API-Integrated Analytics Dashboard

A live, multi-source analytics dashboard that auto-refreshes data from 3+ public APIs - zero manual data entry.

🔗 Live Demo →

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Problem Statement

Business analysts waste hours every week copying data from multiple sources into spreadsheets - news sites, financial data portals, economic databases - before any analysis even begins. By the time a report is ready, the data is already stale.

The core question: Can we build a single dashboard that pulls live data from multiple APIs, processes it automatically, and presents decision-ready insights - refreshed on demand, with no manual work?

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Objective

1. Connect to 3+ real-world public APIs and unify their data in a single interface
2. Apply real-time analytics logic (aggregations, trend detection, alerting)
3. Build an interactive dashboard requiring zero manual data entry
4. Demonstrate that automation removes the "data collection" bottleneck entirely
5. Design the UX so a non-technical user can extract insights without any help

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Data Sources (APIs Integrated)

API	Data Type	Refresh Rate	Use in Dashboard
Alpha Vantage / Yahoo Finance	Stock prices, FX rates	Real-time	Price tracker, % change
World Bank Open Data API	Macro indicators (GDP, inflation, unemployment)	Monthly	Economic context panel
NewsAPI / GNews	Financial news headlines	Hourly	Sentiment + news feed
Open Exchange Rates	Currency exchange rates	Real-time	FX rate table
REST Countries API	Country metadata	Static	Geo context labels

All APIs use free tiers - no paid subscription required to run this project.

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Dashboard Panels

Panel 1 - Market Overview

• Live price cards: selected tickers with % change (daily / weekly)
• Sparkline charts for 30-day price history
• Volume trend bars

Panel 2 - Macro Intelligence

• GDP growth rates: top 10 economies (bar chart, sortable)
• Inflation vs. unemployment scatter plot (Phillips Curve visualisation)
• Country-level macro table with conditional formatting

Panel 3 - FX Rate Monitor

• Live currency rate table vs. USD base
• Heatmap of % change across major pairs (last 7 days)
• Trend line: user-selected pair over custom date range

Panel 4 - News & Sentiment Feed

• Latest 20 financial headlines (auto-refreshed)
• Automated sentiment scoring per headline (positive / neutral / negative) using VADER
• Sentiment trend chart: proportion of positive vs. negative news over time
• Keyword frequency bar chart from headlines (topic tracking)

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Methodology

Step 1 - API Integration Layer

• Built a modular api_client.py with dedicated fetch functions per source
• Implemented caching (@st.cache_data with TTL) to avoid redundant API calls
• Error handling for rate limits, timeouts, and malformed responses
• .env file for secure API key management

Step 2 - Data Processing Pipeline

• Normalised all JSON responses into Pandas DataFrames
• Aligned timestamps across sources (UTC standardisation)
• Computed derived metrics: 7-day rolling average, % change, z-score for anomaly flagging

Step 3 - Sentiment Analysis

• Applied VADER (Valence Aware Dictionary Sentiment Reasoner) to news headlines
• Classified each headline as Positive / Neutral / Negative
• Aggregated sentiment scores by day and ticker mention

Step 4 - Dashboard Build (Streamlit)

• Modular panel structure: each panel is an independent component
• User controls: date range picker, ticker selector, country filter, refresh button
• Colour-coded conditional formatting (green = positive, red = negative)
• Mobile-responsive layout using Streamlit columns

Step 5 - Deployment

• Deployed to Streamlit Community Cloud (free hosting)
• Environment variables managed via Streamlit Secrets
• Refresh interval: on-demand (button) + auto-refresh every 15 minutes

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Tools & Technologies

Category	Tools
Language	Python 3.10
Dashboard	Streamlit
Visualisation	Plotly Express, Plotly Graph Objects
API Handling	Requests, httpx
Data	Pandas, NumPy
NLP / Sentiment	NLTK (VADER)
Env Management	python-dotenv
Deployment	Streamlit Community Cloud
Version Control	Git / GitHub

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Key Insights

1. API reliability varies significantly. World Bank API is highly stable but slow (~2–3 seconds). Alpha Vantage free tier has rate limits (5 calls/min) - required smart caching to maintain UX.

2. Sentiment diverges from price movement ~30% of the time. Negative headlines often coincided with positive price action - a useful contrarian signal worth exploring further.

3. GDP data lags by 1–2 quarters in the World Bank API - crucial for users to understand so macro context is not mistaken for current conditions. Added a clear data freshness label.

4. Single-source dashboards miss the story. The most valuable insights emerge at the intersection of news sentiment + price movement + macro backdrop - something impossible to see when data lives in three separate tools.

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Visualisations Included

• Live Price Sparklines - 30-day mini-charts per ticker
• FX Change Heatmap - Major currency pairs, 7-day % change
• Sentiment Timeline - Daily sentiment ratio chart
• GDP Bubble Chart - Economy size vs. growth rate
• Keyword Word Cloud - Most discussed topics in news headlines
• Macro Comparison Table With conditional cell colouring

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Business Impact

Stakeholder	Value Delivered
Analyst / Trader	Replaces 2–3 hours/day of manual data collection
Portfolio Manager	Single-view macro + market context for morning briefings
Business Development	Country-level economic signals for market entry decisions
Content Creator / Writer	Live data and sentiment trends for market commentary

This type of integrated dashboard is used by Bloomberg Terminal users, FX desks, and BI teams at major banks — replicated here using entirely free tools.

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Repository Structure

api-analytics-dashboard/
│
├── api/
│   ├── market_data.py        # Alpha Vantage / Yahoo Finance client
│   ├── macro_data.py         # World Bank API client
│   ├── news_data.py          # NewsAPI client
│   └── fx_data.py            # Open Exchange Rates client
│
├── processing/
│   ├── clean.py              # Data normalisation
│   ├── sentiment.py          # VADER sentiment scoring
│   └── metrics.py            # Derived metrics (% change, rolling avg)
│
├── dashboard/
│   ├── panels/
│   │   ├── market_overview.py
│   │   ├── macro_panel.py
│   │   ├── fx_panel.py
│   │   └── news_panel.py
│   └── app.py                # Main Streamlit entry point
│
├── .env.example              # API key template (never commit real keys)
├── requirements.txt
└── README.md

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How to Run

git clone https://github.com/yourusername/api-analytics-dashboard.git
cd api-analytics-dashboard

pip install -r requirements.txt

# Set up your API keys
cp .env.example .env
# Edit .env and add your API keys

# Launch dashboard
streamlit run dashboard/app.py

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Future Improvements

• Alert System - Email/Slack notifications when a macro indicator or price crosses a threshold
• Database Layer - Store fetched data in SQLite for historical backtesting
• Multi-Currency Portfolio Tracker - Add portfolio input and PnL calculation
• AI Summary Button - Use GPT-4 to auto-generate a daily market summary from live data
• User Authentication - Personalised watchlists and saved preferences via Streamlit Auth

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License

This project is licensed under the MIT License.

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_{Built by [Your Name] · Chennai, India · 2024}