Basics of Algorithmic Trading: Concepts and Examples

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Basics of Algorithmic Trading: Concepts and Examples

To conduct a deal, algorithmic trading (also known as automated trading, black-box trading, or orgo-trading) employs a computer software that follows a specific set of instructions (an algorithm). In principle, the deal may create profits at a pace and frequency that a human trader cannot match.

Timing, pricing, quantity, or any mathematical model are used to define sets of instructions. Aside from profit prospects, algo-trading makes markets more liquid and trading more methodical by removing the influence of human emotions on trading activity.

Key Takeaways

  • To execute deals at exact times, algorithmic trading integrates computer programming and financial markets.
  • Algorithmic trading seeks to remove emotions from deals, provides the most efficient execution of a trade, instantly places orders, and may minimize trading costs.
  • Trend-following methods, arbitrage possibilities, and index fund rebalancing are all common trading tactics.
  • Algorithmic trading may also be based on trade volume (volume-weighted average price) or time (time-weighted average price).
  • To get started with algorithmic trading, you’ll need a computer, a network connection, financial market expertise, and coding skills.

Basics Of Algorithmic Trading

Algorithmic Trading in Practice

Suppose a trader follows these simple trade criteria:

  • When a stock’s 50-day moving average exceeds its 200-day moving average, buy 50 shares. (A moving average is a weighted average of previous data points that smooths out day-to-day price volatility and so reveals patterns.)
  • When the stock’s 50-day moving average falls below the 200-day moving average, sell shares.

A computer software will automatically watch the stock price (and the moving average indicators) and make buy and sell orders when the preset circumstances are satisfied using these two simple commands. The trader no longer has to watch live prices and graphs or manually enter orders. This is done automatically by the algorithmic trading system by accurately detecting the trading opportunity.

Benefits of Algorithmic Trading

Algo-trading provides the following benefits:

  • Trades are carried out at the most competitive costs.
  • Tradeorder placing is quick and precise (there is a high chance of execution at the desired levels).
  • To prevent substantial price swings, trades are timed precisely and promptly.
  • Reduced transaction costs.
  • Automated checks on several market situations at the same time.
  • Reduced the possibility of human mistakes while making transactions.
  • Backtesting algo trading utilizing accessible historical and real-time data to determine if it is a feasible trading method.
  • Reduced the probability of human traders making errors based on emotional and psychological variables.

The majority of algo trading nowadays is high-frequency trading (HFT), which tries to profit by placing a large number of orders at fast speeds across numerous markets and decision factors based on preprogrammed instructions.

Algo-trading is utilized in a variety of trading and financial operations, such as:

  • When mid- to long-term investors or buy-side firms—pension funds, mutual funds, insurance companies—do not wish to affect stock prices with discrete, big-volume transactions, they utilize algo-trading to acquire equities in enormous numbers.
  • Short-term traders and sell-side participants—market makers (such as brokerage houses), speculators, and arbitrageurs—benefit from automated trade execution; also, algo trading assists in providing enough liquidity for market sellers.
  • Systematic traders—trend followers, hedge funds, or 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 program trade automatically is far more efficient.
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Algorithmic trading is a more methodical approach to active trading than trader intuition or instinct.

Algorithmic Trading Strategies

Any algorithmic trading strategy necessitates the identification of a favorable opportunity in terms of increased revenues or cost reduction. The following are some of the most prevalent trading methods utilized in algo trading:

Trend-Following Strategies

Moving averages, channel breakouts, price level fluctuations, and associated technical indicators are the most often used algorithmic trading systems. Because these methods do not need any predictions or price projections, they are the easiest and simplest to apply using algorithmic trading. Trades are made in response to the occurrence of favorable trends, which are simple and easy to apply using algorithms without delving into the complexities of predictive analysis. A common trend-following method is to use 50- and 200-day moving averages.

Arbitrage Opportunities

Purchasing a dual-listed stock at a cheaper price in one market and concurrently selling it at a higher price in another market results in a risk-free profit or arbitrage. As price differentials arise from time to time, the same technique may be performed for stocks vs. futures products. Implementing an algorithm to detect such price differentials and effectively placing orders provides for lucrative chances.

Index Fund Rebalancing

Index funds have rebalancing periods to bring their holdings up to line with their respective benchmark indexes. This opens up attractive possibilities for algorithmic traders, who may benefit from predicted transactions that give 20 to 80 basis points gains depending on the number of stocks in the index fund immediately before index fund rebalancing. Algorithmic trading algorithms conduct such deals to ensure timely execution and the best pricing.

Algorithmic trading enables traders to execute high-frequency transactions. High-frequency trading used to be measured in milliseconds. They may now be measured in microseconds or nanoseconds (billionths of a second).

Mathematical Model-Based Strategies

Proven mathematical methods, such as the delta-neutral trading technique, enable trading on options and the underlying securities. (Delta neutral is a portfolio strategy that consists of multiple positions with offsetting positive and negative deltas—a ratio that compares the change in price of an asset, usually a marketable security, to the corresponding change in price of its derivative—so that the overall delta of the assets in question is zero.)

Trading Range (Mean Reversion)

The mean reversion method is based on the idea that an asset’s high and low values are only transient and will eventually return to its mean value (average value). Identifying and establishing a price range, as well as creating an algorithm based on it, enables trades to be conducted automatically when an asset’s price breaks in and out of its stated range.

