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本策略每隔1个月定时触发,根据Fama-French三因子模型对每只股票进行回归，得到其alpha值。假设Fama-French三因子模型可以完全解释市场，则alpha为负表明市场低估该股，因此应该买入。 策略思路：- 计算市场收益率、个股的账面市值比和市值,并对后两个进行了分类,
- 根据分类得到的组合分别计算其市值加权收益率、SMB和HML.
- 对各个股票进行回归(假设无风险收益率等于0)得到alpha值.
- 选取alpha值小于0并为最小的10只股票进入标的池
- 平掉不在标的池的股票并等权买入在标的池的股票
回测数据:SHSE.000300的成份股回测时间:2017-07-01 08:00:00到2017-10-01 16:00:0
- # coding=utf-8
- from __future__ import print_function, absolute_import, unicode_literals
- import numpy as np
- from gm.api import *
- from pandas import DataFrame
- '''
- '''
- def init(context):
- # 每月第一个交易日的09:40 定时执行algo任务
- schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')
- print(order_target_percent(symbol='SHSE.600000', percent=0.5, order_type=OrderType_Market,
- position_side=PositionSide_Long))
- # 数据滑窗
- context.date = 20
- # 设置开仓的最大资金量
- context.ratio = 0.8
- # 账面市值比的大/中/小分类
- context.BM_BIG = 3.0
- context.BM_MID = 2.0
- context.BM_SMA = 1.0
- # 市值大/小分类
- context.MV_BIG = 2.0
- context.MV_SMA = 1.0
- # 计算市值加权的收益率,MV为市值的分类,BM为账目市值比的分类
- def market_value_weighted(stocks, MV, BM):
- select = stocks[(stocks.NEGOTIABLEMV == MV) & (stocks.BM == BM)]
- market_value = select['mv'].values
- mv_total = np.sum(market_value)
- mv_weighted = [mv / mv_total for mv in market_value]
- stock_return = select['return'].values
- # 返回市值加权的收益率的和
- return_total = []
- for i in range(len(mv_weighted)):
- return_total.append(mv_weighted[i] * stock_return[i])
- return_total = np.sum(return_total)
- return return_total
- def algo(context):
- # 获取上一个交易日的日期
- last_day = get_previous_trading_date(exchange='SHSE', date=context.now)
- # 获取沪深300成份股
- context.stock300 = get_history_constituents(index='SHSE.000300', start_date=last_day,
- end_date=last_day)[0]['constituents'].keys()
- # 获取当天有交易的股票
- not_suspended = get_history_instruments(symbols=context.stock300, start_date=last_day, end_date=last_day)
- not_suspended = [item['symbol'] for item in not_suspended if not item['is_suspended']]
- fin = get_fundamentals(table='tq_sk_finindic', symbols=not_suspended, start_date=last_day, end_date=last_day,
- fields='PB,NEGOTIABLEMV', df=True)
- # 计算账面市值比,为P/B的倒数
- fin['PB'] = (fin['PB'] ** -1)
- # 计算市值的50%的分位点,用于后面的分类
- size_gate = fin['NEGOTIABLEMV'].quantile(0.50)
- # 计算账面市值比的30%和70%分位点,用于后面的分类
- bm_gate = [fin['PB'].quantile(0.30), fin['PB'].quantile(0.70)]
- fin.index = fin.symbol
- x_return = []
- # 对未停牌的股票进行处理
- for symbol in not_suspended:
- # 计算收益率
- close = history_n(symbol=symbol, frequency='1d', count=context.date + 1, end_time=last_day, fields='close',
- skip_suspended=True, fill_missing='Last', adjust=ADJUST_PREV, df=True)['close'].values
- stock_return = close[-1] / close[0] - 1
- pb = fin['PB'][symbol]
- market_value = fin['NEGOTIABLEMV'][symbol]
- # 获取[股票代码. 股票收益率, 账面市值比的分类, 市值的分类, 流通市值]
- if pb < bm_gate[0]:
- if market_value < size_gate:
- label = [symbol, stock_return, context.BM_SMA, context.MV_SMA, market_value]
- else:
- label = [symbol, stock_return, context.BM_SMA, context.MV_BIG, market_value]
- elif pb < bm_gate[1]:
- if market_value < size_gate:
- label = [symbol, stock_return, context.BM_MID, context.MV_SMA, market_value]
- else:
- label = [symbol, stock_return, context.BM_MID, context.MV_BIG, market_value]
- elif market_value < size_gate:
