Risks And Rewards Of Becoming An Algo Trader (2024)

Updated: March 2023

It’s no secret that automation tools are a big factor in how many fields operate now. As new tools come out, more routine tasks become the domain of software and machines rather than people.

These facts apply to the world of trading, leading to the rise in automated trading software and algorithms. Algorithms are behind “algo” trading and without them, this practice would not exist.

But how can you use algorithms to trade effectively? How does this so-called algo trading even work? Let’s look at algo trading and how a trader can make the most out of this growing subset of financial automation tools.

What Is Algo Trading?

Algo trading is the shorthand term for algorithmic trading, a trading strategy that uses a computer program with specific instructions to engage in automatic trading based on its instruction. Algo trading can also go by the terms automated trading and black-box trading.

Algo trading programs offer several benefits over human-controlled trades, mostly in the speed and liquidity of trade.

Automated trading operates on price and quantity parameters and initiates trade as soon as the market meets those parameters. Thus, algo trading allows trades to happen faster and introduce more liquidity into the markets.

Here is a simple algo, it buys when the price crosses a moving average, and has a stop loss of $1000. First I’ll give you rules in plain English, then I’ll provide rules in Tradestation Easy Language format (the software I use to build and test my algos):

Plain English: If close crosses above the 10 bar moving average of the close price, buy at the open of the next bar. Exit if a loss of more then $1000 occurs.

Tradestation Easy Language:
If close crosses above average(close,10) then buy next bar at market;
Setstoploss(1000);

Is Algo Trading Legal?

While many nations allow any trader to take advantage of algo trading, some countries make algo trading illegal for unlicensed traders and the publishing of certain trading results.

Due to the technical knowledge and hardware requirements to set up algorithmic trading bots, these nations believe licensed individuals should be the ones taking on this technical task.

Can You Make Money With Algo Trading?

How Algorithmic Trading Works

Algo trading works by programming an algorithm with conditions to watch for in the market. Once these conditions occur, the program initiates and executes the order based on the instructions coded by the developer.

This process means that a trading algorithm needs more than just the means to calculate technical indicators to watch out for on the market. It may also require an application programming interface (API) that connects to market data and a place for its rules to be set as a reference point for the code.

These two features allow the algorithm to interact with the market in a way that aligns with the goals of the algo trader.

Algo Trading Tech Requirements

The difference between algo trading and standard trading comes down to the use of coding and technology in the trading practice. Thus, if a trader doesn’t have any coding knowledge, they will not get everything that algo trading offers. Or, they could find they lose money on a faulty algorithm.

Thus, the big question is: does an algo trader learn the coding needed to create their algorithm, or do they buy a premade algorithmic trading software to use?

Both routes have their pros and cons. A custom-built algorithm will align specifically with the trading goals the algo trader has in mind. However, pre-built software will allow them to get into the algo trading space faster and with less effort, but at the cost of money.

Regardless of which route they go, an algo trader will want these features out of their software:

Access to market and company data to have data to review and use in technical factor calculations
Connectivity to multiple markets for arbitrage trades
Low latency order placement and execution
Configuration options
Backtesting features and historical data to backtest with
Trading platform integrations
Coding that doesn’t require a specific platform to create and edit

The Pros and Cons of Algo Trading

Algo trading isn’t like the trading you can do on most retail brokerage apps today. This trading process requires a set of technical skills and resources that won’t be available to the average person.

These facts make algo trading something that many traders will have an interest in, but never follow through with.

The problem with that conclusion is that it ignores the benefits that algo trading can have for a trader. Access to the tools and techniques that a trading algorithm can provide gives traders several benefits, some of which include:

Fast trade executions: Because trades execute by algo trades happen quickly, most algo traders have their orders executed with the best price possible.
Low order latency: The speed that algo trading happens at also ensures that orders enter the market faster and that the execution amount in these orders stays accurate and filled as desired.
Possibly Lower transaction costs: Compared to going through a human-run brokerage, algo trading costs less in transaction fees thanks to the fact that algo traders can utilize a low cost broker, as long as they can connect their algos.
Minimizes human error: Between the increased accuracy of the executed orders and the removal of emotional and psychological factors of human trading, algo trading has several upsides over traditional human traders.
Backtesting possibilities: In addition to testing against current market conditions, you can put algo trading commands against historical data to ensure that the trading parameters work under several types of market conditions.

However, there is more to algo trading than its upsides. This trading practice will not work for all applications, individual circ*mstances, or market conditions. Thus, if you want to get into algo trading, you’ll want to keep some of these cons in mind:

Risk of Black Swan events: Because algo trading requires strict rules to execute, its lack of flexibility opens these scripts up to sudden downturns and other negative news that drastically affects the market.
Reliance on technology: Because technology is not infallible, relying on algo trading for all your trading needs can halt operations in times when the computers, network, or Internet access go down.
High upfront costs: The hardware and development time necessary to create new algorithms can cost a lot of money, something that not all traders will have access to.
Limited customization once built: Changes to the code and rules of an established algorithm can be difficult to make happen without mistakes, preventing these tools from adapting quickly to new market conditions.
Limited human responsiveness: While humans are adaptable, machines are not. This fact means that an algo trading mechanism will fail to respond to market conditions that a human can adapt to in a short time.
High market impacts: Market volatility concerns, especially in flash crashes, have made people aware of the impact algo trading can have on market conditions.
Regulation concerns: In areas with regulations on algorithm upkeep or transparency, traders might find themselves stuck with extra licensing or maintenance fees to comply with government requirements.

How To Start Algo Trading

For the traders out there that want to explore algo trading for themselves, there is a lot to learn about building these pieces of code and running them well. There are also several factors you need to consider when constructing the algorithm.

Overall, you should look at these factors when getting into algo trading.

Algo Trading Time Scales
Algo trading, much like human-managed trading, should have a time scale in mind before its construction.

Because an algorithm can be tough to change once implemented, having a clear idea of your time scale during the development cycle will prevent any mismatch between your financial goals and the algorithm itself.

Most developers will find that the majority of the algo trading going on now is in the high-frequency trading sector. This trading strategy uses the algorithm to place dozens of orders at rapid speeds in several markets to capitalize on disparities between those markets. As a retail trader, I recommend you stay away from high frequency trading! Retail traders simply cannot compete in this realm.

This strategy allows the algo trader to create small instances of profit for each trade, generating their income slowly over the day rather than all at once with one large trader.

Still, algo trading has its use across the standard market time scales:

Short-term: In addition to creating liquidity as market makers, many short-term algorithms see use in arbitrage houses, speculator funds, and brokerage houses to create profits from trading in the short term
Mid- to long-term: Pension funds, mutual funds, and other large financial institutions use algo trading as a way to buy stocks in large amounts in a way that doesn’t affect the price of the stock too much
Systematic: Trend followers, hedge funds, and pair traders use algo trading to automate their trades across correlated assets, allowing them to make their trade and take profit regardless of when their market conditions come about

Thus, when creating a new algo trading measure, the developer needs to consider how long they want to hold the asset. Since algo trading allows for automation in trading, it is a versatile tool that can meet the needs of the desired time scale.

Programming for Algo Trading
As mentioned, algo trading requires someone with coding knowledge to complete the construction of the trading algorithm.

This professional enters the conditions set by the financial experts into the trading script, targeting the assets that will be affected by these trade executions set by the program.

However, there is more to these algorithms than some computer code. Most advanced setups use a sequence of computers or instances to have a high response rate and high order volume.

This multiple-instance setup gives these algorithms the hardware resources they need to execute the high volume of orders most algorithms process today. As a retail trader, get yourself a good trading platform such as Tradestation and you will be in good shape.

The calculations in algo strategies look at known metrics for success in the markets to establish certain market conditions as buy or sell triggers. The algorithm then follows the orders given to it based on these calculations.

Thus, the hardware running these algorithms will need the space and software needed to run these calculations.

Finally, developers and traders will want to review these algorithms against current market trends during the testing phase of their construction. Backtesting is a common strategy for success in algo development thanks to the amount of historical data available to traders from previous trading years.

So, to make the most out of the programming for your trading algorithm, you need to keep these factors in mind:

Programming knowledge: Without any coding knowledge, you cannot create the rules and executables for the algorithm
Network availability: A synced network of computers will help the algorithm handle the high order count it will handle. Retail traders can rely of the trading platform for this.
Automated trading platforms: These trading platforms allow traders to connect their algorithm for trading on their platform
Technical analysis measures: Automated or human-managed analysis will help create a strong foundation for the algorithm and create a good comparator for future iterations
Backtesting infrastructure: Running the beta versions of the algorithm through backtesting data will show how the algorithm runs, giving developers a chance to find and correct any flaws

Trading Strategies
Algo traders have several strategies they can align their algorithms with, depending on their time scale and what factors they focus their algorithm on. However, not all trading strategies will work well with an algorithm.

Strategies that focus on speed and calculable metrics work best with algo trading due to the way computer work. Without extensive work, computer algorithms have a hard time distinguishing nuance in data. Instead, they work best with firm rules.

Thus, when constructing a new trading algorithm, an algo trader will most likely rely on these strategies.

