tensortrade | An open source reinforcement learning framework for training, evaluating, and deploying robust tradi | Machine Learning library

 by   tensortrade-org Python Version: 1.0.3 License: Apache-2.0

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.
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            kandi-support Support

              tensortrade has a medium active ecosystem.
              It has 4192 star(s) with 997 fork(s). There are 239 watchers for this library.
              OutlinedDot
              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

            kandi-Quality Quality

              tensortrade has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse Reuse

              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

            TensorTrade Experiments
            Jupyter Notebookdot img1Lines of Code : 53dot img1no licencesLicense : No License
            copy iconCopy
            # 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  
            Portfolio Allocation with TensorTrade,Commands
            Pythondot img2Lines of Code : 3dot img2License : Permissive (MIT)
            copy iconCopy
            $ python -m penv.tune --num-samples=4 --num-workers=8
            
            $ python -m penv.train --num-workers=8
            
            $ python -m penv.evaluate --price-type=sine
              
            Portfolio Allocation with TensorTrade,Docker
            Pythondot img3Lines of Code : 2dot img3License : Permissive (MIT)
            copy iconCopy
            $ docker build -t penv .
            $ docker run -it -v $PWD:/app --entrypoint /bin/bash penv
              

            Community Discussions

            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 14:44

            You have infinite value in your data, remove them with this:

            Source https://stackoverflow.com/questions/65703709

            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 12:34

            For test multiple values is possible use in with list:

            Source https://stackoverflow.com/questions/65701941

            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.
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            Install
          • PyPI

            pip install tensortrade

          • CLONE
          • HTTPS

            https://github.com/tensortrade-org/tensortrade.git

          • CLI

            gh repo clone tensortrade-org/tensortrade

          • sshUrl

            git@github.com:tensortrade-org/tensortrade.git

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