Slater-Victoroff

class MissingKeyException(Exception):
	"""
	If we can't figure out what the key is of an item being saved, we just say so.
 
	Should probably make a better error message that says what we can figure out and what they can do about it.
	"""
 
	def __init__(self, data_structure):
		self.ds = data_structure
 

Slater-Victoroff

import json

from boto.s3.connection import S3Connection, OrdinaryCallingFormat
from boto.s3.key import Key


class BotoSave(object):
 
	def __init__(self, bucket, filename=None):
		"""

Slater-Victoroff

@BotoSave('bucketname')
def method():
	return {<key>: <data>}

Slater-Victoroff

import os

CREDENTIALS = ['AWSAccessKeyId', 'AWSSecretKey']


def credentials(filename=None):
	"""
	Generally made for standard AWS export format. Turns conf file into python dict
 
	If no filename is given, will attempt to read environment variables instead, then global variables

Slater-Victoroff

import json, os

from boto.s3.connection import S3Connection, OrdinaryCallingFormat
from boto.s3.key import Key

CREDENTIALS = ['AWSAccessKeyId', 'AWSSecretKey']

class MissingCredentialsException(Exception):

	def __init__(self, credentials_dict):

Slater-Victoroff

from zipline.algorithm import TradingAlgorithm
from zipline.transforms import MovingAverage
from zipline.utils.factory import load_from_yahoo
from zipline.finance.slippage import FixedSlippage
from zipline.finance.commission import PerShare
from datetime import datetime
import matplotlib.pyplot as plt

class DualMovingAverage(TradingAlgorithm):

Slater-Victoroff

def normalize(vector, span=(0,1)):
    minimum, maximum = (np.min(vector), np.max(vector))
    scaling = (span[1] - span[0]) / (maximum - minimum)
    return ((vector - minimum) * scaling) + span[0]

Slater-Victoroff

import numpy as np

def normalize(vector, span=(0,1)):
    minimum, maximum = (np.min(vector), np.max(vector))
    scaling = (span[1] - span[0]) / (maximum - minimum)
    return ((vector - minimum) * scaling) + span[0]

class NeuralNet(object):
    
    def __init__(self, layer_sizes=(10,50,20), threshold=0.5):

Slater-Victoroff

import numpy as np

def transfer_function(input_vector, weight_matrix, threshold):
    return np.dot(input_vector, weight_matrix) > threshold

Slater-Victoroff

import numpy as np

def neural_logic_gate(input_vector, weight_matrix=[0.5, 0.5], threshold=0.75): 
    return np.dot(input_vector, weights) > threshold