Fourier For Loop [BackQuant]Fourier For Loop
PLEASE Read the following, as understanding an indicator's functionality is essential before integrating it into a trading strategy. Knowing the core logic behind each tool allows for a sound and strategic approach to trading.
Introducing BackQuant's Fourier For Loop (FFL) — a cutting-edge trading indicator that combines Fourier transforms with a for-loop scoring mechanism. This innovative approach leverages mathematical precision to extract trends and reversals in the market, helping traders make informed decisions. Let's break down the components, rationale, and potential use-cases of this indicator.
Understanding Fourier Transform in Trading
The Fourier Transform decomposes price movements into their frequency components, allowing for a detailed analysis of cyclical behavior in the market. By transforming the price data from the time domain into the frequency domain, this indicator identifies underlying patterns that traditional methods may overlook.
In this script, Fourier transforms are applied to the specified calculation source (defaulted to HLC3). The transformation yields magnitude values that can be used to score market movements over a defined range. This scoring process helps uncover long and short signals based on relative strength and trend direction.
Why Use Fourier Transforms?
Fourier Transforms excel in identifying recurring cycles and smoothing noisy data, making them ideal for fast-paced markets where price movements may be erratic. They also provide a unique perspective on market volatility, offering traders additional insights beyond standard indicators.
Calculation Logic: For-Loop Scoring Mechanism
The For Loop Scoring mechanism compares the magnitude of each transformed point in the series, summing the results to generate a score. This score forms the backbone of the signal generation system.
Long Signals: Generated when the score surpasses the defined long threshold (default set at 40). This indicates a strong bullish trend, signaling potential upward momentum.
Short Signals: Triggered when the score crosses under the short threshold (default set at -10). This suggests a bearish trend or potential downside risk.'
Thresholds & Customization
The indicator offers customizable settings to fit various trading styles:
Calculation Periods: Control how many periods the Fourier transform covers.
Long/Short Thresholds: Adjust the sensitivity of the signals to match different timeframes or risk preferences.
Visualization Options: Traders can visualize the thresholds, change the color of bars based on trend direction, and even color the background for enhanced clarity.
Trading Applications
This Fourier For Loop indicator is designed to be versatile across various market conditions and timeframes. Some of its key use-cases include:
Cycle Detection: Fourier transforms help identify recurring patterns or cycles, giving traders a head-start on market direction.
Trend Following: The for-loop scoring system helps confirm the strength of trends, allowing traders to enter positions with greater confidence.
Risk Management: With clearly defined long and short signals, traders can manage their positions effectively, minimizing exposure to false signals.
Final Note
Incorporating this indicator into your trading strategy adds a layer of mathematical precision to traditional technical analysis. Be sure to adjust the calculation start/end points and thresholds to match your specific trading style, and remember that no indicator guarantees success. Always backtest thoroughly and integrate the Fourier For Loop into a balanced trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future .
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Cerca negli script per "Cycle"
ln(close/20 sma) adjusted for time (BTC)(This indicator was designed for the BTC index chart)
Designed for Bitcoin. Plots the log of the close/20W SMA with a linear offset m*t, where m is the gradient I've chosen and t is the candle index. Anything above 1 is a mania phase/market cycle top. If it peaks around 0.92 and rolls over, it could be a local/market cycle top.
This will obviously not work at all in the long term as Bitcoin will not continue following the trend line on the log plot (you can even see it start to deviate in the Jan-Feb 2021 peaks where the indicator went to 1.15).
It identifies the 2011, 2013 (both of them), 2017 tops as being just above 1. It also identifies the 2019 local peak and 2021 market cycle top at ~0.94.
Feel free to change the gradient or even add a function to curve the straight line eventually. I made this for fun, feel free to use it as you wish.
Dividers Timeframe/Session/Calendar-Based [ARTech]Dividers Timeframe/Session/Calendar-Based
This indicator provides customizable visual dividers for multiple timeframes, trading sessions, and calendar-based periods (daily, weekly, monthly). It helps traders visually separate chart areas by key time boundaries without cluttering the chart with price lines.
Key Features:
Multi-Timeframe Dividers: Select up to 4 timeframes (e.g., 60 min, 4 hours, daily, weekly) to display customizable lines marking the start of each timeframe’s candle.
Session Dividers: Define up to 4 trading sessions with user-defined time zones, colors, and active weekdays. The indicator highlights the session’s highest and lowest price range using a box, and compares the session’s opening and closing prices. Based on this comparison, it displays a green or red emoji to indicate bullish or bearish sessions, making it easy to identify session momentum visually.
Calendar-Based Dividers: Enable daily, weekly, or monthly background color zones, with individual toggles and color settings for each day, week, or month. Perfect for visually distinguishing trading periods.
Why use this indicator?
Divider Indicator helps keep your chart organized by visually segmenting timeframes, sessions, and calendar periods, aiding in better analysis of price action relative to important time boundaries.
How to Use
███████ Timezone ███████
A valid timezone name exactly as it appears in the chart’s lower-right corner (e.g. New York, London).
A valid UTC offset in ±H:MM or ±HH:MM format. Hours: 0–14 (zero-padded or not, e.g. +1:30, +01:30, -0:00). Minutes: Must be 00, 15, 30, or 45.
Examples;
UTC → ✅ Valid
Exchange → ✅ Valid
New York → ✅ Valid
London → ✅ Valid
Berlin → ✅ Valid
America/New York → ❌ Invalid. (Use "New York" instead)
+1:30 → ✅ Valid offset with single-digit hour
+01:30 → ✅ Valid offset with zero-padded hour
-05:00 → ✅ Valid negative offset
-0:00 → ✅ Valid zero offset
+1:1 → ❌ Invalid (minute must be 00, 15, 30, or 45)
+2:50 → ❌ Invalid (minute must be 00, 15, 30, or 45)
+15:00 → ❌ Invalid (hour must be 14 or below)
███████ Timeframe ███████
Use this section to display vertical divider lines at the opening of higher timeframe candles (e.g., 1H, 4H, Daily, Weekly). This helps visually separate price action according to larger market structures.
1. Enable a Timeframe:
Turn on one or more timeframes (e.g., 60, 240, D, W) by checking their respective toggle boxes.
2. Lines Mark Candle Opens:
Each active timeframe will draw a vertical line at the start of its candle , making it easier to align intraday moves with larger timeframe shifts.
3. Customize Line Style:
For each timeframe, you can individually set:
Line Style: Solid, dashed, or dotted.
Line Width: From 1 to 10 pixels.
Line Color: Pick any color to match your chart theme.
Opacity: Use transparent colors to avoid overwhelming the chart.
4. Use Multiple Timeframes Together:
You can enable multiple timeframe dividers simultaneously. To maintain clarity:
Use distinct colors for each timeframe.
Use thinner or dotted lines for lower timeframes and bolder lines for higher ones.
Match line style and color intensity to reflect timeframe importance. (e.g., a thick green solid line for Weekly, a thin gray dotted line for 1H)
5. Visual Tip:
These dividers are especially useful for identifying higher timeframe candle opens during intraday trading, spotting breaks above/below previous candle ranges, or aligning session-based strategies with higher timeframe trends.
███████ Session ███████
Use this section to visually highlight specific trading sessions (e.g., London, New York, Tokyo, Sydney) on your chart using time zones, session ranges, and optional weekday filters. This helps focus your analysis on active market hours.
1. Enable a Session:
Activate up to 4 separate trading sessions. Each session can be named (e.g., "London") and customized independently.
2. Set Session Time and Days:
Define session time using the hhmm-hhmm format. (e.g., 0800-1700)
Select which days of the week the session applies to (Sunday through Saturday)
Set your preferred time zone (UTC, Exchange, etc.) from the global settings.
3. Session Box Drawing:
For each active session, the indicator will:
Draw a background-colored box from the session’s start to end time.
Stretch the box to fit the highest and lowest price within that time window.
Draw an outline using customizable border style and width.
4. Session Labels and Directional Hints:
Optionally display the session’s name as a label.
The indicator compares the session’s opening and closing prices . Based on the result:
📈 Green emoji shows a bullish session (close >= open)
📉 Red emoji shows a bearish session (close < open)
5. Display Options:
Show all sessions, only the last session, or a specific number of previous sessions.
Customize label size, location (top/bottom), and whether it appears inside or outside the box.
Adjust background opacity to blend the sessions neatly into your chart.
6. Visual Tip:
Session boxes are particularly useful for:
Spotting repeated highs/lows during active trading hours.
Recognizing session-based breakouts or consolidations.
Comparing performance across different markets and time zones.
███████ Calendar-Based ███████
This section helps you visually segment your chart based on calendar periods: daily, weekly, and monthly. You can enable background color highlighting for individual days, weeks, or months to better track price movements within these time frames.
1. Enable Daily, Weekly, or Monthly Highlighting:
Toggle on the options for Daily, Weekly, and/or Monthly highlighting according to your needs.
2. Select Specific Days, Weeks, or Months:
For Daily, enable any combination of days (up to 7) to color-code.
For Weekly, enable up to 5 weeks per month to cover partial weeks.
For Monthly, enable up to 12 months with individual toggles and colors.
3. Customize Colors for Each Period:
Assign distinct colors to each day, week, or month for easy differentiation. Choose hues that stand out but avoid colors that are too close in tone for adjacent periods.
4. Background Opacity:
Adjust the opacity level of the background coloring to ensure it complements your chart without obscuring price data.
