Different Ways People Use Algorithmic Trading
Algorithmic trading is not one specific type of trading strategy. It is a way of using clearly defined rules so a computer can perform part of the trading process consistently.
Those rules can be used for very different jobs. An algorithm might look for trading opportunities, make trading decisions, manage an existing position, control how much is traded, or help test a strategy before it is used with real money.
Understanding these different uses is important because algorithmic trading does not automatically mean fully automated trading. You can use an algorithm for one part of the process or combine several parts into a complete trading system.
Using Algorithms to Find Trading Opportunities
One of the simplest uses of an algorithm is to search for conditions that may be interesting to a trader.
Instead of watching charts continuously, a computer can check market data against a set of rules. For example, the rules might look for a particular price level, an indicator condition, or a combination of several conditions.
When the required conditions appear, the algorithm can identify that situation as something worth paying attention to.
An algorithm can simply identify a condition and leave the final decision to a person or another system.
Using Algorithms to Make Trading Decisions
An algorithm can go one step further and apply rules to decide what should happen when certain market conditions occur.
For example, a strategy could define that a trade should only be considered when several conditions are true at the same time. If they are not all true, the algorithm does nothing.
This is useful because the same rules are applied every time. The computer does not change its decision because the previous trade won or lost.
The important part is that the decision comes from rules that were defined beforehand.
Using Algorithms to Execute Trades
An algorithm can also be used to handle the mechanical part of trading after a decision has been made.
This is where an application programming interface (API) becomes useful. An API allows one software system to communicate with another. In trading, an execution system can use an exchange's API to send trading instructions to the exchange.
A simple workflow can look like this:
The execution part can therefore be automated even when the strategy itself was created separately.
For example, a TradingView strategy can generate an alert when its rules produce a signal. That alert can be sent through a webhook to an automated trading system. A webhook is a way for one application to send information to another application over the internet.
With TurboBridge, a TradingView alert can be sent through a webhook to a configured TurboBridge bot, which connects the signal to the configured Binance Futures trading workflow.
If you want to understand that connection in more detail, see How TurboBridge Works.
Using Algorithms to Manage an Open Trade
An algorithm does not have to stop working after a trade is opened.
Rules can also be used to manage what happens while a position is open. A position is simply a trade that is currently open in the market.
For example, a strategy may define a profit target or a stop-loss level. A profit target specifies a price at which the position should be closed to take a planned profit. A stop loss specifies a price at which the position should be closed to limit the loss.
This means an algorithm can handle both sides of the process: deciding when a position should be opened and defining what should happen after it is open.
Using Algorithms to Control Trading Risk
Algorithms can also be used to enforce risk rules.
For example, a trading system can have rules about how much money should be used for a trade, when trading should be allowed, or when a position should be closed.
This can be useful because a computer can apply the same limit every time instead of relying on someone to remember it during a fast-moving market.
An algorithm can enforce a risk rule, but it cannot make a risky strategy safe or guarantee that a trade will be profitable. The quality of the rules still matters.
Using Algorithms to Test Trading Ideas
Algorithms can also be useful before any real trade is placed.
A trading strategy can be tested against historical market data to see how its rules would have behaved in the past. This is commonly called backtesting.
Backtesting lets you take a set of trading rules and apply them to historical data as though the strategy had been running at that time. The resulting trades can then be studied to understand how the rules behaved.
A Simple Backtesting Example
Imagine a very simple strategy that says:
- Buy when a 20-period moving average crosses above a 50-period moving average.
- Exit when the 20-period moving average crosses back below the 50-period moving average.
A moving average is a line calculated from past prices that smooths some of the day-to-day price movement, making the general direction of the market easier to see.
The strategy can then be applied to historical price data. Every time the rules would have been satisfied, the backtest records what would have happened if the strategy had taken the trade.
The results can include information such as the number of trades, winning and losing trades, overall return, and periods of decline known as drawdown.
This gives the trader something concrete to study instead of judging the strategy only by looking at a few charts.
A strategy that performed well on historical data is not guaranteed to perform well in the future. Markets change, and historical results can make a strategy look better than it actually is.
You Can Experiment Without Using Real Money
Backtesting is not the only way to test an idea. Paper trading lets you simulate trades using virtual funds instead of risking real money.
TradingView Paper Trading is one option for practicing trades in a simulated environment.
If you want to explore algorithm development and backtesting more deeply, QuantConnect provides tools for research, backtesting, paper trading, and algorithm deployment.
Another option is Alpaca Paper Trading, which provides a simulated trading environment that can also be accessed through an API.
Creating a Strategy to Test
To backtest a TradingView strategy, the trading idea needs to be expressed as rules that TradingView can execute. Pine Script is TradingView's programming language for creating indicators and strategies.
If you do not want to start from an empty script, TurboBridge also provides ready-to-use Pine Script examples. Where an example is provided as a TradingView strategy, it can be opened in TradingView and used as a starting point for experimenting and backtesting its rules.
You can explore the available examples and create your own Pine Scripts here: Create Pine Scripts with TurboBridge.
The useful learning path is therefore:
Testing first does not guarantee success, but it gives you a way to find problems in your rules before moving toward live trading.
One Algorithm Can Do Several Jobs
These uses do not have to exist separately.
A single trading system can combine several of them. It might look for a particular market condition, make a decision when its rules are satisfied, determine the trade size, execute the trade, and then manage the open position using predefined exit rules.
In other words, algorithmic trading can cover a small part of the trading process or several parts working together.
The important idea is that algorithmic trading is about applying defined rules to a trading process. How much of that process is automated depends on the system being built.
Algorithmic Trading Does Not Always Mean Fully Automated Trading
This distinction is easy to miss.
You can have an algorithm that only identifies opportunities and sends an alert to a person. You can have another system that makes the decision and leaves execution to a trader. Or you can connect the different parts so that the process runs automatically.
The word algorithmic describes the use of defined rules. The word automated describes how much of the process is carried out by software without manual action.
Learn how TradingView signals, webhooks, bots, and exchange execution can work together.
See How TurboBridge Works