github.com/firmai/financial-machine-learning @main sqlite
README
🌟 We Are Growing!
We're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.
🚀 About Sov.ai
Sov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with 3 of the top 10 quantitative hedge funds, and with many mid-sized and boutique firms.
Our platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.
By joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.
🔍 Research and Project Opportunities
We offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:
- Predictive Modeling with GitHub Logs: Develop models to predict market trends and investment opportunities using GitHub activity and developer data.
- Satallite Data Analysis: Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.
- Data Imputation Techniques: Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.
Please visit docs.sov.ai for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.
🌐 Why Join Sov.ai?
- Innovative Environment: Engage with the latest technologies and methodologies in machine learning and finance.
- Collaborative Team: Work alongside a team of experts passionate about driving innovation in investment insights.
- Flexible Projects: Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.
- Experienced Researchers: Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.
- Post Research: Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.
🤝 How to Apply
If you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at research@sov.ai with your resume and a brief description of your research interests.
Join us in shaping the future of investment insights and making a meaningful impact in the world of finance!
So what is ML-Quant.com then?
It is our firehose of daily research, serving as an internal knowledge base and client resource while also acting as a marketing channel to showcase our expertise and attract potential clients in the machine learning and quantitative finance space.
Financial Machine Learning and Data Science
- All repos/links status including last commit date is updated daily
- Only 15 Highest ranked repos/links for each section are displayed on main README.md and full list is available within the wiki page
- Both Wikis/README.md is updated in realtime as soon as new information are pushed to the repo
Trading
Deep Learning & Reinforcement Learning (Wiki)
| repo | comment | created_at | last_commit | star_count | repo_status | rating |
|---|---|---|---|---|---|---|
| FinRL-Library | started by Columbia university engineering students and designed as an end to end deep reinforcement learning library for automated trading platform. Implementation of DQN DDQN DDPG etc using PyTorch and gym use pyfolio for showing backtesting stats. Big contributions on Proximal Policy Optimization (PPO) advantage actor critic (A2C) and Deep Deterministic Policy Gradient (DDPG) agents for trading | 2020-07-26 13:18:16 | 2024-09-28 02:56:03 | 9697.0 | :heavy_check_mark: | :star:x5 |
| Stock-Prediction-Models | very good curated list of notebooks showing deep learning + reinforcement learning models. Also contain topics on outlier detections/overbought oversold study/monte carlo simulartions/sentiment analysis from text (text storage/parsing is not detailed but it mentioned using BERT) | 2017-12-18 10:49:59 | 2021-01-05 10:31:50 | 7924.0 | :heavy_multiplication_x: | :star:x5 |
| AI Trading | AI to predict stock market movements. | 2019-01-09 08:02:47 | 2019-02-11 16:32:47 | 4094.0 | :heavy_multiplication_x: | :star:x5 |
| Deep Learning IV | Bulbea: Deep Learning based Python Library. | 2017-03-09 06:11:06 | 2017-03-19 07:42:49 | 2032.0 | :heavy_multiplication_x: | :star:x5 |
| RLTrader | predecessor to tensortrade uses open api gym and neat way to render matplotlib plots in real time. Also explains LSTM/data stationarity/Bayesian optimization using Optuna etc. | 2019-04-27 18:35:15 | 2019-10-17 16:25:49 | 1731.0 | :heavy_multiplication_x: | :star:x5 |
| Deep Learning III | Algorithmic trading with deep learning experiments. | 2016-06-18 18:23:06 | 2018-08-07 15:24:45 | 1429.0 | :heavy_multiplication_x: | :star:x5 |
| Personae | implementation of deep reinforcement learning and supervised learnings covering areas: deep deterministic policy gradient (DDPG) and DDQN etc. Data are being pulled from rqalpha which is a python backtest engine and have a nice docker image to run training/testing | 2018-03-10 11:22:00 | 2018-09-02 17:21:38 | 1340.0 | :heavy_multiplication_x: | :star:x5 |
| RL Trading | A collection of 25+ Reinforcement Learning Trading Strategies -Google Colab. | nan | nan | nan | :heavy_check_mark: | :star:x4 |
| Neural Network | Neural networks to predict stock prices. |
Core symbols most depended-on inside this repo
search_repo_multiple_termsShape
Languages
Modules by API surface
For agents
$ claude mcp add financial-machine-learning \
-- python -m otcore.mcp_server <graph>