How Automated Trading Systems Work and Their Advantages

Automated trading systems are computer programs that use algorithms to make trading decisions based on market data. They can execute trades faster and more accurately than human traders.

These systems can analyze vast amounts of data in real-time, including charts, news, and economic indicators. This allows them to identify patterns and trends that may not be apparent to human traders.

Automated trading systems can be programmed to follow a specific strategy or set of rules, reducing the risk of emotional decision-making. They can also be designed to adjust their trading parameters based on changing market conditions.

By automating the trading process, individuals can potentially earn consistent profits with less time and effort required.

Readers also liked: Traders Day

Automated trading is a method of participating in financial markets by using a program that executes trades based on predetermined entry and exit conditions.

Automated trading means you can carry out many trades in a small amount of time, with the added benefit of taking the emotion out of your trading decisions.

Recommended read: Currency Carry Trade Definition

Automated trading systems are used by a large percentage of market participants, including trading firms, banks, hedge funds, and pension funds.

These systems can simplify the complexities of executing trades by relying on pre-set rules and algorithms, making it easier to enter the world of trading with efficiency and discipline.

Automated trading systems can be developed in-house or obtained from third-party providers, with varying degrees of automation depending on the system and regulatory environment.

With automated trading, you can predict the rise or fall of the underlying market price using CFD trades, and even use your pre-determined strategies to follow trends and trade accordingly.

Automated trading systems typically involve quantitative modelling and risk monitoring to manage the flow of orders in the markets without human intervention.

Intriguing read: Pre Market Trading Stocks

Automated trading systems can be categorized into several types, each with its own strengths and weaknesses. Trend following is one such approach, primarily employed on longer time frames to allow for potentially larger price movements.

Trend following strategies can generate significant price moves, but they also involve risks such as finance fees and rollover costs. To mitigate these risks, traders need to be selective of which currency pair or instrument they are trading.

Moving averages are another standard trading system approach, where price interaction with a moving average or a combination of multiple moving average crosses is used as a point of entry and exit. These strategies can be vulnerable to whipsaws, so traders may use filters to minimize their impact.

Here are some common types of automated trading systems:

Technical patterns or breakout systems have become increasingly accessible with the abundance of advanced software solutions available, such as the Autoc Chartist MT4 Plugin or the TradingView cChart pattern indicator.

An automated trading system determines whether an order should be submitted based on the current market price of an option and theoretical buy and sell prices. Theoretical buy and sell prices are derived from the current market price of the underlying security.

A fresh viewpoint: Do Debt Collectors Buy Debt

A distributed processing on-line automated trading system uses structured messages to represent each stage in the negotiation between a market maker and a potential buyer or seller.

The system can store a range of theoretical buy and sell prices for a given range of current market prices of the underlying security in a look-up table. This avoids calculations that would otherwise slow automated trading decisions.

To set up your trading strategy, it's essential to find the right trading style and timeframe for you. There are different ways to trade, based on the timeframe you'd like to keep your positions open for, including day trading, swing trading, scalping, and position trading.

Here are some key strategies to consider:

A trend following strategy can be represented by the formula: PVWAP = ∑ ∑ jPj⋅ ⋅ Qj / ∑ ∑ jQj, where PVWAP is the volume-weighted average price.

The automated trading system determines whether an order should be submitted based on the current market price of an option and theoretical buy and sell prices.

These theoretical buy and sell prices are derived from the current market price of the security underlying the option.

A look-up table stores a range of theoretical buy and sell prices for a given range of current market prices of the underlying security.

As the price of the underlying security changes, a new theoretical price may be indexed in the look-up table, thereby avoiding calculations that would otherwise slow automated trading decisions.

A distributed processing on-line automated trading system uses structured messages to represent each stage in the negotiation between a market maker (quoter) and a potential buyer or seller (requestor).

Trend following is a trading strategy that bases buying and selling decisions on observable market trends. It's a popular strategy among speculators, but it remains reliant on manual human judgment to configure trading rules and entry/exit conditions.

The Turtle Trader software program is an example of a trend following strategy. It identifies a trend early in the day and then trades automatically according to a predefined strategy.

Finding the optimal initial strategy is essential for trend following. It's limited by market volatility and the difficulty of accurately identifying trends.

A formula used for trend following strategy is the Volume-weighted average price (VWAP) formula: PVWAP = ∑ ∑ jPj⋅ Qj∑ ∑ jQj. This formula calculates the average price of a security over a specific period of time, taking into account the volume of trades.

A continuous mean-reverting time series can be represented by an Ornstein-Uhlenbeck stochastic differential equation: dXt = θ (μ - Xt) dt + σ dWt. This equation describes how a process reverts to its mean value over time.

To set up a trading strategy, it's essential to find the right trading style and timeframe for you. This can include day trading, swing trading, scalping, or position trading.

You might enjoy: Equity Market Opening Time

The Event Processing Engine is the brain of your strategy, responsible for working with data as per your strategy, including statistical calculations, comparisons with historical data, and decision-making for order generation.

