Commodities Quantitative Risk Engine (CQRE)

Overview

The Commodities Quantitative Risk Engine (CQRE) is a robust backend system designed to analyze real-time and historical data of WTI and Brent crude oil prices. The system employs advanced financial models such as Black-Scholes Option Pricing and Mean Reversion Spread Analysis to generate actionable trading signals and evaluate strategy performance.

This project demonstrates:

• Integration of real-time data processing using Kafka.
• Advanced financial modeling with Python.
• API-driven architecture for analytical insights.
• Scalable backend setup for commodities trading.

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Features

1. Real-Time Data Analysis:

   • Fetches live WTI and Brent prices through a Kafka producer.
   • Processes the prices via a Kafka consumer to calculate spreads and generate trading signals.

2. Financial Models:

   • Black-Scholes Option Pricing: Calculates the fair value of options based on WTI/Brent prices.
   • Mean Reversion Analysis: Detects price deviations and generates buy/sell signals.

3. APIs:

   • Historical Spread Data: Fetch stored spread data from PostgreSQL.
   • Black-Scholes Pricing: API to calculate the price of options.
   • Mean Reversion: API to use the mean reversion strategy.

4. Data Storage:

   • Stores real-time price and spread data in a PostgreSQL database for historical analysis.

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Technology Stack

• Programming Language: Python
• Message Broker: Apache Kafka
• Database: PostgreSQL
• Backend Framework: Flask
• Data Models:
  • Black-Scholes Option Pricing for financial analysis.
  • Mean Reversion Spread Analysis for trading signals.

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APIs

1. Historical Spread Data

• Endpoint: GET /api/historical-spreads
• Description: Fetch historical WTI and Brent price spreads.
• Response:

  [
    {"timestamp": "2024-12-08T10:00:00", "wti_price": 75.32, "brent_price": 78.54, "spread": 3.22},
    {"timestamp": "2024-12-08T10:05:00", "wti_price": 76.10, "brent_price": 79.80, "spread": 3.70}
  ]

2. Black-Scholes Pricing

• Endpoint: POST /api/option-pricing
• Description: Calculate the fair value of an option using Black-Scholes.
• Request:

  {
    "option_type": "call",
    "S": 75.0,
    "K": 80.0,
    "T": 0.5,
    "r": 0.02,
    "sigma": 0.3
  }

• Response:

  {
    "price": 5.34
  }

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How It Works

1. Kafka Producer:
   • Produces random or real-time WTI and Brent prices every 1 second.
2. Kafka Consumer:
   • Consumes prices, calculates spreads, stores data in PostgreSQL, and generates trading signals.
3. API Layer:
   • Exposes historical data, Mean Reversion and Black-Scholes calculations via Flask endpoints.

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Requirements

• Python: Version 3.11 or higher.
• Kafka: Apache Kafka should be installed and running on localhost:9092.
• PostgreSQL: Database should be running on localhost:5432.
• Python Packages:
  • Install dependencies from requirements.txt:

    pip install -r requirements.txt

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Setup and Run Instructions

1. Install Prerequisites

• Install Python 3.11 or higher.

• Install PostgreSQL and create a database:

  CREATE DATABASE cqre;
  CREATE USER cqre WITH PASSWORD 'cqre';
  GRANT ALL PRIVILEGES ON DATABASE cqre TO cqre;

• Start Kafka:

  kafka-server-start.sh config/server.properties

2. Set Environment Variables

Create a .env file or export the following:

DATABASE_URL=postgresql://cqre:cqre@localhost:5432/cqre
KAFKA_BOOTSTRAP_SERVERS=localhost:9092