from matplotlib.finance import quotes_historical_yahoo_ochl as quotes_yahoo
# Input symbol file
symbol_file = 'symbol_map.json'
# Choose a time period
start_date = datetime.datetime(2004, 4, 5)
end_date = datetime.datetime(2007, 6, 2)
# Load the symbol map
with open(symbol_file, 'r') as f:
symbol_dict = json.loads(f.read())
symbols, names = np.array(list(symbol_dict.items())).T
quotes = [quotes_yahoo(symbol, start_date, end_date, asobject=True)
for symbol in symbols]
# Extract opening and closing quotes
opening_quotes = np.array([quote.open for quote in quotes]).astype(np.float)
closing_quotes = np.array([quote.close for quote in quotes]).astype(np.float)
# The daily fluctuations of the quotes
delta_quotes = closing_quotes - opening_quotes
# Build a graph model from the correlations
edge_model = covariance.GraphLassoCV()
# Standardize the data
X = delta_quotes.copy().T
X /= X.std(axis=0)
import datetime
import warnings
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo_ochl as quotes_yahoo
from hmmlearn.hmm import GaussianHMM
#载入历史股票数据
start =datetime.date(1970,9,4)
end = datetime.date(2016,5,17)
stock_quotes =quotes_yahoo('INTC',start,end)
#提取每天的收盘情况
closing_quotes =np.array([quote[2] for quote in stock_quotes])
volumes =np.array([quote[5] for quote in stock_quotes])[1:]
diff_percentage =100.0*np.diff(closing_quotes)/closing_quotes[:-1]
# 重第二个值开始取日期
dates = np.array([quotes[0] for quote in stock_quotes],dtype=np.int)[1:]
#使用不同的大量值进行训练
training_data = np.column_stack([diff_percentage,volumes])
hmm = GaussianHMM(n_components =7,covariance_type='diag',n_iter=1000)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
hmm.fit(training_data)
#使用HMM生成数据
