-chain metrics into a single, normalized score.
By standardizing Bitcoin’s market behavior, this tool helps traders and investors make informed decisions based on historical statistical extremes.
Backtesting and validation are essential before using this indicator in live trading. While it enhances market analysis, it should be used alongside other tools and strategies.
Disclaimer: No indicator can guarantee future performance. Always use appropriate risk management and perform due diligence before trading.
Global Liquidity IndexThis custom indicator provides a composite measure of global liquidity by combining key central bank balance sheet data with additional liquidity proxies. The script aggregates asset data from major economies—including the United States, Japan, China, and the Eurozone—converting non-USD values into U.S. dollars using real-time exchange rates. It then subtracts selected liability measures (such as reverse repurchase agreements and other adjustments) to approximate net central bank liquidity.
Key features include:
• Multi-Regional Coverage:
Incorporates data from the U.S. Federal Reserve, Bank of Japan, Chinese central bank proxies, and the European Central Bank, allowing you to gauge liquidity across major global markets.
• Dynamic Currency Conversion:
Uses live exchange rates (JPY/USD, CNY/USD, EUR/USD) to ensure that all regional figures are consistently expressed in U.S. dollars.
• Customizable Weighting:
Assign adjustable weights to each region’s data, so you can reflect economic size or your own view of their relative importance.
• Additional Liquidity Proxies:
Optionally integrates measures for global money supply and global credit/repo activity (or other proxies of your choice) with user-defined scaling factors.
• User-Friendly Configuration:
All key parameters—including weights and scaling factors—are available as inputs, making the indicator flexible and easy to tailor to your analysis needs.
This indicator is designed for traders and analysts seeking a broad view of global monetary conditions. Whether you’re tracking shifts in central bank policies or assessing global market liquidity, the Global Liquidity Index provides an insightful, customizable tool to help you visualize and interpret liquidity trends over time.
US vs EU Interest Rate SpreadThis script plots the difference (Spread) between the US-Interest Rate (Symbol USINTR) and the EU Interest Rate (Symbol: EUINTR) and plots it in a seperate pane. Areas where the background is green are times were the spread was positive (US interest rate higher than EU interest rate), a red background indicates a higher EU interest rate than US interest rate.
Blockchain Fundamentals: Global LiquidityGlobal Liquidity Indicator Overview
This indicator provides a comprehensive technical analysis of liquidity trends by deriving a Global Liquidity metric from multiple data sources. It applies a suite of technical indicators directly on this liquidity measure, rather than on price data. When this metric is expanding Bitcoin and crypto tends to bullish conditions.
Features:
1. Global Liquidity Calculation
Data Integration: Combines multiple market data sources using a ratio-based formula to produce a unique liquidity measure.
Custom Metric: This liquidity metric serves as the foundational input for further technical analysis.
2. Timeframe Customization
User-Selected Period: Users can select the data timeframe (default is 2 months) to ensure consistency and flexibility in analysis.
3. Additional Technical Indicators
RSI, Momentum, ROC, MACD, and Stochastic:
Each indicator is computed using the Global Liquidity series rather than price.
User-selectable toggles allow for enabling or disabling each individual indicator as desired.
4. Enhanced MACD Visualization
Dynamic Histogram Coloring:
The MACD histogram color adjusts dynamically: brighter hues indicate rising histogram values while darker hues indicate falling values.
When the histogram is above zero, green is used; when below zero, red is applied, offering immediate visual insight into momentum shifts.
Conclusion
This indicator is an enlightening tool for understanding liquidity dynamics, aiding in macroeconomic analysis and investment decision-making by highlighting shifts in liquidity conditions and market momentum.
Turtle Soup Model [PhenLabs]📊 Turtle Soup Model
Version: PineScript™ v6
Description
The Turtle Soup Model is an innovative technical analysis tool that combines market structure analysis with inter-market comparison and gap detection. Unlike traditional structure indicators, it validates market movements against a comparison symbol (default: ES1!) to identify high-probability trading opportunities. The indicator features a unique “soup pattern” detection system, comprehensive gap analysis, and real-time structure breaks visualization.
