Enhanced Doji Candle StrategyYour trading strategy is a Doji Candlestick Reversal Strategy designed to identify potential market reversals using Doji candlestick patterns. These candles indicate indecision in the market, and when detected, your strategy uses a Simple Moving Average (SMA) with a short period of 20 to confirm the overall market trend. If the price is above the SMA, the trend is considered bullish; if it's below, the trend is bearish.
Once a Doji is detected, the strategy waits for one or two consecutive confirmation candles that align with the market trend. For a bullish confirmation, the candles must close higher than their opening price without significant bottom wicks. Conversely, for a bearish confirmation, the candles must close lower without noticeable top wicks. When these conditions are met, a trade is entered at the market price.
The risk management aspect of your strategy is clearly defined. A stop loss is automatically placed at the nearest recent swing high or low, with a tighter distance of 5 pips to allow for more trading opportunities. A take-profit level is set using a 2:1 reward-to-risk ratio, meaning the potential reward is twice the size of the risk on each trade.
Additionally, the strategy incorporates an early exit mechanism. If a reversal Doji forms in the opposite direction of your trade, the position is closed immediately to minimize losses. This strategy has been optimized to increase trade frequency by loosening the strictness of Doji detection and confirmation conditions while still maintaining sound risk management principles.
The strategy is coded in Pine Script for use on TradingView and uses built-in indicators like the SMA for trend detection. You also have flexible parameters to adjust risk levels, take-profit targets, and stop-loss placements, allowing you to tailor the strategy to different market conditions.
Analisi fondamentale
Quantum Liquidity Fractal Dynamics (QLFD) v2.1The Quantum Liquidity Fractal Dynamics (QLFD) v2.1 is an advanced multi-dimensional market analysis too l engineered for professional traders seeking to identify high-probability liquidity-driven reversals. Built upon a proprietary Fractal-Liquidity Convergence Model (FLCM), QLFD v2.1 leverages quantum-phase liquidity oscillations and institutional absorption mapping to dynamically assess order flow efficiency within multi-timeframe market structures.
Core Algorithmic Methodology
QLFD v2.1 integrates a Hybridized Recursive Liquidity Matrix (HRLM) with High-Frequency Adaptive EMA Displacement (HFAED) to model non-linear liquidity density clusters. This proprietary framework is further reinforced by a Multi-Layered RSI Vorticity Filter (MLRVF), enhancing the signal integrity by filtering out stochastic noise anomalies.
The EMA-200 Rejection Dynamics, combined with the Vortex RSI Momentum Refraction Index (VRMRI), allow the system to isolate institutional footprint imbalances. By capturing transient liquidity voids and microstructure inefficiencies, QLFD v2.1 enables traders to position themselves ahead of high-probability liquidity sweeps.
Signal Efficiency & Institutional Calibration
While QLFD v2.1 exhibits an exceptionally high accuracy rate in identifying potential reversal vectors, it is imperative for traders to exercise institutional-grade signal filtration. The indicator autonomously detects Phase-Induced False Signal Clusters (PIFSCs), yet discretion remains paramount in avoiding transient liquidity mirages—a common occurrence in markets exhibiting hyper-fractalized liquidity dislocations.
For optimal performance, professional traders must apply a Multi-Stage Confirmation Protocol (MSCP), leveraging additional confluence layers such as:
Order Flow Delta Cohesion (OFDC)
Gamma-Weighted Imbalance Deviation (GWID)
Synthetic Volume Shockwave Ratio (SVSR)
These advanced methodologies ensure that traders engage only with high-probability fractal reversals, filtering out structurally unreliable signals induced by inter-market arbitrage distortions.
Final Thoughts
QLFD v2.1 is not designed for retail-grade signal chasing. It is an institutional-grade analytical framework tailored for professionals who understand the fractal complexity of modern liquidity landscapes. Mastering the art of discretionary filtration—by distinguishing true liquidity-driven reversals from algorithmically-induced decoy impulses—is the key to leveraging this system’s full potential.
Stock Earnings Viewer for Pine ScreenerThe script, titled "Stock Earnings Viewer with Surprise", fetches actual and estimated earnings, calculates absolute and percent surprise values, and presents them for analysis. It is intended to use in Pine Screener, as on chart it is redundant.
How to Apply to Pine Screener
Favorite this script
Open pine screener www.tradingview.com
Select "Stock Earnings Viewer with Surprise" in "Choose indicator"
Click "Scan"
Data
Actual Earnings: The reported earnings per share (EPS) for the stock, sourced via request.earnings().
Estimated Earnings: Analyst-predicted EPS, accessed with field=earnings.estimate.
Absolute Surprise: The difference between actual and estimated earnings (e.g., actual 1.2 - estimated 1.0 = 0.2).
Percent Surprise (%): The absolute surprise as a percentage of estimated earnings (e.g., (0.2 / 1.0) * 100 = 20%). Note: This may return NaN or infinity if estimated earnings are zero, due to division by zero.
Practical Use
This screener script allows users to filter stocks based on earnings metrics. For example, you could screen for stocks where Percent Surprise > 15 to find companies exceeding analyst expectations significantly, or use Absolute Surprise < -0.5 to identify underperformers.
EPS Line Indicator - cristianhkrOverview
The EPS Line Indicator displays the Earnings Per Share (EPS) of a publicly traded company directly on a TradingView chart. It provides a historical trend of EPS over time, allowing investors to track a company's profitability per share.
Key Features
📊 Plots actual EPS data for the selected stock.
📅 Updates quarterly as new EPS reports are released.
🔄 Smooths missing values by holding the last reported EPS.
🔍 Helps track long-term profitability trends.
How It Works
The script retrieves quarterly EPS using request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "Q", barmerge.gaps_off).
If EPS data is missing for a given period, the last available EPS value is retained to maintain continuity.
The EPS values are plotted as a continuous green line on the chart.
A baseline at EPS = 0 is included to easily identify profitable vs. loss-making periods.
How to Use This Indicator
If the EPS line is trending upwards 📈 → The company is growing earnings per share, a strong sign of profitability.
If the EPS line is declining 📉 → The company’s EPS is shrinking, which may indicate financial weakness.
If EPS is negative (below zero) ❌ → The company is reporting losses per share, which can be a warning sign.
Limitations
Only works with stocks that report EPS data (not applicable to cryptocurrencies or commodities).
Does not adjust for stock splits or other corporate actions.
Best used on daily, weekly, or monthly charts for clear earnings trends.
Conclusion
This indicator is a powerful tool for investors who want to visualize earnings per share trends directly on a price chart. By showing how EPS evolves over time, it helps assess a company's profitability trajectory, making it useful for both fundamental analysis and long-term investing.
🚀 Use this indicator to track EPS growth and make smarter investment decisions!
CAPE / Shiller PE Ratio - cristianhkrThe Cyclically Adjusted Price-to-Earnings Ratio (CAPE Ratio), also known as the Shiller P/E Ratio, is a long-term valuation measure for stocks. It was developed by Robert Shiller and smooths out earnings fluctuations by using an inflation-adjusted average of the last 10 years of earnings.
This TradingView Pine Script indicator calculates the CAPE Ratio for a specific stock by:
Fetching historical Earnings Per Share (EPS) data using request.earnings().
Adjusting the EPS for inflation by dividing it by the Consumer Price Index (CPI).
Computing the 10-year (40-quarter) moving average of the inflation-adjusted EPS.
Calculating the CAPE Ratio as (Stock Price) / (10-year Average EPS adjusted for inflation).
Plotting the CAPE Ratio on the chart with a reference line at CAPE = 20, a historically significant threshold.
Revenue & Net IncomeRevenue & Net Income Indicator
This indicator provides a clear visual representation of a company's revenue and net income, with the flexibility to switch between Trailing Twelve Months (TTM) and Quarterly data. Values are automatically converted into billions and displayed in both an area chart and a dynamic table.
Features:
TTM & Quarterly Data: Easily toggle between financial periods.
Intuitive Visuals: Semi-transparent area charts make trends easy to spot.
Smart Number Formatting: Revenue below 1B is shown with two decimals (e.g., "0.85B"), while larger values use one decimal (e.g., "1.2B").
Customizable Table: Displays the most recent revenue and net income figures, with adjustable position and text size.
Light Mode: Switch table text to black with a white header for better readability on light backgrounds.
This indicator is freely available and open-source on TradingView for all. It is designed to help traders enhance their market analysis and strategic decision-making.
