Is your backtest accurate? Backtesting Challenges in Deep Learning (Stock Market)
artificial intelligence and machine learning
Deep learning deals with algorithms inspired by the biological structure and function of the brain to aid machine intelligence.
In addition, its “grandfather”, artificial intelligence, is the quality of intelligence introduced into machines. Machines themselves are stupid and humans inject some kind of intelligence to allow them to think independently. Its “father”, machine learning, is the process of inducing intelligence into machines without explicit programming. For clarity, explicit programming means programming done by the programmer, and implicit programming means programming done by the Java Virtual Machine, not the programmer.
An example of machine learning is a system that can predict wine prices by learning from past wine quality and other relevant variables. The system is not encoded with a comprehensive list of all possible rules. Instead, it learns independently based on patterns it identifies from past training data. Well, the “son” is deep learning. Machine learning can go awry in certain cases that are easy for humans. For example, images, audio, and some unstructured data types have poor performance.
Ideas exist to mimic the biological processes of the human brain in solving this problem.
People can create systems consisting of billions of neurons connected to learn new things. In other words, deep learning is the field of machine learning and artificial intelligence, which deals with algorithms inspired by the human brain to assist machines with intelligence without explicit programming. Examples of using Deep His Learning in everyday life include virtual assistants like Siri, Tesla’s self-driving cars, and Netflix’s product recommendations. Deep learning is now making inroads into virtually every industry, including healthcare detecting cancer, aviation optimizing fleets, and banking and financial services detecting fraud.
Henry Vandyke Carter and another writer – Henry Gray (1918) human anatomy(See “Books” section below) Bartleby.com: Gray’s Anatomy,
Backtesting Deep Learning in the Stock Market
In the field of financial analysis using stock market data, backtesting is a key tool for achieving explainability during real-world adoption. It refers to retrospectively assessing the viability of a model using historical data, and is based on the intuitive idea that strategies that have worked well in the past are likely to work well in the future. . Machine learning backtesting typically splits the data into a training set and a test set during modeling. The goal of this process is to determine accuracy and evaluate performance. However, in financial market modeling, performance is measured by model profitability or model volatility.
Machine learning for decision making raises concerns about statistical bias and lack of due process. This is related to selection biases that can affect research results in the financial field. In the context of deep learning in the stock market, backtesting involves building models that simulate trading strategies using historical data. Consider model performance. It also helps prevent selection bias by discarding inappropriate models and strategies. Moreover, to backtest properly, you need to test enough unbiased historical data from different samples.
Deep learning backtesting challenges in the stock market
The first challenge of backtesting in deep learning is the availability of historical market data. Critical to conducting stock market analysis is the availability of constantly updated historical data, but such data is not readily available. Paywalls often restrict access to such data, complicating its use in academic research. Agencies such as Wharton Research Data Services work with academic institutions to provide access to these types of data, but your subscription level determines the degree of access. The data remains broadly inaccessible to more financial institutions, with either inconsistent published market data or paying a premium.
JayHenry – Homebrew
A second challenge is access to supplemental data. Access to related data types is closely related to the previous issue and can be used to improve the performance of modeling tasks involving financial data. These data include basic data, such as a company’s annual report, and alternative data, such as financial profits that appear in news articles. Distinguishing between these types of data is essential, as the sources are usually different than those responsible for market data. There are many types of potential supplemental data, and work remains to reach the ready availability of such data.
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A third challenge is the long-term investment horizon. Some of the studies reviewed consider relatively short investment horizons of a few days to a few months. Many stock market investments are associated with portfolios that span decades, making it attractive to buy and hold growth investments. Growth investments expect above-average returns to young listed companies and expect significant future growth. Had we been able to identify growth investment opportunities early on, we would have had higher than average returns.
Such patterns can be discovered using supplemental data. Modeling similar past growth investments as part of your investment strategy may help you identify new investments that can generate significant returns over the long term.
For example, in a generative approach, inverse probabilities are estimated and combined with prior probabilities using Bayes’ law.
Explanation of Bayesian statistics for beginners!
The last one is a financial deep learning framework. Many popular machine learning and deep learning frameworks have improved state-of-the-art capabilities. These frameworks come up frequently in academic and industrial research, but implementations generally cater to financial considerations. We have not seen any real attempt to extend the existing framework with improvements based on these expert works. The stock market machine learning problem involves stepwise learning with time series data.
Some machine learning frameworks dedicated to learning research have tools and considerations for concept drift and upfront evaluation built into the framework. The lack of such financial machine learning frameworks means that individual research teams have to implement ideas without trying to integrate them into open source frameworks.
Moreover, an accessible framework focused on deep learning research using financial data would enable the promotion of such ideas and allow research in this area to adhere more closely to established industry practices. to It also enables researchers to provide specific implementations to improve state-of-the-art technology.
Charles H Martin, PhD and CEO of Calculation Consulting, one of our favorite deep learning minds, said:
“The unique challenge of financial research is that unlike other industry problems such as recommender systems, search relevance, and ad click prediction, in finance past performance does not reflect future results.
For this reason, special care must be taken not to overfit historical data.
For that, try applying the open-source weightwatcher tool to detect if your layers show signs of overfitting. ”
I recommend checking out Dr. Martin’s site: https://weightwatcher.ai
Is your backtest accurate? Backtesting Challenges in Deep Learning (Stock Market)
Is your backtest accurate? Backtesting Challenges in Deep Learning (Stock Market)
Written by Louise Lee
Is your backtest accurate? Backtesting Challenges in Deep Learning (Stock Market)
artificial intelligence and machine learning
