量化策略系列教程：13布林強盜系統

又是嶄新的一天~小哥今天就只上一個策略哈~今天有點忙~證經社量化社區 - 證經社

1.策略原理及邏輯 1.1策略原理

布林帶(BOLL)是由John Bollinger在20世紀60年代創建的。最初，布林帶是用來判斷市場走勢的邊界，現在國內很多人也依然這麼用，即當價格移動到上軌或下軌附近後，預測價格將會回歸到中軌。但是經過測試，已經發現將上、下軌作為突破指標的效果要遠好於做為阻力指標。

布林強盜交易系統是一個中長線策略（本例假想的使用周期是日線），將採用後者的規則，價格超過50日移動平均線上方1個標準差作為買進信號的標準，跌破50日移動平均線下方1個標準差作為賣出信號的標準（主體交易條件）。

持有頭寸的時間每多一天，計算移動平均線的天數減一。持有頭寸時間越長，我們越容易帶

著利潤離場。計算移動平均線的天數最小可以遞減到10。如果達到10，則不再遞減。除了主

條件，次交易為如果持多倉，移動平均低於上軌發出平倉信號；加入這個離場條件是為了

防止布林強盜系統在止損之後重複入場。如果我們不使用這個離場條件，當移動平均在

上軌上方時，多頭入場條件仍然成立，因此多頭頭寸將會建立。

1.2策略邏輯

入場條件：

當前價格突破布林帶上軌，收盤價大於之前第30個交易日的收盤價；

boll bandit周期的均價大於下軌且當前價格小於均價；

出場條件：

當前價格低於之前第30個交易日的close，且低於了下軌；

boll bandit周期的均價低於下軌且當前價格小於均價；

2.代碼解讀

2.1配置文件【bollinger_bandit.ini】（提示ini配置文件，需要保存成UTF8格式）

[strategy]nusername=npassword=n;回測模式nmode=4ntd_addr=localhost:8001nstrategy_id=n;訂閱代碼注意及時更新nsubscribe_symbols=nn[backtest]nstart_time=2014-03-01 09:00:00nend_time=2016-03-18 15:00:00nn;策略初始資金ninitial_cash=1000000nn;委託量成交比率，默認=1（每個委託100%成交）ntransaction_ratio=1nn;手續費率，默認=0（不計算手續費）ncommission_ratio=0.0003nn;滑點比率，默認=0（無滑點）nslippage_ratio=0.00246nn;行情復權模式,0=不復權,1=前復權nprice_type=1nn;基準nbench_symbol=SHSE.000903nn[para]n;數據訂閱周期nbar_type=86400nn;boll bandit指標參數nn;boll bandit周期nboll_bandit_period=50nroc_period=30nn#止損周期nliq_days=10nn;上下軌標準差係數nup_ratio=1.25ndown_ratio=1.25nnn#止盈止損n;是否固定止盈止損nis_fixation_stop=0n;是否移動止盈nis_movement_stop=1nn;移動盈利開始比率及固定盈利比率nstop_fixation_profit=0.20n;虧損比率nstop_fixation_loss=0.068nn;移動止盈比率nstop_movement_profit=0.068nn;開倉量nopen_vol=2000nn;累計開倉距離當前的最大交易日n;若開倉距今超過這個日期，則認為未開過倉nopen_max_days=22nn##############################################################n# logger settingsn##############################################################n[loggers]nkeys=rootnn[logger_root]nlevel=INFOnhandlers=filenn[handlers]nkeys=filenn[handler_file]nclass=handlers.RotatingFileHandlernargs=(bollinger_bandit.log,a,1000,5)nformatter=simplenn[handler_console]nclass=StreamHandlernargs = (sys.stdout,)nformatter=simplenn[formatters]nkeys = simplenn[formatter_simple]nformat=%(asctime)s - %(name)s - %(levelname)s - %(message)sndatefmt=n

