用Ta-Lib计算6日的RSI值和24日的RSI值
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
(‘RSI6period‘, 6),
(‘RSI24period‘, 24),
)
def log(self, txt, dt=None):
‘‘‘ Logging function fot this strategy‘‘‘
dt = dt or self.datas[0].datetime.date(0)
print(‘%s, %s‘ % (dt.isoformat(), txt))
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
# To keep track of pending orders and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# 添加Ta-Lib RSI指标
self.RSI6=bt.talib.RSI(self.data, timeperiod= self.p.RSI6period)
self.RSI24=bt.talib.RSI(self.data, timeperiod= self.p.RSI24period)
self.crossover = bt.ind.CrossOver(self.RSI6, self.RSI24) # 交叉信号
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
‘BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f‘ %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log(‘SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f‘ %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log(‘Order Canceled/Margin/Rejected‘)
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log(‘OPERATION PROFIT, GROSS %.2f, NET %.2f‘ %
(trade.pnl, trade.pnlcomm))
def next(self):
# Simply log the closing price of the series from the reference
self.log(‘Close, %.2f‘ % self.dataclose[0])
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
if not self.position:
if self.crossover > 0:
self.order = self.buy()
elif self.RSI6[0] < 30:
self.order = self.buy()
else:
if self.crossover < 0:
self.order=self.close()
elif self.RSI6[0] > 80:
self.order = self.close()
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, ‘600519-1.csv‘)
# Create a Data Feed
data = bt.feeds.GenericCSVData(
dataname = datapath,
nullvalue = 0.0,
dtformat = (‘%Y-%m-%d‘),
datetime = 0,
open = 1,
high = 2,
low = 3,
close = 4,
volume = 5,
openinterest = -1
)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# 设置交易单位大小 A股100股为一手
cerebro.addsizer(bt.sizers.FixedSize, stake = 100)
# Print out the starting conditions
print(‘Starting Portfolio Value: %.2f‘ % cerebro.broker.getvalue())
# Run over everything
cerebro.run()
# Print out the final result
print(‘Final Portfolio Value: %.2f‘ % cerebro.broker.getvalue())
# Plot the result
cerebro.plot()
结果如下，是盈利的
Final Portfolio Value: 119192.00
短期RSI对于价格的变化情况反应比较敏感，RSI取值波动较大，长期RSI值反应相对迟钝，其波动相对较小。对于交易信号一，超买和超卖现象，使用6日的RSI值和20日的RSI值之间用哪一个时间跨度得到的收益最大那。Backtrader里有Optimize功能
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
params = (
(‘RSIperiod‘, 6),
(‘printlog‘, False),
)
def log(self, txt, dt=None,doprint = False):
‘‘‘ Logging function fot this strategy‘‘‘
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
print(‘%s, %s‘ % (dt.isoformat(), txt))
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
# To keep track of pending orders and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# 添加Ta-Lib RSI指标
self.RSI=bt.talib.RSI(
self.data, timeperiod= self.p.RSIperiod)
#
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
# Buy/Sell order submitted/accepted to/by broker - Nothing to do
return
# Check if an order has been completed
# Attention: broker could reject order if not enough cash
if order.status in [order.Completed]:
if order.isbuy():
self.log(
‘BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f‘ %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else: # Sell
self.log(‘SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f‘ %
(order.executed.price,
order.executed.value,
order.executed.comm))
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log(‘Order Canceled/Margin/Rejected‘)
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log(‘OPERATION PROFIT, GROSS %.2f, NET %.2f‘ %
(trade.pnl, trade.pnlcomm))
def next(self):
# Simply log the closing price of the series from the reference
self.log(‘Close, %.2f‘ % self.dataclose[0])
# Check if an order is pending ... if yes, we cannot send a 2nd one
if self.order:
return
# Check if we are in the market
if not self.position:
if self.RSI[0] < 30:
# BUY, BUY, BUY!!! (with default parameters)
self.log(‘BUY CREATE, %.2f‘ % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.buy()
# 如果已经在场内，则可以进行卖出操作
else:
# Already in the market ... we might sell
if self.RSI[0] > 70:
# SELL, SELL, SELL!!! (with all possible default parameters)
self.log(‘SELL CREATE, %.2f‘ % self.dataclose[0])
# Keep track of the created order to avoid a 2nd order
self.order = self.sell()
def stop(self):
self.log(‘(RSI period %2d) Ending Value %.2f‘ %
(self.p.RSIperiod, self.broker.getvalue()), doprint = True)
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
strats = cerebro.optstrategy(
TestStrategy,
RSIperiod = range(2, 10))
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, ‘C:/Users/taohj/python-work/CopperBat-量化/600519-1.csv‘)
# Create a Data Feed
data = bt.feeds.GenericCSVData(
dataname = datapath,
nullvalue = 0.0,
dtformat = (‘%Y-%m-%d‘),
datetime = 0,
open = 1,
high = 2,
low = 3,
close = 4,
volume = 5,
openinterest = -1
)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# 设置交易单位大小 A股100股为一手
cerebro.addsizer(bt.sizers.FixedSize, stake = 100)
# Run over everything
cerebro.run(maxcpus = 1)
结果如下
2015-07-01, (RSI period 2) Ending Value 107234.00
2015-07-01, (RSI period 3) Ending Value 105259.00
2015-07-01, (RSI period 4) Ending Value 105226.00
2015-07-01, (RSI period 5) Ending Value 106260.00
2015-07-01, (RSI period 6) Ending Value 104142.00
2015-07-01, (RSI period 7) Ending Value 105521.00
2015-07-01, (RSI period 8) Ending Value 102944.00
2015-07-01, (RSI period 9) Ending Value 102944.00
看来选2日的RSI值作为交易信号一的时间跨度有更高的收益。
当然超买线和超卖线，可以取值70和30，也可以取值80和20，或者90和10.
交易信号二的短线RSI和长线RSi也可以取不同的时间跨度。
希望大家多给我意见。本人没有什么编程背景，有任何问题，欢迎在本文下方留言，或者将问题发送至邮箱： taohjk@hotmail.com
谢谢大家！
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本内容仅作为学术参考和代码实例，仅供参考，不对投资决策提供任何建议。本人不承担任何人因使用本系列中任何策略、观点等内容造成的任何直接或间接损失。
原文：https://www.cnblogs.com/CopperBat/p/12733702.html