pkhamre/reddit_stock_analyzer
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format doc.

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Pål-Kristian Hamre
2025-07-28 12:24:14 +02:00
parent 55ea5d187f
commit 3c2a38d1a1
1 changed files with 148 additions and 64 deletions
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180
rstat_tool/main.py
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@@ -18,18 +18,20 @@ from .ticker_extractor import extract_tickers
from .sentiment_analyzer import get_sentiment_score
from .logger_setup import setup_logging, logger as log
def load_subreddits(filepath):
"""Loads a list of subreddits from a JSON file."""
try:
with open(filepath, 'r') as f:
with open(filepath, "r") as f:
return json.load(f).get("subreddits", [])
except (FileNotFoundError, json.JSONDecodeError) as e:
log.error(f"Error loading config file '{filepath}': {e}")
return None
def get_reddit_instance():
"""Initializes and returns a PRAW Reddit instance."""
env_path = Path(__file__).parent.parent / '.env'
env_path = Path(__file__).parent.parent / ".env"
load_dotenv(dotenv_path=env_path)
client_id = os.getenv("REDDIT_CLIENT_ID")
client_secret = os.getenv("REDDIT_CLIENT_SECRET")
@@ -37,7 +39,10 @@ def get_reddit_instance():
if not all([client_id, client_secret, user_agent]):
log.error("Error: Reddit API credentials not found in .env file.")
return None
return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)
return praw.Reddit(
client_id=client_id, client_secret=client_secret, user_agent=user_agent
)
def fetch_financial_data(ticker_symbol):
"""
@@ -46,17 +51,18 @@ def fetch_financial_data(ticker_symbol):
"""
try:
ticker = yf.Ticker(ticker_symbol)
market_cap = ticker.info.get('marketCap')
market_cap = ticker.info.get("marketCap")
data = ticker.history(period="2d", auto_adjust=False)
closing_price = None
if not data.empty:
last_close_raw = data['Close'].iloc[-1]
last_close_raw = data["Close"].iloc[-1]
if pd.notna(last_close_raw):
closing_price = float(last_close_raw)
return ticker_symbol, {"market_cap": market_cap, "closing_price": closing_price}
except Exception:
return ticker_symbol, None
def _process_submission(submission, subreddit_id, conn, comment_limit):
"""
Processes a single Reddit submission to find and save mentions.
@@ -72,12 +78,24 @@ def _process_submission(submission, subreddit_id, conn, comment_limit):
# Process title mentions first
if tickers_in_title:
log.info(f" -> Title Mention(s): {', '.join(tickers_in_title)}. Attributing all comments.")
log.info(
f" -> Title Mention(s): {', '.join(tickers_in_title)}. Attributing all comments."
)
post_sentiment = get_sentiment_score(submission.title)
for ticker_symbol in tickers_in_title:
ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
ticker_id = database.get_or_create_entity(
conn, "tickers", "symbol", ticker_symbol
)
ticker_id_cache[ticker_symbol] = ticker_id
database.add_mention(conn, ticker_id, subreddit_id, submission.id, 'post', int(submission.created_utc), post_sentiment)
database.add_mention(
conn,
ticker_id,
subreddit_id,
submission.id,
"post",
int(submission.created_utc),
post_sentiment,
)
# Process comments
for comment in all_comments:
@@ -86,30 +104,63 @@ def _process_submission(submission, subreddit_id, conn, comment_limit):
# If title has tickers, every comment is a mention for them
for ticker_symbol in tickers_in_title:
ticker_id = ticker_id_cache[ticker_symbol] # Guaranteed to be in cache
database.add_mention(conn, ticker_id, subreddit_id, submission.id, 'comment', int(comment.created_utc), comment_sentiment)
database.add_mention(
conn,
ticker_id,
subreddit_id,
submission.id,
"comment",
int(comment.created_utc),
comment_sentiment,
)
else:
# Otherwise, only direct mentions in comments count
tickers_in_comment = set(extract_tickers(comment.body))
if tickers_in_comment:
all_tickers_found_in_post.update(tickers_in_comment)
for ticker_symbol in tickers_in_comment:
ticker_id = database.get_or_create_entity(conn, 'tickers', 'symbol', ticker_symbol)
database.add_mention(conn, ticker_id, subreddit_id, submission.id, 'comment', int(comment.created_utc), comment_sentiment)
ticker_id = database.get_or_create_entity(
conn, "tickers", "symbol", ticker_symbol
)
database.add_mention(
conn,
ticker_id,
subreddit_id,
submission.id,
"comment",
int(comment.created_utc),
comment_sentiment,
)
# Save deep dive analysis (this is separate from mention counting)
all_comment_sentiments = [get_sentiment_score(c.body) for c in all_comments]
avg_sentiment = sum(all_comment_sentiments) / len(all_comment_sentiments) if all_comment_sentiments else 0
avg_sentiment = (
sum(all_comment_sentiments) / len(all_comment_sentiments)
if all_comment_sentiments
else 0
)
post_analysis_data = {
"post_id": submission.id, "title": submission.title,
"post_url": f"https://reddit.com{submission.permalink}", "subreddit_id": subreddit_id,
"post_timestamp": int(submission.created_utc), "comment_count": len(all_comments),
"avg_comment_sentiment": avg_sentiment
"post_id": submission.id,
"title": submission.title,
"post_url": f"https://reddit.com{submission.permalink}",
"subreddit_id": subreddit_id,
"post_timestamp": int(submission.created_utc),
"comment_count": len(all_comments),
"avg_comment_sentiment": avg_sentiment,
}
database.add_or_update_post_analysis(conn, post_analysis_data)
return all_tickers_found_in_post
def scan_subreddits(reddit, subreddits_list, post_limit=100, comment_limit=100, days_to_scan=1, fetch_financials=True):
def scan_subreddits(
reddit,
subreddits_list,
post_limit=100,
comment_limit=100,
days_to_scan=1,
fetch_financials=True,
):
"""
Scans subreddits to discover mentions, then performs a single batch update for financials if enabled.
