Comments (6)
Just wondering if you can give a few sentence crash course on what this actually does?
This is a simple simulator for training reinforcement learning agents to trade.
Just wondering, what does this actually do? Is the model.zip something that can be used with a running strategy?
Yes!
I updated the repo.
It now comes with a LoadRLModel strategy.
You can try it with your trained model.
What I had hoped that this would do, looking at the code, is look at the different TA methods in IndicatorforRL.py under populate_indicators, and output what the best combo it found was.
Well, it might be. because the agent will change the model weights to achieve this goal.
Or you can try other algorithms agent on this project.
For example, I have tried the NEAT(NeuroEvolution of Augmenting Topologies) algorithm, and it works great.
Feel free to ask if you got any questions.
And have fun!
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@Payback80 I used NEAT-Python to create the trading agent.
If you are interested. I can update the code to the repo.
Sure, I can find some time to work on a RLLIB example.
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@Payback80 I used NEAT-Python to create the trading agent.
If you are interested. I can update the code to the repo.
Sure, I can find some time to work on a RLLIB example.
Well i'm not the biggest fan of NEAT family but why not? If u want to exchange ideas feel free to DM me, u did a great job btw
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Or you can try other algorithms agent on this project.
For example, I have tried the NEAT(NeuroEvolution of Augmenting Topologies) algorithm, and it works great.Feel free to ask if you got any questions.
And have fun!
How could have used neat, since it's not in openbaselines? interesting project, do you mind create an interface for RLLIB?
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Thanks for the update with example.
I was able to get it to run, and get to backtesting. I've backtested with exact dates of timerange in config_rl.json (updated to be 2021 data). My tests show negative performance, even using the exact dates for what was used to train the model.
Again--complete newb. My question is--should this have shown a positive performance, or is there more editing that would need to be done to create a profitable model? (I am just trying to test and see that what is happening "works". With negative performance on exact dates for backtesting downloaded data, it makes me think I might be doing something wrong?)
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My question is--should this have shown a positive performance, or is there more editing that would need to be done to create a profitable model? (I am just trying to test and see that what is happening "works". With negative performance on exact dates for backtesting downloaded data, it makes me think I might be doing something wrong?)
This is absolutely normal.
A profitable model isn't easy to build. You need to do more research on the models, the features, and data...etc. And do more experiments.
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Related Issues (20)
- some questions HOT 9
- Tensorflow Version HOT 3
- Can you advise on how to load the checkpoints from RLLib HOT 1
- The best backtests results HOT 3
- Adding multiple tickers to white pairlist HOT 1
- Adding Trade Information to the observation space HOT 2
- How to use RLlib model for trading? HOT 1
- 'IndicatorforRL' object has no attribute 'ohlcvdata_to_dataframe' HOT 2
- The file model could not be found HOT 4
- neat_config not found on the repo HOT 4
- Profitable training
- which model should I pass as a parameter to ACER
- AttributeError: 'IndicatorforRL' object has no attribute 'ohlcvdata_to_dataframe' HOT 5
- Example for RLlib HOT 2
- ValueError: numpy.ndarray size changed, may indicate binary incompatibility. HOT 2
- Problem while executing python deep_rl.py command HOT 5
- error while running python neat_trade.py
- ImportError: DLL load failed while importing MPI: HOT 2
- don't work whit tensorflow 2 HOT 1
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