在交易函数的context中可以点出哪些属性(记录一次调试过程）
由bq07v232创建，最终由small_q 被浏览 38 用户
我想将选取前十名股票的选股策略的基础上，融合亏损5%则卖出； 获利10%则止盈，换股的策略。
同一个问题，因为刚上手，原来可以1分钟解决的搞了1天，其实一开始文心一言就已经帮我改正了错误，只是我认为不是这个问题。
第一步
第二部
在修改交易代码时，遇到将原来的context.df = context.options['data'].read_df()与positions_cost = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}
同时使用时，后者报错：AttributeError: 'DataSource' object has no attribute 'positions'
代码如下：
# 交易引擎：初始化函数，只执行一次
def m4_initialize_bigquant_run(context):
# 加载预测数据
context.df = context.options['data'].read_df()
# 交易引擎：每个单位时间开盘前调用一次。
def m4_before_trading_start_bigquant_run(context, data):
# 盘前处理，订阅行情等
pass
# 交易引擎：tick数据处理函数，每个tick执行一次
def m4_handle_tick_bigquant_run(context, tick):
pass
# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m4_handle_data_bigquant_run(context, data):
# 获取当前日期
dt = data.current_dt.strftime('%Y-%m-%d')
# 获取当前日期的所有股票市值信息，并排序
df = context.df[context.df['date'] == dt].sort_values('total_market_cap', ascending=True)
# 获取市值排名前10的股票代码集合
instruments = set(list(df['instrument'])[:10])
# 获取当前持仓信息
holding = context.get_account_positions()
# 将持仓股票代码转换为集合，方便后续比较
holding_list = list(holding.keys())
holding_num = len(holding_list)
holding_list = set(holding.keys())
# 初始化止损止盈列表
current_stopwin_stock = []
current_stoploss_stock = []
# 获取持仓成本
print("a--------------")
print(context)
print(context.options['data'].read_df())
print(type(context))
print("b--------------")
#positions_cost = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}
positions_cost = {e.symbol: p.cost_basis for e, p in context.options['data'].positions.items()}
# 检查持仓并执行止损止盈操作
if len(holding) > 0:
for instrument, position in holding.items(): # 假设holding.items()返回(instrument, position)对
stock_cost = positions_cost[instrument] # 获取持仓成本
stock_market_price = data.current(context.symbol(instrument), 'price') # 获
# 检查是否可以交易，并执行止盈操作
if (stock_market_price / stock_cost - 1 >= 0.09) and data.can_trade(context.symbol(instrument)):
context.order_target_percent(context.symbol(instrument), 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
current_stopwin_stock.append(instrument) # 添加到止盈列表
continue # 继续下一个循环，不再检查止损条件
# 检查是否可以交易，并执行止损操作
if (stock_market_price / stock_cost - 1 <= -0.05) and data.can_trade(context.symbol(instrument)):
context.order_target_percent(context.symbol(instrument), 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
current_stoploss_stock.append(instrument) # 添加到止损列表
continue # 继续下一个循环
# 更新已卖出股票列表（假设stock_sold在外部已定义）
stock_sold.extend(current_stopwin_stock + current_stoploss_stock) # 合并止盈止损列表并添加到已卖出列表
# 根据市值排名调整持仓
buy_list = instruments - holding_list # 需要买入的股票列表（排名前10但不在持仓中的）
sell_list = holding_list - instruments # 需要卖出的股票列表（在持仓中但不在排名前10的）
# 卖出不在股票池的股票
for ins in sell_list:
context.order_target_percent(ins, 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
# 买入最新的股票，但不超过10只股票的总持仓限制
for ins in buy_list:
if holding_num >= 10: # 如果当前持仓数量小于等于10，则不再买入新的股票
break # 跳出循环，不再继续买入操作
context.order_target_percent(ins, 1/10) # 买入股票，假设等权重分配资金
holding_num += 1 # 更新持仓数量（注意：这里可能有问题，因为实际买入可能需要时间确认，这里只是模拟逻辑）
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m4_handle_trade_bigquant_run(context, trade):
pass
# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m4_handle_order_bigquant_run(context, order):
pass
# 交易引擎：盘后处理函数，每日盘后执行一次
def m4_after_trading_bigquant_run(context, data):
pass
m4 = M.hftrade.v2(
instruments=instruments,
options_data=df,
start_date='',
end_date='',
initialize=m4_initialize_bigquant_run,
before_trading_start=m4_before_trading_start_bigquant_run,
handle_tick=m4_handle_tick_bigquant_run,
handle_data=m4_handle_data_bigquant_run,
handle_trade=m4_handle_trade_bigquant_run,
handle_order=m4_handle_order_bigquant_run,
after_trading=m4_after_trading_bigquant_run,
capital_base=1000000,
frequency='daily',
price_type='真实价格',
product_type='股票',
before_start_days='0',
volume_limit=1,
order_price_field_buy='open',
order_price_field_sell='close',
benchmark='000300.SH',
plot_charts=True,
disable_cache=False,
replay_bdb=False,
show_debug_info=False,
backtest_only=False
)
报错如下：
date instrument total_market_cap
0 2020-01-02 603269.SH 1.282409e+09
1 2020-01-02 603991.SH 1.302126e+09
2 2020-01-02 603029.SH 1.379565e+09
3 2020-01-02 002112.SZ 1.399104e+09
4 2020-01-02 600561.SH 1.401048e+09
... ... ... ...