2.2策略文件【bollinger_bandit.py】

#!/usr/bin/env pythonn# encoding: utf-8nnimport sysnimport loggingnimport logging.confignimport configparsernimport csvnimport numpy as npnimport datetimenimport talibnimport arrownfrom gmsdk import *nnEPS = 1e-6nINIT_CLOSE_PRICE = 0nnnclass Bollinger_Bandit(StrategyBase):n cls_config = Nonen cls_user_name = Nonen cls_password = Nonen cls_mode = Nonen cls_td_addr = Nonen cls_strategy_id = Nonen cls_subscribe_symbols = Nonen cls_stock_pool = []nn cls_backtest_start = Nonen cls_backtest_end = Nonen cls_initial_cash = 1000000n cls_transaction_ratio = 1n cls_commission_ratio = 0.0n cls_slippage_ratio = 0.0n cls_price_type = 1n cls_bench_symbol = Nonenn def __init__(self, *args, **kwargs):n super(Bollinger_Bandit, self).__init__(*args, **kwargs)n self.cur_date = Nonen self.dict_close = {}n self.dict_open_close_signal = {}n self.dict_position_period = {}n self.dict_entry_high_low = {}n self.dict_last_factor = {}n self.dict_open_cum_days = {}nn @classmethodn def read_ini(cls, ini_name):n """n 功能：讀取策略配置文件n """n cls.cls_config = configparser.ConfigParser()n cls.cls_config.read(ini_name)nn @classmethodn def get_strategy_conf(cls):n """n 功能：讀取策略配置文件strategy段落的值n """n if cls.cls_config is None:n returnnn cls.cls_user_name = cls.cls_config.get(strategy, username)n cls.cls_password = cls.cls_config.get(strategy, password)n cls.cls_strategy_id = cls.cls_config.get(strategy, strategy_id)n cls.cls_subscribe_symbols = cls.cls_config.get(strategy, subscribe_symbols)n cls.cls_mode = cls.cls_config.getint(strategy, mode)n cls.cls_td_addr = cls.cls_config.get(strategy, td_addr)n if len(cls.cls_subscribe_symbols) <= 0:n cls.get_subscribe_stock()n else:n subscribe_ls = cls.cls_subscribe_symbols.split(,)n for data in subscribe_ls:n index1 = data.find(.)n index2 = data.find(., index1 + 1, -1)n cls.cls_stock_pool.append(data[:index2])nn returnnn @classmethodn def get_backtest_conf(cls):n """n 功能：讀取策略配置文件backtest段落的值n """n if cls.cls_config is None:n returnnn cls.cls_backtest_start = cls.cls_config.get(backtest, start_time)n cls.cls_backtest_end = cls.cls_config.get(backtest, end_time)n cls.cls_initial_cash = cls.cls_config.getfloat(backtest, initial_cash)n cls.cls_transaction_ratio = cls.cls_config.getfloat(backtest, transaction_ratio)n cls.cls_commission_ratio = cls.cls_config.getfloat(backtest, commission_ratio)n cls.cls_slippage_ratio = cls.cls_config.getfloat(backtest, slippage_ratio)n cls.cls_price_type = cls.cls_config.getint(backtest, price_type)n cls.cls_bench_symbol = cls.cls_config.get(backtest, bench_symbol)nn returnnn @classmethodn def get_stock_pool(cls, csv_file):n """n 功能：獲取股票池中的代碼n """n csvfile = open(csv_file, r)n reader = csv.reader(csvfile)n for line in reader:n cls.cls_stock_pool.append(line[0])nn returnnn @classmethodn def get_subscribe_stock(cls):n """n 功能：獲取訂閱代碼n """n cls.get_stock_pool(stock_pool.csv)n bar_type = cls.cls_config.getint(para, bar_type)n if 86400 == bar_type:n bar_type_str = .bar. + dailyn else:n bar_type_str = .bar. + %d % cls.cls_config.getint(para, bar_type)n cls.cls_subscribe_symbols = ,.join(data + bar_type_str for data in cls.cls_stock_pool)n returnnn def utc_strtime(self, utc_time):n """n 功能：utc轉字元串時間n """n str_time = %s % arrow.get(utc_time).to(local)n str_time.replace(T, )n str_time = str_time.replace(T, )n return str_time[:19]nn def get_para_conf(self):n """n 功能：讀取策略配置文件para(自定義參數)段落的值n """n if self.cls_config is None:n returnnn self.boll_bandit_period = self.cls_config.getint(para, boll_bandit_period)n self.up_ratio = self.cls_config.getfloat(para, up_ratio)n self.down_ratio = self.cls_config.getfloat(para, down_ratio)n