Python量化交易-源码.rar

# 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__': run(strategy_id='63e92f59-1386-11e8-bbe9-902b3463caf1', filename='多因子选股.py', mode=MODE_BACKTEST, token='a71a8083b68e73817e93f7f196b030482abe5939', backtest_start_time='2017-01-03 08:00:00', backtest_end_time='2017-12-31 16:00:00', backtest_adjust=ADJUST_PREV, backtest_initial_cash=100000, )