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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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35
rstat_tool/dashboard.py
View File

@@ -1,12 +1,14 @@
# rstat_tool/dashboard.py
from flask import Flask, render_template
# --- FIX #1: Import the new function we need ---
from datetime import datetime, timedelta
from .database import (
get_overall_summary,
get_subreddit_summary,
get_all_scanned_subreddits,
get_deep_dive_details
get_deep_dive_details,
get_image_view_summary,
get_weekly_summary_for_subreddit
)
app = Flask(__name__, template_folder='../templates')
@@ -50,6 +52,35 @@ def deep_dive(symbol):
posts = get_deep_dive_details(symbol)
return render_template("deep_dive.html", posts=posts, symbol=symbol)
@app.route("/image/<name>")
def image_view(name):
"""The handler for the image-style dashboard."""
tickers = get_image_view_summary(name)
current_date = datetime.utcnow().strftime("%Y-%m-%d")
return render_template(
"image_view.html",
tickers=tickers,
subreddit_name=name,
current_date=current_date
)
@app.route("/image/weekly/<name>")
def weekly_image_view(name):
"""The handler for the WEEKLY image-style dashboard."""
tickers = get_weekly_summary_for_subreddit(name)
# Create the date range string for the title
end_date = datetime.utcnow()
start_date = end_date - timedelta(days=7)
date_range_str = f"{start_date.strftime('%b %d')} - {end_date.strftime('%b %d, %Y')}"
return render_template(
"weekly_image_view.html",
tickers=tickers,
subreddit_name=name,
date_range=date_range_str
)
def start_dashboard():
"""The main function called by the 'rstat-dashboard' command."""
print("Starting Flask server...")

