ml-finance-python

python scripts for finance machine learning

git clone https://9o.is/git/ml-finance-python.git

broker.py

(1238B)

      1 import time
      2 import json
      3 from datetime import datetime as dt
      4 import requests
      5 import pandas as pd
      6 
      7 
      8 def get_access_token():
      9     with open('/home/user/trading/quantopian/research_public/scratch/credentials', 'r') as file:
     10         data = file.read()
     11     obj = json.loads(data)
     12     return obj['access_token']
     13 
     14 
     15 def get_pricing(symbol, start_date, end_date):
     16     start = str(int(time.mktime(dt.strptime(start_date, '%Y-%m-%d').timetuple()) * 1000))
     17     end = str(int(time.mktime(dt.strptime(end_date, '%Y-%m-%d').timetuple()) * 1000))
     18 
     19     url = 'https://api.tdameritrade.com/v1/marketdata/' + symbol + '/pricehistory'
     20 
     21     headers = {
     22         'Authorization': 'Bearer ' + get_access_token()
     23     }
     24 
     25     params = (
     26         ('apikey', 'UHQLAYUDK3GCXEDLJBFXMUTEDNOCCBL4'),
     27         ('periodType', 'month'),
     28         ('frequencyType', 'daily'),
     29         ('frequency', '1'),
     30         ('endDate', end),
     31         ('startDate', start),
     32     )
     33 
     34     response = requests.get(url=url, headers=headers, params=params)
     35 
     36     content = [{
     37         'date': dt.fromtimestamp(int(tick.get('datetime')) / 1000),
     38         'price': tick.get('close'),
     39     } for tick in response.json()['candles']]
     40 
     41     df = pd.DataFrame(content)
     42     df = df.set_index('date')
     43 
     44     return df