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Machine learning regression algorithm on cryptocurrency stock price for the next 30 days.

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Cryptocurrency Investment Analysis and Modeling

This repository contains the entire analysis and modeling of cryptocurrency performance in the stock market, in the form of Jupyter notebooks.

For those couldn't load Plotly properly, I encourage you to check the notebook via nbviewer: https://nbviewer.jupyter.org/github/jieyima/Cryptocurrency_Investment_Analysis_and_Modeling/blob/master/Analysis_of_Cryptocurrency_Investments.ipynb

CryptocurrencyGeneral

About

This is UC Davis BAX452 Machine Learning Autodesk Group Project.

The objective of this project is to predict 30-days price of the most popular cryptocurrencies given their historical variations. To achieve this, several regression techniques are explored. In the end, Gradient Boosting Regressor yields to the best prediction result for Ripple, and ExtraTrees Regressor performs the best for the rest of cryptocurrencies (e.g., Bitcoin, Ethereum, Litecoin, etc.).

Cryptocurrencies mentioned in this Notebook have the following abbreviation:

  • Bitcoin (BTC)
  • Ethereum (ETH)
  • Litecoin (LTC)
  • Bitcoin Cash (BCH)
  • Ripple (XRP)
  • Monero (XMR)
  • Zcash (ZEC)

How to Navigate this Notebook

Here is the table of content for you to navigate:

1. Prepare Data Set

2. Data Quality Assessment

3. Exploratory Data Analysis and Feature Selection

4. Building Models - Predicting Price for Cryptocurrencies

5. Conclusion - Which Cryptocurrencies to Invest

6. Limitations

7. Future Work

8. Reference

Visualization Excerpts

1. Bitcoin Price Simulation (2017.1 - 2018.3)

This notebook used plotly to visually show bitcoin stock price during the period from Jan. 2017 to Mar. 2018. This plot illustrates the moving average, volume of transaction, opening price, Bollinger Bands, as well as whether the price increases (blue) or decreases (grey).

plotly

2. Heatmap

Below is a correlation heatmap between eight most popular cryptocurrencies. One of the interesting findings is that all of them have a highly positive correlation coefficience (≥0.75), indicating all the cryptocurrencies generally move in the same direction together along with the market.

Monero has the highest correlation coefficient of more than 0.85 with other cryptocurrencies. If the stocks of Bitcoin/Ethereum/Litcoin/Zcash pick up drastically, it is most likely that Monero will experience a similar boost as its fee-driven income picks up and positive earnings reports encourage investors.

Source: How does correlation affect the stock market? | Investopedia

heatmap

Investment Advice: The principal is that inclusion of negatively correlated assets in a portfolio allows individuals to reduce the overall risk while still allowing for a positive return. Therefore, to be able to build a diversified portfolio, investors are not advised to put all their money into cryptocurrencies market, rather they should diversify their assets into stock market, mutual funds and bank savings.

3. 30-days Bitcoin Price Prediction

bitcoin prediction

Investment Advice: Our 30-days forecast of bitcion price begins from Feb 25 2018, during which the predicted price is still volatile. Downward trending follows a short-term increasing momentum. Investors need to be cautious with the drastic price fluctuation as the price has already gone very high to a point of nearly $1,9000. A short-term put option and a long-term call option could be the potential preference of investors.