backtrader绘图

基于Backtrader实现的多资产趋势轮动策略，结合分段趋势模拟数据，通过SMA比率打分与动态权重分配进行定期再平衡。
回测
多资产轮动
Backtrader
趋势跟踪
资产配置
Published

2025-10-04

1 数据准备

import backtrader as bt
import backtrader.feeds as btfeeds
import pandas as pd
import numpy as np

# ----------------------------------------------------
# 1) 多标的轮动策略（使用 bt.talib 的 SMA 打分）
#    - 定期再平衡（rebalance_days）
#    - 使用 order_target_percent 设置目标权重（非全买全卖）
# ----------------------------------------------------
class MultiAssetRotation(bt.Strategy):
    params = dict(
        fast_period=10,
        slow_period=40,
        top_n=2,               # 选择前N个标的
        rebalance_days=5,      # 每N天再平衡一次
        max_gross_leverage=1.0 # 总权重上限（100%资金）
    )

    def __init__(self):
        # 通过 bt.talib 计算每个 data 的 SMA（需安装 TA-Lib）
        self.sma_fast = {}
        self.sma_slow = {}
        for d in self.datas:
            self.sma_fast[d] = bt.talib.SMA(d.close, timeperiod=self.p.fast_period)
            self.sma_slow[d] = bt.talib.SMA(d.close, timeperiod=self.p.slow_period)

        self.day_count = 0

    def next(self):
        self.day_count += 1
        if self.day_count % self.p.rebalance_days != 0:
            return

        # 打分：fast/slow - 1
        scores = []
        for d in self.datas:
            fast = self.sma_fast[d][0]
            slow = self.sma_slow[d][0]
            if slow == 0 or np.isnan(fast) or np.isnan(slow):
                score = np.nan
            else:
                score = fast / slow - 1.0
            scores.append((d, score))

        scores = [(d, s) for d, s in scores if not np.isnan(s)]
        if not scores:
            return

        scores.sort(key=lambda x: x[1], reverse=True)
        winners = scores[: self.p.top_n]

        # 将正分数归一化为权重（负分数为0），并限制总权重
        raw = np.array([max(0.0, s) for _, s in winners], dtype=float)
        if raw.sum() <= 0:
            target_weights = {d: 0.0 for d in self.datas}
        else:
            scaled = raw / raw.sum() * self.p.max_gross_leverage
            target_weights = {d: 0.0 for d in self.datas}
            for (d, _), w in zip(winners, scaled):
                target_weights[d] = float(w)

        # 调整到目标仓位（部分调仓，不是一次性清空/满仓）
        for d in self.datas:
            self.order_target_percent(data=d, target=target_weights.get(d, 0.0))

    def notify_order(self, order):
        if order.status in [order.Completed, order.Partial]:
            pass

    def notify_trade(self, trade):
        if trade.isclosed:
            print(f"交易已平仓: {trade.data._name} PnL: {trade.pnl:.2f}, 净: {trade.pnlcomm:.2f}")


# ----------------------------------------------------
# 2) 生成多标的、分段趋势/震荡的模拟数据（非纯随机漫步）
#    - 分段 drift（正/负/震荡）
#    - 市场共同因子 + 个体噪声，形成相关性
# ----------------------------------------------------
def generate_multi_asset_data(n_assets=5, periods=500, seed=20251004):
    rng = np.random.default_rng(seed)
    dates = pd.date_range(start='2020-01-01', periods=periods, freq='B')

    segments = [int(periods * 0.2), int(periods * 0.3), periods - int(periods * 0.5)]
    assert sum(segments) == periods

    market_drifts = [0.0008, -0.0005, 0.0002]
    market_vol = 0.01

    asset_dfs = []
    for i in range(n_assets):
        id_drift_offsets = rng.normal(0.0, 0.0004, size=3)
        id_vol = 0.012 + 0.003 * rng.random()

