Using deep learning to trade
practical lessons for technologists
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Why use Deep Learning?
The problem
Assets managed globally: $164 trillion
Fees charged : more than $1 trillion a year
Yet, we are no closer to solving the problem
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Towards a science of investing
In future, we should be investing with a trustworthy tool and not experts.
The tool should look at every aspect of the data.
The tool should be affordable and efficient.
The tool should know what we have learned already."
- Benjamin Graham
... and it should keep learning
The answer is obvious ... Deep Learning
How do we use DL at qplum?
Kinds of DL that we found useful and blind alleys that we have gone down
A better relative value trade
A lot more unsupervised learning
A better global macro trade
Understanding the yield curve
.. the way humans do
Technical aspects of DL at qplum
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Technical and operational aspects
Supervised / unsupervised
Number of layers
What sort of data
Key bottlenecks in training and inference
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Software hardware and deplopyment
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Keras / Tensorflow / Caffe 2
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GPUs / CPUs - how many?
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Instance type and Devops
Business drivers for DL in Trading:
Why now?
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1987, 1997, 2007, 2017?
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Why now? - Lots of data
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High Frequency trading and systematic trading, in general, has led to a lot of data. We are generating more data in one day now than we were in the entire decade of 1990s.
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The traditional quant approach does not spend as much time in discarding noise.
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Why now? - Hardware and Software optimized for DL
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GPUs and customized hardware that allows us to solve problems in hours that would have taken weeks a year or two ago.
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Software like Tensorflow/PyTorch and MapReduce make all of this cheap enough for small companies to innovate with.
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Why now? - ML in social sciences
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Trading is a social science and until recently all machine learning was focused on pure sciences.
Deep Learning is perfect for social sciences.
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Why now? - ML is better than traders
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A shift in power from star traders to complex systems.
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Five years ago, no serious money manager would let us touch their money with DL
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Why now? - Because of us
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Availability of talented engineers in DevOps, Data Infrastructure and Machine Learning who can make it happen, who want to make inroads into this last bastion of inequality, who want to stop people from selling crap to investors.
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How is it different from the traditional quant approach?
The next ten years - Deep Learning
Why is Deep Learning better than ML?
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The traditional quant approach
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Hire lots of quants.
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They all think of trading strategies.
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They backtest them
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The firm invests in the strategies that have the best returns.
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Problem: Too much data at every step
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This requires hiring a lot of quants
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They will then make millions and billions of features.
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Challenge then is to pick the needle in a haystack of trading strategies, with very little data.
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Investing is a science, a utility, not a game or competition, but a genuinely inclusive process
Email: contact@qplum.co Ph: 1-888-QPLUM 4U
| qplum LLC is a registered investment adviser. 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, unless otherwise stated, are not 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. |