719 lines
24 KiB
Python
719 lines
24 KiB
Python
### -------------------- LIBARIES --------------------
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import datetime
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import time
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import json
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import yfinance as yf
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import pandas as pd
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import requests
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import config
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### -------------------- FUNCTIONS --------------------
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# ------------------#
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# LEVEL 1 FUNCTIONS #
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# ------------------#
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# CALCULATE THE IRR
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def calculate_irr(date_now, date_open, value_now, value_open):
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error = False
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irr = 0.0
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try:
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# Count the number in days
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a = date_now - date_open
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a = a.days
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# Am Tag des Kaufs selbst, liegt das Delta in Tagen bei 0
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# Um dennoch einen IRR kalkulieren zu können, wird das Delta auf 1 gsetzt
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if a == 0:
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a = 1
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a = a / 365 # Umrechnung auf Jahresanteil, um auch den Jahreszinssatz zu bekommen
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b = value_now / value_open
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# Catch negative IRRs
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if b < 0:
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b = b * (-1)
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irr = b**(1/a) # matematisch identisch zur b-ten Wurzel von a
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irr = irr * (-1)
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else:
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irr = b**(1/a) # matematisch identisch zur b-ten Wurzel von a
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except:
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error = True
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# Return data if successful
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if error == True:
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print("[ERROR] Calculation of irr")
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return error
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else:
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return irr
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# GET THE DAY OF THE OLDEST TRADE
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def get_date_open_oldest_trade(trades):
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# Identify the open date for the oldest trade
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date_open_oldest_trade = datetime.date.today()
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for i in trades:
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if trades[i]["date_open"] < date_open_oldest_trade:
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date_open_oldest_trade = trades[i]["date_open"]
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return date_open_oldest_trade
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# CREATES LIST OF UNIQUE TICKERS
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def filter_list_of_tickers(trades):
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tickers = []
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try:
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for i in trades:
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# Fetch ticker belonging to trade
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ticker = trades[i]['ticker']
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# Add ticker to list, if not already present
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if ticker not in tickers:
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tickers.append(ticker)
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print("[SUCCESS] Creating a list of {} tickers".format(len(tickers)))
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return tickers
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except:
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print("[ERROR] Creating a list of tickers")
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return tickers
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# NOTION FETCH PAGES
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def notion_get_pages(db_id_trades, num_pages=None):
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try:
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# ------------------ FETCH THE FIRST 100 PAGES FROM A DB
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# Prepare Request
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url = f"https://api.notion.com/v1/databases/{db_id_trades}/query"
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get_all = num_pages is None # If num_pages is None, get all pages, otherwise just the defined number.
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page_size = 100 if get_all else num_pages
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payload = {"page_size": page_size}
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# Make Request
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raw_response = requests.post(url, json=payload, headers=config.notion_headers)
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# Process Reply
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parsed_response = raw_response.json()
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result = parsed_response["results"]
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# ------------------ FETCH 100 MORE PAGES AS OFTEN AS REQUIRED
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while parsed_response["has_more"] and get_all:
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# Prepare Request
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payload = {"page_size": page_size, "start_cursor": parsed_response["next_cursor"]}
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url = f"https://api.notion.com/v1/databases/{db_id_trades}/query"
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# Make Request
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raw_response = requests.post(url, json=payload, headers=config.notion_headers)
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# Process Reply
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parsed_response = raw_response.json()
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result.extend(parsed_response["results"])
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# Logging
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print("[SUCCESS] Fetching Data from Notion for database: {}".format(db_id_trades))
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return result
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except Exception:
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print("[ERROR] Fetching Data from Notion with error: {}".format(Exception))
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return True # Return True when there was an error
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# NOTION UPDATE PAGES
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def notion_update_page(page_id: str, data: dict):
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url = f"https://api.notion.com/v1/pages/{page_id}"
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payload = {"properties": data}
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results = requests.patch(url, json=payload, headers=config.notion_headers)
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return results
