成交单分析功能¶

版本v1.0

以"交割单模板.csv"为例，展示了成交单分析功能, 具体的csv文件格式可以参考帖子'交割单模板.csv'

1、成交单csv数据读取¶

输入：交割单模板.csv 指定：

成交日期列的列名，本例的csv文件中日期列表头为"成交日期"
证券代码列名，本例的csv文件中证券代码列表头为"证券代码"
买卖标志列名，本例的csv文件中买卖标志列表头为"买卖标志"
成交价格列名，本例的csv文件中成交价格列表头为"成交价格"
成交数量列名，本例的csv文件中成交价格列表头为"成交数量"

此模块会将每日的交易记录读取至DataFrame，并将信号时间前移1天来实现模拟当日成交，输出股票代码列表、起止时间和前移1日后的交易信号数据(包括日期、代码、买卖方向、成交价格和数量)。

2、回测¶

在初始化函数中，通过设置固定滑点模型实现按照指定价格交易，设置手续费
在主函数中，首先获取当日的交易信号数据trade_data，然后针对每行交易信号数据执行买卖动作：

context.order(context.symbol(trade_data['instrument'].iloc[i]), volume, limit_price=trade_data['trade_price'].iloc[i])

即实现了对指定的一行交易记录的股票trade_data['instrument'].iloc[i],使用指定的价格trade_data['trade_price'].iloc[i],执行买入/卖出volume股的操作
同时设置成交率限制为0，以保证所有下单能够成交。

3、下单检查¶

我们通过回测结果中的交易详情可以看到每笔单子的下单时间、股票代码、买卖方向、成交数量、成交价和佣金。可以检查这些记录与成交单记录的csv文件的一致性。

4、结果分析¶

通过回测结果可以看到绩效曲线、成交记录和策略的阿尔法、夏普比率等统计指标；
从画布左侧模块列表的“共享模块”列表中拖入 “最近N日绩效评估”、“Brinson绩效评估” 和 “每日持仓分析” 三个模块。运行后可以查看相关的阶段绩效、Brinson收益归因和每日持仓股票/行业等信息。
通过调用m2.pyfolio_full_tear_sheet()查看收益分析，通过m2.risk_analyze()查看风险分析

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按日期过滤得到今日收盘后的下单股票\n trade_data = context.trade_data[context.trade_data.date == data.current_dt.strftime('%Y-%m-%d')]\n\n for i in range(0, len(trade_data)):\n volume = trade_data['trade_volume'].iloc[i]\n if trade_data['side'].iloc[i] in ['BUY', '配售中签', '新股入帐']:\n pass\n elif trade_data['side'].iloc[i] == 'SELL':\n volume = -volume\n else:\n print('警告：未知的买卖标志 %s' % (trade_data['side'].iloc[i]))\n try:\n context.order(context.symbol(trade_data['instrument'].iloc[i]), volume, limit_price=trade_data['trade_price'].iloc[i])\n except Exception as e:\n #print('警告：%s' % (e))\n pass","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 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。\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":"0","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"后复权","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-1259"},{"name":"options_data","node_id":"-1259"},{"name":"history_ds","node_id":"-1259"},{"name":"benchmark_ds","node_id":"-1259"},{"name":"trading_calendar","node_id":"-1259"}],"output_ports":[{"name":"raw_perf","node_id":"-1259"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-6539","module_id":"BigQuantSpace.N_days_performance_statistics.N_days_performance_statistics-v5","parameters":[{"name":"N","value":"5","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"backtest_ds","node_id":"-6539"}],"output_ports":[{"name":"evaluation_of_perf_indicator","node_id":"-6539"},{"name":"analysis_of_stage_return_rat","node_id":"-6539"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-129","module_id":"BigQuantSpace.order_record_input.order_record_input-v5","parameters":[{"name":"trading_records_filename","value":"模板策略/股票/常用工具/交割单模板.csv","type":"Literal","bound_global_parameter":null},{"name":"date_col","value":"成交日期","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"证券代码","type":"Literal","bound_global_parameter":null},{"name":"side_col","value":"买卖标志","type":"Literal","bound_global_parameter":null},{"name":"trade_price_col","value":"成交价格","type":"Literal","bound_global_parameter":null},{"name":"trade_volume_col","value":"成交数量","type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"instruments_ds","node_id":"-129"},{"name":"trading_records_ds","node_id":"-129"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-1259' Position='224,293,200,200'/><node_position Node='-6539' Position='-195,500,200,200'/><node_position Node='-129' Position='239,105,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

In [2]:

# 本代码由可视化策略环境自动生成 2021年12月31日 15:44
# 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 m2_initialize_bigquant_run(context):
    context.trade_data = context.options['data'].read_df()
    context.set_long_only()
    # 设置按制定价格交易
    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = order.limit
            return (price, order.amount)
    fix_slippage = FixedPriceSlippage()
    fix_slippage._price_field_buy = 'low'
    fix_slippage._price_field_sell = 'high'
    fix_slippage = FixedPriceSlippage(price_field_buy='low', price_field_sell='high')
    context.set_slippage(us_equities=fix_slippage)
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
# 回测引擎：每日数据处理函数，每天执行一次
def m2_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日收盘后的下单股票
    trade_data = context.trade_data[context.trade_data.date == data.current_dt.strftime('%Y-%m-%d')]