5. Handling Partial Weeks and Overlaps:
The weekly highlighting accounts for months that span 4 to 6 weeks by allowing toggles up to 5 weeks, including weeks that may partially overlap with previous or next months.
6. Visual Tip:
Calendar-based backgrounds are excellent for:
Quickly identifying price behavior within specific calendar units.
Comparing price action across days, weeks, or months.
Spotting seasonal trends or recurring patterns tied to calendar cycles.
Long Term RSILong-Term RSI:
Purpose:
To identify trends and potential entry/exit points within the context of longer-term market cycles.
DTT Yearly Volatility Grid [Pro+] (NINE/ANARR)Introduction :
This tool is designed to automate the Digital Time Theory (DTT) framework created by Ivan and Anarr and applies the DTT Yearly Volatility Grid to uncover swing trading opportunities by analyzing Time-based statistical market behavior across the 4H to Daily chart.
Description:
Built upon the proprietary Digital Time Theory (DTT) , this advanced version is tailored for traders seeking multi-day to multi-week moves . It equips swing traders with an edge by analyzing macro Time intervals and volatility behavior across higher Timeframes. Applicable to all major asset classes, including stocks, crypto, forex, and futures , this script breaks down the entire yearly range into Higher-Time Frame Time Models and statistical zones .
This version uses daily intervals to track broader volatility waves, highlight the DTT framework, and pinpoint premium/discount areas across swing cycles. Powered by Time-driven data insights, this tool assists traders in anticipating expansions, understanding long-range Time distortions, and positioning around statistically significant zones in the higher-Time frame narrative.
Key Features:
Time-Based Models and Macro Volatility Awareness:
Automatically populates the chart with DTT Yearly Time Models (4H, Daily), engineered to spotlight macro volatility events across broader market sessions. Helps swing traders identify potential inflection points, reversals, or trend continuation zones.
Average Model Range Probability (AMRP):
Measure the average volatility expected over higher Time-based models. Use AMRP Levels and Projections to assess the range potential of each Yearly Model Time window—vital for monitoring reversals, breakouts, or continuation plays across several sessions or weeks.
Digital Root Candles and HTF Liquidity Draws:
For DTT Yearly Models, the Digital Root Candles are calculated as a specific Daily candle, and can be viewed on the Daily or 4H Timeframe. Analysts can frame premium and discount zones, based on where price is trading in relation to the current or previous model's Digital Roots. These areas also act as anchors for institutional price movement, often serving as bases for accumulation/distribution periods or large impulse moves.
Extended Visualization:
Track and project prior model ranges (high, low, equilibrium) into the current swing window. This helps visualize macro support/resistance , range expansion, failure zones, and price gravitation levels for longer-term trade planning.
Lookback Periods and Model Count
Utilize adjustable lookback periods to control the number of past DTT Yearly Models displayed—ideal for swing traders and quarterly outlooks. Whether you’re reviewing one yearly model to focus on the present range or several months’ worth of data for backtesting and confluence, this feature keeps charts clean, structured, and aligned with your preferred historical perspective.
By tailoring how many previous Time-based models appear on the chart, traders can better visualize and backtest repeated behaviors, major volatility clusters, and how key levels evolve over Time.
Detailed Data Table:
View statistical AMRP data for multiple DTT Yearly Models in real-Time. The data table helps confirm whether current price movement exceeds, respects, or fails to reach historical volatility ranges—key for analyzing market compression or expansion phases.
Customization Options:
Toggle inner Time interval, calculate AMRP utilizing a custom model lookback, and display styles (solid/dotted lines), including color coordination per drawing. Easily customize your charts and settings to fit your swing trading system or macro analysis.
How Swing Traders Can Use DTT Yearly Volatility Grid Effectively
Identify Swing Premium and Discount Zones:
Use Root Candles and Yearly Time Model AMRP Zones to evaluate where price is positioned in the current Time Model. Using this tool, traders can plan trades with a longer term horizon for a minimum of 1 to 2-weeks or manage entries/exits around market structure shifts and liquidity pools
Expect Macro Volatility Shifts:
Use the HTF models to forecast when and which volatility models are historically known to create larger market impulses . These tools help spot periods of potential exhaustion or breakout, especially near key economic releases, quarterly closes , or macro liquidity zones .
Avoid Low Volatility Consolidations:
AMRP helps you detect when the market is compressing or coiling within a DTT Yearly Model. If price is trading between Digital Root Candles or the AMRP zones, analysts are likely to notice periods of consolidation, and the inability to reach their historical volatility averages.
Usage Guidance:
Add DTT Yearly Volatility Grid (NINE/ANARR) to your TradingView chart.
Make sure to be on the 4H, or Daily Timeframes depending on your asset class and analysis.
Use the DTT Model elements and the Data Table to track expansion zones, premium/discount extremes, and model range behavior.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
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HinduTime Choghadiya(Dynamic Day & Night)🕉️ HinduTime Choghadiya (Dynamic Day & Night) — Visualize real-time Choghadiya Muhurat across global timezones with dynamic sunrise/sunset-based day & night cycles. Perfect for astrology-based or Vedic timing strategies.
How to Use:
Add to Chart: Click "Add to chart" from the TradingView script panel.
Select Your Timezone: Use the dropdown to choose your local timezone (e.g., Asia/Kolkata).
Customize Sunrise/Sunset:
Set "Day Start Hour" (typically 6 AM).
Set "Night Start Hour" (typically 6 PM).
Visual Choghadiya Overlay:
Background color represents the current Choghadiya (e.g., Amrit, Shubh, Rog).
Adjusts dynamically by weekday and day/night period.
Use for Timing Entries:
Favorable: Amrit, Shubh, Labh
Neutral: Chal
Avoid: Rog, Kal, Udveg
AlphaSync | QuantEdgeB📢 Introducing AlphaSync by QuantEdgeB
🛠️ Overview
AlphaSync is a comprehensive medium-term market guidance system designed for major assets such as BTC, ETH, and SOL. This system helps traders determine the overall market direction by integrating three universal strategies (EvolveXSync, ApexSync, QBHV Sync) and a Hybrid strategy (HybridSync).
🚀 What Makes AlphaSync Unique?
✅ Multi-Strategy Fusion → A robust blend of technical, economic, on-chain, and volatility-driven insights.
✅ HybridSync Component (90% Non-Price Factors) → Incorporates macro and liquidity signals to balance pure price-based models.
✅ Structured Decision-Making → The Trend Confluence score aggregates all sub-strategies, providing a unified market signal.
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✨ Key Features
🔹 HybridSync (Hybrid Model)
Utilizes on-chain, economic, liquidity, and volatility factors to provide a fundamental market risk outlook. Unlike technical models, it derives signals primarily from macroeconomic indicators, risk appetite gauges, and capital flows.
🔹 EvolveXSync, & ApexSync (Technical Strategies)
Both strategies are purely price-based, relying on volatility-adjusted trend models, adaptive moving averages, and statistical deviations to confirm bullish or bearish trends.
🔹 QBHV Sync (Momentum & Deviation-Based System)
A fusion of momentum-deviation and a volatility-driven trend confirmation model, designed to detect shifts in momentum while filtering out market noise.
🔹 Trend Confluence (Final Aggregated Signal)
A weighted combination of all four models, delivering a single, structured signal to eliminate conflicting indicators and refine decision-making.
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📊 How It Works
1️⃣ HybridSync – Non-Price Market Structure Analysis
HybridSync is an economic and liquidity-based framework, integrating macro variables, credit spreads, volatility indices, capital flows, and on-chain dynamics to assess risk-on/risk-off conditions.
📌 Key Components:
✔ On-Chain Metrics → Tracks investor behavior, exchange flows, and market cap ratios.
✔ Liquidity Indicators → Monitors global money supply (M2), Federal Reserve balance sheet, credit markets, and capital flows.
✔ Volatility & Risk Metrics → Uses MOVE, VIX, VVIX ratios, and bond market stress indicators to identify risk sentiment shifts.
🔹 Why HybridSync?
• Price alone does not dictate the market; macro liquidity and risk factors are often leading indicators of price movement, especially when it comes to risk assets such as cryptocurrencies.
• Improves decision-making in uncertain market environments, particularly during high-volatility or trendless conditions.
2️⃣ EvolveXSync, & ApexSync – Trend-Following & Volatility Models
Both EvolveXSync, & ApexSync are technical strategies, independently designed to capture trend strength and volatility dynamics.
📌 Core Mechanisms:
✔ VIDYA-Based Trend Detection → Adaptive moving averages adjust dynamically to price swings.
✔ SD-Filtered EMA Models → Uses normalized standard deviation levels to confirm trend validity.
✔ ATR-Adjusted Breakout Filters → Prevents false signals by incorporating dynamic volatility assessments.
🔹 Why Two UniStrategies?
• EvolveXSync, & ApexSync have different calculation methods, providing diverse perspectives on trend confirmation.
• Ensures robustness by mitigating overfitting to a single price-based model.
3️⃣ QBHV Sync – Momentum Deviation & Trend Confirmation
This component blends Bollinger Momentum Deviation (BMD) with a percentile-based trend model to confirm trend shifts.
📌 Core Components:
✔ Bollinger Momentum Deviation → A normalized SMA-SD filter detects overbought/oversold conditions.
✔ Percentile-Based Trend Confirmation → Ensures trends align with long-term volatility structure.
