What we want to do is to help users sleep well amidst volatility.

Article author: 0x9999in1, MetaEra

Having worked in the digital currency industry for many years, Mr. Ice Fire Jason has experienced the ups and downs of several bull and bear cycles. This veteran, who once worked in strategy and operations at Binance Earn, is now leading the OlaXBT team to explore a new path—using AI and blockchain technology to create an innovative MCP-driven market. He understands that retail investors often miss opportunities due to information lag and inefficient operations, even becoming 'chives' in the market. It is this pain point that drives him to deeply integrate his past data processing experience with AI technology, building OlaXBT's core competitiveness.

Breaking through data silos, capturing on-chain whales.

Jason admits that his experience on centralized platforms made him realize the critical importance of data integration. 'In the past at Binance, data from different business lines was scattered like islands, and users had to manually piece together fragmented information.' Now, OlaXBT connects on-chain and off-chain data through blockchain technology, allowing users to not only track cross-market capital flows in real-time but also capture significant movements of whale addresses. The platform integrates various data sources, such as market sentiment, whale movements, news, and a series of quantitative trading indicators, and uses a proprietary multi-factor analysis model to deduce trading factors highly correlated with individual cryptocurrencies (this model is currently under patent application in the U.S.).

AI agent: From 'Q&A' to 'action'.

OlaXBT uses Model Context Protocol (MCP) as its technological foundation to develop its flagship AI agent (AI Market Maker). Based on reinforcement learning technology, by analyzing on-chain data, implied volatility of options, and social media sentiment, the AI agent that generates real-time alpha signals is reshaping the trading experience. When users anxiously ask late at night, 'Can I bottom-fish BTC now?', the AI agent no longer just sends vague analysis reports, but acts like an experienced trader, retrieving historical price trends, scanning the options market (such as the fear index), analyzing spot premiums on major exchanges, and even tracing large transfer records from the last three hours. Subsequently, users can adjust strategies according to their personal trading habits and risk tolerance. This ability to 'ask and trade' comes from the MCP framework's deep integration of fragmented data. For example, when a certain whale address transfers a large amount of BTC to an exchange, the system combines on-chain data, implied volatility of options, and social media sentiment to offer a suggestion like 'short-term selling pressure is high, consider accumulating in batches', freeing retail investors from tedious information filtering. More importantly, the system can automatically trigger trades: users just need to click confirm, and the AI agent will execute the strategy through smart contracts, truly achieving 'ask and trade'.

Lowering the barrier: Interactive tutorials guide users.

Although the tools are powerful, Jason is acutely aware of the balancing act between 'professional' and 'user-friendly'. He shares a product detail: the team has designed a layered guidance mechanism.

・Newbie protection: A real account loss exceeding 50% triggers 'comfort mode', popping up funny memes to relieve anxiety.

・Mandatory learning: The system checks risk scores before real trading; users scoring below 60 must complete a 3-minute instructional video.

・Efficient adaptation: A bilingual interface, preset trading templates, and interactive tutorials help users quickly master advanced functions such as 'creating a personal AI agent' and 'setting up automated vaults'.

Our target users are not complete novices, but those with a foundation who are burdened by manual operations, Jason emphasized.

Data-driven collaboration of a diverse background team.

OlaXBT's team members come from diverse backgrounds: former investment bank traders are responsible for optimizing low-latency systems, while quantitative analysts focus on strategy backtesting. Jason emphasizes that team communication follows the principle of 'data-driven decision-making'. For example, before product iteration, new features are first tested in small groups, collecting user click heatmaps and operation duration data, before deciding whether to fully launch. 'Is there controversy? Then let the A/B test results speak for themselves.' He jokes that this agile development model allows the 'rapid response' of former investment bank traders to complement the 'modeling ability' of quantitative experts, even giving rise to a congestion warning mechanism—alerting users to adjust their strategies before the network is about to congest.

Ambition in the Web2.5 era.

Looking ahead, Jason confidently answers: 'The boundaries between traditional finance and DeFi will become increasingly blurred.' He reveals that OlaXBT is exploring two major innovations: tagging U.S. stock earnings report data and developing cross-market hedging tools. For example, in the future, users may trade Tesla stock tokens directly using USDC, while AI will simultaneously analyze the semantics of earnings call meetings (such as the CEO's degree of pessimism), generating (for example) reduction prompts. In his view, the combination of the transparent data layer provided by blockchain and the efficient decision-making capability of AI will accelerate the disintermediation of financial markets. 'Individual investors can capture opportunities such as cross-border exchange rate differences and pricing deviations of derivatives through AI agents, breaking the information monopoly of traditional institutions.'

When the transparent data layer of blockchain meets the efficient decision-making of AI, the real financial democratization begins.

Risk control: From 'luck' to 'calculable security'.

In the face of high volatility in the digital asset market, Jason emphasizes that 'security comes from technology, not luck.'

OlaXBT's AI risk control system provides users with triple protection through historical backtesting and personalized risk analysis.

1. Personalized strategy matching: Frequent bottom fishers are pushed high volatility strategies, while conservatives are offered low-risk arbitrage options.

2. Dynamic position limits: For traders who continuously make counter-trend purchases for three days, individual positions are automatically compressed to within 5% of total assets.

3. Crisis warning: The system detects whale movements in advance and sends users a 'liquidity withdrawal' alert.

We want users to sleep soundly amidst volatility—this sense of security comes from technology, not luck, Jason confidently concludes.

Conclusion

From the centralized strategy battlefield to OlaXBT's innovative testing ground, Jason Chan has always been solving the same problem: how to help ordinary investors survive in the jungle of digital assets. Today, his 'weapons' have been upgraded to a double-edged sword of AI and blockchain, and the endgame of this battle, perhaps as he puts it, is that 'when technology bridges the information gap, the true democratization of financial markets will arrive.'

About OlaXBT

Official tool: https://t.me/OlaXBT_bot

OlaXBT is an innovative MCP-driven market, its AI agents enhance cryptocurrency trading through reinforcement learning technology, real-time alpha signals, and actionable insights. Users can track KOL sentiment, identify emerging trends, gain early access to high-potential tokens, trade through automated vaults, and create their own AI agents, all based on on-chain precision, making trading faster, smarter, and data-driven.