Python Quant/Backend Developer: Real-Time Finance Risk Engine
I am looking for a high-level Python Backend Developer to build the core intelligence engine for an internal finance dashboard. This is not a standard CRUD app; it is a financial decision engine focused on advanced Greeks (Charm, Vanna), real-time risk analysis, and automated rule-book enforcement.
The goal is to build a "brain" that pulls data from the Massive.com API, performs complex options calculations, and stores them in a PostgreSQL database. The final product will eventually be plugged into a React-based UI.
Key Responsibilities
API Integration: Connect and optimize real-time data streaming from Massive.com.
Quant Logic: Implement Black-Scholes and higher-order Greek calculations (specifically Charm and Vanna).
Risk Engine: Build a "Strategy Aggregator" that can combine multiple option legs into a single P&L curve and risk profile.
Database Design: Architect a robust PostgreSQL (via Supabase) schema to log positions, historical Greeks, and intelligence alerts.
Required Skill Set
Language: Expert-level Python (Asynchronous programming/FastAPI preferred).
Database: Advanced SQL/PostgreSQL (Relational data design is critical here).
Calculations: Proficiency with NumPy, SciPy, or specific options libraries like Mibian or QuantLib.
Experience: Previous experience building trading tools, fintech dashboards, or algorithmic systems.
Apply Now
Apply Now
The goal is to build a "brain" that pulls data from the Massive.com API, performs complex options calculations, and stores them in a PostgreSQL database. The final product will eventually be plugged into a React-based UI.
Key Responsibilities
API Integration: Connect and optimize real-time data streaming from Massive.com.
Quant Logic: Implement Black-Scholes and higher-order Greek calculations (specifically Charm and Vanna).
Risk Engine: Build a "Strategy Aggregator" that can combine multiple option legs into a single P&L curve and risk profile.
Database Design: Architect a robust PostgreSQL (via Supabase) schema to log positions, historical Greeks, and intelligence alerts.
Required Skill Set
Language: Expert-level Python (Asynchronous programming/FastAPI preferred).
Database: Advanced SQL/PostgreSQL (Relational data design is critical here).
Calculations: Proficiency with NumPy, SciPy, or specific options libraries like Mibian or QuantLib.
Experience: Previous experience building trading tools, fintech dashboards, or algorithmic systems.
Apply Now
Apply Now