Collaborative Stock Trading with P2P-Based Recommendation System: A Proposed Approach, Study Guides, Projects, Research of Computer Science

Download Collaborative Stock Trading with P2P-Based Recommendation System: A Proposed Approach and more Study Guides, Projects, Research Computer Science in PDF only on Docsity! Collaborative Stock Trading with P2P-Based Recommendation System Ali Hisham Malik Introduction Impact of internet has been undeniable in the financial market. Taking into account the opportunity of trading on different stock markets around the world with one click of a button with thousands of stocks being traded everyday, it is difficult if not impossible to keep track of 'moving' stocks without the use of automated monitoring system. There are companies that have automated trading system. Yet the design of such systems remain proprietary, and is somewhat black magic. It is difficult to find open literature and publications about design of such systems. One area of services that has not been researched in the financial trading markets is the use of P2P networks. In a P2P environment, the network itself can act as one giant 'trade guru' informing users about which stocks to sell, buy or keep an eye for throughout the world. P2P networks are most suitable for handling the scalability issues involved in tracking information in such a global environment. P2P systems are able to spread information quickly which is non-trivial in a fast-changing environment such as the financial market. This allows us to devise mechanism to collect the statistical information with little bias that would be useful for making good calls. Related Work Many websites provide financial news, stock prices, technical analysis charts, and other useful information and data at little or no charge. Most brokerage companies provide web accounts allowing customers to trade online. More sophisticated brokers provide API connectivity for allowing customers to plug-in their favourite trading software or in- house system to the brokerage company, and place trades through it. There are still other websites provide functionality to devise strategies for trading and placing automated trades based on them. Some other services provided by online brokerage companies include chat rooms where customers can exchange information amongst each other. 'Stock screeners' is another service which allows user to screen the stocks by specifying criteria for certain market indicators, such as average daily volume, market capitalization, P-E ratio etc. If customer wants more, then for a price, one can get access to a successful trade guru's actual market portfolio. Following that market portfolio, one maybe able to make successful trades without needing to do much research or have knowledge about the market. A trade guru may have done extensive research in a certain market, but may lack information about the other markets. Thus this technique, is definitely not scalable when 1 one wants to trade internationally. Also a true measurement the credibility of trade gurus is a research itself as all of them highlight the good calls they have made and hide the bad ones. Goals: I plan on creating the platform for a P2P based universal recommendation and trading system broadly handling the following four issues: 1. Recommendation System 2. Collaborative Trading Platform 3. Financial Data Collection and Sharing 4. Connectivity for Datamining and Automated Trading For the purpose of this course, I would focus on building the Recommendation System. In a parallel effort, Collaborative Trading Platform will be. Support for collection of historical data for yahoo finance has already been incorporated in a previous effort. 1. Recommendation System Peers in the system would be able to share statistical information about the trading activity of each other. Desirable features include an extensible mechanism for statistical collection of peer activity. Initially statistics such as, which stocks are been bought and sold, or watched, volume of traded stocks, frequency of trading, etc. will be collected. Other desirable statistical include a measure of a peer to make successful trades. For collection of HeyLighen and Bollen[1] propose a set of Hebbian algorithms for constructing a recommendation system based on metadata about the documents in a digital library. My goal is to use these algorithms in the context of financial stocks where the number of stocks being bought, sold and watched serve as the metadata. 2. Collaborative Trading Platform An unbiased collection of statistical data for sharing amongst the peers would be difficult without having an actual trading component in the system. Again the challenge here to allow the peers to be able to choose different brokers for placing trades. The challenge here is to allow peers in the network to be able to 1) connect to online brokers, 2) be able to create groups within the network to perform trading using a single brokerage account, and 3) peers in the group should be able to authorize a single peer to manage the multiple brokerage accounts. For creating peer groups, a peer authentication and approval system would need to be devised. Furthermore, for secure sharing of trading accounts in a peer network, mechanism for ensuring consistency and security need to be in place. 2