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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# print(today,\"cash_avg=\",cash_avg,\"context.portfolio.cash=\",context.portfolio.cash,\"cash_for_buy=\",cash_for_buy,\"cash_for_sell=\",cash_for_sell)\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","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 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bigquant_run(bq_graph, inputs):\n g = bq_graph\n #原始因子\n features = [\n 'std(close_0, 5)',\n 'std(low_0, 5)',\n 'std(turn_0, 5)',\n 'std(return_0, 5)',\n 'return_5',\n 'avg_turn_5',\n 'avg_amount_0/avg_amount_5',\n 'rank_avg_amount_0/rank_avg_amount_5',\n 'rank_return_0',\n 'rank_return_5',\n 'rank_return_0/rank_return_5',\n 'pe_ttm_0',\n 'rank_fs_roe_0',\n 'mf_net_amount_5'\n ]\n \n\n parameters_list = []\n \n i = 0\n #循环减因子\n for feature in features: \n print(i,\" ------------ \",feature)\n parameters = {}\n temp = []\n temp = list(set(features) - set([feature]))\n parameters['m3.features'] = '\\n'.join(temp)\n parameters_list.append({'parameters': parameters})\n i+=1\n\n \n print( parameters_list)\n print('==='*20, len(parameters_list))\n \n def run(parameters):\n try:\n print(parameters)\n result = g.run(parameters)\n return result\n except Exception as e:\n print('ERROR --------', e)\n return None\n\n results = T.parallel_map(run, parameters_list, max_workers=4,remote_run=True, silent=True,backend='threading')\n \n return results\n","type":"Literal","bound_global_parameter":null},{"name":"run_now","value":"True","type":"Literal","bound_global_parameter":null},{"name":"bq_graph","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"bq_graph_port","node_id":"-1389"},{"name":"input_1","node_id":"-1389"},{"name":"input_2","node_id":"-1389"},{"name":"input_3","node_id":"-1389"}],"output_ports":[{"name":"result","node_id":"-1389"}],"cacheable":false,"seq_num":11,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='327,104,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='231,206,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='721,22,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='688,505,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='393,329,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='851,566,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='429,388,200,200'/><node_position Node='-86' Position='1073,379,200,200'/><node_position Node='-215' Position='521,184,200,200'/><node_position Node='-222' Position='499,269,200,200'/><node_position Node='-231' Position='1078,236,200,200'/><node_position Node='-238' Position='1076,308,200,200'/><node_position Node='-250' Position='849,632,200,200'/><node_position Node='-1237' Position='458,460,200,200'/><node_position Node='-1597' Position='1077,441,200,200'/><node_position Node='-1389' Position='419,573,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
[2022-12-29 17:35:50.968023] WARNING: AI: 当前可运行1个高级AI任务,购买资源获取更多高级AI任务位[url="https://bigquant.com/account/big_member/?from=navigation" style="display: inline-block;padding: 5px 7px;border-radius: 2px;background: #F0BC41;color: white"]购买高级AI任务位[/url]
[2022-12-29 17:35:50.969592] INFO: AI: 开始并行运算, remote_run=True, workers=1 ..
[2022-12-29 17:35:50.971056] INFO: AI: [ParallelEx(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[2022-12-29 17:35:51.172431] INFO: cached.v2.2ec9bbb0: 任务状态: Pending
[2022-12-29 17:36:01.209895] INFO: cached.v2.2ec9bbb0: 任务状态: Running
[2022-12-29 17:36:31.349706] INFO: cached.v2.2ec9bbb0: 任务状态: Succeeded
[2022-12-29 17:36:31.357653] INFO: AI: [ParallelEx(n_jobs=1)]: Done 1 out of 1 | elapsed: 40.4s remaining: 0.0s
[2022-12-29 17:36:31.611805] INFO: cached.v2.46dd0dce: 任务状态: Pending
[2022-12-29 17:36:41.644994] INFO: cached.v2.46dd0dce: 任务状态: Running
[2022-12-29 17:37:01.719062] INFO: cached.v2.46dd0dce: 任务状态: Succeeded
[2022-12-29 17:37:01.725073] INFO: AI: [ParallelEx(n_jobs=1)]: Done 2 out of 2 | elapsed: 1.2min remaining: 0.0s
