Authors: Arjun Ramesh, A S Lakshmi, Chris Joseph Thomas, C Sivaram Krishnan , Revathy Prasannan
Certificate: View Certificate
The idea of this project is to automate the process of investing or trading based on users choice , by providing references to financial markets trajectory. This project helps a retail investor to meet his financial goals by automating the process and thereby remains manageable for them. The current projects usually reside on the principle of predicting the trend of the market or particular financial instrument .This can be done by using LSTM or prophet seasonality prediction model to predict non-linear trend in the market. Designing a bot to invest in instruments based on the users requirements will further help in automating the entire process. This ideated system is thought by integrating the smart predicting feature of the machine.
Investors are constantly seeking out ways to make more informed investment decisions. One popular approach is using mathematical models, known as quantitative strategies, to predict the likelihood of success. However, machine learning in investing offers a more efficient and hands-off approach for making better investment decisions. By using machine learning algorithms, investors can rely on automated processes to analyze data and make informed predictions about the success of their investments .
The idea of this project is to automate the process of investing or trading based on users choice , by providing references to financial markets trajectory. Previous successful approaches to the problem of algorithmic trading that do not rely on forecasting future prices and are fully based on machine learning have treated the problem as a Reinforcement Learning (RL) issue. These model-free, fully machine-learning methods use RL algorithms to analyze data and make decisions about trading actions. This ideated system is thought to be built by integrating the smart predicting feature of machine learning predicting trends along with bot for automating the process of investing.
II. LITERATURE SURVEY
There are many research papers and articles that have been published on the topic of investment portfolio management. Some areas of research include portfolio optimization, asset allocation, risk management, and performance evaluation. Here are a few examples of research in these areas:
These are just a few examples of the many research works on investment portfolio management.
A. LSTM Model
Recurrent neural networks (RNNs) can be enhanced by using the long short term memory (LSTM) paradigm, which acts as a form of short-term memory by allowing previously determined information to be used in the current neural network. This is useful for urgent tasks that require the use of earlier data. However, it is possible that we may not have a complete list of all the earlier data for a particular neural node. LSTMs are a popular type of neural network within RNNs and are used for a variety of sequence modeling tasks across a range of application areas, including natural language processing, video, geographic information systems, and time series analysis. One of the main challenges with RNNs is the vanishing gradient problem, which occurs due to the repetitive use of the same parameters at each time step. To address this problem, various techniques can be applied at each time step.
In these cases, we aim to find a balance by generalizing variable-length sequences while still maintaining a fixed number of learnable parameters. To do this, we introduce unique parameters at each step and use gated RNN cells such as LSTM and GRU. This allows us to effectively handle variable-length sequences while still maintaining a fixed number of learnable parameters overall.
B. Portfolio Management Challenge
The challenge that the modern youth face when it comes to the subject of finance is based on how they manage their portfolio while striving for financial independence. Strategy is an important
aspect for meeting criteria of successful portfolio. Hence,Taking into consideration a diverse portfolio paired with the ever-changing dynamics of the market presents a new challenge: determining an investment strategy to match your company’s goals. Not every investment strategy is the same. Some investors and companies may be seeking a high-risk portfolio, bringing them the highest return available in the market.
Others may be long term investors, strategizing for the future.Financial challenges also go beyond the numbers to evaluating the structure and workflow of the personnel and management. Inefficiency and ineffective strategies being deployed can be the cause of financial loss to a portfolio, just as severely as investing in an equity projected to be bearish. Wasted money needs to be identified and new tactics must be developed for growth.There is a large proportion of beginners entering the arena of investment , most of whom don't have enough knowledge or market exposure . This could lead to an increased number of failures when it comes to the percentage of the population profiting from investment.
In recent years, there has been increasing interest in using machine learning algorithms to automate investment portfolio management. Machine learning algorithms are designed to learn from data and improve their performance over time, which makes them well-suited for tasks such as analyzing financial data and making investment decisions.
There are several ways in which machine learning can be applied to portfolio management. For example, machine learning algorithms can be used to analyze financial data and identify patterns or trends that may be useful for making investment decisions. They can also be used to build models that can predict the performance of different investments or predict market movements.
There are several potential benefits to using machine learning for portfolio management. For example, machine learning algorithms can process large amounts of data quickly and accurately, which can help investment professionals to identify trends and patterns that might be difficult to detect manually.
Additionally, machine learning algorithms can be trained to make decisions based on multiple factors, which can help to improve the diversification of a portfolio and reduce risk.
Overall, the use of machine learning in portfolio management is an active area of research and development, and it is likely that we will see continued growth and innovation in this area in the coming years.
D. Machine Learning Techniques
Machine learning is a term that has been in focus for many decades now. Machine learning facilitates traders more than traditional algorithmic trading The field of machine learning is receiving a lot of attention due to the current interest in artificial intelligence and its various applications.
