607 lines
25 KiB
Python
607 lines
25 KiB
Python
import praw
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import orjson
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from datetime import datetime
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import os
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from dotenv import load_dotenv
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import time
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import ujson
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import argparse
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import argparse
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def parse_args():
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parser = argparse.ArgumentParser(description='Post Type')
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parser.add_argument('--post_type', choices=['earnings', 'stock-list', 'premarket', 'aftermarket',"option"],
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type=str, default='earnings',
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help='Post type: "earnings" (default), "stock-list", "premarket", or "aftermarket"')
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return parser.parse_args()
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def get_current_weekday():
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"""Return the current weekday name."""
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return datetime.now().strftime("%A")
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def format_time(time_str):
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"""Format time string to AM/PM format"""
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if not time_str:
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return ""
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try:
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time_parts = time_str.split(':')
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hours = int(time_parts[0])
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minutes = int(time_parts[1])
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period = "AM" if hours < 12 else "PM"
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if hours > 12:
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hours -= 12
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elif hours == 0:
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hours = 12
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return f"{hours:02d}:{minutes:02d} {period}"
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except:
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return ""
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def format_number(num, decimal=False):
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"""Abbreviate large numbers with B/M suffix"""
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if decimal:
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if num >= 1_000_000_000:
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return f"{num / 1_000_000_000:.2f}B"
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elif num >= 1_000_000:
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return f"{num / 1_000_000:.2f}M"
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return f"{num:,.0f}"
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else:
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if num >= 1_000_000_000:
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return f"{num / 1_000_000_000:,.2f}B"
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elif num >= 1_000_000:
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return f"{num / 1_000_000:,.2f}M"
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return f"{num:,.0f}" # Format smaller numbers with commas
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def calculate_yoy_change(current, prior):
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"""Calculate year-over-year percentage change"""
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if prior and prior != 0:
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return ((current / prior - 1) * 100)
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return 0
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def get_market_timing(time_str):
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"""Determine if earnings are before, after, or during market hours"""
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if not time_str:
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return ""
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try:
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time_parts = time_str.split(':')
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hours = int(time_parts[0])
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minutes = int(time_parts[1])
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if hours < 9 or (hours == 9 and minutes <= 30):
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return "before market opens."
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elif hours >= 16:
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return "after market closes."
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else:
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return "during market."
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except:
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return ""
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def format_upcoming_earnings_data(earnings_data):
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"""Format earnings data into Reddit-friendly markdown with hyperlinks."""
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formatted_items = []
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for item in earnings_data:
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symbol = item.get('symbol', None)
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if symbol is not None:
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name = item.get('name', 'Unknown')
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market_timing = get_market_timing(item.get('time'))
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revenue_formatted = format_number(item.get('revenueEst', 0))
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revenue_yoy = calculate_yoy_change(item.get('revenueEst', 0), item.get('revenuePrior', 1)) # Avoid division by zero
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eps_yoy = calculate_yoy_change(item.get('epsEst', 0), item.get('epsPrior', 1)) # Avoid division by zero
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# Determine reporting time text
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if item.get('isToday'):
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report_timing = "will report today"
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else:
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current_day = datetime.now().strftime('%A')
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report_timing = "will report tomorrow" if current_day in ['Monday', 'Tuesday', 'Wednesday', 'Thursday'] else "will report Monday"
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# Create hyperlink for symbol
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symbol_link = f"[{symbol}](https://stocknear.com/stocks/{symbol})"
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# Format the entry text
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entry = (
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f"* **{name}** ({symbol_link}) {report_timing} {market_timing} "
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f"Analysts estimate {revenue_formatted} in revenue ({revenue_yoy:.2f}% YoY) and "
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f"${item.get('epsEst', 0):.2f} in earnings per share ({eps_yoy:.2f}% YoY).\n\n"
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)
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formatted_items.append(entry)
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support_message = "\nInvest in yourself and embrace data-driven decisions to minimize losses, identify opportunities and achieve consistent growth with [Stocknear](https://stocknear.com/pricing) 🚀"
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return "".join(formatted_items) + support_message
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def format_recent_earnings_data(earnings_data):
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"""Format earnings data into Reddit-friendly markdown with bullet points."""