self.roc_period = self.cls_config.getint(para, roc_period)n self.liq_days = self.cls_config.getint(para, liq_days)n self.open_vol = self.cls_config.getint(para, open_vol)n self.open_max_days = self.cls_config.getint(para, open_max_days)nn self.is_fixation_stop = self.cls_config.getint(para, is_fixation_stop)n self.is_movement_stop = self.cls_config.getint(para, is_movement_stop)nn self.stop_fixation_profit = self.cls_config.getfloat(para, stop_fixation_profit)n self.stop_fixation_loss = self.cls_config.getfloat(para, stop_fixation_loss)nn self.stop_movement_profit = self.cls_config.getfloat(para, stop_movement_profit)nn returnnn def init_strategy(self):n """n 功能：策略啟動初始化操作n """n if self.cls_mode == gm.MD_MODE_PLAYBACK:n self.cur_date = self.cls_backtest_startn self.end_date = self.cls_backtest_endn else:n self.cur_date = datetime.date.today().strftime(%Y-%m-%d) + 08:00:00n self.end_date = datetime.date.today().strftime(%Y-%m-%d) + 16:00:00nn self.dict_open_close_signal = {}n self.dict_entry_high_low = {}n self.get_last_factor()n self.init_data()n self.init_entry_high_low()n returnnn def init_data(self):n """n 功能：獲取訂閱代碼的初始化數據n """n for ticker in self.cls_stock_pool:n # 初始化倉位操作信號字典n self.dict_open_close_signal.setdefault(ticker, False)n self.dict_position_period.setdefault(ticker, self.boll_bandit_period)nn daily_bars = self.get_last_n_dailybars(ticker, self.boll_bandit_period - 1, self.cur_date)n if len(daily_bars) <= 0:n continuenn end_daily_bars = self.get_last_n_dailybars(ticker, 1, self.end_date)n if len(end_daily_bars) <= 0:n continuenn if ticker not in self.dict_last_factor:n continuenn end_adj_factor = self.dict_last_factor[ticker]n cp_ls = [data.close * data.adj_factor / end_adj_factor for data in daily_bars]n cp_ls.reverse()nn # 留出一個空位存儲當天的一筆數據n cp_ls.append(INIT_CLOSE_PRICE)n close = np.asarray(cp_ls, dtype=np.float)nn # 存儲歷史的closen self.dict_close.setdefault(ticker, close)nn def init_data_newday(self):n """n 功能：新的一天初始化數據n """n # 新的一天，去掉第一筆數據,並留出一個空位存儲當天的一筆數據n for key in self.dict_close:n if len(self.dict_close[key]) >= self.boll_bandit_period and abs(n self.dict_close[key][-1] - INIT_CLOSE_PRICE) > EPS:n self.dict_close[key] = np.append(self.dict_close[key][1:], INIT_CLOSE_PRICE)n elif len(self.dict_close[key]) < self.boll_bandit_period and abs(n self.dict_close[key][-1] - INIT_CLOSE_PRICE) > EPS:n self.dict_close[key] = np.append(self.dict_close[key][:], INIT_CLOSE_PRICE)nn # 初始化倉位操作信號字典n for key in self.dict_open_close_signal:n self.dict_open_close_signal[key] = Falsenn # 持倉周期n for key in self.dict_position_period:n index = key.find(.)n exchange = key[:index]n sec_id = key[index:]n pos = self.get_position(exchange, sec_id, OrderSide_Bid)nn if pos is not None and pos.volume > 0 and self.dict_position_period[key] > self.liq_days:n self.dict_position_period[key] = self.dict_position_period[key] - 1n else:n self.dict_position_period[key] = self.boll_bandit_periodnn # 開倉後到當前的交易日天數n keys = list(self.dict_open_cum_days.keys())n for key in keys:n if self.dict_open_cum_days[key] >= self.open_max_days:n del self.dict_open_cum_days[key]n else:n self.dict_open_cum_days[key] += 1nn def get_last_factor(self):n """n 功能：獲取指定日期最新的復權因子n """n for ticker in self.cls_stock_pool:n daily_bars = self.get_last_n_dailybars(ticker, 1, self.end_date)n if daily_bars is not None and len(daily_bars) > 0:n self.dict_last_factor.setdefault(ticker, daily_bars[0].adj_factor)nn def init_entry_high_low(self):n """n 功能：獲取進場後的最高價和最低價,模擬或實盤交易啟動時載入n """n pos_list = self.get_positions()n high_list = []n low_list = []n for pos in pos_list:n symbol = pos.exchange + . + pos.sec_idn init_time = self.utc_strtime(pos.init_time)nn cur_time = datetime.datetime.now().strftime(%Y-%m-%d %H:%M:%S)nn daily_bars = self.get_dailybars(symbol, init_time, cur_time)nn high_list = [bar.high for bar in daily_bars]n low_list = [bar.low for bar in daily_bars]nn if len(high_list) > 0:n highest = np.max(high_list)n else:n highest = pos.vwapnn if len(low_list) > 0:n lowest = np.min(low_list)n else:n lowest = pos.vwapnn self.dict_entry_high_low.setdefault(symbol, [highest, lowest])nn def on_bar(self, bar):n if self.cls_mode == gm.MD_MODE_PLAYBACK:n if bar.strtime[0:10] != self.cur_date[0:10]:n self.cur_date = bar.strtime[0:10] + 08:00:00n # 新的交易日n self.init_data_newday()nn symbol = bar.exchange + . + bar.sec_idnn self.movement_stop_profit_loss(bar)n self.fixation_stop_profit_loss(bar)nn # 填充價格n if symbol in self.dict_close:n self.dict_close[symbol][-1] = bar.closenn pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)nn if self.dict_open_close_signal[symbol] is False:n # 代碼持倉為空且當天未有對該代碼開、平倉n if symbol in self.dict_close and len(self.dict_close[symbol]) >= self.boll_bandit_period:n average_close = np.average(self.dict_close[symbol])n average_stddev = np.std(self.dict_close[symbol])n upper_band = average_close + average_stddev * self.up_ration down_band = average_close - average_stddev * self.down_ration roc_calc = bar.close - self.dict_close[symbol][self.boll_bandit_period - self.roc_period]n average_stop_loss = np.average(n self.dict_close[symbol][self.boll_bandit_period - self.dict_position_period[symbol]:])nn if pos is None and symbol not in self.dict_open_cum_days n and ((roc_calc > EPS and bar.close > upper_band) n or (average_close > upper_band and bar.close <= average_close)):nn # 有開倉機會則設置已開倉的交易天數n self.dict_open_cum_days[symbol] = 0nn cash = self.get_cash()n cur_open_vol = self.open_voln if cash.available / bar.close > self.open_vol:n cur_open_vol = self.open_voln else:n cur_open_vol = int(cash.available / bar.close / 100) * 100nn if cur_open_vol == 0:n print(no available cash to buy, available cash: %.2f % cash.available)n else:n # 當前價格大於roc周期的close，且上穿過了上軌n # 或者boll bandit周期的均價大於下軌且當前價格小於均價n self.open_long(bar.exchange, bar.sec_id, bar.close, cur_open_vol)n self.dict_open_close_signal[symbol] = Truen logging.info(open long, symbol:%s, time:%s, price:%.2f % (symbol, bar.strtime, bar.close))n elif pos is not None:n if (roc_calc < EPS and bar.close < down_band) n or (average_close < down_band and bar.close >= average_close):n # 當前價格低於roc周期的close，且低於了下軌n # 或者boll bandit周期的均價低於下軌且當前價格小於均價n vol = pos.volume - pos.volume_todayn if vol > 0:n self.close_long(bar.exchange, bar.sec_id, bar.close, vol)n self.dict_open_close_signal[symbol] = Truen logging.info(close long, symbol:%s, time:%s, price:%.2f, vwap: %.2f % (symbol,n bar.strtime,n bar.close,n pos.vwap))nn elif bar.low < average_stop_loss:n # 止損n vol = pos.volume - pos.volume_todayn if vol > 0:n stop_loss_price = average_stop_lossn if stop_loss_price > bar.open:n stop_loss_price = bar.openn self.close_long(bar.exchange, bar.sec_id, stop_loss_price, vol)n logging.info(n stop loss by lip days,close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f % (n symbol,n bar.strtime, stop_loss_price, pos.vwap))nn def on_order_filled(self, order):n symbol = order.exchange + . + order.sec_idn if order.position_effect == PositionEffect_CloseYesterday n and order.side == OrderSide_Bid:n pos = self.get_position(order.exchange, order.sec_id, order.side)n if pos is None and self.is_movement_stop == 1:n self.dict_entry_high_low.pop(symbol)nn def fixation_stop_profit_loss(self, bar):n """n 功能：固定止盈、止損,盈利或虧損超過了設置的比率則執行止盈、止損n """n if self.is_fixation_stop == 0:n returnnn symbol = bar.exchange + . + bar.sec_idn pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)n if pos is not None:n if pos.fpnl > 0 and pos.fpnl / pos.cost >= self.stop_fixation_profit:n self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)n self.dict_open_close_signal[symbol] = Truen logging.info(n fixnation stop profit: close long, symbol:%s, time:%s, price:%.2f, vwap: %s, volume:%s % (symbol,n bar.strtime,n bar.close,n pos.vwap,n pos.volume))n elif pos.fpnl < 0 and pos.fpnl / pos.cost <= -1 * self.stop_fixation_loss:n self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)n self.dict_open_close_signal[symbol] = Truen logging.info(n fixnation stop loss: close long, symbol:%s, time:%s, price:%.2f, vwap:%s, volume:%s % (symbol,n bar.strtime,n bar.close,n pos.vwap,n pos.volume))nn def movement_stop_profit_loss(self, bar):n """n 功能：移動止盈, 移動止盈止損按進場後的最高價乘以設置的比率與當前價格相比，n 並且盈利比率達到設定的盈虧比率時，執行止盈n """n if self.is_movement_stop == 0:n returnnn entry_high = Nonen entry_low = Nonen pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)n symbol = bar.exchange + . + bar.sec_idnn is_stop_profit = Truenn if pos is not None and pos.volume > 0:n if symbol in self.dict_entry_high_low:n if self.dict_entry_high_low[symbol][0] < bar.close:n self.dict_entry_high_low[symbol][0] = bar.closen is_stop_profit = Falsen if self.dict_entry_high_low[symbol][1] > bar.close:n self.dict_entry_high_low[symbol][1] = bar.closen [entry_high, entry_low] = self.dict_entry_high_low[symbol]nn else:n self.dict_entry_high_low.setdefault(symbol, [bar.close, bar.close])n [entry_high, entry_low] = self.dict_entry_high_low[symbol]n is_stop_profit = Falsenn if is_stop_profit:n # 移動止盈n if bar.close <= (n 1 - self.stop_movement_profit) * entry_high and pos.fpnl / pos.cost >= self.stop_fixation_profit:n if pos.volume - pos.volume_today > 0:n self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)n self.dict_open_close_signal[symbol] = Truen logging.info(n movement stop profit: close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f, volume:%s % (n symbol,n bar.strtime, bar.close, pos.vwap, pos.volume))nn # 止損n if pos.fpnl < 0 and pos.fpnl / pos.cost <= -1 * self.stop_fixation_loss:n self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)n self.dict_open_close_signal[symbol] = Truen logging.info(n movement stop loss: close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f, volume:%s % (symbol,n bar.strtime,n bar.close,n pos.vwap,n pos.volume))nnnif __name__ == __main__:n print(get_version())n logging.config.fileConfig(bollinger_bandit.ini)n Bollinger_Bandit.read_ini(bollinger_bandit.ini)n Bollinger_Bandit.get_strategy_conf()nn bollinger_bandit = Bollinger_Bandit(username=Bollinger_Bandit.cls_user_name,n password=Bollinger_Bandit.cls_password,n strategy_id=Bollinger_Bandit.cls_strategy_id,n subscribe_symbols=Bollinger_Bandit.cls_subscribe_symbols,n mode=Bollinger_Bandit.cls_mode,n td_addr=Bollinger_Bandit.cls_td_addr)nn if Bollinger_Bandit.cls_mode == gm.MD_MODE_PLAYBACK:n Bollinger_Bandit.get_backtest_conf()n ret = bollinger_bandit.backtest_config(start_time=Bollinger_Bandit.cls_backtest_start,n end_time=Bollinger_Bandit.cls_backtest_end,n initial_cash=Bollinger_Bandit.cls_initial_cash,n transaction_ratio=Bollinger_Bandit.cls_transaction_ratio,n commission_ratio=Bollinger_Bandit.cls_commission_ratio,n slippage_ratio=Bollinger_Bandit.cls_slippage_ratio,n price_type=Bollinger_Bandit.cls_price_type,n bench_symbol=Bollinger_Bandit.cls_bench_symbol)nn bollinger_bandit.get_para_conf()n bollinger_bandit.init_strategy()n ret = bollinger_bandit.run()nnprint(run result %s % ret)n

