I was speaking with a friend of mine about what techniques are used for quantitative investment management, and he told me that, when assuming active positions on the market, even in high-frequency trading, the most valuable knowledge is about time series analysis, data mining and machine learning. Now, apart from time series analysis, I don't know well the other two aforementioned fields. Can anybody suggest references for getting better in those areas?


3 Answers 3


The classic text for machine learning is 'The Elements of Statistical Learning' by Tibshirani et al. I believe the term "data mining" is often used synonymously with "machine learning".


I second Tibshirani's book. There is an another edition you can download free on internet : http://www-bcf.usc.edu/~gareth/ISL/


Buy side techniques such as machine learning might be useful but i have not seen any trader applying this method. (see market wizards book series) Some tried but stopped using.

Larry Williams, and seasonal, cyclical traders are using patterns based on month of day, days of week etc... you can analyze them as well. There is also best know 6 months of year pattern (see Traders Almanac)

You must know how to backtest any trading/investing strategy before applying them even you read them in a book from a master trader. You have to analyse the strategy by yourself. Do not forget that the markets do not behave the same as they where before.

Some of the key metrics to analyse any daily pattern or trading/investing strategy are:

  • No Trades (must be greater than 30 – pref. much more)
  • No. Trades/ Year/ Month (whatever time period is relevant)
  • % Long Trades/ % Short Trades (is there a bias?)
  • Win%/ Success Rate
  • Average R:R
  • Expectancy (Average profit per dollar risked) – Must be a positive number!
  • Avg. no days in trade (Carry costs/ duration exposed to risk)
  • Maximum R multiple drawdown measured
  • Min/ Max/ Avg. Pips Risked

(see http://blog.cmcmarkets.com.au/analysts/ric-spooner/backtesting-trading-strategies/)

To avoid any biases. You have to test your method by using different time periods such as insample outof sample etc... to validate its validity. blindly apply datamining, or machine learning will not guide you anywhere.


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