Innovation Points:
First indicator to combine structure analysis with gap detection and inter-market validation
Advanced memory management system for efficient long-term analysis
Sophisticated pattern recognition with multi-market confirmation
Real-time structure break detection with comparative validation
🔧 Core Components
Structure Analysis: Advanced pivot detection with inter-market validation
Gap Detection: Sophisticated gap identification and classification system
Inversion Patterns: “Soup pattern” recognition for reversal opportunities
Visual System: Dynamic rendering of structure levels and gaps
Alert Framework: Multi-condition notification system
🚨 Key Features 🚨
The indicator provides comprehensive analysis through:
Structure Levels: Validated support and resistance zones
Gap Patterns: Identification of significant market gaps
Inversion Signals: Detection of potential reversal points
Real-time Comparison: Continuous inter-market analysis
Visual Alerts: Dynamic structure break notifications
📈 Visualization
Structure Lines: Color-coded for highs and lows
Gap Boxes: Visual representation of gap zones
Inversion Patterns: Clear marking of potential reversal points
Comparison Overlay: Inter-market divergence visualization
Alert Indicators: Visual signals for structure breaks
💡Example
📌 Usage Guidelines
The indicator offers multiple customization options:
Structure Settings:
Pivot Period: Adjustable for different market conditions
Comparison Symbol: Customizable reference market
Visual Style: Configurable colors and line widths
Gap Analysis:
Signal Mode: Choice between close and wick-based signals
Box Rendering: Automatic gap zone visualization
Middle Line: Reference point for gap measurements
✅ Best Practices:
🚨Use comparison symbol from related market🚨
Monitor both structure breaks and gap inversions
Combine signals for higher probability trades
Pay attention to inter-market divergences
⚠️ Limitations
Requires comparison symbol data
Performance depends on market correlation
Best suited for liquid markets
What Makes This Unique
Inter-market Validation: Uses comparison symbol for signal confirmation
Gap Integration: Combines structure and gap analysis
Soup Pattern Detection: Identifies specific reversal patterns
Dynamic Structure Management: Automatically updates and removes invalid levels
Memory-Efficient Design: Optimized for long-term chart analysis
🔧 How It Works
The indicator processes market data through three main components:
1. Structure Analysis:
Detects pivot points with comparison validation
Tracks structure levels with array management
Identifies and processes structure breaks
2. Gap Analysis:
Identifies significant market gaps
Processes gap inversions
Manages gap zones visualization
3. Pattern Recognition:
Detects “soup” patterns
Validates with comparison market
Generates structure break signals
💡 Note: The indicator performs best when used with correlated comparison symbols and appropriate timeframe selection. Its unique inter-market validation system provides additional confirmation for traditional structure-based trading strategies.
Interest Rate & CPI Differential By King OsamaINTEREST RATE & CPI Differential Indicator By King Osama
A must-have tool for forex traders and macro analysts, this indicator tracks interest rate differentials, real interest rate gaps, and CPI (inflation) differences to provide a fundamental edge in trading.
Key Features:
✅ Interest Rate Differential (Rate Diff) – Measures the gap between base and quote currency interest rates. Higher rates attract capital, influencing currency strength. Ideal for carry trade opportunities.
✅ Real Interest Rate Differential (Real Rate Diff) – Adjusts interest rates for inflation (CPI) to reflect the true return on holding a currency. A more accurate indicator of long-term strength.
✅ CPI Differential (CPI Diff) – Compares inflation rates between two economies, helping traders anticipate central bank actions (rate hikes/cuts) based on inflation trends.
✅ Dynamic Table & Bias Signals – Clear LONG/SHORT indications, historical tracking, and real-time updates for macro-driven forex decisions.
