1047 lines
37 KiB
Python
1047 lines
37 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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# HELPER FUNCTIONS #
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# ---------------- #
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# LOGGING / PRINTING TO TERMINAL
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def logging(message = "", logging_level = "", new_line = True):
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# Take the selected logging level in the config file
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# Look this up in the list of all available logging levels in the config file
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# Return the index number
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config_logging_level = config.logging_levels.index(config.selected_logging_level)
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try:
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# Take the logging level of the text to print
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# Look this up in the list of all available logging levels in the config file
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# Return the index number
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message_logging_level = config.logging_levels.index(logging_level)
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except:
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# Fallback to the least important logging level
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# Solved by checking the lenght of the available logging levels
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message_logging_level = len(config.logging_levels)
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# Check for false new_line entries
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if new_line is not bool:
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new_line = True
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# Check if the warning should be printed
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if message_logging_level <= config_logging_level:
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# Geting the log color
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log_color = getattr(config.log_colors, logging_level)
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# Construct the logging-text incl. color
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log_text = str(log_color + "[" + logging_level + "] " + config.log_colors.endcode + message)
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# Check if the warning should end with a new-line
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# Printing the text
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if new_line == True:
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print(log_text)
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else:
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print(log_text, end=" ", flush=True)
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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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# Main Logging
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logging(logging_level="success")
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logging(logging_level="info", message=f"{len(tickers)} tickers found")
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return tickers
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except Exception as error_message:
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logging(logging_level="error")
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logging(logging_level="error", message=f"Failed with error: {error_message}")
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return False
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# CREATE LIST OF WEEKLY DATES
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def create_list_filtered_dates(trades, days_seperation):
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stop_date = get_date_open_oldest_trade(trades)
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index_date = datetime.date.today()
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try:
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# Create reversed list (1st entry is today going back in time)
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list_filtered_dates = []
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while index_date >= stop_date:
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list_filtered_dates.append(index_date.isoformat())
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index_date = index_date - datetime.timedelta(days=days_seperation)
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# Reverse the list, so that the frist entry is the oldest one
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list_filtered_dates.reverse()
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# Main Logging
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logging(logging_level="success")
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logging(logging_level="info", message=f"{len(list_filtered_dates)} dates in weekly list")
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return list_filtered_dates
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except Exception as error_message:
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logging(logging_level="error")
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logging(logging_level="error", message=f"Failed with error: {error_message}")
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return False
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# FETCH THE LAST INDEX FROM A DICT
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def fetch_last_key_from_dict(dict):
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key_list = list(dict.keys()) # Extract the keys and convert them to a list
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last_key = key_list[-1] # select the last entry from the list as it is the most current entry
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return last_key
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# ADD BENCHMARK-TICKER TO TICKER-DICT
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def add_benchmark_ticker(tickers, ticker_benchmarkt):
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tickers.append(ticker_benchmarkt)
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logging(logging_level="success")
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# CREATE BENCHMARK TRADES
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def create_benchmark_trades(trades):
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try:
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benchmark_trades = trades
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for trade_id in benchmark_trades:
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benchmark_trades[trade_id]['ticker'] = config.ticker_benchmark
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logging(logging_level="success")
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return benchmark_trades
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except Exception as error_message:
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logging(logging_level="error")
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logging(logging_level="error", message=f"Failed with error: {error_message}")
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return False
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# -------------------------- #
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# NETWORK DOWNLOAD FUNCTIONS #
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# -------------------------- #
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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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return result
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except Exception:
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return True # Return True when there was an error
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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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fetch_error = False
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format_errors = 0
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number_of_trades = 0
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error_message = ""
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# Download data from notion
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data = notion_get_pages(db_id_trades)
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# Check, if cuccessfull
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if data is True:
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fetch_error = True
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else:
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# Format the recieved data
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for i in data:
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# Count for stratistics
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number_of_trades = number_of_trades + 1
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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 Exception as e:
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format_errors = format_errors + 1
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error_message = e
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# Main Logging
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if fetch_error == False & format_errors == 0:
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logging(logging_level="success")
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logging(logging_level="info", message=f"{number_of_trades} trades recieved and formated")
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return trades
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elif fetch_error == False & format_errors > 0:
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logging(logging_level="warning")
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logging(logging_level="warning", message=f"{format_errors} trades out of {number_of_trades} skiped...maybe due to missing values?")
