WAGMI LAB Trend Reversal Indicator HMA-Kahlman (m15)WAGMI HMA-Kahlman Trend Reversal Indicator
This indicator combines the Hull Moving Average (HMA) with the Kahlman filter to provide a dynamic trend reversal signal, perfect for volatile assets like Bitcoin. The strategy works particularly well on lower timeframes, making it ideal for intraday trading and fast-moving markets.
Key Features:
Trend Detection: It uses a blend of HMA and Kahlman filters to detect trend reversals, providing more accurate and timely signals.
Volatility Adaptability: Designed with volatile assets like Bitcoin in mind, this indicator adapts to rapid price movements, offering smoother trend detection during high volatility.
Easy Visualization: Buy (B) and Sell (S) signals are clearly marked with labels, helping traders spot trend shifts quickly and accurately.
Trendlines Module: The indicator plots trendlines based on pivot points, highlighting important support and resistance levels. This helps traders understand the market structure and identify potential breakout or breakdown zones.
Customizable: Adjust the HMA and Kahlman parameters to fit different assets or trading styles, making it flexible for various market conditions.
Usage Tips:
Best Timeframes: The indicator performs exceptionally well on lower timeframes (such as 15-minute to 1-hour charts), making it ideal for scalping and short-term trading strategies.
Ideal for Volatile Assets: This strategy is perfect for highly volatile assets like Bitcoin, but can also be applied to other cryptocurrencies and traditional markets with high price fluctuations.
Signal Confirmation: Use the trend signals (green for uptrend, red for downtrend) along with the buy/sell labels to help you confirm potential entries and exits. It's also recommended to combine the signals with other technical tools like volume analysis or RSI for enhanced confirmation.
Trendline Analysis: The plotted trendlines provide additional visual context to identify key market zones, supporting your trading decisions with a clear view of ongoing trends and possible reversal areas.
Risk Management: As with any strategy, always consider proper risk management techniques, such as stop-loss and take-profit levels, to protect against unforeseen market moves.
Bitcoin (Kriptopara)
Wagmi Lab- Bitcoin H4 Buy Sell Signals This indicator, designed primarily for Bitcoin on the H4 timeframe, is a versatile tool that can also be applied to other assets and timeframes by adjusting its parameters. It combines Exponential Moving Averages (EMAs), MACD (Moving Average Convergence Divergence), and a crossover filtering mechanism to generate reliable buy and sell signals. The indicator is ideal for traders looking to identify trend direction and potential entry/exit points with added precision.
Key Features:
Customizable EMAs and MACD:
Fast EMA (default: 12): Tracks short-term price momentum.
Slow EMA (default: 26): Tracks long-term price momentum.
Signal SMA (default: 9): Smooths the MACD line to generate the signal line.
MACD Crossover Signals:
The indicator calculates the MACD line and signal line to identify potential buy and sell opportunities.
Buy signals are generated when the MACD line crosses above the signal line, indicating bullish momentum.
Sell signals are generated when the MACD line crosses below the signal line, indicating bearish momentum.
Crossover Strength Filter:
A minimum crossover distance percentage (default: 0.1%) ensures that only significant crossovers are considered, reducing false signals.
This filter helps traders avoid weak or insignificant crossovers that may not lead to strong price movements.
Trend Visualization:
The indicator highlights the trend direction by filling the area between the fast and slow EMAs with colors:
Green: Uptrend (MACD > Signal Line).
Red: Downtrend (MACD < Signal Line).
Buy/Sell Signal Markers:
Buy signals are marked with green circles below the price bars.
Sell signals are marked with red circles above the price bars.
These markers provide clear visual cues for potential entry and exit points.
Adaptable to Other Timeframes and Assets:
While optimized for the H4 timeframe, the indicator can be adjusted for other timeframes (e.g., M15, H1, D1) by modifying the EMA and SMA settings.
It can also be applied to other assets, such as stocks, forex, or commodities, by tweaking the parameters to suit the asset's volatility and characteristics.
How to Use:
Identify Trends:
Use the colored areas (green for uptrend, red for downtrend) to determine the overall market direction.
Wait for Confirmation:
Look for buy or sell signals (green or red circles) that align with the trend direction.
Ensure the crossover meets the minimum distance requirement to filter out weak signals.
Enter and Exit Trades:
Enter a long position when a buy signal appears during an uptrend.
Enter a short position or exit a long position when a sell signal appears during a downtrend.
Adjust Settings for Other Timeframes/Assets:
Experiment with the EMA and SMA periods to optimize the indicator for different timeframes or assets.
Why Use This Indicator?
Precision: The crossover strength filter reduces noise and false signals.
Versatility: Works across multiple timeframes and assets with customizable settings.
Visual Clarity: Clear trend visualization and signal markers make it easy to interpret.
This indicator is a powerful tool for traders seeking to capitalize on Bitcoin's volatility or other assets' price movements, providing a structured approach to identifying trends and potential trading opportunities.
Mxwll Hedge Suite [Mxwll]Hello Traders!
The Mxwll Hedge Suite determines the best asset to hedge against the asset on your chart!
By determining correlation between the asset on your chart and a group of internally listed assets, the Mxwll Hedge Suite determines which asset from the list exhibits the highest negative correlation, and then determines exactly how many coins/shares/contracts of the asset must be bought to achieve a perfect 1:1 hedge!
The image above exemplifies the process!
The purple box on the chart shows the eligible price action used to determine correlation between the asset on my chart (BTCUSDT.P) and the list of cryptocurrencies that can be used as a hedge!
From this price action, the coin determined to have to greatest negative correlation to BTCUSDT.P is FTMUSD.
