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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 [email protected] 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!
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
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
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)
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.
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
curated list of papers/repos on topics like CNN/LSTM/GAN/Reinforcement Learning etc. Categorized as deep learning for now but there are other topics here. Manually maintained by cbailes
Implementation of deep reinforcement learning using Deep Q Network (DQN). Only supports single security at the moment. Idea is roughly based here and uses tensorflow/keras. Interesting helper python libraries used here are tqdm for console based progress bar and altair for declarative visualization in python
Retrieve limit order book level data from coinbase pro and bitfinex -> record in arctic timeseries database then implemented trend following strategies (market orders) and market making (limit orders). Uses reinforcement learning (DQN) keras-rl to create agents and uses openai gym to implement POMDP (partially observable markov decision process)
docker based platfrom for developing algo trading strategies. Very interesting combinations of open source components were used including backtrader for backtest strategies / mlflow for managing the machine learning model life cycle (i.e. training and developing machine learning models) / airflow used as workflow management including schedule data download etc. / superset web data visualization tool similar to tableau / minio for fast object storage (i.e. storing saved models and model artifacts) / postgresql used to store security master and daily and minute data. Also contains some details on deployment on cloud
repo for book hands-on-machine learning for algorithmic trading covering topic from data/unsupervised learning/NPL/RNN & CNN/reinforcement learning etc. Leverage zipline/alphalens/sklearn/openai-gym etc as well. Good references to have
accompanying materials for book Machine Learning and Data Science Blueprints for Finance on top of basic machine learning models i.e. nlp/reinforcement learning/supervised & unsupervised learning it covers wider topics including robo-advisors/fraud detection/loan default/derivative pricing/yield curve construction.
machine learning framework built on sklearn and pandas. Support pyfolio/xgboost/lightgmb/catboost(gradient boosting on decision tress) etc. Examples include financial market prediction/sports prediction/kaggle. Configurations are set though yaml file for all model process including feature selection/grid search on parameters and aggregate results for each model
list of technical indicators implemented in c#. Full list and explanation available here. This list contains several indicators that ta-lib does not cover
Research in investment finance for long term forecasts and a curated list of notebooks. Each topic contains a youtube video explaining in details. Interesting topics including using price per book ratio and other multiples for future return prediction and portfolio optimization. data sourced form simfin yahoo finance and s&p 500 earnings and estimate report etc.
lstm model using keras to predict msft prices. Data is from alphavantage which provides some free data through web services. Showing how to use concatenation layer to join timeseries data with TA data. Might be abit of overfitting on the model though
repo for book machine learning for finance with heavier focus on machine learning and less on finance. Topics covered including computer vision/time series/nlp/generative models (i.e. autoencoder)/reinforcement learning/debugging ml systems
base framework trading bot for crypto. Stores data in local mongodb instance and supports backtest and live trading on poloniex and bittrex which are 12-15th ranked crypto exchanges by volume. Leverage talib for ta data and plotly for visualization
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Data Processing Techniques and Transformations (Wiki)
data processing platform which stream data from kafka. The example shows two incoming data stream stock vs tweets and two spark streams are created to consume the kafka data then end results are stored in cassandra. Older tech stacks were used and not actively maintained.
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted.
