422 lines
16 KiB
Python
422 lines
16 KiB
Python
# rstat_tool/database.py
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import sqlite3
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import time
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from .ticker_extractor import COMMON_WORDS_BLACKLIST
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from datetime import datetime, timedelta
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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 ---
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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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closing_price REAL,
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last_updated INTEGER
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)
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""")
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# --- Create subreddits table ---
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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 ---
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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_type TEXT NOT NULL,
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mention_sentiment REAL, -- Renamed from sentiment_score for clarity
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post_avg_sentiment REAL, -- NEW: Stores the avg sentiment of the whole post
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mention_timestamp INTEGER NOT NULL,
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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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)
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""")
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS posts (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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post_id TEXT NOT NULL UNIQUE,
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title TEXT NOT NULL,
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post_url TEXT,
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subreddit_id INTEGER,
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post_timestamp INTEGER,
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comment_count INTEGER,
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avg_comment_sentiment REAL,
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FOREIGN KEY (subreddit_id) REFERENCES subreddits (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 clean_stale_tickers():
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"""
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Removes tickers and their associated mentions from the database
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if the ticker symbol exists in the COMMON_WORDS_BLACKLIST.
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"""
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print("\n--- Cleaning Stale Tickers from Database ---")
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conn = get_db_connection()
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cursor = conn.cursor()
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placeholders = ','.join('?' for _ in COMMON_WORDS_BLACKLIST)
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query = f"SELECT id, symbol FROM tickers WHERE symbol IN ({placeholders})"
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cursor.execute(query, tuple(COMMON_WORDS_BLACKLIST))
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stale_tickers = cursor.fetchall()
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if not stale_tickers:
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print("No stale tickers to clean.")
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conn.close()
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return
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for ticker in stale_tickers:
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ticker_id = ticker['id']
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ticker_symbol = ticker['symbol']
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print(f"Removing stale ticker '{ticker_symbol}' (ID: {ticker_id})...")
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cursor.execute("DELETE FROM mentions WHERE ticker_id = ?", (ticker_id,))
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cursor.execute("DELETE FROM tickers WHERE id = ?", (ticker_id,))
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deleted_count = conn.total_changes
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conn.commit()
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conn.close()
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print(f"Cleanup complete. Removed {deleted_count} records.")
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def clean_stale_subreddits(active_subreddits):
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"""
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Removes all data associated with subreddits that are NOT in the active list.
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"""
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print("\n--- Cleaning Stale Subreddits from Database ---")
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conn = get_db_connection()
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cursor = conn.cursor()
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cursor.execute("SELECT id, name FROM subreddits")
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db_subreddits = cursor.fetchall()
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stale_sub_ids = []
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for sub in db_subreddits:
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if sub['name'] not in active_subreddits:
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print(f"Found stale subreddit to remove: r/{sub['name']}")
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stale_sub_ids.append(sub['id'])
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if not stale_sub_ids:
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print("No stale subreddits to clean.")
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conn.close()
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return
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for sub_id in stale_sub_ids:
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print(f" -> Deleting associated data for subreddit ID: {sub_id}")
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cursor.execute("DELETE FROM mentions WHERE subreddit_id = ?", (sub_id,))
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cursor.execute("DELETE FROM posts WHERE subreddit_id = ?", (sub_id,))
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cursor.execute("DELETE FROM subreddits WHERE id = ?", (sub_id,))
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conn.commit()
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conn.close()
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print("Stale subreddit cleanup complete.")
