saeed349
Welcome to Saeed's laboratory. Life purpose: Making sense out of data using glorified function approximators.
New York, USA
Pinned Repositories
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.
AlgoTrading
Use the zipline and pyfolio to analyze trades.
alphalens
Performance analysis of predictive (alpha) stock factors
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.
financial-machine-learning
A curated list of practical financial machine learning tools and applications.
LLM-Powered-Metadata-Graph
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.
Microservices-Based-Algorithmic-Trading-System-V-2.0
MBATS is a docker based platform for developing, testing and deploying Algorthmic Trading strategies with a focus on Machine Learning based algorithms. This repository is an advanced version of the MBATS infrastructure without any of the business logic. Compared to MBATs, here are the changes that are made in this version.
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.
saeed349's Repositories
saeed349/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.
saeed349/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.
saeed349/Microservices-Based-Algorithmic-Trading-System-V-2.0
MBATS is a docker based platform for developing, testing and deploying Algorthmic Trading strategies with a focus on Machine Learning based algorithms. This repository is an advanced version of the MBATS infrastructure without any of the business logic. Compared to MBATs, here are the changes that are made in this version.
saeed349/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.
saeed349/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.
saeed349/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.
saeed349/AlgoTrading
Use the zipline and pyfolio to analyze trades.
saeed349/financial-machine-learning
A curated list of practical financial machine learning tools and applications.
saeed349/LLM-Powered-Metadata-Graph
saeed349/alphalens
Performance analysis of predictive (alpha) stock factors
saeed349/Dash-Analytics-App
Dash Analytics App
saeed349/Dash-Multi-Page-App-Template
saeed349/Monte-Carlo-simulation-using-Heston-model-in-GPU
saeed349/pandas-ml-quant
Master repository for the pandas-ml modules
saeed349/R-Projects
saeed349/Think-or-swim-trade-analysis
Success leaves traits, let me see if I can find that traits of the competition winners
saeed349/Two-Sigma-Competition
saeed349/applied-ml
📚 Papers and blogs by organizations sharing their work on data science & machine learning in production.
saeed349/arbitrage_research
saeed349/arbitragelab
ArbitrageLab is a python library that enables traders who want to exploit mean-reverting portfolios by providing a complete set of algorithms from the best academic journals.
saeed349/elk-stack-docker
saeed349/M4-methods
Data, Benchmarks, and methods submitted to the M4 forecasting competition
saeed349/Machine-Learning-Nanodegree-Program
Projects carried out as part of the Machine Learning Nanodegree program offered by Udacity.com
saeed349/pipeline-live
Pipeline Extension for Live Trading
saeed349/pyfolio
Portfolio and risk analytics in Python
saeed349/research_public
Quantitative research and educational materials
saeed349/simple-python-docker-debugger
saeed349/stock-pattern-recorginition
saeed349/talk-like-a-graph
saeed349/Trading-Gym
Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.