Analyze trading RFQ competitiveness data
Overview
This question evaluates a candidate's skills in exploratory data analysis, Excel-based feature engineering, basic statistical modeling, and interpretation of RFQ competitiveness and win-rate relationships.
RFQ Competitiveness Analysis in Excel
You are given an Excel file containing a few months of trading RFQ (request-for-quote) data for a sales & trading desk. The data includes RFQs from 3 clients on 3 different assets.
Each row in the dataset corresponds to a single RFQ and has (at least) the following columns:
-
timestamp– time of the RFQ -
client_id– which of the 3 clients sent the RFQ -
asset_id– which of the 3 assets the RFQ is for -
rfq_side– buy or sell -
rfq_size– requested notional/quantity -
our_quote– the price (or spread) your firm quoted -
market_reference_price– the mid‑market or reference price at RFQ time -
is_competitive– Boolean flag: whether our quote was considered competitive (e.g., inside some benchmark or relative to competitors) -
won_trade– Boolean flag: whether your firm ultimately won the RFQ (the client traded with you) or not
You are told only to “analyze the data”; there is no further guidance. You can use only Excel (including functions, pivot tables, charts, etc.).
Task:
Describe, in detail, how you would approach this analysis to help the trading desk understand and potentially improve its quoting and bidding strategy. In particular:
- What questions would you try to answer from this dataset (e.g., about win rates, pricing competitiveness, client behavior, asset behavior)?
- What data cleaning or preprocessing steps would you perform in Excel?
- What descriptive statistics, aggregations, or pivot tables would you build, and why?
- How would you quantify the relationship between quote competitiveness and probability of winning a trade (overall and by client/asset)?
-
What simple models or rules of thumb (they can be statistical but must be implementable/inspectable in Excel) might you build to:
- predict the chance of winning given an RFQ and a chosen quote; and
- suggest how aggressive the quote should be for different clients/assets/sizes?
- How would you summarize and present your findings and recommendations back to traders or sales in a clear, actionable way?
Solution
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