Seite 7 | Scripts Suchergebnis für "2025年+A股+股票推荐"

Multi-indicator Signal Builder [Skyrexio]Overview Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof. It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label. Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions. This script aims to simplify strategy creation and analysis, making it a powerful toolkit for technical traders. Indicators Overview 1. RSI (Relative Strength Index) Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale. 2. Ultimate Oscillator (UO) Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences. 3. Bollinger %B Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold). 4. Moving Average (MA) Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers. 5. ADX (Average Directional Index) Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend. 6. Stochastic Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals. 7. MACD (Moving Average Convergence/Divergence) Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers. 8. PSAR (Parabolic SAR) Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR. 9. MFI (Money Flow Index) Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold. 10. CCI (Commodity Channel Index) Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe. 11. Heikin Ashi A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends. 12. TV Screener A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired. Unique Features 1. Multi-Indicator Entry and Exit You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers. 2. Single-Entry / Single-Exit with Intrabar SL/TP The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets. 3. Time Window Filter Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading. 4. Condition Table and Statistics A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc. 5. Seamless Alerts and Automation Configure alerts in TradingView using “Any alert() function call.” The script sends JSON alert messages you can route to your own webhook. The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges 6. Optional MA/PSAR Plots For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators. Methodology 1. Multi-Indicator Entry Logic When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers. 2. Single-Entry Intrabar SL/TP One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close. Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end. 3. Exit Logic All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close. 4. Time Filter Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval. Justification of Methodology Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals. Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios. Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots). Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods. How to Use 1. Add the Script to Your Chart In TradingView, open Indicators , search for “Multi-indicator Signal Builder”. Click to add it to your chart. 2. Configure Inputs Time Filter: Set a start and end date for trades. Alerts Messages: Input any JSON or text payload needed by your external service or bot. Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry. Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels. 3. Set Up Alerts In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script. Entry Alert: Triggers on the script’s entry signal. Close Alert: Triggers on the script’s close signal (or if SL/TP is hit). Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker). 4. Visual Reference A condition table at the bottom summarizes active signals. Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics. Backtesting Guidelines Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing. Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions. Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance. Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability. Example of statistic calculation Test Period: 2023-01-01 to 2025-12-31 Initial Capital: $1,000 Commission: 0.1%, Slippage ~5 ticks Trade Count: 468 (varies by strategy conditions) Win rate: 76% (varies by strategy conditions) Net Profit: +96.17% (varies by strategy conditions) Disclaimer This indicator is provided strictly for informational and educational purposes . It does not constitute financial or trading advice. Past performance never guarantees future results. Always test thoroughly in demo environments before using real capital. Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!

