算法交易入门
从零搭建你的量化策略

# 安装核心库
pip install pandas numpy matplotlib

# 安装金融数据获取库
pip install yfinance akshare

# 安装回测框架（二选一即可）
pip install backtrader
# 或者
pip install zipline-reloaded

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 生成一些示例数据
dates = pd.date_range('2025-01-01', periods=100)
prices = np.random.randn(100).cumsum() + 100

# 画个折线图
plt.plot(dates, prices)
plt.title('测试：随机价格走势')
plt.xlabel('日期')
plt.ylabel('价格')
plt.show()

print('环境搭建成功！股老师为你骄傲 🎉')

import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt

# 1. 获取数据——以贵州茅台为例
ticker = yf.Ticker("600519.SS")
data = ticker.history(period="1y")  # 最近1年数据

# 2. 计算均线
data['MA5'] = data['Close'].rolling(window=5).mean()
data['MA20'] = data['Close'].rolling(window=20).mean()

# 3. 生成信号
data['Signal'] = 0
data.loc[data['MA5'] > data['MA20'], 'Signal'] = 1  # 金叉信号
data['Position'] = data['Signal'].diff()  # 1表示买入，-1表示卖出

# 4. 画出结果
plt.figure(figsize=(14, 7))
plt.plot(data.index, data['Close'], label='收盘价', color='#94a3b8', alpha=0.7)
plt.plot(data.index, data['MA5'], label='MA5', color='#6366f1', linewidth=1.5)
plt.plot(data.index, data['MA20'], label='MA20', color='#f59e0b', linewidth=1.5)

# 标记买入点（绿色箭头）
buy_signals = data[data['Position'] == 1]
plt.scatter(buy_signals.index, buy_signals['Close'], 
            color='#22c55e', marker='^', s=120, label='买入信号', zorder=5)

# 标记卖出点（红色箭头）
sell_signals = data[data['Position'] == -1]
plt.scatter(sell_signals.index, sell_signals['Close'], 
            color='#ef4444', marker='v', s=120, label='卖出信号', zorder=5)

plt.title('双均线交叉策略信号 (贵州茅台)', fontsize=16, fontweight='bold')
plt.legend()
plt.grid(alpha=0.1)
plt.show()

print(f"买入信号次数: {len(buy_signals)}")
print(f"卖出信号次数: {len(sell_signals)}")

import backtrader as bt
import yfinance as yf
import pandas as pd

# 1. 定义策略类
class GoldenCrossStrategy(bt.Strategy):
    params = (
        ('fast', 5),    # 短期均线周期
        ('slow', 20),   # 长期均线周期
    )

    def __init__(self):
        self.fast_ma = bt.indicators.SMA(self.data.close, period=self.params.fast)
        self.slow_ma = bt.indicators.SMA(self.data.close, period=self.params.slow)
        self.crossover = bt.indicators.CrossOver(self.fast_ma, self.slow_ma)

    def next(self):
        if self.crossover > 0:  # 金叉
            self.buy()
        elif self.crossover < 0:  # 死叉
            self.sell()

# 2. 获取数据
ticker = yf.Ticker("600519.SS")
data = ticker.history(period="2y")
data_feed = bt.feeds.PandasData(dataname=data)

# 3. 配置回测引擎
cerebro = bt.Cerebro()
cerebro.addstrategy(GoldenCrossStrategy)
cerebro.adddata(data_feed)
cerebro.broker.setcash(100000.0)  # 初始资金10万
cerebro.broker.setcommission(commission=0.0003)  # 佣金万分之三

# 4. 添加分析器
cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')

# 5. 运行回测
print('初始资金: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
print('最终资金: %.2f' % cerebro.broker.getvalue())

# 6. 输出评估指标
strat = results[0]
sharpe = strat.analyzers.sharpe.get_analysis()
returns = strat.analyzers.returns.get_analysis()
drawdown = strat.analyzers.drawdown.get_analysis()

print(f"\n=== 回测评估报告 ===")
print(f"夏普比率: {sharpe.get('sharperatio', 0):.2f}")
print(f"年化收益率: {returns.get('rnorm100', 0):.2f}%")
print(f"最大回撤: {drawdown.get('max', {}).get('drawdown', 0):.2f}%")

# 7. 画图
cerebro.plot()

import backtrader as bt

class BollingerBreakout(bt.Strategy):
    params = (
        ('period', 20),
        ('devfactor', 2),
    )

    def __init__(self):
        self.boll = bt.indicators.BollingerBands(
            self.data.close, period=self.params.period, 
            devfactor=self.params.devfactor
        )

    def next(self):
        # 价格突破上轨，买入
        if self.data.close[0] > self.boll.lines.top[0] and not self.position:
            self.buy()
        
        # 价格跌破中轨，卖出
        if self.data.close[0] < self.boll.lines.mid[0] and self.position:
            self.sell()

class RSIStrategy(bt.Strategy):
    params = (
        ('rsi_period', 14),
        ('oversold', 30),
        ('overbought', 70),
    )

    def __init__(self):
        self.rsi = bt.indicators.RSI(self.data.close, period=self.params.rsi_period)

    def next(self):
        # RSI低于超卖线，买入
        if self.rsi[0] < self.params.oversold and not self.position:
            self.buy()
        
        # RSI高于超买线，卖出
        if self.rsi[0] > self.params.overbought and self.position:
            self.sell()

class TurtleStrategy(bt.Strategy):
    params = (
        ('entry_period', 20),   # 突破买入周期
        ('exit_period', 10),    # 跌破卖出周期
    )

    def __init__(self):
        self.entry_high = bt.indicators.Highest(
            self.data.high, period=self.params.entry_period
        )
        self.exit_low = bt.indicators.Lowest(
            self.data.low, period=self.params.exit_period
        )

    def next(self):
        if self.data.close[0] > self.entry_high[-1] and not self.position:
            self.buy()
        
        if self.data.close[0] < self.exit_low[-1] and self.position:
            self.sell()