Back-End Developer (Machine Learning & Analytics) in Reva O.V., FOP

vacancy : Full-Stack developer (Machine Learning & Analytics)

about The project:

We are developing an automated quantum trading system that uses signals generated on the TradeingView platform to automatically execute orders on cryptocurrency (or other financial) exchanges. 

Our goal is to create a reliable, safe and scalable system that automates trading strategies, minimizes the human factor and increases trade efficiency. We move from monolithic architecture to microservice, and we need an experienced developer who will help us in this process, especially in terms of machine learning and analysts.

Basic responsibilities:

• Development and support of microservice Architectures:
  • Participation in the design and development of microservice for the system.
  • ensuring the scalability and reliability of microservice.
  • Interaction between microservice (API, queue, queue Messages).
  • Integration of machine learning (ML):
    • Development and integration of ML models and generating trading signals.
    • Deep integration of ML dialects with databases (for example, for example, (for example,, PostgreSQL) and API Exchange.
    • Using libraries of the PSYCOPG2 or SQLALCHEMY type for working with databases.
    • Development and support of ML services that work with real queries for the database or API. PSYCOPG2 or SQLALCHEMY.
  • Analytics and data processing:
    • Analysis of large volumes of financial data to identify laws and trends.
    • Development of tools for visualization and analysis Data.
    • Implementation of the algorithms of statistical analysis and forecasting.
  • Integration with redis (caching):
    • Implementation of signal caching to prevent duplication and optimization productivity.
    • TTL (Time to Live) for cash data.
    • Caching performance monitoring (HIT RATE, EVICTION RATE).
  • • limiting the maximum size of a position (as a percentage of available capital).
    • Automatic calculation and setting order of stop-loss and teik-profite (several options for the percentage of entrance and atr). Risk limits, not enough funds).
  • Implementation of the basic backing (based on historical data):
    • obtaining historical data (from the API exchange or local storage). Data.
    • Calculation of the main metrics (total profit/loss, number of transactions, Win Rate, DRAWDown).
    • Monitoring setting (Prometheus/Grafana):
      • Basic collection of bases metric (number of webhuk requests, errors, processing delays, error API Exchange).
      • Setting Grafana for visualizing metrics and creating basic dashboards.
      • Setting up the basic munition (alertmanager).
      • Setting Prometheus for metric.
      • writing Tests:
        • unit tests for the main components (risk management, data processing, ml models).
        • Functional tests for webhuk server and microservice. (Locust).

      Technical requirements:

      • Experience with Python (Flask/Fastapi).
      • Deep knowledge of the principles of REST API and Webhukov.
      • Experience with databases (redis, postgresql).
      • Experience with microservice architecture.
      • Experience with ml libraries (Pandas, Scikit-Learn, Ect.)
      • deep knowledge Statistics.
      • Experience with monitoring systems (Prometheus/Grafana). principles CI/CD.

      Additional wishes:

      • Experience with cloud platforms (AWS, Google Cloud, Azure).
      • Experience in writing unit tests and functional tests.
      • Experience with kubernetes.
      • Experience as Full-Stack+

      Personal qualities:

      • Analytical mindset.
      • Responsibility and attentiveness to details.
      • The desire to develop and study new technologies.