✔ Adaptive Signal Filtering → Prevents unnecessary trade signals by refining thresholds dynamically.
🔹 Why QBHV Sync?
• Adds a statistical layer to trend assessment, preventing whipsaws in volatile conditions.
• Complements HybridSync by ensuring price movements align with broader market forces.
4️⃣ Trend Confluence – The Final Aggregated Signal
AlphaSync blends HybridSync, EvolveXSync, ApexSync, and QBHV Sync into one final output.
📌 How It’s Weighted ? Equal Weight to remove any bias and over-reliance on one input.
✔ HybridSync (Macro & On-Chain Factors) → 25% Weight
✔ UniStrat V1 (Pure Trend) → 25% Weight
✔ UniStrat V2 (Trend + ATR) → 25% Weight
✔ QBHV Sync (Momentum & Deviation) → 25% Weight
🔹 Why Merge These Into One System?
The core philosophy behind AlphaSync is to create a holistic, structured decision-making framework that eliminates the weaknesses of single-method trading approaches. Instead of relying solely on technical indicators, which can lag or fail in macro-driven markets, AlphaSync blends price-based trend signals with macroeconomic, liquidity, and risk-adjusted models.
This multi-layered approach ensures that the system:
✔ Adapts dynamically to different market environments.
✔ Eliminates conflicting signals by creating a structured confluence score.
✔ Prevents over-reliance on a single market model, improving robustness.
📌 Final Signal Interpretation:
✅ Long Signal → AlphaSync Score > Long Threshold
❌ Short Signal → AlphaSync Score < Short Threshold
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👥 Who Should Use AlphaSync?
✅ Medium-Term Traders & Portfolio Managers → Ideal for traders who require macro-confirmed trend signals.
✅ Systematic & Quantitative Traders → Designed for algorithmic integration and structured decision-making.
✅ Long-Term Position Traders → Helps identify major trend shifts and capital rotation opportunities.
✅ Risk-Conscious Investors → Incorporates macro volatility assessments to minimize unnecessary risk exposure.
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📊 Backtest Mode - Evaluating Historical Performance
AlphaSync includes a fully integrated backtest module, allowing traders to assess its historical performance metrics.
🔹 Backtest Metrics Displayed:
✔ Equity Max Drawdown → Measures historical peak loss.
✔ Profit Factor → Evaluates profitability vs. loss ratio.
✔ Sharpe & Sortino Ratios → Risk-adjusted return metrics.
✔ Total Trades & Win Rate → Performance across different market cycles.
✔ Half Kelly Criterion → Optimal position sizing based on historical returns.
📌 Disclaimer:Backtest results are based on past performance and do not guarantee future success. Always incorporate real-time validation and risk management in live trading.
🚀 Why This Matters?
✅ Strategy Validation → See how AlphaSync performs across various market conditions.
✅ Customizable Analysis → Adjust parameters and observe real-time backtest results.
✅ Risk Awareness → Understand potential drawdowns before deploying capital.
Behavior Across Crypto Majors:
BTC
ETH
SOL
📌 Disclaimer: Backtest results are based on historical data and past market behavior. Performance is not indicative of future results and should not be considered financial advice. Always conduct your own backtests and research before making any investment decisions. 🚀
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📌 Customization & Default Settings
📌 AlphaSync Input Parameters & Default Values
🔹 Strategy Configuration
• Color Mode → "Strategy"
• Extra Plots → true
• Long/Cash Signal Label → false
• AlphaSync Dashboard → true
• Enable BackTest Table → false
• Enable Equity Curve → false
• Table Position → "Bottom Left"
• Start Date → '01 Jan 2018 00:00'
• AlphaSync Long Threshold → 0.00
• AlphaSync Short Threshold → 0.00
🔹 QBHV.Sync
• DEMA Source → close
• DEMA Length → 14
• Percentile Length → 35
• ATR Length → 14
• Long Multiplier (ATR Up) → 1.8
• Short Multiplier (ATR Down) → 2.5
• Momentum Length → 8
• Momentum Source → close
• Base Length (SMA Calculation) → 40
• Source for BMD → close
• Standard Deviation Length → 30
• SD Multiplier → 0.7
• Long Threshold → 72
• Short Threshold → 59
🔹 EvolveXSync Configuration
• VIDYA Loop Length → 2
• VIDYA Loop Hist Length → 5
• Vidya Loop Long Threshold → 40
• Vidya Loop Short Threshold → 10
• Dynamic EMA Length → 12
• Dynamic EMA SD Length → 30
• Dynamic EMA Upper SD Weight → 1.032
• Dynamic EMA Lower SD Weight → 1.02
• SD Median Length → 12
• Normalized Median Length → 20
• Median SD Length → 30
• Median Long SD Weight → 0.98
• Median Short SD Weight → 1.04
🔹ApexSync Configuration
• DEMA Length → 30
• DEMA ATR Length → 14
• DEMA ATR Multiplier → 1.0
• G-VIDYA Length → 9
• G-VIDYA Hist Length → 30
• VIDYA ATR Length → 14
• VIDYA ATR Multiplier → 1.7
• SD Kijun Length → 24
• Normalized Kijun Length → 50
• KIJUN SD Length → 32
• KIJUN Long SD Weight → 0.98
• KIJUN Short SD Weight → 1.02
🔹 Risk Mosaic (Macro & Liquidity Component)
• Risk Signal Smoothing Length (EMA) → 8
🚀 AlphaSync is fully customizable to match different market conditions and trading styles
🚀 By default, AlphaSync is optimized for structured, medium-term market guidance.
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📌 Conclusion
AlphaSync redefines medium-term trend analysis by merging technical, fundamental, and quantitative models into one unified system. Unlike traditional strategies that rely solely on price action, AlphaSync incorporates macroeconomic and liquidity factors, ensuring a more holistic market view.
🔹 Key Takeaways:
1️⃣ Hybrid + Technical Fusion – Balances macro & price-based strategies for stronger decision-making.
2️⃣ Multi-Factor Trend Aggregation – Reduces false signals by merging independent methodologies.
3️⃣ Structured, Data-Driven Approach – Designed for quantitative trading and risk-aware portfolio allocation.
📌 Master the market with precision and confidence | QuantEdgeB
🔹 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.
Simple Parallel Channel TrackerThis script will automatically draw price channels with two parallel trends lines, the upper trendline and lower trendline. These lines can be changed in terms of appearance at any time.
The Script takes in fractals from local and historic price action points and connects them over a certain period or amount of candles as inputted by the user. It tracks the most recent highs and lows formed and uses this data to determine where the channel begins.
The Script will decide whether to use the most recent high, or low, depending on what comes first.
Why is this useful?
Often, Traders either have no trend lines on their charts, or they draw them incorrectly. Whichever category a trader falls into, there can only be benefits from having Trend lines and Parallel Channels drawn automatically.
Trends naturally occur in all Markets, all the time. These oscillations when tracked allow for a more reliable following of Markets and management of Market cycles.
DTT Weekly Volatility Grid [Pro+] (NINE/ANARR)Introduction:
Automate Digital Time Theory (DTT) Weekly Models with the DTT Weekly Volatility Grid , leveraging the proprietary framework developed by Nine and Anarr. This tool allows to navigate the advanced landscape of Time-based statistical trading for futures, crypto, and forex markets.
Description:
Built on the Digital Time Theory (DTT), this script provides traders with a structured view of time and price interactions, ideal for swing insights. It divides the weekly range into Time models and inner intervals, empowering traders with data-driven insights to anticipate market expansions, detect Time-based distortions, and understand volatility fluctuations at specific Times during the trading week.
Key Features:
Time-Based Weekly Models and Volatility Awareness: The DTT Weekly Time Models automatically map onto your chart, highlighting critical volatility points in weekly sessions. These models help traders recognize potential shifts in the market, ideal for identifying larger, swing-oriented moves.
Average Model Range Probability (AMRP): The AMRP feature calculates the historical probability of reaching previous DTT Weekly Model Ranges. With AMRP and Standard Deviation metrics, traders can evaluate the likelihood of DTT model continuations or breaks, aligning their strategy with higher Timeframe volatility trends.
Root Candles and Liquidity Draws: Visualize Root Candles as liquidity draws, emphasizing premium and discount areas and marking the origin of a Time-based price movement. The tool allows traders to toggle features like opening prices and equilibrium points of each Root Candle. Observing accumulation or distribution zones around these candles provides crucial reference points for strategic swing entries and exits.
Extended Visualization of Weekly Model Ranges: Leverage previous weekly model ranges within the current Time model to observe historical high, low, and equilibrium levels. This feature aids traders in visualizing premium and discount ranges of prior models, pinpointing areas of liquidity and imbalance to watch.
Customization Options: Tailor Time intervals with a variety of line styles (solid, dashed, dotted) and colours to customize each model. Adjust settings to display specific historical weekly models, apply custom labels, and create a personalized view that suits your trading style and focus.
Lookback Periods and Model Count: Select customizable lookback periods to display past models, offering insights into market behaviour over a chosen historical range. This feature enables clean, organized charts and allows analysts to add more models for detailed backtesting and analysis.
Detailed Real-Time Data Table: The live data table provides easy access to AMRP and range data for selected models. This table highlights model targets and anticipated ranges, offering insights into whether previous models have exceeded historical volatility expectations or remained within them.