🔹 Perfect for swing traders combining fundamentals with technicals! 🚀
Cross Market Ratio with Time DelayEnglish Description
Cross Market Ratio with Time Delay
This indicator computes the ratio between two market prices, where Ticker 1 is taken from the current chart and Ticker 2 is specified via the settings (for example, NYMEX:CL1! for crude oil). It features a configurable time delay (in days) for Ticker 2, making it ideal for analyzing cross-market relationships with a lag effect.
Key features include:
• Time Delay & Bars per Day: Apply a delay to Ticker 2’s price (calculated as delayDays × barsPerDay) so that you can study historical relationships between the two markets.
• Smoothing Options: Choose between SMA and EMA to smooth the ratio over a defined Evaluation Period. This helps reduce noise and highlight underlying trends.
• Z‑Score Calculation: The indicator calculates a z‑score based on the standard deviation of the ratio. This measures how many standard deviations the current ratio deviates from its moving average.
• Scaled Index: The z‑score is converted into a scaled index where 50 represents the average, and each standard deviation corresponds to 10 index points. The index is clamped between 0 and 100.
• Dynamic Normalization (Optional): When enabled, the indicator re‐scales the index dynamically over a chosen period to adapt to recent market conditions.
• Visual Aids: Horizontal reference lines at 70 (indicating potential overvaluation) and 30 (indicating potential undervaluation) are displayed. Optionally, you can also plot the raw ratio and the z‑score for deeper insight.
• Ticker Labels: Both ticker symbols are displayed on the chart (with an adjustable offset) to keep you informed about the instruments being compared.
This tool is especially useful for traders looking to explore inter-market dynamics and to identify potential divergences or shifts in relative performance.
Deutsche Beschreibung
Cross Market Ratio with Time Delay
Dieser Indikator berechnet das Verhältnis zwischen den Preisen zweier Märkte, wobei Ticker 1 aus dem aktuellen Chart übernommen wird und Ticker 2 in den Einstellungen frei wählbar ist (zum Beispiel NYMEX:CL1! für Öl). Dank eines einstellbaren Zeitversatzes (in Tagen) für Ticker 2 eignet er sich hervorragend zur Analyse marktübergreifender Zusammenhänge mit Verzögerungseffekt.
Wichtige Funktionen im Überblick:
• Zeitversatz & Balken pro Tag: Wenden Sie einen Zeitversatz auf den Preis von Ticker 2 an (berechnet als delayDays × barsPerDay), um historische Beziehungen zwischen den beiden Märkten zu untersuchen.
• Glättungsmethoden: Wählen Sie zwischen SMA und EMA, um das Verhältnis über einen definierten Evaluationszeitraum zu glätten. Dies reduziert das Rauschen und hebt die zugrunde liegenden Trends hervor.
• Berechnung des Z‑Scores: Der Indikator ermittelt den z‑Score basierend auf der Standardabweichung des Verhältnisses. Dieser zeigt an, um wie viele Standardabweichungen der aktuelle Wert vom gleitenden Durchschnitt abweicht.
• Skalierter Index: Der z‑Score wird in einen Index umgerechnet, bei dem 50 dem Durchschnitt entspricht und jede Standardabweichung 10 Indexpunkte ausmacht. Der Index wird dabei auf einen Bereich von 0 bis 100 begrenzt.
• Dynamische Normalisierung (Optional): Bei Aktivierung passt der Indikator den Index dynamisch über einen festgelegten Zeitraum an die aktuellen Marktbedingungen an.
• Visuelle Unterstützung: Horizontale Referenzlinien bei 70 (potenzielle Überbewertung) und 30 (potenzielle Unterbewertung) werden angezeigt. Zusätzlich können Sie optional den Rohwert des Ratios und den z‑Score mitplotten.
• Ticker-Labels: Beide Ticker-Symbole werden im Chart (mit einstellbarem Versatz) angezeigt, sodass Sie stets wissen, welche Instrumente verglichen werden.
Dieser Indikator eignet sich besonders für Trader, die intermarktliche Dynamiken analysieren und potenzielle Divergenzen oder Veränderungen in der relativen Performance frühzeitig erkennen möchten.
