🧙 Welcome to our GitHub org
"Final Project",UNAM (https://www.unam.mx/) -TICs-Distributed Computing 2023-1 class,taught by Dr. Victor de la Luz
- Team_Members
- License
- Project Overview
- Introduction
- Justification
- Goals
- Objectives of Each Phase
- Metodology
- Libreries
- Packages
- Deployment
- Conclusions
- Executing_Software_Intructions
- References
- José Ignacio Ireta. (}ignacio-ireta)
- Lucrecia Beltz. . (LucreciaBeltz)
- Gerardo. . (ZabdielZ)
- Miguel Ángel Zamorano Presa. (miguelzpresa)
Copyright © 2023 <mikezpresa@gmail.com,ignacio.ireta@outlook.com>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Welcome to our project on analyzing cryptocurrency data for trading purposes! In this project, we aim to process historical data of cryptocurrencies to identify patterns and trends within their behavior, and use this information to make informed trading decisions.
The world of cryptocurrency is highly volatile, with prices fluctuating wildly within a matter of hours. To be successful in this space, traders must have a deep understanding of the behavior of each cryptocurrency they are trading. This is where data analysis comes in - by analyzing large amounts of historical data, we can identify patterns and trends that may not be visible through simple observation.
We have chosen to focus on several key indices, including: IRS, Pivot Point, Fibonacci retracement, EMA, and stochastic oscillator, as these indices have proven to be effective in predicting market trends and making informed trading decisions. By calculating these indices for various cryptocurrencies, we can identify potential entry and exit points for trades, and make more profitable trading decisions.
In this project, we created a system that processes historical data of the behavior of the XRP cryptocurrency price and then sends it to our website. The project is divided into 5 phases, each with its own objectives and challenges.
Cryptocurrencies have experienced rapid growth and adoption since the creation of Bitcoin in 2009 by Satoshi Nakamoto. This innovative digital money system emerged as a decentralized alternative to the traditional financial system, offering fast, secure and low-cost transactions. Over the years, the cryptocurrency ecosystem has evolved, giving rise to thousands of digital coins and tokens, with XRP being one of the best-known cryptocurrencies with the highest market capitalization.
In the current context of 2023, the importance of studying cryptocurrencies has become even more relevant, due to recent events in the traditional financial system. Just a week ago, two big banks, Silvergate and Silicon Valley Bank, went out of business, and Credit Suisse is facing serious financial problems. These events have generated uncertainty in the financial sphere and have led to an increase in interest in cryptocurrencies as a more solid and decentralized alternative.
The study of cryptocurrencies allows us to understand their behavior in the market, identify trends and make informed decisions in the field of investment. Through the analysis of prices and other relevant indicators, such as RSI, MACD, volume, Parabolic Sar and Bollinger bands, machine learning models can be generated to predict the future behavior of cryptocurrencies and offer useful recommendations for investors.
The TICSonomics project will focus on the analysis of the XRP cryptocurrency from August 2013 to date, using the information provided by the Coingecko API. The main objective is to create an artificial intelligence model that processes and stores this data to make decisions based on machine learning techniques and, finally, deploy the model in a web app accessible to users.
The specific tools for the development and deployment of the project will be left to the discretion of the development team, allowing flexibility in the selection and adaptation of the technologies used. This approach guarantees that the project can adapt to changes in the technological environment and optimize its performance based on the needs of the market and users.
In summary, the importance of studying cryptocurrencies lies in their potential as a decentralized and solid alternative to the traditional financial system, especially in times of economic uncertainty. The TICSonomics project aims to seize this opportunity to develop an artificial intelligence model that facilitates the analysis of the XRP cryptocurrency and supports investors in making informed decisions.
The comparison between the TICSonomics project and existing alternatives such as TradingView, TrendSpider, Koyfin, among others, lies mainly in the focus and automation of technical analysis. These existing platforms offer a wide range of tools and charts to perform fundamental and technical analysis of different assets, including cryptocurrencies. However, these tools require users to perform the analysis manually, which can result in a slower and error-prone process due to human subjectivity.
The main benefit of a tool like TICSonomics, which automates technical analysis based on indicators and uses artificial intelligence, is the ability to process and analyze large amounts of data, quickly and efficiently. This automation allows users to focus on making informed decisions instead of spending time and effort manually analyzing data.
In addition, the incorporation of artificial intelligence in the TICSonomics project provides a competitive advantage over current alternatives. By using machine learning techniques to analyze data and predict the future behavior of cryptocurrencies, TICSonomics can offer more accurate and timely recommendations, improving investment performance.
Another benefit of TICSonomics is the possibility of customizing and adapting the artificial intelligence model to the specific needs of each user. As the model learns and adapts over time, its accuracy and relevance can improve, offering a more personalized and effective approach compared to standard manual tools available on platforms like TradingView or Koyfin.
However, it is important to note that manual fundamental and technical analysis tools remain valuable for investors who prefer a more controlled and detailed approach to their investment strategy. TICSonomics does not intend to completely replace these platforms, but to complement them and offer a more automated and AI-based solution for those looking to optimize their cryptocurrency investments.
