pkhamre/reddit_stock_analyzer
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Added better dashboarding.

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This commit is contained in:
Pål-Kristian Hamre
2025-07-21 23:06:29 +02:00
parent 0c90fed0eb
commit d375f4ef38
10 changed files with 513 additions and 145 deletions
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92
rstat_tool/database.py
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@@ -3,6 +3,7 @@
import sqlite3
import time
from .ticker_extractor import COMMON_WORDS_BLACKLIST
from datetime import datetime, timedelta
DB_FILE = "reddit_stocks.db"
@@ -25,6 +26,7 @@ def initialize_db():
id INTEGER PRIMARY KEY AUTOINCREMENT,
symbol TEXT NOT NULL UNIQUE,
market_cap INTEGER,
closing_price REAL,
last_updated INTEGER
)
""")
@@ -44,11 +46,12 @@ def initialize_db():
ticker_id INTEGER,
subreddit_id INTEGER,
post_id TEXT NOT NULL,
mention_type TEXT NOT NULL, -- Can be 'post' or 'comment'
mention_timestamp INTEGER NOT NULL,
sentiment_score REAL,
FOREIGN KEY (ticker_id) REFERENCES tickers (id),
FOREIGN KEY (subreddit_id) REFERENCES subreddits (id),
UNIQUE(ticker_id, post_id, sentiment_score)
UNIQUE(ticker_id, post_id, mention_type, sentiment_score)
)
""")
@@ -103,13 +106,12 @@ def clean_stale_tickers():
print(f"Cleanup complete. Removed {deleted_count} records.")
def add_mention(conn, ticker_id, subreddit_id, post_id, timestamp, sentiment):
"""Adds a new mention with its sentiment score to the database."""
def add_mention(conn, ticker_id, subreddit_id, post_id, mention_type, timestamp, sentiment):
cursor = conn.cursor()
try:
cursor.execute(
"INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_timestamp, sentiment_score) VALUES (?, ?, ?, ?, ?)",
(ticker_id, subreddit_id, post_id, timestamp, sentiment)
"INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_type, mention_timestamp, sentiment_score) VALUES (?, ?, ?, ?, ?, ?)",
(ticker_id, subreddit_id, post_id, mention_type, timestamp, sentiment)
)
conn.commit()
except sqlite3.IntegrityError:
@@ -127,11 +129,14 @@ def get_or_create_entity(conn, table_name, column_name, value):
conn.commit()
return cursor.lastrowid
def update_ticker_market_cap(conn, ticker_id, market_cap):
"""Updates the market cap and timestamp for a specific ticker."""
def update_ticker_financials(conn, ticker_id, market_cap, closing_price):
"""Updates the financials and timestamp for a specific ticker."""
cursor = conn.cursor()
current_timestamp = int(time.time())
cursor.execute("UPDATE tickers SET market_cap = ?, last_updated = ? WHERE id = ?", (market_cap, current_timestamp, ticker_id))
cursor.execute(
"UPDATE tickers SET market_cap = ?, closing_price = ?, last_updated = ? WHERE id = ?",
(market_cap, closing_price, current_timestamp, ticker_id)
)
conn.commit()
def get_ticker_info(conn, ticker_id):
@@ -169,27 +174,28 @@ def generate_summary_report(limit=20):
conn.close()
def get_overall_summary(limit=50):
"""Gets the top tickers across all subreddits for the dashboard."""
conn = get_db_connection()
query = """
SELECT
t.symbol, t.market_cap, COUNT(m.id) as mention_count,
t.symbol, t.market_cap, t.closing_price, -- Added closing_price
COUNT(m.id) as mention_count,
SUM(CASE WHEN m.sentiment_score > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
SUM(CASE WHEN m.sentiment_score < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
SUM(CASE WHEN m.sentiment_score BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
FROM mentions m JOIN tickers t ON m.ticker_id = t.id
GROUP BY t.symbol, t.market_cap ORDER BY mention_count DESC LIMIT ?;
GROUP BY t.symbol, t.market_cap, t.closing_price -- Added closing_price
ORDER BY mention_count DESC LIMIT ?;
"""
results = conn.execute(query, (limit,)).fetchall()
conn.close()
return results
def get_subreddit_summary(subreddit_name, limit=50):
"""Gets the top tickers for a specific subreddit for the dashboard."""
