Case
Crypto Invest Bot
Technology Stack:
Infrastructure:
Hetzner, AWS, OpenAI
Backend:
Node.js, PHP, Express.js, RabbitMQ
Databases:
PostgreSQL, MongoDB, Clickhouse, Redis
Frontend:
React.js, Material UI, TailwindCSS
Project Overview:
The agent, built on a large language model, ingests market feeds (Binance, Coinbase), on‑chain analytics (Glassnode), social signals (Twitter, Reddit) and macro indicators (the Fear & Greed Index). Using ML forecasts from LSTM and Prophet models, it recommends the optimal asset mix. The LLM then generates human‑readable explanations and delivers them as reports or via a chat bot.
01.
System Workflow
Collect market prices, on‑chain metrics, social data and macro indicators.
Normalise the data and compute RSI, MACD and moving averages.
Produce a 30‑day forecast with LSTM and Prophet, scoring each scenario by Sharpe ratio.
Let the LLM craft clear explanations for the user.
Present a rebalance recommendation with an option for automatic execution.
02.
Automation and Optimisation
The agent continuously analyses incoming data and produces recommendations, eliminating the need for manual market watching. Investors receive an immediate, plain‑language brief and can rebalance with one click or enable auto-orders.
03.
Example Scenario
Your portfolio currently holds 40% in Bitcoin, 25% in Ether, 25% in Solana and 10% in USDT. Over the coming month Solana shows the strongest upside - about ten percent - but also the highest volatility, while Ether is projected to rise five percent on a steadier path and Bitcoin may dip a little.
The agent therefore proposes two quick moves. First, trim Solana by ten percent of total portfolio value and channel that capital into Ether. Second, shift five percent from Bitcoin into USDT as a short‑term cushion until market swings subside.
If executed, these steps should lift the portfolio’s expected monthly return from roughly three to four percent, raise its Sharpe ratio from 1.0 to 1.3, and shave about two percentage points off the worst‑case drawdown risk.
04.
Results
Higher portfolio returns thanks to better asset allocation.
Fully automated monitoring of news and social sentiment.
Clear, understandable forecasts for the investor.
Lower overall risk through timely recommendations.
Other Cases
The agent, built on a large language model, ingests market feeds (Binance, Coinbase), on‑chain analytics (Glassnode), social signals (Twitter, Reddit) and macro indicators (the Fear & Greed Index). Using ML forecasts from LSTM and Prophet models, it recommends the optimal asset mix. The LLM then generates human‑readable explanations and delivers them as reports or via a chat bot.
Country:
Germany
Project:
Crypto Invest Bot
Team size:
4
Duration:
11 months
Budget:
NDA
Industry:
Crypto, Investments
Technologies:
OpenAI API, Node.js, PHP, Express.js, RabbitMQ, PostgreSQL, MongoDB, Clickhouse, Redis, React.js, Material UI, TailwindCSS
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Cyprus
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Team size:
4
Duration:
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Budget:
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Technologies:
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Project:
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Project:
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NodeJS, OpenAI API, React.js, MongoDB, PostgreSQL, Redis, RabbitMQ
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Country:
Lithuania
Project:
Lumiveo
Team size:
5
Duration:
8 months
Budget:
NDA
Industry:
E-commerce / Home Decor
Technologies:
UI/UX
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Svitlana Rakova
Head of Sales
Business acceleration starts today.
Leveraging AI to make things done
Leveraging AI to make things done