backend/app/cron_reddit_bot.py

import praw
import orjson
from datetime import datetime
import os
from dotenv import load_dotenv
import time
import ujson
import argparse


def parse_args():
    parser = argparse.ArgumentParser(description='Market Status')
    parser.add_argument('--market_status', choices=[0, 1, 2], type=int, default=0,
                        help='Market status: 0 for Open (default), 1 for Premarket, 2 for Afterhours')
    return parser.parse_args()


def format_time(time_str):
    """Format time string to AM/PM format"""
    if not time_str:
        return ""
    
    try:
        time_parts = time_str.split(':')
        hours = int(time_parts[0])
        minutes = int(time_parts[1])
        
        period = "AM" if hours < 12 else "PM"
        if hours > 12:
            hours -= 12
        elif hours == 0:
            hours = 12
            
        return f"{hours:02d}:{minutes:02d} {period}"
    except:
        return ""


def format_number(num):
    """Abbreviate large numbers with B/M suffix"""
    if num >= 1_000_000_000:
        return f"${num / 1_000_000_000:.2f}B"
    elif num >= 1_000_000:
        return f"${num / 1_000_000:.2f}M"
    return f"${num:,.0f}"

def calculate_yoy_change(current, prior):
    """Calculate year-over-year percentage change"""
    if prior and prior != 0:
        return ((current / prior - 1) * 100)
    return 0

def get_market_timing(time_str):
    """Determine if earnings are before, after, or during market hours"""
    if not time_str:
        return ""
    
    try:
        time_parts = time_str.split(':')
        hours = int(time_parts[0])
        minutes = int(time_parts[1])
        
        if hours < 9 or (hours == 9 and minutes <= 30):
            return "before market opens."
        elif hours >= 16:
            return "after market closes."
        else:
            return "during market."
    except:
        return ""

def format_upcoming_earnings_data(earnings_data):
    """Format earnings data into Reddit-friendly markdown with hyperlinks."""
    formatted_items = []
    
    for item in earnings_data:
        symbol = item.get('symbol', None)
        if symbol is not None:
            name = item.get('name', 'Unknown')
            market_timing = get_market_timing(item.get('time'))
            revenue_formatted = format_number(item.get('revenueEst', 0))
            revenue_yoy = calculate_yoy_change(item.get('revenueEst', 0), item.get('revenuePrior', 1))  # Avoid division by zero
            eps_yoy = calculate_yoy_change(item.get('epsEst', 0), item.get('epsPrior', 1))  # Avoid division by zero

            # Determine reporting time text
            if item.get('isToday'):
                report_timing = "will report today"
            else:
                current_day = datetime.now().strftime('%A')
                report_timing = "will report tomorrow" if current_day in ['Monday', 'Tuesday', 'Wednesday', 'Thursday'] else "will report Monday"
            
            # Create hyperlink for symbol
            symbol_link = f"[{symbol}](https://stocknear.com/stocks/{symbol})"

            # Format the entry text
            entry = (
                f"* **{name}** ({symbol_link}) {report_timing} {market_timing} "
                f"Analysts estimate {revenue_formatted} in revenue ({revenue_yoy:.2f}% YoY) and "
                f"${item.get('epsEst', 0):.2f} in earnings per share ({eps_yoy:.2f}% YoY).\n\n"
            )
            formatted_items.append(entry)
    
    return "".join(formatted_items)

def format_recent_earnings_data(earnings_data):
    """Format earnings data into Reddit-friendly markdown with bullet points."""
    formatted_items = []
    
    for item in earnings_data:
        symbol = item.get('symbol', None)
        if symbol is not None:
            name = item.get('name', 'Unknown')
            time = format_time(item.get('time', ''))
            
            # Financial calculations
            revenue = item.get('revenue', 0)  # Changed from revenueEst to revenue for actual results
            revenue_prior = item.get('revenuePrior', 1)
            revenue_surprise = item.get('revenueSurprise', 0)
            eps = item.get('eps', 0)  # Changed from epsEst to eps for actual results
            eps_prior = item.get('epsPrior', 1)
            eps_surprise = item.get('epsSurprise', 0)
            
            # Calculate YoY changes
            revenue_yoy = calculate_yoy_change(revenue, revenue_prior)
            eps_yoy = calculate_yoy_change(eps, eps_prior)
            
