def stocks_all():
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')
exceptionDF = pd.read_csv('nasdaq/exceptionList.csv',
sep=',',
header=0,
engine='python')
stock_df = pd.DataFrame(listOfStocks)
stock_df_wo_rating = pd.merge(stock_df,
exceptionDF,
how='inner',
on=['Symbol', 'Symbol'])
print("stock_df shpe: ", stock_df.shape)
print("shape: ", stock_df_wo_rating.shape)
stock_df = pd.concat([stock_df, stock_df_wo_rating,
stock_df_wo_rating]).drop_duplicates(keep=False)
print("stock_df shpe: ", stock_df.shape)
stock_df.insert(2, "Market", "NASDAQ", True)
response = stock_df.to_json(orient='records')
return Response(response, mimetype='text/plain')
def getStockSector(stock_symbol):
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')
print(listOfStocks['Sector'].unique())
df = listOfStocks.loc[listOfStocks['Symbol'] == stock_symbol]
response = df.to_json(orient='records')
return Response(response, mimetype='text/plain')
def listOfStocks():
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')
listOfStocks_res = listOfStocks['Symbol'] + ': ' + listOfStocks[
'Security Name'].map(lambda x: x.split('-')[0].strip())
print(listOfStocks[0:5])
response = listOfStocks_res.to_json()
return Response(response, mimetype='text/plain')
def getRecommendationList_Test(stock_symbol):
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')
RatingList = ['BUY', 'SELL', 'HOLD']
stock_df = pd.DataFrame(
columns=['seq', 'stockSymbol', 'stockName', 'sector', 'rating'])
for x in range(0, 5):
randomRow = random.randint(1, len(listOfStocks))
randomRating = random.randint(0, 2)
print(
'reco: ', randomRow, randomRating, RatingList[randomRating],
('' if isNaN(listOfStocks.iloc[randomRow, 1]) else
listOfStocks.iloc[randomRow, 1].tostring().split('-')[0].strip()),
listOfStocks.iloc[randomRow, 2])
stock_df = stock_df.append(
{
'seq':
x + 1,
'stockSymbol':
listOfStocks.iloc[randomRow, 0],
'stockName':
('' if isNaN(listOfStocks.iloc[randomRow, 1]) else
listOfStocks.iloc[randomRow, 1].split('-')[0].strip()),
'sector': ('' if isNaN(listOfStocks.iloc[randomRow, 2]) else
listOfStocks.iloc[randomRow, 2]),
'rating':
RatingList[randomRating]
},
ignore_index=True)
response = stock_df.to_json(orient='records')
return Response(response, mimetype='text/plain')
def stock_list_t(stockFirstChar):
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')['Symbol'].tolist()
exceptionDF = pd.read_csv('nasdaq/exceptionList.csv',
sep=',',
header=0,
engine='python')
exceptionList = exceptionDF['Symbol'].tolist()
relevantStocks = []
# input is a stock symbol and not starting character
if len(stockFirstChar) > 1:
relevantStocks = [stockFirstChar]
if stockFirstChar.isalnum():
for item in listOfStocks:
if item[0].lower() == stockFirstChar and item not in exceptionList:
relevantStocks.append(item)
else:
for item in listOfStocks:
if not item[0].isalpha() and item not in exceptionList:
relevantStocks.append(item)
return relevantStocks
def getRecommendationList(stock_symbol):
listOfStocks = lst.main('nasdaq/nasdaq_result_list.csv')
listOfStocks.set_index('Symbol', inplace=True)
# print(listOfStocks.head(5))
recoDF = reco.main(stock_symbol)
recoList = recoDF['Symbol'].values.tolist()
xrow = listOfStocks.index.isin(recoList)
nasdaqDF = listOfStocks[xrow]
# print(nasdaqDF.head(5))
ratingDict = {-1: 'SELL', 0: 'HOLD', 1: 'BUY'}
stock_df = pd.DataFrame(
columns=['seq', 'stockSymbol', 'stockName', 'sector', 'rating'])
stock_df['stockSymbol'] = recoDF['Symbol']
stock_df['rating'] = recoDF['analyst_rating'].map(lambda x: ratingDict[x])
stock_df['stockName'] = nasdaqDF['Security Name'].map(
lambda x: x.split('-')[0].strip())
stock_df['sector'] = nasdaqDF['Sector']
stock_df.reset_index(level=None,
drop=False,
inplace=True,
col_level=0,
col_fill='')
stock_df['seq'] = stock_df.index
print(stock_df.head(5))
response = stock_df.to_json(orient='records')
return Response(response, mimetype='text/plain')