conn = get_db_connection()
query = """
SELECT
t.symbol, t.market_cap, COUNT(m.id) as mention_count,
t.symbol, t.market_cap, t.closing_price, -- Added closing_price
COUNT(m.id) as mention_count,
SUM(CASE WHEN m.sentiment_score > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
SUM(CASE WHEN m.sentiment_score < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
SUM(CASE WHEN m.sentiment_score BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
@@ -197,7 +203,8 @@ def get_subreddit_summary(subreddit_name, limit=50):
JOIN tickers t ON m.ticker_id = t.id
JOIN subreddits s ON m.subreddit_id = s.id
WHERE s.name = ?
GROUP BY t.symbol, t.market_cap ORDER BY mention_count DESC LIMIT ?;
GROUP BY t.symbol, t.market_cap, t.closing_price -- Added closing_price
ORDER BY mention_count DESC LIMIT ?;
"""
results = conn.execute(query, (subreddit_name, limit)).fetchall()
conn.close()
@@ -205,7 +212,6 @@ def get_subreddit_summary(subreddit_name, limit=50):
def get_all_scanned_subreddits():
"""Gets a unique list of all subreddits we have data for."""
# --- THIS IS THE CORRECTED LINE ---
conn = get_db_connection()
results = conn.execute("SELECT DISTINCT name FROM subreddits ORDER BY name ASC;").fetchall()
conn.close()
@@ -245,4 +251,60 @@ def get_deep_dive_details(ticker_symbol):
"""
results = conn.execute(query, (ticker_symbol,)).fetchall()
conn.close()
return results
def get_image_view_summary(subreddit_name):
"""
Gets a summary of tickers for the image view, including post, comment,
and sentiment counts.
"""
conn = get_db_connection()
# This query now also counts sentiment types
query = """
SELECT
t.symbol,
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
COUNT(CASE WHEN m.sentiment_score > 0.1 THEN 1 END) as bullish_mentions,
COUNT(CASE WHEN m.sentiment_score < -0.1 THEN 1 END) as bearish_mentions
FROM mentions m
JOIN tickers t ON m.ticker_id = t.id
JOIN subreddits s ON m.subreddit_id = s.id
WHERE s.name = ?
GROUP BY t.symbol
ORDER BY (post_mentions + comment_mentions) DESC
LIMIT 10;
"""
results = conn.execute(query, (subreddit_name,)).fetchall()
conn.close()
return results
def get_weekly_summary_for_subreddit(subreddit_name):
"""
Gets a weekly summary for a specific subreddit for the image view.
"""
conn = get_db_connection()
# Calculate the timestamp for 7 days ago
seven_days_ago = datetime.utcnow() - timedelta(days=7)
seven_days_ago_timestamp = int(seven_days_ago.timestamp())
# The query is the same as before, but with an added WHERE clause for the timestamp
query = """
SELECT
t.symbol,
COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
COUNT(CASE WHEN m.sentiment_score > 0.1 THEN 1 END) as bullish_mentions,
COUNT(CASE WHEN m.sentiment_score < -0.1 THEN 1 END) as bearish_mentions
FROM mentions m
JOIN tickers t ON m.ticker_id = t.id
JOIN subreddits s ON m.subreddit_id = s.id
WHERE s.name = ? AND m.mention_timestamp >= ?
GROUP BY t.symbol
ORDER BY (post_mentions + comment_mentions) DESC
LIMIT 10;
"""
results = conn.execute(query, (subreddit_name, seven_days_ago_timestamp)).fetchall()
conn.close()
return results