- 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 @@
-'''
+import pandas as pd
-On your terminal run:
+import numpy as np
-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 matplotlib.pyplot as plt
 
-# Your key here
+def ema(data, key, alpha=0.5, ic=None):
-key = '0UO7Z2MVZ2YSQSVE'
+	result = []
-# Chose your output format, or default to JSON (python dict)
+	if ic is None:
-ts = TimeSeries(key, output_format='pandas')
+		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
+	return np.array(result)
-# aapl_data is a pandas dataframe, aapl_meta_data is a dict
-aapl_data, aapl_meta_data = ts.get_daily(symbol='AAPL')
 
+def colsum(data, keys):
+	result = np.zeros(data.shape[0])
+	for key in keys:
+		result += data[key]
 
-# Visualization
+	return result
-figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')
+
-aapl_data['4. close'].plot()
+store = pd.HDFStore('test.h5')
-dataArray = aapl_data['4. close'].to_numpy()
+if 1:
-plt.tight_layout()
+	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()

pandas_eval.py

@@ -20,82 +20,124 @@ import datetime
 
 key = '0UO7Z2MVZ2YSQSVE'
 
-
+def ema(data, key, alpha=0.5, ic=None):
-def sin(f, a, N):
+	result = []
-	result = np.empty(0)
+	if ic is None:
-	for n in range(0, N):
+		r = data[key][0]
-		v = a*math.sin(2*math.pi*f*n/N)
+	else:
-		result = np.append(result, v)
+		r = ic
-
+	for v in data[key]:
-	return result
-
-def ema(data, alpha=0.75):
-	result = np.empty(0)
-	r = data[0]
-
-	for v in data:
 		r = alpha*r + (1-alpha)*v
-		result = np.append(result, r)
+		result.append(r)
+
+	return np.array(result)
+
+def sma(data, key, window_days, ic=None):
+	mem = [0] * window_days
+	result = []
+	k=0
+	cumsum = 0
+	for v in data[key]:
+		cumsum += (v - mem[k])
+		mem[k] = v
+		k += 1
+		if k >= window_days:
+			k=0
+
+		result.append(cumsum/window_days)
 
 	return result
 
-def macd(data):
+def sd(data, key, window_days, ic=None):
-	ema_short = ema(data, alpha=0.85)
+	mem = [0] * window_days
-	ema_long = ema(data, alpha=0.925)
+	result = []
+	k=0
+	cumsum = 0
+	data_mean = data[key] - sma(data, key, window_days, ic)
+	for v in data_mean:
+		v2 = v * v
+		cumsum += (v2 - mem[k])
+		mem[k] = v2
+		k += 1
+		if k >= window_days:
+			k=0
+
+		var = max(0, cumsum) / window_days
+		result.append(math.sqrt(var))
+
+	return result
+
+
+def colsum(data, keys):
+	result = np.zeros(data.shape[0])
+	for key in keys:
+		result += data[key]
+
+	return result
+
+def macd(data, key):
+	ema_short = ema(data, key, alpha=0.85)
+	ema_long = ema(data, key, alpha=0.925)
 
 	return ema_short - ema_long
 
+def bollinger(data, window_days, f=2):
+	tp_ser = colsum(data, keys=['high', 'low', 'close']) / 3
+	tp_df = tp_ser.to_frame(name='tp')
+	stddev = np.array(sd(tp_df, 'tp', window_days))
+
+	mid = np.array(sma(tp_df, key='tp', window_days=window_days))
+	upper = mid + f * stddev
+	lower = mid - f * stddev
+	print (mid)
+	print (upper)
+	print (lower)
+	result = pd.DataFrame(upper, index=data.index, columns=['upper'])
+	result['lower'] = lower
+	result['mid'] = mid
+	return result
+
 data = {}
+title = 'AAPL'
+
 if 0:
 	# Get stock price via data reader
 	start = datetime.datetime(2016,1,1)
 	end = datetime.date.today()
-	data = web.DataReader("AAPL", "av-monthly", start, end, api_key=key)
+	data = web.DataReader(title, "av-daily", start, end, api_key=key)
-#	data = web.DataReader("AAPL", "stooq", start, end)
+#	data = web.DataReader(title, "stooq", start, end)
 
-	type(data)
+	print(data)
-	hdf = pd.HDFStore('aapl.h5')
+	hdf = pd.HDFStore(title + '.h5')
-	hdf['open'] = data['open']
+	hdf[title] = data
-	hdf['close'] = data['close']
-	hdf['high'] = data['high']
-	hdf['low'] = data['low']
-	hdf.close()
 
 else:
-	hdf = pd.HDFStore('aapl.h5', 'r')
+	hdf = pd.HDFStore(title + '.h5', 'r')
-	data['open'] = hdf['open']
+	data = hdf[title]
-	data['close'] = hdf['close']
-	data['high'] = hdf['high']
-	data['low'] = hdf['low']
-	hdf.close()
 
-df = pd.DataFrame({"A": ["a", "b", "c", "a"]})
+data['ema'] = ema(data, key='close', alpha=0.75)
-print(df)
+data['macd'] = macd(data, key='close')
-
+data['sma'] = sma(data, key='close', window_days=30)
-v = data.values()
+boll = bollinger(data, window_days=30)
-i = data.items()
+data['boll(up)'] = boll['upper']
-print(data)
+data['boll(mid)'] = boll['mid']
-
+data['boll(low)'] = boll['lower']
-data['ema'] = ema(data['close'], alpha=0.75)
-
-data['open'].plot()
-data['close'].plot()
-data['high'].plot()
-data['low'].plot()
-plt.legend()
-plt.grid()
-plt.show()
-
-np_data = np.flip(data['close'].to_numpy())
 
 plt.subplot(211)
-plt.plot(np_data, label='Price')
+data['close'].plot()
-plt.plot(ema(np_data), label='EMA')
+#data['ema'].plot()
+#data['sma'].plot()
+data['boll(up)'].plot()
+data['boll(mid)'].plot()
+data['boll(low)'].plot()
 plt.legend()
 plt.grid()
 
 plt.subplot(212)
-plt.plot(macd(np_data), label='MACD')
+data['macd'].plot()
 plt.legend()
 plt.grid()
+
 plt.show()
+
+hdf.close()