A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Overview

AutoTrader Logo

AutoTrader

Latest version Total downloads Monthly downloads Build Status Documentation Status

AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. From simple indicator-based strategies, to complex non-directional hedging strategies, AutoTrader can do it all. If you prefer a more hands-on approach to trading, AutoTrader can also assist you by notifying you of price behaviour, ensuring you never miss a signal again. A basic level of experience with Python is recommended for using AutoTrader, but the docs aim to make using it as easy as possible with detailed tutorials and documentation.

Features

Installation

AutoTrader can be installed using pip:

pip install autotrader

Updating

AutoTrader can be updated by appending the --upgrade flag to the install command:

pip install autotrader --upgrade

Documentation

AutoTrader is very well documented in-code and on Read the Docs. There is also a detailed walthrough, covering everything from strategy concept to livetrading.

Example Strategies

Example strategies can be found in the demo repository. You can also request your own strategy to be built here.

Backtest Demo

The chart below is produced by a backtest of the MACD trend strategy documented in the tutorials (and available in the demo repository). Entry signals are defined by MACD crossovers, with exit targets defined by a 1.5 risk-to-reward ratio. Stop-losses are automatically placed using the custom swing detection indicator, and position sizes are dynamically calculated based on risk percentages defined in the strategy configuration.

Running this strategy with AutoTrader in backtest mode will produce the following interactive chart.

MACD-backtest-demo

Note that stop loss and take profit levels are shown for each trade taken. This allows you to see how effective your exit strategy is - are you being stopped out too early by placing your stop losses too tight? Are you missing out on otherwise profitable trades becuase your take profits are too far away? AutoTrader helps you visualise your strategy and answer these questions.

Legal

License

AutoTrader is licensed under the GNU General Public License v3.0.

Disclaimer

This platform is currently under heavy development and should not be considered stable for livetrading until version 1.0.0 is released.

Never risk money you cannot afford to lose. Always test your strategies on a paper trading account before taking it live.

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.

Jaeyeon Ahn 2 Mar 22, 2022
PyTorch Implementation of Backbone of PicoDet

PicoDet-Backbone PyTorch Implementation of Backbone of PicoDet Original Implementation is implemented on PaddlePaddle. Example picodet_l_backbone = ES

Yonghye Kwon 7 Jul 12, 2022
Dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

Nest Protect integration for Home Assistant Custom component for Home Assistant to interact with Nest Protect devices via an undocumented and unoffici

Mick Vleeshouwer 175 Dec 29, 2022
Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite.

TFLite-MobileStereoNet Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite. Stereo depth estimati

Ibai Gorordo 4 Feb 14, 2022
Fluency ENhanced Sentence-bert Evaluation (FENSE), metric for audio caption evaluation. And Benchmark dataset AudioCaps-Eval, Clotho-Eval.

FENSE The metric, Fluency ENhanced Sentence-bert Evaluation (FENSE), for audio caption evaluation, proposed in the paper "Can Audio Captions Be Evalua

Zhiling Zhang 13 Dec 23, 2022
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement

Kai Zhu 78 Dec 02, 2022
Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

QSORT QSORT(Quick + Simple Online and Realtime Tracking) is a simple online and realtime tracking algorithm for 2D multiple object tracking in video s

Yonghye Kwon 8 Jul 27, 2022
Pytorch implementation of the paper Time-series Generative Adversarial Networks

TimeGAN-pytorch Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19. Jinsung Yoon, Daniel Jarrett

Zhiwei ZHANG 21 Nov 24, 2022
git《Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser》(2021) GitHub: [fig5]

Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser Abstract The success of deep denoisers on real-world colo

Yue Cao 51 Nov 22, 2022
Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Sh

Syed Waqas Zamir 859 Dec 22, 2022
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

Gradient Institute 127 Dec 12, 2022
Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Drone Detection using Thermal Signature This repository highlights the work for night-time drone detection using a using an Optris PI Lightweight ther

Chong Yu Quan 6 Dec 31, 2022
Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

Implementation of "Distribution Alignment: A Unified Framework for Long-tail Visual Recognition"(CVPR 2021)

105 Nov 07, 2022
Lava-DL, but with PyTorch-Lightning flavour

Deep learning project seed Use this seed to start new deep learning / ML projects. Built in setup.py Built in requirements Examples with MNIST Badges

Sami BARCHID 4 Oct 31, 2022
《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]

Unsupervised 3D Human Pose Representation [Paper] The implementation of our paper Unsupervised 3D Human Pose Representation with Viewpoint and Pose Di

42 Nov 24, 2022
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Few-Shot-Intent-Detection Few-Shot-Intent-Detection is a repository designed for few-shot intent detection with/without Out-of-Scope (OOS) intents. It

Jian-Guo Zhang 73 Dec 26, 2022
Depth-Aware Video Frame Interpolation (CVPR 2019)

DAIN (Depth-Aware Video Frame Interpolation) Project | Paper Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang IEEE C

Wenbo Bao 7.7k Dec 31, 2022
MLJetReconstruction - using machine learning to reconstruct jets for CMS

MLJetReconstruction - using machine learning to reconstruct jets for CMS The C++ data extraction code used here was based heavily on that foundv here.

ALPhA Davidson 0 Nov 17, 2021