Machine learning techniques can be broadly classified into three categories based on the type of data they use and whether they require input from an external agent: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves the use of labeled data to make predictions or decisions, unsupervised learning involves the use of unlabeled data to identify patterns or groupings within the data, and reinforcement learning involves the use of an external agent to take actions within an environment in order to maximize a reward or objective Before diving into the machine learning preferred as such ,it's important to know about how such algorithms help in predicting.
Further details about how data for training is given in the upcoming section. Machine learning applies a process to detect the hidden patterns in dataset from various data sources. Earlier machines were designed to solve specific problems by following a set of instructions programmed by humans. In contrast, modern machine learning systems use algorithms that allow the machine to learn from data and make decisions on its own. This means that machine learning systems are able to adapt and improve their performance over time, without the need for explicit human guidance.
Market data contains a range of data such as price, open, high, low, close, volume etc. Market data refers to the information and statistics that are generated by financial markets, including stock prices, indices, exchange rates, and commodity prices. This data is available for a wide range of global markets, covering various asset classes such as stocks, indices, forex, and commodities.
Market data is used by traders to assess the worth of assets of various companies, and will give them an idea about when to buy or sell stocks. The Aim of using market data is to get as much information about the asset the trader is planning on buying.
Market data is generated in real time,which can be used to make quick but informed decisions. Market
Data can be also used to access historical prices ,these prices are a crucial part of technical analysis, and can be used when creating a strategy for future .
F. Technical Indicators
The system is then configured to meet specific needs, including setting up user accounts, defining asset classes, and establishing risk management parameters. Data integration is crucial, ensuring accurate and reliable information from various sources. If transitioning from an existing system, data migration is performed. Thorough testing and quality assurance are conducted to identify and resolve any issues. User training is provided to ensure effective utilization of the system. Once tested and trained, the system is deployed in a production environment, with ongoing maintenance and support to address any issues and stay updated with upgrades and security patches.
V. TOOLS AND SOFTWARE
VI. RESULT & ACCURACY
The system utilizes technical analysis, which involves analyzing historical price patterns and market trends to identify potential entry and exit points for investments. It also employs machine learning forecasting using Long Short-Term Memory (LSTM) models to predict future stock prices based on historical data and patterns. To gather relevant information about trending stocks, the system includes web scraping modules.
These modules retrieve data from online sources such as news articles, financial reports, and social media sentiment analysis, providing valuable insights for investment decisions. However, it is emphasized that investing in the stock market carries inherent risks. Therefore, the system should incorporate robust risk management strategies.
This may involve diversifying the portfolio, setting risk limits, regularly monitoring investments, and implementing stop-loss measures. Since the system is in the beta state, ongoing testing, evaluation, and refinement are necessary to improve its performance, accuracy, and overall effectiveness. Continuous updates are important to address any limitations or areas for enhancement that are identified during testing. We give some of the outcomes from using the algorithm on stocks traded on the Australian Stock Exchange in this section. The adaptive network is tested using two alternative setups. The first maintains a portfolio using a variable rulebase, and the second utilizes a fixed rulebase that does not adjust to changing market conditions. The weighted return on investment fitness objective is used by both.
Stocks traded on the Australian Stock Exchange are used to create portfolios using the system discussed in the previous sections.
Exchange (hereinafter ASX) (hereafter ASX). From August 2001 to December 2006, two distinct portfolios are created each month. The first one, known as the "Adaptive EA" portfolio, is made via a full adaptive evolutionary process.
A rule base is constructed for the first window and used for the remainder of the simulation to create the second one using a static rule base (see II-F). This portfolio, referred to as the "Static EA," serves as a benchmark for comparison when evaluating the benefits of utilizing an adaptive rule base.
VII. IMPLICATION AND MITIGATION STRATEGIES
A. Data Security
B. System Reliability and Availability
C. Client Communication and Transparency
D. Operational Efficiency
This study focuses on machine learning-based investment strategies that automate the process of trading or investing depending on user preferences by using historical data on financial markets. The work\'s success demonstrates unequivocally that machine learning techniques may be utilized to create varied portfolios that outperform the marketplace indices. Therefore with the help of machine learning we can guide the broker api to invest in security assets based on his/her relevant ROI he set up. This idea is believed to help millennial investors to help achieve financial freedom and earn part time income from this source. In this project we plan to utilize a single machine learning algorithm , this could lead to just adequate performance ,so it would be good enough by adding more than two algorithms and optimizing the result to lead to even better performance of the systems prediction
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Copyright © 2023 Arjun Ramesh, A S Lakshmi, Chris Joseph Thomas, C Sivaram Krishnan , Revathy Prasannan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.