VIP
Build strategies just by chatting
First-week offer
NT$ 749 / mo
Regular NT$888
Minimum term: 12-month minimum
Included
- AI optimization: about 10 runs / month
- US and Taiwan data with full backtesting
- Cloud scheduling runs strategies daily
Use AI to draft Python strategies, then validate them with backtests, debugging, and risk metrics before automation enters the workflow.
US trend rotation strategy using momentum, industry strength, and risk adjustment
Baseline: rank the full market by momentum to establish the shape of a US trend strategy.
Add institutional flow filters to capture stronger sponsorship momentum
ResearchReplace full-market ranking with industry relative strength to improve rotation
ResearchAdjust position count dynamically based on market volatility or regime
ResearchValuation denoising: add value factors and dynamic position sizing to improve drawdowns.
Add valuation filters to avoid chasing extremely expensive stocks
ResearchIntroduce dynamic position sizing based on market risk
ResearchCombine industry relative strength with stock-level leadership selection
ResearchMulti-factor blend: combine flows, valuation, and technical signals to improve risk-adjusted returns.
Use flow factors as a ranking or filter layer
ResearchAdd valuation screens to avoid overvalued names
ResearchAdd RSI or MACD to refine entry timing
ResearchPricing
Built-in AI cloud research can use full data, up to each dataset's latest published date. VIP: latest published data; daily prices normally cover the previous trading day. Free downloads and manual runs: Taiwan historical data through 2023-12-31 (last trading day: 2023-12-29). See the data catalog for update schedules and open-data exceptions.
No credit card. Start in 30 seconds, then upgrade after the idea checks out.
VIP
Build strategies just by chatting
Minimum term: 12-month minimum
Included
AI
Precise AI optimization
Minimum term: 9-month minimum
Included
AI Max
High-volume AI quant workflows
Minimum term: 6-month minimum
Included
Trusted by
Chosen by investors and developers
“The course is systematic, from zero to a complete stock-selection strategy. It focuses on Taiwan equities and provides a full Python workflow and database.”
Dec 2024
“It saves a huge amount of time by combining a ready-to-use database, code examples, a backtesting platform, and documentation.”
Apr 2024
“The examples helped me build candidate stock lists faster and learn how to check investment hypotheses with backtests and risk metrics.”
Jan 2024
“A strong starting point for quant beginners: setup is straightforward, and a backtest can quickly show whether an idea is worth pursuing.”
Aug 2023
“FinLab provides much more than code snippets. The course and platform resources are rich, and questions continue to get answered.”
Jul 2023
“The course is systematic, from zero to a complete stock-selection strategy. It focuses on Taiwan equities and provides a full Python workflow and database.”
Dec 2024
“It saves a huge amount of time by combining a ready-to-use database, code examples, a backtesting platform, and documentation.”
Apr 2024
“The examples helped me build candidate stock lists faster and learn how to check investment hypotheses with backtests and risk metrics.”
Jan 2024
“A strong starting point for quant beginners: setup is straightforward, and a backtest can quickly show whether an idea is worth pursuing.”
Aug 2023
“FinLab provides much more than code snippets. The course and platform resources are rich, and questions continue to get answered.”
Jul 2023
“From EDA to stock-selection strategies, it teaches practical Python skills that remain useful for years.”
Jul 2023
“The modular design is easy to start with, and backtesting makes the investment rationale much clearer.”
Nov 2022
“A rare quantitative investing resource for Taiwan, with clear steps for building your own strategy.”
Nov 2022
“Clear explanations with complete code examples that can be used immediately.”
Aug 2023
“The course is systematic, from zero to a complete stock-selection strategy. It focuses on Taiwan equities and provides a full Python workflow and database.”
Dec 2024
“It saves a huge amount of time by combining a ready-to-use database, code examples, a backtesting platform, and documentation.”
Apr 2024
“From EDA to stock-selection strategies, it teaches practical Python skills that remain useful for years.”
Jul 2023
“The modular design is easy to start with, and backtesting makes the investment rationale much clearer.”
Nov 2022
“A rare quantitative investing resource for Taiwan, with clear steps for building your own strategy.”
Nov 2022
“Clear explanations with complete code examples that can be used immediately.”
Aug 2023
“The course is systematic, from zero to a complete stock-selection strategy. It focuses on Taiwan equities and provides a full Python workflow and database.”
Dec 2024
“It saves a huge amount of time by combining a ready-to-use database, code examples, a backtesting platform, and documentation.”
Apr 2024
FinLab does not promise performance. It puts data, backtesting, risk metrics, and AI collaboration in one workflow so every strategy is checked before it runs.
Quant strategy transparency statement
Data integrityPoint-in-time data and explicit backtest settings reduce look-ahead risk.
Logic defenseDebugging, risk metrics, and trading rules expose strategy blind spots.
ExplainabilityReturns, drawdowns, win rate, and holdings show where performance came from.
No credit card required