700145 2022-01-20 600036.SH 1.295543e+12
700146 2022-01-20 300750.SZ 1.326557e+12
700147 2022-01-20 601939.SH 1.532567e+12
700148 2022-01-20 601398.SH 1.692930e+12
700149 2022-01-20 600519.SH 2.474107e+12
700150 rows × 3 columns
日志 47 条 ▼
[2024-01-04 13:33:26.928239] INFO: moduleinvoker:3951888879.py:98:<module> hfbacktest.v1 开始运行..
[2024-01-04 13:33:26.949984] INFO hfbacktest: biglearning V1.5.6
[2024-01-04 13:33:26.953957] INFO hfbacktest: bigtrader v1.10.6 2023-12-28
[2024-01-04 13:33:26.991327] INFO: moduleinvoker:3951888879.py:98:<module> cached.v2 开始运行..
[2024-01-04 13:33:26.999547] INFO: moduleinvoker:3951888879.py:98:<module> 命中缓存
[2024-01-04 13:33:27.004425] INFO: moduleinvoker:3951888879.py:98:<module> cached.v2 运行完成[0.0131s].
[2024-01-04 13:33:27.115882] INFO: moduleinvoker:3951888879.py:98:<module> cached.v2 开始运行..
[2024-01-04 13:33:27.131427] INFO: moduleinvoker:3951888879.py:98:<module> 命中缓存
[2024-01-04 13:33:27.136301] INFO: moduleinvoker:3951888879.py:98:<module> cached.v2 运行完成[0.020436s].
a--------------
strategy(account_id:bkt000,custom_id:strategy,inited:True,trading:True)
date instrument total_market_cap
0 2020-01-02 603269.SH 1.282409e+09
1 2020-01-02 603991.SH 1.302126e+09
2 2020-01-02 603029.SH 1.379565e+09
3 2020-01-02 002112.SZ 1.399104e+09
4 2020-01-02 600561.SH 1.401048e+09
... ... ... ...
700145 2022-01-20 600036.SH 1.295543e+12
700146 2022-01-20 300750.SZ 1.326557e+12
700147 2022-01-20 601939.SH 1.532567e+12
700148 2022-01-20 601398.SH 1.692930e+12
700149 2022-01-20 600519.SH 2.474107e+12
[700150 rows x 3 columns]
<class 'bigtrader2.bigtrader.strategy.strategy_context.StrategyContext'>
b--------------
2024-01-04 13:34:04.546975 strategy strategy exception:Traceback (most recent call last):
File "bigtrader/strategy/engine.py", line 608, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
File "bigtrader/strategy/strategy_base.py", line 2236, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data
File "/tmp/ipykernel_2046/3951888879.py", line 44, in m4_handle_data_bigquant_run
positions_cost = {e.symbol: p.cost_basis for e, p in context.options['data'].positions.items()}
AttributeError: 'DataSource' object has no attribute 'positions'
event engine process events exception err='DataSource' object has no attribute 'positions'
Traceback (most recent call last):
File "bigtrader/event/event_engine.py", line 83, in bigtrader2.bigtrader.event.event_engine.EventEngine.process_events
File "bigtrader/event/event_engine.py", line 131, in bigtrader2.bigtrader.event.event_engine.EventEngine._process_without_lock
File "bigtrader/strategy/engine.py", line 197, in bigtrader2.bigtrader.strategy.engine.StrategyEngine.process_bars_event
File "bigtrader/strategy/engine.py", line 617, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
File "bigtrader/strategy/engine.py", line 608, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
File "bigtrader/strategy/strategy_base.py", line 2236, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data
File "/tmp/ipykernel_2046/3951888879.py", line 44, in m4_handle_data_bigquant_run
positions_cost = {e.symbol: p.cost_basis for e, p in context.options['data'].positions.items()}
AttributeError: 'DataSource' object has no attribute 'positions'
[2024-01-04 13:34:04.567409] ERROR: moduleinvoker:3951888879.py:98:<module> module name: hfbacktest, module version: v1, trackeback: AttributeError: 'DataSource' object has no attribute 'positions'
