RSI Oversold Entries and Overbought Exits for Stocks
Article Strategy library · Author: Myquant
Summary
This stock strategy uses the Relative Strength Index to time long trades. It opens a position when RSI falls below a configurable oversold threshold, provided the symbol has no current position and is outside a post-entry holding window. It closes a long position when RSI rises above an overbought threshold. The implementation tracks daily closing prices and adjusts historical prices using adjustment factors before calculating RSI.
The system also includes optional fixed profit and loss stops and a moving profit stop based on the highest price reached since entry. Position size is capped by a configured share amount and reduced to fit available cash, rounded to board lots. Parameters cover the RSI period and thresholds, history length, holding days, and stop settings. The document provides implementation details, but no reported backtest results or evidence that the rules are profitable. Results would depend on parameter choices, execution costs, data quality, and how the stop and holding-period logic behaves in practice.
Key ideas
- The strategy opens long positions when RSI falls below an oversold threshold and no position or holding lock is active.
- It exits when RSI exceeds an overbought threshold, with optional fixed and trailing profit or loss stops.
- Historical closes are adjusted using stock adjustment factors before RSI is calculated.
- Position size is constrained by a configured share cap and available cash, with purchases rounded to board lots.
- The source describes implementation mechanics but supplies no performance evidence.
Tags
Full text
# RSI_STOCK
# RSI_STOCK
## 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 RSI_STOCK(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(RSI_STOCK, self).__init__(*args, **kwargs)
self.cur_date = None
self.dict_close = {}
self.dict_open_close_signal = {}
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.rsi_period = self.cls_config.getint('para', 'rsi_period')
self.over_buy = self.cls_config.getint('para', 'over_buy')
self.over_sell = self.cls_config.getint('para', 'over_sell')
self.hist_size = self.cls_config.getint('para', 'hist_size')
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)
daily_bars = self.get_last_n_dailybars(ticker, self.hist_size - 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)
# end = time.clock()
# logging.info('init_data cost time: %f s' % (end - start))
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.hist_size 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.hist_size 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
# 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:
# 当天未有对该代码开、平仓
if symbol in self.dict_close:
rsi_index = talib.RSI(self.dict_close[symbol], timeperiod=self.rsi_period)
if pos is None and symbol not in self.dict_open_cum_days \
and rsi_index[-1] < self.over_sell:
# 超卖
# 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:
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 and rsi_index[-1] > self.over_buy:
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' % (symbol, bar.strtime, bar.close))
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('rsi_stock.ini')
RSI_STOCK.read_ini('rsi_stock.ini')
RSI_STOCK.get_strategy_conf()
rsi_stock = RSI_STOCK(username=RSI_STOCK.cls_user_name,
password=RSI_STOCK.cls_password,
strategy_id=RSI_STOCK.cls_strategy_id,
subscribe_symbols=RSI_STOCK.cls_subscribe_symbols,
mode=RSI_STOCK.cls_mode,
td_addr=RSI_STOCK.cls_td_addr)
if RSI_STOCK.cls_mode == gm.MD_MODE_PLAYBACK:
RSI_STOCK.get_backtest_conf()
ret = rsi_stock.backtest_config(start_time=RSI_STOCK.cls_backtest_start,
end_time=RSI_STOCK.cls_backtest_end,
initial_cash=RSI_STOCK.cls_initial_cash,
transaction_ratio=RSI_STOCK.cls_transaction_ratio,
commission_ratio=RSI_STOCK.cls_commission_ratio,
slippage_ratio=RSI_STOCK.cls_slippage_ratio,
price_type=RSI_STOCK.cls_price_type,
bench_symbol=RSI_STOCK.cls_bench_symbol)
rsi_stock.get_para_conf()
rsi_stock.init_strategy()
ret = rsi_stock.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.