def sample_b3_4(): """ 【示例4】abu量化系统选股结合相关性,编写相关性选股策略 AbuPickSimilarNTop源代码请自行阅读,只简单示例使用。 :return: """ from abupy import AbuPickSimilarNTop from abupy import AbuPickStockWorker from abupy import AbuBenchmark, AbuCapital, AbuKLManager benchmark = AbuBenchmark() # 选股因子AbuPickSimilarNTop, 寻找与usTSLA相关性不低于0.95的股票 # 这里内部使用以整个市场作为观察者方式计算,即取值范围0-1 stock_pickers = [{ 'class': AbuPickSimilarNTop, 'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95 }] # 从这几个股票里进行选股,只是为了演示方便,一般的选股都会是数量比较多的情况比如全市场股票 choice_symbols = [ 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS' ] capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=choice_symbols, stock_pickers=stock_pickers) stock_pick.fit() print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols) """ 通过选股因子first_choice属性执行批量优先选股操作,具体阅读源代码 """ # 选股因子AbuPickSimilarNTop, 寻找与usTSLA相关性不低于0.95的股票 # 通过设置'first_choice':True,进行优先批量操作,默认从对应市场选股 stock_pickers = [{ 'class': AbuPickSimilarNTop, 'first_choice': True, 'similar_stock': 'usTSLA', 'threshold_similar_min': 0.95 }] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=None, stock_pickers=stock_pickers) stock_pick.fit() print('stock_pick.choice_symbols:\n', stock_pick.choice_symbols)
def sample_821_2(): """ 8.2.1_2 ABuPickStockExecute :return: """ stock_pickers = [{ 'class': AbuPickRegressAngMinMax, 'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': False }] choice_symbols = [ '601398', '601988', '601939', '603993', '600999', '300059', '600900', '601328', '601288', '600887', '600029', '000002' ] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) print( 'ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers)) kl_pd_sfun = kl_pd_manager.get_pick_stock_kl_pd('601398') print('sfun 选股周期内角度={}'.format( round(ABuRegUtil.calc_regress_deg(kl_pd_sfun.close), 3)))
def sample_823(): """ 8.2.3 使用并行来提升回测运行效率 :return: """ from abupy import EMarketSourceType abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx abupy.env.disable_example_env_ipython() benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) # 首先随抽取50支股票 choice_symbols = ABuMarket.choice_symbols(50) # 股价在15-50之间 stock_pickers = [{ 'class': AbuPickStockPriceMinMax, 'threshold_price_min': 15.0, 'threshold_price_max': 50.0, 'reversed': False }] cs = AbuPickStockMaster.do_pick_stock_with_process(capital, benchmark, stock_pickers, choice_symbols) print('len(cs):', len(cs)) print('cs:\n', cs)
def __init__(self, read_cash, buy_factors, sell_factors): # self.read_cash = read_cash # self.read_cash = 200000 # 买入因子使用60日向上和42日突破因子 # self.buy_factors = [{'fast': 5, 'slow': 20, 'class': AbuDoubleMaBuy}, {'xd': 60, 'class': AbuFactorBuyBreak}, # {'xd': 42, 'class': AbuFactorBuyBreak, # 'position': {'class': AbuKellyPosition}, }] self.buy_factors = buy_factors # 趋势跟踪策略止盈要大于止损设置值,这里1.0,3.0 # 卖出因子并行生效 # self.sell_factors = [ # {'stop_loss_n': 1.0, 'stop_win_n': 3.0, # 'class': AbuFactorAtrNStop}, # {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5}, # {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} # ] self.sell_factors = sell_factors # self.sell_factors = [{'fast': 5, 'slow': 20, 'class': AbuDoubleMaSell}, # {'stop_loss_n': 1.0, 'stop_win_n': 3.0, # 'class': AbuFactorAtrNStop}, # {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5}, # {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}] abupy.env.g_enable_ml_feature = True abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN self.commission_dict = { 'buy_commission_func': self.buy_commission_ch, 'sell_commission_func': self.sell_commission_ch } self.benchmark = AbuBenchmark() self.capital = AbuCapital(self.read_cash, self.benchmark, self.commission_dict) self.kl_pd_manager = AbuKLManager(self.benchmark, self.capital)
