Before we wrote a single line of code, we lived the markets.
This is the story of how pain became purpose.
在写下第一行代码之前，我们已在市场中浸润多年。
这是一个从痛点到使命的故事。
Years ago, we were retail traders staring at the same screens everyone else was. We read the same headlines, followed the same gurus, made the same emotional decisions.
We lost money. A lot of it.
Not because we were stupid — but because we were playing a game where the rules were written for someone else.
多年前，我们和所有散户一样，盯着同样的屏幕。看同样的新闻，跟同样的“大神”，犯同样的情绪化错误。
我们亏了很多钱。
不是因为我们不够聪明 — 而是因为我们在玩一个规则为别人而写的游戏。
We studied how Renaissance Technologies, Two Sigma, and DE Shaw operated. We saw the gap: institutional quant infrastructure was locked behind $10M minimums and PhD hiring pipelines.
99% of traders had no access to any of it.
我们研究了 Renaissance Technologies、Two Sigma 和 DE Shaw 的运作方式。我们看到了差距：机构级量化基础设施被锁在 1000 万美元的门槛和博士招聘管道之后。
99%的交易者无法接触到这些。
• Gut feelings & tips from Twitter
• No backtesting capability
• Manual, emotional execution
• Excel spreadsheets for "analysis"
• Average holding: panic sell at -15%
• PhD-built factor models
• Millisecond backtesting on tick data
• Systematic, emotion-free execution
• Bloomberg + proprietary data feeds
• Risk-managed with Sharpe > 2.0
The gap isn't talent. It's access.
• 凭感觉和推特上的小道消息
• 没有回测能力
• 手动、情绪化执行
• 用 Excel 做“分析”
• 平均持仓：亏15%就恐慌卖出
• 博士构建的因子模型
• 毫秒级的Tick数据回测
• 系统化、无情绪执行
• Bloomberg + 专有数据源
• 风控管理，Sharpe > 2.0
差距不在天赋，而在资源获取。
So we taught ourselves to code. We learned Python, then C++. We consumed every quant finance paper we could find. We built our first backtest engine in a cramped apartment in Singapore.
It was ugly. It was slow. But it worked.
For the first time, we could test ideas against 10 years of data before risking a single dollar. Our win rate went from 45% to 72%. Our drawdowns shrank by half.
于是我们自学编程。先学 Python，再学 C++。我们阅读了所有能找到的量化金融论文。在新加坡一间狭小的公寓里，我们建造了第一个回测引擎。
它很粗糙。它很慢。但它有效。
第一次，我们可以用10年数据测试策略，而不用拿一分钱冒险。胜率从45%提升到72%。回撤减半。
We looked around and saw millions of traders across Asia — smart, hungry, disciplined — still trading like it was 2010. Not because they lacked ambition, but because nobody had built the bridge between institutional quant and the everyday trader.
我们看到亚洲数百万交易者 — 聪明、勤奋、自律 — 仍在用2010年的方式交易。不是因为他们缺乏雄心，而是因为没有人在机构量化和普通交易者之间架起桥梁。
We're building the platform we wished existed when we were starting out. A platform where a university student in Jakarta has the same quant tools as a portfolio manager on Wall Street.
我们正在构建当初我们刚入行时梦寐以求的平台。一个让雅加达的大学生也能拥有和华尔街基金经理同样量化工具的平台。
From learning to execution, every layer was designed because we needed it ourselves.
从学习到执行，每一层都是因为我们自己需要而设计。
Every segment represents a version of ourselves at a different stage of the journey.
That was us at Day 1. Curious, eager, but drowning in noise. They need a clear path from casual to systematic.
That was us at Year 2. Profitable but inconsistent. They have edge but lack the infrastructure to scale it.
That's us now. They need raw speed, clean data, and a C++ engine that doesn't make them compromise.
每个用户群体都代表着我们在不同阶段的自己。
这是第一天的我们。好奇、热切，但淹没在噪音中。他们需要一条从随意到系统的清晰路径。
这是第二年的我们。盈利但不稳定。有优势但缺乏规模化的基础设施。
这是现在的我们。需要极致速度、干净数据和不将就的C++引擎。
We're not outside observers building for traders. We are traders building for ourselves.
Every feature was born from a real trading problem. We don't build what sounds cool in a pitch deck — we build what we'd actually use at 3am during a volatility spike.
Our C++ engine isn't a marketing claim. It's a necessity. When you're testing 10,000 parameter combinations, the difference between 30 seconds and 0.3 seconds changes everything.
We didn't bolt AI onto an existing platform. We built AI into the foundation. The agent doesn't just chat — it builds, tests, and iterates strategies autonomously.
MAS-regulated, Asia-first, global ambition. We're at the crossroads of the fastest-growing retail trading markets in the world.
我们不是局外人为交易者建产品。我们就是交易者，为自己而建。
每个功能都诞生于真实的交易痛点。我们不做PPT上好看的东西 — 我们做凌晨3点波动率飙升时真正会用的东西。
我们的C++引擎不是营销噪头。这是必须的。当你测试1万组参数组合时，30秒和0.3秒的差别改变一切。
我们不是在现有平台上加AI。我们把AI建在地基里。智能体不只是聊天 — 它自主构建、测试和迭代策略。
MAS监管，亚洲优先，全球雄心。我们位于全球增长最快的零售交易市场的十字路口。
The convergence of AI breakthroughs, zero-commission trading, and a new generation of data-literate investors has created a once-in-a-decade window.
The question isn't if quant goes mainstream. It's who builds the platform that takes it there.
AI突破、零佣金交易和新一代数据素养投资者的汇聚，创造了十年一遇的窗口。
问题不是量化会不会主流化，而是谁来建造带它到那里的平台。
Q1-Q2 2026
Core platform launch
500 beta users
Community seeding
Content engine live
Q3 2026
Public launch
5,000 users
KOL partnerships
Paid subscriptions
Q4 2026 – Q2 2027
50,000 users
Multi-market expansion
Live trading integration
Series A fundraise
2027+
SEA & APAC expansion
Institutional tier
500K+ users
Category leadership
2026 Q1-Q2
核心平台上线
500名测试用户
社区建设
内容引擎启动
2026 Q3
公开发布
5,000用户
KOL合作
付费订阅
2026 Q4 – 2027 Q2
50,000用户
多市场扩展
实盘交易集成
A轮融资
2027+
东南亚与亚太扩张
机构版
50万+用户
品类领导者
We started as traders who were tired of losing to systems we couldn't access.
Now we're building the system that levels the playing field.
Not for the privileged few. For everyone.
Varsity Tech — Singapore — 2026
我们曾是厄倦于输给无法触及的系统的交易者。
现在我们正在构建拉平竞争场的系统。
不是为少数特权者。而是为每一个人。
Varsity Tech — 新加坡 — 2026