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What is Algorithmic Trading - Automation for Forex Trading
Algorithmic trading relies on programming and computers to implement automated trading. Automated trading makes feasible the process of trading without human participation.
Algorithmic trading essentially involves using programming tools to execute various algorithmic trading strategies after analyses of historical data by the computer verify conditions for application of those trading strategies are met.
In other words, automated trading systems that employ algorithmic trading involve computer programs that initiate position entry and exit orders for deals they identify via data analyses based on various trading strategies and submit the orders for execution.
What is Algorithmic Trading
Most trading strategies can be implemented in algorithmic trading. The success of algorithmic trading in generating positive returns for traders depends on the ability of trading strategies they are based on to generate positive returns.
And while algorithmic trading strategies pass extensive tests on historical data to test their ability to generate positive returns, actual returns that are being achieved via automated trading are usually lower than such back-tests indicate.
VWAP Strategy
Volume-weighted average price (VWAP) is a widely used benchmark designed to reflect the average asset price where the asset sales prices are weighted by percentages of total sales volume (for the day) at that prices. Thus, the calculation uses intraday data only.
This measure helps traders evaluate the current price of an asset and determine whether it is relatively overpriced or underpriced compared to the volume-weighted average price for the day.
One volume-weighted average price strategy then is go long only when the price is below the VWAP and go short when the price is above VWAP. The idea is that benchmark-beating buyers are more likely to create support when the price is below VWAP, than when it is above it, and similarly are more likely to create resistance when the price is above VWAP, than when it is below it. This strategy would work well for days with relatively sideways price action.
Institutional investors, who often trade in large orders, use VWAP as a benchmark to determine the quality of executions.
TWAP Strategy
Time-Weighted Average Price (TWAP) is another algorithmic trading strategy based on weighted average price. TWAP is used to calculate the average price of an asset over a set period. TWAP is calculated by summing prices at multiple points across a set period and then dividing this total by the total number of price points:
TWAP = (TP1+ TP2… + TPn) / n,
where;
TP1 is the price at the first timepoint,
n is the total number of timepoints.
The main use of TWAP strategy is in executing bigger orders with the aim of avoiding excessive impact on the market price. The most common use of TWAP is for distributing big orders throughout the trading day.
For example let’s say a hedge fund needs to buy 200,000 shares of Goldman Sachs. Putting one such a big order would materially impact the market and the price most likely would start to raise. To avoid such a market impact, the investor can specify time period in TWAP Strategy over which the shares will be bought. It will slice evenly a big order into smaller ones and execute them over defined period.
Traders however are advised to not trade in such a predictable way – it can lead to situations where other traders or algorithms would identify such a strategy and start to “game” it. Adding some randomness to the strategy is suggested as a solution to the issue.
Arbitrage Trading Strategy
Arbitrage trading aims to profit from an asset mispricing (pricing inefficiency). It involves also profiting from the price difference between identical or related financial instruments.
In arbitrage trading, historical data research is employed to uncover a mispricing of one or multiple assets trading currently on markets. Once a historical asset price behavior indicates a relationship between assets, a deviation from such relationship can be exploited for profit opportunity.
For example, currency data research indicates that the AUDUSD pair has a positive correlation with the NZDUSD (New Zealand dollar and the US dollar) pair.
If one currency pair is rising, the other is also rising. Consider a divergence in the behavior of these positively correlated pairs. Specifically, let us say while AUDUSD was rising, NZDUSD was falling.
Data indicate this divergence in currencies’ behavior is rather an exception than a rule, which means that we can expect the pairs to converge again. That means we could expect either a decrease in AUDUSD, or an increase in NZDUSD or a combination of both.
As a consequence, the optimal strategy in this case is to open a Short position in AUDUSD and, at the same time, open a Long position in NZDUSD.
Trend Following Strategy
A trend following strategy involves capturing a significant move up or down in a financial asset. When prices tend to keep on going in the same direction, a trend following strategy aims to capture most of such moves.
