How to download quarterly reports from IEX Cloud

Signals
1

Hi,

wanted to share a simple script on how to download all available SEC 10-Q filings from IEX Cloud for all US companies.

Might be useful for you models :wink:
Can be obtained for ~$70 from IEX

Have fun!

import requests
import json
import pandas as pd
import numerapi

# In[ select tickers]

napi = numerapi.SignalsAPI()

ticker_map = pd.read_csv('https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv')
ticker_map.to_csv('signals_ticker_map_w_bbg.csv', index=False)
print(f"Number of tickers in map: {len(ticker_map)}")

# read in list of active Signals tickers which can change slightly era to era
eligible_tickers = pd.Series(napi.ticker_universe(), name='numerai_ticker')
print(f"Number of eligible tickers: {len(eligible_tickers)}")

# filter on US stocks only
eligible_tickers = eligible_tickers[eligible_tickers.str.contains(' US')]

# map eligible numerai tickers to yahoo finance tickers
yfinance_tickers = eligible_tickers.map(dict(zip(ticker_map['bloomberg_ticker'], ticker_map['yahoo']))).dropna()
numerai_tickers = ticker_map['bloomberg_ticker']
print(f'Number of eligible, mapped tickers: {len(yfinance_tickers)}')

# In[ Load data]

sandbox = True
domain = 'https://sandbox.iexapis.com/stable' if sandbox else 'https://cloud.iexapis.com/stable'
api_token = 'xxx' if sandbox else 'xxx'

session = requests.session()
params = {}
params["token"] = api_token
headers = {"project": "sandbox"}
full_data = []

for symbol in yfinance_tickers:
    
    url = domain + f'/time-series/reported_financials/{symbol}/10-Q/?from=2005-01-01&format=json'
    response = session.get(url=url, params=params, headers=headers)
    
    if response.status_code == 200:      
    
        df = pd.DataFrame( json.loads(response.text) )
        df['ticker'] = symbol        
        full_data.append(df)
        
        print(f'RESPONSE {symbol}: {response.status_code}, cols: {len(df.columns)}')

    else:
        print(f"RESPONSE {symbol}: {response.status_code}")
        print(f'error: {response}')
        break
        
# In[ Save last step ]

print('Saving...')
full_data_df = pd.concat(full_data)
counts = full_data_df.isna().sum().sort_values()
indexes = counts[counts<full_data_df.shape[0]*0.8].index
full_data_df = full_data_df[indexes]

full_data_df.to_csv('financials_full_final_sandbox.csv')
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