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return trades
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else:
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logging(logging_level="error")
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logging(logging_level="error", message=f"Failed with error: {error_message}")
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return False
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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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fetch_error = False
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format_errors = 0
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number_of_investments = 0
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# Download data & check for success
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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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# Format recieved data
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for i in data:
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# Count up for statistics
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number_of_investments = number_of_investments + 1
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try:
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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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# Skip this entry, if errors show up
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except:
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format_errors = format_errors + 1
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# Main Logging
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if fetch_error == False & format_errors == 0:
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logging(logging_level="success")
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logging(logging_level="info", message=f"{number_of_investments} trades recieved and formated")
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return investments
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elif fetch_error == False & format_errors > 0:
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logging(logging_level="warning")
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logging(logging_level="warning", message=f"{format_errors} trades out of {number_of_investments} skiped...maybe due to missing values?")
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return investments
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else:
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logging(logging_level="error")
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return False
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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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fetch_errors = 0
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format_errors = 0
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number_of_tickers = 0
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# Download data for each ticker seperately
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for i in tickers:
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number_of_tickers = number_of_tickers +1
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skip_formating = False # Helper varianbel (see flow logik)
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ticker = i
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# Catch errors during the download
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try:
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# Download data
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api = yf.Ticker(ticker)
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data = api.history(period="max")
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except:
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# Store error for later logging
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fetch_errors = fetch_errors + 1
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skip_formating = True
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# If the download was successfull:
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if skip_formating == False:
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# Try formating the data
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try:
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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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# Delete these 2 columns, if they exist
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if 'Stock Splits' in data.columns:
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del data['Stock Splits']
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if 'Capital Gains' in data.columns:
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del data['Capital Gains']
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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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# Handle formating errors
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except:
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format_errors = format_errors +1
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# in case of an error the entry never get's added to the yf_data object
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# Wait for the API to cool down
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print(".", end="", flush=True)
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time.sleep(config.api_cooldowm_time)
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# Main Logging
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print(" ", end="", flush=True)
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if fetch_errors == 0 & format_errors == 0:
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logging(logging_level="success")
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logging(logging_level="info", message=f"{number_of_tickers} tickers recieved and formated")
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return yf_data
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elif fetch_errors == 0 & format_errors > 0:
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logging(logging_level="warning")
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logging(logging_level="warning", message=f"{format_errors} tickers out of {number_of_tickers} skiped")
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return yf_data
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else:
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logging(logging_level="error")
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logging(logging_level="error", message=f"Failed with error: {number_of_tickers}")
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print("\n")
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return False
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# ------------------------ #
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# NETWORK UPLOAD FUNCTIONS #
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# ------------------------ #
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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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# 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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number_of_uploads = 0
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for notion_page_id in trades:
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number_of_uploads = number_of_uploads+1
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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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print(".", end="", flush=True)
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time.sleep(config.api_cooldowm_time)
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# Logging
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print(" ", end="", flush=True)
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if error_count == 0:
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logging(logging_level="success")
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elif error_count < number_of_uploads:
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logging(logging_level="warning")
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logging(logging_level="success", message=f"Updating notion trades failed for {error_count} out of {number_of_uploads} entries")
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else:
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logging(logging_level="error")
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logging(logging_level="success", message=f"Updating notion trades failed for all {error_count} entries")
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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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number_of_uploads = 0
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for notion_page_id in investments:
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number_of_uploads = number_of_uploads+1
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# Try uploading an update
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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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},
|
|
"Dividends (€)": {
|
|
"number": investments[notion_page_id]['total_dividends']
|
|
}
|
|
}
|
|
# Update the properties of the corresponding notion-page
|
|
notion_update_page(notion_page_id, notion_update)
|
|
except:
|
|
error_count = error_count + 1