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formatted_items = []
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for item in earnings_data:
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symbol = item.get('symbol', None)
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if symbol is not None:
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name = item.get('name', 'Unknown')
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time = format_time(item.get('time', ''))
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# Financial calculations
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revenue = item.get('revenue', 0) # Changed from revenueEst to revenue for actual results
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revenue_prior = item.get('revenuePrior', 1)
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revenue_surprise = item.get('revenueSurprise', 0)
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eps = item.get('eps', 0) # Changed from epsEst to eps for actual results
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eps_prior = item.get('epsPrior', 1)
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eps_surprise = item.get('epsSurprise', 0)
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# Calculate YoY changes
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revenue_yoy = calculate_yoy_change(revenue, revenue_prior)
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eps_yoy = calculate_yoy_change(eps, eps_prior)
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# Format numbers
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revenue_formatted = format_number(revenue)
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revenue_surprise_formatted = format_number(abs(revenue_surprise))
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# Determine growth/decline text
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revenue_trend = "growth" if revenue_yoy >= 0 else "decline"
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eps_trend = "growth" if eps_yoy >= 0 else "decline"
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# Create hyperlink for symbol
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symbol_link = f"[{symbol}](https://stocknear.com/stocks/{symbol})"
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# Format the entry text with nested bullet points
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entry = (
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f"**{name}** ({symbol_link}) has released its quarterly earnings at {time}:\n\n"
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f"* Revenue of {revenue_formatted} "
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f"{'exceeds' if revenue_surprise > 0 else 'misses'} estimates by {revenue_surprise_formatted}, "
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f"with {revenue_yoy:.2f}% YoY {revenue_trend}.\n\n"
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f"* EPS of ${eps:.2f} "
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f"{'exceeds' if eps_surprise > 0 else 'misses'} estimates by ${abs(eps_surprise):.2f}, "
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f"with {eps_yoy:.2f}% YoY {eps_trend}.\n\n"
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)
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formatted_items.append(entry)
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support_message = "\nInvest in yourself and embrace data-driven decisions to minimize losses, identify opportunities and achieve consistent growth with [Stocknear](https://stocknear.com/pricing) 🚀"
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return "".join(formatted_items) + support_message
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def format_afterhour_market():
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try:
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# Load gainers data
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with open("json/market-movers/afterhours/gainers.json", 'r') as file:
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data = ujson.load(file)
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gainers = [
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{'symbol': item['symbol'], 'name': item['name'], 'price': item['price'],
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'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']}
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for item in data[:5]
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]
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# Load losers data
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with open("json/market-movers/afterhours/losers.json", 'r') as file:
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data = ujson.load(file)
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losers = [
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{'symbol': item['symbol'], 'name': item['name'], 'price': item['price'],
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'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']}
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for item in data[:5]
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]
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market_movers = {'gainers': gainers, 'losers': losers}
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except Exception as e:
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print(f"Error loading market data: {e}")
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market_movers = {'gainers': [], 'losers': []}
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# Create Gainers Table
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gainers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
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gainers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for gainer in market_movers["gainers"]:
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gainers_table += (
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f"| [{gainer['symbol']}](https://stocknear.com/stocks/{gainer['symbol']}) | {gainer['name'][:30]} | "
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f"{gainer['price']:.2f} | +{gainer['changesPercentage']:.2f}% | "
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f"{format_number(gainer['marketCap'])} |\n"
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)
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# Create Losers Table
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losers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
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losers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for loser in market_movers["losers"]:
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losers_table += (
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f"| [{loser['symbol']}](https://stocknear.com/stocks/{loser['symbol']}) | {loser['name'][:30]} | "
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f"{loser['price']:.2f} | {loser['changesPercentage']:.2f}% | "
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f"{format_number(loser['marketCap'])} |\n"
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)
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# Construct final markdown text
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return f"""
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Here's a summary of today's After-Hours Gainers and Losers, showcasing stocks that stood out after the market closed.