92
rstat_tool/database.py
View File

@@ -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

84
rstat_tool/format_blacklist.py
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@@ -1,46 +1,52 @@
# The initial unsorted set of words.
# Note: In Python, a 'set' is inherently unordered, but we define it here for clarity.
COMMON_WORDS_BLACKLIST = {
"401K", "403B", "457B", "ABOUT", "ABOVE", "ADAM", "AEDT", "AEST", "AH", "AI",
"ALL", "ALPHA", "ALSO", "AM", "AMA", "AMEX", "AND", "ANY", "AR", "ARE",
"AROUND", "ASAP", "ASS", "ASSET", "AT", "ATH", "ATL", "ATM", "AUD", "BABY",
"BAG", "BAGS", "BE", "BEAR", "BELOW", "BETA", "BIG", "BIS", "BLEND", "BOE",
"BOJ", "BOMB", "BOND", "BOTH", "BOTS", "BRB", "BRL", "BS", "BST", "BTC",
"BTW", "BULL", "BUST", "BUT", "BUY", "BUZZ", "CAD", "CALL", "CAN", "CAP",
"CEO", "CEST", "CET", "CFO", "CHF", "CHIPS", "CIA", "CLOSE", "CNY", "COME",
"COST", "COULD", "CPAP", "CPI", "CST", "CTB", "CTO", "CYCLE", "CZK", "DAO",
"DATE", "DAX", "DAY", "DCA", "DD", "DEBT", "DIA", "DIV", "DJIA", "DKK",
"DM", "DO", "DOE", "DOGE", "DONT", "DR", "EACH", "EARLY", "EARN", "ECB",
"EDGAR", "EDIT", "EDT", "END", "EOD", "EOW", "EOY", "EPS", "ER", "ESG",
"EST", "ETF", "ETH", "EU", "EUR", "EV", "EVEN", "EVERY", "FAQ", "FAR",
"FAST", "FBI", "FDA", "FIHTX", "FINRA", "FINT", "FINTX", "FINTY", "FOMC", "FOMO",
"FOR", "FRAUD", "FRG", "FROM", "FSPSX", "FTSE", "FUCK", "FUD", "FULL", "FUND",
"FXAIX", "FXIAX", "FY", "FYI", "FZROX", "GAIN", "GBP", "GDP", "GET", "GL",
"GLHF", "GMT", "GO", "GOAL", "GOAT", "GOING", "GPT", "GPU", "GRAB", "GTG",
"HALF", "HAS", "HATE", "HAVE", "HEAR", "HEDGE", "HIGH", "HINT", "HKD", "HODL",
"HOLD", "HOUR", "HSA", "HUF", "IF", "II", "IMHO", "IMO", "IN", "INR",
"IP", "IPO", "IRA", "IRS", "IS", "ISM", "IST", "IT", "ITM", "IV",
"IVV", "IWM", "JPOW", "JPY", "JST", "JUST", "KARMA", "KEEP", "KNOW", "KO",
"KRW", "LARGE", "LAST", "LATE", "LATER", "LBO", "LEAP", "LEAPS", "LETS", "LFG",
"LIKE", "LIMIT", "LMAO", "LOL", "LONG", "LOOK", "LOSS", "LOVE", "LOW", "M&A",
"MA", "MAKE", "MAX", "MC", "ME", "MID", "MIGHT", "MIN", "ML", "MOASS",
"MONTH", "MORE", "MSK", "MUST", "MXN", "MY", "NATO", "NEAR", "NEED", "NEVER",
"NEW", "NEXT", "NFA", "NFT", "NGMI", "NIGHT", "NO", "NOK", "NONE", "NOT",
"NOW", "NSA", "NULL", "NYSE", "NZD", "OEM", "OF", "OK", "OLD", "ON",
"OP", "OR", "OS", "OTC", "OTM", "OUGHT", "OUT", "OVER", "OWN", "PC",
"PDT", "PE", "PEAK", "PEG", "PEW", "PLAN", "PLN", "PM", "PMI", "POS",
"PPI", "PR", "PRICE", "PROFIT", "PSA", "PST", "PT", "PUT", "Q1", "Q2",
"Q3", "Q4", "QQQ", "RBA", "RBNZ", "RE", "REAL", "REIT", "REKT", "RH",
"RIP", "RISK", "ROE", "ROFL", "ROI", "ROTH", "RSD", "RUB", "RULE", "SAVE",
"401K", "403B", "457B", "ABOUT", "ABOVE", "ADAM", "ADX", "AEDT", "AEST", "AH",
"AI", "ALL", "ALPHA", "ALSO", "AM", "AMA", "AMEX", "AND", "ANY", "AR",
"ARE", "ARK", "AROUND", "ASAP", "ASS", "ASSET", "AT", "ATH", "ATL", "ATM",
"AUD", "AWS", "BABY", "BAG", "BAGS", "BE", "BEAR", "BELOW", "BETA", "BIG",
"BIS", "BLEND", "BOE", "BOJ", "BOLL", "BOMB", "BOND", "BOTH", "BOTS", "BRB",
"BRL", "BS", "BST", "BSU", "BTC", "BTW", "BULL", "BUST", "BUT", "BUY",
"BUZZ", "CAD", "CALL", "CAN", "CAP", "CBS", "CCI", "CEO", "CEST", "CET",
"CEX", "CFD", "CFO", "CHF", "CHIPS", "CIA", "CLOSE", "CNBC", "CNY", "COKE",
"COME", "COST", "COULD", "CPAP", "CPI", "CSE", "CST", "CTB", "CTO", "CYCLE",
