Bull Bear Trend IndicatorIntroduction: Origin of the Swing Point Indicator
In the quest for a reliable indicator that accurately predicts trend directions and identifies valid highs and lows, the genesis of the Swing Point Indicator emerged. Faced with the challenge of finding a tool that provided comprehensive market analysis and actionable insights, the need for a novel solution became evident. Combining insights gleaned from market analysis and innovative algorithmic approaches, the Swing Point Indicator was born.
Enhanced Feature: Highs and Lows Labeling in Trend Direction
In addition to its core functionalities, the Swing Point Indicator incorporates an advanced feature that enhances the visualization of trend direction. This feature provides further clarity by selectively labeling highs and lows based on the prevailing trend, reinforcing the identification of higher highs and lower lows in uptrends and downtrends, respectively. Overlapping labels on highs and lows signify a potential trend change, providing traders with valuable insight into market reversals.
Detailed Description:
1. Uptrend Labeling:
- Higher Highs (Green Label with Price): In an uptrend, where higher highs are observed, the indicator labels these points with vibrant green color and includes the corresponding price value. This visually highlights the significance of higher highs as pivotal points in the upward trajectory of prices.
- Higher Lows (Red Marker without Text or Diamond): To complement the identification of higher highs, higher lows are marked with a distinct red marker or diamond, devoid of any accompanying text. While these points are crucial in delineating the ascending trend, their emphasis lies in their role as support levels, providing a foundation for upward price movements.
2. Downtrend Labeling:
- Lower Lows (Red Label with Price): Conversely, in a downtrend characterized by lower lows, the indicator labels these points with conspicuous red color, accompanied by the corresponding price value. Lower lows signify critical levels of downward price momentum, acting as indicators of potential bearish continuation.
- Lower Highs (Green Marker without Text or Diamond): Lower highs, indicative of downward retracements in a downtrend, are marked by distinctive green markers or diamonds without accompanying text. While these points denote temporary pauses or pullbacks in the bearish trend, their emphasis lies in their role as resistance levels, impeding upward price movements.
Functionality and Utility:
- Customizable Lookback Candle Count: Traders have the option to adjust the lookback candle count, which is set by default at 108 candles in the settings. This flexibility allows traders to tailor the indicator to their specific trading preferences and timeframes.
- Equal Highs or Lows Option: When enabled, the Swing Point Indicator can identify equal highs or equal lows, providing traders with additional insight into market dynamics.
- Formation Confirmation: A new higher high along with its higher low or a new lower low along with its lower high is confirmed after two candles have closed following the swing point candle. This ensures the reliability of the identified trend direction.
Conclusion:
The incorporation of selective labeling for highs and lows based on trend direction, alongside the introduction of customizable settings and formation confirmation criteria, enhances the effectiveness of the Swing Point Indicator. This feature-rich tool empowers traders with a nuanced understanding of market dynamics, highlighting critical price levels and trend reversals. By offering enhanced visualization, customizable options, and confirmation criteria, the Swing Point Indicator equips traders with the confidence and precision needed to navigate the markets successfully, contributing to more informed and profitable trading strategies.
Cycles
Volume Profile / Order Blocks + Demandas e Ofertas FortesThis indicator combines two powerful technical analysis tools into one: the Volume Profile Bar-Magnified Order Blocks and Strong Demands and Offers.
The Volume Profile Bar-Magnified Order Blocks identifies and highlights significant areas of volume and price on the chart, helping traders identify zones of high liquidity and potential trend reversal areas. With advanced customization features such as choice of mitigation method and grid adjustments, traders can tailor the indicator to their individual preferences.
Alongside the Volume Profile, Strong Demands and Offers add an additional layer of analysis, highlighting points of interest where buying or selling pressure is strongest. This helps traders identify key areas where the balance of power may shift, providing potential entry or exit signals.
Key Features:
Automatic identification of significant volume areas.
Highlighting of zones of high liquidity and potential trend reversal areas.
Advanced customization, including choice of mitigation method and grid adjustments.
Highlighting of strong demands and offers to identify key areas of buying or selling pressure.
How to Use:
Add the indicator to your chart.
Adjust the parameters according to your preferences.
Observe the highlighted areas of volume and price on the chart.
Look for entry or exit signals based on the identified areas of interest.
This indicator is a valuable tool for traders looking to enhance their technical analysis based on volume and market dynamics. Try it out in your trading strategy and discover how it can help you make more informed and accurate decisions.
Heat Map SeasonsHeat Map Seasons indicator
Indicator offers traders a unique perspective on market dynamics by visualizing seasonal trends and deviations from typical price behavior. By blending regression analysis with a color-coded heat map, this indicator highlights periods of heightened volatility and helps identify potential shifts in market sentiment.
Summer:
In the context of the indicator, "summer" represents a period of heightened volatility and upward price momentum in the market. This is analogous to the warmer months of the year when activities are typically more vibrant and energetic. During the "summer" phase indicated by the indicator, traders may observe strong bullish trends, increased trading volumes, and larger price movements. It suggests a favorable environment for bullish strategies, such as trend following or momentum trading. However, traders should exercise caution as heightened volatility can also lead to increased risk and potential drawdowns.
