packages = [ "numpy", "pandas", "matplotlib", "scipy", "requests", "asyncio" ]

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import pyscript from js import document from pyodide.ffi import create_proxy import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import curve_fit from js import fetchQuote, fetchIncome, fetchProfile, fetchBalance, fetchCF, fetchHP, createMultiplesTable, fetchPeers, fetchExecutives import asyncio from js import JSON import json from js import console from js import subscriptionPlan # Hide loading spinner when pyscript is ready document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Ready!" # Set global variables global ticker ticker = "" global financials financials = pd.DataFrame() global reported_currency reported_currency = "" global market_currency market_currency = "" global reported_to_market_currency reported_to_market_currency = "" global user_set_shAmountChange user_set_shAmountChange = False global user_set_fcfMargin user_set_fcfMargin = False global user_set_growthRate user_set_growthRate = False # Define the asynchronous function that fetches quote async def call_fetch_quote(ticker): data = await fetchQuote(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches income async def call_fetch_income(ticker): data = await fetchIncome(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches profile async def call_fetch_profile(ticker): data = await fetchProfile(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches peers async def call_fetch_peers(ticker): data = await fetchPeers(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches executives async def call_fetch_executives(ticker): data = await fetchExecutives(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches balance async def call_fetch_balance(ticker): data = await fetchBalance(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches cash flow async def call_fetch_cf(ticker): data = await fetchCF(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Define the asynchronous function that fetches historical price async def call_fetch_hp(ticker): data = await fetchHP(ticker) try: # Convert JS object to python python_data = data.to_py() df = pd.DataFrame(python_data) return df except Exception as e: # data could not be parsed or converted to a DataFrame return f"Failed to create DataFrame: {e}" # Function doing the DCF calculations when analyze is pressed async def calculate(e): # Show loading spinner document.getElementById("loadingSpinner").style.display = "inline-block" document.getElementById("spinnerLabel").textContent = "Analyzing..." # Clear error messages and plots, and hide suggestions document.getElementById("errorMessage").textContent = "" document.getElementById('plot-container').innerHTML = "" document.getElementById('log-plot-container').innerHTML = "" document.getElementById('historical-plot-container').innerHTML = "" document.getElementById('lin-historical-plot-container').innerHTML = "" document.getElementById('log-ratios-plot').innerHTML = "" document.getElementById('lin-ratios-plot').innerHTML = "" document.getElementById("suggestionsBox").style.display = "none" document.getElementById('lin-balance-plot-container').innerHTML = "" document.getElementById('log-balance-plot-container').innerHTML = "" document.getElementById('lin-return-plot-container').innerHTML = "" document.getElementById('log-return-plot-container').innerHTML = "" document.getElementById('lin-margin-plot-container').innerHTML = "" document.getElementById('log-margin-plot-container').innerHTML = "" # Check if user has an active subscription plan free_stocks = [] tickerCheck = document.getElementById("ticker").value if subscriptionPlan not in ['monthly','yearly']: if tickerCheck not in free_stocks: document.getElementById("errorMessage").textContent = "SimplicityFTW is currently closed for public access." #"Tickers available to guests and free users are: MSFT, AAPL, GOOGL, TEL.OL, ABB.ST, NOVO-B.CO, KNEBV.HE, SHOP.TO, BRK-B" document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Access Denied" exit() try: # Load global variables global ticker global financials global reported_currency global market_currency global reported_to_market_currency global user_set_shAmountChange global user_set_fcfMargin global user_set_growthRate # If user is still working with the same data, avoid unnecesary API calls if ticker == document.getElementById("ticker").value: new_stock = False # Get Quote and update company name quote = await asyncio.ensure_future(call_fetch_quote(ticker)) document.getElementById("companyName").textContent = quote['name'][0] else: new_stock = True ticker = document.getElementById("ticker").value financials = pd.DataFrame() # Get Quote and update company name quote = await asyncio.ensure_future(call_fetch_quote(ticker)) try: document.getElementById("companyName").textContent = quote['name'][0] except: # Display error message, stop spinner, give failed status. document.getElementById("errorMessage").textContent = "Error: Invalid ticker, did not find any company associated with this ticker. The stock you want to look up may need an additional ending to their ticker. For example, stocks listed at the Oslo Stock Exchange has '.OL' added to their ticker." document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Failed" exit() # Get income and update latest report date income = await asyncio.ensure_future(call_fetch_income(ticker)) document.getElementById("dateNote").textContent = income['date'][0]; document.getElementById("numPeriodsAvailable").textContent = income.shape[0]; # Get profile and update website link profile = await asyncio.ensure_future(call_fetch_profile(ticker)) document.getElementById("linkToWebsite").href = profile['website'][0]; document.getElementById("linkToWebsite").textContent = profile['website'][0]; document.getElementById("linkToWebsite2").href = profile['website'][0]; document.getElementById("linkToWebsite2").textContent = profile['website'][0]; # Fill in information contents document.getElementById("country").textContent = "" document.getElementById("sector").textContent = "" document.getElementById("industry").textContent = "" document.getElementById("exchange").textContent = "" document.getElementById("companyDescription").textContent = "" document.getElementById("country").textContent = profile['country'][0]; document.getElementById("sector").textContent = profile['sector'][0]; document.getElementById("industry").textContent = profile['industry'][0]; document.getElementById("exchange").textContent = profile['exchange'][0]; document.getElementById("companyDescription").textContent = profile['description'][0]; # Build executive table document.getElementById('executiveTable').getElementsByTagName('tbody')[0].innerHTML = '' executives = await asyncio.ensure_future(call_fetch_executives(ticker)) # Get peers document.getElementById("peers").textContent = "" try: peers = await asyncio.ensure_future(call_fetch_peers(ticker)) peers2 = peers['peersList'][0] peerscontent = "" for a in range (0,len(peers2)): if peers2[a] == peers2[-1]: peerscontent += peers2[a] else: peerscontent += peers2[a] + ", " document.getElementById("peers").textContent = peerscontent except: document.getElementById("peers").textContent = "-" # Get necessary data to calculate present fair value balance = await asyncio.ensure_future(call_fetch_balance(ticker)) cashFlow = await asyncio.ensure_future(call_fetch_cf(ticker)) #enterpriseValue = await asyncio.ensure_future(call_fetch_ev(ticker)) # Get historical data for converting report currency to market currency reported_currency = income['reportedCurrency'][0] market_currency = profile['currency'][0] if reported_currency == market_currency: reported_to_market_currency = 1 else: currencies = reported_currency + market_currency reported_to_market_currency = await asyncio.ensure_future(call_fetch_hp(currencies)) reported_to_market_currency['date'] = pd.to_datetime(reported_to_market_currency['date']) # Extract necessary data into a df and convert to numeric financials['date'] = pd.to_datetime(income['date']) financials['reportedCurrency'] = income['reportedCurrency'] financials['fiscalQuarter'] = income['period'] financials['fiscalYear'] = pd.to_datetime(income['calendarYear']) financials['revenue'] = pd.to_numeric(income['revenue']) financials['result'] = pd.to_numeric(income['netIncome']) financials['ebit'] = pd.to_numeric(income['incomeBeforeTax']) financials['R&Dexpenses'] = pd.to_numeric(income['researchAndDevelopmentExpenses']) financials['cash&equivalents'] = pd.to_numeric(balance['cashAndShortTermInvestments']) financials['assets'] = pd.to_numeric(balance['totalAssets']) financials['liabilities'] = pd.to_numeric(balance['totalLiabilities']) financials['currentLiabilities'] = pd.to_numeric(balance['totalCurrentLiabilities']) financials['stockholdersEquity'] = pd.to_numeric(balance['totalStockholdersEquity']) financials['totalEquity'] = pd.to_numeric(balance['totalEquity']) financials['totalDebt'] = pd.to_numeric(balance['totalDebt']) financials['freeCashFlow'] = pd.to_numeric(cashFlow['freeCashFlow']) financials['netOCF'] = pd.to_numeric(cashFlow['netCashProvidedByOperatingActivities']) financials['operatingCashFlow'] = pd.to_numeric(cashFlow['netCashProvidedByOperatingActivities']) financials['dividends'] = -pd.to_numeric(cashFlow['dividendsPaid']) financials['sharesOutstanding'] = pd.to_numeric(income['weightedAverageShsOut']) # Get model parameters if document.getElementById("perpetualGrowthRate").value == "": perpetual_growth_rate = 0 document.getElementById("perpetualGrowthRate").value = 0 else: perpetual_growth_rate = float(document.getElementById("perpetualGrowthRate").value)/100 periods = int(document.getElementById("periods").value) if periods > financials.shape[0]: document.getElementById("errorMessage").textContent = "Error: The number of periods must be less than the number of available reports." # Hide loading spinner document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Failed" exit() elif periods < 4: document.getElementById("errorMessage").textContent = "Error: The number of periods must be at least 4." # Hide loading spinner document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Failed" exit() if document.getElementById("requiredReturn").value == "": document.getElementById("errorMessage").textContent = "No required return was set. Default value restored." document.getElementById("requiredReturn").value = 10 requiredReturn = float(document.getElementById("requiredReturn").value)/100 if document.getElementById("safetyMargin").value == "": document.getElementById("safetyMargin").value = 0 safetyMargin = float(document.getElementById("safetyMargin").value)/100 # Get additional data entered by user num_extra_reports = int(document.getElementById("numColumns").value) if num_extra_reports > 0: extra_revenue = [] extra_fcf = [] extra_equity = [] extra_shAmount = [] for l in range(1, num_extra_reports+1): extra_revenue.append(float(document.getElementById("cellj"+str(l)+"k1").value)) if float(document.getElementById("cellj"+str(l)+"k2").value) == 0: if float(document.getElementById("cellj"+str(l)+"k3").value) == 0 and float(document.getElementById("cellj"+str(l)+"k4").value) == 0: extra_fcf.append(float(document.getElementById("cellj"+str(l)+"k5").value)) else: extra_fcf.append(float(document.getElementById("cellj"+str(l)+"k3").value)-float(document.getElementById("cellj"+str(l)+"k4").value)) else: extra_fcf.append(float(document.getElementById("cellj"+str(l)+"k2").value)) if float(document.getElementById("cellj"+str(l)+"k6").value) == 0: extra_equity.append(float(document.getElementById("cellj"+str(l)+"k7").value)-float(document.getElementById("cellj"+str(l)+"k8").value)) else: extra_equity.append(float(document.getElementById("cellj"+str(l)+"k6").value)) extra_shAmount.append(float(document.getElementById("cellj"+str(l)+"k9").value)) if 0 in extra_revenue or 0 in extra_fcf or 0 in extra_equity or 0 in extra_shAmount: document.getElementById("errorMessage").textContent = "Error: The additional financial numbers you entered may be insufficient. This error message triggers if revenues, (calculated) free cash flow, (calculated) shareholders' equity or outstanding shares are 0." # Get fcf margin if num_extra_reports > 0: fcf_list = [] for i in range(0,num_extra_reports): fcf_list.append(extra_fcf[-i]) for i in range(0,periods-num_extra_reports): fcf_list.append(financials['freeCashFlow'].iloc[i]) revenue_list = [] for i in range(0,num_extra_reports): revenue_list.append(extra_revenue[-i]) for i in range(0,periods-num_extra_reports): revenue_list.append(financials['revenue'].iloc[i]) else: fcf_list = financials['freeCashFlow'].iloc[0:periods] revenue_list = financials['revenue'].iloc[0:periods] if