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Planetary Time Intervals [Javonnii]🪐 Planetary Time Intervals Indicator 🪐 This innovative planetary time-based market timing indicator enables traders to analyze and project planetary degree intervals directly onto any chart with exceptional accuracy. Built on a custom planetary framework, it calculates planetary positions down to the arc-second across all timeframes, accommodating up to 500 future bars. This capability allows for both clean intraday analysis and long-term market timing. The framework operates autonomously and requires no external data or updates. It reliably computes planetary positions across centuries and supports seamless analysis across all markets—whether 24/7 assets like crypto or session-based instruments like stocks—with smooth, gap-free planetary data on any ticker or timeframe. Perfect for time-based market studies on deep historical datasets such as the Dow Jones. 🔶 FEATURES Key planetary timing tools include: - Standard Degree Interval Plotting (Fixed Increments) - Price-to-Planetary Degree Conversions (360 Units Reduction & 1/10 Price Method) for Direct Price-to-Time Harmonics - Geocentric & Heliocentric Models (Including Moon for Lunar Cycles) - Absolute Degree Handling for Retrogrades - Visual Projections: Background Highlights, Vertical Lines, and Degree Labels - Math Display for Calculation Transparency - Scaling Adjustments for Volatile Assets (e.g., Crypto, Indices) - Multi-Timeframe Support Up to 500 Bars - Alert Conditions for Interval Projections - Customizable Interval Logic and Styling - + More (Check changelog for updates) 🔶 BASIC DEMONSTRATION On a daily stock chart, select a key low as your reference point (datetime and price). Choose a 30° interval for Mercury, and the indicator projects vertical lines forward at each degree milestone, with shaded backgrounds indicating potential cycle zones. Labels display traversed degrees, highlighting alignments in real-time. For the 360 Conversion, input a price like $5,000—it reduces to 320° via modular arithmetic (5000 mod 360 = 320°), converting the price directly into a planetary time interval that may sync with numerological patterns or past turns. 🔶 USAGE & EXAMPLES In the image below, the indicator is applied to a long-term weekly BTCUSD chart from 2015 to 2026. Selecting a historical low point (price = 198.02) as the reference, the 360 Conversion method derives the interval directly from the price. Since 198.02 is already under 360, the interval is simply 198.02° (no further reduction needed). Vertical yellow lines project these Sun-based intervals forward, aligning with a few key market turns, bull runs, and corrections. The math is displayed in the label "198.02° Low = 198.02° 360 Conversion 198.02°", illustrating the price-to-planetary time conversion process. Numerology enthusiasts might reduce 198.02° further (1+9+8+0+2=20, then 2+0=2) for symbolic insights into market balance or duality. Supported planets: Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto. 🔶 SETTINGS - Timing Method: Select Standard Interval, 360 Conversion, or 1/10 Price. - Planetary View: Geocentric/Heliocentric; Toggle Moon Inclusion. - Interval Setup: Enter degrees (e.g., 45°); Enable Absolute for Retrogrades. - Base Reference: Input datetime and price from high/low. - Visuals: Toggle highlights, lines, math labels; Set colors/styles. - Scaling: Adjust for asset ranges (e.g., multipliers for high or low prices). - Limits: Bar count (up to 500) - Styling: Label formats, projection options. 🔶 CONCLUSION Planetary and numerological timing offers a unique lens on market rhythms, blending celestial mechanics with number-based insights for deeper analysis. This indicator is an original creation providing precise, flexible tools to explore these concepts, with a strong focus on converting price data into actionable planetary time intervals. As with all trading, past patterns aren't guarantees—pair with sound risk management and your overall approach. 🔶 HOW TO GET ACCESS This public invite-only script is for planetary traders, market analysts, numerology enthusiasts, and anyone interested in harmonic degree-based timing. Reach out to the author for access details to this indicator and related resources.
Pine Script® indicator
by BlueprintResearch
5
Bitcoin Basket [100Zabaan]🟢🟢 Bitcoin Basket 🟢🟢 🟡 Overview This indicator is a long-term analytical tool for Bitcoin investment, designed by drawing inspiration from historical halving cycles, historical peak growths and deepest declines, and the overall price growth trend. The main goal of this indicator is to provide a strategic perspective to investors so they can better identify key market phases, such as periods of major selling and major buying of Bitcoin. 🟡 This tool visually compares two scenarios: Hold Strategy : The strategy of buying and holding Bitcoin from the time of investment until today ( Bitcoin Holding Strategy ). Active Investment Strategy : An active investment strategy that cautiously buys and sells based on market cycle-driven signals ( Active Bitcoin Trading Strategy ). This comparison helps you make more informed decisions regarding your long-term capital management. 🟡 Key Features of the Indicator Performance Comparison : Displays the current value of your investment based on two strategies: Bitcoin Holding Strategy : If you had invested an amount on your chosen date, how much Bitcoin (equivalent to how many dollars) would you have today. Active Bitcoin Trading Strategy : How your capital would have grown if you had traded based on the indicator's buy and sell signals. Also, in the status line section, you can see your asset amount (in USD) at each candle and compare the two strategies. Identification of Buy and Sell Periods : Using colored boxes (red and green), it identifies time periods that have historically been suitable for selling or buying. Identification of Suitable Price Ranges in Buy and Sell Periods : With a horizontal line within the red boxes, it informs us that prices above this line may be worth selling. With a horizontal line within the green boxes, it informs us that prices below this line may be worth buying. Halving Display: Shows the exact time of each halving along with the block reward for each block produced during that halving. Display of Maximum Drawdown During the Investment Period: In the provided table, you can see the maximum loss incurred in each of the two strategies during your hypothetical investment period, on what date this occurred, and what your capital was before and after in each of the two scenarios. Display of Buy and Sell Suggestions: You can also see the suggested amount of Bitcoin to buy and sell at what prices, based on your investment amount. Alarm: This indicator usually provides an alarm one or more days before the start of a selling period, notifying you that a sell signal will be issued soon. Customization Options: In this indicator, you can customize your investment date and amount. You can also determine the display of label text (including price and buy/sell amount) and its size. This indicator also supports the Persian language. 🟡 How it Works and Signal Issuance Mechanism This indicator uses three main methods for calculations: Deceleration of Overall Price Growth : This indicator has found that the price of Bitcoin grows and fluctuates around an overall axis, and the intensity of this upward axis's growth gradually decreases. Halving Impact : This indicator has found that the price of Bitcoin has grown from approximately one year before a halving and this growth continues for at least one year after the halving. It has also found that the price experiences a sharp one-year decline in the range between two halvings. Consequently, time-wise, based on halving, it displays a selling period (as a red box) on the chart. Considering the Bitcoin price growth explained in the previous point, it draws a line in the middle of the red box, identifying prices above that line as a suitable selling area. The inverse of this process is considered for buying. Historical Peak Growths and Deepest Declines : This indicator analyzes Bitcoin's historical peak growths and deepest declines. Based on this, when declines are relatively large compared to what has occurred in the past, it issues the first buy suggestion. If the price decline continues, it sequentially issues the second and finally the third buy suggestion. The inverse of this process is followed for issuing sell suggestions. 🟡 Usage Guide Add the indicator to your chart Go to the indicator's settings section In the Inputs tab, you can adjust the following values: Set the initial investment amount in USD Set the investment start date, from which calculations will begin Set the language for displaying information on the chart, which is English by default Display or hide labels for price and buy/sell volume on candles The indicator will automatically display the results on the chart and in its information panel 🟡 Important Notes and Limitations Compatibility : This indicator is specifically designed for the BTCUSD pair. To access the maximum historical data, you must use the INDEX broker chart and the Daily timeframe ; otherwise, the indicator will display a warning message. Long-Term Tool : This indicator is a macro analysis tool. Its signals are rarely issued and are designed to capture large trends spanning several months or years. This tool is by no means suitable for day trading or scalping. Non-Repainting : Buy and sell signals become definitive after the daily candle closes and do not change in the past. This feature increases the validity of backtests. Note Regarding the Source Code : The core logic of this indicator, especially the proprietary formulas used, is the result of personal research and development. To preserve this unique methodology and ensure its integrity for future developments, this version is released as closed-source. However, we have made every effort to fully and transparently describe the indicator's logic and operational process in the explanations. 🔴 Disclaimer This indicator is provided solely for educational, informational, and analytical purposes and should under no circumstances be considered financial advice or a definitive signal for buying and selling. Past market performance is by no means a guarantee of future results. All investment and trading activities involve risk, and the user is solely responsible for any profits or losses. Please conduct your own research and consult with a financial advisor before making any financial decisions. 🔴 Developers: Mr. Mohammad sanaei, Mrs. Hamideh Azari ⭐️⭐️ Feel free to share your feedback in the comments ⭐️⭐️ این اندیکاتور ابزاری تحلیلی و بلندمدت برای سرمایه‌گذاری در بیت‌کوین است که با الهام از چرخه‌های تاریخی هاوینگ، بیشترین رشد و افت ها تاریخی و روند کلی رشد قیمت طراحی شده است. هدف اصلی این اندیکاتور، ارائه یک دیدگاه استراتژیک به سرمایه‌گذاران است تا بتوانند فازهای کلیدی بازار مانند دوره‌های فروش عمده و خرید عمده بیت کوین را بهتر شناسایی کنند. 🔴 توسعه دهندگان: محمد ثنائی، حمیده آذری ⭐️⭐️ لطفاً نظرات خود را در کامنت‌ها با ما در میان بگذارید; از خواندن بازخوردهای شما خوشحال می‌شویم. ⭐️⭐️
Pine Script® indicator
by 100Zabaan
5
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.
Pine Script® indicator
by jtomasfg
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"
Pine Script® indicator
by fikira
5
MoonBoys BTC Production Cost Daily═════════════════════════════════════════════════════════════ MoonBoys BTC PRODUCTION COST DAILY ═════════════════════════════════════════════════════════════ Track Bitcoin's real-time production cost using comprehensive electricity consumption data and mining economics to identify macro support/resistance zones. ═══ OVERVIEW ═══ This indicator calculates Bitcoin's actual cost of production by combining Cambridge Bitcoin Electricity Consumption Index (CBECI) data with electricity pricing models across different mining eras. It reveals where miners are profitable or underwater, providing crucial macro-level support zones that have historically acted as psychological and economic floors. Perfect for: • Identifying long-term accumulation zones • Understanding miner profitability and capitulation risk • Spotting macro support levels during bear markets • Gauging healthy vs. overheated price levels • Planning dollar-cost averaging strategies ═══ KEY FEATURES ═══ 📊 COMPREHENSIVE HISTORICAL DATA └─ 378 data points spanning 2011-2026 └─ Complete CBECI electricity consumption dataset └─ Verified accuracy: all dates and values cross-checked └─ Updates every ~14 days with new CBECI releases ⚡ ELECTRICITY COST MODELING └─ Pre-June 2019: $0.05/kWh (Early mining era) └─ Pre-April 2021: $0.04/kWh (China dominance period) └─ Post-May 2021: $0.05/kWh (China exodus, Western migration) └─ Post-April 2024: $0.05/kWh (Post-4th halving era) └─ Fully customizable for scenario analysis 🎯 DUAL COST CURVES └─ Red line: Pure electricity cost per BTC └─ Purple line: Total production cost (electricity + operations) └─ Green line: Miner price (spot + transaction fee revenue) └─ Pink fill: Zones where miners are losing money 📈 AUTOMATIC HALVING ADJUSTMENTS └─ Integrates all Bitcoin halvings (2012, 2016, 2020, 2024) └─ Block reward automatically adjusts: 50 → 25 → 12.5 → 6.25 → 3.125 └─ Accurate per-day BTC production calculations 💰 PROFIT MARGIN TRACKING └─ Annual profit margin labels (optional) └─ Shows miner profitability percentage └─ Appears on chart at electricity cost level └─ Calculated using 365-day moving average ═══ HOW TO READ IT ═══ ┌─────────────────────────────────────────────────────────┐ │ INDICATOR │ MEANING │ ├─────────────────────────────────────────────────────────┤ │ 🟢 Green Line │ Miner Price (BTC price + fee revenue)│ │ (above purple) │ → Miners profitable, healthy market │ ├─────────────────────────────────────────────────────────┤ │ 🟢 Green Line │ Price below production cost │ │ (below purple) │ → Miner capitulation zone │ │ │ → Strong historical buy signal │ ├─────────────────────────────────────────────────────────┤ │ 🔴 Red Line │ Pure electricity cost per BTC │ │ │ → Absolute minimum mining cost │ ├─────────────────────────────────────────────────────────┤ │ 🟣 Purple Line │ Total production cost │ │ │ → Break-even for miners (60% elec) │ ├─────────────────────────────────────────────────────────┤ │ 🌸 Pink Fill │ Below-cost territory │ │ │ → Miners selling at a loss │ │ │ → Historical accumulation zone │ └─────────────────────────────────────────────────────────┘ ═══ TRADING APPLICATIONS ═══ 🐻 BEAR MARKET BOTTOMS → Price touching or breaking below production cost = high probability bottom → Extended periods below cost = miner capitulation → Historical bottoms: Nov 2011, Jan 2015, Dec 2018, Nov 2022 → Strongest buy signal in macro Bitcoin investing 📈 BULL MARKET HEALTH CHECKS → Distance above production cost = market heat level → 100-200% above cost = healthy bull market → 500%+ above cost = euphoric/bubble territory → Use as take-profit reference points 💎 ACCUMULATION STRATEGY → DCA when price approaches production cost → Increase buy size when price drops below cost → Maximum allocation when 10-20% below cost → Layer entry as margin shows negative percentages ⚖️ SUPPORT/RESISTANCE ZONES → Production cost acts as macro support in downtrends → Often becomes resistance after prolonged bear markets → Price reclaiming cost = bullish structural shift → Failed reclaims = continued weakness 🔄 HALVING CYCLE ANALYSIS → Cost doubles after each halving (supply cut) → Price typically consolidates near new cost basis → Historic pattern: break above cost = new bull cycle → Track 6-12 months post-halving for trend confirmation ═══ SETTINGS GUIDE ═══ ⚡ ELECTRICITY COST ASSUMPTIONS (USD/kWh) ├─ Pre-June 2019 (0.05): Early mining era, hobby miners ├─ Pre-China Exodus 2021 (0.04): Cheap Chinese hydropower ├─ Post-May 2021 (0.05): Western migration, higher costs └─ Post-April 2024 (0.05): Current era post-4th halving 💡 Adjust these for "what if" scenarios or local costs 💰 ELECTRICITY PERCENTAGE (Default: 60%) └─ Electricity as % of total mining costs └─ Remaining 40% = hardware, labor, rent, maintenance └─ Lower % = higher total cost (more conservative) └─ Typical range: 50-70% 🎨 VISUAL TOGGLES ├─ Plot BTC Miner Price: Show/hide green line │ └─ Includes transaction fee revenue per BTC ├─ Plot Production Cost Curves: Show/hide red & purple lines └─ Plot Annual Profit Margin Labels: Show/hide margin % └─ Appears annually (Jan 1st) and on last bar ═══ HOW IT WORKS ═══ 1. ELECTRICITY CONSUMPTION DATA • Cambridge Bitcoin Electricity Consumption Index (CBECI) • Actual network-wide energy usage in TWh (terawatt-hours) • Updated bi-weekly with real hash rate data • 378 historical data points (Aug 2011 - Jan 2026) 2. COST CALCULATION FORMULA Electricity Cost per BTC = (TWh per year / 365.25 days) × (10^9 to convert to kWh) / (BTC mined per day) × (Electricity price per kWh) Total Cost per BTC = Electricity Cost / (Electricity % of total costs) 3. BTC MINED PER DAY Blocks per day (144) × Block reward • 2009-2012: 50 BTC/block = 7,200 BTC/day • 2012-2016: 25 BTC/block = 3,600 BTC/day • 2016-2020: 12.5 BTC/block = 1,800 BTC/day • 2020-2024: 6.25 BTC/block = 900 BTC/day • 2024+: 3.125 BTC/block = 450 BTC/day 4. MINER PRICE METRIC Spot close price + (Transaction fees per day / BTC mined per day) • Currently simplified with fees = 0 • Shows true revenue per BTC for miners 5. PROFIT MARGIN CALCULATION ((Miner Price / Total Cost) - 1) × 100 • Smoothed with 365-day SMA • Shows sustainable annual profitability ═══ BEST PRACTICES ═══ ✅ DO: • Use on DAILY timeframe for accuracy (designed for daily data) • Combine with on-chain metrics (SOPR, MVRV, Puell Multiple) • Layer with traditional TA for entry/exit timing • Understand this is AVERAGE global cost (varies by miner) • Use as macro filter, not short-term trading signal • Check profit margins during capitulation events ❌ DON'T: • Use as sole indicator for short-term trades • Ignore that efficient miners have much lower costs • Forget that cost is constantly rising (hash rate + difficulty) • Assume price can't go below cost (it can temporarily) • Trade based only on cost - liquidity events can wick lower • Expect instant reversals at cost levels ═══ HISTORICAL PERFORMANCE ═══ Major Bitcoin bottoms near/below production cost: 📅 November 2011 Price: ~$2 | Cost: ~$5 → 60% below cost, -95% drawdown → Bottom signal ✓ 📅 January 2015 Price: ~$150 | Cost: ~$180 → 17% below cost, -85% drawdown → Bottom signal ✓ 📅 December 2018 Price: ~$3,200 | Cost: ~$3,500 → 9% below cost, -84% drawdown → Bottom signal ✓ 📅 November 2022 Price: ~$15,500 | Cost: ~$17,000 → 9% below cost, -77% drawdown → Bottom signal ✓ Pattern: When price trades below production cost, accumulation zone. ═══ TECHNICAL NOTES ═══ • Built with Pine Script v5 • Data source: Cambridge Centre for Alternative Finance (CBECI) Updated Quaterly by Request • All dates/values verified against official CSV dataset • Electricity price adjusts based on major mining regime shifts • Uses series variables for proper historical calculation • Forward-fills data between CBECI update periods • Accounts for all 4 halvings in BTC history ═══ DATA PERIODS EXPLAINED ═══ 🏭 PRE-JUNE 2019 ($0.05/kWh) Early mining era, distributed hobby miners, average global cost 🇨🇳 JUNE 2019 - APRIL 2021 ($0.04/kWh) China dominance period, cheap hydropower in Sichuan/Yunnan 🌍 MAY 2021 - APRIL 2024 ($0.05/kWh) China ban, Western migration, renewable energy transition 🔮 POST-APRIL 2024 ($0.05/kWh) 4th halving, institutional mining, current era ═══ UNDERSTANDING MINING ECONOMICS ═══ ⚡ ELECTRICITY = 60% OF COST (Default) └─ Largest variable expense for miners └─ Directly tied to hash rate and difficulty 🔧 OTHER COSTS = 40% ├─ ASIC hardware (depreciation) ├─ Facility rent and cooling ├─ Labor and maintenance ├─ Internet and infrastructure └─ Insurance and legal 💰 REVENUE SOURCES ├─ Block subsidy (newly minted BTC) └─ Transaction fees (variable, usually 2-10% of revenue) 📉 MINER BEHAVIOR • Profitable: Accumulate BTC, expand operations • Break-even: Hold BTC, maintain operations • Unprofitable: Forced selling, potential capitulation ═══ ADVANCED USE CASES ═══ 🔬 SCENARIO ANALYSIS → Adjust electricity costs to model different regions → US miners: $0.05-0.07/kWh → Nordic miners: $0.02-0.04/kWh → Middle East: $0.01-0.03/kWh 📊 COMBINE WITH ON-CHAIN DATA → Miner Net Position Change (selling pressure) → Hash Ribbons (miner capitulation indicator) → Difficulty Ribbon (hash rate compression) → Puell Multiple (miner revenue extremes) 🎯 MULTI-TIMEFRAME CONFLUENCE → Weekly chart: macro trend and cost support → Daily chart: precise entry/exit near cost → 4H chart: short-term reactions at cost levels 🌐 CORRELATION TRADING → Miner stocks (MARA, RIOT, CLSK) vs BTC cost → When BTC < cost, miner stocks typically -30-50% → Energy prices (oil, nat gas) affect mining costs ═══ LIMITATIONS & CONSIDERATIONS ═══ ⚠️ AVERAGE COST, NOT ACTUAL • Large miners with PPAs have costs as low as $0.02-0.03/kWh • Inefficient miners may have costs 2-3x the average • This shows network-wide average for reference ⚠️ ELECTRICITY PRICE ASSUMPTIONS • Static periods vs. dynamic energy markets • Renewable energy % growing = lower average cost over time • Geographic distribution matters (Texas vs. Kazakhstan) ⚠️ DOESN'T INCLUDE • ASIC efficiency improvements (more hash/watt) • Stranded energy and flare gas mining • Government subsidies or penalties • Seasonal variations (wet/dry seasons) ⚠️ LAGGING INDICATOR • CBECI data updates every ~14 days • Historical data, not forward-looking • Cost always rises, but at variable rate ═══ DISCLAIMER ═══ This indicator visualizes Bitcoin's estimated global average production cost based on publicly available electricity consumption data and modeled pricing assumptions. It does NOT: • Guarantee future price movements or bottoms • Account for individual miner profitability variations • Include all operational costs (simplified to electricity %) • Predict miner capitulation or selling pressure • Constitute financial advice or buy/sell signals Production cost is A REFERENCE POINT, not a hard floor. Price can and has traded below cost during extreme capitulation events. Market liquidity, macro conditions, and sentiment often override cost-basis logic in the short term. Always conduct your own research and use proper risk management. 📚 EDUCATIONAL USE ONLY | NOT FINANCIAL ADVICE ═══ RESOURCES ═══ Cambridge Bitcoin Electricity Consumption Index (CBECI) → ccaf.io Bitcoin Mining Economics → insights.braiins.com Block Reward Halving Schedule → www.bitcoinblockhalf.com Difficulty & Hash Rate Charts → www.blockchain.com Understanding ASIC Mining → academy.binance.com Mining Profitability Calculator → www.coinwarz.com On-Chain Miner Metrics → cryptoquant.com Energy & Mining Data → hashrateindex.com ═══════════════════════════════════════════════════════════ Built for the Bitcoin community 🚀 Because understanding the cost of production is fundamental analysis 💎 ═══════════════════════════════════════════════════════════
Pine Script® indicator
by VickzinBK
Quant Seasonality ProQuant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze precisly historical data. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation. Hope you enjoy it
Pine Script® indicator
by dinisfranco2006
Seasonality (100% reliable for the W)Quant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze historical data precisly. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation.
