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.

[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
[CVPR 2021] Monocular depth estimation using wavelets for efficiency

Single Image Depth Prediction with Wavelet Decomposition Michaël Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit and Daniyar Turmukhambeto

Niantic Labs 205 Jan 02, 2023
AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil

3.4k Jan 07, 2023
Official implementation for: Blended Diffusion for Text-driven Editing of Natural Images.

Blended Diffusion for Text-driven Editing of Natural Images Blended Diffusion for Text-driven Editing of Natural Images Omri Avrahami, Dani Lischinski

328 Dec 30, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

CIFS This repository provides codes for CIFS (ICML 2021). CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Sel

Hanshu YAN 19 Nov 12, 2022
My take on a practical implementation of Linformer for Pytorch.

Linformer Pytorch Implementation A practical implementation of the Linformer paper. This is attention with only linear complexity in n, allowing for v

Peter 349 Dec 25, 2022
Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Light-SERNet This is the Tensorflow 2.x implementation of our paper "Light-SERNet: A lightweight fully convolutional neural network for speech emotion

Arya Aftab 29 Nov 12, 2022
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

PySlowFast PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficie

Meta Research 5.3k Jan 03, 2023
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

NeRF-pytorch NeRF (Neural Radiance Fields) is a method that achieves state-of-the-art results for synthesizing novel views of complex scenes. Here are

Yen-Chen Lin 3.2k Jan 08, 2023
An Intelligent Self-driving Truck System For Highway Transportation

Inceptio Intelligent Truck System An Intelligent Self-driving Truck System For Highway Transportation Note The code is still in development. OS requir

InceptioResearch 11 Jul 13, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Wilson 1.7k Dec 30, 2022
Official PyTorch implementation for "Low Precision Decentralized Distributed Training with Heterogenous Data"

Low Precision Decentralized Training with Heterogenous Data Official PyTorch implementation for "Low Precision Decentralized Distributed Training with

Aparna Aketi 0 Nov 23, 2021
Official Pytorch implementation of "Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes", CVPR 2022

Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes / 3DCrowdNet News 💪 3DCrowdNet achieves the state-of-the-art accuracy on 3D

Hongsuk Choi 113 Dec 21, 2022
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

Why, hello there! This is the supporting notebook for the research paper — Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomal

2 Dec 14, 2021
This repository compare a selfie with images from identity documents and response if the selfie match.

aws-rekognition-facecompare This repository compare a selfie with images from identity documents and response if the selfie match. This code was made

1 Jan 27, 2022
Koopman operator identification library in Python

pykoop pykoop is a Koopman operator identification library written in Python. It allows the user to specify Koopman lifting functions and regressors i

DECAR Systems Group 34 Jan 04, 2023
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Nonuniform-to-Uniform Quantization This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quanti

Zechun Liu 60 Dec 28, 2022