使用stockranker创建的AI策略是每天执行 小白请教如何改成多天执行一次


(shinefuture) #1

每天操作不太实际,比较不是专职炒股,持有多天,一次卖出,再次买入比较合适
想对生成的代码修改,但是基础不行,无从下手,有没有人可以指点下,谢谢

# 回测引擎:每日数据处理函数,每天执行一次
def handle_data(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.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)

(小Q) #2

可以参考下这个策略。

固定一个礼拜调仓,每次调仓的时候卖出所有持仓,然后根据当天的股票排序结果买入股票。持有至下礼拜再重复这样的调仓动作。

克隆策略
In [4]:
# 基础参数配置
class conf:
    start_date = '2010-01-01'
    end_date='2017-01-01'
    # split_date 之前的数据用于训练,之后的数据用作效果评估
    split_date = '2015-01-01'
    # D.instruments: https://bigquant.com/docs/data_instruments.html
    instruments = D.instruments(start_date, split_date)

    # 机器学习目标标注函数
    # 如下标注函数等价于 min(max((持有期间的收益 * 100), -20), 20) + 20 (后面的M.fast_auto_labeler会做取整操作)
    # 说明:max/min这里将标注分数限定在区间[-20, 20],+20将分数变为非负数 (StockRanker要求标注分数非负整数)
    label_expr = ['return * 100', 'where(label > {0}, {0}, where(label < -{0}, -{0}, label)) + {0}'.format(20)]
    # 持有天数,用于计算label_expr中的return值(收益)
    hold_days = 7

    # 特征 https://bigquant.com/docs/data_features.html,你可以通过表达式构造任何特征
    features = [
       'market_cap_0',
    ]

# 给数据做标注:给每一行数据(样本)打分,一般分数越高表示越好
m1 = M.fast_auto_labeler.v5(
    instruments=conf.instruments, start_date=conf.start_date, end_date=conf.split_date,
    label_expr=conf.label_expr, hold_days=conf.hold_days,
    benchmark='000300.SHA', sell_at='open', buy_at='open')
# 计算特征数据
m2 = M.general_feature_extractor.v5(
    instruments=conf.instruments, start_date=conf.start_date, end_date=conf.split_date,
    features=conf.features)
# 数据预处理:缺失数据处理,数据规范化,T.get_stock_ranker_default_transforms为StockRanker模型做数据预处理
m3 = M.transform.v2(
    data=m2.data, transforms=T.get_stock_ranker_default_transforms(),
    drop_null=True, astype='int32', except_columns=['date', 'instrument'],
    clip_lower=0, clip_upper=200000000)
# 合并标注和特征数据
m4 = M.join.v2(data1=m1.data, data2=m3.data, on=['date', 'instrument'], sort=True)
# StockRanker机器学习训练
m5 = M.stock_ranker_train.v2(training_ds=m4.data, features=conf.features)


# prepare data
def prepare(context):
    # 计算预测数据
    # context.start_date / end_date,回测的时候,为trader传入参数;在实盘运行的时候,由系统替换为实盘日期
    n1 = M.general_feature_extractor.v5(
        instruments=D.instruments(start_date=context.start_date, end_date=context.end_date), start_date=context.start_date, end_date=context.end_date,
        model_id=context.options['model_id'])
    n2 = M.transform.v2(
        data=n1.data, transforms=T.get_stock_ranker_default_transforms(),
        drop_null=True, astype='int32', except_columns=['date', 'instrument'],
        clip_lower=0, clip_upper=200000000)
    n3 = M.stock_ranker_predict.v2(model_id=context.options['model_id'], data=n2.data)
    context.ranker_prediction = n3.predictions.read_df()
    # context.instruments:设置回测/实盘运行时需要的股票代码
    context.instruments = n3.instruments
    
## 量化回测 https://bigquant.com/docs/strategy_backtest.html
# 回测引擎:初始化函数,只执行一次
def initialize(context):
    
     # 调仓规则(每个礼拜调用一次rebalance)
    context.schedule_function(rebalance, date_rule=date_rules.week_start(days_offset=0))         

    # 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 = 0.6

# handle_data 函数就是空函数   
def handle_data(context, data):
    pass

# 换仓函数(本例的换仓规则是,每一个礼拜调整一下持仓,调整日卖出所有持仓,再根据当日的预测股票排序结果,按权重依次买入股票)
def rebalance(context, data):
    positions = context.perf_tracker.position_tracker.positions
    
