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# 本代码由可视化策略环境自动生成 2018年1月4日 16:10
# 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。


m1 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2016-01-01',
    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) - 1
shift(close, -5) / shift(open, -1) - 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10个分类
all_wbins(label, 10)

# 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,
    user_functions={}
)

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
close_0 / close_1
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
pe_ttm_0
beta_csi800_5_0
swing_volatility_5_0
ta_macd_macd_12_26_9_0
ta_willr_14_0
ta_mom_10_0
ta_sar_0
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"""
)

m4 = M.general_feature_extractor.v6(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m5 = M.derived_feature_extractor.v2(
    input_data=m4.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

m6 = M.join.v3(
    data1=m2.data,
    data2=m5.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m7 = M.dropnan.v1(
    input_data=m6.data
)

m8 = M.random_forest_train.v2(
    training_ds=m7.data,
    features=m3.data,
    n_estimators=15,
    max_features='auto',
    max_depth=25,
    min_samples_leaf=200,
    n_jobs=1,
    algo='regressor'
)

m9 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m10 = M.general_feature_extractor.v6(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m11 = M.derived_feature_extractor.v2(
    input_data=m10.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

m12 = M.dropnan.v1(
    input_data=m11.data
)

m13 = M.random_forest_predict.v2(
    model=m8.model,
    data=m12.data,
    date_col='date',
    instrument_col='instrument',
    sort=True
)

# 回测引擎：每日数据处理函数，每天执行一次
def m14_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()}
    
    '''
    date = data.current_dt.strftime('%Y-%m-%d')   
    #------------------------------------------止损模块START--------------------------------------------
    current_stoploss_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 亏3%就止损
            if (stock_market_price - stock_cost) / stock_cost <= -0.03:   
                context.order_target_percent(context.symbol(i),0)     
                current_stoploss_stock.append(i)
                #print('日期：',date,'股票：',i,'出现止损状况')
    #-------------------------------------------止损模块END---------------------------------------------
    '''
    
    # 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 m14_prepare_bigquant_run(context):
    pass

# 回测引擎：初始化函数，只执行一次
def m14_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.exp(i) 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：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m14_before_trading_start_bigquant_run(context, data):
    pass

m14 = M.trade.v3(
    instruments=m9.data,
    options_data=m13.predictions,
    start_date='',
    end_date='',
    handle_data=m14_handle_data_bigquant_run,
    prepare=m14_prepare_bigquant_run,
    initialize=m14_initialize_bigquant_run,
    before_trading_start=m14_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    benchmark='000300.SHA',
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    plot_charts=True,
    backtest_only=False,
    amount_integer=False
)

[2018-01-04 14:54:29.434248] INFO: bigquant: instruments.v2 开始运行..
[2018-01-04 14:54:29.438649] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.439599] INFO: bigquant: instruments.v2 运行完成[0.005406s].
[2018-01-04 14:54:29.448660] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
[2018-01-04 14:54:29.451582] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.452529] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.003865s].
[2018-01-04 14:54:29.457999] INFO: bigquant: input_features.v1 开始运行..
[2018-01-04 14:54:29.461224] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.462437] INFO: bigquant: input_features.v1 运行完成[0.004467s].
[2018-01-04 14:54:29.471448] INFO: bigquant: general_feature_extractor.v6 开始运行..
[2018-01-04 14:54:29.474809] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.476027] INFO: bigquant: general_feature_extractor.v6 运行完成[0.00459s].
[2018-01-04 14:54:29.483184] INFO: bigquant: derived_feature_extractor.v2 开始运行..
[2018-01-04 14:54:29.486810] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.487947] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.004739s].
[2018-01-04 14:54:29.496281] INFO: bigquant: join.v3 开始运行..
[2018-01-04 14:54:29.499481] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.500717] INFO: bigquant: join.v3 运行完成[0.00445s].
[2018-01-04 14:54:29.508383] INFO: bigquant: dropnan.v1 开始运行..
[2018-01-04 14:54:29.511351] INFO: bigquant: 命中缓存
[2018-01-04 14:54:29.512720] INFO: bigquant: dropnan.v1 运行完成[0.004384s].
[2018-01-04 14:54:29.521122] INFO: bigquant: random_forest_train.v2 开始运行..
[2018-01-04 15:26:57.981913] INFO: random_forest_train: 模型在训练集分数是:0.16
[2018-01-04 15:26:58.035136] INFO: bigquant: random_forest_train.v2 运行完成[1948.513938s].
[2018-01-04 15:26:58.054860] INFO: bigquant: instruments.v2 开始运行..
[2018-01-04 15:26:58.059209] INFO: bigquant: 命中缓存
[2018-01-04 15:26:58.060758] INFO: bigquant: instruments.v2 运行完成[0.005922s].
[2018-01-04 15:26:58.070541] INFO: bigquant: general_feature_extractor.v6 开始运行..
[2018-01-04 15:26:58.073871] INFO: bigquant: 命中缓存
[2018-01-04 15:26:58.075293] INFO: bigquant: general_feature_extractor.v6 运行完成[0.004759s].
[2018-01-04 15:26:58.083648] INFO: bigquant: derived_feature_extractor.v2 开始运行..
[2018-01-04 15:26:58.087495] INFO: bigquant: 命中缓存
[2018-01-04 15:26:58.088976] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.005351s].
[2018-01-04 15:26:58.096653] INFO: bigquant: dropnan.v1 开始运行..
[2018-01-04 15:26:58.099882] INFO: bigquant: 命中缓存
[2018-01-04 15:26:58.100913] INFO: bigquant: dropnan.v1 运行完成[0.004286s].
[2018-01-04 15:26:58.108382] INFO: bigquant: random_forest_predict.v2 开始运行..
[2018-01-04 15:27:05.315420] INFO: bigquant: random_forest_predict.v2 运行完成[7.206957s].
[2018-01-04 15:27:05.352793] INFO: bigquant: backtest.v7 开始运行..
[2018-01-04 15:27:05.467240] INFO: algo: set price type:backward_adjusted
[2018-01-04 15:27:43.244830] INFO: Performance: Simulated 244 trading days out of 244.
[2018-01-04 15:27:43.246365] INFO: Performance: first open: 2016-01-04 01:30:00+00:00
[2018-01-04 15:27:43.247355] INFO: Performance: last close: 2016-12-30 07:00:00+00:00

[2018-01-04 15:27:45.296911] INFO: bigquant: backtest.v7 运行完成[39.944128s].