Hello there! I am Caner Uçar. I have graduated from Koç University by completing double major program in Industrial Engineering and Business Administration in 2014. I am currently working as a Business Operations Specialist in multi-national brokerage group companies which run the business in the forex market. In this job, I have been doing daily operational activities related to customer’s balance, providing support to customer representatives and accounts team, solving customer issues from Asian and European markets and preparing management information system report presented to the COO by the manager. Throughout my academical and professional career, I studied and worked about financial instruments. Since I am interested in financial markets and trading, I want to improve myself on data analytics side of financial markets. In the near future, I want to be able to write codes and try to forecast price movements of financial instruments. In the far future, I want to program high accuracy trading algorithm by stochastic modeling.
Contact: LinkedIn
This presentation has been occurred on R/Medicine 2020 Virtual Conference. In the video, the presenter talks about University of Wisconsin health system structure, Applied Data Science Team in the university, how Covid-19 Model Team was created when the pandemic showed up, and how they used R Studio Server Pro as the main platform while working on Covid related data. The Covid team in collaboration with other analysts, researchers, clinicians and professors from UW-Madison studied on predicting future scenarios by gathering, analyzing and visualizing publicly available Covid related data and their local data with the help of R Studio Server Pro. As a result, action plans were taken more effectively by inferring meaningful insights.
This post explains five R tools and packages when coding in finance. The first package is tidyverse. It involves useful packages for finance related data science. Also, it helps to create plots with all the parameters. Second one is xts (and zoo). It helps to create xts objects which are normal R matrices. Third one is quantmod which implements trading rules and indicators. It creates a lot of repetitive parts of the backtest. Fourth one is the tidyquant. It helps to make quantmod, TTR and performance analysis easier. Last one is the Shiny. It enables to create web based dashboards.
This article shows that why traders should learn R and explains new tools for traders such as R and Python. Also, author clarifies that why Excel is not useful like R and Python by giving an example. For illustrate, loading of big data sets are not allowed in Excel but R or Python plays vital role in big data sets. Moreover, building complex relationships between data sets are difficult in Excel. Traders could do anything in R what they can do in Excel. Author indicates that R is the best programming tool for financial traders which are using big data sets. Therefore, learning R or Python could open new doors in the business.
This article explains how to make prediction about stock prices by R and H2O.ai Framework. Author of the article Pedro Lealdino Filho shows the 10 steps of how to predict stock prices and explains them step by step. These steps are collecting data, importing data, cleaning and manipulating data, splitting test, choosing a model, training the model, applying the model, evaluating the results and enhancing the model. He indicates that machine learning model could handle with big financial data.