Financial Data Analysis Pipeline: From API to Automated Insights

Project Overview

This is my roadmap project where I aim to design and build an end-to-end financial data pipeline. The pipeline will cover sourcing financial data, cleaning and engineering features, applying statistical models, simulating trading strategies, and deploying the solution with automation. I’m using this to sharpen my skills in financial data analysis, automation, and modeling using Python and modern data engineering tools.

Project Goals

• Extract and clean financial data using APIs.
• Engineer predictive features using time, technical indicators, and business rules.
• Model trends and growth using regression and classification techniques.
• Simulate practical investment strategies and measure outcomes.
• Automate the prediction and reporting process using scheduled jobs and pipelines.

Roadmap Notes

Note 1: Data Extraction and Setup

• Define analysis goals and identify the relevant data sources.
• Evaluate and choose financial APIs for data retrieval.
• Write scripts to download historical data.
• Tools: Python, Finance APIs, VS Code.

Note 2: Data Preparation and Feature Engineering

• Clean raw datasets and handle missing or inconsistent data.
• Join multiple datasets for a complete view.
• Create time-based features: day, hour, week, month.
• Generate technical indicators using TaLib.
• Create future growth targets over selected time periods.
• Tools: Pandas, NumPy, TaLib.

Note 3: Modeling and Time-Series Forecasting

• Frame hypotheses around market behavior.
• Explore time-series trends, seasonality, and decomposition.
• Build regression models for trend analysis.
• Use classification to predict movement direction.
• Tools: scikit-learn, statsmodels, optional neural networks.

Note 4: Strategy Simulation

• Translate predictions into investment decisions.
• Simulate various strategies:
  • Single stock buy-and-hold
  • Diversified portfolios
  • Long/short market-neutral strategies
  • Event-based and mean reversion
• Measure outcomes: returns, risk, and efficiency.
• Tools: Custom Python logic, Matplotlib/Plotly.

Note 5: Deployment and Automation

• Convert notebooks to .py scripts for execution.
• Store outputs in local files or SQLite.
• Use cron to automate daily/weekly tasks.
• Optionally use Airflow for scheduling workflows.
• Implement email notifications for alerts or summaries.
• Tools: Python, SQLite, cron, Airflow (optional).

Tools and Technologies

• Python, Pandas, SQL, TaLib, scikit-learn
• Matplotlib, Seaborn, Plotly
• SQLite, Docker, Airflow, cron
• VS Code

Setup Instructions

• Clone this repository
• Follow each note sequentially as modules
• Install dependencies listed in requirements.txt
• Execute scripts in the order of the pipeline

Connect

• LinkedIn: www.linkedin.com/in/adeyanjuteslimuthman
• Portfolio: www.adeyanjuteslim.co.uk
• Email: info@adeyanjuteslim.co.uk

If you’re working on something similar or want to collaborate—let’s connect!