1
/
of
1
store-traders-com
Stocks & Commodities V. 33:08 (6062): Shannon Entropy by Stephen Massel
Stocks & Commodities V. 33:08 (6062): Shannon Entropy by Stephen Massel
Regular price
$2.95 USD
Regular price
Sale price
$2.95 USD
Quantity
Couldn't load pickup availability
Shannon Entropy by Stephen Massel
Here, we define Shannon entropy and show how you can apply it to your trading results and derive a measure of the uncertainty or predictability of your method.
There are many different trading systems and strategies that can be used to trade the markets, and these systems (including discretionary trading) all generate trade records that can be analyzed, whether generated from backtested results or live trading. There are also many techniques that can be used to analyze these trades (for more on this, see my March 2011 Stocks & Commodities article on mathematical expectation, What Can You Expect, Mathematically?).
Of course, you can review the profit & loss of these trade records as well as many other metrics, but is there a formal way to quantitatively measure the structure (nonrandomness) of the data and compare it to the purely random case in order to provide some insight into the predictability of the method? Welcome to the world of Shannon entropy!
Here, we define Shannon entropy and show how you can apply it to your trading results and derive a measure of the uncertainty or predictability of your method.
There are many different trading systems and strategies that can be used to trade the markets, and these systems (including discretionary trading) all generate trade records that can be analyzed, whether generated from backtested results or live trading. There are also many techniques that can be used to analyze these trades (for more on this, see my March 2011 Stocks & Commodities article on mathematical expectation, What Can You Expect, Mathematically?).
Of course, you can review the profit & loss of these trade records as well as many other metrics, but is there a formal way to quantitatively measure the structure (nonrandomness) of the data and compare it to the purely random case in order to provide some insight into the predictability of the method? Welcome to the world of Shannon entropy!
Share