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# TRMNL UPDATE DIAGRAMMS
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'''
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def push_trmnl_update_chart(wklydict_numbers, dict_chart, trmnl_page_id):
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# --------------------------------------------------------------------------------- #
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# REQUIREMENTS:
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# The first-level-entry of dict_numbers needs be the "key" for referencing in TRMNL
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# At least 3 keys are expected for numbers to fill the screen
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# The json_chart data does not need a key
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# Instead, each first-level-entry represents a series in the chart by name
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# --------------------------------------------------------------------------------- #
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# Create the data-structure for the chart-object
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payload = {}
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payload = dict_numbers
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# Create the data-structure for the chart-object
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chart = []
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for entry in json_chart:
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series = {}
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name = entry[0]
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dict_chart_values = entry[1]
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series["name"] = name
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series["data"] = dict_chart_values
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chart.append(series)
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# {"name" : name, "data" : [[date, value], [date, value]]}
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try:
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data = json.dumps(json_data, indent=2) # Converts a python-dictionary into a json
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url = trmnl_base_url + trmnl_page_id
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reply = requests.post(url, data=data, headers = trmnl_headers)
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if reply.status_code == 200:
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print("[SUCCESS] Pushing data to TRMNL")
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elif reply.status_code == 429:
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print("[WARNING] Exceeded TRMNL's API rate limits")
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else:
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print(f"[ERROR] Pushing data to TRMNL with server reply code: {reply.status_code}")
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except Exception as e:
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print("[ERROR] Pushing data to TRMNL with error code: {}".format(e))
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'''
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# ------------------#
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# LEVEL 2 FUNCTIONS #
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# ------------------#
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# CREATE LIST OF WEEKLY DATES
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def create_list_wkl_dates(trades):
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index_date = get_date_open_oldest_trade(trades)
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# Move start date from weekend to weekday
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while index_date.weekday() > 5:
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index_date = index_date + datetime.timedelta(days=1)
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# Create list
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list_wkl_dates = []
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while index_date <= datetime.date.today():
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list_wkl_dates.append(index_date.isoformat())
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index_date = index_date + datetime.timedelta(days=7)
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# Logging
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print("[SUCCESS] Generating list of weekly dates with {} entries".format(len(list_wkl_dates)))
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return list_wkl_dates
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# NOTION FETCH & FORMAT TRADES
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def fetch_format_notion_trades(db_id_trades):
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trades = {}
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error = False
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data = notion_get_pages(db_id_trades)
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if data is True:
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error = True
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else:
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for i in data:
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# Each page is loaded as a dictionary
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notion_page = dict(i)
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# Handling desired missing entries
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try:
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date_close = notion_page["properties"]["Close"]["date"]
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date_close = date_close["start"]
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date_close = datetime.date(*map(int, date_close.split('-')))
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except:
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date_close = 0
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# Handeling non-desired missing entries (by skipping this trade)
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try:
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# Try extracting values
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trade = {}
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# Format date-open
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date_open = notion_page["properties"]["Open"]["date"]
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date_open = date_open["start"]
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date_open = datetime.date(*map(int, date_open.split('-')))
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# Combine data into json structure
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trade = {
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'ticker' : notion_page["properties"]["Ticker"]["select"]["name"],
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'date_open' : date_open,
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'date_close' : date_close,
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'course_open' : notion_page["properties"]["Open (€)"]["number"],
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'course_close' : notion_page["properties"]["Close (€)"]["number"],
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'course_current' : notion_page["properties"]["Current (€)"]["number"],
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'irr' : notion_page["properties"]["IRR (%)"]["number"],
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'units' : notion_page["properties"]["Units"]["number"],
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'dividends' : notion_page["properties"]["Dividends (€)"]["number"]
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}
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# Save values
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notion_page_id = notion_page["id"] # Use as key for the dictionary
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trades[notion_page_id] = trade
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except:
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print("[ERROR] Skipped an entry in the notion trades-db - Missing values?")
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# Return data if successful
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if error == True:
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return error
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else:
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return trades
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# NOTION FETCH & FORMAT INVESTMENT OVERVIEW
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def fetch_format_notion_investments(db_id_investments):
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investments = {}
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error = False
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data = notion_get_pages(db_id_investments)
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if data is True:
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error = True
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else:
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for i in data:
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# Each page is loaded as a dictionary
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notion_page = dict(i)
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# Extract values
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notion_page_id = notion_page["id"] # Use as key for the dictionary
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investments[notion_page_id] = {}
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investments[notion_page_id]["ticker"] = notion_page["properties"]["Ticker"]["select"]["name"]
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investments[notion_page_id]["total_dividends"] = notion_page["properties"]["Dividends (€)"]["number"]
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investments[notion_page_id]["current_value"] = notion_page["properties"]["Current (€)"]["number"]
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investments[notion_page_id]["current_irr"] = notion_page["properties"]["IRR (%)"]["number"]
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investments[notion_page_id]["total_performanance"] = notion_page["properties"]["Performance (€)"]["number"]
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# Return data if successful
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if error == True:
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return error
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else:
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return investments
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# YFINANCE FETCH & FORMAT DATA
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def fetch_format_yf_data(tickers):
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yf_data = {}
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error_count = 0
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for i in tickers:
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ticker = i
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try:
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api = yf.Ticker(ticker)
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data = api.history(period="max")
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# Convert to Pandas DataFrame
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data = pd.DataFrame(data)
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# Delete the columns "Stock Splits", "High", "Low" and "Open"
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del data['Open']
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del data['Low']
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del data['High']
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del data['Volume']
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try:
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del data['Stock Splits']
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del data['Capital Gains']
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except:
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time.sleep(0)
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# Get the Number of rows in data
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data_rows = data.shape[0]
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# Create new index without the time from the existing datetime64-index
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old_index = data.index
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new_index = []
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x = 0
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while x < data_rows:
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date = pd.Timestamp.date(old_index[x]) # Converts the "Pandas Timestamp"-object to a "date" object
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new_index.append(date)
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x+=1
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# Add the new index to the dataframe and set it as the index
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data.insert(1, 'Date', new_index)
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data.set_index('Date', inplace=True)
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# Save the data-frame to the yf_data dict
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yf_data[ticker] = data
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# Wait for the API to cool down
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time.sleep(config.api_cooldowm_time)
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except:
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error_count = error_count + 1
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print("[ERROR] Fetching data from yahoo-finance for ticker: {}".format(ticker))
