详细逻辑¶

1、获取账号及基本信息

2、获取持仓及成本价

3、获取当前时间

4、获取高开低收

5、持久化中间变量

6、加止盈止损

7、计算技术指标

8、修改手续费

9、常用下单API

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target_hold_instruments:\n context.order_target_percent(instrument, 0)\n \n # 买入目标持有列表中的股票\n for instrument in target_hold_instruments - current_hold_instruments:\n context.order_target_percent(instrument, context.target_percent_per_instrument)\n\n \n \n if context.trading_day_index == 5:\n #获取当前时间 pydatetime 类型\n time = data.current_dt \n #将时间格式转为年月日\n date = data.current_dt.date()\n print('date:', date)\n \n cash = context.portfolio.cash\n print('cash:', cash)\n \n pos = context.get_account_positions()\n print('positions:', pos)\n \n \n instrument = '000002.SZ'\n stock_market_price = data.current(instrument, 'price') \n print('最新价格:', stock_market_price)\n\n \n df = data.history(instrument, [\"close\", \"volume\"], 30, '1d')\n print('k 线数据:', df)\n\n \n \n","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, order):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎：盘后处理函数，每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"100000","type":"Literal","bound_global_parameter":null},{"name":"frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"0","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":1,"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":"open","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SH","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"True","type":"Literal","bound_global_parameter":null},{"name":"debug","value":"False","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data","node_id":"-52"},{"name":"options_data","node_id":"-52"},{"name":"history_ds","node_id":"-52"},{"name":"benchmark_ds","node_id":"-52"}],"output_ports":[{"name":"raw_perf","node_id":"-52"}],"cacheable":true,"seq_num":3,"comment":"交易，日线，设置初始化函数和K线处理函数，以及初始资金、基准等","comment_collapsed":false,"x":-54.444740295410156,"y":308.632080078125}],"node_layout":"<node_postions><node_position Node='-42' Position='-230.81489825248718,-6,200,200'/><node_position Node='-41' Position='-132,132,200,200'/><node_position Node='-52' Position='-54.444740295410156,308.632080078125,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

In [7]:

# 本代码由可视化策略环境自动生成 2023年12月14日 18:06
# 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。
 
# 显式导入 BigQuant 相关 SDK 模块
from bigdatasource.api import DataSource
from bigdata.api.datareader import D
from biglearning.api import M
from biglearning.api import tools as T
from biglearning.module2.common.data import Outputs
 
import pandas as pd
import numpy as np
import math
import warnings
import datetime
 
from zipline.finance.commission import PerOrder
from zipline.api import get_open_orders
from zipline.api import symbol
 
from bigtrader.sdk import *
from bigtrader.utils.my_collections import NumPyDeque
from bigtrader.constant import OrderType
from bigtrader.constant import Direction

# <aistudiograph>

# @param(id="m3", name="initialize")
# 交易引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
    # 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))

    # 持有期/调仓周期，1天，3天，5天等
    context.holding_days = 1
    # 设置买入股票数量
    context.target_hold_count = 10
    # 每只股票的目标权重
    context.target_percent_per_instrument = 1.0 / context.target_hold_count

# @param(id="m3", name="before_trading_start")
# 交易引擎：每个单位时间开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    pass

# @param(id="m3", name="handle_tick")
# 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

# @param(id="m3", name="handle_data")
def m3_handle_data_bigquant_run(context, data):
    
    # 止盈止损
    # stop_pos(context, data)

    # 每 context.holding_days 个交易日调仓一次
    if context.trading_day_index % context.holding_days != 0:
        return

    
    
    # 获取当前日期
    current_date = data.current_dt.strftime("%Y-%m-%d")
    # 获取当日数据
    current_day_data = context.data[context.data["date"] == current_date]
    # 取前10只
    current_day_data = current_day_data.head(context.target_hold_count)
    # 获取当日目标持有股票
    target_hold_instruments = set(current_day_data["instrument"])
    # 获取当前已持有股票
    current_hold_instruments = set(context.get_account_positions().keys())

    # 卖出不在目标持有列表中的股票
    for instrument in current_hold_instruments - target_hold_instruments:
        context.order_target_percent(instrument, 0)
        
    # 买入目标持有列表中的股票
    for instrument in target_hold_instruments - current_hold_instruments:
        context.order_target_percent(instrument, context.target_percent_per_instrument)

