【mindgo】彼得林奇PEG价值选股策略

'''
策略名称: 彼得林奇PEG价值选股策略策略思路:
1.选择PEG < 0.5, 即稳定成长且价值被低估的股票其中PEG = PE / growth_rate
2.使用ES风险平价配权
3.根据组合的日内波动小于3%的条件, 与货币基金组合配资
4.最大持仓5只股票和1只货币基金, 优先买入市值小的, 15天调仓一次
5.剔除了周期性和项目类行业(该部分对改善回撤有明显的效果)
'''import pandas as pd
import numpy as np
import datetime as dtdef init(context):# 当前持仓数：0context.num = 0# 最大持仓股票数：5context.stock_max_num = 5# 每只股票的最大风险敞口context.risk_limit = 0.03 / context.stock_max_num# 已持仓天数：0天# 持仓周期：15天context.hold_days, context.hold_cycle = 0, 15# 计算ES的置信度context.confidenceLevel = 0.05# 计算ES的历史回测长度context.lag = 180# 每个交易日09:31运行run_daily(func=fun_main, reference_security='000001.SZ')######################  主函数
def fun_main(context, bar_dict):flag = fun_needRebalance(context)if flag:last_date = get_last_datetime().strftime('%Y%m%d')universe = list(get_all_securities('stock', date = last_date).index)# stock_list = universestock_list = fun_unpaused(universe,bar_dict)stock_list = fun_remove_new(stock_list, 60)stock_list = fun_remove_periodic_industry(stock_list, last_date)line = '###############' + str(last_date) + ': ' + str(len(universe)) + ' / ' + str(len(stock_list))print(line)# 计算单季度增长率df_growth = fun_cal_growth_rate(bar_dict, stock_list, last_date)# 计算PEGdf_all = fun_cal_PEG(bar_dict, df_growth.copy(), last_date)# 选股buy_list = fun_get_buy_list(context, bar_dict, df_all.copy(), context.stock_max_num, last_date)# 计算股票权重stock_weight = fun_cal_stock_weight(buy_list, context.lag, context.confidenceLevel, last_date, 'ES')print('stock_weight: ')print(stock_weight)bond_weight = {'511880.OF': 1.0}# 计算最终权重（考虑组合风险 + 债券）trade_ratio = fun_cal_position(context, bar_dict, stock_weight, bond_weight)print('trade_ratio: ')print(trade_ratio)# 下单fun_do_trade(context, bar_dict, trade_ratio)context.num = len(context.portfolio.stock_account.positions)def handle_bar(context,bar_dict):pass#### 1.判断是否调仓函数 #########################################
def fun_needRebalance(context):# 条件1: 持仓股数为0时，重新调仓if context.num == 0:context.hold_days = 0return True# 条件2：持仓到调仓周期时，重新调仓elif context.hold_days == context.hold_cycle:context.hold_days = 0return Trueelse:context.hold_days += 1return False#### 2.剔除停牌股票函数 #########################################
def fun_unpaused(_stock_list,bar_dict):return [stock for stock in _stock_list if not bar_dict[stock].is_paused] #### 3.剔除上市不到60天的新股 ###################################
def fun_remove_new(_stock_list, days):deltaDate = get_datetime() - dt.timedelta(days)stock_list = []for stock in _stock_list:if get_security_info(stock).start_date < deltaDate:stock_list.append(stock)return stock_list#### 4.剔除周期性行业 ###########################################
def fun_remove_periodic_industry(stock_list, last_date):periodic_industry = [#'T0101', # 种植业与林业#'T0102', # 养殖业#'T0103', # 农产品加工#'T0104', # 农业服务'T0201', # 煤炭开采'T0202', # 石油矿业开采'T0203', # 采掘服务'T0301', # 基础化学'T0302', # 化学制品'T0303', # 化工新材料'T0304', # 化工合成材料'T0401', # 钢铁'T0501', # 有色冶炼加工'T0502', # 新材料'T0601', # 建筑材料'T0602', # 建筑装饰'T0701', # 通用设备'T0702', # 专用设备'T0703', # 仪器仪表'T0704', # 电气设备#'T0801', # 半导体及元件#'T0802', # 光学光电子#'T0803', # 其他电子#'T0804', # 电子制造'T0901', # 汽车整车'T0902', # 汽车零部件'T0903', # 非汽车交运'T0904', # 交运设备服务#'T1001', # 通信设备#'T1002', # 计算机设备'T1101', # 白色家电'T1102', # 视听器材#'T1201', # 饮料制造#'T1202', # 食品加工制造#'T1301', # 防止制造#'T1302', # 服装家纺#'T1401', # 造纸#'T1402', # 包装印刷#'T1403', # 家用轻工#'T1501', # 化学制药#'T1502', # 中药#'T1503', # 生物制品#'T1504', # 医药商业#'T1505', # 医疗器械服务'T1601', # 电力'T1602', # 燃气水务'T1603', # 环保工程'T1701', # 港口航运'T1702', # 公路铁路运输#'T1703', # 公交#'T1704', # 机场航运#'T1705', # 物流'T1801', # 房地产开发'T1802', # 园区开发'T1901', # 银行'T1902', # 保险及其他'T1903', # 证券#'T2001', # 零售'T2002', # 贸易#'T2101', # 景点及旅游#'T2102', # 酒店及餐饮#'T2201', # 通信服务#'T2202', # 计算机应用#'T2203', # 传媒#'T2301', # 综合#'T2401'  # 国防军工]for industry in periodic_industry:stocks = get_industry_stocks(industry, last_date)stock_list = list(set(stock_list).difference(set(stocks)))return stock_list#### 5.计算单季度增长率函数 ####################################################