How Traders Can Use The DTT Weekly Volatility Grid Effectively:
Identifying Premium and Discount Zones: Track weekly ranges using Root Candles and previous model equilibrium levels to assess if prices are trading in premium or discount areas. This information helps framing the broader swing outlook.
Timing Trades Based on Volatility: Recognize potential exhaustion points through AMRP insights or completed model distortions that may signal new expansions. By observing inner intervals and Root Candles, traders can identify periods of high market activity, assisting in Timing weekly entries and exits.
Avoiding Low Volatility Phases: AMRP calculations can indicate periods when price action may slow or become choppy. If price remains within AMRP deviations or near them, traders can adjust risk or step aside, awaiting more favourable conditions for volatility-driven trades as new inner intervals or model roots appear.
Designed for Swing Traders and Higher Timeframes: The Weekly DTT Models are suited for those looking to study higher timeframe trends across futures, forex, and crypto markets. This tool equips traders with volatility-aware, and data-driven insights during extended market cycles.
Usage Guidance:
Add DTT Weekly Volatility Grid (NINE/ANARR) to your TradingView chart.
Customize your preferred time intervals, model history, and visual settings for your session.
Use the data table to track average model ranges and probabilities, ensuring you align your trades with key levels.
Incorporate DTT Weekly Volatility Grid (NINE/ANARR) into your existing strategies to fine-tune your view through based on data-driven insights into volatility and price behaviour.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
Abdozo - Highlight First DaysAbdozo - Highlight First Days Indicator
This Pine Script indicator helps traders easily identify key timeframes by highlighting the first trading day of the week and the first day of the month. It provides visual markers directly on your chart, helping you stay aware of potential market trends and turning points.
Features:
- Highlight First Day of the Week (Monday): Automatically marks Mondays to help you track weekly market cycles.
- Highlight First Day of the Month: Spot the start of each month with ease to analyze monthly performance and trends.
B4Signals Ichimoku Premium Addon Kyushu Sushi RollKyushu Ashi, one of the Ichimoku strategies, was initially presented in the Ichimoku Kinko Hyo Weekly book by Goichi Hosoda.
<< Historical Context >>
During his time contributing market analysis to the Miyako Newspaper, traders faced the laborious task of manually recording daily open, close, high, and low price levels, alongside the five price values of Ichimoku (Tenkan sen, Kijun sen, Chiko span, Senko span A, Senko span B). With the absence of personal computers, this process was notably cumbersome. In response to traders' requests for a simplified analysis method, Goichi Hosoda introduced the Kyushu Ashi technique.
<< About Kyushu Ashi >>
Derived from Japanese, where "Kyu" denotes nine, "Shu" refers to week, and "Ashi" translates to candles, Kyushu Ashi aims to identify market reversals and trend continuations by leveraging Kihon Suchi time cycles. However, as stated in the original book, trading solely with Kyushu Ashi is not advisable; it is recommended to combine it with the Ichimoku's five lines for a comprehensive trading approach.
Our indicator enhances the Kyushu Ashi strategy by incorporating the original signals in a simplified manner showing up or down carrets in the legs. Additionally, it colors candles with no signals, enabling traders to spot areas of consolidation, continuation, and trend reversal effectively. Drawing inspiration from Fisher's Sushi Roll indicator, our tool identifies these trend changes and adds a trend cloud for the prevailing trend. This comprehensive approach maximizes the effectiveness of the Kyushu Ashi strategy and assists traders in making informed trading decisions.
This is an advanced version with the following features:
- Shows Kyushu Legs
- Shows Kyushu Leg signals
- Draws trend cloud based on Kyushu Legs
- Sends alerts when no Kyushu signal is present, alerting trader of consolidation
- Colors No Kyushu signal candles
- Draws dynamic consolidation zones for breakout identification
Market Health MonitorThe Market Health Monitor is a comprehensive tool designed to assess and visualize the economic health of a market, providing traders with vital insights into both current and future market conditions. This script integrates a range of critical economic indicators, including unemployment rates, inflation, Federal Reserve funds rates, consumer confidence, and housing market indices, to form a robust understanding of the overall economic landscape.
Drawing on a variety of data sources, the Market Health Monitor employs moving averages over periods of 3, 12, 36, and 120 months, corresponding to quarterly, annual, three-year, and ten-year economic cycles. This selection of timeframes is specifically chosen to capture the nuances of economic movements across different phases, providing a balanced view that is sensitive to both immediate changes and long-term trends.
Key Features:
Economic Indicators Integration: The script synthesizes crucial economic data such as unemployment rates, inflation levels, and housing market trends, offering a multi-dimensional perspective on market health.
Adaptability to Market Conditions: The inclusion of both short-term and long-term moving averages allows the Market Health Monitor to adapt to varying market conditions, making it a versatile tool for different trading strategies.
Oscillator Thresholds for Recession and Growth: The script sets specific thresholds that, when crossed, indicate either potential economic downturns (recessions) or periods of growth (expansions), allowing traders to anticipate and react to changing market conditions proactively.
Color-Coded Visualization: The Market Health Monitor employs a color-coding system for ease of interpretation:
-- A red background signals unhealthy economic conditions, cautioning traders about potential risks.
-- A bright red background indicates a confirmed recession, as declared by the NBER, signaling a critical time for traders to reassess risk exposure.
-- A green background suggests a healthy market with expected economic expansion, pointing towards growth-oriented opportunities.
Comprehensive Market Analysis: By combining various economic indicators, the script offers a holistic view of the market, enabling traders to make well-informed decisions based on a thorough understanding of the economic environment.
Key Criteria and Parameters:
Economic Indicators:
Labor Market: The unemployment rate is a critical indicator of economic health.
High or rising unemployment indicates reduced consumer spending and economic stress.
Inflation: Key for understanding monetary policy and consumer purchasing power.
Persistent high inflation can lead to economic instability, while deflation can signal weak
demand.
Monetary Policy: Reflected by the Federal Reserve funds rate.
Changes in the rate can influence economic activity, borrowing costs, and investor
sentiment.
Consumer Confidence: A predictor of consumer spending and economic activity.
Reflects the public’s perception of the economy
Housing Market: The housing market often leads the economy into recession and recovery.
Weakness here can signal broader economic problems.
Market Data:
Stock Market Indices: Reflect overall investor sentiment and economic
expectations. No gains in a stock market could potentially indicate that economy is
slowing down.
Credit Conditions: Indicated by the tightness of bank lending, signaling risk
perception.
Commodity Insight:
Crude Oil Prices: A proxy for global economic activity.
Indicator Timeframe:
A default monthly timeframe is chosen to align with the release frequency of many economic indicators, offering a balanced view between timely data and avoiding too much noise from short-term fluctuations. Surely, it can be chosen by trader / analyst.
The Market Health Monitor is more than just a trading tool—it's a comprehensive economic guide. It's designed for traders who value an in-depth understanding of the economic climate. By offering insights into both current conditions and future trends, it encourages traders to navigate the markets with confidence, whether through turbulent times or in periods of growth. This tool doesn't just help you follow the market—it helps you understand it.
Dark Energy Divergence OscillatorThe Dark Energy Divergence Oscillator (DEDO)
What makes The Universe grow at an accelerating pace?
Dark Energy.
What makes The Economy grow at an accelerating pace?
Debt.
Debt is the Dark Energy of The Economy.
I pronounce DEDO "Deed-oh", but variations are fine with me.
Note: The Pine Script version of DEDO is improved from the original formula, which used a constant all-time high calculation in the normalization factor. This was technically not as accurate for calculating liquidity pressure in historical data because it meant that historical prices were being tested against future liquidity factors. Now using Pine, the functions can be normalized for the bar at the time of calculation, so the liquidity factors are normalized per candle, not across the entire series, which feels like an improvement to me.
Thought Process:
It's all about the liquidity. What I started with is a correlation between major stock indices such as SPX and WRESBAL , a balance sheet metric on FRED
After September 2008, when QE was initiated, many asset valuations started to follow more closely with liquidity factors. This led me to create a function that could combine asset prices and liquidity in WRESBAL , in order to calculate their divergence and chart the signal in TradingView.
The original formula:
First, we don't want "non-QE" data. we only want data for the market affected by QE .
So, find SPX on the day of pre-QE: 1255.08 and subtract that from the 2022 top 4818.62 = 3563.54
With this post-QE SPX range, now you can normalize the price level simply by dividing by the range = ( SPX -1255.08)/3563.54)
Normalization produces values from 0 to 1 so that they can be compared with other normalized figures.
In order to test the 0 to 1 normalized SPX range measure against the liquidity number, WRESBAL , it's the same idea: normalize it using the max as the denominator and you get a 0 to 1 liquidity index:
( WRESBAL /4276000000000)
Subtract one from the other to get the divergence:
(( WRESBAL /4276000000000)-(( SPX -1255.08)/3563.54))*10
x10 to reduce decimal places, but this option is configurable in DEDO's input settings tab.
Positive values indicate there's ample liquidity to hold up price or even create bullish momentum in some cases. Negative values mean price levels are potentially extended beyond what liquidity levels can support.
Note: many viewers of the charts on social media wanted the values to go down in alignment with price moving down, so inverting the chart is what I do with Option + I. I like the fact that negative values represent a deficit in liquidity to hold up price but that's just me.
Now with Pine Script and some help from other liquidity focused accounts on TradingView , I was able to derive a script that includes central bank liquidity and Reverse Repo liquidity drain, all in one algorithm, with adjustable settings.
