Implementing calculation of a performance per ticker

main.py

@@ -5,7 +5,6 @@ import json
 import yfinance as yf
 import pandas as pd
 import requests
-import os
 
 
 
@@ -227,6 +226,7 @@ def push_trmnl_update_chart(wklydict_numbers, dict_chart, trmnl_page_id):
 '''
 
 
+
 # ------------------#
 # LEVEL 2 FUNCTIONS #
 # ------------------#
@@ -430,6 +430,7 @@ def calc_current_value_per_trade(trades, history_per_trade):
 	return trades
 
 
+
 # ------------------#
 # LEVEL 3 FUNCTIONS #
 # ------------------#
@@ -577,33 +578,32 @@ def calc_history_per_ticker(history_per_trade, tickers, trades):
 	
 	# ------------------ CREATE JSON OBJECT 
 	# Create the json-dict
-	history_per_trade = {}
+	history_per_ticker = {}
 		
 	# Loop over each date entry in the history
 	for date_entry in history_per_trade:
 
 		# Set initial values to avoid NULL errors
 		initial_values = {}
-		initial_values["invested"] = 0
-		initial_values["dividends"] = 0
-		initial_values["value"] = 0
-		initial_values["irr"] = 0
-		initial_values["course"] = 0
-		initial_values["performance"] = 0
+		initial_values["current_invested"] = 0
+		initial_values["total_dividends"] = 0
+		initial_values["current_value"] = 0
+		initial_values["current_irr"] = 0
+		initial_values["total_performanance"] = 0
 		
 		# Create a dict to store the results per day and ticker
 		dict_daily = {}
 		for ticker in tickers:
 			dict_daily[ticker] = initial_values
 			# Added only for ticker entries, not for the "total" value
-			dict_daily[ticker]["amount"] = 0
-			initial_values["course"] = 0
+			dict_daily[ticker]["current_amount"] = 0
+			initial_values["current_course"] = 0
 		
 		# Add values for a ticker called "total"
 		dict_daily["total"] = initial_values
 
 		# Loop over each trade-entry for that day
-		for trade_id in date_entry:
+		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']
@@ -615,7 +615,7 @@ def calc_history_per_ticker(history_per_trade, tickers, trades):
 			trade_performanance = history_per_trade[date_entry][trade_id]['total_performanance']
 
 			# Lookup the ticker by the trade-id
-			ticker = trades[trade_id]
+			ticker = trades[trade_id]["ticker"]
 
 			# Extract data from the history_per_ticker
 			ticker_amount = dict_daily[ticker]['current_amount']
@@ -630,10 +630,14 @@ def calc_history_per_ticker(history_per_trade, tickers, trades):
 			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]['current_irr'] = (ticker_irr * ticker_invested + trade_irr * trade_amount) / (ticker_invested + trade_amount)
-			# --> 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
-			dict_daily[ticker]['current_course'] = trade_course # Simple overwrite is fine, as the course is the same for all trades
 			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:
@@ -651,15 +655,21 @@ def calc_history_per_ticker(history_per_trade, tickers, trades):
 			# Extracting the values before rewriting them, to preserve them for the IRR calculation
 			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)
+			if ticker_invested == 0 and total_invested == 0:
+				dict_daily["total"]['current_irr'] = 0
+			else:
+				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_trade[date_entry] = dict_daily
-		print("[INFO] Calculated total history for date{}".format(date_entry))
+		history_per_ticker[date_entry] = dict_daily
 
 	# Logging
-	print("[SUCCESS] Calculating history overall")
-	return history_total
+	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
+
 
 
 ### -------------------- MAIN PROGRAMM --------------------
@@ -699,10 +709,6 @@ while True:
 		# This object stores notion pages with all properties
 		# By "overwriting" the output properties (i.e. irr) and then overwriting the notion-pages, i can store fresh data
 		trades = calc_current_value_per_trade(trades, history_per_trade)
-
-	if error == False:
-		# Updates / replaces all pages in the notion "trades"
-		push_notion_trades_update(trades)
 	
 	if error == False:
 		# Calculates & stores a history per ticker AND a total across all tickers indexed by the ticker name
@@ -722,6 +728,10 @@ while True:
 	if error == False:
 		history_total_wkl = filter_history_by_list(history_total, list_wkl_dates)
 
+	if error == False:
+		# Updates / replaces all pages in the notion "trades"
+		push_notion_trades_update(trades)
+
 	# if error == False:
 		# push_trmnl_update(test_data, trmnl_id)