Bollinger Band and Momentum Stock Strategy with Risk Controls
Article Strategy library · Author: Myquant
Summary
This Python strategy framework is designed to trade a configurable stock universe using rolling daily closing prices. The visible code loads strategy and backtest settings, adjusts historical closes for corporate actions, and maintains per-symbol price histories. Its parameters include a Bollinger-style lookback, upper and lower band multipliers, a rate-of-change period, liquidity and volume thresholds, holding-period controls, and fixed or trailing profit and loss settings. These indicate a system combining price bands, momentum and trade management.
The supplied source is incomplete, so the entry and exit rules cannot be reconstructed with confidence from the excerpt. It describes implementation plumbing for configuration, market data, positions and backtest setup, but provides no performance results or worked examples. Traders should treat the strategy logic and any claimed behavior as unverified until they inspect the complete source and configuration. The visible design also depends on daily data, stock-pool selection and external broker or platform libraries, which may affect portability and the realism of any backtest.
Key ideas
- The framework tracks adjusted daily closing prices for a configurable stock universe.
- Its parameters combine price-band thresholds with momentum, liquidity and volume settings.
- Position handling includes holding-period tracking and fixed or moving profit and loss controls.
- The source excerpt is incomplete and does not establish exact entry rules or strategy performance.
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Full text
# Bollinger_Bandit
# Bollinger_Bandit
## Source (Apache-2.0)
```python
#!/usr/bin/env python
# encoding: utf-8
import sys
import logging
import logging.config
import configparser
import csv
import numpy as np
import datetime
import talib
import arrow
from gmsdk import *
EPS = 1e-6
INIT_CLOSE_PRICE = 0
class Bollinger_Bandit(StrategyBase):
cls_config = None
cls_user_name = None
cls_password = None
cls_mode = None
cls_td_addr = None
cls_strategy_id = None
cls_subscribe_symbols = None
cls_stock_pool = []
cls_backtest_start = None
cls_backtest_end = None
cls_initial_cash = 1000000
cls_transaction_ratio = 1
cls_commission_ratio = 0.0
cls_slippage_ratio = 0.0
cls_price_type = 1
cls_bench_symbol = None
def __init__(self, *args, **kwargs):
super(Bollinger_Bandit, self).__init__(*args, **kwargs)
self.cur_date = None
self.dict_close = {}
self.dict_open_close_signal = {}
self.dict_position_period = {}
self.dict_entry_high_low = {}
self.dict_last_factor = {}
self.dict_open_cum_days = {}
@classmethod
def read_ini(cls, ini_name):
"""
功能：读取策略配置文件
"""
cls.cls_config = configparser.ConfigParser()
cls.cls_config.read(ini_name)
@classmethod
def get_strategy_conf(cls):
"""
功能：读取策略配置文件strategy段落的值
"""
if cls.cls_config is None:
return
cls.cls_user_name = cls.cls_config.get('strategy', 'username')
cls.cls_password = cls.cls_config.get('strategy', 'password')
cls.cls_strategy_id = cls.cls_config.get('strategy', 'strategy_id')
cls.cls_subscribe_symbols = cls.cls_config.get('strategy', 'subscribe_symbols')
cls.cls_mode = cls.cls_config.getint('strategy', 'mode')
cls.cls_td_addr = cls.cls_config.get('strategy', 'td_addr')
if len(cls.cls_subscribe_symbols) <= 0:
cls.get_subscribe_stock()
else:
subscribe_ls = cls.cls_subscribe_symbols.split(',')
for data in subscribe_ls:
index1 = data.find('.')