It's called a Complex Event Processing (CEP) System, which processes events in real-time, making it essential for automated trading systems to detect good opportunities quickly.

A complex event is a set of other events that together imply an occurrence of something of significance, such as stock trends, market movements, or news.

CEP systems comprise four parts: CEP engine, CEP rules, CEP WS, and CEP result interface.

The CEP engine processes incoming events based on CEP rules, which are mentioned in the trading system (trading strategy).

The two primary components of any CEP system are the CEP engine and the set of CEP rules, which are crucial for making trading decisions.

The faster the processing of events, the better a CEP system is, as it enables automated trading systems to detect good opportunities in real-time.

Here are the four parts of a CEP system:

Having a standardized protocol in place can greatly simplify the process of connecting to different destinations and vendors.

The FIX trading protocol is a prominent example of a standard protocol that makes it easy to connect to various destinations and vendors. It drastically reduces the go-to-market time when integrating with a new destination.

Multiple adapters are required to connect to different exchanges, each with its own protocol. This can be a hassle to manage, especially when adding a new exchange to the automated trading system.

Standard protocols like FIX make it possible to integrate with third-party vendors for analytics or market data feeds, making the market more efficient.

Receiving data from the real market and sending orders to a simulator can be done using the FIX protocol, making simulation much easier.

The FIX protocol also makes it possible to replay recorded data with adaptors that are agnostic to the source of the data.

Explore further: Equity Market Making

An automated trading system can help traders eliminate emotional trading decisions or biases they may have towards a specific market move or fundamental expectation.

One of the key benefits of using an automated trading system is that it can process vast amounts of data and execute trades much faster than humans. This allows traders to explore and consider market approaches they may not have previously considered.

Automated trading systems can be tested using historical data to evaluate their performance before being implemented in live trading. This is known as backtesting, and it helps traders validate and optimize their strategies.

Backtesting can also enable traders to employ or apply strategies to various market instruments that exhibit distinct price action behavior. For example, if the US dollar's price action is trending due to a fundamental market event, traders can observe price movements for cross-pairs that do not include the US dollar.

A 24/5 operation is also a benefit of automated trading systems, as algorithms can run continuously, allowing for trading around the clock. However, this also escalates the risk exposure, as the system becomes vulnerable to any market-impacting event.

Here are some of the key advantages of using automated trading systems:

Automated trading systems can also save time, eliminate stress, and save money. They can help traders meet their investment objectives since all of their decisions are based on formulas developed by an experienced trader.

Automated trading systems can be a game-changer for traders, but they're not without their challenges. One of the biggest disadvantages is system failures due to technical glitches, bugs, or network issues that can result in errors or data loss.

Market volatility can also catch traders off guard, triggering unexpected algorithm behavior and losses. A surprise tariff announcement by the US president or an unexpected interest rate decision by a major central bank can be particularly problematic.

Over-optimization is another pitfall to watch out for. Algorithms that are too closely fitted to historical data may look great in backtesting, but may not perform well in live trading. This is known as 'curve fitting,' where the algorithm learns the random fluctuations of past data instead of identifying true market patterns.

Regulatory risks are also a concern, as rules and regulations differ between jurisdictions. In the US, for example, algorithmic traders must follow the "First In First Out" (FIFO) rule, which requires exiting equal-sized trades on the same currency pair in the same order in which they were initially opened.

Check this out: Hft Algo

The automated trading system itself can also be a disadvantage, coming with an expense that may be a barrier for some traders. Additionally, connectivity issues can lead to losses during order placements, especially in remote regions with frequent connectivity disturbances.

Here are some of the common disadvantages of automated trading systems:

Setting up and launching an automated trading system can seem daunting, but it's a crucial step in taking your trading strategy to the next level.

First, you need to take your automated system live, which means you're confident in its performance and ready to trade with it.

To get started, you'll need to subscribe to a system that meets your trading objectives, open and fund your trading account, and then sit back while the system auto-executes your strategy.

Here are the specific steps to follow:

Before taking your system live, it's essential to backtest it on historical data to ensure its performance and make any necessary adjustments.

You can also build your own automated trading system from scratch, which requires coding the strategy in a programming language, backtesting it on historical data, paper trading, and then live trading.

Worth a look: Live Equity Market Watch

You can choose from various platforms to start your automated trading system. The most popular ones include ProRealTime, MetaTrader4 (MT4), and APIs.

ProRealTime offers a straightforward process to get started, where you'll activate it from My IG after opening your live account, then launch the platform.

MetaTrader4 is another powerful option that allows you to create your own expert trading algorithms, indicators, and place orders. You can even import Expert Advisors (EAs) to help you find opportunities according to your predefined parameters.

Here are the main options for using MetaTrader4:

The advancement of artificial intelligence has made it easier to create algorithmic trading robots, known as Expert Advisers or EAs. You can now input your trading parameters and rules to an AI platform in plain English and download a ready-made EA file, which can be applied to MetaTrader for algo trading.

APIs offer a flexible way to trade, allowing you to either use ProRealTime or build your own API platform.