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if error_count == 0:
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print("[SUCCESS] Fetching data from yahoo-finance")
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else:
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print("[ERROR] Fetching data from yahoo-finance in {} cases".format(error_count))
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# Return data if successful
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return yf_data
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# UPDATE NOTION-TRADES-DATABASE
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def push_notion_trades_update(trades):
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# Logging
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error_count = 0
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for notion_page_id in trades:
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try:
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# The irr is stored in the format 1.2534
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# Notion need the format 0,2534
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irr_notion = trades[notion_page_id]['irr'] - 1
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irr_notion = round(irr_notion, 4)
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# Construct Notion-Update-Object
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notion_update = {
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"Current (€)": {
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"number": trades[notion_page_id]['course_current']
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},
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"IRR (%)": {
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"number": irr_notion
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},
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"Dividends (€)": {
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"number": trades[notion_page_id]['dividends']
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}
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}
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# Update the properties of the corresponding notion-page
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notion_update_page(notion_page_id, notion_update)
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except:
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error_count = error_count + 1
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# Wait for the API to cool off
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time.sleep(config.api_cooldowm_time)
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# Logging
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if error_count == 0:
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print ("[SUCCESS] Updating notion trades")
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else:
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print("[[SUCCESS] Updating notion trades in {} cases".format(error_count))
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return trades
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# UPDATE NOTION-INVESTMENT-OVERVIEW
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def push_notion_investment_update(investments):
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# Logging
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error_count = 0
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for notion_page_id in investments:
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try:
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# The irr is stored in the format 1.2534
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# Notion need the format 0,2534
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irr_notion = investments[notion_page_id]['current_irr'] - 1
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irr_notion = round(irr_notion, 4)
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# Construct Notion-Update-Object
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notion_update = {
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"Current (€)": {
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"number": investments[notion_page_id]['current_value']
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},
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"IRR (%)": {
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"number": irr_notion
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},
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"Performance (€)": {
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"number": investments[notion_page_id]['total_performanance']
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},
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"Dividends (€)": {
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"number": investments[notion_page_id]['total_dividends']
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}
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}
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# Update the properties of the corresponding notion-page
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notion_update_page(notion_page_id, notion_update)
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except:
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error_count = error_count + 1
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# Wait for the API to cool off
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time.sleep(config.api_cooldowm_time)
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# Logging
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if error_count == 0:
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print ("[SUCCESS] Updating notion investments")
