How To Do Algorithmic Trading: Ultimate Guide For New Traders

How To Do Algorithmic Trading: Ultimate Guide For New Traders

What’s up Traders, in this article, we’re going to be talking about Algorithmic Trading. The algorithmic trading is the practise of making automatic trading choices by utilising a computer programme that follows instructions based on mathematical formulae.

The computer makes judgments for the trader on whether to buy or sell in various financial markets, generally by monitoring price charts, by following the algorithm’s instructions. It will leave the slot once the algorithm’s requirements are met.

Did you know that machine-based, algorithmic trading, or algo trading robots account for more than 80% of all stock and forex market fluctuations in the United States?

Fortunately, thanks to significant technological advancements, algorithmic trading strategies are now available to all types of traders across nearly all major markets. 

Including Forex algorithmic trading and stock market algorithmic trading, which is just one of the reasons this type of trading is becoming more popular.

You’ll learn about the numerous sorts of algo trading strategies you may use, the finest Forex algorithmic trading strategy software to utilise.

And how to get started without knowing a single line of code or programming language in this Ultimate Guide to Algorithmic Trading Strategies!


What is the Algorithmic Trading?


  • Algorithmic Trading: An overview
  • Algorithmic Trading for Dummies
  • Algorithmic Trading’s Benefits and Drawbacks

The practise of executing orders using automated and pre-programmed trading instructions to account for variables such as price, timing, and volume is known as algorithmic trading. 

A set of instructions for solving a problem is known as an algorithm. Over time, computer algorithms send little chunks of the entire order to the market.

Algorithmic trading makes choices to buy or sell financial securities on an exchange using complex calculations, mathematical models, and human oversight.

High-frequency trading technology, which allows a company to make tens of thousands of deals per second, is frequently used by algorithmic traders. 

Order execution, arbitrage, and trend trading methods are all examples of scenarios where algorithmic trading can be applied.

Algorithmic trading is the application of process- and rules-based algorithms to trade execution strategies. 

Since the early 1980s, it has increased in prominence and is now utilised by institutional investors and huge trading firms for a number of objectives.

While algorithmic trading has benefits like as faster execution and lower costs, it can also accentuate the market’s bearish inclinations by triggering flash crashes and immediate liquidity loss.


Algorithmic Trading: An overview

After computerised trading systems were introduced in American financial markets in the 1970s, the use of algorithms in trading increased. 

The Designated Order Turnaround (DOT) system was created by the New York Stock Trading in 1976 to route orders from traders to specialists on the exchange floor.

In the decades that followed, exchanges improved their ability to handle electronic trading, and by 2009, computers had conducted about 60% of all trades in the United States.

When author Michael Lewis published the best-selling book Flash Boys, which chronicled the lives of Wall Street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in America, he brought high-frequency, algorithmic trading to the public’s attention.

His book contended that these firms were in a race to construct ever faster computers that could interface with exchanges ever faster in order to obtain a speed advantage over competitors by employing order types that benefited them at the expense of average investors.


Algorithmic Trading for Dummies

Do-it-yourself algorithmic trading has become increasingly popular in recent years. 

Hedge funds like Quantopian, for example, crowdsource algorithms from amateur programmers who compete to write the most profitable code in order to win commissions.

The expansion of high-speed internet and the development of ever-faster computers at comparatively low rates have made this technique practicable. 

Quantiacs, for example, has sprung up to help day traders who want to try their hand at algorithmic trading.

Machine learning is another emerging technology on Wall Street. Artificial intelligence advances have enabled computer programmers to create systems that can learn to improve themselves through an iterative process known as deep learning. 

Traders are creating deep learning-based algorithms to improve their profitability.


Algorithmic Trading’s Benefits and Drawbacks

Algorithmic trading is mostly utilised by institutional investors and large brokerage firms to reduce trading expenses.

According to studies, algorithmic trading is particularly effective for big order sizes, which can account for up to 10% of total trading volume. To create liquidity, market makers typically utilise algorithmic trading.

