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Paweł Skrzypek, AI Investments LTD
Complex
AI Forecasting Methods
for Investments Portfolio Optimization
• The Story - why AI for investing now
• General solution architecture
• Financial timeseries forecasting
• MCTS neural networks - portfolio optimization
• Summary
AGENDA
THE AI STORY
Alan Turing publishes “Computing
Machinery and Intelligence”.
Alan Turing creates his Turing Test to
determine whether or not a computer has
human-like intelligence.
The term AI was used later - in 1955 by
John McCarthy.
The birth of AI
Geoffrey Hinton coins the term deep
learning, to explain new algorithms that
empower computers to distinguish objects,
images and video.
Deep learning
A convolutional neural network designed by researchers at the University of Toronto
achieves an error rate of only 16% in the ImageNet Large Scale Visual Recognition
Challenge, a significant improvement over the 25% error rate achieved by the best
entry the year before.
Convolutional neural network
The side effect of so good efficiency in analysis and generating images and videos are
so called “deep fakes”, using AI for creating realistic images and videos.
Deep fake
The biggest success story of the year
was AlphaGo by DeepMind, a
Reinforcement Learning agent that
beats the world’s best Go
player Kie Je.
AlphaGo
Revolutionary reinforcement learning method which could achieve super human level
performance in GO, chess and shoggi without human knowledge.
AlphaZero
New, human attention based architecture
which achieves extraordinary results in
natural language translation.
Transformer
Never send human
to do AI machine job.
Science-fiction
It’s time to send AI
to do investing.
…became reality.
AI INVESTMENTS SOLUTION
MACD
moving averages
price actions
breakout
MA+Boillingers Band
trend following
RSI
stochastic
chaos theory
Algorithmic transaction systems
Based on the data, AI will learn both - the
method and patterns of the transaction
system.
AI transaction systems
• Algorithmic systems - the method and system parameters are
selected by human and therefore are deterministic
• AI – the system recognizes patterns, selects the method and
determines the parameters all by itself
AI vs algorithmic systems
Analyst - Portfolio manager - Trader
Analyst
Portfolio
manager
Trader
Financial time series
forecasting
Trading strategies
Portfolio optimization
Monte Carlo Tree
Search with neural
networks
Trade execution on
over 200 markets,
integration with
2 brokers
FINANCIAL TIME SERIES FORECASTING
• Time series - ordered in time list of values of given attribute
• Time series forecasting - forecasting of future, not known values of time
series
• Hybrid time series forecasting methods - methods of time series
forecasting based on combination of machine learning and statistical
methods
Time series - definitions
• Regression: linear, logistic, polnynomial
• ARMA, ARIMA and different variants
• ARCH/GARCH - and different variants
• Exponential smoothing - Holt-Winters
• Theta method
• Ensemble of methods
Review of fundamental statistical forecasting methods
• M Competition - most prestigious and scientifically backed competition in time
series forecasting
• Organised by University of Nicosia and prof. Spyros Makridakis
• First and second place was won by hybrid methods
In the latest edition, M4 Competition was won by hybrid methods - combination of
statistical and machine learning methods. Accuracy has been evaluated on 100 000
of different time series.
M4 Competition - breakthrough in forecasting
• Data preprocessing - Exponential Smoothing
• Neural networks: LSTM - residual, dilated, attentions
• Model’s ensembling
• Parameters of preprocessing per each series, shared models
Data preprocessing and neural network LSTM in one dynamic computational
graph. Parameters of Exponential Smoothing are trained with neural networks
weight together.
ES Hybrid Method - winning method from M4
Source: https://eng.uber.com/m4-forecasting-competition/
ES Hybrid Method - winning method from M4
ES Hybrid Method - practical usage
• Unique and innovative approach for forecasting
• Stochastic Learning Automata algorithm for forecasting
• Dynamically managed probabilistic distributions
• Model’s ensembling
Dynamically learnt probabilistic distributions.
Tsetlin machines
Tsetlin
machines
Tsetlin machines - usage
• Chaotic time series forecasting
• Radom reservoir of neurons
• Input/output layers weights are only trained
• Neurons are connected together - no layers
Being trained is only input/output layer based on the random reservoir.
Echo State Networks
Source: https://tex.stackexchange.com/questions/190914/drawing-an-echo-state-network
Echo State Networks
• Hybrid methods - ones of the most advanced class of forecasting methods
• For financial time series accuracy over 60% for long term
It very significant edge in investing.
Forecasting - summary
FUTURE PORTFOLIO OPTIMIZATION
MONTE CARLO TREE SEARCH WITH NEURAL NETWORKS
• Reinforcement learning - self-learning algorithms
• Managing the exposure for instruments
• Managing the risk exposure
• Total exposure and risk level controlled by man
Investing with AI tools is the future of financial markets.