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def get_db_connection():
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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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conn = get_db_connection()
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cursor = conn.cursor()
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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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closing_price REAL,
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last_updated INTEGER
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)
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""")
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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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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_type TEXT NOT NULL,
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mention_sentiment REAL,
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post_avg_sentiment REAL,
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mention_timestamp INTEGER NOT NULL,
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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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)
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""")
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS posts (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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post_id TEXT NOT NULL UNIQUE,
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title TEXT NOT NULL,
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post_url TEXT,
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subreddit_id INTEGER,
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post_timestamp INTEGER,
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comment_count INTEGER,
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avg_comment_sentiment REAL,
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FOREIGN KEY (subreddit_id) REFERENCES subreddits (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, mention_type, timestamp, mention_sentiment, post_avg_sentiment=None):
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cursor = conn.cursor()
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try:
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cursor.execute(
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"""
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INSERT INTO mentions (ticker_id, subreddit_id, post_id, mention_type, mention_timestamp, mention_sentiment, post_avg_sentiment)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""",
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(ticker_id, subreddit_id, post_id, mention_type, timestamp, mention_sentiment, post_avg_sentiment)
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)
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conn.commit()
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except sqlite3.IntegrityError:
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pass
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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_financials(conn, ticker_id, market_cap, closing_price):
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"""Updates the financials 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 = ?, closing_price = ?, last_updated = ? WHERE id = ?",
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(market_cap, closing_price, 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(limit=20):
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"""Queries the DB to generate a summary for the command-line tool."""
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print(f"\n--- Top {limit} Tickers by Mention Count ---")
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conn = get_db_connection()
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cursor = conn.cursor()
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# --- UPDATED QUERY: Changed m.sentiment_score to m.mention_sentiment ---
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query = """
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SELECT
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t.symbol, t.market_cap, t.closing_price,
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COUNT(m.id) as mention_count,
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SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
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SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
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SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
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FROM mentions m JOIN tickers t ON m.ticker_id = t.id
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GROUP BY t.symbol, t.market_cap, t.closing_price
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ORDER BY mention_count DESC
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LIMIT ?;
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"""
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results = cursor.execute(query, (limit,)).fetchall()
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header = f"{'Ticker':<8} | {'Mentions':<8} | {'Bullish':<8} | {'Bearish':<8} | {'Neutral':<8} | {'Market Cap':<15} | {'Close Price':<12}"
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print(header)
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print("-" * (len(header) + 2)) # Adjusted separator length
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for row in results:
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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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else: market_cap_str = f"${mc/1e6:.2f}M"
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closing_price_str = f"${row['closing_price']:.2f}" if row['closing_price'] else "N/A"
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print(
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f"{row['symbol']:<8} | "
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f"{row['mention_count']:<8} | "
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f"{row['bullish_mentions']:<8} | "
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f"{row['bearish_mentions']:<8} | "
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f"{row['neutral_mentions']:<8} | "
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f"{market_cap_str:<15} | "
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f"{closing_price_str:<12}"
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)
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conn.close()
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def get_all_scanned_subreddits():
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"""Gets a unique list of all subreddits we have data for."""
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conn = get_db_connection()
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results = conn.execute("SELECT DISTINCT name FROM subreddits ORDER BY name ASC;").fetchall()
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conn.close()
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return [row['name'] for row in results]
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def add_or_update_post_analysis(conn, post_data):
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"""
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Inserts a new post analysis record or updates an existing one.
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This prevents duplicate entries for the same post.
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"""
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cursor = conn.cursor()
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# Use the UNIQUE post_id to replace old data with new on conflict
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cursor.execute(
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"""
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INSERT INTO posts (post_id, title, post_url, subreddit_id, post_timestamp, comment_count, avg_comment_sentiment)
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VALUES (:post_id, :title, :post_url, :subreddit_id, :post_timestamp, :comment_count, :avg_comment_sentiment)
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ON CONFLICT(post_id) DO UPDATE SET
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comment_count = excluded.comment_count,
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avg_comment_sentiment = excluded.avg_comment_sentiment;
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""",
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post_data
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)
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conn.commit()
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def get_deep_dive_details(ticker_symbol):
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"""
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Gets all analyzed posts that mention a specific ticker.
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"""
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conn = get_db_connection()
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query = """
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SELECT DISTINCT p.*, s.name as subreddit_name FROM posts p
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JOIN mentions m ON p.post_id = m.post_id
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JOIN tickers t ON m.ticker_id = t.id
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JOIN subreddits s ON p.subreddit_id = s.id
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WHERE LOWER(t.symbol) = LOWER(?)
