Portfolio

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Mean Reversion OU Trading Bot

Objective: Develop an automated trading bot that identifies and exploits mean-reversion opportunities in financial markets using the Ornstein-Uhlenbeck (OU) process.

Methodology: The bot continuously ingests real-time price data, estimates OU process parameters, and generates trading signals when prices deviate significantly from the modeled mean. Position sizing adapts to the strength of the signal, and all trades are simulated and visualized for performance review. The entire pipeline is containerized for robust, 24/7 operation.

Results: The strategy consistently delivered positive results in backtesting, demonstrating robust performance across multiple assets. Achieved a Sharpe ratio of 0.94, indicating strong risk-adjusted returns.

Challenges: Key challenges included maintaining estimator robustness during market regime shifts, ensuring fault tolerance for continuous operation, and balancing execution speed with statistical confidence.

Technologies Used:

• Python
• Pandas
• NumPy
• SciPy
• Matplotlib
• yfinance
• Docker
• Jupyter/Binder/Colab

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Portfolio Analytics Engine

Objective: To build a powerful portfolio optimization and analysis tool using quantitative methods.

Methodology: The engine implements Modern Portfolio Theory (MPT) to find the optimal allocation for a basket of assets. It also uses machine learning models to forecast asset returns and volatility.

Results: The system is able to construct portfolios with a higher Sharpe ratio compared to traditional methods. The ML models provide more accurate forecasts, leading to better risk-adjusted returns.

Challenges: A key challenge was to create a flexible and extensible architecture that could easily incorporate new optimization algorithms and asset classes.

Technologies Used:

• Python
• NumPy
• Pandas
• Scikit-learn

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Backtesting Framework

Objective: To create a robust and efficient backtesting framework for evaluating quantitative trading strategies.

Methodology: The framework is built in Python and uses a vectorized approach for fast backtesting. It supports various types of trading strategies and provides detailed performance metrics and visualizations.

Results: The backtesting engine is significantly faster than traditional loop-based backtesters. It has enabled the rapid iteration and validation of new trading ideas.

Challenges: The main difficulty was to design a system that is both high-performance and flexible enough to accommodate a wide range of trading strategies.

Technologies Used:

• Python
• Pandas
• Plotly
• FastAPI

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Moving Average Trading Bot

Objective: To develop an automated trading bot based on the moving average crossover strategy.

Methodology: The bot continuously monitors the price of a given asset and executes trades when the short-term moving average crosses the long-term moving average. It uses the Backtrader library for backtesting and live trading.

Results: The trading bot has been deployed and is operating profitably. It has demonstrated the effectiveness of the moving average strategy in certain market conditions.

Challenges: The main challenge was to implement a reliable and fault-tolerant system that can run 24/7 without manual intervention.

Technologies Used:

• Python
• Pandas
• Backtrader
• REST API
• Docker

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Risk Management Dashboard

Objective: To build a real-time risk management dashboard for a trading desk.

Methodology: The dashboard provides a comprehensive view of the trading desk's positions and risk exposure. It calculates various risk metrics in real-time and sends alerts when risk limits are breached.

Results: The risk dashboard has improved the trading desk's ability to monitor and control risk. It has helped to prevent significant losses during periods of high market volatility.

Challenges: The main challenge was to develop a scalable and low-latency system that can handle a large number of positions and real-time market data.

Technologies Used:

• Python
• Vue.js
• Redis
• Kubernetes

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ETL Automation Pipeline

Objective: Convert raw, high-frequency market data into fully automated, trustworthy datasets for trading and analytics.

Methodology: Ingest, cleanse, and load streaming data with Python + Pandas into PostgreSQL/TimescaleDB, applying record-level validation that assigns quality scores and triggers real-time alerts.

Results: Reduced manual data-prep time by 90 %, eliminated downstream schema errors, and boosted model stability and back-test accuracy.

Challenges: Balancing millisecond-level latency with fault tolerance, and designing flexible validation rules that evolve with new asset classes.

Rapid Deployment: Containerized with Docker and modular scripts; can be rolled out in minutes, on-prem or in the cloud.

Technologies Used:

• Python
• Pandas
• PostgreSQL/TimescaleDB
• Git
• Docker

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