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876d1aab3e
* Finnhub and finbrain fns * Twitter menu (forex/ba adjusted) * Google menu * Reddit menu pt1 * Reddit commands * Stocktwits commands * Sentimentinvestor commands * Update crypto-dd * Review pt 1 * Hugo server * Review comments - get rid of all prints/errors in model...
268 lines
8.1 KiB
Python
268 lines
8.1 KiB
Python
"""Reddit View"""
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__docformat__ = "numpy"
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import os
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import warnings
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from datetime import datetime
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from typing import Dict
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import finviz
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import praw
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from prettytable import PrettyTable
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from tabulate import tabulate
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.common.behavioural_analysis import reddit_model
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from gamestonk_terminal.helper_funcs import export_data
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def print_and_record_reddit_post(
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submissions_dict: Dict, submission: praw.models.reddit.submission.Submission
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):
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"""Prints reddit submission
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Parameters
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----------
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submissions_dict : Dict
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Dictionary for storing reddit post information
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submission : praw.models.reddit.submission.Submission
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Submission to show
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"""
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# Refactor data
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s_datetime = datetime.utcfromtimestamp(submission.created_utc).strftime(
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"%d/%m/%Y %H:%M:%S"
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)
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s_link = f"https://old.reddit.com{submission.permalink}"
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s_all_awards = "".join(
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f"{award['count']} {award['name']}\n" for award in submission.all_awardings
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)
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s_all_awards = s_all_awards[:-2]
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# Create dictionary with data to construct dataframe allows to save data
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submissions_dict[submission.id] = {
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"created_utc": s_datetime,
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"subreddit": submission.subreddit,
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"link_flair_text": submission.link_flair_text,
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"title": submission.title,
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"score": submission.score,
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"link": s_link,
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"num_comments": submission.num_comments,
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"upvote_ratio": submission.upvote_ratio,
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"awards": s_all_awards,
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}
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# Print post data collected so far
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print(f"{s_datetime} - {submission.title}")
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print(f"{s_link}")
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t_post = PrettyTable(
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["Subreddit", "Flair", "Score", "# Comments", "Upvote %", "Awards"]
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)
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t_post.add_row(
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[
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submission.subreddit,
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submission.link_flair_text,
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submission.score,
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submission.num_comments,
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f"{round(100 * submission.upvote_ratio)}%",
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s_all_awards,
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]
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)
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print(t_post)
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print("\n")
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def display_watchlist(num: int):
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"""Print other users watchlist. [Source: Reddit]
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Parameters
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----------
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num: int
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Maximum number of submissions to look at
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"""
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subs, d_watchlist_tickers, n_flair_posts_found = reddit_model.get_watchlists(num)
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for sub in subs:
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print_and_record_reddit_post({}, sub)
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if n_flair_posts_found > 0:
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lt_watchlist_sorted = sorted(
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d_watchlist_tickers.items(), key=lambda item: item[1], reverse=True
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)
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s_watchlist_tickers = ""
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n_tickers = 0
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for t_ticker in lt_watchlist_sorted:
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try:
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# If try doesn't trigger exception, it means that this stock exists on finviz
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# thus we can print it.
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finviz.get_stock(t_ticker[0])
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if int(t_ticker[1]) > 1:
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s_watchlist_tickers += f"{t_ticker[1]} {t_ticker[0]}, "
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n_tickers += 1
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except Exception:
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# print(e, "\n")
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pass
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if n_tickers:
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print(
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"The following stock tickers have been mentioned more than once across the previous watchlists:"
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)
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print(s_watchlist_tickers[:-2] + "\n")
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print("")
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def display_popular_tickers(
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n_top: int = 10, posts_to_look_at: int = 50, subreddits: str = "", export: str = ""
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):
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"""Print latest popular tickers. [Source: Reddit]
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Parameters
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----------
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n_top : int
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Number of top tickers to get
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posts_to_look_at : int
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How many posts to analyze in each subreddit
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subreddits : str, optional
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String of comma separated subreddits.
