This document contains code snippets in Python for analyzing stock market data, normalizing features, performing grid search for an SVM classifier, and using the classifier to make predictions and tweet the results. The snippets load CSV data, sort values by date, normalize features, tune an SVM using grid search, make predictions, and tweet the prediction using the Twitter API.

1. Shizuoka.py #5 Tetsuya Tatsumi dokechin
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CSV
▸ Quandl)

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CSV
▸ FRB 2000

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NASDAQ SP500 DAX
2002-09-10 9309 120.68 1320.2 909.58 3494
2002-09-11 9400 121.50 1315.0 909.45 3584
2002-09-12 9415 122.17 1280.0 886.90 3421

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CSV
▸ pandas
▸ sort
# read cvs file and sort by specific column
ni225 = pandas.read_csv("YAHOO-INDEX_N225.csv",parse_dates=True)
ni225 = ni225.dropna()
ni225 = ni225.sort_values(by="Date", ascending=True).reset_index(drop=True)

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▸ −
▸ n
# normalizations of features
features = numpy.array(features)
features = (features - features.min(0)) / features.ptp(0)

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▸ SVM
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clf = svm.SVC()
tuned_parameters = {'kernel': ['rbf'], 'gamma': [0.2,0.1,0.08,0.07,0.06,0.04,0.03,0.02],
'C': [10,20,30,40,50]}
grid = grid_search.GridSearchCV(estimator=clf, param_grid=tuned_parameters)
grid.fit(features, labels)
print (grid.grid_scores_)

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▸ configparser
▸ Tweet python-twitter
predict = clf.predict(feature)
inifile = configparser.ConfigParser()
inifile.read("./config.ini")
api = twitter.Api(
consumer_key = inifile.get("twitter","consumer_key"),
consumer_secret = inifile.get("twitter","consumer_secret"),
access_token_key = inifile.get("twitter","access_token_key"),
access_token_secret = inifile.get("twitter","access_token_secret")
)
content = "# # http://niyo.dokechin.com "
if predict == 1:
content +=" "
else:
content +=" "
api.PostUpdate(content)

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