TrendMaster ProTrendMaster Pro: A Comprehensive Trend Analysis Tool for Long-Term Investors
TrendMaster Pro is an advanced technical indicator designed to provide long-term investors with a robust and comprehensive analysis of market trends. This sophisticated tool operates exclusively on daily timeframes, making it ideal for those focused on long-term investment strategies. By combining multiple analytical approaches, TrendMaster Pro offers investors a powerful means to assess trend quality and make informed decisions.
Automatic Trend Detection
At the heart of TrendMaster Pro lies its ability to automatically identify the most statistically significant trend. The indicator analyzes various timeframes ranging from 1000 to 5000 days, selecting the one that exhibits the highest correlation. This feature ensures that investors are always working with the most relevant trend data, eliminating the subjectivity often associated with manual trend identification.
The trend detection algorithm employs a regression analysis approach, evaluating approximately 80,000 different trend alternatives each day. Each potential trend is assigned a score based on criteria such as trend density, deviation from regression, and the number of price points near the trend's floor and ceiling. The trend with the highest score is then selected and displayed on the chart.
Comprehensive Scoring System
TrendMaster Pro employs a multi-faceted scoring system that evaluates four key aspects of a trend, providing a holistic view of its quality and potential. Each aspect is scored on a scale of 0 to 10, with the overall trend quality score being a weighted average of these individual scores.
1. Length Score
The Length Score measures the duration of the detected trend. Longer trends receive higher scores, reflecting increased reliability and significance. This score is calculated by normalizing the auto-selected period (which ranges from 1000 to 5000 days) to a scale of 5 to 10.
For example, if the auto-selected period is 3000 days, it would receive a score of around 7.5. This emphasizes the importance of long-term trends in investment decision-making, as they tend to be more stable and indicative of underlying market forces.
2. Strength Score
The Strength Score utilizes Pearson's Correlation Coefficient to assess trend strength. This statistical measure gauges the linear relationship between price and trend projection. A value closer to 1 indicates a strong positive correlation, reinforcing confidence in the trend direction based on historical price movements.
The indicator translates the Pearson's Correlation Coefficient into a score from 0 to 10. For instance, a correlation coefficient of 0.95 might translate to a Strength Score of 8, indicating a strong and reliable trend.
3. Performance Score
The Performance Score compares the asset's Compound Annual Growth Rate (CAGR) to a chosen benchmark, typically a major index like the S&P 500. This score provides insight into how well the asset is performing relative to the broader market.
The CAGR is calculated using the formula: CAGR = (Ending Value / Beginning Value)^(1/n) - 1, where n is the number of years. The Performance Score is then determined by comparing this CAGR to the benchmark's CAGR over the same period. A higher score indicates outperformance relative to the benchmark.
4. Level Score
The Level Score evaluates the current price position within the trend channel. Lower prices within the channel receive higher scores, suggesting potential value or buying opportunities. This score helps identify possible entry points based on historical trend behavior.
For example, if the current price is near the lower boundary of the trend channel, it might receive a Level Score of 9, indicating a potentially attractive entry point.
Visual Representation
TrendMaster Pro provides a clear visual representation of the detected trend by displaying a regression channel on the chart. This channel consists of three lines: a middle line representing the main trend, and upper and lower lines representing standard deviations from the main trend.
The channel offers a quick visual reference for support and resistance levels, helping investors identify potential entry and exit points. The color and style of these lines can be customized to suit individual preferences.
Detailed Information Table
A comprehensive table presents all scores and relevant data, allowing for quick and easy interpretation of the trend analysis. This table includes:
The auto-selected trend length
The Pearson's Correlation Coefficient
The asset's CAGR and the benchmark's CAGR
Individual scores for Length, Strength, Performance, and Level
The overall Trend Quality Score
This table provides investors with a clear, at-a-glance summary of the trend's key characteristics and quality.
Practical Application
To use TrendMaster Pro effectively, investors should consider the following:
Focus on the overall Trend Quality Score as a primary indicator of trend strength and reliability.
Use the Length Score to gauge the trend's longevity and potential stability.
Pay attention to the Strength Score to assess how well the price action aligns with the identified trend.
Utilize the Performance Score to compare the asset's performance against the broader market.
Consider the Level Score when timing entries, looking for opportunities when prices are relatively low within the trend channel.
Use the visual trend channel as a guide for potential support and resistance levels.
Limitations and Considerations
While TrendMaster Pro offers powerful insights, it's important to remember that no indicator can predict future market movements with certainty. The tool should be used in conjunction with fundamental analysis and other market information.
Additionally, as the indicator is designed for daily charts and long-term analysis, it may not be suitable for short-term trading strategies. Users should also be aware that past performance does not guarantee future results, even with strong trend indications.
Conclusion
TrendMaster Pro represents a significant advancement in trend analysis for long-term investors. By combining automatic trend detection, comprehensive scoring, and benchmark comparison, it offers a powerful tool for those seeking to make informed, data-driven investment decisions. Its ability to objectively assess trend quality across multiple dimensions provides investors with a valuable edge in navigating complex market conditions.
For investors looking to deepen their understanding of market trends and enhance their long-term investment strategies, TrendMaster Pro offers a sophisticated yet accessible solution. As with any investment tool, users are encouraged to thoroughly familiarize themselves with its features and interpret its outputs in the context of their overall investment approach.
Z-score
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
MVRV-Z adjusted EN version (by ilyaevp95)Descriptions:
The MVRV Z-Score indicator is a powerful tool designed by original authors Murad Mahmudov and David Puell for BTC to help traders make informed decisions about their cryptocurrency investments. It is based on the MVRV (Market Value to Realized Value) metric, which measures the relationship between the market capitalization and the realized capitalization of a cryptocurrency. The indicator provides signals for accumulating or selling an asset based on deviations in market capitalization from realized capitalization.
How it works:
Market Capitalization : This is the total value of coins that have been issued at a given point in time. Market capitalization is calculated by multiplying the current price of the asset by the number of coins that have been issued.
Realized Capitalization (Realized Price) : This is the amount of money that has been spent on purchasing a particular asset. In the context of cryptocurrencies, it represents the sum of all transaction values for a specific blockchain. Realized capitalization can be calculated using historical data on transaction prices.