Pine Script® indicator
by dinisfranco2006
Crypto PCA [LuxAlgo]The Crypto PCA indicator provides a sophisticated, multi-asset sentiment gauge by applying Principal Component Analysis (PCA) to a basket of the top 20 cryptocurrencies. By extracting the primary driver of variance across these assets, the tool offers a "market-wide" oscillator that filters out individual coin noise to highlight the dominant trend and sentiment shifts in the crypto space. In modern quantitative finance, PCA is used to reduce dimensionality and identify the underlying factors that move a group of assets. This indicator brings that institutional-grade approach to the retail trader, condensing the price action of Bitcoin, Ethereum, Solana, and 17 other majors into a single, actionable signal. 🔶 USAGE The script serves as a macro-sentiment oscillator, allowing traders to see the "hidden" force driving the crypto market. It is designed to identify when the market is moving in unison and when that collective movement has reached an extreme. 🔹 Identifying Market Regimes The primary use of the PCA line (PC1) is to determine the current market regime. When the oscillator is above the zero line and colored green, it indicates that the majority of the top 20 assets are experiencing positive variance, signaling a broad bullish regime. Conversely, when the line is below zero and colored red, the market is in a collective bearish state. Traders can use this to align their individual trades with the direction of the total market energy. 🔹 Using Snapshot Mode for Situational Analysis While the continuous mode is ideal for long-term trend following, the Snapshot Mode provides a focused view of market dynamics over the most recent lookback window. This mode isolates the current sentiment cycle, allowing traders to see the specific trajectory and "shape" of the latest move without the influence of older historical data. By enabling Snapshot Mode, you can analyze the immediate internal structure of the market. It is particularly useful for identifying whether a recent pump or dump is a coordinated market-wide event or a more fragmented move. This helps in distinguishing between a broad structural shift and a temporary volatility spike. 🔹 Spotting Overextended Sentiment The indicator includes dashed horizontal lines at +2 and -2, representing standard deviation thresholds. Because the assets are standardized before calculation, these levels mark statistical extremes. Overbought Extremes: When the PCA line exceeds +2, the broad market is significantly overextended to the upside. This often precedes a cooling-off period or a mean-reversion event across the entire sector. Oversold Extremes: When the PCA line drops below -2, it suggests a "panic" or exhausted selling state across the basket. This can signal potential bottoming interest or a relief rally. 🔹 Gauging Relative Strength The faint "ghost" lines in the background represent the individual standardized price paths of the 20 included assets. By comparing these to the main PCA line, traders can identify leaders and laggards. An asset line that stays consistently above the PCA line during a rally is exhibiting relative strength, while an asset trailing below the PCA line is underperforming the market average. 🔶 DETAILS The indicator follows a rigorous mathematical pipeline to ensure the data is statistically significant and comparable across assets with different price scales. 🔹 Standardization (Z-Scores) Before performing PCA, every asset must be on the same scale. The script converts the price of all 20 assets into Z-scores based on the user-defined Lookback Period. A Z-score tells us how many standard deviations a price is from its mean. This allows the movement of a high-priced asset like BTC to be mathematically compared to a lower-priced asset like PEPE. 🔹 The Basket & PCA Approximation The indicator includes the following assets: BTC, ETH, BNB, XRP, SOL, TRX, DOGE, ADA, BCH, WBTC, XLM, LTC, HBAR, LINK, AVAX, PEPE, DOT, UNI, NEAR, and ICP. The script uses a correlation-based approximation to find the First Principal Component. It calculates the correlation of each asset to the equally weighted basket and uses these correlations as "loadings" to compute the PC1. This ensures that assets moving in sync with the general market trend are given higher priority in the final oscillator value. 🔹 Why PCA? Most "Crypto Indices" are simply weighted averages. PCA is superior because it identifies the commonality between assets. If 18 coins are moving up and 2 are moving down, PCA gives more weight to the 18 moving together, as they represent the "Principal Component" of the market's current energy. 🔶 SETTINGS 🔹 Main Settings Lookback Period (N): Determines the window used for Z-score standardization and PCA calculation. A shorter period makes the indicator more reactive, while a longer period identifies macro-cycle shifts. Z-Score Smoothing: Applies a Simple Moving Average (SMA) to the standardized asset values before the PCA calculation. This effectively filters out high-frequency noise and produces a smoother principal component line, which is useful for reducing false regime shifts in volatile markets. Enable Snapshot Mode: Switches the visual output from a continuous rolling line to a static view of the PCA over the most recent lookback window. 🔹 Visual Settings Standardized Assets Color: Controls the color and transparency of the 20 individual asset lines. Bull/Bear Colors: Defines the colors used for positive and negative market sentiment. Disclaimer: This indicator is a statistical tool for sentiment analysis and does not constitute financial advice. The PCA approach measures variance and correlation, not guaranteed future direction.
Pine Script® indicator
by LuxAlgo
2
Trend Highway + OB + EMA + Market Structure📘 User Guide: Neon Trend Highway (All-in-One System) "The complete all-in-one solution: Trend, Entries, and Market Structure." This indicator is designed to declutter your chart by combining essential tools into a high-visibility Neon theme. It cuts out the noise, leaving only what is necessary for real trading. 1. Visual Guide (Interface) 🎨 A. The Highway The colored ribbon that follows the price, used to identify the "Main Direction." 🟢 Neon Green: Market is Uptrend → Focus on Buy/Long. 🔴 Neon Red: Market is Downtrend → Focus on Sell/Short. 🟠🟡 Orange / Yellow: Market is Consolidating or Reversing → Caution: Wait for a clear direction. B. Key EMAs Used to filter the major trend and act as dynamic Support/Resistance. 🟡 Yellow Line (EMA 50): Medium-term trend. ⚪️ White Line (EMA 200): Long-term trend (The Bull/Bear Divider). Price above White Line = Bullish bias. Price below White Line = Bearish bias. C. Trend Memory / Chop Filter Small dots appearing above or below candles to confirm momentum strength. 🟢 Green Dot: Confirms Buying Pressure (Support Momentum). 🔴 Red Dot: Confirms Selling Pressure (Resistance Momentum). D. Structure & OB (Smart Money Concept) BOS / CHoCH Lines: Thin lines indicating a Break of Structure, signaling trend continuation. Order Blocks (Boxes): 🟩 Faint Green Box: Demand Zone (Key Support). 🟥 Faint Red Box: Supply Zone (Key Resistance). 2. Entry Signals 🚀 The system automatically calculates and labels entry points: 🟢 Label "BrkUP" (Break Up): Appears when price breaks out of a consolidation range upwards. Action: Consider opening Buy / Long. 🔴 Label "BrkDN" (Break Down): Appears when price breaks out of a consolidation range downwards. Action: Consider opening Sell / Short. ➕ Label "Add+": Signal to "Scale-in" (Add position). Appears when the trend remains strong, price retraces, and then continues. A safe point to increase profit potential. 3. Stop Loss & Exit Strategy 🛡️ ❌ Red Cross (Unified SL): This is the Trailing Stop Loss calculated dynamically by the system. How to use: If a candle "Closes" past this Red Cross, Cut Loss immediately to limit risk. 4. Strategy Checklist ✅ 📈 Bullish Setup (Uptrend) Check 1: Candle is above the EMA 200 (White line). Check 2: The Highway turns Green. Trigger: Wait for a BrkUP or Add+ signal. Confirm: (Optional) Look for Green Dots or a Green OB acting as support below for higher accuracy. SL: Set at the latest Red Cross. 📉 Bearish Setup (Downtrend) Check 1: Candle is below the EMA 200 (White line). Check 2: The Highway turns Red. Trigger: Wait for a BrkDN or Add+ signal. Confirm: (Optional) Look for Red Dots or a Red OB acting as resistance above for higher accuracy. SL: Set at the latest Red Cross. 5. How to Read the Dashboard 📊 Bottom Right: Trend System Stats Win Rate: Win/Loss percentage of past signals (Calculated on bar close). W / L: Total Win count / Loss count. Est. Cycle PNL: Estimated Profit/Loss of the current trend cycle (in Points). If Blue/Positive, the current signal is in profit. Top Right: Market Structure Displays the Market Structure status (Percentage of Bullish vs Bearish) to keep you aligned with the bigger picture. ⚠️ Important Notes: Signals are valid only on "Bar Close" (Do not enter while the candle is still moving/blinking). Avoid trading or reduce position size when the Highway is Orange/Yellow (Sideways market). This system is a decision-support tool; always practice strict Money Management.
