🚀 EVE-Online Market Analyzer & Route Finder

“Spreadsheets in space — but smarter.”
A Python-powered project that uses the EVE Online ESI and EVE Marketer APIs to identify profitable trade opportunities, analyze regional market trends, and calculate safe routes across New Eden.

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Overview

This project automates EVE Online market analytics by pulling live data from:

• EVE Marketer API → market buy/sell orders (region-based)
• EVE ESI API → item metadata and historical pricing trends

It then:

• 💰 Find the best equipment to trade for maximum ISK profit
• 📊 Calculates profitability and market deltas between regions
• 📈 Tracks 13-day average price and volume trends
• 🧮 Outputs a CSV report with rich market analytics per item
• 🛰️ Determine the safest travel routes between systems based on kill statistics

All operations are multi-threaded using python concurrent.futures for speed -- processing hundreds of items simultaneously.

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Usage

Run any ipynb in colab or local machine: change region if needed based on your cuurrent location

Output will be a CSV file (auto-generated by Pandas) containing all analyzed market data.

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How It Works

1. Item Collection

Fetches all item IDs and names from Fuzzwork’s type IDs CSV.
Default region: The Forge (10000002) — home to Jita.

2. Market Data Gathering

For each batch (up to 199 item IDs at once), it requests:

• Global data from EVE Marketer (no region)
• Regional data limited to The Forge

Both responses are zipped together and processed in parallel.

3. Data Analysis

For each item:

• Compares buy/sell percentiles, averages, and min/max values
• Computes profit percentage, price deltas, and market health
• Fetches 13-day history from EVE ESI (/markets/{region}/history/)
• Calculates:
  • 📈 Average daily volume and orders
  • 📅 Recent price change (%) over 13 days
  • 💰 Profitability per unit and per region

4. Output

All results are compiled into a Pandas DataFrame, then exported as a CSV report, including:

• Item name and ID
• Regional and global buy/sell stats
• Profit and percentile comparisons
• 13-day historical metrics
• Market volume and packaged volume

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Key Classes and Methods

Items Class

Handles the entire workflow:

• __init__ — initializes region, headers, and storage
• ItemInfo() — fetches market data (runs in threads)
• makeItemInfoDF() — parses and computes metrics
• start() — orchestrates the full pipeline

Threading Logic

Uses ThreadPoolExecutor for:

• Fetching market data (evemarketerThreads)
• Processing JSONs into final records (evetechThreads)

Example:

itemsInfo.start(199, 37)

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Requirements

• Python 3.x
• Libraries:

  pip install requests pandas numpy

• Internet connection for API access

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⚡ Performance

• Uses threaded requests to process hundreds of items simultaneously.

• Adjustable thread counts:

  itemsInfo.start(evemerketerThreads=199, evetechThreads=37)

• Typical runtime: depends on number of items (thousands of market calls possible).

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