import yfinance as yf import pandas as pd import matplotlib.pyplot as plt from datetime import datetime, timedelta import calendar # --- Configuration Parameters --- # Define the start and end dates for the simulation # The simulation will run from the start of the ''start_date_simulation'' month # until the end of the ''end_date_simulation'' month. start_date_simulation = "2010-08-01" end_date_simulation = "2025-07-31" # Up to the end of the last full month # Define the saving pattern initial_saving_2010 = 50000 # Annual saving in the first year (2010) saving_2024 = 100000 # Annual saving in the year 2024 start_year_saving_increase = 2010 # Year when the linear saving increase begins end_year_saving_increase = 2024 # Year when the linear saving increase ends # S&P 500 ticker symbol sp500_ticker = "^GSPC" # --- Function to calculate annual saving based on linear increase --- def get_annual_saving(year): """ Calculates the annual saving amount for a given year based on a linear increase. Args: year (int): The year for which to calculate the annual saving. Returns: float: The calculated annual saving for the given year. """ # Calculate the total number of years over which the saving linearly increases num_years_increase = end_year_saving_increase - start_year_saving_increase # Calculate the annual rate of increase in savings # Avoid division by zero if the increase period is 0 years annual_saving_increase_per_year = (saving_2024 - initial_saving_2010) / num_years_increase \ if num_years_increase > 0 else 0 if year < start_year_saving_increase: # Before the start of the saving increase period, assume initial saving level return initial_saving_2010 elif year <= end_year_saving_increase: # During the linear increase period return initial_saving_2010 + (year - start_year_saving_increase) * annual_saving_increase_per_year else: # After the end of the linear increase period, assume saving remains at the 2024 level return saving_2024 # --- Fetch S&P 500 historical data --- print(f"Fetching S&P 500 data for {sp500_ticker} from {start_date_simulation} to {end_date_simulation}...") try: # Download daily adjusted close prices for S&P 500 # auto_adjust=True is the default and means ''Close'' column is already adjusted. sp500_data = yf.download(sp500_ticker, start=start_date_simulation, end=end_date_simulation) if sp500_data.empty: raise ValueError("No S&P 500 data downloaded. Check ticker symbol and date range.") print("S&P 500 data fetched successfully.") except Exception as e: print(f"Error fetching S&P 500 data: {e}") print("Please ensure you have an active internet connection and the ticker symbol is correct.") exit() # Exit the program if data cannot be fetched # Calculate monthly returns from daily adjusted close prices # Changed ''Adj Close'' to ''Close'' because yfinance now auto-adjusts the ''Close'' column. # Changed ''M'' to ''ME'' for resample frequency to address FutureWarning. monthly_prices = sp500_data[''Close''].resample(''ME'').last() # Calculate percentage change between consecutive month-end prices monthly_returns = monthly_prices.pct_change().dropna() # Drop the first NaN value from pct_change # Create a dictionary mapping year-month tuples to returns for faster and more robust lookup # Explicitly iterate and cast values to float to ensure scalar types monthly_returns_map = {} for i in range(len(monthly_returns.index)): year = monthly_returns.index.year month = monthly_returns.index.month val = monthly_returns.values monthly_returns_map[(year, month)] = float(val) # Explicitly cast to float # --- Simulate the investment growth --- portfolio_value = 0.0 # Initialize the total portfolio value portfolio_history = [] # List to store portfolio value at each step for plotting dates_history = [] # List to store corresponding dates for plotting # Convert string dates to datetime objects for iteration current_date = datetime.strptime(start_date_simulation, "%Y-%m-%d") end_date_dt = datetime.strptime(end_date_simulation, "%Y-%m-%d") print("\nSimulating investment