Automated trading and strategies

What are algorithms (Algos)?

Algorithms (Algos) are a set of instructions that are introduced to perform a specific task. Algorithms are introduced to automate trading to generate profit at a rate impossible for a human trader. The process is called algorithmic trading and establishes rules based on prices, quantities, times, and other mathematical patterns. Other variations of algorithmic trading include automatic trading and black box trading. Algorithmic trading excludes the human (emotional) impact on trading activities. The use of sophisticated algorithms is common among institutional investors such as investment banks, pension funds, and hedge funds due to the large volumes of stocks traded daily. This allows them to get the best possible price at a minimal cost without significantly affecting the share price.

Algorithmic Trading Strategies

Any good algorithmic trading strategy should aim to improve trading income and reduce trading costs. The most popular strategies are arbitrage, index fund rebalancing, average reversion, and market timing. Other strategies are scalping, reducing transaction costs, and trading pairs. “Rebalancing” creates opportunities for algorithmic traders who capitalize on expected trades based on the number of stocks in the index fund. Transactions are executed by algorithmic trading systems to enable the best prices, low costs and quick results.

Algos and Arbitrage

Arbitrage is the exercise of taking benefit of occasional small marketplace rate discrepancies that rise withinside the marketplace rate of safety this is traded on one-of-a-kind exchanges. Purchasing a dual-indexed inventory at a reduction in Market A and promoting it at a top rate in Market B gives a threat-loose arbitrage possibility to profit.


The exercise may be carried out in buying and selling the S&P 500 futures contracts and S&P 500 shares due to the fact it’s miles not unusual place for moderate rate differentials to rise among the futures rate and the full rate of the real underlying shares. When it occurs, the securities buying and selling on NASDAQ and NYSE both get beforehand or lag in the back of the S&P futures traded withinside the CME marketplace, developing an arbitrage possibility.


For arbitrage to arise, it has to meet 3 conditions. First, the identical property must now no longer exchange at the identical rate on all markets. Second,  properties with identical coin flows must now no longer exchange at an identical rate. Lastly, an asset with a recognized rate withinside the destiny must now no longer exchange these days on the destiny rate, discounted on the threat-loose hobby rate.


Arbitrage is simplest viable with securities and economic merchandise buying and selling electronically. Also, the transactions must arise concurrently to decrease the publicity of marketplace threat or the opportunity that the rate of 1 marketplace can also additionally exalternate earlier than each transaction are complete.

Mean Reversion

Mean Reversion is a mathematical method used in stock investing and calculates the average of a stock’s temporary high and low prices. It involves identifying the trading range of a stock and calculating its average price using analytical techniques. When the current market price lags behind the average price, the stock is seen as attractive, hoping the price will rise.

On the other hand, when the current market prices rise above the average price, the stock is considered undesirable because investors expect the price to fall back towards the average price. The standard deviation of recent stock prices is often used as an indicator to buy or sell. Trading around mean reversion is a common use of algorithms.

Market Timing

Strategies designed to generate alpha are considered market timing strategies and use a method that includes live testing, backtesting and forward testing. Backtesting is the first phase of market timing and involves simulating hypothetical trades throughout sample data.

The next step is to run the optimization for the best results. The second phase of market timing is advanced testing and involves running the algorithms through sample data to ensure that it performs within the backtested expectations.

The last phase is the live test and requires a developer to compare the live transactions with the back-tested and forward-tested models

Advantages of Algorithmic Trading

Below are various advantages of allowing a computer to monitor and execute live trades:

Trading One of the advantages of trading with algorithms is the ability to minimize emotions during the trading process since trades are limited to a set of predefined instructions. Human commerce is susceptible to emotions such as fear and greed which can lead to poor decision-making. Thanks to automated trading, traders can easily stick to the plan.

Automating the process also helps to curb excessive trading, where some traders may buy and sell at every opportunity, reducing the chance of human-made errors.

Trading with algorithms (Algos) also helps to achieve consistency. The biggest challenge in the business process is business planning and negotiating plans. Failure to follow all the rules is likely to adversely affect a trader’s every chance, even though the trading plan may be profitable.

Although losses are part of trading, human traders can become discouraged after suffering two or more consecutive losses and not moving on to the next trade. By falling in the middle of the process, the trader destroys any chance of winning in other rounds of trading. Automated trading helps achieve consistency, trade according to plan, and increase the odds of winning.

In trading, every second counts and the speed of algorithmic trading makes it a favourable option for investing. Computers react instantly to changing market conditions and help generate orders as soon as the criteria are met, much faster than anyone who can recognize a change in the market and manually enter trading orders.

Also, exiting or entering too early or too late can make a big difference in day trading, and automating the process helps correct human errors.

Disadvantages of Algorithmic Trading

Like other mechanical processes, algorithmic trading is sophisticated and prone to failure.

Internet connectivity issues, power cuts, and computer crashes can result in incorrect orders, duplicate orders, and even missing orders that may not be sent to the marketplace.

In addition, there may be a difference between the operations generated by the trading strategy and the actual results of automated trading systems. Automated trading systems should be monitored at all times to avoid mechanical breakdowns.

Traders who use backtesting techniques to optimize their systems can create systems that look good on paper but don’t work in a real market. The problem can arise due to over-optimization, where traders create overfitting of the curve which produces a trading plan carefully tailored to past market price behaviour but unreliable in current active markets.

Some traders assume that a trading plan should generate 100% profitable trades without leaving room for drawdowns.

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