policy
Ensemble_Stock_Trading_with-DRL_optimized
RonVest92 · PyTorch
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Overview
Name
Ensemble_Stock_Trading_with-DRL_optimized
Author
RonVest92
Framework
PyTorch
License
MIT
Skill type
other
Evidence level
untested
Task description
Deep Reinforcement Learning Ensemble for Automated Stock Trading This project implements an ensemble trading strategy using five DRL agents — PPO, DDPG, TD3, SAC, and A2C — to trade the Dow 30 constituents. The system trains each agent on in-sample data, selects the one with the highest Sharpe ratio
Spaces
Action space
other · 0-dim · 0Hz
Observation space
- type: other
Links
HuggingFace repo
null
Paper (arXiv)
null
Compatible robots
20anybotics-anymal-cnot in seedalohanot in seedgoogle-barkour-vbnot in seedboston-dynamics-spotnot in seedfranka-fr3not in seedgoogle-barkour-v0not in seedagilex-pipernot in seedberkeley-humanoidnot in seedbitcraze-crazyflie-2not in seedanybotics-anymal-bnot in seedagility-cassienot in seedarx-l5not in seedbooster-t1not in seedfranka-emika-pandanot in seedfranka-fr3-v2not in seeddynamixel-2rnot in seedflexiv-rizon4not in seedassetsnot in seedapptronik-apollonot in seedfourier-n1not in seed
Compatible environments
0No environments list Ensemble_Stock_Trading_with-DRL_optimized yet.
Datasets that reference this policy
0No datasets reference Ensemble_Stock_Trading_with-DRL_optimized yet.