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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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This repository presents our work during a project realized in the context of the IEOR 8100 RL Class at Columbia University.
| Date | Stars |
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| 2026-07-31 | 257 |
| 2026-08-04 | 257 |
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| 2026-08-06 | 257 |
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# A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem [](https://zenodo.org/badge/latestdoi/140182058) This repository presents our work during a project realized in the context of the [IEOR 8100 Reinforcement Leanrning](https://github.com/ieor8100/rl) at Columbia University. This Deep Policy Network Reinforcement Learning project is our implementation and further research of the original paper [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (Jiang et al. 2017)](https://arxiv.org/abs/1706.10059). <b>Objective:</b> The problem is the one of automated portfolio management: given a set of stocks, how to best allocate money through time to maximize returns at the end of a certain number of timesteps. In this way, we aim to build an automated agent which best allocates the weights of its investment between different stocks. <b>Data:</b> Jiang et al. use 13 crypto-currencies from the Poloniex exchange. They take into account the open, high, low, close (OHLC) prices, minute per minute. They allow a portfolio rebalance every 30 minutes. They reprocess the data and create a tensor based on the last 50 time-steps. We extend the experiment to the stock market, using the framework on daily data and intraday data with a daily rebalance. The project is decomposed in 3 parts: - Data Preprocessing - Environment Set-up - Deep policy network design - Training and Testing of the agent The files are: - data_pipe.ipynb: pre-processing of S&P500 data - data_pipe_poloniex.ipynb: pre-processing of Poloniex data - environment.py: trading environment render - DPM.ipynb: main notebook - individual_stocks_5yr: folder with the raw [S&P500 data](https://www.kaggle.com/camnugent/sandp500) - np_data: folder with the processed input data/used by the DPM notebook - poloniex_data: poloniex crypto-currencies data - Poster.pdf: poster shown during final evaluation session ## Data Preprocessing For each stock, the input is a raw time series of the prices (High, Low, Open, Close). The output is a matrix of 4 rows (3 in the case of the cryptocurrencies - Open(t) = Close(t-1) - the market never closes) and n (number of available data points) columns. <br> The columns correspond to: - Close(t-1)/Open(t-1) - High(t-1)/Open(t-1) - Low(t-1)/Open(t-1) - Open(t)/Open(t-1)  ## Deep Policy Network Design ### RL Framework The portfolio manager agent is set-up in the way: - The <b>state</b> (at time t) is the input matrix and the previous portfolio weights (at time t-1). - The <b>action</b> is the vector of investment weight (at time t). - The <b>reward function</b> is defined such as it is the agent's return minus a baseline’s return (baseline is an equi weighted agent - invest in all the possible stocks in the same way) minus a term proportional to the maximum of the weight (this term is set-up to make the agent avoids to invest fully in one stock)   ### Deep Policy Function Architecture The policy function is designed through a deep neural network which takes as input the input tensor (shape m x 50 x (3 or 4)) composed of : - the m traded stocks - the 3/4 matrix columns (processed OHLC) - 5O previous time steps A first convolution is realized resulting in a smaller tensor. Then, a second convolution is made resulting in 20 vector of shape (m x 1 x 1). The previous output vector is stacked. The last layer is a terminate convolution resulting in a unique m vector. Then, a cash bias is added and a softmax applied. The output of the neural network is the vector of the actions the agent will take. Then, the environment can compute the new vector of weights, the new portfolio and instant reward.  ## Training and Testing of the agent This part is still in progre
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b279f05370c203a2, llm:Description: 'project ... in the context of the IEOR 8100 RL Class' and repo name 'Deep-Portfolio-Management-Reinforcement-Learning' indicating reinforcement learning for portfolio management (finance).
matched fp:b279f05370c203a2, llm:Description: 'project ... in the context of the IEOR 8100 RL Class' and repo name 'Deep-Portfolio-Management-Reinforcement-Learning' indicating reinforcement learning for portfolio management (finance).
matched fp:b279f05370c203a2, llm:Description: 'project ... in the context of the IEOR 8100 RL Class' and repo name 'Deep-Portfolio-Management-Reinforcement-Learning' indicating reinforcement learning for portfolio management (finance).