复制链接

克隆策略

In [ ]:

PIT(point in time)

TTM:过去12个月单季度财务数据的加总，净利润：Q3,Q2
mrq:
lf:
ly:
_n:np_ly_0/np_ly_1 -1

ROIC¶

ROIC 衡量的是股东和债权人投入的本钱，到底获取了多少回报？假如企业赚到的钱是一个蛋糕，它最后会被三个人分走，支付的利息费用被债主拿走了，缴的税被国家拿走了，剩下的利润被股东拿走了。

ROE = 净利润/净资产：1.净利润容易被操控；2.净资产没有考虑债权人的利润 ROA衡量所有资产

【ROIC】投入资本回报率 |释义|：ROIC是生产经营活动中所有投入资本赚取的收益率，而不论这些投入资本是被称为债务还是权益。分子是指公司如果完全按权益筹资所应报告的税后利润，分母是指公司所有要求回报的现金来源的总和。。相对而言ROIC是比ROA和ROE更全面、更好的度量盈利能力的工具，是衡量一家公司真实经营业绩最好的度量指标，与ROE相比，ROIC去除了公司不同的融资结构对盈利能力造成的影响，使用ROIC将有助于投资者将关注的核心聚焦在核心业务的盈利能力上。 |公式|：EBIT反推法(1-有效税率)2／(期初全部投入资本+期末全部投入资本)

【有效税率】 |公式|：有效税率：当所得税>0时，为所得税/利润总额，否则为0

【EBIT正推法】（营业总收入-营业税金及附加）-（营业成本+利息支出+手续费及佣金支出+销售费用+管理费用+研发费用+存货跌价损失+信用减值损失）+其他收益

【EBIT反推法】 |公式|：利润总额+利息费用（不含资本化利息支出）如果财务报告中公布了财务费用明细，则“利息费用=（利息支出-财务费用明细.利息资本化金额）-利息收入”；如果财务报告中未公布财务费用明细，则以“利润表.利息费用 -利润表.利息收入 ”替代。一般而言，中期报告和年度报告中会公布财务费用明细。

【投入资本】 |释义|：投入资本指所有投资者(股权人、债权人)投入的资金总和，这些资金都是意图分享企业经营回报的。投入资本与总资产的核心差别在于投资资本中不包括无息负债。股东权益(含少数股东权益)+负债合计-无息流动负债-无息长期负债

【无息流动负债】 |释义|：无息流动负债在资产负债表中主要表现为应付账款、应付工资、其他应付等。无息流动负债也是企业对其他方资金的一种占用，但这部分资金并不来自于投资人，这种资金占用只是企业正常经营活动中的一个必要环节，一种循环占用。 |公式|：应付帐款及应付票据+预收款项+应付职工薪酬+应交税费+其他应付款合计+预提费用+递延收益.流动负债+合同负债+其他流动负债-短期融资债(其他流动负债)+衍生金融负债

【无息非流动负债】 |释义|：无息非流动负债在资产负债表中主要体现为预计负债、长期应付款等。为了谨慎起见，我们采用间接法计算无息非流动负债。 |公式|：非流动负债合计-长期借款-应付债券

In [9]:

df = m5.data_1.read()[['date','instrument','roic_ttm_0']]
df[df.instrument=='000063.SZA']

Out[9]:

	date	instrument	roic_ttm_0
2867	2019-10-08	000063.SZA	0.053537
2868	2019-10-09	000063.SZA	0.053537
2869	2019-10-10	000063.SZA	0.053537
2870	2019-10-11	000063.SZA	0.053537
2871	2019-10-14	000063.SZA	0.053537
...	...	...	...
238420	2020-12-25	000063.SZA	0.043285
238421	2020-12-28	000063.SZA	0.043285
238422	2020-12-29	000063.SZA	0.043285
238423	2020-12-30	000063.SZA	0.043285
238424	2020-12-31	000063.SZA	0.043285

