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Fusion

Fusion MFI RSIHello fellas, This superb indicator summons two monsters called Relative Strength Index (RSI) and Money Flow Index (MFI) and plays the Yu-Gi-Oh! card "Polymerization" to combine them. Overview The Fusion MFI RSI Indicator is an advanced analytical tool designed to provide a nuanced understanding of market dynamics by combining the Relative Strength Index (RSI) and the Money Flow Index (MFI). Enhanced with sophisticated smoothing techniques and the Inverse Fisher Transform (IFT), this indicator excels in identifying key market conditions such as overbought and oversold states, trends, and potential reversal points. Key Features (Brief Overview) Fusion of RSI and MFI: Integrates momentum and volume for a comprehensive market analysis. Advanced Smoothing Techniques: Employs Hann Window, Jurik Moving Average (JMA), T3 Smoothing, and Super Smoother to refine signals. Inverse Fisher Transform (IFT) Enhances the clarity and distinctiveness of indicator outputs. Detailed Feature Analysis Fusion of RSI and MFI RSI (Relative Strength Index): Developed by J. Welles Wilder Jr., the RSI measures the speed and magnitude of directional price movements. Wilder recommended using a 14-day period and identified overbought conditions above 70 and oversold conditions below 30. MFI (Money Flow Index): Created by Gene Quong and Avrum Soudack, the MFI combines price and volume to measure trading pressure. It is typically calculated using a 14-day period, with over 80 considered overbought and under 20 as oversold. Application in Fusion: By combining RSI and MFI, the indicator leverages RSI's sensitivity to price changes with MFI's volume-weighted confirmation, providing a robust analysis tool. This combination is particularly effective in confirming the strength behind price movements, making the signals more reliable. Advanced Smoothing Techniques Hann Window: Traditionally used to reduce the abrupt data discontinuities at the edges of a sample, it is applied here to smooth the price data. Jurik Moving Average (JMA): Known for preserving the timing and smoothness of the data, JMA reduces market noise effectively without significant lag. T3 Smoothing: Developed to respond quickly to market changes, T3 provides a smoother response to price fluctuations. Super Smoother: Filters out high-frequency noise while retaining important trends. Application in Fusion: These techniques are chosen to refine the output of the combined RSI and MFI values, ensuring the indicator remains responsive yet stable, providing clearer and more actionable signals. Inverse Fisher Transform (IFT): Developed by John Ehlers, the IFT transforms oscillator outputs to enhance the clarity of extreme values. This is particularly useful in this fusion indicator to make critical turning points more distinct and actionable. Mathematical Calculations for the Fusion MFI RSI Indicator RSI (Relative Strength Index) The RSI is calculated using the following steps: Average Gain and Average Loss: First, determine the average gain and average loss over the specified period (typically 14 days). This is done by summing all the gains and losses over the period and then dividing each by the period. Average Gain = (Sum of Gains over the past 14 periods) / 14 Average Loss = (Sum of Losses over the past 14 periods) / 14 Relative Strength (RS): This is the ratio of average gain to average loss. RS = Average Gain / Average Loss RSI: Finally, the RSI is calculated using the RS value: RSI = 100 - (100 / (1 + RS)) MFI (Money Flow Index) The MFI is calculated using several steps that incorporate both price and volume: Typical Price: Calculate the typical price for each period. Typical Price = (High + Low + Close) / 3 Raw Money Flow: Multiply the typical price by the volume for the period. Raw Money Flow = Typical Price * Volume Positive and Negative Money Flow: Compare the typical price of the current period to the previous period to determine if the money flow is positive or negative. If today's Typical Price > Yesterday's Typical Price, then Positive Money Flow = Raw Money Flow; Negative Money Flow = 0 If today's Typical Price < Yesterday's Typical Price, then Negative Money Flow = Raw Money Flow; Positive Money Flow = 0 Money Flow Ratio: Calculate the ratio of the sum of Positive Money Flows to the sum of Negative Money Flows over the past 14 periods. Money Flow Ratio = (Sum of Positive Money Flows over 14 periods) / (Sum of Negative Money Flows over 14 periods) MFI: Finally, calculate the MFI using the Money Flow Ratio. MFI = 100 - (100 / (1 + Money Flow Ratio)) Fusion of RSI and MFI