def get_data(symbol, start_date=datetime(year=2015, month=1, day=1)):
data = DataReader(symbol, 'yahoo', start_date).reset_index()
data['Daily_Returns'] = data['Adj Close'].pct_change()
data['Daily_Log_Returns'] = np.log(data['Adj Close']) - np.log(
data['Adj Close'].shift(1))
data.drop(data.index[:1], inplace=True)
data.columns = StockDataDownloader.COLUMNS
return data
import pandas as pd import pandas_ta as ta from pandas_datareader import DataReader import datetime import matplotlib.pyplot as plt # Load data symbol = 'AAPL' num_of_years = 1 start_date = datetime.datetime.now() - datetime.timedelta(days=int(365.25 * num_of_years)) end_date = datetime.date.today() df = DataReader(symbol, 'yahoo' ,start_date, end_date) df.columns = map(str.lower, df.columns) df.ta.strategy(name='all') # Sanity check. Make sure all the columns are there print(df.columns) print(df.tail()) plt.plot(df['bbands'])
import pandas as pd
from pandas_datareader import DataReader
import datetime as dt
from stockstats import StockDataFrame
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
ticker = 'AAPL'
num_of_years = 1
start = dt.datetime.now() - dt.timedelta(int(365.25 * num_of_years))
now = dt.datetime.now()
df = DataReader(ticker, 'yahoo', start, now)
df.columns = ['high', 'low', 'open', 'close', 'volume', 'adj close']
df = df[['open', 'close', 'high', 'low', 'volume']]
stock = StockDataFrame.retype(df)
print(stock['rsi_12'])
plt.gcf()
plt.plot(stock['boll'])
plt.plot(stock['boll_ub'])
plt.plot(stock['boll_lb'])
HMM在股票市场中的应用
我们假设隐藏状态数量是6，即假设股市的状态有6种，虽然我们并不知道每种状态到底是什么，
但是通过后面的图我们可以看出那种状态下市场是上涨的，哪种是震荡的，哪种是下跌的。
可观测的特征状态我们选择了3个指标进行标示，进行预测的时候假设假设所有的特征向量的状态服从高斯分布
'''
from hmmlearn.hmm import GaussianHMM
import numpy as np
from matplotlib import cm, pyplot as plt
import matplotlib.dates
import pandas as pd
import datetime
from pandas_datareader import DataReader
# from sklearn.datasets import
# 测试时间从2005年1月1日到2015年12月31日，拿到每日沪深300的各种交易数据。
# beginDate = '2005-01-01'
# endDate ='2015-12-31'
beginDate = datetime.datetime(2015, 1, 1)
endDate = datetime.datetime(2015, 12, 31)
n = 6 # 6个隐藏状态
df = DataReader('XOM', 'yahoo', beginDate, endDate)
print(df.head())
df.columns = [
'OpeningPx', 'HighPx', 'LowPx', 'ClosingPx', 'Adj Close',
'TotalVolumeTraded'
]
print(df.head())