The image above further outlines the hedge table located in the bottom-right corner of your chart!
The hedge table shows exactly how many coins you’d need to purchase for the hedge asset at various leverages to achieve a perfect 1:1 hedge!
Hedge Suite works on any asset on any timeframe!
And that’s all! A short and sweet script that is hopefully helpful to traders looking to hedge their positions with a negatively correlated asset!
Thank you, Traders!
[ADDYad] Google Search Trends - Bitcoin (2012 Jan - 2025 Jan)This Pine Script shows the Google Search Trends as an indicator for Bitcoin from January 2012 to January 2025, based on monthly data retrieved from Google Trends. It calculates and displays the relative search interest for Bitcoin over time, offering a historical perspective on its popularity mainly built for BITSTAMP:BTCUSD .
Important note: This is not a live indicator. It visualizes historical search trends based on Google Trends data.
Key Features:
Data Source : Google Trends (Last retrieved in January 10 2025).
Timeframe : The script is designed to be used on a monthly chart, with the data reflecting monthly search trends from January 2012 to January 2025. For other timeframes, the data is linearly interpolated to estimate the trends at finer resolutions.
Purpose : This indicator helps visualize Bitcoin's search interest over the years, offering insights into public interest and sentiment during specific periods (e.g., major price movements or news events).
Data Handling : The data is interpolated for use on non-monthly timeframes, allowing you to view search trends on any chart timeframe. This makes it versatile for use in longer-term analysis or shorter timeframes, despite the raw data being available only on a monthly basis. However, it is most relevant for Monthly, Weekly, and Daily timeframes.
How It Works:
The script calculates the number of months elapsed since January 1, 2012, and uses this to interpolate Google Trends data values for any given point in time on the chart.
The linear interpolation function adjusts the monthly data to provide an approximate trend for intermediate months.
Why It's Useful:
Track Bitcoin's historic search trends to understand how interest in Bitcoin evolved over time, potentially correlating with price movements.
Correlate search trends with price action and other market indicators to analyze the effects of public sentiment and sentiment-driven market momentum.
Final Notes:
This script is unique because it shows real-world, non-financial dataset (Google Trends) to understand price action of Bitcoin correlating with public interest. Hopefully is a valuable addition to the TradingView community.
ADDYad
4-Year Cycles [jpkxyz]Overview of the Script
I wanted to write a script that encompasses the wide-spread macro fund manager investment thesis: "Crypto is simply and expression of macro." A thesis pioneered by the likes of Raoul Pal (EXPAAM) , Andreesen Horowitz (A16Z) , Joe McCann (ASYMETRIC) , Bob Loukas and many more.
Cycle Theory Background:
The 2007-2008 financial crisis transformed central bank monetary policy by introducing:
- Quantitative Easing (QE): Creating money to buy assets and inject liquidity
- Coordinated global monetary interventions
Proactive 4-year economic cycles characterised by:
- Expansionary periods (low rates, money creation)
- Followed by contraction/normalisation
Central banks now deliberately manipulate liquidity, interest rates, and asset prices to control economic cycles, using monetary policy as a precision tool rather than a blunt instrument.
Cycle Characteristics (based on historical cycles):
- A cycle has 4 seasons (Spring, Summer, Fall, Winter)
- Each season with a cycle lasts 365 days
- The Cycle Low happens towards the beginning of the Spring Season of each new cycle
- This is followed by a run up throughout the Spring and Summer Season
- The Cycle High happens towards the end of the Fall Season
- The Winter season is characterised by price corrections until establishing a new floor in the Spring of the next cycle
Key Functionalities
1. Cycle Tracking
- Divides market history into 4-year cycles (Spring, Summer, Fall, Winter)
- Starts tracking cycles from 2011 (first cycle after the 2007 crisis cycle)
- Identifies and marks cycle boundaries
2. Visualization
- Colors background based on current cycle season
- Draws lines connecting:
- Cycle highs and lows
- Inter-cycle price movements
- Adds labels showing:
- Percentage gains/losses between cycles
- Number of days between significant points
3. Customization Options
- Allows users to customize:
- Colors for each season
- Line and label colors
- Label size
- Background opacity
Detailed Mechanism
Cycle Identification
- Uses a modulo calculation to determine the current season in the 4-year cycle
- Preset boundary years include 2015, 2019, 2023, 2027
- Automatically tracks and marks cycle transitions
Price Analysis
- Tracks highest and lowest prices within each cycle
- Calculates percentage changes:
- Intra-cycle (low to high)
- Inter-cycle (previous high to current high/low)
Visualization Techniques
- Background color changes based on current cycle season
- Dashed and solid lines connect significant price points
- Labels provide quantitative insights about price movements
Unique Aspects
1. Predictive Cycle Framework: Provides a structured way to view market movements beyond traditional technical analysis
2. Seasonal Color Coding: Intuitive visual representation of market cycle stages
3. Comprehensive Price Tracking: Captures both intra-cycle and inter-cycle price dynamics
4. Highly Customizable: Users can adjust visual parameters to suit their preferences
Potential Use Cases
- Technical analysis for long-term investors
- Identifying market cycle patterns
- Understanding historical price movement rhythms
- Educational tool for market cycle theory
Limitations/Considerations
- Based on a predefined 4-year cycle model (Liquidity Cycles)
- Historic Cycle Structures are not an indication for future performance
- May not perfectly represent all market behavior
- Requires visual interpretation
This script is particularly interesting for investors who believe in cyclical market theories and want a visual, data-driven representation of market stages.
