add retail volume

This commit is contained in:
MuslemRahimi 2024-06-15 15:17:25 +02:00
parent 660109daba
commit 95b4cd9439
2 changed files with 120 additions and 0 deletions

102
app/cron_retail_volume.py Normal file
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@ -0,0 +1,102 @@
import ujson
import asyncio
import aiohttp
import sqlite3
from datetime import datetime,timedelta
from tqdm import tqdm
from dotenv import load_dotenv
import os
load_dotenv()
api_key = os.getenv('NASDAQ_API_KEY')
# Get today's date
today = datetime.now()
# Calculate the date six months ago
six_months_ago = today - timedelta(days=6*30) # Rough estimate, can be refined
async def save_json(symbol, data):
with open(f"json/retail-volume/companies/{symbol}.json", 'w') as file:
ujson.dump(data, file)
# Function to filter the list
def filter_past_six_months(data):
filtered_data = []
for entry in data:
entry_date = datetime.strptime(entry['date'], '%Y-%m-%d')
if entry_date >= six_months_ago:
filtered_data.append(entry)
sorted_data = sorted(filtered_data, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
return sorted_data
async def get_data(ticker_list):
ticker_str = ','.join(ticker_list)
async with aiohttp.ClientSession() as session:
url = f"https://data.nasdaq.com/api/v3/datatables/NDAQ/RTAT?api_key={api_key}&ticker={ticker_str}"
async with session.get(url) as response:
if response.status == 200:
data = (await response.json())['datatable']['data']
return data
else:
return []
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")
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
chunk_size = len(total_symbols) // 70 # Divide the list into N chunks
chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
most_retail_volume = []
for chunk in tqdm(chunks):
data = await get_data(chunk)
# Transforming the list of lists into a list of dictionaries
transformed_data = [
{
'date': entry[0],
'symbol': entry[1],
'traded': entry[2]*30*10**9, #data is normalized to $30B per day
'sentiment': entry[3]
}
for entry in data
]
for symbol in chunk:
try:
filtered_data = [item for item in transformed_data if symbol == item['symbol']]
res = filter_past_six_months(filtered_data)
most_retail_volume.append({'assetType': 'stocks' if res[-1]['symbol'] in stocks_symbols else 'etf','symbol': res[-1]['symbol'], 'traded': res[-1]['traded'], 'sentiment': res[-1]['sentiment']})
await save_json(symbol, res)
except:
pass
most_retail_volume = sorted(most_retail_volume, key=lambda x: x['traded'], reverse=True)[:100] # top 100 retail volume stocks
with open(f"json/retail-volume/data.json", 'w') as file:
ujson.dump(most_retail_volume, file)
try:
asyncio.run(run())
except Exception as e:
print(e)

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@ -2820,6 +2820,24 @@ async def get_most_shorted_stocks():
except: except:
res = [] res = []
redis_client.set(cache_key, ujson.dumps(res))
redis_client.expire(cache_key, 3600*3600) # Set cache expiration time to 1 day
return res
@app.post("/retail-volume")
async def get_retail_volume(data:TickerData):
ticker = data.ticker.upper()
cache_key = f"retail-volume-{ticker}"
cached_result = redis_client.get(cache_key)
if cached_result:
return ujson.loads(cached_result)
try:
with open(f"json/retail-volume/companies/{ticker}.json", 'r') as file:
res = ujson.load(file)
except:
res = []
redis_client.set(cache_key, ujson.dumps(res)) redis_client.set(cache_key, ujson.dumps(res))
redis_client.expire(cache_key, 3600*3600) # Set cache expiration time to 1 day redis_client.expire(cache_key, 3600*3600) # Set cache expiration time to 1 day
return res return res