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# 本代码由可视化策略环境自动生成 2020年3月31日 16:14
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import talib

def self_diy(df,volume_0): 
    volume = [float(x) for x in df['volume_0']]
    df['volume_0']=(df['volume_0']>mean(volume,136)).astype(int)
    return df['volume_0']
   #return pd.rolling_apply(df['close_0'], 5,np.mean)
m19_user_functions_bigquant_run = {
    'self_diy':  self_diy
}

import talib

def self_diy(df,volume_0): 
    volume = [float(x) for x in df['volume_0']]
    df['volume_0']=(df['volume_0']>mean(volume,136)).astype(int)
    return df['volume_0']
   #return pd.rolling_apply(df['close_0'], 5,np.mean)
m22_user_functions_bigquant_run = {
    'self_diy':  self_diy
}

#import talib #这是引入一个计算技术指标的库，可以查一下论坛 基本能满足你的常用技术指标定义需求
#def self_diy(df,close_0): 
#    close = [float(x) for x in df['close_0']]
#    df['condition']=(df['close_0']>talib.MA(np.array(close), timeperiod=5)).astype(int)
#    return df['condition']
#上面这几句是定义你的自定义函数具体计算内容，也就是指标值condition，这里计算的是当收盘价>5日均线时就是1，否则就是0
#m5_user_functions_m22_user_functions_bigquant_run = {
#    'self_diy':  self_diy
#}
#函数返回值会传给self_diy这个列
# 回测引擎：初始化函数，只执行一次
def m23_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 = 2
    # 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 = 0.5
    context.options['hold_days'] = 3

# 回测引擎：每日数据处理函数，每天执行一次
def m23_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.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，按机器学习算法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：按机器学习算法预测的排序，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))*1:
            # 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))*1
        if cash > 0:
            #以下是整百买卖的代码
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100*1) * 100
            context.order(context.symbol(instrument), amount)


# 回测引擎：准备数据，只执行一次
def m23_prepare_bigquant_run(context):
    pass
#    bm_price = D.history_data(['000300.SHA'], start_date=context.start_date , end_date=context.end_date, fields=['close'])
#    #bm_price['sma'] = bm_price['close'].rolling(5).mean()
    #bm_price['lma'] = bm_price['close'].rolling(20).mean()
    #bm_price['gold_cross_status'] = bm_price['sma'] > bm_price['lma']
#    bm_price['ma10'] = bm_price['close'].rolling(10).mean()    
#    bm_price['ma10_status'] = bm_price['close'] > bm_price['ma10']
#    bm_price['pos_percent'] = np.where(bm_price['ma10_status'],1,0)
#    context.pos_df = bm_price[['date', 'pos_percent']].set_index('date')
#    context.pos_df = hs300_pos11.bm_price


m1 = M.instruments.v2(
    start_date='2010-01-01',
    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.input_features.v1(
    features_ds=m3.data,
    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
#self_diy(volume_0)
#market_cap_float_0/close_0*adjust_factor_0
#mean(volume_0,136)
st_status_0 #st状态
market_cap_float_0/close_0*adjust_factor_0 #流通股数
list_days_0 #上市天数
"""
)

m18 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m15.data,
    start_date='',
    end_date='',
    before_start_days=100
)

m19 = M.derived_feature_extractor.v3(
    input_data=m18.data,
    features=m15.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m19_user_functions_bigquant_run
)

m16 = M.filter.v3(
    input_data=m19.data,
    expr='st_status_0==0',
    output_left_data=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
)

m20 = M.stock_ranker_train.v6(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2017-01-01'),
    end_date=T.live_run_param('trading_date', '2018-05-15'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m21 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m15.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m22 = M.derived_feature_extractor.v3(
    input_data=m21.data,
    features=m15.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions=m22_user_functions_bigquant_run
)

m17 = M.filter.v3(
    input_data=m22.data,
    expr='list_days_0<180 and st_status_0==0 and market_cap_float_0/close_0*adjust_factor_0 <300000000',
    output_left_data=False
)

m14 = M.dropnan.v1(
    input_data=m17.data
)

m8 = M.stock_ranker_predict.v5(
    model=m20.model,
    data=m14.data,
    m_lazy_run=False
)

m23 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m23_initialize_bigquant_run,
    handle_data=m23_handle_data_bigquant_run,
    prepare=m23_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='前复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)