Volume-Weighted Average Price (VWAP)

Using stock-specific historical volume profiles, the volume-weighted average pricing technique splits up a big order and releases dynamically determined smaller parts of the order to the market. The goal is to execute the order as close to the volume-weighted average price as possible (VWAP).

Time Weighted Average Price (TWAP)

The time-weighted average pricing technique divides a big order into smaller parts and delivers them to the market in dynamically set time windows between a start and finish time. The goal is to execute the order around the average price between the start and finish timings, minimizing market effect.

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Percentage of Volume (POV)

This algorithm keeps delivering partial orders based on the specified participation ratio and the volume traded in the markets until the trade order is completely filled. The associated “steps approach” sends orders at a user-defined percentage of market volumes, and this participation rate rises or decreases when the stock price hits user-defined thresholds.

Implementation Shortfall

The implementation deficit technique tries to reduce an order’s execution cost by trading off the real-time market, saving money on the order and profiting from the opportunity cost of delayed execution. The technique will raise the desired participation rate when the stock price rises and lower it when the stock price falls.

Beyond the Usual Trading Algorithms

There are a few algorithms that try to detect “happenings” on the opposite side. These “sniffing algorithms,” which are deployed by a sell-side marketmaker, have the intelligence to detect the presence of any algorithms on the purchase side of a huge transaction. Such identification through algorithms will assist marketmakers in identifying significant order possibilities and allowing them to profit by filling the orders at a higher price. This is frequently referred to as “high-tech front-running.” Front-running is generally unlawful, depending on the circumstances, and is strictly controlled by the Financial Industry Regulatory Authority (FINRA).

According to a Securities and Exchange Commission research published in 2018, “electronic trading and algorithmic trading are both prevalent and essential to the functioning of our capital market.”

Technical Requirements for Algorithmic Trading

The last component of algorithmic trading is the implementation of the algorithm using a computer program, which is followed by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable).The task at hand is to convert the outlined approach into an integrated automated procedure with access to a trading account for placing orders. The prerequisites for algorithmic trading are as follows:

  • Knowledge of computer programming to program the needed trading strategy, paid programmers, or pre-made trading software
  • Access to trading platforms and network connection to place orders
  • Access to market data sources that the algorithm will watch for chances to make orders.
  • The ability and infrastructure to backtest the system once it has been constructed before going live on actual markets.
  • Depending on the intricacy of the rules employed in the algorithm, historical data is available for backtesting.

An Example of Algorithmic Trading

Royal Dutch Shell (RDS) is a publicly traded company on the Amsterdam Stock Exchange (AEX) and the London Stock Exchange (LSE).We begin by developing an algorithm to detect arbitrage possibilities. Here are some noteworthy observations:

  • The AEX exchange deals in euros, while the LSE trades in British pounds sterling.
  • Because of the one-hour time difference, AEX begins an hour before LSE, followed by both exchanges trading concurrently for the following several hours and then trading solely in LSE for the last hour when AEX shuts.

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

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  • A computer software that is capable of reading current market values.
  • Both the LSE and the AEX provide price feeds.
  • GBP-EUR Aforex (foreign currency) rate feed
  • Capability to place orders and route them to the appropriate exchange.
  • Backtesting on past price feeds is possible.

The computer program should perform the following:

  • Read the RDS stock price feed from both exchanges.
  • Convert the price of one currency to the other using the available foreign exchange rates.
  • If there is a significant enough price difference (after deducting brokerage expenses) that leads to a lucrative opportunity, the software should place the purchase order on the lower-priced exchange and the sell order on the higher-priced exchange.
  • If the orders are executed correctly, the arbitrage profit will be realized.

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 transaction, so can the rest of the market. As a result, prices swing in milliseconds and even microseconds. What happens in the above example if a purchase transaction is performed but a sell trade is not because the sale prices have changed by the time the order reaches the market? The arbitrage approach will be rendered useless since the trader will be left with an open position.

Other risks and obstacles include system failure, network connection failures, time gaps between trade orders and execution, and, most importantly, poor algorithms. The more sophisticated an algorithm, the more severe the backtesting required before it is implemented.

Is Algorithmic Trading Legal?

Yes, algorithmic trading is permitted. Trading algorithms are not restricted by any regulations or laws. Some investors may argue that this style of trading generates an unfair trading environment that harms market performance. However, it is not unlawful in any way.

How Do I Learn Algorithmic Trading?

Algorithmic trading is largely reliant on quantitative analysis or simulation. Because you’ll be investing in the stock market, you’ll require trading expertise or understanding of financial markets. Finally, since algorithmic trading often depends on technology and computers, you’ll most likely need a coding or programming expertise.

What Programming Language Do Algorithmic Traders Use?

C++ is a popular programming language among algorithmic traders due to its excellent efficiency in processing large amounts of data. However, since C and C++ are both more sophisticated and demanding languages, financial professionals interested in getting into programming may be better off starting with a more accessible language like Python.

The Bottom Line

Algorithmic trading brings together computer software, and financial markets to initiate and settle deals based on programmed code. Investors and traders may determine when they want deals initiated or closed. They may also harness processing power to undertake high-frequency trading. With a number of tactics traders may utilize, algorithmic trading is prominent in financial markets today. To get started, be equipped with computer hardware, programming abilities, and financial market knowledge.

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