簡述

這其實是在策略四的前一個版本。
這個是沒有手動實現beta對沖的策略。風險會更大。收益會更低。

代碼

# 導入函數庫 import statsmodels.api as sm from statsmodels import regression import numpy as np import pandas as pd import time from datetime import date# 一鍵回測說明： # 百度聚寬-》注冊賬號-》我的策略里面創建策略-》復制代碼到里面 # 右邊回測 開始時間：2017-1-1 終止時間：今天 資金：10000000# 初始化函數，設定基準等等 def initialize(context):g.tc = 2 # 調倉頻率# 下面是框架固定部分，不需要修改 g.N = 50 # 持倉數目g.t = 0 # 記錄運行的天數g.weight_list = [1] # 因子的權重參數log.set_level('order', 'error')set_option('use_real_price', True) # 用真實價格交易set_slippage(FixedSlippage(0)) # 將滑點設置為0set_commission(PerTrade(buy_cost=0.0000, sell_cost=0.000, min_cost=0)) # 手續費設置為0# set_benchmark('000905.XSHG') #中證500為業績基準set_benchmark('000300.XSHG') # 滬深300為業績基準# 選股范圍為全市場選股：上證+深證股票# g.stockrange= get_index_stocks('000001.XSHG')+get_index_stocks('399106.XSHE')g.stockrange = get_index_stocks('000300.XSHG')init_cash = context.portfolio.starting_cash/2# init_cash = context.portfolio.starting_cash# 操作期貨set_subportfolios([SubPortfolioConfig(cash=init_cash, type='stock'),])# 多少比例的資金用來對沖betag.rate = 0.5# 歷史最高收益率g.returnsRate = 0# 調整臨界限比率g.changeLimiteRate = 0.2# 調整比例g.changeRate = 0# g.betaName = 'IF9999.CCFX'## 每根日線運行一次 def handle_data(context, data):if g.t == 0:# 設置可行股票池：用set_feasible_stocks函數剔除當前或者計算樣本期間停牌的股票g.all_stocks = pickStock(context, g.stockrange)previousStr = str(context.previous_date)# adjustedBeta# adjustedBeta(context)stock_sort = get_all_cleaned_factor_ranked(g.all_stocks, g.weight_list, previousStr)# 調倉rebalance_position(context, stock_sort)# order_stock_sell(context,data,stock_sort)# order_stock_buy(context,data,stock_sort) # g.t+=1 # speed upg.t = (g.t + 1) % g.tcdef get_all_cleaned_factor_ranked(stocks, weight_list, previousStr):value = my_apha1(stocks)value = (-value).argsort()[:g.N]return list(map(lambda x: stocks[x], value))''' 調整對沖beta的量 '''def adjustedBeta(context):current_returns = context.portfolio.returnsif current_returns <= g.returnsRate * ( 1 - g.changeLimiteRate):g.changeRate += 1g.rate = 0.5 + (0.5 / (1+np.exp(-g.changeRate)))elif current_returns >= g.returnsRate * ( 1 + g.changeLimiteRate):g.changeRate -= 1g.rate = 0.5 + (0.5 / (1+np.exp(-g.changeRate)))if current_returns > g.returnsRate:g.returnsRate = current_returns''' END 調整對沖beta的量 '''''' factor1 '''def single_alpha1(stock):df = get_price(stock, count=26, fields=['close'])returns = (df[1:] - df[:-2]) / df[:-2] # 25 linesvalue = [0] * 5for i in range(5):if np.array(returns).tolist()[-1 - i] < 0:value[i] = returns.ix[-20 - i:-1 - i].std() ** 2else:value[i] = df['close'][-1 - i] ** 2return 5 - np.array(value).argmax()def my_apha1(stocks):value = list(map(single_alpha1, stocks))MAX = max(value)MIN = min(value)value = (np.array(value) - MIN) / (MAX - MIN)return value''' END factor1 '''''' 初步篩選股票 '''# pick stocks def pickStock(context, stocks):# universe = set_feasible_stocks(stocks)universe = filter_specials(stocks, context)# 過濾上市時間小于60天的股票for stock in universe:days_public = (context.current_dt.date() - get_security_info(stock).start_date).daysif days_public < 60:universe.remove(stock)g.lenth = len(universe)return universe# 過濾停牌的股票 def set_feasible_stocks(stock_list):current_data = get_current_data()stock_list = [stock for stock in stock_list if not current_data[stock].paused] # 不考慮停盤的股票return stock_list# 將多因子的dataframe進行排序，并且將有空值的行去掉 def rank_stock(all_factor, weight_list):C = len(all_factor.columns)ranked = all_factor.iloc[:, 0].rank() * weight_list[0]if C > 1:for j in range(1, C):ranked = all_factor.iloc[:, j].rank() * weight_list[j] + rankedranked = pd.DataFrame(ranked)ranked.columns = ['rank']one_sort = ranked.sort('rank', ascending=g.ascending)stock_sort = one_sort.index[:g.N]return stock_sort#過濾退市，停牌，STdef filter_specials(stock_list,context):curr_data = get_current_data()stock_list = [stock for stock in stock_list if \(not curr_data[stock].paused) # 未停牌and (not curr_data[stock].is_st) # 非STand ('ST' not in curr_data[stock].name)and ('*' not in curr_data[stock].name)and ('退' not in curr_data[stock].name)and (curr_data[stock].low_limit < curr_data[stock].day_open < curr_data[stock].high_limit) ]return stock_list''' END 初步篩選股票 '''''' 調倉 '''def rebalance_position(context, stocks_list):current_holding = context.subportfolios[0].positions.keys()stocks_to_sell = list(set(current_holding) - set(stocks_list))# 賣出bulk_orders(stocks_to_sell, 0)total_value = context.subportfolios[0].total_value# set_option('futures_margin_rate', g.rate)# bete_order(context,context.subportfolios[1].total_value / g.rate)# 買入 bulk_orders(stocks_list, total_value / len(stocks_list))# 批量買賣股票 def bulk_orders(stocks_list, target_value):for i in stocks_list:order_target_value(i, target_value, pindex=0)def bete_order(context, money):amount = int( money / get_current_data()[g.betaName].last_price)order_target(g.betaName, -amount , side='long', pindex=1) # 做空''' END 調倉 '''''' 調倉初版 '''##獲得賣出信號，并執行賣出操作 # 輸入：context, data，已排序股票列表stock_sort-list類型 # 輸出：none def order_stock_sell(context, data, stock_sort):# 對于不需要持倉的股票，全倉賣出for stock in context.portfolio.positions:# 除去排名前g.N個股票（選股！）if stock not in stock_sort:stock_sell = stockorder_target_value(stock_sell, 0)# 獲得買入信號，并執行買入操作 # 輸入：context, data，已排序股票列表stock_sort-list類型 # 輸出：none def order_stock_buy(context, data, stock_sort):# 對于需要持倉的股票，按分配到的份額買入for stock in stock_sort:stock_buy = stockorder_target_value(stock_buy, g.everyStock)''' END 調倉初版 '''

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