Portfolio management and exposure
AI INVESTMENTS SOLUTION
HOW IT WORKS LIVE
Period: 2018.10 - 2019.06 - 34 weeks, Return in period: 32%
34 weeks live results
Paweł Skrzypek
CEO and CTO at AI Investments
pawel.skrzypek@aiinvestments.pl
www.aiinvestments.pl

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Complex AI forecasting methods for investments portfolio optimization - Pawel Skrzypek

  • 1. Paweł Skrzypek, AI Investments LTD Complex AI Forecasting Methods for Investments Portfolio Optimization
  • 2. • The Story - why AI for investing now • General solution architecture • Financial timeseries forecasting • MCTS neural networks - portfolio optimization • Summary AGENDA
  • 4. Alan Turing publishes “Computing Machinery and Intelligence”. Alan Turing creates his Turing Test to determine whether or not a computer has human-like intelligence. The term AI was used later - in 1955 by John McCarthy. The birth of AI
  • 5. Geoffrey Hinton coins the term deep learning, to explain new algorithms that empower computers to distinguish objects, images and video. Deep learning
  • 6. A convolutional neural network designed by researchers at the University of Toronto achieves an error rate of only 16% in the ImageNet Large Scale Visual Recognition Challenge, a significant improvement over the 25% error rate achieved by the best entry the year before. Convolutional neural network
  • 7. The side effect of so good efficiency in analysis and generating images and videos are so called “deep fakes”, using AI for creating realistic images and videos. Deep fake
  • 8. The biggest success story of the year was AlphaGo by DeepMind, a Reinforcement Learning agent that beats the world’s best Go player Kie Je. AlphaGo
  • 9. Revolutionary reinforcement learning method which could achieve super human level performance in GO, chess and shoggi without human knowledge. AlphaZero
  • 10. New, human attention based architecture which achieves extraordinary results in natural language translation. Transformer
  • 11. Never send human to do AI machine job. Science-fiction
  • 12. It’s time to send AI to do investing. …became reality.
  • 14. MACD moving averages price actions breakout MA+Boillingers Band trend following RSI stochastic chaos theory Algorithmic transaction systems
  • 15. Based on the data, AI will learn both - the method and patterns of the transaction system. AI transaction systems
  • 16. • Algorithmic systems - the method and system parameters are selected by human and therefore are deterministic • AI – the system recognizes patterns, selects the method and determines the parameters all by itself AI vs algorithmic systems
  • 17. Analyst - Portfolio manager - Trader Analyst Portfolio manager Trader Financial time series forecasting Trading strategies Portfolio optimization Monte Carlo Tree Search with neural networks Trade execution on over 200 markets, integration with 2 brokers
  • 18. FINANCIAL TIME SERIES FORECASTING
  • 19. • Time series - ordered in time list of values of given attribute • Time series forecasting - forecasting of future, not known values of time series • Hybrid time series forecasting methods - methods of time series forecasting based on combination of machine learning and statistical methods Time series - definitions
  • 20. • Regression: linear, logistic, polnynomial • ARMA, ARIMA and different variants • ARCH/GARCH - and different variants • Exponential smoothing - Holt-Winters • Theta method • Ensemble of methods Review of fundamental statistical forecasting methods
  • 21. • M Competition - most prestigious and scientifically backed competition in time series forecasting • Organised by University of Nicosia and prof. Spyros Makridakis • First and second place was won by hybrid methods In the latest edition, M4 Competition was won by hybrid methods - combination of statistical and machine learning methods. Accuracy has been evaluated on 100 000 of different time series. M4 Competition - breakthrough in forecasting
  • 22. • Data preprocessing - Exponential Smoothing • Neural networks: LSTM - residual, dilated, attentions • Model’s ensembling • Parameters of preprocessing per each series, shared models Data preprocessing and neural network LSTM in one dynamic computational graph. Parameters of Exponential Smoothing are trained with neural networks weight together. ES Hybrid Method - winning method from M4
  • 24. ES Hybrid Method - practical usage
  • 25. • Unique and innovative approach for forecasting • Stochastic Learning Automata algorithm for forecasting • Dynamically managed probabilistic distributions • Model’s ensembling Dynamically learnt probabilistic distributions. Tsetlin machines
  • 28. • Chaotic time series forecasting • Radom reservoir of neurons • Input/output layers weights are only trained • Neurons are connected together - no layers Being trained is only input/output layer based on the random reservoir. Echo State Networks
  • 30. • Hybrid methods - ones of the most advanced class of forecasting methods • For financial time series accuracy over 60% for long term It very significant edge in investing. Forecasting - summary
  • 31. FUTURE PORTFOLIO OPTIMIZATION MONTE CARLO TREE SEARCH WITH NEURAL NETWORKS
  • 32. • Reinforcement learning - self-learning algorithms • Managing the exposure for instruments • Managing the risk exposure • Total exposure and risk level controlled by man Investing with AI tools is the future of financial markets. Portfolio management and exposure
  • 33.
  • 35. Period: 2018.10 - 2019.06 - 34 weeks, Return in period: 32% 34 weeks live results
  • 36. Paweł Skrzypek CEO and CTO at AI Investments pawel.skrzypek@aiinvestments.pl www.aiinvestments.pl