For example, a current position value is computed by multiplying a quantity by a quote for a particular instrument. An account investment value is the sum of all current position values. And an account total value is the sum of a cash balance and an investment value. Last but not least, a trade amount is the product of a price and a number of shares.
In general, while some derived attribute values can be stored in a database, others can be dynamically computed by an application. An application workflow is designed with the goal of understanding data access patterns for a data-driven application. Its visual representation consists of application tasks, dependencies among tasks, and data access patterns.
A Data Model for Trading Stocks, Funds, and Cryptocurrencies
Ideally, each data access pattern should specify what attributes to search for, search on, order by, or do aggregation on. The application workflow has an entry-point task that shows all investment accounts of a user. This task requires data access pattern Q 1 as shown on the diagram.
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Next, an application can either display current positions in a selected account, which requires data access pattern Q 2 , or display information about trade history for a selected account, which requires data access pattern Q 3. Furthermore, Q 3 is broken down into five more manageable data access patterns Q 3. All in all, there are seven data access patterns for a database to support. A logical data model results from a conceptual data model by organizing data into Cassandra-specific data structures based on data access patterns identified by an application workflow.
Logical data models can be conveniently captured and visualized using Chebotko Diagrams that can feature tables, materialized views, indexes and so forth. The logical data model for investment portfolio data is represented by the shown Chebotko Diagram. There are six tables that are designed to support seven data access patterns. All the tables have compound primary keys, consisting of partition and clustering keys, and therefore can store multiple rows per partition.
Column name is a static column as it describes a user who is uniquely identified by the table partition key. All positions in a particular account can be retrieved from one partition with multiple rows. All four tables have the same partition keys, which represent account numbers, and are designed to retrieve and present trades in the descending order of their dates. Note that not all information from the conceptual data model got transferred to the logical data model. Out of the four derived attribute types, only a trade amount will be stored in a database.
Once a trade is completed, its amount is immutable and it makes sense to store it. In contrast, current position values, account investment values and account total values change frequently and it is better to compute them dynamically in an application. Info lainnya About The Authors. Manuscript Template. Abstract This research was conducted at Utama Aluminum Store which is a companyengaged in general trading or general supplier and manufacture of various doors, frames, windows, X Panda, mosquito wire, and window displays.
A Data Model for Trading Stocks, Funds, and Cryptocurrencies | Vertabelo Database Modeler
Full Text: PDF. References Diana, Anastasia. Sistem Akuntansi edisi ke 4 ed. This is redundant data, but it can prove to be very useful when querying historical prices which happens often, as traders are extremely interested in trends. All attributes in this table other than the primary key and the foreign keys can be NULL. This will be the case when we insert a record for a new trading period — there are no trades so far. The final updated value will also be the final report for that day.
Note that the currency table used here is just a copy. The central table is the trader table. The remaining two tables are directly related to offers and trades. When a matching offer appears, the trade event will happen.
Erd price analysis
Any trader can place an offer to buy or sell items. To make this happen, we need to store the following details:. The final table in our model contains data related to the trading event. Trading takes place between two users after they both place an offer. The price used could be the first price offered or the current price, depending on what we want to implement in our application.
This is just the bare bones of the model; there are a bunch of other details we could add. What would you add? Or maybe remove?
Please tell us in the comments. The Data Model.
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