Winter:
Conversely, "winter" signifies a period of decreased volatility and potentially sideways or bearish price action in the market. Similar to the colder months of the year when activities tend to slow down, the "winter" phase in the indicator suggests a quieter market environment with subdued price movements and lower trading volumes. During this phase, traders may encounter choppy price action, consolidation patterns, or even downtrends. It indicates a challenging environment for trend-following strategies and may require a more cautious approach, such as range-bound or mean-reversion trading strategies.
In summary, the "summer" and "winter" phases in the "Heat Map Seasons" indicator provide traders with valuable insights into the prevailing market sentiment and can help inform their trading decisions based on the observed levels of volatility and price momentum.
How to Use:
Watch for price bars that deviate significantly from the regression line , as these may signal potential trading opportunities.
Use the seasonal gauge to gauge the current market sentiment and adjust trading strategies accordingly.
Experiment with different settings for Length and Heat Sensitivity to customize the indicator to your trading style and preferences.
The "Heat Map Seasons" indicator can potentially identify overheated market tops and bottoms on a weekly timeframe by detecting significant deviations from the regression line and observing extreme color gradients in the heat map. Here's how it can be used for this purpose:
Observing Extreme Color Gradients:
When the market is overheated and reaches a potential top, you may observe extremely warm colors (e.g., deep red) in the heat map section of the indicator.
Traders can interpret this as a warning sign of a potential market top, indicating that bullish momentum may be reaching unsustainable levels.
Conversely, when prices deviate too far below the regression line, it may indicate oversold conditions and a potential bottom.
Potential Tops and Bottoms:
User Inputs:
Length: Determines the length of the regression analysis period.
Heat Sensitivity: Controls the sensitivity of the heat map to deviations from the regression line.
Show Regression Line: Option to display or hide the regression line on the chart
Note: This indicator is best used in conjunction with other technical analysis tools and should not be relied upon as the sole basis for trading decisions.
Relative Volume (RV)Relative Volume take the volume at a given time of day and compares it to the average volume at that time of day. You can either use the current volume or the cumulative volume in this analysis. You have the option to either see the average and the current volume or a comparative view where you see the percent difference between now and the average.
My implementation of relative volume uses a key-value pair to simplify the process of getting the average volume for the time of day. This reduces the lines of code needed and makes it easier to understand. I have added the normal features you would find in a relative volume indicator with the addition of an average above/below average section for comparing the significance of above/below average moves are. I hope this script is not only useful but educational.
Enjoy
Hurst Future Lines of Demarcation StrategyJ. M. Hurst introduced a concept in technical analysis known as the Future Line of Demarcation (FLD), which serves as a forward-looking tool by incorporating a simple yet profound line into future projections on a financial chart. Specifically, the FLD is constructed by offsetting the price half a cycle ahead into the future on the time axis, relative to the Hurst Cycle of interest. For instance, in the context of a 40 Day Cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart, offering an idea of future price movement anticipations.
The utility of FLDs extends into three critical areas of insight, which form the backbone of the FLD Trading Strategy:
A price crossing the FLD signifies the confirmation of either a peak or trough formation, indicating pivotal moments in price action.
Such crossings also help determine precise price targets for the upcoming peak or trough, aligned with the cycle of examination.
Additionally, the occurrence of a peak in the FLD itself signals a probable zone where the price might experience a trough, helping to anticipate of future price movements.
These insights by Hurst in his "Cycles Trading Course" during the 1970s, are instrumental for traders aiming to determine entry and exit points, and to forecast potential price movements within the market.
To use the FLD Trading Strategy, for example when focusing on the 40 Day Cycle, a trader should primarily concentrate on the interplay between three Hurst Cycles:
The 20 Day FLD (Signal) - Half the length of the Trade Cycle
The 40 Day FLD (Trade) - The Cycle you want to trade
The 80 Day FLD (Trend) - Twice the length of the Trade Cycle
Traders can gauge trend or consolidation by watching for two critical patterns:
Cascading patterns, characterized by several FLDs running parallel with a consistent separation, typically emerge during pronounced market trends, indicating strong directional momentum.
Consolidation patterns, on the other hand, occur when multiple FLDs intersect and navigate within the same price bandwidth, often reversing direction to traverse this range multiple times. This tangled scenario results in the formation of Pause Zones, areas where price momentum is likely to temporarily stall or where the emergence of a significant trend might be delayed.
This simple FLD indicator provides 3 FLDs with optional source input and smoothing, A-through-H FLD interaction background, adjustable “Close the Trade” triggers, and a simple strategy for backtesting it all.
The A-through-H FLD interactions are a framework designed to classify the different types of price movements as they intersect with or diverge from the Future Line of Demarcation (FLD). Each interaction (designated A through H by color) represents a specific phase or characteristic within the cycle, and understanding these can help traders anticipate future price movements and make informed decisions.
The adjustable “Close the Trade” triggers are for setting the crossover/under that determines the trade exits. The options include: Price, Signal FLD, Trade FLD, or Trend FLD. For example, a trader may want to exit trades only when price finally crosses the Trade FLD line.
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 @Hpotter
👏 @parisboy
EMA 9/13/18/25 + Bollinger BandThe indicator combines two components: Exponential Moving Averages (EMAs) and Bollinger Bands.
