Machine Learning with Elixir and Phoenix

Slides for my ElixirConf EU 2017 talk on machine learning.

Transcript

1. def talk(struct) do
   struct
   |> title("Machine Learning with Elixir and Phoenix")
   |> speaker("Eric Weinstein")
   |> conf("ElixirConf EU")
   |> city("Barcelona, España")
   |> date("4 May 2017")
   end
   

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2. for Joshua
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3. Part 0: Hello!
   |###..........................................| 7%
   

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4. May the 4th Be With You
   |#####........................................| 11%
   

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5. About Me
   eric_weinstein =
   %{employer: "Hulu",
   github: "ericqweinstein",
   twitter: "ericqweinstein",
   website: "ericweinste.in"}
   30% off with EURORUBY30!
   |#######......................................| 16%
   

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6. Agenda
   • Machine learning and neural networks
   • A wee bit o’ finance
   • The Phoenix application
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7. Part 1: Machine Learning
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8. What is it?
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9. In a word:
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10. Generalization
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11. What’s Supervised Learning?
    Classification or regression, generalizing from
    labeled data to unlabeled data
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12. Our Data
    • Data from the S&P 500 from 2015 and 2016
    • 3 features, 252 instances
    • https://finance.yahoo.com/quote/SPY/history?
    p=SPY
    |##################...........................| 40%
    

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13. Features && Labels
    • Bollinger Bands®
    • Simple moving average (SMA)
    • Relative strength index (RSI)
    • 20-day return
    |####################.........................| 44%
    

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14. Features && Labels
    • Bollinger Bands®
    • Simple moving average (SMA)
    • Relative strength index (RSI)
    • 20-day return
    |#####################........................| 47%
    

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15. Neural Networks
    • Computational model based on biological brains
    • Can model any continuous function (with at
    least two layers), relatively fast to query,
    and can model interaction among inputs
    • However, they are also finicky (often requiring
    lots of tuning and are prone to overfitting),
    expensive to train, and black boxes
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16. |########################.....................| 53%
    Visualization
    Image credit: https://en.wikipedia.org/wiki/Artificial_neural_network
    

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17. Training Rules
    • Buy when predicted 20-day return is positive
    • Sell when predicted 20-day return is negative
    • Do nothing when predicted 20-day return is
    zero (or very close to it)
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18. Part 2: Finan¢e
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19. Caveat Emptor
    • I am not a financial professional
    • No guarantees or warrantees, express or
    implied, about the completeness or correctness
    of my experiments
    • If you repeat them, you can (and probably
    will) lose money at some point
    |#############################................| 64%
    

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20. Some Jargon
    S&P 500: American index comprising 500 large companies
    Adjusted close: closing price adjusted for historical
    events (such as stock splits)
    Stock split: Event where a company splits each
    existing share into multiple shares (e.g. 2:1)
    Lookahead bias: Bias introduced by using data that
    wouldn’t have yet been available in a simulation
    |##############################...............| 67%
    

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21. Our Indicators
    Simple moving average: Arithmetic moving average
    over a specific window
    Bollinger Bands®: N standard deviations above
    and below the SMA (oftentimes N = 2)
    RSI: Momentum indicator that measures speed and
    change of price movements
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22. Part 3:
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23. |###################################..........| 77%
    

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24. |####################################.........| 80%
    

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25. |#####################################........| 83%
    

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26. |#######################################......| 87%
    

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27. How’d We Do?
    • S&P 500 2015 return: -0.73%
    • Our return (training): 2.9%
    • S&P 500 2016 return: 9.84%
    • Our return (testing): 16.0%
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28. Summary
    • Machine learning is fun and practical
    • It’s super doable with Elixir + BEAM
    • Library support is what we really need
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29. Takeaways (TL;DPA)
    • Be the change you want to see in the
    community!
    • https://github.com/ericqweinstein/elixirconfeu
    • http://quantsoftware.gatech.edu
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30. IO.puts("Thanks!")
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