Trading Sessions Background ColorTrading Sessions Background Color
This indicator provides a visual representation of the major trading sessions — Asia, London, and USA — by applying distinct background colors to the chart. It allows traders to easily identify active market hours and session overlaps.
Features:
Customizable Sessions: Users can modify time ranges, and colors according to their preferences.
Predefined Major Trading Sessions: The indicator includes Asia, London, and USA sessions by default.
Time Zone Adjustment: A configurable UTC offset ensures accurate session display.
Clear Visual Differentiation: Background colors indicate when each session is active.
Usage Instructions:
Apply the indicator to a TradingView chart.
Adjust session settings and time zone offset as needed.
The chart background will update dynamically to reflect the active trading session.
Monthly Buy IndicatorIt shows us the the total balance when buying monthly, ploting the total invested amount and total current balance along the time.
Opening the Data Window, it displays the profit (%) and the number of trades.
The "Allow Fractional Purchase" flag can be used to check the the performance of the ticker, disregarding how much the monthly amount is set vs the price of the ticker.
The trades are considering buying the available amount on the 1st candle of each month, at the Open price. The "Total Balance" considers the close price of each candle.
RV- Intrinsic Value AnalyzerWhy These Metrics Matter in IVA Pro (Intrinsic Value Analyzer)?
The IVA Pro consolidates key valuation, profitability, and efficiency metrics into a single, easy-to-read table. These indicators provide a comprehensive view of a company’s financial health, helping traders and investors make informed decisions based on growth potential, profitability, and valuation. The color-coded signals (green for strong, orange for moderate, and red for weak values) simplify fundamental analysis and enable quick comparisons across different stocks.
Key Fundamental Parameters in IVA Pro
Market Capitalization (Market Cap): Measures a company's total market value, helping assess size, stability, and growth potential.
Earnings Yield (TTM): Indicates how much profit a company generates relative to its stock price—useful for comparing against bonds and other assets.
Return on Capital Employed (ROCE): Shows how efficiently a company generates profits using its capital—a key profitability metric.
Return on Equity (ROE): Evaluates how well a company uses shareholder funds to generate earnings.
Price-to-Earnings Ratio (PE): Helps determine whether a stock is overvalued or undervalued based on earnings.
Price-to-Book Ratio (PB): Assesses if a stock is trading above or below its net asset value—useful for asset-heavy industries.
Price-to-Sales Ratio (PS): Helps evaluate revenue potential, particularly for growth-stage companies.
PEG Ratio: Enhances PE ratio by factoring in earnings growth—ideal for identifying undervalued growth stocks.
Forward PE Ratio: Provides a future-looking valuation based on projected earnings.
Forward PS Ratio: Helps evaluate future revenue potential and overall stock valuation.
Value Area - Day Trading SuiteValue Area Day Trading Suite
A professional-grade indicator designed specifically for day traders who utilize Volume Profile and Auction Market Theory. This suite provides tracking of previous day's value areas, helping traders identify how current price interacts with these established institutional levels.
It tracks how much time has spent within the value area without Level to Help Measure Acceptance
Key Features
- Previous Day's Value Area tracking (VAH, POC, VAL)
- Precise time-in-value-area measurement
- Cash session integration with major market timezones
- Value Area acceptance alerts
Trading Applications
Perfect for day traders who:
- Trade using previous day's Volume Profile levels
- Focus on institutional price acceptance/rejection
- Trade market structure using confirmed value areas
- Want to automate their value area analysis
- Trade during specific market sessions
Value Area Analysis
The indicator tracks how long price stays within the previous day's value area, helping traders:
- Identify Potential Support / Resistance Levels
- Spot acceptance of established levels
- Find high-probability trading opportunities
- Time their entries and exits more effectively
Professional Tools
- Customizable cash session times for different markets
- Multiple timezone support
- Flexible dashboard positioning
- Clean, professional appearance with adjustable colors
- Alert system for value area acceptance
Built for day traders who utilize value areas
Tolga's Market Radar - DashboardTolga's Market Radar - Dashboard – The Only TradingView Dashboard Showing Both Daily Performance & Daily Range Together.
This unique dashboard concisely displays both the daily percentage change and the daily range position for up to 14 user-selected tickers, all in one clear, easy-to-follow table.
A customizable dashboard to track daily percentage changes and daily range positions for up to 14 user‐defined symbols. This script displays a color‐coded table (teal for bullish moves, red for bearish moves) that updates in real‐time, helping you quickly gauge market conditions across various assets.