"CZK", "DAO", "DATE", "DAX", "DAY", "DCA", "DD", "DEBT", "DEX", "DIA",
"DIV", "DJIA", "DKK", "DM", "DO", "DOE", "DOGE", "DOJ", "DONT", "DR",
"EACH", "EARLY", "EARN", "ECB", "EDGAR", "EDIT", "EDT", "EMA", "END", "EOD",
"EOW", "EOY", "EPA", "EPS", "ER", "ESG", "EST", "ETF", "ETFS", "ETH",
"EU", "EUR", "EV", "EVEN", "EVERY", "FAQ", "FAR", "FAST", "FBI", "FD",
"FDA", "FIHTX", "FINRA", "FINT", "FINTX", "FINTY", "FIRST", "FOMC", "FOMO", "FOR",
"FOREX", "FRAUD", "FRG", "FROM", "FSPSX", "FTSE", "FUCK", "FUD", "FULL", "FUND",
"FXAIX", "FXIAX", "FY", "FYI", "FZROX", "GAAP", "GAIN", "GBP", "GDP", "GET",
"GL", "GLHF", "GMT", "GO", "GOAL", "GOAT", "GOING", "GPT", "GPU", "GRAB",
"GTG", "HALF", "HAS", "HATE", "HAVE", "HEAR", "HEDGE", "HELP", "HIGH", "HINT",
"HKD", "HODL", "HOLD", "HOUR", "HSA", "HUF", "IF", "II", "IKZ", "IMHO",
"IMO", "IN", "INR", "IP", "IPO", "IRA", "IRS", "IS", "ISA", "ISM",
"IST", "IT", "ITM", "IV", "IVV", "IWM", "JD", "JPOW", "JPY", "JST",
"JUST", "KARMA", "KEEP", "KNOW", "KO", "KRW", "LANGT", "LARGE", "LAST", "LATE",
"LATER", "LBO", "LEAP", "LEAPS", "LETS", "LFG", "LIKE", "LIMIT", "LLC", "LLM",
"LMAO", "LOKO", "LOL", "LONG", "LOOK", "LOSS", "LOVE", "LOW", "M&A", "MA",
"MACD", "MAKE", "MAX", "MC", "ME", "MEME", "MERK", "MEXC", "MID", "MIGHT",
"MIN", "MIND", "ML", "MOASS", "MONTH", "MORE", "MSK", "MUSIC", "MUST", "MXN",
"MY", "NATO", "NEAR", "NEED", "NEVER", "NEW", "NEXT", "NFA", "NFC", "NFT",
"NGMI", "NIGHT", "NO", "NOK", "NONE", "NOT", "NOW", "NSA", "NULL", "NUT",
"NYSE", "NZD", "OBV", "OEM", "OF", "OG", "OK", "OLD", "ON", "ONE",
"ONLY", "OP", "OPEX", "OR", "OS", "OSCE", "OTC", "OTM", "OUGHT", "OUT",
"OVER", "OWN", "PANIC", "PC", "PDT", "PE", "PEAK", "PEG", "PEW", "PLAN",
"PLN", "PM", "PMI", "POC", "POS", "PPI", "PR", "PRICE", "PROFIT", "PSA",
"PST", "PT", "PUT", "Q1", "Q2", "Q3", "Q4", "QQQ", "QR", "RBA",
"RBNZ", "RE", "REAL", "REIT", "REKT", "RH", "RIGHT", "RIP", "RISK", "ROCK",
"ROE", "ROFL", "ROI", "ROTH", "RSD", "RSI", "RUB", "RULE", "SAME", "SAVE",
"SCALP", "SCAM", "SCHB", "SEC", "SEE", "SEK", "SELL", "SEP", "SGD", "SHALL",
"SHARE", "SHORT", "SL", "SMALL", "SO", "SOLIS", "SOME", "SOON", "SP", "SPAC",
"SPEND", "SPLG", "SPX", "SPY", "START", "STILL", "STOCK", "STOP", "SWING", "TAKE",
"TERM", "THAT", "THE", "THINK", "THIS", "TIME", "TITS", "TL", "TL;DR", "TLDR",
"TO", "TODAY", "TOTAL", "TRADE", "TREND", "TRUE", "TRY", "TTYL", "TWO", "UI",
"UK", "UNDER", "UP", "US", "USA", "USD", "UTC", "VALUE", "VOO", "VR",
"VTI", "WAGMI", "WANT", "WATCH", "WAY", "WE", "WEB3", "WEEK", "WHO", "WHY",
"WILL", "WORTH", "WOULD", "WSB", "WTF", "YES", "YET", "YIELD", "YOLO", "YOU",
"YOUR", "YTD", "ZAR"
"SHARE", "SHORT", "SL", "SMA", "SMALL", "SO", "SOLIS", "SOME", "SOON", "SP",
"SPAC", "SPEND", "SPLG", "SPX", "SPY", "START", "STILL", "STOCK", "STOP", "STOR",
"SWING", "TA", "TAG", "TAKE", "TERM", "THANK", "THAT", "THE", "THINK", "THIS",
"TIME", "TITS", "TL", "TL;DR", "TLDR", "TO", "TODAY", "TOTAL", "TRADE", "TREND",
"TRUE", "TRY", "TTYL", "TWO", "UI", "UK", "UNDER", "UP", "US", "USA",
"USD", "UTC", "VALUE", "VOO", "VP", "VR", "VTI", "WAGMI", "WANT", "WATCH",
"WAY", "WE", "WEB3", "WEEK", "WHALE", "WHO", "WHY", "WIDE", "WILL", "WORDS",
"WORTH", "WOULD", "WSB", "WTF", "XRP", "YES", "YET", "YIELD", "YOLO", "YOU",
"YOUR", "YOY", "YT", "YTD", "ZAR", "ZEN", "ZERO"
}
def format_and_print_list(word_set, words_per_line=10):