"""
@@ -125,30 +176,42 @@ def scan_subreddits(reddit, subreddits_list, post_limit=100, comment_limit=100,
for subreddit_name in subreddits_list:
try:
normalized_sub_name = subreddit_name.lower()
subreddit_id = database.get_or_create_entity(conn, 'subreddits', 'name', normalized_sub_name)
subreddit_id = database.get_or_create_entity(
conn, "subreddits", "name", normalized_sub_name
)
subreddit = reddit.subreddit(normalized_sub_name)
log.info(f"Scanning r/{normalized_sub_name}...")
for submission in subreddit.new(limit=post_limit):
if (current_time - submission.created_utc) > post_age_limit:
log.info(f" -> Reached posts older than the {days_to_scan}-day limit.")
log.info(
f" -> Reached posts older than the {days_to_scan}-day limit."
)
break
tickers_found = _process_submission(submission, subreddit_id, conn, comment_limit)
tickers_found = _process_submission(
submission, subreddit_id, conn, comment_limit
)
if tickers_found:
all_tickers_to_update.update(tickers_found)
except Exception as e:
log.error(f"Could not scan r/{normalized_sub_name}. Error: {e}", exc_info=True)
log.error(
f"Could not scan r/{normalized_sub_name}. Error: {e}", exc_info=True
)
conn.close()
log.critical("\n--- Reddit Scan Complete ---")
if fetch_financials and all_tickers_to_update:
log.critical(f"\n--- Starting Batch Financial Update for {len(all_tickers_to_update)} Discovered Tickers ---")
log.critical(
f"\n--- Starting Batch Financial Update for {len(all_tickers_to_update)} Discovered Tickers ---"
)
tickers_from_db = {t['symbol']: t['id'] for t in database.get_all_tickers()}
tickers_needing_update_symbols = [symbol for symbol in all_tickers_to_update if symbol in tickers_from_db]
tickers_from_db = {t["symbol"]: t["id"] for t in database.get_all_tickers()}
tickers_needing_update_symbols = [
symbol for symbol in all_tickers_to_update if symbol in tickers_from_db
]
financial_data_batch = {}
with ThreadPoolExecutor(max_workers=10) as executor:
@@ -161,9 +224,10 @@ def scan_subreddits(reddit, subreddits_list, post_limit=100, comment_limit=100,
conn = database.get_db_connection()
for symbol, financials in financial_data_batch.items():
database.update_ticker_financials(
conn, tickers_from_db[symbol],
financials.get('market_cap'),
financials.get('closing_price')
conn,
tickers_from_db[symbol],
financials.get("market_cap"),
financials.get("closing_price"),
)
conn.close()
log.critical("--- Batch Financial Update Complete ---")
@@ -247,14 +311,20 @@ def main():
top_weekly = database.get_top_weekly_ticker_symbols()
all_sub_names = database.get_all_scanned_subreddits()
for sub_name in all_sub_names:
top_daily.extend(database.get_top_daily_ticker_symbols_for_subreddit(sub_name))
top_weekly.extend(database.get_top_weekly_ticker_symbols_for_subreddit(sub_name))
top_daily.extend(
database.get_top_daily_ticker_symbols_for_subreddit(sub_name)
)
top_weekly.extend(
database.get_top_weekly_ticker_symbols_for_subreddit(sub_name)
)
tickers_to_update = sorted(list(set(top_daily + top_weekly)))
if not tickers_to_update:
log.info("No top tickers found in the last week. Nothing to update.")
else:
log.info(f"Found {len(tickers_to_update)} unique top tickers to update. Fetching in parallel...")
log.info(
f"Found {len(tickers_to_update)} unique top tickers to update. Fetching in parallel..."
