From fundamental ratios, technical indicators to news headlines and insider ... Extract stock sentiments from financial news headlines in FinViz website using Python ... An example of the news headlines section for Amazon (with ticker 'AMZN') ... to add the stock ticker at the end of this url 'https://finviz.com/quote.ashx?t=' to ...
Sentiments analysis of news has become one of the most robust ways of generating buy/sell signals for stocks in all major developed and major emerging markets. The idea is simple, a cumulative sentiments score of the news articles mentioning a companies name, brand, stock ticker, etc. will serve as a great indicator for the next days’ closing stock price.
This only works with stocks that have high trading volumes and active news coverage across major outlets. Generally speaking, constituent stocks of major market indices such as NASDAQ, Dow Jones, or S&P 500 will all satisfy these criteria.
In this article, we will discuss the steps necessary for building such a sentiments analysis pipeline for amazon.com stock.
You will have to select which portions of the page you want to extract. Typically, people want to extract author names, dates, titles, and full text of the news article.
Before we get into actually getting sentiments data, let us first get stock market data for Amazon stock price (AMZN). I like to use Alphavantage’s API to get stock market data; it’s free to use but you will have to generate an API key.
import requests import json from dateutil import parser import requests import json test_url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=AMZN&outputsize=full&apikey=' + API_KEY + '&datatype=csv' r = requests.get(url = test_url) print("Status Code: ", r.status_code) print("*"*20) print(r.headers) html_response = r.text with open("amazon_stock.csv", "w") as outfile: outfile.write(html_response) from dateutil import parser datetime_obj = lambda x: parser.parse(x) df2 = pd.read_csv("amazon_stock.csv", parse_dates=['timestamp'], date_parser=datetime_obj) #df2 = df2[(df2["timestamp"] >= start_date) & (df2["timestamp"] <= end_date)] df2.head(1) ## Output timestamp open high low close adjusted_close volume dividend_amount split_coefficient 0 2020-09-28 3148.85 3175.04 3117.1684 3174.05 3174.05 4224165 0.0 1.0
Next, we will plot this so that we can visually see the price movements. As you might already know, the stock has had a strong rally due to covid-19 pandemic.
import matplotlib.pyplot as plt import seaborn as sns top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4) top.plot(df2['timestamp'], df2['close'], label = 'Closing price') plt.title('Amazon Close Price') plt.legend(loc=2) bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4) bottom.bar(df2["timestamp"], df2["volume"]) plt.title('Amazon Daily Trading Volume') plt.gcf().set_size_inches(12,8) plt.subplots_adjust(hspace=0.75)
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