复制链接

克隆策略

In [9]:

# 本代码由可视化策略环境自动生成 2022年12月30日 17:01
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


def m3_initialize_bigquant_run(context):
    msg = "initialize:" 
    context.PRINT = 1
    context.write_log(msg, stdout=context.PRINT)
    print("initializing:")
    context.all_data = context.options["data"].read()
    context.all_data.set_index("instrument", inplace=True)
    
    context.rebalance_period = 22  # 调仓周期设置
    context.long_short = 1  # 多空策略选择: 0为多空策略; 1为纯多头策略
    context.num_trades = 5  # 每层交易品种数量: 若context.long_short为0, 则总共交易标的有2*context.num_trades; 若为1, 则为context.num_trades
    context.mono = True
    context.lev = 1
    
    context.set_slippage_value(slippage_type=SlippageType.PERCENT, slippage_value=0)  # 交易滑点设置
def m3_before_trading_start_bigquant_run(context, data):
    context.today = data.trading_day_dt.strftime('%Y-%m-%d')
    context.today_data = context.all_data[context.all_data.date==context.today]

    if context.trading_day_index % context.rebalance_period == 0:
        # print("check date, ", context.today)
        r1 = context.today_data
        r1.sort_values(by=["alpha0001"], ascending=context.mono, inplace=True)
        context.ins_to_long = r1.index[:context.num_trades].tolist()  # 做多因子排序后靠前的品种
        context.ins_to_short = r1.index[-context.num_trades:].tolist()  # 做空因子排序后靠后的品种

        context.dominant_to_long = context.get_dominant(context.ins_to_long)
        context.dominant_to_short = context.get_dominant(context.ins_to_short)
        if context.trading_day_index == 0:
            context.extension.pre_dominant_to_long = context.dominant_to_long
            context.extension.pre_dominant_to_short = context.dominant_to_short
    else:
        context.ins_to_long = context.extension.pre_ins_to_long
        context.ins_to_short = context.extension.pre_ins_to_short

        context.dominant_to_long = context.get_dominant(context.ins_to_long)
        context.dominant_to_short = context.get_dominant(context.ins_to_short)
    
    context.ins = context.ins_to_long+context.ins_to_short
    if not context.long_short:
        context.cash_per_constract = context.portfolio.portfolio_value / (context.num_trades * 2)
    else:
        context.cash_per_constract = context.portfolio.portfolio_value / context.num_trades
# 交易引擎：tick数据处理函数，每个tick执行一次
def m3_handle_tick_bigquant_run(context, tick):
    pass

def m3_handle_data_bigquant_run(context, data):
    if len(context.today_data) == 0:
        return
    
    ##--------- 调整标的池之后, 未入选新标的池的合约进行平仓处理 ---------##
    if len(context.get_account_positions()) > 0:
        ins_pc = [i.split(".")[0][:-4] for i in context.ins]  # 当前日期交易品种池的product_codes
        for instrument, _ in context.get_account_positions().items():
            pc = instrument.split(".")[0][:-4]
            if pc not in ins_pc:
                price = data.current(instrument, "close")
                rv = context.order_target(instrument, 0, price, order_type=OrderType.MARKET)
    ##--------- 调整标的池之后, 未入选新标的池的合约进行平仓处理 ---------##
    
    # if context.trading_day_index % context.rebalance_period == 0:
    for instrument in context.ins:
        pc = instrument.split(".")[0][:-4]
        dominant_code = [i for i in context.dominant_to_long+context.dominant_to_short if pc == i.split(".")[0][:-4]][0]
        pre_dominant_list = [i for i in context.extension.pre_dominant_to_long+context.extension.pre_dominant_to_short if pc == i.split(".")[0][:-4]]

        if not len(pre_dominant_list):
            pre_dominant_code = dominant_code
        else:
            pre_dominant_code = pre_dominant_list[0]
        
        price = data.current(instrument, "close")  # 合约价格
        multiplier = context.get_contract(instrument).multiplier  # 合约乘数
        long_position = context.get_account_position(pre_dominant_code, direction=Direction.LONG).avail_qty  # 多头持仓
        short_position = context.get_account_position(pre_dominant_code, direction=Direction.SHORT).avail_qty  # 空头持仓
        curr_position = short_position + long_position  # 总持仓
        
        order_num = context.lev * (context.cash_per_constract // (price * multiplier))
        
        if curr_position==0 and context.trading_day_index % context.rebalance_period == 0:
            if not context.long_short:
                if instrument in context.ins_to_long:
                    rv1 = context.order_target(dominant_code, order_num, price, order_type=OrderType.MARKET)
                elif instrument in context.ins_to_short:
                    rv1 = context.order_target(dominant_code, -order_num, price, order_type=OrderType.MARKET)
            else:
                if instrument in context.ins_to_long:
                    rv1 = context.order_target(dominant_code, order_num, price, order_type=OrderType.MARKET)
        else:
            if short_position > 0 and dominant_code != pre_dominant_code:
                rv1 = context.order_target(pre_dominant_code, 0, price, order_type=OrderType.MARKET)
                rv2 = context.order_target(dominant_code, -short_position, price, order_type=OrderType.MARKET)
            elif long_position > 0 and dominant_code != pre_dominant_code:
                rv1 = context.order_target(pre_dominant_code, 0, price, order_type=OrderType.MARKET)
                rv2 = context.order_target(dominant_code, long_position, price, order_type=OrderType.MARKET)
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m3_handle_trade_bigquant_run(context, data):
#     msg = "handle_trade data:{}".format(data.log_str())
#     context.write_log(msg, stdout=context.PRINT)
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m3_handle_order_bigquant_run(context, data):
#     msg = "handle_order data:{}".format(data.log_str())
#     context.write_log(msg, stdout=context.PRINT)
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m3_after_trading_bigquant_run(context, data):
    context.extension.pre_ins_to_long = context.ins_to_long
    context.extension.pre_ins_to_short = context.ins_to_short