Pine Script® indicator
by AOM-TAWAN
Skylark Digital Assets Monthly FLPSkylark Digital Assets’ Monthly Financial Liquidity Proxy (FLP) is a monthly, regime-focused macro indicator designed to summarize broad financial conditions into a single, stable signal. This version is the core Monthly FLP only—intended for straightforward liquidity regime tracking—without the additional seasonal classification logic used in other variants. What you see Monthly FLP (confirmed): A consolidated monthly liquidity gauge that is held stable on intramonth bars to avoid “mid-month” distortions. The series is designed to reflect the underlying state of conditions at the monthly level rather than short-term noise. Optional Monthly FLP EMA: A smoothing/trend filter that helps highlight structural shifts and reduces month-to-month volatility. Midline reference: A neutral reference level for quick above/below regime interpretation. How to use it Macro regime context: Use the Monthly FLP as a higher-timeframe backdrop for understanding when conditions are broadly improving or tightening. Cycle confirmation: The monthly timeframe reduces noise and is best suited for identifying longer-cycle transitions rather than short-term trades. Asset overlays: Add the FLP to any chart (crypto, equities, FX, rates, commodities) to compare whether price is moving with or against the broader liquidity regime. Notes This script is intended for research and visualization. It is not a trading strategy and does not provide guaranteed signals. Always apply independent confirmation and risk management.
Pine Script® indicator
by Skylark_Digital_Assets
BTC Macro Regime & Stoch RSI SignalThis script is designed for traders who want to combine macro context with precise momentum timing on Bitcoin. Instead of looking at isolated indicators, it aggregates several independent data streams into a single bias score and then uses a stochastic RSI engine to time entries and exits. The goal is simple: Filter the market into long / neutral / short regimes, Only act when momentum aligns with the current regime, Avoid overtrading in noisy or highly leveraged conditions. What the indicator does (conceptually) The script builds an internal “macro + momentum score” for BTC by combining: Cycle / valuation regime Tracks where BTC sits in the broader cycle (discount vs overheating) and whether conditions are favorable for medium-term upside or downside. Network & miner trend regime Monitors the health and trend of network activity / miners to detect stress, capitulation and recovery phases. Derivatives / leverage regime Looks at futures positioning to identify when leverage is excessive or has been flushed out, acting as a risk filter rather than a standalone entry trigger. Price momentum (Stochastic RSI) Uses a Stochastic RSI engine on price to capture short-term swings and turning points, especially at extremes. All of this is compressed into a single score between −1 and +1, where: Values near +1 indicate a supportive macro environment with bullish momentum, Values near −1 indicate a risk-off / bearish environment, Values around 0 indicate indecision or transition. The exact construction, weights and thresholds are handled internally by the script. Signals & visuals The indicator provides: A Total Score line: Above a configurable upper level → long bias Below a configurable lower level → short bias In between → neutral / low conviction Background shading: Green tint when the regime favors long setups Red tint when the regime favors short setups Stochastic RSI panel: K and D lines plotted on a 0–100 scale The 0–10 zone is highlighted in green (deep oversold) The 90–100 zone is highlighted in red (deep overbought) Extreme cross markers: A green marker when Stoch RSI crosses up in the lower extreme zone A red marker when Stoch RSI crosses down in the upper extreme zone Entry / exit markers (optional visual guide): Long entry markers appear when: The macro score is in long-bias territory, and Stoch RSI confirms an upward cross from oversold conditions. Short entry markers appear when: The macro score is in short-bias territory, and Stoch RSI confirms a downward cross from overbought conditions. Exit markers appear when momentum flips against the active bias or the macro score degrades. These markers are guides, not mechanical trading rules. How to use it Typical workflow: Start with the Total Score Use it as a regime filter: Only look for longs when the score shows a sustained positive bias. Only look for shorts when the score shows a sustained negative bias. Then look at Stoch RSI Use it to time pullbacks and reversals within the current bias: In a long bias → favor bullish crosses from low levels. In a short bias → favor bearish crosses from high levels. Respect leverage conditions When the internal risk engine flags crowded leverage, treat signals more conservatively: Reduce size, Tighten risk, Or skip trades entirely if conditions look unstable. This script is not meant to create a high-frequency scalping system. It is a context + timing framework for swing and positional trades on BTC. Timeframes The macro components are designed with higher-timeframe logic in mind. Recommended: Use 1D as the primary perspective for the bias, Optionally refine entries on 4H if you want more precise timing. Important notes & disclaimer This tool is specifically tuned for Bitcoin, not for altcoins. The internal logic, data processing and weighting are intentionally abstracted to keep the focus on the final score and signals rather than on raw formulas. As with any indicator, it can produce false signals and whipsaws, especially during violent news events or regime shifts. This is not financial advice. Always combine the script with your own analysis, sound risk management and position sizing. Use at your own risk.
Pine Script® indicator
by Metina
Altcoin Relative Macro StrengthAltcoin Relative Macro Strength Overview The Altcoin Relative Macro Strength indicator measures the altcoin market's price performance relative to global macroeconomic conditions. By comparing TOTAL3ES (total altcoin market capitalization excluding Bitcoin, Ethereum and stable coins) against a composite macro trend, the indicator identifies periods of relative overvaluation and undervaluation. Methodology Global Macro Trend Calculation: The macro trend synthesizes three primary components: - ISM PMI – A proxy for the business cycle phase - Global Liquidity – An aggregate measure of major central bank balance sheets and broad money supply - IWM (Russell 2000) – Small-cap equity exposure, reflecting risk-on/risk-off market sentiment Global Liquidity is calculated as: Fed Balance Sheet - Reverse Repo - Treasury General Account + U.S. M2 + China M2 The final Global Macro Trend is: ISM PMI × Global Liquidity × IWM Theoretical Framework: The global macro trend integrates liquidity expansion/contraction with business cycle dynamics and small-cap equity performance. The inclusion of IWM reflects altcoins' tendency to behave as high-beta risk assets, exhibiting sensitivity similar to small-cap equities. This composite exhibits strong directional correlation with altcoin market movements, capturing the risk-on/risk-off dynamics that drive altcoin performance. Interpretation Primary Signal: The histogram displays the rolling percentage change of TOTAL3ES relative to the global macro trend (default: 21-period average). Positive divergence indicates altcoins are outperforming macro conditions; negative divergence suggests underperformance relative to the underlying economic and risk environment. Data Tables: Alts/Macro Change – Percentage deviation of the altcoin market's average value from the Global Macro Trend's average over the specified period Macro Trend – Directional assessment of the macro trend based on slope and trend agreement: 🔵 BULLISH ▲ – Positive slope with upward trend ⚪ NEUTRAL → – Slope and trend direction disagree 🟣 BEARISH ▼ – Negative slope with downward trend Macro Slope – Percentage rate of change in the global macro trend Altcoin Valuation – Relative valuation category based on TOTAL3/Macro deviation: 🟢 Extreme Discount / Deep Discount / Discount 🟡 Fair Value 🔴 Premium / Large Premium / Extreme Premium TOTAL3ES Mcap – Current total altcoin market capitalization (in billions) Visual Components: 📊 Histogram: Alts/Macro Change 🟢 Green = Positive deviation (altcoins outperforming) 🔴 Red = Negative deviation (altcoins underperforming) 📈 Macro Slope Line Color-coded to match trend assessment Scaled for visibility (adjustable in settings) Application This indicator is designed to identify mean reversion opportunities by highlighting periods when the altcoin market materially diverges from fundamental macro and risk conditions. Extreme positive values may indicate overvaluation; extreme negative values may signal undervaluation relative to the prevailing economic and risk appetite backdrop. Strategy Considerations: - Identify extremes: Look for periods when the histogram reaches elevated positive or negative levels - Assess valuation: Use the Altcoin Valuation reading to gauge relative over/undervaluation Confirm with risk sentiment: Check whether macro conditions and risk appetite support or contradict current price levels - Mean reversion: Consider that significant deviations from trend historically tend to revert Note: This indicator identifies relative valuation based on macro conditions and risk sentiment—it does not predict price direction or timing. Settings Lookback Period – 21 bars (default) – Number of bars for calculating rolling averages Macro Slope Scale – 3.0 (default) – Multiplier for macro slope line visibility
Pine Script® indicator
by DamonAndTheSea
