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Quantitative & Algo

Turn a market hypothesis into a research workflow.

Define the question, inspect the data and reason through the logic. Use Trezable to develop research and code with assumptions you can examine.

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t.Trezable /Quant workspaceInteractive example
RESEARCH LOGIC / NOT EXECUTED

Make the method visible.

Python

Define before measuring

Hypothesis: a trailing moving average may describe trend persistence. Specify the universe and horizon before examining the outcome.

moving_average_example
# Input: sorted closing-price series
average = close.rolling(20).mean()
signal = (close > average).astype(int)

# Use the previous observation’s signal
position = signal.shift(1)

# No fills, costs or returns modeled
No backtest executed. No performance or profitability claim.
Illustrative research preview. Not live market data.

Built around your research.

Web researchFiles & documentsStructured analysisYour judgment
A CLEARER PERSPECTIVE

Start with a hypothesis. Earn the conclusion.

Bring a specific question. Build the context. Leave with a clearer understanding of what to check next.

01

Make the idea testable

Translate a market intuition into explicit rules, inputs, assumptions and conditions that would challenge the hypothesis.

02

Develop the analysis

Explore code, inspect supplied datasets and discuss methodology. Review generated code before running it in your own environment.

03

Question the result

Look for look-ahead bias, survivorship bias, overfitting and missing costs. Separate a historical observation from an investable strategy.

HOW IT COMES TOGETHER

From a question
to a working view.

The quantitative research notebook. Powered by the same Trezable chat, with a workflow built around your market.

Try it in chat
  1. 01

    Specify the hypothesis

    Define the universe, signal, timing and outcome you want to study before selecting favorable examples.

  2. 02

    Audit the inputs

    Check timestamps, missing values, corporate actions and whether the data was available at each decision point.

  3. 03

    Review the method

    Examine code, assumptions, transaction costs and out-of-sample validation. Treat every result as something to challenge.

START WITH SOMETHING REAL

A good question goes a long way.

Open an example in chat, edit it for your context, and send when you’re ready.

01 / STARTING QUESTION

Help me turn a moving-average idea into a testable research specification. Define inputs, decision timing and invalidation criteria. Address look-ahead bias, missing data and costs.

02 / STARTING QUESTION

Review the Python research code I will provide. Look for look-ahead bias, data leakage, survivorship bias and incorrect return calculations. Explain each issue without inventing results.

03 / STARTING QUESTION

Design an out-of-sample validation plan for a trading hypothesis. Explain walk-forward evaluation, transaction costs and the risk of overfitting. Do not claim a strategy is profitable.

BEFORE YOU BEGIN

Frequently Asked Questions

Is this a strategy execution platform?

No. These pages lead to research in chat. The preview does not deploy algorithms, connect brokers or execute trades.

Can Trezable help write research code?

Yes. Explain your data and intended method in chat. Inspect and test generated code in your own environment; generated code can contain errors.

Are there backtest results on this page?

No. The code is illustrative and is not executed here. Any real evaluation needs a specified dataset, methodology, costs and independent validation.

CONNECTED WORKFLOWS
YOUR NEXT QUESTION STARTS HERE

Bring your curiosity.
Build your conviction carefully.

A research partner for the questions behind your financial decisions.

Open TrezableAI can make mistakes. Verify sources, data and calculations. Research support, not personalized financial advice.