Resources
This is where the Stretus team writes about how automated trading actually works: how strategies are expressed as rules, how backtests are run and where they mislead, and what the engineering beneath a live trading system has to get right.
Algorithmic trading executes trades from predefined rules rather than discretion. How it works, what it does not promise, and where it fits.
Algorithmic trading means a computer places orders from rules you defined in advance, not from judgement in the moment.
A side-by-side comparison of discretionary and rule-based trading, and a straightforward way to decide which one your approach actually needs.
The question is not which is better, but whether your decisions can be written down as rules.
Subject hubs that collect everything written on one theme. Each hub opens with a plain definition of the term.
Rule-based execution: how automated strategies are built, validated and deployed, and what automation does and does not change.
Explore topicTesting a strategy against history: how engines work, why results flatter, and what validation can and cannot establish.
Explore topicPosition sizing, exposure limits, order gates and kill switches, how risk is enforced by systems rather than intention.
Explore topicWhat the algorithmic trading framework requires of brokers and of the vendors they empanel, with every obligation traced to its circular.
Explore topicThe engineering beneath automated trading: data pipelines, order management, reconciliation, risk gates and monitoring.
Explore topicHow automated orders actually reach the market: authorisation, idempotency, reconciliation and the permissions never to grant.
Explore topicExplainers and practical guides. Written to be read once and understood.
Algorithmic trading executes trades from predefined rules rather than discretion. How it works, what it does not promise, and where it fits.
A side-by-side comparison of discretionary and rule-based trading, and a straightforward way to decide which one your approach actually needs.
Backtesting replays a strategy against historical data. What the process does, why results look better than they were, and how to read a report honestly.
Deep-dives for engineers and quantitative readers. Every article cites its sources.
The six subsystems between a strategy definition and a filled order, what each is responsible for, and where the hard failure modes live.
Inside a backtest: the event loop, how fills are simulated, why lookahead bias appears, and what separates a useful engine from a plausible one.
Who registers the algorithm, whose static IP it is, and who carries the liability, answered from the circulars, with every reference attached.
Describe a strategy in plain language, backtest it against historical data, and paper trade it before any capital is committed.