Algorithmic trading also provides for faster and easier order execution, which appeals to exchanges. As a result, traders and investors can take advantage of modest price movements to benefit quickly.

Because scalping includes quick purchasing and selling of stocks at small price increments, algorithms are frequently used.

When numerous orders are performed concurrently without human interaction, the speed of order execution, which is advantageous in most cases, might become a problem. Algorithmic trading has been implicated for the flash crash of 2010.

Another downside of algorithmic trading is that liquidity, which is established by rapid buy and sell orders, can vanish in an instant, preventing traders from profiting from price fluctuations.

It can also result in an immediate loss of liquidity. According to research, algorithmic trading was a significant contributor to the loss of liquidity in currency markets after the Swiss franc dropped its Euro peg in 2015.


If you are looking to find a suitable forex broker, be sure to read the following guides:

10 Best Forex Brokers That Give The Most Value To Traders

9 Best Forex Brokers That Are Recommended For Day Trading

9 Best Forex Brokers That Are Recommended For Scalping

My reviews about the best forex brokers in the world that offer the most value and facilities to traders.

Read About:

IRONFX Review                                      BLACKBULL MARKETS Review
XM Review                                               PUPRIME Review
INSTAFOREX Review                             TRADEVIEW MARKETS Review
VANTAGE Review                                   SUPERFOREX Review
INFINOX Review                                    AVATRADE Review


Algorithmic Trading Fundamentals: concepts and examples


  • Practice of Algorithmic Trading
  • Algorithmic Trading’s advantages
  • About using Algorithms Trading Strategies 
  • Algorithmic Trading Technical requirements
  • An Algorithmic Trading example

Algorithmic trading (also known as automated trading, black-box trading, or algo-trading) involves placing a deal using a computer programme that follows a set of instructions (an algorithm). 

In principle, the deal can make profits at a rate and frequency that a human trader could never achieve.

Timing, price, quantity, or any mathematical model are used to define the sets of instructions. 

Apart automating providing profit opportunities for traders, algo-trading makes markets more liquid and trading more methodical by eliminating the impact of human emotions on trading.


Practice of Algorithmic Trading

Consider the following simple trading criteria:

*When a stock’s 50-day moving average crosses above its 200-day moving average, buy 50 shares. 

(A moving average smooths out day-to-day price volatility and so finds trends by taking an average of previous data points.)

*When the stock’s 50-day moving average falls below the 200-day moving average, sell it.

A computer programme will automatically watch the stock price (and the moving average indicators) and make buy and sell orders when the preset circumstances are met using these two simple commands. 

The trader no longer needs to manually enter orders or check live prices and graphs. This is done automatically by the algorithmic trading system accurately detecting the trade opportunity.


Algorithmic Trading’s advantages

Algorithmic trading has the following advantages:

*The best potential pricing are used to conduct trades.

*The placement of trade orders is quick and precise (there is a high chance of execution at the desired levels).

*To avoid substantial price swings, trades are timed precisely and promptly.

*Transaction costs are lower.

*Automated checks on multiple market conditions at the same time.

*When placing trades, the possibility of manual errors is reduced.

*To see if algorithmic trading is a feasible trading method, it can be backtested using historical and real-time data.

*Reduced the risk of human traders making mistakes due to emotional and psychological variables.

The majority of today’s algo trading is high-frequency trading (HFT), which tries to profit from placing a large number of orders at fast speeds across numerous marketplaces and decision factors according on preprogrammed instructions.

Many types of trading and financial operations use algo-trading, including:

*When mid- to long-term investors or buy-side firms—pension funds, mutual funds, and insurance companies—do not aim to affect stock prices with discrete, large-volume investments, they use algo-trading.

*Automated trade execution benefits short-term traders and sell-side participants—market makers (such as brokerage houses), speculators, and arbitrageurs; in addition, algo-trading aids in creating sufficient liquidity for market sellers.