BTC - VERI - Valuation & Entity Ratio IndexVERI: Valuation & Entity Ratio IndexObservation-only. Data: IntoTheBlock. Overview & Philosophy The name VERI is derived from the Latin Veritas (Truth). In a crypto market often driven by deceptive speculative noise, this indicator seeks to establish the "On-Chain Truth" of a price trend. It operates on the thesis that price action is only sustainable when verified by high-conviction capital flows.VERI is a fundamental composite oscillator that fuses Entity Behavior (Who is holding?) with Network Valuation (Is the price fair?) to identify Bitcoin market cycle extremes. The "Alpha" Why this Composite stands out: on-chain metrics often tell only half the story. MVRV tells you if the price is cheap, but not if anyone is actually buying. Whale Activity tells you if large players are moving, but not if they are accumulating at a value discount. VERI fuses these two dimensions into a single Z-Score. It identifies the rare, high-probability moments where Smart Money Conviction intersects with Deep Value. Methodology The Mathematics of VERI: The indicator constructs a composite index using three fundamental metrics from IntoTheBlock: The "Who" (Entity Ratio) : We calculate the flow ratio between Whales (>1% supply holders) and Retail (<0.1% supply holders). A rising ratio indicates supply is transferring from weak hands to strong hands. The "Why" (Valuation Multiplier) : We utilize the MVRV (Market Value to Realized Value) ratio. To isolate value opportunities, we use the inverse (1 / MVRV). The Fusion : These factors are multiplied to create the raw VERI index. Normalization & Inversion We apply a rolling Z-Score (standard deviation from the mean) and invert the result. How to Interpret the Indicator Because the output is inverted, the visual logic matches price action intuitively: 🟥 Distribution Zone (High Values > 1.5): The Signal: "Low Conviction Overvaluation." Context: The price is historically expensive relative to the cost basis (High MVRV), and Whales are distributing coins to Retail.Implication: Historically precedes macro tops or deep corrections. 🟩 Accumulation Zone (Low Values < -1.5): The Signal: "High Conviction Undervaluation."Context: The price is historically cheap (Low MVRV), and Whales are aggressively accumulating relative to Retail. Implication: Historically precedes macro bottoms and generational entry points. Zero Line : Represents the historical baseline. A crossover of the zero line often confirms a regime shift (e.g., from Bear to Bull). Visual Guide & Features Dynamic Coloring: The line turns Red in the Distribution Zone, Blue in the Accumulation Zone, and Orange during neutral trends. Zone Labels: Static labels are pinned to the left side of the chart for immediate context. The "Data Check" Monitor (Status Table): Since this indicator relies on third-party fundamental data, we have included a diagnostic table in the bottom-right corner. Data Check Monitor Guide STATUS: LIVE (Green): The indicator is functioning correctly. All data feeds (Whales, Retail, MVRV) are being retrieved successfully. STATUS: WAIT (Red): The indicator cannot retrieve data. This might happen for some reasons, e.g. your TradingView plan may not support IntoTheBlock integration. Settings Lookback Period (Default: 365): The window used for Z-Score normalization. We use a full year to smooth out seasonal volatility. Smoothing (Default: 7): A 7-day smoothing is applied to the signal to filter out daily noise. Zone Thresholds: Users can customize the specific Z-Score levels for the Distribution and Accumulation bands. Disclaimer This script is for research and educational purposes only. It uses historical on-chain data to visualize market structure and does not constitute financial advice. Past performance of whale entities does not guarantee future results. Tags bitcoin, btc, on-chain, mvrv, whales, valuation, fundamentals, cycle, oscillator, veri
Pine Script® indicator
by Rob_Maths
Nexural Fisher ProNexural Fisher Transform Ultimate This is my take on what the Fisher Transform should have been from the start. Not a repaint of the standard indicator with a fresh coat of paint, but a ground-up rebuild using techniques from John Ehlers and quantitative finance that actually make a difference. What This Indicator Does The Fisher Transform converts price into a Gaussian normal distribution, which sounds complicated but really just means it gives you clear overbought and oversold readings with sharp turning points. The problem with the standard Fisher Transform is that it was designed decades ago and has some serious limitations. This version addresses those limitations directly. The Core Improvements Adaptive Period via Dominant Cycle Measurement Instead of guessing what lookback period works best, this indicator measures the dominant cycle in the market using autocorrelation and adjusts itself automatically. In choppy markets it becomes more responsive. In smooth trends it slows down to avoid false signals. You no longer have to optimize the length setting for every instrument and timeframe because the indicator does it for you. Tanh Normalization The standard Fisher Transform has a nasty habit of spiking to extreme values during volatile moves. You have probably seen Fisher readings of plus or minus 8 or even higher which makes the indicator almost unreadable. This version uses hyperbolic tangent normalization to mathematically bound the output. The indicator stays within a consistent range regardless of market conditions so your overbought and oversold levels actually mean something. Efficiency Ratio Regime Detection This is the feature that changes how you use the indicator. The Efficiency Ratio measures whether the market is trending or ranging on a scale from zero to one. A high reading means price is moving efficiently in one direction which indicates a trend. A low reading means price is chopping around which indicates a range. Why does this matter? Because oscillators work completely differently in trends versus ranges. In a ranging market you want to fade overbought and oversold readings. In a trending market those same signals will destroy you as the market stays overbought or oversold for extended periods while continuing in the trend direction. The info panel shows you the current regime so you know how to interpret the signals. When it says Trending you trust the direction and ignore the zones. When it says Ranging you trust the zones and fade the extremes. Volume Weighted Calculation High volume bars have more influence on the Fisher calculation than low volume bars. This means the indicator responds more to moves that have real participation behind them and filters out low volume noise. The weighting is clamped to prevent any single bar from dominating the calculation. Ehlers Super Smoother Rather than using a simple moving average or exponential moving average for smoothing, this indicator uses the Ehlers Super Smoother which is a two pole filter specifically designed to remove noise without adding lag. The math behind it is based on signal processing theory and it genuinely works better than traditional smoothing methods. How To Use It The indicator shows two lines. The main Fisher line changes color based on direction. Green means bullish momentum and red means bearish momentum. The white trigger line is the previous bar value and crossovers between the two lines indicate momentum shifts. The info panel in the corner gives you everything at a glance. State tells you the current direction. Zone tells you if the indicator is overbought, oversold, or neutral. Regime tells you if the market is trending, ranging, or mixed. ER shows you the raw Efficiency Ratio value. Period shows you the current adaptive lookback being used. Fisher shows you the exact indicator value. For ranging markets look for bullish crosses when the indicator is in oversold territory and bearish crosses when the indicator is in overbought territory. These are mean reversion setups. For trending markets use the indicator to confirm trend direction and look for pullback entries when the indicator dips toward the zero line without reaching oversold or overbought extremes. Strengths The adaptive period is the biggest advantage. Most traders either use a default setting that works sometimes or spend hours optimizing settings that stop working when market conditions change. This indicator handles that problem automatically. The regime detection is genuinely useful. Knowing whether to fade extremes or ride momentum is half the battle with any oscillator and this indicator tells you directly. The bounded output means your levels are consistent. When the indicator hits 1.5 it means the same thing today as it did last month. You are not constantly adjusting your interpretation based on recent volatility. The volume weighting adds a layer of confirmation that most oscillators lack entirely. Moves on high volume carry more weight which aligns with how markets actually work. Weaknesses No indicator predicts the future. This indicator tells you about momentum and regime but it does not tell you where to place your stop or what your target should be. It is a tool not a strategy. The adaptive period can occasionally shift during volatile transitions which may cause the indicator to behave slightly differently than expected. The smoothing minimizes this but it can still happen. Like all oscillators this indicator is better at identifying conditions than timing exact entries. A cross into oversold does not mean price stops falling immediately. It means conditions favor a bounce but the timing of that bounce requires additional analysis. The regime detection has a slight lag because it needs data to determine whether the market is trending or ranging. At the exact moment of a regime change the indicator may still show the previous state for a few bars. What This Is Not This is not a signal service. There are no buy and sell arrows because markets are more nuanced than that. A bullish cross in a range means something completely different than a bullish cross in a trend and treating them the same is how traders lose money. This is not a holy grail. It will not turn a losing trader into a winning trader on its own. It is a well built tool that gives you better information than the standard Fisher Transform. This is not overfitted to historical data. The techniques used here are based on principles that have been validated across decades of market data. There is no curve fitting or optimization to make backtests look good. Final Thoughts I built this because I got tired of indicators that looked good in marketing but fell apart in live trading. The standard Fisher Transform has real value but the standard implementation has real problems. This version solves those problems using math that actually makes sense. If you understand what the indicator is telling you and combine it with solid risk management and market structure analysis, it will serve you well. If you are looking for something to tell you exactly when to buy and sell, keep looking because that does not exist. This is an excellent indicator on the 5-15 time frame. Use it wisely.
