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Developer-Trader

Analysts who write code and want to build, test and document data-driven rules and models.

Handle market data in code, extend rules with code nodes, validate ML studies without leakage and run your own model inside a strategy.

New to markets? Start with Start Here first.

Start here

What a backtest can prove, and what it can't

A backtest shows how a precisely defined rule would have behaved on past data under stated assumptions. It cannot show the rule will behave that way again.

In the app: Quantitative testing (in development)

Foundations

  1. 1

    Exchanges, brokers and data feeds: who does what

    Who supplies the data your code reads.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon
  2. 2

    Where market data comes from

    Where market data comes from.

    • Beginner
    • Explainer
    • 3 min
    • Coming soon
  3. 3

    Data quality: gaps, spikes and duplicates

    Data faults code must catch.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon
  4. 4

    Discretionary vs rules-based research

    Rules versus judgement.

    • Beginner
    • Explainer
    • 3 min
    • Coming soon
  5. 5

    From idea to tested rule

    From idea to testable rule.

    • Beginner
    • Explainer
    • 3 min
    • Coming soon
  6. 6

    Anatomy of a rule: entry, exit, size, filter

    Parts of a rule.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon
  7. 7

    Your first visual strategy graph

    Visual graph that code nodes extend.

    • Beginner
    • Research recipe
    • 5 min
    • Coming soon
  8. 8

    What a backtest can prove, and what it can't

    Limits of backtests.

    • Beginner
    • Explainer
    • 3 min
  9. 9

    Your first backtest, step by step

    First backtest run.

    • Beginner
    • Research recipe
    • 5 min
    • Coming soon
  10. 10

    Reading a backtest report: metrics that matter

    Reading the report.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon
  11. 11

    Returns: simple, log and percentage points

    Returns, the first calculation in code.

    • Beginner
    • Explainer
    • 3 min
    • Coming soon
  12. 12

    Standard deviation: volatility as a number

    Volatility as a number.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon
  13. 13

    Correlation without the confusion

    Correlation before regression.

    • Beginner
    • Visual guide
    • 3 min
    • Coming soon

Core

  1. 14

    Python for market data: your first script

    First Python script on market data.

    • Intermediate
    • Research recipe
    • 5 min
    • Coming soon
  2. 15

    Python for analysts cheat sheet

    Python operations cheat sheet.

    • Intermediate
    • Cheat sheet
    • 2 min
    • Coming soon
  3. 16

    Build a custom indicator in code

    Custom indicator verified by hand.

    • Intermediate
    • Research recipe
    • 5 min
    • Coming soon
  4. 17

    Code nodes: when visual logic isn't enough

    Code nodes inside a graph.

    • Intermediate
    • Research recipe
    • 5 min
    • Coming soon
  5. 18

    Rule playground: wire a rule, see the signals

    Interactive rule playground.

    • Intermediate
    • Interactive tool
    • 3 min
    • Coming soon
  6. 19

    Regression basics: lines through noisy data

    Regression before ML.

    • Intermediate
    • Visual guide
    • 3 min
    • Coming soon
  7. 20

    Machine learning for markets: what it can and can't do

    What ML can and cannot do.

    • Intermediate
    • Explainer
    • 5 min
    • Coming soon
  8. 21

    Features and labels: framing an ML question

    Framing features and labels.

    • Intermediate
    • Explainer
    • 5 min
    • Coming soon
  9. 22

    Look-ahead bias: the backtest that read tomorrow

    Look-ahead bias, the root of leakage.

    • Intermediate
    • Explainer
    • 5 min
    • Coming soon
  10. 23

    Overfitting in one picture

    Overfitting in one picture.

    • Intermediate
    • Visual guide
    • 3 min
    • Coming soon
  11. 24

    In-sample vs out-of-sample

    In-sample versus out-of-sample.

    • Intermediate
    • Visual guide
    • 3 min
    • Coming soon
  12. 25

    Leakage: how ML studies accidentally cheat

    Leakage in ML studies.

    • Intermediate
    • Explainer
    • 5 min
    • Coming soon

Advanced

  1. 26

    Time-series cross-validation done right

    Time-series cross-validation.

    • Advanced
    • Visual guide
    • 4 min
    • Coming soon
  2. 27

    Trees and boosting on tabular market data

    Tree models and tuning risk.

    • Advanced
    • Explainer
    • 6 min
    • Coming soon
  3. 28

    Export a model to ONNX for on-device inference

    Export and verify a model.

    • Advanced
    • Research recipe
    • 6 min
    • Coming soon
  4. 29
    Capstone

    Use your own ML model inside a strategy graph

    Capstone recipe: your own model in a strategy graph.

    • Advanced
    • Research recipe
    • 6 min
    • Coming soon
Optional reading: 9
  • Time zones and session boundaries in data
  • JavaScript in the research workspace
  • Pandas time-series essentials for analysts
  • Code as a transparent calculation aid
  • Checking an AI-generated indicator formula
  • Model cards: document a model before you use it
  • Reproducible notebooks and environments
  • Spot the leak: an ML study quiz
  • Why a node graph

In the app

The Analess features these lessons use most. Availability is shown on each feature’s page.