def sample_821_1(): """ 8.2.1_1 选股使用示例 :return: """ # 选股条件threshold_ang_min=0.0, 即要求股票走势为向上上升趋势 stock_pickers = [{'class': AbuPickRegressAngMinMax, 'threshold_ang_min': 0.0, 'reversed': False}] # 从这几个股票里进行选股,只是为了演示方便 # 一般的选股都会是数量比较多的情况比如全市场股票 choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS'] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) stock_pick = AbuPickStockWorker(capital, benchmark, kl_pd_manager, choice_symbols=choice_symbols, stock_pickers=stock_pickers) stock_pick.fit() # 打印最后的选股结果 print('stock_pick.choice_symbols:', stock_pick.choice_symbols) # 从kl_pd_manager缓存中获取选股走势数据,注意get_pick_stock_kl_pd为选股数据,get_pick_time_kl_pd为择时 kl_pd_noah = kl_pd_manager.get_pick_stock_kl_pd('usNOAH') # 绘制并计算角度 deg = ABuRegUtil.calc_regress_deg(kl_pd_noah.close) print('noah 选股周期内角度={}'.format(round(deg, 3)))
def sample_815(): """ 8.1.5 自定义仓位管理策略的实现 :return: """ metrics = sample_814(False) print('\nmetrics.gains_mean:{}, -metrics.losses_mean:{}'.format(metrics.gains_mean, -metrics.losses_mean)) from abupy import AbuKellyPosition # 42d使用AbuKellyPosition,60d仍然使用默认仓位管理类 buy_factors2 = [{'xd': 60, 'class': AbuFactorBuyBreak}, {'xd': 42, 'position': AbuKellyPosition, 'win_rate': metrics.win_rate, 'gains_mean': metrics.gains_mean, 'losses_mean': -metrics.losses_mean, 'class': AbuFactorBuyBreak}] sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak} sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop} sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0} sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] benchmark = AbuBenchmark() choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS'] capital = AbuCapital(1000000, benchmark) orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols, benchmark, buy_factors2, sell_factors, capital, show=False) print(orders_pd[:10].filter(['symbol', 'buy_cnt', 'buy_factor', 'buy_pos']))
def sample_816(): """ 8.1.6 多支股票使用不同的因子进行择时 :return: """ # 选定noah和sfun target_symbols = ['usSFUN', 'usNOAH'] # 针对sfun只使用42d向上突破作为买入因子 buy_factors_sfun = [{'xd': 42, 'class': AbuFactorBuyBreak}] # 针对sfun只使用60d向下突破作为卖出因子 sell_factors_sfun = [{'xd': 60, 'class': AbuFactorSellBreak}] # 针对noah只使用21d向上突破作为买入因子 buy_factors_noah = [{'xd': 21, 'class': AbuFactorBuyBreak}] # 针对noah只使用42d向下突破作为卖出因子 sell_factors_noah = [{'xd': 42, 'class': AbuFactorSellBreak}] factor_dict = dict() # 构建SFUN独立的buy_factors,sell_factors的dict factor_dict['usSFUN'] = {'buy_factors': buy_factors_sfun, 'sell_factors': sell_factors_sfun} # 构建NOAH独立的buy_factors,sell_factors的dict factor_dict['usNOAH'] = {'buy_factors': buy_factors_noah, 'sell_factors': sell_factors_noah} # 初始化资金 benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) # 使用do_symbols_with_diff_factors执行 orders_pd, action_pd, all_fit_symbols = ABuPickTimeExecute.do_symbols_with_diff_factors( target_symbols, benchmark, factor_dict, capital) print('pd.crosstab(orders_pd.buy_factor, orders_pd.symbol):\n', pd.crosstab(orders_pd.buy_factor, orders_pd.symbol))
