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Building Your Own  Automated Trade Strategy Todd Hanson Ph.D. www.livetradesignals.com
Agenda ,[object Object],[object Object],[object Object],[object Object]
Todd Hanson Ph.D. in the News
Interest in Automated Trading ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Automated Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Black Box Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Risks of Commoditization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Build Your Own
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Build Your Own
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Build Your Own
Algorithmic War ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ideas for New Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Automated Trade Signals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Automated Trade Signals  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
LTS Java Trader
[object Object],Trade Signals on the Go
Trader Tools
Full Help Files Built In
We will be continuing to host free seminars on Livetradesignals Product Suite. Please sign up at our website www.livetradesignals.com [email_address]

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Automated Trade Strategy by Todd Hanson PhD

Editor's Notes

  1. Algorithmic trading is generating massive interest. On the Buyside, the interest is due to the competitive advantage that can be gained through algorithmic trading strategies like VWAP, pairs trading, index arbitrage etc. You have an advantage over regular traders if you can devise or use an algorithm that beats the rest. And if you can develop that algorithm yourself then you can go direct market access and avoid the fees that your brokerage firm would charge for executing the trade for you with their black box. On the sellside, the interest is due to the buyside’s excitement about algorithmic trading. Sellside firms looking for prime brokerage contracts must offer algorithmic trading capabilities in order to increase their trading volumes and thus their revenues, and to attract and retain customers.
  2. However, the current way in which many users are accessing algorithmic trading is presenting some problems to their visions of competitive advantage, leveraging their skills and the cost advantages. Firstly, let us look at the problems associated with “black box” strategies – i.e. parameterisable algo strategies made available by brokers or vendors. Firstly, with these strategies, if everyone has access to the same strategies then this to a large extent cancels out the competitive advantage. These black boxes are closed systems – you are relying on the algorithm inside being right, with no knowledge of how it works. Not being able to go inside is limiting your ability to use your skills to influence the algorithm. And if you see that there is some kind of disadvantage with the algorithm then you are unable to do anything about it directly. Using black boxes through your broker is also expensive. And if you are using a third party vendor’s black box then it may be that you have to use their entire market access platform, which you might not want to do. Secondly, you could build your own trading strategies in house. This is what the big banks and quant funds have always done. But this approach has a number of disadvantages. Apart from being particularly unattractive to those buyside firms with little IT capability, for any firm building strategies using traditional development approaches (C++, Java, VB, Excel etc.) involves a long IT cycle – and by the time the strategy is delivered the opportunity in the market may have disappeared – so there is an opportunity cost here. Also, the integration and maintenance issues are sizeable – and if the relevant people leave it can be difficult to work out how the systems work.
  3. However, the current way in which many users are accessing algorithmic trading is presenting some problems to their visions of competitive advantage, leveraging their skills and the cost advantages. Firstly, let us look at the problems associated with “black box” strategies – i.e. parameterisable algo strategies made available by brokers or vendors. Firstly, with these strategies, if everyone has access to the same strategies then this to a large extent cancels out the competitive advantage. These black boxes are closed systems – you are relying on the algorithm inside being right, with no knowledge of how it works. Not being able to go inside is limiting your ability to use your skills to influence the algorithm. And if you see that there is some kind of disadvantage with the algorithm then you are unable to do anything about it directly. Using black boxes through your broker is also expensive. And if you are using a third party vendor’s black box then it may be that you have to use their entire market access platform, which you might not want to do. Secondly, you could build your own trading strategies in house. This is what the big banks and quant funds have always done. But this approach has a number of disadvantages. Apart from being particularly unattractive to those buyside firms with little IT capability, for any firm building strategies using traditional development approaches (C++, Java, VB, Excel etc.) involves a long IT cycle – and by the time the strategy is delivered the opportunity in the market may have disappeared – so there is an opportunity cost here. Also, the integration and maintenance issues are sizeable – and if the relevant people leave it can be difficult to work out how the systems work.
  4. However, the current way in which many users are accessing algorithmic trading is presenting some problems to their visions of competitive advantage, leveraging their skills and the cost advantages. Firstly, let us look at the problems associated with “black box” strategies – i.e. parameterisable algo strategies made available by brokers or vendors. Firstly, with these strategies, if everyone has access to the same strategies then this to a large extent cancels out the competitive advantage. These black boxes are closed systems – you are relying on the algorithm inside being right, with no knowledge of how it works. Not being able to go inside is limiting your ability to use your skills to influence the algorithm. And if you see that there is some kind of disadvantage with the algorithm then you are unable to do anything about it directly. Using black boxes through your broker is also expensive. And if you are using a third party vendor’s black box then it may be that you have to use their entire market access platform, which you might not want to do. Secondly, you could build your own trading strategies in house. This is what the big banks and quant funds have always done. But this approach has a number of disadvantages. Apart from being particularly unattractive to those buyside firms with little IT capability, for any firm building strategies using traditional development approaches (C++, Java, VB, Excel etc.) involves a long IT cycle – and by the time the strategy is delivered the opportunity in the market may have disappeared – so there is an opportunity cost here. Also, the integration and maintenance issues are sizeable – and if the relevant people leave it can be difficult to work out how the systems work.
  5. However, the current way in which many users are accessing algorithmic trading is presenting some problems to their visions of competitive advantage, leveraging their skills and the cost advantages. Firstly, let us look at the problems associated with “black box” strategies – i.e. parameterisable algo strategies made available by brokers or vendors. Firstly, with these strategies, if everyone has access to the same strategies then this to a large extent cancels out the competitive advantage. These black boxes are closed systems – you are relying on the algorithm inside being right, with no knowledge of how it works. Not being able to go inside is limiting your ability to use your skills to influence the algorithm. And if you see that there is some kind of disadvantage with the algorithm then you are unable to do anything about it directly. Using black boxes through your broker is also expensive. And if you are using a third party vendor’s black box then it may be that you have to use their entire market access platform, which you might not want to do. Secondly, you could build your own trading strategies in house. This is what the big banks and quant funds have always done. But this approach has a number of disadvantages. Apart from being particularly unattractive to those buyside firms with little IT capability, for any firm building strategies using traditional development approaches (C++, Java, VB, Excel etc.) involves a long IT cycle – and by the time the strategy is delivered the opportunity in the market may have disappeared – so there is an opportunity cost here. Also, the integration and maintenance issues are sizeable – and if the relevant people leave it can be difficult to work out how the systems work.
  6. e.g. front running VWAP
  7. Need to update – trying to do so
  8. I recommend we pull from slide show
  9. Need to review the help files to see if we keep or dump