user_set_fcfMargin == False: FCFmargin = sum(fcf_list)/sum(revenue_list) document.getElementById("fcfMargin").value = round(FCFmargin*100,1) else: if document.getElementById("fcfMargin").value == "": FCFmargin = 0 document.getElementById("fcfMargin").value = 0 else: FCFmargin = float(document.getElementById("fcfMargin").value)/100 # Order revenue numbers chronologically revenues = revenue_list[::-1] # Downscale numbers to now overflow the curve_fit function for i in range(0,len(revenues)): revenues[i] = revenues[i] / 10**len(str(financials['revenue'].iloc[0])) # Fit the curve x = np.arange(periods) try: popt, pcov = curve_fit(lambda x,b,m: b*m**x,x,revenues,maxfev = 500) curve_type = 'exponential' except: print('Exponental growth curve does not fit, trying linear instead.') try: popt, pcov = curve_fit(lambda x,b,m: b+m*x,x,revenues,maxfev = 500) curve_type = 'linear' except: print('Failed to find best fit growth rate') # Calculate growth rates and now-revenue estimates. Upscale revenues. quarterly_gr = popt[1] annual_gr = quarterly_gr**4 if curve_type == 'exponential': now_revenue_estimate_quarterly = popt[0]*popt[1]**x[-1] * 10**len(str(financials['revenue'].iloc[0])) best_fit_line = popt[0]*popt[1]**x * 10**len(str(financials['revenue'].iloc[0])) if curve_type == 'linear': now_revenue_estimate_quarterly = popt[0]+popt[1]*x[-1] * 10**len(str(financials['revenue'].iloc[0])) best_fit_line = popt[0]+popt[1]*x * 10**len(str(financials['revenue'].iloc[0])) # Check for user set growth rate if user_set_growthRate == False: # Update growth rate based on data document.getElementById("growthRate").value = round((annual_gr-1)*100,1) else: if document.getElementById("growthRate").value == "": quarterly_gr = 1 document.getElementById("growthRate").value = 0 else: quarterly_gr = (1+float(document.getElementById("growthRate").value)/100)**(1/4) # Project future revenues and free cash flow for an equal period of time to the period projected_quarterly_revenues = [] projected_quarterly_fcf = [] for j in range(1,(periods+1)): if curve_type == 'exponential': projected_quarterly_revenues.append(now_revenue_estimate_quarterly*quarterly_gr**j) if curve_type == 'linear': projected_quarterly_revenues.append(now_revenue_estimate_quarterly*quarterly_gr**j) projected_quarterly_fcf = np.multiply(projected_quarterly_revenues,FCFmargin) # Calculate discounted terminal value final_projected_year_fcf = sum(projected_quarterly_fcf[(len(projected_quarterly_fcf)-4):len(projected_quarterly_fcf)]) discounted_TV = (final_projected_year_fcf*(1+perpetual_growth_rate) / (requiredReturn-perpetual_growth_rate))/((1+requiredReturn)**(periods/4)) # Calculate discounted cash flows discounted_FCF = 0 for j in range(0,periods): discounted_FCF = discounted_FCF + projected_quarterly_fcf[j]/(((1+requiredReturn)**(1/4))**(j+1)) # Calculate dilution/contraction if num_extra_reports > 0: latest_share_amount = extra_shAmount[-1] earliest_share_amount = financials['sharesOutstanding'][periods-1-num_extra_reports] else: latest_share_amount = financials['sharesOutstanding'][0] earliest_share_amount = financials['sharesOutstanding'][periods-1] if user_set_shAmountChange == False: change_in_shares = latest_share_amount/earliest_share_amount document.getElementById("shAmountChange").value = round((change_in_shares-1)*100,1) else: if document.getElementById("shAmountChange").value == "": change_in_shares = 1 document.getElementById("shAmountChange").value = 0 else: change_in_shares = 1+float(document.getElementById("shAmountChange").value)/100 # Get latest stockholder's equity if entered if num_extra_reports > 0: latest_stockholders_equity = extra_equity[-1] else: latest_stockholders_equity = financials['stockholdersEquity'][0] # Calculate present fair value if reported_currency == market_currency: present_fair_value = ((discounted_FCF + discounted_TV + latest_stockholders_equity)/latest_share_amount) else: present_fair_value = ((discounted_FCF + discounted_TV + latest_stockholders_equity)/latest_share_amount)*reported_to_market_currency['vwap'].iloc[0] # Adjust fair value for dilution/contraction diluted_fair_value = present_fair_value * 1/change_in_shares # Adjust fair value for safety margin safe_fair_value = diluted_fair_value * (1-safetyMargin) # Update fair value estimate and current price if safe_fair_value < 0: document.getElementById("errorMessage").textContent = "Warning: The average free cash flow margin of the company is negative for the chosen number of periods. The discounted free cash flow model is not suitable to value the company. The runway estimate is an important number to consider for companies like this. Please be extra careful when you consider investing in this company." document.getElementById("fairValueEstimate").textContent = 'N/A' else: document.getElementById("fairValueEstimate").textContent = str(round(safe_fair_value*0.9625,1)) + " - " + str(round(safe_fair_value*1.0375,1)) + " " + market_currency document.getElementById("stockPrice").textContent = str(quote['price'][0]) + " " + market_currency # Calculate and update liquidation value if reported_currency == market_currency: liquidation_value = ((latest_stockholders_equity)/latest_share_amount) else: liquidation_value = ((latest_stockholders_equity)/latest_share_amount)*reported_to_market_currency['vwap'].iloc[0] document.getElementById("liquidationValue").textContent = str(round(liquidation_value,1)) + " " + market_currency # Calculate runway if sum(financials['result'].iloc[0:4]) > 0 or financials['cash&equivalents'].iloc[0] >= financials['cash&equivalents'].iloc[1]: document.getElementById("runwayEstimate").textContent = 'N/A' elif financials['cash&equivalents'].iloc[0] >= financials['cash&equivalents'].iloc[4]: runway_Q = (financials['cash&equivalents'].iloc[0]/-(financials['cash&equivalents'].iloc[0]-financials['cash&equivalents'].iloc[1]))*3 runway_A = "N/A" document.getElementById("runwayEstimate").textContent = str(round(runway_Q,1)) + " months - " + runway_A + " months" else: runway_Q = (financials['cash&equivalents'].iloc[0]/-(financials['cash&equivalents'].iloc[0]-financials['cash&equivalents'].iloc[1]))*3 runway_A = (financials['cash&equivalents'].iloc[0]/-(financials['cash&equivalents'].iloc[0]-financials['cash&equivalents'].iloc[4]))*12 document.getElementById("runwayEstimate").textContent = str(round(runway_Q,1)) + " months - " + str(round(runway_A,1)) + " months" # Plots start here ##################################################################################################################################################################################################################################################################################################################################################################### # Plot revenues, free cash flows, best fit line and projections in linear scale. # Use the 'ggplot' style for aesthetics plt.style.use('ggplot') # Create a figure with a specified size plt.figure(figsize=(12, 4)) # Plot data markersize = 400/(financials['revenue'].shape[0] + periods) plt.plot(np.arange(financials['revenue'].shape[0]), financials['revenue'][::-1], label='Revenue', color='blue', linewidth=1, linestyle='none', marker='o', markersize=markersize) plt.plot(np.arange(financials['revenue'].shape[0]), financials['freeCashFlow'][::-1], label='Free Cash Flow', color='green', linewidth=1, linestyle='none', marker='o', markersize=markersize) plt.plot(np.arange(financials['revenue'].shape[0],financials['revenue'].shape[0] + periods), projected_quarterly_revenues, label='Projected Revenue', color='lightblue', linewidth=1, linestyle='none', marker='o', markersize=markersize) plt.plot(np.arange(financials['revenue'].shape[0],financials['revenue'].shape[0] + periods), projected_quarterly_fcf, label='Projected Free Cash Flow', color='lightgreen', linewidth=1, linestyle='none', marker='o', markersize=markersize) plt.plot(np.arange(financials['revenue'].shape[0] - periods,financials['revenue'].shape[0]), best_fit_line, label='Best Fit line', color='red', linewidth=1, linestyle='-') # Add a legend to differentiate the lines plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=5) # Add title and labels with increased font size plt.title('Linear plot of revenues, free cash flow and projections', fontsize=14) x_tick_locs = [0,financials['revenue'].shape[0]//2,financials['revenue'].shape[0]-1] x_tick_labels = [financials['date'].iloc[financials['revenue'].shape[0]-1].date(), financials['date'].iloc[financials['revenue'].shape[0]//2].date(), financials['date'].iloc[0].date()] plt.xticks(x_tick_locs, x_tick_labels, fontsize=12) plt.ylabel(reported_currency, fontsize=12) # Add a grid and set the background color plt.grid(True) plt.gca().set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('plot.svg', transparent=True) document.getElementById('plot-container').innerHTML = open('plot.svg').read() # Plot revenues, free cash flows, best fit line and projections in logarithmic scale. # Set the Y-axis to a logarithmic scale plt.yscale('log') # Add title and labels with increased font size plt.title('Logarithmic plot of revenues, free cash flow and projections', fontsize=14) # Save the plot to a HTML element plt.savefig('log-plot.svg', transparent=True) document.getElementById('log-plot-container').innerHTML = open('log-plot.svg').read() #plt.close() # Plotting historical FV vs price ############################################################################################################################################################################################################################################## # Get data for historical plots historicalPrice = await asyncio.ensure_future(call_fetch_hp(ticker)) historical_date = pd.to_datetime(historicalPrice['date']) historical_vwap = pd.to_numeric(historicalPrice['vwap']) # Calculate historical fair value fair_value_estimates = [] historical_liquidation_value = [] historical_zero_growth_scenario = [] fair_value_dates = pd.to_datetime(financials['date'].iloc[0:(financials['date'].shape[0]-periods)]) for i in range(0,financials['revenue'].shape[0]-periods): # Get fcf margin revenue_list = financials['revenue'].iloc[i:(periods+i)] fcf_list = financials['freeCashFlow'].iloc[i:(periods+i)] if sum(revenue_list) == 0: i_rev_zero = i break FCFmargin = sum(fcf_list)/sum(revenue_list) # Order revenue numbers chronologically revenues = revenue_list[::-1] revenues = revenues.reset_index(drop=True) # Downscale numbers to now overflow the curve_fit function for j in range(0,len(revenues)): revenues[j] = revenues[j] / 10**len(str(financials['revenue'].iloc[i])) # Fit the curve # x = np.arange(periods), already set further up try: popt, pcov = curve_fit(lambda x,b,m: b*m**x,x,revenues,maxfev = 500) curve_type = 'exponential' except: print('Exponental growth curve does not fit, trying linear instead.') try: popt, pcov = curve_fit(lambda x,b,m: b+m*x,x,revenues,maxfev = 500) curve_type = 'linear' except: print('Failed to find best fit growth rate') # Calculate growth rates and now-revenue estimates. Upscale revenues. quarterly_gr = popt[1] annual_gr = quarterly_gr**4 if curve_type == 'exponential': now_revenue_estimate_quarterly = popt[0]*popt[1]**x[-1] * 10**len(str(financials['revenue'].iloc[i])) if curve_type == 'linear': now_revenue_estimate_quarterly = popt[0]+popt[1]*x[-1] * 10**len(str(financials['revenue'].iloc[i])) # Project future revenues and free cash flow for an equal period of time to the period projected_quarterly_revenues = [] projected_quarterly_fcf = [] for j in range(1,(periods+1)): if curve_type == 'exponential': projected_quarterly_revenues.append(now_revenue_estimate_quarterly*quarterly_gr**j) if curve_type == 'linear': projected_quarterly_revenues.append(now_revenue_estimate_quarterly*quarterly_gr**j) projected_quarterly_fcf = np.multiply(projected_quarterly_revenues,FCFmargin) # Calculate discounted terminal value final_projected_year_fcf = sum(projected_quarterly_fcf[(len(projected_quarterly_fcf)-4):len(projected_quarterly_fcf)]) discounted_TV = (final_projected_year_fcf*(1+perpetual_growth_rate) / (requiredReturn-perpetual_growth_rate))/((1+requiredReturn)**(periods/4)) zero_growth_TV = ((sum(fcf_list)/periods) / (requiredReturn))/((1+requiredReturn)**(periods/4)) # Calculate discounted cash flows discounted_FCF = 0 for j in range(0,periods): discounted_FCF = discounted_FCF + projected_quarterly_fcf[j]/(((1+requiredReturn)**(1/4))**(j+1)) # Calculate dilution/contraction latest_share_amount = financials['sharesOutstanding'].iloc[i] earliest_share_amount = financials['sharesOutstanding'].iloc[periods-1+i] change_in_shares = latest_share_amount/earliest_share_amount # Get latest stockholder's equity if entered latest_stockholders_equity = financials['stockholdersEquity'][i] # Calculate present fair value if reported_currency == market_currency: present_fair_value = ((discounted_FCF + discounted_TV + latest_stockholders_equity)/latest_share_amount) historical_liquidation_value.append((financials['stockholdersEquity'].iloc[i] / financials['sharesOutstanding'].iloc[i])) historical_zero_growth_scenario.append((zero_growth_TV + latest_stockholders_equity)/latest_share_amount) else: try: # Calculate the absolute difference between each value and the target value reported_to_market_currency['abs_diff'] = abs(reported_to_market_currency['date'] - financials['date'].iloc[i]) # Find closest match date_match_index = reported_to_market_currency['abs_diff'].idxmin() # Calculate present fair value adjusted for reported to market currency present_fair_value = ((discounted_FCF + discounted_TV + latest_stockholders_equity)/latest_share_amount)*reported_to_market_currency['vwap'].iloc[(date_match_index-42):(date_match_index+1)].mean() # Calculate historical liquidation value adjusted for reported to market currency historical_liquidation_value.append((financials['stockholdersEquity'].iloc[i] / financials['sharesOutstanding'].iloc[i]) * reported_to_market_currency['vwap'].iloc[(date_match_index-42):(date_match_index+1)].mean()) # Calculate historical zero growth scenario adjusted for reported to market currency historical_zero_growth_scenario.append((zero_growth_TV + latest_stockholders_equity)/latest_share_amount * reported_to_market_currency['vwap'].iloc[(date_match_index-42):(date_match_index+1)].mean()) except: #If unable to find the date_match_index, use the last available vwap console.log('Date not matched for report date in currency exchange rate dates: ' + str(financials['date'].iloc[i])) present_fair_value = ((discounted_FCF + discounted_TV + latest_stockholders_equity)/latest_share_amount)*reported_to_market_currency['vwap'].iloc[0] historical_liquidation_value.append((financials['stockholdersEquity'].iloc[i] / financials['sharesOutstanding'].iloc[i]) * reported_to_market_currency['vwap'].iloc[0]) historical_zero_growth_scenario.append((zero_growth_TV + latest_stockholders_equity)/latest_share_amount * reported_to_market_currency['vwap'].iloc[0]) # Adjust fair value for dilution/contraction diluted_fair_value = present_fair_value * 1/change_in_shares # Adjust fair value for safety margin safe_fair_value = round(diluted_fair_value * (1-safetyMargin),1) # Save value to fair value estimate df fair_value_estimates.append(safe_fair_value) # Plot historical price and fair value estimate on linear scale # Create a figure with a specified size fig, axs = plt.subplots(1, 1, figsize=(12, 4)) # Plot data axs.plot(historical_date, historical_vwap, label='Volume Weighted Average Price', color='blue', linewidth=1) try: axs.plot(fair_value_dates.iloc[0:i_rev_zero], historical_liquidation_value, label='Liquidation Value', color='green', linestyle='none', marker='_', markersize=min(400/len(fair_value_dates),10)) axs.plot(fair_value_dates.iloc[0:i_rev_zero], fair_value_estimates, label='Fair Value Estimate', color='red', linestyle='none', marker='_', markersize=min(400/len(fair_value_dates),10)) except: axs.plot(fair_value_dates, historical_liquidation_value, label='Liquidation Value', color='green', linestyle='none', marker='_', markersize=min(400/len(fair_value_dates),10)) axs.plot(fair_value_dates, historical_zero_growth_scenario, label='Zero Growth Value', color='orange', linestyle='none', marker='_', markersize=min(400/len(fair_value_dates),10)) axs.plot(fair_value_dates, fair_value_estimates, label='Fair Value Estimate', color='red', linestyle='none', marker='_', markersize=min(400/len(fair_value_dates),10)) # Add a legend to differentiate the lines axs.legend(loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=5) # Add title and labels with increased font size axs.set_title('Linear plot of historical data', fontsize=14) axs.set_ylabel(market_currency, fontsize=12) # Add a grid and set the background color axs.grid(True) axs.set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('historical_plot.svg', transparent=True) document.getElementById('lin-historical-plot-container').innerHTML = open('historical_plot.svg').read() # Plot historical price and fair value estimate on log scale axs.set_title('Logarithmic plot of historical data', fontsize=14) axs.set_yscale('log') # Save the plot to a HTML element plt.savefig('historical_plot.svg', transparent=True) document.getElementById('historical-plot-container').innerHTML = open('historical_plot.svg').read() #plt.close() # Plot Multiples ############################################################################################################################################################################################################################################## createMultiplesTable() # Get historical multiples quarterly_dates = pd.to_datetime(financials['date']) #.iloc[0] is the most recent, same for historical_date historical_earnings_ttm = [] historical_fcf_ttm = [] historical_B = financials['stockholdersEquity'] # historcal_vwap, historical_date # Calculate TTM values for j in range(0,len(quarterly_dates)-4): historical_earnings_ttm.append(sum(financials['result'].iloc[j:j+4])) historical_fcf_ttm.append(sum(financials['freeCashFlow'].iloc[j:j+4])) # Calculate P/ values historical_PE = [] historical_PFCF = [] historical_PB = [] for j in range(0,len(historical_date)): try: quarterly_index = (quarterly_dates >= historical_date.iloc[j]).idxmin() historical_PE.append(historical_vwap[j] / (historical_earnings_ttm[quarterly_index] / financials['sharesOutstanding'].iloc[quarterly_index])) historical_PFCF.append(historical_vwap[j] / (historical_fcf_ttm[quarterly_index] / financials['sharesOutstanding'].iloc[quarterly_index])) historical_PB.append(historical_vwap[j] / (historical_B.iloc[quarterly_index] / financials['sharesOutstanding'].iloc[quarterly_index])) except: end_j = j break # Clean infinite values: historical_PE = np.where(np.isinf(historical_PE), 0, historical_PE) historical_PFCF = np.where(np.isinf(historical_PFCF), 0, historical_PFCF) historical_PB = np.where(np.isinf(historical_PB), 0, historical_PB) # Plot historical multiples in linear scale # Create a figure with a specified size fig, axs = plt.subplots(1, 1, figsize=(12, 4)) # Plot data try: axs.plot(historical_date.iloc[0:end_j], historical_PE, label='P/E', color='blue', linewidth=1) axs.plot(historical_date.iloc[0:end_j], historical_PFCF, label='P/FCF', color='green', linewidth=1) axs.plot(historical_date.iloc[0:end_j], historical_PB, label='P/B', color='red', linewidth=1) except: axs.plot(historical_date, historical_PE, label='P/E', color='blue', linewidth=1) axs.plot(historical_date, historical_PFCF, label='P/FCF', color='green', linewidth=1) axs.plot(historical_date, historical_PB, label='P/B', color='red', linewidth=1) # Add a legend to differentiate the lines axs.legend(loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=3) # Add title and labels with increased font size axs.set_title('Linear plot of historical multiples', fontsize=14) axs.set_ylabel('Multiple', fontsize=12) axs.set_ylim(0-max(max(historical_PE),max(historical_PFCF),max(historical_PB))*0.05, max(max(historical_PE),max(historical_PFCF),max(historical_PB))*1.05) # Add a grid and set the background color axs.grid(True) axs.set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('lin-multiples-plot.svg', transparent=True) document.getElementById('lin-multiples-plot').innerHTML = open('lin-multiples-plot.svg').read() # Create log plot axs.set_ylim(0, max(max(historical_PE),max(historical_PFCF),max(historical_PB))*1.05) axs.set_title('Logarithmic plot of historical multiples', fontsize=14) axs.set_yscale('symlog') # Save the plot to a HTML element plt.savefig('log-multiples-plot.svg', transparent=True) document.getElementById('log-multiples-plot').innerHTML = open('log-multiples-plot.svg').read() #plt.close() # Plot key financials (prev. key metrics) ############################################################################################################################################################################################################################################## # Find margins Quarterly_fcf_margin_ttm = [] Quarterly_profit_margin_ttm = [] Quarterly_ocf_margin_ttm = [] Quarterly_fcf_to_debt_ttm = [] Quarterly_ocf_to_debt_ttm = [] Quarterly_profit_to_debt_ttm = [] Quarterly_dilution = [] for j in range(0,financials['revenue'].shape[0]-4): Quarterly_fcf_margin_ttm.append((sum(financials['freeCashFlow'].iloc[j:(j+4)]) / sum(financials['revenue'].iloc[j:(j+4)])) * 100) Quarterly_profit_margin_ttm.append((sum(financials['result'].iloc[j:(j+4)]) / sum(financials['revenue'].iloc[j:(j+4)])) * 100) Quarterly_ocf_margin_ttm.append((sum(financials['operatingCashFlow'].iloc[j:(j+4)]) / sum(financials['revenue'].iloc[j:(j+4)])) * 100) Quarterly_fcf_to_debt_ttm.append((sum(financials['freeCashFlow'].iloc[j:(j+4)]) / financials['liabilities'].iloc[j]) * 100) Quarterly_ocf_to_debt_ttm.append((sum(financials['operatingCashFlow'].iloc[j:(j+4)]) / financials['liabilities'].iloc[j]) * 100) Quarterly_profit_to_debt_ttm.append((sum(financials['result'].iloc[j:(j+4)]) / financials['liabilities'].iloc[j]) * 100) Quarterly_dilution.append(((financials['sharesOutstanding'].iloc[j] / financials['sharesOutstanding'].iloc[j+1]) - 1) * 100) # Plot historical balance ####################################################################################################################################################################################################### # Create a figure with a specified size fig, axs = plt.subplots(1, 1, figsize=(12, 4)) axs2 = axs.twinx() # Plot data axs.plot(financials['date'],financials['cash&equivalents'], label='Cash & Equivalents', color='green', linestyle='-', linewidth=1) axs.plot(financials['date'],financials['liabilities'], label='Liabilities', color='red', linestyle='-', linewidth=1) axs.plot(financials['date'],financials['assets'], label='Assets', color='blue', linestyle='-', linewidth=1) axs.plot(financials['date'],financials['dividends'], label='Dividends', color='black', linestyle='none', marker='_', markersize=markersize) axs.plot(financials['date'],financials['stockholdersEquity'], label="Stockholder's Equity", color='purple', linestyle='-', linewidth=1) axs.plot(financials['date'],financials['R&Dexpenses'], label='R&D Expenses', color='orange', linestyle='-', linewidth=1) axs2.plot(financials['date'], financials['sharesOutstanding'], label='Shares Outstanding', color='black', linestyle='-', linewidth=1) # Place the legend below the plot handles1, labels1 = axs.get_legend_handles_labels() handles2, labels2 = axs2.get_legend_handles_labels() handles = handles1 + handles2 labels = labels1 + labels2 axs.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=7) # Add title and labels with increased font size axs.set_title('Linear Key Balance Sheet', fontsize=14) axs.set_ylabel(reported_currency, fontsize=12) axs.set_ylim(0-max(max(financials['cash&equivalents']), max(financials['liabilities']), max(financials['assets']), max(financials['dividends']), max(financials['stockholdersEquity']), max(financials['R&Dexpenses']))*0.05, max(max(financials['cash&equivalents']), max(financials['liabilities']), max(financials['assets']), max(financials['dividends']), max(financials['stockholdersEquity']), max(financials['R&Dexpenses']))*1.05) shares_min, shares_max = financials['sharesOutstanding'].min(), financials['sharesOutstanding'].max() ticks_to_use = [shares_min, shares_max] axs2.set_yticks(ticks_to_use) axs2.set_ylim(shares_min*0.95, shares_max*1.05) axs2.set_ylabel('Number of Shares', fontsize=12) # Add a grid and set the background color axs.grid(True) axs.set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('lin-balance-plot-container').innerHTML = open('metrics_plot.svg').read() # Create log plot axs.set_ylim(0, max(max(financials['cash&equivalents']), max(financials['liabilities']), max(financials['assets']), max(financials['dividends']), max(financials['stockholdersEquity']), max(financials['R&Dexpenses']))*1.05) axs.set_title('Logarithmic Key Balance Sheet', fontsize=14) axs.set_yscale('symlog') axs2.set_yscale('symlog') # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('log-balance-plot-container').innerHTML = open('metrics_plot.svg').read() #plt.close() # Plot historical margins ############################################################################################################################################################################################################## # Create a figure with a specified size fig, axs = plt.subplots(1, 1, figsize=(12, 