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vine-prices-estimation

Check prices for vine purchases on keepa. I assume you are using Windows and have Chrome installed.

step 1

Go to https://www.amazon.de/hz/privacy-central/data-requests/preview.html and request your order data. (Meine Bestellungen)

step 2

Download and unzip the data provided by Amazon. Check the Retail.OrderHistory-folders and look for the csv file that contains your Vine orders. Copy the path to this csv file.

step 3

Install python, then

pip install -r requirements.txt

step 4

Run download_data.py <path_to_csv_or_json>

You can run

python download_data.py --help

to see possible filter options and reduce the amount of data being downloaded. Already downloaded keepa-pages will be skipped. Please make sure you have the permission from keepa to scrape their webpage before using this code.

The program will connect to keepa using selenium and download price information for your orders with value = 0€. It takes around 1 hour for 200 orders. For each item, a text file will be stored with the source code of the html page as well as the product price history in json file format.

step 5

Run evaluate.py to show some stats and export an Excel sheet.

If you run

python evaluate.py plot

(needs matplotlib installed), it will create keepa-typical plots with your time of purchase for each ASIN.

step 6

If you want to update with new orders, but don't want to download new data from the GDPR export from amazon (they might block you if you do it too often), you can download the order data using this plugin for Chrome:

https://chrome.google.com/webstore/detail/amazon-order-history-repo/mgkilgclilajckgnedgjgnfdokkgnibi

You will need to check the options "Show items not orders" and "Show ASIN in items view" so that the export will have the information we need to combine the informations. After running through your orders, download it as csv. Then run the downloader again:

python download_data.py <path_to_your_gdpr_output.csv> <path_to_your_orderhistory_csv>´

So you can add as many csv files as you wish to this command and it will delete duplicates based on the amazon order id.

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