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Basics of Algorithmic Trading: Concepts and Examples

2017-05-03 03:23 | Network |

An algorithm is a specific set of clearly defined instructions aimed to carry out a task or process.

Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. The defined sets of rules are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algo-trading makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities. (For more, check out Picking the Right Algorithmic Trading Software.)

Suppose a trader follows these simple trade criteria:

Using this set of two simple instructions, it is easy to write a computer program which will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. The trader no longer needs to keep a watch for live prices and graphs, or put in the orders manually. The algorithmic trading system automatically does it for him, by correctly identifying the trading opportunity. (For more on moving averages, see Simple Moving Averages Make Trends Stand Out.)

Benefits of Algorithmic Trading

Algo-trading provides the following benefits:

The greatest portion of present day algo-trading is high frequency trading (HFT), which attempts to capitalize on placing a large number of orders at very fast speeds across multiple markets and multiple decision parameters, based on pre-programmed instructions. (For more on high frequency trading, see Strategies and Secrets of High Frequency Trading (HFT) Firms.)

Algo-trading is used in many forms of trading and investment activities, including:

Algorithmic trading provides a more systematic approach to active trading than methods based on a human trader's intuition or instinct.

Algorithmic Trading Strategies

Any strategy for algorithmic trading requires an identified opportunity which is profitable in terms of improved earnings or cost reduction. The following are common trading strategies used in algo-trading:

Trend Following Strategies:

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. The above mentioned example of 50 and 200 day moving average is a popular trend following strategy. (For more on trend trading strategies, see: Simple Strategies for Capitalizing on Trends.)

Arbitrage Opportunities:

Buying a dual listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments, as price differentials do exists from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows profitable opportunities in efficient manner.

Index Fund Rebalancing:

Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20-80 basis points profits depending upon the number of stocks in the index fund, just prior to index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and best prices.

Mathematical Model Based Strategies:

A lot of proven mathematical models, like the delta-neutral trading strategy, which allow trading on combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.

Trading Range (Mean Reversion):

Mean reversion strategy is based on the idea that the high and low prices of an asset are a temporary phenomenon that revert to their mean value periodically. Identifying and defining a price range and implementing algorithm based on that allows trades to be placed automatically when price of asset breaks in and out of its defined range.

Volume Weighted Average Price (VWAP):

Volume weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock specific historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby benefiting on average price.

Time Weighted Average Price (TWAP):

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