import backtrader as bt
import yfinance as yf
# 定义BIAS指标
class BIAS(bt.Indicator):
lines = ('bias',)
params = (('period', 20),)
def __init__(self):
self.lines.bias = (self.data.close - bt.indicators.SMA(period=self.params.period)) / bt.indicators.SMA(period=self.params.period) * 100
# 定义交易策略
class BIASStrategy(bt.Strategy):
params = (('bias_upper', 10), ('bias_lower', -10),)
def __init__(self):
self.bias = BIAS(self.data)
self.order = None
def next(self):
if not self.position:
if self.bias[0] > self.params.bias_upper:
pass # 不进行操作
elif self.bias[0] < self.params.bias_lower:
commission_info = self.broker.getcommissioninfo(self.data)
cash = self.broker.get_cash()
size = int(cash / (self.data.close[0] * (1 + commission_info.p.commission)))
self.order = self.buy(size=size)
print(f'BUY: {size} shares')
else:
if self.order and self.order.isbuy() and self.bias[0] > self.params.bias_upper:
self.close()
self.order = None # 重置self.order为None
print(f'SELL: {self.position.size} shares')
elif self.order and self.order.issell() and self.bias[0] < self.params.bias_lower:
self.close()
self.order = None # 重置self.order为None
print(f'BUY: {self.position.size} shares')
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
return
if order.status in [order.Completed]:
if order.isbuy():
print(f'BUY executed at {self.data.num2date(order.executed.dt).date()}, Price: {order.executed.price:.2f}, Cost: {order.executed.value:.2f}, Comm: {order.executed.comm:.2f}')
elif order.issell():
cost = order.executed.value
profit = order.executed.value - order.created.size * order.created.price
profit_percent = (profit / cost) * 100
print(f'SELL executed at {self.data.num2date(order.executed.dt).date()}, Price: {order.executed.price:.2f}, Cost: {cost:.2f}, Profit: {profit:.2f}, Profit %: {profit_percent:.2f}%')
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
print('Order Canceled/Margin/Rejected')
# 创建Cerebro引擎
cerebro = bt.Cerebro()
# 设置初始资金
cerebro.broker.setcash(100000.0)
# 下载苹果股票数据
data = yf.download('AAPL', '2020-01-01', '2023-12-30')
data = data.dropna()
# 将数据添加到Cerebro引擎中
data = bt.feeds.PandasData(dataname=data)
cerebro.adddata(data)
# 添加MACD策略
cerebro.addstrategy(BIASStrategy)
# 设置佣金为0.1%
cerebro.broker.setcommission(commission=0.001)
# 添加分析指标
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
# 运行回测
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
# 获取回测结果
strat = results[0]
returns = strat.analyzers.returns.get_analysis()
sharpe = strat.analyzers.sharpe.get_analysis()
drawdown = strat.analyzers.drawdown.get_analysis()
# 打印回测指标
print('Annualized Return: %.2f%%' % (returns['rnorm100']))
print('Sharpe Ratio: %.2f' % (sharpe['sharperatio']))
print('Max Drawdown: %.2f%%' % (drawdown['max']['drawdown']))
print('Max Drawdown Period: %s' % (drawdown['max']['len']))
# 绘制回测结果
cerebro.plot()
