背景最近网上看到有人问豆包定投的问题验证1豆包的计算正确吗我拿同样的问题问了DeepSeek感觉Deepseek不太准截至到2026年4月来计算投入金额应该在60w左右。验证2再问问ChatgptChatgpt投入金额与豆包计算结果相同但持有的总市值本金收益与豆包计算的结果要差一半到底哪个是对的验证3我让Chatgpt写了一段代码以 $纳指100ETF-Invesco(QQQ)$ 为标的看看20年究竟能收益多钱标的: QQQ投资起始: 2006-01-01当前日期: 2026-05-28累计投入本金: 602,000.00累计持仓份额: 5,652.1816当前价格: 729.45当前市值: 4,122,983.94累计收益: 3,520,983.94总收益率: 584.88%年化收益率(XIRR): 19.28%当前市值为412w收益额为352w足够直观20年接近6倍的收益年化收益率约20%非常强业绩超过了80%的基金经理。代码import yfinance as yf import pandas as pd from dateutil.relativedelta import relativedelta from scipy.optimize import newton # # 参数配置 # TICKER SPY FX_TICKER CNYX # USD/CNY START_DATE 2006-01-01 INITIAL_MONTHLY_RMB 1000 YEARLY_INCREMENT_RMB 150 # # 下载数据 # def download_price_data( ticker, start_date ): df yf.download( ticker, startstart_date, auto_adjustTrue, progressFalse ) if df.empty: raise ValueError(f{ticker} 没有下载到数据) else: print(f获取到{len(df)}条数据) # 修复 MultiIndex if isinstance(df.columns, pd.MultiIndex): df.columns df.columns.get_level_values(0) df df[[Close]].copy() df.index pd.to_datetime(df.index) return df # # 生成定投日期 # def generate_invest_dates(df): start_dt df.index.min() end_dt df.index.max() current pd.Timestamp( yearstart_dt.year, monthstart_dt.month, day1 ) invest_dates [] while current end_dt: invest_dates.append(current) current relativedelta(months1) return invest_dates # # 获取实际交易日 # def get_actual_trade_date(df, invest_date): future_dates df.index[df.index invest_date] if len(future_dates) 0: return None return future_dates[0] # # XIRR # def xnpv(rate, cashflows): t0 cashflows[0][0] return sum( cf / ((1 rate) ** ((t - t0).days / 365.0)) for t, cf in cashflows ) def xirr(cashflows, guess0.1): return newton( lambda r: xnpv(r, cashflows), guess ) # # 定投模拟加入汇率 # def simulate_dca_with_fx( etf_df, fx_df, invest_dates, initial_monthly_rmb, yearly_increment_rmb ): total_shares 0.0 total_invested_rmb 0.0 records [] cashflows [] for invest_date in invest_dates: actual_date get_actual_trade_date( etf_df, invest_date ) if actual_date is None: break # # 获取 ETF 价格 # etf_price_usd float( etf_df.loc[actual_date, Close] ) # # 获取汇率 # CNYX 表示: # 1 USD ? RMB # fx_rate float( fx_df.loc[actual_date, Close] ) # # 计算定投金额 # years_passed actual_date.year - 2006 monthly_rmb ( initial_monthly_rmb years_passed * yearly_increment_rmb ) # RMB - USD monthly_usd monthly_rmb / fx_rate # 买入份额 shares_bought monthly_usd / etf_price_usd total_shares shares_bought total_invested_rmb monthly_rmb # 现金流人民币 cashflows.append( (actual_date, -monthly_rmb) ) records.append({ date: actual_date, monthly_rmb: monthly_rmb, fx_rate: fx_rate, monthly_usd: monthly_usd, etf_price_usd: etf_price_usd, shares_bought: shares_bought, total_shares: total_shares }) # # 当前资产 # latest_etf_price float( etf_df[Close].iloc[-1] ) latest_fx_rate float( fx_df[Close].iloc[-1] ) # ETF 市值美元 current_value_usd ( total_shares * latest_etf_price ) # 转回人民币 