Disclosures: All investments carry risk. Information presented is for educational purposes only and does not intend to make an offer or solicitation for the sale or purchase of any specific securities, investments, or investment strategies. Investments involve risk and are never guaranteed. Be sure to first consult with a qualified financial adviser and/or tax professional before implementing any strategy discussed herein. Past performance is not indicative of future performance. Important information relating to qplum and its registration with the Securities and Exchange Commission (SEC), and the National Futures Association (NFA) is available here and here.
Quantitative Portfolio Manager
Evolution in trading and machine learning
Data Augmentation in Deep Learning
How to generate hypothetical data for trading strategies ?
Stylized facts of time series of asset returns
Improving deep learning models for Finance
Future Testing Strategies
Ten Year Cycles of Innovations in Trading
Source : May 2017 Research, qplum
Evolution of Machine Learning
Tremendous Success of Deep Learning
Image
Text
Speech
Abundance of Data!
Source : Andrew Ng, Coursera Deep learning specialization
Data Augmentation for Deep Learning
Even more data!
Source : Bharat Raj, Data Augmentation | How to use Deep Learning when you have Limited Data
Much harder in Finance!
Generating Hypothetical Data for Trading
Step 1 : Generate 15-minute contiguous non-overlapping samples from high frequency tick-data
Step 2 : Compute Mean and Variance for daily returns as Daily_Mean and Daily_Variance
Step 3 : Transform High Frequency Data to have the mean as Daily_Mean and variance as Daily_Variance
Absence of Autocorrelations
Heavy Tails
Volatility Clustering
Leverage Effect
Cross Correlation vs Volatility
Autocorrelation of returns is low both in daily and high frequency data
Autocorrelation
Autocorrelation
Lag
Lag
Source : qplum Research
High frequency data exhibits divergence from normal similar to daily returns
The magnitude of the divergence, however, is much more pronounced for daily returns
Source : qplum Research
As opposed to returns, volatility of daily returns shows significant autocorrelation, which is retained in high frequency data as well
Autocorrelation
Autocorrelation
Lag
Lag
Source : qplum Research
...refers to the observed tendency of an asset's volatility to be negatively correlated with the asset's returns
Correlation of volatility with returns for US Total Market Index (VTI)
Leverage effect is also exhibited by high frequency data but to a smaller extent
Correlation
Source : qplum Research
Mean of 30-day rolling correlation of different assets with US Total Stock Market (VTI)
Standard Deviation is shown as error bars.
Correlation ( Mean and Variance )
Source : qplum Research
Correlation of 30-day rolling correlation of log returns of different assets vs US Total Stock Market against the volatility of US Total Stock Market.
Correlation
Source : qplum Research
Source : Arden Dertat, Applied Deep Learning
Source : Arden Dertat, Applied Deep Learning
Daily EOD Data
Training
3000 pts
Validation
1000 pts
Testing
1000 pts
Large High Frequency Data Corpus
( 32000 pts )
Training
(High Frequency + Daily)
Validation
Out of Sample
Evaluation
Source : qplum Research
Model complexity alone doesn't help much!
One Hidden Layer : 62 -> 3 -> 62
Two Hidden Layers : 62 -> 10 -> 3 -> 10 -> 62
Three Hidden Layers : 62 -> 20 -> 10 -> 3 -> 10 -> 20 -> 62
Four Hidden Layers : 62 -> 100 -> 20 -> 10 -> 3 -> 10 -> 20 -> 100 -> 62
Source : qplum Research
However....more data does!
Architecture Used : 62 -> 100 -> 20 -> 10 -> 3 -> 10 -> 20 -> 100 -> 62
Source : qplum Research
Scenario Analysis
Helps to be data-driven with more data!
Cross-Validating Hyper-parameters
Helps to be data-driven with more data!
" It is far better to forsee even without certainty than not to forsee at all " - Henri Poincare
Deep learning works best when you have lots of data
Adapting high frequency returns is one of the easiest ways of augmenting daily returns data
Hypothetical data can be used to stress test strategy performance
Hypothetical data can be used to develop better and more robust strategies
Disclosures: All investments carry risk. Information presented is for educational purposes only and does not intend to make an offer or solicitation for the sale or purchase of any specific securities, investments, or investment strategies. Investments involve risk and are never guaranteed. Be sure to first consult with a qualified financial adviser and/or tax professional before implementing any strategy discussed herein. Past performance is not indicative of future performance. Important information relating to qplum and its registration with the Securities and Exchange Commission (SEC), and the National Futures Association (NFA) is available here and here.
By Gaurav Chakravorty
Over the last decade deep learning has had tremendous success in pushing state of the art in numerous domains such as computer vision, natural language processing, machine translation and speech recognition. All of these domains are characterized by large quantities of data. In finance, however, even 20 years of end-of-day data is merely 5,000 points and any data-driven trading strategy is only as good as the data itself. In order to leverage deep learning research to the fullest, many progressive asset managers are experimenting with different approaches to generate and use hypothetical data so that the models can learn what to do in scenarios that the markets haven't seen yet. In this webinar, we will discuss use of synthetic/hypothetical data that can potentially solve this problem.
