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I used the dataset for Facebook and Microsoft 2014-2018 stock values to analyze the price differences, returns, direction, and the moving averages. After I calculated the moving averages. I graphed the fast signal and the slow signal and visualized the profitability of the trend trading strategy over time.
This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.
This repository is related to all about Computer Vision - an A-Z guide to the world of Computer Vision. This supplement contains the implementation of algorithms, statistical methods, and techniques (in Python)
Data analysis and data filtering on IoT devices on a time series in a unique C++ library, Data Tome. Focus on the developer's experience and performance. It is the successor to the MovingAveragePlus library.
The Python project written on Jupyter includes technical analysis using various indicators, developing trading strategies based on the indicators, visualizing the outcomes, and testing the strategies. The indicators applied in the project are Simple Moving Average 200, Bollinger Bands, and RSI - Relative Strength Index.