Pine Script® indicator
by Nexural
BTC Energy + HR + Longs + M2 BTC Energy Ratio + Hashrate + Longs + M2 The #1 Bitcoin Macro Weapon on TradingView 🚀🔥 If you’re tired of getting chopped by fakeouts, ETF noise, and Twitter hopium — this is the one chart that finally puts you on the right side of every major move. What you’re looking at: Orange line → Bitcoin priced in real-world mining energy (Oil × Gas + Uranium × Coal) × 1000 → The true fundamental floor of BTC Blue line → Scaled hashrate trend (miner strength & capex lag) Green line → Bitfinex longs EMA (leveraged bull sentiment) Purple line → Global M2 money supply (US+EU+CN+JP) with 10-week lead (the liquidity wave BTC rides) Why this indicator prints money: Most tools react to price. This one predicts where price is going based on energy, miners, leverage, and liquidity — the only four things that actually drive Bitcoin long-term. It has nailed: 2022 bottom at ~924 📉 2024 breakout above 12,336 🚀 2025 top at 17,280 🏔️ And right now it’s flashing generational accumulation at ~11,500 (Nov 2025) 13 permanent levels with right-side labels — no guessing what anything means: 20,000 → 2021 Bull ATH 17,280 → 2025 ATH 15,000 → 2024 High Resist 14,000 → Overvalued Zone 13,000 → 2024 Breakout 12,336 → Bull/Bear Line (the most important level) 12,000 → 2024 Volume POC 10,930 → Key Support 2024 9,800 → Strong Buy Fib 8,000 → Deep Support 2023 6,000 → 2021 Mid-Cycle 4,500 → 2023 Accum Low 924 → 2022 Bear Low Live dashboard tells you exactly what to do — no thinking required: Current ratio (updates live) Hashrate + 24H % Longs trend Risk Mode → Orange vs Hashrate (RISK ON / RISK OFF) 180-day correlation RSI 13-tier Zone + SIGNAL (STRONG BUY / ACCUMULATE / HOLD / DISTRIBUTE / EXTREME SELL) Dead-simple rules that actually work: Weekly timeframe = cleanest view Blue peaking + orange holding support → miner pain = next leg up Green spiking + orange failing → overcrowded longs = trim Purple rising → liquidity coming in = ride the wave Risk Mode = RISK OFF → price is cheap vs miners → buy Set these 3 alerts and walk away: Ratio > 12,336 → Bull confirmed → add Ratio > 14,000 → Start scaling out Ratio < 9,800 → Generational buy → back up the truck No repainting • Fully open-source • Forced daily data • Works on any TF Energy is the only real backing Bitcoin has. Hashrate lag is the best leading indicator. Longs show greed. M2 is the tide. This chart combines all four — and right now it’s screaming ACCUMULATE. Load it. Trust it. Stop trading hope. Start trading reality. DYOR • NFA • For entertainment purposes only 😎 #bitcoin #macro #energy #hashrate #m2 #cycle #riskon #riskoff
Pine Script® indicator
by AndyHansen
NEESON Plus Crypto Market Sentiment IndicatorCore Features 1. Multi-Factor Sentiment Scoring System Comprehensive Algorithm: Combines 6 different market indicators Weighted Scoring: Each factor contributes with different weights Real-time Calculation: Updates with every new bar Smoothing Mechanism: Triple EMA smoothing for stable signals 2. Advanced Technical Indicators Integration Multi-Timeframe RSI: 1H, 4H, and Daily RSI analysis Volume Analysis: Volume spikes and decline detection ATR Volatility: Market volatility assessment MACD Momentum: Trend momentum confirmation Bollinger Bands: Price position analysis 3. Proprietary Indicator Calculations AHR999 Proxy: Enhanced version for crypto markets Puell Multiple Proxy: Dynamic calculation with RSI adjustment PI Cycle Top: Multi-moving average cycle analysis CBBI Enhanced: Crypto Bull Bear Index with momentum Market Volatility Sentiment: Volatility-based sentiment scoring Volume Sentiment: Volume-based market sentiment Signal Generation System 4. Multi-Condition Signal Filters Strong Buy/Sell Signals: Multiple confirmation requirements Warning Signals: Early entry/exit indications Confirmation Bars: User-configurable signal confirmation Trend Filter: Optional trend alignment requirement Volume Filter: Volume spike confirmation Volatility Filter: ATR-based market condition filtering Momentum Filter: MACD momentum confirmation 5. Advanced Signal Management Signal State Tracking: Maintains current position state Duration Tracking: Tracks how long signals have been active Entry Score Recording: Records sentiment score at entry Consecutive Signal Counting: Prevents signal flipping Exit Conditions: Multiple exit criteria for risk management Visualization Features 6. Professional Chart Display Dual Score Plotting: Comprehensive and raw sentiment scores Color-Coded Background: Real-time market sentiment coloring Threshold Lines: Clear visual reference levels Area Fills: Colored zones for different sentiment levels Signal Markers: Visual indicators for buy/sell signals 7. Information Panel Real-time Data Display: Current scores and signals Position Tracking: Duration and entry information Performance Metrics: Floating P/L calculation Market Status: RSI, Volume, Volatility, MACD status Configuration Status: Current filter settings Customization Options 8. User-Configurable Parameters Threshold Settings: Adjustable buy/sell/exit levels Filter Toggles: Enable/disable various filters Indicator Periods: Customizable calculation periods Color Settings: Fully customizable color scheme Signal Duration: Minimum signal duration requirements 9. Alert System Strong Buy/Sell Alerts: Immediate notification for strong signals Warning Alerts: Early signal notifications Custom Alert Messages: Clear, descriptive alert texts Multiple Timeframe Compatibility: Works across all timeframes Risk Management Features 10. Built-in Protection Mechanisms Signal Confirmation: Prevents false signals Exit Triggers: Multiple exit conditions Position Duration Limits: Automatic exit after prolonged periods Profit/Loss Tracking: Real-time performance monitoring Volatility Adjustment: Adapts to market conditions Technical Specifications 11. Performance Optimization Efficient Calculation: Optimized for real-time performance Multi-Timeframe Support: Works on all chart timeframes Resource Management: Controlled line and label counts Precision Control: Adjustable decimal precision 12. Compatibility Cryptocurrency Focus: Specifically designed for crypto markets Multi-Asset Support: Works with all TradingView symbols Platform Compatibility: Fully compatible with TradingView platform Mobile Support: Responsive design for mobile devices Usage Benefits Comprehensive Analysis: Single indicator providing multiple insights Clear Signals: Easy-to-understand buy/sell indications Customizable: Adaptable to different trading styles Risk-Aware: Built-in risk management features Professional Grade: Institutional-level analysis tools User-Friendly: Intuitive visual interface Educational: Helps understand market sentiment dynamics This indicator is designed to provide traders with a comprehensive market sentiment analysis tool specifically optimized for cryptocurrency markets, combining traditional technical analysis with crypto-specific metrics.