Mean Reversion Pro Strategy [tradeviZion]Mean Reversion Pro Strategy : User Guide A mean reversion trading strategy for daily timeframe trading. Introduction Mean Reversion Pro Strategy is a technical trading system that operates on the daily timeframe. The strategy uses a dual Simple Moving Average (SMA) system combined with price range analysis to identify potential trading opportunities. It can be used on major indices and other markets with sufficient liquidity. The strategy includes: Trading System Fast SMA for entry/exit points (5, 10, 15, 20 periods) Slow SMA for trend reference (100, 200 periods) Price range analysis (20% threshold) Position management rules Visual Elements Gradient color indicators Three themes (Dark/Light/Custom) ATR-based visuals Signal zones Status Table Current position information Basic performance metrics Strategy parameters Optional messages 📊 Strategy Settings Main Settings Trading Mode Options: Long Only, Short Only, Both Default: Long Only Position Size: 10% of equity Starting Capital: $20,000 Moving Averages Fast SMA: 5, 10, 15, or 20 periods Slow SMA: 100 or 200 periods Default: Fast=5, Slow=100 🎯 Entry and Exit Rules Long Entry Conditions All conditions must be met: Price below Fast SMA Price below 20% of current bar's range Price above Slow SMA No existing position Short Entry Conditions All conditions must be met: Price above Fast SMA Price above 80% of current bar's range Price below Slow SMA No existing position Exit Rules Long Positions Exit when price crosses above Fast SMA No fixed take-profit levels No stop-loss (mean reversion approach) Short Positions Exit when price crosses below Fast SMA No fixed take-profit levels No stop-loss (mean reversion approach) 💼 Risk Management Position Sizing Default: 10% of equity per trade Initial capital: $20,000 Commission: 0.01% Slippage: 2 points Maximum one position at a time Risk Control Use daily timeframe only Avoid trading during major news events Consider market conditions Monitor overall exposure 📊 Performance Dashboard The strategy includes a comprehensive status table displaying: Strategy Parameters Current SMA settings Trading direction Fast/Slow SMA ratio Current Status Active position (Flat/Long/Short) Current price with color coding Position status indicators Performance Metrics Net Profit (USD and %) Win Rate with color grading Profit Factor with thresholds Maximum Drawdown percentage Average Trade value 📱 Alert Settings Entry Alerts Long Entry (Buy Signal) Short Entry (Sell Signal) Exit Alerts Long Exit (Take Profit) Short Exit (Take Profit) Alert Message Format Strategy name Signal type and direction Current price Fast SMA value Slow SMA value 💡 Usage Tips Consider starting with Long Only mode Begin with default settings Keep track of your trades Review results regularly Adjust settings as needed Follow your trading plan ⚠️ Disclaimer This strategy is for educational and informational purposes only. It is not financial advice. Always: Conduct your own research Test thoroughly before live trading Use proper risk management Consider your trading goals Monitor market conditions Never risk more than you can afford to lose 📋 Release Notes 14 January 2025 Added New Fast & Slow SMA Options: Fibonacci-based periods: 8, 13, 21, 144, 233, 377 Additional period: 50 Complete Fast SMA options now: 5, 8, 10, 13, 15, 20, 21, 34, 50 Complete Slow SMA options now: 100, 144, 200, 233, 377 Bug Fixes: Fixed Maximum Drawdown calculation in the performance table Now using strategy.max_drawdown_percent for accurate DD reporting Previous version showed incorrect DD values Performance metrics now accurately reflect trading results Performance Note: Strategy tested with Fast/Slow SMA 13/377 Test conducted with 10% equity risk allocation Daily Timeframe For Beginners - How to Modify SMA Levels: Find this line in the code: fastLength = input.int(title="Fast SMA Length", defval=5, options= ) To add a new Fast SMA period: Add the number to the options list, e.g., To remove a Fast SMA period: Remove the number from the options list For Slow SMA, find: slowLength = input.int(title="Slow SMA Length", defval=100, options= ) Modify the options list the same way ⚠️ Note: Keep the periods that make sense for your trading timeframe 💡 Tip: Test any new combinations thoroughly before live trading "Trade with Discipline, Manage Risk, Stay Consistent" - tradeviZion

4EMAs+OpenHrs+FOMC+CPI This script displays 4 custom EMAs of your choice based on the Pine script standard ema function. Additionally the following events are shown 1. Opening hours for New York Stock exchange 2. Opening Time for London Stock exchange 3. US CPI Release Dates 4. FOMC press conference dates 5. FOMC meeting minutes release dates I have currently added FOMC and CPI Dates for 2025 but will keep updating in January of every year (at least as long as I stay in the game :D)