growth...") while current_date <= end_date_dt: year = current_date.year month = current_date.month # Calculate the annual and monthly saving for the current year annual_saving = get_annual_saving(year) monthly_saving = float(annual_saving / 12) # Ensure monthly_saving is a float # Add the monthly saving to the portfolio portfolio_value += monthly_saving # Determine the end of the current month to find the corresponding S&P 500 return # This date is used purely for lookup, not for the plot''s x-axis directly last_day_of_month = calendar.monthrange(year, month)[1] month_end_date = current_date.replace(day=last_day_of_month) # Find the S&P 500 return for the current month using the pre-built map target_year_month = (month_end_date.year, month_end_date.month) return_for_month = monthly_returns_map.get(target_year_month) if return_for_month is not None: # Apply the monthly return to the portfolio value portfolio_value *= (1 + return_for_month) else: # This warning will appear if there''s no S&P 500 data for a particular month # within the specified range, which is rare but possible with partial months # or data gaps. # print(f"Warning: No S&P 500 return data found for {month_end_date.strftime(''%Y-%m'')}. Portfolio not adjusted for this month.") pass # Continue without applying return if not found # Store the current portfolio value and date for plotting portfolio_history.append(portfolio_value) dates_history.append(current_date) # Move to the first day of the next month if current_date.month == 12: current_date = current_date.replace(year=current_date.year + 1, month=1, day=1) else: current_date = current_date.replace(month=current_date.month + 1, day=1) print("Simulation complete. Generating plot...") # --- Plotting the results --- plt.figure(figsize=(14, 7)) # Set the figure size for better readability plt.plot(dates_history, portfolio_history, label=''Total Portfolio Value'', color=''#1f77b4'', linewidth=2) # Add title and labels plt.title(''Investment Growth in S&P 500 (2010-2025)'', fontsize=16, fontweight=''bold'') plt.xlabel(''Date'', fontsize=12) plt.ylabel(''Portfolio Value ($)'', fontsize=12) # Customize the grid and legend plt.grid(True, linestyle=''--'', alpha=0.7) plt.legend(fontsize=10) # Improve x-axis date formatting plt.gcf().autofmt_xdate() # Automatically format the date labels # Add a text annotation for the final portfolio value if portfolio_history: final_value = portfolio_history[-1] final_date = dates_history[-1] plt.annotate(f''Final Value: ${final_value:,.2f}'', xy=(final_date, final_value), xytext=(final_date + timedelta(days=60), final_value * 0.9), # Adjust text position arrowprops=dict(facecolor=''black'', shrink=0.05), fontsize=10, color=''green'', fontweight=''bold'') plt.tight_layout() # Adjust layout to prevent labels from overlapping plt.show() print(f"\nSimulation finished. Final Portfolio Value: ${portfolio_value:,.2f}")
假设这家人15年前25岁开始工作，每年攒5万。现在挣30万到手20万，花10万存10万。每年能存的钱线性增加，一直定投大盘。Gemini写的code：
import yfinance as yf import pandas as pd import matplotlib.pyplot as plt from datetime import datetime, timedelta import calendar
# --- Configuration Parameters --- # Define the start and end dates for the simulation # The simulation will run from the start of the ''start_date_simulation'' month # until the end of the ''end_date_simulation'' month. start_date_simulation = "2010-08-01" end_date_simulation = "2025-07-31" # Up to the end of the last full month
# Define the saving pattern initial_saving_2010 = 50000 # Annual saving in the first year (2010) saving_2024 = 100000 # Annual saving in the year 2024 start_year_saving_increase = 2010 # Year when the linear saving increase begins end_year_saving_increase = 2024 # Year when the linear saving increase ends
# S&P 500 ticker symbol sp500_ticker = "^GSPC"
# --- Function to calculate annual saving based on linear increase --- def get_annual_saving(year): """ Calculates the annual saving amount for a given year based on a linear increase.
Args: year (int): The year for which to calculate the annual saving.