Central bank assets included in this version:
-JPY (Japan)
-CNY (China)
-UK (British Pound)
-SNB (Swiss National Bank)
-ECB (European Central Bank )
Central Bank assets can be adjusted to an allocation % so that the formula is adjusted for the market cap of the asset.
A handy table in the lower right corner displays useful information about the asset market cap, and percentage it represents in the liquidity pool.
Reverse repo soak is also an optional addition in the Input settings using the RRPONTSYD value from FRED. This value is subtracted from global liquidity used to determine divergence since it is swept away from markets when residing in the Fed's reverse repo facility.
There is an option to draw a line at the Zero bound. This provides a convenience so that the line doesn't keep having to be redrawn on every chart. The normalized equation produces a value that should oscillate around zero, as price/valuation grows past liquidity support, falls under it, and repeats in cycles.
S&P 500 Quandl Data & RatiosTradingView has a little-known integration that allows you to pull in 3rd party data-sets from Nasdaq Data Link, also known as Quandl. Today, I am open-sourcing for the community an indicator that uses the Quandl integration to pull in historical data and ratios on the S&P500. I originally coded this to study macro P/E ratios during peaks and troughs of boom/bust cycles.
The indicator pulls in each of the following datasets, as defined and provided by Quandl. The user can select which datasets to pull in using the indicator settings:
Dividend Yield : S&P 500 dividend yield (12 month dividend per share)/price. Yields following June 2022 (including the current yield) are estimated based on 12 month dividends through June 2022, as reported by S&P. Sources: Standard & Poor's for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.
Price Earning Ratio : Price to earnings ratio, based on trailing twelve month as reported earnings. Current PE is estimated from latest reported earnings and current market price. Source: Robert Shiller and his book Irrational Exuberance for historic S&P 500 PE Ratio.
CAPE/Shiller PE Ratio : Shiller PE ratio for the S&P 500. Price earnings ratio is based on average inflation-adjusted earnings from the previous 10 years, known as the Cyclically Adjusted PE Ratio (CAPE Ratio), Shiller PE Ratio, or PE 10 FAQ. Data courtesy of Robert Shiller from his book, Irrational Exuberance.
Earnings Yield : S&P 500 Earnings Yield. Earnings Yield = trailing 12 month earnings divided by index price (or inverse PE) Yields following March, 2022 (including current yield) are estimated based on 12 month earnings through March, 2022 the latest reported by S&P. Source: Standard & Poor's
Price Book Ratio : S&P 500 price to book value ratio. Current price to book ratio is estimated based on current market price and S&P 500 book value as of March, 2022 the latest reported by S&P. Source: Standard & Poor's
Price Sales Ratio : S&P 500 Price to Sales Ratio (P/S or Price to Revenue). Current price to sales ratio is estimated based on current market price and 12 month sales ending March, 2022 the latest reported by S&P. Source: Standard & Poor's
Inflation Adjusted SP500 : Inflation adjusted SP500. Other than the current price, all prices are monthly average closing prices. Sources: Standard & Poor's Robert Shiller and his book Irrational Exuberance for historic S&P 500 prices, and historic CPIs.
Revenue Per Share : Trailing twelve month S&P 500 Sales Per Share (S&P 500 Revenue Per Share) non-inflation adjusted current dollars. Source: Standard & Poor's
Earnings Per Share : S&P 500 Earnings Per Share. 12-month real earnings per share inflation adjusted, constant August, 2022 dollars. Sources: Standard & Poor's for current S&P 500 Earnings. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Earnings.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Fair Value Trend Model [SiDec]ABSTRACT
This pine script introduces the Fair Value Trend Model, an on-chart indicator for TradingView that constructs a continuously updating "fair-value" estimate of an asset's price via a logarithmic regression on historical data. Specifically, this model has been applied to Bitcoin (BTC) to fully grasp its fair value in the cryptocurrency market. Symmetric channel bands, defined by fixed percentage offsets around this central fair-value curve, provide a visual band within which normal price fluctuations may occur. Additionally, a short-term projection extends both the fair-value trend and its channel bands forward by a user-specified number of bars.
INTRODUCTION
Technical analysts frequently seek to identify an underlying equilibrium or "fair value" about which prices oscillate. Traditional approaches-moving averages, linear regressions in price-time space, or midlines-capture linear trends but often misrepresent the exponential or power-law growth patterns observable in many financial markets. The Fair Value Trend Model addresses this by performing an ordinary least squares (OLS) regression in log-space, fitting ln(Price) against ln(Days since inception). In practice, the primary application has been to Bitcoin, aiming to fully capture Bitcoin's underlying value dynamics.
The result is a curved trend line in regular (price-time) coordinates, reflecting Bitcoin's long-term compounding characteristics. Surrounding this fair-value curve, symmetric bands at user-specified percentage deviations serve as dynamic support and resistance levels. A simple linear projection extends both the central fair-value and its bands into the immediate future, providing traders with a heuristic for short-term trend continuation.
This exposition details:
Data transformation: converting bar timestamps into days since first bar, then applying natural logarithms to both time and price.
Regression mechanics: incremental (or rolling-window) accumulation of sums to compute the log-space fit parameters.
Fair-value reconstruction: exponentiation of the regression output to yield a price-space estimate.
Channel-band definition: establishing ±X% offsets around the fair-value curve and rendering them visually.
Forecasting methodology: projecting both the fair-value trend and channel bands by extrapolating the most recent incremental change in price-space.
Interpretation: how traders can leverage this model for trend identification, mean-reversion setups, and breakout analysis, particularly in Bitcoin trading.
Analysing the macro cycle on Bitcoin's monthly timeframe illustrates how the fair-value curve aligns with multi-year structural turning points.
DATA TRANSFORMATION AND NOTATION
1. Timestamp Baseline (t0)
Let t0 = timestamp of the very first bar on the chart (in milliseconds). Each subsequent bar has a timestamp ti, where ti ≥ t0.
2. Days Since Inception (d(t))
Define the “days since first bar” as
d(t) = max(1, (t − t0) / 86400000.0)
Here, 86400000.0 represents the number of milliseconds in one day (1,000 ms × 60 seconds × 60 minutes × 24 hours). The lower bound of 1 ensures that we never compute ln(0).
3. Logarithmic Coordinates:
Given the bar’s closing price P(t), define:
xi = ln( d(ti) )
yi = ln( P(ti) )
Thus, each data point is transformed to (xi, yi) in log‐space.
REGRESSION FORMULATION
We assume a log‐linear relationship:
yi = a + b·xi + εi
where εi is the residual error at bar i. Ordinary least squares (OLS) fitting minimizes the sum of squared residuals over N data points. Define the following accumulated sums:
Sx = Σ for i = 1 to N
Sy = Σ for i = 1 to N
Sxy = Σ for i = 1 to N
Sx2 = Σ for i = 1 to N
N = number of data points
The OLS estimates for b (slope) and a (intercept) are:
b = ( N·Sxy − Sx·Sy ) / ( N·Sx2 − (Sx)^2 )
a = ( Sy − b·Sx ) / N
All‐Time Versus Rolling‐Window Mode:
All-Time Mode:
Each new bar increments N by 1.
Update Sx ← Sx + xN, Sy ← Sy + yN, Sxy ← Sxy + xN·yN, Sx2 ← Sx2 + xN^2.
Recompute a and b using the formulas above on the entire dataset.
Rolling-Window Mode:
Fix a window length W. Maintain two arrays holding the most recent W values of {xi} and {yi}.
On each new bar N:
Append (xN, yN) to the arrays; add xN, yN, xN·yN, xN^2 to the sums Sx, Sy, Sxy, Sx2.
If the arrays’ length exceeds W, remove the oldest point (xN−W, yN−W) and subtract its contributions from the sums.
Update N_roll = min(N, W).
Compute b and a using N_roll, Sx, Sy, Sxy, Sx2 as above.
This incremental approach requires only O(1) operations per bar instead of recomputing sums from scratch, making it computationally efficient for long time series.
FAIR‐VALUE RECONSTRUCTION
Once coefficients (a, b) are obtained, the regressed log‐price at time t is:
ŷ(t) = a + b·ln( d(t) )
Mapping back to price space yields the “fair‐value”:
F(t) = exp( ŷ(t) )
= exp( a + b·ln( d(t) ) )
= exp(a) · ^b
In other words, F(t) is a power‐law function of “days since inception,” with exponent b and scale factor C = exp(a). Special cases:
If b = 1, F(t) = C · d(t), which is an exponential function in original time.
If b > 1, the fair‐value grows super‐linearly (accelerating compounding).
If 0 < b < 1, it grows sub‐linearly.
If b < 0, the fair‐value declines over time.
CHANNEL‐BAND DEFINITION
To visualise a “normal” range around the fair‐value curve F(t), we define two channel bands at fixed percentage offsets:
1. Upper Channel Band
U(t) = F(t) · (1 + α_upper)
where α_upper = (Channel Band Upper %) / 100.
2. Lower Channel Band
L(t) = F(t) · (1 − α_lower)
where α_lower = (Channel Band Lower %) / 100.
For example, default values of 50% imply α_upper = α_lower = 0.50, so:
U(t) = 1.50 · F(t)
L(t) = 0.50 · F(t)
When “Show FV Channel Bands” is enabled, both U(t) and L(t) are plotted in a neutral grey, and a semi‐transparent fill is drawn between them to emphasise the channel region.