120
rstat_tool/main.py
View File

@@ -15,21 +15,23 @@ from .sentiment_analyzer import get_sentiment_score
load_dotenv()
MARKET_CAP_REFRESH_INTERVAL = 86400
POST_AGE_LIMIT = 86400
def load_subreddits(filepath):
try:
with open(filepath, 'r') as f:
return json.load(f).get("subreddits", [])
except (FileNotFoundError, json.JSONDecodeError) as e:
print(f"Error loading config: {e}")
print(f"Error loading config file '{filepath}': {e}")
return None
def get_market_cap(ticker_symbol):
def get_financial_data(ticker_symbol):
try:
ticker = yf.Ticker(ticker_symbol)
return ticker.fast_info.get('marketCap')
data = { "market_cap": ticker.fast_info.get('marketCap'), "closing_price": ticker.fast_info.get('previousClose') }
return data
except Exception:
return None
return {"market_cap": None, "closing_price": None}
def get_reddit_instance():
client_id = os.getenv("REDDIT_CLIENT_ID")
@@ -38,59 +40,55 @@ def get_reddit_instance():
if not all([client_id, client_secret, user_agent]):
print("Error: Reddit API credentials not found in .env file.")
return None
# --- THIS IS THE CORRECTED LINE ---
# The argument is 'client_secret', not 'secret_client'.
return praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)
def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
"""Scans subreddits, analyzes posts and comments, and stores results in the database."""
def scan_subreddits(reddit, subreddits_list, post_limit=100, comment_limit=100, days_to_scan=1):
conn = database.get_db_connection()
post_age_limit = days_to_scan * 86400
current_time = time.time()
print(f"\nScanning {len(subreddits_list)} subreddits (Top {post_limit} posts, {comment_limit} comments/post)...")
print(f"\nScanning {len(subreddits_list)} subreddit(s) for NEW posts in the last {days_to_scan} day(s)...")
for subreddit_name in subreddits_list:
try:
subreddit_id = database.get_or_create_entity(conn, 'subreddits', 'name', subreddit_name)
subreddit = reddit.subreddit(subreddit_name)
print(f"Scanning r/{subreddit_name}...")
for submission in subreddit.hot(limit=post_limit):
# --- LOGIC PART 1: PROCESS INDIVIDUAL MENTIONS ---
# 1a. Process the Post Title and Body for mentions
for submission in subreddit.new(limit=post_limit):
if (current_time - submission.created_utc) > post_age_limit:
print(f" -> Reached posts older than the {days_to_scan}-day limit. Moving to next subreddit.")
break
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)
database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(submission.created_utc), post_sentiment)
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'])
# 1b. Process Comments for mentions
submission.comments.replace_more(limit=0)
for comment in submission.comments.list()[:comment_limit]:
tickers_in_comment = extract_tickers(comment.body)
if not tickers_in_comment:
continue
comment_sentiment = get_sentiment_score(comment.body)
for ticker_symbol in set(tickers_in_comment):
if tickers_in_post:
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)
database.add_mention(conn, ticker_id, subreddit_id, submission.id, int(comment.created_utc), comment_sentiment)
# --- LOGIC PART 2: DEEP DIVE ANALYSIS ---
database.add_mention(conn, ticker_id, subreddit_id, submission.id, 'post', int(submission.created_utc), post_sentiment)
ticker_info = database.get_ticker_info(conn, ticker_id)
if not ticker_info['last_updated'] or (current_time - ticker_info['last_updated'] > MARKET_CAP_REFRESH_INTERVAL):
print(f" -> Fetching financial data for {ticker_symbol}...")
financials = get_financial_data(ticker_symbol)
database.update_ticker_financials(
conn, ticker_id,
financials['market_cap'] or ticker_info['market_cap'],
financials['closing_price'] or ticker_info['closing_price']
)
submission.comments.replace_more(limit=0)
all_comment_sentiments = []
for comment in submission.comments.list()[:comment_limit]:
all_comment_sentiments.append(get_sentiment_score(comment.body))
tickers_in_comment = extract_tickers(comment.body)
if tickers_in_comment:
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)
database.add_mention(conn, ticker_id, subreddit_id, submission.id, 'comment', int(comment.created_utc), comment_sentiment)
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}",
@@ -98,7 +96,7 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
"comment_count": len(all_comment_sentiments), "avg_comment_sentiment": avg_sentiment
}
database.add_or_update_post_analysis(conn, post_analysis_data)
except Exception as e:
print(f"Could not scan r/{subreddit_name}. Error: {e}")
@@ -106,23 +104,47 @@ def scan_subreddits(reddit, subreddits_list, post_limit=25, comment_limit=100):
print("\n--- Scan Complete ---")
def main():
"""Main function to run the Reddit stock analysis tool."""
parser = argparse.ArgumentParser(description="Analyze stock ticker mentions on Reddit.", formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("config_file", help="Path to the JSON file containing subreddits.")
parser.add_argument("-p", "--posts", type=int, default=25, help="Number of posts to scan per subreddit.\n(Default: 25)")
parser.add_argument("--config", default="subreddits.json", help="Path to the JSON file containing subreddits.\n(Default: subreddits.json)")
parser.add_argument("--subreddit", help="Scan a single subreddit, ignoring the config file.")
parser.add_argument("--days", type=int, default=1, help="Number of past days to scan for new posts.\n(Default: 1 for last 24 hours)")
parser.add_argument("-p", "--posts", type=int, default=200, help="Max posts to check per subreddit.\n(Default: 200)")
parser.add_argument("-c", "--comments", type=int, default=100, help="Number of comments to scan per post.\n(Default: 100)")
parser.add_argument("-l", "--limit", type=int, default=20, help="Number of tickers to show in the final report.\n(Default: 20)")
parser.add_argument("-l", "--limit", type=int, default=20, help="Number of tickers to show in the CLI report.\n(Default: 20)")
args = parser.parse_args()
# --- THIS IS THE CORRECTED LOGIC BLOCK ---
if args.subreddit:
# If --subreddit is used, create a list with just that one.
subreddits_to_scan = [args.subreddit]
print(f"Targeted Scan Mode: Focusing on r/{args.subreddit}")
else:
# Otherwise, load from the config file.
print(f"Config Scan Mode: Loading subreddits from {args.config}")
# Use the correct argument name: args.config
subreddits_to_scan = load_subreddits(args.config)
if not subreddits_to_scan:
print("Error: No subreddits to scan. Please check your config file or --subreddit argument.")
return
# --- Initialize and Run ---
database.initialize_db()
database.clean_stale_tickers()
subreddits = load_subreddits(args.config_file)
if not subreddits: return
reddit = get_reddit_instance()
if not reddit: return
scan_subreddits(reddit, subreddits, post_limit=args.posts, comment_limit=args.comments)
scan_subreddits(
reddit,
subreddits_to_scan,
post_limit=args.posts,
comment_limit=args.comments,
days_to_scan=args.days
)
database.generate_summary_report(limit=args.limit)
if __name__ == "__main__":

