Category: Market Analysis
-
How to translate complex crypto analytics into clear, actionable insights
Understanding the journey from data to decisions Historical background of crypto analytics In the early Bitcoin era, “analysis” mostly meant staring at price charts and arguing on forums. Order books were thin, data providers were rare, and almost nobody talked about fundamentals beyond hash rate and difficulty. As liquidity grew, exchanges began exposing APIs, and…
-
Practical approaches to crypto data normalization pipelines for reliable analytics
Why crypto data normalization became a thing By 2025, nobody is surprised that the same Bitcoin trade can have three different prices depending on the exchange, timestamp format and quote currency. Early crypto desks in 2015–2017 tried to glue CSV exports together in Excel; later everyone rushed to build home‑grown scripts pulling from every cryptocurrency…
-
How to use on-chain signals to time crypto entries and exits effectively
Why on-chain signals matter for timing exits and entries От рыночного шума к поведенческим данным When you trade only off price charts, you’re basically watching the shadow of what’s happening on-chain. On‑chain signals go one level deeper: they show capital flows, holder behavior, and network usage directly in the ledger. That’s why on-chain analytics for…
-
How to create verifiable crypto research through blockchain proofs
Why verifiable crypto research suddenly matters in 2025 In 2025 никто уже не верит скриншотам, PDF и «доверяйте, братцы». Рынок стал слишком быстрым: инсайты устаревают за часы, а фейки разлетаются мгновенно. Поэтому идея делать исследования, которые любой может перепроверить через блокчейн-пруфы, перестала быть нишевой. Если вы аналитик, инвестор или фаундер, вам нужна инфраструктура, где каждое…
-
How to measure defi liquidity quality: key metrics and practical methods
Why liquidity quality matters more than “TVL looks big” When people talk about DeFi liquidity, they usually point at TVL or 24h volume and call it a day. That’s dangerously shallow. Quality liquidity is about how easily size can move through a pool without wrecking the price, how fast that liquidity can disappear in stress,…
-
How Ai can accelerate crypto due diligence and reduce risk
From spreadsheets to smart agents: короткая история крипто‑due diligence До середины 2010‑х проверка криптопроектов выглядела по‑старинке: аналитики вручную листали whitepaper, таблицы в Excel и чаты в Telegram. В 2020–2022 годах появились первые crypto due diligence software решения, но они были скорее «агрегаторами данных»: тянули ончейн‑метрики, новости и базовую аналитику. К 2025 году всё сместилось в…
-
How to balance speed and accuracy in crypto reporting for reliable insights
Why speed vs accuracy became the core issue in 2025 By 2025, крипто-журналисты живут в режиме чата с рынком: твит Илона, баг в смарт-контракте или внезапный регуляторный анонс — и за минуты улетают миллиарды. Исследования отрасли показывают, что почти 70% розничных трейдеров принимают решения, ориентируясь на новости и соцсети, а не только на графики, поэтому…
-
Investor guide: using on-chain data to make smarter venture bets
Investor guide: using on-chain data for venture bets Why on-chain data стала критичной для венчурных ставок On-chain данные — это не модный аксессуар, а реальный способ увидеть экономику проекта без презентаций и маркетинга. Смарт‑контракты, транзакции, движение токенов и активность кошельков дают «телеметрию» продукта в реальном времени. По оценкам аналитических провайдеров, сейчас отслеживаются десятки миллионов активных…
-
Practical guide to auditing Ai crypto research products for risk and accuracy
Why auditing AI crypto research products actually matters Before you wire money into anything “AI + crypto”, you need to assume one thing: most marketing decks dramatically overstate what the models can really do. Auditing AI crypto research products isn’t about distrusting technology; it’s about forcing the vendor to prove their system is robust, testable,…
-
Ethics and accountability in ai-assisted market research: principles and risks
Why ethics and accountability suddenly matter so much in AI-assisted market research AI has turned market research from slow and expensive into fast and always-on. That’s powerful—and risky. When algorithms decide which customers to listen to, which segments matter, and which insights drive spend, ethical blind spots quickly turn into reputation, legal, and trust problems….