[2024-01-04 13:34:04.573471] ERROR: moduleinvoker:3951888879.py:98:<module> module name: hftrade, module version: v2, trackeback: AttributeError: 'DataSource' object has no attribute 'positions'
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[7], line 98
94 def m4_after_trading_bigquant_run(context, data):
95 pass
---> 98 m4 = M.hftrade.v2(
99 instruments=instruments,
100 options_data=df,
101 start_date='',
102 end_date='',
103 initialize=m4_initialize_bigquant_run,
104 before_trading_start=m4_before_trading_start_bigquant_run,
105 handle_tick=m4_handle_tick_bigquant_run,
106 handle_data=m4_handle_data_bigquant_run,
107 handle_trade=m4_handle_trade_bigquant_run,
108 handle_order=m4_handle_order_bigquant_run,
109 after_trading=m4_after_trading_bigquant_run,
110 capital_base=1000000,
111 frequency='daily',
112 price_type='真实价格',
113 product_type='股票',
114 before_start_days='0',
115 volume_limit=1,
116 order_price_field_buy='open',
117 order_price_field_sell='close',
118 benchmark='000300.SH',
119 plot_charts=True,
120 disable_cache=False,
121 replay_bdb=False,
122 show_debug_info=False,
123 backtest_only=False
124 )
File module2/common/modulemanagerv2.py:88, in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__()
File module2/common/moduleinvoker.py:370, in biglearning.module2.common.moduleinvoker.module_invoke()
File module2/common/moduleinvoker.py:292, in biglearning.module2.common.moduleinvoker._invoke_with_cache()
File module2/common/moduleinvoker.py:253, in biglearning.module2.common.moduleinvoker._invoke_with_cache()
File module2/common/moduleinvoker.py:210, in biglearning.module2.common.moduleinvoker._module_run()
File module2/modules/hftrade/v2/__init__.py:417, in biglearning.module2.modules.hftrade.v2.__init__.bigquant_run()
File module2/modules/hftrade/v2/__init__.py:257, in biglearning.module2.modules.hftrade.v2.__init__.bigquant_run.do_backtest_run()
File module2/common/modulemanagerv2.py:88, in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__()
File module2/common/moduleinvoker.py:370, in biglearning.module2.common.moduleinvoker.module_invoke()
File module2/common/moduleinvoker.py:292, in biglearning.module2.common.moduleinvoker._invoke_with_cache()
File module2/common/moduleinvoker.py:253, in biglearning.module2.common.moduleinvoker._invoke_with_cache()
File module2/common/moduleinvoker.py:212, in biglearning.module2.common.moduleinvoker._module_run()
File module2/modules/hfbacktest/v1/__init__.py:614, in biglearning.module2.modules.hfbacktest.v1.__init__.BigQuantModule.run()
File module2/modules/hfbacktest/v1/__init__.py:551, in biglearning.module2.modules.hfbacktest.v1.__init__.BigQuantModule.run_algo()
File /var/app/enabled/bigtrader2/bigtrader/run_trading.py:650, in run_backtest(start_date, end_date, strategy, strategy_setting, capital_base, product_type, frequency, instruments, options_data, **kwargs)
634 capital_base = kwargs.pop("capital")
636 rt = RunTrading(RunMode.BACKTEST,
637 acct_type=acct_type,
638 account_id=account_id,
(...)
648 options_data=options_data,
649 **kwargs)
--> 650 return rt.run(**kwargs)
File /var/app/enabled/bigtrader2/bigtrader/run_trading.py:401, in RunTrading.run(self, **kwargs)
398 return
400 if self.trading_env.run_mode == RunMode.BACKTEST:
--> 401 return self._run_backtest(**kwargs)
402 else:
403 if not self.trading_env.account_id:
File /var/app/enabled/bigtrader2/bigtrader/run_trading.py:428, in RunTrading._run_backtest(self, **kwargs)
426 debug_print("run_backtest() running...")
