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雷达卡
用支持向量机-回归算法实现A股股票选股
- # 回测引擎：初始化函数，只执行一次
- def m6_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 = 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 = 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.hold_days = 5
- # 回测引擎：每日数据处理函数，每天执行一次
- def m6_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')]
- # 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.hold_days # 是否在建仓期间（前 hold_days 天）
- cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m6_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 m6_before_trading_start_bigquant_run(context, data):
- pass
- m1 = M.instruments.v2(
- start_date='2014-01-01',
- end_date='2015-01-01',
- 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)
- shift(close, -5) / shift(open, -1)
- # 极值处理：用1%和99%分位的值做clip
- clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
- # 将分数映射到分类，这里使用20个分类
- all_wbins(label, 20)
- # 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
- where(shift(high, -1) == shift(low, -1), NaN, label)
- """,
- 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', '2016-01-01'),
- 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.mlp_regressor.v1(
- training_ds=m13.data,
- features=m3.data,
- predict_ds=m14.data,
- hidden_layer_sizes='100',
- activation='relu',
- solver='adam',
- alpha=0.0001,
- batch_size=200,
- learning_rate_init=0.001,
- max_iter=200,
- key_cols='date,instrument',
- other_train_parameters={}
- )
- m6 = M.trade.v4(
- instruments=m9.data,
- options_data=m4.predictions,
- start_date='',
- end_date='',
- initialize=m6_initialize_bigquant_run,
- handle_data=m6_handle_data_bigquant_run,
- prepare=m6_prepare_bigquant_run,
- before_trading_start=m6_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='后复权',
- product_type='股票',
- plot_charts=True,
- backtest_only=False,
- benchmark=''
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