Analyze bid-ask spreads, liquidity, and volume patterns for a stock
A notebook or dashboard with data cleaning, strategy logic, backtest results, risk metrics, and limitations.
Build
Model, memo, dashboard, notebook, or deck.
Explain
Clear assumptions, insight, and recommendation.
Show
Resume bullet and interview story.
What you are doing
You are working as a Capital Markets and Sales Trading. Your manager asks you to use Market microstructure to answer a real business or investment question and present a decision-ready output.
Show that you can apply Market microstructure in a practical analyst workflow, not only explain the theory.
What to make
A notebook or dashboard with data cleaning, strategy logic, backtest results, risk metrics, and limitations.
Deliverables
- Brief
- Model or notebook
- Charts or dashboard
- Resume bullet
- Source and assumption log
- One-page executive summary
- Final output file
How to start
- Define the hypothesis before coding.
- Select universe, date range, frequency, and benchmark.
- Create a reproducible notebook structure: import, clean, signal, test, evaluate, explain.
Step-by-step execution
- Clean and validate the dataset.
- Build signals or factors with no look-ahead bias.
- Run backtest with transaction-cost assumptions.
- Calculate returns, volatility, drawdown, Sharpe, hit rate, beta, and benchmark comparison.
- Write a limitations section explaining survivorship, data quality, and overfitting risk.
Data and sources
- Yahoo Finance or Stooq price data
- NSE/BSE public data
- FRED or World Bank macro data
- Company fundamentals from public filings
- Synthetic data when licensing blocks redistribution
Tools to use
- NSE/BSE data
- Python
- Excel or Google Sheets
- Google Docs
- Public sources
Quality rubric
- No look-ahead bias or hidden future data.
- Benchmark and costs are included.
- Metrics show risk as well as return.
- Code is reproducible and readable.
- Conclusion admits limitations instead of overclaiming.
Resume bullet
Built a a notebook or dashboard with data cleaning, strategy logic, backtest results, risk metrics, and limitations. for Market microstructure, using NSE/BSE data, Python to convert raw information into a decision-ready finance output.
Interview talk track
- Problem: explain the business question and why it matters for Capital Markets and Sales Trading.
- Method: describe the data collected, assumptions made, and analysis performed.
- Decision: state the recommendation, key risk, and what would change your view.