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01

Introduction to Automated Trading

SFZ Pulsar is built around one discipline: turn a market idea into a testable process before capital is exposed. The product teaches that workflow by forcing structure into strategy design, backtesting, execution, and review.

3
Modules 3
Lessons Idea -> Rule -> Test
Focus
Module 01 · Basics

What automated trading actually means inside SFZ

The platform is not a signal vending machine. It is a workflow engine for structured decision-making.

Rule set
A bot is a policy, not a prediction

The edge comes from rules that behave consistently across enough market conditions, not from one forecast that happens to be right today.

Control
Risk limits stay first-class

Capital limits, drawdown awareness, and execution constraints are configured alongside the strategy rather than added after the fact.

Workflow
Learning mirrors product onboarding

The best way to understand automation is to follow the same sequence you will use in the platform: create, test, launch, review.

Why this matters

Every idea in this path reduces to one question: what are the explicit rules, and what stops the trade if the thesis is wrong? Keep that lens as you read.

Module 02 · Core Concepts

The four moves every automated system must survive

If one of these steps is weak, the system is still discretionary even if it looks automated on the surface.

Step 01
Define the market behavior

State what condition you think exists: trend, mean reversion, breakout, volatility compression, or event response.

Step 02
Express it as a rule

Convert the idea into entries, exits, invalidation logic, and position sizing that the creator can enforce without improvisation.

Step 03
Test the claim

Run historical validation with realistic capital assumptions, symbols, and bad periods instead of trusting the first attractive equity curve.

Step 04
Supervise the live process

Automation reduces repetitive decisions, but the operator still owns monitoring, escalation, and shutdown criteria.

Module 03 · Practical Understanding

What to lock in before you call anything automated

These habits prevent the most common beginner mistakes in SFZ and in live trading generally.

Keep the human role clear
  • You still choose the logic, capital budget, and shutdown conditions.
  • You still verify that the strategy matches the market it trades.
  • You still decide when a bot should be retired rather than patched.
Start with process metrics
  • Ask whether the rules are explicit enough to test.
  • Ask whether the expected drawdown is tolerable before looking at return.
  • Ask whether you could explain the system to another operator without guessing.
Treat SFZ like an operating desk
  • Use the creator to remove ambiguity.
  • Use the backtester to reject weak ideas early.
  • Use the terminal and analytics screens to supervise, not to hope.
Try it — position sizing

Sizing is risk ÷ stop distance. Adjust the inputs and watch how many shares the same risk budget buys.

Risk budget
€100
Position size
€2,000
% of account
20%
What this page should change

Do not automate vague pattern recognition you cannot define.

  • Do not confuse historical correlation with a durable edge.
  • Do not move forward until the rules are testable and reviewable.
Test your knowledge

Check whether the operating logic is clear before you move into strategy design.

Question 1

What makes a trading idea ready for automation inside SFZ?

Question 2

What should a backtest do at the fundamentals stage?

Question 3

Which statement matches the SFZ workflow best?

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