def chat(msg):
global dictionary,pending,pending_action,state,my_friend
print(msg)
usermessage=txt_recog(msg)
if usermessage=='quit':
my_friend.send(get_chatbotanswer('See you.'))
sys.exit()
intent=interprete(interpreter,usermessage)['intent']['name']
entities=extract_entities(nlp,usermessage)
Org=Check_Org(entities)
intent_=(check_intents(intent))
dictionary,pending,pending_action,state=bot_reply(intent_,state,pending,Org,pending_action,dictionary)
if state==END:
my_friend.send(get_chatbotanswer('See you.'))
sys.exit()
action=pending_actions()
if pending is not 0:
my_friend.send(get_chatbotanswer(action[pending_action]))
if state is not 0 and state is not 3 and state is not 5:
my_friend.send(get_chatbotanswer(state_change_action(state)))
if (dictionary['function'] is not None) and (dictionary['company']is not None):
CPN=Stock(get_ticker_symbol(dictionary['company']))
if dictionary['function']=='b':
if error_check1(CPN)==1:
p_CPN=CPN.get_price()
state=FOUND
my_friend.send(get_chatbotanswer('Ok,I have found it! The price of {0} is {1}$'.format(dictionary['company'],p_CPN) ))
else:
my_friend.send(get_chatbotanswer('Information of company not found'))
state=NOTFOUND
if dictionary['function']=='c':
if error_check2(CPN)==1:
mv_CPN=CPN.get_volume()
my_friend.send(get_chatbotanswer('Ok,I have found it!'))
state=FOUND
my_friend.send(get_chatbotanswer('The volume of {0} is {1}'.format(dictionary['company'],mv_CPN) ))
else:
my_friend.send(get_chatbotanswer('Information of company not found'))
state=NOTFOUND
if dictionary['function']=='d':
if error_check3(CPN)==1:
mc_CPN=CPN.get_market_cap()
my_friend.send(get_chatbotanswer('Ok,I have found it!'))
state=FOUND
my_friend.send(get_chatbotanswer('The market value of {0} is {1}$'.format(dictionary['company'],mc_CPN)))
else:
my_friend.send(get_chatbotanswer('Information not found'))
state=NOTFOUND
if state == FOUND or state==NOTFOUND :
my_friend.send(get_chatbotanswer('Do you have other questions? Say "quit" to quit'))
state=INIT
class TestFieldMethodsBatch(object):
def setup_class(self):
self.batch = Stock(["AAPL", "TSLA"])
self.batch2 = Stock(["AAPL", "TSLA"], output_format='pandas')
self.batch4 = Stock(["AAPL", "TSLA"],
json_parse_int=Decimal,
json_parse_float=Decimal)
def test_get_company_name(self):
data = self.batch.get_company_name()
assert isinstance(data, dict)
assert data["AAPL"] == "Apple Inc."
data2 = self.batch2.get_company_name()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_primary_exchange(self):
data = self.batch.get_primary_exchange()
assert isinstance(data, dict)
assert data["AAPL"] == "Nasdaq Global Select"
data2 = self.batch2.get_primary_exchange()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_sector(self):
data = self.batch.get_sector()
assert isinstance(data, dict)
assert data["AAPL"] == "Technology"
data2 = self.batch2.get_sector()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
def test_get_open(self):
data = self.batch.get_open()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_open()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_close(self):
data = self.batch.get_close()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_close()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_years_high(self):
data = self.batch.get_years_high()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_years_high()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_years_low(self):
data = self.batch.get_years_low()
assert isinstance(data, dict)
assert data["AAPL"] > 0
data2 = self.batch2.get_years_low()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_ytd_change(self):
data = self.batch.get_ytd_change()
assert isinstance(data, dict)
data2 = self.batch2.get_ytd_change()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
def test_get_volume(self):
data = self.batch.get_volume()
assert isinstance(data, dict)
assert data["AAPL"] > 50000
data2 = self.batch2.get_volume()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_market_cap(self):
data = self.batch.get_market_cap()
assert isinstance(data, dict)
assert data["AAPL"] > 1000000
data2 = self.batch2.get_market_cap()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_beta(self):
data = self.batch.get_beta()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_beta()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_beta()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_short_interest(self):
data = self.batch.get_short_interest()
assert isinstance(data, dict)
assert data["AAPL"] > 50000
data2 = self.batch2.get_short_interest()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_short_ratio(self):