def sample_812(): """ 8.1.2 卖出因子的实现 :return: """ # 120天向下突破为卖出信号 sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak} # 趋势跟踪策略止盈要大于止损设置值,这里0.5,3.0 sell_factor2 = { 'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop } # 暴跌止损卖出因子形成dict sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0} # 保护止盈卖出因子组成dict sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} # 四个卖出因子同时生效,组成sell_factors sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] # buy_factors 60日向上突破,42日向上突破两个因子 buy_factors = [{ 'xd': 60, 'class': AbuFactorBuyBreak }, { 'xd': 42, 'class': AbuFactorBuyBreak }] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors( ['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=True)
def sample_814(show=True): """ 8.1.4 对多支股票进行择时 :return: """ sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak} sell_factor2 = {'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop} sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0} sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] benchmark = AbuBenchmark() buy_factors = [{'xd': 60, 'class': AbuFactorBuyBreak}, {'xd': 42, 'class': AbuFactorBuyBreak}] choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS'] capital = AbuCapital(1000000, benchmark) orders_pd, action_pd, all_fit_symbols_cnt = ABuPickTimeExecute.do_symbols_with_same_factors(choice_symbols, benchmark, buy_factors, sell_factors, capital, show=False) metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark) metrics.fit_metrics() if show: print('orders_pd[:10]:\n', orders_pd[:10].filter( ['symbol', 'buy_price', 'buy_cnt', 'buy_factor', 'buy_pos', 'sell_date', 'sell_type_extra', 'sell_type', 'profit'])) print('action_pd[:10]:\n', action_pd[:10]) metrics.plot_returns_cmp(only_show_returns=True) return metrics
def sample_811(): """ 8.1.1 买入因子的实现 :return: """ # buy_factors 60日向上突破,42日向上突破两个因子 buy_factors = [{ 'xd': 60, 'class': AbuFactorBuyBreak }, { 'xd': 42, 'class': AbuFactorBuyBreak }] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) # 获取TSLA的交易数据 kl_pd = kl_pd_manager.get_pick_time_kl_pd('usTSLA') abu_worker = AbuPickTimeWorker(capital, kl_pd, benchmark, buy_factors, None) abu_worker.fit() orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders, kl_pd, draw=True) ABuTradeExecute.apply_action_to_capital(capital, action_pd, kl_pd_manager) capital.capital_pd.capital_blance.plot() plt.show()
def sample_822(): """ 8.2.2 多个选股因子并行执行 :return: """ # 选股list使用两个不同的选股因子组合,并行同时生效 stock_pickers = [{ 'class': AbuPickRegressAngMinMax, 'threshold_ang_min': 0.0, 'reversed': False }, { 'class': AbuPickStockPriceMinMax, 'threshold_price_min': 50.0, 'reversed': False }] choice_symbols = [ 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS' ] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) print( 'ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
def sample_821_2(): """ 8.2.1_2 ABuPickStockExecute :return: """ stock_pickers = [{ 'class': AbuPickRegressAngMinMax, 'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': False }] choice_symbols = [ 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS' ] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) print( 'ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers)) kl_pd_sfun = kl_pd_manager.get_pick_stock_kl_pd('usSFUN') print('sfun 选股周期内角度={}'.format( round(ABuRegUtil.calc_regress_deg(kl_pd_sfun.close), 3)))