            # Format numbers
            revenue_formatted = format_number(revenue)
            revenue_surprise_formatted = format_number(abs(revenue_surprise))
            
            # Determine growth/decline text
            revenue_trend = "growth" if revenue_yoy >= 0 else "decline"
            eps_trend = "growth" if eps_yoy >= 0 else "decline"
            
            # Create hyperlink for symbol
            symbol_link = f"[{symbol}](https://stocknear.com/stocks/{symbol})"
            
            # Format the entry text with nested bullet points
            entry = (
                f"**{name}** ({symbol_link}) has released its quarterly earnings at {time}:\n\n"
                f"* Revenue of {revenue_formatted} "
                f"{'exceeds' if revenue_surprise > 0 else 'misses'} estimates by {revenue_surprise_formatted}, "
                f"with {revenue_yoy:.2f}% YoY {revenue_trend}.\n\n"
                f"* EPS of ${eps:.2f} "
                f"{'exceeds' if eps_surprise > 0 else 'misses'} estimates by ${abs(eps_surprise):.2f}, "
                f"with {eps_yoy:.2f}% YoY {eps_trend}.\n\n"
            )

            formatted_items.append(entry)
    
    return "".join(formatted_items)

def format_afterhour_market():
    try:
        # Load gainers data
        with open("json/market-movers/afterhours/gainers.json", 'r') as file:
            data = ujson.load(file)
            gainers = [
                {'symbol': item['symbol'], 'name': item['name'], 'price': item['price'], 
                 'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']} 
                for item in data[:5]
            ]
        
        # Load losers data
        with open("json/market-movers/afterhours/losers.json", 'r') as file:
            data = ujson.load(file)
            losers = [
                {'symbol': item['symbol'], 'name': item['name'], 'price': item['price'], 
                 'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']} 
                for item in data[:5]
            ]
        
        market_movers = {'gainers': gainers, 'losers': losers}
    
    except Exception as e:
        print(f"Error loading market data: {e}")
        market_movers = {'gainers': [], 'losers': []}
    
    # Create Gainers Table
    gainers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
    gainers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
    for gainer in market_movers["gainers"]:
        gainers_table += (
            f"| [{gainer['symbol']}](https://stocknear.com/stocks/{gainer['symbol']}) | {gainer['name'][:30]} | "
            f"{gainer['price']:.2f} | +{gainer['changesPercentage']:.2f}% | "
            f"{format_number(gainer['marketCap'])} |\n"
        )

    # Create Losers Table
    losers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
    losers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
    for loser in market_movers["losers"]:
        losers_table += (
            f"| [{loser['symbol']}](https://stocknear.com/stocks/{loser['symbol']}) | {loser['name'][:30]} | "
            f"{loser['price']:.2f} | {loser['changesPercentage']:.2f}% | "
            f"{format_number(loser['marketCap'])} |\n"
        )
    # Construct final markdown text
    return f"""

Here's a summary of today's After-Hours Gainers and Losers, showcasing stocks that stood out after the market closed.

### 📈 After-Hours Gainers

{gainers_table}

### 📉 After-Hours Losers

{losers_table}

More info can be found here: [After-Hours Gainers and Losers](https://stocknear.com/market-mover/afterhours/gainers)
"""

def format_premarket_market():
    try:
        # Load gainers data
        with open("json/market-movers/premarket/gainers.json", 'r') as file:
            data = ujson.load(file)
            gainers = [
                {'symbol': item['symbol'], 'name': item['name'], 'price': item['price'], 
                 'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']} 
                for item in data[:5]
            ]
        
        # Load losers data
        with open("json/market-movers/premarket/losers.json", 'r') as file:
            data = ujson.load(file)
            losers = [
                {'symbol': item['symbol'], 'name': item['name'], 'price': item['price'], 
                 'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']} 
                for item in data[:5]
            ]
        
        market_movers = {'gainers': gainers, 'losers': losers}
    
    except Exception as e:
        print(f"Error loading market data: {e}")
        market_movers = {'gainers': [], 'losers': []}
    
    # Create Gainers Table
    gainers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
    gainers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
    for gainer in market_movers["gainers"]:
        gainers_table += (
            f"| [{gainer['symbol']}](https://stocknear.com/stocks/{gainer['symbol']}) | {gainer['name'][:30]} | "
            f"{gainer['price']:.2f} | +{gainer['changesPercentage']:.2f}% | "
            f"{format_number(gainer['marketCap'])} |\n"
        )

    # Create Losers Table
    losers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
    losers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
    for loser in market_movers["losers"]:
        losers_table += (
            f"| [{loser['symbol']}](https://stocknear.com/stocks/{loser['symbol']}) | {loser['name'][:30]} | "
            f"{loser['price']:.2f} | {loser['changesPercentage']:.2f}% | "
            f"{format_number(loser['marketCap'])} |\n"
        )
    # Construct final markdown text
    return f"""

Here's a summary of today's Premarket Gainers and Losers, showcasing stocks that stood out before the market opened.

