125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
import json
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import re
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import requests
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import praw
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from datetime import datetime, timedelta
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from collections import defaultdict
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import os
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from dotenv import load_dotenv
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import sqlite3
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con = sqlite3.connect('stocks.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks")
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stock_symbols = [row[0] for row in cursor.fetchall()]
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etf_con = sqlite3.connect('etf.db')
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etf_cursor = etf_con.cursor()
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etf_cursor.execute("PRAGMA journal_mode = wal")
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etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
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etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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con.close()
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etf_con.close()
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load_dotenv()
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client_key = os.getenv('REDDIT_API_KEY')
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client_secret = os.getenv('REDDIT_API_SECRET')
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user_agent = os.getenv('REDDIT_USER_AGENT')
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# Initialize Reddit instance
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reddit = praw.Reddit(
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client_id=client_key,
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client_secret=client_secret,
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user_agent=user_agent
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)
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# Function to save data
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def save_data(data, filename):
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with open(f'json/reddit-tracker/wallstreetbets/{filename}', 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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def compute_daily_statistics(file_path):
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# Load the data from the JSON file
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# Initialize a defaultdict to store daily statistics
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daily_stats = defaultdict(lambda: {
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'post_count': 0,
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'total_comments': 0,
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'ticker_mentions': defaultdict(int),
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'unique_tickers': set()
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})
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# Compile regex pattern for finding tickers
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ticker_pattern = re.compile(r'\$([A-Z]+)')
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# Process each post
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for post in data:
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# Convert UTC timestamp to datetime object
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post_date = datetime.utcfromtimestamp(post['created_utc']).date()
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# Update statistics for this day
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daily_stats[post_date]['post_count'] += 1
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daily_stats[post_date]['total_comments'] += post['num_comments']
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# Find ticker mentions in title and selftext
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text_to_search = post['title'] + ' ' + post['selftext']
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tickers = ticker_pattern.findall(text_to_search)
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for ticker in tickers:
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daily_stats[post_date]['ticker_mentions'][ticker] += 1
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daily_stats[post_date]['unique_tickers'].add(ticker)
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# Calculate averages and format the results
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formatted_stats = []
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for date, stats in sorted(daily_stats.items(), reverse=True):
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formatted_stats.append({
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'date': date.isoformat(),
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'totalPosts': stats['post_count'],
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'totalComments': stats['total_comments'],
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'totalMentions': sum(stats['ticker_mentions'].values()),
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'companySpread': len(stats['unique_tickers']),
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'tickerMentions': dict(stats['ticker_mentions']) # Optional: include detailed ticker mentions
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})
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return formatted_stats, daily_stats
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# Function to compute trending tickers
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def compute_trending_tickers(daily_stats):
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today = datetime.now().date()
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seven_days_ago = today - timedelta(days=14)
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trending = defaultdict(int)
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for date, stats in daily_stats.items():
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if seven_days_ago <= date <= today:
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for ticker, count in stats['ticker_mentions'].items():
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trending[ticker] += count
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trending_list = [{'symbol': symbol, 'count': count} for symbol, count in trending.items()]
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trending_list.sort(key=lambda x: x['count'], reverse=True)
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for item in trending_list:
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symbol = item['symbol']
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if symbol in stock_symbols:
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item['assetType'] = 'stocks'
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elif symbol in etf_symbols:
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item['assetType'] = 'etf'
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else:
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item['assetType'] = ''
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return trending_list
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# Usage
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file_path = 'json/reddit-tracker/wallstreetbets/data.json'
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daily_statistics, daily_stats_dict = compute_daily_statistics(file_path)
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save_data(daily_statistics, 'stats.json')
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# Compute and save trending tickers
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trending_tickers = compute_trending_tickers(daily_stats_dict)
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save_data(trending_tickers, 'trending.json') |