Bitcoin (BTC) and Ethereum (ETH) are two of the most popular cryptocurrencies in the world, with a combined market capitalization of over $1 trillion USD. The prices of BTC and ETH are known to be volatile and are affected by various factors such as market sentiment, news events, and government regulations and simply hype. However, one important factor that can have a significant impact on the prices of BTC and ETH is the price of the US dollar (USD). Understanding the relationship between the USD and BTC/ETH prices is crucial for investors and traders in the cryptocurrency market.
The aim of this project is to analyze the relationship between the price of the USD and the prices of BTC and ETH. We will collect historical data on the prices of BTC, ETH, and the USD and perform analysis to identify any correlations between these variables. We will use various data visualization techniques to present the results and provide insights into the relationship between the USD and BTC/ETH prices.
This project will have significant implications for investors and traders in the cryptocurrency market. By understanding how the price of the USD affects the prices of BTC and ETH, investors can make informed decisions about when to buy or sell these cryptocurrencies. Additionally, this project will provide insights into the broader cryptocurrency market and how it is influenced by global economic trends.
We plan to use regression analysis to analyze the data on the price of the US dollar and predict the prices of BTC and ETH. To do this, we will include features such as date, closing price, and opening price of the DXY - US dollar ticker on Yahoo Finance. These features will be used to identify any linear relationships between the USD price and the prices of BTC and ETH.
We will use historical price data for BTC, ETH, and the USD from a reliable cryptocurrency data providers such as Yahoo Finance. The dataset will include daily closing prices for BTC, ETH, and the USD for the period November 2017 to February 21 2023.
The data will be collected by downloading the selected data from the websites mentioned. We will use Python programming language and Pandas library to clean and process the data. We will analise and identify any relationships between the USD and BTC/ETH prices. We will also use data visualization tools such as Matplotlib and Seaborn to present the results in an intuitive and meaningful way. Finally, we will write a report detailing our findings and conclusions.
Lower you can see tables of the data that will be used.
import pandas as pd
df = pd.read_csv("eth-usd.csv")
df.head(10)
| Date | Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|---|
| 0 | 2017-11-09 | 308.644989 | 329.451996 | 307.056000 | 320.884003 | 320.884003 | 893249984 |
| 1 | 2017-11-10 | 320.670990 | 324.717987 | 294.541992 | 299.252991 | 299.252991 | 885985984 |
| 2 | 2017-11-11 | 298.585999 | 319.453003 | 298.191986 | 314.681000 | 314.681000 | 842300992 |
| 3 | 2017-11-12 | 314.690002 | 319.153015 | 298.513000 | 307.907990 | 307.907990 | 1613479936 |
| 4 | 2017-11-13 | 307.024994 | 328.415009 | 307.024994 | 316.716003 | 316.716003 | 1041889984 |
| 5 | 2017-11-14 | 316.763000 | 340.177002 | 316.763000 | 337.631012 | 337.631012 | 1069680000 |
| 6 | 2017-11-15 | 337.963989 | 340.911987 | 329.812988 | 333.356995 | 333.356995 | 722665984 |
| 7 | 2017-11-16 | 333.442993 | 336.158997 | 323.605988 | 330.924011 | 330.924011 | 797254016 |
| 8 | 2017-11-17 | 330.166992 | 334.963989 | 327.523010 | 332.394012 | 332.394012 | 621732992 |
| 9 | 2017-11-18 | 331.980011 | 349.615997 | 327.687012 | 347.612000 | 347.612000 | 649638976 |
df2 = pd.read_csv("btc-usd.csv")
df2.head(10)
| Date | Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|---|
| 0 | 2017-11-09 | 7446.830078 | 7446.830078 | 7101.520020 | 7143.580078 | 7143.580078 | 3226249984 |
| 1 | 2017-11-10 | 7173.729980 | 7312.000000 | 6436.870117 | 6618.140137 | 6618.140137 | 5208249856 |
| 2 | 2017-11-11 | 6618.609863 | 6873.149902 | 6204.220215 | 6357.600098 | 6357.600098 | 4908680192 |
| 3 | 2017-11-12 | 6295.450195 | 6625.049805 | 5519.009766 | 5950.069824 | 5950.069824 | 8957349888 |
| 4 | 2017-11-13 | 5938.250000 | 6811.189941 | 5844.290039 | 6559.490234 | 6559.490234 | 6263249920 |
| 5 | 2017-11-14 | 6561.479980 | 6764.979980 | 6461.750000 | 6635.750000 | 6635.750000 | 3197110016 |
| 6 | 2017-11-15 | 6634.759766 | 7342.250000 | 6634.759766 | 7315.540039 | 7315.540039 | 4200880128 |
| 7 | 2017-11-16 | 7323.240234 | 7967.379883 | 7176.580078 | 7871.689941 | 7871.689941 | 5123809792 |
| 8 | 2017-11-17 | 7853.569824 | 8004.589844 | 7561.089844 | 7708.990234 | 7708.990234 | 4651670016 |
| 9 | 2017-11-18 | 7697.209961 | 7884.990234 | 7463.439941 | 7790.149902 | 7790.149902 | 3667190016 |
df4 = pd.read_csv("DX-Y.NYB.csv")
df4.head(10)
| Date | Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|---|
| 0 | 2017-11-09 | 94.879997 | 94.959999 | 94.419998 | 94.440002 | 94.440002 | 0.0 |
| 1 | 2017-11-10 | 94.500000 | 94.650002 | 94.260002 | 94.389999 | 94.389999 | 0.0 |
| 2 | 2017-11-12 | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 2017-11-13 | 94.410004 | 94.639999 | 94.400002 | 94.489998 | 94.489998 | 0.0 |
| 4 | 2017-11-14 | 94.519997 | 94.540001 | 93.750000 | 93.830002 | 93.830002 | 0.0 |
| 5 | 2017-11-15 | 93.860001 | 93.910004 | 93.400002 | 93.809998 | 93.809998 | 0.0 |
| 6 | 2017-11-16 | 93.839996 | 94.000000 | 93.769997 | 93.930000 | 93.930000 | 0.0 |
| 7 | 2017-11-17 | 93.870003 | 93.930000 | 93.510002 | 93.660004 | 93.660004 | 0.0 |
| 8 | 2017-11-19 | NaN | NaN | NaN | NaN | NaN | NaN |
| 9 | 2017-11-20 | 93.709999 | 94.099998 | 93.580002 | 94.080002 | 94.080002 | 0.0 |
The limited availability of data from only 2017-11-09 may negatively impact the quality of our results. Nevertheless, we must work with the data we have. It's important to acknowledge that Ethereum is still a relatively new cryptocurrency, and this fact should be taken into account when making predictions and searching for correlations.