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"
Murad Picks Target MCThe Murad Picks Target Market Cap Indicator is a custom TradingView tool designed for crypto traders and enthusiasts tracking tokens in the Murad Picks list. This indicator dynamically calculates and visualizes the price targets based on Murad Mahmudov's projected market capitalizations, allowing you to gauge each token's growth potential directly on your charts.
Indicator support tokens:
- SPX6900
- GIGA
- MOG
- POPCAT
- APU
- BITCOIN
- RETARDIO
- LOCKIN
Key Features :
Dynamic Target Price Lines:
- Displays horizontal lines representing the price when the token reaches its projected market cap.
- Automatically adjusts for the active chart symbol (e.g., SPX, MOG, APU, etc.).
X Multiplier Calculation:
- Shows how many times the current price must multiply to achieve the target price.
- Perfect for understanding relative growth potential.
Customizable Inputs:
- Easily update target market caps and circulating supply for each token.
- Adjust visuals such as line colors and styles.
Seamless Integration:
- Automatically adapts to the token you’re viewing (e.g., SPX, MOG, APU).
- Clean and visually intuitive, with labels marking targets.
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
Bitcoin Events HistoryWith this tool, you can travel back to Bitcoin’s very first price quote and retrace its entire history directly on your chart. Major events are plotted as labels or markers, providing context for how significant moments shaped Bitcoin’s journey.
Key Features
Comprehensive Event Coverage: From Bitcoin’s inception to the most recent updates.
Custom View: Change label colors, styles, sizes, and fonts using the script’s settings.
Regular Updates: New events are added regularly to keep the history current.
Replay History
Use Bar Replay Mode to step through Bitcoin’s price history and see events unfold in sequence.
Follow the on-screen instructions for a more immersive experience.
Community Contributions
If you notice a significant event missing or misplaced on a particular date, feel free to leave a comment! Your suggestions will be considered for the next update.
To all Bitcoin enthusiasts, traders, and anyone eager to explore the history of cryptocurrency from its inception, I hope you enjoy this indicator :)
Simple Moving Average with Regime Detection by iGrey.TradingThis indicator helps traders identify market regimes using the powerful combination of 50 and 200 SMAs. It provides clear visual signals and detailed metrics for trend-following strategies.
Key Features:
- Dual SMA System (50/200) for regime identification
- Colour-coded candles for easy trend visualisation
- Metrics dashboard
Core Signals:
- Bullish Regime: Price < 200 SMA
- Bearish Regime: Price > 200 SMA
- Additional confirmation: 50 SMA Cross-over or Cross-under (golden cross or death cross)
Metrics Dashboard:
- Current Regime Status (Bull/Bear)
- SMA Distance (% from price to 50 SMA)
- Regime Distance (% from price to 200 SMA)
- Regime Duration (bars in current regime)
Usage Instructions:
1. Apply the indicator to your chart
2. Configure the SMA lengths if desired (default: 50/200)
3. Monitor the color-coded candles:
- Green: Bullish regime
- Red: Bearish regime
4. Use the metrics dashboard for detailed analysis
Settings Guide:
- Length: Short-term SMA period (default: 50)
- Source: Price calculation source (default: close)
- Regime Filter Length: Long-term SMA period (default: 200)
- Regime Filter Source: Price source for regime calculation (default: close)
Trading Tips:
- Use bullish regimes for long positions
- Use bearish regimes for capital preservation or short positions
- Consider regime duration for trend strength
- Monitor distance metrics for potential reversals
- Combine with other systems for confluence
#trend-following #moving average #regime #sma #momentum
Risk Management:
- Not a standalone trading system
- Should be used with proper position sizing
- Consider market conditions and volatility
- Always use stop losses
Best Practices:
- Monitor multiple timeframes
- Use with other confirmation tools
- Consider fundamental factors
Version: 1.0
Created by: iGREY.Trading
Release Notes
// v1.1 Allows table overlay customisation
// v1.2 Update to v6 pinescript
Crypto Wallets Profitability & Performance [LuxAlgo]The Crypto Wallets Profitability & Performance indicator provides a comprehensive view of the financial status of cryptocurrency wallets by leveraging on-chain data from IntoTheBlock. It measures the percentage of wallets profiting, losing, or breaking even based on current market prices.
Additionally, it offers performance metrics across different timeframes, enabling traders to better assess market conditions.
This information can be crucial for understanding market sentiment and making informed trading decisions.
🔶 USAGE
🔹 Wallets Profitability
This indicator is designed to help traders and analysts evaluate the profitability of cryptocurrency wallets in real-time. It aggregates data gathered from the blockchain on the number of wallets that are in profit, loss, or breaking even and presents it visually on the chart.
Breaking even line demonstrates how realized gains and losses have changed, while the profit and the loss monitor unrealized gains and losses.
The signal line helps traders by providing a smoothed average and highlighting areas relative to profiting and losing levels. This makes it easier to identify and confirm trading momentum, assess strength, and filter out market noise.
🔹 Profitability Meter
The Profitability Meter is an alternative display that visually represents the percentage of wallets that are profiting, losing, or breaking even.
🔹 Performance
The script provides a view of the financial health of cryptocurrency wallets, showing the percentage of wallets in profit, loss, or breaking even. By combining these metrics with performance data across various timeframes, traders can gain valuable insights into overall wallet performance, assess trend strength, and identify potential market reversals.
🔹 Dashboard
The dashboard presents a consolidated view of key statistics. It allows traders to quickly assess the overall financial health of wallets, monitor trend strength, and gauge market conditions.