Precious Metals & GSR (Zeiierman)█ Overview
The Precious Metals & GSR (Zeiierman) is designed to provide traders and investors with a comprehensive view of the Gold-Silver Ratio (GSR) and other precious metal relationships. This tool helps evaluate the relative strength between different metals by analyzing their price ratios over historical periods, using quantile-based analysis and trend interpretation tables to highlight key insights.
The Gold-Silver Ratio (GSR) is a widely utilized metric in precious metals trading, representing the number of silver ounces required to purchase one ounce of gold. Historically, this ratio has fluctuated, providing traders with insights into the relative value of these two metals. By analyzing the GSR, traders can identify potential trading opportunities based on historical patterns and market dynamics.
By integrating customizable percentile bands, gradient coloring for performance visualization, and dynamic ratio analysis, this indicator assists in understanding how one metal is performing relative to another, making it useful for trend tracking, risk management, and portfolio allocation.
█ How It Works
The Precious Metals & GSR Indicator operates by fetching the latest prices of the selected precious metals in the user's chosen currency. It then calculates the ratio between two selected metals (Metal 1 and Metal 2) and analyzes this ratio over a specified period. By computing quantile bands and high/low bands, the indicator provides insights into the historical performance and current standing of the ratio.
⚪ Ratio Calculation
The core of this indicator is the metal ratio, calculated by dividing the price of Metal 1 by Metal 2.
A rising ratio means Metal 1 is outperforming Metal 2.
A falling ratio means Metal 2 is outperforming Metal 1.
The indicator automatically retrieves live market prices of Gold, Silver, Platinum, and Palladium to compute the ratio.
⚪ Quantile Ratio Bands
The indicator calculates the highest (max) and lowest (min) ratio levels over a user-defined period.
It also plots quantile bands at the 10th, 25th, 50th (median), 75th, and 90th percentiles, providing deeper statistical insights into how extreme or average the current ratio is.
The median (Q50) acts as a reference level, showing whether the ratio is above or below its historical midpoint.
⚪ Interpretation Table
The Ratio Interpretation Table provides a text-based summary of the ratio’s strength.
It detects whether Metal 1 is at a historical high, low, or within common ranges.
This helps traders and investors make informed decisions on whether the ratio is overextended, mean-reverting, or trending.
⚪ Precious Metals Table
Displays live market prices for Gold, Silver, Platinum, and Palladium.
Prices are shown in different units (oz, kg, grams, and troy ounces) based on user preferences.
A color-coded system highlights price changes, making it easier to track market movements.
⚪ Physical Holding Calculator
Users can enter their precious metal holdings to estimate their current value.
The system adjusts calculations based on weight, purity (24K, 22K, etc.), and unit of measurement.
The holding value is displayed in the selected currency (USD, EUR, GBP, etc.).
█ How to Use
⚪ Trend Identification
If the ratio is increasing, Metal 1 is gaining strength relative to Metal 2 → Possible Long Position on Metal 1 / Short on Metal 2
If the ratio is decreasing, Metal 2 is gaining strength relative to Metal 1 → Possible Short Position on Metal 1 / Long on Metal 2
⚪ Mean Reversion Strategy
When the ratio reaches the 90th percentile, Metal 1 is historically overextended (expensive) compared to Metal 2.
Traders may look to sell Metal 1 and buy Metal 2, expecting the ratio to decline back toward its historical average.
Example (Gold/Silver Ratio): If the GSR is above the 90th percentile, gold is very expensive relative to silver, suggesting a potential buying opportunity in silver and/or a selling opportunity in gold.
When the ratio reaches the 10th percentile, Metal 1 is historically undervalued (cheap) compared to Metal 2.
Traders may look to buy Metal 1 and sell Metal 2, expecting the ratio to rise back toward its historical average.
Example (Gold/Silver Ratio): If the GSR is below the 10th percentile, gold is very cheap relative to silver, suggesting a potential buying opportunity in gold and/or a selling opportunity in silver.
