Buffett Quality Score [Materials]The Buffett Quality Score tailored for the Materials sector aims to assess the financial strength and quality of companies within this industry. Each selected financial ratio is strategically chosen to align with the unique characteristics and challenges prevalent in the Materials sector.
Selected Financial Ratios and Criteria:
1. Asset Turnover > 0.8
Relevance: In the Materials sector, efficient asset utilization is crucial for productivity and profitability. A high Asset Turnover (>0.8) indicates effective management of resources and operational efficiency.
2. Current Ratio > 1.5
Relevance: Materials companies often require adequate liquidity to manage inventory and operational expenses. A Current Ratio > 1.5 ensures sufficient short-term liquidity to support ongoing operations and investments.
3. Debt to Equity Ratio < 1.0
Relevance: Given the capital-intensive nature of Materials projects, maintaining a low Debt to Equity Ratio (<1.0) signifies prudent financial management with reduced reliance on debt financing, essential for stability amid industry fluctuations.
4. Gross Margin > 25.0%
Relevance: Materials companies deal with varying production costs and market pricing. A Gross Margin exceeding 25.0% reflects effective cost management and pricing strategies, critical for profitability in a competitive market.
5. EBITDA Margin > 15.0%
Relevance: Strong EBITDA margins (>15.0%) indicate robust operational performance and profitability, essential for sustaining growth and weathering industry-specific challenges.
6. Interest Coverage Ratio > 3.0
Relevance: The Materials sector is subject to market cyclicality and commodity price fluctuations. An Interest Coverage Ratio > 3.0 ensures the company's ability to service debt obligations, safeguarding against financial risks.
7. EPS One-Year Growth > 5.0%
Relevance: EPS growth > 5.0% demonstrates the company's ability to generate sustainable earnings amidst industry dynamics, reflecting positive investor sentiment and potential future prospects.
8. Revenue One-Year Growth > 5.0%
Relevance: Materials companies require consistent revenue growth (>5.0%) to support expansion initiatives and capitalize on market opportunities, indicative of operational resilience and adaptability.
9. Return on Assets (ROA) > 5.0%
Relevance: ROA > 5.0% showcases efficient asset utilization and profitability, essential metrics for evaluating performance and competitive positioning within the Materials industry.
10. Return on Equity (ROE) > 10.0%
Relevance: ROE > 10.0% reflects effective capital deployment and shareholder value creation, crucial for sustaining long-term growth and investor confidence in Materials sector investments.
Score Interpretation:
0-4 Points: Signals potential weaknesses across critical financial aspects, requiring in-depth analysis and risk assessment.
5 Points: Represents average performance based on sector-specific criteria.
6-10 Points: Indicates strong financial health and quality, demonstrating robustness and resilience within the demanding Materials industry landscape.
Development and Context:
The selection and weighting of these specific financial metrics underwent meticulous research and consideration to ensure relevance and applicability within the Materials sector. This scoring framework aims to provide actionable insights for stakeholders navigating investment decisions and evaluating company performance in the Materials industry.
Disclaimer: This information serves as an educational resource on financial evaluation methodology tailored for the Materials sector. It does not constitute financial advice or a guarantee of future performance. Consult qualified professionals for personalized financial guidance based on your specific circumstances and investment objectives.
Statistics
Buffett Quality Score [Energy]The Buffett Quality Score for the Energy sector is designed to meticulously evaluate the financial health and quality of companies operating within this dynamic industry. Each selected financial ratio is specifically chosen based on its relevance and significance within the Energy sector context.
Selected Financial Ratios and Criteria:
1. Return on Assets (ROA) > 5%
Relevance: In the Energy sector, where asset-intensive operations are common (e.g., oil exploration and infrastructure), a robust ROA above 5% indicates efficient asset utilization, crucial for profitability.
2. Debt to Equity Ratio < 1.0
Relevance: Energy companies often require substantial capital for projects and operations. A low Debt to Equity Ratio (<1.0) suggests prudent financial management with less reliance on debt financing, vital in a capital-intensive industry vulnerable to economic cycles.
3.Interest Coverage Ratio > 3.0
Relevance: Given the capital-intensive nature of Energy projects, maintaining a healthy Interest Coverage Ratio (>3.0) ensures the company's ability to service debt obligations, particularly important during periods of economic volatility affecting commodity prices.
4. Gross Margin % > 25%
Relevance: Energy companies face varying production costs and pricing pressures. A Gross Margin exceeding 25% reflects efficient cost management and pricing power, critical in mitigating volatility in commodity prices.
5. Current Ratio > 1.5
Relevance: Energy projects often require substantial working capital. A Current Ratio > 1.5 indicates sufficient liquidity to cover short-term obligations, essential for operational continuity in an industry susceptible to market fluctuations.
6. EBITDA Margin % > 15%
Relevance: Energy companies must manage operating costs effectively. An EBITDA Margin > 15% signifies strong operational efficiency and profitability, crucial for sustaining growth amidst market uncertainties.
7. Altman Z-Score > 2.0
Relevance: The Energy sector experiences cyclical downturns and price volatility. An Altman Z-Score > 2.0 indicates financial stability and resilience, vital for weathering industry-specific challenges.
8. EPS Basic One-Year Growth % > 5%
Relevance: Energy companies' earnings growth is closely tied to commodity prices and market demand. EPS growth > 5% indicates positive momentum and adaptability to industry shifts.
9. Revenue One-Year Growth % > 5%
Relevance: Energy companies operate in a dynamic market influenced by geopolitical factors and global demand. Revenue growth > 5% reflects market adaptability and expansion potential.
10. Piotroski F-Score > 6
Relevance: Fundamental strength is paramount in the Energy sector, characterized by capital-intensive projects. A Piotroski F-Score > 6 highlights solid operational and financial performance, critical for long-term sustainability.
Score Interpretation:
0-4 Points: Indicates potential weaknesses across critical financial areas, necessitating closer scrutiny.
5 Points: Suggests average performance based on industry-specific criteria.
6-10 Points: Signifies strong overall financial health and quality, aligning with the demanding requirements of the Energy sector.
Development and Context:
The selection and weighting of these specific financial metrics underwent rigorous industry-specific research to ensure their applicability and reliability within the unique operational environment of the Energy sector. This scoring framework aims to provide actionable insights for stakeholders navigating the complexities of Energy industry investments and operations.
Disclaimer: This information serves as an educational resource on financial evaluation methodology tailored for the Energy sector. It does not constitute financial advice or a guarantee of future performance. Consult qualified professionals for personalized financial guidance based on your specific circumstances and investment objectives.
Buffett Quality Score [Industry]The Buffett Quality Score is a composite indicator developed to assess the financial health and quality of companies operating within the Industrial sector. It combines a carefully selected set of financial ratios, each weighted with specific thresholds, to provide a comprehensive evaluation of company performance.
Selected Financial Ratios and Criteria:
1. Return on Assets (ROA) > 5%
ROA measures a company's profitability by evaluating how effectively it utilizes its assets. An ROA exceeding 5% earns 1 point.
2. Debt to Equity Ratio < 1.0
The Debt to Equity Ratio reflects a company's leverage. A ratio below 1.0 earns 1 point, indicating lower reliance on debt financing.
3. Interest Coverage Ratio > 3.0
The Interest Coverage Ratio assesses a company's ability to meet interest payments. A ratio above 3.0 earns 1 point, indicating strong financial health.
4. Gross Margin % > 25%
Gross Margin represents the profitability of sales after deducting production costs. A margin exceeding 25% earns 1 point, indicating better pricing power.