82
rstat_tool/ticker_extractor.py
View File

@@ -5,48 +5,54 @@ import re
# A set of common English words and acronyms that look like stock tickers.
# This helps reduce false positives.
COMMON_WORDS_BLACKLIST = {
"401K", "403B", "457B", "ABOUT", "ABOVE", "ADAM", "AEDT", "AEST", "AH", "AI",
"ALL", "ALPHA", "ALSO", "AM", "AMA", "AMEX", "AND", "ANY", "AR", "ARE",
"AROUND", "ASAP", "ASS", "ASSET", "AT", "ATH", "ATL", "ATM", "AUD", "BABY",
"BAG", "BAGS", "BE", "BEAR", "BELOW", "BETA", "BIG", "BIS", "BLEND", "BOE",
"BOJ", "BOMB", "BOND", "BOTH", "BOTS", "BRB", "BRL", "BS", "BST", "BTC",
"BTW", "BULL", "BUST", "BUT", "BUY", "BUZZ", "CAD", "CALL", "CAN", "CEO",
"CEST", "CET", "CFO", "CHF", "CHIPS", "CIA", "CLOSE", "CNY", "COME", "COST",
"COULD", "CPI", "CST", "CTB", "CTO", "CYCLE", "CZK", "DAO", "DATE", "DAX",
"DAY", "DCA", "DD", "DEBT", "DIA", "DIV", "DJIA", "DKK", "DM", "DO",
"DOE", "DOGE", "DONT", "DR", "EACH", "EARLY", "EARN", "ECB", "EDGAR", "EDIT",
"EDT", "END", "EOD", "EOW", "EOY", "EPS", "ER", "ESG", "EST", "ETF",
"ETH", "EU", "EUR", "EV", "EVEN", "EVERY", "FAQ", "FAR", "FAST", "FBI",
"FDA", "FIHTX", "FINRA", "FINT", "FINTX", "FINTY", "FOMC", "FOMO", "FOR", "FRAUD",
"FRG", "FROM", "FSPSX", "FTSE", "FUCK", "FUD", "FULL", "FUND", "FXAIX", "FXIAX",
"FY", "FYI", "FZROX", "GAIN", "GBP", "GDP", "GET", "GL", "GLHF", "GMT",
"GO", "GOAL", "GOAT", "GOING", "GPU", "GRAB", "GTG", "HALF", "HAS", "HATE",
"HAVE", "HEAR", "HEDGE", "HIGH", "HINT", "HKD", "HODL", "HOLD", "HOUR", "HSA",
"HUF", "IF", "II", "IMHO", "IMO", "IN", "INR", "IP", "IPO", "IRA",
"IRS", "IS", "ISM", "IST", "IT", "ITM", "IV", "IVV", "IWM", "JPOW",
"JPY", "JST", "JUST", "KARMA", "KEEP", "KNOW", "KO", "KRW", "LARGE", "LAST",
"LATE", "LATER", "LBO", "LEAP", "LEAPS", "LETS", "LFG", "LIKE", "LIMIT", "LMAO",
"LOL", "LONG", "LOOK", "LOSS", "LOVE", "LOW", "M&A", "MA", "MAKE", "MAX",
"MC", "ME", "MID", "MIGHT", "MIN", "ML", "MOASS", "MONTH", "MORE", "MSK",
"MUST", "MXN", "MY", "NATO", "NEAR", "NEED", "NEVER", "NEW", "NEXT", "NFA",
"NFT", "NGMI", "NIGHT", "NO", "NOK", "NONE", "NOT", "NOW", "NSA", "NULL",
"NYSE", "NZD", "OEM", "OF", "OK", "OLD", "ON", "OP", "OR", "OS",
"OTC", "OTM", "OUGHT", "OUT", "OVER", "OWN", "PC", "PDT", "PE", "PEAK",
"PEG", "PEW", "PLAN", "PLN", "PM", "PMI", "POS", "PPI", "PR", "PRICE",
"PROFIT", "PSA", "PST", "PUT", "Q1", "Q2", "Q3", "Q4", "QQQ", "RBA",
"RBNZ", "RE", "REAL", "REIT", "REKT", "RH", "RIP", "RISK", "ROE", "ROFL",
"ROI", "ROTH", "RSD", "RUB", "RULE", "SAVE", "SCALP", "SCAM", "SCHB", "SEC",
"SEE", "SEK", "SELL", "SEP", "SGD", "SHALL", "SHARE", "SHORT", "SL", "SO",
"SOLIS", "SOME", "SOON", "SP", "SPAC", "SPEND", "SPLG", "SPX", "SPY", "START",
"STILL", "STOCK", "STOP", "SWING", "TAKE", "TERM", "THAT", "THE", "THINK", "THIS",
"401K", "403B", "457B", "ABOUT", "ABOVE", "ADAM", "ADX", "AEDT", "AEST", "AH",
"AI", "ALL", "ALPHA", "ALSO", "AM", "AMA", "AMEX", "AND", "ANY", "AR",
"ARE", "ARK", "AROUND", "ASAP", "ASS", "ASSET", "AT", "ATH", "ATL", "ATM",
"AUD", "AWS", "BABY", "BAG", "BAGS", "BE", "BEAR", "BELOW", "BETA", "BIG",
"BIS", "BLEND", "BOE", "BOJ", "BOLL", "BOMB", "BOND", "BOTH", "BOTS", "BRB",
"BRL", "BS", "BST", "BSU", "BTC", "BTW", "BULL", "BUST", "BUT", "BUY",
"BUZZ", "CAD", "CALL", "CAN", "CAP", "CBS", "CCI", "CEO", "CEST", "CET",