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### 📈 After-Hours Gainers
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{gainers_table}
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### 📉 After-Hours Losers
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{losers_table}
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More info can be found here: [After-Hours Gainers and Losers](https://stocknear.com/market-mover/afterhours/gainers)
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"""
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def format_premarket_market():
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try:
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# Load gainers data
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with open("json/market-movers/premarket/gainers.json", 'r') as file:
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data = ujson.load(file)
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gainers = [
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{'symbol': item['symbol'], 'name': item['name'], 'price': item['price'],
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'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']}
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for item in data[:5]
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]
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# Load losers data
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with open("json/market-movers/premarket/losers.json", 'r') as file:
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data = ujson.load(file)
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losers = [
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{'symbol': item['symbol'], 'name': item['name'], 'price': item['price'],
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'changesPercentage': item['changesPercentage'], 'marketCap': item['marketCap']}
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for item in data[:5]
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]
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market_movers = {'gainers': gainers, 'losers': losers}
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except Exception as e:
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print(f"Error loading market data: {e}")
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market_movers = {'gainers': [], 'losers': []}
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# Create Gainers Table
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gainers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
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gainers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for gainer in market_movers["gainers"]:
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gainers_table += (
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f"| [{gainer['symbol']}](https://stocknear.com/stocks/{gainer['symbol']}) | {gainer['name'][:30]} | "
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f"{gainer['price']:.2f} | +{gainer['changesPercentage']:.2f}% | "
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f"{format_number(gainer['marketCap'])} |\n"
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)
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# Create Losers Table
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losers_table = "| Symbol | Name | Price | Change (%) | Market Cap |\n"
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losers_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for loser in market_movers["losers"]:
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losers_table += (
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f"| [{loser['symbol']}](https://stocknear.com/stocks/{loser['symbol']}) | {loser['name'][:30]} | "
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f"{loser['price']:.2f} | {loser['changesPercentage']:.2f}% | "
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f"{format_number(loser['marketCap'])} |\n"
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)
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# Construct final markdown text
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return f"""
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Here's a summary of today's Premarket Gainers and Losers, showcasing stocks that stood out before the market opened.
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### 📈 Premarket Gainers
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{gainers_table}
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### 📉 Premarket Losers
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{losers_table}
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More info can be found here: [Premarket Gainers and Losers](https://stocknear.com/market-mover/premarket/gainers)
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"""
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def format_option_data():
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try:
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with open("json/stocks-list/list/highest-option-premium.json", 'r') as file:
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data = ujson.load(file)
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highest_premium = [
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{'symbol': item['symbol'],
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'changesPercentage': item['changesPercentage'], 'totalPrem': item['totalPrem'],'totalOI': item['totalOI'],'ivRank':item['ivRank']}
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for item in data[:5]
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]
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with open("json/stocks-list/list/highest-option-iv-rank.json", 'r') as file:
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data = ujson.load(file)
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highest_iv_rank = [
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{'symbol': item['symbol'],
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'changesPercentage': item['changesPercentage'], 'totalPrem': item['totalPrem'],'totalOI': item['totalOI'],'ivRank':item['ivRank']}
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for item in data[:5]
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]
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with open("json/stocks-list/list/highest-open-interest-change.json", 'r') as file:
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data = ujson.load(file)
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highest_change_oi = [
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{'symbol': item['symbol'],
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'changesPercentage': item['changesPercentage'], 'totalPrem': item['totalPrem'],'changeOI': item['changeOI'],'ivRank':item['ivRank']}
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for item in data[:5]
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]
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combined_data = {'highest_premium': highest_premium, 'highest_iv_rank': highest_iv_rank, 'highest_change_oi': highest_change_oi}
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except Exception as e:
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print(f"Error loading market data: {e}")
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combined_data = {'highest_premium': [], 'highest_iv_rank': []}
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# Create highest_premium Table
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highest_premium_table = "| Symbol | Change (%) | Total Prem | IV Rank | Total OI |\n"
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highest_premium_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for item in combined_data["highest_premium"]:
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highest_premium_table += (
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f"| [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | {item['changesPercentage']:.2f}% | "
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f"{format_number(item['totalPrem'])} | {item['ivRank']:.2f} | "
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f"{format_number(item['totalOI'])} |\n"
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)
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# Create highest_iv_rank Table
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highest_iv_rank_table = "| Symbol | Change (%) | Total Prem | IV Rank | Total OI |\n"
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highest_iv_rank_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for item in combined_data["highest_iv_rank"]:
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highest_iv_rank_table += (
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f"| [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | {item['changesPercentage']:.2f}% | "
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f"{format_number(item['totalPrem'])} | {item['ivRank']:.2f} | "
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f"{format_number(item['totalOI'])} |\n"
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)
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# Create highest_iv_rank Table
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highest_change_oi_table = "| Symbol | Change (%) | Total Prem | IV Rank | OI Change|\n"
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highest_change_oi_table += "|:------:|:-----|------:|-----------:|-----------:|\n"
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for item in combined_data["highest_change_oi"]:
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highest_change_oi_table += (
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f"| [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | {item['changesPercentage']:.2f}% | "
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f"{format_number(item['totalPrem'])} | {item['ivRank']:.2f} | "
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f"{format_number(item['changeOI'], decimal=True)} |\n"
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)
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# Construct final markdown text
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return f"""
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Here's a quick overview of the top companies that led the market today with the highest options premium, IV rank and notable open interest (OI) changes—highlighting key stocks that gained attention.
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### Highest Options Premium
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{highest_premium_table}
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### Top IV Rank Leaders
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{highest_iv_rank_table}
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### Hottest Companies with highest OI Change
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{highest_change_oi_table}
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More info can be found at [Stocknear](https://stocknear.com/list)
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"""
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def create_post(post_data):
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include_rsi = False
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include_volume = post_data['data_type'] == 'penny-stocks'
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try:
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# Use the parameter passed to the function
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with open(f"json/stocks-list/list/{post_data['data_type']}.json", 'r') as file:
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data = ujson.load(file)
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# Limit to first 5 items and select specific fields
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include_rsi = post_data['data_type'] in ["overbought-stocks", "oversold-stocks"]
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data = [
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{
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'rank': item['rank'],
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'symbol': item['symbol'],
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'price': item['price'],
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'changesPercentage': item['changesPercentage'],
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'marketCap': item['marketCap'],
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'rsi': item.get('rsi') if include_rsi else None,
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'volume': item.get('volume') if include_volume else None
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}
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for item in data[:5]
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]