Key Features:
Dual Metrics: Simultaneously shows daily performance (percentage change) and range position.
Customizable: Set your preferred tickers and labels, with an option to invert for quote-driven pairs.
Color-Coded Clarity: Uses a teal/red scheme to instantly highlight bullish or bearish moves.
Flexible Timeframes: Adjust the resolution to suit your trading strategy.
Ideal for traders who want a compact, real-time snapshot of market moves without clutter. Enjoy a streamlined view that uniquely combines both performance and range data in one dashboard!
Two Measurements
Daily % Change: (Close - Previous Close) / Previous Close × 100.
Daily Range Position: Where today’s close sits relative to the day’s high/low, expressed as a percentage (0% = at low, 100% = at high).
Configure Symbols
For each row (Group 1 for the top row, Group 2 for the bottom row), enter:
Symbol Label: The text you want displayed in the table header.
Symbol Ticker: The TradingView ticker (e.g., FX:AUDUSD, TVC:USOIL, etc.).
Invert?: Toggle this on if you want to flip the percentage change (for pairs like USDCAD, turning it into CAD’s perspective).
Interpretation of the Table
Each cell shows two lines:
Daily % Change (top line).
Daily Range Position (bottom line).
The background color instantly tells you if the asset is bullish or bearish and whether the close is near the top or bottom of the day’s range.
Notes & Disclaimers:
This script is designed for informational and educational purposes. Always do your own due diligence before making trading decisions.
Performance may vary depending on your TradingView plan and the number of tickers you request.
MosaicMix V3 | QuantEdgeBIntroducing MosaicMix V3 by QuantEdgeB
🔹 Overview
MosaicMix V3 is an advanced multi-dimensional trend-following indicator designed for investors, traders, and analysts looking to assess market cycles, identify strategic entry/exit points, and optimize risk management. This system integrates on-chain data, macroeconomic insights, and technical analysis into a unified scoring model to provide a data-driven approach to market decision-making.
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🛠️ Who is it for?
✔ Long-Term Investors – Helps in understanding deep-cycle valuation trends for Bitcoin and other assets highly influenced by global macroeconomic dynamics.
✔ Swing & Position Traders – Offers trend-based buy and sell signals, combining risk-adjusted momentum with on chain indicators.
✔ Risk-Averse Traders – Uses a systematic approach to risk quantification, making it useful for those aiming to avoid high-risk trades.
✔ Long-Term Horizon – Tailored for those managing positions over months to years, particularly in global risk assets like Bitcoin, where macroeconomic and market cycle dynamics play a crucial role.
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🔑 Key Features
✔ 📊 On-Chain Intelligence – Incorporates fundamental blockchain metrics, including MVRV momentum, profit/loss ratios, and MVRV Rate of Change, to assess valuation trends and market strength.
✔ 🌍 Macro & Risk Integration – Uses global economic indicators such as China’s equity index, PMI data, Baltic Dry Index, and the Silver-to-BTC ratio to capture external market influences.
✔ 📈 Adaptive Trend Identification – Implements DEMA, WMA, and EMA smoothing techniques to refine trend direction and momentum shifts, reducing noise and improving trade reliability.
✔ 📏 Risk-Weighted Mosaic Model – Merges technical and fundamental indicators into a single, risk-adjusted composite score, enabling a structured assessment of market conditions.
✔ 🛠️ Customizable Inputs & Dashboard – Traders can adjust on-chain parameters, macro settings, and smoothing techniques, ensuring flexibility across different market environments.
✔ ⚡ Signal-Based Trading Alerts – Automatic buy/sell signals triggered based on risk conditions and on-chain trend changes, with real-time alerts for trade execution.
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🔍 How It Works
1️⃣ On-Chain Market Evaluation – The system derives trend momentum using MVRV momentum, profit/loss ratios, and MVRV Rate of Change (RoC) to assess underlying market movements.
2️⃣ Macroeconomic Risk Mosaic – A composite Z-score model evaluates external influences like equity performance, supply metrics, and commodity ratios to refine long-term positioning.
3️⃣ Risk-Adjusted Filtering – The model smooths signals with exponential averaging and volatility normalization to ensure stable and consistent trade execution.