        rets = []
        for seg_len, mdrift, off in zip(segments, market_drifts, id_drift_offsets):
            eps = rng.normal(0, 1, size=seg_len)
            r = np.zeros(seg_len)
            phi = 0.2  # 轻微自相关，形成段内连贯
            for t in range(seg_len):
                prev = r[t-1] if t > 0 else 0.0
                r[t] = mdrift + off + phi * prev + market_vol * 0.5 * eps[t] + id_vol * 0.5 * rng.normal()
            rets.append(r)
        rets = np.concatenate(rets)

        price = 100.0 * np.exp(np.cumsum(rets))

        close = price
        open_ = close * (1 + rng.normal(0, 0.001, size=periods))
        high = np.maximum(open_, close) * (1 + rng.random(size=periods) * 0.002)
        low = np.minimum(open_, close) * (1 - rng.random(size=periods) * 0.002)
        volume = rng.integers(5e4, 2e5, size=periods).astype(int)

        df = pd.DataFrame({'open': open_, 'high': high, 'low': low, 'close': close, 'volume': volume}, index=dates)
        df.index.name = 'datetime'
        asset_dfs.append(df)

    return asset_dfs

2 默认绘图

# ----------------------------------------------------
# 3) 回测入口
# ----------------------------------------------------
%matplotlib inline 
def run_backtest_multi():
    asset_dfs = generate_multi_asset_data(n_assets=5, periods=500, seed=20251004)

    cerebro = bt.Cerebro()
    cerebro.addstrategy(MultiAssetRotation,
                        fast_period=10,
                        slow_period=40,
                        top_n=2,
                        rebalance_days=5,
                        max_gross_leverage=1.0)

    for i, df in enumerate(asset_dfs):
        data = btfeeds.PandasData(
            dataname=df,
            open='open', high='high', low='low', close='close', volume='volume', openinterest=-1
        )
        cerebro.adddata(data, name=f"Asset_{i+1}")

    cerebro.broker.setcash(100000.0)
    cerebro.broker.setcommission(commission=0.001)
    cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')

    print(f"起始资金: {cerebro.broker.getvalue():.2f}")
    results = cerebro.run()

    cerebro.plot(style='candlestick', iplot=False) 
    
    final_value = cerebro.broker.getvalue()
    returns = results[0].analyzers.returns.get_analysis()
    print("-" * 32)
    print(f"最终资金: {final_value:.2f}")
    print(f"总收益(%) : {returns.get('rtot', 0.0) * 100:.2f}")
    print(f"年化(%)  : {returns.get('rnorm100', 0.0):.2f}")
    print("-" * 32)

    return cerebro, results


# 直接运行回测（适合 Quarto 单元）
cerebro, _ = run_backtest_multi()
起始资金: 100000.00
交易已平仓: Asset_2 PnL: 328.78, 净: 270.90
交易已平仓: Asset_5 PnL: 1138.57, 净: 1031.69
交易已平仓: Asset_1 PnL: -3239.77, 净: -3333.58
交易已平仓: Asset_4 PnL: 3268.02, 净: 3137.72
交易已平仓: Asset_2 PnL: 806.99, 净: 736.14
交易已平仓: Asset_5 PnL: -4234.12, 净: -4360.99
交易已平仓: Asset_1 PnL: -437.95, 净: -543.20
交易已平仓: Asset_3 PnL: -948.34, 净: -1065.55
交易已平仓: Asset_4 PnL: 297.70, 净: 233.94
交易已平仓: Asset_5 PnL: -5732.46, 净: -5857.85
交易已平仓: Asset_3 PnL: -100.70, 净: -118.39
交易已平仓: Asset_1 PnL: -1125.54, 净: -1285.70
交易已平仓: Asset_2 PnL: 213.27, 净: 154.34
交易已平仓: Asset_2 PnL: 115.30, 净: 105.80
交易已平仓: Asset_1 PnL: 8420.36, 净: 8226.38
交易已平仓: Asset_2 PnL: 26.74, 净: 14.13
交易已平仓: Asset_4 PnL: -3278.24, 净: -3455.59
交易已平仓: Asset_1 PnL: 66.76, 净: -40.77
交易已平仓: Asset_2 PnL: 9661.58, 净: 9523.66
交易已平仓: Asset_1 PnL: -2156.63, 净: -2293.11
交易已平仓: Asset_3 PnL: -3162.97, 净: -3271.07
--------------------------------
最终资金: 97723.70
总收益(%) : -2.30
年化(%)  : -1.15
--------------------------------