There are many practical methods for identifying presence of a trend while there is no exact universal definition of what a trend is. For example, many define the trend in the stock market by using the 200-day moving average of the closing price. If the price is above the average, the trend is up, and vice versa.
Bollinger Channel breakout strategy is an example of a trend following strategy. A Bollinger Band channel is formed by adding a band of 2 standard deviations to a simple moving average. For stocks 200-day moving average is usually used. A long trade is entered on the open if the previous day’s close exceeded the top of the channel. A short trade is entered if the close is below the bottom band.
While it is relatively simple to devise a trend following trading system, traders testify that while they look easy on a back-test, trend following strategies have a low win ratio: most of trades end up as losses. On the positive side, trend following strategies frequently have more big winners than big losers.
Trend followers employ different time frames and many asset classes to diversify their trading portfolio in order to avoid big drawdowns. Having different strategies is important for a trend follower.
Iceberg Order Strategy
Iceberg orders are a type of limit order used by institutional traders to execute large-volume trades. They are also called reserve orders as a large order is split up, with only a piece of it getting displayed on the limit book, while a large undisplayed reserve is held back. As the smaller order is executed, another is pulled from the reserve and appears on the book.
Iceberg orders prevent unfavorable price movements in order execution by reducing the risk of revealing investors intent as they are carried out.
Scalping Strategy
Algorithmic trading makes feasible high frequency trading, and scalping is a particular type of high frequency trading where one strives to earn a few percentages in point (pips) by beating the bid/ask spread. It is a short-term intra-day strategy meaning that positions are closed before the end of the trading day or session.
Essentially, once a position is opened a scalping trading strategy aims to close it as soon as the price change exceeds the bid/ask spread by a few ticks.
In order to gain profit from small price changes within the shortest time frame possible, scalping strategy require large enough position sizes.
Machine Learning-based Strategies
Algorithmic trading is based on computer programs that execute algorithms to automate some or all elements of trading. Machine learning employs various algorithms that learn from data, build the model and generate trading profits with minimal drawdowns in back tests on historical data.
Machine learning is used in algorithmic trading for several specific tasks as mentioned below:
Pattern Recognition
Detecting chart pattern formations for subsequent successful trading is one of main tasks that machine learning is employed for. Machine learning can rapidly analyze huge amounts of historical data within seconds and implement trading strategies based on recognized patterns.
Sentiment Prediction
Machine learning is used also in forecasting price direction on the basis of different analyses including news headlines, media comments, and other platforms.
Pair Trading Strategy
Pairs trading strategy involves simultaneously buying and selling two highly correlated financial assets. When two highly correlated assets are trading at a price relationship that is out of their historical trading range, pair trading strategy can be implemented by buying the undervalued security while short-selling the overvalued security.
The essence of the strategy is betting on the convergence between the two assets when the spread in prices is high. PepsiCo and Coca Cola are an example of highly correlated assets pair: the three months correlation between PepsiCo and Coca is 0.85. So if PepsiCo and Coca Cola prices typically move together but then unexpectedly move away from each other, this may be a temporarily exploitable opportunity.
Pairs trading strategy would short the stock moving up and buy the one moving down. In this strategy, the bet is not on the overall direction of both stocks, but rather on the convergence of prices.
Advantages and Disadvantages of Algorithmic Trading
A major advantage of algorithmic trading is that it takes out emotions from trading process. Many traders know how much discipline it takes in actual trading to follow the rules one had decided to adhere to when designing the strategies to be implemented.
Designing algorithms for automated forex trading and using automated trading software make trading without interference of emotions possible: an algorithmic trading system ensures that all trades adhere to a predetermined set of rules.
It is important to realize however that while an algorithmic strategy may perform well in testing on historical data (known as back-test) it still may not work well in real trading. So there is no guarantee that an algorithmic trading system will actually generate a positive return over time.
