Spaces:
Sleeping
Sleeping
Robert Castagna
commited on
Commit
•
50bed66
1
Parent(s):
fbe72f1
rollback
Browse files- .gitignore +4 -1
- app.py +25 -6
- fin_data_api.py +52 -18
- requirements.txt +2 -1
.gitignore
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@@ -1,2 +1,5 @@
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secrets.json
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fin_data.db
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secrets.json
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fin_data.db
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streamlit_config.json
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edgar-crawler/
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.venv/
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app.py
CHANGED
@@ -2,15 +2,23 @@ import streamlit as st
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import sqlite3
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import pandas as pd
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import streamlit as st
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st.
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conn = sqlite3.connect('fin_data.db')
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c = conn.cursor()
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c.execute("""
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select * from
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""")
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rows = c.fetchall()
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# Extract column names from the cursor
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column_names = [description[0] for description in c.description]
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# Create a DataFrame
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df = pd.DataFrame(rows, columns=column_names)
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st.dataframe(df)
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import sqlite3
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import pandas as pd
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import streamlit as st
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import pygwalker as pyg
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st.set_page_config(
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page_title="Financial Data",
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page_icon="📈",
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layout="wide",
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initial_sidebar_state="expanded",
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)
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st.set_title('Financial Data')
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conn = sqlite3.connect('fin_data.db')
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c = conn.cursor()
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c.execute("""
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select * from company_news
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""")
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rows = c.fetchall()
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# Extract column names from the cursor
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column_names = [description[0] for description in c.description]
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conn.commit()
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conn.close()
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# Create a DataFrame
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df = pd.DataFrame(rows, columns=column_names)
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# setup pygwalker configuration: https://github.com/Kanaries/pygwalker
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def load_config(file_path):
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with open(file_path, 'r') as config_file:
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config_str = config_file.read()
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return config_str
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config = load_config('config.json')
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pyg.walk(df, env='Streamlit', dark='dark', spec=config)
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# show the dataframe just to test
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st.dataframe(df)
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fin_data_api.py
CHANGED
@@ -30,38 +30,72 @@ def sentiment_analysis(headline:str) -> str:
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:param1 headline: Text string: 'Apple is the best company in the world'
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"""
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nlp = pipeline("sentiment-analysis")
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return nlp(headline)
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## get basic financials
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#res_basic_fins = get_finnhub_data('/stock/metric?symbol=AAPL&metric=all')
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#print(res_basic_fins['metric'].keys())
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#print(res_basic_fins['series']['annual'].keys())
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#print(res_basic_fins['series']['quarterly'].keys())
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#res_sentiment = get_finnhub_data('/stock/insider-sentiment?symbol=AAPL')
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#print(res_sentiment['data'][0].keys())
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# # put data in database after figuring out what data we want to store
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# conn = sqlite3.connect('fin_data.db')
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# c = conn.cursor()
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# c.execute("""
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# insert into test values (2, 'test value two')
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# """)
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:param1 headline: Text string: 'Apple is the best company in the world'
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"""
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nlp = pipeline("sentiment-analysis", model="ProsusAI/finbert")
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return nlp(headline)
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# --------------------------------- get news articles for a company --------------------------------- #
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conn = sqlite3.connect('fin_data.db')
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c = conn.cursor()
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c.execute("""create table if not exists company_news (
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id text primary key,
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ticker text,
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category text,
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headline text,
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date_stamp text,
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sentiment_label text,
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sentiment_score real
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)""")
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res_news = get_finnhub_data('/company-news?symbol=AAPL&from=2023-08-15&to=2023-08-17')
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print(res_news[0].keys())
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for item in res_news:
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dt_object = datetime.datetime.fromtimestamp(item['datetime']).strftime("%Y-%m-%d")
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sentiment = sentiment_analysis(item['headline'])
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sentiment_label = sentiment[0]['label']
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sentiment_score = sentiment[0]['score']
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print(item['headline'], dt_object, sentiment[0]['label'])
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# Prepare your query and data
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query = """
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INSERT INTO company_news
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VALUES (?, ?, ?, ?, ?, ?, ?)
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"""
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data = (item['id'], 'AAPL', item['category'], item['headline'], dt_object, sentiment_label, sentiment_score)
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# Execute the query with the data
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#c.execute(query, data)
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rows = c.execute("""
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select * from company_news
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""")
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column_names = [description[0] for description in c.description]
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# Create a DataFrame
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df = pd.DataFrame(rows, columns=column_names)
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print(df)
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# --------------------------------- get basic financials ---------------------------------#
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#res_basic_fins = get_finnhub_data('/stock/metric?symbol=AAPL&metric=all')
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#print(res_basic_fins['metric'].keys())
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#print(res_basic_fins['series']['annual'].keys())
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#print(res_basic_fins['series']['quarterly'].keys())
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# --------------------------------- get insider sentiment --------------------------------- #
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#res_sentiment = get_finnhub_data('/stock/insider-sentiment?symbol=AAPL')
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#print(res_sentiment['data'][0].keys())
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conn.commit()
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conn.close()
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requirements.txt
CHANGED
@@ -17,4 +17,5 @@ transformers
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requests
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datetime
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pysqlite3 == 0.5.2
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streamlit
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requests
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datetime
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pysqlite3 == 0.5.2
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streamlit
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pygwalker
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