An algorithm is a detailed procedure to accomplish a task. Algorithmic trading (also known as automated trading, black-box trading, or algo-trading) uses a computer program that follows a defined set of instructions (also known as an algorithm) to place a trade.

Thus, algorithmic trading is the process of using a computer program to follow a defined set of instruction for placing trades to generate profit. This process is executed at a speed and frequency that is beyond human capability. The set of instruction is based on timing, price, quantity and any other mathematical models.

For example, a trader is looking to buy ten stocks of a company when the 30-day moving average for the stock crosses above the 50-day moving average mark. The trader also intends to sell the scrip when the 30-day moving average moves below the 50-day moving average.

A computer program is designed in a manner that monitors the prices and place the orders when conditions are met. The trades are executed by the system and not the trader. Thus, manual intervention is reduced significantly.

Benefits of Algorithmic Trading

Algorithmic trading provides the following benefits;

  • Trades are executed at best possible price
  • Trade is placed instantly and accurately with a high chance of execution at the desired level
  • Trade is timed correctly and immediately to avoid price change
  • Low transaction cost
  • Simultaneous automated checks on multiple market conditions.
  • Low risk of manual error while placing orders
  • The method can be backtested using available historical and real-time data to check the viability of the trading strategy
  • Reduced possibility of mistakes due to less human interference. Human traders are generally influenced by emotional and psychological factors which are not the case with algorithmic trading.

Risks Involved in the Trading System

Trading comes with a risk. Risk includes

  • System failures or issues due to network connectivity
  • Time lags between orders and execution

Utility

The method is used in multiple forms of trading and investment activities. Following are some – Mid to long-term investors
Buy-side firms such as pension funds, mutual funds, insurance companies, etc.

  • Short-term traders
  • Sell-side participants such as brokerage houses, speculators, and arbitrageurs
  • Systematic traders that follow trend
  • Hedge funds
  • Pairs traders

Strategies in Algorithmic Trading

Every strategy for implementing algorithmic trading requires an identified opportunity that is profitable in terms of improved earning or cost reduction.

Following are the most used strategies of algorithmic trading;

1. Trend Following Strategy

The trend is the most commonly used trading strategy.

The trends used are moving averages, breakout, price level movement, etc. This is the most straightforward strategy to implement, as the strategy does not require any prediction of price.

Trades are executed based on a popular trend that is easy and straightforward to implement. For example, 30-day, 50-day, and 200-day moving average are the most popular trends used.

2.Index Fund Rebalancing Strategy

 

Index funds have a defined period of rebalancing.

This helps the holdings at par with the respective benchmark indices. This method creates an opportunity for algorithmic traders.

The traders tend to capitalize on expected trades that offer around 25-75 basis points profit, depending on the number of stocks in the index before rebalancing.

3. Mathematical Model Based Strategy

Some of the models such as delta-neutral, allow trading on a combination of options and underlying security.

For novice readers, delta neutral is a portfolio strategy that comprises of positions offsetting the positive and negative delta. Delta is the ratio that compares the change in the price of the asset to its corresponding derivative.

4. Mean Reversion

The said strategy is based on the concept of high and low price of an asset which is temporary and the price reverts to the mean value over time. In this strategy, the main component is to identify and define the price range and thereby implementing the algorithm.

5. Volume-Weighted Average Price (VWAP)

The strategy breaks a large order and releases a smaller chunk of order using historical volume profile for every stock. It seeks to execute the order close to the volume-weighted average price (VWAP).

6. Time-weighted Average Price (TWAP)

The strategy breaks a large order and releases a smaller chunk of order using evenly divided time slots between a start and an end time. The strategy seeks to execute the order close to the average price between the start and end times.

7. Percentage of Volume (POV)

In the strategy, the algorithm sends partial orders according to the defined participation ratio and volume traded in the market.

Requirement for Algorithmic Trading

Implementing the method of algorithmic trading requires a computer program. A computer program accompanied by backtesting completes the need from an execution standpoint.

However, the challenge is to transform the strategies mentioned above into an integrated computerized process including access to the trading account for placing orders.

Following are the technical requirements of algorithmic trading – computer programming – required to program the trading strategy using any language. One can use an existing trading platform as well.

  • Network connectivity with access to the trading platform to place order
  • Access to market data by way of feeds. This is generally monitored by the algorithm to scout for opportunities for placing orders
  • Infrastructure to backtest the system before it goes live or trade in the live market
  • Access to historical data for backtesting

Happy Investing!

Disclaimer: The views expressed in this post are that of the author and not those of Groww