The final Fusion MFI RSI value could be calculated by averaging the IFT-transformed values of RSI and MFI, providing a single oscillator value that reflects both momentum and volume-weighted price action: Fusion MFI RSI = (MFI weight * MFI) + (RSI weight * RSI) Suggested Settings and Trading Rules Original Usage RSI: Wilder suggested buying when the RSI moves above 30 from below (enter long) and selling when the RSI moves below 70 from above (enter short). He recommended exiting long positions when the RSI reaches 70 or higher and exiting short positions when the RSI falls below 30. MFI: Quong and Soudack recommended buying when the MFI is below 20 and starts rising (enter long), and selling when it is above 80 and starts declining (enter short). They suggested exiting long positions when the MFI reaches 80 or higher and exiting short positions when the MFI falls below 20. Fusion Application Settings: Use a 14-day period for this indicator's calculations to maintain consistency with the original settings suggested by the inventors. Trading Rules: Enter Long Signal: Consider entering a long position when both RSI and MFI are below their respective oversold levels and begin to rise. This indicates strong buying pressure supported by both price momentum and volume. Exit Long Signal: Exit the long position when either RSI or MFI reaches its respective overbought threshold, suggesting a potential reversal or decrease in buying pressure. Enter Short Signal: Consider entering a short position when both indicators are above their respective overbought levels and begin to decline, suggesting that selling pressure is mounting. Exit Short Signal: Exit the short position when either RSI or MFI falls below its respective oversold threshold, indicating diminishing selling pressure and a potential upward reversal. How to Use the Indicator Select Source and Timeframe: Choose the data source and the timeframe for analysis. Configure Fusion Settings: Adjust the weights for RSI and MFI. Choose Smoothing Technique: Select and configure the desired smoothing method to suit the market conditions and personal preference. Enable Fisherization: Optionally apply the Inverse Fisher Transform to enhance signal clarity. Customize Visualization: Set up gradient coloring, background plots, and bands according to your preferences. Interpret the Indicator: Use the Fusion value and visual cues to identify market conditions and potential trading opportunities. Conclusion The Fusion MFI RSI Indicator integrates classical and modern technical analysis concepts to provide a comprehensive tool for market analysis. By combining RSI and MFI with advanced smoothing techniques and the Inverse Fisher Transform, this indicator offers enhanced insights, aiding traders in making more informed and timely trading decisions. Customize the settings to align with your trading strategy and leverage this powerful tool to navigate financial markets effectively. Best regards, simwai --- Credits to: @loxx – T3 @everget – JMA @cheatcountry – Hann Window
Pine Script™ indicator
by simwai
3
🇨🇭Advanced Fusion MetricsIndicator Overview The "Advanced Fusion Metrics Indicator" is a comprehensive trading tool designed for TradingView that combines several technical analysis methods to assist traders in identifying potential buy and sell opportunities in financial markets. Key Components Moving Averages (MA): Uses two Simple Moving Averages (SMA) with periods defined by the user (default 10 and 20). The indicator generates buy signals when the shorter MA (MA 10) crosses above the longer MA (MA 20) and sell signals when it crosses below, helping to pinpoint trend reversals. Relative Strength Index (RSI): A momentum oscillator that helps identify overbought or oversold conditions, adding a layer of confirmation to the signals generated by the moving averages. Exponential Moving Average (EMA 50): Used to gauge the medium-term trend direction. The color of the EMA line changes based on whether the trend is up (green) or down (red), providing a visual representation of the market trend. Average True Range (ATR): This component measures market volatility. Signals are only generated when the ATR confirms significant market movement relative to the EMA50, enhancing the reliability of the signals during volatile conditions. How It Works Signal Generation: The core of the indicator is based on the crossover of two SMAs. A buy signal is issued when the short-term MA crosses above the long-term MA during sufficient market volatility (confirmed by ATR). Conversely, a sell signal is triggered when the short-term MA crosses below the long-term MA under similar