Exponential Moving Averages (EMAs): The indicator calculates four EMAs with different periods: 9, 13, 18, and 25. An Exponential Moving Average is a type of moving average that places a greater weight and significance on the most recent data points. As the name suggests, it's an average of the asset's price over a certain period, with recent prices given more weight in the calculation, making it more responsive to recent price changes.
Bollinger Bands: Bollinger Bands consist of a simple moving average (the basis) and two standard deviations plotted away from it. The standard deviations are multiplied by a factor (usually 2) to determine the distance from the basis. These bands dynamically adjust themselves based on recent price movements. The upper band represents the highest price level reached in the given period, while the lower band represents the lowest price level.
Combining these components provides traders with insights into both trend direction and volatility. The EMAs help identify trends by smoothing out price data, while the Bollinger Bands offer insights into volatility and potential price reversal points. Traders often use the crossovers of EMAs and interactions with Bollinger Bands to make trading decisions. For example, when the price touches the upper Bollinger Band, it may indicate overbought conditions, while touching the lower band may suggest oversold conditions. Additionally, crossovers of EMAs (such as the shorter-term EMA crossing above or below the longer-term EMA) may signal changes in trend direction.
Sequencer [LuxAlgo]The Sequencer indicator is a tool that is able to highlight sequences of prices based on their relative position to past prices, which allows a high degree of customization from the user.
Two phases are included in this script, a "Preparation" phase and a "Lead-Up" phase, each with a customizable amount of steps, as well as other characteristics.
Users can also highlight the last step leading to each phase completion with a level, this level can eventually be used as a key price point.
🔶 USAGE
The script highlights two phases, each being based on a sequence of events requiring prices to be higher/lower than prices various bars ago.
The completion of the preparation phase will lead to the evaluation of the lead-up phase, however, it isn't uncommon to see a reversal occurring after the completion of a preparation phase. In the script, bullish preparations are highlighted in green, while bearish preparations are highlighted in red.
Completion of a "Lead-Up" phase is indicative of a potential reversal, with a bullish reversal for the completion of a bullish lead-up (in blue), and a bearish reversal for the completion of a bearish lead-up (in orange).
Using a higher length for the preparation/lead-up phases can allow the detection of longer-term reversals.
Users wishing to display levels based on specific phases completion can do so from the settings in the "Preparation/Lead-Up Completion Levels" settings group.
The "Show Last" settings determine the amount of respective levels to display on the chart.
🔶 PREPARATION PHASE
The "Preparation" phase precedes the "Lead-Up" phase. The completion of this phase requires N successive prices to be lower than the closing price P bars ago for a bullish phase, and for prices to be higher than the closing price P bars ago for a bearish phase, where N is the user set "Preparation Phase Length" and P the user set "Comparison Period".
🔹 Refined Preparations
Sequences of the preparation phase can either be "Standard" or "Refined". Unlike the standard preparation previously described a refined preparation requires the low prices from the user-specified steps in "Refined Preparation Steps" to be above the low price of the last step for a bullish preparation phase, and for the high prices specified in the refined preparation steps to be below the high price of the last step for a bearish preparation phase.
🔶 LEAD-UP PHASE
The "Lead-Up" phase is initiated by the completion of the "Preparation" phase.
Completion of this phase requires the price to be lower than the low price P bars ago N times for a bullish phase, and for prices to be higher than the high price P bars ago N times for a bearish phase, where N is the user set "Lead-Up Phase Length" and P the user set "Comparison Period".
Unlike with the "Preparation" phase these conditions don't need to be successive for them to be valid and can occur at any time.
🔹 Lead-Up Cancellation
Incomplete "Lead-Up" phases can be canceled and removed from the chart once a preparation of the opposite sentiment is completed, avoiding lead-ups to be evaluated after completion of complete preparations.
This can be disabled by toggling off "Apply Cancellation".
🔹 Lead-Up Suspension
Like with refined preparations, we can require specific steps from the lead-up phase to be higher/lower than the price on the last step. This can be particularly important since we do not require lead-up steps to be successive.
For a bullish lead-up, the low of the last step must be lower than the minimum closing prices of the user-specified steps for it to be valid, while for a bearish lead-up, the high of the last step must be higher than the maximum closing prices of the user-specified steps for it to be valid.
This effectively allows for eliminating lead-up phases getting completed on opposite trends.
🔶 SETTINGS
🔹 Preparation Phase
Preparation Phase Length: Length of the "Preparation" phase.
Comparison Period: Offset used to compare current prices to past ones.
Preparation Type: Type of preparation to evaluate, options include "Standard" or "Refined"
Refined Preparations Steps: Steps to evaluate when preparation type is "Refined"
🔹 Lead-Up Phase
Lead-Up Phase Length: Length of the "Lead-Up" phase.
Comparison Period: Offset used to compare current prices to past ones.
Suspension: Applies suspension rule to evaluate lead-up completion.
Suspension Steps: Specifies the steps evaluated to determine if the lead-up referral is respected. Multiple steps are supported and should be comma-separated.
Apply Cancellation: Cancellation will remove any incomplete lead-up upon the completion of a new preparation phase of the opposite sentiment.
🔹 Levels
Bullish Preparations Levels: When enabled display price levels from completed bullish preparations.
Show Last: Number of most recent bullish preparations levels to display.
Bearish Preparations Levels: When enabled display price levels from completed bearish preparations.