304 rows × 3 columns

{"description":"实验创建于2022/11/18","graph":{"edges":[{"to_node_id":"-168:features","from_node_id":"-155:data"},{"to_node_id":"-492:features","from_node_id":"-155:data"},{"to_node_id":"-168:instruments","from_node_id":"-159:data"},{"to_node_id":"-512:instruments","from_node_id":"-159:data"},{"to_node_id":"-492:input_data","from_node_id":"-168:data"},{"to_node_id":"-684:input_1","from_node_id":"-492:data"},{"to_node_id":"-1947:input_ds","from_node_id":"-684:data_1"},{"to_node_id":"-1623:input_data","from_node_id":"-684:data_1"},{"to_node_id":"-512:options_data","from_node_id":"-1947:sorted_data"},{"to_node_id":"-1598:user_factor_data","from_node_id":"-1623:data"},{"to_node_id":"-1598:features","from_node_id":"-1626:data"}],"nodes":[{"node_id":"-155","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# 所得税费用\nincome_tax_cost_ttm_0\nincome_tax_cost_ttm_4\n# 利润总额\ntotal_profit_ttm_0\ntotal_profit_ttm_4\n# 财务费用：利息费用\ninterest_fee_ttm_0\ninterest_fee_ttm_4\n# 财务费用：利息收入\nfc_interest_income_ttm_0\nfc_interest_income_ttm_4\n# 财务费用\nfinancing_expenses_ttm_0\nfinancing_expenses_ttm_4\n# 所有者权益\ntotal_owner_equity_lf_0\ntotal_owner_equity_lf_4\n# 负债合计\ntotal_liab_lf_0\ntotal_liab_lf_4\n# 应收账款及应付票据\nbill_and_account_payable_lf_0\nbill_and_account_payable_lf_4\n# 预收款项\nadvance_payment_lf_0\nadvance_payment_lf_4\n# 应付职工薪酬\npayroll_payable_lf_0\npayroll_payable_lf_4\n# 应交税费\ntax_payable_lf_0\ntax_payable_lf_4\n# 其他应付款合计\nother_payables_sum_lf_0\nother_payables_sum_lf_4\n# 递延收益.流动负债\ndiffered_income_current_liab_lf_0\ndiffered_income_current_liab_lf_4\n# 合同负债\ncontract_liab_lf_0\ncontract_liab_lf_4\n# 其他流动负债\nother_current_liab_lf_0\nother_current_liab_lf_4\n# 衍生金融负债\nderivative_fnncl_liab_lf_0\nderivative_fnncl_liab_lf_4\n# 非流动负债合计\ntotal_noncurrent_liab_lf_0\ntotal_noncurrent_liab_lf_4\n# 长期借款\nlt_loan_lf_0\nlt_loan_lf_4\n# 应付债券\nbond_payable_lf_0\nbond_payable_lf_4","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-155"}],"output_ports":[{"name":"data","node_id":"-155"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-159","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-159"}],"output_ports":[{"name":"data","node_id":"-159"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-168","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-168"},{"name":"features","node_id":"-168"}],"output_ports":[{"name":"data","node_id":"-168"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-492","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-492"},{"name":"features","node_id":"-492"}],"output_ports":[{"name":"data","node_id":"-492"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-684","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n df = input_1.read()\n df.replace(np.nan, round(0, 1), inplace=True)\n \n # 无息流动负债\n df['noninterest_current_liab_lf_0'] = df['bill_and_account_payable_lf_0'] + df['advance_payment_lf_0'] + df['payroll_payable_lf_0'] + df['tax_payable_lf_0'] + df['other_payables_sum_lf_0'] + df['differed_income_current_liab_lf_0'] + df['contract_liab_lf_0'] + df['other_current_liab_lf_0'] + df['derivative_fnncl_liab_lf_0']\n df['noninterest_current_liab_lf_4'] = df['bill_and_account_payable_lf_4'] + df['advance_payment_lf_4'] + df['payroll_payable_lf_4'] + df['tax_payable_lf_4'] + df['other_payables_sum_lf_4'] + df['differed_income_current_liab_lf_4'] + df['contract_liab_lf_4'] + df['other_current_liab_lf_4'] + df['derivative_fnncl_liab_lf_4']\n\n # 无息非流动负债\n df['noninterest_noncurrent_liab_lf_0'] = df['total_noncurrent_liab_lf_0'] - df['lt_loan_lf_0'] - df['bond_payable_lf_0']\n df['noninterest_noncurrent_liab_lf_4'] = df['total_noncurrent_liab_lf_4'] - df['lt_loan_lf_4'] - df['bond_payable_lf_4']\n\n # 全部投入资本\n df['total_paidin_capital_lf_0'] = df['total_owner_equity_lf_0'] + (df['total_liab_lf_0'] - df['noninterest_current_liab_lf_0'] - df['noninterest_noncurrent_liab_lf_0'])\n df['total_paidin_capital_lf_4'] = df['total_owner_equity_lf_4'] + (df['total_liab_lf_4'] - df['noninterest_current_liab_lf_4'] - df['noninterest_noncurrent_liab_lf_4'])\n\n # 平均全部投入资本\n df['total_paidin_capital_ttm_mean'] = (df['total_paidin_capital_lf_0'] + df['total_paidin_capital_lf_4']) / 2\n \n # 是否有财务费用明细的数据打标签\n df['nan_tag_fs_interest_income_ttm_0'] = 0 # 财务费用明细有数据则为0，则不用财务费用科目的数据\n df.loc[(df['interest_fee_ttm_0']==0)&(df['fc_interest_income_ttm_0']==0), 'nan_tag_fs_interest_income_ttm_0'] = 1\n df['EBIT_ttm_0'] = df['total_profit_ttm_0'] + (df['interest_fee_ttm_0'] - df['fc_interest_income_ttm_0']) + df['financing_expenses_ttm_0'] * df['nan_tag_fs_interest_income_ttm_0']\n \n # 给利润总额为负的数据打标签\n df['neg_tag_total_profit'] = 1\n df.loc[df['total_profit_ttm_0']<0, 'neg_tag_total_profit'] = 0\n df['effective_tax_rate_ttm_0'] = (df['income_tax_cost_ttm_0'] * df['neg_tag_total_profit']) / df['total_profit_ttm_0']\n \n \n # 计算ROIC\n df['roic_ttm_0'] = (df['EBIT_ttm_0'] * (1 - df['effective_tax_rate_ttm_0'])) / df['total_paidin_capital_ttm_mean']\n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-684"},{"name":"input_2","node_id":"-684"},{"name":"input_3","node_id":"-684"}],"output_ports":[{"name":"data_1","node_id":"-684"},{"name":"data_2","node_id":"-684"},{"name":"data_3","node_id":"-684"}],"cacheable":true,"seq_num":5,"comment":"计算EBIT和有效税率","comment_collapsed":true},{"node_id":"-1947","module_id":"BigQuantSpace.sort.sort-v5","parameters":[{"name":"sort_by","value":"roic_ttm_0","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-1947"},{"name":"sort_by_ds","node_id":"-1947"}],"output_ports":[{"name":"sorted_data","node_id":"-1947"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-512","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只\n stock_count = 50\n # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n \n ranker_prediction = ranker_prediction.sort_values('roic_ttm_0',ascending=False)\n \n # 1. 