Enhanced Retail vs Institutional Activity This script highlights market activity in real-time, making it easier to infer the type of market participants driving price and volume changes. Here’s a list of what the script analyzes: Volume: Current volume of the candle. Moving average of volume over a specified number of periods. Volume spikes: Current volume compared to a threshold multiple of the moving average. Price Movement: Percentage change in price between the current and previous candle. Identifies significant price changes based on a user-defined threshold. Institutional Activity: High volume spikes combined with significant price movements. Retail Activity: Periods without volume spikes or significant price changes. VWAP (Volume-Weighted Average Price): The average traded price over a specified lookback period, weighted by volume, used as a benchmark. Market Context Visualization: Background colors to differentiate institutional (red) and retail (green) activity. Overlays for: -Volume bars. -Average volume line. -VWAP line. In summary: Red = Institutional activity: High volume + significant price change. Green = Retail activity: Low volume or insignificant price change. --------------------------------------------------------------------------------------------------------------------- Analysis Explanation: I’m forecasting that Bitcoin will retest its November 12th low (~$85,098.75) around January 20th, 2025, where the horizontal support line intersects with the downtrend line. This conclusion is based on the following: Trend Analysis: The chart shows a clear downtrend with price respecting the descending trendline. The intersection of the horizontal support and the downtrend line on January 20th indicates a confluence point where price action may gravitate. Volume and Activity Insights: Using the Retail vs Institutional Activity indicator, the chart highlights periods dominated by institutional (red background) or retail (green background) activity. Current price action is in a green zone, suggesting predominantly retail participation with lower volume and insignificant price movements. Retail vs Institutional Dynamics: Institutional activity (red zones) aligns with significant price movements and volume spikes, often marking key turning points or trends. The recent green retail-dominated periods suggest a lack of strong momentum, which may lead to continued price decline until institutions re-enter around the confluence area. Volume Observations: Volume remains relatively low during the current retail phase, indicating weak buying pressure. A potential surge in institutional activity (red zones) near the support level could trigger a rebound or breakdown. I expect Bitcoin’s price to drop further and test the November 12th low near $85,098.75 on January 20th, 2025. This projection is supported by the convergence of the downtrend line and horizontal support, low retail-driven volume, and historical institutional activity patterns observed using the "Retail vs Institutional Activity" indicator.

Winter Is Coming (Snowflake)While attempting to draw a star using Pine Script, I ended up creating another nonsense indicator 🙂 How to Draw a Dynamic Snowflake? 🤦‍♂️ This indicator provides a customizable snowflake pattern that can be displayed on either a linear or logarithmic chart. Users can change the number of vertices and notches to make the pattern dynamic and versatile. (For added fun, the skull emojis that appear on each tick can be replaced with other symbols, like 🍺—because, hey, it’s Christmas!) What Can You Learn? Curious users analyzing this script can uncover practical answers to these questions: How can line and label drawings be constructed using array functions? How can trigonometric and logarithmic calculations be implemented effectively? Details: The snowflake is composed of symmetrical branches radiating from a central point. Each branch includes adjustable notches along its length, allowing users to control both their count and spacing. At the center of the snowflake, an n-point star is drawn (parameter: gon). This star's outer and inner vertices are aligned with the notches, ensuring perfect harmony with the snowflake’s overall geometry. The star is evenly spaced, with each of its points separated by 360/n degrees, resulting in a visually balanced and symmetrical design. Best Wishes I hope 2025 will be the year when we can create more peace, more freedom and more time to drink beer for the whole planet! Happy New Year everyone!