def fun_cal_growth_rate(bar_dict, stock_list, last_date):# 计算单季度净收益函数def fun_get_quarterly_net_profit(current_quarter, last_quarter, stock_list):q = query(income.symbol,income.net_profit).filter(income.symbol.in_(stock_list))# 当季度累计净利润df1 = get_fundamentals(q, statDate = current_quarter)df1.columns = ['symbol', 'total_net_profit']# 上季度累计净利润df2 = get_fundamentals(q, statDate = last_quarter)df2.columns = ['symbol', 'last_total_net_profit']df = pd.merge(df1, df2, on='symbol')# 当前单季度净利润df['quarterly_net_profit'] = df['total_net_profit'] - df['last_total_net_profit']del df['total_net_profit']del df['last_total_net_profit']return df########## 获取财报季度日期 q = query(income.symbol, income.stat_date).filter(income.symbol.in_(stock_list))df = get_fundamentals(q, date = last_date)df = df.sort_values(['income_stat_date'], ascending=False)last_statDate = df.iloc[0,1]# 剔除未按时公布报表的公司，避免未来函数df[df['income_stat_date'] != last_statDate] = Nonedf = df.dropna()stock_list = list(df['income_symbol'].values)the_year = int(str(last_statDate)[0:4])the_month = str(last_statDate)[5:7]########## 获取财报单季度净利润增长率'''quarter_1:  当季度quarter_2:  上季度quarter_3:  同比上季度quarter_4:  去年上季度'''if the_month == '03':# 因为一季度的报表是单季度表，所以需要单独处理quarter_1 = str(the_year)     + 'q1'quarter_3 = str(the_year - 1) + 'q1'q = query(income.symbol,income.net_profit).filter(income.symbol.in_(stock_list))df1 = get_fundamentals(q, statDate = quarter_1)df1.columns = ['symbol', 'current_net_profit']df2 = get_fundamentals(q, statDate = quarter_3)df2.columns = ['symbol', 'last_net_profit']else:if the_month == '12':quarter_1 = str(the_year)     + 'q4'quarter_2 = str(the_year)     + 'q3'quarter_3 = str(the_year - 1) + 'q4'quarter_4 = str(the_year - 1) + 'q3'elif the_month == '09':quarter_1 = str(the_year)     + 'q3'quarter_2 = str(the_year)     + 'q2'quarter_3 = str(the_year - 1) + 'q3'quarter_4 = str(the_year - 1) + 'q2'elif the_month == '06':quarter_1 = str(the_year)     + 'q2'quarter_2 = str(the_year)     + 'q1'quarter_3 = str(the_year - 1) + 'q2'quarter_4 = str(the_year - 1) + 'q1'else:print('There is something wrong with the stat_date.')# 计算当期单季度净利润df1 = fun_get_quarterly_net_profit(quarter_1, quarter_2, stock_list)df1.columns = ['symbol', 'current_net_profit']# 计算同比上期单季度净利润df2 = fun_get_quarterly_net_profit(quarter_3, quarter_4, stock_list)df2.columns = ['symbol', 'last_net_profit']df_growth = pd.merge(df1, df2, on='symbol')# 增长率单位为%df_growth['growth_rate'] = (df_growth['current_net_profit'] / df_growth['last_net_profit'] - 1) * 100return df_growth#### 6.计算PEG函数 ############################################################
def fun_cal_PEG(bar_dict, df_growth, last_date):stock_list = stock_list = list(df_growth['symbol'].values)q = query(valuation.symbol,valuation.pe_ttm,).filter(valuation.symbol.in_(stock_list))df_pe = get_fundamentals(q, date = last_date)df_pe.columns = ['symbol', 'pe_ttm']# 剔除PE值为负的股票df_pe[df_pe['pe_ttm'] < 0] = Nonedf_pe = df_pe.dropna()# 使用中位数去极值法df_pe = winsorize(df_pe, 'pe_ttm')df_pe = df_pe.dropna()# 彼得林奇的文章中提到：增长率>50的公司，高增长不可持续df_growth[df_growth['growth_rate'] > 50] = None# 剔除增长率为负的公司df_growth[df_growth['growth_rate'] <= 0] = Nonedf_growth = df_growth.dropna()del df_growth['current_net_profit']del df_growth['last_net_profit']df_all = pd.merge(df_pe, df_growth, on='symbol')df_all['PEG'] = df_all['pe_ttm'] / df_all['growth_rate']return df_all#### 7.根据PEG选股函数 ###########################################################