conditions. Trend Confirmation: The EMA50 helps confirm the broader market trend, while the ATR ensures that the crossover signals occur during periods of meaningful price movement, filtering out noise and less significant price movements. Use Case For Traders: The indicator is ideal for traders who need clear, actionable signals combined with an assessment of market conditions. It’s particularly useful in markets where understanding volatility and momentum is crucial, such as in cryptocurrencies and forex. Benefits Comprehensive Analysis: Combines trend, momentum, and volatility analysis in one tool, providing a multifaceted approach to the markets. Enhanced Decision-Making: By integrating multiple indicators, it reduces the likelihood of false signals and enhances decision-making confidence. Customizable and Dynamic: Allows for easy adjustment of parameters to fit different trading styles and market conditions. This indicator equips traders with a powerful blend of tools to analyze price movements and make informed trading decisions based on a combination of trend, momentum, and volatility insights.
Pine Script™ indicator
by swisscover
Fusion: Machine Learning SuiteThe Fusion: Machine Learning Suite combines multiple technical analysis dimensions and harnesses the predictive power of machine learning, seamlessly integrating a diverse array of classic and novel indicators to deliver precision, adaptability, and innovation. Features and Capabilities Multidimensional Analysis: Fusion: MLS integrates various technical analysis dimensions to offer a more comprehensive perspective. Machine Learning Integration: Utilizing ML algorithms, Fusion: MLS offers adaptability to market changes. Custom Indicators: Including dimensions like "Moon Lander", "Cap Line" and "Z-Pack" the indicator expands the scope of traditional technical analysis methods. Tailored Customization: With customization options, Fusion: MLS allows traders to configure the tool to suit their specific strategies and market focus. In the following sections, we'll explore the features and settings of Fusion: MLS in detail, providing insights into how it can be utilized. Major Features and Settings The indicator consists of several core components and settings, each designed to provide specific functionalities and insights. Here's an in-depth look: Machine Learning Component Distance Classifier: A Strategic Approach to Market Analysis In the world of trading and investment, the ability to classify and predict price movements is paramount. Machine learning offers powerful tools for this purpose. The Fusion: MLS indicator among others incorporates an Approximate Nearest Neighbors (ANN)* algorithm, a machine learning classification technique, and allows the selection of various distance functions . This flexibility sets Fusion: MLS apart from existing solutions. The available distance functions include: Euclidean: Standard distance metric, commonly used as a default. Chebyshev: Also known as maximum value distance. Manhattan: Sum of absolute differences. Minkowski: Generalized metric that includes Euclidean and Manhattan as special cases. Mahalanobis: Measures distance between points in a correlated space. Lorentzian: Known for its robustness to outliers and noise. *For a deeper understanding of the Approximate Nearest Neighbors (ANN) algorithm, traders are encouraged to refer to the relevant articles that can be found in the public domain. Alternative scoring system Fusion: MLS also includes a custom scoring alternative based on directional price action. "Combined: Directional" and "Alpha: Directional" scoring types represent our own directional change algorithm, simple yet effective in displaying trend direction changes early on. They are visualized by color changes when scoring becomes below or above zero. Changes in scoring quickly reflect shifts in buyer and seller sentiment. Traders may choose signals by Color Change in the indicator settings to get alerts when scoring color shifts, not waiting until the histogram crosses the zero level. Application in Trading Machine learning classification has become an integral part of modern trading, offering innovative ways to analyze and interpret financial data. Many algorithmic trading systems leverage ML classification to automate trading decisions. By continuously learning from real-time data, these systems can adapt to changing market conditions and execute trades with increased efficiency and accuracy. ML classification allows for the development of tailored trading strategies as traders can select specific algorithms, dimensions, and filters that align with their trading