⚪ Common Strategy Based on GSR Insights
A common approach involves monitoring the ratio for extreme values based on historical data. When the ratio reaches historically high levels, it suggests that gold is expensive relative to silver, potentially indicating a buying opportunity for silver and/or a selling opportunity for gold. Conversely, when the ratio is at historically low levels, silver is expensive relative to gold, suggesting a potential buying opportunity for gold and/or selling opportunity for silver. This mean-reversion strategy relies on the tendency of the GSR to return to its historical average over time.
⚪ Hedging & Portfolio Diversification
If Gold is strongly outperforming Silver, investors may shift allocations to balance risk.
If Silver is rapidly gaining on Gold, it may indicate increased industrial demand or speculative interest.
⚪ Inflation & Economic Cycles
A rising Gold-Silver ratio often correlates with economic downturns and increased risk aversion.
A falling Gold-Silver ratio may signal stronger economic growth and higher inflation expectations.
█ Settings
Precious Metals Table
Select which metals to display (Gold, Silver, Platinum, Palladium)
Choose measurement units (oz, kg, grams, troy ounces)
Ratio Analysis
Select Metal 1 & Metal 2 for ratio calculation
Set historical length for quantile calculations
Interpretation Table
Enable automated insights based on ratio levels
Physical Holdings Calculator
Enter metal weight, purity, and unit
Select calculation currency
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Simple Time-Based Strategy(Price Action Hypothesis)Core Theory: Trend Continuation Pattern Recognition**
1. **Price Action Hypothesis**
The strategy is built on the assumption that consecutive price movements (3-bar patterns) indicate momentum continuation:
- *Long Pattern*: Three consecutive higher closes combined with ascending highs
- *Short Pattern*: Three consecutive lower closes combined with descending lows
This reflects a belief that sustained directional price movement creates self-reinforcing trends that can be captured through simple pattern recognition.
2. **Time-Based Risk Management**
Implements a dynamic exit mechanism:
- *Training Phase*: 5-bar holding period (quick turnover)
- *Testing Phase*: 10-bar holding period (extended exposure)
This dual timeframe approach suggests the hypothesis that market conditions may require different holding durations in different market eras.
3. **Adaptive Market Hypothesis**
The structure incorporates two distinct phases:
- *Training Period (11 years)*: Pattern recognition without stop losses
- *Testing Period*: Pattern recognition with stop losses
This assumes markets may change character over time, requiring different risk parameters in different epochs.
4. **Asymmetric Risk Control**
Implements stop-losses only in the testing phase:
- Fixed 500-pip (point) stop distance
- Activated post-training period
This reflects a belief that historical patterns might need different risk constraints than real-time trading.
5. **Dual-Path Validation**
The split between training/testing phases suggests:
- Pattern validity should first be confirmed without protective stops
- Real-world implementation requires added risk constraints
6. **Market Efficiency Paradox**
The simultaneous use of both long/short entries assumes:
- Markets exhibit persistent inefficiencies
- These inefficiencies manifest differently in bullish/bearish conditions
- A symmetric approach can capture opportunities in both directions
7. **Behavioral Finance Elements**
The 3-bar pattern recognition potentially exploits:
- Herd mentality in trend formation
- Delayed reaction to price momentum
- Cognitive bias in trend confirmation
8. **Quantitative Time Segmentation**
The annual-based period division (training vs testing) implies:
- Market cycles operate on multi-year timeframes
- Strategy robustness requires validation across different market regimes
- Parameter sensitivity needs temporal validation
This strategy combines elements of technical pattern recognition, temporal adaptability, and phased risk management to create a systematic approach to trend exploitation. The theoretical framework suggests markets exhibit persistent but evolving patterns that can be systematically captured through rule-based execution.
Prev Day & Curr Day H/L + Opening Range (9:30, 5min)Script Description:
This TradingView Pine Script is designed for use on a 5‑minute chart and plots key price levels for daily trading analysis. It automatically draws:
• Previous Day High/Low Lines:
These lines mark the previous day’s regular trading hours (RTH) high and low levels, with labels (“PDH” and “PDL”) for easy identification.