Trend following: One of the most popular ways to program an algorithm involves looking at known trend parameters like moving averages and breakouts. These trends are easy to program due to the simplistic way computers can calculate these parameters and apply them to code in the algorithm
Arbitrage: Another easy trading strategy for algorithms, this strategy has the code place a buy order from one market and a sell order on another market. Doing this strategy allows the code to find disparities between markets and pocket the difference as profit
Index fund rebalancing: When index funds rebalance their holdings, algo traders can use these opportunities to complete buys and sells to align with these big-name funds. The timeliness of algo trading allows for the trader to get the best price and beat out manual traders
Mean reversion: Based on the idea that highs and lows represent the outliers of an asset’s price, this strategy defines a price range for the algorithm to buy and sell an asset. As the price climbs, the algorithm sells the asset, while a falling price increases the holdings. Over time, this creates an increased holding and total value of the held asset
Volume-weighted average price (VWAP): By weighing the price of an asset over the day against its volume, this strategy looks to execute more sell orders the price approaches or exceeds this value and execute buy orders when the price goes below this value.
Time-weighted average price (TWAP): This strategy introduces buy and sell orders at even intervals over the day. Doing this process prevents one seller from swinging the market too far in one direction, causing disruptions that might affect the trader’s profit margin
Percentage of volume: The creators of this algorithm set a percentage of the total trading volume they want to hold or participate in, creating a trading bot that sends partial orders until the algorithm meets this parameter
Implementation shortfall: Because the order placement and execution happen faster with algorithms, this strategy seeks to reduce the price difference between the order price and the execution price to prevent any sudden losses for the trader

Testing Strategies
Once the strategy and algorithm have their final parameters set, savvy algo traders will put the algorithm under testing to ensure that the program works as it should.

In addition to finding any bugs in the execution code for the algorithm, this process will also show how the software performs in the market.

However, you will want to do this in an environment that doesn’t introduce a way to lose money. Rather than plugging directly into the market, many algo devs will instead connect the algorithm into a test environment loaded with market data and provide the software with test funds to debug with.

The market data provided to the algorithm can affect the results and parameters of the software you want to test. The two main datasets a new algorithm goes into testing with are out-of-sample sets and backtesting datasets.

Out-of-sample dataset testing refers to testing with data the algorithm did not see before. Testing with data like this allows you to see how the software will react to new market conditions once plugged into the market.

Backtesting datasets use historical data to see how your algorithm responds to known quantities.

Because this data occurred already, results from these tests are easy to calculate based on the parameters set by the developer, meaning any discrepancy in results and the calculation is a result of an error in coding.

Ideally, testing should continue until you have a model that works. However, many models that make it out of rigorous testing fail in the open market due to the unpredictability of the real-world market. This unpredictability is why the ability to adjust the code easily was critical when choosing your platform to create the algorithm.

FAQs - Algo Trading

If you’re still wondering about algo trading and would like to learn more, here are some answers to other common questions out there about algo trading.

What do algo traders do?

Ideally, algo traders use algorithmic trading to make profitable trades. These traders combine financial expertise and an understanding of the markets with coding skills to create automatic trading bots that can place and execute trading orders faster than a human can.
These traders also adjust their algorithms as market and regulatory conditions change.

Does algo trading work?

Algo trading can work well as long as the trading parameters follow good financial fundamentals. Frequent testing and adjustments also improve the chances that an algorithm improves the success rate for an algo trader.
While these won’t remove the chance of random downturns in the market, they improve the chances the code runs well during standard markets.

Is algo trading illegal?

Algo trading is legal in most nations. While many nations allow all traders to create and host their personalized algorithms, some nations require that licensed or institutional traders be the only ones allowed access to automated trading measures.
Regulatory measures may adjust how algo trading operates in the future, though.

What skills do you need for algo trading?

Skill #1: Understand what markets you are trading. Skill #2: Know your trading platform for charting, executing trades, etc. Skill #3: Know how to program algo strategies into your trading platform. Like all forms of trading, algo trading requires a strong understanding of the trading markets to take advantage of the benefits of automated tools. However, algo trading requires solid mathematical and programming skills to create and test the algo trading scripts they create.

How long does it take to learn algorithmic trading?

How long algorithmic trading takes to learn depends on where you start as a trader. Novice traders will need to learn market fundamentals first, while expert traders only need to pick up the coding and computer knowledge needed to create the algorithm.
Most courses on algo trading have a six-month course length, meaning it takes the better part of a year to learn the computer side of algo trading for most traders.
In my Platinum Strategy Factory course, I provide traders with 12 months of my personal e-mail support. Many students still have some questions during the last few months of that support window. Algo trading is not an “overnight success” endeavor!

Closing Thoughts On Algorithmic Trading

Overall, algo trading offers automation for a lot of trading strategies that rely on mathematical or data-driven factors. The automation speeds these orders along to reduce the overall fees a trader will pay.

But not every trader will have access to the tools, knowledge, and time to create a custom algorithm. While premade solutions exist, they may not match the specific needs of every trader.

Thus, traders should look into everything they can when trying to change their trading habits and plans. Reviewing the most successful trading algorithms and the strategies they use will help algo traders of all kinds improve their dealings and software. Learning how to properly develop algo strategies is key.

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About Author: Kevin Davey is an award winning private futures, forex and commodities trader. He has been trading for over 25 years.Three consecutive years, Kevin achieved over 100% annual returns in a real time, real money, year long trading contest, finishing in first or second place each of those years.

Kevin is the author of the highly acclaimedalgorithmic tradingbook "Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" (Wiley 2014). Kevin provides a wealth of trading information at his website: http://www.kjtradingsystems.com