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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 # 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.portfolio.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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":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 = 5\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'] = 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Outputs(data_1=data_1)\n","ValueType":"Literal","LinkedGlobalParameter":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","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"input_1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{\n 'N': 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[2019-03-04 21:13:00.455481] INFO: bigquant: instruments.v2 开始运行..
[2019-03-04 21:13:00.462917] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.464463] INFO: bigquant: instruments.v2 运行完成[0.008984s].
[2019-03-04 21:13:00.468210] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
[2019-03-04 21:13:00.474532] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.476384] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.008169s].
[2019-03-04 21:13:00.479292] INFO: bigquant: input_features.v1 开始运行..
[2019-03-04 21:13:00.486145] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.487641] INFO: bigquant: input_features.v1 运行完成[0.008346s].
[2019-03-04 21:13:00.496190] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-03-04 21:13:00.503530] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.504910] INFO: bigquant: general_feature_extractor.v7 运行完成[0.00872s].
[2019-03-04 21:13:00.508034] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-03-04 21:13:00.513708] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.514985] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.006963s].
[2019-03-04 21:13:00.520559] INFO: bigquant: cached.v3 开始运行..
[2019-03-04 21:13:00.526124] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.527696] INFO: bigquant: cached.v3 运行完成[0.007131s].
[2019-03-04 21:13:00.530527] INFO: bigquant: instruments.v2 开始运行..
[2019-03-04 21:13:00.536581] INFO: bigquant: 命中缓存
[2019-03-04 21:13:00.537941] INFO: bigquant: instruments.v2 运行完成[0.007408s].
[2019-03-04 21:13:00.543890] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-03-04 21:13:02.822150] INFO: 基础特征抽取: 年份 2017, 特征行数=90630
[2019-03-04 21:13:05.139529] INFO: 基础特征抽取: 年份 2018, 特征行数=816987
[2019-03-04 21:13:05.433983] INFO: 基础特征抽取: 年份 2019, 特征行数=135421
[2019-03-04 21:13:05.456965] INFO: 基础特征抽取: 总行数: 1043038
[2019-03-04 21:13:05.461393] INFO: bigquant: general_feature_extractor.v7 运行完成[4.917493s].
[2019-03-04 21:13:05.464685] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-03-04 21:13:06.162338] INFO: general_feature_extractor: 提取完成 amount_0/deal_number_0, 0.005s
[2019-03-04 21:13:06.168474] INFO: general_feature_extractor: 提取完成 high_0/low_0, 0.004s
[2019-03-04 21:13:06.269672] INFO: general_feature_extractor: /y_2017, 90630
[2019-03-04 21:13:06.489065] INFO: general_feature_extractor: /y_2018, 816987
[2019-03-04 21:13:07.061414] INFO: general_feature_extractor: /y_2019, 135421
[2019-03-04 21:13:07.186475] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.721767s].
[2019-03-04 21:13:07.193601] INFO: bigquant: cached.v3 开始运行..
[2019-03-04 21:14:49.908546] INFO: bigquant: cached.v3 运行完成[102.714939s].
[2019-03-04 21:14:49.911778] INFO: bigquant: input_features.v1 开始运行..
[2019-03-04 21:14:49.917222] INFO: bigquant: 命中缓存
[2019-03-04 21:14:49.918583] INFO: bigquant: input_features.v1 运行完成[0.006802s].
[2019-03-04 21:14:49.921243] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-03-04 21:14:50.246435] INFO: general_feature_extractor: 提取完成 M_high-M_low, 0.004s
[2019-03-04 21:14:50.517684] INFO: general_feature_extractor: /data, 1043038
[2019-03-04 21:14:51.298537] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.377279s].
[2019-03-04 21:14:51.302174] INFO: bigquant: dropnan.v1 开始运行..
[2019-03-04 21:14:52.239643] INFO: dropnan: /data, 974832/1043038
[2019-03-04 21:14:52.264528] INFO: dropnan: 行数: 974832/1043038
[2019-03-04 21:14:52.311534] INFO: bigquant: dropnan.v1 运行完成[1.009347s].
[2019-03-04 21:14:52.314700] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-03-04 21:14:53.534912] INFO: general_feature_extractor: 提取完成 M_high-M_low, 0.011s
[2019-03-04 21:14:54.262990] INFO: general_feature_extractor: /data, 1954477
[2019-03-04 21:14:55.767063] INFO: bigquant: derived_feature_extractor.v3 运行完成[3.452293s].
[2019-03-04 21:14:55.770986] INFO: bigquant: join.v3 开始运行..
[2019-03-04 21:15:00.644274] INFO: join: /data, 行数=1919027/1954477, 耗时=3.471797s
[2019-03-04 21:15:00.887898] INFO: join: 最终行数: 1919027
[2019-03-04 21:15:00.891642] INFO: bigquant: join.v3 运行完成[5.120647s].
[2019-03-04 21:15:00.894727] INFO: bigquant: dropnan.v1 开始运行..
[2019-03-04 21:15:03.331397] INFO: dropnan: /data, 1862934/1919027
[2019-03-04 21:15:03.678394] INFO: dropnan: 行数: 1862934/1919027
[2019-03-04 21:15:03.783786] INFO: bigquant: dropnan.v1 运行完成[2.889044s].
[2019-03-04 21:15:03.789544] INFO: bigquant: stock_ranker_train.v5 开始运行..
[2019-03-04 21:15:04.424133] INFO: StockRanker: 特征预处理 ..
[2019-03-04 21:15:04.659584] INFO: StockRanker: prepare data: training ..
[2019-03-04 21:15:05.082711] INFO: StockRanker: sort ..
[2019-03-04 21:15:20.653960] INFO: StockRanker训练: 85cb0ab0 准备训练: 1862934 行数
[2019-03-04 21:15:20.675383] INFO: StockRanker训练: 正在训练 ..
[2019-03-04 21:19:23.779793] INFO: bigquant: stock_ranker_train.v5 运行完成[259.99024s].
[2019-03-04 21:19:23.783348] INFO: bigquant: stock_ranker_predict.v5 开始运行..
[2019-03-04 21:19:24.376599] INFO: StockRanker: prepare data: prediction ..
[2019-03-04 21:19:32.239614] INFO: stock_ranker_predict: 准备预测: 974832 行
[2019-03-04 21:19:32.245512] INFO: stock_ranker_predict: 正在预测 ..
[2019-03-04 21:28:18.759319] INFO: bigquant: stock_ranker_predict.v5 运行完成[534.975942s].
[2019-03-04 21:28:18.783207] INFO: bigquant: backtest.v8 开始运行..
[2019-03-04 21:28:18.786261] INFO: bigquant: biglearning backtest:V8.1.11
[2019-03-04 21:28:18.788101] INFO: bigquant: product_type:stock by specified
[2019-03-04 21:28:27.867982] INFO: bigquant: 读取股票行情完成:1791327
[2019-03-04 21:28:47.179898] INFO: algo: TradingAlgorithm V1.4.7
[2019-03-04 21:28:59.009171] INFO: algo: trading transform...
[2019-03-04 21:29:07.417244] INFO: Performance: Simulated 281 trading days out of 281.
[2019-03-04 21:29:07.418930] INFO: Performance: first open: 2018-01-02 09:30:00+00:00
[2019-03-04 21:29:07.420387] INFO: Performance: last close: 2019-03-01 15:00:00+00:00
[2019-03-04 21:29:10.085246] INFO: bigquant: backtest.v8 运行完成[51.302034s].
- 收益率8.06%
- 年化收益率7.2%
- 基准收益率-6.97%
- 阿尔法0.14
- 贝塔0.89
- 夏普比率0.28
- 胜率0.52
- 盈亏比1.02
- 收益波动率28.62%
- 信息比率0.05
- 最大回撤33.74%