The Benefits of Algorithmic TradingAlgorithmic trading potentially provides the following benefits following the rules of the trading strategy without human Psychology decision or indecision getting in the way:
- Trades executed at the best possible prices as per the trading strategy,
- Instant and accurate trade order placement (thereby high chances of execution at desired levels),
- Trades timed correctly as per the defined rules and instantly, to avoid significant price changes,
- Reduced transaction costs,
- Simultaneous automated checks on multiple market conditions,
- Reduced risk of manual errors in placing the trades,
- Back test the algorithm, based on available historical and real-time data,
- Reduced possibility of mistakes by human traders based on emotional and psychological factors.
The following are common trading strategies used in algorithmic trading:
- Trend Following Strategies, which is very common and follows trends in moving averages, channel breakouts, price level movements and other technical indicators. These are the easiest and simplest strategies to implement because they do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of popular trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis.
- Arbitrage Opportunities, which involve buying a dual listed stock at a lower price in
one market and simultaneously selling it at a higher price in another market. These opportunities offer the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks versus futures instruments, as price differentials do exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders allows pro table opportunities in an efficient manner.
- Index Fund Rebalancing, which has defined periods of rebalancing to bring their holdings on par with their respective benchmark indices. This creates pro table opportunities for algorithmic traders. Such trades are initiated via algorithmic trading systems for timely execution and best prices.
- Mathematical Model Based Strategies, where a lot of proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and its underlying security. Trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
- Trading Range (Mean Reversion); this is based on the idea that the high and low prices of an asset are a temporary phenomenon which reverts to their mean value periodically. Identifying and defining a price range and implementing an algorithm based on that which allows trades to be placed automatically when the amount of an asset breaks in and out of its defined range.
- Volume-Weighted Average Price (VWAP) strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock particular historical volume profiles. The aim is to execute the order close to the Volume Weighted Average Price (VWAP), thereby bene ting on average price.
- Time Weighted Average Price (TWAP) strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimising market impact.
- Percentage of Volume (POV) requires the trade order to be fully filled. This algorithm continues sending partial orders, according to the de ned participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-de ned levels.
- Implementation Shortfall strategy aims at minimizing the execution cost of an order
by trading off the real-time market, thereby saving on the expense of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favourably and decreases it when the stock price moves adversely.
Technical Requirements for Algorithmic Trading
Implementing the algorithm using a computer program is the last part, clubbed with back testing. The challenge is to transform the identified strategy into an integrated computerized process that has access to a trading account for placing orders. The following are needed:
- Computer programming knowledge to program the required trading strategy, hired programmers or pre-made trading software;
- Network connectivity and access to trading platforms for placing the orders;
- Access to market data feeds that will be monitored by the algorithm for opportunities to place orders;
- The ability and infrastructure to back test the system once built, before it goes live on real markets;
- Available historical data for back testing, depending upon the complexity of rules implemented in the algorithm.
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