The ARESS financial data processing procedure

Financial data, more accurate than ever

ARESS gathers financial data from trusted sources, keeps the raw data, and runs control and validation processes, turning what the capital market needs into data you can rely on.

Choosing sources
Receiving the data
Keeping the raw data
Control and validation
Processing and analysis
Delivering the information
01 · Data source
02 · Data ingestion
03 · Raw data
04 · Validation
05 · Processing
06 · Output
The data processing procedure

Our process starts with raw data

The quality of financial information depends on more than its source: how the data is received, kept, checked and processed all shape it too. ARESS takes data from trusted sources and, after storing, validating and processing it, turns it into information that financial products and services can use.

01
Choosing sources
Receiving data from official, trusted sources connected to the capital market and economic institutions.
02
Receiving the data
Data arrives through defined channels and enters the processing pipeline.
03
Keeping the raw data
Incoming data is kept raw, before any processing, so it can always be checked against the original source.
04
Control and validation
Stored data is compared against the original sources, and periodic tests are run.
05
Processing
Raw data is processed according to defined methods, formulas and logic.
06
Producing the output
Processed data is made ready for use in financial products, services and systems.
Scroll down to enter the process ↓
01Choosing sourcesReceiving data from official, trusted sources connected to the capital market and economic institutions.
02Receiving the dataData arrives through defined channels and enters the processing pipeline.
03Keeping the raw dataIncoming data is kept raw, before any processing.
04Control and validationStored data is compared against the original sources.
05ProcessingRaw data is processed according to defined methods, formulas and logic.
06Producing the outputProcessed data is made ready for use in products and services.
01 · Choosing the source

Data starts with trusted sources

Choosing the source is the first step of data quality control. We use official, trusted websites connected to the capital market and economic institutions, and we treat their data as reliable on the strength of the source itself.

We review the list of sources and the standing of each one regularly, and we assess every new source for how official it is and how steadily it publishes before letting it into the process.

Securities and Exchange Organization Subsidiaries of the Securities and Exchange Organization Licensed financial institutions The Official Gazette Ministry of Economic Affairs and Finance Central Bank Statistical Center of Iran
Securities and Exchange Organization Central Bank Ministry of Economic Affairs and Finance Statistical Center of Iran The Official Gazette Licensed financial institutions Subsidiaries of the Securities and Exchange Organization The sources ARESS uses
The sources ARESS uses Securities and Exchange Organization Central Bank Ministry of Economic Affairs and Finance Statistical Center of Iran The Official Gazette Licensed financial institutions Subsidiaries of the Securities and Exchange Organization
02 · Receiving the data

Data ingestion through defined and controlled channels

Data enters the processing pipeline through defined channels, and access to those channels is part of the financial data processing infrastructure.

We build the receiving and storage flow in modules, so each part can be reviewed and tested on its own, by hand and automatically. We check that ingestion, storage and processing are correct with unit tests and integration tests in the code itself.

API keys we receive from various sources and institutions are kept encrypted whenever they are stored in a database or a file. Access passwords are stored using one-way algorithms.

Securities and Exchange Organization Central Bank Statistical Center Financial systems Ingestion · Initial validation · Routing The ARESS financial data processing infrastructure
Securities and Exchange Organization · Central Bank · Statistical Center · Financial systems ↓ Ingestion · Initial validation · Routing ↓ The ARESS financial data processing infrastructure
03 · Raw data

The raw data is kept before any processing

Data we take from trusted sources is stored raw in the database, and every piece of processing runs on that stored copy.

We change nothing before the raw copy is stored. As long as that copy stays untouched, any final figure can be traced back to its original source and checked. Without it, a processing error can no longer be traced.

RAW · PRESERVED Preserved, unchanged Processing runs on a separate copy
↓ RAW · PRESERVED ↓
Preserved, unchanged Processing runs on a separate copy
04 · Control and validation

Data control is not a one-off stage

No more than three months pass between two validation tests. The point is to see whether the data we received and stored still matches the original sources: in each test we take at least 10 percent of the stored data at random and compare it against the original source.