5. Current Ratio > 1.5
The Current Ratio evaluates a company's liquidity by comparing current assets to current liabilities. A ratio above 1.5 earns 1 point, indicating sufficient short-term liquidity.
6. EBITDA Margin % > 15%
EBITDA Margin measures operating profitability, excluding non-operating expenses. A margin exceeding 15% earns 1 point, indicating efficient operations.
7. Altman Z-Score > 2.0
The Altman Z-Score predicts bankruptcy risk based on profitability, leverage, liquidity, solvency, and activity. A score above 2.0 earns 1 point, indicating financial stability.
8. EPS Basic One-Year Growth % > 5%
EPS One-Year Growth reflects the percentage increase in earnings per share over the past year. Growth exceeding 5% earns 1 point, indicating positive earnings momentum.
9. Revenue One-Year Growth % > 5%
Revenue One-Year Growth represents the percentage increase in revenue over the past year. Growth exceeding 5% earns 1 point, indicating healthy sales growth.
10. Piotroski F-Score > 6
The Piotroski F-Score evaluates fundamental strength based on profitability, leverage, liquidity, and operating efficiency. A score above 6 earns 1 point, indicating strong fundamental performance.
Score Calculation Process:
Each company is evaluated against these criteria.
For every criterion met or exceeded, 1 point is assigned.
The total points accumulated determine the Buffett Quality Score out of a maximum of 10.
Interpretation of Scores:
0-4 Points: Indicates potential weaknesses across multiple financial areas.
5 Points: Suggests average performance based on the selected criteria.
6-10 Points: Signifies strong overall financial health and quality, meeting or exceeding most of the performance thresholds.
Research and Development:
The selection and weighting of these specific financial ratios underwent extensive research to ensure relevance and applicability to the Industrial sector. This scoring methodology aims to provide valuable insights for investors and analysts seeking to evaluate company quality and financial robustness within the Industrial landscape.
The information provided about the Buffett Quality Score is for educational purposes only. This document serves as an illustrative example of financial evaluation methodology and should not be construed as financial advice, investment recommendation, or a guarantee of future performance. Actual results may vary based on individual circumstances and specific factors affecting each company. We recommend consulting qualified professionals for personalized financial advice tailored to your individual situation.
Seasonality Widget [LuxAlgo]The Seasonality Widget tool allows users to easily visualize seasonal trends from various data sources.
Users can select different levels of granularity as well as different statistics to express seasonal trends.
🔶 USAGE
Seasonality allows us to observe general trends occurring at regular intervals. These intervals can be user-selected from the granularity setting and determine how the data is grouped, these include:
Hour
Day Of Week
Day Of Month
Month
Day Of Year
The above seasonal chart shows the BTCUSD seasonal price change for every hour of the day, that is the average price change taken for every specific hour. This allows us to obtain an estimate of the expected price move at specific hours of the day.
Users can select when data should start being collected using the "From Date" setting, any data before the selected date will not be included in the calculation of the Seasonality Widget.
🔹 Data To Analyze
The Seasonality Widget can return the seasonality for the following data:
Price Change
Closing price minus the previous closing price.
Price Change (%)
Closing price minus the previous closing price, divided by the
previous closing price, then multiplied by 100.
Price Change (Sign)
Sign of the price change (-1 for negative change, 1 for positive change), normalized in a range (0, 100). Values above 50 suggest more positive changes on average.
Range
High price minus low price.
Price - SMA
Price minus its simple moving average. Users can select the SMA period.
Volume
Amount of contracts traded. Allow users to see which periods are generally the most /least liquid.
Volume - SMA
Volume minus its simple moving average. Users can select the SMA period.
🔹 Filter
In addition to the "From Date" threshold users can exclude data from specific periods of time, potentially removing outliers in the final results.
The period type can be specified in the "Filter Granularity" setting. The exact time to exclude can then be specified in the "Numerical Filter Input" setting, multiple values are supported and should be comma separated.
For example, if we want to exclude the entire 2008 period we can simply select "Year" as filter granularity, then input 2008 in the "Numerical Filter Input" setting.
Do note that "Sunday" uses the value 1 as a day of the week.
🔶 DETAILS
🔹 Supported Statistics
Users can apply different statistics to the grouped data to process. These include:
Mean
Median
Max
Min
Max-Min Average
Using the median allows for obtaining a measure more robust to outliers and potentially more representative of the actual central tendency of the data.
Max and Min do not express a general tendency but allow obtaining information on the highest/lowest value of the analyzed data for specific periods.
🔶 SETTINGS
Granularity: Periods used to group data.
From Data: Starting point where data starts being collected
🔹 Data
Analyze: Specific data to be processed by the seasonality widget.
SMA Length: Period of the simple moving average used for "Price - SMA" and "Volume - SMA" options in "Analyze".
Statistic: Statistic applied to the grouped data.
🔹 Filter
Filter Granularity: Period type to exclude in the processed data.
Numerical Filter Input: Determines which of the selected hour/day of week/day of month/month/year to exclude depending on the selected Filter Granularity. Only numerical inputs can be provided. Multiple values are supported and must be comma-separated.
Simple Volatility MomentumOverview:
The Simple Volatility Momentum indicator calculates the mean and standard deviation of the changes of price (returns) using various types of moving averages (Incremental, Rolling, and Exponential). With quantifying the dispersion of price data around the mean, statistical insights are provided on the volatility and the movements of price and returns. The indicator also ranks the mean absolute value of the changes of price over a specified time period which helps you assess the strength of the "trend" and "momentum" regardless of the direction of returns.
Simple Volatility Momentum
This indicator can be used for mean reversion strategies and "momentum" or trend based strategies.
The indicator calculates the average return as the momentum metric and then gets the moving average of the average return and standard deviations from average return average. On the options you can determine if you want to use 1 or 2 standard deviation bands or have both of them enabled.
Settings:
Source: By default it's at close.
M Length: This is the length of the "momentum".
Rank Length: This is the length of the rank calculation of absolute value of the average return
MA Type: This is the different type of calculations for the mean and standard deviation. By default its at incremental.
Smoothing factor: (Only used if you choose the exponential MA type.)
The absolute value of the average return helps you see the strength of the "momentum" and trend. If there is a low ranking of the absolute value of the average return then you can eventually expect it to increase which means that the average return is trending, leading to trending price moves. If the Mean ABS rank value is at or near the maximum value 100 and the average return is at -2 standard deviation from the mean, you can see it as the negative momentum or trend being "finished". Similarly, if the Mean ABS value is near or at the maximum value 100 and the average return is at +2 standard deviation from the mean, you can view the uptrend, as "finished" and the Mean ABS rank can't really go higher than 100.
Moving Average Calculations type:
Incremental: Incremental moving averages use an incremental approach to update the moving average by adding the newest data point and subtracting the oldest one.
Exponential: The exponential moving average gives more weight to recent data points while still considering older ones. This is achieved by applying a smooth factor to the previous EMA value and the current data point. EMA's react more quickly to recent changes in the data compared to simple moving averages, making them useful for short term trends and momentum in financial markets.
Rolling: The moving average is calculated by taking the average of a fixed number of data points within a defined window. As new data becomes available, the window moves forward and the average is recalculated. Rolling Moving Averages are useful for smoothing out short-fluctuations and identifying trends over time.
Important thing to note about indicators involving bands and "momentum" or "trend" or prices:
For the explanation we will assume that stock returns follow a normal distribution and price follows a log normal distribution. Please note that in the live market this assumption isn't always true. Many people incorrectly use standard deviations on prices and trade them as mean reversion strategies or overbought or oversold levels which is not what standard deviations are meant for. Assuming you have applied the log transformation on the standard deviation bands (if your input is raw price then you should use a log transformation to remove the skewness of price), and you have a range of 2 standard deviations from the mean, under the empirical rule with enough occurrences 95% of the values will be within the 2 standard deviation range. This doesn't mean that if price falls to the bottom of the 2 standard deviation bound, there is a 95% chance it will revert back to mean, this is incorrect and not how standard deviations or mean reversion works.
"MOMENTUM"
In finance "momentum" refers to the rate of change of a time series data point. It shows the persistence or tendency for a data series to continue moving in its current direction. In finance, "momentum" based strategies capitalize on the observed tendency of assets that have performed well (or poorly) in the recent past to continue performing well (or poorly) in the near future. This persistence is often observed in various financial instruments including stocks, currencies and commodities.
"Momentum" is commonly calculated with the average return, and relies on the assumption that assets with positive "momentum" or a positive average return will likely continue to perform well in the short to medium term, while assets with a negative average return are expected to continue underperforming. This average return or expected value is derived from historical observations and statistical analysis of previous price movements. However, real markets are subject to levels of efficiencies, market fluctuations, randomness, and may not always produce consistent returns over time involving momentum based strategies.
Mean Reversion:
In finance, the average return is an important parameter in mean reversion strategies. Using statistical methodologies, mean reversion strategies aim to exploit the deviations from the historical average return by identifying instances where current prices and their changes diverge from their expected levels based on past performance. This approach involves statistical analysis and predictive modelling techniques to check where and when the average rate of change is likely to revert towards the mean. It's important to know that mean reversion is a temporary state and will not always be present in a specific timeseries.
Using the average return over price offers several advantages in finance and trading since it is less sensitive to extreme price movements or outliers compared to raw price data. Price itself contains a distribution that is usually positively-skewed and has no upper bound. Mean reversion typically occurs in distributions where extreme values are followed by a tendency for the variable to return towards its mean over time, however the probability distribution of price has no tendency for values to revert towards any specific level. Instead, values may continue to increase without a bound. Returns themself contain more stationary behavior than price levels. Mean reversion strategies rely on the assumption that deviations from the mean will eventually revert back to the mean. Returns, being more likely to exhibit stationary, are better suited for mean reversion based strategies.
The distribution of returns are often more symmetrically distributed around their mean compared to price distributions. This symmetry makes it easier to identify deviations from the mean and assess the likelihood of mean reversion occurrence. Returns are also less sensitive to trends and long-term price movements compared to price levels. Mean reversion strategies aim to exploit deviations from mean, which can be obscured when analyzing raw price data since raw price is almost always trending. Returns can filter out the trend component of price movements, making it easier to identify opportunities.
Stationary Process: Implication that properties like mean and variance remain relatively constant over time.
Profitability Power RatioProfitability Power Ratio
The Profitability Power Ratio is a financial metric designed to assess the efficiency of a company's operations by evaluating the relationship between its Enterprise Value (EV) and Return on Equity (ROE). This ratio provides insights into how effectively a company generates profits relative to its equity and overall valuation.
Qualities and Interpretations:
1. Efficiency Benchmark: The Profitability Power Ratio serves as a benchmark for evaluating how efficiently a company utilizes its equity capital to generate profits. A higher ratio indicates that the company is generating significant profits relative to its valuation, reflecting efficient use of invested capital.
2. Financial Health Indicator: This ratio can be used as an indicator of financial health. A consistently high or improving ratio over time suggests strong operational efficiency and sustainable profitability.
3. Investment Considerations: Investors can use this ratio to assess the attractiveness of an investment opportunity. A high ratio may signal potential for good returns, but it's important to consider the underlying reasons for the ratio's level to avoid misinterpretation.
4. Risk Evaluation: An excessively high Profitability Power Ratio could also signal elevated risk. It may indicate aggressive financial leveraging or unsustainable growth expectations, which could pose risks during economic downturns or market fluctuations.
Interpreting the Ratio:
1. Higher Ratio: A higher Profitability Power Ratio typically signifies efficient capital utilization and strong profitability relative to the company's valuation.
2. Lower Ratio: A lower ratio may suggest inefficiencies in capital allocation or lower profitability relative to enterprise value.
3. Benchmarking: Compare the company's ratio with industry peers and historical performance to gain deeper insights into its financial standing and operational efficiency.
Using the Indicator:
The Profitability Power Ratio is plotted on a chart to visualize trends and fluctuations over time. Users can customize the color of the plot to emphasize this metric and integrate it into their financial analysis toolkit for comprehensive decision-making.
Disclaimer: The Profitability Power Ratio is a financial metric designed for informational purposes only and should not be considered as financial or investment advice. Users should conduct thorough research and analysis before making any investment decisions based on this indicator. Past performance is not indicative of future results. All investments involve risks, and users are encouraged to consult with a qualified financial advisor or professional before making investment decisions.
Dividend-to-ROE RatioDividend-to-ROE Ratio Indicator
The Dividend-to-ROE Ratio indicator offers valuable insights into a company's dividend distribution relative to its profitability, specifically comparing the Dividend Payout Ratio (proportion of earnings as dividends) to the Return on Equity (ROE), a measure of profitability from shareholder equity.
Interpretation:
1. Higher Ratio: A higher Dividend-to-ROE Ratio suggests a stable dividend policy, where a significant portion of earnings is returned to shareholders. This can indicate consistent dividend payments, often appealing to income-seeking investors.
2. Lower Ratio: Conversely, a lower ratio implies that the company retains more earnings for growth, potentially signaling a focus on reinvestment for future expansion rather than immediate dividend payouts.
3. Excessively High Ratio: An exceptionally high ratio may raise concerns. While it could reflect a generous dividend policy, excessively high ratios might indicate that a company is distributing more earnings than it can sustainably afford. This could potentially hinder the company's ability to reinvest in its operations, research, or navigate economic downturns effectively.
Utility and Applications:
The Dividend-to-ROE Ratio can be particularly useful in the following scenarios:
1. Income-Oriented Investors: For investors seeking consistent dividend income, a higher ratio signifies a company's commitment to distributing profits to shareholders, potentially aligning with income-oriented investment strategies.
2. Financial Health Assessment: Analysts and stakeholders can use this ratio to gauge a company's financial health and dividend sustainability. It provides insights into management's capital allocation decisions and strategic focus.
3. Comparative Analysis: When comparing companies within the same industry, this ratio helps in benchmarking dividend policies and identifying outliers with unusually high or low ratios.
Considerations:
1. Contextual Analysis: Interpretation should be contextualized within industry standards and the company's financial history. Comparing the ratio with peers in the same sector can provide meaningful insights.
2. Financial Health: It's crucial to evaluate this indicator alongside other financial metrics (like cash flow, debt levels, and profit margins) to grasp the company's overall financial health and sustainability of its dividend policy.
Disclaimer: This indicator is for informational purposes only and does not constitute financial advice. Investors should conduct thorough research and consult with financial professionals before making investment decisions based on this ratio.
Fourier Adjusted Average True Range [BackQuant]Fourier Adjusted Average True Range
1. Conceptual Foundation and Innovation
The FA-ATR leverages the principles of Fourier analysis to dissect market prices into their constituent cyclical components. By applying Fourier Transform to the price data, the FA-ATR captures the dominant cycles and trends which are often obscured in noisy market data. This integration allows the FA-ATR to adapt its readings based on underlying market dynamics, offering a refined view of volatility that is sensitive to both market direction and momentum.