427 t0 = time.time()
--> 428 bkt_engine.run()
429 cost_time = round(time.time() - t0, 3)
430 if show_debug_info:
File bigtrader/strategy/backtest_engine.py:318, in bigtrader2.bigtrader.strategy.backtest_engine.BacktestEngine.run()
File bigtrader/strategy/backtest_engine.py:549, in bigtrader2.bigtrader.strategy.backtest_engine.BacktestEngine.transform()
File bigtrader/strategy/backtest_engine.py:518, in bigtrader2.bigtrader.strategy.backtest_engine.BacktestEngine.transform.replay_bars_dt()
File bigtrader/event/event_engine.py:88, in bigtrader2.bigtrader.event.event_engine.EventEngine.process_events()
File bigtrader/event/event_engine.py:83, in bigtrader2.bigtrader.event.event_engine.EventEngine.process_events()
File bigtrader/event/event_engine.py:131, in bigtrader2.bigtrader.event.event_engine.EventEngine._process_without_lock()
File bigtrader/strategy/engine.py:197, in bigtrader2.bigtrader.strategy.engine.StrategyEngine.process_bars_event()
File bigtrader/strategy/engine.py:617, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func()
File bigtrader/strategy/engine.py:608, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func()
File bigtrader/strategy/strategy_base.py:2236, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data()
Cell In[7], line 44, in m4_handle_data_bigquant_run(context, data)
41 print("b--------------")
43 #positions_cost = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}
---> 44 positions_cost = {e.symbol: p.cost_basis for e, p in context.options['data'].positions.items()}
46 # 检查持仓并执行止损止盈操作
47 if len(holding) > 0:
AttributeError: 'DataSource' object has no attribute 'positions'
日志 1 条 ▼
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 1
----> 1 print(type(context))
NameError: name 'context' is not defined
这时我还在想文档上看起来两个context是同一个东西，实际上我觉得不是，后来发现是同一个东西。
第三步
第四步
第五步
又回到了一开始的问题
\
# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m4_handle_data_bigquant_run(context, data):
# 获取当前日期
dt = data.current_dt.strftime('%Y-%m-%d')
# 获取当前日期的所有股票市值信息，并排序
df = context.df[context.df['date'] == dt].sort_values('total_market_cap', ascending=True)
# 获取市值排名前10的股票代码集合
instruments = set(list(df['instrument'])[:10])
# 获取当前持仓信息
holding = context.get_account_positions()
# 将持仓股票代码转换为集合，方便后续比较
holding_list = list(holding.keys())
holding_num = len(holding_list)
holding_list = set(holding_list)
# 初始化止损止盈列表
current_stopwin_stock = []
current_stoploss_stock = []
# 获取持仓成本
#print(context.portfolio)
positions_cost = {e: p.cost_basis for e, p in context.portfolio.positions.items()}
# 检查持仓并执行止损止盈操作
if len(holding) > 0:
for instrument, position in holding.items(): # 假设holding.items()返回(instrument, position)对
stock_cost = positions_cost[instrument] # 获取持仓成本
stock_market_price = data.current(context.symbol(instrument), 'price') # 获
# 检查是否可以交易，并执行止盈操作
if (stock_market_price / stock_cost - 1 >= 0.09) and data.can_trade(context.symbol(instrument)):
context.order_target_percent(context.symbol(instrument), 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
current_stopwin_stock.append(instrument) # 添加到止盈列表
continue # 继续下一个循环，不再检查止损条件
# 检查是否可以交易，并执行止损操作
if (stock_market_price / stock_cost - 1 <= -0.05) and data.can_trade(context.symbol(instrument)):
context.order_target_percent(context.symbol(instrument), 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
current_stoploss_stock.append(instrument) # 添加到止损列表
continue # 继续下一个循环
# 更新已卖出股票列表（假设stock_sold在外部已定义）
#stock_sold.extend(current_stopwin_stock + current_stoploss_stock) # 合并止盈止损列表并添加到已卖出列表
# 根据市值排名调整持仓
buy_list = instruments - holding_list # 需要买入的股票列表（排名前10但不在持仓中的）
sell_list = holding_list - instruments # 需要卖出的股票列表（在持仓中但不在排名前10的）
# 卖出不在股票池的股票
for ins in sell_list:
context.order_target_percent(ins, 0) # 卖出股票
holding_num -= 1 # 更新持仓数量
# 买入最新的股票，但不超过10只股票的总持仓限制
for ins in buy_list:
if holding_num <= 10: # 如果当前持仓数量小于等于10，则不再买入新的股票
#break # 跳出循环，不再继续买入操作
context.order_target_percent(ins, 1/10) # 买入股票，假设等权重分配资金
holding_num += 1 # 更新持仓数量（注意：这里可能有问题，因为实际买入可能需要时间确认，这里只是模拟逻辑）
\