Returns: float: The calculated annual saving for the given year. """ # Calculate the total number of years over which the saving linearly increases num_years_increase = end_year_saving_increase - start_year_saving_increase
# Calculate the annual rate of increase in savings # Avoid division by zero if the increase period is 0 years annual_saving_increase_per_year = (saving_2024 - initial_saving_2010) / num_years_increase \ if num_years_increase > 0 else 0
if year < start_year_saving_increase: # Before the start of the saving increase period, assume initial saving level return initial_saving_2010 elif year <= end_year_saving_increase: # During the linear increase period return initial_saving_2010 + (year - start_year_saving_increase) * annual_saving_increase_per_year else: # After the end of the linear increase period, assume saving remains at the 2024 level return saving_2024
# --- Fetch S&P 500 historical data --- print(f"Fetching S&P 500 data for {sp500_ticker} from {start_date_simulation} to {end_date_simulation}...") try: # Download daily adjusted close prices for S&P 500 # auto_adjust=True is the default and means ''Close'' column is already adjusted. sp500_data = yf.download(sp500_ticker, start=start_date_simulation, end=end_date_simulation) if sp500_data.empty: raise ValueError("No S&P 500 data downloaded. Check ticker symbol and date range.") print("S&P 500 data fetched successfully.") except Exception as e: print(f"Error fetching S&P 500 data: {e}") print("Please ensure you have an active internet connection and the ticker symbol is correct.") exit() # Exit the program if data cannot be fetched
# Calculate monthly returns from daily adjusted close prices # Changed ''Adj Close'' to ''Close'' because yfinance now auto-adjusts the ''Close'' column. # Changed ''M'' to ''ME'' for resample frequency to address FutureWarning. monthly_prices = sp500_data[''Close''].resample(''ME'').last() # Calculate percentage change between consecutive month-end prices monthly_returns = monthly_prices.pct_change().dropna() # Drop the first NaN value from pct_change
# Create a dictionary mapping year-month tuples to returns for faster and more robust lookup # Explicitly iterate and cast values to float to ensure scalar types monthly_returns_map = {} for i in range(len(monthly_returns.index)): year = monthly_returns.index.year month = monthly_returns.index.month val = monthly_returns.values monthly_returns_map[(year, month)] = float(val) # Explicitly cast to float
# --- Simulate the investment growth --- portfolio_value = 0.0 # Initialize the total portfolio value portfolio_history = [] # List to store portfolio value at each step for plotting dates_history = [] # List to store corresponding dates for plotting
# Convert string dates to datetime objects for iteration current_date = datetime.strptime(start_date_simulation, "%Y-%m-%d") end_date_dt = datetime.strptime(end_date_simulation, "%Y-%m-%d")
print("\nSimulating investment growth...") while current_date <= end_date_dt: year = current_date.year month = current_date.month
# Calculate the annual and monthly saving for the current year annual_saving = get_annual_saving(year) monthly_saving = float(annual_saving / 12) # Ensure monthly_saving is a float
# Add the monthly saving to the portfolio portfolio_value += monthly_saving
# Determine the end of the current month to find the corresponding S&P 500 return # This date is used purely for lookup, not for the plot''s x-axis directly last_day_of_month = calendar.monthrange(year, month)[1] month_end_date = current_date.replace(day=last_day_of_month)
# Find the S&P 500 return for the current month using the pre-built map target_year_month = (month_end_date.year, month_end_date.month) return_for_month = monthly_returns_map.get(target_year_month) if return_for_month is not None: # Apply the monthly return to the portfolio value portfolio_value *= (1 + return_for_month) else: # This warning will appear if there''s no S&P 500 data for a particular month # within the specified range, which is rare but possible with partial months # or data gaps. # print(f"Warning: No S&P 500 return data found for {month_end_date.strftime(''%Y-%m'')}. Portfolio not adjusted for this month.") pass # Continue without applying return if not found
# Store the current portfolio value and date for plotting portfolio_history.append(portfolio_value) dates_history.append(current_date)
# Move to the first day of the next month if current_date.month == 12: current_date = current_date.replace(year=current_date.year + 1, month=1, day=1) else: current_date = current_date.replace(month=current_date.month + 1, day=1)
print("Simulation complete. Generating plot...")