2. Technical Composition and Calculation
The core of the FA-ATR involves calculating the traditional ATR, which measures market volatility by decomposing the entire range of price movements. The FA-ATR extends this by incorporating a Fourier Transform of price data to assess cyclical patterns over a user-defined period 'N'. This process synthesizes both the magnitude of price changes and their rhythmic occurrences, resulting in a more comprehensive volatility indicator.
Fourier Transform Application: The Fourier series is calculated using price data to identify the fundamental frequency of market movements. This frequency helps in adjusting the ATR to reflect more accurately the current market conditions.
Dynamic Adjustment: The ATR is then adjusted by the magnitude of the dominant cycle from the Fourier analysis, enhancing or reducing the ATR value based on the intensity and phase of market cycles.
3. Features and User Inputs
Customizability: Traders can modify the Fourier period, ATR period, and the multiplication factor to suit different trading styles and market environments.
Visualization : The FA-ATR can be plotted directly on the chart, providing a visual representation of volatility. Additionally, the option to paint candles according to the trend direction enhances the usability and interpretative ease of the indicator.
Confluence with Moving Averages: Optionally, a moving average of the FA-ATR can be displayed, serving as a confluence factor for confirming trends or potential reversals.
4. Practical Applications
The FA-ATR is particularly useful in markets characterized by periodic fluctuations or those that exhibit strong cyclical trends. Traders can utilize this indicator to:
Adjust Stop-Loss Orders: More accurately set stop-loss orders based on a volatility measure that accounts for cyclical market changes.
Trend Confirmation: Use the FA-ATR to confirm trend strength and sustainability, helping to avoid false signals often encountered in volatile markets.
Strategic Entry and Exit: The indicator's responsiveness to changing market dynamics makes it an excellent tool for planning entries and exits in a trend-following or a breakout trading strategy.
5. Advantages and Strategic Value
By integrating Fourier analysis, the FA-ATR provides a volatility measure that is both adaptive and anticipatory, giving traders a forward-looking tool that adjusts to changes before they become apparent through traditional indicators. This anticipatory feature makes it an invaluable asset for traders looking to gain an edge in fast-paced and rapidly changing market conditions.
6. Summary and Usage Tips
The Fourier Adjusted Average True Range is a cutting-edge development in technical analysis, offering traders an enhanced tool for assessing market volatility with increased accuracy and responsiveness. Its ability to adapt to the market's cyclical nature makes it particularly useful for those trading in highly volatile or cyclically influenced markets.
Traders are encouraged to integrate the FA-ATR into their trading systems as a supplementary tool to improve risk management and decision-making accuracy, thereby potentially increasing the effectiveness of their trading strategies.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
CAPEX RatioUnderstanding the CAPEX Ratio: An Essential Financial Metric
Introduction
In the world of finance, understanding how companies allocate their resources and reinvest their earnings is crucial for investors and analysts. One fundamental metric used to assess a company's investment behavior is the CAPEX Ratio. This article delves into what the CAPEX Ratio signifies, its advantages, and how to interpret its implications.
What is the CAPEX Ratio?
The CAPEX Ratio, short for Capital Expenditure Ratio, is a financial indicator that measures the proportion of a company's capital expenditures (CAPEX) relative to various financial metrics such as revenue, free cash flow, net income, or total assets. CAPEX represents investments made by a company to acquire or maintain its physical assets.
Interpreting the Results
Each variant of the CAPEX Ratio provides unique insights into a company's financial strategy:
• CAPEX to Revenue Ratio: This ratio shows what portion of a company's revenue is being reinvested into capital investments. A higher ratio might indicate aggressive expansion plans or a need for infrastructure upgrades.
• CAPEX to Free Cash Flow Ratio: By comparing CAPEX with free cash flow, this ratio reveals how much of a company's available cash is dedicated to capital investments. It helps assess financial health and sustainability.
• CAPEX to Net Income Ratio: This ratio measures how much of a company's net income is being channeled back into capital expenditures. A high ratio relative to net income could signal a company's commitment to growth and development.
• CAPEX to Total Assets Ratio: This metric assesses the proportion of total assets being allocated towards capital expenditures. It provides a perspective on the company's investment intensity relative to its overall asset base.
Advantages of Using CAPEX Ratios
• Insight into Investment Strategy: Helps investors understand where a company is directing its resources.
• Evaluation of Financial Health: Indicates how efficiently a company is reinvesting profits or available cash.
• Comparative Analysis: Enables comparisons across companies or industries to gauge investment priorities.
How to Use the CAPEX Ratio
• Comparative Analysis: Compare the CAPEX Ratios over time or against industry peers to spot trends or outliers.
• Investment Decision-Making: Consider CAPEX Ratios alongside other financial metrics when making investment decisions.
Conclusion
In conclusion, the CAPEX Ratio is a valuable financial metric that offers deep insights into a company's investment behavior and financial health. By analyzing different variants of this ratio, investors and analysts can make informed decisions about a company's growth prospects and financial stability.
Dynamic Date and Price Tracker with Entry PriceThe Dynamic Date and Price Tracker indicator is a simple tool designed for traders to visualize and monitor their trade's progress in real-time from a specified starting point.
This tool provides an intuitive graphical representation of your trade's profitability based on a custom entry date and price.
Features:
-Starting Date Selection: Choose a specific starting date, after which the indicator begins tracking your trade's performance.
-Custom Entry Price: Input a starting price to accurately reflect your actual entry price for performance tracking across different timeframes.
-Real-Time Tracking: As new bars form, the indicator automatically adjusts a dynamic line to the current closing price.
-Profit/Loss Color Coding: The dynamic line color changes based on whether the current price is above (green for profit) or below (red for loss) your specified entry price.
-Performance Label: A real-time label displays the absolute and percentage change in price since your initial entry, color-coded for positive (green) or negative (red) performance.
-Entry Price Line: The horizontal line marks your starting price for easy visual comparison.
OrderFlow [Probabilities] | FractalystWhat's the indicator's purpose and functionality?
The indicator is designed to incorporate probabilities with buyside and sellside liquidity, as well as premium and discount ranges within the market. It also provides traders with a multi-timeframe functionality for observing liquidity levels and probabilities across two timeframes without the need to manually switch between them.
These levels are often used in smart money trading concepts for identifying key areas of interest, such as potential reversal points, areas of accumulation or distribution, and zones of high liquidity.
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What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside , Sellside and Equilibrium levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
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How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
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What does the multi-timeframe functionality offer?
Enabling and selecting a higher timeframe in the indicator's user-input settings allows you to access not only the current range information but also the liquidity sides, status, price levels, and probabilities of a higher timeframe without needing to switch between timeframes and mark up the levels manually.
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What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and requests the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
Non-repainting Security Function with Lookahead ON
//Function to fetch data for a given timeframe
getHTFData(timeframe_,exp_) =>
request.security(syminfo.tickerid, timeframe_,exp_ ,lookahead = barmerge.lookahead_on)
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How to use the indicator?
1. Add the indicator to your TradingView chart.
2. Choose the pair you want to analyze/trade.
3. Enable the HTF in user-input settings and choose a timeframe as for your higher timeframe bias.
4. (Important) : Ensure that the probabilities on both timeframes are aligned in one direction. If not, switch between timeframes until you find a pair of timeframes that are in line with each other and have higher probabilities on one liquidity side.
For Swing traders:
Use Hourly timeframes (1H/2H/4H/8H/12H) as your current timeframe and 1D/3D/1W/2W for your higher timeframe (HTF).
Entry: Hourly Equilibrium level. (Limit order)
Stoploss: Place it on the side where the probability is lower than 50%.
Break-even level/TP1: Hourly breakout of the liquidity.
TP2: Target the Higher Timeframe (HTF) liquidity level where the probability is higher than 50%.
2H/1D COINBASE:BTCUSD
For Day traders:
Use minutely timeframes (5m/15m/30m) as your current timeframe and 1H/2H/4H/8H/12H for your higher timeframe (HTF).
Entry: Minutely Equilibrium level. (Limit order)
Stoploss: Place it on the side where the probability is lower than 50%.
Break-even level/TP1: Minutely breakout of the liquidity.
TP2: Target the Higher Timeframe (HTF) liquidity level where the probability is higher than 50%.
1H/5m COINBASE:BTCUSD
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User-input settings and customizations
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What makes this indicator original?
1. Real-time calculation of probabilities directly on your charts.
2. Multi-timeframe functionality, enabling effortless observation of liquidity levels and probabilities across two timeframes.
3. Status label for clear identification of whether price has reached equilibrium.
4. All levels are updated only upon candle closure above or below liquidity levels, ensuring it remains a non-repainting indicator.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer.
US Net LiquidityAnalysis of US Net Liquidity: A Comprehensive Overview
Introduction:
The "US Net Liquidity" indicator offers a detailed analysis of liquidity conditions within the United States, drawing insights from critical financial metrics related to the Federal Reserve (FED) and other government accounts. This tool enables economists to assess liquidity dynamics, identify trends, and inform economic decision-making.
Key Metrics and Interpretation:
1. Smoothing Period: This parameter adjusts the level of detail in the analysis by applying a moving average to the liquidity data. A longer smoothing period results in a smoother trend line, useful for identifying broader liquidity patterns over time.
2. Data Source (Timeframe): Specifies the timeframe of the data used for analysis, typically daily (D). Different timeframes can provide varying perspectives on liquidity trends.
3. Data Categories:
- FED Balance Sheet: Represents the assets and liabilities of the Federal Reserve, offering insights into monetary policy and market interventions.
- US Treasury General Account (TGA): Tracks the balance of the US Treasury's general account, reflecting government cash management and financial stability.
- Overnight Reverse Repurchase Agreements (RRP): Highlights short-term borrowing and lending operations between financial institutions and the Federal Reserve, influencing liquidity conditions.
- Earnings Remittances to the Treasury: Indicates revenues transferred to the US Treasury from various sources, impacting government cash flow and liquidity.
4. Moving Average Length: Determines the duration of the moving average applied to the data. A longer moving average length smoothens out short-term fluctuations, emphasizing longer-term liquidity trends.
Variation Lookback Length: Specifies the historical period used to assess changes and variations in liquidity. A longer lookback length captures more extended trends and fluctuations.
Interpretation:
1. Data Retrieval: Real-time data from specified financial instruments (assets) is retrieved to calculate balances for each category (FED, TGA, RRP, Earnings Remittances).
2. Global Balance Calculation: The global liquidity balance is computed by aggregating the balances of individual categories (FED Balance - TGA Balance - RRP Balance - Earnings Remittances Balance). This metric provides a comprehensive view of net liquidity.
3. Smoothed Global Balance (SMA): The Simple Moving Average (SMA) is applied to the global liquidity balance to enhance clarity and identify underlying trends. A rising SMA suggests improving liquidity conditions, while a declining SMA may indicate tightening liquidity.
Insight Generation and Decision-Making:
1. Trend Analysis: By analyzing smoothed liquidity trends over time, economists can identify periods of liquidity surplus or deficit, which can inform monetary policy decisions and market interventions.
2. Forecasting: Understanding liquidity dynamics aids in economic forecasting, particularly in predicting market liquidity, interest rate movements, and financial stability.
3. Policy Implications: Insights derived from this analysis tool can guide policymakers in formulating effective monetary policies, managing government cash flow, and ensuring financial stability.
Conclusion:
The "US Net Liquidity" analysis tool serves as a valuable resource for economists, offering a data-driven approach to understanding liquidity dynamics within the US economy. By interpreting key metrics and trends, economists can make informed decisions and contribute to macroeconomic stability and growth.
Disclaimer: This analysis is based on real-time financial data and should be used for informational purposes only. It is not intended as financial advice or a substitute for professional expertise.
[BT] NedDavis Series: CPI Minus 5-Year Moving Average🟧 GENERAL
The script works on the Monthly Timeframe and has 2 main settings (explained in FEATURES ). It uses the US CPI data, reported by the Bureau of Labour Statistics.
🔹Functionality 1: The main idea is to plot the distance between the CPI line and the 5 year moving average of the CPI line. This technique in mathematics is called "deviation from the moving average". This technique is used to analyse how has CPI previously acted and can give clues at what it might do in the future. Economic historians use such analysis, together with specific period analysis to predict potential risks in the future (see an example of such analysis in HOW TO USE section. The mathematical technique is a simple subtraction between 2 points (CPI - 5yr SMA of CPI).
▶︎Interpretation for deviation from a moving average:
Positive Deviation: When the line is above its moving average, it indicates that the current value is higher than the average, suggesting potential strength or bullish sentiment.
Negative Deviation: Conversely, when the line falls below its moving average, it suggests weakness or bearish sentiment as the current value is lower than the average.
▶︎Applications:
Trend Identification: Deviations from moving averages can help identify trends, with sustained deviations indicating strong trends.
Reversal Signals: Significant deviations from moving averages may signal potential trend reversals, especially when combined with other technical indicators.
Volatility Measurement: Monitoring the magnitude of deviations can provide insights into market volatility and price movements.
Remember the indicator is applying this only for the US CPI - not the ticker you apply the indicator on!
🔹Functionality 2: It plots on a new pane below information about the Consumer Price Index. You can also find the information by plotting the ticker symbol USACPIALLMINMEI on TradingView, which is a Monthly economic data by the OECD for the CPI in the US. The only addition you would get from the indicator is the plot of the 5 year Simple Moving Average.
🔹What is the US Consumer Price Index?
Measures the change in the price of goods and services purchased by consumers;
Traders care about the CPI because consumer prices account for a majority of overall inflation. Inflation is important to currency valuation because rising prices lead the central bank to raise interest rates out of respect for their inflation containment mandate;
It is measured as the average price of various goods and services are sampled and then compared to the previous sampling.
Source: Bureau of Labor Statistics;
FEATURES OF INDICATOR
1) The US Consumer Price Index Minus the Five Year Moving Average of the same.
As shown on the picture above and explained in previous section. Here a more detailed view.
2) The actual US Consumer Price Index (Annual Rate of change) and the Five year average of the US Consumer Price Index. Explained above and shown below:
To activate 2) go into settings and toggle the check box.
HOW TO USE
It can be used for a fundamental analysis on the relationship between the stock market, the economy and the Feds decisions to hike or cut rates, whose main mandate is to control inflation over time.
I have created this indicator to show my analysis in this idea:
What does a First Fed Rate cut really mean?
CREDITS
I have seen such idea in the past posted by the institutional grade research of NedDavis and have recreated it for the TradingView platform, open-source for the community.
Range Sentiment | FractalystWhat’s the purpose of this indicator?
This indicator aims to calculate the percentage level of bullish or bearish fluctuations within equally divided price sections, in the latest price range.
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How does this indicator calculate the current sentiment?
This indicator calculates the current sentiment by identifying the highest swing high and the lowest swing low, then evenly dividing the distance between them into percentage amounts. If the price is above the 50% mark, it indicates bullishness, whereas if it's below 50%, it suggests bearishness.
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Is this sentiment the same as the ones on online websites?
No, the sentiment as discussed earlier, is based on analyzing price action on charts and calculating the current range in percentage.
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What methods do other websites typically use to determine market sentiment?
1. Broker Data Aggregation: Compiling data from various forex brokers to assess the percentage of long and short positions taken by traders.
2. Lot Size Analysis: Considering the size of positions taken by traders, where a significant number of large positions in one direction may indicate strong bullish or bearish sentiment.
3. Order Flow Analysis: Tracking the flow of orders in the forex market, with an increase in buy orders suggesting bullish sentiment and a surge in sell orders indicating bearish sentiment.
4. Sentiment Surveys: Conducting surveys among forex traders to gather opinions about future price movements and gauge sentiment.
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What are the options to display the current market sentiment?
1. Table - located at top centre of your charts
2. Label - indicating the 50% level of the current range mentioned above
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How can I analyze how the sentiment worked on historical data?
There are 3 available options:
1. Shapes
2. Candles
3. Background
- White colour/transparent background denote a weak or uncertain trend, where the bullish or bearish degree is less than 60%.
- Blue colour indicates that the price is trading bullish.
- Red colour indicates that the price is trading bearish.
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On what market and timeframe this indicator operates on?
The Range Sentiment indicator is applicable to all markets and timeframes.
When switching between timeframes, the values remain consistent because it utilizes the daily bias to determine the sentiment for the current day.
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How can I use this indicator?
1. Add "Range Sentiment | Fractalyst" to your TradingView chart.
2. Choose the pair you want to analyze or trade intraday/during session.
3. Make sure the sentiment is showing you a bullish or a bearish degree of higher than 60%.
(To avoid potentially choppy price action)
4. Use your favourite entry model to trade in the same direction that the sentiment is showing you.
5. Determine your take profit based on the daily swing level OR previous daily high if the bullish degree is above +60% and previous daily low if the bearish degree is +60%.
6. Risk adjustment and Trade management based on your personal preferences.
Example:
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User-input settings and customizations
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What makes this indicator original?
1. It utilizes the daily timeframe's current range to determine the day's bias degree in percentage.
2. Apart from presenting the current sentiment in a sentimental table, it also visualizes the 50% threshold as a label, indicating where price may shift direction from a sentiment perspective.
3. It offers the feature to analyze historical data, allowing users to observe indicator performance through shapes, candles, and background colors.
4. By integrating market sentiment directly into the charts, it simplifies the trading process, removing the reliance on online websites and services for sentiment analysis, which often rely on limited brokerage data and resources.
5. Unlike most sentiment websites that primarily focus on forex pairs sentiment data, this indicator stands out by its capability to analyze sentiment across all markets. Moreover, it provides real-time updates, ensuring traders have access to the latest sentiment information regardless of the market they are trading in.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer.
RSI Crypto Average W/AnalysisThis is an indicator designed to find the limits and stricture of an array of different crypto coins. It finds the average Relative Strength Index (RSI) across 20 different crypto coins and then plots the average.
This also uses arrays and averages across the coins to output the number of bars the RSI usually stays above or below the specified bands. Using this information you can predict when the RSI is about to start loosing momentum or even crash.
Cryptos included in the average:
Bitcoin (BTC)
Ethereum (ETH)
Ripple (XRP)
Binance Coin (BNB)
Cardano (ADA)
Solana (SOL)
Polkadot (DOT)
Dogecoin (DOGE)
Uniswap (UNI)
Avalanche (AVAX)
Terra (LUNA)
Chainlink (LINK)
Bitcoin Cash (BCH)
Ethereum Classic (ETC)
Filecoin (FIL)
Stellar (XLM)
TRON (TRX)
EOS (EOS)
Cosmos (ATOM)
Tezos (XTZ)
Pivot Profit Target [Mxwll]Introducing the Pivot Profit Target!
This script identifies recent pivot highs/lows and calculates the expected minimum distance for the next pivot, which acts as an approximate profit target.
The image above details the indicator's output.
The image above shows a table consisting of projection statistics.
How to use
The Pivot Profit Targets can be used to approximate a profit target for your trade.
Identify where your entry is relative to the most recent pivot, and assess whether the minimum expected distance for the most recent pivot has been exceeded. Treat the zones as an approximation.
If your trade aligns with the most recent pivot - treat the minimum expected distance zone as a potential profit target area. Of course, price might stop short or continue beyond the projection area!
That's it! Just a short and sweet script; thank you!
Kalman Filter Volume Bands by TenozenHello there! I am excited to introduce a new original indicator, the Kalman Filter Volume Bands. This indicator is calculated using the Kalman Filter, which is an adaptive-based smoothing quantitative tool. The Kalman Filter Volume Bands have two components that support the calculation, namely VWAP and VaR.
VWAP is used to determine the weight of the Kalman Filter Returns, but it doesn't have a significant impact on the calculation. On the other hand, VaR or Value at risk is calculated using the 99th percentile, which means that there is a 1% chance for the returns to exceed the 99th percentile level. After getting the VaR value, I manually adjust the bands based on the current market I'm trading on. I take the highest point (VaR*2) and the lowest point (-(VaR*2)) from the Kalman Filter, and then divide them into segments manually based on my preference.
This process results in 8 segments, where 2 segments near the Kalman Filter are further divided, making a total of 12 segments. These segments classify the current state of the price based on code-based coloring. The five states are very bullish, bullish, very bearish, bearish, and neutral.
I created this indicator to have an adaptive band that is not biased toward the volatility of the market. Most band-based indicators don't capture reversals that well, but the Kalman Filter Volume Bands can capture both trends and reversals. This makes it suitable for both trend-following and reversal trading approaches.
That's all for the explanation! Ciao!
Additional Reminder:
- Please use hourly timeframes or higher as lower timeframes are too noisy for reliable readings of this indicator.
Price alert multi symbols (Miu)This indicator won't plot anything to the chart.
Please follow steps below to set your alarms based on multiple symbols' prices:
1) Add indicator to the chart
2) Go to settings
3) Check symbols you want to receive alerts (choose up to 8 different symbols)
4) Set price for each symbol
5) Once all is set go back to the chart and click on 3 dots to set alert in this indicator, rename your alert and confirm
6) You can remove indicator after alert is set and it'll keep working as expected
What does this indicator do?
This indicator will generate alerts based on following conditions:
- If price set is met for any symbol
Once condition is met it will send an alert with the following information:
- Symbol name (e.g: BTC, ETH, LTC)
- Price reached
This script requests current price for each symbol through request.security() built-in function. It also requests amount of digits (mintick) for each symbol to send alerts with correct value.
This script was developed to attend a demand from a comment in other published script.
Feel free to give feedbacks on comments section below.
Enjoy!
Previous Day and Week RangesI've designed the "Previous Day and Week Ranges" indicator to enhance your trading strategy by clearly displaying daily and weekly price levels. This tool shows Open-Close and High-Low ranges for both daily and weekly timeframes directly on your trading chart.
Key Features :
Potential Support and Resistance: The indicator highlights previous day and week ranges that may serve as key support or resistance levels in subsequent trading sessions.
Customizable Display Options: Offers the flexibility to show or hide daily and weekly ranges based on your trading needs.
Color Customization: Adjust the color settings to differentiate between upward and downward movements, enhancing visual clarity and chart readability.
This indicator is ideal for traders aiming to understand market dynamics better, offering insights into potential pivot points and zones of price stability or volatility.
1 Year Historical Trend AnalyzerHey everyone!
This is a new indicator of mine. If you know me, you know I really like Z-Score and there are a lot of cool things that can be done with Z-Score, especially as it pertains to trading!
This indicator uses Z-Score but in a different way from conventional Z-Score indicators (including mine). It uses Z-Score to plot out the current 1 year trend of a stock. Now, 1 year trend is not year to date (i.e. if we are in April, it is not just looking from January to April), but instead, its taking the last 1 trading year of candle data to plot out the trend, ranges and areas of z-score math based supports and resistances.
How it works:
The indicator will look at the current timeframe you are on, whether it be daily, 1 hour, 4 hours, weekly or even monthly. It will then look back the designated amount of candles that constitute 1 trading year. These are preprogrammed into the indicator so it knows to look back X number of Candles based on Y timeframe. This will give you a standard, scaled version of the past 1 year of trading data.
From there, the indicator will calculate the MAX Z-Score (or the highest Z-Score that the stock reached over the 1 trading year) and the MIN Z-score (or the lowest Z-Score that the stock reached over the 1 trading year). It plots these as a red and green line respectively:
It will then display the price that the MAX and MIN fall at. Keep in mind, the MAX and MIN price will change as the trading time elapses, but the Z-Score will remain the same until the stock does a lower or higher move from that z-score point.
It will then calculate the mean (average) of the Max and Min and then the mid points between the max and mean, and the min and mean. These all represent mathematical areas of support and resistance and key levels to watch when trading.
The indicator also has a table that is optional. The table can be toggled to either Auto or Manual. Auto will automatically calculate 5 Z-Score Points that are within the proximity of the annual trading range. However, you can select manual and input your own Z-Score values to see where the prices will fall based on the 1 year of data.
Some other options:
You can toggle on and off these midline support and resistance levels in the settings menu. Additionally, you can have the indicator plot actual scaled candles of the 1 year trading history. This is a great function to really see how the support and resistance works. Let’s take a look at RIVN, plotted as candles, on the 1 hour timeframe:
In this diagram, we can see two recent points in March where the Z-Score has acted as support for the stock. If we view this in conjunction with the actual ticker, you can see these were great buy points:
Do get this functionality, simply go into the plots menu in the settings menu and select “Plot as Candles”.
How to Use it:
While I have discussed some applications of the indicator, namely identify math supports and resistances, targets and such, there are some key things I really want to emphasize that this indicator excels at. I am going to group them for greater clarity:
All time Highs and All Time Lows:
AXP has recently been pushing ATHs. When a stock breaks an ATH or an ATL, it is said that there is no resistance or support. However, with Z-Score that is never true, there are always areas of math resistance and support. We can use this indicator to identify such areas. Let’s look at AXP:
Using this as a reference, we can see that AXP broke out of a Z-Score resistance level and re-tested the resistance as support. It held and continued up. We can see that the next area of math resistance is at 270:
And 234.65 is support. We would look for the ticker to hold this 234.65 line as support to continue the move up to the 270s.
Similar setup for ATLs with RIVN:
We can see that RIVN can indeed make a new ATL because support isn’t until 7.63.
Technical Tips on How to Use:
Because this indicator uses predefined lookback periods based on timeframes, its important that you are analyzing the data with pre-market turned off. The candles are calculated with the assumption that there is no pre-market data.
As well, the lowest timeframe that can be used to get 1 year worth of data is 1 hour. Anything below 1 hour will require you to manually input a lookback length (default is 252) which will be less than 1 year. This is simply because of the limitations of candle lookbacks through Pinescript.
That is not to say that this is not effective on smaller timeframes, it is! You just need to be sure that you understand you are not looking at a year trend worth of data. You can toggle your manual lookback parameters in the settings menu.
Concluding remarks
And that’s the indicator! I know the explanation is lengthy but I really suggest you read it carefully to understand how the indicator works and how you can best use it to analyze tickers and supplement your strategy.
Thanks for reading and safe trades as always!
Rise Sense Capital - RSI MACD Spot Buying IndicatorToday, I'll share a spot buying strategy shared by a member @KR陳 within the DATA Trader Alliance Alpha group. First, you need to prepare two indicators:
今天分享一個DATA交易者聯盟Alpha群組裏面的群友@KR陳分享的現貨買入策略。
首先需要準備兩個指標
RSI Indicator (Relative Strength Index) - RSI is a technical analysis tool based on price movements over a period of time to evaluate the speed and magnitude of price changes. RSI calculates the changes in price over a period to determine whether the recent trend is relatively strong (bullish) or weak (bearish).
RSI指標，(英文全名:Relative Strength Index)，中文稱為「相對強弱指標」，是一種以股價漲跌為基礎，在一段時間內的收盤價，用於評估價格變動的速度 (快慢) 與變化 (幅度) 的技術分析工具，RSI藉由計算一段期間內股價的漲跌變化，判斷最近的趨勢屬於偏強 (偏多) 還是偏弱 (偏空)。
MACD Indicator (Moving Average Convergence & Divergence) - MACD is a technical analysis tool proposed by Gerald Appel in the 1970s. It is commonly used in trading to determine trend reversals by analyzing the convergence and divergence of fast and slow lines.
MACD 指標 (Moving Average Convergence & Divergence) 中文名為平滑異同移動平均線指標，MACD 是在 1970 年代由美國人 Gerald Appel 所提出，是一項歷史悠久且經常在交易中被使用的技術分析工具，原理是利用快慢線的交錯，藉以判斷股價走勢的轉折。
In MACD analysis, the most commonly used values are 12, 26, and 9, known as MACD (12,26,9). The market often uses the MACD indicator to determine the future direction of assets and to identify entry and exit points.
在 MACD 的技術分析中，最常用的值為 12 天、26 天、9 天，也稱為 MACD (12,26,9)，市場常用 MACD 指標來判斷操作標的的後市走向，確定波段漲幅並找到進、出場點。
Strategy analysis by member KR陳:
策略解析 by群友 KR陳 ：
Condition 1: RSI value in the previous candle is below oversold zone(30).
條件1：RSI 在前一根的數值低於超賣區（30）
buycondition1 = RSI <30
Condition 2: MACD histogram changes from decreasing to increasing.
條件2：MACD柱由遞減轉遞增
buycondition2 = hist >hist and hist <hist
Strategy Effect Display:
策略效果展示：
Slight modification:
稍微修改：
I've added the ATR-MACD, developed earlier, as a filter signal alongside the classic MACD. The appearance of an upward-facing triangle indicates that the ATR MACD histogram also triggers the condition, aiming to serve as a filtering mechanism.
我在經典的macd作爲條件的同時 也加入了之前開發的ATR-MACD作爲過濾信號 出現朝上的三角圖示代表ATR MACD的柱狀圖一樣觸發條件 希望可以以此起到過濾的作用
Asset/Usage Instructions:
使用標的/使用説明
Through backtesting, it's found that it's not suitable for smaller time frames as there's a lot of noise. It's recommended to use it in assets with a long-term bullish view, focusing on time frames of 12 hours or longer such as 12H, 16H, 1D, 1W to find spot buying opportunities.
經過回測發現 并不適用與一些小級別時區 噪音會非常多，建議在一些長期看漲的標的中切入12小時以上的時區如12H，16H, 1D, 1W 中間尋找現貨買入的機會。
A few thoughts:
Overall, it's a very good indicator strategy for spot buying in the physical market. Thanks to member @KR陳 for sharing!
