1. Oluwafikayo Sanni Presenting: O.P Akomolafe, Oluwafikayo Sanni
Cryptocurrency Price Prediction Using Long Short Term Memory Modeling and Social Media Sentiment
O.P Akomolafe, Oluwafikayo Sanni
Presenting:
Oluwafikayo Sanni
3rd Biennial Conference on TOKI
(Transition From Observation To Knowledge To Intelligence)
15th to 16th August 2019
New Engineering Lecture Theatre, University of Lagos

2. Introduction
Cryptocurrencies are digital assets designed to work as mediums of exchange which use cryptography to
Secure their transactions
Control the creation of additional units
Verify the transfer of assets.
Cryptocurrencies work using a decentralized/distributed ledger system called a blockchain

3. Introduction
There currently exists more than 1800 cryptocurrencies of which the most popular three are
Bitcoin
Ripple
Ethereum
Each and every token has a value attached to it. This value is in terms of fiat currency and exchange rates for other tokens
A distributed global market exists where you can trade cryptocurrencies for fiat values assigned or other cryptocurrencies
This market is decentralized and spans multiple platforms. It exists in a state of constant change

4. Literature Review TOPIC AUTHOR & YEAR CONTRIBUTION LIMITATIONS
Cryptocurrency Price Prediction Using News and Social Media Sentiment
Lamon, Nielsen & Redondo
2017
Used supervised learning algorithm for text-based sentiment analysis.
Daily news and social media data were labeled with actual price changes one day in the future for each coin, rather than on positive or negative sentiment.
Simple binary classifiers were built. Some of which had up to a 75.8% accuracy predicting price increases.
Some also had as low has 16.1% accuracy prediciting price reductions
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis
Colianni et al 2015
Reported up 90% accuracy in predicting price fluctuations using similar supervised learning algorithms on test data sets
Data was labeled using an online text sentiment API.
It could predict price increase or decreases but couldn’t tell by how much
Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence
Laskowski, Kim, & Ieee 2016
This work found a direct correlations between the number of mentions on twitter and the price of bitcoin
Limited scope to only bitcoin and dogecoin prices. It only found correlations between mentions of twitter and prices of bitcoin and dogecoin
Classification Based Financial Markets Prediction using Deep Neural Networks
Dixon, Klabjan, & Bang 2017
Properly describes how to apply DNNs to Algorithmic Trading Financial Time Series Data
Cryptocurrency price prediction using news and social media sentiment
Lamon etal 2015
Uses multiple models and tries to perform price prediction on cryptocurrencies using social media sentiment and historical prices.
“Sentiment” is evaluated as magnitude of price change rather than actual sentiment polarity of string.

5. Research Motivation
Predicting cryptocurrency prices and market directions is not a new problem, however existing solutions in literature generally have
Wide margins of errors in prediction
Conservative loss functions and auto-regressive models,
A general inability to scale to predict prices and market directions for multiple tokens while keeping performance relatively stable.
This work aims to provide an optimized solution that provides low margins of errors in predictions and scalable performance for multiple crypto-currencies

6. Our Approach Social Media Sentiment Extraction and Analysis
Search Keyword with Date De-limiter
Generate Average Daily Sentiment
Time Series Daily Sentiment Data
Twitter API
Sentiment Polarity Detection with TextBlob
Regular Expression Filtering and Data Cleaning
Daily Time Series Tweets

7. Our Approach Model Flow

8. Our Approach ~Data Collected:
Social Media sentiment Data and Historical price data were collected for 3 tokens, Bitcoin, Ethereum & Ripple
311,810 tweets over a period of 1280 days between June and December This included
128,000 tweets for bitcoin,
77,084 tweets for ethereum and
106,726 tweets for ripple
Prediction features from our Data include Date, Open, High,Low, Close, Volume, MarketCap, Daily Sentiment and Tweet Num

9. Our Approach ~ Training Hyperparameters:
We used a linear activation function, a dropout of 0.25 and an adam optimizer, 40 neurons are used and our choice of loss function was the mean square error
~ Training the LSTM
Training the LSTM was carried over 25 randomizations, each randomization reduced the loss function over 50 epochs. We used random seeds between 775 & 800. Weights were initialized with the simple weight guessing algorithm for each randomization

10. Our Approach ~ Penalizing Conservative Models
We automatically excluded all randomizations of the models whose loss function (MSE) could not be reduced below This was an arbitrary number chosen by the us
~ Averaging the Target Function
After training the 25 random initializations of the LSTM and weeding out the models with loss functions that fell above our arbitrary threshold. Our we averaged the loss functions obtained from all the initializations to get our target function, we used on our test set.

11. Result Summary TOKEN 1% ACC 2% ACC 5% ACC 10% ACC 20% ACC 30% ACC
INC MOE
DEC MOE
INC ACC
DEC ACC
Bitcoin (BTC)
15.94%
33.75%
65.94%
87.81%
98.12%
99.69%
3.72%
6.04%
26.56%
26.25%
Ripple (XRP)
9.68%
20.63%
54.06%
85.63%
97.19
5.72%
5.71%
18.44%
27.19%
Ethereum (ETH)
18.4%
33.44%
69.69%
88.75%
99.06%
100%
4.44%
4.78%
28.75%

12. Result Comparison
The Table Below Shows Optimal Results in Previous Works
Token
INC ACC
DEC ACC
INC MOE
DEC MOE
Model
Bitcoin (BTC)
43.19%
61.9%
42.24%
65.24%
Logistic Regression
Ethereum (ETH)
75.8%
16.1%
48.23%
9.74%
Bernoulli Naive Baye’s

13. Result Comparison, Bitcoin Accuracy

14. Result Comparison, Ethereum Accuracy

15. Result Comparison, Bitcoin Prediction; Margin of Error

16. Result Comparison, Ethereum Prediction; Margin of Error

17. Conclusion
In predicting absolute number of price reductions and increments, our model performed very poorly when compared the best of the existing models.
Our Model however possess a comparative advantage of having a higher accuracy when it comes to predicting the margins of these fluctuations.
Our model also has a relatively uniform performance for all three tokens. Meaning its behaviour could be scalable for predicting other tokens with proper training

18. References
Dixon, M., Klabjan, D., & Bang, J. H. (2017). Classification-based financial markets prediction using deep neural networks. Algorithmic Finance, 6(3–4), 67–77.
Jiang, Z., & Liang, J. (2016). Cryptocurrency Portfolio Management with Deep Reinforcement Learning. arXiv. Retrieved from
Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–
Laskowski, M., Kim, H. M., & Ieee. (2016). Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence. Proceedings of 2016 Ieee 17th International Conference on Information Reuse and Integration (Ieee Iri), 448–

19. References
Xing, F. Z., Cambria, E., & Welsch, R. E. (2017). Natural language based financial forecasting: a survey. Artificial Intelligence Review, pp. 1–25.
Evita Stenqvtst, Jacob Lnn Predicting Bitcoin price fluctuation with Twitter sentiment analysis
Kareem Hegazy and Samuel Mumford. Comparitive Automated Bitcoin Trading Strategies. CS229 Project,
Dixon, Klabjan, & Bang. Classification Based Financial Markets Prediction using Deep Neural Networks. alg=1&pos=8
Lamon, Nielsen & Redondo, Cryptocurrency Price Prediction Using News and Social Media Sentiment (2015)