2.3[stock_pool.csv]文件

SHSE.600000nSHSE.600010nSHSE.600011nSHSE.600015nSHSE.600016nSHSE.600018nSHSE.600019nSHSE.600023nSHSE.600028nSHSE.600030nSHSE.600031nSHSE.600036nSHSE.600048nSHSE.600050nSHSE.600104nSHSE.600111nSHSE.600115nSHSE.600150nSHSE.600276nSHSE.600340nSHSE.600372nSHSE.600398nSHSE.600485nSHSE.600518nSHSE.600519nSHSE.600585nSHSE.600637nSHSE.600690nSHSE.600705nSHSE.600795nSHSE.600837nSHSE.600886nSHSE.600887nSHSE.600893nSHSE.600900nSHSE.600958nSHSE.600959nSHSE.600999nSHSE.601006nSHSE.601018nSHSE.601088nSHSE.601111nSHSE.601166nSHSE.601169nSHSE.601186nSHSE.601211nSHSE.601225nSHSE.601288nSHSE.601318nSHSE.601328nSHSE.601336nSHSE.601377nSHSE.601390nSHSE.601398nSHSE.601600nSHSE.601601nSHSE.601618nSHSE.601628nSHSE.601633nSHSE.601668nSHSE.601669nSHSE.601688nSHSE.601727nSHSE.601766nSHSE.601788nSHSE.601800nSHSE.601808nSHSE.601818nSHSE.601857nSHSE.601898nSHSE.601899nSHSE.601901nSHSE.601939nSHSE.601985nSHSE.601988nSHSE.601989nSHSE.601998nSHSE.603288nSZSE.000001nSZSE.000002nSZSE.000063nSZSE.000069nSZSE.000166nSZSE.000333nSZSE.000538nSZSE.000625nSZSE.000651nSZSE.000725nSZSE.000776nSZSE.000858nSZSE.000895nSZSE.002024nSZSE.002252nSZSE.002304nSZSE.002415nSZSE.002594nSZSE.002736nSZSE.002739nSZSE.300059nSZSE.300104n

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