MT

Trade Me AnalysisData Scientist · Senior Data Analyst

Python · Pandas · SQL Trade Me Market Intelligence Pipeline online ← Back to home
1,683Stores tracked
6,308,302Listings captured
208,226Listing details
1.9 GBRaw dataset
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Stock Movement Monitor

Daily quantity snapshots in SQLite — what's selling most, by member and category.

Open monitor →
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★ Flagship Data ready

Trade Me Competitors Analysis

6.3M rows

End-to-end competitor intelligence across 1,683 Trade Me stores — what is selling most, at what price, and which member is behind each listing. Built to be explored like a senior analyst would present it: demand, pricing bands, sellers, category mix and map-level trends.

Ask about Trade Me data

Pick a view above, or ask a question.

Results come from trademe.db (read-only). The first query may take a few seconds.
The brief above is the exact scope for this tool — copy it to kick off the dashboard build.
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Dashboard

Market & Demand Insights

Where the volume actually sits — listing distribution by NZ region, pulled live from the scraped detail set.

208klistings
Auckland 48.7%
Other 12.1%
Canterbury 11.1%
Waikato 5.1%
Wellington 5.0%
Rest of NZ 18.0%
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Members

Top Sellers by Listings

The members holding the most inventory — who dominates each category before you price against them.

Big Face644,008
Geek Store252,165
Rolan223,773
SmartFox220,909
The Nile215,054
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Source of truth

Raw Data

Every layer of the collection, checkpointed and resumable. Click to open or download.

⚙️ Scraper pipeline · tm_scrape.py 01 Stores 1,683 02 Counts 1,459 03 Listings 6.31M 04 Details 208k 05 CSV export 1.9 GB Checkpointed & resumable · ~150 req/s