def __init__(self):

self.search_url = 'http://biz.finance.sina.com.cn/suggest/lookup_n.php?country=&q={}&name={}&t=keyword&c=all&k={}&range=all&col=1_7&from=channel'

self.trend_url = 'http://vip.stock.finance.sina.com.cn/quotes_service/view/CN_TransListV2.php?symbol={}&rn={}'

def searchFromCode(self, code):

search_url = self.search_url.format(code, code, code)

search = requests.get(search_url)

search.encoding = 'gb2312'

search_text = search.text

search_soup = BeautifulSoup(search_text, 'html.parser')

list = search_soup.select('.list')[0]

search_result = list.select('a')[0]['href']

return search_result

def getStockTrend(self, code):

trend_url = self.trend_url.format(code, time.time())

trend = requests.get(trend_url).text.split(';')[1:-2]

data = []

for item in trend:

item = float(item.split(',')[2][2:-1])

data.append(item)

return data

def func(x, p):

"""

A, k, theta = p

return A * np.sin(2 * np.pi * k * x + theta)

def drewGraph(self, data):

y = np.array(data)

index = y.shape[0]

x = np.arange(1, index + 1, 1)

features = [tf.contrib.layers.real_valued_column("x", dimension=1)]

estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, batch_size=4674,

num_epochs=10000)

estimator.fit(input_fn=input_fn, steps=10000)

print(estimator.evaluate(input_fn=input_fn))

predict = list(estimator.predict(input_fn=input_fn))

plt.figure(figsize=(60, 5))

plt.plot(x, y, 'b')

plt.plot(predict, 'r')