def fun_get_buy_list(context, bar_dict, df_all, n, last_date):# 获取股票市值信息函数def fun_get_market_cap(df_selected, last_date):stock_list = list(df_selected['symbol'].values)# 获取股票市值q = query(valuation.symbol,valuation.market_cap).filter(valuation.symbol.in_(stock_list))df_cap = get_fundamentals(q, date = last_date)df_cap.columns = ['symbol', 'market_cap']df_selected = pd.merge(df_selected, df_cap, on = 'symbol')df_selected = df_selected.sort_values(['market_cap'], ascending = True)df_selected = df_selected.reset_index()del df_selected['index']return df_selected# PEG 需小于 0.5df_selected = df_all[df_all['PEG'] < 0.5].copy()# 增添股票的市值信息到df_selecteddf_selected = fun_get_market_cap(df_selected.copy(), last_date)# 获得备选股票列表if len(df_selected) >= n:buy_list = list(df_selected['symbol'][:n].values)for i in range(n):print(str(df_selected['symbol'][i]) + ', PEG = ' + str(df_selected['PEG'][i]))else:print('新股仅： ' + str(len(df_selected)))buy_list = list(df_selected['symbol'].values)for i in range(len(df_selected)):print(str(df_selected['symbol'][i]) + ', PEG = ' + str(df_selected['PEG'][i]))old_stock_list = list(context.portfolio.stock_account.positions.keys())if '511880.OF' in old_stock_list:old_stock_list.remove('511880.OF')if len(old_stock_list) > 0:df_growth_old = fun_cal_growth_rate(bar_dict, old_stock_list, last_date)df_all_old = fun_cal_PEG(bar_dict, df_growth_old.copy(), last_date)df_selected_old = df_all_old[df_all_old['PEG'] < 1.0].copy()df_selected_old = fun_get_market_cap(df_selected_old.copy(), last_date)old_stock_list = list(df_selected_old['symbol'].values)i = len(buy_list)for stock in old_stock_list:if i < n:buy_list.append(stock)print(str(stock) + ', PEG = ' + str(df_selected_old.loc[df_selected_old['symbol'] == stock, 'PEG'].values))i += 1else:breakreturn buy_list#### 8.计算股票仓位 ##############################################################
def fun_cal_stock_weight(stock_list, lag, alpha, last_date, flag=None):# 计算个股ES风险def fun_cal_stockES(stock, lag, alpha, last_date):if lag * alpha < 3: print('The size of lag is too small for the given confidence level.')prices = get_price(stock, start_date=None, end_date=last_date, fre_step='1d', fields=['close'], skip_paused=False, fq='pre', bar_count=lag, is_panel=0)dailyReturns = prices.pct_change().dropna()dailyReturns_sort = dailyReturns.sort_values(['close'], ascending=True)num = round((lag-1) * alpha)ES = dailyReturns_sort['close'][:num].sum() / numreturn ES# ES风险平价配股if flag == 'ES':stock_position = {}total_position = 0for stock in stock_list:risk = fun_cal_stockES(stock, lag, alpha, last_date)stock_position[stock] = 1.0 / risktotal_position += stock_position[stock]stock_real_position = {}for stock in stock_list:stock_real_position[stock] = stock_position[stock] / total_position# 等权重配股else:stock_real_position = {}for stock in stock_list:stock_real_position[stock] = 1.0 / len(stock_list)return stock_real_position#### 9.计算加入货币基金后的资产配置 ##################################################
def fun_cal_position(context, bar_dict, stock_weight, bond_weight, position_ratio = 1.0):# 计算组合收益def fun_get_portfolio_daily_return(bar_dict, stock_weight, lag=180):last_date = get_last_datetime().strftime('%Y%m%d')stock_list = list(stock_weight.keys())df = pd.DataFrame()for stock in stock_list:prices = get_price(stock, start_date=None, end_date=last_date, fre_step='1d', fields=['close'], skip_paused=False, fq='pre', bar_count=lag, is_panel=0)df[stock] = prices['close']df = df.pct_change().dropna()df['portfolio_returns'] = 0for stock in stock_list:df['portfolio_returns'] += df[stock] * stock_weight[stock]del