- added some new indicators

git-svn-id: http://moon:8086/svn/projects/Stock@297 fda53097-d464-4ada-af97-ba876c37ca34

my_test.py

@@ -1,35 +1,44 @@
-'''
-On your terminal run:
-pip install alpha_vantage
-
-This also uses the pandas dataframe, and matplotlib, commonly used python packages
-pip install pandas
-pip install matplotlib
-
-For the develop version run:
-pip install git+https://github.com/RomelTorres/alpha_vantage.git@develop
-'''
-
-from alpha_vantage.timeseries import TimeSeries
-from alpha_vantage.techindicators import TechIndicators
-
-from matplotlib.pyplot import figure
+import pandas as pd
+import numpy as np
 import matplotlib.pyplot as plt
 
-# Your key here
-key = '0UO7Z2MVZ2YSQSVE'
-# Chose your output format, or default to JSON (python dict)
-ts = TimeSeries(key, output_format='pandas')
+def ema(data, key, alpha=0.5, ic=None):
+	result = []
+	if ic is None:
+		r = data[key][0]
+	else:
+		r = ic
+	for v in data[key]:
+		r = alpha*r + (1-alpha)*v
+		result.append(r)
 
-# Get the data, returns a tuple
-# aapl_data is a pandas dataframe, aapl_meta_data is a dict
-aapl_data, aapl_meta_data = ts.get_daily(symbol='AAPL')
+	return np.array(result)
 
+def colsum(data, keys):
+	result = np.zeros(data.shape[0])
+	for key in keys:
+		result += data[key]
 
-# Visualization
-figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')
-aapl_data['4. close'].plot()
-dataArray = aapl_data['4. close'].to_numpy()
-plt.tight_layout()
+	return result
+
+store = pd.HDFStore('test.h5')
+if 1:
+	df = pd.DataFrame(np.array([[1, 2, 3, 101], [4, 5, 6, 102], [7, 8, 9, 103], [10, 11, 12, 104]]),columns=['high', 'low', 'open', 'close'])
+	store['df'] = df
+else:
+	df = store['df']
+
+df['ema(close)'] = ema(df, 'close')
+df['colsum'] = colsum(df, ['high', 'low', 'open', 'close'])
+df['ema(colsum)'] = ema(df, 'colsum')
+
+# Print table
+print(df)
+
+# Plot graph
+df.plot()
+plt.legend()
 plt.grid()
 plt.show()
+
+store.close()