Python Developer for an architectural project on trading strategies (GUI, pandas, simulation)

We are looking for a confident Python developer (Middle–Middle+) to implement a system for simulation and filtering of trading strategies.

The project already has an architecture, codebase, and detailed documentation. We need an engineer who can:

• implement logic for calculating metrics and filtering signals,

• improve the strategy structure (including linear models, levels, new tools),

• accelerate simulations and support multiple symbols.

This is not ordinary automation or a "trading script," but a engineering system that needs to be refined / structured with scalability and maintenance in mind.

📌 Format:

• Fixed technical assignment (already prepared)

• Total project budget: $3,000 (payment based on completed blocks)

• Remote work. Flexible schedule.

🧱 Stack and technologies:

• Python 3.9+

• pandas, numpy — data analysis and processing

• PyQt5 / PySide — interface

• OOP and architectural thinking — mandatory 

• Optional: experience with tradingview / PineScript, backtesting, event-driven logic

🧭 Task:

The goal is to implement architectural blocks of an analytical platform for working with market data and trading signals.

Within the project, it is necessary to:

• Build logic for analyzing market structure: identifying key levels, forming behavioral patterns based on price data (working with linear models based on Close prices).

• Develop algorithms for identifying and filtering market zones, including calculating probabilistic characteristics, visualizing zones on the chart, and their relation to historical price behavior.

• Optimize strategy simulation, including speeding up data loading, caching, supporting multithreading, and running analysis on multiple instruments simultaneously.

• Expand the basic strategy logic: add support for complex levels (fractal, micro/macro), as well as additional price behavior modes and tools.

• Integrate all blocks into the existing system, based on pandas and PyQt, considering the current code and architecture.

📌 Important:

• All calculations and algorithms are performed according to the predefined technical assignment or based on existing system components.

• The developer is not required to invent new metrics or build unique trading models from scratch — the task is in engineering assembly, structuring, and adaptation.