MVRV Metric : The MVRV metric compares market capitalization with realized capitalization, providing a measure of how overvalued or undervalued a cryptocurrency is relative to its historical transaction data. A high MVRV value indicates that the market is overvaluing the asset, while a low MVRV suggests undervaluation.
Z-Score Calculation : The Z-score is a statistical measure that normalizes the deviation of market capitalization from its mean value (realized capitalization) to a standard deviation. This makes it possible to compare assets that have different values and time periods, as it takes into account the volatility of the market.
Note: For accurate Z-score calculation, you need to use the indicator on a chart with a mostly complete historical data set for a specific cryptocurrency.
Signals : Based on the Z-score, the indicator generates signals for accumulation or sale. If the Z-score falls below a certain threshold (negative), it may indicate an opportunity to accumulate the asset. Conversely, if the Z-score rises above a positive threshold, it could suggest a potential sell signal.
The indicator uses a color-coded system to provide traders with visual cues:
Green background indicates a signal to accumulate.
Orange (Red) background indicates a signal to sell.
Deviations exceeding the specified thresholds by 1 and 2 Z (positive direction), 0.5 and 1 Z (negative direction) are highlighted in a brighter color, indicating more extreme deviations.
Note: The signals provided by this indicator should not be considered financial advice. Traders should conduct their own research (DYOR) before making any investment decisions.
Parameters: The indicator provides several parameters for customization:
Blockchain : The blockchain for which the analysis is performed. This allows the user to select the specific blockchain they are interested in analyzing. The default value is BTC.
Z threshold for positive deviations : This parameter sets the threshold above which the deviation will be considered positive. A higher value will result in fewer signals, while a lower value may generate more false signals. The default value is 3.0.
Z threshold for negative deviations : Similar to the previous parameter, this sets the threshold below which the deviation will be considered negative. The default value is 0.
Market Capitalization : There are two types of market capitalization available: Standard and Free float coin capitalization. Free float is calculated by multiplying its current price by the total number of units in free circulation - the number that are not locked in any contracts or other forms of restriction. For DASH, ZEC, BAT and ALGO available only Free float capitalization. The default value is "Standard"
Negative Deviation Filter Mode : When enabled, if the deviation has been positive for a certain number of previous weeks (the default value is 40 weeks), the indicator will not generate a signal to accumulate. This helps to avoid false signals during the start of a bearish market. This may be helpful for volatile coins, whose price can drastically fall below the realized price after the end of a bull market. The default setting is "disabled".
Display Options:
MVRV plot : Displays the MVRV metric for the selected blockchain.
Z-Score plot : Shows the Z-score calculated by the indicator.
Realized Price plot : Provides a visual representation of the realized price of the cryptocurrency on main chart.
S ignal Display : Choose whether to display signals on the main chart or in a separate panel.
Historical mode : Choose whether to show signals for all historical data on the chart or for a certain number of periods. The default setting is "disabled".
Global Financial IndexIntroducing the "Global Financial Index" indicator on TradingView, a meticulously crafted tool derived from extensive research aimed at providing the most comprehensive assessment of a company's financial health, profitability, and valuation. Developed with the discerning trader and investor in mind, this indicator amalgamates a diverse array of financial metrics, meticulously weighted and balanced to yield optimal results.
Financial Strength:
Financial strength is a cornerstone of a company's stability and resilience in the face of economic challenges. It encompasses various metrics that gauge the company's ability to meet its financial obligations, manage its debt, and generate sustainable profits. In our Global Financial Index indicator, the evaluation of financial strength is meticulously crafted to provide investors with a comprehensive understanding of a company's fiscal robustness. Let's delve into the key components and the rationale behind their inclusion:
1. Current Ratio:
The Current Ratio serves as a vital indicator of a company's liquidity position by comparing its current assets to its current liabilities.
A ratio greater than 1 indicates that the company possesses more short-term assets than liabilities, suggesting a healthy liquidity position and the ability to meet short-term obligations promptly.
By including the Current Ratio in our evaluation, we emphasize the importance of liquidity management in sustaining business operations and weathering financial storms.
2. Debt to Equity Ratio:
The Debt to Equity Ratio measures the proportion of a company's debt relative to its equity, reflecting its reliance on debt financing versus equity financing.
A higher ratio signifies higher financial risk due to increased debt burden, potentially leading to liquidity constraints and solvency issues.
Incorporating the Debt to Equity Ratio underscores the significance of balancing debt levels to maintain financial stability and mitigate risk exposure.
3. Interest Coverage Ratio:
The Interest Coverage Ratio assesses a company's ability to service its interest payments with its operating income.
A higher ratio indicates a healthier financial position, as it implies that the company generates sufficient earnings to cover its interest expenses comfortably.
By evaluating the Interest Coverage Ratio, we gauge the company's capacity to manage its debt obligations without compromising its profitability or sustainability.
4. Altman Z-Score:
The Altman Z-Score, developed by Edward Altman, is a composite metric that predicts the likelihood of a company facing financial distress or bankruptcy within a specific timeframe.
It considers multiple financial ratios, including liquidity, profitability, leverage, and solvency, to provide a comprehensive assessment of a company's financial health.
The Altman Z-Score categorizes companies into distinct risk groups, allowing investors to identify potential warning signs and make informed decisions regarding investment or credit exposure.
By integrating the Altman Z-Score, we offer a nuanced perspective on a company's financial viability and resilience in turbulent market conditions.
Profitability Rank:
Profitability rank is a crucial aspect of investment analysis that evaluates a company's ability to generate profits relative to its peers and industry benchmarks. It involves assessing various profitability metrics to gauge the efficiency and effectiveness of a company's operations and management. In our Global Financial Index indicator, the profitability rank segment is meticulously designed to provide investors with a comprehensive understanding of a company's profitability dynamics. Let's delve into the key components and rationale behind their inclusion:
1. Return on Equity (ROE):
Return on Equity measures a company's net income generated relative to its shareholders' equity.
A higher ROE indicates that a company is generating more profits with its shareholders' investment, reflecting efficient capital utilization and strong profitability.
By incorporating ROE, we assess management's ability to generate returns for shareholders and evaluate the overall profitability of the company's operations.
2. Gross Profit Margin:
Gross Profit Margin represents the percentage of revenue retained by a company after accounting for the cost of goods sold (COGS).
