diff --git a/main.py b/main.py index 48b1dd5..99df3e7 100644 --- a/main.py +++ b/main.py @@ -11,29 +11,28 @@ from dotenv import load_dotenv import database from ticker_extractor import extract_tickers -from sentiment_analyzer import get_sentiment_score # <-- IMPORT OUR NEW MODULE +from sentiment_analyzer import get_sentiment_score load_dotenv() MARKET_CAP_REFRESH_INTERVAL = 86400 -# ... (load_subreddits, get_market_cap, get_reddit_instance functions are unchanged) +# (load_subreddits, get_market_cap, get_reddit_instance functions are unchanged) def load_subreddits(filepath): - # ... try: - with open(filepath, 'r') as f: return json.load(f).get("subreddits", []) + with open(filepath, 'r') as f: + return json.load(f).get("subreddits", []) except (FileNotFoundError, json.JSONDecodeError) as e: print(f"Error loading config: {e}") return None def get_market_cap(ticker_symbol): - # ... try: ticker = yf.Ticker(ticker_symbol) return ticker.fast_info.get('marketCap') - except Exception: return None + except Exception: + return None def get_reddit_instance(): - # ... client_id = os.getenv("REDDIT_CLIENT_ID") client_secret = os.getenv("REDDIT_CLIENT_SECRET") user_agent = os.getenv("REDDIT_USER_AGENT") @@ -42,12 +41,12 @@ def get_reddit_instance(): return None return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent) - -def scan_subreddits(reddit, subreddits_list, post_limit=25): - """Scans subreddits, performs sentiment analysis, and stores results in the database.""" +# --- UPDATED: Function now accepts post_limit and comment_limit --- +def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100): + """Scans subreddits, analyzes posts and comments, and stores results in the database.""" conn = database.get_db_connection() - print(f"\nScanning {len(subreddits_list)} subreddits for top {post_limit} posts...") + print(f"\nScanning {len(subreddits_list)} subreddits (Top {post_limit} posts, {comment_limit} comments/post)...") for subreddit_name in subreddits_list: try: subreddit_id = database.get_or_create_entity(conn, 'subreddits', 'name', subreddit_name) @@ -55,34 +54,43 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25): print(f"Scanning r/{subreddit_name}...") for submission in subreddit.hot(limit=post_limit): - # We analyze the title for sentiment as it's often the most concise summary. - # Analyzing all comments could be a future enhancement. - text_to_analyze = submission.title - tickers_in_post = extract_tickers(text_to_analyze + " " + submission.selftext) - - # --- NEW: Get sentiment score for the post's title --- - sentiment = get_sentiment_score(text_to_analyze) + # --- 1. Process the Post Title and Body --- + post_text = submission.title + " " + submission.selftext + tickers_in_post = extract_tickers(post_text) + post_sentiment = get_sentiment_score(submission.title) for ticker_symbol in set(tickers_in_post): ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol) - - # --- NEW: Pass the sentiment score to the database --- - database.add_mention( - conn, - ticker_id=ticker_id, - subreddit_id=subreddit_id, - post_id=submission.id, - timestamp=int(submission.created_utc), - sentiment=sentiment # Pass the score here - ) - - # (The market cap update logic remains the same) + database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(submission.created_utc), post_sentiment) + # (Market cap logic remains the same) ticker_info = database.get_ticker_info(conn, ticker_id) current_time = int(time.time()) if not ticker_info['last_updated'] or (current_time - ticker_info['last_updated'] > MARKET_CAP_REFRESH_INTERVAL): print(f" -> Fetching market cap for {ticker_symbol}...") market_cap = get_market_cap(ticker_symbol) database.update_ticker_market_cap(conn, ticker_id, market_cap or ticker_info['market_cap']) + + # --- 2. Process the Comments --- + # Expand "MoreComments" objects. limit=None means we try to get all, but PRAW is protective. + # A limit of 32 is the max PRAW will do in a single call. We'll iterate to be safe. + submission.comments.replace_more(limit=10) + comment_count = 0 + for comment in submission.comments.list(): + if comment_count >= comment_limit: + break # Stop processing comments for this post if we hit our limit + + tickers_in_comment = extract_tickers(comment.body) + if not tickers_in_comment: + continue # Skip comments that don't mention any tickers + + comment_sentiment = get_sentiment_score(comment.body) + + for ticker_symbol in set(tickers_in_comment): + ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol) + # We use the submission.id as the post_id to group mentions correctly + database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment) + + comment_count += 1 except Exception as e: print(f"Could not scan r/{subreddit_name}. Error: {e}") @@ -91,10 +99,6 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25): print("\n--- Scan Complete ---") def main(): - # --- IMPORTANT: Delete your old DB file before running! --- - # Since we changed the schema and logic, old data won't have sentiment. - # It's best to start fresh. Delete the `reddit_stocks.db` file now. - parser = argparse.ArgumentParser(description="Analyze stock ticker mentions on Reddit.") parser.add_argument("config_file", help="Path to the JSON file containing subreddits.") args = parser.parse_args() @@ -105,7 +109,8 @@ def main(): reddit = get_reddit_instance() if not reddit: return - scan_subreddits(reddit, subreddits) + # We now pass the limits to the scan function + scan_subreddits(reddit, subreddits, post_limit=25, comment_limit=100) database.generate_summary_report() if __name__ == "__main__": diff --git a/subreddits.json b/subreddits.json index 4f7e414..742c938 100644 --- a/subreddits.json +++ b/subreddits.json @@ -1,9 +1,7 @@ { "subreddits": [ - "wallstreetbets", - "stocks", - "investing", "pennystocks", - "Shortsqueeze" + "Shortsqueeze", + "smallstreetbets" ] } diff --git a/ticker_extractor.py b/ticker_extractor.py index 7ef8c12..7cd19cf 100644 --- a/ticker_extractor.py +++ b/ticker_extractor.py @@ -5,47 +5,44 @@ import re # A set of common English words and acronyms that look like stock tickers. # This helps reduce false positives. COMMON_WORDS_BLACKLIST = { - "A", "I", "DD", "CEO", "CFO", "CTO", "EPS", "IPO", "YOLO", "FOMO", - "TLDR", "EDIT", "THE", "AND", "FOR", "ARE", "BUT", "NOT", "YOU", - "ALL", "ANY", "CAN", "HAS", "NEW", "NOW", "OLD", "SEE", "TWO", - "WAY", "WHO", "WHY", "BIG", "BUY", "SELL", "HOLD", "BE", "GO", - "ON", "AT", "IN", "IS", "IT", "OF", "OR", "TO", "WE", "UP", - "OUT", "SO", "RH", "SEC", "IRS", "USA", "UK", "EU", - "AI", "ML", "AR", "VR", "NFT", "DAO", "WEB3", "ETH", "BTC", "DOGE", - "USD", "EUR", "GBP", "JPY", "CNY", "INR", "AUD", "CAD", "CHF", - "RUB", "ZAR", "BRL", "MXN", "HKD", "SGD", "NZD", "RSD", - "JPY", "KRW", "SEK", "NOK", "DKK", "PLN", "CZK", "HUF", "TRY", - "US", "IRA", "FDA", "SEC", "FBI", "CIA", "NSA", "NATO", "FINRA", - "NASDAQ", "NYSE", "AMEX", "FTSE", "DAX", "WSB", "SPX", "DJIA", - "EDGAR", "GDP", "CPI", "PPI", "PMI", "ISM", "FOMC", "ECB", "BOE", - "BOJ", "RBA", "RBNZ", "BIS", "NFA", "P", "VOO", "CTB", "DR", - "ETF", "EV", "ESG", "REIT", "SPAC", "IPO", "M&A", "LBO", "PE", - "Q1", "Q2", "Q3", "Q4", "FY", "FAQ", "ROI", "ROE", "EPS", "P/E", "PEG", - "FRG", "FXAIX", "FXIAX", "FZROX", "BULL", "BEAR", "BULLISH", "BEARISH", - "QQQ", "SPY", "DIA", "IWM", "VTI", "VOO", "IVV", "SCHB", "SPLG", - "ROTH", "IRA", "401K", "403B", "457B", "SEP", "SIMPLE", "HSA", - "LONG", "SHORT", "LEVERAGE", "MARGIN", "HEDGE", "SWING", "DAY", - "GRAB", "GPU", "MY", "PSA", "AMA", "DM", "OP", "SPAC", "FIHTX", - "FINTX", "FINT", "FINTX", "FINTY", "FSPSX", "TOTAL", "LARGE", "MID", "SMALL", - "GROWTH", "VALUE", "BLEND", "INCOME", "DIV", "YIELD", "BETA", "ALPHA", "VOLATILITY", - "RISK", "RETURN", "SHARPE", "SORTINO", "MAX", "MIN", "STDDEV", "VARIANCE", - "PDF", "FULL", "PEAK", "LATE", "EARLY", "MIDDAY", "NIGHT", "MORNING", "AFTERNOON", - "CYCLE", "TREND", "PATTERN", "BREAKOUT", "PULLBACK", "REVERSAL", "CONSOLIDATION", - "OTC", "TRUE", "FALSE", "NULL", "NONE", "ALL", "ANY", "SOME", "EACH", "EVERY", - "STILL", "TERM", "TIME", "DATE", "YEAR", "MONTH", "WEEK", "HOUR", "MINUTE", "SECOND", - "JUST", "ALREADY", "STILL", "YET", "NOW", "LATER", "SOON", "EARLIER", "TODAY", "TOMORROW", - "YESTERDAY", "TONIGHT", "THIS", "LAST", "NEXT", "WOULD", "SHOULD", "COULD", "MIGHT", - "WILL", "CAN", "MUST", "SHALL", "OUGHT", "TAKE", "MAKE", "HAVE", "GET", "DO", "BE", - "GO", "COME", "SEE", "LOOK", "WATCH", "HEAR", "YES", "NO", "OK", "LIKE", "LOVE", "HATE", - "WANT", "NEED", "THINK", "BELIEVE", "KNOW", "PRICE", "COST", "VALUE", "WORTH", - "EXPENSE", "SPEND", "SAVE", "EARN", "PROFIT", "LOSS", "GAIN", "DEBT", "CREDIT", - "BOND", "STOCK", "SHARE", "FUND", "ASSET", "LIABILITY", "BUZZ", "UNDER", "OVER", "BETWEEN", - "FRAUD", "SCAM", "RISK", "REWARD", "RETURN", "INVEST", "TRADE", "BUY", "SELL", "HOLD", - "SHORT", "LONG", "LEVERAGE", "MARGIN", "HEDGE", "SCALP", "POSITION", - "PLAN", "GOAL", "WILL", "FAST", "HINT", "ABOVE", "BELOW", "AROUND", "NEAR", "FAR", - "TL", + "401K", "403B", "457B", "ABOVE", "AI", "ALL", "ALPHA", "AMA", "AMEX", + "AND", "ANY", "AR", "ARE", "AROUND", "ASSET", "AT", "ATH", "ATL", "AUD", + "BE", "BEAR", "BELOW", "BETA", "BIG", "BIS", "BLEND", "BOE", "BOJ", + "BOND", "BRB", "BRL", "BTC", "BTW", "BULL", "BUT", "BUY", "BUZZ", "CAD", + "CAN", "CEO", "CFO", "CHF", "CIA", "CNY", "COME", "COST", "COULD", "CPI", + "CTB", "CTO", "CYCLE", "CZK", "DAO", "DATE", "DAX", "DAY", "DCA", "DD", + "DEBT", "DIA", "DIV", "DJIA", "DKK", "DM", "DO", "DOGE", "DR", "EACH", + "EARLY", "EARN", "ECB", "EDGAR", "EDIT", "EPS", "ESG", "ETF", "ETH", + "EU", "EUR", "EV", "EVERY", "FAQ", "FAR", "FAST", "FBI", "FDA", "FIHTX", + "FINRA", "FINT", "FINTX", "FINTY", "FOMC", "FOMO", "FOR", "FRAUD", + "FRG", "FSPSX", "FTSE", "FUD", "FULL", "FUND", "FXAIX", "FXIAX", "FY", + "FYI", "FZROX", "GAIN", "GDP", "GET", "GBP", "GO", "GOAL", "GPU", "GRAB", + "GTG", "HAS", "HAVE", "HATE", "HEAR", "HEDGE", "HINT", "HKD", "HODL", + "HOLD", "HOUR", "HSA", "HUF", "IMHO", "IMO", "IN", "INR", "IPO", "IRA", + "IRS", "IS", "ISM", "IT", "IV", "IVV", "IWM", "JPY", "JUST", "KNOW", + "KRW", "LARGE", "LAST", "LATE", "LATER", "LBO", "LIKE", "LMAO", "LOL", + "LONG", "LOOK", "LOSS", "LOVE", "M&A", "MAKE", "MAX", "MC", "MID", "MIGHT", + "MIN", "ML", "MOASS", "MONTH", "MUST", "MXN", "MY", "NATO", "NEAR", + "NEED", "NEW", "NEXT", "NFA", "NFT", "NGMI", "NIGHT", "NO", "NOK", "NONE", + "NOT", "NOW", "NSA", "NULL", "NZD", "NYSE", "OF", "OK", "OLD", "ON", + "OP", "OR", "OTC", "OUGHT", "OUT", "OVER", "PE", "PEAK", "PEG", + "PLAN", "PLN", "PMI", "PPI", "PRICE", "PROFIT", "PSA", "Q1", "Q2", "Q3", + "Q4", "QQQ", "RBA", "RBNZ", "REIT", "REKT", "RH", "RISK", "ROE", "ROFL", + "ROI", "ROTH", "RSD", "RUB", "SAVE", "SCALP", "SCAM", "SCHB", "SEC", + "SEE", "SEK", "SELL", "SEP", "SGD", "SHALL", "SHARE", "SHORT", "SO", + "SOME", "SOON", "SPAC", "SPEND", "SPLG", "SPX", "SPY", "STILL", "STOCK", + "SWING", "TAKE", "TERM", "THE", "THINK", "THIS", "TIME", "TL", "TL;DR", + "TLDR", "TODAY", "TO", "TOTAL", "TRADE", "TREND", "TRUE", "TRY", "TTYL", + "TWO", "UK", "UNDER", "UP", "US", "USA", "USD", "VTI", "VALUE", "VOO", + "VR", "WAGMI", "WANT", "WATCH", "WAY", "WE", "WEB3", "WEEK", "WHO", + "WHY", "WILL", "WORTH", "WOULD", "WSB", "YET", "YIELD", "YOLO", "YOU", + "ZAR", + "KARMA", "OTM", "ITM", "ATM", "JPOW", "OPEN", "CLOSE", "HIGH", "LOW", + "RE", "BS", "ASAP", "RULE", "REAL", "LIMIT", "STOP", "END", "START", "BOTS", + "UTC", "AH", "PM", "PR", "GMT", "EST", "CST", "PST", "BST", "AEDT", "AEST", + "CET", "CEST", "EDT", "IST", "JST", "MSK", "PDT", "PST", "YES", "NO", "OWN", + "BOMB", } - def extract_tickers(text): """ Extracts potential stock tickers from a given piece of text. @@ -54,7 +51,7 @@ def extract_tickers(text): # Regex to find potential tickers: # 1. Words prefixed with $: $AAPL, $TSLA # 2. All-caps words between 1 and 5 characters: GME, AMC - ticker_regex = r"\$[A-Z]{1,5}\b|\b[A-Z]{1,5}\b" + ticker_regex = r"\$[A-Z]{1,5}\b|\b[A-Z]{2,5}\b" potential_tickers = re.findall(ticker_regex, text)