When developing an automatic trading system the following aspects are important:
- Data feeds and data ingestion. It can be a fairly independent component which collects data from different sources (might be even discussion forums) making it available to other components in some uniform format
- Feature generation. The source data is rarely used in its original form for decision making and having good (informative) features is frequently the primary factor of success. Moving averages is an example but nowadays this will hardly work
- Signal generation. Here some logic should be applied in order to emit discrete decisions and such models are heavily parameterized with thresholds.
- Real trading and order management as well as coordination of all activities.
The article sheds some light on the technological aspects and the general pipeline used to process the data and manage orders. Although it might be interesting indeed, I would expect more details about how to scale the solution and how to implement it asynchronously. Especially if it uses Go which has a special construct for that purpose - channels.
I understand that it is not the focus of the article, but having some general information about its trading logic and how to plug new and parameterize existing strategies would help. Some links at the end are quite interesting for me because I am developing an intelligent trading bot based on ML and feature engineering (https://github.com/asavinov/intelligent-trading-bot) for which such articles might be quite important
- Data feeds and data ingestion. It can be a fairly independent component which collects data from different sources (might be even discussion forums) making it available to other components in some uniform format
- Feature generation. The source data is rarely used in its original form for decision making and having good (informative) features is frequently the primary factor of success. Moving averages is an example but nowadays this will hardly work
- Signal generation. Here some logic should be applied in order to emit discrete decisions and such models are heavily parameterized with thresholds.
- Real trading and order management as well as coordination of all activities.
The article sheds some light on the technological aspects and the general pipeline used to process the data and manage orders. Although it might be interesting indeed, I would expect more details about how to scale the solution and how to implement it asynchronously. Especially if it uses Go which has a special construct for that purpose - channels.
I understand that it is not the focus of the article, but having some general information about its trading logic and how to plug new and parameterize existing strategies would help. Some links at the end are quite interesting for me because I am developing an intelligent trading bot based on ML and feature engineering (https://github.com/asavinov/intelligent-trading-bot) for which such articles might be quite important