def perform_transaction(username, symbol, shares, transaction_type):
user = Users.objects.get(username=username)
#stock = Stocks.objects.get(symbol = symbol)
share = Shares.objects.get(username=username)
new_val = Transactions(user_id=username,
stock_id=symbol,
timestamp=datetime.datetime.now(),
number_of_shares=shares,
transaction_type=transaction_type)
new_val.save()
if transaction_type == "+":
#subtract stock amount purchased from users wallet
user.cash_amount -= stock_info.getStockPrice(symbol) * shares
#add number of shares on the given stock in Shares table
share.__dict__[symbol] += shares
else:
#add stock amount sold to users wallet
user.cash_amount += stock_info.getStockPrice(symbol) * shares
#subtract number of shares from the given stock in Shares table
share.__dict__[symbol] -= shares
user.save()
share.save()
def generate_test(user_data): '''Generate test data based on user account status''' test_data = [] risk_profile = RISK_PROFILES[user_data[1]] stocks = user_data[3:] for i, stock in enumerate(stocks): symbol = STOCK_SYMBOLS[i] owned = 'not owned' # check if the user owns the current stock if stock != 0: owned = 'owned' # identify the relationship between the current stock price and vwap price = stock_info.getStockPrice(symbol) vwap = stock_info.getStockVWAP(symbol) if vwap > price: vwap_price = 'greater' elif vwap < price: vwap_price = 'less' else: vwap_price = 'equal' # add preprocess stock info to test data test_data.append([risk_profile, owned, vwap_price]) return test_data
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 dash(request):
user = request.user
user = get_user(user.username)
U_stocks = get_user_stocks_and_price(user.values_list()[0][0])
stocks = []
names = []
for i, k in U_stocks:
stocks.append(float(k) * (stock_info.getStockPrice(i)))
names.append(i)
plt.pie(stocks,
labels=names,
autopct='%.1f%%',
startangle=90,
shadow=False)
plt.savefig("main/static/pie.jpg")
ai = get_recommendations(user.values_list()[0][0])
return render(request, 'main/dash.html', {
"stocks": U_stocks,
"cash": user.values_list()[0][5],
"reccomend": ai
})
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))