    for i in range(0, len(trade_data)):
        volume = trade_data['trade_volume'].iloc[i]
        if trade_data['side'].iloc[i] in ['BUY', '配售中签', '新股入帐']:
            pass
        elif trade_data['side'].iloc[i] == 'SELL':
            volume = -volume
        else:
            print('警告：未知的买卖标志 %s' % (trade_data['side'].iloc[i]))
        try:
            context.order(context.symbol(trade_data['instrument'].iloc[i]), volume, limit_price=trade_data['trade_price'].iloc[i])
        except Exception as e:
            #print('警告：%s' % (e))
            pass
# 回测引擎：准备数据，只执行一次
def m2_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 m2_before_trading_start_bigquant_run(context, data):
    pass


m4 = M.order_record_input.v5(
    trading_records_filename='模板策略/股票/常用工具/交割单模板.csv',
    date_col='成交日期',
    instrument_col='证券代码',
    side_col='买卖标志',
    trade_price_col='成交价格',
    trade_volume_col='成交数量',
    m_cached=False
)

m2 = M.trade.v4(
    instruments=m4.instruments_ds,
    options_data=m4.trading_records_ds,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    prepare=m2_prepare_bigquant_run,
    before_trading_start=m2_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

m5 = M.N_days_performance_statistics.v5(
    backtest_ds=m2.raw_perf,
    N=5
)

[2021-12-31 15:43:26.598690] INFO: moduleinvoker: order_record_input.v5 开始运行..

[2021-12-31 15:43:27.067995] INFO: moduleinvoker: order_record_input.v5 运行完成[0.469299s].

[2021-12-31 15:43:28.956159] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-12-31 15:43:28.961271] INFO: backtest: biglearning backtest:V8.6.1

[2021-12-31 15:43:28.962759] INFO: backtest: product_type:stock by specified

[2021-12-31 15:43:29.040719] INFO: moduleinvoker: cached.v2 开始运行..

[2021-12-31 15:43:29.828823] INFO: backtest: 读取股票行情完成:118871

[2021-12-31 15:43:30.086147] INFO: moduleinvoker: cached.v2 运行完成[1.045433s].

[2021-12-31 15:43:30.262990] INFO: algo: TradingAlgorithm V1.8.6

[2021-12-31 15:43:30.380360] INFO: algo: trading transform...

[2021-12-31 15:43:31.186528] INFO: Performance: Simulated 45 trading days out of 45.

[2021-12-31 15:43:31.188056] INFO: Performance: first open: 2018-07-02 09:30:00+00:00

[2021-12-31 15:43:31.189213] INFO: Performance: last close: 2018-08-31 15:00:00+00:00

[2021-12-31 15:43:32.222931] INFO: moduleinvoker: backtest.v8 运行完成[3.266762s].

[2021-12-31 15:43:32.225165] INFO: moduleinvoker: trade.v4 运行完成[5.139956s].

[2021-12-31 15:43:32.242086] INFO: moduleinvoker: N_days_performance_statistics.v5 开始运行..

[2021-12-31 15:43:32.464719] INFO: moduleinvoker: N_days_performance_statistics.v5 运行完成[0.222635s].

收益率-8.91%
年化收益率-40.71%
基准收益率-5.03%
阿尔法-0.19
贝塔0.96
夏普比率-1.5
胜率0.65
盈亏比1.83
收益波动率33.17%
信息比率-0.05
最大回撤17.78%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f2c2b592a90e4f579add214dda625100"}/bigcharts-data-end

                     最近5日策略绩效统计
return_ratio          -0.064695
annual_return_ratio   -0.965641
beta                   2.327051
alpha                 -0.918770
sharp_ratio           -8.866525
sortino               -9.296394
return_volatility      0.375235
max_drawdown          -0.086538

Pyfolio收益分析¶

In [14]:

m2.pyfolio_full_tear_sheet()

Entire data start date: 2018-07-02
Entire data end date: 2018-08-31


Backtest Months: 2

Performance statistics	Backtest
cum_returns_final	-0.08
annual_return	-0.39
annual_volatility	0.28
sharpe_ratio	-1.65
calmar_ratio	-2.57
stability_of_timeseries	0.40
max_drawdown	-0.15
omega_ratio	0.76
sortino_ratio	-2.15
skew	-0.09
kurtosis	-0.37
tail_ratio	0.85
common_sense_ratio	0.52
information_ratio	-0.11
alpha	-0.35
beta	0.95

Worst Drawdown Periods	net drawdown in %	peak date	valley date	recovery date	duration
0	15.22	2018-07-26	2018-08-31	NaT	NaN
1	4.61	2018-07-13	2018-07-19	2018-07-24	8
2	0.93	2018-07-03	2018-07-05	2018-07-09	5
3	0.00	2018-07-02	2018-07-02	2018-07-02	1
4	0.00	2018-07-02	2018-07-02	2018-07-02	1


[-0.037 -0.096]

Top 10 long positions of all time	max
Equity(46 [000820.SZA])	12.84%
Equity(6 [002122.SZA])	12.21%
Equity(49 [002124.SZA])	8.81%
Equity(22 [002247.SZA])	8.67%
Equity(113 [002600.SZA])	7.64%
Equity(74 [600734.SHA])	6.07%
Equity(99 [600532.SHA])	5.94%
Equity(76 [002604.SZA])	5.79%
Equity(100 [603555.SHA])	4.93%
Equity(78 [601111.SHA])	4.83%

risk_analyze风险分析¶

In [15]:

m2.risk_analyze()