data = self.batch.get_short_ratio()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_short_ratio()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_short_ratio()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_latest_eps(self):
data = self.batch.get_latest_eps()
assert isinstance(data, dict)
assert isinstance(data["TSLA"], float)
data2 = self.batch2.get_latest_eps()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["TSLA"].dtype == "float64"
data4 = self.batch4.get_latest_eps()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
def test_get_shares_outstanding(self):
data = self.batch.get_shares_outstanding()
assert isinstance(data, dict)
assert data["AAPL"] > 100000
data2 = self.batch2.get_shares_outstanding()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_float(self):
data = self.batch.get_float()
assert isinstance(data, dict)
assert data["AAPL"] > 1000000
data2 = self.batch2.get_float()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "int64"
def test_get_eps_consensus(self):
data = self.batch.get_eps_consensus()
assert isinstance(data, dict)
assert isinstance(data["AAPL"], float)
data2 = self.batch2.get_eps_consensus()
assert isinstance(data2, pd.DataFrame)
assert_index_equal(data2.index, pd.Index(self.batch2.symbols))
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.batch4.get_eps_consensus()
assert isinstance(data4, dict)
assert isinstance(data4["AAPL"], Decimal)
class TestFieldMethodsShare(object):
def setup_class(self):
self.share = Stock("AAPL")
self.share2 = Stock("AAPL", output_format='pandas')
self.share4 = Stock("AAPL",
json_parse_int=Decimal,
json_parse_float=Decimal)
self.share5 = Stock("TSLA")
def test_get_company_name(self):
data = self.share.get_company_name()
print(type(data))
assert isinstance(data, six.string_types)
assert data == "Apple Inc."
data2 = self.share2.get_company_name()
assert isinstance(data2, pd.DataFrame)
def test_get_primary_exchange(self):
data = self.share.get_primary_exchange()
assert isinstance(data, six.string_types)
assert data == "Nasdaq Global Select"
data2 = self.share2.get_primary_exchange()
assert isinstance(data2, pd.DataFrame)
def test_get_sector(self):
data = self.share.get_sector()
assert isinstance(data, six.string_types)
assert data == "Technology"
data2 = self.share2.get_sector()
assert isinstance(data2, pd.DataFrame)
def test_get_open(self):
data = self.share.get_open()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_open()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_open()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_close(self):
data = self.share.get_close()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_close()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_close()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_years_high(self):
data = self.share.get_years_high()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_years_high()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_years_high()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_years_low(self):
data = self.share.get_years_low()
assert isinstance(data, float)
assert data > 0
data2 = self.share2.get_years_low()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_years_low()
assert isinstance(data4, Decimal)
assert data4 > 0
def test_get_ytd_change(self):
data = self.share.get_ytd_change()
assert isinstance(data, float)
data2 = self.share2.get_ytd_change()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_ytd_change()
assert isinstance(data4, Decimal)
def test_get_volume(self):
data = self.share.get_volume()
assert isinstance(data, int)
assert data > 1000
data2 = self.share2.get_volume()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_volume()
assert isinstance(data4, Decimal)
assert data4 > 1000
def test_get_market_cap(self):
data = self.share.get_market_cap()
assert isinstance(data, int)
data2 = self.share2.get_market_cap()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_market_cap()
assert isinstance(data4, Decimal)
def test_get_beta(self):
data = self.share.get_beta()
assert isinstance(data, float)
data2 = self.share2.get_beta()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_beta()
assert isinstance(data4, Decimal)
def test_get_short_interest(self):
data = self.share.get_short_interest()
assert isinstance(data, int)
data2 = self.share2.get_short_interest()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_short_interest()
assert isinstance(data4, Decimal)
def test_get_short_ratio(self):
data = self.share.get_short_ratio()
assert isinstance(data, float)
data2 = self.share2.get_short_ratio()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_short_ratio()
assert isinstance(data4, Decimal)
def test_get_latest_eps(self):
data = self.share5.get_latest_eps()
assert isinstance(data, float)
data4 = self.share4.get_latest_eps()
assert isinstance(data4, Decimal)
def test_get_shares_outstanding(self):
data = self.share.get_shares_outstanding()
assert isinstance(data, int)
data2 = self.share2.get_shares_outstanding()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_shares_outstanding()
assert isinstance(data4, Decimal)
def test_get_float(self):
data = self.share.get_float()
assert isinstance(data, int)
data2 = self.share2.get_float()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "int64"
data4 = self.share4.get_float()
assert isinstance(data4, Decimal)
def test_get_eps_consensus(self):
data = self.share.get_eps_consensus()
assert isinstance(data, float)
data2 = self.share2.get_eps_consensus()
assert isinstance(data2, pd.DataFrame)
assert data2.loc["AAPL"].dtype == "float64"
data4 = self.share4.get_eps_consensus()
assert isinstance(data4, Decimal)
def analyse_data(self, filename, coin, formatted_cryptocurrency_tweets,
j_info):
tweetsInHour = []
print("Number of unique tweets in an hour for " + coin + " is " +
str(len(formatted_cryptocurrency_tweets)))
file_exists = os.path.isfile(filename)
if sentiment_config.TYPE == "CRYPTO":
change = j_info['ticker']['change']
volume = j_info['ticker']['volume']
self.price = j_info['ticker']['price']
timestamp = j_info['timestamp']
else:
stock = Stock(sentiment_config.EXCHANGE)
change = stock.get_price() - self.price
volume = stock.get_volume()
self.price = stock.get_price()
timestamp = datetime.datetime.now().strftime("%H:%M:%S")
try:
# average compound
average_compound = float(
sum(d['sentiment']['compound']
for d in formatted_cryptocurrency_tweets)) / len(
formatted_cryptocurrency_tweets)
cryptoFeature = {
'TimeStamp': [timestamp],
'Sentiment': [average_compound],
'Volume': [volume],
'Change': [change],
'Price': [self.price],
'NoOfTweets': [len(formatted_cryptocurrency_tweets)]
}
formatted_cryptocurrency_tweets = []
pd.DataFrame.from_dict(data=cryptoFeature,
orient='columns').to_csv(
filename,
mode='a',
header=not file_exists)
# Make Predictions
linear_predict_change = predict.generate_linear_prediction_model(
cryptoFeature, filename)
multi_linear_predict_change = predict.generate_multi_linear_prediction_model(
cryptoFeature, filename)
tree_predict_change = predict.generate_tree_prediction_model(
cryptoFeature, filename)
forest_predict_change = predict.generate_forest_prediction_model(
cryptoFeature, filename)
if linear_predict_change is not None and multi_linear_predict_change is not None\
and tree_predict_change is not None and forest_predict_change is not None:
#get average prediction
average_prediction = (float(linear_predict_change) +
float(multi_linear_predict_change) +
float(tree_predict_change) +
float(forest_predict_change)) / 4
self.notify_user(str(average_prediction),
str(cryptoFeature['Sentiment'][0]))
#Plotting
self.cryptocurrency_current_price.append(self.price)
self.cryptocurrency_plot_time.append(
datetime.datetime.now().strftime("%H:%M:%S"))
print("Linear " + linear_predict_change)
print("Multi Linear" + multi_linear_predict_change)
print("Forest" + forest_predict_change)
self.plot(linear_predict_change, multi_linear_predict_change,
tree_predict_change, forest_predict_change)
else:
print("Still building set")
except Exception as e:
print("No tweets in the last hour", str(e))
print('Tesla: open price %f, current price %f' % (opn, pri))
# look at AA (Alcoa?)
aa = Stock('AA')
eps = aa.get_latest_eps()
pri = aa.get_price()
print('AA: latest EPS %f, current price %f' % (eps, pri))
# show symbols and company name for each
# for s in symbols:
# if s['isEnabled'] == True: print(s['symbol'] + ', ' + s['name'])
# Apple
apple = Stock('AAPL')
apple.get_key_stats()
apple.get_volume()
apple.get_earnings()
apple.get_quote()
apple.get_quote()['peRatio']
apple.get_quote()['close']
def pStock(symbol):
priceCriteria = 5.00
data = Stock(symbol)
quote = data.get_quote()
pe = quote['peRatio']
close = quote['close']
sts = data.get_key_stats()
cName = sts['companyName']
from iexfinance import Stock
from iexfinance import get_historical_data
from datetime import datetime
tsla = Stock('TSLA')
print("open", tsla.get_open())
print("volume", tsla.get_volume())
print("price", tsla.get_price())
print("close", tsla.get_close())
start = datetime(2018,10,11)
end = datetime(2018,10,13)
df = get_historical_data("AAPL", start = start, end = end, output_format = 'pandas')
print(df.head())