"CEX", "CFD", "CFO", "CHF", "CHIPS", "CIA", "CLOSE", "CNBC", "CNY", "COKE",
"COME", "COST", "COULD", "CPAP", "CPI", "CSE", "CST", "CTB", "CTO", "CYCLE",
"CZK", "DAO", "DATE", "DAX", "DAY", "DCA", "DD", "DEBT", "DEX", "DIA",
"DIV", "DJIA", "DKK", "DM", "DO", "DOE", "DOGE", "DOJ", "DONT", "DR",
"EACH", "EARLY", "EARN", "ECB", "EDGAR", "EDIT", "EDT", "EMA", "END", "EOD",
"EOW", "EOY", "EPA", "EPS", "ER", "ESG", "EST", "ETF", "ETFS", "ETH",
"EU", "EUR", "EV", "EVEN", "EVERY", "FAQ", "FAR", "FAST", "FBI", "FD",
"FDA", "FIHTX", "FINRA", "FINT", "FINTX", "FINTY", "FIRST", "FOMC", "FOMO", "FOR",
"FOREX", "FRAUD", "FRG", "FROM", "FSPSX", "FTSE", "FUCK", "FUD", "FULL", "FUND",
"FXAIX", "FXIAX", "FY", "FYI", "FZROX", "GAAP", "GAIN", "GBP", "GDP", "GET",
"GL", "GLHF", "GMT", "GO", "GOAL", "GOAT", "GOING", "GPT", "GPU", "GRAB",
"GTG", "HALF", "HAS", "HATE", "HAVE", "HEAR", "HEDGE", "HELP", "HIGH", "HINT",
"HKD", "HODL", "HOLD", "HOUR", "HSA", "HUF", "IF", "II", "IKZ", "IMHO",
"IMO", "IN", "INR", "IP", "IPO", "IRA", "IRS", "IS", "ISA", "ISM",
"IST", "IT", "ITM", "IV", "IVV", "IWM", "JD", "JPOW", "JPY", "JST",
"JUST", "KARMA", "KEEP", "KNOW", "KO", "KRW", "LANGT", "LARGE", "LAST", "LATE",
"LATER", "LBO", "LEAP", "LEAPS", "LETS", "LFG", "LIKE", "LIMIT", "LLC", "LLM",
"LMAO", "LOKO", "LOL", "LONG", "LOOK", "LOSS", "LOVE", "LOW", "M&A", "MA",
"MACD", "MAKE", "MAX", "MC", "ME", "MEME", "MERK", "MEXC", "MID", "MIGHT",
"MIN", "MIND", "ML", "MOASS", "MONTH", "MORE", "MSK", "MUSIC", "MUST", "MXN",
"MY", "NATO", "NEAR", "NEED", "NEVER", "NEW", "NEXT", "NFA", "NFC", "NFT",
"NGMI", "NIGHT", "NO", "NOK", "NONE", "NOT", "NOW", "NSA", "NULL", "NUT",
"NYSE", "NZD", "OBV", "OEM", "OF", "OG", "OK", "OLD", "ON", "ONE",
"ONLY", "OP", "OPEX", "OR", "OS", "OSCE", "OTC", "OTM", "OUGHT", "OUT",
"OVER", "OWN", "PANIC", "PC", "PDT", "PE", "PEAK", "PEG", "PEW", "PLAN",
"PLN", "PM", "PMI", "POC", "POS", "PPI", "PR", "PRICE", "PROFIT", "PSA",
"PST", "PT", "PUT", "Q1", "Q2", "Q3", "Q4", "QQQ", "QR", "RBA",
"RBNZ", "RE", "REAL", "REIT", "REKT", "RH", "RIGHT", "RIP", "RISK", "ROCK",
"ROE", "ROFL", "ROI", "ROTH", "RSD", "RSI", "RUB", "RULE", "SAME", "SAVE",
"SCALP", "SCAM", "SCHB", "SEC", "SEE", "SEK", "SELL", "SEP", "SGD", "SHALL",
"SHARE", "SHORT", "SL", "SMA", "SMALL", "SO", "SOLIS", "SOME", "SOON", "SP",
"SPAC", "SPEND", "SPLG", "SPX", "SPY", "START", "STILL", "STOCK", "STOP", "STOR",
"SWING", "TA", "TAG", "TAKE", "TERM", "THANK", "THAT", "THE", "THINK", "THIS",
"TIME", "TITS", "TL", "TL;DR", "TLDR", "TO", "TODAY", "TOTAL", "TRADE", "TREND",
"TRUE", "TRY", "TTYL", "TWO", "UI", "UK", "UNDER", "UP", "US", "USA",
"USD", "UTC", "VALUE", "VOO", "VR", "VTI", "WAGMI", "WANT", "WATCH", "WAY",
"WE", "WEB3", "WEEK", "WHO", "WHY", "WILL", "WORTH", "WOULD", "WSB", "WTF",
"YES", "YET", "YIELD", "YOLO", "YOU", "YOUR", "YTD", "ZAR"
"USD", "UTC", "VALUE", "VOO", "VP", "VR", "VTI", "WAGMI", "WANT", "WATCH",
"WAY", "WE", "WEB3", "WEEK", "WHALE", "WHO", "WHY", "WIDE", "WILL", "WORDS",
"WORTH", "WOULD", "WSB", "WTF", "XRP", "YES", "YET", "YIELD", "YOLO", "YOU",
"YOUR", "YOY", "YT", "YTD", "ZAR", "ZEN", "ZERO"
}
def extract_tickers(text):
"""
Extracts potential stock tickers from a given piece of text.

