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mrC0dermrC0der
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Initial (#30)
* added action * added stock grabber * added yfin script
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‎500.csv

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‎Pipfile

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@@ -8,6 +8,7 @@ streamlit = "*"
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pandas = "*"
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requests = "*"
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lxml = "*"
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yfinance = {extras = ["nospam", "repair"], version = "*"}
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[dev-packages]
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‎Pipfile.lock

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‎README.md

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Stock Analysis App
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## Goals
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### Leading Indicators
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1. [ ] **Moving Averages**: Longer-term moving averages to confirm a market trend.
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2. [ ] **Volume**: Trading volume following a price move.
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3. [ ] **EPS (Earnings Per Share)**: Historical earnings growth can be indicative of future performance but is a lagging indicator.
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4. **P/E Ratio**: Indicates how much investors are willing to pay for a company’s earnings.
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5. **Dividend Yields**: Higher yields may signify a mature, established company.
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6. **Balance Sheet Metrics**: Debt-to-equity ratio, working capital, etc.
@@ -116,3 +118,4 @@ Understanding the layers of bonds or funds requires digging into their structure
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7. [ ] **Intrinio**: Provides various types of financial market data, including information on ETFs and mutual funds, through their API.
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8. [ ] **Zacks Investment Research**: Their API provides detailed mutual fund data including holdings, but typically requires a subscription.
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9. [ ] **Interactive Brokers API**: Known for its powerful trading API, it also allows you to fetch quite a bit of fund data if you have an account with them.
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‎app.py

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"""midas.py"""
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import streamlit as st
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import requests
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# from lxml import etree
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import pandas as pd
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def fetch_thirteen_f(url):
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"""doc str."""
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response = requests.get(url, timeout=30)
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if response.status_code == 200:
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return response.content
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else:
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return None
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def parse_thirteen_f(xml_data):
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"""doc str."""
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# tree = etree.fromstring(xml_data)
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tree = f"{xml_data}"
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rows = [xml_data, tree]
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# for info_table in tree.xpath("//infoTable"):
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# row = {}
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# for element in info_table:
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# row[element.tag] = element.text
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# rows.append(row)
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return pd.DataFrame(rows)
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# ------ Data Fetching Functions ------
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def fetch_stock_data(ticker):
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"""doc str."""
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print(ticker)
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return {} # Logic to fetch stock data
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def fetch_economic_data(indicator):
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"""doc str."""
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print(indicator)
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return {} # Logic to fetch economic data
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# ------ Analysis Functions ------
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# ---- Leading Indicators ----
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def calculate_moving_average(data):
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"""doc str."""
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print(data)
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return {} # Logic for Moving Average
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def calculate_macd(data):
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"""doc str."""
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print(data)
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return {} # Logic for MACD
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def analyze_social_media_sentiment(data):
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"""doc str."""
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print(data)
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return {} # Logic for social media sentiment
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# ---- Lagging Indicators ----
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def calculate_rsi(data):
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"""doc str."""
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print(data)
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return {} # Logic for Relative Strength Index
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def calculate_bollinger_bands(data):
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"""doc str."""
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print(data)
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return {} # Logic for Bollinger Bands
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# ---- Economic Indicators ----
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def analyze_gdp(data):
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"""doc str."""
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print(data)
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return {} # Logic for GDP Analysis
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def analyze_interest_rates(data):
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"""doc str."""
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print(data)
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return {} # Logic for Interest Rates Analysis
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def analyze_unemployment(data):
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"""doc str."""
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print(data)
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return {} # Logic for Unemployment Analysis
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# ------ Display Functions ------
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def display_stock_data(data):
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"""doc str."""
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print(data)
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return {} # Logic to display stock data
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def display_economic_data(data):
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"""doc str."""
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print(data)
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return {} # Logic to display economic data
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def display_thirteen_f(data):
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"""doc str."""
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print(data)
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return {} # Logic to display 13F data
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def display_analysis(data):
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"""doc str."""
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print(data)
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return {} # Logic to display analyzed data
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# ------ Main App Function ------
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def main():
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"""main."""
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st.title("Financial Analytics Dashboard")
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# User Inputs
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ticker = st.text_input("Enter stock ticker", "AAPL")
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economic_indicator = st.selectbox(
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"Choose an Economic Indicator", ["GDP", "Interest Rates", "Unemployment"]
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)
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# Fetch Data
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stock_data = fetch_stock_data(ticker)
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economic_data = fetch_economic_data(economic_indicator)
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sec_thirteen_f_data = fetch_thirteen_f(ticker)
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# Analyze Data
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moving_avg = calculate_moving_average(stock_data)
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macd = calculate_macd(stock_data)
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social_media_sentiment = analyze_social_media_sentiment(ticker)
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rsi = calculate_rsi(stock_data)
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bollinger_bands = calculate_bollinger_bands(stock_data)
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gdp_analysis = analyze_gdp(economic_data)
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interest_rates_analysis = analyze_interest_rates(economic_data)
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unemployment_analysis = analyze_unemployment(economic_data)
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# Display Data and Analysis
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display_stock_data(stock_data)
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display_economic_data(economic_data)
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display_thirteen_f(sec_thirteen_f_data)
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display_analysis(
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{
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"Moving Average": moving_avg,
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"MACD": macd,
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"Social Media Sentiment": social_media_sentiment,
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"RSI": rsi,
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"Bollinger Bands": bollinger_bands,
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"GDP": gdp_analysis,
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"Interest Rates": interest_rates_analysis,
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"Unemployment": unemployment_analysis,
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}
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)
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if __name__ == "__main__":
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main()
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if __name__ == "__main__":
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main()