4️⃣ Unified Signal Scoring – The final signal integrates on-chain data and macro risk factors into a single trend indication, highlighting favorable long or short opportunities.
5️⃣ Visual & Data Dashboard – The embedded dashboard provides real-time updates on macro risk, on-chain trends, and technical positioning to inform decision-making.
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Behaviour across Crypto Majors
BTC
ETH
SOL
Note: Past behaviour/performance is not indicative of future results. Always conduct thorough testing and risk management before making trading decisions.
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Signal Generation
✅ Long Signals:
A Long Signal is triggered when the Final Section in the dashboard is positive, indicating a favorable macro and on-chain environment.
✅ Short Signals:
A Short Signal (or Cash Mode) is triggered when the Final Section turns negative, signaling potential downside risk or unfavorable conditions.
- Signal confirmation is visually represented through color changes on the chart, with bullish (long) and bearish (cash) states clearly distinguished.
- When Label Display is turned off, the signals "Long" and "Cash" will still appear directly on the chart to ensure clear trade visibility.
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⚙️ Default Settings
🔹 On-Chain Section:
- MVRV Momentum: 20 (DEMA)
- MVRV WMA Smoothing: 300
- Profit/Loss Lookback: 50
- MVRV RoC Period: 110
🔹 Macro Section:
- RSI Length: 30
- China Equity Index Length: 40
- BTC Supply Length: 40
- Silver Ratio Length: 40
- Baltic Dry Index Length: 60
- SOPR Length: 150
- RiskMosaic Smoothing: 24
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Conclusion
MosaicMix V3 is a powerful fundamental trend-following system that merges on-chain analysis, macroeconomic insights, and technical momentum into a comprehensive risk-adjusted model. It is particularly well-suited and optimized for Bitcoin and macro-aware traders, but can also be applied to other assets with sufficient historical data.
Whether you are an investor seeking long-term value signals, a trader optimizing for risk-adjusted trend entries, or an analyst monitoring multi-dimensional market conditions, MosaicMix V3 provides a systematic, structured approach to market navigation.
🔹 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.
TradFi Fundamentals: Enhanced Macroeconomic Momentum Trading Introduction
The "Enhanced Momentum with Advanced Normalization and Smoothing" indicator is a tool that combines traditional price momentum with a broad range of macroeconomic factors. I introduced the basic version from a research paper in my last script. This one leverages not only the price action of a security but also incorporates key economic data—such as GDP, inflation, unemployment, interest rates, consumer confidence, industrial production, and market volatility (VIX)—to create a comprehensive, normalized momentum score.
Previous indicator
Explanation
In plain terms, the indicator calculates a raw momentum value based on the change in price over a defined lookback period. It then normalizes this momentum, along with several economic indicators, using a method chosen by the user (options include simple, exponential, or weighted moving averages, as well as a median absolute deviation (MAD) approach). Each normalized component is assigned a weight reflecting its relative importance, and these weighted values are summed to produce an overall momentum score.
To reduce noise, the combined momentum score can be further smoothed using a user-selected method.
Signals
For generating trade signals, the indicator offers two modes:
Zero Cross Mode: Signals occur when the smoothed momentum line crosses the zero threshold.
Zone Mode: Overbought and oversold boundaries (which are user defined) provide signals when the momentum line crosses these preset limits.
Definition of the Settings
Price Momentum Settings:
Price Momentum Lookback: The number of days used to compute the percentage change in price (default 50 days).
Normalization Period (Price Momentum): The period over which the price momentum is normalized (default 200 days).
Economic Data Settings:
Normalization Period (Economic Data): The period used to normalize all economic indicators (default 200 days).
Normalization Method: Choose among SMA, EMA, WMA, or MAD to standardize both price and economic data. If MAD is chosen, a multiplier factor is applied (default is 1.4826).
Smoothing Options:
Apply Smoothing: A toggle to enable further smoothing of the combined momentum score.
Smoothing Period & Method: Define the period and type (SMA, EMA, or WMA) used to smooth the final momentum score.
Signal Generation Settings:
Signal Mode: Select whether signals are based on a zero-line crossover or by crossing user-defined overbought/oversold (OB/OS) zones.
OB/OS Zones: Define the upper and lower boundaries (default upper zones at 1.0 and 2.0, lower zones at -1.0 and -2.0) for zone-based signals.