        
        
    if context.trading_day_index == 5:
         #获取当前时间 pydatetime 类型
        time = data.current_dt 
        #将时间格式转为年月日
        date = data.current_dt.date()
        print('date:', date)
        
        cash  = context.portfolio.cash
        print('cash:', cash)
        
        pos = context.get_account_positions()
        print('positions:', pos)
        
        
        instrument = '000002.SZ'
        stock_market_price = data.current(instrument, 'price') 
        print('最新价格:', stock_market_price)

        
        df = data.history(instrument, ["close", "volume"], 30, '1d')
        print('k 线数据:', df)

        
        

# @param(id="m3", name="handle_trade")
# 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, trade):
    pass

# @param(id="m3", name="handle_order")
# 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, order):
    pass

# @param(id="m3", name="after_trading")
# 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):
    pass


# @module(position="-230.81489825248718,-6", comment='通过SQL调用数据、因子和表达式等构建策略逻辑', comment_collapsed=False)
m1 = M.input_features_dai.v6(
    sql="""-- 使用DAI SQL获取数据，构建因子等，如下是一个例子作为参考
-- DAI SQL 语法: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-sql%E5%85%A5%E9%97%A8%E6%95%99%E7%A8%8B

SELECT
    -- 【在这里输入因子表达式】
    -- DAI SQL 算子/函数: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-%E5%87%BD%E6%95%B0
    -- 数据&字段: 数据文档 https://bigquant.com/data/home
    -- 使用在时间截面的total_market_cap排名、五日日均成交量m_avg(amount_0, 5)作为本模版的两个因子
    -- 这里使用了DAI提供的normalize函数对因子进行z-score标准化处理以在多因子线性合成时不被量纲影响
    -- 还可以增加系数调节因子权重，这里对第二个因子简单地添加了0.9的系数，在这个示例中没有经济学意义
    -- 另外可以使用DAI提供的cut_outliers函数去极值，c_indneutralize、c_neutralize函数进行行业、行业市值中性化
    0.4*normalize(total_market_cap) + 0.6*normalize(m_avg(amount_0, 5)) AS score,

    -- 日期，这是每个股票每天的数据
    date,
    -- 股票代码，代表每一支股票
    instrument
-- 预计算因子和数据 cn_stock_factors https://bigquant.com/data/datasources/cn_stock_factors
FROM cn_stock_factors

-- where 数据过滤
WHERE
   st_status = 0
   
-- QUALIFY 数据过滤，支持过滤窗口函数，在 WHERE 之后才执行窗口函数。这里简化了一下，都放到QUALIFY了。对于专业用户建议分开WHERE和QUALIFY，有更好的升性能和准确性
QUALIFY
    -- 剔除ST股票
    st_status = 0
    -- 上市天数 > 270, 过滤掉新股
    AND list_days > 270
    -- 要求 pe > 0，-- 表示注释
    -- AND pe_ttm > 0
    -- 非停牌股
    AND suspended = 0
    -- 不属于北交所
    AND list_sector < 4
    -- 去掉有空值的行
    AND COLUMNS(*) IS NOT NULL

-- 按因子值排名，从小到大
ORDER BY date, score
"""
)

# @module(position="-132,132", comment='抽取数据，设置数据开始时间和结束时间，并绑定模拟交易', comment_collapsed=False)
m2 = M.extract_data_dai.v7(
    sql=m1.data,
    start_date='2023-01-01',
    start_date_bound_to_trading_date=True,
    end_date='2023-10-31',
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    debug=False
)

# @module(position="-54.444740295410156,308.632080078125", comment='交易，日线，设置初始化函数和K线处理函数，以及初始资金、基准等', comment_collapsed=False)
m3 = M.bigtrader.v7(
    data=m2.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=100000,
    frequency='daily',
    product_type='股票',
    before_start_days=0,
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='open',
    benchmark='000300.SH',
    plot_charts=True,
    disable_cache=True,
    debug=False,
    backtest_only=False
)
# </aistudiograph>

[2023-12-14 18:06:38.374987] INFO: moduleinvoker:3658027457.py:127: input_features_dai.v6 开始运行..
[2023-12-14 18:06:38.393990] INFO: moduleinvoker:3658027457.py:127: 命中缓存
[2023-12-14 18:06:38.398018] INFO: moduleinvoker:3658027457.py:127: input_features_dai.v6 运行完成[0.02305s].
[2023-12-14 18:06:38.420887] INFO: moduleinvoker:3658027457.py:173: extract_data_dai.v7 开始运行..
[2023-12-14 18:06:38.435373] INFO: moduleinvoker:3658027457.py:173: 命中缓存
[2023-12-14 18:06:38.439142] INFO: moduleinvoker:3658027457.py:173: extract_data_dai.v7 运行完成[0.018253s].
[2023-12-14 18:06:38.474906] INFO: moduleinvoker:3658027457.py:184: bigtrader.v7 开始运行..

