def get_recommendations(username): user_data = dbTesting.get_user_stocks_raw(username) # generate training and test data training_data = generate_training() test_data = generate_test(user_data) dt = build_tree(training_data) # perform recommendations recommendations = [] for i, row in enumerate(test_data): result = predict(row, dt) if result != 'do nothing': symbol = STOCK_SYMBOLS[i] stock_name = stock_info.getStockCompany(symbol) recommendation = [symbol, stock_name, result] recommendations.append(recommendation) return recommendations
def get_user_stocks(username):
column = 1
shares = Shares.objects.filter(username=username).values_list()
shares_array = shares[::1]
user_stocks = []
for i in shares_array:
for j in i:
if column > 3 and j != 0:
user_stocks.append([
stocks[column - 4],
stock_info.getStockCompany(stocks[column - 4]),
str(j),
str(stock_info.getStockPrice(stocks[column - 4]))
])
column += 1
column = 0
return user_stocks
def create_all_stocks():
for x in stocks:
create_stock(x, stock_info.getStockCompany(x),
stock_info.getStockPrice(x),
stock_info.getStockPercentChange(x),
stock_info.getStockVWAP(x))