Automated Trading Strategies, Their Types & Importance

Automated Trading Strategies: Their Types & Importance

In the world of trading, many different strategies can be used to help you make a profit. Some traders prefer to use manual strategies, while others use automated trading strategies. However, both approaches come with their own pros & cons. 

Considering Manual trading strategies can be time-consuming and may require more experience and knowledge to be successful. Whereas, Automated trading strategies are computer-based plans that automatically initiate and execute trades based on pre-determined criteria. These criteria can be based on technical analysis or fundamental analysis.

  • Technical analysis is the study of past market data to identify patterns that can be used to predict future market movements. Also called tactical asset revesting, which provides above-average growth and capital protection.
  • Fundamental analysis is the study of economic indicators to determine the health of a country’s economy and its impact on the markets.

In this article, we’ll be going through every detail of Automated trading strategies including history, beneficial features, critical pros, planning & future. Keep reading every detail!

A Brief History Of Automated Trading Stategies

Developing Trading Algorithms :How To Create a Trading Algorithm??

Getting started with the history of Automated trading strategies, the origins of automated trading can be traced back to the early days of market trading. One of the first examples of automated trading was developed by Richard Donchian in 1949. Donchian’s system was based on a set of rules for buying and selling funds.

The concept of rule-based trading became more popular in the 1980s when famous traders like John Henry began to use such strategies. In the mid-1990s, some models were available for purchase. Also, improvements in technology increased the accessibility for retail investors.

The early form of an Automated Trading System was composed of software based on algorithms that have historically been used by financial managers and brokers. This type of software was used to automatically manage clients’ portfolios.

However, the first service to the free market without any supervision was first launched in 2008 by Jon Stein. Since then, this system has been improving with the development of the IT industry. Now, Automated Trading System is managing huge assets all around the globe. 

This system is typically divided into different types, to know about types of Automated trading strategies go ahead.

Check out: Investors/Traders: Wake Up

Types of Automated Trading Strategies

Automated Trading Strategies, Their Types & Importance

With the advent of technology, there has been a significant evolution in the field of finance and trading. This has given rise to various Types of Algorithmic Trading Strategies that make use of different technologies like rule-based trading, machine learning, statistical arbitrage, and high-frequency trading. 

So without further ado let’s check out How many types of algorithmic trading are there?

Rule-Based Trading:

In recent years, rule-based trading has become increasingly popular among traders. This type of trading involves the use of a preprogrammed set of instructions or rules, which are executed by a  computing system to place orders and carry out the trades as specified. Rule-based trading has several advantages. 

  • It can take emotions out of the equation, as trades are executed automatically according to the rules that have been set up. This can be especially helpful in volatile markets, where emotions can often cloud judgment.
  • Rule-based trading can help to ensure that trades are executed promptly and efficiently. This is because the rules that have been set up will be followed without exception, and there is no need to wait for a human trader to execute the trade.
  • This type of Automated trading strategies,  can be used to backtest trading strategies. This is a process whereby a best algo trading strategy is tested on historical data to see how it would have performed. This can be very useful in discovering whether a particular strategy is likely to be successful in the future.

Statistical Arbitrage

To earn extremely short-term returns, statistical arbitrage trading is a sort of trading technique that makes use of vast, varied portfolios. This type of trading is based on the concept of arbitrage, which is the practice of taking advantage of price differences to make a profit. 

While Statistical arbitrage trading strategies are complex and can be difficult to understand. However, they can be extremely profitable for those who can execute them successfully. 

Arbitrage opportunities can arise when two identical products are being offered at different prices. For example, if identical shares of stock are being traded on two different exchanges, and the price of the stock on one exchange is higher than the price on the other exchange, then a trader could buy the stock on the cheaper exchange.

Related: What type of trader or investor are you?

Machine Learning-Based Trading

Machine learning-based trading is a form of trading that uses algorithms to make decisions based on data. These algorithms can learn from data and improve their decision-making over time.

Machine learning-based trading is a relatively new field, and it is growing in popularity as more and more traders look for ways to use data to their advantage. There are a few different ways that machine learning can be used in trading, and we will discuss some of the most popular methods below.

  • Develop Trading Strategies.
  • Create Models That Predict Market Movements.
  • Improve The Execution Of Trades.
  • Automate Trade Decision-Making.
  • Detect And Exploit Market Inefficiencies.

Each of these methods has its benefits and drawbacks, and it is important to understand all of them before deciding if machine learning-based trading is right for you.

High-Frequency Trading

In the world of finance, high-frequency trading (HFT) is a type of trading that uses powerful computer programs. This is to transact a large number of orders in fractions of a second. It is a highly automated and algorithmic trading approach that relies on speed and real-time market data to make decisions. 

HFT can be employed in various types of trading strategies, including market making, arbitrage, and trend following. Market makers provide liquidity to the markets by buying and selling securities, thus reducing the bid-ask spread. 

Arbitrageurs take advantage of price discrepancies across different markets to make a profit. Trend followers seek to profit from market trends. HFT comes with a range of favourable pros & Cons:

  • It allows traders to take advantage of very small fluctuations in stock prices. These small price movements, or “spreads,” can add up to big profits when they are executed at high volume.
  • It can help to provide liquidity in the markets. When there are more buyers and sellers in the market, it helps to ensure that prices remain stable.
  • Of course, high-frequency trading is not without its risks. One of the biggest risks is that HFT orders can sometimes overwhelm the markets, resulting in what’s known as a “flash crash.” A flash crash is a sudden and dramatic drop in stock prices that can happen when HFT orders are executed in large volumes.

Building an Automated Trading Strategy

Before venturing into the world of automated trading, there are a few important factors to consider. Building Automated trading strategies requires significant time and effort but can be a rewarding experience. Here are the key steps to take when constructing an automated trading strategy.

1. Choosing a trading platform: 

This is perhaps the most important step in the process. There are a variety of different platforms available, and it is important to choose one that is compatible with your broker. Additionally, make sure to consider the fees associated with each platform.

2. Backtesting and simulation: 

Once you have chosen a platform, it is time to test your strategy. Backtesting involves running your strategy through historical data to see how it would have performed. Simulation is similar but uses artificial data to test your strategy. These steps are important to ensure that your strategy is viable.

3. Implementation and execution: 

After you have backtested and simulated your strategy, it is time to put it into action. This requires opening a live trading account and connecting it to your chosen platform. Once your strategy is up and running, it will begin executing trades automatically.

By following these steps, you can build an automated trading strategy that has a good chance of success. However, it is important to keep in mind that no strategy is perfect and there is always risk involved in trading.

Common Pitfalls to Avoid

When it comes to Automated trading strategies, there are a few common pitfalls that can trip up even the most experienced trader. Below, we will take a look at three of the most common pitfalls and how you can avoid them.


One of the most common mistakes made in automated trading strategy is overfitting. Overfitting occurs when a trader uses too much data in their model and as a result, the model becomes too complex and starts to fit the data too closely. This can lead to problems when the market changes and the model are no longer accurate.

To avoid overfitting, you need to use a method known as cross-validation. Cross-validation is a way of splitting up your data into multiple parts and testing the model on each part. This way, you can be sure that the model is not overfitting the data.

Lack of diversification

Another common mistake made in automated trading strategy is a lack of diversification. When trading stocks, it is important to diversify your portfolio so that you are not too exposed to any one company. The same is true for automated trading.

If you rely on a single trading strategy, you are putting all your eggs in one basket. If that strategy fails, you could lose all your money. To avoid this, you need to have multiple trading strategies that you can use. This way, if one strategy fails, you have others to fall back on.


Over-optimization occurs when a trader adjusts their trading strategy too frequently in an attempt to improve performance. This can lead to curve-fitting, which is where a trading strategy is artificially tuned to fit past data, rather than being based on sound trading principles. This can ultimately lead to a trading strategy that performs well in backtesting but fails in live trading.

Failing To Account For Transaction Costs

Another common pitfall is failing to account for transaction costs. When trading with real money, there will always be costs associated with each trade, such as spreads, commissions, and slippage. 

These costs can eat into your profits, so it is important to factor them into your trading strategy. Many trading platforms have built-in transaction cost analysis tools that can help you to estimate these costs.

Not Properly Managing Your Risk

Failure to appropriately manage your risk is another possible pitfall. This includes both position sizing and risk management. Position sizing is the process of determining how much of your capital you should risk on each trade. 

Risk management is the process of managing your overall risk exposure. This includes using stop-losses and setting limits on your loss-per-trade.

Backtesting Your Strategy:

Not properly backtesting your plan is a typical error. Just because a strategy works in real-time does not mean it is profitable. You need to test your strategy in various market conditions to ensure it is robust. This includes testing it in both up and down markets, as well as in different timeframes.

Other mistakes to avoid include failing to diversify your portfolio, over-leveraging your position, and trading too frequently. These can all lead to significant losses if not properly managed. By avoiding these common pitfalls, you can increase your chances of success with automated trading.

Related: Why Technical Analysis Is Critical for the Success of Traders and Investors 


In conclusion, automated trading strategies are a critical tool for modern investors. There are a variety of strategies available, each with different strengths and weaknesses. Understanding the key characteristics of each strategy is important for investors to make informed decisions about which strategy to use.

The future outlook for automated trading strategies is very positive. There is a lot of interest in these strategies from both individual investors and institutions. The main attraction of these strategies is that they can take into account a lot of data and make decisions very quickly. This is something that human traders simply cannot do.


1- What Are Algorithmic Trading Strategies?

Algorithmic trading strategies are those that are programmed into an automated system that trades on behalf of the trader. These strategies can be based on several different factors, including technical indicators, market conditions, and even specific news events. 

Many traders who use these strategies do so to take advantage of the speed and accuracy of the computerized system to make more profits than they would be able to with manual trading.

2- What Is The Best Automated Trading Strategy?

There is no single “best” automated trading strategy, as different traders have different preferences and goals. However, some general tips for developing an effective strategy include focusing on a specific market or markets, using clear and objective rules for entry and exit points, and testing the strategy extensively before using it live. 

Additionally, it is important to continuously monitor and adjust the strategy as needed, to adapt to changing market conditions.

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