Comments (2)
Hi @lamhk , I haven't specifically made the testing module for agent. On the dev branch of the repository, I use samre12/gym-cryptotrading
to train the agent which is Gym
based environment to train the agent.
gym-cryptotrading
can be used for testing the agent learned using the current repository.
from deep-trading-agent.
Just make sure that the same environment type and parameters are used to train and test the agent. 😄
from deep-trading-agent.
Related Issues (20)
- Price gap between historical dataset and live API requests HOT 4
- not an issue - fyi dockerfile + ssh daemon HOT 1
- Why On Earth would you use python 2.7 ? that's backward HOT 3
- enhancement - risk reward / position sizing HOT 2
- Nan in summary histogram for: q_network/avg_q_summary/q/2 HOT 10
- AttributeError: 'Agent' object has no attribute 'q_gru_keep_prob' HOT 1
- Running the docker container throws `killed` in preprocess.py HOT 2
- ERROR: INVALID_TIMESTEP HOT 1
- Add trading activity as an input HOT 1
- Performance issue HOT 6
- Example Config HOT 2
- Some Issues - Pycharm W10
- Unable to understand the Reward Function
- TypeError: unhashable type: 'numpy.ndarray' HOT 3
- ModuleNotFoundError: No module named 'ConfigParser' HOT 2
- training issue HOT 5
- any plan to add SMA, RSI, ATR indices? HOT 28
- InvalidArgumentError (see above for traceback): Input to reshape is a tensor wit h 80640 values, but the requested shape has 115200 HOT 1
- ERROR: No section: 'global' HOT 5
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from deep-trading-agent.