Types of algorithmic trading strategies

Author: turkey-in On: 12.06.2017

The phrase holds true for Algorithmic Trading Strategies. The term Algorithmic trading strategies might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear.

In this article, I will be telling you about algorithmic trading strategies with some interesting examples. If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading. Let me start with a very simple trading strategy.

Those who are already into trading would know about S. A is Simple Moving Average. A can be calculated using any predefined and fixed number of days. An algorithmic trading strategy based on S. A can be simplified in these four simple steps:. This was just a simple example.

Even if it were, then be prepared for the thorns.

types of algorithmic trading strategies

In everyday trading, far more complex trading algorithms are used to generate algorithmic trading strategies. Algorithmic Trading Strategies Click To Tweet.

All the algorithmic trading strategies that are being used today can be classified broadly into the following categories:. Assuming that there is a particular trend in the market. As an algo trader, you are following that trend. Further to our assumption, the markets fall within the week.

Now, you can use stats to determine if this trend is going to continue. Or if it will change in the coming weeks. Accordingly, you will make your next move.

You have based your algorithmic trading strategy on the market trends which you determined by using statistics. If we assume that a pharma-corp is to be bought by another company, then the stock price of our corp could go up. This is triggered by the acquisition which is a corporate event. If you are planning to invest based on the pricing inefficiencies that may happen during a corporate event before or after , then you are using an event-driven strategy.

Bankruptcy, acquisition, merger, spin-offs etc could be the event that drives such kind of an investment strategy. When an arbitrage opportunity arises because of misquoting in prices, it can be very advantageous to algo trading strategy. Although such opportunities exist for a very short duration as the prices in the market get adjusted quickly. A market maker or liquidity provider is a company, or an individual, that quotes both a buy and a sell price in a financial instrument or commodity held in inventory, hoping to make a profit on the bid-offer spread, or turn.

Market making provides liquidity to securities which are not frequently traded on the stock exchange. The market maker can enhance the demand-supply equation of securities. Let me give you an example:. He will give you a bid-ask quote of Rs. The profit of Rs. When Martin takes a higher risk then the profit is also higher. Check it out after you finish reading this article.

Reading this article on Automated Trading with Interactive Brokers using Python will be very beneficial for you. You can read the article here. Now that I have introduced you to algorithmic trading strategies, I will be throwing some light on the strategy paradigms and modeling ideas pertaining to each strategy.

As I had mentioned earlier, the primary objective of Market making is to infuse liquidity in securities that are not traded on stock exchanges. In order to measure the liquidity, we take the bid-ask spread and trading volumes into consideration. The trading algorithms tend to profit from the bid-ask spread. I will be referring to our buddy, Martin, again in this section.

Martin being a market maker is a liquidity provider who can quote on both buy and sell side in a financial instrument hoping to profit from the bid-offer spread. Martin will accept the risk of holding the securities for which he has quoted the price for and once the order is received, he will often immediately sell from his own inventory.

He might seek an offsetting offer in seconds and vice versa. When it comes to illiquid securities, the spreads are usually higher and so are the profits. Martin will take a higher risk in this case. Several segments in the market lack investor interest due to lack of liquidity as they are unable to gain exit from several small- and mid-cap stocks at any given point in time. Market Makers like Martin are helpful as they are always ready to buy and sell at the price quoted by them.

In fact, much of high frequency trading HFT is passive market making. The strategies are present on both sides of the market often simultaneously competing with each other to provide liquidity to those who need.

This strategy is profitable as long as the model accurately predicts the future price variations. The bid-ask spread and trade volume can be modeled together to get the liquidity cost curve which is the fee paid by the liquidity taker.

If the liquidity taker only executes orders at the best bid and ask, the fee will be equal to the bid ask spread times the volume. When the traders go beyond best bid and ask taking more volume, the fee becomes a function of the volume as well. Trade volume is difficult to model as it depends on the liquidity takers execution strategy.

The objective should be to find a model for trade volumes that is consistent with price dynamics. Market making models are usually based on one of the two:. If Market making is the strategy that makes use of the bid-ask spread, Statistical Arbitrage seeks to profit from statistical mispricing of one or more assets based on the expected value of these assets.

A more academic way to explain statistical arbitrage is to spread the risk among thousand to million trades in a very short holding time to, expecting to gain profit from the law of large numbers.

Statistical Arbitrage Algorithms are based on mean reversion hypothesis, mostly as a pair. Pairs trading is one of the several strategies collectively referred to as Statistical Arbitrage Strategies.

In pairs trade strategy, stocks that exhibit historical co-movement in prices are paired using fundamental or market-based similarities. The strategy builds upon the notion that the relative prices in a market are in equilibrium, and that deviations from this equilibrium eventually will be corrected. When one stock outperforms the other, the outperformer is sold short and the other stock is bought long with the expectation that the short term diversion will end in convergence.

This often hedges market risk from adverse market movements i. However, the total market risk of a position depends on the amount of capital invested in each stock and the sensitivity of stocks to such risk. Momentum Strategies seek to profit from the continuance of existing trend by taking advantage of market swings. In this particular algo-trading strategy we will take short-term positions in stocks that are going up or down until they show signs of reversal.