These tests run automatically, and they are deliberately written in a different language or library from the tools in the storage path, so that a fault in the storage process is not repeated inside the test that is meant to catch it. We report each period's result to the board by email or messenger.

Interval: three months at most Sample: at least 10% at random Execution: automatic, independent of the storage path Reporting: to the board
Reference data
✓
✓
✓
✓
VALIDATE
Processed data
✓
✓
✓
✓
✓Validation passed
Now
3 months
6 months
9 months
Validation report → the board
05 · Processing

Processing built on methods that can be checked

We process raw data according to defined methods, formulas and logic. In the mathematics we aim to keep our methods consistent with financial standards and with the teaching of bodies such as the CFA Institute and GARP, and with recognised academic sources.

Wherever we take a formula, a methodology or a particular encoding from an outside source, we check that source's standing and record the reference both in the product documentation and in the code itself. The formulas and mathematical relationships are documented as an appendix to the procedure.

CFA Institute GARP Recognised academic sources Source references in the code and the documentation
Validated data Cleaning Standardisation Calculation Analysis Processed data
06 · Producing the output

Data that is ready to use

The processed data is the information layer beneath the ARESS financial products, services and systems.

Whoever builds a part of this path documents it themselves once it is implemented: comments in the code, a flowchart, and a written explanation in the product documentation. That documentation is what makes the output reviewable.

Following this procedure is mandatory for all financial data ingestion and processing activities and for the company's specialists and managers. It can only be changed on the managing director's proposal and with the board's approval.

Processed data Market data Information relating to financial markets Data for financial products The information layer beneath the products Historical data Examining trends and past analyses Processed data After the required calculations and logic
Processed data ↓
Market dataInformation relating to financial markets
Data for financial productsThe information layer beneath ARESS products and services
Historical dataUsable for examining trends and past analyses
Processed dataAfter the required calculations and logic have been applied
Access security

Security is part of the data processing chain

Access to the data sources and to the stored data is part of the financial data processing infrastructure. Access keys and sensitive information are kept with appropriate security measures.

Keys and passwords

Secure Credentials

Authentication data kept safely using one-way methods

Encrypted storage

Encrypted Storage

Encrypted storage of keys and sensitive information

Controlled access

API Access

Controlled access to the data sources

Method and documentation

Data that can be checked and relied on

A financial figure earns its standing from several independent layers: choosing the source, keeping the raw data, periodic checks, access security and documentation. Wherever processing follows a particular formula, methodology or encoding, we check that source and record the reference in the process documentation and in the code. In the mathematics we aim to keep our methods consistent with financial standards and with the teaching of sources such as the CFA Institute and GARP.

Why data quality does not rest on the source alone
Trusted sources
Choosing official, reliable sources
Keeping the raw data
Recording the data before processing
Periodic validation
Checking it still matches the original sources
Access security
Keeping keys and information safely
Documentation
Recording the process, the methods and the sources
Every stage traceable and documented
Source referenceSource Reference

A reference to the original source of a formula or methodology

Formula Methodology Source Reference
Process flowchartProcess Flow

A flowchart of the collection and processing stages

RAW DATA
→
VALIDATE
→
PROCESS
→
OUTPUT
Documentation in the codeCode Documentation

Notes on the implementation, in the code

1
2
3
4 // Process financial data validate(data) process(data) return result
From data to decision

Data is worth something once it can be used

In the capital market, financial decisions take shape on top of information. ARESS covers that distance by combining collection, storage, control, processing and delivery: an infrastructure for products and services that need structured financial data they can rely on.

Who uses this data

Investment funds

Access to accurate, structured data for better analysis and decisions

Asset management companies

Support for professional analysis and asset management built on data you can rely on

Brokerages

Current, consistent data for trading and analytical services

Financial institutions

Stronger transparency, reporting and compliance with supervisory standards

Professional investors

Access to reliable information for deep analysis and informed decisions