df[stock]df = df.sort_values(['portfolio_returns'], ascending = True)return df# 计算组合ES值def fun_get_portfolio_ES(dailyReturns, alpha):lag = len(dailyReturns)num = round(lag * alpha)ES = - (dailyReturns['portfolio_returns'][:num].sum() / num)if ES < 0:ES = 0print('ES: ' + str(ES))return ES# 计算组合VaR值def fun_get_portfolio_VaR(dailyReturns, alpha):z_score = {0.05: 1.65,0.04: 1.75,0.01: 2.33,0.0001: 3.7}VaR = - (dailyReturns['portfolio_returns'].mean() - z_score[alpha] * dailyReturns['portfolio_returns'].std())if VaR < 0:VaR = 0print('VaR: ' + str(VaR))return VaR# 计算股票组合在给定风险损失的情况下最大持仓金额    def fun_get_equity_value(bar_dict, stock_weight, risk_money, max_risk_money, alpha, position_ratio):# 计算组合每日收益df_daily_returns = fun_get_portfolio_daily_return(bar_dict, stock_weight)# 计算组合ES值portfolio_ES = fun_get_portfolio_ES(df_daily_returns.copy(), alpha)# 计算组合VaR值portfolio_VaR = fun_get_portfolio_VaR(df_daily_returns.copy(), alpha)# 组合ES和VaR风险均为0if (portfolio_ES) == 0 and (portfolio_VaR) == 0:equity_value = context.positions_value * position_ratioprint('组合风险评估为0， 请检查数据。')return equity_valueif portfolio_ES == 0:print('ES = 0')equity_value = risk_money / portfolio_VaRelif portfolio_VaR == 0:print('VaR = 0')equity_value = max_risk_money / portfolio_ESelse:equity_value = min(risk_money / portfolio_VaR, max_risk_money / portfolio_ES)return equity_valuestock_num = len(stock_weight)risk_money = context.portfolio.portfolio_value * stock_num * context.risk_limit * position_ratiomax_risk_money = risk_money * 1.5# 股票组合在给定风险损失的情况下，最大持仓金额stock_value = 0if stock_weight:stock_value = fun_get_equity_value(bar_dict, stock_weight, risk_money, max_risk_money, context.confidenceLevel, position_ratio)stock_ratio = 0    # 股票持仓比例bond_ratio = 0     # 债券持仓比例total_value = context.portfolio.portfolio_value * position_ratio    # 最大持仓金额（包括股票和债券）if stock_value > total_value:bond_ratio = 0stock_ratio = 1.0 * position_ratioelse:stock_ratio = (stock_value / total_value) * position_ratiobond_ratio = (1 - (stock_value / total_value)) * position_ratioprint('stock_value: ' + str(stock_value))print('total_value: ' + str(total_value))trade_ratio = {}for stock in stock_weight:if stock in trade_ratio:trade_ratio[stock] += round((stock_weight[stock] * stock_ratio), 3)else:trade_ratio[stock] = round((stock_weight[stock] * stock_ratio), 3)for stock in bond_weight:if stock in trade_ratio:trade_ratio[stock] += round((bond_weight[stock] * bond_ratio), 3)else:trade_ratio[stock] = round((bond_weight[stock] * bond_ratio), 3)return trade_ratio#### 10.根据指定的仓位下单 #############################################################
def fun_do_trade(context, bar_dict, stock_position):for stock in list(context.portfolio.stock_account.positions.keys()):if stock not in stock_position:order_target_percent(stock, 0)else:order_target_percent(stock, stock_position[stock])for stock in stock_position:if stock not in list(context.portfolio.stock_account.positions.keys()):order_target_percent(stock, stock_position[stock])print('当前持仓: ')print(list(context.portfolio.stock_account.positions.keys()))#### 11.中位数去极值函数 ################################################################
def winsorize(df, factor, n=20):'''df为bar_dictFrame数据factor为需要去极值的列名称n 为判断极值上下边界的常数'''# 提取该列的数据ls_raw = np.array(df[factor].values)# 排序ls_raw.sort(axis = 0)# 获取中位数D_M = np.median(ls_raw)# 计算离差值ls_deviation = abs(ls_raw - D_M)# 排序ls_deviation.sort(axis = 0)# 获取离差中位数D_MAD = np.median(ls_deviation)# 将大于中位数n倍离差中位数的值赋为NaNdf.loc[df[factor] >= D_M + n * D_MAD, factor] = None# 将小于中位数n倍离差中位数的值赋为NaNdf.loc[df[factor] <= D_M - n * D_MAD, factor] = Nonereturn df

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