以下是源代码： def simulate_tradeself trade_dict trade_dates close_dict # 模拟交易过程并获取每个交易日和持有日的资金列表和资产列表返回这两个列表 position = # 持仓字典格式为股票1 持仓数量 cash = 100000 # 初始资金单位为元 cash_list =

def simulate_trade(self, trade_list, trade_dates, close_dict, max_num=None): # 模拟交易过程，并获取每个交易日和持有日的资金列表和资产列表，返回这两个列表 position = {} # 持仓字典，格式为{股票1: 持仓数量,} cash = 100000 # 初始资金，单位为元 cash_list = [cash] # 资金列表，记录每个交易日的资金情况 asset_list = [cash] # 资产列表，记录每个交易日和持有日的总资产情况 date_list = [] # 将trade_list转换成买入字典和卖出字典 buy_dict = {} # 买入字典，格式为{日期: [股票1, 股票2],} sell_dict = {} # 卖出字典，格式为{日期: [股票1, 股票2],} for buy_date, sell_date, codes in trade_list: for code in codes: buy_dict.setdefault(buy_date, []).append(code) sell_dict.setdefault(sell_date, []).append(code) date_list.append(buy_date) date_list.append(sell_date) date_list = sorted(list(set(date_list))) # 遍历每个交易日和持有日，模拟交易过程 for date in trade_dates: # 检查是否是交易日 if date in date_list: # 检查是否有需要卖出的股票 for code in sell_dict.get(date, []): # 获取当天该股票的收盘价作为卖出价 if code not in close_dict: continue sell_price = close_dict[code].loc[date] # 检查是否可以交易 while np.isnan(sell_price): date = pd.to_datetime(date) + pd.Timedelta(days=1) date = date.strftime('%Y-%m-%d') if date not in close_dict: break sell_price = close_dict[code].loc[date] if np.isnan(sell_price): continue # 计算卖出所得的资金，并更新持仓和资金 if code in position: sell_amount = position[code] * sell_price else: print('%s持仓中找不到%s:\n%s' % (date, code, ', '.join(position.keys()))) continue cash += sell_amount position.pop(code) # 检查是否有需要买入的股票 for code in buy_dict.get(date, []): # 获取当天该股票的收盘价作为买入价 if code not in close_dict: continue buy_price = close_dict[code].loc[date] # 检查是否可以交易 while np.isnan(buy_price): date = pd.to_datetime(date) + pd.Timedelta(days=1) date = date.strftime('%Y-%m-%d') if date not in close_dict: break buy_price = close_dict[code].loc[date] if np.isnan(buy_price): continue # 计算可以买入的数量，并更新持仓和资金 buy_amount = cash / len(buy_dict[date]) # 平均分配资金 buy_volume = int(buy_amount / buy_price / 100) * 100 # 向下取整到100的倍数 if max_num is not None: # 检查是否需要限制买入股票数量 buy_volume = min(buy_volume, int(cash / max_num / buy_price / 100) * 100) if code in position: max_volume = int(0.1 * cash / position[code] / buy_price / 100) * 100 # 检查是否需要限制股票的仓位 buy_volume = min(buy_volume, max_volume) if buy_volume > 0: # 检查资金是否足够买入 cash -= buy_volume * buy_price position[code] = position.get(code, 0) + buy_volume if max_num is not None and len(position) > max_num: # 检查是否需要卖出股票 excess_codes = sorted(position, key=lambda x: position[x], reverse=True)[max_num:] for code in excess_codes: sell_price = close_dict[code].loc[date] if np.isnan(sell_price) or code not in position: continue sell_volume = position[code] sell_amount = sell_volume * sell_price cash += sell_amount position.pop(code) # 计算当天的总资产 cash_list.append(cash) total_asset = cash for code, volume in position.items(): if code not in close_dict: continue if date in close_dict[code].index: price = close_dict[code].loc[date] else: sub = close_dict[code].loc[:date] if len(sub) == 0: logger.info('can not find %s for %s in\n%s' % (date, code, sub)) price = sub.iloc[-1] total_asset += volume * price # 将当天的总资产加入到资金列表和资产列表中 asset_list.append(total_asset) return cash_list, asset_lis