Show Last: Number of most recent bearish preparations levels to display.
Fibonacci Entry SignalsЭтот индикатор "Fibonacci Entry Signals" помогает определить точки входа в покупку и продажу на основе уровней Фибоначчи и скользящей средней.
Ключевые особенности:
Уровни Фибоначчи: Индикатор рисует на графике несколько уровней Фибоначчи, таких как 38.2%, 50% и 61.8%. Эти уровни определяются на основе крайних точек цены за последние 100 баров. Уровень 0% соответствует самой низкой точке, а уровень 100% - самой высокой.
Точки входа: При наступлении условий входа в покупку или продажу, индикатор отмечает на графике точки входа в виде треугольников вверх или вниз соответственно. Условия входа в покупку определяются, когда цена пересекает уровень Фибоначчи 38.2% вверх и находится выше скользящей средней за последние 50 баров. Условия входа в продажу аналогичны, но цена пересекает уровень Фибоначчи 61.8% вниз.
Этот индикатор может быть полезен для трейдеров, которые используют уровни Фибоначчи в своей торговой стратегии и ищут точки входа в рынок. Однако, перед использованием на реальном счете, рекомендуется провести тщательное тестирование и адаптацию к своим индивидуальным потребностям и стратегии торговли.
Давайте рассмотрим, как пользоваться индикатором "Fibonacci Entry Signals":
Установка на график: Для начала необходимо добавить индикатор на график вашего торгового актива. Вы можете сделать это, выбрав его из списка индикаторов в торговой платформе.
Интерпретация уровней Фибоначчи: Индикатор отображает на графике несколько уровней Фибоначчи, таких как 38.2%, 50% и 61.8%. Понимание этих уровней поможет вам определить возможные точки входа в рынок. Обычно, уровень 38.2% используется как уровень поддержки, а уровень 61.8% - как уровень сопротивления.
Точки входа в покупку и продажу: Индикатор помогает определить моменты, когда можно войти в покупку или продажу. При наступлении условий входа в покупку, индикатор отмечает на графике точку входа в виде зеленого треугольника вверх. При наступлении условий входа в продажу, индикатор отмечает точку входа на графике в виде красного треугольника вниз.
Управление позициями: После получения сигнала от индикатора, решение о входе в позицию остается за вами. Важно помнить, что индикатор является только инструментом, который помогает в принятии решений, и его сигналы всегда следует подтверждать другими аспектами вашей торговой стратегии, такими как подтверждение другими индикаторами или анализ фундаментальных данных.
Тестирование и адаптация: Прежде чем использовать индикатор на реальном счете, рекомендуется провести тщательное тестирование на исторических данных и адаптировать его к своим индивидуальным торговым потребностям и стратегии.
Индикатор "Fibonacci Entry Signals" может быть полезным инструментом для трейдеров, которые используют уровни Фибоначчи в своей торговой стратегии и ищут точки входа в рынок.
his "Fibonacci Entry Signals" indicator helps identify entry points for buying and selling based on Fibonacci levels and moving averages.
Key features:
Fibonacci Levels: The indicator draws several Fibonacci levels on the chart, such as 38.2%, 50%, and 61.8%. These levels are determined based on the extreme price points over the last 100 bars. The 0% level corresponds to the lowest point, and the 100% level corresponds to the highest point.
Entry Points: When conditions for buying or selling occur, the indicator marks entry points on the chart as upward or downward triangles, respectively. Entry conditions for buying are determined when the price crosses above the 38.2% Fibonacci level and remains above the 50-bar moving average. Entry conditions for selling are similar, but the price crosses below the 61.8% Fibonacci level.
This indicator can be useful for traders who use Fibonacci levels in their trading strategy and look for entry points in the market. However, before using it on a real account, it is recommended to conduct thorough testing and adaptation to your individual trading needs and strategy.
Let's look at how to use the "Fibonacci Entry Signals" indicator:
Installation on the Chart: First, you need to add the indicator to the chart of your trading asset. You can do this by selecting it from the list of indicators in the trading platform.
Interpreting Fibonacci Levels: The indicator displays several Fibonacci levels on the chart, such as 38.2%, 50%, and 61.8%. Understanding these levels will help you identify potential entry points in the market. Usually, the 38.2% level is used as support, and the 61.8% level is used as resistance.
Entry Points for Buying and Selling: The indicator helps identify moments when you can enter buying or selling positions. When conditions for buying occur, the indicator marks the entry point on the chart as a green upward triangle. When conditions for selling occur, the indicator marks the entry point on the chart as a red downward triangle.
Position Management: After receiving a signal from the indicator, the decision to enter a position is up to you. It's important to remember that the indicator is just a tool to help you make decisions, and its signals should always be confirmed by other aspects of your trading strategy, such as confirmation from other indicators or fundamental analysis.
Testing and Adaptation: Before using the indicator on a real account, it's recommended to conduct thorough testing on historical data and adapt it to your individual trading needs and strategy.
The "Fibonacci Entry Signals" indicator can be a useful tool for traders who use Fibonacci levels in their trading strategy and look for entry points in the market.
Session LiquidityDescribes if markets are liquid enough for institutions to manipulate. Its often difficult to determine if markets will trend or chop, but by looking at how much volume we have at the open, we can determine of the session will be choppy or trendy, and take trades based on that.