‎yfin.py

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import yfinance as yf
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from yfinance import shared
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import json
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import pandas as pd
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import time
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def get_stock_data(symbol):
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print(f'Getting stock data for {symbol}')
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data ={}
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try:
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stock_ticker = yf.Ticker(symbol)
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except Exception as err:
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print('Error: %s', err)
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# get all stock info
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try:
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data['info'] = stock_ticker.info
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except Exception as err:
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print('Error: %s', err)
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try:
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# get historical market data
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mo1 = stock_ticker.history(period="1mo")
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data['1mo_hist'] = pd.DataFrame(mo1).to_json()
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except Exception as err:
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print('Error: %s', err)
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try:
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# show meta information about the history (requires history() to be called first)
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data['history_metadata'] = stock_ticker.history_metadata
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except Exception as err:
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print('Error: %s', err)
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try:
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# show actions (dividends, splits, capital gains)
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data['actions'] = pd.DataFrame(stock_ticker.actions).to_json()
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data['dividends'] = pd.DataFrame(stock_ticker.dividends).to_json()
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data['splits'] = pd.DataFrame(stock_ticker.splits).to_json()
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except Exception as err:
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print('Error: %s', err)
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try:
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data['capital_gains'] = pd.DataFrame(stock_ticker.capital_gains).to_json() # only for mutual funds & etfs
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except Exception as err:
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print('Error: %s', err)
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try:
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# show share count
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get_shares_full = stock_ticker.get_shares_full(start="2022-01-01", end=None)
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df = pd.DataFrame(get_shares_full)
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df.reset_index(inplace=True)
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data['get_shares_full'] = pd.DataFrame(df).to_json()
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except Exception as err:
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print('Error: %s', err)
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# show financials:
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# - income statement
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try:
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income_stmt = stock_ticker.income_stmt
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data['income_stmt'] = pd.DataFrame(income_stmt).to_json()
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data['quarterly_income_stmt'] = pd.DataFrame(stock_ticker.quarterly_income_stmt).to_json()
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# - balance sheet
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data['balance_sheet'] = pd.DataFrame(stock_ticker.balance_sheet).to_json()
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data['quarterly_balance_sheet'] = pd.DataFrame(stock_ticker.quarterly_balance_sheet).to_json()
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# - cash flow statement
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data['cashflow'] = pd.DataFrame(stock_ticker.cashflow).to_json()
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data['quarterly_cashflow'] = pd.DataFrame(stock_ticker.quarterly_cashflow).to_json()
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# see `Ticker.get_income_stmt()` for more options
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except Exception as err:
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print('Error: %s', err)
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try:
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# show holders
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data['major_holders'] = pd.DataFrame(stock_ticker.major_holders).to_json()
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data['institutional_holders'] = pd.DataFrame(stock_ticker.institutional_holders).to_json()
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data['mutualfund_holders'] = pd.DataFrame(stock_ticker.mutualfund_holders).to_json()
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data['insider_transactions'] = pd.DataFrame(stock_ticker.insider_transactions).to_json()
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data['insider_purchases'] = pd.DataFrame(stock_ticker.insider_purchases).to_json()
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data['insider_roster_holders'] = pd.DataFrame(stock_ticker.insider_roster_holders).to_json()
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except Exception as err:
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print('Error: %s', err)
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try:
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# show recommendations
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data['recommendations'] = pd.DataFrame(stock_ticker.recommendations).to_json()
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data['recommendations_summary'] = pd.DataFrame(stock_ticker.recommendations_summary).to_json()
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data['upgrades_downgrades'] = pd.DataFrame(stock_ticker.upgrades_downgrades).to_json()
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except Exception as err:
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print('Error: %s', err)
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# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
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# Note: If more are needed use stock_ticker.get_earnings_dates(limit=XX) with increased limit argument.
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# data['earnings_dates'] = pd.DataFrame(stock_ticker.earnings_dates).to_json()
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# show ISIN code - *experimental*
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# ISIN = International Securities Identification Number
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try:
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data['isin'] = stock_ticker.isin
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except Exception as err:
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print('Error: %s', err)
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try:
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# show options expirations
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data['options'] = stock_ticker.options
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# FIXME: Get the options data into the json dict so we can publish to mongo
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# data['options_data'] = {}
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# for opt in data['options']:
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# print(f'Getting options for {opt}')
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# # data['options_data'][opt] = stock_ticker.option_chain(opt)
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# # data['options_data'][opt] = stock_ticker.option_chain(opt)
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# data['options_data'][f'{opt}_calls'] = pd.DataFrame(stock_ticker.option_chain(opt)).to_json()
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# data['options_data'][f'{opt}_puts'] = pd.DataFrame(stock_ticker.option_chain(opt)).to_json()
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except Exception as err:
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print('Error: %s', err)
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try:
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# show news
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data['news'] = stock_ticker.news
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except Exception as err:
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print('Error: %s', err)
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#TODO: This is only used when debuggins Json not Serializable errors
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# with open(f'data/{symbol}.txt', 'w') as f:
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# print(data, file=f)
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with open(f'data/{symbol}.json', 'w') as file:
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file.write(json.dumps(data))
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# return data
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# csv_data = pd.read_csv('https://gist.githubusercontent.com/yongghongg/4fa63c26369f844f22fed6121a24e04f/raw/8bda30f0b3ffbc82e563821ec131ebd62126c20d/SAP500_symbol_list.csv', index_col='Symbol')
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csv_data = pd.read_csv('500.csv', index_col='Symbol')
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for symbol in csv_data.iterrows():
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# print(symbol[0])
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get_stock_data(symbol[0])
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time.sleep(5)
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