Weights:
Each component (price momentum, GDP, inflation, unemployment, interest rates, consumer confidence, industrial production, and VIX) has an associated weight that determines its contribution to the overall score. These can be adjusted to reflect different market views or risk preferences.
Visual Aspects
The indicator plots the smoothed combined momentum score as a continuous blue line against a dotted zero-line reference. If the Zone signal mode is selected, the indicator also displays the upper and lower OB/OS boundaries as horizontal lines (red for overbought and green for oversold). Buy and sell signals are marked by small labels ("B" for buy and "S" for sell) that appear at the bottom or top of the chart when the score crosses the defined thresholds, allowing traders to quickly identify potential entry or exit points.
Conclusion
This enhanced indicator provides traders with a robust approach to momentum trading by integrating traditional price-based signals with a suite of macroeconomic indicators. Its normalization and smoothing techniques help reduce noise and mitigate the effects of outliers, while the flexible signal generation modes offer multiple ways to interpret market conditions. Overall, this tool is designed to deliver a more nuanced perspective on market momentum.
Cryptolabs Global Liquidity Cycle Momentum IndicatorCryptolabs Global Liquidity Cycle Momentum Indicator (LMI-BTC)
This open-source indicator combines global central bank liquidity data with Bitcoin price movements to identify medium- to long-term market cycles and momentum phases. It is designed for traders who want to incorporate macroeconomic factors into their Bitcoin analysis.
How It Works
The script calculates a Liquidity Index using balance sheet data from four central banks (USA: ECONOMICS:USCBBS, Japan: FRED:JPNASSETS, China: ECONOMICS:CNCBBS, EU: FRED:ECBASSETSW), augmented by the Dollar Index (TVC:DXY) and Chinese 10-year bond yields (TVC:CN10Y). This index is:
- Logarithmically scaled (math.log) to better represent large values like central bank balances and Bitcoin prices.
- Normalized over a 50-period range to balance fluctuations between minimum and maximum values.
- Compared to prior-year values, with the number of bars dynamically adjusted based on the timeframe (e.g., 252 for 1D, 52 for 1W), to compute percentage changes.
The liquidity change is analyzed using a Chande Momentum Oscillator (CMO) (period: 24) to measure momentum trends. A Weighted Moving Average (WMA) (period: 10) acts as a signal line. The Bitcoin price is also plotted logarithmically to highlight parallels with liquidity cycles.
Usage
Traders can use the indicator to:
- Identify global liquidity cycles influencing Bitcoin price trends, such as expansive or restrictive monetary policies.
- Detect momentum phases: Values above 50 suggest overbought conditions, below -50 indicate oversold conditions.
- Anticipate trend reversals by observing CMO crossovers with the signal line.
It performs best on higher timeframes like daily (1D) or weekly (1W) charts. The visualization includes:
- CMO line (green > 50, red < -50, blue neutral), signal line (white), Bitcoin price (gray).
- Horizontal lines at 50, 0, and -50 for improved readability.
Originality
This indicator stands out from other momentum tools like RSI or basic price analysis due to:
- Unique Data Integration: Combines four central bank datasets, DXY, and CN10Y as macroeconomic proxies for Bitcoin.
- Dynamic Prior-Year Analysis: Calculates liquidity changes relative to historical values, adjustable by timeframe.
- Logarithmic Normalization: Enhances visibility of extreme values, critical for cryptocurrencies and macro data.
This combination offers a rare perspective on the interplay between global liquidity and Bitcoin, unavailable in other open-source scripts.
Settings
- CMO Period: Default 24, adjustable for faster/slower signals.
- Signal WMA: Default 10, for smoothing the CMO line.
- Normalization Window: Default 50 periods, customizable.
Users can modify these parameters in the Pine Editor to tailor the indicator to their strategy.
Note
This script is designed for medium- to long-term analysis, not scalping. For optimal results, combine it with additional analyses (e.g., on-chain data, support/resistance levels). It does not guarantee profits but supports informed decisions based on macroeconomic trends.
Data Sources
- Bitcoin: INDEX:BTCUSD
- Liquidity: ECONOMICS:USCBBS, FRED:JPNASSETS, ECONOMICS:CNCBBS, FRED:ECBASSETSW
- Additional: TVC:DXY, TVC:CN10Y
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.
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.
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.