Santa's Secrets | Fractalyst Santa’s Secrets is a visually engaging trading tool that infuses holiday cheer into your charts. Inspired by the enchanting, mysterious vibes of the holiday season, this indicator overlays price charts with dynamic, multi-colored glitches that sync with market data, delivering a festive and whimsical visual experience. The indicator brings a magical touch to your charts, featuring characters from classic holiday themes (e.g., Santa, reindeer, snowflakes, gift boxes) to create a fun and festive “glitch effect.” Users can select a theme for their matrix characters, adding a holiday twist to their trading visuals. As the market data moves, these themed characters are randomly picked and displayed on the chart in a colorful cascade. Underlying Calculations and Logic 1.Character Management: The indicator uses arrays to manage different sets of holiday-themed characters, such as Santa’s sleigh, snowflakes, and reindeer. These arrays allow dynamic selection and update of characters as the market moves, mimicking a festive glitch effect. 2. Current and Previous States: Arrays track the current and previous states of characters, ensuring smooth transitions between visual updates. This dual-state management enables the effects to look like a magical, continuous movement, just like Santa’s sleigh cruising through the winter night. 3. Transparency Control: Transparency levels are controlled through arrays, adjusting opacity to create subtle fading effects or more intense visual appearances. The result is a festive glow that can fade or intensify depending on the market’s volatility. 4. Rain Effect Simulation: To create the “snowfall” or “glitching lights” effect, the indicator manages arrays that simulate falling characters, like snowflakes or candy canes, continuously updating their position and visibility. As new characters enter the top of the screen, older ones disappear from the bottom, with fading transparency to simulate a seamless flow. 5. Operational Flow: • Initialization: Arrays initialize the characters and transparency controls, readying the script for smooth and continuous updates during trading. • Updates: During each cycle, new characters are selected and the old ones shift, with updates in both content and appearance ensuring the matrix effect is visually appealing. • Rendering: The arrays control how the characters are rendered, ensuring the magical holiday effect stays lively and eye-catching without interrupting the trading flow. How to Use Santa’s Secrets Indicator 1. Apply the Indicator to Your Charts: Add the Santa’s Secrets indicator to your chart, activating the holiday-themed visual effect on your selected trading instrument or time frame. 2. Select Your Holiday Theme: In the settings, choose the holiday theme or character set. Whether it’s Santa’s sleigh, reindeer, snowflakes, or gift boxes, pick the one that brings the most festive cheer to your charts. 3. Choose Your Visual Effect (Snowfall or Glitch Burst): Select between the “Snowfall” effect, where characters gently drift down the chart like snowflakes, or the “Glitch Burst” effect, where characters explode outward in a burst of holiday cheer, representing bursts of market volatility. 4. Adjust the Color for Holiday Vibes: Customize the color of the characters to match your chart’s aesthetic or reflect different market conditions. Choose from red for a downtrend, green for an uptrend, or opt for a gradient of colors to capture a true holiday spirit. 5. Fit the Matrix to Your Display: Adjust the width and height of the matrix display to make sure it fits perfectly with your chart layout. Ensure it doesn’t obscure your view while still providing the holiday-themed magic. What Makes Santa’s Secrets Indicator Unique? Holiday Theme Selection: Santa’s Secrets allows traders to choose from a variety of holiday-themed characters. Whether you prefer the traditional Santa’s sleigh, snowflakes, reindeer, or gift boxes, you can bring the festive spirit into your trading. This personalized touch adds a fun, holiday twist to your charts and keeps you engaged during the festive season. Dynamic Effects: Choose between two exciting visual modes – Snowfall Mode or Glitch Burst Mode. The Snowfall Mode brings a gentle, peaceful effect with characters cascading down the chart like snowflakes, while Glitch Burst Mode creates a more intense effect, radiating characters outward in an explosive, holiday-themed display. Customizable Holiday Colors: Traders can fully customize the color of the matrix characters to match their trading environment. Whether you want a traditional red and green for a Christmas mood or a blue and white snow effect, Santa’s Secrets allows you to create the perfect holiday atmosphere while you trade. Universal Display Compatibility: No matter what screen or device you’re using – whether it’s a large monitor, laptop, or mobile – Santa’s Secrets is fully adjustable to fit your screen size. The holiday effect remains visually striking without compromising the integrity of your chart data. Wishing you a happy year filled with success, growth, and profitable trades.🎅🎁 Let's kick off the new year strong with Santa's Secrets! 🚀🎄

