517 lines
20 KiB
Python
517 lines
20 KiB
Python
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"""
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backtest_all_groups.py — 对所有组的当前最优因子做完整回测验证
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用法：
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python backtest_all_groups.py --offline
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分别加载 forex / metals_comm / index 三组的最优因子，
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在全量历史数据上回测，输出：回测摘要、品种级详情、前后半段一致性、资金曲线图。
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"""
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import sys, json, math
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from pathlib import Path
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import numpy as np
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import torch
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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sys.path.insert(0, str(Path(__file__).parent))
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from config import Config
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from data_pipeline.data_manager import MT5DataManager
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from data_pipeline.fetcher import MT5DataFetcher
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from model_core.vocab import FORMULA_VOCAB, VOCAB_VERSION
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from model_core.vm import StackVM
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from model_core.features import MT5FeatureEngineer
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from strategy_manager.signal import compute_target_positions_stateless
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_H1_PER_YEAR = 6240
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OUTPUT_DIR = "backtest_output"
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GROUP_CONFIG = {
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"forex": {"symbols": Config.SYMBOL_GROUPS["forex"], "cost_rate": 0.00015},
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"metals_comm": {"symbols": Config.SYMBOL_GROUPS["metals_comm"], "cost_rate": 0.00025},
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"index": {"symbols": Config.SYMBOL_GROUPS["index"], "cost_rate": 0.00030},
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}
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def decode(toks):
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names = FORMULA_VOCAB.token_names
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return " -> ".join(names[t] if 0 <= t < len(names) else f"?{t}" for t in toks)
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def calc_sharpe(pnl):
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m, s = pnl.mean(), pnl.std()
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return float(m / (s + 1e-10) * math.sqrt(_H1_PER_YEAR))
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def calc_sortino(pnl):
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m = pnl.mean()
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down = pnl[pnl < 0]
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ds = down.std() if len(down) > 0 else 1e-10
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ds = max(ds, abs(m), 1e-10)
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return float(np.clip(m / ds * math.sqrt(_H1_PER_YEAR), -20, 20))
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def calc_mdd(cum_pnl):
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peak = np.maximum.accumulate(cum_pnl)
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return float((peak - cum_pnl).max())
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def calc_ic(factor, target_ret):
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N, T = factor.shape
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ic_list = []
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for n in range(N):
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x = factor[n, :-1]
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y = target_ret[n, 1:]
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xm = x - x.mean()
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ym = y - y.mean()
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sx = np.sqrt((xm**2).mean())
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sy = np.sqrt((ym**2).mean())
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if sx < 1e-6 or sy < 1e-6:
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continue
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ic = (xm * ym).mean() / (sx * sy + 1e-8)
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ic_list.append(float(ic))
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return float(np.mean(ic_list)) if ic_list else 0.0
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def calc_annual_return(total_ret, T):
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years = T / _H1_PER_YEAR
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return float(total_ret / years) if years > 0 else 0.0
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def calc_calmar(total_ret, mdd, T):
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ann = calc_annual_return(total_ret, T)
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return float(ann / mdd) if mdd > 1e-8 else 0.0
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def _max_consecutive_loss(pnl):
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max_streak = 0
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current = 0
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for p in pnl:
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if p < 0:
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current += 1
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max_streak = max(max_streak, current)
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else:
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current = 0
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return max_streak
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def backtest_one(formula, feat, target_ret, symbols, cost_rate):
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vm = StackVM()
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factor = vm.execute(formula, feat)
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if factor is None:
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return None