style, goals, and the particular market they are operating. We have integrated ML classification with traditional trading tools, such as moving averages and technical indicators. This fusion creates a more robust analysis framework, combining the strengths of classical techniques with the adaptability of machine learning. Whether used independently or in conjunction with other tools, ML classification represents a significant advancement in trading technology, opening new avenues for exploration, innovation, and success in the financial world. ML: Weighting System The Fusion: MLS indicator introduces a unique weighting system that allows traders to customize the influence of various technical indicators in the machine learning process. This feature is not only innovative but also provides a level of control and adaptability that sets it apart from other indicators. Customizable Weights The weighting system allows users to assign specific weights to different indicators such as Moon Lander, RSI, MACD, Money Flow, Bollinger Bands, Cap Line, Z-Pack, Squeeze Momentum*, and MA Crossover. These weights can be adjusted manually, providing the ability to emphasize or de-emphasize specific indicators based on the trader's strategy or market conditions. *Note, we determined via testing that the popular "Squeeze" indicator can actually be well replicated by simply using inputs of 15 & 199 in the bedrock indicator - MACD ; while we employed the standard "Squeeze" formula (developed by J. Carter ) in Fusion: MLS, traders are hereby made aware of our research findings regarding such. The weighting system's importance lies in its ability to provide a more nuanced and personalized analysis. By adjusting the weights of different indicators a trader focusing on momentum strategies might assign higher weights to the Squeeze Momentum and MA Crossover indicators, while a trader looking for volatility might emphasize RSI and Bollinger Bands. The ability to customize weights adds a layer of complexity and adaptability that is rare in standard machine-learning indicators. Custom Indicators: Moon Lander The "Moon Lander" is not just a catchy name; it's a robust feature inspired by principles from aerospace engineering and offers a unique perspective on trading analysis. Here's a conceptual overview: Fast EMA and Kalman Matrix "Moon Lander" incorporates both a Fast Exponential Moving Average (EMA) and a Kalman Matrix in its design. These two elements are combined to create a histogram, providing a specific approach to data analysis. The Kalman Matrix, or Kalman Filter, is a mathematical concept used for estimating variables that can be measured indirectly and contain noise or uncertainty. It's a standard tool in machine learning and control systems, known for its ability to provide optimal estimates based on observed data. Kalman Filter: A Navigational Tool The Kalman filter, an essential part of "Moon Lander," is a mathematical concept known for its applications in navigation and control systems used by NASA in the apollo program : Guidance in Uncertainty: Just as the Kalman filter helped guide complex aerospace missions through uncertain paths, it assists traders in navigating the often unpredictable financial markets. Filtering Noise: In trading, the Kalman filter serves to filter out market noise, allowing traders to focus on the underlying trends. Predictive Capabilities: Its ability to predict future states makes it a valuable tool for forecasting market movements and trend directions. Custom Indicators: Cap Line and Z-Pack Fusion: MLS integrates our additional proprietary custom indicators that have been published on TradingView earlier: Cap Line: Delve into the specific functionalities and applications of our proprietary "Cap Line" indicator in the published description on TradingView. Z-Pack: Explore the analytical perspectives, focused on the z-score methodology, and custom "Z-Pack" indicator by reviewing the published description on TradingView. Buy/Sell Signal Generation Algorithms Fusion: MLS offers various options for generating buy/sell signals, tailored to different trading strategies and perspectives: Fusion: Allows traders to select any number of dimensions to receive buy/sell signals from, offering customized signal generation. ML: Utilizes the machine learning ANN distance for signal generation. Color Change: Generates signals by selected scoring type color change. Displayed Dimension, Alpha Dimension: Generate signals based on specific selected dimensions. These algorithms provide flexibility in determining buy/sell signals, catering to different trading styles and market conditions. Filters