Initial import

git-svn-id: http://moon:8086/svn/projects/Stock@291 fda53097-d464-4ada-af97-ba876c37ca34

aapl.csv

@@ -0,0 +1,100 @@
+2019-12-06,270.71
+2019-12-05,265.58
+2019-12-04,261.74
+2019-12-03,259.45
+2019-12-02,264.16
+2019-11-29,267.25
+2019-11-27,267.84
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+2019-11-19,266.29
+2019-11-18,267.1
+2019-11-15,265.76
+2019-11-14,262.64
+2019-11-13,264.47
+2019-11-12,261.96
+2019-11-11,262.2
+2019-11-08,260.14
+2019-11-07,259.43
+2019-11-06,257.24
+2019-11-05,257.13
+2019-11-04,257.5
+2019-11-01,255.82
+2019-10-31,248.76
+2019-10-30,243.26
+2019-10-29,243.29
+2019-10-28,249.05
+2019-10-25,246.58
+2019-10-24,243.58
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+2019-10-03,220.82
+2019-10-02,218.96
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+2019-09-30,223.97
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+2019-09-26,219.89
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+2019-09-20,217.73
+2019-09-19,220.96
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+2019-09-17,220.7
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+2019-09-06,213.26
+2019-09-05,213.28
+2019-09-04,209.19
+2019-09-03,205.7
+2019-08-30,208.74
+2019-08-29,209.01
+2019-08-28,205.53
+2019-08-27,204.16
+2019-08-26,206.49
+2019-08-23,202.64
+2019-08-22,212.46
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+2019-07-24,208.67
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+2019-07-22,207.22
+2019-07-19,202.59
+2019-07-18,205.66

my_test.py

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+'''
+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 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')
+
+# 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')
+
+
+# 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()
+plt.grid()
+plt.show()

pandas_eval.py

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+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+
+# https://www.fmlabs.com/reference/default.htm?url=SimpleMA.htm
+
+def sin(f, a, N):
+	result = np.empty(0)
+	for n in range(0, N):
+		v = a*math.sin(2*math.pi*f*n/N)
+		result = np.append(result, v)
+
+	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)
+
+	return result
+
+def macd(data):
+	ema_short = ema(data, alpha=0.85)
+	ema_long = ema(data, alpha=0.925)
+
+	return ema_short - ema_long
+
+pd_data = pd.read_csv("aapl.csv", names=['Dates', 'Price'])
+np_data = np.flip(pd_data['Price'].to_numpy())
+a = sin(2, 1, 100)
+
+plt.plot(np_data, label='Price')
+plt.plot(ema(np_data), label='EMA')
+plt.plot(macd(np_data), label='MACD')
+plt.legend()
+plt.grid()
+plt.show()

test.py

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+'''
+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 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')
+ti = TechIndicators(key, output_format='pandas')
+
+# 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')
+# aapl_sma is a dict, aapl_meta_sma also a dict
+aapl_sma, aapl_meta_sma = ti.get_sma(symbol='AAPL')
+
+aapl_data['4. close'].to_csv("aapl.csv")
+
+# Visualization
+figure(num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')
+aapl_data['4. close'].plot()
+#aapl_sma['SMA'].plot()
+
+plt.tight_layout()
+plt.grid()
+plt.show()