1. # coding=utf-8
      2. from __future__ import print_function, absolute_import, unicode_literals
      3. import numpy as np
      4. from gm.api import *
      5. from pandas import DataFrame
      7. '''
      8. 本策略以0.8为初始权重跟踪指数标的沪深300中权重大于0.35%的成份股.
      9. 个股所占的百分比为(0.8*成份股权重)*100%.然后根据个股是否:
      10. 1.连续上涨5天 2.连续下跌5天
      11. 来判定个股是否为强势股/弱势股,并对其把权重由0.8调至1.0或0.6
      12. 回测时间为:2017-07-01 08:50:00到2017-10-01 17:00:00
      13. '''
      16. def init(context):
      17.     # 资产配置的初始权重,配比为0.6-0.8-1.0
      18.     context.ratio = 0.8
      19.     # 获取沪深300当时的成份股和相关数据
      20.     stock300 = get_history_constituents(index='SHSE.000300', start_date='2017-06-30', end_date='2017-06-30')[0][
      21.         'constituents']
      22.     stock300_symbol = []
      23.     stock300_weight = []
      25.     for key in stock300:
      26.         # 保留权重大于0.35%的成份股
      27.         if (stock300[key] / 100) > 0.0035:
      28.             stock300_symbol.append(key)
      29.             stock300_weight.append(stock300[key] / 100)
      31.     context.stock300 = DataFrame([stock300_weight], columns=stock300_symbol, index=['weight']).T
      32.     print('选择的成分股权重总和为: ', np.sum(stock300_weight))
      33.     subscribe(symbols=stock300_symbol, frequency='1d', count=5, wait_group=True)
      36. def on_bar(context, bars):
      37.     # 若没有仓位则按照初始权重开仓
      38.     for bar in bars:
      39.         symbol = bar['symbol']
      40.         position = context.account().position(symbol=symbol, side=PositionSide_Long)
      41.         if not position:
      42.             buy_percent = context.stock300['weight'][symbol] * context.ratio
      43.             order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
      44.                                  position_side=PositionSide_Long)
      45.             print(symbol, '以市价单开多仓至仓位:', buy_percent)
      46.         else:
      47.             # 获取过去5天的价格数据,若连续上涨则为强势股,权重+0.2;若连续下跌则为弱势股,权重-0.2
      48.             recent_data = context.data(symbol=symbol, frequency='1d', count=5, fields='close')['close'].tolist()
      49.             if all(np.diff(recent_data) > 0):
      50.                 buy_percent = context.stock300['weight'][symbol] * (context.ratio + 0.2)
      51.                 order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
      52.                                      position_side=PositionSide_Long)
      53.                 print('强势股', symbol, '以市价单调多仓至仓位:', buy_percent)
      54.             elif all(np.diff(recent_data) < 0):
      55.                 buy_percent = context.stock300['weight'][symbol] * (context.ratio - 0.2)
      56.                 order_target_percent(symbol=symbol, percent=buy_percent, order_type=OrderType_Market,
      57.                                      position_side=PositionSide_Long)
      58.                 print('弱势股', symbol, '以市价单调多仓至仓位:', buy_percent)
      61. if __name__ == '__main__':
      62.     '''
      63.     strategy_id策略ID,由系统生成
      64.     filename文件名,请与本文件名保持一致
      65.     mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
      66.     token绑定计算机的ID,可在系统设置-密钥管理中生成
      67.     backtest_start_time回测开始时间
      68.     backtest_end_time回测结束时间
      69.     backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
      70.     backtest_initial_cash回测初始资金
      71.     backtest_commission_ratio回测佣金比例
      72.     backtest_slippage_ratio回测滑点比例
      73.     '''
      74.     run(strategy_id='strategy_id',
      75.         filename='main.py',
      76.         mode=MODE_BACKTEST,
      77.         token='token_id',
      78.         backtest_start_time='2017-07-01 08:50:00',
      79.         backtest_end_time='2017-10-01 17:00:00',
      80.         backtest_adjust=ADJUST_PREV,
      81.         backtest_initial_cash=10000000,
      82.         backtest_commission_ratio=0.0001,
      83.         backtest_slippage_ratio=0.0001)

    指数增强(股票)

    原文: https://www.myquant.cn/docs/python_strategyies/105