"""
回测可视化 — 权益曲线 + 统计图表
生成HTML报告，包含:
1. 权益曲线（对数尺度）
2. 回撤曲线
3. 月度盈亏柱状图
4. 信号类型分布
"""
import os
import pandas as pd
import numpy as np
from typing import List
def generate_report_html(trades: list, initial_capital: float,
output_path: str = "backtest_report.html"):
"""
从交易记录生成完整HTML回测报告
Args:
trades: List of TradeRecord
initial_capital: 初始资金
output_path: 输出HTML路径
"""
if not trades:
print("没有交易记录，无法生成报告")
return
df = pd.DataFrame([t.__dict__ for t in trades])
# ===== 计算统计数据 =====
total = len(df)
wins = len(df[df['pnl_dollar'] > 0])
losses = total - wins
win_rate = wins / total * 100
final_balance = df['balance_after'].iloc[-1]
multiple = final_balance / initial_capital
avg_win = df[df['pnl_dollar'] > 0]['pnl_pct'].mean() * 100 if wins > 0 else 0
avg_loss = abs(df[df['pnl_dollar'] <= 0]['pnl_pct'].mean()) * 100 if losses > 0 else 1
payoff = avg_win / avg_loss if avg_loss > 0 else 0
gross_profit = df[df['pnl_dollar'] > 0]['pnl_dollar'].sum()
gross_loss = abs(df[df['pnl_dollar'] <= 0]['pnl_dollar'].sum())
pf = gross_profit / gross_loss if gross_loss > 0 else float('inf')
equity = df['balance_after'].values
peak = np.maximum.accumulate(equity)
drawdown = (peak - equity) / peak * 100
max_dd = drawdown.max()
# ===== 权益曲线数据 =====
equity_dates = [t.strftime('%Y-%m-%d') for t in pd.to_datetime(df['exit_time'])]
equity_values = [round(v, 2) for v in equity]
dd_values = [round(v, 2) for v in drawdown]
# ===== 月度盈亏 =====
df['month'] = pd.to_datetime(df['entry_time']).dt.tz_localize(None).dt.to_period('M').astype(str)
monthly = df.groupby('month')['pnl_dollar'].sum()
monthly_labels = list(monthly.index)
monthly_values = [round(v, 2) for v in monthly.values]
monthly_colors = ['#22c55e' if v >= 0 else '#ef4444' for v in monthly.values]
# ===== 年度汇总 =====
df['year'] = pd.to_datetime(df['entry_time']).dt.year
yearly_rows = ""
for year, grp in df.groupby('year'):
n = len(grp)
w = len(grp[grp['pnl_dollar'] > 0])
l = n - w
pnl = grp['pnl_dollar'].sum()
end_bal = grp['balance_after'].iloc[-1]
wr = w / n * 100 if n > 0 else 0
yearly_rows += f"""
| {year} | {n} | {w}/{l} |
{wr:.1f}% |
${pnl:,.0f} |
${end_bal:,.0f} |
"""
# ===== 币种汇总 =====
coin_rows = ""
for coin, grp in df.groupby('coin'):
n = len(grp)
w = len(grp[grp['pnl_dollar'] > 0])
pnl = grp['pnl_dollar'].sum()
wr = w / n * 100 if n > 0 else 0
coin_rows += f"""
| {coin.replace('USDT','')} | {n} |
{wr:.1f}% |
${pnl:,.0f} |
"""
# ===== 信号类型汇总 =====
type_rows = ""
for stype, grp in df.groupby('signal_type'):
n = len(grp)
w = len(grp[grp['pnl_dollar'] > 0])
pnl = grp['pnl_dollar'].sum()
wr = w / n * 100 if n > 0 else 0
type_rows += f"""
| {stype} | {n} |
{wr:.1f}% |
${pnl:,.0f} |
"""
html = f"""
浪浪AI 回测报告
浪浪AI 回测报告
BTC 10x + ETH/山寨 5x | 做多+做空 | 12币种
初始资金
${initial_capital:,.0f}
最终资金
${final_balance:,.0f}
权益曲线 (对数尺度)
回撤曲线
月度盈亏
年度表现
| 年份 | 交易数 | 盈/亏 | 胜率 | 净盈亏 | 年末余额 |
|---|
{yearly_rows}
"""
with open(output_path, 'w', encoding='utf-8') as f:
f.write(html)
print(f"报告已生成: {output_path}")