资金分配\n # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金\n # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"1000000","type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-512"},{"name":"options_data","node_id":"-512"},{"name":"history_ds","node_id":"-512"},{"name":"benchmark_ds","node_id":"-512"},{"name":"trading_calendar","node_id":"-512"}],"output_ports":[{"name":"raw_perf","node_id":"-512"}],"cacheable":false,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-1598","module_id":"BigQuantSpace.factorlens.factorlens-v2","parameters":[{"name":"title","value":"因子分析: {factor_name}","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-10-01","type":"Literal","bound_global_parameter":null},{"name":"rebalance_period","value":22,"type":"Literal","bound_global_parameter":null},{"name":"delay_rebalance_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"rebalance_price","value":"close_0","type":"Literal","bound_global_parameter":null},{"name":"stock_pool","value":"全市场","type":"Literal","bound_global_parameter":null},{"name":"quantile_count","value":5,"type":"Literal","bound_global_parameter":null},{"name":"commission_rate","value":0.0016,"type":"Literal","bound_global_parameter":null},{"name":"returns_calculation_method","value":"累乘","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"无","type":"Literal","bound_global_parameter":null},{"name":"drop_new_stocks","value":60,"type":"Literal","bound_global_parameter":null},{"name":"drop_price_limit_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_st_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"drop_suspended_stocks","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cutoutliers","value":"True","type":"Literal","bound_global_parameter":null},{"name":"normalization","value":"True","type":"Literal","bound_global_parameter":null},{"name":"neutralization","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22displayValue%22%3A%22%E8%A1%8C%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22displayValue%22%3A%22%E5%B8%82%E5%80%BC%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%A8%E7%8E%B0%E6%A6%82%E8%A7%88%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E8%A1%8C%E4%B8%9A%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%B8%82%E5%80%BC%E5%88%86%E5%B8%83%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22IC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E4%B9%B0%E5%85%A5%E4%BF%A1%E5%8F%B7%E9%87%8D%E5%90%88%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E4%BC%B0%E5%80%BC%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E6%8B%A5%E6%8C%A4%E5%BA%A6%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22displayValue%22%3A%22%E5%9B%A0%E5%AD%90%E5%80%BC%E6%9C%80%E5%A4%A7%2F%E6%9C%80%E5%B0%8F%E8%82%A1%E7%A5%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22displayValue%22%3A%22%E8%A1%A8%E8%BE%BE%E5%BC%8F%E5%9B%A0%E5%AD%90%E5%80%BC%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22displayValue%22%3A%22%E5%A4%9A%E5%9B%A0%E5%AD%90%E7%9B%B8%E5%85%B3%E6%80%A7%E5%88%86%E6%9E%90%22%2C%22selected%22%3Atrue%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"factor_coverage","value":0.5,"type":"Literal","bound_global_parameter":null},{"name":"user_data_merge","value":"left","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features","node_id":"-1598"},{"name":"user_factor_data","node_id":"-1598"}],"output_ports":[{"name":"data","node_id":"-1598"},{"name":"save_data","node_id":"-1598"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-1623","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-1623"},{"name":"features","node_id":"-1623"}],"output_ports":[{"name":"data","node_id":"-1623"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-1626","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"roic_ttm_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-1626"}],"output_ports":[{"name":"data","node_id":"-1626"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-155' Position='388,357,200,200'/><node_position Node='-159' Position='62,356,200,200'/><node_position Node='-168' Position='209,497,200,200'/><node_position Node='-492' Position='250,591,200,200'/><node_position Node='-684' Position='249,706,200,200'/><node_position Node='-1947' Position='240,814,200,200'/><node_position Node='-512' Position='135,938,200,200'/><node_position Node='-1598' Position='623,931,200,200'/><node_position Node='-1623' Position='616,814,200,200'/><node_position Node='-1626' Position='609,693,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

