指數投資方式中有四種基本的方法，分別是定期定額、定期不定額、不定期定額和不定期不定額，這四種方式投資效果不同，對投資者的要求也不同，定期定額最簡單，但收益不算高，不定期不定額最復雜，對投資者的要求最高，特別是對情緒的要求非常高，同時收益也是最好的。

這里先介紹第一種定期定額的情況，下面會通過量化的過程來反應投資的整體過程。

定期定額就是按日、按周或者按月進行投資，每次投資的資金是一樣的，比如每周買入1000塊的滬深300基金，這種方式是不管指數漲跌，到點就買；

假設每次投入的資金是1000塊，按周定投，下面是通過量化的過程跑出來的情況（源碼附在后面），這里既然是定期定額就不考慮賣出。數據是通過中證全指（指數代碼1000002）進行計算的。

上半部分的圖中藍線是中證全指的走勢圖，紅點是每周定投的位置，下半部分的圖中藍線是累計投入的資金，紅線是持有基金的市值，整個過程投入的總資金是500000，最終的基金總市值是680424.75，最后獲得的收益是36.08%，從下半部分圖中可以很清晰的看出，當指數下跌的過程，基金市值會低于投入的資金，這個過程收益為負，隨著指數的上漲，基金市值上漲的幅度會高于投入的資金，然后在某些點超過投入資金的總值，這個過程收益就會轉負為正，這就是在低點積累的份額更多造成的。