*Trend followers, hedge funds, and pairs traders (a market-neutral trading strategy that matches a long position with a short position in a pair of highly correlated instruments such as two stocks, exchange-traded funds (ETFs), or currencies) find that programming their trading rules and letting the programme trade automatically is much more efficient.

Compared to strategies relying on trader intuition or instinct, algorithmic trading offers a more methodical approach to active trading.


About using Algorithms Trading Strategies 


  • Trend-following techniques
  • Arbitrage possibilities
  • Rebalancing Index Funds
  • Model-based Mathematical Strategies
  • Trading area (Mean Reversion)
  • The volume-weighted Average Price (VWAP)
  • The time weighted Average Price (TWAP)
  • The percentage of volume (POV)
  • Implementation defects
  • Trading Algorithms that aren’t typical

Any algorithmic trading strategy must start with a profitable opportunity in terms of increased earnings or lower costs. The following are some of the most common algo trading strategies:


Trend-following techniques

Moving averages, channel breakouts, price level fluctuations, and other technical indicators are used in the most common algorithmic trading techniques.

Because these methods do not require any predictions or price forecasts, they are the easiest and simplest to implement using algorithmic trading.

Without entering into the complexities of predictive analysis, trades are made based on the occurrence of favourable patterns, which are simple and basic to apply through algorithms. 

A popular trend-following method is to use 50- and 200-day moving averages.


Arbitrage possibilities

Buying a dual-listed stock at a lower price in one market and selling it at a higher price in another market provides a risk-free profit or arbitrage opportunity.

Because price differentials actually happen from time to time, the identical technique can be performed for stocks vs. futures products. 

Profitable chances can be found by using an algorithm to identify price differentials and placing orders quickly.


Rebalancing Index Funds

To bring their holdings up to par with their respective benchmark indexes, index funds have set rebalancing periods. 

This generates attractive opportunities for algorithmic traders, who earn from projected trades that yield 20 to 80 basis points profits right before index fund rebalancing, depending on the number of stocks in the index fund. 

For timely execution and the best prices, such deals are initiated using algorithmic trading algorithms.


Model-based Mathematical Strategies

Trading on a mix of options and the underlying security is possible thanks to mathematical models like the delta-neutral trading technique. 

(A portfolio strategy known as delta neutral consists of multiple positions with offsetting positive and negative deltas—a ratio comparing the change in the price of an asset, usually a marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question is zero.)


Trading area (Mean Reversion)

The mean reversion method is based on the idea that an asset’s high and low values are only transient fluctuations that eventually return to its mean value (average value). 

Identifying and defining a price range, as well as designing an algorithm based on it, allows transactions to be executed automatically when an asset’s price moves inside or outside of its stated range.


The volume-weighted Average Price (VWAP)

Using stock-specific historical volume profiles, the volume-weighted average pricing technique splits up a large order and releases dynamically determined smaller parts of the order to the market. 

The goal is to fill the order as near to the volume-weighted average pricing as possible (VWAP).


The time weighted Average Price (TWAP)

Using evenly divided time intervals between a start and finish time, the time-weighted average pricing technique breaks up a large order and releases dynamically determined smaller parts of the order to the market. 

The goal is to execute the order as close to the average price between the start and end timings as possible in order to minimise market impact.


The percentage of volume (POV)

This algorithm continues sending partial orders until the trade order is entirely filled, based on the defined participation ratio and the volume transacted in the markets.

When the stock price reaches user-defined levels, the “steps approach” sends orders at a user-defined percentage of market volume and increases or decreases this participation rate.


Implementation defects

The implementation shortfall technique tries to reduce an order’s execution cost by trading off the real-time market, saving money on the order and taking advantage of the opportunity cost of delayed execution. 

When the stock price moves in a positive direction, the approach will increase the desired participation rate and decrease it when the stock price moves in a negative direction.


Trading Algorithms that aren’t typical

There are a few different types of algorithms that try to find “happenings” on the other side. These “sniffing algorithms,” which are commonly utilised by sell-side market makers, have the intelligence to detect any algorithms on the buy side of a huge order.