🔶 DETAILS
🔹 The Chart Occupation Option
The chart occupation option adjusts the occupation percentage of the chart to balance the visibility of the indicator.
🔹 The Height in Performance Options
Crypto markets often experience significant volatility, leading to rapid and substantial gains or losses. Hence, plotting performance graphs on top of the chart alongside other indicators can result in a cluttered display. The height option allows you to adjust the plotting for balanced visibility, ensuring a clearer and more organized chart.
🔶 SETTINGS
The script offers a range of customizable settings to tailor the analysis to your trading needs.
Chart Occupation %: Adjust the occupation percentage of the chart to balance the visibility of the indicator.
🔹 Profiting Wallets
Profiting Percentage: Toggle to display the percentage of wallets in profit.
Smoothing: Adjust the smoothing period for the profiting percentage line.
Signal Line: Choose a signal line type (SMA, EMA, RMA, or None) to overlay on the profiting percentage.
🔹 Losing Wallets
Losing Percentage: Toggle to display the percentage of wallets in loss.
Smoothing: Adjust the smoothing period for the losing percentage line.
Signal Line: Choose a signal line type (SMA, EMA, RMA, or None) to overlay on the losing percentage.
🔹 Breaking Even Wallets
Breaking-Even Percentage: Toggle to display the percentage of wallets breaking even.
Smoothing: Adjust the smoothing period for the breaking-even percentage line.
🔹 Profitability Meter
Profitability Meter: Enable or disable the meter display, set its width, and adjust the offset.
🔹 Performance
Performance Metrics: Choose the timeframe for performance metrics (Day to Date, Week to Date, etc.).
Height: Adjust the height of the chart visuals to balance the visibility of the indicator.
🔹 Dashboard
Block Profitability Stats: Toggle the display of profitability stats.
Performance Stats: Toggle the display of performance stats.
Dashboard Size and Position: Customize the size and position of the performance dashboard on the chart.
🔶 RELATED SCRIPTS
Market-Sentiment-Technicals
Multi-Chart-Widget
Pulse DPO: Major Cycle Tops and Bottoms█ OVERVIEW
Pulse DPO is an oscillator designed to highlight Major Cycle Tops and Bottoms .
It works on any market driven by cycles. It operates by removing the short-term noise from the price action and focuses on the market's cyclical nature.
This indicator uses a Normalized version of the Detrended Price Oscillator (DPO) on a 0-100 scale, making it easier to identify major tops and bottoms.
Credit: The DPO was first developed by William Blau in 1991.
█ HOW TO READ IT
Pulse DPO oscillates in the range between 0 and 100. A value in the upper section signals an OverBought (OB) condition, while a value in the lower section signals an OverSold (OS) condition.
Generally, the triggering of OB and OS conditions don't necessarily translate into swing tops and bottoms, but rather suggest caution on approaching a market that might be overextended.
Nevertheless, this indicator has been customized to trigger the signal only during remarkable top and bottom events.
I suggest using it on the Daily Time Frame , but you're free to experiment with this indicator on other time frames.
The indicator has Built-in Alerts to signal the crossing of the Thresholds. Please don't act on an isolated signal, but rather integrate it to work in conjunction with the indicators present in your Trading Plan.
█ OB SIGNAL ON: ENTERING OVERBOUGHT CONDITION
When Pulse DPO crosses Above the Top Threshold it Triggers ON the OB signal. At this point the oscillator line shifts to OB color.
When Pulse DPO enters the OB Zone, please beware! In this Area the Major Players usually become Active Sellers to the Public. While the OB signal is On, it might be wise to Consider Selling a portion or the whole Long Position.
Please note that even though this indicator aims to focus on major tops and bottoms, a strong trending market might trigger the OB signal and stay with it for a long time. That's especially true on young markets and on bubble-mode markets.
█ OB SIGNAL OFF: EXITING OVERBOUGHT CONDITION
When Pulse DPO crosses Below the Top Threshold it Triggers OFF the OB signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OB Zone, please beware because a Major Top might just have occurred. In this Area the Major Players usually become Aggressive Sellers. They might wind up any remaining Long Positions and Open new Short Positions.
This might be a good area to Open Shorts or to Close/Reverse any remaining Long Position. Whatever you choose to do, it's usually best to act quickly because the market is prone to enter into panic mode.
█ OS SIGNAL ON: ENTERING OVERSOLD CONDITION
When Pulse DPO crosses Below the Bottom Threshold it Triggers ON the OS signal. At this point the oscillator line shifts to OS color.
When Pulse DPO enters the OS Zone, please beware because in this Area the Major Players usually become Active Buyers accumulating Long Positions from the desperate Public.
While the OS signal is On, it might be wise to Consider becoming a Buyer or to implement a Dollar-Cost Averaging (DCA) Strategy to build a Long Position towards the next Cycle. In contrast to the tops, the OS state usually takes longer to resolve a major bottom.
█ OS SIGNAL OFF: EXITING OVERSOLD CONDITION
When Pulse DPO crosses Above the Bottom Threshold it Triggers OFF the OS signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OS Zone, please beware because a Major Bottom might already be in place. In this Area the Major Players become Aggresive Buyers. They might wind up any remaining Short Positions and Open new Long Positions.
This might be a good area to Open Longs or to Close/Reverse any remaining Short Positions.
█ WHY WOULD YOU BE INTERESTED IN THIS INDICATOR?
This indicator is built over a solid foundation capable of signaling Major Cycle Tops and Bottoms across many markets. Let's see some examples:
Early Bitcoin Years: From 0 to 1242
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling the major early highs from 9-Jun-2011 at 31.50, to the next one on 9-Apr-2013 at 240 and the epic top from 29-Nov-2013 at 1242.
