Machine learning & Time Series Analysis
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This document discusses machine learning techniques for time series analysis and financial applications. It introduces common machine learning models like neural networks, convolutional neural networks, and LSTM networks. It also covers important concepts like feature engineering, labeling financial data, model evaluation techniques like cross validation, and analyzing feature importance. The document provides an overview of machine learning fundamentals and challenges in applying these techniques to time series and trading applications.
![Labeling generation for financial price
• Triple barrier [Prado 2018]
• Continuous trading signals [Dash 2016]
• Trading Point decision [Chang 2009]
[Prado 2018] Advances in Financial Machine Learning
[Tsantekidis 2017] Using Deep Learning to Detect Price Change Indications in Financial Markets
[Dash 2016] A hybrid stock trading framework integrating technical analysis with machine learning techniques
[Chang 2009] Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-18-320.jpg)
![Triple barrier [Prado 2018]
price
time
1
0
-1
𝑝 𝑡 + 𝑤𝑝 𝑡
𝑝 𝑡 + 𝑤 + 𝜏1
𝑝 𝑡 + 𝑤 − 𝜏2
• Labels according to the first barrier touched out of three barriers
• horizontal barriers are defined by profit-taking and stop- loss limit
• 𝜏1 and 𝜏2 are dynamic according to estimated volatility](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-19-320.jpg)
![Continuous trading signals [Dash 2016]
time
price
𝑝 𝑡 + 𝑤𝑝 𝑡
𝑝𝑡,𝑡+𝑤
max
𝑝𝑡,𝑡+𝑤
min
𝑝𝑡,𝑡+𝑤
min
𝑝𝑡,𝑡+𝑤
max
0.5
1
0.5
0
• Using momentum of the stock price
• y(t)’s are continuous
• Provides more detailed information](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-20-320.jpg)
![Trading point decision [Chang 2009]
• Find the local minimum and maximum points
• Divide the time series into subsegments
• Threshold value d length of trend](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-21-320.jpg)
![Trading point decision [Chang 2009]
• Find the local minimum and maximum points
• Divide the time series into subsegments
• Threshold value d length of trend](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-22-320.jpg)
![Trading point decision [Chang 2009]
• Find the local minimum and maximum points
• Divide the time series into subsegments
• Threshold value d length of trend](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-23-320.jpg)
![Neural Network [McCulloch 1943]
• Built to model the human brain
• Designed to recognize patterns
• Interpret numeric data through a kind of machine perception
Human neuron structure Single neuron model
y1= g (w1x1+w2x2+
v
g(v)
1
w0
w1x1
x2
g
w2
y1
[McCulloch 1943] A Logical Calculus of Ideas Immanent in Nervous Activity](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-27-320.jpg)
![Neural network [McCulloch 1943]
Single node in neural network
g y1
1
w0
w1x1
x2
w2
v
g(v)](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-28-320.jpg)
![Neural network [McCulloch 1943]
Simplified expression
y1
1
w0
w1x1
x2
w2](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-29-320.jpg)
![Neural network [McCulloch 1943]
A layer contain multiple neurons
1
x1
x2
y2
y1
y3
y4](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-30-320.jpg)
![Long short term memory neural network(LSTM)
[Hochreiter 1997]
• Can process sequence of data
• LSTM deals with the exploding and vanishing gradient problems
Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780.