[2022-12-29 17:37:02.117392] INFO: cached.v2.58f8ab3a: 任务状态: Pending
[2022-12-29 17:37:12.175330] INFO: cached.v2.58f8ab3a: 任务状态: Running
[2022-12-29 17:37:32.269838] INFO: cached.v2.58f8ab3a: 任务状态: Succeeded
[2022-12-29 17:37:32.273922] INFO: AI: [ParallelEx(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.7min remaining: 0.0s
[2022-12-29 17:37:32.547339] INFO: cached.v2.6b2fe160: 任务状态: Pending
[2022-12-29 17:37:42.595401] INFO: cached.v2.6b2fe160: 任务状态: Running
[2022-12-29 17:38:22.854464] INFO: cached.v2.6b2fe160: 任务状态: Succeeded
[2022-12-29 17:38:22.860327] INFO: AI: [ParallelEx(n_jobs=1)]: Done 4 out of 4 | elapsed: 2.5min remaining: 0.0s
[2022-12-29 17:38:23.200516] INFO: cached.v2.89529fe8: 任务状态: Pending
[2022-12-29 17:38:33.241907] INFO: cached.v2.89529fe8: 任务状态: Running
[2022-12-29 17:39:03.357773] INFO: cached.v2.89529fe8: 任务状态: Succeeded
[2022-12-29 17:39:03.363366] INFO: AI: [ParallelEx(n_jobs=1)]: Done 5 out of 5 | elapsed: 3.2min remaining: 0.0s
[2022-12-29 17:39:03.648160] INFO: cached.v2.a1769c8c: 任务状态: Pending
[2022-12-29 17:39:13.684149] INFO: cached.v2.a1769c8c: 任务状态: Running
[2022-12-29 17:39:33.762316] INFO: cached.v2.a1769c8c: 任务状态: Succeeded
[2022-12-29 17:39:33.766623] INFO: AI: [ParallelEx(n_jobs=1)]: Done 6 out of 6 | elapsed: 3.7min remaining: 0.0s
[2022-12-29 17:39:34.041438] INFO: cached.v2.b39857c0: 任务状态: Pending
[2022-12-29 17:39:44.102889] INFO: cached.v2.b39857c0: 任务状态: Running
[2022-12-29 17:39:54.151957] INFO: cached.v2.b39857c0: 任务状态: Succeeded
[2022-12-29 17:39:54.158646] INFO: AI: [ParallelEx(n_jobs=1)]: Done 7 out of 7 | elapsed: 4.1min remaining: 0.0s
[2022-12-29 17:39:54.525050] INFO: cached.v2.bfbde1d2: 任务状态: Pending
[2022-12-29 17:40:04.568271] INFO: cached.v2.bfbde1d2: 任务状态: Running
[2022-12-29 17:40:24.670429] INFO: cached.v2.bfbde1d2: 任务状态: Succeeded
[2022-12-29 17:40:24.677042] INFO: AI: [ParallelEx(n_jobs=1)]: Done 8 out of 8 | elapsed: 4.6min remaining: 0.0s
[2022-12-29 17:40:25.019850] INFO: cached.v2.d1ef5f8e: 任务状态: Pending
[2022-12-29 17:40:35.060421] INFO: cached.v2.d1ef5f8e: 任务状态: Running
[2022-12-29 17:41:05.301101] INFO: cached.v2.d1ef5f8e: 任务状态: Succeeded
[2022-12-29 17:41:05.312018] INFO: AI: [ParallelEx(n_jobs=1)]: Done 9 out of 9 | elapsed: 5.2min remaining: 0.0s
[2022-12-29 17:41:05.778681] INFO: cached.v2.ea2ab756: 任务状态: Pending
[2022-12-29 17:41:15.831995] INFO: cached.v2.ea2ab756: 任务状态: Running
[2022-12-29 17:41:35.906366] INFO: cached.v2.ea2ab756: 任务状态: Succeeded
[2022-12-29 17:41:35.910878] INFO: AI: [ParallelEx(n_jobs=1)]: Done 10 out of 10 | elapsed: 5.7min remaining: 0.0s
[2022-12-29 17:41:36.198351] INFO: cached.v2.fc62f2da: 任务状态: Pending
[2022-12-29 17:41:46.244406] INFO: cached.v2.fc62f2da: 任务状态: Running
[2022-12-29 17:42:16.351207] INFO: cached.v2.fc62f2da: 任务状态: Succeeded
[2022-12-29 17:42:16.356170] INFO: AI: [ParallelEx(n_jobs=1)]: Done 11 out of 11 | elapsed: 6.4min remaining: 0.0s
[2022-12-29 17:42:16.680842] INFO: cached.v2.14801686: 任务状态: Pending
[2022-12-29 17:42:26.715711] INFO: cached.v2.14801686: 任务状态: Running
[2022-12-29 17:42:56.829510] INFO: cached.v2.14801686: 任务状态: Succeeded
[2022-12-29 17:42:56.835180] INFO: AI: [ParallelEx(n_jobs=1)]: Done 12 out of 12 | elapsed: 7.1min remaining: 0.0s
[2022-12-29 17:42:57.140659] INFO: cached.v2.2ca4abd2: 任务状态: Pending
[2022-12-29 17:43:07.180670] INFO: cached.v2.2ca4abd2: 任务状态: Running
0 ------------ std(close_0, 5)
1 ------------ std(low_0, 5)
2 ------------ std(turn_0, 5)
3 ------------ std(return_0, 5)
4 ------------ return_5
5 ------------ avg_turn_5
6 ------------ avg_amount_0/avg_amount_5
7 ------------ rank_avg_amount_0/rank_avg_amount_5
8 ------------ rank_return_0
9 ------------ rank_return_5
10 ------------ rank_return_0/rank_return_5
11 ------------ pe_ttm_0
12 ------------ rank_fs_roe_0
13 ------------ mf_net_amount_5
[{'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'rank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}]
============================================================ 14