4)) # Plot data axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_fcf_margin_ttm, label='FCF Margin TTM', color='green', linestyle='-', linewidth=1) axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_profit_margin_ttm, label='Profit Margin TTM', color='blue', linestyle='-', linewidth=1) axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_ocf_margin_ttm, label='OCF Margin TTM', color='red', linestyle='-', linewidth=1) #axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_fcf_to_debt_ttm, label='TTM FCF/Liabilities', color='orange', linestyle='-', linewidth=1) #axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_profit_to_debt_ttm, label='TTM Profit/Liabilities', color='purple', linestyle='-', linewidth=1) #axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_ocf_to_debt_ttm, label='TTM OCF/Liabilities', color='gray', linestyle='-', linewidth=1) # Add a legend to differentiate the lines axs.legend(loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=6) # Add title and labels with increased font size axs.set_title('Linear Key Margins', fontsize=14) axs.set_ylabel('%', fontsize=12) #axs.set_ylim(0-max(max(Quarterly_fcf_margin_ttm), max(Quarterly_profit_margin_ttm), max(Quarterly_ocf_margin_ttm), max(Quarterly_fcf_to_debt_ttm), max(Quarterly_profit_to_debt_ttm), max(Quarterly_ocf_to_debt_ttm))*0.05, max(max(Quarterly_fcf_margin_ttm), max(Quarterly_profit_margin_ttm), max(Quarterly_ocf_margin_ttm), max(Quarterly_fcf_to_debt_ttm), max(Quarterly_profit_to_debt_ttm), max(Quarterly_ocf_to_debt_ttm))*1.05) axs.set_ylim(0-max(max(Quarterly_fcf_margin_ttm), max(Quarterly_profit_margin_ttm), max(Quarterly_ocf_margin_ttm))*0.05, max(max(Quarterly_fcf_margin_ttm), max(Quarterly_profit_margin_ttm), max(Quarterly_ocf_margin_ttm))*1.05) # Add a grid and set the background color axs.grid(True) axs.set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('lin-margin-plot-container').innerHTML = open('metrics_plot.svg').read() # Create log plot axs.set_ylim(0, max(max(Quarterly_fcf_margin_ttm), max(Quarterly_profit_margin_ttm), max(Quarterly_ocf_margin_ttm), max(Quarterly_fcf_to_debt_ttm), max(Quarterly_profit_to_debt_ttm), max(Quarterly_ocf_to_debt_ttm))*1.05) axs.set_title('Logarithmic Key Margins', fontsize=14) axs.set_yscale('symlog') # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('log-margin-plot-container').innerHTML = open('metrics_plot.svg').read() #plt.close() # Plot historical return on... ############################################################################################################################################################################################################## # Find returns Quarterly_roic_ttm = [] Quarterly_roe_ttm = [] Quarterly_roa_ttm = [] Quarterly_roce_ttm = [] for j in range(0,financials['revenue'].shape[0]-4): Quarterly_roe_ttm.append(sum(financials['result'].iloc[j:(j+4)]) / financials['stockholdersEquity'].iloc[j] * 100) Quarterly_roa_ttm.append(sum(financials['result'].iloc[j:(j+4)]) / financials['assets'].iloc[j] * 100) Quarterly_roce_ttm.append(sum(financials['ebit'].iloc[j:(j+4)]) / (financials['assets'].iloc[j] - financials['currentLiabilities'].iloc[j]) * 100) tax_rate = financials['result'].iloc[j] / financials['ebit'].iloc[j] Quarterly_roic_ttm.append(sum(financials['netOCF'].iloc[j:(j+4)]) * tax_rate / (financials['totalEquity'].iloc[j] + financials['totalDebt'].iloc[j]) * 100) # Create a figure with a specified size fig, axs = plt.subplots(1, 1, figsize=(12, 4)) # Plot data axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_roic_ttm, label='ROIC TTM', color='green', linestyle='-', linewidth=1) axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_roe_ttm, label='ROE TTM', color='blue', linestyle='-', linewidth=1) axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_roa_ttm, label='ROA TTM', color='red', linestyle='-', linewidth=1) axs.plot(financials['date'].iloc[0:(financials['revenue'].shape[0]-4)], Quarterly_roce_ttm, label='ROCE TTM', color='orange', linestyle='-', linewidth=1) # Add a legend to differentiate the lines axs.legend(loc='upper center', bbox_to_anchor=(0.5, -0.075), ncol=4) # Add title and labels with increased font size axs.set_title('Linear Key Return Ratios', fontsize=14) axs.set_ylabel('%', fontsize=12) axs.set_ylim(0-max(max(Quarterly_roic_ttm), max(Quarterly_roe_ttm), max(Quarterly_roa_ttm), max(Quarterly_roce_ttm))*0.05, max(max(Quarterly_roic_ttm), max(Quarterly_roe_ttm), max(Quarterly_roa_ttm), max(Quarterly_roce_ttm))*1.05) # Add a grid and set the background color axs.grid(True) axs.set_facecolor('#f9fafb') plt.tight_layout(pad=1.0) # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('lin-return-plot-container').innerHTML = open('metrics_plot.svg').read() # Create log plot axs.set_ylim(0, max(max(Quarterly_roic_ttm), max(Quarterly_roe_ttm), max(Quarterly_roa_ttm), max(Quarterly_roce_ttm))*1.05) axs.set_title('Logarithmic Key Return Ratios', fontsize=14) axs.set_yscale('symlog') # Save the plot to a HTML element plt.savefig('metrics_plot.svg', transparent=True) document.getElementById('log-return-plot-container').innerHTML = open('metrics_plot.svg').read() #plt.close() # Update dividend information ################################################################################################################################################################### document.getElementById("dividendYield1Year").textContent = "-" document.getElementById("dividendYield3Year").textContent = "-" document.getElementById("dividendYield5Year").textContent = "-" document.getElementById("dividendYield10Year").textContent = "-" document.getElementById("cagr1YearDiv").textContent = "-" document.getElementById("cagr3YearDiv").textContent = "-" document.getElementById("cagr5YearDiv").textContent = "-" document.getElementById("cagr10YearDiv").textContent = "-" if reported_currency == market_currency: try: DY1Y = round(sum(financials['dividends'].iloc[0:4]) / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield1Year").textContent = str(DY1Y) + ' %' except: document.getElementById("dividendYield1Year").textContent = 'N/A' try: DY3Y = round(sum(financials['dividends'].iloc[0:12]) / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield3Year").textContent = str(DY3Y) + ' %' except: document.getElementById("dividendYield3Year").textContent = 'N/A' try: DY5Y = round(sum(financials['dividends'].iloc[0:20]) / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield5Year").textContent = str(DY5Y) + ' %' except: document.getElementById("dividendYield5Year").textContent = 'N/A' try: DY10Y = round(sum(financials['dividends'].iloc[0:40]) / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield10Year").textContent = str(DY10Y) + ' %' except: document.getElementById("dividendYield10Year").textContent = 'N/A' else: try: DY1Y = round(sum(financials['dividends'].iloc[0:4]) * reported_to_market_currency['vwap'].iloc[0] / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield1Year").textContent = str(DY1Y) + ' %' except: document.getElementById("dividendYield1Year").textContent = 'N/A' try: DY3Y = round(sum(financials['dividends'].iloc[0:12]) * reported_to_market_currency['vwap'].iloc[0] / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield3Year").textContent = str(DY3Y) + ' %' except: document.getElementById("dividendYield3Year").textContent = 'N/A' try: DY5Y = round(sum(financials['dividends'].iloc[0:20]) * reported_to_market_currency['vwap'].iloc[0] / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield5Year").textContent = str(DY5Y) + ' %' except: document.getElementById("dividendYield5Year").textContent = 'N/A' try: DY10Y = round(sum(financials['dividends'].iloc[0:40]) * reported_to_market_currency['vwap'].iloc[0] / quote['marketCap'][0] * 100, 2) document.getElementById("dividendYield10Year").textContent = str(DY10Y) + ' %' except: document.getElementById("dividendYield10Year").textContent = 'N/A' try: DY1YCAGR = round((sum(financials['dividends'].iloc[0:4]) / sum(financials['dividends'].iloc[4:8]) - 1) * 100, 2) document.getElementById("cagr1YearDiv").textContent = str(DY1YCAGR) + ' %' except: document.getElementById("cagr1YearDiv").textContent = 'N/A' try: DY3YCAGR = round(((sum(financials['dividends'].iloc[0:4]) / sum(financials['dividends'].iloc[12:16]))**(1/3) - 1) * 100, 2) document.getElementById("cagr3YearDiv").textContent = str(DY3YCAGR) + ' %' except: document.getElementById("cagr3YearDiv").textContent = 'N/A' try: DY5YCAGR = round(((sum(financials['dividends'].iloc[0:4]) / sum(financials['dividends'].iloc[20:24]))**(1/5) - 1) * 100, 2) document.getElementById("cagr5YearDiv").textContent = str(DY5YCAGR) + ' %' except: document.getElementById("cagr5YearDiv").textContent = 'N/A' try: DY10YCAGR = round(((sum(financials['dividends'].iloc[0:4]) / sum(financials['dividends'].iloc[40:44]))**(1/10) - 1) * 100, 2) document.getElementById("cagr10YearDiv").textContent = str(DY10YCAGR) + ' %' except: document.getElementById("cagr10YearDiv").textContent = 'N/A' # Hide loading spinner document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Done!" except Exception as e: #If there is an unhandled error, display the error message console.log(str(e)) document.getElementById("loadingSpinner").style.display = "none" document.getElementById("spinnerLabel").textContent = "Failed" if document.getElementById("ticker").value == "": document.getElementById("errorMessage").textContent = "Error: No ticker entered." else: document.getElementById("errorMessage").textContent = "Error: " + str(e) + ". Please report it to help us find all ways the script can fail. Thank you!" # Run when analyze button is pressed calculate_button_proxy = create_proxy(calculate) calculate_button = document.getElementById("Calculatebtn") calculate_button.addEventListener("click", calculate_button_proxy) # Handle custom values for growth rate, fcfMargin or share amount change # Function locking custom shAmountChange def lock_shAmountChange(e): # Load global variables global user_set_shAmountChange # Show icon document.getElementById("shAmountChangeIcon").style.display = "inline-block" # Update global variable user_set_shAmountChange = True # Listen for entry in shAmountChange shAmountChange_entry_proxy = create_proxy(lock_shAmountChange) shAmountChange_entry = document.getElementById("shAmountChange") shAmountChange_entry.addEventListener("change", shAmountChange_entry_proxy) # Function for unlocking custom shAmountChange def unlock_shAmountChange(e): # Load global variables global user_set_shAmountChange # Hide icon document.getElementById("shAmountChangeIcon").style.display = "none" # Update global variable user_set_shAmountChange = False # Listen for click on share amount changeicon shAmountChange_icon_proxy = create_proxy(unlock_shAmountChange) shAmountChange_icon = document.getElementById("shAmountChangeIcon") shAmountChange_icon.addEventListener("click", shAmountChange_icon_proxy) # Function locking custom fcfMargin def lock_fcfMargin(e): # Load global variables global user_set_fcfMargin # Show icon document.getElementById("fcfMarginIcon").style.display = "inline-block" # Update global variable user_set_fcfMargin = True # Listen for entry in fcfMargin fcfMargin_entry_proxy = create_proxy(lock_fcfMargin) fcfMargin_entry = document.getElementById("fcfMargin") fcfMargin_entry.addEventListener("change", fcfMargin_entry_proxy) # Function for unlocking custom shAmountChange def unlock_fcfMargin(e): # Load global variables global user_set_fcfMargin # Hide icon document.getElementById("fcfMarginIcon").style.display = "none" # Update global variable user_set_fcfMargin = False # Listen for click on fcfMargin icon fcfMargin_icon_proxy = create_proxy(unlock_fcfMargin) fcfMargin_icon = document.getElementById("fcfMarginIcon") fcfMargin_icon.addEventListener("click", fcfMargin_icon_proxy) # Function locking custom growthRate def lock_growthRate(e): # Load global variables global user_set_growthRate # Show icon document.getElementById("growthRateIcon").style.display = "inline-block" # Update global variable user_set_growthRate = True # Listen for entry in growthRate growthRate_entry_proxy = create_proxy(lock_growthRate) growthRate_entry = document.getElementById("growthRate") growthRate_entry.addEventListener("change", growthRate_entry_proxy) # Function for unlocking custom growthRate def unlock_growthRate(e): # Load global variables global user_set_growthRate # Hide icon document.getElementById("growthRateIcon").style.display = "none" # Update global variable user_set_growthRate = False # Listen for click on growthRate icon growthRate_icon_proxy = create_proxy(unlock_growthRate) growthRate_icon = document.getElementById("growthRateIcon") growthRate_icon.addEventListener("click", growthRate_icon_proxy)