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export : str
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Format to export dataframe
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"""
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popular_tickers_df = reddit_model.get_popular_tickers(
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n_top, posts_to_look_at, subreddits
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)
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if not popular_tickers_df.empty:
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print(f"\nThe following TOP {n_top} tickers have been mentioned:")
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if gtff.USE_TABULATE_DF:
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print(
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tabulate(
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popular_tickers_df,
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headers=popular_tickers_df.columns,
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tablefmt="fancy_grid",
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showindex=False,
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)
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)
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else:
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print(popular_tickers_df.to_string())
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else:
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print("No tickers found")
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print("")
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export_data(
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export,
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os.path.dirname(os.path.abspath(__file__)),
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"popular",
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popular_tickers_df,
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)
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def display_spac_community(limit: int = 10, popular: bool = False):
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"""Look at tickers mentioned in r/SPACs [Source: Reddit]
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Parameters
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----------
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limit: int
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Number of posts to look through
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popular: bool
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Search by popular instead of new
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"""
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subs, d_watchlist_tickers = reddit_model.get_spac_community(limit, popular)
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for sub in subs:
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print_and_record_reddit_post({}, sub)
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if d_watchlist_tickers:
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lt_watchlist_sorted = sorted(
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d_watchlist_tickers.items(), key=lambda item: item[1], reverse=True
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)
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s_watchlist_tickers = ""
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n_tickers = 0
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for t_ticker in lt_watchlist_sorted:
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try:
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# If try doesn't trigger exception, it means that this stock exists on finviz
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# thus we can print it.
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finviz.get_stock(t_ticker[0])
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if int(t_ticker[1]) > 1:
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s_watchlist_tickers += f"{t_ticker[1]} {t_ticker[0]}, "
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n_tickers += 1
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except Exception:
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# print(e, "\n")
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pass
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if n_tickers:
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print(
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"The following stock tickers have been mentioned more than once across the previous SPACs:"
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)
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print(s_watchlist_tickers[:-2])
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print("")
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def display_spac(limit: int = 5):
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"""Look at posts containing 'spac' in top communities
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Parameters
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----------
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limit: int
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Number of posts to get from each subreddit
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"""
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warnings.filterwarnings("ignore") # To avoid printing the warning
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subs, d_watchlist_tickers, n_flair_posts_found = reddit_model.get_spac(limit)
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for sub in subs:
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print_and_record_reddit_post({}, sub)
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if n_flair_posts_found > 0:
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lt_watchlist_sorted = sorted(
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d_watchlist_tickers.items(), key=lambda item: item[1], reverse=True
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)
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s_watchlist_tickers = ""
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n_tickers = 0
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for t_ticker in lt_watchlist_sorted:
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try:
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# If try doesn't trigger exception, it means that this stock exists on finviz
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# thus we can print it.
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finviz.get_stock(t_ticker[0])
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if int(t_ticker[1]) > 1:
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s_watchlist_tickers += f"{t_ticker[1]} {t_ticker[0]}, "
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n_tickers += 1
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except Exception:
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pass
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if n_tickers:
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print(
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"The following stock tickers have been mentioned more than once across the previous SPACs:"
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)
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print(s_watchlist_tickers[:-2])
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print("")
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def display_wsb_community(limit: int = 10, new: bool = False):
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"""Show WSB posts
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Parameters
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----------
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limit : int, optional
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Number of posts to look at, by default 10
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new : bool, optional
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Flag to sort by new instead of hot, by default False
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"""
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subs = reddit_model.get_wsb_community(limit, new)
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for sub in subs:
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print_and_record_reddit_post({}, sub)
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def display_due_diligence(
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ticker: str, limit: int = 10, n_days: int = 3, show_all_flairs: bool = False
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):
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"""Display Reddit due diligence data for a given ticker
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Parameters
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----------
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ticker: str
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Stock ticker
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limit: int
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Number of posts to get
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n_days: int
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Number of days back to get posts
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show_all_flairs: bool
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Search through all flairs (apart from Yolo and Meme)
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"""
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subs = reddit_model.get_due_dilligence(ticker, limit, n_days, show_all_flairs)
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for sub in subs:
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print_and_record_reddit_post({}, sub)
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if not subs:
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print(f"No DD posts found for {ticker}\n")
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