By Rishikesh Mahadevan Imagine you have to backtest a strategy on 50 stocks and for that you have to download price data of 50 stocks. But traditionally you have to download ticker by ticker. This sequential download process can be painfully slow, especially when each API call requires waiting for external servers to respond. What … Read more
By Manusha Rao Pre-requisites for learning from this blog: https://blog.quantinsti.com/python-programming/ https://blog.quantinsti.com/set-up-python-system/ https://blog.quantinsti.com/python-data-structures/ https://blog.quantinsti.com/python-data-types-variables-tutorial/ Level of your blog: Intermediate Here is a table with the Python libraries for a quick look. Python libraries Python is widely used to develop trading algorithms due to its extensive ecosystem of libraries tailored to finance and trading. In this article, … Read more
In the context of autoregressive (AR) models, the coefficients represent the weights assigned to the lagged values of the time series to predict the current value. These coefficients capture the relationship between the current observation and its past values. The goal is to find the coefficients that best fit the historical data, allowing the model … Read more
By Rekhit Pachanekar The origin of the Relative Strength Index (RSI) indicator is quite interesting. Created in 1986 by J. Welles Wilder, who was an engineer, land developer first and then a commodities trader, the RSI indicator has come a long way since its origin. Even though J. Welles Wilder was working on commodities when … Read more
By Ajay Pawar Have you ever noticed how a model that once predicted stock prices with pinpoint accuracy suddenly starts missing the mark? This isn’t just bad luck—it’s often the result of concept drift or model drift, common challenges in the ever-evolving world of quantitative finance. Financial markets are anything but static; their dynamic nature … Read more