def train_classifier(self, X_train, X_test, y_train, y_test):
classifiers = [
svm.SVR(),
linear_model.SGDRegressor(),
linear_model.BayesianRidge(),
linear_model.LassoLars(),
linear_model.ARDRegression(),
linear_model.PassiveAggressiveRegressor(),
linear_model.TheilSenRegressor(),
linear_model.LinearRegression()
]
best_accuracy = self.lowest_accuracy_wanted
best_classifier = None
for classifier in classifiers:
classifier.fit(X_train, y_train)
prediction_score = classifier.score(X_test, y_test)
if prediction_score > best_accuracy:
best_accuracy = prediction_score
best_classifier = classifier
if best_classifier:
print(
f"{classifier} performed the best with an accuracy of: {best_accuracy}"
)
joblib.dump(
classifier,
relative_path(
f'models/files/predict_{self.prediction_column_name}.pkl'))
def __get_reddit_df(path):
with open(relative_path(path)) as f:
data = json.load(f)
dict_collection = []
for company in data['companies'].keys():
for date in data['companies'][company].keys():
row = data['companies'][company][date].copy()
row['company'] = company
row['date'] = date
dict_collection.append(row)
return pd.DataFrame(dict_collection)
def __get_companies_related_reddit_submissions(companies):
for company in companies:
submission_dict_collection = RedditInterface.get_all_submissions_for(company)
DataFileInterface.write_to_file(relative_path('data/files/stock_submissions.csv'), submission_dict_collection)
def __get_historical_data_for_all_tech_companies(companies):
for company in companies:
print(f"getting historical data for company: {company}")
company_historical_data_collection = IEXCloudInterface.get_historical_data(company, '1m')
DataFileInterface.write_to_file(relative_path('data/files/stock_data.csv'), company_historical_data_collection)
def __get_stats_for_all_tech_companies(companies):
for company in companies:
print(f"getting stat data for company: {company}")
company_stat_data_collection = IEXCloudInterface.get_stats(company)
DataFileInterface.write_to_file(relative_path('data/files/stock_stats.csv'), company_stat_data_collection)
def __get_news_data_for_all_tech_companies(companies):
for company in companies:
company_news_collection = IEXCloudInterface.get_news(company, 'last-week')
DataFileInterface.write_to_file(relative_path('data/files/news.csv'), company_news_collection)
row['company'] = company
row['date'] = date
dict_collection.append(row)
return pd.DataFrame(dict_collection)
if __name__ == '__main__':
if '--get-data' in sys.argv:
companies = __get_companies()
__retrieve_data(companies)
if '--analyze-text' in sys.argv:
reddit_submissions = pd.read_csv(relative_path('data/files/stock_submissions.csv'))
ssm = SubmissionSentimentModel(reddit_submissions)
ssm.analyze(relative_path('data/files/reddit_submissions_analyzed.json'))
if '--train' in sys.argv:
prediction_column_names = ['high', 'low', 'close']
stock_stats_df = pd.read_csv(relative_path('data/files/stock_stats.csv'))
spm = StockPriceModel()
for prediction_column_name in prediction_column_names:
stock_price_movement_df = pd.read_csv(relative_path('data/files/stock_data.csv'))
stock_price_movement_df = stock_price_movement_df[['company', 'date', 'open', prediction_column_name]].copy()
reddit_df = __get_reddit_df('data/files/reddit_submissions_analyzed.json')