index2 = data.find('.', index1 + 1, -1)
cls.cls_stock_pool.append(data[:index2])
return
@classmethod
def get_backtest_conf(cls):
"""
功能：读取策略配置文件backtest段落的值
"""
if cls.cls_config is None:
return
cls.cls_backtest_start = cls.cls_config.get('backtest', 'start_time')
cls.cls_backtest_end = cls.cls_config.get('backtest', 'end_time')
cls.cls_initial_cash = cls.cls_config.getfloat('backtest', 'initial_cash')
cls.cls_transaction_ratio = cls.cls_config.getfloat('backtest', 'transaction_ratio')
cls.cls_commission_ratio = cls.cls_config.getfloat('backtest', 'commission_ratio')
cls.cls_slippage_ratio = cls.cls_config.getfloat('backtest', 'slippage_ratio')
cls.cls_price_type = cls.cls_config.getint('backtest', 'price_type')
cls.cls_bench_symbol = cls.cls_config.get('backtest', 'bench_symbol')
return
@classmethod
def get_stock_pool(cls, csv_file):
"""
功能：获取股票池中的代码
"""
csvfile = open(csv_file, 'r')
reader = csv.reader(csvfile)
for line in reader:
cls.cls_stock_pool.append(line[0])
return
@classmethod
def get_subscribe_stock(cls):
"""
功能：获取订阅代码
"""
cls.get_stock_pool('stock_pool.csv')
bar_type = cls.cls_config.getint('para', 'bar_type')
if 86400 == bar_type:
bar_type_str = '.bar.' + 'daily'
else:
bar_type_str = '.bar.' + '%d' % cls.cls_config.getint('para', 'bar_type')
cls.cls_subscribe_symbols = ','.join(data + bar_type_str for data in cls.cls_stock_pool)
return
def utc_strtime(self, utc_time):
"""
功能：utc转字符串时间
"""
str_time = '%s' % arrow.get(utc_time).to('local')
str_time.replace('T', ' ')
str_time = str_time.replace('T', ' ')
return str_time[:19]
def get_para_conf(self):
"""
功能：读取策略配置文件para(自定义参数)段落的值
"""
if self.cls_config is None:
return
self.boll_bandit_period = self.cls_config.getint('para', 'boll_bandit_period')
self.up_ratio = self.cls_config.getfloat('para', 'up_ratio')
self.down_ratio = self.cls_config.getfloat('para', 'down_ratio')
self.roc_period = self.cls_config.getint('para', 'roc_period')
self.liq_days = self.cls_config.getint('para', 'liq_days')
self.open_vol = self.cls_config.getint('para', 'open_vol')
self.open_max_days = self.cls_config.getint('para', 'open_max_days')
self.is_fixation_stop = self.cls_config.getint('para', 'is_fixation_stop')
self.is_movement_stop = self.cls_config.getint('para', 'is_movement_stop')
self.stop_fixation_profit = self.cls_config.getfloat('para', 'stop_fixation_profit')
self.stop_fixation_loss = self.cls_config.getfloat('para', 'stop_fixation_loss')
self.stop_movement_profit = self.cls_config.getfloat('para', 'stop_movement_profit')
return
def init_strategy(self):
"""
功能：策略启动初始化操作
"""
if self.cls_mode == gm.MD_MODE_PLAYBACK:
self.cur_date = self.cls_backtest_start
self.end_date = self.cls_backtest_end
else:
self.cur_date = datetime.date.today().strftime('%Y-%m-%d') + ' 08:00:00'
self.end_date = datetime.date.today().strftime('%Y-%m-%d') + ' 16:00:00'
self.dict_open_close_signal = {}
self.dict_entry_high_low = {}
self.get_last_factor()
self.init_data()
self.init_entry_high_low()
return
def init_data(self):
"""
功能：获取订阅代码的初始化数据
"""
for ticker in self.cls_stock_pool:
# 初始化仓位操作信号字典
self.dict_open_close_signal.setdefault(ticker, False)
self.dict_position_period.setdefault(ticker, self.boll_bandit_period)
daily_bars = self.get_last_n_dailybars(ticker, self.boll_bandit_period - 1, self.cur_date)
if len(daily_bars) <= 0:
continue
end_daily_bars = self.get_last_n_dailybars(ticker, 1, self.end_date)
if len(end_daily_bars) <= 0:
continue
if ticker not in self.dict_last_factor:
continue
end_adj_factor = self.dict_last_factor[ticker]
cp_ls = [data.close * data.adj_factor / end_adj_factor for data in daily_bars]
cp_ls.reverse()
# 留出一个空位存储当天的一笔数据
cp_ls.append(INIT_CLOSE_PRICE)
close = np.asarray(cp_ls, dtype=np.float)
# 存储历史的close
self.dict_close.setdefault(ticker, close)
def init_data_newday(self):
"""
功能：新的一天初始化数据
"""
# 新的一天，去掉第一笔数据,并留出一个空位存储当天的一笔数据
for key in self.dict_close:
if len(self.dict_close[key]) >= self.boll_bandit_period and abs(
self.dict_close[key][-1] - INIT_CLOSE_PRICE) > EPS:
self.dict_close[key] = np.append(self.dict_close[key][1:], INIT_CLOSE_PRICE)
elif len(self.dict_close[key]) < self.boll_bandit_period and abs(
self.dict_close[key][-1] - INIT_CLOSE_PRICE) > EPS:
self.dict_close[key] = np.append(self.dict_close[key][:], INIT_CLOSE_PRICE)
# 初始化仓位操作信号字典
for key in self.dict_open_close_signal:
self.dict_open_close_signal[key] = False
# 持仓周期
for key in self.dict_position_period:
index = key.find('.')
