import numpy as np import pandas as pd #初始化函数 def initialize(context): # 设定基准 set_benchmark('000300.XSHG') # 用真实价格交易 set_option('use_real_price', True) # 打开防未来函数 set_option("avoid_future_data", True) # 设置滑点 https://www.joinquant.com/view/community/detail/a31a822d1cfa7e83b1dda228d4562a70 set_slippage(FixedSlippage(0.000)) # 设置交易成本 set_order_cost(OrderCost(open_tax=0, close_tax=0, open_commission=0.0002, close_commission=0.0002, close_today_commission=0, min_commission=5), type='fund') # 过滤一定级别的日志 log.set_level('system', 'error') # 参数 g.etf_pool = [ '518880.XSHG', #黄金ETF（大宗商品） '513100.XSHG', #纳指100（海外资产） '159915.XSHE', #创业板100（成长股，科技股，中小盘） '510180.XSHG', #上证180（价值股，蓝筹股，中大盘） ] g.m_days = 25 #动量参考天数 run_daily(trade, '9:30') #每天运行确保即时捕捉动量变化 # 基于年化收益和判定系数打分的动量因子轮动 https://www.joinquant.com/post/26142 def get_rank(etf_pool): score_list = [] for etf in etf_pool: df = attribute_history(etf, g.m_days, '1d', ['close']) y = df['log'] = np.log(df.close) x = df['num'] = np.arange(df.log.size) slope, intercept = np.polyfit(x, y, 1) annualized_returns = math.pow(math.exp(slope), 250) - 1 r_squared = 1 - (sum((y - (slope * x + intercept))**2) / ((len(y) - 1) * np.var(y, ddof=1))) score = annualized_returns * r_squared score_list.append(score) df = pd.DataFrame(index=etf_pool, data={'score':score_list}) df = df.sort_values(by='score', ascending=False) rank_list = list(df.index) print(df) record(黄金 = round(df.loc['518880.XSHG'], 2)) record(纳指 = round(df.loc['513100.XSHG'], 2)) record(成长 = round(df.loc['159915.XSHE'], 2)) record(价值 = round(df.loc['510180.XSHG'], 2)) return rank_list # 交易 def trade(context): # 获取动量最高的一只ETF target_num = 1 target_list = get_rank(g.etf_pool)[:target_num] # 卖出 hold_list = list(context.portfolio.positions) for etf in hold_list: if etf not in target_list: order_target_value(etf, 0) print('卖出' + str(etf)) else: print('继续持有' + str(etf)) # 买入 hold_list = list(context.portfolio.positions) if len(hold_list) < target_num: value = context.portfolio.available_cash / (target_num - len(hold_list)) for etf in target_list: if context.portfolio.positions[etf].total_amount == 0: order_target_value(etf, value) print('买入' + str(etf))