What you will learn
- Define algorithmic trading
- Distinguish systematic from discretionary trading
- Know the genuine advantages of automation
- Understand that automation executes an edge but does not create one
Everything you have learned so far, valuing assets, reading markets, managing risk, and pricing derivatives, can be encoded into rules that a computer executes automatically. That is algorithmic trading, the systematic, rules-based approach behind modern quantitative finance and the strategy testing on this platform. This unit takes you from a trading idea to a tested, executable strategy, and it begins with what algorithmic trading actually is.
Algorithmic trading explained (IG)
A clear overview of what algorithmic trading is and how it works. Good orientation for the unit.
Trading by rules, executed by computers
Algorithmic trading is the use of computer programs to execute trades automatically according to predefined rules. Rather than a human deciding each trade in the moment, a set of precise instructions, based on price, timing, quantity, or any mathematical model, determines when and how to buy and sell, and a computer carries them out. The rules can be simple, such as buying when a price crosses a threshold, or sophisticated systematic strategies drawing on the statistics and models from earlier units. What unites them is that the logic is specified in advance and executed mechanically.
Key terms
- Algorithmic trading
- Using computer programs to execute trades automatically according to predefined rules.
- Systematic trading
- Following predefined rules consistently, with no in-the-moment improvisation.
- Discretionary trading
- Relying on a human's judgment in each individual situation.
- Edge
- A genuine, repeatable source of predictive value. Automation executes an edge but cannot create one.
Systematic versus discretionary
The fundamental distinction is between systematic trading, which follows predefined rules consistently, and discretionary trading, which relies on human judgment in each situation. Algorithmic trading is inherently systematic: every decision flows from the rules, with no room for improvisation. This is both its great strength and its central limitation, because the rules capture exactly what was programmed and nothing more. A systematic approach trades the flexibility of human judgment for the consistency and discipline of mechanical execution.
Why algorithmic trading is powerful
- Speed: computers react and execute in fractions of a second, faster than any human can.
- Discipline: rules remove emotion, addressing exactly the panic and greed that Unit 6 called the greatest threat to investors.
- Scale: an algorithm can monitor many markets and instruments at once, far beyond human capacity.
- Testability: because the rules are precise, they can be tested on historical data through backtesting, which the rest of this unit develops.
Automation does not create an edge, it executes one. A flawed strategy simply loses money faster when a computer runs it.
Does automating it help?
A trader has a strategy that loses money on average. They automate it so a computer runs it faster and around the clock. What happens?
Automation adds speed, discipline, and scale, but it does not create an edge. A computer executes the given logic faithfully, so a losing strategy simply loses money faster. The edge must come from the genuine predictive value of the rules themselves.Demystifying algorithmic trading (QuantInsti)
Reinforces what algo trading really involves and what it does not promise. Watch for the honest reality.
The honest reality and the scope
Be clear about what algorithmic trading provides. Automation adds speed, discipline, and scale, and it removes emotional mistakes, which is genuinely useful. But automation does not, by itself, create a profitable edge. An algorithm executes whatever logic it is given, so a flawed strategy executes flawed logic faster. The edge, if it exists, must come from the quality of the rules, not from automating them. The field spans a wide spectrum: execution algorithms that place large orders efficiently, systematic strategies that decide what and when to trade (the main subject here), and high-frequency trading where microseconds are the whole game. This unit shows how a trading idea becomes a complete strategy, how to test it honestly against history, and how to guard against the many ways testing can deceive you, which is the hardest skill in quantitative trading.
Systematic or discretionary?
What automation does and does not do
In your own words, explain what algorithmic trading genuinely adds and why it cannot make a bad strategy good.
Write an answer before comparing it with the model response.
Model answer
Algorithmic trading genuinely adds speed, so it can react and execute far faster than a person, discipline, because it follows the rules exactly and removes the emotional mistakes like panic and greed that hurt discretionary traders, scale, since it can watch many markets at once, and testability, because precise rules can be backtested against history. What it cannot do is create an edge. A computer only executes the logic it is given, so if the underlying strategy has no real predictive value, automating it just runs a losing idea faster and more efficiently. The hard part, having a genuine and robust idea, remains as difficult as ever, and automation is a tool for executing and testing ideas rather than a source of profit by itself.
Algorithmic trading executes rules mechanically, but a real strategy is far more than a single rule. The next lesson breaks down the full anatomy of a systematic strategy.