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else:
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print("[[SUCCESS] Updating notion investments in {} cases".format(error_count))
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return investments
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# SELECT CURRENT VALUES PER TRADE
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def select_current_value_per_trade(trades, history_per_trade):
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# Logging
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error_count = 0
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# Loop over all trades
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for trade_id in trades:
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try:
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# Determine, what values to fetch based on whether the trade was closed already
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date_closed = trades[trade_id]["date_close"]
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if date_closed == 0:
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# If trade still open, use performance data from today
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index_date_iso = datetime.date.today().isoformat()
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else:
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# If trade closed, use performance data from close-date
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index_date_iso = date_closed.isoformat()
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# Fetch data from history and save for this trade
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trades[trade_id]["course_current"] = history_per_trade[index_date_iso][trade_id]['current_course']
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trades[trade_id]["irr"] = history_per_trade[index_date_iso][trade_id]['current_irr']
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trades[trade_id]["dividends"] = history_per_trade[index_date_iso][trade_id]['total_dividends']
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except:
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error_count = error_count + 1
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if error_count == 0:
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print ("[SUCCESS] Selecting current value per trade")
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else:
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print("[ERROR] Selecting current value per trade for {} cases".format(error_count))
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return trades
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# SELECT CURRENT VALUES PER TRADE
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def select_current_value_per_ticker(investments, history_per_ticker):
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# Logging
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error_count = 0
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# Loop over all investments
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for investment_id in investments:
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try:
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# Generate the iso-date of today as the required index
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index_date_iso = datetime.date.today().isoformat()
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# Get the ticker corresponding to the investment
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ticker = investments[investment_id]["ticker"]
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# Select latest data from history and save for this investment
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investments[investment_id]["total_dividends"] = history_per_ticker[index_date_iso][ticker]['total_dividends']
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investments[investment_id]["current_value"] = history_per_ticker[index_date_iso][ticker]['current_value']
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investments[investment_id]["current_irr"] = history_per_ticker[index_date_iso][ticker]['current_irr']
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investments[investment_id]["total_performanance"] = history_per_ticker[index_date_iso][ticker]['total_performanance']
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except:
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error_count = error_count + 1
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if error_count == 0:
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print ("[SUCCESS] Selecting current value per investment")
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else:
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print("[ERROR] Selecting current value per investment for {} cases".format(error_count))
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return investments
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# ------------------#
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# LEVEL 3 FUNCTIONS #
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# ------------------#
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# FILTER ANY HISTORY OBJECT TO SELECTED DATES
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def filter_history_by_list(history, dates_list):
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filtered_history = {}
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# Loop over all days
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for index_date in history:
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# Check, if the history-date is in the filter-list
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if index_date in dates_list:
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# If so, add this date-entry to the filtered history object
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filtered_history[index_date] = history[index_date]
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# Logging
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print ("[SUCCESS] Filtering History")
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return filtered_history
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# CALC HISTORY PER TRADE
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def calc_history_per_trade(trades, yf_data):
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# Create support variables
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history_per_trade = {}
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total_dividends = 0
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warning_count = 0
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date_open_oldest_trade = get_date_open_oldest_trade(trades)
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# ------------------ LOOP OVER ALL TRADES
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for trade_id in trades:
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# ------------------ PREPARE FOR THE (NEXT) LOOP OVER ALL DAYS
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# Set / Reset the index-date to the oldest trade day
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# Resetting is required so that the calculations for the next trade start with day 1
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index_date = date_open_oldest_trade
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|
|
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# Set the initial value for the course on the previous day to 0
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# Just in case the very first trade was made on a weekend somehow, where there is no yfinance data available
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|
previous_course = 0.0
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|
|
|
# Check, if the trade was closed already
|
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# If it was not, set the closure date to the future (Trick 17)
|
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if trades[trade_id]["date_close"] == 0:
|
|
date_close = datetime.date.today() + datetime.timedelta(days=1)
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else:
|
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date_close = trades[trade_id]["date_close"]
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|
date_open = trades[trade_id]["date_open"]
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|
|
|
# Keep ticker for connecting performance later
|
|
ticker = trades[trade_id]['ticker']
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|
|
|
# ------------------ DETERMINE THE COUSE PER DAY
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|
while index_date != datetime.date.today() + datetime.timedelta(days=1):
|
|
|
|
# Fetch course for the day & eventual dividends from yf_data
|
|
try:
|
|
current_course = yf_data[ticker].at[index_date, 'Close']
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|
current_dividends_per_ticker = yf_data[ticker].at[index_date, 'Dividends']
|
|
|
|
# Catch missing yf-data (eg. for weekends) by reusing course from previous day
|
|
except:
|
|
current_course = previous_course
|
|
current_dividends_per_ticker = 0.0 # there are never dividends on non-trading days
|
|
warning_count = warning_count +1 # Increase the warning count
|
|
|
|
# Catch the special case of the day when the trade was closed
|
|
# In this case, the current course needs to be overwritten with the sell-value
|
|
if date_close == index_date:
|
|
current_course = trades[trade_id]['course_close']
|
|
|
|
# Save the result for the next iteration
|
|
# This setup also makes it possible, that a previous course is passed down across mutiple days
|
|
# This makes sense is case i.e. for a weekend
|
|
previous_course = current_course
|
|
|
|
# ------------------ CALCULATE PERFORMANCE IF REQUIRED
|
|
if index_date >= date_open and index_date <= date_close:
|
|
# Calculate performance values
|
|
current_amount = trades[trade_id]['units']
|
|
current_invested = current_amount * trades[trade_id]['course_open']
|
|
total_dividends = total_dividends + current_amount * current_dividends_per_ticker
|
|
current_value = current_amount * current_course
|
|
current_value_with_dividends = current_value + total_dividends
|
|
current_irr = calculate_irr(index_date, date_open, current_value_with_dividends, current_invested)
|
|
total_performanance = current_value_with_dividends - current_invested
|
|
|
|
if current_value_with_dividends == 0:
|
|
print("0-value Error with ticker: {}".format(ticker))
|
|
|
|
else:
|
|
# Write 0, if trade is not relevant for current timeframe
|
|
current_amount = 0
|
|
current_invested = 0.00
|
|
total_dividends = 0.00
|
|
current_value = 0.00
|
|
current_irr = 0.00
|
|
total_performanance = 0.0
|
|
|
|
# ------------------ STORE RESULTS
|
|
index_date_iso = index_date.isoformat()
|
|
|
|
# Store all values into a dict
|
|
dict_a = {}
|
|
dict_a['current_amount'] = current_amount
|
|
dict_a['current_invested'] = current_invested
|
|
dict_a['total_dividends'] = total_dividends
|
|
dict_a['current_value'] = current_value
|
|
dict_a['current_irr'] = current_irr
|
|
dict_a['current_course'] = current_course
|
|
dict_a['total_performanance'] = total_performanance
|
|
|
|
# Check if the date is already present
|
|
if index_date_iso in history_per_trade:
|
|
dict_b = history_per_trade[index_date_iso]
|
|
else:
|
|
dict_b = {}
|
|
# Add the values to the trade_id value-pair
|
|
dict_b[trade_id] = dict_a
|
|
|
|
# Update the hostory_per_trade
|
|
history_per_trade.update({index_date_iso : dict_b})
|
|
|
|
# ------------------ NEXT ITERATION
|
|
