During a recent discussion about investments and savings for the future with a teammate, we turned our attention to cryptocurrency, the stock market, and time series data. We both shared concerns about the consistency and assurance of cryptocurrencies, and my teammate had two outstanding questions related to Bitcoin price prediction:
How to forecast/predict the future price of the commodity?
How to get the public opinion of the cryptocurrency/Stock?
This discussion leads to this blog, and I going to answer these questions. Here I’m considering the most expensive and powerful cryptocurrency, “Bitcoin” for the forecast/prediction. Before digging into the core part, we need to have some intro on cryptocurrencies and Bitcoin.
Cryptocurrency
A cryptocurrency is a type of digital or virtual money that uses cryptography to protect it from counterfeiting or double spending. Based on blockchain technology, which is a distributed record maintained by a dispersed network of computers, many cryptocurrencies are decentralized networks. The fact that cryptocurrencies are typically not issued by any central authority makes them potentially immune to interference from or manipulation by governments.
Why Cryptocurrency
The crypto exchange market will enter a new development phase. According to statistics, global investment in blockchain projects has rapidly increased since 2017. The total amount of money invested has increased from 8 billion U.S dollars in 2016 to 400 billion U.S dollars in 2017.
Bitcoin
Bitcoin(BTC) is a peer-to-peer digital currency, or “cryptocurrency,” created in 2009 at the height of the global financial crisis. It is based on the ideas in a white paper by Satoshi Nakamoto, whose true identity remains unknown.
Unlike regular money, Bitcoin is operated by a decentralized authority, allowing its owners anonymous, untraceable, and non-taxable transactions.
How does it work?
A bitcoin transaction is a transfer of value between bitcoin wallets that are recorded on a shared public ledger in the cloud, called BlockChain.
What is bitcoin mining?
Bitcoin mining secures and verifies bitcoin transactions and is used to generate new bitcoins, and bitcoin miners are rewarded with bitcoins in exchange.
Why is bitcoin volatile?
Due to the lack of liquidity in the market and no central authority to influence supply and demand, Bitcoin is vulnerable to speculation and manipulation.
Done. Now it’s the time to explain the plan with Bitcoin
The goal is to take a step towards cryptocurrency by making a dashboard with vital insights about current price trends. As we know, the cryptocurrency market is growing gradually. I’m trying to give an idea to users who do not have that much deep knowledge of the price variation when to buy a Bitcoin and predict from people’s perspective. We will have a dashboard for showcasing the cryptocurrency miners to buy or sell their stock without losing money using a machine learning model that can forecast the price. We take people’s sentiment towards their coin in price variation by doing a sentimental analysis. Sentiment analysis takes humanly readable language and can rate it on various qualities, such as positive/negative sentiment and the objectivity/subjectivity of statements. Cryptocurrency prices are highly driven by speculation, so combining sentiment analysis with traditional historical price analysis improves prediction.
The first and foremost step for this is the ‘data’. Without proper data or content, we can not go further.
Read More : Crypto Exchange App Development: An Ultimate Guide for 2023
Bitcoin Price Prediction :
Data Collection
Bitcoin historical price data(Day wise price) is freely available from different sources. We collected the current data for Bitcoin from Yahoo finance. And also available in different python libraries as API to get the historical data.
We can fetch the data using the yfinance library in python. A command for that is given below.
| import yfinance as yf data = yf.download(‘BTC-USD’,start=’2014-09-17′,end = ‘2022-10-17’) |
Data Preparation
It starts with the date, close price, price open, price low, price high, adj. close, and volume. For more accurate predictions, day-wise data was selected. This would require more time and processing power.
Here, close price data is used for the machine learning model. Then selected data to undergo data quality and preprocessing steps such as missing data and outlier checks.
| import numpy as np import matplotlib.pyplot as plt plt.style.use(‘fivethirtyeight’) # Removing the missing fields df.dropna()# Checking the distribution of the data plt.figure() counts, bins, bars = plt.hist(df[‘Close’])  # Setting forecast duration future_days = 30 # create a new column for regressor df[str(future_days)+’Days_price_forecast’] = df[[‘Close’]].shift(-future_days) # shifting 30 rows up # New column name for shifted price df[[‘Close’,str(future_days)+’Days_price_forecast’]] # Preparing data for model training X = np.array(df[[‘Close’]]) X = X[:df.shape[0]-future_days] y = np.array(df[str(future_days)+’Days_price_forecast’]) y = y[:-future_days] |
Model Selection
Support Vector Regression(SVR)
The SVR is an extended technique of the “Support Vector Machine” (SVM). SVR is a supervised learning algorithm that is used to predict discrete values. Support Vector Regression uses the same principle as the SVMs. The basic idea behind SVR is to find the best-fit line. In SVR, the best-fit line is the hyperplane that has the maximum number of points.
SVR applies the Linear, Polynomial, and “Radial Basis Function” (RBF) kernels to predict the prices of the three major cryptocurrencies, Bitcoin, XRP, and Ethereum, using a bivariate time series method employing the cryptocurrency (daily-Closed Price) as the continuous dependent variable and the shifted price as the predictor variable.
Training the model
I split the data into train & test, train the model on train data and predict the model on test data & see how well the model performs by comparing actual & predicted/forecasted data.
| # Split the data from sklearn.model_selection import train_test_split # Spliting train and test data x_train, x_test, y_train, y_test = train_test_split(X,y, test_size=0.2) # model preparation svr_rbf = SVR(kernel=’rbf’, C=1e4, gamma=0.000000008) svr_rbf.fit(x_train,y_train) svr_accuracy = svr_rbf.score(x_test,y_test) print(“SVR Accuracy : “, svr_accuracy) |
The model gives an accuracy of 92.57 and now I’m going to visualize the forecasted data. Here it is.
