Production-style, enterprise-grade Mutual Fund Analytics Platform demonstrating end-to-end Data Engineering, Advanced Analytics, Cloud Architecture, and Executive BI Reporting.
Built by: Sumit Kumar Prajapat | GitHub: Skpkush
Status: ✅ Complete — End-to-end pipeline live (ingestion → Azure ADF → Star Schema → Power BI)
🔴 Live Demo: mf-analytics-platform.streamlit.app
| Metric | Value |
|---|---|
| NAV rows processed | ~100K NAV observations |
| Fund schemes covered | 14,384 |
| Funds with computed metrics | 16 (Yahoo ETF universe) |
| AMCs covered | 51 |
| Power BI dashboard pages | 4 |
| Cloud ETL | Azure Data Factory pipeline (✅ Succeeded) |
| DAX measures authored | 25+ |
| Data model | Star schema — 5 dimension + 4 fact tables |
Asset Management Companies, Wealth Advisors, and FinTech firms need a unified analytics platform that ingests multi-source mutual fund data, computes risk-adjusted performance metrics, segments investors, forecasts NAV trends, and surfaces executive-level insights — all in a scalable cloud architecture.
This platform delivers exactly that.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Data Sources │───▶│ Azure Blob │───▶│ Azure Data │
│ Yahoo Finance │ │ (Raw Layer) │ │ Factory │
│ AMFI India │ └─────────────────┘ │ (✅ Succeeded) │
│ Kaggle │ └─────────────────┘
└─────────────────┘ │
▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Streamlit App │◀───│ Power BI │◀───│ Azure SQL DB │
│(Streamlit Cloud)│ │ 4-Page │ │ Star Schema │
└─────────────────┘ │ Dashboard │ │ 5 Dim + 4 Fact │
▲ └─────────────────┘ └─────────────────┘
│ │
│ ▼
│ ┌─────────────────┐
└─────────────────────────────────────│ Python ML │
│ Prophet NAV │
│ Forecasting │
└─────────────────┘
Data flow: Raw multi-source data lands in Azure Blob → Azure Data Factory orchestrates ingestion → transformation/cleaning in Python → load into a star schema (5 dimension + 4 fact tables) on Azure SQL Database (PostgreSQL locally) → 25+ DAX measures power a 4-page Power BI dashboard → Prophet forecasts NAV trajectories → Streamlit surfaces client-facing analytics.
Full diagram: docs/architecture/architecture.md
- Dimensions:
Dim_Date,Dim_AMC,Dim_Category,Dim_Fund,Dim_Investor - Facts:
Fact_NAV,Fact_Transactions,Fact_SIP,Fact_Returns
DDL: scripts/sql/ddl/ · Views: scripts/sql/views/ · Procs: scripts/sql/procs/
| Layer | Technology |
|---|---|
| Cloud | Azure (Blob Storage, SQL Database, Data Factory, Functions, Key Vault) |
| Database | Azure SQL Database (cloud) + PostgreSQL 15 (local/fallback) |
| ETL | Azure Data Factory + Python (pandas 2.x, sqlalchemy) |
| Analytics | Python (numpy, scipy, statsmodels) |
| ML | Prophet (NAV forecasting) |
| BI | Power BI Desktop — 4 pages, 25+ DAX measures |
| App | Streamlit (deployed on Streamlit Community Cloud) |
| Orchestration | ADF pipelines + Azure Functions |
| Version Control | Git + GitHub |
📥 Download: Power BI Dashboard (.pbix) (Git LFS) · 🖼️ Screenshots: docs/screenshots/
- Executive Overview — AUM, top funds, market summary, KPIs
- Fund Performance Analytics — CAGR, rolling returns, benchmark comparison
- Investor Analytics — SIP trends, segmentation, retention
- Risk & Volatility — Sharpe heatmaps, drawdown, risk-return scatter
Page 1 — Executive Overview
Page 2 — Fund Performance Analytics
Page 3 — Investor Analytics
Page 4 — Risk & Volatility
Azure Data Factory Pipeline — Succeeded
- CAGR (Compound Annual Growth Rate)
- Rolling Returns (1Y, 3Y, 5Y)
- Alpha, Beta (vs Nifty 50 benchmark)
- Sharpe Ratio, Sortino Ratio, Treynor Ratio
- Standard Deviation, Volatility
- Maximum Drawdown
- Information Ratio
NAV Forecasting (Prophet) — Forecasts 30/60/90-day NAV trajectories with confidence-interval bands for the Yahoo ETF/benchmark universe (≥ 200 trading days of history). Implemented in scripts/ml/forecast_nav.py and served live through the Streamlit app (streamlit/), deployed on Streamlit Community Cloud with a Supabase PostgreSQL backend.