11
subreddits.json
View File

@@ -4,6 +4,15 @@
"Shortsqueeze",
"smallstreetbets",
"wallstreetbets",
"Wallstreetbetsnew"
"Wallstreetbetsnew",
"wallstreetbets2",
"stocks",
"RobinHoodPennyStocks",
"StocksAndTrading",
"investing",
"WallStreetBetsELITE",
"ValueInvesting",
"Daytrading",
"Tollbugatabets"
]
}

116
templates/image_view.html Normal file
View File

@@ -0,0 +1,116 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>r/{{ subreddit_name }} Mentions</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap" rel="stylesheet">
<style>
body {
margin: 0;
padding: 2rem;
font-family: 'Inter', sans-serif;
background: #1a1a1a;
display: flex;
justify-content: center;
align-items: center;
min-height: 100vh;
}
.image-container {
width: 650px; /* Increased width to accommodate new column */
background: linear-gradient(145deg, #4d302d, #1f2128);
color: #ffffff;
border-radius: 16px;
padding: 2.5rem;
box-shadow: 0 10px B30px rgba(0,0,0,0.5);
text-align: center;
}
header {
display: flex;
justify-content: space-between;
align-items: flex-start;
margin-bottom: 2rem;
}
.title-block { text-align: left; }
.title-block h1 { font-size: 2.5rem; font-weight: 800; margin: 0; line-height: 1; }
.title-block h2 { font-size: 1.25rem; font-weight: 600; margin: 0.5rem 0 0; color: #b0b0b0; }
.date { font-size: 1.1rem; font-weight: 600; color: #c0c0c0; letter-spacing: 0.02em; }
table { width: 100%; border-collapse: collapse; text-align: left; }
th, td { padding: 1rem 0.5rem; border-bottom: 1px solid rgba(255, 255, 255, 0.1); }
th { font-weight: 700; text-transform: uppercase; font-size: 0.8rem; color: #a0a0a0; }
td { font-size: 1.1rem; font-weight: 600; }
tr:last-child td { border-bottom: none; }
td.rank { font-weight: 700; color: #d0d0d0; width: 8%; }
td.ticker { width: 30%; }
td.mentions { text-align: center; width: 18%; }
td.sentiment { text-align: center; width: 26%; } /* New width */
/* Sentiment Colors */
.sentiment-bullish { color: #28a745; font-weight: 700; }
.sentiment-bearish { color: #dc3545; font-weight: 700; }
.sentiment-neutral { color: #9e9e9e; font-weight: 600; }
/* Row colors */
tr:nth-child(1) td.ticker { color: #d8b4fe; } tr:nth-child(6) td.ticker { color: #fca5a5; }
tr:nth-child(2) td.ticker { color: #a3e635; } tr:nth-child(7) td.ticker { color: #fdba74; }
tr:nth-child(3) td.ticker { color: #67e8f9; } tr:nth-child(8) td.ticker { color: #6ee7b7; }
tr:nth-child(4) td.ticker { color: #fde047; } tr:nth-child(9) td.ticker { color: #93c5fd; }
tr:nth-child(5) td.ticker { color: #fcd34d; } tr:nth-child(10) td.ticker { color: #d1d5db; }
footer { margin-top: 2.5rem; }
.brand-name { font-size: 1.75rem; font-weight: 800; letter-spacing: -1px; }
.brand-subtitle { font-size: 1rem; color: #b0b0b0; }
</style>
</head>
<body>
<div class="image-container">
<header>
<div class="title-block">
<h1>Reddit Mentions</h1>
<h2>r/{{ subreddit_name }}</h2>
</div>
<div class="date">{{ current_date }}</div>
</header>
<table>
<thead>
<tr>
<th class="rank">Rank</th>
<th class="ticker">Ticker</th>
<th class="mentions">Posts</th>
<th class="mentions">Comments</th>
<!-- UPDATED: Added Sentiment column header -->
<th class="sentiment">Sentiment</th>
</tr>
</thead>
<tbody>
{% for ticker in tickers %}
<tr>
<td class="rank">{{ loop.index }}</td>
<td class="ticker">{{ ticker.symbol }}</td>
<td class="mentions">{{ ticker.post_mentions }}</td>
<td class="mentions">{{ ticker.comment_mentions }}</td>
<!-- UPDATED: Added Sentiment data cell -->
<td class="sentiment">
{% if ticker.bullish_mentions > ticker.bearish_mentions %}
<span class="sentiment-bullish">Bullish</span>
{% elif ticker.bearish_mentions > ticker.bullish_mentions %}
<span class="sentiment-bearish">Bearish</span>
{% else %}
<span class="sentiment-neutral">Neutral</span>
{% endif %}
</td>
</tr>
{% endfor %}
</tbody>
</table>
<footer>
<div class="brand-name">RSTAT</div>
<div class="brand-subtitle">Reddit Stock Analysis Tool</div>
</footer>
</div>
</body>
</html>

9
templates/index.html
View File

@@ -10,6 +10,7 @@
<th>Ticker</th>
<th>Mentions</th>
<th>Market Cap</th>
<th>Closing Price</th>
<th>Sentiment</th>
</tr>
</thead>
@@ -19,6 +20,14 @@
<td><strong><a href="/deep-dive/{{ ticker.symbol }}">{{ ticker.symbol }}</a></strong></td>
<td>{{ ticker.mention_count }}</td>
<td>{{ ticker.market_cap | format_mc }}</td>
<!-- NEW COLUMN FOR CLOSING PRICE -->
<td>
{% if ticker.closing_price %}
${{ "%.2f"|format(ticker.closing_price) }}
{% else %}
N/A
{% endif %}
</td>
<td>
{% if ticker.bullish_mentions > ticker.bearish_mentions %}
<span class="sentiment-bullish">Bullish</span>