    # 换仓日期卖出所有持仓股票
    for e, p in positions.items():
            context.order_target(context.symbol(e.symbol), 0)
    
    # 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')]
    
    # 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 = context.portfolio.portfolio_value * 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)
            
# 调用交易引擎
m6 = M.trade.v1(
    instruments=None,
    start_date=conf.split_date,
    end_date=conf.end_date,
    volume_limit=0,
    prepare=prepare,
    initialize=initialize,
    handle_data=handle_data,
    order_price_field_buy='open',       # 表示 开盘 时买入
    order_price_field_sell='close',     # 表示 收盘 前卖出
    capital_base=1000000,               # 初始资金
    benchmark='000300.SHA',             # 比较基准,不影响回测结果
    # 通过 options 参数传递预测数据和参数给回测引擎
    options={'hold_days': conf.hold_days, 'model_id': m5.model_id}
)
[2017-07-05 10:58:06.837319] INFO: bigquant: fast_auto_labeler.v5 start ..
[2017-07-05 10:58:06.839517] INFO: bigquant: hit cache
[2017-07-05 10:58:06.843742] INFO: bigquant: fast_auto_labeler.v5 end [0.006445s].
[2017-07-05 10:58:06.848120] INFO: bigquant: general_feature_extractor.v5 start ..
[2017-07-05 10:58:06.850173] INFO: bigquant: hit cache
[2017-07-05 10:58:06.851291] INFO: bigquant: general_feature_extractor.v5 end [0.00317s].
[2017-07-05 10:58:06.857431] INFO: bigquant: transform.v2 start ..
[2017-07-05 10:58:06.859706] INFO: bigquant: hit cache
[2017-07-05 10:58:06.861239] INFO: bigquant: transform.v2 end [0.00381s].
[2017-07-05 10:58:06.866283] INFO: bigquant: join.v2 start ..
[2017-07-05 10:58:06.868107] INFO: bigquant: hit cache
[2017-07-05 10:58:06.869019] INFO: bigquant: join.v2 end [0.002712s].
[2017-07-05 10:58:06.873889] INFO: bigquant: stock_ranker_train.v2 start ..
[2017-07-05 10:58:06.875759] INFO: bigquant: hit cache
[2017-07-05 10:58:06.876889] INFO: bigquant: stock_ranker_train.v2 end [0.003023s].
[2017-07-05 10:58:06.892758] INFO: bigquant: backtest.v6 start ..
[2017-07-05 10:58:07.021686] INFO: bigquant: general_feature_extractor.v5 start ..
[2017-07-05 10:58:07.023804] INFO: bigquant: hit cache
[2017-07-05 10:58:07.024697] INFO: bigquant: general_feature_extractor.v5 end [0.003017s].
[2017-07-05 10:58:07.029467] INFO: bigquant: transform.v2 start ..
[2017-07-05 10:58:07.030957] INFO: bigquant: hit cache
[2017-07-05 10:58:07.031723] INFO: bigquant: transform.v2 end [0.002253s].
[2017-07-05 10:58:07.034968] INFO: bigquant: stock_ranker_predict.v2 start ..
[2017-07-05 10:58:07.039464] INFO: bigquant: hit cache
[2017-07-05 10:58:07.040639] INFO: bigquant: stock_ranker_predict.v2 end [0.005664s].
[2017-07-05 10:58:42.474283] INFO: Performance: Simulated 488 trading days out of 488.
[2017-07-05 10:58:42.475622] INFO: Performance: first open: 2015-01-05 14:30:00+00:00
[2017-07-05 10:58:42.477045] INFO: Performance: last close: 2016-12-30 20:00:00+00:00
  • 收益率288.7%
  • 年化收益率101.59%
  • 基准收益率-6.33%
  • 阿尔法1.05
  • 贝塔0.95
  • 夏普比率1.71
  • 收益波动率57.47%
  • 信息比率2.15
  • 最大回撤63.57%
[2017-07-05 10:58:44.303473] INFO: bigquant: backtest.v6 end [37.410728s].

(shinefuture) #3

非常感谢!