current_value_rmb ( current_value_usd * latest_fx_rate ) profit_rmb ( current_value_rmb - total_invested_rmb ) cashflows.append( ( etf_df.index[-1], current_value_rmb ) ) annual_return xirr(cashflows) result { total_invested_rmb: total_invested_rmb, total_shares: total_shares, current_value_usd: current_value_usd, current_value_rmb: current_value_rmb, profit_rmb: profit_rmb, xirr: annual_return, records: pd.DataFrame(records) } return result # # 主程序 # def main(): print(下载 ETF 数据...) etf_df download_price_data( TICKER, START_DATE ) print(下载汇率数据...) fx_df download_price_data( FX_TICKER, START_DATE ) # 对齐日期 common_index etf_df.index.intersection( fx_df.index ) etf_df etf_df.loc[common_index] fx_df fx_df.loc[common_index] invest_dates generate_invest_dates( etf_df ) result simulate_dca_with_fx( etf_dfetf_df, fx_dffx_df, invest_datesinvest_dates, initial_monthly_rmbINITIAL_MONTHLY_RMB, yearly_increment_rmbYEARLY_INCREMENT_RMB ) # # 输出 # print( * 60) print(fETF: {TICKER}) print( * 60) print( f累计投入(RMB): f{result[total_invested_rmb]:,.2f} ) print( f累计持仓份额: f{result[total_shares]:,.4f} ) print( f当前市值(USD): f{result[current_value_usd]:,.2f} ) print( f当前市值(RMB): f{result[current_value_rmb]:,.2f} ) print( f累计收益(RMB): f{result[profit_rmb]:,.2f} ) print( f总收益率: f{result[profit_rmb] / result[total_invested_rmb] * 100:.2f}% ) print( f年化收益率(XIRR): f{result[xirr] * 100:.2f}% ) print( * 60) # # 启动 # if __name__ __main__: main()
背景最近网上看到有人问豆包定投的问题验证1豆包的计算正确吗我拿同样的问题问了DeepSeek感觉Deepseek不太准截至到2026年4月来计算投入金额应该在60w左右。验证2再问问ChatgptChatgpt投入金额与豆包计算结果相同但持有的总市值本金收益与豆包计算的结果要差一半到底哪个是对的验证3我让Chatgpt写了一段代码以 $纳指100ETF-Invesco(QQQ)$ 为标的看看20年究竟能收益多钱标的: QQQ投资起始: 2006-01-01当前日期: 2026-05-28累计投入本金: 602,000.00累计持仓份额: 5,652.1816当前价格: 729.45当前市值: 4,122,983.94累计收益: 3,520,983.94总收益率: 584.88%年化收益率(XIRR): 19.28%当前市值为412w收益额为352w足够直观20年接近6倍的收益年化收益率约20%非常强业绩超过了80%的基金经理。代码import yfinance as yf import pandas as pd from dateutil.relativedelta import relativedelta from scipy.optimize import newton # # 参数配置 # TICKER SPY FX_TICKER CNYX # USD/CNY START_DATE 2006-01-01 INITIAL_MONTHLY_RMB 1000 YEARLY_INCREMENT_RMB 150 # # 下载数据 # def download_price_data( ticker, start_date ): df yf.download( ticker, startstart_date, auto_adjustTrue, progressFalse ) if df.empty: raise ValueError(f{ticker} 没有下载到数据) else: print(f获取到{len(df)}条数据) # 修复 MultiIndex if isinstance(df.columns, pd.MultiIndex): df.columns df.columns.get_level_values(0) df df[[Close]].copy() df.index pd.to_datetime(df.index) return df # # 生成定投日期 # def generate_invest_dates(df): start_dt df.index.min() end_dt df.index.max() current pd.Timestamp( yearstart_dt.year, monthstart_dt.month, day1 ) invest_dates [] while current end_dt: invest_dates.append(current) current relativedelta(months1) return invest_dates # # 获取实际交易日 # def get_actual_trade_date(df, invest_date): future_dates df.index[df.index invest_date] if