Modularized the tool.
This commit is contained in:
142
database.py
142
database.py
@@ -1,142 +0,0 @@
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# database.py
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import sqlite3
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import time
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DB_FILE = "reddit_stocks.db"
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def get_db_connection():
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"""Establishes a connection to the SQLite database."""
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conn = sqlite3.connect(DB_FILE)
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conn.row_factory = sqlite3.Row
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return conn
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def initialize_db():
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"""
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Initializes the database and creates the necessary tables if they don't exist.
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"""
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conn = get_db_connection()
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cursor = conn.cursor()
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# --- Create tickers table (This is the corrected section) ---
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS tickers (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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symbol TEXT NOT NULL UNIQUE,
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market_cap INTEGER,
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last_updated INTEGER
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)
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""")
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# --- Create subreddits table (This is the corrected section) ---
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS subreddits (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL UNIQUE
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)
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""")
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# --- Create mentions table with sentiment_score column ---
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS mentions (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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ticker_id INTEGER,
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subreddit_id INTEGER,
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post_id TEXT NOT NULL,
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mention_timestamp INTEGER NOT NULL,
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sentiment_score REAL,
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FOREIGN KEY (ticker_id) REFERENCES tickers (id),
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FOREIGN KEY (subreddit_id) REFERENCES subreddits (id),
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UNIQUE(ticker_id, post_id)
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)
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""")
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conn.commit()
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conn.close()
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print("Database initialized successfully.")
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def add_mention(conn, ticker_id, subreddit_id, post_id, timestamp, sentiment):
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"""Adds a new mention with its sentiment score to the database."""
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cursor = conn.cursor()
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try:
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cursor.execute(
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"INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_timestamp, sentiment_score) VALUES (?, ?, ?, ?, ?)",
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(ticker_id, subreddit_id, post_id, timestamp, sentiment)
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)
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conn.commit()
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except sqlite3.IntegrityError:
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pass # Ignore duplicate mentions
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def get_or_create_entity(conn, table_name, column_name, value):
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"""Generic function to get or create an entity and return its ID."""
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cursor = conn.cursor()
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cursor.execute(f"SELECT id FROM {table_name} WHERE {column_name} = ?", (value,))
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result = cursor.fetchone()
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if result:
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return result['id']
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else:
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cursor.execute(f"INSERT INTO {table_name} ({column_name}) VALUES (?)", (value,))
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conn.commit()
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return cursor.lastrowid
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def update_ticker_market_cap(conn, ticker_id, market_cap):
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"""Updates the market cap and timestamp for a specific ticker."""
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cursor = conn.cursor()
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current_timestamp = int(time.time())
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cursor.execute(
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"UPDATE tickers SET market_cap = ?, last_updated = ? WHERE id = ?",
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(market_cap, current_timestamp, ticker_id)
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)
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conn.commit()
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def get_ticker_info(conn, ticker_id):
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"""Retrieves all info for a specific ticker by its ID."""
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cursor = conn.cursor()
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cursor.execute("SELECT * FROM tickers WHERE id = ?", (ticker_id,))
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return cursor.fetchone()
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def generate_summary_report():
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"""Queries the DB to generate a summary with market caps and avg. sentiment."""
