Skip to main content

A trading system building blocks

Project description


> Automated quantitative trading kit, for hackers



**Intuition** is an engine, some building bricks and a set of tools meant to
let you efficiently and intuitively make your own **automated quantitative trading
system**. It is designed to let traders, developers and scientists explore,
improve and deploy market technical hacks.

While the project is still at an early stage, you can already write, use, combine
**signal detection algorithms, portfolio allocation strategies, data sources
and contexts configurators**. Just plug your strategies and analyze
**backtests** or monitor **live trading sessions**.

In addition I work on facilities to build a distributed system and
21st century application (big data, fat computations, d3.js and other html5
stuff), tools to mix languages like Python, node.js and R and a financial
library. You will find some goodies like machine learning forecast, markowitz
portfolio optimization, genetic optimization, sentiment analysis from twitter, ...


* Highly configurable trading environment, powered by [zipline](
* From instant kickstart to full control
* Made to let you tweak algorithms, portfolio manager, data sources, contexts and plugins
* Already includes many
* Experimental live trading on different markets (Nyse, Nasdaq, CAC40 and Forex for now)
* Experimental R integration in your algorithms
* Results analyser
* Mail and Android notifications (for now with the help of freely available [NotifyMyAndroid]( or [PushBullet](
* Financial library, with common used trading functions, data fetchers, ... used for example to solve Coursera econometrics assignments
* Easy to use data management, powered by [rethinkdb](
* [Docker]( support for development workflow and deployment
* Kind of a CI showcase as I am testing [travis](, [wercker](, [shippable](, [](, [coveralls]( and [landscape](


[![Latest Version](](
<!--[![wercker status]( "wercker status")](>
[![wercker status]( "wercker status")](
[![Build Status](](
[![Build Status](](
[![Coverage Status](](
[![Code Health](](
[![Requirements Status](](

[Development Board][1]

**Attention** Project is in an *early alpha*, and under heavy development.
The new version 0.3.0 revises a lot of code :

* Algoithms, managers and data sources have their [own repository][2]
* More powerful API to build custom versions of them
* The context module now handles configuration
* [Shiny]( interface, [Dashboard]( and clustering will have their intuition-plugins repository (soon)
* ZeroMQ messaging is for now removed but might be back for inter-algo communication
* So is MySQL, that has been removed and will be re-implemented as a [data plugin](
* But currently it has been replaced by [Rethinkdb](
* Installation is much simpler and a docker image is available for development and deployment
* More intuitive configuration splitted between the context mentioned, command line argument and environment variables
* And a lot (I mean A LOT) of house keeping and code desgin stuff


You are just a few steps away from algoritmic trading. Choose one of the
following installation method

* The usual way

$ pip install intuition
$ # Optionnaly, install offcial algorithms, managers, ...
$ pip install insights

* One-liner for the full installation (i.e. with packages and official

$ wget -qO- | sudo FULL_INTUITION=true bash
$ # ... Go grab a coffee

* From source

$ git clone
$ cd intuition && sudo make

* Sexy, early-adopter style

$ docker pull hivetech/intuition

Getting started

Intuition wires 4 primitives to build up the system : A data source generates
events, processed by the algorithm, that can optionnaly use a portfolio manager
to compute assets allocation. They are configured through a Context, while
third party services use environment variables (take a look in

The following example trades in real time forex, with a simple buy and hold
algorithm and a portfolio manager that allocates same amount for each asset.
Their configuration below is stored in a json file. The `--bot` flag allows
the portfolio to process orders on its own.

$ intuition --context file::liveForex.json --id chuck --showlog --bot

"id": "liveForex",
"end": "22h",
"universe": "forex,5",
"algorithm": {
"notify": "",
"save": false
"manager": {
"cash": 10000,
"buy_scale": 150,
"max_weight": 0.3
"modules": {
"algorithm": "insights.algorithms.buyandhold.BuyAndHold",
"data": "",
"manager": "insightsmanagers.fair.Fair"

Note that in the current implementation, Nasdaq, Nyse, Cac 40 and Forex markets
are available.

Alternatively you can use docker. Here we also fire up a [rethinkdb](
database to store portfolios while trading, and
[mongodb]( to store configurations.

$ docker run -d -name mongodb -p 27017:27017 -p 28017:28017 waitingkuo/mongodb

$ docker run -d -name rethinkdb crosbymichael/rethinkdb --bind all

$ docker run \
-e LOG=debug \
-e LANGUAGE="fr_FR.UTF-8" \
-e LANG="fr_FR.UTF-8" \
-e LC_ALL="fr_FR.UTF-8" \
-name trade_box hivetech/intuition \
intuition --context mongodb::${host_ip}:27017/backtestNasdaq --showlog

For Hackers

You can easily work out and plug your own strategies :

* [Algorithm API](
* [Portfolio API](
* [Data API](
* [Contexts](
* [Middlewares](

Either clone the [insights repository][2]
and hack it or start from scratch. Just make sure the modules paths you give in
the configuration are in the python path.

The [provided](
``intuition`` command does already a lot of things but why not improve it or
write your own. Here is a minimal implementation, assuming you installed

from datetime import datetime
from intuition.core.engine import Simulation

data = {'universe': 'nasdaq,10',
'index': pd.date_range(, datetime(2014, 1, 7))}

modules = {
'algorithm': 'algorithms.movingaverage.DualMovingAverage',
'manager': 'managers.gmv.GlobalMinimumVariance',
'data': ''}})

engine = Simulation()

# Use the configuration to prepare the trading environment
engine.configure_environment(data['index'][-1], 'nasdaq')'chuck_norris', modules)
analyzes =, auto=True)

# Explore the analyzes object
print analyzes.overall_metrics('one_month')
print analyzes.results.tail()


> Fork, implement, add tests, pull request, get my everlasting thanks and a
> respectable place here [=)](


Copyright 2014 Xavier Bruhiere
Intuition is available under the [Apache License, Version 2.0](



* [Zipline](
* [Quantopian](
* [Pandas](
* [R-bloggers](
* [QSTK](
* [Coursera](
* [Udacity](
* [Babypips](
* [GLMF](


Project details

Release history Release notifications

History Node


History Node


History Node


History Node


History Node


This version
History Node


History Node


History Node

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
intuition-0.3.1.tar.gz (34.5 kB) Copy SHA256 hash SHA256 Source None Jan 15, 2014

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page