整體來說定期定額的投資效果并不是很好，接下來還會分享定期不定額、不定期定額和不定期不定額來進行比較。

源碼

import pandas as pd import numpy as np import matplotlib.pyplot as plt import math as mathname_index = 'lxr_1000002' name_index_g = 'g_lxr' all_data_index = pd.read_csv('./exportfile/indexDataAll/' + name_index + '.csv') all_data_index_g = pd.read_csv('./importfile/indexSeries/indexValuation/g/' + name_index_g + '.csv')calc_range = 2500 calc_gap = 5 data_index_p = all_data_index['close'].values[len(all_data_index['close']) - calc_range:len(all_data_index['close']):calc_gap] data_index_g = all_data_index_g['pe'].values[len(all_data_index_g['pe']) - calc_range:len(all_data_index_g['pe']):calc_gap] val_percentage_list = list()sell_flag_no_regular_no_quota = [0, 0] sell_flag_regular_quota = 0 sell_flag_regular_no_quota = 0 sell_flag_no_regular_quota = 0def RegularQuota(val_percentage, val_data_p, buy_cnt, buy_total_share):global sell_flag_regular_quotaif val_percentage <= 1:sell_flag_regular_quota = 0buy_each_regular_quota = 1000buy_each_share = buy_each_regular_quota / val_data_pbuy_total_share = buy_total_share + buy_each_sharebuy_cnt = buy_cnt + 1plot_y = val_data_pplot_x = iplot_flag = 1else:if sell_flag_regular_quota == 0:sell_flag_regular_quota = 1buy_each_share = -buy_total_sharebuy_total_share = 0plot_y = val_data_pplot_x = iplot_flag = -1else:buy_each_share = 0plot_y = val_data_pplot_x = iplot_flag = 0return buy_each_share, buy_cnt, [plot_flag, plot_x, plot_y], buy_total_sharegap = 5 # invest each week cnt = 0buy_each_share_regular_quota = np.zeros((len(data_index_p), 1)) buy_total_share_list_regular_quota = np.zeros((len(data_index_p), 1)) buy_total_money_list_regular_quota = np.zeros((len(data_index_p), 1)) buy_cnt_regular_quota = 0 plot_regular_quota = np.zeros((len(data_index_p), 3))buy_each_share_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_total_share_list_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_total_money_list_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_cnt_regular_no_quota = 0 plot_regular_no_quota = np.zeros((len(data_index_p), 3))buy_each_share_no_regular_quota = np.zeros((len(data_index_p), 1)) buy_total_share_list_no_regular_quota = np.zeros((len(data_index_p), 1)) buy_total_money_list_no_regular_quota = np.zeros((len(data_index_p), 1)) buy_cnt_no_regular_quota = 0 plot_no_regular_quota = np.zeros((len(data_index_p), 3))buy_each_share_no_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_total_share_list_no_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_total_money_list_no_regular_no_quota = np.zeros((len(data_index_p), 1)) buy_cnt_no_regular_no_quota = 0 plot_no_regular_no_quota = np.zeros((len(data_index_p), 3))# idx_start = 974 #2011-1-4 idx_start = 1 for i in range(len(data_index_p)):valuation_len = all_data_index_g['pe'].values[len(all_data_index['close']) - calc_range-500:len(all_data_index['close']) - calc_range+i*calc_gap:calc_gap]val_loc = np.where(valuation_len < data_index_g[i])val_percentage = len(val_loc[0]) / (len(valuation_len))val_percentage_list.append(val_percentage)buy_each_regular_quota = 1000buy_each_share_regular_quota[i], buy_cnt_regular_quota, plot_regular_quota[i], buy_total_share_regular_quota\= RegularQuota(val_percentage, data_index_p[i], buy_cnt_regular_quota, sum(buy_each_share_regular_quota))buy_total_share_list_regular_quota[i] = sum(buy_each_share_regular_quota) * data_index_p[i]buy_total_money_list_regular_quota[i] = buy_cnt_regular_quota * buy_each_regular_quotaearn_total_money_no_regular_quota = np.zeros((len(data_index_p), 1)) money_sell_no_regular_quota = 0 for i in range(len(data_index_p)):if buy_each_share_no_regular_quota[i] < 0:money_sell_no_regular_quota = money_sell_no_regular_quota - buy_each_share_no_regular_quota[i] * data_index_p[i]earn_total_money_no_regular_quota[i] = sum(buy_each_share_no_regular_quota[0:i+1]) * data_index_p[i] + money_sell_no_regular_quotaearn_total_money_regular_no_quota = np.zeros((len(data_index_p), 1)) money_sell_regular_no_quota = 0 for i in range(len(data_index_p)):if buy_each_share_regular_no_quota[i] < 0:money_sell_regular_no_quota = money_sell_regular_no_quota - buy_each_share_regular_no_quota[i] * data_index_p[i]earn_total_money_regular_no_quota[i] = sum(buy_each_share_regular_no_quota[0:i+1]) * data_index_p[i] + money_sell_regular_no_quotaearn_total_money_regular_quota = np.zeros((len(data_index_p), 1)) money_sell_regular_quota = 0 for i in range(len(data_index_p)):if buy_each_share_regular_quota[i] < 0:money_sell_regular_quota = money_sell_regular_quota - buy_each_share_regular_quota[i] * data_index_p[i]print('')earn_total_money_regular_quota[i] = sum(buy_each_share_regular_quota[0:i+1]) * data_index_p[i] + money_sell_regular_quotaprint('')earn_total_money_no_regular_no_quota = np.zeros((len(data_index_p), 1)) money_sell_no_regular_no_quota = 0 for i in range(len(data_index_p)):if buy_each_share_no_regular_no_quota[i] < 0:money_sell_no_regular_no_quota = money_sell_no_regular_no_quota - buy_each_share_no_regular_no_quota[i] * data_index_p[i]earn_total_money_no_regular_no_quota[i] = sum(buy_each_share_no_regular_no_quota[0:i+1]) * data_index_p[i] + money_sell_no_regular_no_quotaplt_gap = 10 size_title = 28 size_label = 15 size_line = 3 size_rotation = 15 size_buy_plot = 5# ------------------------------------------------------------- #plt.figure() plt.rcParams["axes.grid"] = True plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams["grid.linestyle"] = (3, 5) plt.subplot(211)income = 100 * (earn_total_money_regular_quota[-1][0] - buy_total_money_list_regular_quota[-1][0]) / buy_total_money_list_regular_quota[-1][0] plt.title('定期定額 | 投資收益 = ' + str("{:.2f}".format(income)) + '%',size=15)v_max = max(data_index_p) v_min = min(data_index_p)for i in range(len(plot_regular_quota)):if plot_regular_quota[i][0] == 1:plt.plot(plot_regular_quota[i][1], plot_regular_quota[i][2],color='tomato',marker='o',ms=(size_buy_plot*v_max/plot_regular_quota[i][2]))elif plot_regular_quota[i][0] == -1:plt.plot(plot_regular_quota[i][1], plot_regular_quota[i][2], color='purple', marker='o',ms=10) plt.plot(data_index_p) plt_xticks = all_data_index['date'].values[len(all_data_index['close']) - calc_range:len(all_data_index['close']):calc_gap].tolist() plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=size_rotation) plt.tick_params(labelsize=size_label)plt.subplot(212) plt.plot(buy_total_share_list_regular_quota,color='tomato') font = {'size': 15, 'color': 'tomato', 'weight': 'black'} plt.text(len(buy_total_share_list_regular_quota), buy_total_share_list_regular_quota[-1][0], str("{:.2f}".format(buy_total_share_list_regular_quota[-1][0])), fontdict=font) plt.plot(len(buy_total_share_list_regular_quota)-1,buy_total_share_list_regular_quota[-1][0], color='tomato', marker='o')plt.plot(buy_total_money_list_regular_quota,color='cornflowerblue') font = {'size': 15, 'color': 'cornflowerblue', 'weight': 'black'} plt.text(len(buy_total_money_list_regular_quota), buy_total_money_list_regular_quota[-1][0], str("{:.2f}".format(buy_total_money_list_regular_quota[-1][0])), fontdict=font) plt.plot(len(buy_total_money_list_regular_quota)-1,buy_total_money_list_regular_quota[-1][0], color='cornflowerblue', marker='o')plt.plot(earn_total_money_regular_quota,color='red') font = {'size': 15, 'color': 'red', 'weight': 'black'} plt.text(len(earn_total_money_regular_quota), earn_total_money_regular_quota[-1][0], str("{:.2f}".format(earn_total_money_regular_quota[-1][0])), fontdict=font) plt.plot(len(earn_total_money_regular_quota)-1,earn_total_money_regular_quota[-1][0], color='red', marker='o')plt_xticks = all_data_index['date'].values[len(all_data_index['close']) - calc_range:len(all_data_index['close']):calc_gap].tolist() plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=size_rotation) plt.tick_params(labelsize=size_label)# ----------------------------------------------------------------- #plt.show()

文中用到的兩個文件下載鏈接: https://pan.baidu.com/s/13alPKvTP7Rw061UMcgtMXQ?pwd=s6dr 提取碼: s6dr

程序中用到的數據如果有問題，大家可以留言獲取也可以添加小將前行的微信xjqx_666進行獲取，歡迎大家一起交流溝通

課程參考：基于Python的量化指數基金投資

總結

以上是生活随笔為你收集整理的基于Python的指数基金量化投资 - 指数投资技巧（一）定期定额的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。