exchange = key[:index]
sec_id = key[index:]
pos = self.get_position(exchange, sec_id, OrderSide_Bid)
if pos is not None and pos.volume > 0 and self.dict_position_period[key] > self.liq_days:
self.dict_position_period[key] = self.dict_position_period[key] - 1
else:
self.dict_position_period[key] = self.boll_bandit_period
# 开仓后到当前的交易日天数
keys = list(self.dict_open_cum_days.keys())
for key in keys:
if self.dict_open_cum_days[key] >= self.open_max_days:
del self.dict_open_cum_days[key]
else:
self.dict_open_cum_days[key] += 1
def get_last_factor(self):
"""
功能：获取指定日期最新的复权因子
"""
for ticker in self.cls_stock_pool:
daily_bars = self.get_last_n_dailybars(ticker, 1, self.end_date)
if daily_bars is not None and len(daily_bars) > 0:
self.dict_last_factor.setdefault(ticker, daily_bars[0].adj_factor)
def init_entry_high_low(self):
"""
功能：获取进场后的最高价和最低价,仿真或实盘交易启动时加载
"""
pos_list = self.get_positions()
high_list = []
low_list = []
for pos in pos_list:
symbol = pos.exchange + '.' + pos.sec_id
init_time = self.utc_strtime(pos.init_time)
cur_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
daily_bars = self.get_dailybars(symbol, init_time, cur_time)
high_list = [bar.high for bar in daily_bars]
low_list = [bar.low for bar in daily_bars]
if len(high_list) > 0:
highest = np.max(high_list)
else:
highest = pos.vwap
if len(low_list) > 0:
lowest = np.min(low_list)
else:
lowest = pos.vwap
self.dict_entry_high_low.setdefault(symbol, [highest, lowest])
def on_bar(self, bar):
if self.cls_mode == gm.MD_MODE_PLAYBACK:
if bar.strtime[0:10] != self.cur_date[0:10]:
self.cur_date = bar.strtime[0:10] + ' 08:00:00'
# 新的交易日
self.init_data_newday()
symbol = bar.exchange + '.' + bar.sec_id
self.movement_stop_profit_loss(bar)
self.fixation_stop_profit_loss(bar)
# 填充价格
if symbol in self.dict_close:
self.dict_close[symbol][-1] = bar.close
pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)
if self.dict_open_close_signal[symbol] is False:
# 代码持仓为空且当天未有对该代码开、平仓
if symbol in self.dict_close and len(self.dict_close[symbol]) >= self.boll_bandit_period:
average_close = np.average(self.dict_close[symbol])
average_stddev = np.std(self.dict_close[symbol])
upper_band = average_close + average_stddev * self.up_ratio
down_band = average_close - average_stddev * self.down_ratio
roc_calc = bar.close - self.dict_close[symbol][self.boll_bandit_period - self.roc_period]
average_stop_loss = np.average(
self.dict_close[symbol][self.boll_bandit_period - self.dict_position_period[symbol]:])
if pos is None and symbol not in self.dict_open_cum_days \
and ((roc_calc > EPS and bar.close > upper_band) \
or (average_close > upper_band and bar.close <= average_close)):
# 有开仓机会则设置已开仓的交易天数
self.dict_open_cum_days[symbol] = 0
cash = self.get_cash()
cur_open_vol = self.open_vol
if cash.available / bar.close > self.open_vol:
cur_open_vol = self.open_vol
else:
cur_open_vol = int(cash.available / bar.close / 100) * 100
if cur_open_vol == 0:
print('no available cash to buy, available cash: %.2f' % cash.available)
else:
# 当前价格大于roc周期的close，且上穿过了上轨
# 或者boll bandit周期的均价大于下轨且当前价格小于均价
self.open_long(bar.exchange, bar.sec_id, bar.close, cur_open_vol)
self.dict_open_close_signal[symbol] = True
logging.info('open long, symbol:%s, time:%s, price:%.2f' % (symbol, bar.strtime, bar.close))
elif pos is not None:
if (roc_calc < EPS and bar.close < down_band) \
or (average_close < down_band and bar.close >= average_close):
# 当前价格低于roc周期的close，且低于了下轨
# 或者boll bandit周期的均价低于下轨且当前价格小于均价
vol = pos.volume - pos.volume_today
if vol > 0:
self.close_long(bar.exchange, bar.sec_id, bar.close, vol)
self.dict_open_close_signal[symbol] = True
logging.info('close long, symbol:%s, time:%s, price:%.2f, vwap: %.2f' % (symbol,
bar.strtime,
bar.close,
pos.vwap))
elif bar.low < average_stop_loss:
# 止损
vol = pos.volume - pos.volume_today
if vol > 0:
stop_loss_price = average_stop_loss
if stop_loss_price > bar.open:
stop_loss_price = bar.open
self.close_long(bar.exchange, bar.sec_id, stop_loss_price, vol)
logging.info(