Settings predefined for 1m timeframe on SPY. May work with other tickers, but I have not tested it out yet.
Designed for stocks(as of now, may update later)
Dynamic Candle Balance Indicator (Binary)
Dynamic Candle Balance Indicator
The Dynamic Candle Balance Indicator is a powerful tool designed to identify imbalances in candle colors on a chart, which can indicate potential reversals or changes in market direction. This indicator is specifically developed for traders operating on short timeframes, such as 1-minute candles, and is particularly useful for identifying opportunities in binary options.
How to Use:
Set Parameters
Initial Position: Specify the number of initial candles to be considered for calculation.
Count: Determine the total number of candles to be analyzed, including the initial position.
Interpret Results:
Green: Indicates the number of bullish candles (where the closing price is higher than the opening price).
Red: Indicates the number of bearish candles (where the closing price is lower than the opening price).
Absent: Indicates the number of candles that were not considered due to the selected interval.
Performance Analysis:
The indicator calculates the percentage of green and red candles relative to the total number of analyzed candles, providing insights into market balance or imbalance.
Identify Trading Opportunities:
Significant imbalances between candle colors can indicate potential reversals or changes in market direction.
Traders can use this information to make informed decisions about their trading strategies, such as identifying entry or exit points.
Example:
In the last 40 candles, there were 13 green candles and 27 red candles, indicating a higher likelihood of the next candle being green.
Usage Tips:
The indicator is most effective when used on a 1-minute timeframe for binary options trading, especially during periods of high imbalance.
Adjust the parameters according to your trading strategy and the timeframe being analyzed.
Combine the Dynamic Candle Balance Indicator with other technical analysis tools to confirm trading signals.
Legal Disclaimer:
This indicator is provided for educational and informational purposes only. It represents a theory and should be used as part of a comprehensive trading strategy. Past performance is not indicative of future results. Traders should always conduct their own analysis before making trading decisions.
Try out the Dynamic Candle Balance Indicator and leverage its functionalities to identify trading opportunities on short-term charts, especially in 1-minute timeframes for binary options trading during periods of high imbalance. Remember to test the indicator on a practice account before using it on a real account.
Adaptive Schaff Trend Cycle (STC) [AlgoAlpha]Introducing the Adaptive Schaff Trend Cycle by AlgoAlpha: Elevate Your Trading Strategies 🚀
Discover precision and adaptability with the Adaptive Schaff Trend Cycle 🎯, meticulously crafted for traders seeking an edge in the markets. This advanced tool integrates sophisticated algorithms to offer clear insights and real-time analytics 📈.
Key Features:
⚙️Adaptive Signal Processing: Utilizes evolving calculations to adjust to market changes, offering highly responsive signals.
🔍Enhanced MACD Analysis: Innovates on the traditional MACD, providing new insights into market dynamics through an adaptive lens.
🎨Customizable Visual Experience: Features customizable up and down colors for tailored chart analysis.
🔔Real-Time Alerts: Stay informed with instant alerts on indicator changes.
Quick Guide to Using the Adaptive STC Indicator
1. 🔧 Adding the Indicator: Search for "Adaptive Schaff Trend Cycle (STC) " within TradingView's Indicators & Strategies and apply it to your chart. Customize the settings according to your trading style for optimum results.
2.👀 Market Analysis: Monitor the STC and Histogram values closely. The indicator's color gradients provide a visual representation of momentum shifts, helping you to identify trends more clearly.
3. 🚨 Set Alerts: Enable alerts for specific conditions like significant moves up or down, or when the histogram crosses zero. This feature ensures you never miss a potential trading opportunity.
How It Works:
The Adaptive Schaff Trend Cycle by AlgoAlpha introduces a dynamic approach to market analysis, refining traditional indicators through adaptive logic to align with fluctuating market conditions. Here's a concise overview of its operation:
🔄 Adaptive MACD Adjustment: The foundation of the indicator is an enhanced MACD calculation, which dynamically adjusts its parameters based on real-time market trends and momentum. This algorithmic adjustment aims to ensure the MACD's responsiveness to market changes, adapting its sensitivity to offer timely insights .
🌟 Integration of Schaff Trend Cycle (STC): After adjusting the MACD, the indicator calculates STC values to provide a smoothed representation of market trends. By normalizing and smoothing the MACD values on a scale from 0 to 100, the STC method helps in identifying market phases with a clear visualization. The smoothing process is designed to mitigate noise and focus on significant market movements .
📊 Visualization and Alerts: To aid in the interpretation of these insights, the Adaptive Schaff Trend Cycle employs color gradients and customizable visual settings to indicate momentum shifts. These visual cues, combined with alert functionalities, are structured to assist traders in monitoring market developments, enabling them to make informed decisions based on the presented data .
🛠️The Adaptive Schaff Trend Cycle thus merges adaptive MACD adjustments with STC methodology, supported by visual and alert features, to create a tool aimed at enhancing market analysis. By focusing on adaptability and current market conditions, it provides a nuanced view of market trends, intended to support traders in their decision-making processes without promising predictive accuracy or reliability .
Buffett IndicatorThis is an open-source version of the Buffett indicator. The old version was code-protected and broken, so I created another version.
It's computed simply as the entire SPX 500 capitalization divided by the US GDP. Since TradingView does not have data for the SPX 500 capitalization, I used quarterly values of SPX devisors as a proxy.
I tried to create another version of the Buffett indicator for other countries/indexes, but I can't find the data. If you can help me find data for index divisors, I can add more choices to this indicator.
It's interesting to see how this indicator's behavior has changed in the last few years. Levels that looked crazy are not so crazy anymore.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Temporal Value Tracker: Inception-to-Present Inflation Lens!What we're looking at here is a chart that does more than just display the price of gold. It offers us a time-traveling perspective on value. The blue line, that's our nominal price—it's the straightforward market price of gold over time. But it's the red line that takes us on a deeper journey. This line adjusts the nominal price for inflation, showing us the real purchasing power of gold.
Now, when we talk about 'real value,' we're not just philosophizing. We're anchoring our prices to a point in time when the journey began—let's say when gold trading started on the markets, or any inception point we choose. By 'shadowing' certain years—say, from the 1970s when the gold standard was abandoned—we can adjust this chart to reflect what the inflation-adjusted price means since that key moment in history.
By doing so, we're effectively isolating our view to start from that pivotal year, giving us insight into how gold, or indeed any asset, has held up against the backdrop of economic changes, policy shifts, and the inevitable rise in the cost of living. If you're analyzing a stock index like the S&P 500, you might begin your inflation-adjusted view from the index's inception date, which allows you to measure the true growth of the market basket from the moment it started.
This adjustment isn't just academic. It influences how we perceive value and growth. Consider a period where the nominal price skyrockets. We might toast to our brilliance in investment! But if the inflation-adjusted line lags, what we're seeing is nominal growth without real gains. On the other hand, if our red line outpaces the blue even during stagnant market periods, we're witnessing real growth—our asset is outperforming the eroding effects of inflation.
Every asset class can be evaluated this way. Stocks, bonds, real estate—they all have their historical narratives, and inflation adjustment tells us if these stories are tales of genuine growth or illusions masked by inflation.
So, as informed traders and investors, we need to keep our eyes on this inflation-adjusted line. It's our measure against the silent thief that is inflation. It ensures we're not just keeping up with the Joneses of the market, but actually outpacing them, building real wealth over time
Daily Close GAP Detector [Yosiet]User Manual for "Daily Close GAP Detector "
Overview
This script is designed to help traders identify and react to significant gaps in daily market prices. It plots daily open and close prices and highlights significant gaps with a cross. The script is particularly useful for identifying potential breakouts or reversals based on these gaps.
Configuration
GAP Close Threshold: This input allows you to set a threshold for the gap size that you consider significant. The default value is 0.001.
Timeframe Seeker: This input lets you choose the timeframe for the gap detection. The default is 'D' for daily.
Features
Daily Open and Close Lines: The script plots daily open and close prices. If the close price is lower than the open price, the line is colored red; otherwise, it's green.
Gap Detection: It calculates the difference between the current day's close and the previous day's close, both adjusted for the selected timeframe. If this difference exceeds the threshold, it's considered a significant gap.
Significant Gap Indicator: A cross is plotted on the chart to indicate significant gaps. The color of the cross indicates whether the gap is a short or long gap: red for short gaps and green for long gaps.
Alert Conditions: The script sets up alert conditions for short and long gap breakouts. You can customize the alert messages to include details like the ticker symbol, interval, price, and exchange.
How to Use
Add the Script to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Configure Inputs: Adjust the "GAP Close Threshold" and "Timeframe Seeker" inputs as needed.
Review the Chart: The script will overlay daily open and close prices on your chart, along with crosses indicating significant gaps.
Set Alerts: Use the script's alert conditions to set up alerts for short and long gap breakouts. You can customize the alert messages to suit your trading strategy.
Extending the Code
To extend this script, you can modify the gap detection logic, add more indicators, or integrate it with other scripts for a more comprehensive trading strategy. Remember to test any changes thoroughly before using them in live trading.
Pi Cycle Indicator Low and High
The Pi Cycle Indicator is a technical analysis tool used in finance, particularly within cryptocurrency markets, to identify potential market tops or bottoms. It is based on two moving averages: the 111-day moving average and the 350-day moving average of Bitcoin's price. The indicator suggests that when these two moving averages converge or cross each other, it may signal significant market turning points. The name "Pi Cycle" comes from the mathematical relationship between these two moving averages, roughly equivalent to the mathematical constant Pi (3.14). Traders and analysts use this indicator to gauge potential trend reversals and make informed decisions regarding their trading strategies. However, like any technical analysis tool, it should be used in conjunction with other indicators and fundamental analysis for a comprehensive understanding of market conditions.
Vertical line at 8 AMThis indicator plots a blue vertical line on the chart when it's 8 AM, providing a clear visual reference of this time point on the TradingView chart.
Yeong RRGThe code outlines a trading strategy that leverages Relative Strength (RS) and Rate of Change (RoC) to make trading decisions. Here's a detailed breakdown of the tactic described by the code:
Ticker and Period Selection: The strategy begins by selecting a stock ticker symbol and defining a period (len) for the calculations, which defaults to 14 but can be adjusted by the user.
Stock and Index Data Retrieval: It fetches the closing price (stock_close) of the chosen stock and calculates its 25-period exponential moving average (stock_ema). Additionally, it retrieves the closing price of the S&P 500 Index (index_close), used as a benchmark for calculating Relative Strength.
