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According to Arthur Samuel (1959), Machine Learning (ML) is a field of scientific study that empowers a computer the ability to learn and act without being explicitly programmed. ML is a subset of Artificial Intelligence (AI), which involves more specific algorithm and advanced technique that enables our computer to think like human as well as in decision making. It is so pervasive nowadays and sometimes we are even unable to notice its existence for any application we have used in our daily life such as microwave (refer to IoTs). Over the past two decades, AI technology has been booming in each and every part of industry, with no doubt, we believe financial industry is the most pervasive among the others, as each nanosecond in financial market nowadays matters in being “cutting edge” especially in High Frequency Trading (HFT), which empower the computerized trading algorithm to gain from any riskless arbitrage (or opportunity) with the unparalleled edge over the speed and accuracy that any human could not surpass. What we are eagerly to solve here is the similar headache of investor (or trader) as we’ve encountered before, over data collecting (e.g. news) and analyzing information (e.g. statistical tools) for determining any investment opportunity (low risk, high return) in the financial market as well as, attached with customization support to any specific need of what our users desire to, at lowest cost as we could provide, with the extensive coverage to Mobile Apps (Coming Soon) for being kept up-to-date at anywhere and anytime.
Big Data Analytic Learning Model (BDALM) refers to our database system that enables analyst saving tons of repeatable and unproductive work especially in preparing various types of data and information into presentable and readable form at a very short time period (especially in earning release season) for analytical purposes. Our system is also able to generate various types of infographics from our data visualization model database, which would enhance analyst’s efficiency and effectiveness with a better user experience in conducting research analysis and constructive judgement based on those presentable and clear-cut infographics instead of solely numerical data form format for more accurate yet faster flow of decision making process. (Coming Soon)
Reader Analytic Learning Model (RALM) is mainly used for investment analysis modelling in Q&A manner. It is our Machine Learning (ML) model that empowers our system to read, comprehend and analyze each and every textual paragraph word by word that retrieved from multiple uploaded documents at once, such as financial statement, regulatory filling, news and articles, analyst reports and any other readable material on a large scale, which enables the users to do any kind of textual enquiry to our system and return with the most relevant answer in human understandable textual form. Our RALM system contains tons of qualitative and quantitative database, from which our system could scan, search, highlight, analyze and finally capture the most relevant information for answering any enquiry requested from our users, which would surpass any human capability to digest tons of reading material in such an efficient (time saving) and effective (accurate) manner. The key technology may refer to Machine reading comprehension (MRC). (Coming Soon)
Image Analytic Learning Model (IALM) acts as our artificial eyes that could automatically recognize any image uploaded from our user-end such as a screenshot of stock chart, logo, etc., from which our ML system will start to analyze and match the uploaded image with the most similar pattern in graphical image searching from our infographic database system and then retrieve the most accurate image to our user-end on iterative basis. This image recognition technology enables our users to effortlessly filter out any desired chart pattern and automatically put into their watchlist on daily up-to-date basis, without any human work to screen through each and every stock chart over 20,000 stocks in US Stock Market every day, as well as, giving our users the “edge” to capture any other graphical information that might be relevant to a specific investment decision. (Coming Soon)
Backtesting Analytic Learning Model (BALM) refers to our trading strategy assessment system on any algorithm trading model based on historical data and basically used to evaluate whether the predictive result of a specific trading strategy model is still relevant (or accurate) to actual results when it applies to current market situation. Backtesting gives us a basic understanding of an overall win/loss ratio in our trading strategy , as well as tracking any particular characteristic of specific financial instrument (like WTI Crude Oil) in more scientific approach without any biased assumption involved, which enables us to see a more comprehensive picture when applying our own strategy in real market, after taking consideration of Winning Percentage (%), Profit Factor, Average Net Trade Profit (including fees and charges), Maximum Drawdown, etc. from past performance, in order to determine the exact execution order (long or short) of the algorithm for each trading signal is triggered in future. The backtesting methodology could optimize the overall risk and reward ratio of our trading strategy (through strategy adjustment approach) and avoid the most irreversible risk on our trading account to being blown out with any emotional trade plays. Besides, we also provide backtesting report for some basic trading strategy (like 20 MA) at all kind of financial instrument on daily basis that allows us to be well prepared to coming market condition. (Coming Soon)
Refers to our ML analytical system on macroeconomic data on any specific country fundamental strength and weakness in wider range of assessment coverage in term of Demographic, Monetary Policy, Fiscal Policy, Political Preferences, Economic Structure, Household Income, Population Pyramid, etc., which would imply any possible changes of a specific country in policies or development direction in future that might prepare us to be fully aware of any risk or opportunity that starts sprouting from any specific country at early stage . (Coming Soon)
Only for limited invitation to our collaborative partner upon a special request in customized system development. (Coming Soon)