Swissquote Client Integration

This project implements a multi-threaded data integration pipeline for interacting with the Swissquote API, retrieving financial data for managed clients and injecting the data into a Microsoft SQL Server database. It is designed for financial analysts, advisors, and platform engineers looking to automate portfolio and transaction monitoring at scale.

Overview

Purpose

This application automates the following tasks:

• Authenticates with the Swissquote REST API using a bearer token.
• Retrieves data across several dimensions (clients, positions, transactions, static lists).
• Parallelizes API calls across data types using Python threading.
• Transforms the nested API responses into structured tabular formats.
• Inserts the transformed data into a SQL Server database.

The solution is intended to enable seamless integration of Swissquote data into a financial platform or analytics pipeline.

Source of Data

Data is collected from the Swissquote Asset Management API, specifically:

• Managed clients and associated metadata.
• Current holdings (positions) by client.
• Historical transactions paginated per client.
• Static lists (securities metadata and taxonomy).

Base URL:

https://bankingapi.swissquote.ch/am-interface-v2/api/v1/

A valid API token is required for access.

Application Flow

The orchestration begins in main.py, executing the following sequence:

1. Initialization:

   • The App class initializes an internal SwissQuote client instance.
   • Configurations like retry/backoff are applied to ensure robust API interaction.

2. Data Fetching:

   • A list of client accounts is fetched first.
   • Threads are launched to pull positions, transactions, and static lists concurrently.
   • Transactions are paginated client-by-client for completeness.

3. Transformation:

   • The raw nested responses are passed to a transformer.Agent module.
   • Data is flattened and filtered to retain relevant fields only.

4. Storage:

   • Final cleaned data is inserted into respective SQL Server tables.
   • The insert_data() utility handles upsert/append logic.

Project Structure

swissquote-client-main/
├── swissquote/               # API interaction logic and app orchestration
│   ├── client.py             # Raw endpoint wrappers
│   └── app.py                # Threaded data fetch logic
├── config/                   # Environment setup and logging
├── database/                 # MSSQL connectivity and helpers
├── transformer/              # Data cleaning and shaping
├── main.py                   # Pipeline entry point
├── .env.sample               # Example environment configuration
├── Dockerfile                # Containerization for deployment

Environment Variables

Create a .env file based on .env.sample. Required variables include:

Variable	Description
LOG_LEVEL	Logging verbosity
TOKEN	Swissquote API bearer token
STATICLISTS_OUTPUT_TABLE, TRANSACTIONS_OUTPUT_TABLE, ...	Output MSSQL table names
MSSQL_*	SQL Server authentication parameters
INSERTER_MAX_RETRIES, REQUEST_MAX_RETRIES, REQUEST_BACKOFF_FACTOR	Retry behavior and exponential backoff settings

Docker Support

This project supports containerized deployment using Docker.

Build

docker build -t swissquote-client .

Run

docker run --env-file .env swissquote-client

Requirements

Install Python dependencies using pip:

pip install -r requirements.txt

Key libraries include:

• requests: API communication
• pandas: Data transformation
• SQLAlchemy: Database interaction
• fast-to-sql: High-speed SQL insertion

Running the App

Ensure .env is correctly set with all required variables, then:

python main.py

Logs will reflect API calls, data transformations, and database writes.

License

This project is licensed under the MIT License. Access and use of Swissquote APIs must comply with their official data licensing and privacy terms.