A higher gross profit margin indicates that a company is effectively managing its production costs and pricing strategies, leading to greater profitability.
By analyzing gross profit margin, we evaluate a company's pricing power, cost efficiency, and competitive positioning within its industry.
3. Operating Profit Margin:
Operating Profit Margin measures the percentage of revenue that remains after deducting operating expenses, such as salaries, rent, and utilities.
A higher operating profit margin signifies that a company is efficiently managing its operating costs and generating more profit from its core business activities.
By considering operating profit margin, we assess the underlying profitability of a company's operations and its ability to generate sustainable earnings.
4. Net Profit Margin:
Net Profit Margin measures the percentage of revenue that remains as net income after deducting all expenses, including taxes and interest.
A higher net profit margin indicates that a company is effectively managing its expenses and generating greater bottom-line profitability.
By analyzing net profit margin, we evaluate the overall profitability and financial health of a company, taking into account all expenses and income streams.
Valuation Rank:
Valuation rank is a fundamental aspect of investment analysis that assesses the attractiveness of a company's stock price relative to its intrinsic value. It involves evaluating various valuation metrics to determine whether a stock is undervalued, overvalued, or fairly valued compared to its peers and the broader market. In our Global Financial Index indicator, the valuation rank segment is meticulously designed to provide investors with a comprehensive perspective on a company's valuation dynamics. Let's explore the key components and rationale behind their inclusion:
1. Price-to-Earnings (P/E) Ratio:
The Price-to-Earnings ratio is a widely used valuation metric that compares a company's current stock price to its earnings per share (EPS).
A lower P/E ratio may indicate that the stock is undervalued relative to its earnings potential, while a higher ratio may suggest overvaluation.
By incorporating the P/E ratio, we offer insight into market sentiment and investor expectations regarding a company's future earnings growth prospects.
2. Price-to-Book (P/B) Ratio:
The Price-to-Book ratio evaluates a company's market value relative to its book value, which represents its net asset value per share.
A P/B ratio below 1 may indicate that the stock is trading at a discount to its book value, potentially signaling an undervalued opportunity.
Conversely, a P/B ratio above 1 may suggest overvaluation, as investors are paying a premium for the company's assets.
By considering the P/B ratio, we assess the market's perception of a company's tangible asset value and its implications for investment attractiveness.
3. Dividend Yield:
Dividend Yield measures the annual dividend income received from owning a stock relative to its current market price.
A higher dividend yield may indicate that the stock is undervalued or that the company is returning a significant portion of its profits to shareholders.
Conversely, a lower dividend yield may signal overvaluation or a company's focus on reinvesting profits for growth rather than distributing them as dividends.
By analyzing dividend yield, we offer insights into a company's capital allocation strategy and its implications for shareholder returns and valuation.
4. Discounted Cash Flow (DCF) Analysis:
Discounted Cash Flow analysis estimates the present value of a company's future cash flows, taking into account the time value of money.
By discounting projected cash flows back to their present value using an appropriate discount rate, DCF analysis provides a fair value estimate for the company's stock.
Comparing the calculated fair value to the current market price allows investors to assess whether the stock is undervalued, overvalued, or fairly valued.
By integrating DCF analysis, we offer a rigorous framework for valuing stocks based on their underlying cash flow generation potential.
Earnings Transparency:
Mitigating the risk of fraudulent financial reporting is crucial for investors. The indicator incorporates the Beneish M-Score, a robust model designed to detect earnings manipulation or financial irregularities. By evaluating various financial ratios and metrics, this component provides valuable insights into the integrity and transparency of a company's financial statements, aiding investors in mitigating potential risks.
Overall Score:
The pinnacle of the "Global Financial Index" is the Overall Score, a comprehensive amalgamation of financial strength, profitability, valuation, and manipulation risk, further enhanced by the inclusion of the Piotroski F-Score. This holistic score offers investors a succinct assessment of a company's overall health and investment potential, facilitating informed decision-making.
The weighting and balancing of each metric within the indicator have been meticulously calibrated to ensure accuracy and reliability. By amalgamating these diverse metrics, the "Global Financial Index" empowers traders and investors with a powerful tool for evaluating investment opportunities with confidence and precision.
This indicator is provided for informational purposes only and does not constitute financial advice, investment advice, or any other type of advice. The information provided by this indicator should not be relied upon for making investment decisions. Trading and investing in financial markets involves risk, and you should carefully consider your financial situation and consult with a qualified financial advisor before making any investment decisions. Past performance is not necessarily indicative of future results. The creator of this indicator makes no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability with respect to the indicator or the information contained herein. Any reliance you place on such information is therefore strictly at your own risk. By using this indicator, you agree to assume full responsibility for any and all gains and losses, financial, emotional, or otherwise, experienced, suffered, or incurred by you.
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.
Stochastic Z-Score Oscillator Strategy [TradeDots]The "Stochastic Z-Score Oscillator Strategy" represents an enhanced approach to the original "Buy Sell Strategy With Z-Score" trading strategy. Our upgraded Stochastic model incorporates an additional Stochastic Oscillator layer on top of the Z-Score statistical metrics, which bolsters the affirmation of potential price reversals.
We also revised our exit strategy to when the Z-Score revert to a level of zero. This amendment gives a much smaller drawdown, resulting in a better win-rate compared to the original version.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
Following this, the Stochastic Oscillator is utilized to affirm the Z-Score overbought and oversold indicators. This indicator operates within a 0 to 100 range, so a base adjustment to match the Z-Score scale is required. Post Stochastic Oscillator calculation, we recalibrate the figure to lie within the -4 to 4 range.
Finally, we compute the average of both the Stochastic Oscillator and Z-Score, signaling overpriced or underpriced conditions when the set threshold of positive or negative is breached.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURAUD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Stochastic Length: 14
Stochastic Smooth Period: 7
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 40%
FURTHER IMPLICATION
The Stochastic Oscillator imparts minimal impact on the current strategy. As such, it may be beneficial to adjust the weightings between the Z-Score and Stochastic Oscillator values or the scale of Stochastic Oscillator to test different performance outcomes.
Alternative momentum indicators such as Keltner Channels or RSI could also serve as robust confirmations of overbought and oversold signals when used for verification.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Price Based Z-Trend - Strategy [presentTrading]█ Introduction and How it is Different
Z-score: a statistical measurement of a score's relationship to the mean in a group of scores.
Simple but effective approach.
The "Price Based Z-Trend - Strategy " leverages the Z-score, a statistical measure that gauges the deviation of a price from its moving average, normalized against its standard deviation. This strategy stands out due to its simplicity and effectiveness, particularly in markets where price movements often revert to a mean. Unlike more complex systems that might rely on a multitude of indicators, the Z-Trend strategy focuses on clear, statistically significant price movements, making it ideal for traders who prefer a streamlined, data-driven approach.
BTCUSD 6h LS Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Z-score
"Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean."
The Z-score is central to this strategy. It is calculated by taking the difference between the current price and the Exponential Moving Average (EMA) of the price over a user-defined length, then dividing this by the standard deviation of the price over the same length:
z = (x - μ) /σ
Local
🔶 Trading Signals
Trading signals are generated based on the Z-score crossing predefined thresholds:
- Long Entry: When the Z-score crosses above the positive threshold.
- Long Exit: When the Z-score falls below the negative threshold.
- Short Entry: When the Z-score falls below the negative threshold.
- Short Exit: When the Z-score rises above the positive threshold.
█ Trade Direction
The strategy allows users to select their preferred trading direction through an input option.
█ Usage
To use this strategy effectively, traders should first configure the Z-score thresholds according to their risk tolerance and market volatility. It's also crucial to adjust the length for the EMA and standard deviation calculations based on historical performance and the expected "noise" in price data.
The strategy is designed to be flexible, allowing traders to refine settings to better capture profitable opportunities in specific market conditions.
█ Default Settings
- Trade Direction: Both
- Standard Deviation Length: 100
- Average Length: 100
- Threshold for Z-score: 1.0
- Bar Color Indicator: Enabled
These settings offer a balanced starting point but can be customized to suit various trading styles and market environments. The strategy's parameters are designed to be adjusted as traders gain experience and refine their approach based on ongoing market analysis.
Z-score is a must-learn approach for every algorithmic trader.
Buy Sell Strategy With Z-Score [TradeDots]The "Buy Sell Strategy With Z-Score" is a trading strategy that harnesses Z-Score statistical metrics to identify potential pricing reversals, for opportunistic buying and selling opportunities.
HOW DOES IT WORK
The strategy operates by calculating the Z-Score of the closing price for each candlestick. This allows us to evaluate how significantly the current price deviates from its typical volatility level.
The strategy first takes the scope of a rolling window, adjusted to the user's preference. This window is used to compute both the standard deviation and mean value. With these values, the strategic model finalizes the Z-Score. This determination is accomplished by subtracting the mean from the closing price and dividing the resulting value by the standard deviation.
This approach provides an estimation of the price's departure from its traditional trajectory, thereby identifying market conditions conducive to an asset being overpriced or underpriced.
APPLICATION
Firstly, it is better to identify a stable trading pair for this technique, such as two stocks with considerable correlation. This is to ensure conformance with the statistical model's assumption of a normal Gaussian distribution model. The ideal performance is theoretically situated within a sideways market devoid of skewness.
Following pair selection, the user should refine the span of the rolling window. A broader window smoothens the mean, more accurately capturing long-term market trends, while potentially enhancing volatility. This refinement results in fewer, yet precise trading signals.
Finally, the user must settle on an optimal Z-Score threshold, which essentially dictates the timing for buy/sell actions when the Z-Score exceeds with thresholds. A positive threshold signifies the price veering away from its mean, triggering a sell signal. Conversely, a negative threshold denotes the price falling below its mean, illustrating an underpriced condition that prompts a buy signal.
Within a normal distribution, a Z-Score of 1 records about 68% of occurrences centered at the mean, while a Z-Score of 2 captures approximately 95% of occurrences.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
The following is the default setup on EURUSD 1h timeframe
Rolling Window: 80
Z-Score Threshold: 2.8
Signal Cool Down Period: 5
Commission: 0.03%
Initial Capital: $10,000
Equity per Trade: 30%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Ohlson O-Score IndicatorThe Ohlson O-Score is a financial metric developed by Olof Ohlson to predict the probability of a company experiencing financial distress. It is widely used by investors and analysts as a key tool for financial analysis.
Inputs:
Period: Select the financial period for analysis, either "FY" (Fiscal Year) or "FQ" (Fiscal Quarter).
Country: Specify the country for Gross Net Product data. This helps in tailoring the analysis to specific economic conditions.
Gross Net Product : Define the number of years back for the index to be set at 100. This parameter provides a historical context for the analysis.
Table Display : Customize the display of various tables to suit your preference and analytical needs.
Key Features:
Predictive Power : The Ohlson O-Score is renowned for its predictive power in assessing the financial health of a company. It incorporates multiple financial ratios and indicators to provide a comprehensive view.
Financial Distress Prediction : Use the O-Score to gauge the likelihood of a company facing financial distress in the future. It's a valuable tool for risk assessment.
Country-Specific Analysis : Tailor the analysis to the economic conditions of a specific country, ensuring a more accurate evaluation of financial health.
Historical Context : Set the Gross Net Product index at a specific historical point, allowing for a deeper understanding of how a company's financial health has evolved over time.
How to Use:
Select Period : Choose either Fiscal Year or Fiscal Quarter based on your preference.
Specify Country : Input the country for country-specific Gross Net Product data.
Set Historical Context : Determine the number of years back for the index to be set at 100, providing historical context to your analysis.
Custom Table Display : Personalize the display of various tables to focus on the metrics that matter most to you.
Calculation and component description
Here is the description of O-score components as found in orginal Ohlson publication :
1. SIZE = log(total assets/GNP price-level index). The index assumes a base value of 100 for 1968. Total assets are as reported in dollars. The index year is as of the year prior to the year of the balance sheet date. The procedure assures a real-time implementation of the model. The log transform has an important implication. Suppose two firms, A and B, have a balance sheet date in the same year, then the sign of PA - Pe is independent of the price-level index. (This will not follow unless the log transform is applied.) The latter is, of course, a desirable property.
2. TLTA = Total liabilities divided by total assets.
3. WCTA = Working capital divided by total assets.
4. CLCA = Current liabilities divided by current assets.
5. OENEG = One if total liabilities exceeds total assets, zero otherwise.
6. NITA = Net income divided by total assets.
7. FUTL = Funds provided by operations divided by total liabilities
8. INTWO = One if net income was negative for the last two years, zero otherwise.
9. CHIN = (NI, - NI,-1)/(| NIL + (NI-|), where NI, is net income for the most recent period. The denominator acts as a level indicator. The variable is thus intended to measure change in net income. (The measure appears to be due to McKibben ).
Interpretation
The foundational model for the O-Score evolved from an extensive study encompassing over 2000 companies, a notable leap from its predecessor, the Altman Z-Score, which examined a mere 66 companies. In direct comparison, the O-Score demonstrates significantly heightened accuracy in predicting bankruptcy within a 2-year horizon.
While the original Z-Score boasted an estimated accuracy of over 70%, later iterations reached impressive levels of 90%. Remarkably, the O-Score surpasses even these high benchmarks in accuracy.
It's essential to acknowledge that no mathematical model achieves 100% accuracy. While the O-Score excels in forecasting bankruptcy or solvency, its precision can be influenced by factors both internal and external to the formula.
For the O-Score, any results exceeding 0.5 indicate a heightened likelihood of the firm defaulting within two years. The O-Score stands as a robust tool in financial analysis, offering nuanced insights into a company's financial stability with a remarkable degree of accuracy.
Z-score changeAs a wise man once said that:
1. beginners think in $ change
2. intermediates think in % change
3. pros think in Z change
Here is the "Z-score change" indicator that calculates up/down moves normalized by standard deviation (volatility) displayed as bar chart with 1,2 and 3 stdev levels.
MVRV Z-ScoreThe MVRV ratio was created by Murad Mahmudov & David Puell. It simply compares Market Cap to Realised Cap, presenting a ratio (MVRV = Market Cap / Realised Cap). The MVRV Z-Score is a later version, refining the metric by normalising the peaks and troughs of the data.
Z-Scored Volume [KFB Quant]The Z-Scored Volume (CSV) indicator is designed to make it easier to identity potential market extremes.
What is the Z-Score?
The Z-Score is a statistical measure that quantifies how far a particular data point is from the mean of a group of data. It's expressed in terms of standard deviations from the mean.
How to calculate the Z-Score?
Z-Score = (Value - Mean) / StDev
How the script works
In this script we calculate the Z-Score of the charts volume.
We get the Mean of the predefined period in the Length Configuration tab by using the ta.sma function.
We get the StDev of the predefined period in the Length Configuration tab by using the ta.stdev function.
The default period is 360.
Past performance does not guarantee future results. This indicator is for informational & educational purposes only.
Z-ScoreThe "Z-Score" indicator is a unique and powerful tool designed to help traders identify overbought and oversold conditions in the market. Below is an explanation of its features, usefulness, and what makes it special:
Features:
Z-Score Calculation: The indicator calculates the Z-Score, a statistical measure that represents how far the current price is from the moving average (MA) in terms of standard deviations. It helps identify extreme price movements.
Customizable Parameters: Traders can adjust key parameters such as the Z-Score threshold, the type of MA (e.g., SMA, EMA), and the length of the moving average to suit their trading preferences.
Signal Options: The indicator offers flexibility in terms of signaling. Traders can choose whether to trigger signals when the Z-Score crosses the specified threshold or when it moves away from the threshold.
Visual Signals : Z-Score conditions are represented visually on the chart with color-coded background highlights. Overbought conditions are marked with a red background, while oversold conditions are indicated with a green background.
Information Table: A dynamic information table displays essential details, including the MA type, MA length, MA value, standard deviation, current price, and Z-Score. This information table helps traders make informed decisions.
Usefulness:
Overbought and Oversold Signals: Z-Score is particularly valuable for identifying overbought and oversold market conditions. Traders can use this information to potentially enter or exit positions.
Statistical Analysis: The Z-Score provides a statistical measure of price deviation, offering a data-driven approach to market analysis.
Customization: Traders can customize the indicator to match their trading strategies and preferences, enhancing its adaptability to different trading styles.
Visual Clarity: The visual signals make it easy for traders to quickly spot potential trade opportunities on the price chart.
In summary, the Z-Score indicator is a valuable tool for traders looking to incorporate statistical analysis into their trading strategies. Its customizability, visual signals, and unique statistical approach make it an exceptional choice for identifying overbought and oversold market conditions and potential trading opportunities.
Z-Score - AsymmetrikZ-Score-Asymmetrik User Manual
Introduction
The Z-Score Indicator is a powerful tool used in technical analysis to measure how far a data point is from the mean value of a dataset, measured in terms of standard deviations. This indicator helps traders identify potential overbought or oversold conditions in the market.
This user manual provides a comprehensive guide on how to use the Z-Score Indicator in TradingView.
0. Quickstart
- Set the thresholds based on your asset (number of standard deviations that you consider being extreme for this asset / timeframe).
- Red background indicates a possible overbought situation, green background an oversold one.
- The color and direction of the Z-Score Line acts as a confirmation of the trend reversal.
1. Indicator Overview
The Z-Score Indicator, also known as the Z-Score Oscillator, is designed to display the Z-Score of a selected financial instrument on your TradingView chart. The Z-Score measures how many standard deviations an asset's price is from its mean (average) price over a specified period.
The indicator consists of the following components:
- Z-Score Line: This line represents the Z-Score value and is displayed on the indicator panel.
- Background Color: The background color of the indicator panel changes based on user-defined thresholds.
2. Inputs
The indicator provides several customizable inputs to tailor it to your specific trading preferences:
- Number of Periods: This input allows you to define the number of periods over which the Z-Score will be calculated. A longer period will provide a smoother Z-Score line but may be less responsive to recent price changes.
- Z-Score Low Threshold: Sets the lower threshold value for the Z-Score. When the Z-Score crosses below this threshold, the background color of the indicator panel changes accordingly.
- Z-Score High Threshold: Sets the upper threshold value for the Z-Score. When the Z-Score crosses above this threshold, the background color of the indicator panel changes accordingly.
3. How to Use the Indicator
Here are the steps to use the Z-Score Indicator:
- Adjust Parameters: Modify the indicator's inputs as needed. You can change the number of periods for the Z-Score calculation and set your desired low and high thresholds.
- Interpret the Indicator: Observe the Z-Score line on the indicator panel. It fluctuates above and below zero. Pay attention to the background color changes when the Z-Score crosses your specified thresholds.
4. Interpreting the Indicator
- Z-Score Line: The Z-Score line represents the current Z-Score value. When it is above zero, it suggests that the asset's price is above the mean, indicating potential overvaluation. When below zero, it suggests undervaluation.
- Background Color: The background color of the indicator panel changes based on the Z-Score's position relative to the specified thresholds. Green indicates the Z-Score is below the low threshold (potential undervaluation), while red indicates it is above the high threshold (potential overvaluation).
- Z-Score Line Color: The color of the Z-Score line shows that the Z-Score is trending up compared to its moving average. This can be used as a validation of the background color.
5. Customization Options
You can customize the Z-Score Indicator in the following ways:
- Adjust Inputs: Modify the number of periods and the Z-Score thresholds.
- Change Line and Background Colors: You can customize the colors of the Z-Score line and background by editing the indicator's script.
6. Troubleshooting
If you encounter any issues while using the Z-Score Indicator, make sure to check the following:
- Ensure that the indicator is applied correctly to your chart.
- Verify that the indicator's inputs match your intended settings.
- Contact me for more support if needed
7. Conclusion
The Z-Score Indicator is a valuable tool for traders and investors to identify potential overbought and oversold conditions in the market. By understanding how the Z-Score works and customizing it to your preferences, you can integrate it into your trading strategy to make informed decisions.
Remember that trading involves risk, and it's essential to combine technical indicators like the Z-Score with other analysis methods and risk management strategies for successful trading.
Z MomentumOverview
This is a Z-Scored Momentum Indicator. It allows you to understand the volatility of a financial instrument. This indicator calculates and displays the momentum of z-score returns expected value which can be used for finding the regime or for trading inefficiencies.
Indicator Purpose:
The primary purpose of the "Z-Score Momentum" indicator is to help traders identify potential trading opportunities by assessing how far the current returns of a financial instrument deviate from their historical mean returns. This analysis can aid in recognizing overbought or oversold conditions, trend strength, and potential reversal points.
Things to note:
A Z-Score is a measure of how many standard deviations a data point is away from the mean.
EV: Expected Value, which is basically the average outcome.
When the Z-Score Momentum is above 0, there is a positive Z-Score which indicates that the current returns of the financial instrument are above their historical mean returns over the specified return lookback period, which could mean Positive, Momentum, and in a extremely high Z-Score value, like above +2 Standard deviations it could indicate extreme conditions, but keep in mind this doesn't mean price will go down, this is just the EV.
When the Z-Score Momentum is below 0, there is negative Z-Score which indicates that the current returns of the financial instrument are below their historical mean returns which means you could expect negative returns. In extreme Z-Score situations like -2 Standard deviations this could indicate extreme conditions and the negative momentum is coming to an end.
TDLR:
Interpretation:
Positive Z-Score: When the Z-score is positive and increasing, it suggests that current returns are above their historical mean, indicating potential positive momentum.
Negative Z-Score: Conversely, a negative and decreasing Z-score implies that current returns are below their historical mean, suggesting potential negative momentum.
Extremely High or Low Z-Score: Extremely high (above +2) or low (below -2) Z-scores may indicate extreme market conditions that could be followed by reversals or significant price movements.
The lines on the Indicator highlight the Standard deviations of the Z-Score. It shows the Standard deviations 1,2,3 and -1,-2,-3.
VWMA/SMA Delta Volatility (Statistical Anomaly Detector)The "VWMA/SMA Delta Volatility (Statistical Anomaly Detector)" indicator is a tool designed to detect and visualize volatility in a financial market's price data. The indicator calculates the difference (delta) between two moving averages (VWMA/SMA) and uses statistical analysis to identify anomalies or extreme price movements. Here's a breakdown of its components:
Hypothesis:
The hypothesis behind this indicator is that extreme price movements or anomalies in the market can be detected by analyzing the difference between two moving averages and comparing it to a statistically derived normal distribution. When the MA delta (the difference between two MAs: VWMA/SMA) exceeds a certain threshold based on standard deviation and the Z-score coefficient, it may indicate increased market volatility or potential trading opportunities.
Calculation of MA Delta:
The indicator calculates the MA delta by subtracting a simple moving average (SMA) from a volume-weighted moving average (VWMA) of a selected price source. This calculation represents the difference in the market's short-term and long-term trends.
Statistical Analysis:
To detect anomalies, the indicator performs statistical analysis on the MA delta. It calculates a moving average (MA) of the MA delta and its standard deviation over a specified sample size. This MA acts as a baseline, and the standard deviation is used to measure how much the MA delta deviates from the mean.
Delta Normalization:
The MA delta, lower filter, and upper filter are normalized using a function that scales them to a specific range, typically from -100 to 100. Normalization helps in comparing these values on a consistent scale and enhances their visual representation.
Visual Representation:
The indicator visualizes the results through histograms and channels:
The histogram bars represent the normalized MA delta. Red bars indicate negative and below-lower-filter values, green bars indicate positive and above-upper-filter values, and silver bars indicate values within the normal range.
It also displays a Z-score channel, which represents the upper and lower filters after normalization. This channel helps traders identify price levels that are statistically significant and potentially indicative of market volatility.
In summary, the "MA Delta Volatility (Statistical Anomaly Detector)" indicator aims to help traders identify abnormal price movements in the market by analyzing the difference between two moving averages and applying statistical measures. It can be a valuable tool for traders looking to spot potential opportunities during periods of increased volatility or to identify potential market anomalies.
Realized Profit & Loss [BigBeluga]The Realized Loss & Profit indicator aims to find potential dips and tops in price by utilizing the security function syminfo.basecurrency + "_LOSSESADDRESSES".
The primary objective of this indicator is to present an average, favorable buying/selling opportunity based on the number of people currently in profit or loss.
The script takes into consideration the syminfo.basecurrency, so it should automatically adapt to the current coin.
🔶 USAGE
Users have the option to enable the display of either Loss or Profit, depending on their preferred visualization.
Examples of displaying Losses:
Example of displaying Profits:
🔶 CONCEPTS
The concept aims to assign a score to the data in the ticker representing the realized losses. This score will provide users with an average of buying/selling points that are better to the typical investor.
🔶 SETTINGS
Users have complete control over the script settings.
🔹 Calculation
• Profit: Display people in profit on an average of the selected length.
• Loss: Display people in loss on an average of the selected length.
🔹 Candle coloring
• True: Color the candle when data is above the threshold.
• False: Do not color the candle.
🔹 Levels
- Set the level of a specific threshold.
• Low: Low losses (green).
• Normal: Low normal (yellow).
• Medium: Low medium (orange).
• High: Low high (red).
🔹 Z-score Length: Length of the z-score moving window.
🔹 Threshold: Filter out non-significant values.
🔹 Histogram width: Width of the histogram.
🔹 Colors: Modify the colors of the displayed data.
🔶 LIMITATIONS
• Since the ticker from which we obtain data works only on the daily timeframe, we are
restricted to displaying data solely from the 1D timeframe.
• If the coin does not have any realized loss data, we can't use this script.
MEO Reversal and AlertHello; This indicator offers a suite of diverse analytical features. These features are typically triggered in unusual overbought and oversold conditions and are primarily used to identify excessive buying or selling and for general monitoring in suspicious cases.
Below is a general overview of the various features of this indicator:
RSI Overbought and Oversold Zones: This feature determines whether the RSI is in the overbought or oversold zones.
RSI Peak and Trough Points: Identifies the peak and trough points of the RSI.
Stoch RSI Peak and Trough Points: Identifies the peak and trough points of the Stoch RSI.
MACD Peak and Trough Points: Identifies the peak and trough points of the MACD.
MACD Overflow Points: Detects the overflow points of the MACD.
WaveTrend Reversal Points: Identifies the reversal points of the WaveTrend.
Money Flow Index (MFI) Potential Reversals: Determines the potential reversal points of the MFI.
Z-Score Outliers: Identifies the deviation points of the Z-Score.
Momentum Reversal Points: Identifies the reversal points of Momentum.
SR Support Resistance Breakouts: Determines the breakout points of support and resistance.
Rate of Change (ROC) Rapid Price Change Points: Identifies the rapid price change points of the ROC.
You can set alert conditions for each feature.
The inspiration for this indicator came from the idea of making a few indicators easier and faster to use together. Instead of tracking three basic indicators as shown in the image, I thought it might be more straightforward to follow the Reversal indicator. I imagined this could generally be a handy tip-off indicator and wanted to share it with you. Please write if you have any questions or if there's something you'd like to ask.
However, remember that this should not be considered as investment advice and should not be used for direct buying or selling operations. Each trade is under the individual user's responsibility.
For frequently asked questions, you can check the TradingView support page here: tr.tradingview.com
Extreme Reversal SignalThe Extreme Reversal Signal is designed to signal potential pivot points when the price of an asset becomes extremely overbought or oversold. Extreme conditions typically signal a brief or extensive price reversal, offering valuable entry or exit points. It's important to note that this indicator may produce multiple signals, making it essential to corroborate these signals with other forms of analysis to determine their validity. While the default settings provide valuable insights, it might be beneficial to experiment with different configurations to ensure the indicator's efficacy.
Two primary conditions define extremely overbought and oversold states. The first condition is that the price must deviate by two standard deviations from the 20-day Simple Moving Average (SMA). The second condition is that the 3-day SMA of the 14-day Stochastic Oscillator (STO) derived from the 14-day Relative Strength Index (RSI) is above or below the upper or lower limit.
Oversold states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI falls below the lower limit, suggesting a buy signal. These are visually represented by green triangles below the price bars. Overbought states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI rises above the upper limit, suggesting a sell signal. These are visually represented by red triangles above the price bars. It's also possible to set up automated alerts to get notifications when either of these two conditions is met to avoid missing out.
While this indicator has traditionally identified overbought and oversold conditions in various different assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, trend indicators can greatly improve the decision-making process when planning for entries and exit points.
Volume-Weighted Trend Filter CloudThe Volume-Weighted Trend Filter Cloud is a powerful technical analysis tool designed to identify trend directions and potential buy/sell signals in a trading instrument. The indicator combines volume-weighted moving averages, average true range (ATR), and cloud plotting techniques to provide a comprehensive view of the market trend.
Inputs:
Length: Specifies the length of Algo used for trend analysis. Default value is 14.
Multiplier: Adjusts the width of the trend filter bands based on the ATR. Default value is 2.0.
Tenkan-sen Period: Defines the period for calculating the Tenkan-sen line. Default value is 200.
Kijun-sen Period: Sets the period for calculating the Kijun-sen line. Default value is 400.
Senkou Span Period: Determines the period for calculating the Senkou Span A and Senkou Span B lines. Default value is 600.
Calculation:
Average True Range (ATR): The indicator calculates the ATR based on the specified moving average length.
Trend Filter Bands: The basic upper and lower bands are calculated using the highest high and lowest low values, respectively, along with the multiplier and ATR. These bands are then adjusted to create the final upper and lower bands, taking into account the previous values.
Trend Direction: The indicator determines the trend direction by comparing the close price with the lower and upper bands. If the close price is above the lower band, it indicates an upward trend (trendUp = 1). If the close price is below the upper band, it indicates a downward trend (trendDown = 1).
Volume-Weighted Z-Score: The indicator calculates the volume-weighted Z-Score by determining the mean and standard deviation of the close price with volume weighting. The Z-Score represents the deviation of the close price from the mean in terms of standard deviations.
Tenkan-sen, Kijun-sen, Senkou Span A, and Senkou Span B: These lines are calculated using the respective periods and the average of the high and low prices.
Sigmoid Transformation: The indicator applies the sigmoid function to the Z-Score values to obtain sigmoid-transformed values for open, high, low, and close prices. These transformed values help in visualizing the trend strength.
Plotting:
Trend Filter: The trend filter is plotted as a line that changes color based on the trend direction. The lower band is displayed for an upward trend, while the upper band is displayed for a downward trend.
Trend Cloud: The cloud plot represents the Senkou Span A and Senkou Span B lines. The cloud color changes based on the trend direction, providing a visual representation of the market trend.
Buy and Sell Signals: The indicator generates buy and sell signals based on the crossover of fast and slow moving averages, Z-Score values, trend direction, and other conditions. These signals are labeled on the chart, indicating potential entry points for traders.
The indicator generates buy and sell signals based on specific conditions, including the intersection of fast and slow moving averages, Z-Score values, trend direction, and more.
Buy signals are described as a "buy signal" on the chart, which indicates potential entry points for buy trades.
Sell signals are described as a "sell signal", which indicates potential entry points for sell trades. The signals in light color represent that they are signals in the opposite direction of the cloud that can be considered as exit points
The Z-score The Z-score, also known as the standard score, is a statistical measurement that describes a value's relationship to the mean of a group of values. It's measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
The concept of Z-score was introduced by statistician Carl Friedrich Gauss as part of his "method of the least squares," which was an important step in the development of the normal distribution and Z-score tables. It's a key concept in statistics and is used in various statistical tests.
In financial analysis, Z-scores are used to determine whether a data point is usual or unusual. You can think of it as a measure of how many standard deviations an element is from the mean. For instance, a Z-score of 1.0 would denote a value that is one standard deviation from the mean. Z-scores are also used to predict probabilities, with Z-scores having a distribution that is expected to be normal.
In trading, a Z-score is used to determine how often a trading system may produce a string of winners or losers. It can help a trader to understand whether the losses or profits they see are something that the system would most likely produce, or if it's a once in a blue moon situation. This helps traders make decisions about when to start or stop a system.
I just wanted to play a bit with the Z-score I guess.
Feel free to share your findings if you discover additional applications for this strategy or identify timeframes where it appears to perform more optimally.
How it works:
This strategy is based on a statistical concept called Z-score, which measures the number of standard deviations a data point is from the mean. In other words, it helps determine how unusual or usual a data point is.
In the context of this strategy, Z-score is applied to a 10-period EMA (Exponential Moving Average) of Heikin-Ashi candlestick close prices. The Z-score is calculated over a look-back period of 25 bars.
The EMA of the Z-score is then calculated over a 20-bar period, and the upper and lower thresholds (bounds for buy and sell signals) are defined using the 90th and 10th percentiles of this EMA score.
Long positions are taken when the Z-score crosses above the lower threshold or crosses above the mid-line (50th percentile). An additional long entry is made when the Z-score crosses above the highest value the EMA has been in the past 100 periods.
Short positions are initiated when the EMA crosses below the upper threshold, lower threshold or the highest value the EMA has been in the past 100 periods.
Positions are closed when opposing entry conditions are met, for example, a long position is closed when the short entry condition is true, and vice versa.
Set your desired start date for the strategy. This can be modified in the timestamp("YYYY MM DD") function at the top of the script.
SwingConfidence ScoreSwingConfidence is a scoring system that helps us quantitatively manage risk & position size in swing trading.
SwingConfidence uses T3 moving average to determine the swing state in which the instrument is in. So, this is supposed to be used with my previously posted Simple Swing with T3MA indicator . The T3MA ribbon consists of a fast and a slow moving average (MA). The ribbon is green when the fast MA is above the slow MA. This green ribbon represents the upswing. Similarly, the red ribbon represents the downswing.
The score takes into account the swing state of 2 chosen benchmark indices (by default, these are NIFTY & CNXSMALLCAP). It has 2 components:
- Weekly Swing
- Daily Swing
Weekly Swing
The script uses the Simple Swing indicator on weekly charts of of 2 benchmark indices to determine whether the index is in a weekly upswing or downswing.
- If the color of the weekly ribbon is green, we are in a weekly Upswing.
- If the color of the weekly ribbon is red, we are in a weekly Downswing.
Daily Swing
The script uses the Simple Swing indicator on daily charts of 2 benchmark indices to determine the daily swing state. There can be any one of total 6 swing states on a daily chart:
- Early Upswing (close above red ribbon)
- Confirmed upswing (green ribbon)
- Upswing under strain (close inside green ribbon)
- Early Downswing (close below green ribbon)
- Confirmed downswing (red ribbon)
- Downswing under strain (close inside red ribbon)
SwingConfidence Scoring
The script prints the Weekly & Daily Swing states, & assigns a score to each index from 0 to 50, where 0 is the most bearish score, & 50 is the most bullish score. The sum of the scores is the final SwingConfidence score. e.g. If both indices are in a confirmed upswing, then the score reads 50 + 50 = 100.
How to use the SwingConfidence score?
There are multiple ways by which we can use the SwingConfidence score:
- If the SwingConfidence value is 100%, then we can go in with the maximum open risk our strategy allows. As the score starts decreasing, we keep on closing/modifying our positions, so as to keep the open risk proportionately down. Once the score reaches to zero, we must not be having any open risk. We can achieve this by either going in all-cash, or bringing the stop losses to breakeven.
- Another way is to use this is via a progressive exposure method. If the SwingConfidence value is 100%, then we go with full position size (e.g. 1% capital-at-risk). If the value is 0%, we sit out in cash. Between these 2 extremes, we reduce/increase our position size accordingly.
Please note that this script will display only on the daily timeframe.
MVRV Z Score and MVRV Free Float Z-ScoreIMPORTANT: This script needs as much historic data as possible. Please run it on INDEX:BTCUSD , BNC:BLX or another chart of sufficient length.
MVRV
The MVRV (Market Value to Realised Value Ratio) simply divides bitcoins market cap by bitcoins realized market cap. This was previously impossible on Tradingview but has now been made possible thanks to Coinmetrics providing us with the realized market cap data.
In the free float version, the free float market cap is used instead of the regular market cap.
Z-Score
The MVRV Z-score divides the difference between Market cap and realized market cap by the historic standard deviation of the market cap.
Historically, this has been insanely accurate at detecting bitcoin tops and bottoms:
A Z-Score above 7 means bitcoin is vastly overpriced and at a local top.
A Z-Score below 0.1 means bitcoin is underpriced and at a local bottom.
In the free float version, the free float market cap is used instead of the regular market cap.
The Z-Score, also known as the standard score is hugely popular in a wide range of mathematical and statistical fields and is usually used to measure the number of standard deviations by which the value of a raw score is above or below the mean value of what is being observed or measured.
Credits
MVRV Z Score initially created by aweandwonder
MVRV initially created by Murad Mahmudov and David Puell