SHORT‐TERM FORECAST PROJECTION
To extend both the fair‐value and its channel bands M bars into the future, the model uses a simple constant‐increment extrapolation in price space. The procedure is:
1. Compute Recent Increments
Let
F_prev = F( t_{N−1} )
F_curr = F( t_N )
Then define the per‐bar change in fair‐value:
ΔF = F_curr − F_prev
Similarly, for channel bands:
U_prev = U( t_{N−1} ), U_curr = U( t_N ), ΔU = U_curr − U_prev
L_prev = L( t_{N−1} ), L_curr = L( t_N ), ΔL = L_curr − L_prev
2. Forecasted Values After M Bars
Assuming the same per‐bar increments continue:
F_future = F_curr + M · ΔF
U_future = U_curr + M · ΔU
L_future = L_curr + M · ΔL
These forecasted values produce dashed lines on the chart:
A dashed segment from (bar_N, F_curr) to (bar_{N+M}, F_future).
Dashed segments from (bar_N, U_curr) to (bar_{N+M}, U_future), and from (bar_N, L_curr) to (bar_{N+M}, L_future).
Forecasted channel bands are rendered in a subdued grey to distinguish them from the current solid bands. Because this method does not re‐estimate regression coefficients for future t > t_N, it serves as a quick visual heuristic of trend continuation rather than a precise statistical forecast.
MATHEMATICAL SUMMARY
Summarising all key formulas:
1. Days Since Inception
d(t_i) = max( 1, ( t_i − t0 ) / 86400000.0 )
x_i = ln( d(t_i) )
y_i = ln( P(t_i) )
2. Regression Summations (for i = 1..N)
Sx = Σ
Sy = Σ
Sxy = Σ
Sx2 = Σ
N = number of data points (or N_roll if using rolling‐window)
3. OLS Estimator
b = ( N · Sxy − Sx · Sy ) / ( N · Sx2 − (Sx)^2 )
a = ( Sy − b · Sx ) / N
4. Fair‐Value Computation
ŷ(t) = a + b · ln( d(t) )
F(t) = exp( ŷ(t) ) = exp(a) · ^b
5. Channel Bands
U(t) = F(t) · (1 + α_upper)
L(t) = F(t) · (1 − α_lower)
with α_upper = (Channel Band Upper %) / 100, α_lower = (Channel Band Lower %) / 100.
6. Forecast Projection
ΔF = F_curr − F_prev
F_future = F_curr + M · ΔF
ΔU = U_curr − U_prev
U_future = U_curr + M · ΔU
ΔL = L_curr − L_prev
L_future = L_curr + M · ΔL
IMPLEMENTATION CONSIDERATIONS
1. Time Precision
Timestamps are recorded in milliseconds. Dividing by 86400000.0 yields days with fractional precision.
For the very first bar, d(t) = 1 ensures x = ln(1) = 0, avoiding an undefined logarithm.
2. Incremental Versus Sliding Summation
All‐Time Mode: Uses persistent scalar variables (Sx, Sy, Sxy, Sx2, N). On each new bar, add the latest x and y contributions to the sums.
Rolling‐Window Mode: Employs fixed‐length arrays for {x_i} and {y_i}. On each bar, append (x_N, y_N) and update sums; if array length exceeds W, remove the oldest element and subtract its contribution from the sums. This maintains exact sums over the most recent W data points without recomputing from scratch.
3. Numerical Robustness
If the denominator N·Sx2 − (Sx)^2 equals zero (e.g., all x_i identical, as when only one day has passed), then set b = 0 and a = Sy / N. This produces a constant fair‐value F(t) = exp(a).
Enforcing d(t) ≥ 1 avoids attempts to compute ln(0).
4. Plotting Strategy
The fair‐value line F(t) is plotted on each new bar. Its color depends on whether the current price P(t) is above or below F(t): a “bullish” color (e.g., green) when P(t) ≥ F(t), and a “bearish” color (e.g., red) when P(t) < F(t).
The channel bands U(t) and L(t) are plotted in a neutral grey when enabled; otherwise they are set to “not available” (no plot).
A semi‐transparent fill is drawn between U(t) and L(t). Because the fill function is executed at global scope, it is automatically suppressed if either U(t) or L(t) is not plotted (na).
5. Forecast Line Management
Each projection line (for F, U, and L) is created via a persistent line object. On successive bars, the code updates the endpoints of the same line rather than creating a new one each time, preserving chart clarity.
If forecasting is disabled, any existing projection lines are deleted to avoid cluttering the chart.
INTERPRETATION AND APPLICATIONS
1. Trend Identification
The fair‐value curve F(t) represents the best‐fit long‐term trend under the assumption that ln(Price) scales linearly with ln(Days since inception). By capturing power‐law or exponential patterns, it can more accurately reflect underlying compounding behavior than simple linear regressions.
When actual price P(t) lies above U(t), it may be considered “overextended” relative to its long‐term trend; when price falls below L(t), it may be deemed “oversold.” These conditions can signal potential mean‐reversion or breakout opportunities.
2. Mean‐Reversion and Breakout Signals
If price re‐enters the channel after touching or slightly breaching L(t), some traders interpret this as a mean‐reversion bounce and consider initiating a long position.
Conversely, a sustained move above U(t) can indicate strong upward momentum and a possible bullish breakout. Traders often seek confirmation (e.g., price remaining above U(t) for multiple bars, rising volume, or corroborating momentum indicators) before acting.
3. Rolling Versus All‐Time Usage
All‐Time Mode: Captures the entire dataset since inception, focusing on structural, long‐term trends. It is less sensitive to short‐term noise or volatility spikes.
Rolling‐Window Mode: Restricts the regression to the most recent W bars, making the fair‐value curve more responsive to changing market regimes, sudden volatility expansions, or fundamental shifts. Traders who wish to align the model with local behaviour often choose W so that it approximates a market cycle length (e.g., 100–200 bars on a daily chart).
4. Channel Percentage Selection
A wider band (e.g., ±50 %) accommodates larger price swings, reducing the frequency of breaches but potentially delaying actionable signals.
A narrower band (e.g., ±10 %) yields more frequent “overbought/oversold” alerts but may produce more false signals during normal volatility. It is advisable to calibrate the channel width to the asset’s historical volatility regime.
5. Forecast Cautions
The short‐term projection assumes that the last single‐bar increment ΔF remains constant for M bars. In reality, trend acceleration or deceleration can occur, rendering the linear forecast inaccurate.
As such, the forecast serves as a visual guide rather than a statistically rigorous prediction. It is best used in conjunction with other momentum, volume, or volatility indicators to confirm trend continuation or reversal.
LIMITATIONS AND CONSIDERATIONS
1. Power‐Law Assumption
By fitting ln(P) against ln(d), the model posits that P(t) ≈ C · ^b. Real markets may deviate from a pure power‐law, especially around significant news events or structural regime changes. Temporary misalignment can occur.
2. Fixed Channel Width
Markets exhibit heteroskedasticity: volatility can expand or contract unpredictably. A static ±X % band does not adapt to changing volatility. During high‐volatility periods, a fixed ±50 % may prove too narrow and be breached frequently; in unusually calm periods, it may be excessively broad, masking meaningful variations.
3. Endpoint Sensitivity
Regression‐based indicators often display greater curvature near the most recent data, especially under rolling‐window mode. This can create sudden “jumps” in F(t) when new bars arrive, potentially confusing users who expect smoother behaviour.
4. Forecast Simplification
The projection does not re‐estimate regression slope b for future times. It only extends the most recent single‐bar change. Consequently, it should be regarded as an indicative extension rather than a precise forecast.
PRACTICAL IMPLEMENTATION ON TRADINGVIEW
1 Adding the Indicator
In TradingView’s “Indicators” dialog, search for Fair Value Trend Model or visit my profile, under "scripts" add it to your chart.
Add it to any chart (e.g., BTCUSD, AAPL, EURUSD) to see real‐time computation.
2. Configuring Inputs
Show Forecast Line: Toggle on or off the dashed projection of the fair‐value.
Forecast Bars: Choose M, the number of bars to extend into the future (default is often 30).
Forecast Line Colour: Select a high‐contrast colour (e.g., yellow).
Bullish FV Colour / Bearish FV Colour: Define the colour of the fair‐value line when price is above (e.g., green) or below it (e.g., red).
Show FV Channel Bands: Enable to display the grey channel bands around the fair‐value.
Channel Band Upper % / Channel Band Lower %: Set α_upper and α_lower as desired (defaults of 50 % create a ±50 % envelope).
Use Rolling Window?: Choose whether to restrict the regression to recent data.
Window Bars: If rolling mode is enabled, designate W, the number of bars to include.
3. Visual Output
The central curve F(t) appears on the price chart, coloured green when P(t) ≥ F(t) and red when P(t) < F(t).
If channel bands are enabled, the chart shows two grey lines U(t) and L(t) and a subtle shading between them.
If forecasting is active, dashed extensions of F(t), U(t), and L(t) appear, projecting forward by M bars in neutral hues.
CONCLUSION
The Fair Value Trend Model furnishes traders with a mathematically principled estimate of an asset’s equilibrium price curve by fitting a log‐linear regression to historical data. Its channel bands delineate a normal corridor of fluctuation based on fixed percentage offsets, while an optional short‐term projection offers a visual approximation of trend continuation.