In short, TICSonomics sets itself apart from current alternatives by offering an automated, AI-based solution for cryptocurrency technical analysis. This can result in faster and more accurate decision making, as well as a more personalized and adaptable approach for investors compared to the manual tools offered by platforms such as TradingView, TrendSpider, and Koyfin.
Facilitate and accelerate the decision making of minority investors in the cryptocurrency market, especially in volatile environments and subject to market manipulation.
Develop and apply an artificial intelligence model that allows analyzing technical and fundamental indicators in real time, providing valuable and updated information to support decision-making in cryptocurrency investments.
Leveling the playing field for minority investors by providing them with access to advanced analysis and forecasting tools that were previously reserved for institutional investors with greater computational, financial and human resources.
Create an easy-to-use and accessible platform that allows users, regardless of their level of experience, to take full advantage of artificial intelligence and machine learning in cryptocurrency analysis.
Promote transparency and fairness in the cryptocurrency market, countering market manipulation techniques and giving minority investors a greater ability to anticipate and respond to changes in the correlations between assets and market events.
Customize and adapt the artificial intelligence model to the specific needs of each user, continuously improving its accuracy and relevance based on the preferences and investment objectives of the users.
Integrate and complement existing technical and fundamental analysis tools, offering a more complete and efficient solution for investors interested in the cryptocurrency market.
Obtain data on the XRP cryptocurrency from Coin Market Cap api. Standardize the obtained data for use in the system.
Design and configure a suitable database for storing the obtained data. Implement the necessary mechanisms to maintain data integrity.
Design and implement a data processing system to analyze the data obtained from the XRP cryptocurrency. Develop algorithms and analysis techniques to extract relevant information from the data.
Design and implement a website to display the information obtained and analyzed in the previous phases. Develop a clear and intuitive data visualization system for users.
Integrate all the previous phases into a functional and efficient system. Conduct exhaustive testing to ensure that the system works correctly in all situations.
During the first iterations of the project, free software tools for information acquisition and storage will be considered. Seeking to be able to count from the beginning with the minimum information essential for the project such as the opening price, maximum, minimum, closing and volume of the asset of interest. Currently, various sources of information have already been considered, with historical prices dating back to the inception of the first asset to be analyzed, the Ripple cryptocurrency with XRP ticker, which have public APIs, which, although free, do not seem to provide adequate access to the information. necessary information in its free versions, however our specialists ensure that it is possible to obtain them from various sources or derive them from those already mentioned.
data lake Data warehousing Possible tools to use for data storage: -Apache Hive -Apache Spark In this stage, a division will be made in the database to store all the types of data and the different phases in which they are found. The first division of the database will be to store the data provided by the data acquisition system, these data will be the ones that are worked on during the processing stage and the results of this stage will be stored in the second division of the database. of data so that they can be used in the publication stage
In this project, we aim to process historical data of cryptocurrencies to identify patterns and trends within their behavior, and use this information to make informed trading decisions.
We have chosen to focus on several key indices, including: IRS, Pivot Point, Fibonacci retracement, EMA, and stochastic oscillator, as these indices have proven to be effective in predicting market trends and making informed trading decisions. By calculating these indices for various cryptocurrencies, we can identify potential entry and exit points for trades, and make more profitable trading decisions.
Exploratory Data Analysis (EDA) : We will begin by performing exploratory data analysis of the provided historical data to identify any trends or patterns in the time series data. This will involve visualizing the data, analyzing the distribution of the data, and identifying any outliers or anomalies.
Statistical Techniques: We plan to use a variety of statistical techniques to analyze the time series data, including time series decomposition, autocorrelation analysis, and statistical modeling. These techniques will help us to identify any trends, seasonal patterns, or other time-dependent relationships within the data.
Applying Indices: We will apply various indices such as IRS, Fibonacci, EMA, and stochastic oscillator to the time series data at different time frames (e.g., daily, weekly, monthly, etc.). This will help us to identify potential entry and exit points for trades, as well as to identify patterns of trend, consolidation, resistance breakthroughs, and momentum.
Pattern Identification: Once we have applied the indices, we will use them to identify patterns of trend, consolidation, resistance breakthroughs, and momentum within the data. This will involve identifying support and resistance levels, analyzing trends and patterns over time, and identifying potential breakouts or breakdowns in the data.
Creation of container images
We start creating container images for each of the subsystems. It must be ensured that the images are compatible with the deployment environment and meet the system requirements. A version control system like Git can be used to manage code and configurations.
Deployment of containers
Once container images have been created, they must be deployed. We will use Docker Compose for deployment. It must be ensured that the containers are deployed correctly and running optimally.
System configuration and testing
After the containers have been deployed correctly, the system must be configured. This may include configuring databases, web pages, and connections between subsystems. Tests must be performed to ensure that everything is working correctly, and the system is ready to use.
Monitoring and maintenance of the system
Once the system is in production, it must be ensured that it remains up-to-date and running optimally. This may include software updates, security patches, and troubleshooting errors. A monitoring system must also be implemented to keep track of any issues that may arise.