    context.extension.pre_dominant_to_long = context.dominant_to_long
    context.extension.pre_dominant_to_short = context.dominant_to_short

m1 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2022-11-30'),
    market='CN_FUTURE',
    instrument_list='',
    max_count=0
)

m10 = M.futures_forward_extractor.v11(
    input_1=m1.data,
    before_days=50,
    product_filter=["BB", "LR", "JR", "FB", "RI",
 "WR", "RS", "PM", "WT", "TC",
 "RO", "ER", "WS", "B", "FU",
 "LU", "SC", "L", "ME", "WH",
 "FB", "FBA"],
    if_CFX=True,
    set_enable_trade=False,
    output_type='主力连续'
)

m2 = M.use_datasource.v2(
    instruments=m10.data_2,
    datasource_id='bar1d_CN_FUTURE',
    start_date='',
    end_date='',
    before_start_days=0
)

m5 = M.sort.v5(
    input_ds=m2.data,
    sort_by='date',
    group_by='--',
    keep_columns='--',
    ascending=True
)

m7 = M.input_features.v1(
    features='alpha0001 = high / (ts_min(high, 5) / close)'
)

m4 = M.derived_feature_extractor.v3(
    input_data=m5.sorted_data,
    features=m7.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m19 = M.dropnan.v2(
    input_data=m4.data
)

m3 = M.hftrade.v2(
    instruments=m10.data_1,
    options_data=m19.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    handle_tick=m3_handle_tick_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    handle_trade=m3_handle_trade_bigquant_run,
    handle_order=m3_handle_order_bigquant_run,
    after_trading=m3_after_trading_bigquant_run,
    capital_base=20000000,
    frequency='daily',
    price_type='真实价格',
    product_type='期货',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='open',
    benchmark='',
    plot_charts=True,
    disable_cache=True,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2022-12-30 15:00:36.434255] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-30 15:00:37.116624] INFO: moduleinvoker: instruments.v2 运行完成[0.682347s].

[2022-12-30 15:00:37.145446] INFO: moduleinvoker: futures_forward_extractor.v11 开始运行..

[2022-12-30 15:00:40.071676] INFO: moduleinvoker: futures_forward_extractor.v11 运行完成[2.926235s].

[2022-12-30 15:00:40.079513] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2022-12-30 15:00:41.120266] INFO: moduleinvoker: use_datasource.v2 运行完成[1.04078s].

[2022-12-30 15:00:41.126336] INFO: moduleinvoker: sort.v5 开始运行..

[2022-12-30 15:00:41.564287] INFO: moduleinvoker: sort.v5 运行完成[0.43795s].

[2022-12-30 15:00:41.572286] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-12-30 15:00:41.581813] INFO: moduleinvoker: 命中缓存

[2022-12-30 15:00:41.584127] INFO: moduleinvoker: input_features.v1 运行完成[0.011851s].

[2022-12-30 15:00:41.594126] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-12-30 15:00:41.936727] INFO: derived_feature_extractor: 提取完成 alpha0001 = high / (ts_min(high, 5) / close), 0.039s

[2022-12-30 15:00:42.130842] INFO: derived_feature_extractor: /data, 54736

[2022-12-30 15:00:42.223882] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.629748s].

[2022-12-30 15:00:42.233798] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-12-30 15:00:42.586286] INFO: dropnan: /data, 54536/54736

[2022-12-30 15:00:42.625497] INFO: dropnan: 行数: 54536/54736

[2022-12-30 15:00:42.631576] INFO: moduleinvoker: dropnan.v2 运行完成[0.397784s].

[2022-12-30 15:00:42.665010] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-12-30 15:00:42.671253] INFO: hfbacktest: biglearning V1.4.19

[2022-12-30 15:00:42.674824] INFO: hfbacktest: bigtrader v1.9.11 2022-12-22

[2022-12-30 15:01:28.688997] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(1d460bfdc20c483b9a79e56dd5d5e482T)

[2022-12-30 15:01:35.297474] INFO: moduleinvoker: hfbacktest.v1 运行完成[52.632458s].

[2022-12-30 15:01:35.299797] INFO: moduleinvoker: hftrade.v2 运行完成[52.660181s].

2022-12-30 15:00:47.937579 strategy(bktfut,): initialize: 
initializing:

收益率118.07%
年化收益率17.13%
基准收益率0.0%
阿尔法nan
贝塔nan
夏普比率0.94
胜率0.63
盈亏比2.59
收益波动率15.73%
最大回撤21.02%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-625c121e233b477cbb9f31e8d8f7a08b"}/bigcharts-data-end

In [ ]: