Build an AI-Powered Stock Analysis Dashboard with ChatGPT and EODHD Financial API
Kevin Meneses
SFMC Consultant|SAP CX Senior Consultant |SAP Sales and Service Cloud|CPI|CDC|Qualtrics|Data Analyst and ETL|Marketing Automation|SAPMarketing Cloud and Emarsys
“The stock market is a device for transferring money from the impatient to the patient.” — Warren Buffett
The Problem: Drowning in Financial Data
If you’re an investor today, you’re overwhelmed.
Even for experienced traders, turning raw numbers into actionable insights is time-consuming. For beginners? It’s almost impossible.
Losing Opportunities
I’ll be honest: In 2024, I missed a major breakout on a stock I’d been tracking for months. The signals were there: dividends were rising, RSI was flashing oversold, moving averages were converging… but by the time I put it all together, the move had already happened.
“He who lives by the golden rule makes the golden rules.” — Ray Dalio
That was the turning point. I needed a system to do the heavy lifting — one that could:
The Solution: An AI-Powered Investor Dashboard
So I built it.
A Streamlit dashboard that combines:
“Know what you own, and know why you own it.” — Peter Lynch
And it doesn’t just crunch numbers — it explains them like a seasoned analyst.
Step 1: Test the EODHD API
Before building the dashboard, we test our endpoints.
Code to check fundamentals, dividends, and historical prices:
import requests, json
API_KEY = "67c1af0e6f0185.67184735"
TICKER = "AAPL.US"
# Fundamentals
fund_url = f"https://eodhd.com/api/fundamentals/{TICKER}?api_token={API_KEY}&fmt=json"
fund_data = requests.get(fund_url).json()
print("Fundamentals sample:", json.dumps(fund_data.get('General', {}), indent=2))
# Dividends
div_url = f"https://eodhd.com/api/div/{TICKER}?api_token={API_KEY}&fmt=json"
div_data = requests.get(div_url).json()
print("Dividends sample:", div_data[:3])
# Historical Prices
eod_url = f"https://eodhd.com/api/eod/{TICKER}?api_token={API_KEY}&fmt=json&period=6mo"
eod_data = requests.get(eod_url).json()
print("Price sample:", eod_data[:3])
For fundamental data, you get
fundamentals sample: {
"Code": "AAPL",
"Type": "Common Stock",
"Name": "Apple Inc",
"Exchange": "NASDAQ",
"CurrencyCode": "USD",
"CurrencyName": "US Dollar",
"CurrencySymbol": "$",
"CountryName": "USA",
"CountryISO": "US",
"OpenFigi": "BBG000B9XRY4",
"ISIN": "US0378331005",
"LEI": "HWUPKR0MPOU8FGXBT394",
"PrimaryTicker": "AAPL.US",
"CUSIP": "037833100",
"CIK": "0000320193",
"EmployerIdNumber": "94-2404110",
"FiscalYearEnd": "September",
"IPODate": "1980-12-12",
"InternationalDomestic": "International/Domestic",
"Sector": "Technology",
"Industry": "Consumer Electronics",
"GicSector": "Information Technology",
"GicGroup": "Technology Hardware & Equipment",
"GicIndustry": "Technology Hardware, Storage & Peripherals",
"GicSubIndustry": "Technology Hardware, Storage & Peripherals",
"HomeCategory": "Domestic",
"IsDelisted": false,
"Description": "Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide. The company offers iPhone, a line of smartphones; Mac, a line of personal computers; iPad, a line of multi-purpose tablets; and wearables, home, and accessories comprising AirPods, Apple TV, Apple Watch, Beats products, and HomePod. It also provides AppleCare support and cloud services; and operates various platforms, including the App Store that allow customers to discover and download applications and digital content, such as books, music, video, games, and podcasts, as well as advertising services include third-party licensing arrangements and its own advertising platforms. In addition, the company offers various subscription-based services, such as Apple Arcade, a game subscription service; Apple Fitness+, a personalized fitness service; Apple Music, which offers users a curated listening experience with on-demand radio stations; Apple News+, a subscription news and magazine service; Apple TV+, which offers exclusive original content; Apple Card, a co-branded credit card; and Apple Pay, a cashless payment service, as well as licenses its intellectual property. The company serves consumers, and small and mid-sized businesses; and the education, enterprise, and government markets. It distributes third-party applications for its products through the App Store. The company also sells its products through its retail and online stores, and direct sales force; and third-party cellular network carriers, wholesalers, retailers, and resellers. Apple Inc. was founded in 1976 and is headquartered in Cupertino, California.",
"Address": "One Apple Park Way, Cupertino, CA, United States, 95014",
"AddressData": {
"Street": "One Apple Park Way",
"City": "Cupertino",
"State": "CA",
"Country": "United States",
"ZIP": "95014"
},
"Listings": {
"0": {
"Code": "0R2V",
"Exchange": "LSE",
"Name": "Apple Inc."
},
"1": {
"Code": "AAPL",
"Exchange": "BA",
"Name": "Apple Inc DRC"
},
"2": {
"Code": "AAPL34",
"Exchange": "SA",
"Name": "Apple Inc"
}
},
"Officers": {
"0": {
"Name": "Mr. Timothy D. Cook",
"Title": "CEO & Director",
"YearBorn": "1961"
},
"1": {
"Name": "Mr. Kevan Parekh",
"Title": "Senior VP & CFO",
"YearBorn": "1972"
},
"2": {
"Name": "Molly Thompson",
"Title": "Head of Documentaries for Apple TV+",
"YearBorn": "NA"
},
"3": {
"Name": "Mr. Matthew Cherniss",
"Title": "Head of Programming for Apple TV+",
"YearBorn": "1973"
},
"4": {
"Name": "Ashish Chowdhary",
"Title": "Head of Indian Business",
"YearBorn": "NA"
},
"5": {
"Name": "Mr. Roger Rosner",
"Title": "Vice President of Applications",
"YearBorn": "NA"
},
"6": {
"Name": "Mr. Kevin M. Lynch",
"Title": "Vice President of Technology",
"YearBorn": "1967"
},
"7": {
"Name": "Mr. Wyatt Mitchell",
"Title": "Director of App Design",
"YearBorn": "NA"
},
"8": {
"Name": "Ms. Jennifer Bailey",
"Title": "Vice President of Pay",
"YearBorn": "1963"
},
"9": {
"Name": "Ms. Ann Thai",
"Title": "Director of Product Management for the App Store",
"YearBorn": "NA"
}
},
"Phone": "(408) 996-1010",
"WebURL": "https://www.apple.com",
"LogoURL": "/img/logos/US/aapl.png",
"FullTimeEmployees": 164000,
"UpdatedAt": "2025-07-31"
}
For dividends data, you get
Dividends sample: [{'date': '1987-05-11', 'declarationDate': None, 'recordDate': None, 'paymentDate': None, 'period': None, 'value': 0.00054, 'unadjustedValue': 0.12096, 'currency': 'USD'}, {'date': '1987-08-10', 'declarationDate': None, 'recordDate': None, 'paymentDate': None, 'period': None, 'value': 0.00054, 'unadjustedValue': 0.06048, 'currency': 'USD'}, {'date': '1987-11-17', 'declarationDate': None, 'recordDate': None, 'paymentDate': None, 'period': None, 'value': 0.00071, 'unadjustedValue': 0.07952, 'currency': 'USD'}]
For historical data, you get
Price sample: [{'date': '1980-12-12', 'open': 28.7392, 'high': 28.8736, 'low': 28.7392, 'close': 28.7392, 'adjusted_close': 0.0986, 'volume': 469033600}, {'date': '1980-12-15', 'open': 27.3728, 'high': 27.3728, 'low': 27.2608, 'close': 27.2608, 'adjusted_close': 0.0935, 'volume': 175884800}, {'date': '1980-12-16', 'open': 25.3792, 'high': 25.3792, 'low': 25.2448, 'close': 25.2448, 'adjusted_close': 0.0866, 'volume': 105728000}]
Step 2: Build the AI Dashboard
Here’s the core idea:
The Full Python Code
We’ll use Streamlit for the interface and Plotly for interactive charts:
import requests
import streamlit as st
import openai
import plotly.graph_objects as go
import pandas as pd
# --- API KEYS ---
EODHD_API_KEY = "YOUR_API_KEY"
openai.api_key = OPENAI_API_KEY
# --- API Functions ---
def get_fundamentals(ticker):
url = f"https://eodhd.com/api/fundamentals/{ticker}?api_token={EODHD_API_KEY}&fmt=json"
r = requests.get(url)
return r.json() if r.status_code == 200 else {}
def get_dividends(ticker):
url = f"https://eodhd.com/api/div/{ticker}?api_token={EODHD_API_KEY}&fmt=json"
r = requests.get(url)
return r.json() if r.status_code == 200 else {}
def get_eod_data(ticker, period="6mo"):
url = f"https://eodhd.com/api/eod/{ticker}?api_token={EODHD_API_KEY}&fmt=json&period={period}"
r = requests.get(url)
return r.json() if r.status_code == 200 else []
# --- Technical calculations ---
def calculate_technicals(df):
df = pd.DataFrame(df)
df['date'] = pd.to_datetime(df['date'])
df['close'] = df['close'].astype(float)
df.sort_values('date', inplace=True)
df['MA50'] = df['close'].rolling(window=50).mean()
df['MA200'] = df['close'].rolling(window=200).mean()
delta = df['close'].diff()
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(window=14).mean()
avg_loss = loss.rolling(window=14).mean()
rs = avg_gain / avg_loss
df['RSI'] = 100 - (100 / (1 + rs))
return df
# --- ChatGPT summarizer ---
def summarize_with_gpt(ticker, fundamentals, dividends, techs):
key_data = {
"Market Cap": fundamentals.get("General", {}).get("MarketCapitalization"),
"P/E Ratio": fundamentals.get("Highlights", {}).get("PERatio"),
"Dividend Yield": fundamentals.get("Highlights", {}).get("DividendYield"),
"Beta": fundamentals.get("Highlights", {}).get("Beta"),
"52W High": fundamentals.get("Technicals", {}).get("52WeekHigh"),
"52W Low": fundamentals.get("Technicals", {}).get("52WeekLow")
}
rsi = round(techs['RSI'].iloc[-1], 2) if 'RSI' in techs else None
ma50 = round(techs['MA50'].iloc[-1], 2) if 'MA50' in techs else None
ma200 = round(techs['MA200'].iloc[-1], 2) if 'MA200' in techs else None
prompt = f"""
Analyze {ticker} for an investor.
Key fundamentals: {key_data}
Latest RSI: {rsi}, MA50: {ma50}, MA200: {ma200}
Recent dividends: {dividends[:3]}.
Give:
1. Attractiveness score (1-10).
2. Suggested entry price, stop loss, take profit levels.
3. A brief analysis of trend (bullish/bearish/neutral).
4. Explain your reasoning in simple terms.
"""
response = openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "system", "content": "You are a financial analyst providing clear, actionable insights."},
{"role": "user", "content": prompt}],
max_tokens=500
)
return response.choices[0].message.content
# --- Streamlit UI ---
st.set_page_config(page_title="Investor AI Dashboard", layout="wide")
st.title("📊 Investor AI Dashboard")
st.write("Real-time **EODHD** data + **ChatGPT** insights for smarter investing.")
# Sidebar inputs
ticker = st.sidebar.text_input("Enter Ticker (e.g., AAPL.US):", value="AAPL.US")
period = st.sidebar.selectbox("Select Period:", ["1mo", "3mo", "6mo", "ytd", "1y", "2y"])
if st.sidebar.button("Fetch Data"):
with st.spinner("Fetching data..."):
fundamentals = get_fundamentals(ticker)
dividends = get_dividends(ticker)
eod_data = get_eod_data(ticker, period)
if eod_data:
df = calculate_technicals(eod_data)
# --- Price Chart ---
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['close'], mode='lines', name='Close Price'))
fig.add_trace(go.Scatter(x=df['date'], y=df['MA50'], mode='lines', name='MA50'))
fig.add_trace(go.Scatter(x=df['date'], y=df['MA200'], mode='lines', name='MA200'))
fig.update_layout(title=f"{ticker} Price & Moving Averages", xaxis_title="Date", yaxis_title="Price")
st.plotly_chart(fig, use_container_width=True)
# --- KPIs ---
st.subheader("Key Metrics")
col1, col2, col3 = st.columns(3)
col1.metric("Market Cap", fundamentals.get("General", {}).get("MarketCapitalization"))
col2.metric("P/E Ratio", fundamentals.get("Highlights", {}).get("PERatio"))
col3.metric("Dividend Yield", fundamentals.get("Highlights", {}).get("DividendYield"))
col4, col5, col6 = st.columns(3)
col4.metric("Beta", fundamentals.get("Highlights", {}).get("Beta"))
col5.metric("52W High", fundamentals.get("Technicals", {}).get("52WeekHigh"))
col6.metric("52W Low", fundamentals.get("Technicals", {}).get("52WeekLow"))
# --- Dividends Section ---
st.subheader("Recent Dividends")
if dividends:
st.table(dividends[:5])
else:
st.write("No dividend data available.")
# --- AI Analysis ---
st.subheader("AI-Powered Investment Insight")
ai_summary = summarize_with_gpt(ticker, fundamentals, dividends, df)
st.write(ai_summary)
else:
st.warning("No price data available.")
Feature-by-Feature: How This AI Dashboard Simplifies Stock Analysis
1. Price Chart with Technical Indicators
A dynamic Plotly chart shows the stock’s closing prices along with the 50-day and 200-day moving averages, helping you quickly assess the trend (bullish, bearish, or sideways). This visual layer makes it easy to identify potential support and resistance levels.
2. Key Metrics at a Glance
The dashboard displays the most important fundamental KPIs in clean, easy-to-read cards:
3. Dividends Overview
For dividend investors, there’s a table of the last 5 dividends, including payment dates and amounts. This helps you track income consistency and plan around upcoming payouts.
4. AI-Powered Summary
This is where the magic happens. ChatGPT analyzes fundamentals, technical indicators, and dividend data to generate a plain-English report that includes:
The Impact
Instead of juggling tools and data sources, you now get:
“Risk comes from not knowing what you’re doing.” — Warren Buffett
Now you don’t have to guess — your dashboard does the heavy lifting.
Final Thoughts
This project shows how AI + Financial APIs can turn complex data into actionable insights. It’s perfect for traders who want clarity, speed, and confidence.
👉 Try EODHD Financial API (10% off) and supercharge your analysis.
“In investing, what is comfortable is rarely profitable.” — Robert Arnott