Due to the massive price movements, the OB condition stays pinned during most of the exponential price action. But as you can see, the OB condition quickly vanishes once the Cycle Top has been reached. As the market matures, the OB condition becomes more exceptional and triggers much closer from the Cycle Top.
With regards to Cycle Bottoms, the early bottom of 2 after having peaked at 31.50 doesn’t get captured by the indicator. That is the only cycle bottom that escapes the Pulse DPO when the bottom threshold is set at a value of 5. In that event, the oscillator low reached 6.95.
Bitcoin Adoption Spreading: From 257 to 73k
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling all the major highs from 17-Dec-2017 at 19k, to the next one on 14-Apr-2021 at 64k and the most recent top from 9-Nov-2021 at 68k.
During the massive run of 2017, the OB condition still stayed triggered for a few weeks on each swing top. But on the next cycles it started to signal only for a few days before each swing top actually happened. The OB condition during the last cycle top triggered only for 3 days. Therefore the signal grows in focus as the market matures.
At the time of publishing this indicator, Bitcoin printed a new All Time High (ATH) on 13-Mar-2024 at 73k. That run didn’t trigger the OB condition. Therefore, if the indicator is correct the Bitcoin market still has some way to grow during the next months.
With regards to Cycle Bottoms, the bottom of 3k after having peaked at19k got captured within the wide OS zone. The bottom of 15k after having peaked at 68k got captured too within the OS accumulation area.
Gold
Pulse DPO behaves surprisingly well on a long standing market such as Gold. Moving back to the 197x years it’s been signaling most Cycle Tops and Bottoms with precision. During the last cycle, it shows topping at 2k and bottoming at 1.6k.
The current price action is signaling OB condition in the range of 2.5k to 2.7k. Looking at past cycles, it tends to trigger on and off at multiple swing tops until reaching the final cycle top. Therefore this might indicate the first wave within a potential gold run.
Oil
On the Oil market, we can see that most of the cycle tops and bottoms since the 80s got signaled. The only exception being the low from 2020 which didn’t trigger.
EURUSD
On Forex markets the Pulse DPO also behaves as expected. Looking back at EURUSD we can see the marketing triggering OB and OS conditions during major cycle tops and bottoms from recent times until the 80s.
S&P 500
On the S&P 500 the Pulse DPO catched the lows from 2016 and 2020. Looking at present price action, the recent ATH didn’t trigger the OB condition. Therefore, the indicator is allowing room for another leg up during the next months.
Amazon
On the Amazon chart the Pulse DPO is mirroring pretty accurately the major swings. Scrolling back to the early 2000s, this chart resembles early exponential swings in the crypto space.
Tesla
Moving onto a younger tech stock, Pulse DPO captures pretty accurately the major tops and bottoms. The chart is shown in logarithmic scale to better display the magnitude of the moves.
█ SETTINGS
This indicator is ideal for identifying major market turning points while filtering out short-term noise. You are free to adjust the parameters to align with your preferred trading style.
Parameters : This section allows you to customize any of the Parameters that shape the Oscillator.
Oscillator Length: Defines the period for calculating the Oscillator.
Offset: Shifts the oscillator calculation by a certain number of periods, which is typically half the Oscillator Length.
Lookback Period: Specifies how many bars to look back to find tops and bottoms for normalization.
Smoothing Length: Determines the length of the moving average used to smooth the oscillator.
Thresholds : This section allows you to customize the Thresholds that trigger the OB and OS conditions.
Top: Defines the value of the Top Threshold.
Bottom: Defines the value of the Bottom Threshold.
Ping Pong Bot StrategyOverview:
The Ping Pong Bot Strategy is designed for traders who focus on scalping and short-term opportunities using support and resistance levels. This strategy identifies potential buy entries when the price reaches a key support area and shows bullish momentum (a green bar). It aims to capitalize on small price movements with predefined risk management and take profit levels, making it suitable for active traders looking to maximize quick trades in trending or ranging markets.
How It Works:
Support & Resistance Calculation:
The strategy dynamically identifies support and resistance levels using the lowest and highest price points over a user-defined period. These levels help pinpoint potential price reversal areas, guiding traders on where to enter or exit trades.
Buy Entry Criteria:
A buy signal is triggered when the closing price is at or below the support level, and the bar is green (i.e., the closing price is higher than the opening price). This ensures that entries are made when prices show signs of upward momentum after hitting support.
Risk Management:
For each trade, a stop loss is calculated based on a user-defined risk percentage, helping to protect against significant drawdowns. Additionally, a take profit level is set at a ratio relative to the risk, ensuring a disciplined approach to exit points.
0.5% Take Profit Target:
The strategy also includes a 0.5% quick take profit target, indicated by an orange arrow when reached. This feature helps traders lock in small gains rapidly, making it ideal for volatile market conditions.
Customizable Inputs:
Length: Adjusts the period for calculating support and resistance levels.
Risk-Reward Ratio: Allows traders to set the desired risk-to-reward ratio for each trade.
Risk Percentage: Defines the risk tolerance for stop loss calculations.
Take Profit Target: Enables the customization of the quick take profit target.
Ideal For:
Traders who prefer an active trading style and want to leverage support and resistance levels for precise entries and exits. This strategy is particularly useful in markets that experience frequent price bounces between support and resistance, allowing traders to "ping pong" between these levels for profitable trades.
Note:
This strategy is developed mainly for the 5-minute chart and has not been tested on longer time frames. Users should perform their own testing and adjustments if using it on different time frames.
