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# 本代码由可视化策略环境自动生成 2023年1月5日 09:42
# 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。


# 回测引擎：初始化函数，只执行一次
def m5_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 m5_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 m5_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 m5_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2022-1-1',
    end_date='2022-1-10',
    market='CN_STOCK_A',
    instrument_list="""002820.SZA
600420.SHA
002421.SZA
601001.SHA
601011.SHA
601015.SHA
600490.SHA
600063.SHA
603011.SHA""",
    max_count=0
)

m2 = M.input_features.v1(
    features="""
#号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
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

"""
)

m4 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m3 = M.chinaa_stock_filter.v1(
    input_data=m4.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=True
)

m6 = M.sort.v5(
    input_ds=m3.data,
    sort_by='return_5',
    group_by='date',
    keep_columns='--',
    ascending=True
)

m5 = M.trade.v4(
    instruments=m1.data,
    options_data=m2.data,
    history_ds=m6.sorted_data,
    start_date='',
    end_date='',
    initialize=m5_initialize_bigquant_run,
    handle_data=m5_handle_data_bigquant_run,
    prepare=m5_prepare_bigquant_run,
    before_trading_start=m5_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='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2023-01-05 09:41:31.613735] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-01-05 09:41:31.620499] INFO: moduleinvoker: 命中缓存

[2023-01-05 09:41:31.622505] INFO: moduleinvoker: instruments.v2 运行完成[0.008779s].

[2023-01-05 09:41:31.627329] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-01-05 09:41:31.633633] INFO: moduleinvoker: 命中缓存

[2023-01-05 09:41:31.635733] INFO: moduleinvoker: input_features.v1 运行完成[0.008408s].

[2023-01-05 09:41:31.652885] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-01-05 09:41:31.659106] INFO: moduleinvoker: 命中缓存

[2023-01-05 09:41:31.660773] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007917s].

[2023-01-05 09:41:31.670331] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-01-05 09:41:31.678002] INFO: moduleinvoker: 命中缓存

[2023-01-05 09:41:31.679684] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.009358s].

[2023-01-05 09:41:31.685660] INFO: moduleinvoker: sort.v5 开始运行..

[2023-01-05 09:41:31.690663] INFO: moduleinvoker: 命中缓存

[2023-01-05 09:41:31.692044] INFO: moduleinvoker: sort.v5 运行完成[0.006383s].