Reading the market from tick and quote data

The problem

Raw tick and quote data is huge and noisy. The signals that explain execution quality, like how wide spreads really are or how much a trade moves the price, have to be computed before anyone can use them.

What I built

• A pipeline that processes tick and quote data in Python, Pandas and kdb+
• Bid-ask spread decomposition and order flow imbalance
• VWAP deviation and short-horizon price impact measures
• Built on public data, on my own time

Why it matters

It gives a clear, measurable view of market behavior and trading costs, the starting point for better execution decisions.