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N, T = factor.shape
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factor_np = factor.detach().numpy()
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target_np = target_ret.detach().numpy()
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pos = compute_target_positions_stateless(factor)
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pos_np = pos.detach().numpy()
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prev = np.zeros_like(pos_np)
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prev[:, 1:] = pos_np[:, :-1]
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turnover = np.abs(pos_np - prev)
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pnl = pos_np * target_np - turnover * cost_rate
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per_sym = {}
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for i, sym in enumerate(symbols[:N]):
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p = pnl[i]
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cum = np.cumsum(p)
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n_trades = int((np.abs(np.diff(pos_np[i])) > 0.5).sum())
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per_sym[sym] = {
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"pnl": p,
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"cum": cum,
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"total_ret": float(cum[-1]),
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"ann_ret": calc_annual_return(float(cum[-1]), T),
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"sharpe": calc_sharpe(p),
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"sortino": calc_sortino(p),
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"mdd": calc_mdd(cum),
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"n_trades": n_trades,
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"avg_hold": float(T / max(1, n_trades)) if n_trades > 0 else float(T),
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"ic": calc_ic(factor_np[i:i+1], target_np[i:i+1]),
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}
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port_pnl = pnl.mean(axis=0)
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port_cum = np.cumsum(port_pnl)
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ic = calc_ic(factor_np, target_np)
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split = T // 2
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p1 = port_pnl[:split]
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p2 = port_pnl[split:]
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port_total_ret = float(port_cum[-1])
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port_mdd = calc_mdd(port_cum)
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port_ann_ret = calc_annual_return(port_total_ret, T)
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port_calmar = calc_calmar(port_total_ret, port_mdd, T)
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return {
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"formula": formula,
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"readable": decode(formula),
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"per_sym": per_sym,
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"port_pnl": port_pnl,
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"port_cum": port_cum,
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"port_total_ret": port_total_ret,
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"port_ann_ret": port_ann_ret,
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"port_sharpe": calc_sharpe(port_pnl),
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"port_sortino": calc_sortino(port_pnl),
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"port_mdd": port_mdd,
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"port_calmar": port_calmar,
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"ic": ic,
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"n_pos_syms": sum(1 for d in per_sym.values() if d["total_ret"] > 0),
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"n_syms": N,
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"half1_sharpe": calc_sharpe(p1),
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"half2_sharpe": calc_sharpe(p2),
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"half1_sortino": calc_sortino(p1),
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"half2_sortino": calc_sortino(p2),
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"half1_ret": float(np.cumsum(p1)[-1]),
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"half2_ret": float(np.cumsum(p2)[-1]),
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"avg_turnover": float(turnover.mean()),
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"avg_hold_h": float(np.mean([d["avg_hold"] for d in per_sym.values()])),
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"max_consec_loss": _max_consecutive_loss(port_pnl),
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}
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def plot_group(result, symbols, group_name, output_dir, cost_rate):
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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fig = plt.figure(figsize=(18, 12), dpi=110)
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gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 2], hspace=0.15)
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ax_eq = fig.add_subplot(gs[0])
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ax_dd = fig.add_subplot(gs[1], sharex=ax_eq)
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ax_sym = fig.add_subplot(gs[2], sharex=ax_eq)
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T = len(result["port_pnl"])
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x = np.arange(T)
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port_cum = result["port_cum"]
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ax_eq.plot(x, port_cum, linewidth=2.0, color="#1565c0",
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label=f"Portfolio (AnnRet={result['port_ann_ret']:+.4f}, Sortino={result['port_sortino']:+.2f}, MDD={result['port_mdd']:.3f})")
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ax_eq.axhline(0, color="gray", linewidth=0.5, linestyle="--")
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ax_eq.set_ylabel("Cumulative Return", fontsize=10)
