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Re: current capabilities of algoquant – questions

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#2174
haksunli
Keymaster

Hello ba201,

Thank you very much for your interest in AlgoQuant.

A few clarifications –

AlgoQuant is not an application but rather a programming library. AlgoQuant is a collection of data structures and algorithms in algorithmic or quantitative trading. So, in theory, the backtesting can be as fast as you make it. In reality, however, currently, AlgoQuant comes with a few simulators, each designed to simulate a different market situation.

We do try to make AlgoQuant run very fast. Because we rely on Monte Carlo simulation and bootstrapping to backtest and validate a trading strategy, we make AlgoQuant run in as many parallel cores as possible. This is a still working objective in progress.

Yes, as of now, AlgoQuant work with milliseconds. We will have to extend the library if to work with microseconds.

The AlgoQuant features and priorities are driven by the demand of the subscribers to the library. The short term modules scheduled to be released are:

1. Monte Carlo backtesting
2. Bootstrapping backtesting
3. Auto backtesting report generation
4. A downloader of Yahoo and Google financial data
5. Better parallel processing capability

I would like to emphasize that the selling point of AlgoQuant is not yet another backtester. Backtesting is never the reason why we create AlgoQuant. Rather, the advantage of AlgoQuant is to let user code up complex mathematical trading strategies relatively easier. It is those mathematical models that we are selling so you don’t need to reproduce them yourself. They are like the arsenal and tools in your warehouse. For upcoming releases, we have worked on the following math modules:

For mean-reversion trading,
1. Identifying small mean-reverting portfolios. Alexandre D’Aspremont. Quantitative Finance, Volume 11 Issue 3 2011.
2. Optimal Pairs Trading: A Stochastic Control Approach. Mudchanatongsuk, S., Primbs, J.A., Wong, W. Dept. of Manage. Sci. & Eng., Stanford Univ., Stanford, CA.

For portfolio allocation,
3. Mean–variance portfolio optimization when means and covariances are unknown. Tze Leung Lai, Haipeng Xing, Zehao Chen.

For stop-losing a strategy,
4. a change point detection algorithm