Leonid's Bitcoin Sharpe RatioThe Sharpe ratio is an old formula used to value the risk-adjusted return of an asset. It was developed by Nobel Laureate William F. Sharpe. In this case, I have applied it to Bitcoin with an adjustable look-back date.
The Sharpe Ratio shows you the average return earned after subtracting out the risk-free rate per unit of volatility (I've defaulted this to 0.02 ).
Volatility is a measure of the price fluctuations of an asset or portfolio. Subtracting the risk-free rate from the mean return allows you to understand what the extra returns are for taking the risk.
If the indicator is flashing red, Bitcoin is temporarily overbought (expensive).
If the indicator is flashing green, Bitcoin is temporarily oversold (cheap).
The goal of this indicator is to signal out local tops & bottoms. It can be adjusted as far as the lookback time but I have found 25-26 days to be ideal.
Bitcoin Logarithmic Growth Curve 2024The 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)
Combined Bitcoin CME Gaps and Weekend DaysScript Description: Combined Bitcoin CME Gaps and Weekend Days
Author: NeoButane (Bitcoin CME Gaps), JohnIsTrading (Day of Week),
Contributor : MikeTheRuleTA (Combined and optimizations)
This Pine Script indicator provides a combined view of Bitcoin CME gaps and customizable weekend day backgrounds on your chart. It’s designed to help traders visualize CME gaps along with customizable weekend day highlights.
Features:
CME Gaps Visualization:
Enable CME Gaps: Toggle the display of CME gaps on your chart.
Show Real vs. CME Price: Choose whether to display chart prices or CME prices for gap analysis.
Weekend Gaps Only: Filter to show only weekend gaps for a cleaner view (note: this may miss holidays).
CME Gaps Styling:
Weekend Background Highlighting:
Enable Weekend Background: Toggle the weekend day background highlight on or off.
Timezone Selection: Choose the relevant timezone for accurate weekend highlighting.
Customizable Weekend Colors: Define colors for Saturday and Sunday backgrounds.
How It Works:
CME Gaps: The script identifies gaps between CME and chart prices when the CME session is closed. It plots these gaps with customizable colors and line widths.
You can choose to see gaps based on CME prices or chart prices and decide whether to include only weekends.
Weekend Backgrounds: The script allows for background highlighting of weekends (Saturday and Sunday) on your chart. This can be enabled or disabled and customized with specific colors.
The timezone setting ensures that the background highlights match your local time settings.
Inputs:
CME Gaps Settings:
Enable CME Gaps
Show Real vs. CME Price
Only Show Weekend Gaps
CME Gaps Style:
Gap Fill Color Up
Gap Fill Color Down
Gap Fill Transparency
Weekend Settings:
Enable Weekend Background
Timezone
Enable Saturday
Saturday Color
Enable Sunday
Sunday Color
Usage:
Add this script to your TradingView chart to overlay CME gaps and weekend highlights.
Adjust the settings according to your preferences for a clearer view of gaps and customized weekend backgrounds.
This indicator provides a comprehensive tool for tracking CME gaps and understanding weekend market behaviors through visual enhancements on your trading charts.
BTC Coinbase PremiumThis script is designed to compare the price of Bitcoin on two major exchanges: Coinbase and Binance. It helps you see if there’s a difference in the price of Bitcoin between these two exchanges, which is known as a “premium” or “discount.”
Here’s how it works in simple terms:
Getting the Prices:
The script first fetches the current price of Bitcoin from Coinbase and Binance. It looks at the closing price, which is the price at the end of the selected time period on your chart.
Calculating the Difference:
It then calculates the difference between these two prices. If Bitcoin is more expensive on Coinbase than on Binance, this difference will be positive, indicating a “premium.” If it’s cheaper on Coinbase, the difference will be negative, indicating a “discount.”
Visualizing the Difference:
The script creates a visual chart that shows this price difference over time. It uses green bars to show when there’s a premium (Coinbase is more expensive) and red bars to show when there’s a discount (Coinbase is cheaper).
Optional Table Display:
If you choose to, the script can also show this price difference in a small table at the top right corner of your chart. The table displays the words “Coinbase Premium” and the exact dollar amount of the premium or discount.
Why does it matter?
Traders and investors have spotted a correlation between bullish strength on BTC and a strong Coinbase premium along with the inverse of a strong Coinbase discount and BTC price weakness.
Bitcoin Fundamentals - Bitcoin Block RewardThe Bitcoin Block Reward is the batch of new Bitcoins generated by the miners after solving each block.
The Block Reward is set as a basic rule and cannot be changed without agreement between the entire Bitcoin network. It started at 50 BTC during the first period. Afterwards the Block Reward gets adjusted to half of it value (Halving Event) on each cycle of 210000 blocks mined.
This is the only way that new bitcoins are created. It creates an incentive for miners to secure the network.
Over time the Block Reward will decreases to a value that might not cover the mining costs. At that point, the use of the Bitcoin Network might have increased sufficiently as to generate enough transaction fees to cover the mining costs.
MOTIVATION
Even though this is a very simple indicator, I'm currently missing a data source to compute the Block Reward value within Tradingview. Therefore, I created this indicator and its associated library function to enable its visualization and (eventually) for coders to make use of the source function to power more elaborate scripts related to the Halving Events.
Hope that helps!
