Algorithmic trading, also commonly called algotrading is trading using an automated computer system instead of looking at the graphs using human eyes and pretending you can tell which direction the stock prices are going to go.
There are some places with interesting algotrading discussions.
You won’t get very far in algotrading without good data.
Crypto is appealing for algotrading because the markets are open 24/7, and the data is generally high quality and free.
https://data-api.binance.vision/api
Yahoo Finance is a free source. It’s extremely popular among beginners due to covering a lot of symbols internationally, being free, and having mature libraries for downloading data. A lot of financial programming tutorials use this as a data source.
https://finance.yahoo.com/quote/ENJSA.IS/
The Turkish stock exchange is called BIST (Borsa Istanbul).
This is a very established player. Almost all the banks get their trader application and data from Matriks.
Their website is matriksdata.com. They have an interactive web application with stock data at app.matrikswebtrader.com, the pricing for the application is pretty good.
If you reverse-engineer their web application, or the web application of any of the banks that use their service, you will discover an API service called “dumrul”. This service provides pricing data in CSV and JSON, trading hours, trading holidays, and financial news.
Matriks also sells historical data as CSVs, but the interface for purchasing and downloading this data is terrible. Much easier to get it using the API.
This is the official data store of BIST. It’s extremely cumbersome to use, but you can use it to download a lot of data manually for free.
Link: datastore.borsaistanbul.com
There are daily ZIP files you can download.
I’ve found some online sources that talk about these ZIP files.
tefas.gov.tr is the official website that provides information and statistics about investment funds in Turkey. If you look around, you will find a lot of endpoints with useful data.
TCMB is Turkey’s central bank. A lot of numerical and natural-language data to be found here.
The Information Coefficient (IC) is a statistical measure used in finance and algorithmic trading to quantify the predictive power of a model in forecasting financial returns. It represents the correlation between predicted and actual returns over a specific time period, ranging from -1 to +1.
A high positive IC value indicates strong predictive power, while a negative IC indicates inversely related predictions. A value of 0 suggests no predictive ability. The IC is particularly useful for quantitative analysts and portfolio managers who need to evaluate the quality of their trading signals before implementation.
By measuring the strength and consistency of predictive relationships, IC helps practitioners determine which signals are most likely to generate profitable trading opportunities and maintain their effectiveness over time. It is crucial for validating trading models, refining strategies, benchmarking performance, and managing risks in algorithmic trading.
Organize later