def test_get_most_recent_sentiment_by_symbol_id(self):
data = PyStockTwitData()
recent_sentiment = data.get_most_recent_sentiment_by_symbol_id('AAPL')
# Since the recent msg always changes, just testing if a dict
# is returned
self.assertEqual(type({}), type(recent_sentiment))
def test_get_all_msgs_with_sentiment_by_symbol_id(self):
data = PyStockTwitData()
msgs, sentiment = data.get_all_msgs_with_sentiment_by_symbol_id(
'AAPL', limit=3)
# Check if the length equals the limit
self.assertEqual(3, len(msgs))
self.assertEqual(3, len(sentiment))
def test_get_most_recent_msg_by_symbol_id(self):
data = PyStockTwitData()
recent_msg = data.get_most_recent_msg_by_symbol_id('AAPL')
# Since the recent msg always changes, just testing if a string
# is returned
self.assertEqual(type('test'), type(recent_msg))
def test_extract_sentiment_statements_basic(self):
data = PyStockTwitData()
# Use an example json that this statement parses
example = [{'sentiment': {'basic': 'Bullish'}}, {'sentiment': None}]
parsed_sentiment = data.extract_sentiment_statements_basic(example)
# Check if the parser gets Bullish and None
self.assertEqual('Bullish', parsed_sentiment[0])
self.assertEqual("None", parsed_sentiment[1])
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
import panda as pd
data = PyStockTwitData()
# Get all msgs from this company that is specified
list_of_msgs, list_of_sentiment_json = (
data.get_all_msgs_with_sentiment_by_symbol_id("VEEV"))
# Parse out the Bullish, Bearish, or None Sentiment
list_of_sentiment = (
data.extract_sentiment_statements_basic(list_of_sentiment_json))
# Create a Dataframe
dataframe = pd.DataFrame({'msg': list_of_msgs, 'sentiment': list_of_sentiment})
# Print to see dataframe and save
print(dataframe)
dataframe.to_csv('../sample_csv_output/pystockdataset.csv')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
data = PyStockTwitData()
recent_msg = data.get_most_recent_msg_by_symbol_id('AAPL')
print(recent_msg)
# Sample Output
# $AAPL i'd love to have a 1,000 shares of this at $150 average.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
data = PyStockTwitData()
example = [{'sentiment': {'basic': 'Bullish'}}, {'sentiment': None}]
parsed_sentiment = data.extract_sentiment_statements_basic(example)
print(parsed_sentiment)
# Sample Output
# ['Bullish', 'None']
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
data = PyStockTwitData()
company_list = ['AAPL', 'VEEV', 'DECK', 'MSFT', 'AMZN']
data.stock_csv_list_create(
"../sample_csv_output/"
"stocktwit_csv__list_create_multi.csv",
company_list,
30,
5,
limit=30)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
data = PyStockTwitData()
recent_msg = data.get_most_recent_msg_by_user('170')
print(recent_msg)
# Sample Output
# @howardlindzon Thanks man. How did you get so good at this?
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pystocktwits_data_utils import PyStockTwitData
data = PyStockTwitData()
data.stocktwit_csv_create("../sample_csv_output/stocktwit_csv_create_VEEV.csv",
"VEEV",
30,
5,
limit=30)