16
templates/subreddit.html
View File

@@ -3,13 +3,19 @@
{% block title %}r/{{ subreddit_name }} Dashboard{% endblock %}
{% block content %}
<h1>Top 10 Tickers in r/{{ subreddit_name }}</h1>
<h1>
Top 10 Tickers in r/{{ subreddit_name }}
<a href="/image/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Daily Image)</a>
<!-- ADD THIS NEW LINK -->
<a href="/image/weekly/{{ subreddit_name }}" target="_blank" style="font-size: 0.8rem; margin-left: 1rem; font-weight: normal;">(View Weekly Image)</a>
</h1>
<table>
<thead>
<tr>
<th>Ticker</th>
<th>Mentions</th>
<th>Market Cap</th>
<th>Closing Price</th>
<th>Sentiment</th>
</tr>
</thead>
@@ -19,6 +25,14 @@
<td><strong><a href="/deep-dive/{{ ticker.symbol }}">{{ ticker.symbol }}</a></strong></td>
<td>{{ ticker.mention_count }}</td>
<td>{{ ticker.market_cap | format_mc }}</td>
<!-- NEW COLUMN FOR CLOSING PRICE -->
<td>
{% if ticker.closing_price %}
${{ "%.2f"|format(ticker.closing_price) }}
{% else %}
N/A
{% endif %}
</td>
<td>
{% if ticker.bullish_mentions > ticker.bearish_mentions %}
<span class="sentiment-bullish">Bullish</span>

93
templates/weekly_image_view.html Normal file
View File

@@ -0,0 +1,93 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Weekly Sentiment: r/{{ subreddit_name }}</title>
<!-- All the <style> and <link> tags from image_view.html go here -->
<!-- You can just copy the entire <head> section from image_view.html -->
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap" rel="stylesheet">
<style>
/* This entire style block is IDENTICAL to image_view.html */
body { margin: 0; padding: 2rem; font-family: 'Inter', sans-serif; background: #1a1a1a; display: flex; justify-content: center; align-items: center; min-height: 100vh; }
.image-container { width: 650px; background: linear-gradient(145deg, #4d302d, #1f2128); color: #ffffff; border-radius: 16px; padding: 2.5rem; box-shadow: 0 10px 30px rgba(0,0,0,0.5); text-align: center; }
header { display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: 2rem; }
.title-block { text-align: left; }
.title-block h1 { font-size: 2.5rem; font-weight: 800; margin: 0; line-height: 1; }
.title-block h2 { font-size: 1.25rem; font-weight: 600; margin: 0.5rem 0 0; color: #b0b0b0; }
.date { font-size: 1rem; font-weight: 600; color: #c0c0c0; letter-spacing: 0.02em; }
table { width: 100%; border-collapse: collapse; text-align: left; }
th, td { padding: 1rem 0.5rem; border-bottom: 1px solid rgba(255, 255, 255, 0.1); }
th { font-weight: 700; text-transform: uppercase; font-size: 0.8rem; color: #a0a0a0; }
td { font-size: 1.1rem; font-weight: 600; }
tr:last-child td { border-bottom: none; }
td.rank { font-weight: 700; color: #d0d0d0; width: 8%; }
td.ticker { width: 30%; }
td.mentions { text-align: center; width: 18%; }
td.sentiment { text-align: center; width: 26%; }
.sentiment-bullish { color: #28a745; font-weight: 700; }
.sentiment-bearish { color: #dc3545; font-weight: 700; }
.sentiment-neutral { color: #9e9e9e; font-weight: 600; }
tr:nth-child(1) td.ticker { color: #d8b4fe; } tr:nth-child(6) td.ticker { color: #fca5a5; }
tr:nth-child(2) td.ticker { color: #a3e635; } tr:nth-child(7) td.ticker { color: #fdba74; }
tr:nth-child(3) td.ticker { color: #67e8f9; } tr:nth-child(8) td.ticker { color: #6ee7b7; }
tr:nth-child(4) td.ticker { color: #fde047; } tr:nth-child(9) td.ticker { color: #93c5fd; }
tr:nth-child(5) td.ticker { color: #fcd34d; } tr:nth-child(10) td.ticker { color: #d1d5db; }
footer { margin-top: 2.5rem; }
.brand-name { font-size: 1.75rem; font-weight: 800; letter-spacing: -1px; }
.brand-subtitle { font-size: 1rem; color: #b0b0b0; }
</style>
</head>
<body>
<div class="image-container">
<header>
<div class="title-block">
<!-- UPDATED: Title shows it's a weekly report -->
<h1>Weekly Sentiment</h1>
<h2>r/{{ subreddit_name }} - Top 10</h2>
</div>
<!-- UPDATED: Date now shows the range -->
<div class="date">{{ date_range }}</div>
</header>
<!-- The entire table structure is IDENTICAL to image_view.html -->
<table>
<thead>
<tr>
<th class="rank">Rank</th>
<th class="ticker">Ticker</th>
<th class="mentions">Posts</th>
<th class="mentions">Comments</th>
<th class="sentiment">Sentiment</th>
</tr>
</thead>
<tbody>
{% for ticker in tickers %}
<tr>
<td class="rank">{{ loop.index }}</td>
<td class="ticker">{{ ticker.symbol }}</td>
<td class="mentions">{{ ticker.post_mentions }}</td>
<td class="mentions">{{ ticker.comment_mentions }}</td>
<td class="sentiment">
{% if ticker.bullish_mentions > ticker.bearish_mentions %}
<span class="sentiment-bullish">Bullish</span>
{% elif ticker.bearish_mentions > ticker.bullish_mentions %}
<span class="sentiment-bearish">Bearish</span>
{% else %}
<span class="sentiment-neutral">Neutral</span>
{% endif %}
</td>
</tr>
{% endfor %}
</tbody>
</table>
<footer>
<div class="brand-name">RSTAT</div>
<div class="brand-subtitle">Reddit Stock Analysis Tool</div>
</footer>
</div>
</body>
</html>