From a Spreadsheet to the Crypto Bloomberg
In 2020, there was no tool that told crypto investors what the data actually meant. There were numbers — market caps, transaction volumes, GitHub activity, social metrics. But no one had put them together into a single, scored, comparable view. Chantal Läng built one. In Excel.
Three years later, that spreadsheet became CryptoScores.
100+
Scores
6
Categories
3 years
In the making
2–3 years
Historical data
Chapter 1
The Problem (2020)
The year was 2020. Chantal had been in crypto since 2017, had lived through the 2018 crash, had earned her Certified Bitcoin Professional credential, and had been teaching investors how to read the market for years. She knew what most investors didn't: that the data existed.
Market cap, total value locked, GitHub commit history, Twitter community size, on-chain transaction volume — it was all out there. Scattered. Raw. Uninterpreted.
A Geneva-based investment fund asked for her help. She started where any project manager would: with a spreadsheet. The first version was manual. She collected the numbers herself — Twitter followers divided by market cap (because a large crypto naturally has more followers than a small one), TVL, trading volumes. She sorted them. She applied color codes. Top 10% in dark green. Bottom 10% in dark red. Shades in between.
Her students saw it and immediately understood its value.
“They didn't want the raw data,” she says. “They wanted the scores. Because the scores tell them what the numbers mean.”
She had built something. She just didn't know yet how big it would get.
Chapter 2
When Excel Wasn't Enough (2020–2022)
The spreadsheet grew. She discovered APIs. CoinGecko, then others. The manual data collection became automated — a cron job running on a rented remote server, pulling updated data every hour and feeding the master file. More sources. More cryptos. More columns.
At its largest, the Excel setup involved separate files for each data source — hundreds of columns, thousands of rows — all reconciled into a master file on her computer. When the system worked, it was powerful. When a single cell broke, the whole thing collapsed.
“It was becoming unmanageable,” she says. “Excel is not built for this. One error cascades everywhere.”
She chose to rebuild it.
“My brain is wired for comparisons,” she says. “I find it easy. I find it interesting. So I said: let's make it a proper tool.”
What she imagined as a small project would take three years.
Chapter 3
Building the Software (2022–2025)
In September 2022, Chantal assembled her development team. She found Mathieu through GitHub — his open-source TradingView tools caught her attention because of their technical quality. She hired Jérémy through a developer platform. David joined on Jérémy's recommendation. Sovattha, a longtime friend and technical collaborator, became team lead.
From the beginning, it was clear the hardest problem wasn't the scoring. It was the matching. Every data source uses different identifiers for the same crypto. One source calls it “Joe.” Another calls it “Trader Joe.” Three different projects share the same ticker symbol.
To solve this, the team built a proprietary trust score system. Every piece of identifying information gets assigned a confidence weight: a smart contract address earns 1,000 points, a Twitter handle earns around 20. When multiple cryptos match an identifier, the one with the highest cumulative trust score wins.
The scoring engine came next: 100+ metrics, six categories, weighting logic calibrated through backtesting. The platform was built in French first, then English.
On September 11th, 2023, Chantal purchased CryptoScores.com — listed at $20,000, she offered $1,000. It was accepted.
Key Milestones
Chantal discovers crypto at a banker's conference. Two use cases — refugee aid and gaming authentication — convince her this technology is real.
Passes the CBP on December 31st, after failing the first attempt in May. Continues building knowledge through a bear market no one else was watching.
Co-authors the "Blockchain Project Manager" curriculum with ALYRA. Launches CryptoExperts (late 2020) and a private investor community (February 2021). Rides the 2021 bull run alongside her students.
A Geneva investment fund asks for help. The first color-coded Excel scoring file is built. The idea that becomes CryptoScores is born.
Development team assembled. Sovattha, Mathieu, Jérémy join. David follows.
French-speaking beta goes live on Telegram. First real users, first real feedback.
CryptoScores.com domain acquired.
French v1 officially launches on Telegram.
New metrics and scores ship roughly every two weeks. The platform grows continuously.
Telegram version goes live in English.
Gaétan Läng — Chantal's son — joins to lead growth and business development. Product positioning, user acquisition, and go-to-market become his ground.
Full documentation goes live — every metric on the platform explained, publicly accessible via GitBook.
Advanced filtering and custom column layout launched — users can now tailor the crypto table to their own analysis workflow.
100+ scores across 6 categories. A proprietary matching engine. 2–3 years of historical data.
Programmable alerts — set custom triggers on any score or metric and get notified when a crypto crosses your threshold.
Data-backed buy signals derived from Chantal's backtesting research. Built on the correlation framework published in Fundamental Signals and Cryptocurrency Performance.
A YouTube series explaining every metric on the platform — what it measures and how to use it. Subscribe to stay updated.
What Makes CryptoScores Different
The crypto market has data. What it has never had is a single platform that aggregates, matches, and scores that data across dozens of sources — the way Bloomberg does for equities. That's not an accident. It's hard.
Most platforms that work with crypto data use one or two sources. The matching problem — reconciling inconsistent identifiers across dozens of APIs — is difficult enough that most companies don't attempt it. Their business model doesn't require it. Ours does.
CryptoScores currently aggregates more sources than any comparable tool. It holds 2–3 years of historical data that competitors don't have. And its scoring logic is continuously refined through backtesting: we measure which metrics actually correlate with price behavior, weight them accordingly, and publish our methodology.
The data you can replicate
In the age of AI-assisted development, a motivated team could replicate the software architecture in a few months.
The data you can't replicate
20+ years of project management thinking applied to crypto, seven years of market experience, and three years of data that doesn't exist anywhere else.
That's what CryptoScores is built on.