Such algorithmic detection will assist market makers in identifying huge order possibilities and allowing them to profit by filling the orders at a higher price. This is referred to as “high-tech front-running.”

Front-running is generally deemed criminal, depending on the circumstances, and is strictly controlled by FINRA (Financial Industry Regulatory Authority).


If you are looking to find a suitable forex broker, be sure to read the following guides:

10 Best Forex Brokers That Give The Most Value To Traders

9 Best Forex Brokers That Are Recommended For Day Trading

9 Best Forex Brokers That Are Recommended For Scalping

My reviews about the best forex brokers in the world that offer the most value and facilities to traders.

Read About:

IRONFX Review                                      BLACKBULL MARKETS Review
XM Review                                               PUPRIME Review
INSTAFOREX Review                             TRADEVIEW MARKETS Review
VANTAGE Review                                   SUPERFOREX Review
INFINOX Review                                    AVATRADE Review


Algorithmic Trading Technical requirements

Backtesting and implementing the method using a computer programme are the final steps in algorithmic trading (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable).

The task is to turn the selected approach into an integrated automated procedure with access to a trading account where orders may be placed. 

The requirements for algorithmic trading are as follows:

*Computer programming knowledge, professional programmers, or pre-made trading software are all options for creating the appropriate trading strategy.

*Access to trading platforms and network connectivity are required to place orders.

*Access to market data sources that the algorithm will monitor for order placement chances.

*The ability and infrastructure to backtest the system after it is constructed before it is put into production on real markets.

*Depending on the intricacy of the rules employed in the algorithm, historical data is available for backtesting.


An Algorithmic Trading example

The Amsterdam Stock Exchange (AEX) and the London Stock Exchange (LSE) both list Royal Dutch Shell (RDS) (LSE). 

To find arbitrage possibilities, we first create an algorithm. Here are a few noteworthy findings:

*The AEX trades in euros, whereas the LSE trades in British pounds.

*Due to the one-hour time difference, AEX begins an hour before LSE, with both exchanges trading together for the next few hours until trading exclusively on LSE for the last hour when AEX closes.

Can we look at the possibilities of arbitrage trading on the Royal Dutch Shell shares, which is listed in two different currencies on these two markets?


  • A computer programme that is capable of reading current market prices.
  • Both the LSE and the AEX provide price feeds.
  • A GBP-EUR forex (foreign exchange) rate stream.
  • Ability to place orders and route them to the appropriate exchange.
  • Backtesting on historical price feeds is possible.

This is what the computer software should do:

*Read the incoming RDS stock price feed from both exchanges.

*Convert the price of one currency to another using the available international exchange rates.

*If there is a significant enough price difference (after accounting for brokerage expenses) that results in a profitable opportunity, the programme should buy on the lower-priced exchange and sell on the higher-priced exchange.

*The arbitrage benefit will follow if the orders are executed correctly.

Simple and straightforward! However, algorithmic trading is not an easy technique to manage and perform. Remember that if one investor can execute an algo-generated deal, so can others.

As a result, prices change in milliseconds or even microseconds. What happens if a buy trade is performed but a sell trade is not because the sell prices have changed by the time the order reaches the market in the example above? 

The arbitrage approach will be rendered useless because the trader will be left with an open position.

System failure risks, network connectivity issues, time gaps between trading orders and execution, and, most importantly, flawed algorithms are all risks and obstacles. 

The more complicated an algorithm is, the more rigorous backtesting is required before it is implemented.


Choose the most appropriate Algorithmic Trading Software


  • A Basic Introduction to Algorithmic Trading
  • What is Algorithmic Trading Software used for?
  • Build or Buy Algorithmic Trading Software?
  • Algorithmic Trading Software’s key features
  • Where should I begin?

Traders who use algorithmic trading put their hard-earned money in the hands of their trading software. 

As a result, the right computer software is critical for ensuring effective and precise trade order execution.

On the other hand, especially in the lightning-fast realm of algorithmic trading, malfunctioning software—or software lacking the needed features—can result in massive losses.


A Basic Introduction to Algorithmic Trading

An algorithm is a collection of instructions that must be followed in order to finish a task. 

Whether it’s a simple-yet-addictive computer game like Pac-Man or a spreadsheet with a plethora of functionalities, each software follows a set of instructions based on an underlying algorithm.

When creating an algorithmic trading system, selecting the right software is critical. A trading algorithm is a set of instructions that guides buy and sell orders step by step.

When trading financial markets, faulty software can lead to significant losses. You can purchase algorithmic trading software or design it yourself. 

Free trial versions of algorithmic trading software are frequently available with restricted capability.

Algorithmic trading is the process of making a trade order using a computer programme that follows a set of instructions.

The goal of an algorithmic trading programme is to dynamically find advantageous opportunities and conduct trades in order to earn profits at a pace and frequency that a human trader could never equal.

Trading activities based on computer algorithms have exploded in popularity due to their superior accuracy and lightning-fast execution speed.


What is Algorithmic Trading Software used for?

Large trading firms, such as hedge funds, investment banks, and proprietary trading organisations, dominate algorithmic trading.

Due to their vast scale, such companies typically develop their own custom trading software, which includes massive trading platforms with dedicated data centres and support employees.

Experienced proprietary traders and quants utilise algorithmic trading on an individual basis. 

For their algorithmic trading needs, proprietary traders who are less tech-savvy can acquire ready-made trading software.

Their brokers either sell the software or they buy it from third-party vendors. 

Quants typically have a strong understanding of both trading and computer programming, and they create their own trading software.


Build or Buy Algorithmic Trading Software?

Building or purchasing algorithmic trading software are the two options. 

Buying ready-made software gives you immediate access, whereas developing your own gives you complete customization freedom.

Automated trading software is sometimes expensive to purchase and may contain flaws that, if ignored, can result in losses. 

The hefty cost of the software may reduce your algorithmic trading venture’s realistic earning potential.

Building algorithmic trading software on your own, on the other hand, takes time, effort, and in-depth understanding, and it isn’t always failsafe.


Algorithmic Trading Software’s key features


  • Data about the Market and the Company
  • Access to a variety of Markets
  • The Latency
  • Customization and Configurability
  • Writing custom programs capability
  • Using historical data for backtesting
  • Trading interface integration
  • Integration with plug-and-play
  • Platform-agnostic programming
  • The insider information

Automatic trading carries a high level of risk, which can result in significant losses. Regardless of whether you choose to buy or create, it’s critical to understand the essential elements.


Data about the Market and the Company

Every trading algorithm is built to react to real-time market data and price quotes. 

A few applications are additionally tailored to take into consideration financial data such as earnings and P/E ratios.

A real-time market data feed, as well as a corporate data feed, should be included in any algorithmic trading software. 

It should either be built into the system or have the ability to be easily integrated from other sources.


Access to a variety of Markets

Traders who want to trade numerous markets should be aware that each exchange’s data feed may be delivered in a different format, such as TCP/IP, Multicast, or FIX.

Your software should be able to handle various feed formats. Another alternative is to use third-party data suppliers like as Bloomberg and Reuters, who aggregate market data from several exchanges and present it to end clients in a uniform format.

These aggregated feeds should be processed as needed by algorithmic trading software.


The Latency

This is the most crucial aspect of algorithmic trading. The time delay induced in the movement of data points from one application to the next is known as latency.

Consider the sequence of events below. 

A price quote from the exchange takes 0.2 seconds to reach your software vendor’s data centre (DC). 

0.3 seconds to reach your trading screen, 0.1 seconds for your trading software to process this received quote, 0.3 seconds for it to analyse and place a trade. 

0.2 seconds for your trade order to reach your broker, and 0.3 seconds for your broker to route your order to the exchange.

Total elapsed time = 0.2 + 0.3 + 0.1 + 0.3 + 0.2 + 0.3 = 1.4 seconds

The original price quote would have changed numerous times during this 1.4 second period in today’s dynamic trading market. Your algorithmic trading business could be made or broken by any delay.