- label = [symbol, stock_return, context.BM_BIG, context.MV_SMA, market_value]
- else:
- label = [symbol, stock_return, context.BM_BIG, context.MV_BIG, market_value]
- if len(x_return) == 0:
- x_return = label
- else:
- x_return = np.vstack([x_return, label])
- stocks = DataFrame(data=x_return, columns=['symbol', 'return', 'BM', 'NEGOTIABLEMV', 'mv'])
- stocks.index = stocks.symbol
- columns = ['return', 'BM', 'NEGOTIABLEMV', 'mv']
- for column in columns:
- stocks[column] = stocks[column].astype(np.float64)
- # 计算SMB.HML和市场收益率
- # 获取小市值组合的市值加权组合收益率
- smb_s = (market_value_weighted(stocks, context.MV_SMA, context.BM_SMA) +
- market_value_weighted(stocks, context.MV_SMA, context.BM_MID) +
- market_value_weighted(stocks, context.MV_SMA, context.BM_BIG)) / 3
- # 获取大市值组合的市值加权组合收益率
- smb_b = (market_value_weighted(stocks, context.MV_BIG, context.BM_SMA) +
- market_value_weighted(stocks, context.MV_BIG, context.BM_MID) +
- market_value_weighted(stocks, context.MV_BIG, context.BM_BIG)) / 3
- smb = smb_s - smb_b
- # 获取大账面市值比组合的市值加权组合收益率
- hml_b = (market_value_weighted(stocks, context.MV_SMA, 3) +
- market_value_weighted(stocks, context.MV_BIG, context.BM_BIG)) / 2
- # 获取小账面市值比组合的市值加权组合收益率
- hml_s = (market_value_weighted(stocks, context.MV_SMA, context.BM_SMA) +
- market_value_weighted(stocks, context.MV_BIG, context.BM_SMA)) / 2
- hml = hml_b - hml_s
- close = history_n(symbol='SHSE.000300', frequency='1d', count=context.date + 1,
- end_time=last_day, fields='close', skip_suspended=True,
- fill_missing='Last', adjust=ADJUST_PREV, df=True)['close'].values
- market_return = close[-1] / close[0] - 1
- coff_pool = []
- # 对每只股票进行回归获取其alpha值
- for stock in stocks.index:
- x_value = np.array([[market_return], [smb], [hml], [1.0]])
- y_value = np.array([stocks['return'][stock]])
- # OLS估计系数
- coff = np.linalg.lstsq(x_value.T, y_value)[0][3]
- coff_pool.append(coff)
- # 获取alpha最小并且小于0的10只的股票进行操作(若少于10只则全部买入)
- stocks['alpha'] = coff_pool
- stocks = stocks[stocks.alpha < 0].sort_values(by='alpha').head(10)
- symbols_pool = stocks.index.tolist()
- positions = context.account().positions()
- # 平不在标的池的股票
- for position in positions:
- symbol = position['symbol']
- if symbol not in symbols_pool:
- order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,
- position_side=PositionSide_Long)
- print('市价单平不在标的池的', symbol)
- # 获取股票的权重
- percent = context.ratio / len(symbols_pool)
- # 买在标的池中的股票
- for symbol in symbols_pool:
- order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
- position_side=PositionSide_Long)
- print(symbol, '以市价单调多仓到仓位', percent)
- if __name__ == '__main__':
- '''
- strategy_id策略ID,由系统生成
- filename文件名,请与本文件名保持一致
- mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
- token绑定计算机的ID,可在系统设置-密钥管理中生成
- backtest_start_time回测开始时间
- backtest_end_time回测结束时间
- backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
- backtest_initial_cash回测初始资金
- backtest_commission_ratio回测佣金比例
- backtest_slippage_ratio回测滑点比例
- '''
- run(strategy_id='strategy_id',
- filename='main.py',
- mode=MODE_BACKTEST,
- token='token_id',
- backtest_start_time='2017-07-01 08:00:00',
- backtest_end_time='2017-10-01 16:00:00',
- backtest_adjust=ADJUST_PREV,
- backtest_initial_cash=10000000,
- backtest_commission_ratio=0.0001,
- backtest_slippage_ratio=0.0001)
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