# --- Plotting the results --- plt.figure(figsize=(14, 7)) # Set the figure size for better readability plt.plot(dates_history, portfolio_history, label=''Total Portfolio Value'', color=''#1f77b4'', linewidth=2)
# Add title and labels plt.title(''Investment Growth in S&P 500 (2010-2025)'', fontsize=16, fontweight=''bold'') plt.xlabel(''Date'', fontsize=12) plt.ylabel(''Portfolio Value ($)'', fontsize=12)
# Customize the grid and legend plt.grid(True, linestyle=''--'', alpha=0.7) plt.legend(fontsize=10)
# Improve x-axis date formatting plt.gcf().autofmt_xdate() # Automatically format the date labels
# Add a text annotation for the final portfolio value if portfolio_history: final_value = portfolio_history[-1] final_date = dates_history[-1] plt.annotate(f''Final Value: ${final_value:,.2f}'', xy=(final_date, final_value), xytext=(final_date + timedelta(days=60), final_value * 0.9), # Adjust text position arrowprops=dict(facecolor=''black'', shrink=0.05), fontsize=10, color=''green'', fontweight=''bold'')
plt.tight_layout() # Adjust layout to prevent labels from overlapping plt.show()
print(f"\nSimulation finished. Final Portfolio Value: ${portfolio_value:,.2f}")
这都什么鬼
Who cares
能问一下15年前25岁一年能存五万的有几个。然后现在就是30万了。你知道家庭收入30万是美国前5%了吗？不要总拿马工来说话。你还不如拿搞ai的年薪百万以上的举例。
这是那个lz说的，又不是我说的，自己去看去
https://huaren.us/showtopic.html?topicid=3126338&fid=398
good point about自住房，这个我承认考虑掉了
bad point about收入，你到底看我怎么计算每年的saving了吗？
2000多的房贷应该是当年60w左右买的房。所以算第5年花了12万付了首付，这是现在的情况。
他家2.5M左右，不过，我还是算得保守：他家如果不在加州到手应该是13万，不是10万。
没啥。15年前买入bt coin可解。
show me the data and analysis.
忘记这个了！这是另一种可能的小概率事件。
这是有可能的。我只是按照最简单最简单的投资计算的，我要证明的只是华人上很多人投资理财做的很差，才对30万能攒到3M那么惊奇。
我早就在别的帖说过了，华人上很多大妈对金钱如此的渴望，但是理财知识又是令人叹为观止的贫乏，造成了一对令人费解的矛盾。
板上人民比百分之90以上的人强了。绝大多数人根本没那么多收入也剩不下那么多钱。绝大部分人买房的首付就可以让前五年白干。如果家里再有几个娃。你就算30万了也剩不下10万的。大部分人能老老实实存满401k就不错了。
买房子也可解。
加州的房子，涨幅100%。
当然了 房贷2000 没娃
不花钱大手大脚有什么不可能的？
“现在年入30万又不代表家庭收入一直都是30万。”
回去自己看我的主贴
才看到他们快四十才要第一个孩子，这又是一个华人中的小概率事件。所以总体结论是：他们家的情况是一个小概率事件，没有普遍意义，所以看上去很难让人相信。很多华人三十五岁不到就有两个孩子（aka 碎钞机）了。所以大部分人没有办法beat你假设的saving速度不是理财方法的问题，而是家有“碎钞”机。
这里的平均发帖质量，说实话，是配不上你认真分析的 :)
嗯，我只看了那个楼主的主贴，第一印象就是要么是假的信息，要么有一部分资产是父母给的。
楼主的计算是基于他/她自己的假设。如果都按照她假设的那样发生（每年一半的税后收入都拿去买标普500而且两人从来不瞎折腾自己的账户），那的确会会积累到这么多钱。但我还是认为现实生活中这是小概率事件（基于两人现在的年龄和收入）。
人的消费都是随着收入递增而递增的，你挣5万月光的时候觉得等你挣10万了能存5万，等到挣了10万大概率还是月光，并且也并没有大手大脚。 觉的到手20万就能存10万的也是大致如此。
是的，那个40岁收入10来万，存款300万的楼主，也是春秋笔法，遮遮掩掩好多没说
没想到这个帖子的楼主竟然当真了，一通分析。以后看帖子，也不能太过于相信作者的小作文啊
女方父母给了1.5m
啊？25岁一人存2.5万在正常不过了吧？你只要不住在纽约没啥吃喝玩乐。能花啥钱啊？一般父母凑个首付，两个人一年还5万不就都存下了吗？
你是不是没看原贴啊。人家没娃啊。人家是今年快40了才生的。
我只看到了这个贴，不太清楚具体情况。个人情况不同。选择不同。结果不同。如果认为人人能实现就可以洗洗睡了