Bitcoin Logarithmic Growth Curve 2024 The Bitcoin logarithmic growth curve is a concept used to analyze Bitcoin's price movements over time. The idea is based on the observation that Bitcoin's price tends to grow exponentially, particularly during bull markets. It attempts to give a long-term perspective on the Bitcoin price movements. The curve includes an upper and lower band. These bands often represent zones where Bitcoin's price is overextended (upper band) or undervalued (lower band) relative to its historical growth trajectory. When the price touches or exceeds the upper band, it may indicate a speculative bubble, while prices near the lower band may suggest a buying opportunity. Unlike most Bitcoin growth curve indicators, this one includes a logarithmic growth curve optimized using the latest 2024 price data, making it, in our view, superior to previous models. Additionally, it features statistical confidence intervals derived from linear regression, compatible across all timeframes, and extrapolates the data far into the future. Finally, this model allows users the flexibility to manually adjust the function parameters to suit their preferences. The Bitcoin logarithmic growth curve has the following function: y = 10^(a * log10(x) - b) In the context of this formula, the y value represents the Bitcoin price, while the x value corresponds to the time, specifically indicated by the weekly bar number on the chart. How is it made (You can skip this section if you’re not a fan of math): To optimize the fit of this function and determine the optimal values of a and b, the previous weekly cycle peak values were analyzed. The corresponding x and y values were recorded as follows: 113, 18.55 240, 1004.42 451, 19128.27 655, 65502.47 The same process was applied to the bear market low values: 103, 2.48 267, 211.03 471, 3192.87 676, 16255.15 Next, these values were converted to their linear form by applying the base-10 logarithm. This transformation allows the function to be expressed in a linear state: y = a * x − b. This step is essential for enabling linear regression on these values. For the cycle peak (x,y) values: 2.053, 1.268 2.380, 3.002 2.654, 4.282 2.816, 4.816 And for the bear market low (x,y) values: 2.013, 0.394 2.427, 2.324 2.673, 3.504 2.830, 4.211 Next, linear regression was performed on both these datasets. (Numerous tools are available online for linear regression calculations, making manual computations unnecessary). Linear regression is a method used to find a straight line that best represents the relationship between two variables. It looks at how changes in one variable affect another and tries to predict values based on that relationship. The goal is to minimize the differences between the actual data points and the points predicted by the line. Essentially, it aims to optimize for the highest R-Square value. Below are the results: It is important to note that both the slope (a-value) and the y-intercept (b-value) have associated standard errors. These standard errors can be used to calculate confidence intervals by multiplying them by the t-values (two degrees of freedom) from the linear regression. These t-values can be found in a t-distribution table. For the top cycle confidence intervals, we used t10% (0.133), t25% (0.323), and t33% (0.414). For the bottom cycle confidence intervals, the t-values used were t10% (0.133), t25% (0.323), t33% (0.414), t50% (0.765), and t67% (1.063). The final bull cycle function is: y = 10^(4.058 ± 0.133 * log10(x) – 6.44 ± 0.324) The final bear cycle function is: y = 10^(4.684 ± 0.025 * log10(x) – -9.034 ± 0.063) The main Criticisms of growth curve models: The Bitcoin logarithmic growth curve model faces several general criticisms that we’d like to highlight briefly. The most significant, in our view, is its heavy reliance on past price data, which may not accurately forecast future trends. For instance, previous growth curve models from 2020 on TradingView were overly optimistic in predicting the last cycle’s peak. This is why we aimed to present our process for deriving the final functions in a transparent, step-by-step scientific manner, including statistical confidence intervals. It's important to note that the bull cycle function is less reliable than the bear cycle function, as the top band is significantly wider than the bottom band. Even so, we still believe that the Bitcoin logarithmic growth curve presented in this script is overly optimistic since it goes parly against the concept of diminishing returns which we discussed in this post: This is why we also propose alternative parameter settings that align more closely with the theory of diminishing returns. Our recommendations: Drawing on the concept of diminishing returns, we propose alternative settings for this model that we believe provide a more realistic forecast aligned with this theory. The adjusted parameters apply only to the top band: a-value: 3.637 ± 0.2343 and b-parameter: -5.369 ± 0.6264. However, please note that these values are highly subjective, and you should be aware of the model's limitations. Conservative bull cycle model: y = 10^(3.637 ± 0.2343 * log10(x) - 5.369 ± 0.6264)

Bitcoin Market Cap wave model weekly This Bitcoin Market Cap wave model indicator is rooted in the foundation of my previously developed tool, the : Bitcoin wave model To derive the Total Market Cap from the Bitcoin wave price model, I employed a straightforward estimation for the Total Market Supply (TMS). This estimation relies on the formula: TMS <= (1 - 2^(-h)) for any h.This equation holds true for any value of h, which will be elaborated upon shortly. It is important to note that this inequality becomes the equality at the dates of halvings, diverging only slightly during other periods. Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events. This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023. The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line. The parameters of this model are provided in the script as follows: log(BTCUSD) = 1.48 + 5.44log(h). The secondary concept involves employing the inherent time unit of Bitcoin instead of days: 'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000. Adjustments are made to account for variations in block creation time. The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1. Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes. The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h. Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h. The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived. The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in Total Bitcoin Market Cap ranging between 4B and 5B USD. The projections to the future works well only for weekly timeframe. Enjoy the mathematical insights!

Bitcoin wave model Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events. This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023. The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line. The parameters of this model are provided in the script as follows: log (BTCUSD) = 1.48 + 5.44log(h). The secondary concept involves employing the inherent time unit of Bitcoin instead of days: 'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000. Adjustments are made to account for variations in block creation time. The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1. Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes. The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h. Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h. The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived. The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in prices ranging between 200,000 and 240,000 USD. Enjoy the mathematical insights!