index_date = index_date + datetime.timedelta(days=1)
|
|
|
|
# ------------------ LOGGING
|
|
if warning_count > 0:
|
|
print("[WARNING] Calculating history for trade: {} with ticker: {}".format(trade_id, ticker))
|
|
print(" No yf-data available in {} cases of ticker & date".format(warning_count))
|
|
print(" Probably reason is non-trading-days eg. weekends")
|
|
print(" Used values from previous trade-day instead")
|
|
else:
|
|
print("[SUCCESS] Calculating history for trade: {} with ticker: {}".format(trade_id, ticker))
|
|
|
|
# Reset warning count for the next trade
|
|
warning_count = 0
|
|
|
|
data = json.dumps(history_per_trade, indent=2) # Converts a python-dictionary into a json
|
|
|
|
# Logging
|
|
print("[SUCCESS] Calculating history for trades")
|
|
with open("history_per_trade.json", "w") as f:
|
|
f.write(data)
|
|
|
|
return history_per_trade
|
|
|
|
# CALC THE HISTORY PER TRADE & OVERALL
|
|
def calc_history_per_ticker(history_per_trade, tickers, trades):
|
|
|
|
# ------------------ CREATE JSON OBJECT
|
|
# Create the json-dict
|
|
history_per_ticker = {}
|
|
|
|
# Loop over each date entry in the history
|
|
for date_entry in history_per_trade:
|
|
|
|
# Create a dict to store the results per day and ticker
|
|
dict_daily = {}
|
|
for ticker in tickers:
|
|
dict_daily[ticker] = {}
|
|
dict_daily[ticker]["current_invested"] = 0
|
|
dict_daily[ticker]["total_dividends"] = 0
|
|
dict_daily[ticker]["current_value"] = 0
|
|
dict_daily[ticker]["current_irr"] = 0
|
|
dict_daily[ticker]["current_irr"] = 0
|
|
dict_daily[ticker]["total_performanance"] = 0
|
|
dict_daily[ticker]["current_amount"] = 0 # Added only for ticker entries, not for the "total" value
|
|
dict_daily[ticker]["current_course"] = 0 # Added only for ticker entries, not for the "total" value
|
|
dict_daily["total"] = {}
|
|
dict_daily["total"]["current_invested"] = 0
|
|
dict_daily["total"]["total_dividends"] = 0
|
|
dict_daily["total"]["current_value"] = 0
|
|
dict_daily["total"]["current_irr"] = 0
|
|
dict_daily["total"]["current_irr"] = 0
|
|
dict_daily["total"]["total_performanance"] = 0
|
|
|
|
# Loop over each trade-entry for that day
|
|
for trade_id in history_per_trade[date_entry]:
|
|
|
|
# Extract data from the history_per_trade
|
|
trade_amount = history_per_trade[date_entry][trade_id]['current_amount']
|
|
trade_invested = history_per_trade[date_entry][trade_id]['current_invested']
|
|
trade_dividends = history_per_trade[date_entry][trade_id]['total_dividends']
|
|
trade_value = history_per_trade[date_entry][trade_id]['current_value']
|
|
trade_irr = history_per_trade[date_entry][trade_id]['current_irr']
|
|
trade_course = history_per_trade[date_entry][trade_id]['current_course']
|
|
trade_performanance = history_per_trade[date_entry][trade_id]['total_performanance']
|
|
|
|
# Lookup the ticker by the trade-id
|
|
ticker = trades[trade_id]["ticker"]
|
|
|
|
# Extract data from the history_per_ticker
|
|
ticker_amount = dict_daily[ticker]['current_amount']
|
|
ticker_invested = dict_daily[ticker]['current_invested']
|
|
ticker_dividends = dict_daily[ticker]['total_dividends']
|
|
ticker_value = dict_daily[ticker]['current_value']
|
|
ticker_irr = dict_daily[ticker]['current_irr']
|
|
ticker_performanance = dict_daily[ticker]['total_performanance']
|
|
|
|
# Overwrite the values in the history_per_ticker
|
|
dict_daily[ticker]['current_amount'] = ticker_amount + trade_amount # Simple addition works
|
|
dict_daily[ticker]['current_invested'] = ticker_invested + trade_invested
|
|
dict_daily[ticker]['total_dividends'] = ticker_dividends + trade_dividends
|
|
dict_daily[ticker]['current_value'] = ticker_value + trade_value
|
|
dict_daily[ticker]['total_performanance'] = ticker_performanance + trade_performanance
|
|
dict_daily[ticker]['current_course'] = trade_course # Simple overwrite is fine, as the course is the same for all trades
|
|
if ticker_invested == 0 and trade_invested == 0:
|
|
dict_daily[ticker]['current_irr'] = 0
|
|
# Catch 0 values
|
|
else:
|
|
dict_daily[ticker]['current_irr'] = (ticker_irr * ticker_invested + trade_irr * trade_invested) / (ticker_invested + trade_invested)
|
|
# --> IRR is adjusted based on the trade values. This way a trade of 25% of the initial trade volume has only a 25% influence on the irr
|
|
|
|
# Calculate the "total" entry after finishing with all the trades
|
|
for ticker in tickers:
|
|
|
|
# Same logic as above, but shortended code
|
|
dict_daily["total"]['total_dividends'] = dict_daily["total"]['total_dividends'] + dict_daily[ticker]['total_dividends']
|
|
dict_daily["total"]['current_value'] = dict_daily["total"]['current_value'] + dict_daily[ticker]['current_value']
|
|
dict_daily["total"]['total_performanance'] = dict_daily["total"]['total_performanance'] + dict_daily[ticker]['total_performanance']
|
|
|
|
# Extracting the values before rewriting them, to preserve them for the IRR calculation
|
|
total_invested = dict_daily["total"]['current_invested']
|
|
ticker_invested = dict_daily[ticker]['current_invested']
|
|
dict_daily["total"]['current_invested'] = total_invested + ticker_invested
|
|
|
|
# Extracting the values before rewriting them, to preserve them for the IRR calculation
|
|
if ticker_invested == 0 and total_invested == 0:
|
|
dict_daily["total"]['current_irr'] = 0
|
|
else:
|
|
total_irr = dict_daily["total"]['current_irr']
|
|
ticker_irr = dict_daily[ticker]['current_irr']
|
|
dict_daily["total"]['current_irr'] = (total_irr * total_invested + ticker_irr * ticker_invested) / (total_invested + ticker_invested)
|
|
|
|
# Finally, write the results for this day-entry to the history_per_ticker
|
|
history_per_ticker[date_entry] = dict_daily
|
|
|
|
# Logging
|
|
print("[SUCCESS] Calculating history per ticker")
|
|
data = json.dumps(history_per_ticker, indent=2) # Converts a python-dictionary into a json
|
|
with open("history_per_ticker.json", "w") as f:
|
|
f.write(data)
|
|
return history_per_ticker |