FollowWhat is Algorithmic Trading?
Algorithmic trading has been known as “algo trading” and “blackbox trading” and includes trading systems that use complex mathematical formulas with the use of high-speed computers determining which trading strategies to use. A trading order is entered with a stock trading algorithm that will set off pre-programmed instructions that will account for a set of variables. This strategy is used mainly by investment banks, pension funds, mutual funds and other investor driven traders.
Algorithmic trading is an automatic stock trading strategy defined by a set of rules based on pre-set variables; timing, price and volume and takes the emotional human impact out of the trades. A trader can follow a simple set of trade criteria, and using just two simple instructions; write a computer program that will automatically monitor the stock price. This monitoring will enable them to buy and sell orders when the criteria are met. The trader is then relieved from having to watch live prices and graphs. The trader is also relieved of having to manually place the orders as the system automatically places them.
The benefits of algo-trading:
* Avoid price changes of significant amounts with trades timed instantly and correctly.
* High chances of execution at desired levels.
* Trades are made at the time prices are best.
* Transaction costs are reduced.
* Multiple markets are simultaneously checked.
* Manual errors are reduced.
* The human factor, mistakes made by humans, is reduced.
High frequency trading
(HFT) capitalizes on placing a large number of trades at incredibly fast speeds and is the greatest portion of algo-trading. These transactions go across multiple markets and decision parameters. They are based on pre-programed instructions. Algo-trading (stock trading online) is performed in many forms of investment and trading strategies:
* Mid to long term investors or buy side firms.
* Short term traders and sell-side participants.
* Systematic traders
This method of trading gives a more systematic approach to trading than methods using human trader’s instinct.
Algorithmic strategies follow trends in channel breakouts, price level movements, moving averages and technical indicators. These strategies are the easiest to implement through algorithmic trading. This is due to them not involving predictions or price forecasts. They are implemented based on occurrence of traits. This strategy is called the Trend Following Strategy. These are some other common trading strategies used for algo-trading:
Arbitrage Opportunities:
This strategy involves buying dual stock for a lower price in one market and at the same time selling it for a higher price in different market. This creates a price differential as a profit or arbitrage. When an algorithmic is implemented to identify a price differential such as this, it allows for profitable opportunities in making trades.
Mathematical Model Based Strategies:
One proven mathematical strategy, the delta-neutral allows trading on a combination of options. Trades are placed to offset positive and negative deltas allowing the portfolio delta to maintain at zero.
Volume Weighted Average Price:
This strategy breaks up a large order and releases it in smaller determined chunks to the market by using volume profiles. The goal is to execute trades to enable a benefit of gain on an average price.
Index Fund Re-balancing:
These trades are initiated by algorithmic trading systems and are performed for a precise execution to gain the best prices.
Trading Range:
Another name for this is, Mean Reversion and is based on the idea the highs and lows of an asset are only temporary and they will revert back to their original value. Being able to identify these ranges using algorithm trading allows for trades to be placed when the asset price breaks in and out of the defined ranges.
Percentage of Volume:
This algorithm strategy continues to send partial orders until an order is filled. This is determined by a defined participation ratio set to the volume traded in the markets.
Implementation Shortfall:
This strategy’s goal is to minimize the execution cost of a trade. It trades off the real-time market saving costs of the trade and benefiting from the opportunity of delayed execution. This strategy allows for increased participation when the stock is favorable and decrease of it when the stock price moves unfavorably.
* Time Weighted Average Price: This strategy breaks up a large order and releases it into smaller chunks into the market by using an evenly divided time slot between a start time and end time. The goal is release the order close to the average price to minimize market impact.
* Sniffing Algorithms: These are algorithms that attempt to identify “happenings” on the other side. This enables market makers to identify large order opportunities and allow him to benefit by filling the orders at a higher price.
The practice of algorithms trading is not as simple and easy as it sounds. If you are able to generate an algo-generated trade there are other market participants who can do the same or better. Prices fluctuate in as little time as microseconds and if your trade gets executed, but the sell trade doesn’t as it can change by the time your order hits, you end up with an open position making your strategy worthless. There are other challenges and risks:
* Network connectivity errors
* Time lags between orders and execution
* System failure risks
* Imperfect algorithms
The more complicated the algorithms the more back-testing you need to perform before you put it into action.
Complex mathematical and statistical modeling on how your algorithm is going to perform is a vital role in finding out if it will be successful and beneficial for you. It is very exciting to have a computer make automatic trades for you in the hopes of reaching financial gains, however, it is critical to make sure the system is tested thoroughly and limits are set. Traders who use logic and reason should learn programming and know how to build their own systems. This will enable them to be confident the right strategies are being implemented. When a trader is cautious and thorough profitable opportunities do exist for algo-trading.