Portfolio Manager AI
Introduction
Simulation Environment for stock market and agent which takes the input as Markov decision process. The input is mathematical indicators deriven from price and volume of stock which passes through a neural network that tries to optimize alpha generated and minimize risk.
Installation Guide
TODO
- Simulation environment
- Agent interaction
- W&B data and model versioning
- Streamlit for analysis for training - 25%
- Dynamic Clustering of stocks
- Parallel environment
- UI for agent training
- Pretrain from date to date
- Pretrain with Expert dataset
- Multi input dictionary from env
- Dictionary observation for prices / indicators
- Time window input
- Train process with time window input
- Take flag days out of environment
- Cirriculum learning
- Learn not sell for loss for generalization
- Learn always to keep 50.000$
- Validation of model for different time horizons for PBT
- Day Trading Environment
- MARL
- Ensemble voting for trading strategy
- Kelly Criterion Auxilary task
- Data Selection Add to Preprocess
- Correlation Table as input
- Past prices for the input
- Avg. bought price of stocks in environment as a fuction
- Dynamic Clustering
- Normalized inputs for the model
- Switching stocks and keeping the same stock holding if is in same list
To start
- pip install -r requirements.txt
- StockTradingBot.ipynb notebook is for training process