Bugfix.

2025-07-21 23:44:27 +02:00
parent 728fe43571
commit d330f31950
3 changed files with 190 additions and 96 deletions
--- a/rstat_tool/database.py
+++ b/rstat_tool/database.py
@@ -46,12 +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_type TEXT NOT NULL,
+            mention_sentiment REAL,         -- Renamed from sentiment_score for clarity
+            post_avg_sentiment REAL,      -- NEW: Stores the avg sentiment of the whole post
            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, mention_type, sentiment_score)
+            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
        )
    """)

@@ -105,13 +105,69 @@ def clean_stale_tickers():
    conn.close()
    print(f"Cleanup complete. Removed {deleted_count} records.")

+def get_db_connection():
+    conn = sqlite3.connect(DB_FILE)
+    conn.row_factory = sqlite3.Row
+    return conn

-def add_mention(conn, ticker_id, subreddit_id, post_id, mention_type, timestamp, sentiment):
+def initialize_db():
+    conn = get_db_connection()
+    cursor = conn.cursor()
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS tickers (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            symbol TEXT NOT NULL UNIQUE,
+            market_cap INTEGER,
+            closing_price REAL,
+            last_updated INTEGER
+        )
+    """)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS subreddits (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL UNIQUE
+        )
+    """)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS mentions (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            ticker_id INTEGER,
+            subreddit_id INTEGER,
+            post_id TEXT NOT NULL,
+            mention_type TEXT NOT NULL,
+            mention_sentiment REAL,
+            post_avg_sentiment REAL,
+            mention_timestamp INTEGER NOT NULL,
+            FOREIGN KEY (ticker_id) REFERENCES tickers (id),
+            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
+        )
+    """)
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS posts (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            post_id TEXT NOT NULL UNIQUE,
+            title TEXT NOT NULL,
+            post_url TEXT,
+            subreddit_id INTEGER,
+            post_timestamp INTEGER,
+            comment_count INTEGER,
+            avg_comment_sentiment REAL,
+            FOREIGN KEY (subreddit_id) REFERENCES subreddits (id)
+        )
+    """)
+    conn.commit()
+    conn.close()
+    print("Database initialized successfully.")
+
+def add_mention(conn, ticker_id, subreddit_id, post_id, mention_type, timestamp, mention_sentiment, post_avg_sentiment=None):
    cursor = conn.cursor()
    try:
        cursor.execute(
-            "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)
+            """
+            INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_type, mention_timestamp, mention_sentiment, post_avg_sentiment)
+            VALUES (?, ?, ?, ?, ?, ?, ?)
+            """,
+            (ticker_id, subreddit_id, post_id, mention_type, timestamp, mention_sentiment, post_avg_sentiment)
        )
        conn.commit()
    except sqlite3.IntegrityError:
@@ -150,19 +206,26 @@ def generate_summary_report(limit=20):
    print(f"\n--- Top {limit} Tickers by Mention Count ---")
    conn = get_db_connection()
    cursor = conn.cursor()
+    
+    # --- UPDATED QUERY: Changed m.sentiment_score to m.mention_sentiment ---
    query = """
        SELECT
-            t.symbol, t.market_cap, 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
+            t.symbol, t.market_cap, t.closing_price,
+            COUNT(m.id) as mention_count,
+            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
+            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
+            SUM(CASE WHEN m.mention_sentiment 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
+        ORDER BY mention_count DESC
+        LIMIT ?;
    """
    results = cursor.execute(query, (limit,)).fetchall()
-    header = f"{'Ticker':<8} | {'Mentions':<8} | {'Bullish':<8} | {'Bearish':<8} | {'Neutral':<8} | {'Market Cap':<15}"
+    
+    header = f"{'Ticker':<8} | {'Mentions':<8} | {'Bullish':<8} | {'Bearish':<8} | {'Neutral':<8} | {'Market Cap':<15} | {'Close Price':<12}"
    print(header)
-    print("-" * len(header))
+    print("-" * (len(header) + 2)) # Adjusted separator length
+
    for row in results:
        market_cap_str = "N/A"
        if row['market_cap'] and row['market_cap'] > 0:
@@ -170,45 +233,19 @@ def generate_summary_report(limit=20):
            if mc >= 1e12: market_cap_str = f"${mc/1e12:.2f}T"
            elif mc >= 1e9: market_cap_str = f"${mc/1e9:.2f}B"
            else: market_cap_str = f"${mc/1e6:.2f}M"
-        print(f"{row['symbol']:<8} | {row['mention_count']:<8} | {row['bullish_mentions']:<8} | {row['bearish_mentions']:<8} | {row['neutral_mentions']:<8} | {market_cap_str:<15}")
-    conn.close()

-def get_overall_summary(limit=50):
-    conn = get_db_connection()
-    query = """
-        SELECT
-            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, t.closing_price -- Added closing_price
-        ORDER BY mention_count DESC LIMIT ?;
-    """
-    results = conn.execute(query, (limit,)).fetchall()
-    conn.close()
-    return results
+        closing_price_str = f"${row['closing_price']:.2f}" if row['closing_price'] else "N/A"

-def get_subreddit_summary(subreddit_name, limit=50):
-    conn = get_db_connection()
-    query = """
-        SELECT
-            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
-        JOIN subreddits s ON m.subreddit_id = s.id
-        WHERE s.name = ?
-        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()
+        print(
+            f"{row['symbol']:<8} | "
+            f"{row['mention_count']:<8} | "
+            f"{row['bullish_mentions']:<8} | "
+            f"{row['bearish_mentions']:<8} | "
+            f"{row['neutral_mentions']:<8} | "
+            f"{market_cap_str:<15} | "
+            f"{closing_price_str:<12}"
+        )
    conn.close()
-    return results

 def get_all_scanned_subreddits():
    """Gets a unique list of all subreddits we have data for."""
@@ -253,20 +290,52 @@ def get_deep_dive_details(ticker_symbol):
    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.
-    """
+def get_overall_summary(limit=50):
+    conn = get_db_connection()
+    query = """
+        SELECT
+            t.symbol, t.market_cap, t.closing_price,
+            COUNT(m.id) as mention_count,
+            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
+            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
+            SUM(CASE WHEN m.mention_sentiment 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, t.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):
+    conn = get_db_connection()
+    query = """
+        SELECT
+            t.symbol, t.market_cap, t.closing_price,
+            COUNT(m.id) as mention_count,
+            SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
+            SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
+            SUM(CASE WHEN m.mention_sentiment 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
+        JOIN subreddits s ON m.subreddit_id = s.id
+        WHERE s.name = ?
+        GROUP BY t.symbol, t.market_cap, t.closing_price
+        ORDER BY mention_count DESC LIMIT ?;
+    """
+    results = conn.execute(query, (subreddit_name, limit)).fetchall()
+    conn.close()
+    return results
+
+def get_image_view_summary(subreddit_name):
    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
+            COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
+            COUNT(CASE WHEN m.mention_sentiment < -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
@@ -280,23 +349,16 @@ def get_image_view_summary(subreddit_name):
    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
+            COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
+            COUNT(CASE WHEN m.mention_sentiment < -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