• Current Day High/Low Lines:
As the trading day progresses, the script updates and displays the current day’s RTH high and low levels, labeled as “CDH” and “CDL”.
• Opening Range for 9:30 AM:
The script specifically identifies the first 5‑minute candle at 9:30 AM (using the “America/New_York” time zone) and draws two additional lines at its high and low. These lines are labeled “HighOpen” and “LowOpen” to indicate the opening range.
All lines are drawn with a width of 5 and have configurable colors, styles, and extension lengths. The script automatically resets at the start of each new day, ensuring that the plotted levels are current and relevant for daily trading decisions.
Draw on Liquidity [PhenLabs]📊 Draw on Liquidity (DOL) Indicator
Version: PineScript™ v6
Description
The Draw on Liquidity (DOL) indicator is an advanced technical analysis tool designed to identify and visualize significant liquidity zones in the market. It combines volume analysis, pivot point detection, and real-time proximity alerts to help traders identify potential support and resistance levels where significant trading activity occurs. The indicator features dual display modes, adaptive volume thresholds, and a comprehensive real-time dashboard.
🔧 Components
• Liquidity Detection: Advanced pivot point analysis with volume validation
• Volume Analysis: Adaptive volume threshold system
• Display Modes: Historical and Current visualization options
• Proximity Detection: Real-time price-to-level distance monitoring
• Visual Dashboard: Dynamic status display with alert system
🚨 Important Dashboard Features 🚨
The dashboard provides real-time information about:
• High Draw Zones: Resistance levels with significant liquidity
• Low Draw Zones: Support levels with high trading activity
• Current Price: Real-time price monitoring
• Active Alerts: Proximity warnings when price approaches liquidity zones
📈 Visualization
• Historical Mode: Displays all past and present liquidity zones
• Current Mode: Shows only active, unhit liquidity levels
• Color-coded lines: Blue for high liquidity, Red for low liquidity
• Dynamic line extension: Updates with price movement
• Alert indicators: Visual signals when price approaches zones
Historical Visualization
Current Visualization
📌 Usage Guidelines
The indicator is highly customizable with several key parameters:
Pivot Settings:
• Shorter lengths (3-7): More frequent zones, suitable for scalping
• Longer lengths (7-15): Major zones, better for swing trading
Volume Analysis:
• Lower multiplier (1.5-2.0): More zones, higher sensitivity
• Higher multiplier (2.0-3.0): Major zones only, reduced noise
✅ Best Practices:
• Start with default settings and adjust based on timeframe
• Use Historical mode for analysis, Current mode for active trading
• Monitor dashboard alerts for potential trade setups
• Combine with trend analysis for better entry/exit points
⚠️ Limitations
• Requires sufficient volume data for accurate analysis
• Performance varies with market volatility
• Historical mode may become visually cluttered on longer timeframes
• Best performance during regular market hours
What Makes This Unique
• Dual Display System: Choose between historical analysis and current trading modes
• Volume-Validated Zones: Only marks levels with significant trading activity
• Real-time Proximity Alerts: Dynamic warnings when approaching liquidity zones
• Adaptive Threshold System: Automatically adjusts to market conditions
• Comprehensive Dashboard: All-in-one view of current market status
🔧 How It Works
The indicator processes market data through three main components:
1. Liquidity Detection (40% weight):
• Identifies pivot points using customizable lookback periods
• Validates levels with volume analysis
• Marks significant zones based on combined criteria
2. Volume Analysis (40% weight):
• Calculates dynamic volume thresholds
• Compares current volume to moving average
• Filters out low-volume noise
3. Proximity Analysis (20% weight):
• Monitors price distance to active zones
• Triggers alerts based on customizable thresholds
• Updates dashboard status in real-time
💡 Note: For optimal results, combine with price action analysis and consider using multiple timeframes for confirmation. The indicator performs best in markets with consistent volume and clear trend structure.
