jens
2568f96851
git-svn-id: http://moon:8086/svn/projects/Stock@300 fda53097-d464-4ada-af97-ba876c37ca34
161 lines
3.1 KiB
Python
161 lines
3.1 KiB
Python
import pandas as pd
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import pandas_datareader.data as web
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import numpy as np
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import math
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import matplotlib.pyplot as plt
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import datetime
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# Stock Investors Financial Math
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# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
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# Alpha Vantage
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# https://www.alphavantage.co/documentation/
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# Pandas
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# https://pandas.pydata.org/pandas-docs/stable/index.html
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# Knowledge
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# https://www.investopedia.com/articles/technical/02/050602.asp
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# https://ntguardian.wordpress.com/2016/09/19/introduction-stock-market-data-python-1
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key = '0UO7Z2MVZ2YSQSVE'
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def ema(data, key, alpha=0.5, ic=None):
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result = []
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if ic is None:
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r = data[key][0]
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else:
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r = ic
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for v in data[key]:
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r = alpha*r + (1-alpha)*v
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result.append(r)
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return np.array(result)
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def sma(data, key, window_days, ic=None):
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mem = [0] * window_days
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result = []
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k=0
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cumsum = 0
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for v in data[key]:
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cumsum += (v - mem[k])
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mem[k] = v
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k += 1
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if k >= window_days:
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k=0
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result.append(cumsum/window_days)
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return result
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def sd(data, key, window_days, ic=None):
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mem = [0] * window_days
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result = []
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k=0
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cumsum = 0
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data_mean = data[key] - sma(data, key, window_days, ic)
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for v in data_mean:
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v2 = v * v
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cumsum += (v2 - mem[k])
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mem[k] = v2
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k += 1
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if k >= window_days:
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k=0
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var = max(0, cumsum) / window_days
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result.append(math.sqrt(var))
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return result
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def colsum(data, keys):
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result = np.zeros(data.shape[0])
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for key in keys:
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result += data[key]
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return result
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def macd(data, key):
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ema_short = ema(data, key, alpha=0.85)
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ema_long = ema(data, key, alpha=0.925)
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return ema_short - ema_long
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def bollinger(data, window_days, f=2):
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tp_ser = colsum(data, keys=['high', 'low', 'close']) / 3
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tp_df = tp_ser.to_frame(name='tp')
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stddev = np.array(sd(tp_df, 'tp', window_days))
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mid = np.array(sma(tp_df, key='tp', window_days=window_days))
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upper = mid + f * stddev
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lower = mid - f * stddev
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result = pd.DataFrame(upper, index=data.index, columns=['upper'])
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result['lower'] = lower
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result['mid'] = mid
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return result
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data = {}
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titles = []
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titles.append('AAPL')
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titles.append('XLNX')
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for title in titles:
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fetch = False
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today = datetime.date.today()
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try:
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hdf = pd.HDFStore(title + '.h5', 'r')
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except IOError as e:
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fetch = True
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print (e)
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try:
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data = hdf[title]
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except Exception as e:
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fetch = True
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print (e)
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if fetch:
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print ("Fetching \"{}\"".format(title))
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hdf.close()
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# Get stock price via data reader
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start = datetime.datetime(2016,1,1)
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end = datetime.date.today()
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data = web.DataReader(title, "av-daily", start, end, api_key=key)
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# data = web.DataReader(title, "stooq", start, end)
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hdf = pd.HDFStore(title + '.h5')
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hdf[title] = data
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hdf.close()
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data['ema'] = ema(data, key='close', alpha=0.75)
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data['macd'] = macd(data, key='close')
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data['sma'] = sma(data, key='close', window_days=30)
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boll = bollinger(data, window_days=30)
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plt.subplot(311)
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plt.title(title)
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data['close'].plot()
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data['ema'].plot()
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data['sma'].plot()
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plt.legend()
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plt.grid()
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plt.subplot(312)
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boll['upper'].plot()
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boll['mid'].plot()
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boll['lower'].plot()
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plt.legend()
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plt.grid()
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plt.subplot(313)
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data['macd'].plot()
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plt.legend()
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plt.grid()
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plt.show()
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hdf.close()
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