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ORDER BY p.post_timestamp DESC;
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"""
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results = conn.execute(query, (ticker_symbol,)).fetchall()
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conn.close()
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return results
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def get_overall_summary(limit=50):
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conn = get_db_connection()
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query = """
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SELECT
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t.symbol, t.market_cap, t.closing_price,
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COUNT(m.id) as mention_count,
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SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
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SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
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SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
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FROM mentions m JOIN tickers t ON m.ticker_id = t.id
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GROUP BY t.symbol, t.market_cap, t.closing_price
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ORDER BY mention_count DESC LIMIT ?;
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"""
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results = conn.execute(query, (limit,)).fetchall()
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conn.close()
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return results
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def get_subreddit_summary(subreddit_name, limit=50):
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conn = get_db_connection()
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query = """
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SELECT
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t.symbol, t.market_cap, t.closing_price,
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COUNT(m.id) as mention_count,
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SUM(CASE WHEN m.mention_sentiment > 0.1 THEN 1 ELSE 0 END) as bullish_mentions,
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SUM(CASE WHEN m.mention_sentiment < -0.1 THEN 1 ELSE 0 END) as bearish_mentions,
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SUM(CASE WHEN m.mention_sentiment BETWEEN -0.1 AND 0.1 THEN 1 ELSE 0 END) as neutral_mentions
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FROM mentions m
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JOIN tickers t ON m.ticker_id = t.id
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JOIN subreddits s ON m.subreddit_id = s.id
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WHERE LOWER(s.name) = LOWER(?)
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GROUP BY t.symbol, t.market_cap, t.closing_price
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ORDER BY mention_count DESC LIMIT ?;
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"""
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results = conn.execute(query, (subreddit_name, limit)).fetchall()
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conn.close()
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return results
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def get_image_view_summary(subreddit_name):
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conn = get_db_connection()
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query = """
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SELECT
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t.symbol,
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COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
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COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
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COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
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COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
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FROM mentions m
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JOIN tickers t ON m.ticker_id = t.id
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JOIN subreddits s ON m.subreddit_id = s.id
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WHERE LOWER(s.name) = LOWER(?)
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GROUP BY t.symbol
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ORDER BY (post_mentions + comment_mentions) DESC
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LIMIT 10;
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"""
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results = conn.execute(query, (subreddit_name,)).fetchall()
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conn.close()
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return results
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def get_weekly_summary_for_subreddit(subreddit_name):
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conn = get_db_connection()
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seven_days_ago = datetime.utcnow() - timedelta(days=7)
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seven_days_ago_timestamp = int(seven_days_ago.timestamp())
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query = """
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SELECT
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t.symbol,
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COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
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COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
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COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
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COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
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FROM mentions m
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JOIN tickers t ON m.ticker_id = t.id
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JOIN subreddits s ON m.subreddit_id = s.id
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WHERE LOWER(s.name) = LOWER(?) AND m.mention_timestamp >= ?
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GROUP BY t.symbol
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ORDER BY (post_mentions + comment_mentions) DESC
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LIMIT 10;
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"""
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results = conn.execute(query, (subreddit_name, seven_days_ago_timestamp)).fetchall()
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conn.close()
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return results
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def get_overall_image_view_summary():
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"""
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Gets a summary of top tickers across ALL subreddits for the image view.
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"""
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conn = get_db_connection()
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query = """
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SELECT
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t.symbol,
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COUNT(CASE WHEN m.mention_type = 'post' THEN 1 END) as post_mentions,
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COUNT(CASE WHEN m.mention_type = 'comment' THEN 1 END) as comment_mentions,
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COUNT(CASE WHEN m.mention_sentiment > 0.1 THEN 1 END) as bullish_mentions,
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COUNT(CASE WHEN m.mention_sentiment < -0.1 THEN 1 END) as bearish_mentions
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FROM mentions m
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JOIN tickers t ON m.ticker_id = t.id
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-- No JOIN or WHERE for subreddit, as we want all of them
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GROUP BY t.symbol
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ORDER BY (post_mentions + comment_mentions) DESC
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LIMIT 10;
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"""
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results = conn.execute(query).fetchall()
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conn.close()
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return results |