def batchpick(): start = '20190101' end = '2020-11-06' # 策略 选取符合基本财务业绩指标,且短期与相关系数为负的,思路,庄股,跟大盘反着来的。这种策略适合在大盘下跌时买入 stock_pickers = [{ 'class': KPickStockValue, 'first_choice': True, 'start': start, 'end': end.replace('-', ''), 'roe_dt_2': 13, 'grossprofit_margin': 20, 'ocf_to_opincome': 0.7 }, { 'class': KPickStockStrongShake, 'first_choice': True, 'start': start, 'end': end.replace('-', ''), 'short_relation': -0.25, 'short_range': 30, 'long_range': 300, 'short_scope': 1.15, 'long_scope': 1.7, 'short_range_deg': 5 }] # 策略 选取符合基本财务业绩指标,最近1,2,3周成交量较大盘放大的,思路,选取资金涌入个股,改进(根据涌入个股分析所属板块,再延申选取相应板块优秀个股),自上而下的顺势根据当前资金涌入板块选择标的冲浪 stock_pickers = [{ 'class': KPickStockValue, 'first_choice': True, 'start': start, 'end': end.replace('-', ''), 'roe_dt_2': 8, 'grossprofit_margin': 18, 'ocf_to_opincome': 0.5 }, { 'class': KPickStockVolume, 'first_choice': True, 'start': '20200801', 'end': end.replace('-', '') }] fin_manager = KFinManager() choice_symbols = fin_manager.get_stock_basic().ts_code #choice_symbols = ['688179.SH'] benchmark = AbuBenchmark(start='2018-01-01', end=end) capital = AbuCapital(1000000, benchmark) print( 'ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
def sample_817(): """ 8.1.7 使用并行来提升择时运行效率 :return: """ # 要关闭沙盒数据环境,因为沙盒里就那几个股票的历史数据, 下面要随机做50个股票 from abupy import EMarketSourceType abupy.env.g_market_source = EMarketSourceType.E_MARKET_SOURCE_tx abupy.env.disable_example_env_ipython() # 关闭沙盒后,首先基准要从非沙盒环境换取,否则数据对不齐,无法正常运行 benchmark = AbuBenchmark() # 当传入choice_symbols为None时代表对整个市场的所有股票进行回测 # noinspection PyUnusedLocal choice_symbols = None # 顺序获取市场后300支股票 # noinspection PyUnusedLocal choice_symbols = ABuMarket.all_symbol()[-50:] # 随机获取300支股票 choice_symbols = ABuMarket.choice_symbols(50) capital = AbuCapital(1000000, benchmark) sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak} sell_factor2 = { 'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop } sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0} sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] buy_factors = [{ 'xd': 60, 'class': AbuFactorBuyBreak }, { 'xd': 42, 'class': AbuFactorBuyBreak }] orders_pd, action_pd, _ = AbuPickTimeMaster.do_symbols_with_same_factors_process( choice_symbols, benchmark, buy_factors, sell_factors, capital) metrics = AbuMetricsBase(orders_pd, action_pd, capital, benchmark) metrics.fit_metrics() metrics.plot_returns_cmp(only_show_returns=True) abupy.env.enable_example_env_ipython()
def sample_821_3(): """ 8.2.1_3 reversed :return: """ # 和上面的代码唯一的区别就是reversed=True stock_pickers = [{'class': AbuPickRegressAngMinMax, 'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': True}] choice_symbols = ['usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usTSLA', 'usWUBA', 'usVIPS'] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) print('ABuPickStockExecute.do_pick_stock_work:\n', ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers))