len(future_dates) 0: return None return future_dates[0] # # XIRR # def xnpv(rate, cashflows): t0 cashflows[0][0] return sum( cf / ((1 rate) ** ((t - t0).days / 365.0)) for t, cf in cashflows ) def xirr(cashflows, guess0.1): return newton( lambda r: xnpv(r, cashflows), guess ) # # 定投模拟加入汇率 # def simulate_dca_with_fx( etf_df, fx_df, invest_dates, initial_monthly_rmb, yearly_increment_rmb ): total_shares 0.0 total_invested_rmb 0.0 records [] cashflows [] for invest_date in invest_dates: actual_date get_actual_trade_date( etf_df, invest_date ) if actual_date is None: break # # 获取 ETF 价格 # etf_price_usd float( etf_df.loc[actual_date, Close] ) # # 获取汇率 # CNYX 表示: # 1 USD ? RMB # fx_rate float( fx_df.loc[actual_date, Close] ) # # 计算定投金额 # years_passed actual_date.year - 2006 monthly_rmb ( initial_monthly_rmb years_passed * yearly_increment_rmb ) # RMB - USD monthly_usd monthly_rmb / fx_rate # 买入份额 shares_bought monthly_usd / etf_price_usd total_shares shares_bought total_invested_rmb monthly_rmb # 现金流人民币 cashflows.append( (actual_date, -monthly_rmb) ) records.append({ date: actual_date, monthly_rmb: monthly_rmb, fx_rate: fx_rate, monthly_usd: monthly_usd, etf_price_usd: etf_price_usd, shares_bought: shares_bought, total_shares: total_shares }) # # 当前资产 # latest_etf_price float( etf_df[Close].iloc[-1] ) latest_fx_rate float( fx_df[Close].iloc[-1] ) # ETF 市值美元 current_value_usd ( total_shares * latest_etf_price ) # 转回人民币 current_value_rmb ( current_value_usd * latest_fx_rate ) profit_rmb ( current_value_rmb - total_invested_rmb ) cashflows.append( ( etf_df.index[-1], current_value_rmb ) ) annual_return xirr(cashflows) result { total_invested_rmb: total_invested_rmb, total_shares: total_shares, current_value_usd: current_value_usd, current_value_rmb: current_value_rmb, profit_rmb: profit_rmb, xirr: annual_return, records: pd.DataFrame(records) } return result # # 主程序 # def main(): print(下载 ETF 数据...) etf_df download_price_data( TICKER, START_DATE ) print(下载汇率数据...) fx_df download_price_data( FX_TICKER, START_DATE ) # 对齐日期 common_index etf_df.index.intersection( fx_df.index ) etf_df etf_df.loc[common_index] fx_df fx_df.loc[common_index] invest_dates generate_invest_dates( etf_df ) result simulate_dca_with_fx( etf_dfetf_df, fx_dffx_df, invest_datesinvest_dates, initial_monthly_rmbINITIAL_MONTHLY_RMB, yearly_increment_rmbYEARLY_INCREMENT_RMB ) # # 输出 # print( * 60) print(fETF: {TICKER}) print( * 60) print( f累计投入(RMB): f{result[total_invested_rmb]:,.2f} ) print( f累计持仓份额: f{result[total_shares]:,.4f} ) print( f当前市值(USD): f{result[current_value_usd]:,.2f} ) print( f当前市值(RMB): f{result[current_value_rmb]:,.2f} ) print( f累计收益(RMB): f{result[profit_rmb]:,.2f} ) print( f总收益率: f{result[profit_rmb] / result[total_invested_rmb] * 100:.2f}% ) print( f年化收益率(XIRR): f{result[xirr] * 100:.2f}% ) print( * 60) # # 启动 # if __name__ __main__: main()
