Files
Notion-Performance-Tracker/main program.py
2025-11-30 00:22:09 +01:00

77 lines
2.8 KiB
Python

import functions
import config
while True:
# ------------------------------------------- #
# PART 1: Updating the notion trades database #
# ------------------------------------------- #
# Fetches the list of all trades stored in notion
trades = functions.fetch_format_notion_trades(config.notion_db_id_trades)
# Generates a list with unique tickers and no duplicates to reduce workload for the yfinance api
tickers = functions.filter_list_of_tickers(trades)
# Fetches & formats the complete history per ticker from yfinance
yf_data = functions.fetch_format_yf_data(tickers)
# Calculates & stores a history per trade
history_per_trade = functions.calc_history_per_trade(trades, yf_data)
# Selects the most current values from the history per trade and overwrites them in the "trades" feteched from notion
trades = functions.select_current_value_per_trade(trades, history_per_trade)
# Updates the values in the notion database
functions.push_notion_trades_update(trades)
# ------------------------------------------------ #
# PART 2: Updating the notion investments database #
# ------------------------------------------------ #
# Fetches the list of entries in the investment-overview database stored in notion
investments = functions.fetch_format_notion_investments(config.notion_db_id_investments)
# Calculates & stores a history per ticker AND a total across all tickers indexed by the ticker name
history_per_ticker = functions.calc_history_per_ticker(history_per_trade, tickers, trades)
# Selects the most current values from the history per ticker and overwrites them in the "investments" feteched from notion
investments = functions.select_current_value_per_ticker(investments, history_per_ticker)
# Updates the values in the notion database
functions.push_notion_investment_update(investments)
# --------------------------------- #
# PART 3: Updating the TRMNL Screen #
# --------------------------------- #
# Creates a list containing one date per week
list_wkl_dates = functions.create_list_wkl_dates(trades)
# Filter a weekly snapshot from the history per ticker
history_per_ticker_wkl = functions.filter_history_by_list(history_per_ticker, list_wkl_dates)
# Create a screen for the overall performance
# ......
# Push the weekly screen to TRML
# functions.push_trmnl_update(test_data, trmnl_id)
# --------------------------- #
# PART 4: Cool off and repeat #
# --------------------------- #
# Logging
print("[INFO] Completed cycle at: {}".format(functions.datetime.datetime.now()))
print("[INFO] Waiting a few minutes before the next execution")
print("---------------------------------------------------------------------------")
# Clear variables
trades = {}
yf_data = {}
history_per_trade = {}
tickers = []
# Wait for api-cooldown
functions.time.sleep(config.programm_cooldown_time * 60)