def sample_813(): """ 8.1.3 滑点买入卖出价格确定及策略实现 :return: """ from abupy import AbuSlippageBuyBase # 修改g_open_down_rate的值为0.02 g_open_down_rate = 0.02 # noinspection PyClassHasNoInit class AbuSlippageBuyMean2(AbuSlippageBuyBase): def fit_price(self): if (self.kl_pd_buy.open / self.kl_pd_buy.pre_close) < (1 - g_open_down_rate): # 开盘下跌K_OPEN_DOWN_RATE以上,单子失效 print(self.factor_name + 'open down threshold') return np.inf # 买入价格为当天均价 self.buy_price = np.mean( [self.kl_pd_buy['high'], self.kl_pd_buy['low']]) return self.buy_price # 只针对60使用AbuSlippageBuyMean2 buy_factors2 = [{ 'slippage': AbuSlippageBuyMean2, 'xd': 60, 'class': AbuFactorBuyBreak }, { 'xd': 42, 'class': AbuFactorBuyBreak }] sell_factor1 = {'xd': 120, 'class': AbuFactorSellBreak} sell_factor2 = { 'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop } sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.0} sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors( ['usTSLA'], benchmark, buy_factors2, sell_factors, capital, show=True)
sell_factors = [{ 'xd': 120, 'class': AbuFactorSellBreak }, { 'stop_loss_n': 0.5, 'stop_win_n': 3.0, 'class': AbuFactorAtrNStop }, { 'pre_atr_n': 1.0, 'class': AbuFactorPreAtrNStop }, { 'close_atr_n': 1.5, 'class': AbuFactorCloseAtrNStop }] benchmark = AbuBenchmark() capital = AbuCapital(1000000, benchmark) # assign money ''' # 1) tedious way from abupy import AbuPickTimeWorker; from abupy import AbuKLManager; kl_pd_manager = AbuKLManager(benchmark, capital); kl_pd = kl_pd_manager.get_pick_time_kl_pd('usTSLA'); # load k line of Tesla # simulate with buy factor and sell factor abu_worker = AbuPickTimeWorker(capital, kl_pd, benchmark, buy_factors = buy_factors, sell_factors = sell_factors); abu_worker.fit(); # visualize orders from abupy import ABuTradeProxy; orders_pd, action_pd, _ = ABuTradeProxy.trade_summary(abu_worker.orders, kl_pd, draw = True, show_info = False); # visualize capitalism from abupy import ABuTradeExecute;
def __init__(self, symbol, period, money): self.symbol = symbol self.buy_factors = [{'xd': period, 'class': AbuFactorBuyBreak}] self.benchmark = AbuBenchmark() self.capital = AbuCapital(money, self.benchmark)
def sample_821_3(): """ 8.2.1_2 ABuPickStockExecute :return: """ stock_pickers = [{ 'class': abupy.FuWeekVolumeBoll, 'threshold_ang_min': 0.0, 'threshold_ang_max': 10.0, 'reversed': False }] choice_symbols = [ '601398', '601988', '601939', '603993', '600999', '300059', '600900', '601328', '601288', '600887', '600029', '000002' ] choice_symbols = [ 'sz000983', 'sh600338', 'sh600511', 'sh600196', 'sh600423', 'sz399136', 'sz002044', 'sh601800', 'sz300132', 'sz300133', 'sh000821', 'sz300003', 'sz300009', 'sz200045', 'sh600998', 'sz300313', 'sh601607', 'sz002644', 'sh600697', 'sz000627', 'sh000003', 'sz399302', 'sh600984', 'sz399301', 'sz000916', 'sz000911', 'sz000912', 'sz000688', 'sh600079', 'sh601101', 'sz000861', 'sz000736', 'sz002053', 'sz000048', 'sh600703', 'sh000814', 'sz300015', 'sh000818', 'sz399352', 'sz399356', 'sh900911', 'sh600395', 'sh000075', 'sz002323', 'sh000101', 'sh600285', 'sh600882', 'sz000789', 'sh601398', 'sz000898', 'sh601390', 'sh601009', 'sh601001', 'sz000525', 'sh600713', 'sh601628', 'sz399299', 'sz399298', 'sh600800', 'sh000808', 'sh900909', 'sh900908', 'sh000061', 'sh000068', 'sh000116', 'sz000617', 'sh600535', 'sz000792', 'sz000889', 'sz000065', 'sh601015', 'sz000089', 'sh600871', 'sz002412', 'sz399400', 'sz399402', 'sz399404', 'sh000057', 'sh900930', 'sh900936', 'sh900934', 'sh900935', 'sh600267', 'sz000650', 'sz399978', 'sh600485', 'sh601021', 'sh601601', 'sh600208', 'sh601288', 'sh600062', 'sh600015', 'sh600016', 