insights 0.1.1

Quantitative algorithms, portfolio managers, data sources and contexts for Intuition

Insights
========

[](https://travis-ci.org/hackliff/insights)
[](https://coveralls.io/r/hackliff/insights)
[](https://landscape.io/github/hackliff/insights/develop)

> Plug-and-play building blocks for modern quants

Quantitative algorithms, portfolio managers data sources, contexts and
middlewares for [Intuition](https://github.com/hackliff/intuition)

* [Algorithm API](https://github.com/hackliff/insights/blob/develop/algorithms/readme.md)
* [Portfolio API](https://github.com/hackliff/insights/blob/develop/managers/readme.md)
* [Data API](https://github.com/hackliff/insights/blob/develop/sources/readme.md)
* [Contexts](https://github.com/hackliff/insights/blob/develop/contexts/readme.md)
* [Middlewares](https://github.com/hackliff/insights/blob/develop/contexts/readme.md)


Installation
------------

```console
# apt-get install r-base
# pip install intuition
# pip install insights
```

Or if you plan to hack on it

```console
$ git clone https://github.com/hackliff/insights.git && cd insights
# pip install -r requirements.txt
$ export PYTHONPATH=$PYTHONPATH:$PWD
```

Now use in your *intuition* configuration something like

```yaml
modules:
manager: insights.managers.optimalfrontier.OptimalFrontier
algorithm: insights.algorithms.gradient.StochasticGradientDescent
data: insights.sources.live.EquitiesLiveSource
```


Getting started
---------------

* Here is the Fair manager example, which allocates the same weight to all of your assets:

```python
from intuition.zipline.portfolio import PortfolioFactory

class Fair(PortfolioFactory):
'''
Dispatch equals weigths for buy signals and give up everything on sell ones
'''
def optimize(self, date, to_buy, to_sell, parameters):
allocations = dict()
if to_buy:
fraction = round(1.0 / float(len(to_buy)), 2)
for s in to_buy:
allocations[s] = fraction
for s in to_sell:
allocations[s] = - self.portfolio.positions[s].amount

expected_return = 0
expected_risk = 1
return allocations, expected_return, expected_risk
```

* A classic buy and hold strategy, with a plugin which stores metrics in
[rethinkdb](www.rethinkdb.com):

```python
from intuition.zipline.algorithm import TradingFactory
import insights.plugins.database as database

class BuyAndHold(TradingFactory):
'''
Simpliest algorithm ever, just buy every stocks at the first frame
'''
def initialize(self, properties):

self.save = properties.get('save', False)
if self.save:
self.use(database.RethinkdbBackend(self.identity, reset=True)
.save_portfolio)

def event(self, data):
signals = {}

if self.day == 2:
for ticker in data:
signals[ticker] = data[ticker].price

return signals
```