Relative Strength Calculation: The Relative Strength (rs) is computed by dividing the stock's closing price by the index's closing price, then multiplying by 100 to scale the result. This metric is used to assess the stock's performance relative to the broader market.
Moving RS Ratio and Rate of Change: The strategy calculates a Simple Moving Average (sma) of the RS over the specified period to get the RS Ratio (rs_ratio). It then computes the Rate of Change (roc) of this RS Ratio over the same period to get the RM Ratio (rm_ratio).
Normalization: The RS Ratio and RM Ratio are normalized using a formula that adjusts their values based on the mean and standard deviation of their respective series over the specified window. This normalization process helps in standardizing the indicators, making them easier to interpret and compare.
Indicator Plotting: The normalized RS Ratio (jdk_rs_ratio) and RM Ratio (jdk_rm_ratio) are plotted on the chart with different colors for visual analysis. A horizontal line (hline) at 100 serves as a reference point, indicating a neutral level for the indicators.
State Color Logic: The script includes a logic to determine the state color (statecolor) based on the previous state color and the current values of jdk_rs_ratio and jdk_rm_ratio. This color coding is intended to visually represent different market states: green for bullish, red for bearish, yellow for hold, and blue for watch conditions.
Signal Generation: The strategy generates buy, sell, hold, and watch signals based on the state color and the indicators' values relative to 100. For example, a buy signal is generated when both jdk_rs_ratio and jdk_rm_ratio are above 100, and the background color is set to green to reflect this bullish condition.
Trade Execution: Finally, the strategy executes trades based on the generated signals. A "BUY" trade is entered when a buy signal is present, and it is closed when a sell signal occurs.
Overall, the strategy uses a combination of RS and RoC indicators, normalized for better comparison, to identify potential buy and sell opportunities based on the stock's performance relative to the market and its momentum.
BTC Valuation
The BTC Valuation indicator
is a powerful tool designed to assist traders and analysts in evaluating the current state of Bitcoin's market valuation. By leveraging key moving averages and a logarithmic trendline, this indicator offers valuable insights into potential buying or selling opportunities based on historical price value.
Key Features:
200MA/P (200-day Moving Average to Price Ratio):
Provides a perspective on Bitcoin's long-term trend by comparing the current price to its 200-day Simple Moving Average (SMA).
A positive value suggests potential undervaluation, while a negative value may indicate overvaluation.
50MA/P (50-day Moving Average to Price Ratio):
Focuses on short-term trends, offering insights into the relationship between Bitcoin's current price and its 50-day SMA.
Helps traders identify potential bullish or bearish trends in the near term.
LTL/P (Logarithmic TrendLine to Price Ratio):
Incorporates a logarithmic trendline, considering Bitcoin's historical age in days.
Assists in evaluating whether the current price aligns with the long-term logarithmic trend, signaling potential overvaluation or undervaluation.
How to Use:
Z Score Indicator Integration:
The BTC Valuation indicator leverages the Z Score Indicator to score the ratios in a statistical way.
Statistical scoring provides a standardized measure of how far each ratio deviates from the mean, aiding in a more nuanced and objective evaluation.
Z Score Indicator
This BTC Valuation indicator provides a comprehensive view of Bitcoin's valuation dynamics, allowing traders to make informed decisions.
While indicators like BTC Valuation provide valuable insights, it's crucial to remember that no indicator guarantees market predictions.
Traders should use indicators as part of a comprehensive strategy and consider multiple factors before making trading decisions.
Historical performance is not indicative of future results. Exercise caution and continually refine your approach based on market dynamics.
Bitcoin Bubble Risk (Adjusted for Diminishing Returns)Description:
This indicator offers a unique lens through which traders can assess risk in the Bitcoin market, specifically tailored to recognize the phenomenon of diminishing returns. By calculating the natural logarithm of the price relative to a 20-month Simple Moving Average (SMA) and applying a dynamic normalization process, this tool highlights periods of varying risk based on historical price movements and adjusted returns. The indicator is designed to provide nuanced insights into potential risk levels, aiding traders in their decision-making processes.
Usage:
To effectively use this indicator, apply it to your chart while ensuring that Bitcoin's price is set to display in monthly candles. This setting is vital for the indicator to accurately reflect the market's risk levels, as it relies on long-term data aggregation to inform its analysis.
This tool is especially beneficial for traders focused on medium to long-term investment horizons in Bitcoin, offering insights into when the market may be entering higher or lower risk phases. By incorporating this indicator into your analysis, you can gain a deeper understanding of potential risk exposures based on the adjusted price trends and market conditions.
Originality and Utility:
This script stands out for its innovative approach to risk analysis in the cryptocurrency space. By adjusting for the diminishing returns seen in mature markets, it provides a refined perspective on risk levels, enhancing traditional methodologies. This script is a significant contribution to the TradingView community, offering a unique tool for traders aiming to navigate the complexities of the Bitcoin market with informed risk management strategies.
Important Note:
This indicator is for informational purposes only and should not be considered investment advice. Users are encouraged to conduct their own research and consult with financial professionals before making investment decisions. The accuracy of the indicator's predictions can only be ensured when applied to monthly candlestick charts of Bitcoin.