By operating in log‐space, the model effectively captures exponential or power‐law growth patterns that linear methods overlook. Rolling‐window capability enables responsiveness to regime shifts, whereas all‐time mode highlights broader structural trends. Nonetheless, users should remain mindful of the model’s assumptions—particularly the power‐law form and fixed band percentages—and employ the forecast projection as a supplemental guide rather than a standalone predictor.
When combined with complementary indicators (e.g., volatility measures, momentum oscillators, volume analysis) and robust risk management, the Fair Value Trend Model can enhance market timing, mean‐reversion identification, and breakout detection across diverse trading environments.
REFERENCES
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.
Tsay, R. S. (2014). Introductory Time Series with R (2nd ed.). Springer.
Hull, J. C. (2017). Options, Futures, and Other Derivatives (10th ed.). Pearson.
These references provide background on regression, time-series analysis, and financial modeling.
Modern Economic Eras DashboardOverview
This script provides a historical macroeconomic visualization of U.S. markets, highlighting long-term structural "eras" such as the Bretton Woods period, the inflationary 1970s, and the post-2020 "Age of Disorder." It overlays key economic indicators sourced from FRED (Federal Reserve Economic Data) and displays notable market crashes, all in a clean and rescaled format for easy comparison.
Data Sources & Indicators
All data is loaded monthly from official FRED series and rescaled to improve readability:
🔵 Real GDP (FRED:GDP): Total output of the U.S. economy.
🔴 Inflation Index (FRED:CPIAUCSL): Consumer price index as a proxy for inflation.
⚪ Debt to GDP (FRED:GFDGDPA188S): Federal debt as % of GDP.
🟣 Labor Force Participation (FRED:CIVPART): % of population in the labor force.
🟠 Oil Prices (FRED:DCOILWTICO): Monthly WTI crude oil prices.
🟡 10Y Real Yield (FRED:DFII10): Inflation-adjusted yield on 10-year Treasuries.
🔵 Symbol Price: Optionally overlays the charted asset’s price, rescaled.
Historical Crashes
The dashboard highlights 10 major U.S. market crashes, including 1929, 2000, and 2008, with labeled time spans for quick context.
Era Classification
Six macroeconomic eras based on Deutsche Bank’s Long-Term Asset Return Study (2020) are shaded with background color. Each era reflects dominant economic regimes—globalization, wars, monetary systems, inflationary cycles, and current geopolitical disorder.
Best Use Cases
✅ Long-term macro investors studying structural market behavior
✅ Educators and analysts explaining economic transitions
✅ Portfolio managers aligning strategy with macroeconomic phases
✅ Traders using history for cycle timing and risk assessment
Technical Notes
Designed for monthly timeframe, though it works on weekly.
Uses close price and standard request.security calls for consistency.
Max labels/lines configured for broader history (from 1860s to present).
All plotted series are rescaled manually for better visibility.
Originality
This indicator is original and not derived from built-in or boilerplate code. It combines multiple economic dimensions and market history into one interactive chart, helping users frame today's markets in a broader structural context.
The Mayan CalendarThis indicator displays the current date in the Mayan Calendar, based on real-time UTC time. It calculates and presents:
🌀 Long Count (Baktun.Katun.Tun.Uinal.Kin) – A linear count of days since the Mayan epoch (August 11, 3114 BCE).
🔮 Tzolk'in Date – A 260-day sacred cycle combining a number (1–13) and one of 20 day names (e.g., 4 Ajaw).
🌾 Haab' Date – A 365-day civil cycle divided into 18 months of 20 days + 5 "nameless" days (Wayeb').
The calculations follow Smithsonian standards and align with the Maya Calendar Converter from the National Museum of the American Indian:
👉 maya.nmai.si.edu
The results are shown in a table overlay on your chart's top-right corner. This indicator is great for symbolic traders, astro enthusiasts, or anyone interested in ancient timekeeping systems woven into financial timeframes. Enjoy, time travelers! ⌛
Log Regression OscillatorThe Log Regression Oscillator transforms the logarithmic regression curves into an easy-to-interpret oscillator that displays potential cycle tops/bottoms.
🔶 USAGE
Calculating the logarithmic regression of long-term swings can help show future tops/bottoms. The relationship between previous swing points is calculated and projected further. The calculated levels are directly associated with swing points, which means every swing point will change the calculation. Importantly, all levels will be updated through all bars when a new swing is detected.
The "Log Regression Oscillator" transforms the calculated levels, where the top level is regarded as 100 and the bottom level as 0. The price values are displayed in between and calculated as a ratio between the top and bottom, resulting in a clear view of where the price is situated.
The main picture contains the Logarithmic Regression Alternative on the chart to compare with this published script.
Included are the levels 30 and 70. In the example of Bitcoin, previous cycles showed a similar pattern: the bullish parabolic was halfway when the oscillator passed the 30-level, and the top was very near when passing the 70-level.
🔹 Proactive
A "Proactive" option is included, which ensures immediate calculations of tentative unconfirmed swings.
Instead of waiting 300 bars for confirmation, the "Proactive" mode will display a gray-white dot (not confirmed swing) and add the unconfirmed Swing value to the calculation.
The above example shows that the "Calculated Values" of the potential future top and bottom are adjusted, including the provisional swing.
When the swing is confirmed, the calculations are again adjusted, showing a red dot (confirmed top swing) or a green dot (confirmed bottom swing).
🔹 Dashboard
When less than two swings are available (top/bottom), this will be shown in the dashboard.
The user can lower the "Threshold" value or switch to a lower timeframe.
🔹 Notes
Logarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time, meaning some symbols/tickers will fit better than others.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Users have to check the validity of swings; for example, if the direction of swings is downwards, then the dataset is not fitted for logarithmic regression.
In the example above, the "Threshold" is lowered. However, the calculated levels are unreliable due to the swings, which do not fit the model well.
Here, the combination of downward bottom swings and price accelerates slower at first and faster recently, resulting in a non-fit for the logarithmic regression model.
Note the price value (white line) is bound to a limit of 150 (upwards) and -150 (down)
In short, logarithmic regression is best used when there are enough tops/bottoms, and all tops are around 100, and all bottoms around 0.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 DETAILS
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers (arrays) and returns a single number, the sum of the products of the corresponding entries of the two sequences of numbers.
The usual way is to loop through both arrays and sum the products.
In this case, the two arrays are transformed into a matrix, wherein in one matrix, a single column is filled with the first array values, and in the second matrix, a single row is filled with the second array values.
After this, the function matrix.mult() returns a new matrix resulting from the product between the matrices m1 and m2.
Then, the matrix.eigenvalues() function transforms this matrix into an array, where the array.sum() function finally returns the sum of the array's elements, which is the dot product.
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
🔶 SETTINGS
Threshold: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Proactive: Tentative Swings are included with this setting enabled.
Style: Color Settings
Dashboard: Toggle, "Location" and "Text Size"
Logarithmic Regression AlternativeLogarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time. Bitcoin is a good example.
𝑦 = 𝑎 + 𝑏 * ln(𝑥)
With this logarithmic regression (log reg) formula 𝑦 (price) is calculated with constants 𝑎 and 𝑏, where 𝑥 is the bar_index .
Instead of using the sum of log x/y values, together with the dot product of log x/y and the sum of the square of log x-values, to calculate a and b, I wanted to see if it was possible to calculate a and b differently.
In this script, the log reg is calculated with several different assumed a & b values, after which the log reg level is compared to each Swing. The log reg, where all swings on average are closest to the level, produces the final 𝑎 & 𝑏 values used to display the levels.
🔶 USAGE
The script shows the calculated logarithmic regression value from historical swings, provided there are enough swings, the price pattern fits the log reg model, and previous swings are close to the calculated Top/Bottom levels.
When the price approaches one of the calculated Top or Bottom levels, these levels could act as potential cycle Top or Bottom.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Swings are based on Weekly bars. A Top Swing, for example, with Swing setting 30, is the highest value in 60 weeks. Thirty bars at the left and right of the Swing will be lower than the Top Swing. This means that a confirmation is triggered 30 weeks after the Swing. The period will be automatically multiplied by 7 on the daily chart, where 30 becomes 210 bars.
Please note that the goal of this script is not to show swings rapidly; it is meant to show the potential next cycle's Top/Bottom levels.
🔹 Multiple Levels
The script includes the option to display 3 Top/Bottom levels, which uses different values for the swing calculations.
Top: 'high', 'maximum open/close' or 'close'
Bottom: 'low', 'minimum open/close' or 'close'
These levels can be adjusted up/down with a percentage.
Lastly, an "Average" is included for each set, which will only be visible when "AVG" is enabled, together with both Top and Bottom levels.
🔹 Notes
Users have to check the validity of swings; the above example only uses 1 Top Swing for its calculations, making the Top level unreliable.
Here, 1 of the Bottom Swings is pretty far from the bottom level, changing the swing settings can give a more reliable bottom level where all swings are close to that level.
Note the display was set at "Logarithmic", it can just as well be shown as "Regular"
In the example below, the price evolution does not fit the logarithmic regression model, where growth should accelerate rapidly at first and then slows over time.
Please note that this script can only be used on a daily timeframe or higher; using it at a lower timeframe will show a warning. Also, it doesn't work with bar-replay.
🔶 DETAILS
The code gathers data from historical swings. At the last bar, all swings are calculated with different a and b values. The a and b values which results in the smallest difference between all swings and Top/Bottom levels become the final a and b values.
