Python Quant Developer - Backtesting & Research Engine

I’m building a research-grade analytical trading system and am looking for an experienced Python/quant developer to continue development of an existing backtesting & research platform.

The project already exists and includes:

• Data loading layer (database + APIs)

• Streamlit-based web interface

• Core backtesting logic (partially implemented)

• YAML-based strategy configuration (recently added)

The goal is to evolve this into a robust research platform focused on:

• Correct methodology

• Realistic execution modeling

• Clean, modular architecture

This is not a one-off task. I’m looking for a long-term collaborator who can gradually take ownership of the technical side.

Scope of Work (current and planned):

Backtesting Core

• Declarative strategy definitions (config-driven, YAML/JSON)

• Entry/exit rules using AND / OR logic over indicators

• Single instruments, batch backtesting, and later spreads / pairs

Execution Model

• Configurable execution delay (N bars after signal)

• Market, stop, limit, stop-limit orders

• Commissions, slippage, explicit execution assumptions

Indicators

• External indicator registry

• Dynamic indicator computation

• Adding new indicators without touching core engine

Optimization & Validation

• Parameter optimization (Optuna or similar)

• In-sample/out-of-sample testing

• Walk-forward analysis

Risk & Analysis

• Drawdown, stability metrics

• Monte Carlo on trade sequences

Web Interface

• Strategy builder UI (not only YAML files)

• Ability to configure strategies via web forms

• Visual analytics of results

Important (context):

The project already has a working codebase and Docker setup. The developer is expected to:

• Review existing architecture

• Fix and improve current implementation

• Extend features gradually

• Help shape the final system design

Requirements

• Strong Python (pandas, numpy)

• Experience with backtesting / trading systems

• Understanding of look-ahead bias, OOS validation, overfitting

• Ability to work with an existing codebase

Nice to have

• Experience with Streamlit

• Quant/research background

• Prior work on trading platforms

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