|
|
|
|
# Wait for the API to cool off
|
|
print(".", end="", flush=True)
|
|
time.sleep(config.api_cooldowm_time)
|
|
|
|
# Logging
|
|
print(" ", end="", flush=True)
|
|
if error_count == 0:
|
|
logging(logging_level="success")
|
|
elif error_count < number_of_uploads:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="success", message=f"Updating notion investments failed for {error_count} out of {number_of_uploads} entries")
|
|
else:
|
|
logging(logging_level="error")
|
|
logging(logging_level="success", message=f"Updating notion investments failed for all {error_count} entries")
|
|
|
|
# TRMNL UPDATE DIAGRAMMS
|
|
def push_trmnl_update_chart(trmnl_update_object, trmnl_url, trmnl_headers):
|
|
|
|
# Send the data to TRMNL
|
|
try:
|
|
data = json.dumps(trmnl_update_object, indent=2) # Converts a python-dictionary into a json
|
|
reply = requests.post(trmnl_url, data=data, headers=trmnl_headers)
|
|
|
|
# Logging
|
|
if reply.status_code == 200:
|
|
logging(logging_level="success")
|
|
elif reply.status_code == 429:
|
|
logging_level="success"
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Exceeded TRMNL's API rate limits")
|
|
logging(logging_level="warning", message="Waiting some time should work")
|
|
elif reply.status_code == 422:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Upload successful, but data cannot be displayed correctly")
|
|
logging(logging_level="warning", message="The payload is probably to large in size")
|
|
else:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed pushing data to TRMNL with server reply code: {reply.status_code}")
|
|
logging(logging_level="debug", message=f"Complete server reply message: {reply}")
|
|
except Exception as e:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed pushing data to TRMNL with error code: {e}")
|
|
|
|
|
|
|
|
# ----------------------------- #
|
|
# HISTORY CALCULATION FUNCTIONS #
|
|
# ----------------------------- #
|
|
|
|
# CALC HISTORY PER TRADE
|
|
def calc_history_per_trade(trades, yf_data):
|
|
|
|
# Create support variables
|
|
history_per_trade = {}
|
|
total_dividends = 0
|
|
date_open_oldest_trade = get_date_open_oldest_trade(trades)
|
|
|
|
# Logging & statistics
|
|
missing_day_entrys = 0
|
|
days_formated = 0
|
|
number_of_trades = 0
|
|
|
|
# As this history is so important, it is okay if this functions fails in total if errors araise
|
|
try:
|
|
# ------------------ LOOP OVER ALL TRADES
|
|
for trade_id in trades:
|
|
|
|
# Statistics
|
|
number_of_trades = number_of_trades +1
|
|
|
|
# ------------------ PREPARE FOR THE (NEXT) LOOP OVER ALL DAYS
|
|
# Set / Reset the index-date to the oldest trade day
|
|
# Resetting is required so that the calculations for the next trade start with day 1
|
|
index_date = date_open_oldest_trade
|
|
|
|
# Set the initial value for the course on the previous day to 0
|
|
# Just in case the very first trade was made on a weekend somehow, where there is no yfinance data available
|
|
previous_course = 0.0
|
|
|
|
# Check, if the trade was closed already
|
|
# If it was not, set the closure date to the future (Trick 17)
|
|
if trades[trade_id]["date_close"] == 0:
|
|
date_close = datetime.date.today() + datetime.timedelta(days=1)
|
|
else:
|
|
date_close = trades[trade_id]["date_close"]
|
|
date_open = trades[trade_id]["date_open"]
|
|
|
|
# Keep ticker for connecting performance later
|
|
ticker = trades[trade_id]['ticker']
|
|
|
|
# ------------------ DETERMINE THE COUSE PER DAY
|
|
while index_date != datetime.date.today() + datetime.timedelta(days=1):
|
|
|
|
# Statistics
|
|
days_formated = days_formated +1
|
|
|
|
# Fetch course for the day & eventual dividends from yf_data
|
|
try:
|
|
current_course = yf_data[ticker].at[index_date, 'Close']
|
|
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
|
|
missing_day_entrys = missing_day_entrys +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 & DEBUGING
|
|
# Debug writing history to disk
|
|
if config.selected_logging_level == "debug":
|
|
data = json.dumps(history_per_trade, indent=2) # Converts a python-dictionary into a json
|
|
with open("history_per_trade.json", "w") as f:
|
|
f.write(data)
|
|
|
|
# Logging logging
|
|
if missing_day_entrys == 0:
|
|
logging(logging_level="success")
|
|
logging(logging_level="info", message=f"created a history with {days_formated} across all {number_of_trades} tickers o_O")
|
|
else:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message=f"No yf-data available in {missing_day_entrys} cases accross all {number_of_trades} tickers")
|
|
logging(logging_level="warning", message="Probably reason is non-trading-days eg. weekends")
|
|
logging(logging_level="warning", message="Used values from previous trade-day instead")
|
|
# Return date
|
|
return history_per_trade
|
|
|
|
except Exception as error_message:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed with error message: {error_message}")
|
|
return False
|
|
|
|
# CALC THE HISTORY PER TRADE & OVERALL
|
|
def calc_history_per_ticker(history_per_trade, tickers):
|
|
|
|
# ------------------ CREATE JSON OBJECT
|
|
# Create the json-dict
|
|
history_per_ticker = {}
|
|
|
|
# Logging & statistics
|
|
missing_day_entrys = 0
|
|
days_formated = 0
|
|
|
|
# As this history is so important, it is okay if this functions fails in total if errors araise
|
|
try:
|
|
# Loop over each date entry in the history
|
|
for date_entry in history_per_trade:
|
|
|
|
# Statistics
|
|
days_formated = days_formated +1
|
|
|
|
# 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 & DEBUGING
|
|
# Debugging
|
|
if config.selected_logging_level == "debug":
|
|
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)
|
|
|
|
# Success Logging
|
|
logging(logging_level="success")
|
|
logging(logging_level="info", message=f"created a history with {days_formated} days formated o_O")
|
|
return history_per_ticker
|
|
|
|
# Error Logging
|
|
except Exception as error_message:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed with error message: {error_message}")
|
|
return False
|
|
|
|
# --------------------------- #
|
|
# HISTORY SELECTION FUNCTIONS #
|
|
# --------------------------- #
|
|
|
|
# FILTER ANY HISTORY OBJECT TO SELECTED DATES
|
|
def filter_history_by_list(history, dates_list):
|
|
filtered_history = {}
|
|
try:
|
|
# Loop over all days
|
|
for index_date in history:
|
|
# Check, if the history-date is in the filter-list
|
|
if index_date in dates_list:
|
|
# If so, add this date-entry to the filtered history object
|
|
filtered_history[index_date] = history[index_date]
|
|
# Main Logging
|
|
logging(logging_level="success")
|
|
return filtered_history
|
|
except Exception as error_message:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed with error: {error_message}")
|
|
return False
|
|
|
|
# SELECT CURRENT VALUES PER TRADE
|
|
def select_current_value_per_trade(trades, history_per_trade):
|
|
|
|
# Logging
|
|
format_errors = 0
|
|
|
|
# Loop over all trades
|
|
for trade_id in trades:
|
|
try:
|
|
# Determine, what values to fetch based on whether the trade was closed already
|
|
date_closed = trades[trade_id]["date_close"]
|
|
if date_closed == 0:
|
|
|
|
# If trade still open, use performance data from today
|
|
index_date_iso = datetime.date.today().isoformat()
|
|
|
|
else:
|
|
# If trade closed, use performance data from close-date
|
|
index_date_iso = date_closed.isoformat()
|
|
|
|
# Fetch data from history and save for this trade
|
|
trades[trade_id]["course_current"] = history_per_trade[index_date_iso][trade_id]['current_course']
|
|
trades[trade_id]["irr"] = history_per_trade[index_date_iso][trade_id]['current_irr']
|
|
trades[trade_id]["dividends"] = history_per_trade[index_date_iso][trade_id]['total_dividends']
|
|
except:
|
|
format_errors = format_errors + 1
|
|
|
|
# Logging logging
|
|
if format_errors == 0:
|
|
logging(logging_level="success")
|
|
else:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message=f"Failed updating the current value per trade in {format_errors} cases")
|
|
return trades
|
|
|
|
# SELECT CURRENT VALUES PER TICKER
|
|
def select_current_value_per_ticker(investments, history_per_ticker):
|
|
|
|
# Logging
|
|
format_errors = 0
|
|
|
|
# Loop over all investments
|
|
for investment_id in investments:
|
|
try:
|
|
# Generate the iso-date of today as the required index
|
|
index_date_iso = datetime.date.today().isoformat()
|
|
|
|
# Get the ticker corresponding to the investment
|
|
ticker = investments[investment_id]["ticker"]
|
|
|
|
# Select latest data from history and save for this investment
|
|
investments[investment_id]["total_dividends"] = history_per_ticker[index_date_iso][ticker]['total_dividends']
|
|
investments[investment_id]["current_value"] = history_per_ticker[index_date_iso][ticker]['current_value']
|
|
investments[investment_id]["current_irr"] = history_per_ticker[index_date_iso][ticker]['current_irr']
|
|
investments[investment_id]["total_performanance"] = history_per_ticker[index_date_iso][ticker]['total_performanance']
|
|
except:
|
|
format_errors = format_errors + 1
|
|
|
|
# Logging
|
|
if format_errors == 0:
|
|
logging(logging_level="success")
|
|
else:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message=f"Failed updating the current value per ticker in {format_errors} cases")
|
|
return investments
|
|
|
|
# TRMNL CREATE IRR-UPDATE
|
|
def prep_trmnl_chart_udpate(history_to_show, series_to_show_1 = "total", data_to_show_1 = "current_value", series_to_show_2 = "bechnmark", data_to_show_2 = "current_value"): # default value = current invested
|
|
|
|
# Setup
|
|
dict_big_numbers = {}
|
|
charts_data = []
|
|
chart_1 = {}
|
|
chart_2 = {}
|
|
|
|
try:
|
|
# Fetch the latest date entry from the history
|
|
index_date_iso = fetch_last_key_from_dict(history_to_show)
|
|
|
|
# Select latest data from history for the big-numbers
|
|
current_value = history_to_show[index_date_iso]["total"]["current_value"]
|
|
total_performanance = history_to_show[index_date_iso]["total"]["total_performanance"]
|
|
current_irr = history_to_show[index_date_iso]["total"]["current_irr"]
|
|
current_irr = (current_irr -1) *100
|
|
|
|
# Round the nubers
|
|
dict_big_numbers["current_value"] = str(round(current_value, 0))
|
|
dict_big_numbers["total_performanance"] = str(round(total_performanance, 0))
|
|
dict_big_numbers["current_irr"] = str(round(current_irr, 2))
|
|
|
|
# Catching false inputs for the series to show
|
|
possible_series_to_show = list(history_to_show[index_date_iso].keys()) # Get a list of all the series values, that could be shown
|
|
|
|
if series_to_show_1 not in possible_series_to_show: # checks, if the selected series is not part of the history-object sent to the function
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Selecting 'total' as the series to show, as the input was not valid")
|
|
series_to_show_1 = "total"
|
|
|
|
if series_to_show_2 not in possible_series_to_show: # checks, if the selected series is not part of the history-object sent to the function
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Selecting 'total' as the series to show, as the input was not valid")
|
|
series_to_show_2 = "total"
|
|
|
|
# Catching false inputs for the data to show
|
|
possible_data_to_show = list(history_to_show[index_date_iso][series_to_show_1].keys())
|
|
if data_to_show_1 not in possible_data_to_show:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Selecting 'current invested' as chart data, as the input was not valid")
|
|
data_to_show_1 = "current_value"
|
|
|
|
possible_data_to_show = list(history_to_show[index_date_iso][series_to_show_2].keys())
|
|
if data_to_show_2 not in possible_data_to_show:
|
|
logging(logging_level="warning")
|
|
logging(logging_level="warning", message="Selecting 'current invested' as chart data, as the input was not valid")
|
|
data_to_show_2 = "current_value"
|
|
|
|
# Create space for storing values
|
|
chart_1["data"] = []
|
|
chart_2["data"] = []
|
|
|
|
# Format the chart data into the right data
|
|
for date in history_to_show:
|
|
|
|
# Extract the value to be stored
|
|
value_to_show_1 = history_to_show[date][series_to_show_1][data_to_show_1]
|
|
value_to_show_2 = history_to_show[date][series_to_show_2][data_to_show_2]
|
|
|
|
# Catch the case irr and convert to percent
|
|
if data_to_show_1 == "current_irr":
|
|
value_to_show_1 = (value_to_show_1 -1) *100
|
|
|
|
if data_to_show_2 == "current_irr":
|
|
value_to_show_2 = (value_to_show_2 -1) *100
|
|
|
|
# Round to 2 decimal values
|
|
value_to_show_1 = round(value_to_show_1, 2)
|
|
value_to_show_2 = round(value_to_show_2, 2)
|
|
|
|
# Extend the date by a timestamp
|
|
json_date = datetime.date.fromisoformat(date) # Convert ISO-String to python date-object
|
|
json_date = datetime.datetime.combine(json_date, datetime.datetime.min.time()) # Combine the date with midnight (00:00:00) to create a datetime object
|
|
json_date = json_date.isoformat() # Convert back to ISO-String, now including a time
|
|
|
|
# Store the values together with the corresponding date
|
|
value_1 = [json_date, value_to_show_1]
|
|
value_2 = [json_date, value_to_show_2]
|
|
|
|
# Add the value pair to the list of values for this chart
|
|
chart_1["data"].append(value_1)
|
|
chart_2["data"].append(value_2)
|
|
|
|
# Add the two series to the list of series in the TRML object
|
|
charts_data.append(chart_1)
|
|
charts_data.append(chart_2)
|
|
|
|
# Generating nicer series titels
|
|
if series_to_show_1 == "total":
|
|
series_to_show_1 = "Portfolio"
|
|
if series_to_show_2 == "total":
|
|
series_to_show_2 = "Portfolio"
|
|
|
|
# Generating nicer data titels
|
|
data_to_show_1 = data_to_show_1.replace("_", " ").capitalize()
|
|
data_to_show_2 = data_to_show_2.replace("_", " ").capitalize()
|
|
|
|
# Increase look of IRR even more
|
|
# Funktioniert auch dann, wenn "irr" nicht vorkommt
|
|
data_to_show_1 = data_to_show_1.replace("irr", "IRR")
|
|
data_to_show_2 = data_to_show_2.replace("irr", "IRR")
|
|
|
|
# Generate the chat names / desciptions
|
|
chart_1["name"] = data_to_show_1 + " " + series_to_show_1
|
|
chart_2["name"] = data_to_show_2 + " " + series_to_show_2
|
|
|
|
# Construct the trmnl_object
|
|
trmnl_update_object = {}
|
|
trmnl_update_object["merge_variables"] = {}
|
|
trmnl_update_object["merge_variables"]["big_numbers"] = dict_big_numbers
|
|
trmnl_update_object["merge_variables"]["charts"] = charts_data
|
|
|
|
# Debugging
|
|
if config.selected_logging_level == "debug":
|
|
data = json.dumps(trmnl_update_object, indent=2) # Converts a python-dictionary into a json
|
|
with open("trmnl_update_object.json", "w") as f:
|
|
f.write(data)
|
|
|
|
# Main Logging
|
|
logging(logging_level="success")
|
|
return trmnl_update_object
|
|
except Exception as error_message:
|
|
logging(logging_level="error")
|
|
logging(logging_level="error", message=f"Failed with error: {error_message}")
|
|
return False |