Market Cap & Volume Tracker with TrendsMarket Cap & Volume Tracker with Trends
This indicator provides a compact, at-a-glance view of key market data directly on your chart, specifically focusing on Market Cap, Volume, and Volume Trends over various time intervals. It helps traders and investors monitor price action and volume shifts in real-time.
Key Features:
Market Cap: Displays the market capitalization of the selected asset, calculated as the Close Price multiplied by Volume, and formatted in Millions (M).
Volume Data: Shows the volume for:
Pre-market (the volume before the main market session starts)
Current Volume (real-time volume during the current session)
After-hours Volume (volume traded after the market closes)
All volumes are formatted in Thousands (K) for easy readability.
Volume Trends: Monitors volume movement across multiple time intervals:
15-Minutes
30-Minutes
45-Minutes
1 Hour
The indicator tracks whether the volume is Increasing or Decreasing in each of these time frames to help identify trends and potential market shifts.
Customization:
Easily adjustable colors for the table background, text, and header for clear visibility and user preferences.
Option to choose the display position of the table (top or bottom right corner).
Use Case:
This indicator is ideal for traders who want quick insights into the market's activity without the need to look at multiple charts or external data points. It helps spot volume changes and trends over various time frames and supports decision-making for entries, exits, and overall market sentiment.
Macro-Sentiment Index Model (MSIM)Macro-Sentiment Index Model (MSIM) is a comprehensive trading strategy developed to analyze and interpret the broader macroeconomic and market sentiment. The strategy integrates various quantitative signals, including market volatility, trading volume, market breadth, and economic indicators, to assess the prevailing mood in the financial markets. This sentiment analysis is then used to guide trading decisions, helping identify optimal entry and exit points based on underlying market conditions. The model is specifically designed to capture the shifts in investor sentiment, which have been shown to significantly influence market behavior (Fleming et al., 2001).
The MSIM utilizes a multi-faceted approach to measure sentiment. Drawing from the theory that macroeconomic variables can influence financial markets (Stock & Watson, 2002), the strategy incorporates market volatility (VIX), volume measures, and long-term market trends. These indicators help form a robust view of the market’s risk appetite and potential for price movement. For instance, high volatility often signals increased market uncertainty (Bollerslev, 1986), while volume-based indicators provide insights into investor conviction (Chen, 1991).
Additionally, the model incorporates macroeconomic proxies like GDP growth, interest rates, and unemployment data, leveraging the findings of macroeconomic studies that indicate a direct correlation between these factors and market performance (Hamilton, 1994). By normalizing these economic indicators, the model provides a standardized sentiment score that reflects the aggregated impact of these factors on the market’s outlook.
The MSIM aims to exploit market inefficiencies by responding to shifts in sentiment before they manifest in price movements. Studies have shown that sentiment indicators, such as the Advance-Decline Line and the Stock-Bond Ratio, can be predictive of future price movements (Neely, 2010). The model integrates these indicators into a single composite sentiment score, which is then filtered through momentum signals to refine entry points. This approach is grounded in behavioral finance theory, which suggests that investor sentiment plays a crucial role in driving asset prices, sometimes beyond the reach of fundamental data alone (Shiller, 2000).
The strategy is designed to identify long opportunities when sentiment is particularly favorable, with a focus on minimizing risk during adverse conditions. By analyzing market trends alongside macroeconomic signals, the MSIM helps traders stay aligned with the prevailing market forces.
References:
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Chen, S. S. (1991). The determinants of stock market liquidity. Journal of Financial and Quantitative Analysis, 26(3), 283-305.
• Fleming, M. J., Kirby, C. W., & Ostdiek, B. (2001). The economic value of volatility timing. Journal of Financial and Quantitative Analysis, 36(1), 113-134.
• Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
• Neely, C. J. (2010). The behavior of exchange rates: A survey of recent empirical literature. International Finance Discussion Papers, 981.
• Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press.
• Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
