如题，前阵子看到社区内的RBreaker策略，想到加入长周期的ATR指标来检验波动，只在波动率大于平均波动率时开仓，于是在原策略的模板上进行了一些修改。
但是修改后的策略只能加载历史数据，逐日盯市后成交数量为空。
想请各位大佬帮忙看下代码，是不是我写的有问题？
数据部分使用的是RQData的IC88，时间为2022/1/1 - 2022/7/30
def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
""""""
super(RBreakStrategyPlus, self).__init__(
cta_engine, strategy_name, vt_symbol, setting
)
self.bg1 = BarGenerator(self.on_bar, window=1, on_window_bar=self.on_1min_bar, interval=Interval.MINUTE)
self.bg10 = BarGenerator(self.on_bar, window=10, on_window_bar=self.on_10min_bar, interval=Interval.MINUTE)
self.am1 = ArrayManager()
self.am10 = ArrayManager()
self.bars = []
def on_init(self):
"""
Callback when strategy is inited.
"""
self.write_log("策略初始化")
self.load_bar(10)
def on_start(self):
"""
Callback when strategy is started.
"""
self.write_log("策略启动")
def on_stop(self):
"""
Callback when strategy is stopped.
"""
self.write_log("策略停止")
def on_tick(self, tick: TickData):
"""
Callback of new tick data update.
"""
self.bg1.update_tick(tick)
def on_bar(self,bar:BarData):
"""k线更新,切记先更新长周期，再更新短周期"""
self.bg10.update_bar(bar) #将1分钟K线合成10分钟K线
self.bg1.update_bar(bar)
def on_10min_bar(self, bar: BarData):
"""
Callback of new bar data update.
"""
am = self.am10
am.update_bar(bar)
if not am.inited:
return
# 计算ATR并判断波动大小
atr_array = am.atr(self.atr_length, array=True)
self.atr_value = atr_array[-1]
self.atr_ma = atr_array[-self.atr_ma_length:].mean()
if self.atr_value > self.atr_ma:
self.atr_trend = 1
else:
self.atr_trend = -1
def on_1min_bar(self, bar: BarData):
"""
Callback of new bar data update.
"""
self.cancel_all()
am = self.am1
if not am.inited:
return
self.bars.append(bar)
if len(self.bars) <= 2:
return
else:
self.bars.pop(0)
last_bar = self.bars[-2]
# New Day
if last_bar.datetime.date() != bar.datetime.date():
if self.day_open:
self.buy_setup = self.day_low - self.setup_coef * (self.day_high - self.day_close) # 观察买入价
self.sell_setup = self.day_high + self.setup_coef * (self.day_close - self.day_low) # 观察卖出价
self.buy_enter = (self.enter_coef_1 / 2) * (self.day_high + self.day_low) - self.enter_coef_2 * self.day_high # 反转买入价
self.sell_enter = (self.enter_coef_1 / 2) * (self.day_high + self.day_low) - self.enter_coef_2 * self.day_low # 反转卖出价
self.buy_break = self.buy_setup + self.break_coef * (self.sell_setup - self.buy_setup) # 突破买入价
self.sell_break = self.sell_setup - self.break_coef * (self.sell_setup - self.buy_setup) # 突破卖出价
self.day_open = bar.open_price
self.day_high = bar.high_price
self.day_close = bar.close_price
self.day_low = bar.low_price
# Today
else:
self.day_high = max(self.day_high, bar.high_price)
self.day_low = min(self.day_low, bar.low_price)
self.day_close = bar.close_price
if not self.sell_setup:
return
self.tend_high, self.tend_low = am.donchian(self.donchian_window)
if bar.datetime.time() < self.exit_time:
if self.pos == 0:
self.intra_trade_low = bar.low_price
self.intra_trade_high = bar.high_price
if self.tend_high > self.sell_setup and self.sell_setup > self.buy_setup:
long_entry = max(self.buy_break, self.day_high)
if self.atr_trend > 0:
self.buy(long_entry, self.fixed_size, stop=True)
# self.short(self.sell_enter, self.multiplier * self.fixed_size, stop=True)
elif self.tend_low < self.buy_setup:
short_entry = min(self.sell_break, self.day_low)
if self.atr_trend > 0:
self.short(short_entry, self.fixed_size, stop=True)
# self.buy(self.buy_enter, self.multiplier * self.fixed_size, stop=True)
elif self.pos > 0:
self.intra_trade_high = max(self.intra_trade_high, bar.high_price)
long_stop = self.intra_trade_high * (1 - self.trailing_long / 100)
self.sell(long_stop, abs(self.pos), stop=True)
elif self.pos < 0:
self.intra_trade_low = min(self.intra_trade_low, bar.low_price)
short_stop = self.intra_trade_low * (1 + self.trailing_short / 100)
self.cover(short_stop, abs(self.pos), stop=True)
self.write_log("最低价 = %f" % bar.low_price )
# Close existing position
else:
if self.pos > 0:
self.sell(bar.close_price * 0.99, abs(self.pos))
elif self.pos < 0:
self.cover(bar.close_price * 1.01, abs(self.pos))
self.put_event()