Dickey-Fuller Test for Mean Reversion and Stationarity **IF YOU NEED EXTRA SPECIAL HELP UNDERSTANDING THIS INDICATOR, GO TO THE BOTTOM OF THE DESCRIPTION FOR AN EVEN SIMPLER DESCRIPTION**
Dickey Fuller Test:
The Dickey-Fuller test is a statistical test used to determine whether a time series is stationary or has a unit root (a characteristic of a time series that makes it non-stationary), indicating that it is non-stationary. Stationarity means that the statistical properties of a time series, such as mean and variance, are constant over time. The test checks to see if the time series is mean-reverting or not. Many traders falsely assume that raw stock prices are mean-reverting when they are not, as evidenced by many different types of statistical models that show how stock prices are almost always positively autocorrelated or statistical tests like this one, which show that stock prices are not stationary.
Note: This indicator uses past results, and the results will always be changing as new data comes in. Just because it's stationary during a rare occurrence doesn't mean it will always be stationary. Especially in price, where this would be a rare occurrence on this test. (The Test Statistic is below the critical value.)
The indicator also shows the option to either choose Raw Price, Simple Returns, or Log Returns for the test.
Raw Prices:
Stock prices are usually non-stationary because they follow some type of random walk, exhibiting positive autocorrelation and trends in the long term.
The Dickey-Fuller test on raw prices will indicate non-stationary most of the time since prices are expected to have a unit root. (If the test statistic is higher than the critical value, it suggests the presence of a unit root, confirming non-stationarity.)
Simple Returns and Log Returns:
Simple and log returns are more stationary than prices, if not completely stationary, because they measure relative changes rather than absolute levels.
This test on simple and log returns may indicate stationary behavior, especially over longer periods. (The test statistic being below the critical value suggests the absence of a unit root, indicating stationarity.)
Null Hypothesis (H0): The time series has a unit root (it is non-stationary).
Alternative Hypothesis (H1): The time series does not have a unit root (it is stationary)
Interpretation: If the test statistic is less than the critical value, we reject the null hypothesis and conclude that the time series is stationary.
Types of Dickey-Fuller Tests:
1. (What this indicator uses) Standard Dickey-Fuller Test:
Tests the null hypothesis that a unit root is present in a simple autoregressive model.
This test is used for simple cases where we just want to check if the series has a consistent statistical property over time without considering any trends or additional complexities.
It examines the relationship between the current value of the series and its previous value to see if the series tends to drift over time or revert to the mean.
2. Augmented Dickey-Fuller (ADF) Test:
Tests for a unit root while accounting for more complex structures like trends and higher-order correlations in the data.
This test is more robust and is used when the time series has trends or other patterns that need to be considered.
It extends the regular test by including additional terms to account for the complexities, and this test may be more reliable than the regular Dickey-Fuller Test.
For things like stock prices, the ADF would be more appropriate because stock prices are almost always trending and positively autocorrelated, while the Dickey-Fuller Test is more appropriate for more simple time series.
Critical Values
This indicator uses the following critical values that are essential for interpreting the Dickey-Fuller test results. The critical values depend on the chosen significance levels:
1% Significance Level: Critical value of -3.43.
5% Significance Level: Critical value of -2.86.
10% Significance Level: Critical value of -2.57.
These critical values are thresholds that help determine whether to reject the null hypothesis of a unit root (non-stationarity). If the test statistic is less than (or more negative than) the critical value, it indicates that the time series is stationary. Conversely, if the test statistic is greater than the critical value, the series is considered non-stationary.
This indicator uses a dotted blue line by default to show the critical value. If the test-static, which is the gray column, goes below the critical value, then the test-static will become yellow, and the test will indicate that the time series is stationary or mean reverting for the current period of time.
What does this mean?
This is the weekly chart of BTCUSD with the Dickey-Fuller Test, with a length of 100 and a critical value of 1%.
So basically, in the long term, mean-reversion strategies that involve raw prices are not a good idea. You don't really need a statistical test either for this; just from seeing the chart itself, you can see that prices in the long term are trending and no mean reversion is present.
For the people who can't understand that the gray column being above the blue dotted line means price doesn't mean revert, here is a more simple description (you know you are):
Average (I have to include the meaning because they may not know what average is): The middle number is when you add up all the numbers and then divide by how many numbers there are. EX: If you have the numbers 2, 4, and 6, you add them up to get 12, and then divide by 3 (because there are 3 numbers), so the average is 4. It tells you what a typical number is in a group of numbers.
This indicator checks if a time series (like stock prices) tends to return to its average value or time.
Raw prices, which is just the regular price chart, are usually not mean-reverting (It's "always" positively autocorrelating but this group of people doesn't like that word). Price follows trends.
Simple returns and log returns are more likely to have periods of mean reversion.
How to use it:
Gray Column (the gray bars) Above the Blue Dotted Line: The price does not mean revert (non-stationary).
Gray Column Below Blue Line: The time series mean reverts (stationary)
So, if the test statistic (gray column) is below the critical value, which is the blue dotted line, then the series is stationary and mean reverting, but if it is above the blue dotted line, then the time series is not stationary or mean reverting, and strategies involving mean reversion will most likely result in a loss given enough occurrences.
intellect_city - World Cycle - Ath & Atl - Logarithmic - Signal.Indicator Overview
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - The Pi Cycle Top and Bottom Oscillator is an adaptation of the original Pi Cycle Top chart. It compares the 111-Day Moving Average circle and the 2 * 350-Day Moving Average circle of Bitcoin’s Price. These two moving averages were selected as 350 / 111 = 3.153; An approximation of the important mathematical number Pi.
When the 111-Day Moving Average circle reaches the 2 * 350-Day Moving Average circle, it indicates that the market is becoming overheated. That is because the mid time frame momentum reference of the 111-Day Moving Average has caught up with the long timeframe momentum reference of the 2 * 350-Day Moving Average.