)
financial_data_batch = {}
successful_updates = 0
@@ -264,12 +334,14 @@ def main():
results = executor.map(fetch_financial_data, tickers_to_update)
for symbol, data in results:
# A successful fetch is one where data is returned and has a closing price
if data and data.get('closing_price') is not None:
if data and data.get("closing_price") is not None:
log.info(f" -> SUCCESS: Fetched data for {symbol}")
financial_data_batch[symbol] = data
successful_updates += 1
else:
log.warning(f" -> FAILED: Could not fetch valid financial data for {symbol}")
log.warning(
f" -> FAILED: Could not fetch valid financial data for {symbol}"
)
failed_updates += 1
if not financial_data_batch:
@@ -277,14 +349,15 @@ def main():
else:
conn = database.get_db_connection()
all_tickers_from_db = database.get_all_tickers()
ticker_map = {t['symbol']: t['id'] for t in all_tickers_from_db}
ticker_map = {t["symbol"]: t["id"] for t in all_tickers_from_db}
for symbol, financials in financial_data_batch.items():
if symbol in ticker_map:
database.update_ticker_financials(
conn, ticker_map[symbol],
financials.get('market_cap'),
financials.get('closing_price')
conn,
ticker_map[symbol],
financials.get("market_cap"),
financials.get("closing_price"),
)
conn.close()
@@ -299,17 +372,23 @@ def main():
if update_mode == "ALL_TICKERS":
log.critical("--- Starting Financial Data Update for ALL tickers ---")
all_tickers_from_db = database.get_all_tickers()
tickers_to_update = [t['symbol'] for t in all_tickers_from_db]
tickers_to_update = [t["symbol"] for t in all_tickers_from_db]
else:
ticker_symbol_to_update = update_mode
log.critical(f"--- Starting Financial Data Update for single ticker: {ticker_symbol_to_update} ---")
log.critical(
f"--- Starting Financial Data Update for single ticker: {ticker_symbol_to_update} ---"
)
if database.get_ticker_by_symbol(ticker_symbol_to_update):
tickers_to_update = [ticker_symbol_to_update]
else:
log.error(f"Ticker '{ticker_symbol_to_update}' not found in the database.")
log.error(
f"Ticker '{ticker_symbol_to_update}' not found in the database."
)
if tickers_to_update:
log.info(f"Found {len(tickers_to_update)} unique tickers to update. Fetching in parallel...")
log.info(
f"Found {len(tickers_to_update)} unique tickers to update. Fetching in parallel..."
)
financial_data_batch = {}
successful_updates = 0
@@ -319,12 +398,14 @@ def main():
results = executor.map(fetch_financial_data, tickers_to_update)
for symbol, data in results:
# A successful fetch is one where data is returned and has a closing price
if data and data.get('closing_price') is not None:
if data and data.get("closing_price") is not None:
log.info(f" -> SUCCESS: Fetched data for {symbol}")
financial_data_batch[symbol] = data
successful_updates += 1
else:
log.warning(f" -> FAILED: Could not fetch valid financial data for {symbol}")
log.warning(
f" -> FAILED: Could not fetch valid financial data for {symbol}"
)
failed_updates += 1
if not financial_data_batch:
@@ -332,14 +413,15 @@ def main():
else:
conn = database.get_db_connection()
all_tickers_from_db = database.get_all_tickers()
ticker_map = {t['symbol']: t['id'] for t in all_tickers_from_db}
ticker_map = {t["symbol"]: t["id"] for t in all_tickers_from_db}
for symbol, financials in financial_data_batch.items():
if symbol in ticker_map:
database.update_ticker_financials(
conn, ticker_map[symbol],
financials.get('market_cap'),
financials.get('closing_price')
conn,
ticker_map[symbol],
financials.get("market_cap"),
financials.get("closing_price"),
)
conn.close()
@@ -362,7 +444,8 @@ def main():
return
reddit = get_reddit_instance()
if not reddit: return
if not reddit:
return
scan_subreddits(
reddit,
@@ -370,8 +453,9 @@ def main():
post_limit=args.posts,
comment_limit=args.comments,
days_to_scan=args.days,
fetch_financials=(not args.no_financials)
fetch_financials=(not args.no_financials),
)
if __name__ == "__main__":
main()