RP - Realized Price for Bitcoin (BTC) [Logue]Realized Price (RP) - The RP is summation of the value of each BTC when it last moved divided by the total number of BTC in circulation. This gives an estimation of the average "purchase" price of BTC on the bitcoin network based on when it was last transacted. This indicator tells us if the average network participant is in a state of profit or loss. This indicator is normally used to detect BTC bottoms, but an extension can be used to detect when the bitcoin network is "highly" overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator triggers when the BTC price is above the realized price extension. For the bottoms, the RP is shifted downwards at a default value of 80%. The slope, intercept, and RP bottom shift can all be modified in the script.
CVDD - Coin Value Days Destroyed for Bitcoin (BTC) [Logue]Cumulative Value Days Destroyed (CVDD) - The CVDD was created by Willy Woo and is the ratio of the cumulative value of Coin Days Destroyed in USD and the market age (in days). While this indicator is used to detect bottoms normally, an extension is used to allow detection of BTC tops. When the BTC price goes above the CVDD extension, BTC is generally considered to be overvalued. Because the "strength" of the BTC tops has decreased over the cycles, a logarithmic function for the extension was created by fitting past cycles as log extension = slope * time + intercept. This indicator is triggered for a top when the BTC price is above the CVDD extension. For the bottoms, the CVDD is shifted upwards at a default value of 120%. The slope, intercept, and CVDD bottom shift can all be modified in the script.
PA Helper - Draw Next 5 CandlesA user-friendly tool designed for a quick visual preview of the next 5 candles on your trading chart.
Here's how to use it effortlessly:
Set Open Prices:
Adjust the open prices for the upcoming 5 candles using the inputs labeled Next close #1 to Next close #5.
Toggle Candles:
Use the checkboxes (p1 to p5) to enable or disable the drawing of each corresponding next candle.
Offset Option:
Customize your preview by toggling the offset option:
If offset is set to false, the drawing starts from the current candle's close, providing insight into the next 5 candles relative to the current one.
If offset is set to true, the drawing begins with the next candle, offering a preview of the upcoming 5 candles, effectively skipping the current one.
Visual Representation:
The indicator visually displays the next 5 candles on your chart based on your selected open prices, offering a clear and tailored insight into potential market movements.
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach
THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY.
If you enjoy this software and information, please consider contributing to my lightning address
Prelude
It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated
In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with.
This is a model of sound money, scarcity and subjective value.
Introduction
Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only.
Data preparation
To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps:
Fixing date formats and setting the dataset’s time index
Generating cumulative sums for blocks and halving periods
Calculating daily rewards and total supply
Computing the log-transformed price
Step 1: Building the Base Model
To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch.
where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression.
Base Model Regression
We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”.
The code below shows the regression for the base model coefficients:
\# Run the regression
mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model
reg\_X = df.loc\ [mask, \ \].shift(1).iloc\
reg\_y = df.loc\ .iloc\
reg\_X = sm.add\_constant(reg\_X)
ols = sm.OLS(reg\_y, reg\_X).fit()
coefs = ols.params.values
print(coefs)
The result of this regression gives us the coefficients for the betas of the base model:
\
or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures.
Fundamental Insights from the Base Model
Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend.
Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term.
The momentum effect can be driven by several factors:
Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum.
Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect.
Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum.
Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving.
The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin.
The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred.
Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller.
This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective.
The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions.
The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve.
NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available.
Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate.
In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero.
The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc).
Base Model Regression Diagnostics
Below is a summary of the model generated by the OLS function
OLS Regression Results
\==============================================================================
Dep. Variable: logprice R-squared: 0.999
Model: OLS Adj. R-squared: 0.999
Method: Least Squares F-statistic: 2.041e+06
Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00
Time: 11:06:58 Log-Likelihood: 3001.6
No. Observations: 2182 AIC: -5997.
Df Residuals: 2179 BIC: -5980.
Df Model: 2
Covariance Type: nonrobust
\==============================================================================
coef std err t P>|t| \
\------------------------------------------------------------------------------
const 0.0292 0.009 3.081 0.002 0.011 0.048
logprice 0.9969 0.001 1012.724 0.000 0.995 0.999
phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05
\==============================================================================
Omnibus: 674.771 Durbin-Watson: 1.901
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353
Skew: -0.765 Prob(JB): 0.00
Kurtosis: 19.491 Cond. No. 255.
\==============================================================================
Below we see some regression diagnostics along with the regression itself.
Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code:
\# Calculate the out-of-sample R-squared
oos\_mask = df\ >= 2
oos\_actual = df.loc\
oos\_predicted = df.loc\
residuals\_oos = oos\_actual - oos\_predicted
SSR = np.sum(residuals\_oos \*\* 2)
SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2)
R2\_oos = 1 - SSR/SST
print("Out-of-sample R-squared:", R2\_oos)
The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate.
Step 2: Adding the Damping Function
Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation.
This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin.
Fundamental reasoning behind the addition of a damping function:
Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value.
Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events.
Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts.
Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance —
Financial and physical analogies to the damping function:
Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions.
Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is:
x’’(t) + 2γx’(t) + ω₀²x(t) = 0
where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles.
Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics.
By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence.
Conclusion
In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics.
The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information.
Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange.
The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin.
The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives.
In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
