OPEN-SOURCE SCRIPT
Vo-S-Di-T-I - Volatility Scaled Directional Trend Indicator
This code represents just the foundation for what's to come. It lays the groundwork for a more sophisticated quant trading model, offering a glimpse into the potential of future developments. I hope my contribution to this community will be valued. I'm here for idea exchanges and coding together, with the key emphasis on ensuring everything we do is grounded on a solid statistical basis.
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The developed code is based on a rigorous quantitative approach for analyzing price trends in the equity sector, utilizing advanced statistical methodology to scale returns based on the volatility observed over predefined periods of 20 and 50 days. This technique for normalizing returns allows us to eliminate distortions due to the intrinsic variability of prices and focus on the underlying structure of price behavior. The primary goal of the code is not to speculatively predict future market movements but rather to identify potential reversal trend signals through price dynamics analysis, within an optimized risk and return context.
Our approach is distinguished by the use of statistical decomposition techniques and time series analysis to interpret price variations as indicators of possible shifts in market behavior. This allows distinguishing between random or short-term price movements and true trend changes, providing a solid foundation for more informed investment decisions.
The current code represents the initial phase of a broader project that envisages the integration of machine learning algorithms to further refine the ability to detect significant changes in price trends. Through the application of predictive models and machine learning techniques, we intend to explore complex patterns in historical price data that may precede trend reversals, always respecting the principles of rigorous statistical analysis and risk management. This development and learning path will allow us to continuously improve investment strategies, leveraging the analytical capabilities of modern data science algorithms applied to the financial sector.
HOW TO READ
Simply put, Z values above 0 indicate an uptrend, while values below indicate a downtrend. IMPORTANT: It is not necessary to consider any crosses between Z-Short and Z-Long, but only potential crosses with 0.
The initial values are set at 20 and 50, but everyone is free to choose the most suitable periods, as long as all choices have valid statistical significance. My advice is to use R or MatLab to explore the best correlation between N and price movements. The reason I have set two values for N (Short and Long) is because it's interesting to assess short-term and medium-to-long-term trends to understand if price movements can lead to reversals only in the short term or also in the medium to long term. This idea came to me because I believe all other trend determination systems have too much lag and unpredictability.
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The developed code is based on a rigorous quantitative approach for analyzing price trends in the equity sector, utilizing advanced statistical methodology to scale returns based on the volatility observed over predefined periods of 20 and 50 days. This technique for normalizing returns allows us to eliminate distortions due to the intrinsic variability of prices and focus on the underlying structure of price behavior. The primary goal of the code is not to speculatively predict future market movements but rather to identify potential reversal trend signals through price dynamics analysis, within an optimized risk and return context.
Our approach is distinguished by the use of statistical decomposition techniques and time series analysis to interpret price variations as indicators of possible shifts in market behavior. This allows distinguishing between random or short-term price movements and true trend changes, providing a solid foundation for more informed investment decisions.
The current code represents the initial phase of a broader project that envisages the integration of machine learning algorithms to further refine the ability to detect significant changes in price trends. Through the application of predictive models and machine learning techniques, we intend to explore complex patterns in historical price data that may precede trend reversals, always respecting the principles of rigorous statistical analysis and risk management. This development and learning path will allow us to continuously improve investment strategies, leveraging the analytical capabilities of modern data science algorithms applied to the financial sector.
HOW TO READ
Simply put, Z values above 0 indicate an uptrend, while values below indicate a downtrend. IMPORTANT: It is not necessary to consider any crosses between Z-Short and Z-Long, but only potential crosses with 0.
The initial values are set at 20 and 50, but everyone is free to choose the most suitable periods, as long as all choices have valid statistical significance. My advice is to use R or MatLab to explore the best correlation between N and price movements. The reason I have set two values for N (Short and Long) is because it's interesting to assess short-term and medium-to-long-term trends to understand if price movements can lead to reversals only in the short term or also in the medium to long term. This idea came to me because I believe all other trend determination systems have too much lag and unpredictability.
Skrypt open-source
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