'sz300197', 'sz300199', 'sz399413', 'sz399411', 'sz399416', 'sh000134', 'sh000136', 'sh000139', 'sz002007', 'sh600258', 'sh600123', 'sz000511', 'sh601618', 'sh600745', 'sz399170', 'sh000923', 'sz399319', 'sz399554', 'sz399555', 'sz002530', 'sh000145', 'sz002070', 'sh000149', 'sz399220', 'sh601998', 'sh600111', 'sh600023', 'sz000560', 'sh601699', 'sz399305', 'sz399431', 'sz000766', 'sz399436', 'sz399230', 'sz399237', 'sz002661', 'sz002599', 'sh000155', 'sh000152', 'sh000151', 'sh600806', 'sh601988', 'sh600693', 'sh600699', 'sh600582', 'sz000995', 'sh600566', 'sh601318', 'sz399150', 'sz399441', 'sz399200', 'sh000841', 'sh600917', 'sz002128', 'sh600176', 'sz000968', 'sh600771', 'sh600579', 'sh600578', 'sh600572', 'sh600681', 'sh600680', 'sz399140', 'sz000540', 'sz000545', 'sz200022', 'sz200026', 'sz200025', 'sz399210', 'sz200029', 'sz002601', 'sz002656', 'sz002204', 'sz002737', 'sz000748', 'sh600965', 'sz002135', 'sh000934', 'sh601169', 'sh601899', 'sh601898', 'sh600549', 'sh600546', 'sh600545', 'sz000778', 'sh600141', 'sh600145', 'sh601231', 'sz399139', 'sz000630', 'sz000613', 'sz399137', 'sz399130', 'sz399131', 'sz399132', 'sz399133', 'sz200019', 'sz300146', 'sz300144', 'sz399661', 'sz002701', 'sh600971', 'sz002382', 'sz002385', 'sh600085', 'sh603158', 'sz002602', 'sh601939', 'sh600007', 'sz399645', 'sh600000', 'sh601339', 'sh601336', 'sh000125', 'sz399674', 'sh000974', 'sz399160', 'sz002653', 'sz002717', 'sz200726', 'sz399647', 'sz300294', 'sh000100', 'sz300347', 'sh600348', 'sh000933', 'sh600401', 'sh000109', 'sz000034', 'sh600623', 'sz000581', 'sz000672', 'sz300028', 'sh603368', 'sh000023', 'sh000021', 'sz002198', 'sh600432', 'sh603989', 'sz000937', 'sh600508', 'sh600500', 'sh000159', 'sz000732', 'sh600188', 'sz000598', 'sz000029', 'sz000028', 'sz399394', 'sh000832', 'sz300036', 'sz200053', 'sz300326', 'sz002742', 'sz300253', 'sh000013', 'sh000011' ] # choice_symbols = ['002656', '000903'] benchmark = AbuBenchmark(n_folds=15) capital = AbuCapital(1000000, benchmark) kl_pd_manager = AbuKLManager(benchmark, capital) stock_pickers = ABuPickStockExecute.do_pick_stock_work( None, benchmark, # stock_pickers = ABuPickStockExecute.do_pick_stock_work(choice_symbols, benchmark, capital, stock_pickers) print('ABuPickvStockExecute.do_pick_stock_work:\n', stock_pickers) for stock_symbol in stock_pickers: if ~fetch_stock_base_info(stock_symbol): continue draw_candle(stock_symbol, 15)
} sell_factor3 = {'class': AbuFactorPreAtrNStop, 'pre_art_n': 1.0} sell_factor4 = {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5} # buy_factors 60日向上突破,42日向上突破两个因子 buy_factors = [{ 'slippage': AbuSlippageBuyMean2, 'xd': 60, 'class': AbuFactorBuyBreak }, { 'xd': 42, 'class': AbuFactorBuyBreak }] # 只使用120天向下突破为卖出因子 sell_factors = [sell_factor1, sell_factor2, sell_factor3, sell_factor4] benchmark = AbuBenchmark() # 构造一个字典key='buy_commission_func', value=自定义的手续费方法函数 commission_dict = {'buy_commission_func': calc_commission_us} # 将commission_dict做为参数传入AbuCapital capital = AbuCapital(1000000, benchmark, user_commission_dict=commission_dict) print(benchmark.__str__()) orders_pd, action_pd, _ = ABuPickTimeExecute.do_symbols_with_same_factors( ['usTSLA'], benchmark, buy_factors, sell_factors, capital, show=False) print(orders_pd) print(action_pd) print(capital.commission.commission_df)