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except Exception as e:
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print(f"Error loading data: {e}")
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data = []
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# Create Markdown table headers
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if include_rsi:
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data_table = "| Rank | Symbol | RSI | Price | Change (%) | Market Cap |\n"
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data_table += "|:----:|:------|----:|------:|-----------:|-----------:|\n"
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elif include_volume:
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data_table = "| Rank | Symbol | Price | Change (%) | Volume | Market Cap |\n"
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data_table += "|:----:|:------|------:|-----------:|-------:|-----------:|\n"
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else:
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data_table = "| Rank | Symbol | Price | Change (%) | Market Cap |\n"
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data_table += "|:----:|:------|------:|-----------:|-----------:|\n"
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# Generate table rows
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for item in data:
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if include_rsi:
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data_table += (
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f"| {item['rank']} | [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | "
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f"{item['rsi']:.2f} | {item['price']:.2f} | {'+' if item['changesPercentage'] > 0 else ''}{item['changesPercentage']:.2f}% | "
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f"{format_number(item['marketCap'])} |\n"
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)
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elif include_volume:
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data_table += (
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f"| {item['rank']} | [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | "
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f"{item['price']:.2f} | {'+' if item['changesPercentage'] > 0 else ''}{item['changesPercentage']:.2f}% | "
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f"{format_number(item['volume'])} | {format_number(item['marketCap'])} |\n"
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)
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else:
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data_table += (
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f"| {item['rank']} | [{item['symbol']}](https://stocknear.com/stocks/{item['symbol']}) | "
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f"{item['price']:.2f} | {'+' if item['changesPercentage'] > 0 else ''}{item['changesPercentage']:.2f}% | "
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f"{format_number(item['marketCap'])} |\n"
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)
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# Return the Markdown string
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return f"""
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{data_table}
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The complete list can be found [here]({post_data['url']})
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*{post_data['info_text']}*
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*PS: If you find this post valuable please leave an upvote. Would love to hear what you guys think.*
|
||
"""
|
||
|
||
|
||
|
||
|
||
def post_to_reddit():
|
||
# Load environment variables
|
||
load_dotenv()
|
||
args = parse_args()
|
||
post_type = args.post_type
|
||
|
||
|
||
# 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())
|
||
|
||
if post_type == 'stock-list':
|
||
post_configs = [
|
||
{
|
||
"data_type": "penny-stocks",
|
||
"title": f"Top 5 Actively Traded Penny Stocks by Volume 🚀",
|
||
"url": "https://stocknear.com/list/penny-stocks",
|
||
"info_text": "Penny stocks are generally defined as stocks trading below $5 per share. This list is filtered to show only stocks with a volume over 10K.",
|
||
"flair_id": "b348676c-e451-11ee-8572-328509439585"
|
||
},
|
||
{
|
||
"data_type": "overbought-stocks",
|
||
"title": f"Top 5 Most Overbought Companies 📉",
|
||
"url": "https://stocknear.com/list/overbought-stocks",
|
||
'info_text': "I’ve compiled a list of the top 5 most overbought companies based on RSI (Relative Strength Index) data. For those who don’t know, RSI is a popular indicator that ranges from 0 to 100, with values above 70 typically indicating that a stock is overbought.",
|
||
"flair_id": "b348676c-e451-11ee-8572-328509439585"
|
||
},
|
||
{
|
||
"data_type": "oversold-stocks",
|
||
"title": f"Top 5 Most Oversold Companies 📈",
|
||
"url": "https://stocknear.com/list/oversold-stocks",
|
||
'info_text': "I’ve compiled a list of the top 5 most oversold companies based on RSI (Relative Strength Index) data. For those who don’t know, RSI is a popular indicator that ranges from 0 to 100, with values below 30 typically indicating that a stock is oversold.",
|
||
"flair_id": "b348676c-e451-11ee-8572-328509439585"
|
||
},
|
||
]
|
||
|
||
for item in post_configs:
|
||
formatted_text = create_post(item)
|
||
title = item["title"]
|
||
flair_id = item["flair_id"]
|
||
|
||
# Submit the post
|
||
post = subreddit.submit(
|
||
title=title,
|
||
selftext=formatted_text,
|
||
flair_id=flair_id
|
||
)
|
||
|
||
|
||
if post_type == 'earnings':
|
||
# 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"
|
||
},
|
||
]
|
||
|
||
try:
|
||
# Loop through post configurations to submit each post
|
||
for config in post_configs:
|
||
if len(data.get(config["data_key"], [])) > 0:
|
||
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")
|
||
|
||
except praw.exceptions.PRAWException as e:
|
||
print(f"Error posting to Reddit: {str(e)}")
|
||
except Exception as e:
|
||
print(f"Unexpected error: {str(e)}")
|
||
|
||
if post_type == '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)}")
|
||
|
||
if post_type == '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 post_type == 'option':
|
||
|
||
try:
|
||
formatted_content = format_option_data()
|
||
title = "Top Companies with the Highest Options Premiums, IV Rank and OI Change 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()
|