Editing Technical analysis (section)

==Systematic trading==
{{main|Systematic trading}}

===Neural networks===
Since the early 1990s when the first practically usable types emerged, [[artificial neural network]]s (ANNs) have rapidly grown in popularity. They are [[artificial intelligence]] adaptive software systems that have been inspired by how biological neural networks work. They are used because they can learn to detect complex patterns in data. In mathematical terms, they are universal [[Function approximation|function approximators]],<ref>K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks vol 2, 1989</ref><ref>K. Hornik, Multilayer feed-forward networks are universal approximators, Neural Networks, vol 2, 1989</ref> meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

As ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems.<ref>R. Lawrence. [http://people.ok.ubc.ca/rlawrenc/research/Papers/nn.pdf Using Neural Networks to Forecast Stock Market Prices]</ref><ref>B.Egeli et al. [http://www.hicbusiness.org/biz2003proceedings/Birgul%20Egeli.pdf Stock Market Prediction Using Artificial Neural Networks] {{Webarchive|url=https://web.archive.org/web/20070620024840/http://www.hicbusiness.org/biz2003proceedings/Birgul%20Egeli.pdf |date=20 June 2007 }}</ref><ref>M. Zekić. [http://oliver.efos.hr/nastavnici/mzekic/radovi/mzekic_varazdin98.pdf Neural Network Applications in Stock Market Predictions – A Methodology Analysis] {{Webarchive|url=https://web.archive.org/web/20120424231150/http://oliver.efos.hr/nastavnici/mzekic/radovi/mzekic_varazdin98.pdf |date=24 April 2012 }}</ref>

While the advanced mathematical nature of such adaptive systems has kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly [[neural network software]] has made the technology more accessible to traders.{{Citation needed|date=November 2023}}

===Backtesting/Hindcasting===
[[File:Hindcasting.jpeg|200px|thumb|right|Temporal representation of hindcasting<ref>Taken from p.145 of [https://archive.org/details/TECA2004 Yeates, L.B., ''Thought Experimentation: A Cognitive Approach'',  Graduate Diploma in Arts (By Research) dissertation, University of New South Wales, 2004.]</ref>]]
Systematic trading is most often employed after testing an investment strategy on historic data. This is known as [[Backtesting#Hindcast|backtesting]] (or [[Thought experiment#Hindcasting|hindcasting]]). Backtesting is most often performed for technical indicators combined with volatility but can be applied to most investment strategies (e.g. fundamental analysis). While traditional backtesting was done by hand, this was usually only performed on human-selected stocks, and was thus prone to prior knowledge in stock selection. With the advent of computers, backtesting can be performed on entire exchanges over decades of historic data in very short amounts of time.

The use of computers does have its drawbacks, being limited to algorithms that a computer can perform. Several trading strategies rely on human interpretation,<ref>{{harvp|Elder|1993|pp=54, 116–118}}</ref> and are unsuitable for computer processing.<ref>{{harvp|Elder|1993}}</ref> Only technical indicators which are entirely algorithmic can be programmed for computerized automated backtesting.