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ax_eq.legend(loc="upper left", fontsize=9, framealpha=0.85)
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ax_eq.grid(alpha=0.25)
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ax_eq.set_title(f"{group_name} group backtest | {' + '.join(symbols)} equal weight\n"
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f"Formula: {result['readable']}\n"
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f"Cost: {cost_rate} T={T} bars H1", fontsize=11, pad=8)
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peak = np.maximum.accumulate(port_cum)
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dd = port_cum - peak
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ax_dd.fill_between(x, dd, 0, alpha=0.4, color="#1565c0", label="Drawdown")
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ax_dd.axhline(0, color="gray", linewidth=0.5)
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ax_dd.set_ylabel("Drawdown", fontsize=9)
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ax_dd.grid(alpha=0.2)
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ax_dd.legend(loc="lower left", fontsize=8)
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colors = ["#e65100", "#00897b", "#6a1b9a", "#b71c1c", "#26418f"]
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for i, sym in enumerate(symbols):
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if sym in result["per_sym"]:
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cum = result["per_sym"][sym]["cum"]
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c = colors[i % len(colors)]
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ax_sym.plot(x, cum, linewidth=1.2, color=c, alpha=0.8,
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label=f"{sym} (Ret={result['per_sym'][sym]['total_ret']:+.3f})")
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ax_sym.axhline(0, color="gray", linewidth=0.5, linestyle="--")
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ax_sym.set_ylabel("Per-Symbol Cum Return", fontsize=9)
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ax_sym.set_xlabel("Bar Index (H1)", fontsize=9)
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ax_sym.legend(loc="upper left", fontsize=7, ncol=3, framealpha=0.85)
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ax_sym.grid(alpha=0.2)
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plt.tight_layout()
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plot_path = Path(output_dir) / f"{group_name}_backtest.png"
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fig.savefig(plot_path, bbox_inches="tight")
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plt.close(fig)
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return str(plot_path)
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def run_group_backtest(group_name, group_cfg, fetcher, multi_mgr):
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symbols = group_cfg["symbols"]
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cost_rate = group_cfg["cost_rate"]
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strategy_path = Path(f"strategies/best_{group_name}.json")
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print(f"\n{'='*70}")
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print(f" [{group_name}] group backtest")
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print(f"{'='*70}")
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if not strategy_path.exists():
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print(f" [SKIP] No strategy file: {strategy_path}")
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return None
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data = json.load(open(strategy_path))
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if data.get("vocab_version", "unknown") != VOCAB_VERSION:
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print(f" [SKIP] Vocab mismatch: {data.get('vocab_version')} != {VOCAB_VERSION}")
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return None
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formula = data["formula"]
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best_score = data.get("best_score", 0.0)
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print(f" Formula tokens : {formula}")
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print(f" Readable : {decode(formula)}")
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print(f" Train score : {best_score:.4f}")
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print(f" Symbols : {symbols}")
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print(f" Cost rate : {cost_rate}")
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# Load group data independently
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Config.SYMBOLS = list(symbols)
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group_mgr = MT5DataManager(fetcher)
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group_mgr.load()
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Config.SYMBOLS = list(Config.SYMBOL_GROUPS) and [] # reset
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# Actually restore from the full config
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from config import Config as _Cfg
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_Cfg.SYMBOLS = ["EURUSD", "USDJPY", "XAUUSD", "AAVUSD", "COCOA.c",
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"US30.cash", "US100.cash", "US500.cash", "US2000.cash", "JP225.cash"]
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raw_dict = group_mgr.raw_dict
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syms_loaded = group_mgr.symbols
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T = raw_dict["open"].shape[1]
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print(f" Loaded: {syms_loaded} T={T} bars")
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feat = MT5FeatureEngineer.compute_features(raw_dict)
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target_ret = group_mgr.target_ret
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result = backtest_one(formula, feat, target_ret, syms_loaded, cost_rate)
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if result is None:
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print(f" [ERROR] Factor execution failed!")
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return None
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# Print summary
