Aspiring Data Science

#books #trading

Пробежался по книжке Inside the Black Box. Хорошая.
Выписал некоторые определения.


As Dr. Simons puts it, “The advantage scientists bring into the game is less their mathematical or computational skills than their ability to think scientifically.”

Many successful traders subscribe to the old adage, “Cut losers and ride winners.” However, discretionary investors often find it very difficult to realize losses, whereas they are quick to realize gains. This is a welldocumented behavioral bias known as the disposition effect.

The systematic trader is able to make this “rational” decision at a time when there is no pressure, thereby obviating the need to exercise discipline at a time when most people would find it extraordinarily challenging.

A quant systematically applies an alpha-seeking investment strategy that was specified based on exhaustive research.

Our definition of alpha - which I stress is not conventional - is skill in timing the selection and/or sizing of portfolio holdings.

Alpha, the spelled-out version of the Greek letter α, generally is used as a way to quantify the skill of an investor or the return she delivers independently of the moves in the broader market. By conventional definition, alpha is the portion of the investor's return not due to the market benchmark, or, in other words, the value added (or lost) solely because of the manager. The portion of the return which can be attributed to market factors is then referred to as beta.

The software that a quant builds and uses to conduct this timing systematically is known as an alpha model, though there are many synonyms for this term: forecast, factor, alpha, model, strategy, estimator, or predictor. All successful alpha models are designed to have some “edge,” which allows them to anticipate the future with enough accuracy that, after allowing for being wrong at least sometimes and for the cost of trading, they can still make money. In a sense, of the various parts of a quant strategy, the alpha model is the optimist, focused on making money by predicting the future.

An important and not widely understood fact is that there are only a small number of trading strategies that exist for someone seeking alpha. But these basic strategies can be implemented in many ways, making it possible to create an incredible diversity of strategies from a limited set of core ideas. This distinction between the idea and how it is implemented is important to understand.

Most of what theory-driven quants do can be relatively easily fitted into one of eight classes of phenomena: trend, mean reversion, technical sentiment, value/yield, growth/sentiment, supply/demand, quality, and tactical/events.

www.tgoop.com/AspiringDataScience/2731

67 viewsAnatoly Alekseev, edited  Sep 29 at 03:30

#books #trading

Пробежался по книжке Inside the Black Box. Хорошая.
Выписал некоторые определения.


As Dr. Simons puts it, “The advantage scientists bring into the game is less their mathematical or computational skills than their ability to think scientifically.”

Many successful traders subscribe to the old adage, “Cut losers and ride winners.” However, discretionary investors often find it very difficult to realize losses, whereas they are quick to realize gains. This is a welldocumented behavioral bias known as the disposition effect.

The systematic trader is able to make this “rational” decision at a time when there is no pressure, thereby obviating the need to exercise discipline at a time when most people would find it extraordinarily challenging.

A quant systematically applies an alpha-seeking investment strategy that was specified based on exhaustive research.

Our definition of alpha - which I stress is not conventional - is skill in timing the selection and/or sizing of portfolio holdings.

Alpha, the spelled-out version of the Greek letter α, generally is used as a way to quantify the skill of an investor or the return she delivers independently of the moves in the broader market. By conventional definition, alpha is the portion of the investor's return not due to the market benchmark, or, in other words, the value added (or lost) solely because of the manager. The portion of the return which can be attributed to market factors is then referred to as beta.

The software that a quant builds and uses to conduct this timing systematically is known as an alpha model, though there are many synonyms for this term: forecast, factor, alpha, model, strategy, estimator, or predictor. All successful alpha models are designed to have some “edge,” which allows them to anticipate the future with enough accuracy that, after allowing for being wrong at least sometimes and for the cost of trading, they can still make money. In a sense, of the various parts of a quant strategy, the alpha model is the optimist, focused on making money by predicting the future.

An important and not widely understood fact is that there are only a small number of trading strategies that exist for someone seeking alpha. But these basic strategies can be implemented in many ways, making it possible to create an incredible diversity of strategies from a limited set of core ideas. This distinction between the idea and how it is implemented is important to understand.

Most of what theory-driven quants do can be relatively easily fitted into one of eight classes of phenomena: trend, mean reversion, technical sentiment, value/yield, growth/sentiment, supply/demand, quality, and tactical/events.

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