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Run this module

cd "Python Basics - Pandas"
python "pandas_tutorial.py"

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Python Basics – Pandas

Overview

Covers the Pandas patterns that power real quant research pipelines — from building a synthetic OHLCV DataFrame through rolling indicators, resampling, groupby analysis, and a simple SMA-crossover backtest. Every example is grounded in price data so the link from Pandas API to practical quant work stays concrete.

Concepts Covered

  • Building a date-indexed OHLCV DataFrame with pd.bdate_range
  • pct_change, shift, and log returns on a Series
  • Rolling windows: SMA-20, Bollinger Bands, rolling Sharpe ratio
  • Resampling daily OHLCV to weekly bars with resample().agg()
  • groupby by day-of-week to surface return seasonality patterns
  • Signal generation: SMA crossover with np.where, vectorised P&L, strategy vs buy-and-hold comparison

Files

  • pandas_tutorial.py: Annotated walkthrough script; each section prints labelled DataFrame output alongside the corresponding Pandas call

How to Run

pip install pandas numpy
python pandas_tutorial.py

Sections

Section What it demonstrates
DataFrame basics Shape, head, dtypes, describe on a synthetic 60-day OHLCV frame
Returns and rolling windows pct_change, rolling().mean/std, Bollinger Bands, rolling Sharpe
Resampling Daily → weekly OHLC aggregation with named agg columns
Groupby by weekday Day-of-week return stats as a bias check
Signal generation SMA-5/20 crossover, shifted signal, strategy vs buy-and-hold

Practice Ideas

  • Load real price data via pandas_datareader or a CSV and replace the synthetic DataFrame
  • Add a drawdown column using cummax() and cummin()
  • Extend the groupby section to group by calendar month

Next Steps

  • Apply these patterns to real data in Market Data/
  • Move into more advanced modelling in Quantitative Methods - Time Series/
  • See Strategies - Momentum Trading/ for production-grade signal generation

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