111 lines
4.7 KiB
Python
111 lines
4.7 KiB
Python
import aiohttp
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import ujson
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import sqlite3
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import asyncio
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import pandas as pd
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from tqdm import tqdm
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import orjson
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from collections import defaultdict
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with open(f"json/stock-screener/data.json", 'rb') as file:
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stock_screener_data = orjson.loads(file.read())
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# Convert stock_screener_data into a dictionary keyed by symbol
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stock_screener_data_dict = {item['symbol']: item for item in stock_screener_data}
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def save_as_json(data):
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with open(f"json/industry/overview.json", 'w') as file:
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ujson.dump(data, file)
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#async def get_data():
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def run():
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# Initialize a dictionary to store stock count, market cap, and other totals for each industry
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sector_industry_data = defaultdict(lambda: defaultdict(lambda: {
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'numStocks': 0,
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'totalMarketCap': 0.0,
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'totalPE': 0.0,
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'totalDividendYield': 0.0,
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'totalNetIncome': 0.0,
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'totalRevenue': 0.0,
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'totalChange1M': 0.0,
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'totalChange1Y': 0.0,
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'peCount': 0,
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'dividendCount': 0,
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'change1MCount': 0,
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'change1YCount': 0
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}))
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# Iterate through stock_screener_data to accumulate values
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for stock in stock_screener_data:
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sector = stock.get('sector')
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industry = stock.get('industry')
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market_cap = stock.get('marketCap')
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pe = stock.get('pe')
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dividend_yield = stock.get('dividendYield')
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net_income = stock.get('netIncome')
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revenue = stock.get('revenue')
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change_1_month = stock.get('change1M')
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change_1_year = stock.get('change1Y')
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# Ensure both sector and industry are valid and that market cap is a valid number
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if sector and industry and market_cap is not None:
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# Update stock count and accumulate market cap
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sector_industry_data[sector][industry]['numStocks'] += 1
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sector_industry_data[sector][industry]['totalMarketCap'] += float(market_cap)
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# Accumulate PE ratio if available
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if pe is not None:
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sector_industry_data[sector][industry]['totalPE'] += float(pe)
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sector_industry_data[sector][industry]['peCount'] += 1
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# Accumulate dividend yield if available
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if dividend_yield is not None:
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sector_industry_data[sector][industry]['totalDividendYield'] += float(dividend_yield)
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sector_industry_data[sector][industry]['dividendCount'] += 1
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# Accumulate net income and revenue for profit margin calculation
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if net_income is not None and revenue is not None:
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sector_industry_data[sector][industry]['totalNetIncome'] += float(net_income)
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sector_industry_data[sector][industry]['totalRevenue'] += float(revenue)
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# Accumulate 1-month change if available
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if change_1_month is not None:
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sector_industry_data[sector][industry]['totalChange1M'] += float(change_1_month)
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sector_industry_data[sector][industry]['change1MCount'] += 1
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# Accumulate 1-year change if available
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if change_1_year is not None:
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sector_industry_data[sector][industry]['totalChange1Y'] += float(change_1_year)
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sector_industry_data[sector][industry]['change1YCount'] += 1
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# Prepare the final data in the requested format
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result = {}
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for sector, industries in sector_industry_data.items():
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# Sort industries by stock count in descending order
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sorted_industries = sorted(industries.items(), key=lambda x: x[1]['numStocks'], reverse=True)
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# Add sorted industries with averages to the result for each sector
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result[sector] = [
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{
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'industry': industry,
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'numStocks': data['numStocks'],
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'totalMarketCap': data['totalMarketCap'],
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'pe': round((data['totalMarketCap'] / data['totalNetIncome']),2) if data['totalNetIncome'] > 0 else None,
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'avgDividendYield': round((data['totalDividendYield'] / data['dividendCount']),2) if data['dividendCount'] > 0 else None,
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'profitMargin': round((data['totalNetIncome'] / data['totalRevenue'])*100,2) if data['totalRevenue'] > 0 else None,
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'avgChange1M': round((data['totalChange1M'] / data['change1MCount']),2) if data['change1MCount'] > 0 else None,
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'avgChange1Y': round((data['totalChange1Y'] / data['change1YCount']),2) if data['change1YCount'] > 0 else None
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} for industry, data in sorted_industries
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]
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print(result)
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save_as_json(result)
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run() |