Historically this has occurred within 3 days of the very top of each market cycle.
When the 111 Day Moving Average circle falls back beneath the 2 * 350 Day Moving Average circle, it indicates that the market momentum of that cycle is significantly cooling down. The oscillator drops down into the lower green band shown where the 111 Day Moving Average is moving at a 75% discount relative to the 2 * 350 Day Moving Average.
Historically, this has highlighted broad areas of bear market lows.
IMPORTANT: You need to set a LOGARITHMIC graph. (The function is located at the bottom right of the screen)
IMPORTANT: The INTELLECT_city indicator is made for signal purchases of sales, there is also a strategic one from INTELLECT_city
IMPORTANT: The Chart shows all cycles, both buying and selling.
IMPORTANT: Suitable timeframes are 1 daily (recommended) and 1 weekly
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Описание на русском:
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Обзор индикатора
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - Логарифмический - Сигнал - Осциллятор вершины и основания цикла Пи представляет собой адаптацию оригинального графика вершины цикла Пи. Он сравнивает круг 111-дневной скользящей средней и круг 2 * 350-дневной скользящей средней цены Биткойна. Эти две скользящие средние были выбраны как 350/111 = 3,153; Приближение важного математического числа Пи.
Когда круг 111-дневной скользящей средней достигает круга 2 * 350-дневной скользящей средней, это указывает на то, что рынок перегревается. Это происходит потому, что опорный моментум среднего временного интервала 111-дневной скользящей средней догнал опорный момент импульса длинного таймфрейма 2 * 350-дневной скользящей средней.
Исторически это происходило в течение трех дней после вершины каждого рыночного цикла.
Когда круг 111-дневной скользящей средней опускается ниже круга 2 * 350-дневной скользящей средней, это указывает на то, что рыночный импульс этого цикла значительно снижается. Осциллятор опускается в нижнюю зеленую полосу, показанную там, где 111-дневная скользящая средняя движется со скидкой 75% относительно 2 * 350-дневной скользящей средней.
Исторически это высветило широкие области минимумов медвежьего рынка.
ВАЖНО: Выставлять нужно ЛОГАРИФМИЧЕСКИЙ график. (Находиться функция с правой нижней части экрана)
ВАЖНО: Индикатор INTELLECT_city сделан для сигнальных покупок продаж, есть также и стратегический от INTELLECT_сity
ВАЖНО: На Графике видны все циклы, как на покупку так и на продажу.
ВАЖНО: Подходящие таймфреймы 1 дневной (рекомендовано) и 1 недельный
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Beschreibung - Deutsch
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Indikatorübersicht
INTELLECT_city – Weltzyklus – ATH & ATL – Zeitrahmen 1T und 1W – Logarithmisch – Signal – Der Pi-Zyklus-Top- und Bottom-Oszillator ist eine Anpassung des ursprünglichen Pi-Zyklus-Top-Diagramms. Er vergleicht den 111-Tage-Gleitenden-Durchschnittskreis und den 2 * 350-Tage-Gleitenden-Durchschnittskreis des Bitcoin-Preises. Diese beiden gleitenden Durchschnitte wurden als 350 / 111 = 3,153 ausgewählt; eine Annäherung an die wichtige mathematische Zahl Pi.
Wenn der 111-Tage-Gleitenden-Durchschnittskreis den 2 * 350-Tage-Gleitenden-Durchschnittskreis erreicht, deutet dies darauf hin, dass der Markt überhitzt. Das liegt daran, dass der Momentum-Referenzwert des 111-Tage-Gleitenden-Durchschnitts im mittleren Zeitrahmen den Momentum-Referenzwert des 2 * 350-Tage-Gleitenden-Durchschnitts im langen Zeitrahmen eingeholt hat.
Historisch gesehen geschah dies innerhalb von 3 Tagen nach dem Höhepunkt jedes Marktzyklus.
Wenn der Kreis des 111-Tage-Durchschnitts wieder unter den Kreis des 2 x 350-Tage-Durchschnitts fällt, deutet dies darauf hin, dass die Marktdynamik dieses Zyklus deutlich nachlässt. Der Oszillator fällt in das untere grüne Band, in dem der 111-Tage-Durchschnitt mit einem Abschlag von 75 % gegenüber dem 2 x 350-Tage-Durchschnitt verläuft.
Historisch hat dies breite Bereiche mit Tiefstständen in der Baisse hervorgehoben.
WICHTIG: Sie müssen ein logarithmisches Diagramm festlegen. (Die Funktion befindet sich unten rechts auf dem Bildschirm)
WICHTIG: Der INTELLECT_city-Indikator dient zur Signalisierung von Käufen oder Verkäufen, es gibt auch einen strategischen Indikator von INTELLECT_city
WICHTIG: Das Diagramm zeigt alle Zyklen, sowohl Kauf- als auch Verkaufszyklen.
WICHTIG: Geeignete Zeitrahmen sind 1 täglich (empfohlen) und 1 wöchentlich
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.
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Table - Past and future Bitcoin halvings:
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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
Funding Rate [CryptoSea]The Funding Rate Indicator by is a comprehensive tool designed to analyze funding rates across multiple cryptocurrency exchanges. This indicator is essential for traders who want to monitor funding rates and their impact on market trends.
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Bitcoin Halving CountdownJust a simple Bitcoin halving countdown
Set up for the 2028 halving with the block height set at 1050000
Block height can be setup in the script settings for future halving
We don't have real time on-chain data on TradingView, so the script will update only once a day with a lag of one day (GLASSNODE data) until we get live data.
If you have any suggestions/questions leave a comment :)
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