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print("\n--- Summary Report ---")
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conn = get_db_connection()
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cursor = conn.cursor()
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query = """
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SELECT
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t.symbol,
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t.market_cap,
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COUNT(m.id) as mention_count,
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AVG(m.sentiment_score) as avg_sentiment
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FROM mentions m
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JOIN tickers t ON m.ticker_id = t.id
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GROUP BY t.symbol, t.market_cap
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ORDER BY mention_count DESC
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LIMIT 20;
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"""
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results = cursor.execute(query).fetchall()
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print(f"{'Ticker':<10} | {'Mentions':<10} | {'Sentiment':<18} | {'Market Cap':<20}")
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print("-" * 65)
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for row in results:
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# Format Market Cap
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market_cap_str = "N/A"
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if row['market_cap'] and row['market_cap'] > 0:
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mc = row['market_cap']
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if mc >= 1e12: market_cap_str = f"${mc/1e12:.2f}T"
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elif mc >= 1e9: market_cap_str = f"${mc/1e9:.2f}B"
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elif mc >= 1e6: market_cap_str = f"${mc/1e6:.2f}M"
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else: market_cap_str = f"${mc:,}"
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# Determine Sentiment Label
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sentiment_score = row['avg_sentiment']
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if sentiment_score is not None:
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if sentiment_score > 0.1: sentiment_label = f"Bullish ({sentiment_score:+.2f})"
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elif sentiment_score < -0.1: sentiment_label = f"Bearish ({sentiment_score:+.2f})"
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else: sentiment_label = f"Neutral ({sentiment_score:+.2f})"
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else:
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sentiment_label = "N/A"
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print(f"{row['symbol']:<10} | {row['mention_count']:<10} | {sentiment_label:<18} | {market_cap_str:<20}")
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conn.close()
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117
main.py
117
main.py
@@ -1,117 +0,0 @@
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# main.py
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import argparse
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import json
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import os
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import time
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import praw
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import yfinance as yf
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from dotenv import load_dotenv
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import database
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from ticker_extractor import extract_tickers
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from sentiment_analyzer import get_sentiment_score
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load_dotenv()
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MARKET_CAP_REFRESH_INTERVAL = 86400
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# (load_subreddits, get_market_cap, get_reddit_instance functions are unchanged)
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def load_subreddits(filepath):
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try:
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with open(filepath, 'r') as f:
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return json.load(f).get("subreddits", [])
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except (FileNotFoundError, json.JSONDecodeError) as e:
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print(f"Error loading config: {e}")
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return None
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def get_market_cap(ticker_symbol):
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try:
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ticker = yf.Ticker(ticker_symbol)
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return ticker.fast_info.get('marketCap')
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except Exception:
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return None
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def get_reddit_instance():
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client_id = os.getenv("REDDIT_CLIENT_ID")
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client_secret = os.getenv("REDDIT_CLIENT_SECRET")
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user_agent = os.getenv("REDDIT_USER_AGENT")
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if not all([client_id, client_secret, user_agent]):
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print("Error: Reddit API credentials not found.")
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return None
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return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)
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# --- UPDATED: Function now accepts post_limit and comment_limit ---
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def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
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"""Scans subreddits, analyzes posts and comments, and stores results in the database."""
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conn = database.get_db_connection()
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print(f"\nScanning {len(subreddits_list)} subreddits (Top {post_limit} posts, {comment_limit} comments/post)...")
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for subreddit_name in subreddits_list:
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try:
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subreddit_id = database.get_or_create_entity(conn, 'subreddits', 'name', subreddit_name)
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subreddit = reddit.subreddit(subreddit_name)
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print(f"Scanning r/{subreddit_name}...")
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for submission in subreddit.hot(limit=post_limit):
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# --- 1. Process the Post Title and Body ---
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post_text = submission.title + " " + submission.selftext
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tickers_in_post = extract_tickers(post_text)
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post_sentiment = get_sentiment_score(submission.title)
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for ticker_symbol in set(tickers_in_post):
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ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
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database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(submission.created_utc), post_sentiment)
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# (Market cap logic remains the same)
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ticker_info = database.get_ticker_info(conn, ticker_id)
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current_time = int(time.time())
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if not ticker_info['last_updated'] or (current_time - ticker_info['last_updated'] > MARKET_CAP_REFRESH_INTERVAL):
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print(f" -> Fetching market cap for {ticker_symbol}...")
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market_cap = get_market_cap(ticker_symbol)
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database.update_ticker_market_cap(conn, ticker_id, market_cap or ticker_info['market_cap'])
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# --- 2. Process the Comments ---
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# Expand "MoreComments" objects. limit=None means we try to get all, but PRAW is protective.
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# A limit of 32 is the max PRAW will do in a single call. We'll iterate to be safe.
