01
Multi-factor scoring
Composite of valuation, growth, quality and momentum factors with custom weights. One-click export.
Q
QuantEdge
Beta · invite only
Organize research, test historical assumptions, record your reasoning
QuantEdge is currently invite-only. Enter your code to continue.
Illustrative interface · fictional figures, not actual investment performance or recommendations.
Portfolio Lab
组合净值+18.6%
CAGR13.2%Max DD-8.4%Sharpe1.46
78Score
质量82动量76估值69增长85
Bring screening criteria, historical backtests and a research journal together, so you can revisit the data and assumptions behind a decision. Backtests describe historical outcomes under specific assumptions; they do not predict future returns.
4
core modules
50+
factor library
US · HK
markets covered
Tech Stack
ReactRechartsTailwindPythonyfinance
— Features
What it does.
02
Portfolio backtest
Custom rebalance frequency, transaction costs and slippage assumptions. Outputs CAGR, drawdown, Sharpe.
03
Monitoring and rule alerts
The monitoring page summarizes prices, indicators and triggered rules. Data may update on request, from cache or through scheduled jobs, so delays are possible. It is not exchange real-time data and does not promise continuous alerts after the page is closed.
04
Investing journal
Logs every buy/sell with the rationale at the time — a traceable archive of decisions.
— Why I Built It
Why I built it.
I wanted to bring research scattered across spreadsheets and notes into one place, so I could compare my assumptions with what happened later.
For example, when studying a group of companies, I record the screening criteria, compare historical results under different assumptions, and write down my reasoning. The tool preserves that context; it does not make investment decisions for me.
— Try it
Want to see if it helps?
Click the link below — you can try it in minutes. Got problems or feature requests? Just email me.