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Top 9 Algorithmic Trading Strategies

  • Vrinda Mathur
  • Aug 23, 2024
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Algorithmic trading techniques are simply those that are programmed in a computer language like Python to execute trade orders. The trader codes these techniques to employ a computer's processing capacity to execute transactions more efficiently and with little to no intervention.

 

What do you understand about Algorithmic Trading?

 

An algorithm is essentially a set of instructions or rules that cause the computer to do a task on behalf of the programmer (the person who created the algorithm). In the trading area, a programmer is a trader who is familiar with at least one of the computer programming languages (such as C, C++, Java, Python, etc.).

 

This knowledge of programming language is necessary because the trader must code a set of instructions in a language that the computer understands.

 

Algorithmic trading (also known as automated trading, black-box trading, or algo-trading) is the employment of a computer programme to place a trade based on a predetermined set of instructions. In principle, the deal can earn profits faster and more frequently than a human trader.

 

The predefined sets of instructions are based on timing, pricing, quantity, or any mathematical model. Aside from profit prospects for traders, algo-trading makes markets more liquid and trading more methodical by eliminating the influence of human emotions on trading activity.

 

Also Read | Unlocking More Potential Profit in Trading with AI Based Platform

 

Algorithmic Trading Types

 

Algorithms in financial trading are rules or instructions that automate trading choices. They range from simple single-stock algorithms to more complex black-box algorithms that use market circumstances, price movements, and other financial data to execute trades at the best moments for the lowest cost-to-maximum profit ratio. 

 

The intersection of computer engineering and finance is known for its leaden language, so we won't overload you with terminology here. While some terminology may differ significantly from one trading business to the next, below should give you an understanding of the broad purposes for algorithmic trading.


 

  1. Arrival price algorithms

 

These are intended to execute trades as closely as possible to the stock price at the time the order was made. These are important for reducing market effect and the danger of price movements after an order is placed.

 

  1. Basket algorithms

 

Also known as portfolio algorithms, execute orders while taking into account the implications on other decisions and assets in a portfolio. For example, even if a security is available at the proper price, the algorithm may choose not to trade if doing so would increase overall portfolio risk. The algorithm's constraints include cash balance, self-financing, and minimum and maximum participation rates.

 

  1. Percentage of volume

 

These algorithms change order sizes in response to real-time market trading volume. The goal is to maintain a predetermined percentage of overall market volume while balancing market impact and timing.

 

  1. Time-weighted average price (TWAP)

 

These algorithms distribute trades evenly over a defined period to get an average price that corresponds to the stock's time-weighted average. They are used to reduce market volatility when placing large orders.

 

Top Algorithmic Trading Strategies

 

Algorithmic trading necessitates meticulous programming of each algorithm used to make critical buy and sell decisions. Traders can construct different trading strategies by simply switching from one algorithm to another based on their requirements or preferences.

 

 Here are some of the most widely utilized algorithmic trading strategies and examples.

 

  1. Momentum

 

Momentum trading is a traditional day-trading approach that has produced results for over 80 years. It was just a matter of time before traders decided to combine this investment strategy with algorithmic trading. The underlying notion behind momentum trading is to predict future values based on values that have already been observed.

 

  1. Trend Following

 

Trend following is often referred to as time-series momentum. It is similar to momentum trading in that it aims to make profit by expecting that future asset price returns will be in the same direction as that asset's previous returns.

 

Trend-following strategies rely on well-defined market circumstances such as range breakouts, volume profile skews, and volatility increases. The "simple moving average crossover" is one of the most popular tactics.

 

  1. Risk-on or risk-off

 

Risk-on/risk-off is a technique in which fluctuations in investor risk tolerance are closely tracked in response to global economic trends. Under a risk-on/risk-off strategy, periods when risk is seen to be low require investors to undertake higher-risk investments, while the opposite is also true.

 

This method is difficult to implement in practice since it requires monitoring a variety of elements such as global central bank actions and pronouncements, macroeconomic statistics, business results, and so on. Algorithms can be used to analyze these data points and determine whether the risk in a specific market is increasing or decreasing.

 

  1. Inverse volatility.

 

The inverse volatility approach is frequently employed in conjunction with exchange-traded funds (ETFs). This technique entails purchasing inverse volatility ETFs to mitigate against portfolio risk by increasing exposure to volatility. This eliminates the need for option purchases. Investors can expect significant gains if volatility remains low. This is because an inverse volatility ETF assumes that market stability will prevail.

 

  1. Black Swan Catchers

 

The black swan event is a financial term that refers to an unexpected event that exceeds typical expectations yet has potentially terrible consequences. In his famous writings, Nicholas Taleb refers to the Global Financial Crisis as a "black swan event." A more contemporary example would be the COVID-19 epidemic.

 

  1. Index Fund Rebalancing

 

Index funds are connected to benchmark indices. Each fund has a set period during which it rebalances its holdings to match its index. When this occurs, algorithmic traders can profit from the event. The trades generated by this rebalancing can result in profits ranging from 20 to 80 basis points, depending on the number of stocks in the index fund prior to rebalancing.

 

  1. Arbitrage

 

Arbitrage investment occurs when an investor simultaneously buys and sells the same asset in various marketplaces. Arbitrage seeks to benefit on slight, often minuscule discrepancies in the asset's quoted price. Arbitrage works best when it takes advantage of short-term price fluctuations in the same or nearly similar assets in multiple forms or markets.

 

  1. Mean Reversion

 

Mean reversion tactics rely on the temporal dynamics of high and low asset prices. The idea is that assets will periodically return to their average (or mean) value. The problem, then, is to recognise when such a mean reversion is going to occur and act accordingly. If a reversion is expected to drive the price higher, it is time to buy; conversely, if reversions are expected to lower the price, it is time to sell.

 

  1. Market Timing

 

The goal of market timing strategy is to acquire or sell an asset at the optimal time. This method might be hit or miss, as an investor may wait for an asset to reach what they believe is an all-time low, only to have the price fall even further after buying in it. Similarly, investors may miss out on profits if they sell an item at a perceived high, only to have its value rise after they sell.

 

Also Read | Mastering Market Moves: How Trading Signals Can Elevate Your Strategy

 

Bottom Line

 

Algorithmic trading combines computer software and financial markets to open and close trades using programmed code. Investors and traders can specify when they want trades open or closed. They can also use processing power to execute high-frequency trades. Algorithmic trading, which allows traders to employ a multitude of techniques, is now common in financial markets. To begin, prepare with computer hardware, programming abilities, and financial market experience.

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