This is a collection of projects from me. In applying the Data to investments whether real estate or financial markets such as stock, options, etc..

• This is a project that a finance job interviewer asked me to do. I will explore the datasets to find interesting insights for investment purposes.
• In the first part, I will analyze the datasets to develop trading strategy.
• I found the percentage change of today's closing price from today's morning opening price are correlate with the percentage change of afternoon opening price from today's morning opening price.
• So I got strategy "Buy Afternoon opening price (ATO2), Sell today closing price (ATC)"
• Tool : Python
• Skill : Data analysis, Data visualization, Trading ideas

• In the second part, I will experiment with different types of stock datasets to evaluate the practicality of the trading ideas from the first part.
• I use Linear regression model to predict the target.
• Backtesting result show that Highest profit on mai stock list ( 95%) in 1 year and 7 months
• Tool : Python, Power Point
• Skill : Backtesting, Machine learning, System trading, Presentation

• It's a project about solving the problem of selling my brother's condo. And find opportunities to invest in condos by comparing prices from real estate web sites.
• The market price of my brother’s condo room is 3,130,330 THB, which is information that helps to decide sale price again for my brother, for example, if he want to sell faster, maybe consider a 10%, 20% or 30% reduction from initial price.
• Tool : R, SQL, Tableau
• Skill : Web scraping, Data analysis, Dashboard

• This is the basis for studying important events over the past 43 years, to understand how each event impacted asset prices and to use this information as a guide for investment allocation.
• Tool : Google Sheets, Tableau Public
• Skill : Dashboard

• This is a project about try to predict the Korea's representative implied volatility index (VKOSPI)
• Even though we turn to forecasts based on the rate of change, the R-square value is even lower (0.2041), i.e. we cannot predict VKOPSI this time.
• Tool : Python
• Skill : Machine Learning