一些小感言 綜合來看是一個針對現貨買入非常好的指標策略，感謝群友@KR陳的分享！
Crypto Liquidation Heatmap [LuxAlgo]The Crypto Liquidation Heatmap tool offers real-time insights into the liquidations of the top cryptocurrencies by market capitalization, presenting the current state of the market in a visually accessible format. Assets are sorted in descending order, with those experiencing the highest liquidation values placed at the top of the heatmap.
Additional details, such as the breakdown of long and short liquidation values and the current price of each asset, can be accessed by hovering over individual boxes.
🔶 USAGE
The crypto liquidation heatmap tool provides real-time insights into liquidations across all timeframes for the top 29 cryptocurrencies by market capitalization. The assets are visually represented in descending order, prioritizing assets with the highest liquidation values at the top of the heatmap.
Different colors are used to indicate whether long or short liquidations are dominant for each asset. Green boxes indicate that long liquidations surpass short liquidations, while red boxes indicate the opposite, with short liquidations exceeding long liquidations.
Hovering over each box provides additional details, such as the current price of the asset, the breakdown of long and short liquidation values, and the duration for the calculated liquidation values.
🔶 DETAILS
🔹Crypto Liquidation
Crypto liquidation refers to the process of forcibly closing a trader's positions in the cryptocurrency market. It occurs when a trader's margin account can no longer support their open positions due to significant losses or a lack of sufficient margin to meet the maintenance requirements. Liquidations can be categorized as either a long liquidation or a short liquidation.
A long liquidation occurs when long positions are being liquidated, typically due to a sudden drop in the price of the asset being traded. Traders who were bullish on the asset and had opened long positions will face losses as the market moves against them.
On the other hand, a short liquidation occurs when short positions are being liquidated, often triggered by a sudden spike in the price of the asset. Traders who were bearish on the asset and had opened short positions will face losses as the market moves against them.
🔹Liquidation Data
It's worth noting that liquidation data is not readily available on TradingView. However, we recognize the close correlation between liquidation data, trading volumes, and asset price movements. Therefore, this script analyzes accessible data sources, extracts necessary information, and offers an educated estimation of liquidation data. It's important to emphasize that the presented data doesn't reflect precise quantitative values of liquidations. Traders and analysts should instead focus on observing changes over time and identifying correlations between liquidation data and price movements.
🔶 SETTINGS
🔹Cryptocurrency Asset List
It is highly recommended to select instruments from the same exchange with the same currency to maintain proportional integrity among the chosen assets, as different exchanges may have varying trading volumes.
Supported currencies include USD, USDT, USDC, USDP, and USDD. Remember to use the same currency when selecting assets.
List of Crypto Assets: The default options feature the top 29 cryptocurrencies by market capitalization, currently listed on the Binance Exchange. Please note that only crypto assets are supported; any other asset type will not be processed or displayed. To maximize the utility of this tool, it is crucial to heed the warning message displayed above.
🔹Liquidation Heatmap Settings
Position: Specifies the placement of the liquidation heatmap on the chart.
Size: Determines the size of the liquidation heatmap displayed on the chart.
🔶 RELATED SCRIPTS
Liquidations-Meter
Liquidation-Estimates
Liquidation-Levels
Mxwll Liquidation Ranges - Mxwll CapitalIntroducing: Mxwll Liquidation Ranges
Mxwll Liquidation Ranges gathers data outside of TradingView to provide the highest quality, highest accuracy liquidation levels and ranges for popular crypto currencies.
Features
Real liquidation ranges and levels calculated outside of TradingView.
Real net position delta
Average leverage for long positions
Average leverage for short positions
Real number of bids for the cryptocurrency by the day
Real number of asks for the cryptocurrency by the day
Real Bid/Ask Ratio
Real Bid/Ask Delta
Real number of long market orders
Real number of short market orders
Real number of long limit orders
Real number of short limit orders
How do we obtain this data?
Using a now deprecated feature called "TradingView Pine Seeds", we are able to calculate the metrics listed above outside of TradingView and, consequently, import the data to TradingView for public use.
This means no indicators on TradingView that attempt to show liquidation levels, limit orders, net position delta, etc. can be as accurate as ours.
Why aren't other liquidation ranges indicators on TradingView as accurate as ours?
Simple: the data required to calculate liquidation levels and ranges isn't available on TradingView. No level 2 data, bids, asks, leverage information, pending limit orders, etc. This means any custom-coded indicator on TradingView attempting to use or show this information is just a guess, and is naturally inaccurate.
Mxwll Liquidation Ranges has access to all of the required data outside of TradingView, to which liquidation levels/ranges and other pertinent metrics are calculated and uploaded directly to TradingView using the Pine Seeds feature. This means that all information displayed by our indicator uses legitimate level 2 data outside of TradingView. Which means no "estimates" are required to produce this information. Consequently, unless a custom-coded indicator has access to the Pine Seeds feature and calculates liquidation levels and other level 2 data metrics outside of TradingView, then that indicator is inaccurate.
Liquidation Heatmap
The above image shows our liquidation heatmaps, which are calculated using level 2 data, in action.
Liquidation ranges are color coded. Purple/blue colored ranges indicate a lower number of net liquidations should the range be violated.
Green/yellow ranges indicate a liquidation range where the net number of liquidated positions, should the price range be violated, is substantial. Expect volatile price action around these areas and plan accordingly.
Yellow labels indicate the four highest liquidation ranges for the asset over the period.
Liquidation Levels
In addition to calculating a liquidation heatmap, Mxwll Liquidation Ranges also calculates liquidation levels by leverage. Level 2 data outside of TradingView is used.
Levels are colored coded by leverage used.
Green levels are 25x leverage liquidation areas.
Purple levels are 50x leverage liquidation areas.
Orange levels are 100x leverage liquidation areas.
Use this information to improve your trading plan and better pinpoint entries, exits, and key levels of expected volatility.
Other Metrics
Mxwll Liquidation Ranges uses level 2 data and the orderbook to calculate various metrics.
Average leverage for long positions
Average leverage for short positions
Real number of bids for the cryptocurrency by the day
Real number of asks for the cryptocurrency by the day
Real Bid/Ask Ratio
Real Bid/Ask Delta
Real number of long market orders
Real number of short market orders
Real number of long limit orders
Real number of short limit orders
How To Use
Understanding and interpreting heatmaps for predicting liquidation levels in trading can provide a significant edge. Here’s a basic guide on how to interpret these charts:
Understanding Liquidation Levels: Liquidation levels indicate where traders who are using leverage might be forced to exit their positions due to insufficient margin to cover their trades. These levels are crucial because they can trigger sudden price movements if many positions are liquidated at once.
Clusters on the Heatmap: On the heatmap, clusters of liquidation levels are represented by color-coded areas. These clusters show where significant numbers of leveraged positions are concentrated. The color intensity often indicates the density of liquidation points – darker or brighter colors suggest higher concentrations of liquidation risks.
Price Movements: By knowing where these clusters are, traders can anticipate potential price movements. For example, if a significant price drop moves the market closer to a cluster of liquidation levels, there’s an increased risk of those levels being triggered, potentially causing a sharp further drop due to cascading liquidations.
Strategic Trading: With this information, traders can strategically place their own stop losses or prepare to enter trades. Knowing where others might be forced to close their positions can help in predicting bullish or bearish movements.
Risk Management: Understanding liquidation levels helps in managing your own risk. Setting stop losses away from common liquidation points can avoid being caught in volatile price swings caused by mass liquidations.
- Mxwll Capital