2023-12-14 18:06:38 [info     ] got metadata extra from input datasource
2023-12-14 18:06:38 [info     ] read data ..
2023-12-14 18:06:38 [info     ] start_date='2023-01-01', end_date='2023-10-31', instruments=2988
2023-12-14 18:06:38 [info     ] bigtrader module V2.0.2
2023-12-14 18:06:38 [info     ] bigtrader engine v1.10.6 2023-12-11
date: 2023-01-10
cash: 5543.129999999999
positions: {'300321.SZ': StockPosition(bkt000,300321.SZ,LONG,current_qty:600,avail_qty:600,cost_price:15.148,last_price:15.17,margin:0.0), '603159.SH': StockPosition(bkt000,603159.SH,LONG,current_qty:700,avail_qty:700,cost_price:13.207,last_price:13.26,margin:0.0), '300417.SZ': StockPosition(bkt000,300417.SZ,LONG,current_qty:1000,avail_qty:1000,cost_price:10.005,last_price:9.85,margin:0.0), '300549.SZ': StockPosition(bkt000,300549.SZ,LONG,current_qty:700,avail_qty:700,cost_price:12.737,last_price:12.85,margin:0.0), '603829.SH': StockPosition(bkt000,603829.SH,LONG,current_qty:900,avail_qty:900,cost_price:10.916,last_price:11.11,margin:0.0), '603685.SH': StockPosition(bkt000,603685.SH,LONG,current_qty:1000,avail_qty:1000,cost_price:9.555,last_price:9.65,margin:0.0), '603926.SH': StockPosition(bkt000,603926.SH,LONG,current_qty:1000,avail_qty:1000,cost_price:9.305,last_price:9.43,margin:0.0), '603037.SH': StockPosition(bkt000,603037.SH,LONG,current_qty:600,avail_qty:600,cost_price:16.208,last_price:16.54,margin:0.0), '300509.SZ': StockPosition(bkt000,300509.SZ,LONG,current_qty:1700,avail_qty:1700,cost_price:5.703,last_price:5.69,margin:0.0), '300669.SZ': StockPosition(bkt000,300669.SZ,LONG,current_qty:1200,avail_qty:1200,cost_price:8.324,last_price:8.27,margin:0.0)}
最新价格: 18.8
k 线数据:             close     volume
date                        
2022-12-30  18.20   51486383
2023-01-03  18.23   63639963
2023-01-04  19.07  108714690
2023-01-05  19.33   70524523
2023-01-06  19.22   72123976
2023-01-09  18.78   68506576
2023-01-10  18.80   48208009
2023-12-14 18:06:49 [info     ] backtest done, raw_perf_ds:dai.DataSource("_527cac7fb5ac465198665babeb13e74a")

[2023-12-14 18:06:49.611694] INFO: bigcharts.impl.render:render.py:408:render_chart Data is None, skip loading it to chart.

[2023-12-14 18:06:50.688339] INFO: moduleinvoker:3658027457.py:184: bigtrader.v7 运行完成[12.213433s].

BigTrader(高性能回测/交易)
收益概况
交易详情
每日持仓和收益
输出日志

收益率42.67%
年化收益率54.06%
基准收益率-8.11%
阿尔法0.66
贝塔0.55
夏普比率2.44
胜率0.55
盈亏比1.92
收益波动率17.9%
信息比率0.2
最大回撤8.98%

日期	时间	证券代码	证券名称	买/卖	数量	成交价	成交金额	平仓盈亏	交易费用
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日期	证券代码	证券名称	数量	持仓均价	收盘价	持仓市值	收益
Loading... (need help?)

时间	级别	内容
Loading... (need help?)

In [4]:

def stop_pos(context, data):
    date = data.current_dt.strftime('%Y-%m-%d')
    positions = {e: p.cost_basis  for e, p in context.get_account_positions().items()}
    # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stopwin_stock = []
    current_stoploss_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(i, 'price') 
            # 赚20%就止赢
            if (stock_market_price - stock_cost ) / stock_cost>= 0.2:   
                context.order_target_percent(i, 0)     
                current_stopwin_stock.append(i)
                print('日期：',date,'股票：',i,'出现止盈状况')
            elif (stock_market_price - stock_cost) / stock_cost <= -0.05:   
                context.order_target_percent(i, 0)     
                current_stoploss_stock.append(i)
                print('日期：',date,'股票：',i,'出现止损状况')

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