Readers also liked: Algorithmic Trading Platform

Automated trading systems have a complex architecture that involves multiple components working together seamlessly. The traditional architecture consists of the exchange, server, and application layers, which receive and process market data, store orders, and interact with the user.

The application layer is primarily a view, but some risk checks can be offloaded to it, especially those related to user inputs like fat finger errors. However, most risk checks are performed by a separate Risk Management System (RMS) within the Order Manager (OM).

The RMS involves strategy-level RMS (SLRMS) and global RMS (GRMS), which may also include a UI to view the SLRMS and GRMS. This setup allows for efficient risk management across all logical units and strategies.

In an automated trading system, latency is a critical factor that affects decision-making. Reducing latency has become a necessity, as it's essential for survival in a competitive market. To approach this problem, we need to understand that latency encompasses several different delays.

If this caught your attention, see: Placing Trades with Trading View from Tradestation

The life cycle of an automated trading system involves multiple steps, including data packet publication, transmission, and processing. High latency at any of these steps can ensure high latency for the entire cycle. To optimize latency, we should focus on the steps within our control, such as shortening the distance to the destination.

Colocation is a facility provided by exchanges to host the trading server near the exchange, which can significantly reduce latency. By colocating the server, we can minimize the distance between the server and the exchange, resulting in faster data transmission and processing.

Here's a breakdown of the steps involved in an automated trading system:

Evaluating results is crucial for determining the effectiveness of an automated trading system. This involves looking at several key metrics to assess performance.

Profit/loss is the most basic metric, showing the overall gain or loss. Understanding this metric is essential to determine if the system is profitable or not.

The Sharpe ratio measures risk-adjusted return, indicating how much return is earned for each unit of risk taken. This helps traders understand the risk involved in the system.

Drawdown shows the maximum decline from a peak, highlighting potential exposure. This is a critical metric to analyze the potential risks of the system.

The win rate, average profit, and average loss help analyze the consistency and quality of trades. By analyzing these metrics, traders can refine their strategies and manage risk effectively.

The next step is to test and refine the automated trading system so as to improvise the system in case of any malfunctions. This involves making necessary changes or improvements to ensure the system is set for taking trades to the live market.

A unique perspective: Time-weighted Average Price

The Flash Crash on May 6, 2010, saw the Dow Jones Industrial Average decline by about 1,000 points, or 9 percent, in a matter of minutes. This was the second-largest point swing in the Average's history and led to new regulations to control market access achieved through automated trading.

For more insights, see: Volume-weighted Average Price

The Flash Crash was a wake-up call for regulators and market participants alike, highlighting the potential risks and consequences of automated trading. In fact, Knight Capital Group lost four times its 2011 net income on August 1, 2012, due to a bug in one of its trading algorithms.

This incident showcases the importance of robust testing and validation of automated trading systems to prevent similar errors. Knight shares closed down 62 percent as a result of the trading error, and the firm nearly collapsed.

Here are some key takeaways from these notable examples:

The Flash Crash of 2010 is a notable example of a market disruption. On May 6, 2010, the Dow Jones Industrial Average declined about 1,000 points (about 9 percent) and recovered those losses within minutes.

This was the second-largest point swing (1,010.14 points) and the largest one-day point decline (998.5 points) on an intraday basis in the Average's history. The Flash Crash resulted in U.S. regulators issuing new regulations to control market access achieved through automated trading.

Here's an interesting read: Flash Trading

Knight Capital Group's trading error on August 1, 2012, is another example of a market disruption. The firm lost four times its 2011 net income due to a bug in one of its trading algorithms that submitted erroneous orders to exchanges for nearly 150 different stocks.

Trading volumes soared in so many issues that the SPDR S&P 500 ETF (SPY) became the 52nd-most traded stock on that day, according to Eric Hunsader, CEO of market data service Nanex. Knight shares closed down 62 percent as a result of the trading error and Knight Capital nearly collapsed.

Here are some key statistics from the Knight Capital Group trading error:

These examples illustrate the potential risks and consequences of market disruptions.

Market data feeds are crucial for algorithms to make decisions, providing real-time data on market prices, volumes, and other relevant information.

Real-time data feeds are the most up-to-date market information available, allowing algorithms to make informed decisions.

For your interest: Full Time Day Trader

Infrastructure such as servers, networks, and software are required to run trading algorithms and connect to trading platforms and data feeds.

Servers, networks, and software are the backbone of any trading system, enabling the smooth operation of trading algorithms.

Defining a robust and tested trading strategy is crucial for success, as it provides a clear direction for trading decisions.

A well-defined trading strategy helps traders stay focused and avoid impulsive decisions.

Implementing stop-loss orders and other risk management measures is essential to limit potential losses.

Risk management is a critical aspect of trading, and stop-loss orders can help prevent significant losses.

Algorithms require regular monitoring and maintenance to ensure they function as intended, and any issues should be addressed promptly.

Regular monitoring and maintenance help prevent technical issues from affecting trading performance.

Expand your knowledge: Cascades in Financial Networks