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print(f"\n --- Portfolio Summary ({T} bars H1, {len(syms_loaded)} symbols) ---")
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print(f" Total Return : {result['port_total_ret']:+.4f}")
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print(f" Annual Return: {result['port_ann_ret']:+.4f}")
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print(f" Sharpe : {result['port_sharpe']:+.4f}")
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print(f" Sortino : {result['port_sortino']:+.4f}")
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print(f" Max DD : {result['port_mdd']:.4f}")
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print(f" Calmar : {result['port_calmar']:+.4f}")
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print(f" IC : {result['ic']:.5f}")
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print(f" Avg Turnover : {result['avg_turnover']:.6f}")
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print(f" Avg Hold : {result['avg_hold_h']:.1f}h")
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print(f" Max Consec Loss: {result['max_consec_loss']} bars")
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print(f" Profitable : {result['n_pos_syms']}/{result['n_syms']}")
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print(f"\n --- Half-split Consistency ---")
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print(f" H1: Sharpe={result['half1_sharpe']:+.4f} Ret={result['half1_ret']:+.4f}")
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print(f" H2: Sharpe={result['half2_sharpe']:+.4f} Ret={result['half2_ret']:+.4f}")
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h1, h2 = result["half1_sharpe"], result["half2_sharpe"]
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if h1 > 0 and h2 > 0:
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print(f" [OK] Both halves positive, good consistency")
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elif h1 * h2 > 0:
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print(f" [WARN] Same sign but negative")
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else:
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print(f" [FAIL] Opposite signs, overfitting suspected")
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print(f"\n --- Per-Symbol Detail ---")
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for sym, d in result["per_sym"].items():
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sig = "[OK]" if d["total_ret"] > 0 else "[X]"
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print(f" {sym:14s}: Ret={d['total_ret']:+.4f} Ann={d['ann_ret']:+.4f} "
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f"Sharpe={d['sharpe']:+.4f} Sortino={d['sortino']:+.4f} "
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f"MDD={d['mdd']:.4f} IC={d['ic']:.5f} "
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f"Trades={d['n_trades']} Hold={d['avg_hold']:.0f}h {sig}")
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# FTMO check
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print(f"\n --- FTMO Assessment ---")
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issues = []
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if result["port_mdd"] > 0.10:
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issues.append(f"[WARN] MDD={result['port_mdd']:.4f} > 10% FTMO Max Loss")
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else:
|
||
|
|
print(f" [OK] MDD={result['port_mdd']:.4f} < 10%")
|
||
|
|
if result["port_ann_ret"] > 0:
|
||
|
|
print(f" [OK] AnnRet={result['port_ann_ret']:+.4f} positive")
|
||
|
|
else:
|
||
|
|
issues.append(f"[WARN] Negative annual return")
|
||
|
|
if result["max_consec_loss"] > 500:
|
||
|
|
issues.append(f"[WARN] Max consec loss {result['max_consec_loss']} bars too long")
|
||
|
|
if result["avg_hold_h"] < 2:
|
||
|
|
issues.append(f"[WARN] Avg hold {result['avg_hold_h']:.1f}h too short")
|
||
|
|
if result["n_pos_syms"] < result["n_syms"] // 2:
|
||
|
|
issues.append(f"[WARN] Only {result['n_pos_syms']}/{result['n_syms']} profitable")
|
||
|
|
for iss in issues:
|
||
|
|
print(f" {iss}")
|
||
|
|
if not issues:
|
||
|
|
print(f" [OK] No obvious FTMO violations")
|
||
|
|
|
||
|
|
# Plot
|
||
|
|
plot_path = plot_group(result, syms_loaded, group_name, OUTPUT_DIR, cost_rate)
|
||
|
|
print(f"\n Plot saved -> {plot_path}")
|
||
|
|
|
||
|
|
# Save JSON report
|
||
|
|
report = {
|
||
|
|
"group": group_name,
|
||
|
|
"formula": formula,
|
||
|
|
"readable": result["readable"],
|
||
|
|
"best_score": best_score,
|
||
|
|
"vocab_version": VOCAB_VERSION,
|
||
|
|
"symbols": syms_loaded,
|
||
|
|
"T": T,
|
||
|
|
"cost_rate": cost_rate,
|
||
|
|
"port_total_ret": round(result["port_total_ret"], 6),
|
||
|
|
"port_ann_ret": round(result["port_ann_ret"], 6),
|
||
|
|
"port_sharpe": round(result["port_sharpe"], 4),
|
||
|
|
"port_sortino": round(result["port_sortino"], 4),
|
||
|
|
"port_mdd": round(result["port_mdd"], 6),
|
||
|
|
"port_calmar": round(result["port_calmar"], 4),
|
||
|
|
"ic": round(result["ic"], 6),
|
||
|
|
"avg_turnover": round(result["avg_turnover"], 8),
|
||
|
|
"avg_hold_h": round(result["avg_hold_h"], 1),
|
||
|
|
"max_consec_loss": result["max_consec_loss"],
|
||
|
|
"n_pos_syms": result["n_pos_syms"],
|
||
|
|
"n_syms": result["n_syms"],
|
||
|
|
"half1_sharpe": round(result["half1_sharpe"], 4),
|
||
|
|
"half2_sharpe": round(result["half2_sharpe"], 4),
|
||
|
|
"half1_ret": round(result["half1_ret"], 6),
|
||
|
|
"half2_ret": round(result["half2_ret"], 6),
|
||
|
|
"per_sym": {
|
||
|
|
sym: {
|
||
|
|
"total_ret": round(d["total_ret"], 6),
|
||
|
|
"ann_ret": round(d["ann_ret"], 6),
|
||
|
|
"sharpe": round(d["sharpe"], 4),
|
||
|
|
"sortino": round(d["sortino"], 4),
|
||
|
|
"mdd": round(d["mdd"], 6),
|
||
|
|
"ic": round(d["ic"], 6),
|
||
|
|
"n_trades": d["n_trades"],
|
||
|
|
"avg_hold": round(d["avg_hold"], 1),
|
||
|
|
}
|
||
|
|
for sym, d in result["per_sym"].items()
|
||
|
|
},
|
||
|
|
}
|
||
|
|
report_path = Path(OUTPUT_DIR) / f"{group_name}_backtest.json"
|
||
|
|
with open(report_path, "w") as f:
|
||
|
|
json.dump(report, f, indent=2, ensure_ascii=False)
|
||
|
|
print(f" Report saved -> {report_path}")
|
||
|
|
|
||
|
|
return result
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
offline = "--offline" in sys.argv
|
||
|
|
|
||
|
|
# First, save metals_comm current best from latest checkpoint
|
||
|
|
print("Checking for latest metals_comm checkpoint to save current best...")
