Blueshift Demo Strategies

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Blueshift is a systematic trading development platform with built-in market data covernig multiple markets and asset classes.

This repository lists a number of demo strategies to show the use cases of the Blueshift development platform. This demonstrates the power of Blueshift to model simple and complex strategies and simulate them accurately.

Note: the strategies here are for demonstration purpose only. Possible improvements include optimizing parameters and fine-tuning in trading costs and slippage parameters, among others.

Introduction

Blueshift is a flexible, platform agnostic, trading and backtesting framework for developing systematic investment strategies in Python in a fast and reliable way. This makes developing complex (and simple) strategies easy, and moving a strategy from back-testing to live trading seamless.

Features

• Standard and simple APIs: For fast algo development and deployment (see Bluesfhit API documentation).

• Live ready: Seamlessly move from research/backtesting to taking your strategy live

• Asset agnostic: Can adapt to any asset class you are trading, stocks, FX or cryptos.

• Full-featured: Comes complete with middle office and back-office functionalities integrated.

• Fully-loaded: Includies out-of-the-box supports for many live trading platform and data sources.

• Libraries: Includes a host of useful financial packages, ready to use.

  Blueshift supports Python 3.6 and newer only. We do not have any planned release for earlier versions of Python.

Why Blueshift!

There are many Pythonic backtesting framework out there. A few are excellent for the purpose they were built for. So why another one? The issues we found with existing frameworks are:

• Most are developed for back-testing. Coaxing them in to live trading frameworks is hacky and risky.
• Many from the above are useful only for a first-cut research, but fall behind when we need realistic simulation
• Many are focussed on specific markets, asset classes or even geographies.
• Many are suitable only for certain types of strategies (e.g. using a technical indicator to trade).

We developed Blueshift because when we were looking for a framework, we found none that meets our requirements. It is a full-featured complext event processing engine developed specifically for simulation and live execution of any types of trading strategies. Whatever you can imagine, you can model on Blueshift, only practical limitations being server resource and data.

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