1

main.py

@@ -1,6 +1,22 @@
 from fastapi import FastAPI
+import requests
+from telegram import ChatAction
+import os
+from urllib.request import urlopen, Request
+from bs4 import BeautifulSoup
+import pandas as pd
+import json  # for graph plotting in website
 
-# from typing import Union
+# NLTK VADER for sentiment analysis
+import nltk
+
+nltk.downloader.download("vader_lexicon")
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+import subprocess
+import os
+
+import datetime
 
 app = FastAPI()
 
@@ -10,6 +26,96 @@ def read_root():
     return {"Hello": "World!!!!"}
 
 
-@app.get("/ticker/{item_id}")
-def read_item(item_id: str):
-    return {"item_id": item_id}
+def get_news(ticker):
+    url = finviz_url + ticker
+    req = Request(
+        url=url,
+        headers={
+            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0"
+        },
+    )
+    response = urlopen(req)
+    # Read the contents of the file into 'html'
+    html = BeautifulSoup(response)
+    # Find 'news-table' in the Soup and load it into 'news_table'
+    news_table = html.find(id="news-table")
+    return news_table
+
+
+# parse news into dataframe
+def parse_news(news_table):
+    parsed_news = []
+    today_string = datetime.datetime.today().strftime("%Y-%m-%d")
+
+    for x in news_table.findAll("tr"):
+        try:
+            # read the text from each tr tag into text
+            # get text from a only
+            text = x.a.get_text()
+            # splite text in the td tag into a list
+            date_scrape = x.td.text.split()
+            # if the length of 'date_scrape' is 1, load 'time' as the only element
+
+            if len(date_scrape) == 1:
+                time = date_scrape[0]
+
+            # else load 'date' as the 1st element and 'time' as the second
+            else:
+                date = date_scrape[0]
+                time = date_scrape[1]
+
+            # Append ticker, date, time and headline as a list to the 'parsed_news' list
+            parsed_news.append([date, time, text])
+        except:
+            pass
+
+        # Set column names
+        columns = ["date", "time", "headline"]
+        # Convert the parsed_news list into a DataFrame called 'parsed_and_scored_news'
+        parsed_news_df = pd.DataFrame(parsed_news, columns=columns)
+        # Create a pandas datetime object from the strings in 'date' and 'time' column
+        parsed_news_df["date"] = parsed_news_df["date"].replace("Today", today_string)
+        # parsed_news_df["datetime"] = pd.to_datetime(
+        #     parsed_news_df["date"] + " " + parsed_news_df["time"],
+        #     format="%Y-%m-%d %H:%M",
+        # )
+
+    return parsed_news_df
+
+
+def score_news(parsed_news_df):
+    # Instantiate the sentiment intensity analyzer
+    vader = SentimentIntensityAnalyzer()
+
+    # Iterate through the headlines and get the polarity scores using vader
+    scores = parsed_news_df["headline"].apply(vader.polarity_scores).tolist()
+
+    # Convert the 'scores' list of dicts into a DataFrame
+    scores_df = pd.DataFrame(scores)
+
+    # Join the DataFrames of the news and the list of dicts
+    parsed_and_scored_news = parsed_news_df.join(scores_df, rsuffix="_right")
+    # parsed_and_scored_news = parsed_and_scored_news.set_index("datetime")
+    parsed_and_scored_news = parsed_and_scored_news.drop(["date", "time"], axis=1)
+    parsed_and_scored_news = parsed_and_scored_news.rename(
+        columns={"compound": "sentiment_score"}
+    )
+    return parsed_and_scored_news
+
+
+# for extracting data from finviz
+finviz_url = "https://finviz.com/quote.ashx?t="
+
+
+def get_stock_data(ticker):
+    news_table = get_news(ticker)
+    parsed_news_df = parse_news(news_table)
+    parsed_and_scored_news = score_news(parsed_news_df)
+    return parsed_and_scored_news
+
+
+@app.get("/ticker/{ticker}")
+def read_item(ticker: str):
+    stock_data = get_stock_data(ticker)
+    result = stock_data.to_json(orient="columns")
+    return {"result": result}