'stop loss by lip days,close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f' % (
symbol,
bar.strtime, stop_loss_price, pos.vwap))
def on_order_filled(self, order):
symbol = order.exchange + '.' + order.sec_id
if order.position_effect == PositionEffect_CloseYesterday \
and order.side == OrderSide_Bid:
pos = self.get_position(order.exchange, order.sec_id, order.side)
if pos is None and self.is_movement_stop == 1:
self.dict_entry_high_low.pop(symbol)
def fixation_stop_profit_loss(self, bar):
"""
功能：固定止盈、止损,盈利或亏损超过了设置的比率则执行止盈、止损
"""
if self.is_fixation_stop == 0:
return
symbol = bar.exchange + '.' + bar.sec_id
pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)
if pos is not None:
if pos.fpnl > 0 and pos.fpnl / pos.cost >= self.stop_fixation_profit:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
'fixnation stop profit: close long, symbol:%s, time:%s, price:%.2f, vwap: %s, volume:%s' % (symbol,
bar.strtime,
bar.close,
pos.vwap,
pos.volume))
elif pos.fpnl < 0 and pos.fpnl / pos.cost <= -1 * self.stop_fixation_loss:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
'fixnation stop loss: close long, symbol:%s, time:%s, price:%.2f, vwap:%s, volume:%s' % (symbol,
bar.strtime,
bar.close,
pos.vwap,
pos.volume))
def movement_stop_profit_loss(self, bar):
"""
功能：移动止盈, 移动止盈止损按进场后的最高价乘以设置的比率与当前价格相比，
并且盈利比率达到设定的盈亏比率时，执行止盈
"""
if self.is_movement_stop == 0:
return
entry_high = None
entry_low = None
pos = self.get_position(bar.exchange, bar.sec_id, OrderSide_Bid)
symbol = bar.exchange + '.' + bar.sec_id
is_stop_profit = True
if pos is not None and pos.volume > 0:
if symbol in self.dict_entry_high_low:
if self.dict_entry_high_low[symbol][0] < bar.close:
self.dict_entry_high_low[symbol][0] = bar.close
is_stop_profit = False
if self.dict_entry_high_low[symbol][1] > bar.close:
self.dict_entry_high_low[symbol][1] = bar.close
[entry_high, entry_low] = self.dict_entry_high_low[symbol]
else:
self.dict_entry_high_low.setdefault(symbol, [bar.close, bar.close])
[entry_high, entry_low] = self.dict_entry_high_low[symbol]
is_stop_profit = False
if is_stop_profit:
# 移动止盈
if bar.close <= (
1 - self.stop_movement_profit) * entry_high and pos.fpnl / pos.cost >= self.stop_fixation_profit:
if pos.volume - pos.volume_today > 0:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
'movement stop profit: close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f, volume:%s' % (
symbol,
bar.strtime, bar.close, pos.vwap, pos.volume))
# 止损
if pos.fpnl < 0 and pos.fpnl / pos.cost <= -1 * self.stop_fixation_loss:
self.close_long(bar.exchange, bar.sec_id, 0, pos.volume - pos.volume_today)
self.dict_open_close_signal[symbol] = True
logging.info(
'movement stop loss: close long, symbol:%s, time:%s, price:%.2f, vwap:%.2f, volume:%s' % (symbol,
bar.strtime,
bar.close,
pos.vwap,
pos.volume))
if __name__ == '__main__':
print(get_version())
logging.config.fileConfig('bollinger_bandit.ini')
Bollinger_Bandit.read_ini('bollinger_bandit.ini')
Bollinger_Bandit.get_strategy_conf()
bollinger_bandit = Bollinger_Bandit(username=Bollinger_Bandit.cls_user_name,
password=Bollinger_Bandit.cls_password,
strategy_id=Bollinger_Bandit.cls_strategy_id,
subscribe_symbols=Bollinger_Bandit.cls_subscribe_symbols,
mode=Bollinger_Bandit.cls_mode,
td_addr=Bollinger_Bandit.cls_td_addr)
if Bollinger_Bandit.cls_mode == gm.MD_MODE_PLAYBACK:
Bollinger_Bandit.get_backtest_conf()
ret = bollinger_bandit.backtest_config(start_time=Bollinger_Bandit.cls_backtest_start,
end_time=Bollinger_Bandit.cls_backtest_end,
initial_cash=Bollinger_Bandit.cls_initial_cash,
transaction_ratio=Bollinger_Bandit.cls_transaction_ratio,
commission_ratio=Bollinger_Bandit.cls_commission_ratio,
slippage_ratio=Bollinger_Bandit.cls_slippage_ratio,
price_type=Bollinger_Bandit.cls_price_type,
bench_symbol=Bollinger_Bandit.cls_bench_symbol)
bollinger_bandit.get_para_conf()
bollinger_bandit.init_strategy()
ret = bollinger_bandit.run()
print('run result %s' % ret)
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.