|
||
|
|
import glob
|
||
|
|
ckpt_files = sorted(glob.glob(r"D:\cl\MT5_AlphaGPT\checkpoints\ckpt_metals_comm_step_*.pt"))
|
||
|
|
if ckpt_files:
|
||
|
|
import torch
|
||
|
|
latest_ckpt = ckpt_files[-1]
|
||
|
|
ckpt = torch.load(latest_ckpt, map_location='cpu', weights_only=False)
|
||
|
|
best_formula = ckpt.get('best_formula')
|
||
|
|
best_score = ckpt.get('best_score', 0)
|
||
|
|
step = ckpt.get('step', 0)
|
||
|
|
if best_formula and best_score > 0:
|
||
|
|
# Update strategy file with latest best
|
||
|
|
strategy_path = Path("strategies/best_metals_comm.json")
|
||
|
|
strategy_path.parent.mkdir(exist_ok=True)
|
||
|
|
strategy_path.write_text(json.dumps({
|
||
|
|
"vocab_version": VOCAB_VERSION,
|
||
|
|
"symbol": "metals_comm",
|
||
|
|
"formula": best_formula,
|
||
|
|
"best_score": best_score,
|
||
|
|
"step": step,
|
||
|
|
}, indent=2))
|
||
|
|
print(f" Updated best_metals_comm.json: score={best_score:.4f} step={step} formula={best_formula}")
|
||
|
|
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print(f" All-Groups Backtest Analysis")
|
||
|
|
print(f"{'='*70}")
|
||
|
|
|
||
|
|
with MT5DataFetcher(offline=offline) as fetcher:
|
||
|
|
all_results = {}
|
||
|
|
for gname, gcfg in GROUP_CONFIG.items():
|
||
|
|
result = run_group_backtest(gname, gcfg, fetcher, None)
|
||
|
|
if result:
|
||
|
|
all_results[gname] = result
|
||
|
|
|
||
|
|
# Cross-group summary
|
||
|
|
if all_results:
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print(f" Cross-Group Comparison")
|
||
|
|
print(f"{'='*70}")
|
||
|
|
print(f" {'Group':<14s} {'Score':>8s} {'AnnRet':>9s} {'Sharpe':>8s} {'Sortino':>8s} {'MDD':>8s} {'Calmar':>8s} {'IC':>8s} {'Pos/N':>6s}")
|
||
|
|
print(f" {'-'*14} {'-'*8} {'-'*9} {'-'*8} {'-'*8} {'-'*8} {'-'*8} {'-'*8} {'-'*6}")
|
||
|
|
for gname, r in all_results.items():
|
||
|
|
print(f" {gname:<14s} "
|
||
|
|
f"{r.get('best_score',0):>8.4f} "
|
||
|
|
f"{r['port_ann_ret']:>+9.4f} "
|
||
|
|
f"{r['port_sharpe']:>+8.4f} "
|
||
|
|
f"{r['port_sortino']:>+8.4f} "
|
||
|
|
f"{r['port_mdd']:>8.4f} "
|
||
|
|
f"{r['port_calmar']:>+8.4f} "
|
||
|
|
f"{r['ic']:>8.5f} "
|
||
|
|
f"{r['n_pos_syms']:>2d}/{r['n_syms']:<2d}")
|
||
|
|
|
||
|
|
# Combined portfolio (equal weight across groups)
|
||
|
|
print(f"\n --- Combined Portfolio (equal weight across groups) ---")
|
||
|
|
# Align all groups to min T
|
||
|
|
min_T = min(len(r["port_pnl"]) for r in all_results.values())
|
||
|
|
combined_pnl = np.zeros(min_T)
|
||
|
|
for r in all_results.values():
|
||
|
|
combined_pnl += r["port_pnl"][:min_T]
|
||
|
|
combined_pnl /= len(all_results)
|
||
|
|
|
||
|
|
combined_cum = np.cumsum(combined_pnl)
|
||
|
|
combined_mdd = calc_mdd(combined_cum)
|
||
|
|
combined_ann = calc_annual_return(float(combined_cum[-1]), min_T)
|
||
|
|
combined_sharpe = calc_sharpe(combined_pnl)