Pine Script® indicator
by neeson1987
Multi-Mode Seasonality Map [BackQuant]Multi-Mode Seasonality Map A fast, visual way to expose repeatable calendar patterns in returns, volatility, volume, and range across multiple granularities (Day of Week, Day of Month, Hour of Day, Week of Month). Built for idea generation, regime context, and execution timing. What is “seasonality” in markets? Seasonality refers to statistically repeatable patterns tied to the calendar or clock, rather than to price levels. Examples include specific weekdays tending to be stronger, certain hours showing higher realized volatility, or month-end flow boosting volumes. This tool measures those effects directly on your charted symbol. Why seasonality matters It’s orthogonal alpha: timing edges independent of price structure that can complement trend, mean reversion, or flow-based setups. It frames expectations: when a session typically runs hot or cold, you size and pace risk accordingly. It improves execution: entering during historically favorable windows, avoiding historically noisy windows. It clarifies context: separating normal “calendar noise” from true anomaly helps avoid overreacting to routine moves. How traders use seasonality in practice Timing entries/exits : If Tuesday morning is historically weak for this asset, a mean-reversion buyer may wait for that drift to complete before entering. Sizing & stops : If 13:00–15:00 shows elevated volatility, widen stops or reduce size to maintain constant risk. Session playbooks : Build repeatable routines around the hours/days that consistently drive PnL. Portfolio rotation : Compare seasonal edges across assets to schedule focus and deploy attention where the calendar favors you. Why Day-of-Week (DOW) can be especially helpful Flows cluster by weekday (ETF creations/redemptions, options hedging cadence, futures roll patterns, macro data releases), so DOW often encodes a stable micro-structure signal. Desk behavior and liquidity provision differ by weekday, impacting realized range and slippage. DOW is simple to operationalize: easy rules like “fade Monday afternoon chop” or “press Thursday trend extension” can be tested and enforced. What this indicator does Multi-mode heatmaps : Switch between Day of Week, Day of Month, Hour of Day, Week of Month . Metric selection : Analyze Returns , Volatility ((high-low)/open), Volume (vs 20-bar average), or Range (vs 20-bar average). Confidence intervals : Per cell, compute mean, standard deviation, and a z-based CI at your chosen confidence level. Sample guards : Enforce a minimum sample size so thin data doesn’t mislead. Readable map : Color palettes, value labels, sample size, and an optional legend for fast interpretation. Scoreboard : Optional table highlights best/worst DOW and today’s seasonality with CI and a simple “edge” tag. How it’s calculated (under the hood) Per bar, compute the chosen metric (return, vol, volume %, or range %) over your lookback window. Bucket that metric into the active calendar bin (e.g., Tuesday, the 15th, 10:00 hour, or Week-2 of month). For each bin, accumulate sum , sum of squares , and count , then at render compute mean , std dev , and confidence interval . Color scale normalizes to the observed min/max of eligible bins (those meeting the minimum sample size). How to read the heatmap Color : Greener/warmer typically implies higher mean value for the chosen metric; cooler implies lower. Value label : The center number is the bin’s mean (e.g., average % return for Tuesdays). Confidence bracket : Optional “ ” shows the CI for the mean, helping you gauge stability. n = sample size : More samples = more reliability. Treat small-n bins with skepticism. Suggested workflows Pick the lens : Start with Analysis Type = Returns , Heatmap View = Day of Week , lookback ≈ 252 trading days . Note the best/worst weekdays and their CI width. Sanity-check volatility : Switch to Volatility to see which bins carry the most realized range. Use that to plan stop width and trade pacing. Check liquidity proxy : Flip to Volume , identify thin vs thick windows. Execute risk in thicker windows to reduce slippage. Drill to intraday : Use Hour of Day to reveal opening bursts, lunchtime lulls, and closing ramps. Combine with your main strategy to schedule entries. Calendar nuance : Inspect Week of Month and Day of Month for end-of-month, options-cycle, or data-release effects. Codify rules : Translate stable edges into rules like “no fresh risk during bottom-quartile hours” or “scale entries during top-quartile hours.” Parameter guidance Analysis Period (Days) : 252 for a one-year view. Shorten (100–150) to emphasize the current regime; lengthen (500+) for long-memory effects. Heatmap View : Start with DOW for robustness, then refine with Hour-of-Day for your execution window. Confidence Level : 95% is standard; use 90% if you want wider coverage with fewer false “insufficient data” bins. Min Sample Size : 10–20 helps filter noise. For Hour-of-Day on higher timeframes, consider lowering if your dataset is small. Color Scheme : Choose a palette with good mid-tone contrast (e.g., Red-Green or Viridis) for quick thresholding. Interpreting common patterns Return-positive but low-vol bins : Favorable drift windows for passive adds or tight-stop trend continuation. Return-flat but high-vol bins : Opportunity for mean reversion or breakout scalping, but manage risk accordingly. High-volume bins : Better expected execution quality; schedule size here if slippage matters. Wide CI : Edge is unstable or sample is thin; treat as exploratory until more data accumulates. Best practices Revalidate after regime shifts (new macro cycle, liquidity regime change, major exchange microstructure updates). Use multiple lenses: DOW to find the day, then Hour-of-Day to refine the entry window. Combine with your core setup signals; treat seasonality as a filter or weight, not a standalone trigger. Test across assets/timeframes—edges are instrument-specific and may not transfer 1:1. Limitations & notes History-dependent: short histories or sparse intraday data reduce reliability. Not causal: a hot Tuesday doesn’t guarantee future Tuesday strength; treat as probabilistic bias. Aggregation bias: changing session hours or symbol migrations can distort older samples. CI is z-approximate: good for fast triage, not a substitute for full hypothesis testing. Quick setup Use Returns + Day of Week + 252d to get a clean yearly map of weekday edge. Flip to Hour of Day on intraday charts to schedule precise entries/exits. Keep Show Values and Confidence Intervals on while you calibrate; hide later for a clean visual. The Multi-Mode Seasonality Map helps you convert the calendar from an afterthought into a quantitative edge, surfacing when an asset tends to move, expand, or stay quiet—so you can plan, size, and execute with intent.
Pine Script® indicator
by BackQuant
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.
Pine Script® indicator
by SiDec
3
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! ⌛
Pine Script® indicator
by BarefootJoey
4
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
Pine Script® indicator
by fikira
9
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
Pine Script® indicator
by INTELLECT_city
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).
Pine Script® indicator
by chry5ler
4