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雷达卡
用线性-分类算法实现选股，详细代码如下：
本应用利用BigQuant平台实现，具体可以直接复制到平台。
- # 回测引擎：初始化函数，只执行一次
- def m19_initialize_bigquant_run(context):
- # 加载预测数据
- context.predictions = 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 = 5
- # 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 = [1/stock_count for k in range(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 m19_handle_data_bigquant_run(context, data):
- # 按日期过滤得到今日的预测数据
- daily_prediction = context.predictions[
- context.predictions.date == data.current_dt.strftime('%Y-%m-%d')]
- # 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
- equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
- if equities!={}:
- instruments = [k for k in equities if daily_prediction[daily_prediction.instrument==k].classes_prob_0.values[0]>0.5]
- for instrument in instruments:
- context.order_target(context.symbol(instrument), 0)
- # 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(daily_prediction[daily_prediction.classes_prob_1>0.5].instrument[:len(buy_cash_weights)])
- for i, instrument in enumerate(buy_instruments):
- cash = context.portfolio.cash * buy_cash_weights[i]
- context.order_value(context.symbol(instrument), cash)
- # 回测引擎：准备数据，只执行一次
- def m19_prepare_bigquant_run(context):
- pass
- m1 = M.instruments.v2(
- start_date='2010-01-01',
- end_date='2011-01-01',
- market='CN_STOCK_A',
- instrument_list='',
- max_count=0
- )
- m2 = M.advanced_auto_labeler.v2(
- instruments=m1.data,
- label_expr="""# #号开始的表示注释
- # 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
- # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
- # 添加benchmark_前缀，可使用对应的benchmark数据
- # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
- # 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
- where(shift(close, -5) / shift(open, -1)>0,1,0)
- """,
- start_date='',
- end_date='',
- benchmark='000300.SHA',
- drop_na_label=True,
- cast_label_int=True
- )
- m3 = M.input_features.v1(
- features="""# #号开始的表示注释
- # 多个特征，每行一个，可以包含基础特征和衍生特征
- return_5
- return_10
- return_20
- avg_amount_0/avg_amount_5
- avg_amount_5/avg_amount_20
- rank_avg_amount_0/rank_avg_amount_5
- rank_avg_amount_5/rank_avg_amount_10
- rank_return_0
- rank_return_5
- rank_return_10
- rank_return_0/rank_return_5
- rank_return_5/rank_return_10
- pe_ttm_0
- """
- )
- m15 = M.general_feature_extractor.v7(
- instruments=m1.data,
- features=m3.data,
- start_date='',
- end_date='',
- before_start_days=0
- )
- m16 = M.derived_feature_extractor.v3(
- input_data=m15.data,
- features=m3.data,
- date_col='date',
- instrument_col='instrument',
- drop_na=False,
- remove_extra_columns=False
- )
- m7 = M.join.v3(
- data1=m2.data,
- data2=m16.data,
- on='date,instrument',
- how='inner',
- sort=False
- )
- m13 = M.dropnan.v1(
- input_data=m7.data
- )
- m9 = M.instruments.v2(
- start_date=T.live_run_param('trading_date', '2015-01-01'),
- end_date=T.live_run_param('trading_date', '2015-05-01'),
- market='CN_STOCK_A',
- instrument_list='',
- max_count=0
- )
- m17 = M.general_feature_extractor.v7(
- instruments=m9.data,
- features=m3.data,
- start_date='',
- end_date='',
- before_start_days=0
- )
- m18 = M.derived_feature_extractor.v3(
- input_data=m17.data,
- features=m3.data,
- date_col='date',
- instrument_col='instrument',
- drop_na=False,
- remove_extra_columns=False
- )
- m14 = M.dropnan.v1(
- input_data=m18.data
- )
- m4 = M.kneighbors_classifier.v1(
- training_ds=m13.data,
- features=m3.data,
- predict_ds=m14.data,
- n_neighbors=5,
- weights='uniform',
- algorithm='auto',
- leaf_size=30,
- metric='minkowski',
- key_cols='date,instrument',
- workers=1,
- other_train_parameters={}
- )
- m19 = M.trade.v4(
- instruments=m9.data,
- options_data=m4.predictions,
- start_date='',
- end_date='',
- initialize=m19_initialize_bigquant_run,
- handle_data=m19_handle_data_bigquant_run,
- prepare=m19_prepare_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='后复权',
- product_type='股票',
- plot_charts=True,
- backtest_only=False,
- benchmark='000300.SHA'
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