The ranges of a and b are between -20.000 to +20.000, which means a and b will have the values -20.000, -19.999, -19.998, -19.997, -19.996, ... -> +20.000.
As you can imagine, the number of calculations is enormous. Therefore, the calculation is split into parts, first very roughly and then very fine.
The first calculations are done between -20 and +20 (-20, -19, -18, ...), resulting in, for example, 4.
The next set of calculations is performed only around the previous result, in this case between 3 (4-1) and 5 (4+1), resulting in, for example, 3.9. The next set goes even more in detail, for example, between 3.8 (3.9-0.1) and 4.0 (3.9 + 0.1), and so on.
1) -20 -> +20 , then loop with step 1 (result (example): 4 )
2) 4 - 1 -> 4 +1 , then loop with step 0.1 (result (example): 3.9 )
3) 3.9 - 0.1 -> 3.9 +0.1 , then loop with step 0.01 (result (example): 3.93 )
4) 3.93 - 0.01 -> 3.93 +0.01, then loop with step 0.001 (result (example): 3.928)
This ensures complicated calculations with less effort.
These calculations are done at the last bar, where the levels are displayed, which means you can see different results when a new swing is found.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 SETTINGS
Three sets
High/Low
• color setting
• Swing Length settings for 'High' & 'Low'
• % adjustment for 'High' & 'Low'
• AVG: shows average (when both 'High' and 'Low' are enabled)
Max/Min (maximum open/close, minimum open/close)
• color setting
• Swing Length settings for 'Max' & 'Min'
• % adjustment for 'Max' & 'Min'
• AVG: shows average (when both 'Max' and 'Min' are enabled)
Close H/Close L (close Top/Bottom level)
• color setting
• Swing Length settings for 'Close H' & 'Close L'
• % adjustment for 'Close H' & 'Close L'
• AVG: shows average (when both 'Close H' and 'Close L' are enabled)
Show Dashboard, including Top/Bottom levels of the desired source and calculated a and b values.
Show Swings + Dot size
Intellect_city - Halvings Bitcoin CycleWhat is halving?
The halving timer shows when the next Bitcoin halving will occur, as well as the dates of past halvings. This event occurs every 210,000 blocks, which is approximately every 4 years. Halving reduces the emission reward by half. The original Bitcoin reward was 50 BTC per block found.
Why is halving necessary?
Halving allows you to maintain an algorithmically specified emission level. Anyone can verify that no more than 21 million bitcoins can be issued using this algorithm. Moreover, everyone can see how much was issued earlier, at what speed the emission is happening now, and how many bitcoins remain to be mined in the future. Even a sharp increase or decrease in mining capacity will not significantly affect this process. In this case, during the next difficulty recalculation, which occurs every 2014 blocks, the mining difficulty will be recalculated so that blocks are still found approximately once every ten minutes.
How does halving work in Bitcoin blocks?
The miner who collects the block adds a so-called coinbase transaction. This transaction has no entry, only exit with the receipt of emission coins to your address. If the miner's block wins, then the entire network will consider these coins to have been obtained through legitimate means. The maximum reward size is determined by the algorithm; the miner can specify the maximum reward size for the current period or less. If he puts the reward higher than possible, the network will reject such a block and the miner will not receive anything. After each halving, miners have to halve the reward they assign to themselves, otherwise their blocks will be rejected and will not make it to the main branch of the blockchain.
The impact of halving on the price of Bitcoin
It is believed that with constant demand, a halving of supply should double the value of the asset. In practice, the market knows when the halving will occur and prepares for this event in advance. Typically, the Bitcoin rate begins to rise about six months before the halving, and during the halving itself it does not change much. On average for past periods, the upper peak of the rate can be observed more than a year after the halving. It is almost impossible to predict future periods because, in addition to the reduction in emissions, many other factors influence the exchange rate. For example, major hacks or bankruptcies of crypto companies, the situation on the stock market, manipulation of “whales,” or changes in legislative regulation.
---------------------------------------------
Table - Past and future Bitcoin halvings:
---------------------------------------------
Date: Number of blocks: Award:
0 - 03-01-2009 - 0 block - 50 BTC
1 - 28-11-2012 - 210000 block - 25 BTC
2 - 09-07-2016 - 420000 block - 12.5 BTC
3 - 11-05-2020 - 630000 block - 6.25 BTC
4 - 20-04-2024 - 840000 block - 3.125 BTC
5 - 24-03-2028 - 1050000 block - 1.5625 BTC
6 - 26-02-2032 - 1260000 block - 0.78125 BTC
7 - 30-01-2036 - 1470000 block - 0.390625 BTC
8 - 03-01-2040 - 1680000 block - 0.1953125 BTC
9 - 07-12-2043 - 1890000 block - 0.09765625 BTC
10 - 10-11-2047 - 2100000 block - 0.04882813 BTC
11 - 14-10-2051 - 2310000 block - 0.02441406 BTC
12 - 17-09-2055 - 2520000 block - 0.01220703 BTC
13 - 21-08-2059 - 2730000 block - 0.00610352 BTC
14 - 25-07-2063 - 2940000 block - 0.00305176 BTC
15 - 28-06-2067 - 3150000 block - 0.00152588 BTC
16 - 01-06-2071 - 3360000 block - 0.00076294 BTC
17 - 05-05-2075 - 3570000 block - 0.00038147 BTC
18 - 08-04-2079 - 3780000 block - 0.00019073 BTC
19 - 12-03-2083 - 3990000 block - 0.00009537 BTC
20 - 13-02-2087 - 4200000 block - 0.00004768 BTC
21 - 17-01-2091 - 4410000 block - 0.00002384 BTC
22 - 21-12-2094 - 4620000 block - 0.00001192 BTC
23 - 24-11-2098 - 4830000 block - 0.00000596 BTC
24 - 29-10-2102 - 5040000 block - 0.00000298 BTC
25 - 02-10-2106 - 5250000 block - 0.00000149 BTC
26 - 05-09-2110 - 5460000 block - 0.00000075 BTC
27 - 09-08-2114 - 5670000 block - 0.00000037 BTC
28 - 13-07-2118 - 5880000 block - 0.00000019 BTC
29 - 16-06-2122 - 6090000 block - 0.00000009 BTC
30 - 20-05-2126 - 6300000 block - 0.00000005 BTC
31 - 23-04-2130 - 6510000 block - 0.00000002 BTC
32 - 27-03-2134 - 6720000 block - 0.00000001 BTC
Gann Dates█ INTRODUCTION
This indicator is very easy to understand and simple to use. It indicates important Gann dates in the future based on pivots (highs and lows) or key dates from the past.
According to W.D. Gann the year can be seen as a cycle or one full circle with 365 degrees. The circle can be symmetrically divided into equal sections at angles of 30, 45, 60, etc. The start of the cycle can be a significant key date or a pivot in the chart. Hence there are dates in the calendar, that fall on important angles. According to W.D. Gann those are important dates to watch for significant price movement in either direction.
In combination with other tools, this indicator can help you to time the market and make better risk-on/off decisions.
█ HOW TO USE
ibb.co
You need to adjust the settings depending on the chart. The following parameters can be adjusted:
Gann angles: The script will plot dates that are distant from pivots by a multiple of this.
Gann dates per pivot: The amount of dates that will show.
Search window size for pivots: This is how the local highs and lows are detected in the chart. The smaller this number the more local highs and lows will show.
You also have the option to hide dates derived from lows/highs, or show dates based on two custom key dates.
█ EXAMPLES
The following chart shows the price of Gold in USD with multiples of 20 days from local pivots.
The following chart shows the price of Bitcoin in USD with multiples of 30 weeks from two custom dates (in this case the low in late 2018 and the low in late 2022).
90cycle @joshuuu90 minute cycle is a concept about certain time windows of the day.
This indicator has two different options. One uses the 90 minute cycle times mentioned by traderdaye, the other uses the cls operational times split up into 90 minutes session.
e.g. we can often see a fake move happening in the 90 minute window between 2.30am and 4am ny time.
The indicator draws vertical lines at the start/end of each session and the user is able to only display certain sessions (asia, london, new york am and pm)
For the traderdayes option, the indicator also counts the windows from 1 to 4 and calls them q1,q2,q3,q4 (q-quarter)
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Range Identifier*Re-upload as previous attempt was removed.
An attempt to create a half decent identifier of when the markets are ranging and in a state of choppiness and mean reversion - as opposed to in trending trade conditions.
It's super simple logic just working on some basic price action and market structure operating on higher time frames.
It uses the Donchian Channels but with hlc3 data as opposed to high/lows - and identifies periods in which the baseline is static, or when the channel upper & lower are contracting.
This combination identifies non trending price action with decreasing volatility, which tends to indicate a lot of upcoming chop and ranging/sideways action; especially when intraday trading and applied on the daily timeframe.
The filter increasing results in a decrease of areas identified as choppy by extending the required period of a sideways static basis, I've found values of 2 or 3 to be a nice sweetspot!
Overall should be pretty intuitive to use, when the background changes just consider altering your trading and investing approach. This was created as I've not really seen anything on here that functions quite the same.
I decided to not include the Donchian upper/lower/basis as I found that can often lead to decision bias and being influenced by where these lines are situated causing you to guess on future direction.
It's obviously never going to be perfect, but a nice and unbiased way to quickly check where we may be in a cycle; let me know if there are any issues/questions and please enjoy!