### 📈 Premarket Gainers

{gainers_table}

### 📉 Premarket Losers

{losers_table}

More info can be found here: [Premarket Gainers and Losers](https://stocknear.com/market-mover/premarket/gainers)
"""




def post_to_reddit():
    # Load environment variables
    load_dotenv()
    args = parse_args()
    market_status = args.market_status
    

    # Initialize Reddit instance
    reddit = praw.Reddit(
        client_id=os.getenv('REDDIT_BOT_API_KEY'),
        client_secret=os.getenv('REDDIT_BOT_API_SECRET'),
        username=os.getenv('REDDIT_USERNAME'),
        password=os.getenv('REDDIT_PASSWORD'),
        user_agent=os.getenv('REDDIT_USER_AGENT', 'script:my_bot:v1.0 (by /u/username)')
    )
    
    # Define the subreddit
    subreddit = reddit.subreddit("stocknear")

    flair_choices = subreddit.flair.link_templates  # Get submission flair templates

    # Print all submission flairs
    '''
    print("Submission Flairs:")
    for flair in flair_choices:
        print(f"ID: {flair['id']} | Text: {flair['text']} | CSS Class: {flair['css_class']} | Mod Only: {flair['mod_only']}")
    '''

    
    # Get current date with formatting
    today = datetime.now()
    month_str = today.strftime("%b")
    day = today.day
    year = today.year
    day_suffix = "th" if 11 <= day <= 13 else {1: "st", 2: "nd", 3: "rd"}.get(day % 10, "th")
    formatted_date = f"{month_str} {day}{day_suffix} {year}"
    
    # Load and parse data from JSON file
    with open("json/dashboard/data.json", "rb") as file:
        data = orjson.loads(file.read())
    
    # Define the post configurations
    post_configs = [
        {
            "data_key": "upcomingEarnings",
            "format_func": format_upcoming_earnings_data,
            "title": f"Upcoming Earnings for {formatted_date}",
            "flair_id": "b9f76638-772e-11ef-96c1-0afbf26bd890"
        },
        {
            "data_key": "recentEarnings",
            "format_func": format_recent_earnings_data,
            "title": f"Recent Earnings for {formatted_date}",
            "flair_id": "b9f76638-772e-11ef-96c1-0afbf26bd890"
        },
    ]
    
    if market_status == 0:
        try:
            # Loop through post configurations to submit each post
            for config in post_configs:
                formatted_text = config["format_func"](data.get(config["data_key"], []))
                title = config["title"]
                flair_id = config["flair_id"]
                
                # Submit the post
                post = subreddit.submit(
                    title=title,
                    selftext=formatted_text,
                    flair_id=flair_id
                )
                print(f"Post created successfully: {post.url}")
        
        except praw.exceptions.PRAWException as e:
            print(f"Error posting to Reddit: {str(e)}")
        except Exception as e:
            print(f"Unexpected error: {str(e)}")

    elif market_status == 1: #premarket
        try:
            formatted_content = format_premarket_market()
            title = "Premarket Gainers and Losers for Today 🚀📉"
            post = subreddit.submit(title, selftext=formatted_content, flair_id="b348676c-e451-11ee-8572-328509439585")
            print(f"Post created successfully")
        except Exception as e:
            print(f"Error posting to Reddit: {str(e)}")

    elif market_status == 2: #aftermarket
        try:
            formatted_content = format_afterhour_market()
            title = "Afterhours Gainers and Losers for Today 🚀📉"
            post = subreddit.submit(title, selftext=formatted_content, flair_id="b348676c-e451-11ee-8572-328509439585")
            print(f"Post created successfully")
        except Exception as e:
            print(f"Error posting to Reddit: {str(e)}")
    

if __name__ == "__main__":
    post_to_reddit()