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submission.comments.replace_more(limit=10)
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comment_count = 0
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for comment in submission.comments.list():
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if comment_count >= comment_limit:
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break # Stop processing comments for this post if we hit our limit
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tickers_in_comment = extract_tickers(comment.body)
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if not tickers_in_comment:
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continue # Skip comments that don't mention any tickers
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comment_sentiment = get_sentiment_score(comment.body)
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for ticker_symbol in set(tickers_in_comment):
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ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
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# We use the submission.id as the post_id to group mentions correctly
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database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment)
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comment_count += 1
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except Exception as e:
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print(f"Could not scan r/{subreddit_name}. Error: {e}")
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conn.close()
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print("\n--- Scan Complete ---")
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def main():
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parser = argparse.ArgumentParser(description="Analyze stock ticker mentions on Reddit.")
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parser.add_argument("config_file", help="Path to the JSON file containing subreddits.")
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args = parser.parse_args()
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database.initialize_db()
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subreddits = load_subreddits(args.config_file)
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if not subreddits: return
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reddit = get_reddit_instance()
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if not reddit: return
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# We now pass the limits to the scan function
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scan_subreddits(reddit, subreddits, post_limit=25, comment_limit=100)
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database.generate_summary_report()
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if __name__ == "__main__":
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main()
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@@ -1,19 +0,0 @@
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# sentiment_analyzer.py
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from nltk.sentiment.vader import SentimentIntensityAnalyzer
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# Initialize the VADER sentiment intensity analyzer
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# We only need to create one instance of this.
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_analyzer = SentimentIntensityAnalyzer()
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def get_sentiment_score(text):
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"""
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Analyzes a piece of text and returns its sentiment score.
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The 'compound' score is a single metric that summarizes the sentiment.
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It ranges from -1 (most negative) to +1 (most positive).
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"""
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# The polarity_scores() method returns a dictionary with 'neg', 'neu', 'pos', and 'compound' scores.
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# We are most interested in the 'compound' score.
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scores = _analyzer.polarity_scores(text)
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return scores['compound']
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@@ -1,11 +0,0 @@
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import nltk
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# This will download the 'vader_lexicon' dataset
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# It only needs to be run once
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try:
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nltk.data.find('sentiment/vader_lexicon.zip')
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print("VADER lexicon is already downloaded.")
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except LookupError:
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print("Downloading VADER lexicon...")
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nltk.download('vader_lexicon')
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print("Download complete.")
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@@ -1,65 +0,0 @@
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# ticker_extractor.py
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import re
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# A set of common English words and acronyms that look like stock tickers.
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# This helps reduce false positives.
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COMMON_WORDS_BLACKLIST = {
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"401K", "403B", "457B", "ABOVE", "AI", "ALL", "ALPHA", "AMA", "AMEX",