3 自定义绘图

  • 结论:
    • 1, jupyter + plotly绘制简单图表
    • 2, 前端去做完美的舒服交互
def run_backtest_multi_custom():
    asset_dfs = generate_multi_asset_data(n_assets=5, periods=500, seed=20251004)

    cerebro = bt.Cerebro()
    cerebro.addstrategy(MultiAssetRotation,
                        fast_period=10,
                        slow_period=40,
                        top_n=2,
                        rebalance_days=5,
                        max_gross_leverage=1.0)

    for i, df in enumerate(asset_dfs):
        data = btfeeds.PandasData(
            dataname=df,
            open='open', high='high', low='low', close='close', volume='volume', openinterest=-1
        )
        cerebro.adddata(data, name=f"Asset_{i+1}")

    cerebro.broker.setcash(100000.0)
    cerebro.broker.setcommission(commission=0.001)
    cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')

    print(f"起始资金: {cerebro.broker.getvalue():.2f}")
    results = cerebro.run()

    cerebro.plot(style='candlestick', iplot=False) 
    
    final_value = cerebro.broker.getvalue()
    returns = results[0].analyzers.returns.get_analysis()
    print("-" * 32)
    print(f"最终资金: {final_value:.2f}")
    print(f"总收益(%) : {returns.get('rtot', 0.0) * 100:.2f}")
    print(f"年化(%)  : {returns.get('rnorm100', 0.0):.2f}")
    print("-" * 32)

    return cerebro, results


# 直接运行回测（适合 Quarto 单元）
cerebro, _ = run_backtest_multi_custom()
起始资金: 100000.00
交易已平仓: Asset_2 PnL: 328.78, 净: 270.90
交易已平仓: Asset_5 PnL: 1138.57, 净: 1031.69
交易已平仓: Asset_1 PnL: -3239.77, 净: -3333.58
交易已平仓: Asset_4 PnL: 3268.02, 净: 3137.72
交易已平仓: Asset_2 PnL: 806.99, 净: 736.14
交易已平仓: Asset_5 PnL: -4234.12, 净: -4360.99
交易已平仓: Asset_1 PnL: -437.95, 净: -543.20
交易已平仓: Asset_3 PnL: -948.34, 净: -1065.55
交易已平仓: Asset_4 PnL: 297.70, 净: 233.94
交易已平仓: Asset_5 PnL: -5732.46, 净: -5857.85
交易已平仓: Asset_3 PnL: -100.70, 净: -118.39
交易已平仓: Asset_1 PnL: -1125.54, 净: -1285.70
交易已平仓: Asset_2 PnL: 213.27, 净: 154.34
交易已平仓: Asset_2 PnL: 115.30, 净: 105.80
交易已平仓: Asset_1 PnL: 8420.36, 净: 8226.38
交易已平仓: Asset_2 PnL: 26.74, 净: 14.13
交易已平仓: Asset_4 PnL: -3278.24, 净: -3455.59
交易已平仓: Asset_1 PnL: 66.76, 净: -40.77
交易已平仓: Asset_2 PnL: 9661.58, 净: 9523.66
交易已平仓: Asset_1 PnL: -2156.63, 净: -2293.11
交易已平仓: Asset_3 PnL: -3162.97, 净: -3271.07
--------------------------------
最终资金: 97723.70
总收益(%) : -2.30
年化(%)  : -1.15
--------------------------------