Neural
network
x y
Neural
network
x y
Neural
network
x y
Neural
network
x y
Neural
network
x y
t = 1
t = 2
t = 3
x
y
Input gate +
tanh
LST
M
Forget gate
Output gate](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-33-320.jpg)
![Long short term memory neural network(LSTM)
[Hochreiter 1997]
Train
2006 ~ 2014 2016 ~ 2019-3-1
Validate
2015
Backtest
Features
Scaled Technical Indicators
Asset
Data split
Labels
Fixed time horizon
Taiwan Capitalization
Weighted Stock Index loss
profit
epoch](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-34-320.jpg)
![Convolutional Neural Network [2012 Krizhevsky]
• Commonly applied to computer visual imagery
• Prevent overfitting
Convolutional Layer](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-35-320.jpg)
![Time series to Image conversion Approach
[Sezer 2018]
[Sezer 2018] Algorithmic FinancialTrading with Deep Convolutional Neural Networks:Time Series to Image Conversion Approach](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-36-320.jpg)
![Purged K-fold Cross Validation [Prado 2018]
Price
Price
Before Purging
After Purging](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-43-320.jpg)
![Feature Importance [Liaw 2002]
• Understand features contributed to the performance
• Add some features that strengthen the predictive power
• Opens up the proverbial black box
• How to deal with selection bias
• Evaluate features in multiple assets
• Using different tree base classifier/regressor
• Use random feature to distinguish powerful features](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-44-320.jpg)
![Mean decrease impurity (MDI)
• Adding up information gain for each features
• Simple and efficient to calculate
• inflate importance of continuous feature
Correlation Coefficient
0.94
Importance
indicator ID’s
Humidity
Rain Sunny day
Throw a coin
Rain Sunny day
Importance: technical indicators of TXF1
Importance: technical Indicators of random walk time series
[2013 Louppe] Understanding variable importances
in forests of randomized trees.
[2013 Louppe]](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-45-320.jpg)
![Permutation Importance
• Much more computationally expensive
• Results are more reliable
Color Weight Age
3.2 kg 2
4.2 kg 5
6.2 kg 4
baseline = model.score(X, y)
for each column in X
permutate column in X randomly
reduced_score = model.score(X, y)
recover the column
importance of the feature = baseline – reduced_score
indicator ID’s
Importance: technical indicators of TXF1
Importance: technical Indicators of random walk time series
-0.04
Correlation Coefficient
Importance
[2013 Louppe]
[2013 Louppe] Understanding variable importances in forests of randomized trees](https://image.slidesharecdn.com/finlab-190708070508/85/Machine-learning-Time-Series-Analysis-46-320.jpg)
Machine learning & Time Series Analysis
- 1. Solutions Machine learning Time Series Analysis Challenges Finlab CTO 韓承佑 2019 / 07 / 05
- 2. Outline • Introduction • Background • Motivation • Proposed Method • Conclusion
- 3. Human Evolution Agricultural revolution Industrial revolution i Information revolution 14000 years
- 4. Human vs Computer at Go 4300 years 200 years
- 6. Gartner Hype Cycle For emerging technologies 2018 Deep Learning
- 7. Gartner Hype Cycle For emerging technologies 2015 Machine Learning
- 8. 1948 Technical Indicators 1970 Algorithmic Trading 1980 Personal Computer 1990 High frequency trading History of Trading
- 9. • Mimics the ability to see and hear • Extract rules automatically from data • ML spots patterns in high dimensional data Machine Learning
- 10. ML algorithms in finance?
- 12. What is AI, ML, DL? AI (Artificial Intelligent) ML (Machine Learning) DL (Deep Learning) @ Dog or Cat? Categories Probability Dog 0.9 Cat 0.1
- 13. Supervised Machine Learning Color Weight Age Category 3.2 kg 2 cat 4.2 kg 5 cat 6.2 kg 4 dog features labels ML Model Training Testing Color Weight Age 3.2 kg 2 4.2 kg 5 6.2 kg 4 ML Model True Answer Prediction cat cat cat dog dog dog
- 14. Outline • Machine Learning Models • Training & Testing • Evaluation • Feature Engineering • Data Preprocessing
- 15. Feature Engineering & Data Preprocessing