|
||
|
|
combined_sortino = calc_sortino(combined_pnl)
|
||
|
|
combined_calmar = calc_calmar(float(combined_cum[-1]), combined_mdd, min_T)
|
||
|
|
|
||
|
|
print(f" Total Return : {float(combined_cum[-1]):+.4f}")
|
||
|
|
print(f" Annual Return: {combined_ann:+.4f}")
|
||
|
|
print(f" Sharpe : {combined_sharpe:+.4f}")
|
||
|
|
print(f" Sortino : {combined_sortino:+.4f}")
|
||
|
|
print(f" Max DD : {combined_mdd:.4f}")
|
||
|
|
print(f" Calmar : {combined_calmar:+.4f}")
|
||
|
|
|
||
|
|
# Plot combined
|
||
|
|
fig, axes = plt.subplots(2, 1, figsize=(16, 8), dpi=110, gridspec_kw={'height_ratios': [3, 1], 'hspace': 0.15})
|
||
|
|
x = np.arange(min_T)
|
||
|
|
colors_g = {"forex": "#2e7d32", "metals_comm": "#e65100", "index": "#1565c0"}
|
||
|
|
for gname, r in all_results.items():
|
||
|
|
axes[0].plot(x, np.cumsum(r["port_pnl"][:min_T]), linewidth=1.2, alpha=0.7,
|
||
|
|
color=colors_g.get(gname, "gray"), label=f"{gname}")
|
||
|
|
axes[0].plot(x, combined_cum, linewidth=2.5, color="#d32f2f",
|
||
|
|
label=f"Combined (Ann={combined_ann:+.4f}, Sharpe={combined_sharpe:+.2f}, MDD={combined_mdd:.3f})")
|
||
|
|
axes[0].axhline(0, color="gray", linewidth=0.5, linestyle="--")
|
||
|
|
axes[0].legend(fontsize=9)
|
||
|
|
axes[0].set_title("All Groups Combined Portfolio", fontsize=12)
|
||
|
|
axes[0].grid(alpha=0.25)
|
||
|
|
|
||
|
|
peak = np.maximum.accumulate(combined_cum)
|
||
|
|
dd = combined_cum - peak
|
||
|
|
axes[1].fill_between(x, dd, 0, alpha=0.4, color="#d32f2f")
|
||
|
|
axes[1].axhline(0, color="gray", linewidth=0.5)
|
||
|
|
axes[1].set_ylabel("Drawdown", fontsize=9)
|
||
|
|
axes[1].set_xlabel("Bar Index (H1)", fontsize=9)
|
||
|
|
axes[1].grid(alpha=0.2)
|
||
|
|
|
||
|
|
plt.tight_layout()
|
||
|
|
combined_path = Path(OUTPUT_DIR) / "all_groups_combined.png"
|
||
|
|
fig.savefig(combined_path, bbox_inches="tight")
|
||
|
|
plt.close(fig)
|
||
|
|
print(f"\n Combined plot saved -> {combined_path}")
|
||
|
|
|
||
|
|
# Save combined report
|
||
|
|
combined_report = {
|
||
|
|
"groups": list(all_results.keys()),
|
||
|
|
"T": min_T,
|
||
|
|
"total_ret": round(float(combined_cum[-1]), 6),
|
||
|
|
"ann_ret": round(combined_ann, 6),
|
||
|
|
"sharpe": round(combined_sharpe, 4),
|
||
|
|
"sortino": round(combined_sortino, 4),
|
||
|
|
"mdd": round(combined_mdd, 6),
|
||
|
|
"calmar": round(combined_calmar, 4),
|
||
|
|
}
|
||
|
|
combined_report_path = Path(OUTPUT_DIR) / "all_groups_combined.json"
|
||
|
|
with open(combined_report_path, "w") as f:
|
||
|
|
json.dump(combined_report, f, indent=2, ensure_ascii=False)
|
||
|
|
print(f" Combined report saved -> {combined_report_path}")
|
||
|
|
|
||
|
|
print(f"\n{'='*70}")
|
||
|
|
print(f" All-Groups Backtest Complete")
|
||
|
|
print(f"{'='*70}\n")
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|