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