rl-trading
Practical_RL
| rl-trading | Practical_RL | |
|---|---|---|
| 1 | 2 | |
| 4 | 5,716 | |
| - | 0.5% | |
| 0.0 | 6.0 | |
| over 3 years ago | 19 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | The Unlicense |
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rl-trading
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Recruiters representing Citadel has been aggressively attempting to recruit me as a software developer since mid November, offering to pay $100-150k more than the median for early/mid career developers
In the summer, I did something somewhat similar to what OP suggests above. Here's a link to the corresponding Github repository.
Practical_RL
- [D] implementation of MCTS in Python
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Alternatives to OpenAI’s spinning up?
there is this great github repo where there are lectures and other resources, and have a week by week jupyter notebooks where they explain and code with homeworks at the very end of it. is basics and deepRL, but just dqn and DDPG/ppo but i think will give you good start in the topic for later star working on your own.
What are some alternatives?
Lean - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
webdataset - A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
alphalens - Performance analysis of predictive (alpha) stock factors
FunMatch-Distillation - TF2 implementation of knowledge distillation using the "function matching" hypothesis from https://arxiv.org/abs/2106.05237.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
awesome-rl - Reinforcement learning resources curated
alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
redisai-examples - RedisAI showcase
TensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos