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Your Guide to Algorithmic Trading

Making money is a never-ending game. Trading can be a good way to supplement your income or make enough to strike out on your own. Since day trading opened up to regular people, there have been incredible stories about young entrepreneurs quitting their jobs to become day traders. The software that retail traders use has truly revolutionized the industry. 

Like any type of trading, people have seen huge profits, and they've also seen huge loses. So before you quit your job and head out into this new frontier, it's important to learn about the basics of algorithmic trading, the systems and strategies around algorithmic trading that make a profit, and the different markets you'll come across.

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What Is Algorithmic Trading?

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Let's start with the basics. What is algorithmic trading? How do you start? And how do you do it well? This section of our guide will cover these basics and get you on your way to a trading future.

Definition of Algorithmic Trading

Algorithmic trading goes by many names, including black box trading, automated trading, or algo-trading. Algorithmic trading uses computer programming to place automated trades that generate a profit at a speed that a regular human can't accomplish. The algorithm that places the trade follows a set of rules based on timing, price, quantity, or another standard mathematical model.

There are three main groupings of traders that use algorithmic trading:

  • Mid-to-long-term investors and buy-side firms
  • Short-term traders and buy-side participants
  • Systematic Traders

Mid-to-Long-Term Investors and Buy-Side Firms

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Examples of these types of traders include pension funds, mutual funds, and insurance companies. They use algorithmic trading to purchase large volumes of stock without it affecting the overall stock price.

Short-Term Traders and Buy-Side Participants

Examples of these types of traders include market makers like brokerage houses, speculators and arbitrageurs. They use algorithmic trading for its accurate timing and trade execution and to create sufficient liquidity in the market.

Systematic Traders

Examples of this type of trader include followers, hedge funds, and pair traders. They find programming their trades and letting the program run automatically more efficient.

Benefits of Algorithmic Trading

There are capital gains benefits for the algorithmic trader, but there are also overall benefits that make trading easier and more accurate. Trades are executed at the actual best price instead of what is judged as best. Trade order placement is instant and more accurate than ever before.

A trader avoids significant price changes because of correct timing and the reduced overall risk of manual errors. The algorithm can also simultaneously check on multiple markets and back-test strategies to see if they are viable based on historical data. Algorithmic trading also has benefits for the market because the elimination of human emotion and error makes the market more liquid and systematic.

Strategies for Algorithmic Trading

The main way to start algorithmic trading is by first picking a market you want to trade in and then choosing the trading platform you want to use. Any algorithm that is written for trading will have several key components essential for a good algorithm.

Strategies are one of the main components of an algorithm. They determine what algorithm you will write! Any strategy for algorithmic trading requires an opportunity for either more profit or reduced costs. The strategies below are some of the most commonly used strategies in the industry, but before you implement them, remember you still need to have an opportunity.

Trend-Following Strategies

This is probably the most common algorithmic trading strategy because you are simply following market trends. Moving averages, channel breakouts, and price level movements are technical indicators and common market trends to monitor. Following a trend is also simple on the equation front because you don't need to make predictions or forecast prices. Trades are initiated when the desired trend occurs. Traders often use 50- or 200-day moving averages for this trend.

Arbitrage Opportunities

Arbitrage is when you buy a dual-listed stock in one market and sell that stock for a higher price on another market. Algorithms identify the price differentials in markets and give the trader opportunities for arbitrage.

Index Fund Re-Balancing

There are defined periods where index funds will re-balance for to make sure their holdings are matching their benchmark indices. Algorithmic trading allows a trader to profit off of this re-balancing and can expect between a 20 to an 80 basis point profit.

Mathematical Model Based Strategies

Using a proven mathematical model allows lots of trading options and underlying security. An example of a proven mathematical model trading strategy is the delta neutral trading strategy that takes multiple positions while offsetting positive and negative delta ratios that compare the change in price asset.

Trading Range

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Trading range strategies are also called mean reversion strategies. This strategy is based on the idea that gains and losses of an asset are temporary and will return to the mean value. The algorithm used here trades automatically when the asset price is out of its defined range.

Volume Weighted Average Price Strategy

This strategy breaks up a large order of trades and releases them in smaller chunks based on their historical volume profiles. The goal is to have the order at the Volume Weighted Average Price.

Time Weighted Average Price Strategy

This strategy releases a large order in scheduled time slots. The goal here is to release the order at the stocks average price to minimize market impact.

Percentage of Volume Strategy

This strategy releases partial orders until the order is filled. The percentages are defined by the participation ratio of volume traded on the market.

Implementation Shortfall Strategy

This strategy is used to minimize the execution cost of an order. This is done by trading off the real-time market saving on the cost of the order and benefiting from the delayed execution. The algorithm will increase the targeted participation rate when the stock goes up and decrease it when it goes down.

Considerations for Writing Your Algorithm

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Sniffing Algorithms

A sniffing algorithm attempts to understand what is going on behind the curtain and is a special class of strategy. The algorithms used will identify other algorithms used by the buy side. Through these algorithms, market makers can identify large orders and buy at a higher price. This is also sometimes called high-end front running.


The main coding languages that algorithmic trading uses are Python and Matlab. If you are not fluent in these languages, you can always hire a computer programmer who is or use software for algorithmic trading that makes this part simple. Through the code, you will set everything in the algorithm.


The indicators are the signifiers in the algorithm that tell the program when to trade. There are thousands of indicators available to use, but if you are a beginner algorithmic trader, it is best to stick to a few known indicators to start with. Some good indicator options include Moving Averages, Parabolic SAR, Stochastic, Relative Strength Index, and Relative Vigour Index.


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Inputs are generally assigned to other nodes when you are creating an algorithm. The four main types of inputs that coders use for algorithmic trading are string, integer, Boolean, and number. Using these will get you headed in the right direction for a good algorithm.


Each data type will have its variables. The variables tell the algorithms what to do and when to do it.


The real world market data will be what you use your algorithms on. Historical data is also important because that is what you back-test your algorithm on.


Back-testing is testing your trading strategy based on historical market data. It is the way to ensure your strategy will work in the real market and not just on paper. It helps identify good strategies and strategies that will work on multiple markets. You can also identify biases in your strategy before it makes its market debut. If your back-test doesn't perform well on the historical data, there is a good chance it won't perform well in the real world

Technical Requirements

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.Algorithmic Trading is highly technical and involves coding and computer programming skills. The goal of the whole operation is to take the identified strategy and create a computerized process for that strategy.

Every trader will need the following things to use their algorithms effectively:

  • Computer programming abilities, hired computer programmers, or software that programs for you
  • Access to a network and trading platforms
  • Market data feeds that algorithms can monitor
  • The infrastructure and technical ability to back-test the system
  • Access to historical data


 While highly profitable, there are several skills and necessities to have before you dive into writing an algorithm for trading. After several tries, it will become apparent that while writing the code may get simple and easier, maintaining and executing the system you've built can still be complicated. There are lots of risks and complications to consider.

Everything from other market players to network connectivity can disrupt your system. Do your due diligence and back-test to help mitigate these risks. Happy trading!

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