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Microservices-Based-Algorithmic-Trading-System
MBATS is a docker based platform for developing, testing and deploying Algorthmic Trading strategies with a focus on Machine Learning based algorithms.Deep-Reinforcement-Learning-in-Trading
This repository provides the code for a Reinforcement Learning trading agent with its trading environment that works with both simulated and historical market data. This was inspired by OpenAI Gym framework.Advances-in-Financial-Machine-Learning
Using Dask, a Python framework, I handle 900 million rows of S&P E-mini futures trade tick data directly on a local machine. Through exploratory data analysis, continuous series creation, and bar sampling, inspired by Marcos Lopez de Prado's work, I demonstrate efficient alternatives to costly data processing methods.Quant-Trading-Cloud-Infrastructure
This repository is an advanced version of the MBATS infrastructure that you can use to provision Google Cloud and CloudFlare services so that you could take the different components of MBATS into the cloud.quant_infra
Explore building an advanced infrastructure for enhancing QuantConnect with Snowflake, Databricks, Airflow & AWS. Learn the basics of quant trading workflows, from selecting US cash equities datasets to efficient trade execution. Dive into computing indicators and ML-based signals across thousands of symbols using a distributed framework.AlgoTrading
Use the zipline and pyfolio to analyze trades.Dash-Multi-Page-App-Template
Dash-Analytics-App
Dash Analytics AppR-Projects
Think-or-swim-trade-analysis
Success leaves traits, let me see if I can find that traits of the competition winnersTwo-Sigma-Competition
Monte-Carlo-simulation-using-Heston-model-in-GPU
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