This project involves the development of a high-frequency trading (HFT) strategy using Strategy Studio, a multi-asset class strategy development software. The goal is to capture market inefficiencies through rapid execution and sophisticated algorithms. The project covers the entire journey from initial concept to deployment, including rigorous backtesting, parameter optimization, and results analysis.
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Architected Backtesting Pipelines incorporating Bash scripts, Python automation , C++ Strategy Scripts and Makefile commands for Strategy Studio. Simplified Strategy Management and Parameter Optimization to be run entirely with a single command and parameter files. Also Documented the scripts and instructions for future reference and other students.
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This project utilises various Broker APIs to develop a custom trading engine for day trading Forex, Crypto and Equity products. The engine is capable of handling multiple strategies and deploying them in real-time. It also features a robust backtesting module and cloud deployment capabilities for live trading
This project encompasses various machine learning tasks, including Treasury Squeeze Prediction, Corporate Bond Rating Analysis, Home Price Prediction, Bond Price Prediction, Credit Card Default Prediction, Corporate Bond Credit Rating Classification, and Economic Cycle Prediction.
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Designed, implemented, and backtested Deep Deterministic Policy Gradient (DDPG) Market Making Agent and Deep Q Network (DQN) Market Taking Agent for algorithmic trading. Utilized Strategy Studio for backtesting and validation.
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Repository containing the code for training and fine-tuning large language models like GPT-2, GPT- Bigram, Text Tokenizer models and NLP models for text generation, and sentiment analysis.
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Forex Trading Strategy Development and Optimisation using Quantitative Techniques for Grid & Lot Sizing Optimisation for bp trading
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