reddit_api.py

import argparse
from prettytable import PrettyTable
from datetime import datetime, timedelta
import pandas as pd
from psaw import PushshiftAPI
import praw
from datetime import datetime
from pytz import timezone
from holidays import US as holidaysUS
from helper_funcs import *
import re
import finviz
import config_terminal as cfg

# -------------------------------------------------------------------------------------------------------------------
def due_diligence(l_args, s_ticker):
    parser = argparse.ArgumentParser(prog='red',
                                     description="""Print top stock's due diligence from other users. [Source: Reddit] """)

    parser.add_argument('-l', "--limit", action="store", dest="n_limit", type=check_positive, default=5,
                        help='limit of posts to retrieve.')
    parser.add_argument('-d', "--days", action="store", dest="n_days", type=check_positive, default=3,
                        help="number of prior days to look for.")
    parser.add_argument('-a', "--all", action="store_true", dest="b_all", default=False,
                        help="""search through all flairs (apart from Yolo and Meme), otherwise we focus on
                        specific flairs: DD, technical analysis, Catalyst, News, Advice, Chart """)

    try:
        (ns_parser, l_unknown_args) = parser.parse_known_args(l_args)

        if l_unknown_args:
            print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
            return

        praw_api = praw.Reddit(client_id=cfg.API_REDDIT_CLIENT_ID,
                                client_secret=cfg.API_REDDIT_CLIENT_SECRET,
                                username=cfg.API_REDDIT_USERNAME,
                                user_agent=cfg.API_REDDIT_USER_AGENT,
                                password=cfg.API_REDDIT_PASSWORD)

        psaw_api = PushshiftAPI()

        n_ts_after = int((datetime.today() - timedelta(days=ns_parser.n_days)).timestamp())
        l_flair_text = ['DD', 'technical analysis', 'Catalyst', 'News', 'Advice', 'Chart']
        l_sub_reddits = ['pennystocks', 'RobinHoodPennyStocks', 'Daytrading', 'StockMarket', 'stocks', 'investing', 'wallstreetbets']

        submissions = psaw_api.search_submissions(after=int(n_ts_after),
                                                  subreddit=l_sub_reddits,
                                                  q=s_ticker,
                                                  filter=['id'])
        d_submission = {}
        n_flair_posts_found = 0
        while True:
            submission = next(submissions, None)
            if submission:
                # Get more information about post using PRAW api
                submission = praw_api.submission(id=submission.id)

                # Ensure that the post hasn't been removed in the meanwhile
                if not submission.removed_by_category:

                    # Either just filter out Yolo, and Meme flairs, or focus on DD, based on b_DD flag
                    if (submission.link_flair_text in l_flair_text,
                        submission.link_flair_text not in ['Yolo', 'Meme'])[ns_parser.b_all]:

                            # Refactor data
                            s_datetime = datetime.utcfromtimestamp(submission.created_utc).strftime("%d/%m/%Y %H:%M:%S")
                            s_link = f"https://www.reddit.com{submission.permalink}"
                            s_all_awards = ""
                            for award in submission.all_awardings:
                                s_all_awards += f"{award['count']} {award['name']}\n"
                            s_all_awards = s_all_awards[:-2]

                            # Create dictionary with data to construct dataframe allows to save data
                            d_submission[submission.id] = {
                                                            'created_utc': s_datetime,
                                                            'subreddit': submission.subreddit,
                                                            'link_flair_text': submission.link_flair_text,
                                                            'title':submission.title,
                                                            'score': submission.score,
                                                            'link': s_link,
                                                            'num_comments': submission.num_comments,
                                                            'upvote_ratio': submission.upvote_ratio,
                                                            'awards': s_all_awards
                                                        }

                            # Print post data collected so far
                            print(f"{s_datetime} - {submission.title}")
                            print(f"{s_link}")
                            t_post = PrettyTable(['Subreddit', 'Flair', 'Score', '# Comments', 'Upvote %', "Awards"])
                            t_post.add_row([submission.subreddit, submission.link_flair_text, submission.score,
                                            submission.num_comments,f"{round(100*submission.upvote_ratio)}%", s_all_awards])
                            print(t_post)
                            print("\n")

                            # If needed, submission.comments could give us the top comments

                            # Increment count of valid posts found
                            n_flair_posts_found += 1

                # Check if number of wanted posts found has been reached
                if n_flair_posts_found > ns_parser.n_limit-1:
                    break

            # Check if search_submissions didn't get anymore posts
            else:
                break

        print(f"{('No more posts with specified requirements found.', '')[n_flair_posts_found > ns_parser.n_limit-1]}")
        # Create df with found data. Useful for saving all info in excel file.
        #df_submissions = pd.DataFrame.from_dict(d_submission, orient='index', columns=list(d_submission[next(iter(d_submission.keys()))].keys()))
        #df_submissions.sort_values(by=['created_utc'], inplace=True, ascending=True)

    except:
        print("")