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tensortrade-extras

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Discover a curated list of projects complementing TensorTrade, distinct from those mentioned in its official documentation. Contributions are welcome; if you spot a missing project, please submit a pull request!

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# Technical Analysis
- [TA-lib](https://github.com/mrjbq7/ta-lib): For more technical analysis compared to ta library.
- [Pandas Technical Analysis (Pandas TA)](https://github.com/twopirllc/pandas-ta): An easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions.
- [Unofficial TradingView technical analysis API wrapper](https://github.com/brian-the-dev/python-tradingview-ta)

# Renderers
- [Tensortrade_dashboard](https://github.com/mitcheccles/tensortrade_dashboard): To visualize trades in simulation runs.
- [WalletPlotlyTradingChart](https://github.com/AlexQuant62/test01/blob/main/WalletPlotlyTradingChart.py): Improved PlotlyTradingChart() in ability to generate chart specific for particular exchange/quote_currency.

# Analysis
- [Quantstats](https://github.com/ranaroussi/quantstats): A Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.
- [SHAP (SHapley Additive exPlanations)](https://github.com/slundberg/shap): A game theoretic approach to explain the output of any machine learning model.
- [Backtest trading strategies with Python](https://github.com/kernc/backtesting.py)
- [Visualizer for pandas data structures](https://github.com/man-group/dtale)
- [TensorBoard](https://github.com/tensorflow/tensorboard): For visualizing a TensorTrade model's output.
- [Weights & Biases](https://github.com/wandb/client): Use W&B to organize and analyze machine learning experiments. It's framework-agnostic and lighter than TensorBoard.

# Live Trading
- [CCXT](https://github.com/ccxt/ccxt): A JavaScript / Python / PHP library for cryptocurrency trading and e-commerce with support for many Bitcoin/Ether/altcoin exchange markets and merchant APIs.
- [Binance API](https://github.com/sammchardy/python-binance): This is an unofficial Python wrapper for the Binance exchange REST API v3.
- [Binance Websockets](https://github.com/oliver-zehentleitner/unicorn-binance-websocket-api): An unofficial Python API to use the Binance Websocket APIs.
- [Coinbase Pro API](https://github.com/danpaquin/coinbasepro-python): The unofficial Python client for the Coinbase Pro API.

# Reward calculation
- [Differential Sharpe Ratio (DSR) calculation](https://github.com/AchillesJJ/DSR)

# Data
- [Binance Data](https://github.com/StephanAkkerman/BinanceData): My simple script for fetching data, using the Binance API. There are more time frames possible compared to data of CryptoDataDownload.
- [CCXT Data](https://github.com/StephanAkkerman/Crypto_OHLCV): My newer script for fetching data, using CCXT instead of Binance API to utilize more exchanges. It works the same as BinanceData, but supports more exchanges.
- [Binance Public Data](https://github.com/binance/binance-public-data): Official Binance repo for getting their public data.
- [Binance Harvester](https://github.com/declasm/binance_harvester): A Python 3 script to harvest data from the Binance socket stream and calculate popular TA indicators and produce lists of top trending coins storing data in an SQLite3 database for use by algorithmic and bot traders.

# Ray
- [Stoppers](https://docs.ray.io/en/master/tune/api_docs/stoppers.html): Custom stopping mechanisms to stop trials early.
- [Custom Metrics Example](https://github.com/ray-project/ray/blob/master/rllib/examples/custom_metrics_and_callbacks.py)
- [Schedulers](https://docs.ray.io/en/master/tune/api_docs/schedulers.html): Trial Schedulers can early terminate bad trials, pause trials, clone trials, and alter hyperparameters of a running trial.
- [Tune’s Search Algorithms](https://docs.ray.io/en/master/tune/api_docs/suggestion.html): Wrappers around open-source optimization libraries for efficient hyperparameter selection.
- [Customizing Exploration Behavior](https://docs.ray.io/en/master/rllib-training.html#customizing-exploration-behavior)
- [Curiosity plugin as exploration behavior](https://docs.ray.io/en/master/rllib-algorithms.html#curiosity)

# Stocks
- [Yahoo Finance API](https://github.com/ranaroussi/yfinance): Yahoo! Finance market data downloader, if you want to train your model on stock data.
- [Alpaca's trade API](https://github.com/alpacahq/alpaca-trade-api-python)
- [Alpha Vantage API](https://github.com/RomelTorres/alpha_vantage)

# Examples
- [My Example](https://github.com/StephanAkkerman/TensorTrade): Includes benchmarks to compare net worth performance, and fetching data from Binance.
- [Zhivko's Examples](https://github.com/zhivko/tensortrade/tree/master/examples/myexample): Includes implementation of training and evaluation environments in after.py.
- [Kodiak's Notebook Example](https://colab.research.google.com/drive/1N0gZhsiXT7vwHN__FoX8an-AqxJDSfak): Includes implementation of feature correlation and Optuna search algorithm.
- [Msrparadesi's Notebook Example](https://github.com/msrparadesi/tensortrade/blob/master/examples/TensorTrade_on_SageMaker_Studio.ipynb)
- [Matthew Brulhardt's Example](https://github.com/mwbrulhardt/simple-sine-curve): How to trade on a basic sine curve using TensorTrade and Ray.
- [Matthew Brulhardt's Example](https://github.com/mwbrulhardt/penv): About making highly customized environments in TensorTrade.

# Research
- [Part one of 8ball030's research of technical analysis indicators](https://github.com/8ball030/FTXIndicators)
- [Part two of 8ball030's research of technical analysis indicators](https://github.com/8ball030/indicator_part_2)
- [Awesome Deep Trading](https://github.com/cbailes/awesome-deep-trading): List of code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading.
- [Portfolio Management List](https://github.com/Draichi/Portfolio-Management-list): A list of portfolio management resources, using Reinforcement Learning.

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