python scripts/ml/forecast_nav.py --fund-code NIFTYBEES.NS # CLI forecast
streamlit run streamlit/app.py # interactive app# Clone repo
git clone https://github.com/Skpkush/mf-analytics-platform.git
cd mf-analytics-platform
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Configure environment variables
cp .env.example .env # Windows: copy .env.example .env
# Edit .env with your Azure / PostgreSQL credentialspython scripts/ingestion/fetch_yahoo_finance.py
python scripts/ingestion/fetch_amfi_nav.py# Clean and validate NAV / transaction history
python scripts/transformation/clean_nav.py
python scripts/transformation/clean_transactions.py
# Create the schema (DDL), then load dimensions and facts
python scripts/etl/run_ddl.py
python scripts/etl/load_dimensions.py
python scripts/etl/load_facts.pypython scripts/analytics/metrics_returns.py # CAGR, rolling returns
python scripts/analytics/metrics_risk.py # volatility, drawdown
python scripts/analytics/metrics_risk_adjusted.py # Sharpe, Sortino, Treynor, alpha/beta
python scripts/analytics/metrics_market.py # market/AUM aggregatespython scripts/ml/forecast_nav.py --list # forecastable funds
python scripts/ml/forecast_nav.py --fund-code NIFTYBEES.NS # 30/60/90-day forecast- Power BI: open
powerbi/mf_analytics_dashboard_p4.pbix(apply themepowerbi/theme_mf_analytics.json). - Streamlit:
streamlit run streamlit/app.py→ openhttp://localhost:8501(interactive NAV forecasting; deploy notes instreamlit/README.md).
Cloud path: the Azure Data Factory pipeline orchestrates the ingestion → blob → SQL load in the cloud (pipeline run Succeeded — see screenshot above). Run the cloud ETL via
scripts/etl/run_azure_etl.py/scripts/etl/trigger_adf_pipeline.py.
mf-analytics-platform/
├── azure/
│ ├── adf_pipelines/ # ADF JSON pipeline definitions
│ ├── alerts/ # ADF failure alert configurations
│ ├── arm_templates/ # Infrastructure as code
│ ├── datasets/ # ADF dataset definitions
│ ├── functions/
│ │ └── fn_compute_daily_metrics/ # Azure Function daily refresh
│ ├── pipelines/ # ADF pipeline JSON definitions
│ └── triggers/ # ADF schedule triggers
├── data/
│ ├── processed/ # Cleaned, transformed data (gitignored)
│ └── external/ # Benchmark + risk-free rate data (gitignored)
├── docs/
│ ├── architecture/ # Architecture diagrams + Mermaid
│ ├── case_study/ # Case study PDF (planned)
│ ├── screenshots/ # Dashboard + pipeline screenshots
│ ├── daily_log.md # Day-by-day build log
│ └── data_dictionary.md # Schema + field definitions
├── notebooks/
│ └── 01_data_exploration.ipynb # EDA notebook
├── powerbi/
│ ├── mf_analytics_dashboard_p4.pbix # 4-page dashboard (gitignored — built locally)
│ └── theme_mf_analytics.json # Navy theme file
├── scripts/
│ ├── ingestion/ # AMFI + Yahoo Finance data acquisition
│ ├── transformation/ # Cleaning, validation, feature engineering
│ ├── etl/ # DDL run, dimension/fact load, Azure ETL
│ ├── analytics/ # CAGR, Sharpe, Beta, Drawdown metrics
│ ├── ml/ # Prophet NAV forecasting
│ └── sql/
│ ├── ddl/ # CREATE TABLE scripts (star schema)
│ ├── views/ # Analytical views (vw_fund_performance)
│ └── procs/ # Stored procedures
├── streamlit/
│ ├── .streamlit/ # Theme + secrets template
│ ├── pages/ # 5 app pages (Explorer, Forecast, Risk, SIP, Portfolio)
│ ├── utils/ # DB, metrics, charts utilities
│ ├── app.py # Home page + KPI cards
│ ├── Dockerfile # Container definition
│ ├── docker-compose.yml # VPS deployment config (alternative)
│ ├── requirements.txt # App-only dependencies
│ └── README.md # Deploy instructions
├── tests/
│ ├── conftest.py # Pytest fixtures
│ └── test_data_quality.py # Data quality unit tests
├── .env.example # Environment variable template
├── .gitignore # Secrets + venv excluded
├── CLAUDE.md # Claude Code project context
├── PROJECT_PLAN.md # Week-by-week execution plan
├── requirements.txt # Full Python dependencies
└── README.md
MIT License — see LICENSE file.
Sumit Kumar Prajapat Data Analyst | Analytics Engineer | 📧 sumitkprajapat29@gmail.com 🔗 GitHub
Last updated: June 2026