IBKR TWS Python API – Placing Orders
Visualizations for Algorithmic Trading in R
Installing Python Packages – Part I
The Impact of Crowding on Alternative Risk Premiums
Markov Switching Models for Recession Prediction
Monte Carlo Simulation in R – Part I
Trade Ideas – Algorithmic Trading Elevated
IBKR Python API for Algo Trading
How can you work faster in R Studio? Do you really want to know?
Console History & History pane To refresh your knowledge on how to execute and format R code in R Studio, see the previous article in this series. Everything that you passed to the console doesn’t have to be typed again. Accessing previously executed lines is as easy as navigating with the up and down arrows … Continue reading How can you work faster in R Studio? Do you really want to know?
IBKR Python TWS API
ESG Investing with AI
Environmental, social, and governance (ESG) investing is becoming more popular nowadays and making an impact on mainstream investing. One may find various definitions of ESG, as it is a relatively new concept in investing. Nevertheless, in essence, ESG investing is supposed to adhere to certain socially responsible principles with a focus on environmental, social, and governance criteria.
Detecting Bots On Twitter Using Botometer – Part I
When we perform trading on the basis of market sentiment, we need to fetch data from news sources such as Twitter, Reuters, Bloomberg and Webhosie, etc. Although reading complete articles and gauging their sentiment can be difficult, estimating the sentiment of a tweet is not that complicated.
Towards Better Keras Modeling – Part III
RSI Indicator – Part II
Real world tidy interest rate swap pricing
RSI Indicator – Part I
The Relative Strength Index (RSI) indicator, as the name suggests, tells us the relative strength of the asset. RSI is counted as a robust technical indicator.
Gini Index For Decision Trees – Part III
Calculation of Gini Index of Open Interest for Positive Past Trend
Gini Index For Decision Trees – Part II
Gini Index For Decision Trees – Part I
Decision trees are often used while implementing machine learning algorithms. The hierarchical structure of a decision tree leads us to the final outcome by traversing through the nodes of the tree.