112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
import ujson
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import asyncio
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import aiohttp
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import sqlite3
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from tqdm import tqdm
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from datetime import datetime,timedelta
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import os
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from dotenv import load_dotenv
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from concurrent.futures import ThreadPoolExecutor
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from finra_api_queries import finra_api_queries
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# Load environment variables
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load_dotenv()
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api_key = os.getenv('FINRA_API_KEY')
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api_secret = os.getenv('FINRA_API_SECRET')
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api_token = finra_api_queries.retrieve_api_token(finra_api_key_input=api_key, finra_api_secret_input=api_secret)
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start_date = datetime.today() - timedelta(365)
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end_date = datetime.today()
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start_date = start_date.strftime("%Y-%m-%d")
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end_date = end_date.strftime("%Y-%m-%d")
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dataset_name = "regsho_daily_shorts_volume"
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filtered_columns_input = ['tradeReportDate', 'securitiesInformationProcessorSymbolIdentifier', 'shortParQuantity', 'shortExemptParQuantity', 'totalParQuantity']
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date_filter_inputs = [{'startDate': start_date, 'endDate': end_date, 'fieldName': 'tradeReportDate'}]
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async def get_data(ticker):
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try:
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filters_input = {'securitiesInformationProcessorSymbolIdentifier': [ticker]}
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df = finra_api_queries.retrieve_dataset(
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dataset_name,
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api_token,
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filtered_columns=filtered_columns_input,
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filters = filters_input,
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date_filter=date_filter_inputs)
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df = df.rename(columns={"tradeReportDate": "date","totalParQuantity": "totalVolume", "shortParQuantity": "shortVolume", "securitiesInformationProcessorSymbolIdentifier": "symbol", "shortExemptParQuantity": "shortExemptVolume"})
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summed_df = df.drop('symbol', axis=1).groupby('date').sum().reset_index()
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data = summed_df.to_dict('records')
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# Iterate through the list and calculate the percentages
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for record in data:
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total_volume = record["totalVolume"]
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short_volume = record["shortVolume"]
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short_exempt_volume = record["shortExemptVolume"]
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# Calculate percentages
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short_percent = round((short_volume / total_volume) * 100,2)
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short_exempt_percent = round((short_exempt_volume / total_volume) * 100,2)
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# Add new elements to the dictionary
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record["shortPercent"] = short_percent
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record["shortExemptPercent"] = short_exempt_percent
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return data
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except Exception as e:
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print(f"Error fetching data for {ticker}: {e}")
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return []
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async def save_json(symbol, data):
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# Use async file writing to avoid blocking the event loop
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loop = asyncio.get_event_loop()
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path = f"json/dark-pool/companies/{symbol}.json"
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os.makedirs(os.path.dirname(path), exist_ok=True)
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await loop.run_in_executor(None, ujson.dump, data, open(path, 'w'))
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async def process_ticker(ticker):
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data = await get_data(ticker)
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if len(data)>0:
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await save_json(ticker, data)
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async def run():
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.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 marketCap >= 1E9 AND symbol NOT LIKE '%.%'")
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stocks_symbols = [row[0] for row in cursor.fetchall()]
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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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total_symbols = stocks_symbols+ etf_symbols
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async with aiohttp.ClientSession() as session:
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tasks = []
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for ticker in total_symbols:
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tasks.append(process_ticker(ticker))
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# Run tasks concurrently in batches to avoid too many open connections
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batch_size = 10 # Adjust based on your system's capacity
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for i in tqdm(range(0, len(tasks), batch_size)):
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batch = tasks[i:i + batch_size]
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await asyncio.gather(*batch)
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if __name__ == "__main__":
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try:
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asyncio.run(run())
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except Exception as e:
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print(f"An error occurred: {e}") |