Token Peak Price Analysis
Budget: €30 – €250 EUR
I have a CSV with roughly 1,600 entries. Each row holds the post time (in ISO 8601 format, e.g. 2025-01-01 20:56:35+00:00), the chart link, and the token’s initial price. The tokens live on Ethereum, Binance Smart Chain, Solana and Base.
What I need is an analysis to visit every chart link, and note the local all time high of the token at the beginning of the post time. You can also automate it by code if you can.
• the absolute highest price each token reached after it was called till today
• the gain expressed both as “X” and as a percentage over the initial price (3.77x and 277%)
• how many days (or hours, if you prefer) it took to hit that high
• statistics like: tokens that never managed to trade above the initial price. (for example 150 token didn't reach higher pricem 1400 are in profit with ~200%)
When the run is complete I want two things:
1. A clean, well-labelled data file (CSV or similar) containing the original fields plus your calculated columns.
2. The code you used, with straightforward instructions so I can rerun it on fresh lists later.
Feel free to rely on Python, APIs, browser automation—whatever gets the job done accurately and reproducibly. Speed is welcome, but correctness and clear documentation come first.
Example: $LABUBU posted at 05.05.2025. Price: 0.34$. ATH reached at 25.05.2025 1.50$. 4.4x ...
What I need is an analysis to visit every chart link, and note the local all time high of the token at the beginning of the post time. You can also automate it by code if you can.
• the absolute highest price each token reached after it was called till today
• the gain expressed both as “X” and as a percentage over the initial price (3.77x and 277%)
• how many days (or hours, if you prefer) it took to hit that high
• statistics like: tokens that never managed to trade above the initial price. (for example 150 token didn't reach higher pricem 1400 are in profit with ~200%)
When the run is complete I want two things:
1. A clean, well-labelled data file (CSV or similar) containing the original fields plus your calculated columns.
2. The code you used, with straightforward instructions so I can rerun it on fresh lists later.
Feel free to rely on Python, APIs, browser automation—whatever gets the job done accurately and reproducibly. Speed is welcome, but correctness and clear documentation come first.
Example: $LABUBU posted at 05.05.2025. Price: 0.34$. ATH reached at 25.05.2025 1.50$. 4.4x ...