It is counter-intuitive to almost all other well-known strategies. Value investing is generally based on long-term reversion to mean whereas momentum investing is based on the gap in time before mean reversion occurs. Momentum is chasing performance, but in a systematic way taking advantage of other performance chasers who are making emotional decisions. There are usually two explanations given for any strategy that has been proven to work historically, either the strategy is compensated for the extra risk that it takes or there are behavioral factors due to which premium exists.

There is a long list of behavioral biases and emotional mistakes that investors exhibit due to which momentum works. Momentum trading carries a higher degree of volatility than most other strategies and tries to capitalize on the market volatility. It is important to time the buys and sells correctly to avoid losses by using proper risk management techniques and stop losses.

Momentum investing requires proper monitoring and appropriate diversification to safeguard against such severe crashes. Firstly, you should know how to detect Price momentum or the trends.

As you are already into trading, you know that trends can be detected by following stocks and ETFs that have been continuously going up for days, weeks or even several months in a row. Similarly to spot a shorter trend, include a shorter term price change. If you remember, back in , the oil and energy sector was continuously ranked as one of the top sectors even while it was collapsing.

We can also look at earnings to understand the movements in stock prices. An earnings momentum strategy may profit from the under-reaction to information related to short-term earnings.

In Machine Learning based trading, algorithms are used to predict the range for very short term price movements at a certain confidence interval.

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The advantage of using Artificial Intelligence AI is that humans develop the initial software and the AI itself develops the model and improves it over time. ML based models on the other hand can analyze large amounts of data at high speed and improve themselves through such analysis.

An AI which includes techniques such as evolutionary computation which is inspired by genetics and deep learning might run across hundreds or even thousands of machines. It can create a large and random collection of digital stock traders and test their performance on historical data. These were some important strategy paradigms and modelling ideas.

types of algorithmic trading strategies

Next, we will go through the step by step procedure to build a trading strategy. From algo trading strategies to paradigms and modeling ideas, I come to that section of the article where I will tell you how to build a basic algorithmic trading strategy. That is the first question that must have come to your mind, I presume. The point is that you have already started by knowing the basics and paradigms of algorithmic trading strategies while reading this article. I will explain how an algorithmic trading strategy is built, step by step.

The concise description will give you an idea about the entire process. The first step is to decide the strategy paradigm. It can be Market Making, Arbitrage based, Alpha generating, Hedging or Execution based strategy.

For this particular instance, I will choose pair trading which is a statistical arbitrage strategy that is market neutral Beta neutral and generates alpha, i.

You can decide on the actual securities you want to trade based on market view or through visual correlation in the case of pair trading strategy. Establish if the strategy is statistically significant for the selected securities. For instance, in the case of pair trading, check for co-integration of the selected pairs. Execution strategy to a great extent decides how aggressive or passive your strategy is going to be.

types of algorithmic trading strategies

The choice between the probability of fill and Optimized execution in terms of slippage and timed executive is what this is if I have to put it that way. If you choose to quote, then you need to decide what are quoting for, this is how pair trading works. If you decide to quote for the less liquid security, slippage will be less but the trading volumes will come down liquid securities on the other hand increase the risk of slippage but trading volumes will be high.

Using stats to check causality is another way of arriving at a decision, i. This is where back-testing the strategy comes as an essential tool for estimation of the performance of the designed hypothesis based on historical data.

A strategy can be considered to be good if the backtest results and performance statistics back the hypothesis. Hence, it is important to choose historical data with a sufficient number of data points.

Ensure that you make provision for brokerage and slippage costs as well. This will get you more realistic results but you might still have to make some approximations while backtesting. For instance, while backtesting quoting strategies it is difficult to figure out when you get a fill.

So, the common practice is to assume that the positions get filled with the last traded price. So, you should go for tools which can handle such mammoth load of data. R is excellent for dealing with huge amounts of data and has a high computation power as well.

How to distinguish between different types of algorithmic trading - Quantitative Finance Stack Exchange

Thus, making it one of the better tools for backtesting. Also, R is open source and free of cost. We can use MATLAB as well but it comes with a licensing cost. No matter how confident you seem with your strategy or how successful it might turn out previously, you must go down and evaluate each and everything in detail.

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The entire process of Algorithmic trading strategies does not end here. What I have provided in this article is just the foot of an endless Everest.

In order to conquer this, you must be equipped with the right knowledge and mentored by the right guide. QuantInsti will help you conquer the Everest at the end. If you want to know more about algorithmic trading strategies then you can click here.

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Algorithmic Trading Strategies, Paradigms and Modelling Ideas. Algorithmic Trading Strategies, Paradigms and Modelling Ideas On September 14, By admin In Trading Strategies 0 Comment. A can be simplified in these four simple steps: Decoding the Black Box running Trading Systems Learn Algorithmic Trading: A Step by Step Guide Development of Cloud-Based Automated Trading System with… Why You Should Be Doing Algorithmic Trading? Leave a Reply Cancel reply Your email address will not be published.

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