tensortrade | An open source reinforcement learning framework for training, evaluating, and deploying robust tradi | Machine Learning library
kandi X-RAY | tensortrade Summary
kandi X-RAY | tensortrade Summary
tensortrade is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. tensortrade has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install tensortrade' or download it from GitHub, PyPI.
# TensorTrade: Trade Efficiently with Reinforcement Learning. TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning. The framework focuses on being highly composable and extensible, to allow the system to scale from simple trading strategies on a single CPU, to complex investment strategies run on a distribution of HPC machines. Under the hood, the framework uses many of the APIs from existing machine learning libraries to maintain high quality data pipelines and learning models. One of the main goals of TensorTrade is to enable fast experimentation with algorithmic trading strategies, by leveraging the existing tools and pipelines provided by numpy, pandas, gym, keras, and tensorflow. Every piece of the framework is split up into re-usable components, allowing you to take advantage of the general use components built by the community, while keeping your proprietary features private. The aim is to simplify the process of testing and deploying robust trading agents using deep reinforcement learning, to allow you and I to focus on creating profitable strategies.
# TensorTrade: Trade Efficiently with Reinforcement Learning. TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning. The framework focuses on being highly composable and extensible, to allow the system to scale from simple trading strategies on a single CPU, to complex investment strategies run on a distribution of HPC machines. Under the hood, the framework uses many of the APIs from existing machine learning libraries to maintain high quality data pipelines and learning models. One of the main goals of TensorTrade is to enable fast experimentation with algorithmic trading strategies, by leveraging the existing tools and pipelines provided by numpy, pandas, gym, keras, and tensorflow. Every piece of the framework is split up into re-usable components, allowing you to take advantage of the general use components built by the community, while keeping your proprietary features private. The aim is to simplify the process of testing and deploying robust trading agents using deep reinforcement learning, to allow you and I to focus on creating profitable strategies.
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Security
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tensortrade has a medium active ecosystem.
It has 4192 star(s) with 997 fork(s). There are 239 watchers for this library.
It had no major release in the last 12 months.
There are 46 open issues and 198 have been closed. On average issues are closed in 181 days. There are 6 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of tensortrade is 1.0.3
Quality
tensortrade has 0 bugs and 0 code smells.
Security
tensortrade has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
tensortrade code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
tensortrade is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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tensortrade releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
tensortrade saves you 2857 person hours of effort in developing the same functionality from scratch.
It has 8901 lines of code, 927 functions and 190 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed tensortrade and discovered the below as its top functions. This is intended to give you an instant insight into tensortrade implemented functionality, and help decide if they suit your requirements.
- Transfer quantity from source to target wallet
- Commit a transaction
- Adds a quantity to the exchange
- Remove a quantity from the exchange
- Render an environment
- Returns a new stream with the given dtype
- Slice a sequence of strings
- Create a TradingEnv
- Return the renderer with the given identifier
- Register a new trade
- Calculates the COX - correlation coefficients for a given model
- Observe observations
- Adjust a trade
- Decorator registering methods
- Perform the given action
- Save the figure
- Clamp a stream to a given range
- Decorator for registering methods
- Returns the reward of the given portfolio
- Creates a Limit Order
- Create a limit order
- Render a env
- Execute an order
- Get orders for given action
- Create a market order
- Fetch data from exchange
Get all kandi verified functions for this library.
tensortrade Key Features
No Key Features are available at this moment for tensortrade.
tensortrade Examples and Code Snippets
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# Position-based returns reward scheme
reward_scheme = PBR(price=p)
# Buy, sell, or hold action scheme
action_scheme = BSH(
cash=cash,
asset=asset
).attach(reward_scheme)
# A simple reward scheme that rewards the agent for incremental incre
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$ python -m penv.tune --num-samples=4 --num-workers=8
$ python -m penv.train --num-workers=8
$ python -m penv.evaluate --price-type=sine
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$ docker build -t penv .
$ docker run -it -v $PWD:/app --entrypoint /bin/bash penv
Community Discussions
Trending Discussions on tensortrade
QUESTION
Input contains infinity or a value too large for dtype('float64') error
Asked 2021-Jan-13 at 14:44
Input contains infinity or a value too large for dtype('float64') error shows up when I run this code. How can I solve it?
...ANSWER
Answered 2021-Jan-13 at 14:44You have infinite value in your data, remove them with this:
QUESTION
Excluding two columns in IF functions
Asked 2021-Jan-13 at 12:40
I'm doing a for where I want to exclude both 'date' and 'unix' columns from the data frame.
How can I do it?
...ANSWER
Answered 2021-Jan-13 at 12:34For test multiple values is possible use in
with list
:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install tensortrade
You can get started testing on Google Colab or your local machine, by viewing our many examples.
TensorTrade requires Python >= 3.7 for all functionality to work as expected. You can install TensorTrade both as a pre-packaged solution by running the default setup command. You can then alternatively install TensorTrade directly from the master code repository, pulling directly from the latest commits. This will give you the latest features\fixes, but it is highly untested code, so proceed at your own risk. Alternatively you can clone\download the repository in your local environment an manually install the requirements, either the "base" ones, or the ones that also include requirements to run the examples in the documentation.
To build the HTML documentation, execute the following command.
TensorTrade requires Python >= 3.7 for all functionality to work as expected. You can install TensorTrade both as a pre-packaged solution by running the default setup command. You can then alternatively install TensorTrade directly from the master code repository, pulling directly from the latest commits. This will give you the latest features\fixes, but it is highly untested code, so proceed at your own risk. Alternatively you can clone\download the repository in your local environment an manually install the requirements, either the "base" ones, or the ones that also include requirements to run the examples in the documentation.
To build the HTML documentation, execute the following command.
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To build the HTML documentation, execute the following command.
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