Plug-and-play building blocks for modern quants
Quantitative algorithms, portfolio managers data sources, contexts and middlewares for Intuition
# apt-get install r-base
# pip install intuition
# pip install insightsOr if you plan to hack on it
$ git clone https://github.com/hackliff/insights.git && cd insights
# pip install -r requirements.txt
$ export PYTHONPATH=$PYTHONPATH:$PWDNow use in your intuition configuration something like
modules:
manager: insights.managers.optimalfrontier.OptimalFrontier
algorithm: insights.algorithms.gradient.StochasticGradientDescent
data: insights.sources.live.EquitiesLiveSource- Here is the Fair manager example, which allocates the same weight to all of your assets:
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:
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