Now it’s the turn of public opinion and social media impact on bitcoin. I wish to combine our forecasted output with the result from the sentiment analysis.
Sentiment Analysis
Sentiment Analysis is used as an indicator, using the volume of keywords found and their levels of positivity and negativity. There are many good sources to pull from, such as social media platforms and popular news sites. For the tests seen here, Twitter was used as the source.
The initial step for this process is to set up Twitter API. You can find the documentation here
After setting up the environment we can fetch the text data related to Bitcoin(#BitCoin) from Twitter and then proceed to identify the sentiment on bitcoin. For that, I’m using the re, TextBlob and tweepy libraries
import re from textblob import TextBlob import tweepy consumerKey = “Insert API key here”consumerSecret = “Insert API key secret here” accessToken = “Insert access token here” accessTokenSecret = “Insert access token secret here” # create the authentication object authenticate = tweepy.OAuthHandler(consumerKey, consumerSecret) # set the access token and access token secret authenticate.set_access_token(accessToken, accessTokenSecret) # create the api object api = tweepy.API(authenticate, wait_on_rate_limit=True) # Fetching tweets about bitcoin and filtering the retweets today = str(date.today()) # today’s date to string for in the input of tweepy cursor no_of_tweets = 500 search_term = ‘#bitcoin -filter:retweets’ # Creating a cursor object tweets = tweepy.Cursor(api.search, q = search_term, lang = ‘en’, since = today, tweet_mode = ‘extended’).items(no_of_tweets) # store the tweets in variable all_tweets = [tweet.full_text for tweet in tweets] # Creating a datframe to store the tweets df_twt = pd.DataFrame(all_tweets, columns=[‘Tweets’]) # creating a function clean the tweets def cleanTwt(twt): twt = re.sub(“#bitcoin”, ‘bitcoin’, twt) # removes the ‘#’ from bitcoin twt = re.sub(“#Bitcoin”, ‘Bitcoin’, twt) # removes the ‘#’ from Bitcoin twt = re.sub(‘#[A-Za-z0-9]+’, ”, twt) # removes any string with a ‘#’ twt = re.sub(‘\\n’, ”, twt) # removes the ‘\n’ string twt = re.sub(‘https:\/\/\S+’, ”, twt) # removes any hyperlinks return twt # Cleaning the tweets df_twt[‘Cleaned_Tweets’] = df_twt[‘Tweets’].apply(cleanTwt) # create a function to get subjectivity def getSubjectivity(twt): return TextBlob(twt).sentiment.subjectivity # create a function to get the polarity def getPolarity(twt): return TextBlob(twt).sentiment.polarity # create two new columns called “Subjectivity” & “Polarity” df_twt[‘Subjectivity’] = df_twt[‘Cleaned_Tweets’].apply(getSubjectivity) df_twt[‘Polarity’] = df_twt[‘Cleaned_Tweets’].apply(getPolarity) # create a function get the sentiment text def getSentiment(score): if score < 0: return “Negative” elif score == 0: return “Neutral” else: return “Positive” # Create a column to store the text sentiment df_twt[‘Sentiment’] = df_twt[‘Polarity’].apply(getSentiment) |
The sentimental analysis part is done. Now we can club the forecast output and sentimental analysis results along with the essential data related to the price of the bitcoin. Here is the output for that,
As a conclusion, the dashboard will give you an idea of the trend for Bitcoin price predictions from people’s perspectives. So the ones who do not have much idea about Bitcoin easily decide whether to buy or sell Bitcoin.
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Wrapping up
Accurate bitcoin price predictions are notoriously challenging, given the cryptocurrency’s extreme volatility. However, based on historical data and current market trends, many experts predict that bitcoin’s price will continue to rise, potentially reaching new all-time highs of $100,000 or more in the next few years. If you’re considering investing in bitcoin or other cryptocurrencies, it’s critical to do your research and understand the risks involved.
To ensure that your investment is secure and your data is protected, it’s essential to work with a reputable software product development company like NeoITO. Our team of expert developers can help you create customized software products that integrate with the bitcoin network and other blockchain-based platforms. Additionally, we offer security and data protection services to safeguard your investment.
Ready to explore the potential of bitcoin and blockchain technology for your business? Contact NeoITO today to schedule a consultation and learn more about our bitcoin price prediction services and other blockchain solutions!
FAQs
What factors affect Bitcoin’s price?
Several factors can impact Bitcoin’s price, including supply and demand, investor sentiment, media coverage, government regulations, and technological developments. As a result, Bitcoin’s price can be highly volatile and difficult to predict.
Can Bitcoin’s price reach $100,000?
Many experts believe that Bitcoin’s price could reach $100,000 or more in the next few years. However, it’s important to remember that Bitcoin’s price is notoriously volatile, and there are no guarantees. It’s crucial to do your research and understand the risks before investing.
How accurate are Bitcoin price predictions?
Bitcoin price predictions can vary widely, and accuracy is never guaranteed. However, many experts use a combination of fundamental analysis, technical analysis, and market sentiment to make their predictions.
How can I stay up-to-date on Bitcoin’s price movements?
There are several ways to stay informed about Bitcoin’s price movements, including following cryptocurrency news websites, subscribing to social media channels of cryptocurrency experts, and using real-time Bitcoin price tracker apps or tools.
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