Frame the thesis
Turn a market observation into explicit signals, constraints, assumptions, and falsifiable questions.
Natural language → research specificationIndependent research project · Pre-launch
Turn an investment thesis into a testable strategy.
Tesion Quants is building an AI-assisted workspace for quantitative research—helping independent investors structure ideas, write research code, test assumptions, and understand results.
01
Built for research and decision support. Tesion Quants does not provide personalized investment advice or execute trades.
RESEARCH / MOMENTUM_01
SIMULATION
UNIVERSEUS EQUITIES
WINDOW2018—2026
FREQUENCYDAILY
CUMULATIVE RETURNILLUSTRATIVE
20182020202220242026
Stress-test this thesis across multiple market regimes
↵Concept interface — sample data only
THE PLATFORM
A clearer path from idea to evidence.
Quantitative research often breaks across scattered notebooks, ambiguous assumptions, and results that are hard to explain. Tesion Quants is designed as one guided workflow.
Build the test
Draft transparent research code and define the universe, rebalancing rules, costs, and evaluation window.
Specification → reproducible experimentChallenge the result
Inspect robustness, regime sensitivity, look-ahead risk, and the gap between a backtest and live conditions.
Output → evidence and limitationsWHY CLAUDE
Intelligence across the research loop.
Claude is planned as the reasoning layer that helps translate between investment language, quantitative specifications, code, and plain-English interpretation.
Integration planned for MVP
Research copilot
Structure hypotheses, surface hidden assumptions, and propose validation steps before code is written.
Code assistance
Help draft and review Python research code while keeping calculations inspectable by the user.
Result interpretation
Explain metrics, compare scenarios, and turn test output into a concise research memo with clear caveats.
BUILD STATUS
Early, focused, and transparent.
Tesion Quants is an independent, founder-led project in the concept and prototype stage. The current focus is validating the research workflow before expanding scope.
Concept validation
Define users, research jobs, and responsible product boundaries.
Research prototype
Build the first thesis-to-backtest workflow with Claude.
Private testing
Test usability and analytical reliability with early research users.
PRODUCT PRINCIPLES
Research tools should earn trust.
01
Show the assumptions
Inputs, constraints, and model-generated suggestions should remain visible and editable.
02
Separate evidence from confidence
Backtests are historical experiments—not promises about future performance.
03
Keep humans accountable
Users review the logic and make their own decisions. The platform supports research; it does not replace judgment.
THE NEXT EXPERIMENT
Building the first research workflow.
Tesion Quants is currently preparing its MVP and Claude integration. Partnership and early-user conversations will open as the prototype develops.
Back to the thesis ↑