SlideShare a Scribd company logo
1 of 11
Stock prices analysis and prediction using Deep
Learning
Dmitry Lukovkin, Petr Permyakov
Problem
• Total financial assets of households – USD 250 trillion
• USD 58.7 trillion are belong to High Net Worth Individuals
• USD 106 trillion expected by 2025
• 600 000 000 of private investors worldwide (165 mln in US only)
• Banking deposits rates are close to 0 => investors seek for profitable
yet low risk instruments and strategies
• Quantitative Analysis is not available to the ‘casual’ investor or too
complicated for him
2/11
Product: https://StocksNeural.net
Deep Learning-based stock prices predictions and trading
recommendations published daily on the Web
3/11
How does it work?
4/11
Load data
Preprocess
Train neural
networks
Backtest
Predict
Publish
How does it work?
5/11
Results
6/11
• Top 50 models:
• Average APR – 25.5%
• Average % of successful deals – 75%
• Average Sortino ratio – 4.67
• Ahead of S&P 500
• Tracking by the independent trader:
8.8% of return for the 7 weeks
• Maximal position opened by our client
according to our recommendations:
$500K
• It was profitable
Technology & Features
• Deep Learning-based models:
• Recurrent (RNN) and convolutional (ConvNet) neural networks
• Daily re-training and adaptation to the changing market
• Financial instruments covered
• S&P 500 constituents
• Currency pairs
• Commodity futures
Premium data feeds are being used
• Web-service
• Daily predictions and trade recommendations
• Screening by models accuracy and performance
7/11
Customer traction
• > 1000 registered users
• Mostly males, 25-44
• Avid investors
• Got covered in the business media – RBC.Money, Financial One
• Cooperation & collaboration with experienced individual traders and
institutions:
• Hedge funds/asset management firms – Russia, US, France
• Traders from Canada, Germany, US and Russia
• Energy company from TX, US
8/11
Business Model
• Subscription model for the individual investors
• Partnership with brokers:
• Lead generation
• Integrated trading – share of commissions
• Partnership with institutions:
• Hedge funds, asset management firms – share of management fee
• Tailored models for specific products and markets
9/11
Team
Dmitry Lukovkin
• More than 15 years of IT experience. Serial entrepreneur.
• Projects: DavaiSravnim, Berry.Travel, Za Rabotoy
• Project Management, Web-development, Data Science
• Focusing on machine learning problems during last 3 years. Has a long term
trading experience.
Petr Permyakov
• IT infrastructure specialist with over 9 years hands-on experience in
analysis,architecture design, development, and project management
• Has an experience as an IT Infrastructure Architect in the one of the leading
invest banks, providing and managing grid computing environment for core
business applications
10/11
AI and Deep Learning will
change the people’s lives.
Join us!
http://stocksneural.net
Dmitry Lukovkin
lukovkin@stocksneural.net
Skype: dmitry.lukovkin
T: +7 (903) 750 29 76

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StocksNeural.net - Stock prices analysis and prediction using Deep Learning

  • 1. Stock prices analysis and prediction using Deep Learning Dmitry Lukovkin, Petr Permyakov
  • 2. Problem • Total financial assets of households – USD 250 trillion • USD 58.7 trillion are belong to High Net Worth Individuals • USD 106 trillion expected by 2025 • 600 000 000 of private investors worldwide (165 mln in US only) • Banking deposits rates are close to 0 => investors seek for profitable yet low risk instruments and strategies • Quantitative Analysis is not available to the ‘casual’ investor or too complicated for him 2/11
  • 3. Product: https://StocksNeural.net Deep Learning-based stock prices predictions and trading recommendations published daily on the Web 3/11
  • 4. How does it work? 4/11 Load data Preprocess Train neural networks Backtest Predict Publish
  • 5. How does it work? 5/11
  • 6. Results 6/11 • Top 50 models: • Average APR – 25.5% • Average % of successful deals – 75% • Average Sortino ratio – 4.67 • Ahead of S&P 500 • Tracking by the independent trader: 8.8% of return for the 7 weeks • Maximal position opened by our client according to our recommendations: $500K • It was profitable
  • 7. Technology & Features • Deep Learning-based models: • Recurrent (RNN) and convolutional (ConvNet) neural networks • Daily re-training and adaptation to the changing market • Financial instruments covered • S&P 500 constituents • Currency pairs • Commodity futures Premium data feeds are being used • Web-service • Daily predictions and trade recommendations • Screening by models accuracy and performance 7/11
  • 8. Customer traction • > 1000 registered users • Mostly males, 25-44 • Avid investors • Got covered in the business media – RBC.Money, Financial One • Cooperation & collaboration with experienced individual traders and institutions: • Hedge funds/asset management firms – Russia, US, France • Traders from Canada, Germany, US and Russia • Energy company from TX, US 8/11
  • 9. Business Model • Subscription model for the individual investors • Partnership with brokers: • Lead generation • Integrated trading – share of commissions • Partnership with institutions: • Hedge funds, asset management firms – share of management fee • Tailored models for specific products and markets 9/11
  • 10. Team Dmitry Lukovkin • More than 15 years of IT experience. Serial entrepreneur. • Projects: DavaiSravnim, Berry.Travel, Za Rabotoy • Project Management, Web-development, Data Science • Focusing on machine learning problems during last 3 years. Has a long term trading experience. Petr Permyakov • IT infrastructure specialist with over 9 years hands-on experience in analysis,architecture design, development, and project management • Has an experience as an IT Infrastructure Architect in the one of the leading invest banks, providing and managing grid computing environment for core business applications 10/11
  • 11. AI and Deep Learning will change the people’s lives. Join us! http://stocksneural.net Dmitry Lukovkin lukovkin@stocksneural.net Skype: dmitry.lukovkin T: +7 (903) 750 29 76