To ensure that you obtain the most up-to-date and correct information without a time gap, maintain this latency as low as feasible.

Latency has been decreased to microseconds, and every effort should be made in the trading system to keep it as low as possible.

Having direct connectivity to the exchange to get data faster by eliminating the vendor in between. 

Improving the trading algorithm so that analysis and decision-making take less than 0.1+0.3 = 0.4 seconds. 

Or eliminating the broker and directly sending trades to the exchange to save 0.2 seconds are just a few ways to reduce latency.


Customization and Configurability

Standard built-in trade algorithms, such as those based on a crossover of the 50-day moving average (MA) with the 200-day MA, are available in most algorithmic trading software. 

A trader might want to try combining the 20-day MA with the 100-day MA.

Unless the software allows for such parameter customisation, the trader may be limited by the built-in fixed functionality. 

Whether you’re purchasing or constructing, trading software should be very customizable and configurable.


Writing custom programs capability

The most frequent programming languages used to create trading software are MatLab, Python, C++, JAVA, and Perl. 

The majority of trading software supplied by third-party providers allows you to develop your own own programmes.

This allows a trader to experiment with different trading strategies. Obviously, software that allows you to code in your favourite programming language is recommended.


Using historical data for backtesting

Simulating a trading technique on historical data is known as backtesting. It evaluates the strategy’s feasibility and profitability based on historical data, verifying its viability (or failure or any needed changes).

This need must be backed by the availability of previous data on which backtesting can be conducted.


Trading interface integration

Algorithmic trading software executes trades based on the occurrence of predetermined criteria. 

The software should be able to connect to the broker(s) network to place trades or to transmit trade orders directly to the exchange.


Integration with plug-and-play

A trader may use a Bloomberg terminal for price analysis, a broker’s terminal for trade execution, and a Matlab application for trend analysis all at the same time.

The algorithmic trading programme should have easy plug-and-play integration and open APIs across such widely used trading tools, depending on individual demands. This ensures both scalability and integration.


Platform-agnostic programming

Only a few programming languages require their own platforms. Certain versions of C++, for example, may only run on certain operating systems, but Perl may run on any operating system.

When developing or purchasing trading software, platform independence and support for platform-independent languages should be prioritised. You never know how your trading will progress in a few months.


The insider information

“Even a monkey can click a button to place a trade,” as the cliché goes. Computer dependency should not be taken lightly. The trader should be aware of what is going on behind the scenes.

When purchasing trading software, make sure to ask for (and read through) extensive documentation that explains the algorithmic trading software’s underlying logic. 

Avoid any trading software that purports to be a hidden moneymaking machine but is a total black box.

When developing software, be realistic about what you’re doing and understand the scenarios in which it might fail. Before spending actual money, thoroughly backtest the strategy.


Where should I begin?

Typically, ready-made algorithmic trading software offers free trial versions with restricted capability or limited trial periods with full functionality. 

Before purchasing anything, use these trials to fully explore them. Remember to read over all of the accessible documentation thoroughly.


Final Thoughts

Algorithmic trading software is expensive to buy and complex to create yourself. 

Buying ready-made software provides quick and convenient access, while developing your own gives you complete customization options. 

However, you must first master the essential functioning of the trading software before engaging in real-money algorithmic trading. Failure to do so could result in significant financial losses.


If you are looking to find a suitable forex broker, be sure to read the following guides:

10 Best Forex Brokers That Give The Most Value To Traders

9 Best Forex Brokers That Are Recommended For Day Trading

9 Best Forex Brokers That Are Recommended For Scalping

My reviews about the best forex brokers in the world that offer the most value and facilities to traders.

Read About:

IRONFX Review                                      BLACKBULL MARKETS Review
XM Review                                               PUPRIME Review
INSTAFOREX Review                             TRADEVIEW MARKETS Review
VANTAGE Review                                   SUPERFOREX Review
INFINOX Review                                    AVATRADE Review


Final words

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