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"AND", "ANY", "AR", "ARE", "AROUND", "ASSET", "AT", "ATH", "ATL", "AUD",
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"BE", "BEAR", "BELOW", "BETA", "BIG", "BIS", "BLEND", "BOE", "BOJ",
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"BOND", "BRB", "BRL", "BTC", "BTW", "BULL", "BUT", "BUY", "BUZZ", "CAD",
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"CAN", "CEO", "CFO", "CHF", "CIA", "CNY", "COME", "COST", "COULD", "CPI",
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"CTB", "CTO", "CYCLE", "CZK", "DAO", "DATE", "DAX", "DAY", "DCA", "DD",
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"DEBT", "DIA", "DIV", "DJIA", "DKK", "DM", "DO", "DOGE", "DR", "EACH",
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"EARLY", "EARN", "ECB", "EDGAR", "EDIT", "EPS", "ESG", "ETF", "ETH",
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"EU", "EUR", "EV", "EVERY", "FAQ", "FAR", "FAST", "FBI", "FDA", "FIHTX",
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"FINRA", "FINT", "FINTX", "FINTY", "FOMC", "FOMO", "FOR", "FRAUD",
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"FRG", "FSPSX", "FTSE", "FUD", "FULL", "FUND", "FXAIX", "FXIAX", "FY",
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"FYI", "FZROX", "GAIN", "GDP", "GET", "GBP", "GO", "GOAL", "GPU", "GRAB",
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"GTG", "HAS", "HAVE", "HATE", "HEAR", "HEDGE", "HINT", "HKD", "HODL",
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"HOLD", "HOUR", "HSA", "HUF", "IMHO", "IMO", "IN", "INR", "IPO", "IRA",
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"IRS", "IS", "ISM", "IT", "IV", "IVV", "IWM", "JPY", "JUST", "KNOW",
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"KRW", "LARGE", "LAST", "LATE", "LATER", "LBO", "LIKE", "LMAO", "LOL",
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"LONG", "LOOK", "LOSS", "LOVE", "M&A", "MAKE", "MAX", "MC", "MID", "MIGHT",
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"MIN", "ML", "MOASS", "MONTH", "MUST", "MXN", "MY", "NATO", "NEAR",
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"NEED", "NEW", "NEXT", "NFA", "NFT", "NGMI", "NIGHT", "NO", "NOK", "NONE",
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"NOT", "NOW", "NSA", "NULL", "NZD", "NYSE", "OF", "OK", "OLD", "ON",
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"OP", "OR", "OTC", "OUGHT", "OUT", "OVER", "PE", "PEAK", "PEG",
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"PLAN", "PLN", "PMI", "PPI", "PRICE", "PROFIT", "PSA", "Q1", "Q2", "Q3",
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"Q4", "QQQ", "RBA", "RBNZ", "REIT", "REKT", "RH", "RISK", "ROE", "ROFL",
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"ROI", "ROTH", "RSD", "RUB", "SAVE", "SCALP", "SCAM", "SCHB", "SEC",
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"SEE", "SEK", "SELL", "SEP", "SGD", "SHALL", "SHARE", "SHORT", "SO",
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"SOME", "SOON", "SPAC", "SPEND", "SPLG", "SPX", "SPY", "STILL", "STOCK",
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"SWING", "TAKE", "TERM", "THE", "THINK", "THIS", "TIME", "TL", "TL;DR",
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"TLDR", "TODAY", "TO", "TOTAL", "TRADE", "TREND", "TRUE", "TRY", "TTYL",
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"TWO", "UK", "UNDER", "UP", "US", "USA", "USD", "VTI", "VALUE", "VOO",
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"VR", "WAGMI", "WANT", "WATCH", "WAY", "WE", "WEB3", "WEEK", "WHO",
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"WHY", "WILL", "WORTH", "WOULD", "WSB", "YET", "YIELD", "YOLO", "YOU",
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"ZAR",
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"KARMA", "OTM", "ITM", "ATM", "JPOW", "OPEN", "CLOSE", "HIGH", "LOW",
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"RE", "BS", "ASAP", "RULE", "REAL", "LIMIT", "STOP", "END", "START", "BOTS",
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"UTC", "AH", "PM", "PR", "GMT", "EST", "CST", "PST", "BST", "AEDT", "AEST",
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"CET", "CEST", "EDT", "IST", "JST", "MSK", "PDT", "PST", "YES", "NO", "OWN",
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"BOMB",
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}
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def extract_tickers(text):
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"""
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Extracts potential stock tickers from a given piece of text.
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A ticker is identified as a 1-5 character uppercase word, or a word prefixed with $.
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"""
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# Regex to find potential tickers:
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# 1. Words prefixed with $: $AAPL, $TSLA
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# 2. All-caps words between 1 and 5 characters: GME, AMC
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ticker_regex = r"\$[A-Z]{1,5}\b|\b[A-Z]{2,5}\b"
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potential_tickers = re.findall(ticker_regex, text)
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# Filter out common words and remove the '$' prefix
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tickers = []
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for ticker in potential_tickers:
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cleaned_ticker = ticker.replace("$", "").upper()
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if cleaned_ticker not in COMMON_WORDS_BLACKLIST:
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tickers.append(cleaned_ticker)
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return tickers
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