103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
"""read_progress.py — 读取当前训练进度并做回测分析"""
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import sys, torch
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sys.path.insert(0, '.')
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ckpt = torch.load('checkpoints/ckpt_step_0480.pt', map_location='cpu', weights_only=False)
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h = ckpt['training_history']
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tokens = ckpt['best_formula']
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best_sc = ckpt['best_score']
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from model_core.vocab import FORMULA_VOCAB
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names = FORMULA_VOCAB.token_names
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n = len(h['step'])
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vals = h['val_score']
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bests = h['best_score']
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rews = h['avg_reward']
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ents = h['entropy']
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ics = h['ic_mean']
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print(f"=== 当前训练进度 step={ckpt['step']} ===")
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print(f"全局最优 BestScore : {best_sc:.4f}")
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print(f"末步批次 ValScore : {vals[-1]:+.4f}")
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print(f"末步 Entropy : {ents[-1]:.3f}")
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print(f"末步 IC : {ics[-1]:+.5f}")
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print(f"最优公式 tokens : {tokens}")
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print(f"最优公式 解读 : {' -> '.join(names[t] for t in tokens)}")
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print()
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print("=== BestScore 跳变点 ===")
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prev = bests[0]
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print(f" step 0: {prev:.4f}")
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for i in range(1, n):
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if bests[i] > prev + 0.05:
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print(f" step{h['step'][i]:4d}: {prev:.4f} -> {bests[i]:.4f} (+{bests[i]-prev:.4f})")
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prev = bests[i]
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print()
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print("=== 分阶段统计 ===")
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for s, e in [(0,100),(100,200),(200,300),(300,400),(400,481)]:
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sv = [vals[i] for i in range(n) if s <= h['step'][i] < e]
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sr = [rews[i] for i in range(n) if s <= h['step'][i] < e]
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se = [ents[i] for i in range(n) if s <= h['step'][i] < e]
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sic = [ics[i] for i in range(n) if s <= h['step'][i] < e]
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if sv:
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print(f" {s:3d}-{e}: val_mean={sum(sv)/len(sv):+.3f}"
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f" val_max={max(sv):+.3f}"
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f" H={sum(se)/len(se):.2f}"
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f" IC={sum(sic)/len(sic):+.5f}")
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for i, v in enumerate(vals):
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if v > 0:
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print(f"\n首次 val>0: step {h['step'][i]} (val={v:+.4f})")
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break
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# ── 交易频率分析 ──────────────────────────────────────────────────
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print()
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print("=== 最优公式交易频率 ===")
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from data_pipeline.fetcher import MT5DataFetcher
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from data_pipeline.data_manager import MT5DataManager
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from model_core.vm import StackVM
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with MT5DataFetcher() as fetcher:
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mgr = MT5DataManager(fetcher)
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mgr.load()
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feat = mgr.feat_tensor
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syms = mgr.symbols
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vm = StackVM()
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factor = vm.execute(tokens, feat)
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pos = torch.sign(torch.tanh(factor))
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T = pos.shape[1]
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print(f"数据长度 T={T} bars (H1 交集对齐)")
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print()
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total_trades = 0
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for i, sym in enumerate(syms):
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p = pos[i]
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trades, runs, cur_len, cur_dir, prev = 0, [], 0, 0, 0
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for v in p.tolist():
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vi = int(v)
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if vi != 0:
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if vi == cur_dir:
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cur_len += 1
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else:
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if cur_len > 0: runs.append(cur_len)
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cur_dir, cur_len = vi, 1
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if prev != vi: trades += 1
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else:
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if cur_len > 0: runs.append(cur_len)
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cur_dir, cur_len = 0, 0
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prev = vi
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if cur_len > 0: runs.append(cur_len)
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avg_hold = sum(runs) / len(runs) if runs else 0
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zero_pct = (p == 0).float().mean().item() * 100
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long_pct = (p == 1).float().mean().item() * 100
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total_trades += trades
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print(f" {sym}: {trades}笔 每100bar={trades/T*100:.1f}笔 "
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f"均持仓={avg_hold:.1f}bar 空仓={zero_pct:.0f}% "
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f"多{long_pct:.0f}%/空{100-zero_pct-long_pct:.0f}%")
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avg100 = total_trades / (T * len(syms)) * 100
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print(f"\n 三品种合计: 每100bar={avg100:.1f}笔 每天~{avg100*24/100:.1f}笔")
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