- 16. Feature Source • Difficult to confirm data release date • Missing data is often backfilled • Consider multiple correction • Maybe useful to combine other data types Fundamental data • Trading book • market participant characteristic footprint • Massive amount of data generated in one day Market data Alternative data • news, trend, web, satellites… • Primary data source • Hard to process, difficult to confirm consistency
- 17. Challenging of Labeling the data Time t KD RSI MACD Category 1 -1 2 -1 … 0 t 1 features labels price time 1 0 -1 𝑝 𝑡 + 𝑤 + 𝜏 𝑝 𝑡 + 𝑤 − 𝜏 𝑝 𝑡 + 𝑤𝑝 𝑡 • A popular method in the literature • 𝜏 is a constant regardless of the volatility • Do not have stop-loss limits Fixed time horizon
- 18. Labeling generation for financial price • Triple barrier [Prado 2018] • Continuous trading signals [Dash 2016] • Trading Point decision [Chang 2009] [Prado 2018] Advances in Financial Machine Learning [Tsantekidis 2017] Using Deep Learning to Detect Price Change Indications in Financial Markets [Dash 2016] A hybrid stock trading framework integrating technical analysis with machine learning techniques [Chang 2009] Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction
- 19. Triple barrier [Prado 2018] price time 1 0 -1 𝑝 𝑡 + 𝑤𝑝 𝑡 𝑝 𝑡 + 𝑤 + 𝜏1 𝑝 𝑡 + 𝑤 − 𝜏2 • Labels according to the first barrier touched out of three barriers • horizontal barriers are defined by profit-taking and stop- loss limit • 𝜏1 and 𝜏2 are dynamic according to estimated volatility
- 20. Continuous trading signals [Dash 2016] time price 𝑝 𝑡 + 𝑤𝑝 𝑡 𝑝𝑡,𝑡+𝑤 max 𝑝𝑡,𝑡+𝑤 min 𝑝𝑡,𝑡+𝑤 min 𝑝𝑡,𝑡+𝑤 max 0.5 1 0.5 0 • Using momentum of the stock price • y(t)’s are continuous • Provides more detailed information
- 21. Trading point decision [Chang 2009] • Find the local minimum and maximum points • Divide the time series into subsegments • Threshold value d length of trend
- 22. Trading point decision [Chang 2009] • Find the local minimum and maximum points • Divide the time series into subsegments • Threshold value d length of trend
- 23. Trading point decision [Chang 2009] • Find the local minimum and maximum points • Divide the time series into subsegments • Threshold value d length of trend
- 24. Trading point decision • Find the local minimum and maximum points • Divide the time series into subsegments • Threshold value d length of trend
- 25. Trading point decision • Find the local minimum and maximum points • Divide the time series into subsegments • Threshold value d length of trend
- 27. Neural Network [McCulloch 1943] • Built to model the human brain • Designed to recognize patterns • Interpret numeric data through a kind of machine perception Human neuron structure Single neuron model y1= g (w1x1+w2x2+ v g(v) 1 w0 w1x1 x2 g w2 y1 [McCulloch 1943] A Logical Calculus of Ideas Immanent in Nervous Activity
- 28. Neural network [McCulloch 1943] Single node in neural network g y1 1 w0 w1x1 x2 w2 v g(v)
- 29. Neural network [McCulloch 1943] Simplified expression y1 1 w0 w1x1 x2 w2
- 30. Neural network [McCulloch 1943] A layer contain multiple neurons 1 x1 x2 y2 y1 y3 y4
- 31. Deep Neural network Multi-layer deep neural network 1 x1 x2 y2 y3 y1
- 32. Deep Neural Network Training Result 2018-1-1 2019-7-1 Train 2006 ~ 2014 2016 ~ 2019-3-1 Validate 2015 Backtest Features Scaled Technical Indicators Asset Data split Labels Fixed time horizon Taiwan Capitalization Weighted Stock Index benchmark backtest
- 33. Long short term memory neural network(LSTM) [Hochreiter 1997] • Can process sequence of data • LSTM deals with the exploding and vanishing gradient problems Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780. Neural network x y Neural network x y Neural network x y Neural network x y Neural network x y t = 1 t = 2 t = 3 x y Input gate + tanh LST M Forget gate Output gate
- 34. Long short term memory neural network(LSTM) [Hochreiter 1997] Train 2006 ~ 2014 2016 ~ 2019-3-1 Validate 2015 Backtest Features Scaled Technical Indicators Asset Data split Labels Fixed time horizon Taiwan Capitalization Weighted Stock Index loss profit epoch
- 35. Convolutional Neural Network [2012 Krizhevsky] • Commonly applied to computer visual imagery • Prevent overfitting Convolutional Layer