In [5]:

# 本代码由可视化策略环境自动生成 2022年11月21日 13:45
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m5_run_bigquant_run(input_1, input_2, input_3):
    df = input_1.read()
    df.replace(np.nan, round(0, 1), inplace=True)
    
    # 无息流动负债
    df['noninterest_current_liab_lf_0'] = df['bill_and_account_payable_lf_0'] + df['advance_payment_lf_0'] + df['payroll_payable_lf_0'] + df['tax_payable_lf_0'] + df['other_payables_sum_lf_0'] + df['differed_income_current_liab_lf_0'] + df['contract_liab_lf_0'] + df['other_current_liab_lf_0'] + df['derivative_fnncl_liab_lf_0']
    df['noninterest_current_liab_lf_4'] = df['bill_and_account_payable_lf_4'] + df['advance_payment_lf_4'] + df['payroll_payable_lf_4'] + df['tax_payable_lf_4'] + df['other_payables_sum_lf_4'] + df['differed_income_current_liab_lf_4'] + df['contract_liab_lf_4'] + df['other_current_liab_lf_4'] + df['derivative_fnncl_liab_lf_4']

    # 无息非流动负债
    df['noninterest_noncurrent_liab_lf_0'] = df['total_noncurrent_liab_lf_0'] - df['lt_loan_lf_0'] - df['bond_payable_lf_0']
    df['noninterest_noncurrent_liab_lf_4'] = df['total_noncurrent_liab_lf_4'] - df['lt_loan_lf_4'] - df['bond_payable_lf_4']

    # 全部投入资本
    df['total_paidin_capital_lf_0'] = df['total_owner_equity_lf_0'] + (df['total_liab_lf_0'] - df['noninterest_current_liab_lf_0'] - df['noninterest_noncurrent_liab_lf_0'])
    df['total_paidin_capital_lf_4'] = df['total_owner_equity_lf_4'] + (df['total_liab_lf_4'] - df['noninterest_current_liab_lf_4'] - df['noninterest_noncurrent_liab_lf_4'])

    # 平均全部投入资本
    df['total_paidin_capital_ttm_mean'] = (df['total_paidin_capital_lf_0'] + df['total_paidin_capital_lf_4']) / 2
    
    # 是否有财务费用明细的数据打标签
    df['nan_tag_fs_interest_income_ttm_0'] = 0    # 财务费用明细有数据则为0，则不用财务费用科目的数据
    df.loc[(df['interest_fee_ttm_0']==0)&(df['fc_interest_income_ttm_0']==0), 'nan_tag_fs_interest_income_ttm_0'] = 1
    df['EBIT_ttm_0'] = df['total_profit_ttm_0'] + (df['interest_fee_ttm_0'] - df['fc_interest_income_ttm_0']) + df['financing_expenses_ttm_0'] * df['nan_tag_fs_interest_income_ttm_0']
    
    # 给利润总额为负的数据打标签
    df['neg_tag_total_profit'] = 1
    df.loc[df['total_profit_ttm_0']<0, 'neg_tag_total_profit'] = 0
    df['effective_tax_rate_ttm_0'] = (df['income_tax_cost_ttm_0'] * df['neg_tag_total_profit']) / df['total_profit_ttm_0']
    
    
    # 计算ROIC
    df['roic_ttm_0'] = (df['EBIT_ttm_0'] * (1 - df['effective_tax_rate_ttm_0'])) / df['total_paidin_capital_ttm_mean']
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m5_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m7_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = 50
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.options['hold_days'] = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m7_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
    ranker_prediction = ranker_prediction.sort_values('roic_ttm_0',ascending=False)
    
    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m7_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m7_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.input_features.v1(
    features="""# 所得税费用
income_tax_cost_ttm_0
income_tax_cost_ttm_4
# 利润总额
total_profit_ttm_0
total_profit_ttm_4
# 财务费用：利息费用
interest_fee_ttm_0
interest_fee_ttm_4
# 财务费用：利息收入
fc_interest_income_ttm_0
fc_interest_income_ttm_4
# 财务费用
financing_expenses_ttm_0
financing_expenses_ttm_4
# 所有者权益
total_owner_equity_lf_0
total_owner_equity_lf_4
# 负债合计
total_liab_lf_0
total_liab_lf_4
# 应收账款及应付票据
bill_and_account_payable_lf_0
bill_and_account_payable_lf_4
# 预收款项
advance_payment_lf_0
advance_payment_lf_4
# 应付职工薪酬
payroll_payable_lf_0
payroll_payable_lf_4
# 应交税费
tax_payable_lf_0
tax_payable_lf_4
# 其他应付款合计
other_payables_sum_lf_0
other_payables_sum_lf_4
# 递延收益.流动负债
differed_income_current_liab_lf_0
differed_income_current_liab_lf_4
# 合同负债
contract_liab_lf_0
contract_liab_lf_4
# 其他流动负债
other_current_liab_lf_0
other_current_liab_lf_4
# 衍生金融负债
derivative_fnncl_liab_lf_0
derivative_fnncl_liab_lf_4
# 非流动负债合计
total_noncurrent_liab_lf_0
total_noncurrent_liab_lf_4
# 长期借款
lt_loan_lf_0
lt_loan_lf_4
# 应付债券
bond_payable_lf_0
bond_payable_lf_4"""
)

m2 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2021-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m5 = M.cached.v3(
    input_1=m4.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.sort.v5(
    input_ds=m5.data_1,
    sort_by='roic_ttm_0',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m9 = M.dropnan.v2(
    input_data=m5.data_1
)

m7 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.sorted_data,
    start_date='',
    end_date='',
    initialize=m7_initialize_bigquant_run,
    handle_data=m7_handle_data_bigquant_run,
    prepare=m7_prepare_bigquant_run,
    before_trading_start=m7_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

m10 = M.input_features.v1(
    features='roic_ttm_0'
)

m8 = M.factorlens.v2(
    features=m10.data,
    user_factor_data=m9.data,
    title='因子分析: {factor_name}',
    start_date='2019-01-01',
    end_date='2021-10-01',
    rebalance_period=22,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_suspended_stocks=True,
    cutoutliers=True,
    normalization=True,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)

[2022-11-19 14:58:12.754302] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-11-19 14:58:12.762328] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.766848] INFO: moduleinvoker: input_features.v1 运行完成[0.01254s].

[2022-11-19 14:58:12.781633] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-19 14:58:12.789509] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.792047] INFO: moduleinvoker: instruments.v2 运行完成[0.010426s].

[2022-11-19 14:58:12.815289] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-19 14:58:12.829407] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.832463] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.017222s].

[2022-11-19 14:58:12.846791] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-19 14:58:12.857261] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.859965] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013201s].

[2022-11-19 14:58:12.888500] INFO: moduleinvoker: cached.v3 开始运行..

[2022-11-19 14:58:12.902332] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.905556] INFO: moduleinvoker: cached.v3 运行完成[0.017072s].

[2022-11-19 14:58:12.926025] INFO: moduleinvoker: sort.v5 开始运行..

[2022-11-19 14:58:12.940212] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:12.944862] INFO: moduleinvoker: sort.v5 运行完成[0.018843s].

[2022-11-19 14:58:19.996367] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-11-19 14:58:20.010123] INFO: backtest: biglearning backtest:V8.6.3

[2022-11-19 14:58:20.012108] INFO: backtest: product_type:stock by specified

[2022-11-19 14:58:20.271827] INFO: moduleinvoker: cached.v2 开始运行..

[2022-11-19 14:58:20.284086] INFO: moduleinvoker: 命中缓存

[2022-11-19 14:58:20.286705] INFO: moduleinvoker: cached.v2 运行完成[0.014904s].

[2022-11-19 14:58:33.017985] INFO: backtest: algo history_data=DataSource(8f8166930df248cd94f3bb1ea7cf7beeT)

[2022-11-19 14:58:33.020297] INFO: algo: TradingAlgorithm V1.8.8

[2022-11-19 14:58:42.231725] INFO: algo: trading transform...

[2022-11-19 14:58:44.130029] INFO: algo: handle_splits get splits [dt:2020-01-20 00:00:00+00:00] [asset:Equity(4920 [002299.SZA]), ratio:0.9405704140663147]

[2022-11-19 14:58:44.134807] INFO: Position: position stock handle split[sid:4920, orig_amount:100, new_amount:106.0, orig_cost:25.49999809273436, new_cost:23.9845, ratio:0.9405704140663147, last_sale_price:23.7399959564209]

[2022-11-19 14:58:44.137095] INFO: Position: after split: PositionStock(asset:Equity(4920 [002299.SZA]), amount:106.0, cost_basis:23.9845, last_sale_price:25.23999786376953)

[2022-11-19 14:58:44.139699] INFO: Position: returning cash: 7.5603

[2022-11-19 14:58:44.294605] INFO: algo: handle_splits get splits [dt:2020-01-21 00:00:00+00:00] [asset:Equity(328 [600186.SHA]), ratio:0.9224806427955627]

[2022-11-19 14:58:44.297176] INFO: Position: position stock handle split[sid:328, orig_amount:2400, new_amount:2601.0, orig_cost:2.4500000520141545, new_cost:2.2601, ratio:0.9224806427955627, last_sale_price:2.379999876022339]

[2022-11-19 14:58:44.299356] INFO: Position: after split: PositionStock(asset:Equity(328 [600186.SHA]), amount:2601.0, cost_basis:2.2601, last_sale_price:2.5799999237060547)

[2022-11-19 14:58:44.301461] INFO: Position: returning cash: 1.6198

[2022-11-19 14:58:48.738183] INFO: algo: handle_splits get splits [dt:2020-03-19 00:00:00+00:00] [asset:Equity(72 [002458.SZA]), ratio:0.5697818994522095]

[2022-11-19 14:58:48.740680] INFO: Position: position stock handle split[sid:72, orig_amount:800, new_amount:1404.0, orig_cost:31.904999733080814, new_cost:18.1789, ratio:0.5697818994522095, last_sale_price:18.28999900817871]

[2022-11-19 14:58:48.742976] INFO: Position: after split: PositionStock(asset:Equity(72 [002458.SZA]), amount:1404.0, cost_basis:18.1789, last_sale_price:32.099998474121094)

[2022-11-19 14:58:48.745136] INFO: Position: returning cash: 0.8414

[2022-11-19 14:58:51.986683] INFO: algo: handle_splits get splits [dt:2020-04-24 00:00:00+00:00] [asset:Equity(943 [002746.SZA]), ratio:0.9734218716621399]

[2022-11-19 14:58:51.989678] INFO: Position: position stock handle split[sid:943, orig_amount:200, new_amount:205.0, orig_cost:14.789999962018017, new_cost:14.3969, ratio:0.9734218716621399, last_sale_price:14.649999618530273]

[2022-11-19 14:58:51.992507] INFO: Position: after split: PositionStock(asset:Equity(943 [002746.SZA]), amount:205.0, cost_basis:14.3969, last_sale_price:15.050000190734863)

[2022-11-19 14:58:51.994935] INFO: Position: returning cash: 6.7502

[2022-11-19 14:58:52.248405] INFO: algo: handle_splits get splits [dt:2020-04-28 00:00:00+00:00] [asset:Equity(1782 [603360.SHA]), ratio:0.6991899609565735]

[2022-11-19 14:58:52.250567] INFO: Position: position stock handle split[sid:1782, orig_amount:100, new_amount:143.0, orig_cost:28.549999239231692, new_cost:19.9619, ratio:0.6991899609565735, last_sale_price:19.850004196166992]

[2022-11-19 14:58:52.252338] INFO: Position: after split: PositionStock(asset:Equity(1782 [603360.SHA]), amount:143.0, cost_basis:19.9619, last_sale_price:28.39000129699707)

[2022-11-19 14:58:52.254046] INFO: Position: returning cash: 0.4496

[2022-11-19 14:58:53.172879] INFO: algo: handle_splits get splits [dt:2020-05-08 00:00:00+00:00] [asset:Equity(2452 [300682.SZA]), ratio:0.6643429398536682]

[2022-11-19 14:58:53.175357] INFO: Position: position stock handle split[sid:2452, orig_amount:100, new_amount:150.0, orig_cost:28.90000152700163, new_cost:19.1995, ratio:0.6643429398536682, last_sale_price:19.059999465942383]

[2022-11-19 14:58:53.177461] INFO: Position: after split: PositionStock(asset:Equity(2452 [300682.SZA]), amount:150.0, cost_basis:19.1995, last_sale_price:28.690000534057617)

[2022-11-19 14:58:53.179192] INFO: Position: returning cash: 10.0002

[2022-11-19 14:58:53.304254] INFO: algo: handle_splits get splits [dt:2020-05-11 00:00:00+00:00] [asset:Equity(5154 [002555.SZA]), ratio:0.9920969605445862]

[2022-11-19 14:58:53.307107] INFO: Position: position stock handle split[sid:5154, orig_amount:100, new_amount:100.0, orig_cost:38.22999954231322, new_cost:37.9279, ratio:0.9920969605445862, last_sale_price:37.65999984741211]

[2022-11-19 14:58:53.309639] INFO: Position: after split: PositionStock(asset:Equity(5154 [002555.SZA]), amount:100.0, cost_basis:37.9279, last_sale_price:37.959999084472656)

[2022-11-19 14:58:53.311866] INFO: Position: returning cash: 29.9999

[2022-11-19 14:58:53.881573] INFO: algo: handle_splits get splits [dt:2020-05-18 00:00:00+00:00] [asset:Equity(3923 [000672.SZA]), ratio:0.9675537943840027]

[2022-11-19 14:58:53.884211] INFO: Position: position stock handle split[sid:3923, orig_amount:100, new_amount:103.0, orig_cost:27.61999893197195, new_cost:26.7238, ratio:0.9675537943840027, last_sale_price:26.540000915527344]

[2022-11-19 14:58:53.887487] INFO: Position: after split: PositionStock(asset:Equity(3923 [000672.SZA]), amount:103.0, cost_basis:26.7238, last_sale_price:27.43000030517578)

[2022-11-19 14:58:53.890046] INFO: Position: returning cash: 9.3799

[2022-11-19 14:58:54.168219] INFO: algo: handle_splits get splits [dt:2020-05-20 00:00:00+00:00] [asset:Equity(3783 [002607.SZA]), ratio:0.9916289448738098]

[2022-11-19 14:58:54.170685] INFO: Position: position stock handle split[sid:3783, orig_amount:100, new_amount:100.0, orig_cost:28.29999923757006, new_cost:28.0631, ratio:0.9916289448738098, last_sale_price:28.430002212524414]

[2022-11-19 14:58:54.173159] INFO: Position: after split: PositionStock(asset:Equity(3783 [002607.SZA]), amount:100.0, cost_basis:28.0631, last_sale_price:28.670000076293945)

[2022-11-19 14:58:54.175563] INFO: Position: returning cash: 23.9998

[2022-11-19 14:58:54.887439] INFO: algo: handle_splits get splits [dt:2020-05-29 00:00:00+00:00] [asset:Equity(2163 [002234.SZA]), ratio:0.9733926057815552]

[2022-11-19 14:58:54.889565] INFO: Position: position stock handle split[sid:2163, orig_amount:200, new_amount:205.0, orig_cost:22.750001912820977, new_cost:22.1447, ratio:0.9733926057815552, last_sale_price:21.95000457763672]

[2022-11-19 14:58:54.891793] INFO: Position: after split: PositionStock(asset:Equity(2163 [002234.SZA]), amount:205.0, cost_basis:22.1447, last_sale_price:22.55000114440918)

[2022-11-19 14:58:54.894653] INFO: Position: returning cash: 10.2493

[2022-11-19 14:58:55.527716] INFO: algo: handle_splits get splits [dt:2020-06-04 00:00:00+00:00] [asset:Equity(1941 [300417.SZA]), ratio:0.581839919090271]

[2022-11-19 14:58:55.532336] INFO: Position: position stock handle split[sid:1941, orig_amount:100, new_amount:171.0, orig_cost:33.959999094600946, new_cost:19.7593, ratio:0.581839919090271, last_sale_price:19.479999542236328]

[2022-11-19 14:58:55.535436] INFO: Position: after split: PositionStock(asset:Equity(1941 [300417.SZA]), amount:171.0, cost_basis:19.7593, last_sale_price:33.47999954223633)

[2022-11-19 14:58:55.538059] INFO: Position: returning cash: 16.9199

[2022-11-19 14:58:56.178206] INFO: algo: handle_splits get splits [dt:2020-06-11 00:00:00+00:00] [asset:Equity(766 [300498.SZA]), ratio:0.8027135133743286]

[2022-11-19 14:58:56.180064] INFO: Position: position stock handle split[sid:766, orig_amount:100, new_amount:124.0, orig_cost:27.500000000033538, new_cost:22.0746, ratio:0.8027135133743286, last_sale_price:21.889997482299805]

[2022-11-19 14:58:56.181750] INFO: Position: after split: PositionStock(asset:Equity(766 [300498.SZA]), amount:124.0, cost_basis:22.0746, last_sale_price:27.270000457763672)

[2022-11-19 14:58:56.183401] INFO: Position: returning cash: 12.6403

[2022-11-19 14:58:56.895866] INFO: algo: handle_splits get splits [dt:2020-06-18 00:00:00+00:00] [asset:Equity(4738 [002605.SZA]), ratio:0.9887735843658447]

[2022-11-19 14:58:56.899451] INFO: Position: position stock handle split[sid:4738, orig_amount:100, new_amount:101.0, orig_cost:36.39999771128623, new_cost:35.9914, ratio:0.9887735843658447, last_sale_price:35.230003356933594]

[2022-11-19 14:58:56.901538] INFO: Position: after split: PositionStock(asset:Equity(4738 [002605.SZA]), amount:101.0, cost_basis:35.9914, last_sale_price:35.630001068115234)

[2022-11-19 14:58:56.903919] INFO: Position: returning cash: 4.7697

[2022-11-19 14:58:58.703562] INFO: algo: handle_splits get splits [dt:2020-07-09 00:00:00+00:00] [asset:Equity(1125 [600556.SHA]), ratio:0.9990323781967163]

[2022-11-19 14:58:58.705752] INFO: Position: position stock handle split[sid:1125, orig_amount:200, new_amount:200.0, orig_cost:18.649997711243287, new_cost:18.632, ratio:0.9990323781967163, last_sale_price:20.64999771118164]

[2022-11-19 14:58:58.707865] INFO: Position: after split: PositionStock(asset:Equity(1125 [600556.SHA]), amount:200.0, cost_basis:18.632, last_sale_price:20.669998168945312)

[2022-11-19 14:58:58.709637] INFO: Position: returning cash: 4.0001

[2022-11-19 14:58:59.398834] WARNING: Performance: maybe_close_position no price for asset:Equity(2156 [002604.SZA]), field:price, dt:2020-07-15 15:00:00+00:00

[2022-11-19 14:58:59.674313] INFO: algo: handle_splits get splits [dt:2020-07-20 00:00:00+00:00] [asset:Equity(4486 [000048.SZA]), ratio:0.9712644219398499]

[2022-11-19 14:58:59.676318] INFO: Position: position stock handle split[sid:4486, orig_amount:200, new_amount:205.0, orig_cost:25.489997897492866, new_cost:24.7575, ratio:0.9712644219398499, last_sale_price:25.35000228881836]

[2022-11-19 14:58:59.678011] INFO: Position: after split: PositionStock(asset:Equity(4486 [000048.SZA]), amount:205.0, cost_basis:24.7575, last_sale_price:26.100000381469727)

[2022-11-19 14:58:59.679657] INFO: Position: returning cash: 23.2497

[2022-11-19 14:58:59.908536] WARNING: Performance: maybe_close_position no price for asset:Equity(153 [300104.SZA]), field:price, dt:2020-07-21 15:00:00+00:00

[2022-11-19 14:59:03.159513] INFO: algo: handle_splits get splits [dt:2020-08-24 00:00:00+00:00] [asset:Equity(1502 [603988.SHA]), ratio:0.9882484674453735]

[2022-11-19 14:59:03.162020] INFO: Position: position stock handle split[sid:1502, orig_amount:200, new_amount:202.0, orig_cost:18.29999924261452, new_cost:18.0849, ratio:0.9882484674453735, last_sale_price:17.660001754760742]

[2022-11-19 14:59:03.164353] INFO: Position: after split: PositionStock(asset:Equity(1502 [603988.SHA]), amount:202.0, cost_basis:18.0849, last_sale_price:17.8700008392334)

[2022-11-19 14:59:03.168558] INFO: Position: returning cash: 6.68

[2022-11-19 14:59:06.279744] INFO: algo: handle_splits get splits [dt:2020-09-23 00:00:00+00:00] [asset:Equity(72 [002458.SZA]), ratio:0.9923076629638672]

[2022-11-19 14:59:06.281816] INFO: Position: position stock handle split[sid:72, orig_amount:300, new_amount:302.0, orig_cost:13.049999239428479, new_cost:12.9496, ratio:0.9923076629638672, last_sale_price:12.899999618530273]

[2022-11-19 14:59:06.283835] INFO: Position: after split: PositionStock(asset:Equity(72 [002458.SZA]), amount:302.0, cost_basis:12.9496, last_sale_price:13.0)

[2022-11-19 14:59:06.285465] INFO: Position: returning cash: 4.2001

[2022-11-19 14:59:06.402641] INFO: algo: handle_splits get splits [dt:2020-09-24 00:00:00+00:00] [asset:Equity(425 [002838.SZA]), ratio:0.9916915893554688]

[2022-11-19 14:59:06.404533] INFO: Position: position stock handle split[sid:425, orig_amount:100, new_amount:100.0, orig_cost:30.36999893234706, new_cost:30.1177, ratio:0.9916915893554688, last_sale_price:29.84000015258789]

[2022-11-19 14:59:06.406304] INFO: Position: after split: PositionStock(asset:Equity(425 [002838.SZA]), amount:100.0, cost_basis:30.1177, last_sale_price:30.09000015258789)

[2022-11-19 14:59:06.408384] INFO: Position: returning cash: 25.0

[2022-11-19 14:59:14.259895] INFO: Performance: Simulated 243 trading days out of 243.

[2022-11-19 14:59:14.263412] INFO: Performance: first open: 2020-01-02 09:30:00+00:00

[2022-11-19 14:59:14.265259] INFO: Performance: last close: 2020-12-31 15:00:00+00:00

[2022-11-19 14:59:24.489361] INFO: moduleinvoker: backtest.v8 运行完成[64.493029s].

[2022-11-19 14:59:24.494372] INFO: moduleinvoker: trade.v4 运行完成[71.519969s].

收益率-19.03%
年化收益率-19.66%
基准收益率27.21%
阿尔法-0.27
贝塔0.35
夏普比率-1.19
胜率0.47
盈亏比0.94
收益波动率19.36%
信息比率-0.13
最大回撤28.91%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3d8949f779ab4950a55d72280b6e5302"}/bigcharts-data-end