模拟没有卖出信号

由aimarhu创建，最终由small_q更新于2023-10-09 02:50 被浏览 77 用户

请问这个卖出是否哪里设置不对，用这个trade去跑回测是可以正常运行的 也会买入卖出，但是放到模拟里面 他只买入 不卖出

回测引擎：每日数据处理函数，每天执行一次

def 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')] #------------------------------------------止损模块START-------------------------------------------- date = data.current_dt.strftime('%Y-%m-%d')equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0} # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断 current_stoploss_stock = [] if len(equities) > 0: for i in equities.keys(): stock_market_price = data.current(context.symbol(i), 'price') # 最新市场价格 last_sale_date = equities[i].last_sale_date # 上次交易日期 delta_days = data.current_dt - last_sale_datehold_days = delta_days.days # 持仓天数 # 建仓以来的最高价 highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max() # 确定止损位置 stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.15 record('止损位置', stoploss_line) # 如果价格下穿止损位置 if stock_market_price < stoploss_line: context.order_target_percent(context.symbol(i), 0)current_stoploss_stock.append(i) print('日期：', date , '股票：', i, '出现止损状况') #-------------------------------------------止损模块END-------------------------------------------------- # 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 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.portfolio.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StockRanker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):
        # 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)
    if cash > 0:
        context.order_value(context.symbol(instrument), cash)

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