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import pandas as pd
from datetime import datetime
import yfinance as yf
from charts import update_bar_graph
import json
# Download stock data and format into a DataFrame
def download_data(state):
df = yf.download(state["symbol"], period="max", interval="1d")
df = df.reset_index()
df = df.sort_values(by="Date", ascending=False)
df = df.round({"Open": 2, "High": 2, "Low": 2, "Close": 2, "Adj Close": 2})
df["Date"] = pd.to_datetime(df["Date"])
state["main_df_subset"] = df
state["main_df"] = state["main_df_subset"]
# Download S&P 500 data and format into a DataFrame
def download_sp500(state):
df = yf.download(tickers="^GSPC", period="max", interval="1d")
df = df.reset_index()
df = df.sort_values(by="Date", ascending=False)
df = df.round({"Open": 2, "High": 2, "Low": 2, "Close": 2, "Adj Close": 2})
df["Date"] = pd.to_datetime(df["Date"])
state["another_df"] = df
# Retrieve latest stock news
def stock_news(state):
msft = yf.Ticker(state["symbol"])
articles = {}
data = msft.news
latest_articles = sorted(
data, key=lambda x: x["providerPublishTime"], reverse=True
)[:4]
for item in latest_articles:
provider_publish_time = item.get("providerPublishTime", "")
if provider_publish_time:
# Convert the timestamp to a readable date
formatted_date = datetime.fromtimestamp(provider_publish_time)
readable_date = formatted_date.strftime("%B %d, %Y at %H:%M")
else:
readable_date = "Date not available"
title = item.get("title", "No Title")
articles[title] = {
"source": item.get("publisher", ""),
"published_at": readable_date,
"url": item.get("link", ""),
}
state["articles"] = articles
# Retrieve income statement data
def income_statement(state):
quarterly_income_stmt = yf.Ticker(state["symbol"]).quarterly_income_stmt
df = pd.DataFrame(quarterly_income_stmt)
df.columns = pd.to_datetime(df.columns).strftime("%Y-%m-%d")
state["income_statement_df"] = df
update_bar_graph(state)
show_fin_metrics(state)
# Show financial metrics
def show_fin_metrics(state):
stock = yf.Ticker(state["symbol"])
operating_margins = stock.info['operatingMargins']
gross_margin = stock.info['grossMargins']
ebitda_margin = stock.info['ebitdaMargins']
state["operating_margin"] = f"{operating_margins * 100:.2f}%"
state["gross_margin"] = f"{gross_margin * 100:.2f}%"
state["ebitda_margin"] = f"{ebitda_margin * 100:.2f}%"
def _one_day_data(state):
state["last_24_hours_open"] = round(state["main_df"]["Open"].iloc[0], 2)
state["last_24_hours_high"] = round(state["main_df"]["High"].iloc[0], 2)
state["last_24_hours_low"] = round(state["main_df"]["Low"].iloc[0], 2)
# Retrieve earnings call transcript from JSON file
# You could replace this with a call to an API like Financial Modeling Prep
def earnings_calls(state):
ticker = state["symbol"]
with open('earnings-data.json', 'r') as file:
earnings_transcript = json.load(file)
if earnings_transcript:
for item in earnings_transcript:
if item['symbol'] == ticker:
state["earnings_transcript"] = item["content"]
else:
print("No earnings transcript found.") |