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)