In this blog, we will give you a brief introduction to AgentBoard, an open source AI agent visualization toolkit which can be used the same way as Tensorboard, which helps visaulize Tensors data during model training. AgentBoard aims to visualize key elements and information of AI Agents running loops in development and production stages of real-world scenarios. It provides easy logging APIs in python and can help log multiple data types such as text (prompt), tools use (function calling of LLM API), image, audio and video. With organized logging schemas, agentboard will visualize the agent-loop (PLAN -> ACT -> REFLECT -> REACT etc ), RAG (Retrieval Augumented Generation), Autonomous Agents running, Multi-Agents orchestration, etc. The rest of the blog will cover the basic data types usage, visualization of Agent Loop, Chat History, Tool Use, Functions Calling, RAG, etc.

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.

In this blog, we will summarize the latex code of most popular formulas and equations for Financial Engineering Formula and Equation part I-Forwards, Puts, and Calls. We will cover important topics including Forwards, Put-Call Parity, Calls and Puts with Different Strikes, Calls and Puts Arbitrage, Call and Put Price Bounds, Varying Times to Expiration, Early Exercise for American Options, etc.

In this blog, we will summarize the latex code of most popular formulas and equations for Financial Engineering Formula and Equation part I-Forwards, Puts, and Calls. We will cover important topics including Forwards, Put-Call Parity, Calls and Puts with Different Strikes, Calls and Puts Arbitrage, Call and Put Price Bounds, Varying Times to Expiration, Early Exercise for American Options, etc.

In this blog, we will summarize the latex code of most popular formulas and equations for Microeconomics, Economics. We will cover important topics, including Allocative Efficiency Condition, Annuities Due, Average Fixed Cost, Average Product, Average Revenue, Average Total Cost, Average Variable Cost ,Compound Interest, Cross-Price Elasticity of Demand, Effective Rate, Elasticity of Supply, Factor of Production Hiring Rule, Future Value of Ordinary Annuities Marginal Cost, Marginal Revenue Product, Present Value of Ordinary Annuities, Simple Interest ,Distributive Efficiency Condition, Gini Coefficient, Marginal Factor Cost MFC, Marginal Product of Labor, Marginal Revenue, Marginal Revenue Product of Labor MRPL, Optimal Combination of Resources Condition, Optimal Consumption Rule, Price Elasticity of Demand, Price for a Competitive Firm, Production Efficiency Condition, Profit, Profit-Maximizing Output Level, Slope of the Total Product Curve, Socially Optimal Level of Output, Total Costs, etc.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

In this blog, we will introduce how to use ChatGPT stock analysis chatbot to quickly generate professional charts of financial data. A few AI techniques such as RAG (Retrieval Augmented Generation), Text2SQL, and Larget Language Models (LLM) are used to help users find financial data, draw chart to compare key metrics, and adjust the color and shape of the chart using simple natural language prompts. A typical scenrio for professional financial analysts is to draw chart to compare the market capitalization, PE ratio of stocks from global market in converted currencies (US Dollars for example). This is a frequent scenario of daily workflow. The snapshots of AI generated charts is shown using DeepNLP Financial Assistant. You can try by clicking the button below the page, copy and paste the query in the chatbox and ask AI to generate charts or tables in few seconds, copy the charts to your reports or send to your manager. From the chart, we can see that on January 15th, Microsoft (MSFT) market cap reaches 2,887 Billon US dollars, and Apple (AAPL) reaches 2,874.68 Billion US dollars. Microsoft's market cap surpassed Apple and became the company of largest market capitalization in United States. The real-time stock data is from the web APIs, and converted to SQL using Text2SQL and LLM techniques.

In this blog, we will summarize the latex code for equations of CFA Level I exam, Formula Sheet Equations and Latex Code, and provide Chatbot as AI Assistant to facilitate your reading. You can ask question like what is "Real GDP" in the chatbox. Topics in the blog include three major parts of CFA Level I exam: QUANTITATIVE, ECONOMICS and FINANCIAL REPORTING. Detailed topics include THE TIME VALUE OF MONEY, Future Value, Present Value, Effective Annual Rate, Continuous Compounding, Ordinary Annuity, Annuity Due, Perpetuity, STATISTICAL CONCEPT AND MARKET RETURNS, Fisher Skewness, Kurtosis, Two-asset portfolio, Three-asset portfolio, Microeconomics, Simple Interest, Effective Rate, Future Value of Ordinary Annuities, Annuities Due, Present Value of Ordinary Annuities, Allocative Efficiency Condition, Average Fixed Cost; Macroeconomics Investment, Aggregate Expenditure Without Government or Foreign Sectors, Marginal Propensity to Consume MPC, Marginal Propensity Save MPS, Sum of Marginal Propensity to Save and Marginal Propensity to Consume, Autonomous Spending Multiplier, Balanced Budget Multiplier, Banks Reserve Ratio, Nominal Interest Rate, Real GDP, Real Interest Rate, Tax Multiplier, Unemployment Rate. FINANCIAL REPORTING and ANALYSIS, Basic EPS, Diluted EPS, Balance Sheet, Free Cash Flow to the Firm, Cash Flow Performance Ratio, Cash Flow To Revenue Ratio, Cash Return On Assets, Cash Return On Assets, Cash Return On Equity, Activity Ratio, Inventory Turnover, Days of Inventory On Hand (DOH), Receivables Turnover, Days of Sales Outstanding, etc.

We are witnessing great success in recent development of generative Artificial Intelligence in many fields, such as AI assistant, Chatbot, AI Writer. Among all the AI native products, AI Search Engine such as Perplexity, Gemini and SearchGPT are most attrative to website owners, bloggers and web content publishers. AI Search Engine is a new tool to provide answers directly to users' questions (queries). In this blog, we will give some brief introduction to basic concepts of AI Search Engine, including Large Language Models (LLM), Retrieval-Augmented Generation(RAG), Citations and Sources. Then we will highlight some majors differences between traditional Search Engine Optimization (SEO) and Generative Engine Optimization(GEO). And then we will cover some latest research and strategies to help website owners or content publishers to better optimize their content in Generative AI Search Engines.