- 36. Time series to Image conversion Approach [Sezer 2018] [Sezer 2018] Algorithmic FinancialTrading with Deep Convolutional Neural Networks:Time Series to Image Conversion Approach
- 37. Which time series is random walk? 1 2 3 4 5 6
- 38. Generative Adversarial Networks (GAN) Historical Price data Real/Generated • The Generator is trained to generate data that looks like historical price • The Discriminator is trained to tell the difference between generated and real data https://github.com/nmharmon8/StockMarketGAN
- 39. StockMarketGAN Discriminator features is good for predict the direction of the price Historical Price data Fixed Weights (New features) Rise/Fall https://github.com/nmharmon8/StockMarketGAN
- 40. Backtest • Survivor bias, lookahead bias, training, transection cost, outlier • Finding the lottery tickets that won the last game • Machine learning overfitting • Solutions • Develop model for entire. asset or classes • Use Bootstrap aggregating • Record every backtest conducted • K-fold Cross validation
- 41. K-fold Cross Validation • Determine the generalization error of an ML algorithm • Prevent overfitting • Assume the training set and the testing set are IID
- 42. Drawback Train Test Train Price • Training set contains information that also appears in the testing set • Observations cannot be assumed to be drawn from an IID process • Multiple testing and selection bias
- 43. Purged K-fold Cross Validation [Prado 2018] Price Price Before Purging After Purging
- 44. Feature Importance [Liaw 2002] • Understand features contributed to the performance • Add some features that strengthen the predictive power • Opens up the proverbial black box • How to deal with selection bias • Evaluate features in multiple assets • Using different tree base classifier/regressor • Use random feature to distinguish powerful features
- 45. Mean decrease impurity (MDI) • Adding up information gain for each features • Simple and efficient to calculate • inflate importance of continuous feature Correlation Coefficient 0.94 Importance indicator ID’s Humidity Rain Sunny day Throw a coin Rain Sunny day Importance: technical indicators of TXF1 Importance: technical Indicators of random walk time series [2013 Louppe] Understanding variable importances in forests of randomized trees. [2013 Louppe]
- 46. Permutation Importance • Much more computationally expensive • Results are more reliable Color Weight Age 3.2 kg 2 4.2 kg 5 6.2 kg 4 baseline = model.score(X, y) for each column in X permutate column in X randomly reduced_score = model.score(X, y) recover the column importance of the feature = baseline – reduced_score indicator ID’s Importance: technical indicators of TXF1 Importance: technical Indicators of random walk time series -0.04 Correlation Coefficient Importance [2013 Louppe] [2013 Louppe] Understanding variable importances in forests of randomized trees
- 47. Conclusion • Feature engineering • Labeling • Fixed time horizon, triple barrier, Continuous signal • Multi-asset feature selection • MDI, MDA • Model • Neural network • LSTN • CNN • Cross validation, backtest • Realistic check
- 48. Future work Feature enumeration Stationarize features Feature selection Preprocessing Training Trading strategies development Reality check Multiple assets Strategies Strategy management Live trading
- 49. Q&A Facebook AI CourseFinlab Blog AI Diagnose
Editor's Notes
- #4: 好幾千年的進化， 解決生存問題 - 農業 解決勞動問題 - 工業革命 解決思考問題 - 資訊革命 人類是很懶的生物 懶得出外打獵 懶得勞動 懶得思考 人類思考了這麼久
- #5: 我們如今走到了這裡，200年前真空管發明到現在，電腦取得了巨大的進步
- #9: 要是…就好了 要是…很痛苦 要是…躺著賺
- #10: As emotional beings, subject to fears, hopes, and agendas, humans are not particularly good at making fact-based decisions, particularly when those decisions involve conflicts of interest. Millions of years of evolution (a genetic algorithm) have fine-tuned our ape brains to survive in a hostile 3-dimensional world where the laws of nature are static. Humans are slow learners, which puts us at a disadvantage in a fast-changing world like finance. 10 year is very different The first algorithm trading Technical Trading -> Quantitive trading -> high frequency trading -> machine learning trading