import matplotlib.pyplot as plt
from dataset import Dataset
from models import Linear, Nonlinear
from trainer import AdaGradTrainer
from utils import synthetic, wealth, sharpe
from quote import get
FETCH = False
if FETCH:
start = '2007-07-01'
finish = '2014-02-01'
jnj_data = get('JNJ', start, finish)
cov_data = get('COV', start, finish)
nvs_data = get('NVS', start, finish)
pfe_data = get('PFE', start, finish)
days = len(jnj_data)
jnj = zeros(days)
cov = zeros(days)
nvs = zeros(days)
pfe = zeros(days)
for i in range(days):
jnj[i] = jnj_data[i][1]['c']
cov[i] = cov_data[i][1]['c']
nvs[i] = nvs_data[i][1]['c']
pfe[i] = pfe_data[i][1]['c']
from sys import path
path.append('src/')
import matplotlib.pyplot as plt
from numpy import append, zeros, array
from trainer import ValidatingTrainer
from models import Nonlinear, Linear
from dataset import Dataset
from utils import synthetic, wealth, sharpe
import quote
mxe = [s[1]['c'] for s in quote.get('MXE', '1990-09-01', '2014-05-01')]
mxf = [s[1]['c'] for s in quote.get('MXF', '1990-09-01', '2014-05-01')]
if len(mxe) != len(mxf):
raise RuntimeError("Length mismatch in series.")
dataset = Dataset(mxe, [mxf])
for window in [1000]:
for slide in [200]:
for lookback in [5]:
for delta in [0.0, 0.0001, 0.001, 0.01]:
models = []
for lmb in [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]:
models.append(Linear(delta=delta, lmb=lmb))
trainer = ValidatingTrainer(dataset, models)
import matplotlib.pyplot as plt
from dataset import Dataset
from models import Linear, Nonlinear
from trainer import AdaGradTrainer
from utils import synthetic, wealth, sharpe
from quote import get
FETCH = False
if FETCH:
start = '2007-07-01'
finish = '2014-02-01'
jnj_data = get('JNJ', start, finish)
cov_data = get('COV', start, finish)
nvs_data = get('NVS', start, finish)
pfe_data = get('PFE', start, finish)
days = len(jnj_data)
jnj = zeros(days)
cov = zeros(days)
nvs = zeros(days)
pfe = zeros(days)
for i in range(days):
jnj[i] = jnj_data[i][1]['c']
cov[i] = cov_data[i][1]['c']
nvs[i] = nvs_data[i][1]['c']
pfe[i] = pfe_data[i][1]['c']
from sys import path
path.append('src/')
import matplotlib.pyplot as plt
from numpy import append, zeros, array
from trainer import ValidatingTrainer
from models import Nonlinear, Linear
from dataset import Dataset
from utils import synthetic, wealth, sharpe
import quote
mxe = [s[1]['c'] for s in quote.get('MXE', '1990-09-01', '2014-05-01')]
mxf = [s[1]['c'] for s in quote.get('MXF', '1990-09-01', '2014-05-01')]
if len(mxe) != len(mxf):
raise RuntimeError("Length mismatch in series.")
dataset = Dataset(mxe, [mxf])
for window in [1000]:
for slide in [200]:
for lookback in [5]:
for delta in [0.0, 0.0001, 0.001, 0.01]:
models = []
for lmb in [0.0, 0.0001, 0.001, 0.01, 0.1, 1.0]:
models.append(Linear(delta=delta, lmb=lmb))
trainer = ValidatingTrainer(dataset, models)
