# 推荐Python 3.8+版本

pip install pandas numpy matplotlib tushare

# 回测框架

pip install backtrader==1.9.76.123 # 指定稳定版本

# 技术指标库（Windows专属）

pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl

import tushare as ts

ts.set_token(‘你的token’)

pro = ts.pro_api()

# 获取2015-2025年日线数据

df = pro.fund_daily(ts_code=’510300.SH’, start_date=’20150101′, end_date=’20250301′)

df.to_csv(‘510300.csv’, index=False)

# data_processor.py

def process_data(file_path):

df = pd.read_csv(file_path)

df[‘trade_date’] = pd.to_datetime(df[‘trade_date’], format=’%Y%m%d’)

df.set_index(‘trade_date’, inplace=True)

# 计算唐奇安通道

df[‘high_20’] = df[‘high’].rolling(20).max().shift(1)

df[‘low_10’] = df[‘low’].rolling(10).min().shift(1)

# 计算ATR

df[‘tr1’] = df[‘high’] – df[‘low’]

df[‘tr2’] = abs(df[‘high’] – df[‘close’].shift(1))

df[‘tr3’] = abs(df[‘low’] – df[‘close’].shift(1))

df[‘atr’] = df[[‘tr1′,’tr2′,’tr3’]].max(axis=1).rolling(14).mean()

return df.dropna()

# turtle_strategy.py

class TurtleStrategy(bt.Strategy):

params = (

(‘entry_period’, 20), # 入场周期

(‘exit_period’, 10), # 离场周期

(‘risk_percent’, 0.02),# 单笔风险比例

)

def __init__(self):

# 通道指标

self.entry_high = bt.indicators.Highest(self.data.high(-1), self.p.entry_period)

self.exit_low = bt.indicators.Lowest(self.data.low(-1), self.p.exit_period)

# ATR指标

self.atr = bt.indicators.ATR(self.data, self.p.entry_period)

# 持仓管理

self.order = None

self.position_size = 0

def next(self):

# 入场信号

if not self.position:

if self.data.close[0] > self.entry_high[0]:

self._enter_position()

# 持仓逻辑

else:

self._manage_position()

def _enter_position(self):

risk_capital = self.broker.getvalue() * self.p.risk_percent

unit_size = risk_capital / self.atr[0]

self.buy(size=unit_size)

self.position_size = unit_size

def _manage_position(self):

# 动态加仓

if self.data.close[0] > self.position.price + 0.5 * self.atr[0]:

if self.position.size < 4 * self.position_size:

self.buy(size=self.position_size)

# 止损逻辑

stop_price = self.position.price – 2 * self.atr[0]

if self.data.close[0] < stop_price:

self.close()

# 离场信号

if self.data.close[0] < self.exit_low[0]:

self.close()

# backtest.py

cerebro = bt.Cerebro()

# 加载数据

data = bt.feeds.PandasData(

dataname=processed_data,

datetime=’trade_date’,

open=’open’,

high=’high’,

low=’low’,

close=’close’,

volume=’vol’

)

cerebro.adddata(data)

# 策略配置

cerebro.addstrategy(TurtleStrategy)

# 交易设置

cerebro.broker.setcash(1000000)

cerebro.broker.setcommission(commission=0.0003) # 万3佣金

# 运行回测

print(f’初始资金: {cerebro.broker.getvalue():.2f}’)

cerebro.run()

print(f’最终资金: {cerebro.broker.getvalue():.2f}’)

# 可视化

cerebro.plot(style=’candlestick’, plotdist=0.1, barup=’red’, bardown=’green’)

params = dict(

entry_period = [15,20,25],

exit_period = [8,10],

risk_percent = [0.01,0.02,0.03]

)

# 优化配置

cerebro.optstrategy(

TurtleStrategy,

entry_period=params[‘entry_period’],

exit_period=params[‘exit_period’],

risk_percent=params[‘risk_percent’]

)

# 运行优化

results = cerebro.run(maxcpus=4)

(20,10,0.02)

25.7%

1.8

-18.3%

(15,8,0.03)

28.1%

1.5

-23.7%

(25,10,0.01)

19.2%

2.1

-15.6%