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Learn Algorithmic Trading

03/03/15

Algo trading can be part machine trading alongside human intervention, or it can be fully automated trading software – which requires only to be switched on and takes all trades autonomously.

 

 

 

 

    Advantages of Algorithmic Trading

 

 

 

Advantages of Algorithmic Trading?

In the previous example we highlighted a simple quant strategy. Most traders/investors will conclude the rules in our example have little merit and statistically irrelevant. This may be the case and this is exactly what we suspect; but now the rules have been coded we can apply this algorithmic trading strategy to numerous markets/stocks, this way we can now definitively measure if there is any positive expectation to the rules working.

Once you begin quant trading you can rule out statistically what works and what doesn’t. Sometimes even something crazy and simple may work – you will be surprised what you can find.

 

Don’t Have Time or Effort To Become a Quant

I am sure you can see how advantageous it can be to have fully automated trading software. However, if you do not have the time to create your own algorithmic trading system then you can check out strategies online:

 

So How Do We Begin Learning To Become a Quant Trading expert

First and foremost we need to learn how to code. This is probably the most daunting aspect for most daytraders or even ex floor traders. However, let me assure you that learning to code algorithmic trading strategies can be very simple and easy and most will pick it up within a couple of weeks of learning.

Modern trading software has made it very easy for us to code, something like Multicharts is geared with simple coding language and most technical functions are pre-built into the software. For the average quant you do not require anything more than top of the line retail software such as:

Multicharts

Tradestation

Ninjatrader

Esignal

There is no need to re-invent the wheel and start creating your own software (this takes too much time and effort and advanced computing and coding skills). Why take on the arduous task of creating your own software when there are companies out there working on this for you each and every day. Obviously for very complex trading strategies a custom built platform and software is required but I would say 9 out 10 daytraders needs are fully met by retail software.

In this next example we will begin our journey with Multicharts. We simply download the software, purchase some historical data and load the charts/markets you wish to trade. We start to practice our coding skills – mainly learning how to enter positions, then targets and stops. Software like Multicharts have numerous beginner examples and excellent software support.

Now once you get familiar with some of the coding practices you can then start to transfer some of your ideas into code. Anything and everything can be tested, all you need to do is break it down your rules into lines of code. Be creative and imaginative or logical and mathematical – the biggest advantage is using the computing power to do all the backtesting for you.

To begin with I would simply recommend learning to code quant trading strategies in something simple like Multicharts using their own basic programming language (very similar to JavaScript).

 

So How Do I Find an Algorithmic Trading Strategy?

Let me say the markets even though complex are actually very inefficient (anybody who believes in efficient market hypothesis is usually an academic and not a real daytrader). Anybody who says that it is impossible to beat the market is simply a disgruntled losing trader or misguided academic. Creating your algo trading strategies you can keep to the KISS rules (keep it simple stupid) or go for complexity. What I find is that begin every strategy with a simple concept, something you may have observed the market doing. If you spot a repeatable pattern simply try to code the logic and test it on a very basic level. See our blog articles:

Examples of algorithmic trading strategies can use the following logic:

Pattern recognition

  • Some daytraders feel that markets/stocks are fractal and there is some evidence to suggest this. Markets tend to work in waves/fractals or have other repeatable patterns. Daytraders which adhere to this style of trading often follow patterns such as: head and shoulder, triangles, rectangles, wedges etc.

Order flow analysis

  • By watching the trading tape and following volume opportunities can be spotted (usually scalpers use this method).

Indicators

  • Your basic moving averages, RSI etc. (what we find is that most indicators are completely useless and lag the market. Moving averages can be used as a complementary filter to an otherwise decent algorithmic trading strategy however.

Breakouts, mean reversion, trend following

  • These are the three main themes the market can be in. If you can spot the current market theme you can apply an algorithm to that exploit the market behaviour (hard part is spotting the current theme).

Volatility

  • Most daytraders know that most their money will come from periods of high volatility, this is because on a daytrading level this is when markets are at their most inefficient. Algo trading can perform superbly during these periods because they adhere to strict rules and do not suffer from fear and greed.

Technical Analysis

  • Comprising of numerous features, from patterns to indicators.

High frequency trading

  • The strategies include different forms of arbitrage – index arbitrage, volatility arbitrage, statistical arbitrage and merger arbitrage along with global macro, long/short equity, passive market making, and so on. HFT rely on the ultra fast speed of computer software, data access (NASDAQ TotalView-ITCH, NYSE OpenBook, etc) to important resources and connectivity with minimal latency (delay).

Support/Resistance

  • Can be categorised as technical analysis. Can be useful but very hard to code the logic of support and resistance into a quant trading system

Pairs trading

  • The pairs trade is market-neutral, meaning the direction of the overall market does not affect its win or loss.The goal is to match two trading vehicles that are highly correlated, trading one long and the other short when the pair's price ratio diverges "x" number of standard deviations - "x" is optimized using historical data. If the pair reverts to its mean trend, a profit is made on one or both of the positions.

 

Trade a Style Which Suits Your Personality

Every trader will inform you that ‘oh yeah Technical Analysis does not work, or indicators are junk’. I sometimes fall into this bracket too, it is all to easy to dismiss an idea just because one could not get it to work for them. But as you learn to code, then you can start to test the ideas yourself – you may conclude that indicators work for you or pattern recognition works. As we say ‘there are many ways to skin a cat’, I don’t think there is a right or wrong way to trade – as long as you are net profitable over your timeframe that is all that matters.

Trade a style which suits you and keep testing to find strategies which fit your hypothesis.

 

Books to Find Ideas From?

Let me start out by saying most book on algorithmic trading are useless to the average trader, some are needlessly mathematically complex and others are simply flawed and basic. The only books I found of any use were ones where the backtesting process was rigorously discussed. Books where correct data mining practices and how not to over fit data or curve fit have been useful. Moreover, books wish give you tools to evaluate your algorithmic trading strategy potential success and measure your edge have also prove to be useful.

Books which give you a good story about algorithmic trading or daytrading in general, also can be a fun read as well are:

Other books which you might find algo trading strategy ideas - you can code up the rules and test them out (bear in mind doing this might be good practice but also be aware that hardly any of systems in the books work when tested rigorously; some work for certain periods of time and then fail under different market conditions):

 

Advanced Quantitative books including complex mathematics and understanding behind quant trading:

Very important books on correct procedure on backtesting algo trading strategies:

 

Useful Quant Trading Blogs

The resources on the internet are numerous when it comes to algorithmic trading, the only problem is that some bloggers try to complicate the matter of automated trading too much. Keep in mind that if you are finding repeatable patterns in the markets then nothing will forecast the future with 100% certainty, you simply are playing the odds in your favour over ‘x’ amount of trades. Do not fall for the trap of a fool proof system or the Holy Grail of daytrading systems.

Blogs I have found useful:

 

If You Are a Daytrader or Thinking About Trading Then Get Started Now

I would advise nearly all daytraders or individuals thinking about daytrading to at least attempt to code some of their strategies. I think a lot of daytraders will say they can beat the returns of any algo trading system, hence they don't want or need to learn quant trading. I feel this is often because ultimately they are scared that their strategy does not show any edge and usually they end up trading on gut feeling (gambling). If you are serious about trading and investing then nearly any strategy can be coded and automated.

Computing power is such a huge advantage it would be silly not to use it. Not only can you test 100s of strategies, but when a quant trading strategy has been fully automated it can run live on numerous markets leaving you with free time to test and try out new strategies.

 

 

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