64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
from datetime import datetime
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import orjson
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import sqlite3
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import asyncio
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from tqdm import tqdm
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# Load stock screener data
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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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stock_screener_data_dict = {item['symbol']: item for item in stock_screener_data}
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async def save_json(symbol, data):
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"""Save JSON data to a file."""
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with open(f"json/statistics/{symbol}.json", 'wb') as file:
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file.write(orjson.dumps(data))
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async def get_data(symbol):
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"""Extract specified columns data for a given symbol."""
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columns = ['sharesOutStanding', 'sharesQoQ', 'sharesYoY','institutionalOwnership','floatShares',
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'peg','priceEarningsRatio','forwardPE','priceToSalesRatio','forwardPS','priceToBookRatio','priceToFreeCashFlowsRatio',
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'sharesShort','shortOutStandingPercent','shortFloatPercent','shortRatio',
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'enterpriseValue','evEarnings','evSales','evEBITDA','evEBIT','evFCF',
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'currentRatio','quickRatio','debtRatio','debtEquityRatio','interestCoverage','cashFlowToDebtRatio','totalDebtToCapitalization',
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'returnOnEquity','returnOnAssets','returnOnCapital','revenuePerEmployee','profitPerEmployee',
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'employees','assetTurnover','inventoryTurnover','incomeTaxExpense','effectiveTaxRate','beta','returnOnInvestedCapital',
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'change1Y','sma50','sma200','rsi','avgVolume','revenue','netIncome','grossProfit','operatingIncome','ebitda','ebit','eps',
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'cashAndCashEquivalents','totalDebt','retainedEarnings','totalAssets','workingCapital','operatingCashFlow',
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'capitalExpenditure','freeCashFlow','freeCashFlowPerShare','grossProfitMargin','operatingProfitMargin','pretaxProfitMargin',
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'netProfitMargin','ebitdaMargin','ebitMargin','freeCashFlowMargin','failToDeliver','relativeFTD',
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'annualDividend','dividendYield','payoutRatio','dividendGrowth','earningsYield','freeCashFlowYield','altmanZScore','piotroskiScore',
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'lastStockSplit','splitType','splitRatio','analystRating','analystCounter','priceTarget','upside'
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]
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if symbol in stock_screener_data_dict:
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result = {}
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for column in columns:
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result[column] = stock_screener_data_dict[symbol].get(column, None)
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return result
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return {}
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async def run():
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"""Main function to run the data extraction process."""
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# Connect to SQLite database
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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 WHERE symbol NOT LIKE '%.%'")
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total_symbols = [row[0] for row in cursor.fetchall()]
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con.close()
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# Process symbols with progress bar
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for symbol in tqdm(total_symbols, desc="Extracting data"):
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data = await get_data(symbol)
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if data: # Only save if we have data
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await save_json(symbol, data)
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if __name__ == "__main__":
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loop = asyncio.get_event_loop()
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loop.run_until_complete(run()) |