The importance of having at least rudimentary knowledge of the several types of automated trading strategies cannot be overstated. In this article, we discuss in detail the various ways trading algorithms go about trading. However, to first uncover the basics of automated trading strategies, check out a previous article on what automated trading strategies are and how they are developed.

High-Frequency Trading

By itself, it involves the use of high-powered computers to execute a large number of orders in quick succession. The technicality behind this trading approach is the use of composite algorithms to evaluate various markets and make executions when market conditions and liquidity levels correspond to predefined parameters. The profitability of this automated trading strategy is in the speed of executing orders. Thus, the faster the execution speeds, the higher the returns.

The popularity of high-frequency trading has its roots in the genesis of the era when exchanges started offering incentives to groups that add liquidity to the market. As an example, the NYSE has a regulated assemblage of companies that provide liquidity. These companies (liquidity providers) are referred to as Supplemental Liquidity Providers (SLPs), and their goal is to increase the liquidity and competition for existing quotes on NYSE.

In return, NYSE offers a rebate incentive to the SLPs. Current reports point to the value of the SLP rebate to be $0.0015. Though minute when considered in isolation, the SLR rebate does begin to look more enticing when it is multiplied by millions of transactions per day. For starters, now you know where part of the profits in high-frequency trading is at.

Automated Trading Strategies

This refers to any trading activity influenced by the use of an automated computer system. By definition, it is the process of “placing a buy or sell request of a pre-defined quantity into a quantifiable model that automatically creates the timing and size of orders grounded on preset goals stated by the parameters and controls of the algorithm.” Summarily, “Automated trading refers to the process of using a set of guidelines to carry out trade executions.”

Simple Technical Analysis Program Trading

These automated trading strategies differ from the others in that trades are executed based on trading signals generated by computer software. These signals are customarily fed into the clients’ brokerage infrastructure from the trader’s computer. After which, the order request corresponding to the signal is automatically executed.

Automated Trading Strategies and Trends

The onset of the automated trading strategies drives in trading forever left the market in a state of flux, providing exciting opportunities for resourceful minds. Since the introduction of algorithms, much has changed, so much that it is almost impossible to establish precise foreknowledge of how automated trading will proceed in the future.

Nonetheless, we provide uncommon insight into future automated trading trends. We portend that in the near future, trading firms will continually re-evaluate and rehash their views, asset-class mix, trading strategy, the existential relationship between the buy and sell halves of the market, and the proficiency of those employed to overlook automated trading technology; so as to keep up with the rapidly evolving automated trading landscape.

Automated Trading Migrating to Currencies

The rise of automated trading use in multiple asset classes shows no sign of depreciating soon. To corroborate this fact, there exist weighty indications that point to automated trading snagging a sizeable percentage of trading activities in the $4 trillion-plus global foreign exchange market.

The equities market and FX markets have long seen a number of links drawn between both markets, amongst which are baskets and order slicing. Aside from these, market participants are quick on the mark to identify that automated trading holds promise in the fast-paced FX markets.

Thus, while there is no dispute in asserting that automated trading has a stronger foothold in the equities marketplace than in the FX markets, there is a general consensus that automated trading use in the FX markets will eclipse automated trading use in the equities market.

Fixed Income Next

Automated trading has begun to make relatively slow but significant inroads in the fixed income market. The reduced speed of the permeation of automated trading in fixed income, when compared to its tumultuous speed in FX, is primarily a result of the contrasting market structure between the fixed income and FX markets.

Automated trading is known to perform better in environments that are order-driven and have greater price transparency, virtues that have not been fully extolled by fixed income markets. Notwithstanding, once the vital European markets show increased acceptance of automated trading as is the case across other regional fixed income markets, there is little disputing the forecast that automated trading would have gotten the much-awaited boost to becoming a force in fixed income markets.

Automated Trading Connect Dark Pools Leading to Increased Liquidity

Off-exchange marketplaces that execute orders incognito exist. Technically, these marketplaces can be described as “dark” in that they provide little room for dissipating information.

Crossing networks of this kind are responsible for five to eight percent of buy trades, according to TABB Group. Amongst notable broker-dealer dark books are Credit Suisse’s CrossFinder, Goldman Sachs’ Sigma X, and UBS’ Price Improvement Network (PIN). Within the crossing networks conclave, notable mentions include Instinet Crossing, Pipeline, ITG’s Posit, and Pipeline.

Algorithms are used expansively to match buy and sell orders without publishing quotes by broker-dealers. By exerting greater control over the flow of information, and both the bid and ask halves of a trade, broker algorithms are able to artificially increase the liquidity, share pricing, and broker commissions.

Cross-Asset Trading Adoption of Automated Trading Strategies

Risk-adjusted extra return on investment (Alpha) is an investment prize traders seek to win by exploring cross-asset trading opportunities. Once an elusive form of trading, current technology has surmounted the barriers that used to exist in cross-asset trading by permitting traders to observe and interact in various liquidity pools across multiple asset classes.

Thus, it is possible for a trader to buy equity, hedge with a derivative of the purchased equity, and take a foreign exchange position—all within a single trading strategy.

The future is expected to be rife with sophisticated algorithms that will exploit existing high-frequency cross-asset opportunities. Careful testing and detailed simulation will ensure that these systems see the light of day: and current automated trading platforms are en route to providing these much-needed features through the use of varied tools to profile, back-test, and fine-tune these enterprising automated trading strategies before deployment.

Algorithms for News Analysis

The markets have a soft spot for news, especially when they have economic undertones. Little wonder why automated trading strategies known to evaluate news events and predict their impacts on a firm, industry or the economy; have received much interest from traders and buy-side firms.

The quicker the automated traded strategy can identify the connection, the better and the higher the interest shown. For example, an algorithm could alert a trader about news centering on a company X. An epitome of such an algorithm is the Reuters News Scope Real-time Product (RNSRP). RNSRP provides clients with exclusive access to getting first-hand knowledge of live news content to provide raw feed for automated trading as well as to elicit a timely response to market-inciting events as they occur. On the technical side of things, each news event is ‘meta-tagged’ electronically to specify the sectors, firms, specific data items, or stories that are of particular importance in automated trading.

Automated Trading Strategy Types Conclusion

While we offer expert insight into the various methodologies used in developing automated trading strategies in this article, we must point out that we do not use any exact methodology at Algotrades.net. Our algorithms are slow frequency algorithmic investing systems that exploit intermediate cycle highs and lows in the broad market, specifically using the S&P 500 Index. Find out more about our simple automatic trading system.

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