Filters are used to refine and selectively include or exclude signals based on specific criteria. Rather than generating signals, these filters act as gatekeepers, ensuring that only the signals meeting certain conditions are considered. Here's an overview of the filters used: Dynamic State Predictor (DSP) The DSP employs the Kalman Matrix to evaluate existing signals by comparing the fast and slow-moving averages, both processed through the Kalman Matrix. Based on the relationship between these averages, the DSP may exclude specific signals, depending on whether they align with upward or downward trends. Average Directional Index (ADX) The ADX filter evaluates the strength of existing trends and filters out signals that do not meet the specified ADX threshold and length, focusing on significant market movements. Feature Engineering: RSI Applies a filter to the existing signals, clearing out those that do not meet the criteria for RSI overbought or oversold threshold condition. Feature Engineering: MACD Assesses existing signals to identify changes in the strength, direction, momentum, and duration of a trend, filtering out those that do not align with MACD trend direction. The Visual Component The machine learning component is an internal component. However, the indicator also offers an equally important and useful visual component. It is a graphical representation of the multiple technical analysis dimensions, that can be combined in various ways (where the name "Fusion" comes from), allowing traders to visualize the underlying data and its analysis. Displayed Dimension: Visualization and Normalization The Fusion: MLS indicator offers a "Displayed Dimension" feature that visualizes various dimensions as a histogram. These dimensions may include RSI, MAs, BBs, MACD, etc. RSI Dimension on the image + ML signals Normalization: Each dimension is normalized. If any dimension has extreme values, a Fisher transformation is applied to bring them within a reasonable range. Combined Dimension: When selecting the "Combined" option , the normalized values of the selected dimensions are combined using techniques such as standardization, normalization, or winsorization. This flexibility enables tailored visualization and analysis. Alpha Dimension: Enhancing Analysis The "Alpha Dimension" feature allows traders to select an additional dimension alongside the Displayed Dimension. This facilitates a combined analysis, enhancing the depth of insights. Theme Selection Fusion: MLS offers various themes such as "Sailfish", "Iceberg", "Moon", "Perl", "Candy" and "Monochrome" Traders can select a theme that resonates with their preference, enhancing visual appeal. There is also a "Custom" theme available that allows the user to choose the colors of the theme. Customizing Fusion: MLS for Various Markets and Strategies Fusion: MLS is designed with customization in mind. Traders can tailor the indicator to suit various markets and trading strategies. Selecting specific dimensions allows it to align with individual trading goals. Selecting Dimensions: Choose the dimensions that resonate with your trading approach, whether focusing on trend-following, momentum, or other strategies. Adjusting Parameters: Fine-tune the parameters of each dimension, including custom ones like "Moon Lander," to suit specific market conditions. Theme Customization: Select a theme that aligns with your visual preferences, enhancing your chart's readability and appeal. Utilizing Research: Leverage the underlying algorithms and research, such as machine learning classification by ANN and the Kalman filter, to deepen your understanding and application of Fusion: MLS. Alerts The indicator includes an alerting system that notifies traders when new buy or sell signals are detected. Disclaimer The information provided herein is intended for informational purposes only and should not be construed as investment advice, endorsement, nor a recommendation to buy or sell any financial instruments. Fusion: MLS is a technical analysis tool, and like all tools, it should be used with caution and in conjunction with other forms of analysis. Traders and investors are encouraged to consult with a licensed financial professional and conduct their own research before making any trading or investment decisions. Past performance of the Fusion: MLS indicator or any trading strategy does not guarantee future results, and all trading involves risk. Users of Fusion: MLS should understand the underlying algorithms and assumptions and consider their individual risk tolerance and investment goals when using this tool.
Pine Script™ indicator
by BitcoinSpike
4