Game trading engine: Difference between revisions
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Client: Radmilo Racic, [[IMC (Netherlands)]] (Radmilo.racic@imc.nl) | |||
Data mining and statistical modeling is an essential part of generating successful models. There are various approaches to generate a statistical model, but verification of ideas are usually standardized to a couple of criteria. In this project, the aim is to create a back-testing engine that is capable of evaluating different models according to well-known success metrics such as P&L, and risk metrics like returns, alpha, beta, max drawdown and sharp ratio. The project involves efficient parallelization of experiments with parameter sweeps and visualization of the success of the models. Succinctly put, the input to this engine is a strategy and the output is a vector of success metrics mentioned above. | Data mining and statistical modeling is an essential part of generating successful models. There are various approaches to generate a statistical model, but verification of ideas are usually standardized to a couple of criteria. In this project, the aim is to create a back-testing engine that is capable of evaluating different models according to well-known success metrics such as P&L, and risk metrics like returns, alpha, beta, max drawdown and sharp ratio. The project involves efficient parallelization of experiments with parameter sweeps and visualization of the success of the models. Succinctly put, the input to this engine is a strategy and the output is a vector of success metrics mentioned above. | ||
Revision as of 08:01, 9 October 2013
Client: Radmilo Racic, IMC (Netherlands) (Radmilo.racic@imc.nl)
Data mining and statistical modeling is an essential part of generating successful models. There are various approaches to generate a statistical model, but verification of ideas are usually standardized to a couple of criteria. In this project, the aim is to create a back-testing engine that is capable of evaluating different models according to well-known success metrics such as P&L, and risk metrics like returns, alpha, beta, max drawdown and sharp ratio. The project involves efficient parallelization of experiments with parameter sweeps and visualization of the success of the models. Succinctly put, the input to this engine is a strategy and the output is a vector of success metrics mentioned above.
